Hi friends 👋 ,

Happy Friday, welcome to the 197th Weekly Dose of Optimism, and a very Happy SpaceX IPO Day to those who celebrate!

It’s been a big week over here at Not Boring HQ. On Monday, we published a co-written essay on flying cars with Tsung Xu. On Wednesday, we published one on tokenminning with Markie Wagner. That night, I got to go to the craziest basketball game I’ve ever seen. Yesterday, the World Cup started right here in America.

And today, we have wall-to-wall, handcrafted optimism for you, brought to you by our new friends at Pangram, who are allies in the fight against AI-written slop.

We have a church that has taken lifetimes to build, and a trial of a drug that may help extend our lifetimes. We have Doudna back at it with a cancer-shredding CRISPR. We have drone boats saving soldiers, and autonomous planes that fly right over the water. We have money for robots and Bezos has money for manufacturing. And we have Science Breakthroughs, a view of America through Freddy’s eyes, nuclear batteries, and even more. What a week for the optimists.

Let’s get to it.

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(1) Pope Blesses La Sagrada Família’s Tower of Jesus Christ

This week, to mark the 100th anniversary of the architect Antoni Gaudí's death, Pope Leo XIV blessed the Tower of Jesus Christ at Barcelona’s Sagrada Família basilica. It LSF’s tallest tower, making it the tallest Catholic church in the world, and its central one. There’s more construction to be done on the rest of the church, but the ceremony was beautiful enough to lead off with, given how much I love the story of La Sagrada Família.

I wrote about it in I, Exponential back in August 2023. You can read the LSF section here.

In 1883, a small group of Catholic devotees of Saint Joseph (Josephites) entrusted a young Catalan architect, Antoni Gaudí, to build them a church. As architect Mark Foster Gage writes in a piece for CNN:

Gaudí’s vision of the church was so complex and detailed from the start that at no point could it be physically drawn by hand using the typical scale drawings so common to almost all architectural projects. Instead, it was almost entirely constructed through the making of large plaster models to communicate Gaudí’s desires to the army of stonemasons slowly liberating its form from blocks of local Montjuïc sandstone.

Then Gaudí was hit by a tram car and died in 1926, with the church only 10-15% completed. For the past century, teams of builders and architects and technologists have worked to figure out exactly what he was going for, and then to build it.

The reason I love the story, other than LSF’s beauty and the fact that Barcelona is one of my favorite cities in the world, is that the reason we’ve been able to get it to this point over the past half-century is that the technology finally caught up to what was in the architect’s head.

In 1979, a 22-year-old Kiwi Cambridge grad student, Mark Burry, visited Sagrada Família and interviewed some of Gaudí’s former apprentices. They showed him the boxes of broken model fragments, and offered him an internship. Burry got to work trying to reconstruct the mind of Antoni Gaudí in order to construct the church that lived within.

He tried to hand-draw the “complex intersection of weird shapes, including things like conoids and hyperbolic paraboloids” but he realized that the tool wasn’t up to the task. The computer saved the day.

Burry brought in software used to design airplanes to solve the otherwise-impossible problem of translating Gaudí’s sketches of bone columns into 3D models.

In order to actually construct the building, Burry and team hooked their computers up to a relatively new invention, CNC (computer numerically controlled) machines. CNC machines were themselves a product of a number of technological advances in computing power, data storage, electronics, motors, material science, user interfaces, networking and connectivity, control systems, and software designs. All of those curves converged in time for Burry to feed his 3D models into CNC machines that could precisely carve their designs out of stone.

Today, the team working on Sagrada Família uses a full arsenal of modern technology, from 3D printers to Lidar laser scans, from sensors to VR headsets.

And now, this great tech-enabled architectural wonder of the world has its central tower completed, is nearing full completion, and has the blessing of the Pope.

As impressive as the building itself is, the Pope blessed both it and his Nova Knicks in the same week. He’s the real MVP.

(2) World-first: therapy to make cells young again trialled in a person

Heidi Ledford for Nature

Five years ago, David Sinclair's lab at Harvard made old, blind mice see again by turning back the biological clock in their cells. They coaxed aged optic-nerve neurons to behave young again, and regrow.

This week, for the first time, they did it to a person.

Life Biosciences, the Boston company built on Sinclair's work, announced it has treated the first participant in the world’s first clinical trial of partial cellular reprogramming. The trial is targeting glaucoma, a disease that slowly kills the neurons of the optic nerve, which don’t typically regenerate. They’re betting that the therapy will make the cells young enough to try.

The work is based on Japanese Nobel Laureate Shinya Yamanaka’s eponymous Yakanama Factors, four genes that can rewind any adult cell all the way back to a stem-cell state. The problem is, a reset retina cell forgets it's a retina cell. So Life Bio uses three of the four factors (dropping c-Myc, the one most tied to cancer) and nudges the cells only partway back: younger, but still themselves. In Sinclair's 2020 mouse study, that partial nudge regenerated neurons and reversed vision loss in elderly and glaucomatous mice.

The approach - partial epigentic reprogramming - is similar to the NewLimit approach to curing mouse (and eventually human) hangovers and liver damage that we covered in last week’s Dose.

We flagged the potential for this trial when the FDA cleared it back in January. Now, there's a human being walking around with partially reprogrammed cells in their eye.

Before we get too excited, this is a safety trial, and it’s only in one patient so far. There are real concerns. Push cells too far back, or in the wrong tissue, and you risk tipping them cancerous. Longevity scientist Matt Kaeberlein put it plainly: the upside is big if it can be done safely, but the tech is early and the downside risk is severe.

That’s the only place to start, though, and the potential here is enormous. Two weeks in a row, we’ve covered credible teams pursuing trials to reverse aging, that horrible disease that ultimately kills every human on earth and degrades our quality of life in the process. The sooner we get these therapies, the longer we live younger.

(3) Doudna's Lab Build a CRISPR to Selectively Shred Cancer Cells

Andy Murdock for Innovative Genomics

Speaking of cells… this week Jennifer Doudna's Innovative Genomics Institute published a paper in Nature describing a CRISPR system that does the opposite of what it usually does. Instead of fixing a broken gene, it finds the cells carrying one and destroys them.

Most cancer drugs are inhibitors that tamp down an overactive gene. But the most common cancer driver is the reverse: a tumor-suppressor switch that's been snapped off. p53, the gene that normally keeps cells from turning cancerous, is mutated in roughly half of all cancers and up to 70–90% of the nastiest ones: ovarian, pancreatic, non-small-cell lung. You can't restore a lost function by inhibiting something, which is why, after 35 years of trying, there's still no p53 drug.

So first author Jingkun Zeng went the other way. He engineered CRISPR-Cas12a2 to watch for the RNA signature only a p53-mutant cell produces, and when it sees that signature, the enzyme shreds all the genetic material inside that one cell, killing it while leaving healthy cells alone. In a dish of cells differing by a single DNA letter, it wiped out the mutants and left the normal ones almost untouched. Because it's programmable, a new mutation just means writing a new guide RNA.

The caveat is that this is still cell culture, and as Zeng flags herself, the hard part will be delivery, first in animals, then humans, to get a big genome-cutting enzyme into every target cell in a living body.

But still! People have been trying to target p53 for 35 years. Doudna & Co have figured out a way to just shred it. Say it with me… get fucked, cancer.

(4) Saronic Drone Boat Rescues Downed Apache Crew

Nicholas Kulish and Eric Schmitt for The New York Times

On Monday, Iran downed an American Apache helicopter near the Strait of Hormuz, sending two crew members into the water. That’s bad news, not the stuff of the Dose.

The Dose-worthy part is that those two crew members were rescued by a drone boat, a Saronic Corsair. “It was the first U.S. rescue carried out by an autonomous surface vessel, remotely piloted by a human operator, the Central Command spokesman, Capt. Tim Hawkins, said on Tuesday.”

Per The New York Times, “The vessel carried the Apache’s pilot and gunner to another location, where they were picked up by a helicopter to complete the rescue.”

Again, two American military members were stranded in the water in hostile territory, and they were rescued by drone boats built by an American startup that we’ve covered previously in the Dose, just one week after the same company launched its much larger Marauder, which it built in an American shipyard (America is supposed to be bad at shipbuilding) in under a year.

If that’s what American Dynamism looks like, then all aboard. (jk they’re autonomous)

(5) Standard Bots Raises $200 Million at $1 Billion

Here at the Weekly Dose, we love when good things happen to our friends. A controversial stance, perhaps, but we stand by it.

So we were very happy to see our friend Evan Beard raise $200 million at $1 billion for Standard Bots, America’s largest manufacture of AI-native industrial robots. Evan said that the company is on track to deploy 10% of America’s industrial robots by 2027. That’s not saying as much as it should: last year, China installed 9x more robots than the US.

With the money, Standard Bots plans to manufacture robots - from metal in to robots out - and deploy them in manufacturers across the country. More robots, Evan argues, more competitive American manufacturing.

If you want to learn more about what Standard Bots is building and why their Many Small Steps approach might be the way to actually make robotics happen, check out the essay Evan and I co-wrote in January:

EXTRA DOSES: Science Breakthroughs, Freddy, Poseidon, Prometheus, Zeno Power, Criminals Hate Flock Safety

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Hi friends 👋,

Happy Wednesday and welcome back!

A couple of months ago, my friends Adam and Ben at Genius Ventures asked if they could introduce me to one of their favorite founders, Markie Wagner.

The Markie Wagner? The Choose Good Quests Markie Wagner? The drop an all-timer then go quiet for years, cooking up something spoken of in hushed tones Markie Wagner? The grew up inside of a computer and dreamed as a young girl in Southern California, seriously, of making computers do the work that humans shouldn’t have to Markie Wagner?

Of course I wanted to meet Markie Wagner.

So we met a month ago at Soho Diner and I ordered a milkshake and she asked them to cut up a bowl of fruit. She asked for my lore, which was boring, and I asked for hers, which she weaved non-stop for the next hour, landing so naturally on why she’s building what she’s building that it seemed almost pre-destined.

She also told me, before everyone else came to the same conclusion, that tokenmaxxing was bullshit, because behind closed doors, the Fortune 500 CEOs she works with were all saying some version of “We committed to all this token spend and I have no idea what we’re getting out of it.”

She was right, I think she’s going to be right again, she’s backed by Founders Fund, Kleiner Perkins, Genius Ventures, and OpenAI to go prove it, and now she’s explaining her logic publicly in her first written piece since Good Quests.

So this is where we are heading, according to Markie Wagner.

Let’s get to it.

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Return on Tokens (ROT)

Co-Written with Markie Wagner

The promise of AI is that it will turn businesses into software so that they can evolve over millions of tiny iterations. Beautiful, ideal, complex things can only emerge as the result of tremendous trial and error over time. You cannot build perfection, only discover it.

Capitalism is organizational evolution. Millions of businesses compete in the marketplace with offerings that they think customers will want. Some thrive and grow. Others die. Each company evolves, too. People come and go. An experiment becomes a process, a process becomes a web of tacit knowledge. Products are introduced, and products are retired.

This constant evolution is why we enjoy the standard of living we enjoy today, and why ours will look primitive to future generations. Accelerating it is my Good Quest, because if every business can evolve to its ideal form, it will create trillions of dollars of value and unblock all of the other Good Quests.

I dropped out of the research world because it felt like the wrong hill to climb, and I went out into America to just do work, so that I could figure out how to make computers do the work, so that humans could direct the computers in evolving the work.

What I suspected before and learned in my travels is that the way that the market has implemented AI thus far is the wrong way. It’s not endgame. It is too wasteful, too forgetful, and too imprecise. I’ve been in the fucking Sahara Desert out here fighting demons to learn this wisdom.

Tokenmaxxing Clearly Isn’t It

Tokenmaxxing - literally maximizing the amount of tokens you or your organization spends, tracked in leaderboards and rewarded with trinkets - was a mass delusion, something like a commercial form of AI psychosis.

Tokenmaxxing was a lab-grown supermeme that worked better than the labs could have hoped.

Picture this. Anthropic and OpenAI release a product, Agents, in the form of Claude Code/Cowork and Codex, respectively, that are basically lab employees working inside of customers’ companies and are given company credit cards with no spending limit (tokenmaxxing suggests the more they spend the better they’re doing) to spend on behalf of their real employer, the lab. Anthropic ships a bunch of Agents into, say, KPMG, which commits to a certain spend in exchange for discounts (token commits), KPMG’s employees are encouraged to use Agents to do everything they can possibly think of (lots of dashboards), and then these Agents, which again you can think of as digital Anthropic employees with no-limit KPMG credit cards that they can use to spend on Anthropic, run up token bills to their heart’s content. Employees who direct their Agents to use the most tokens are recognized as AI Innovators.

Certainly, some people recognized that it was a delusion. They would ask questions like, “But are the Agents doing anything useful? Aren’t they just building dashboards? Please can someone show me something useful they’ve built with an Agent?” but those sane few were met with the killer retort: “Skill Issue.”

Some people, they were told, were building immensely valuable things with Agents, the same way that some people had a super hot girlfriend at summer camp but you’ve never met her. If you couldn’t figure out how to do the same, well, welcome to the Permanent Underclass.

Everyone fell for it, for a while. The market incentivized companies to spend tokens, so boards incentivized leaders to spend tokens, so leaders incentivized managers to spend tokens, so managers incentivized employees to spend tokens. Nobody had an incentive to say that the tokens aren’t doing useful stuff.

I talk to these people all the time and every company has some version of the same conversation. Someone who’s running the AI team goes, “We’ve made a ton of progress this quarter. We spent $50 million on tokens.” And everyone nods and claps. “Usage is up. We’ve built 3,000 Agents. We shipped 10 million lines of code.” And you’re like… what? And then I’d ask, “Hey did you measure accuracy for the fraud Agent?” And they’d go, “Yeah… it’s about 50%.” People are at 99%!

But the models improve and everyone’s tokenmaxxing and you don’t want to be the luddite, so you keep lazily throwing Agents at everything and hoping they learn.

All of this happened, by the way, right as the labs switched from subscription-based to consumption-based revenue models, so companies had no time to prepare.

It is no wonder token usage, and therefore lab revenue, went parabolic.

It took Uber, that last era’s poster child of VC-subsidized demand, to break the spell. Its CTO said that the company had burned through its 2026 Claude Code token budget by April. In May, its COO said that the company was having a harder time justifying its AI spend, because the link between AI consumption and shipped features “is not there yet.”

What followed was like that scene in Mean Girls where Tina Fey asks the students to raise their hands if they feel personally victimized by Regina George, and one hand goes up, then all of the rest of the hands go up.

There was the consultant saying his client accidentally burned half a billion dollars on Claude Code. Amazon shut down its AI leaderboard. Legora CTO Jacob Lauritzen told Harry Stebbings that token leaderboards “lead to tokenmaxxing, which is people just burn tokens just to look good. That’s a really stupid way to do anything.” Ramp’s Veeral Patel called it the Token Casino: “useful software wrapped in mechanics that make spend feel like progress. It starts with the oldest trick in the book: abstract the money.” Palantir CEO Alex Karp told the TBPN boys that tokenmaxxing is like “a porn addiction.”

Even Sam Altman, a prominent token vendor himself, admitted on CNBC that “You hear companies saying, ‘I am spending a ton of money on AI, and I know some great stuff is happening, but I know there’s a ton of waste, and you know, when… how long do I have to wait for it to really show up in revenue, and how long do I have to wait to really get the costs under control?’” It had become, he admitted, a “huge issue.”

The issue is the companies have focused on maximizing tokens, assuming that tokens = value.

Every cycle has its dumb metric. In the mid-nineteenth century, the market wanted miles of railroad track as a proxy for future monopoly and the benefits thereof, and so railroads raced to lay miles, often along the same routes as competitors. At the turn of the 21st century, the market wanted eyeballs, and so dot coms attracted eyeballs and served them up on a platter. In the 2010s, the market wanted top-line gross revenue, and so companies like WeWork delivered top line gross revenue.

This cycle has tokenmaxxing.

Which is not to say that tokens can’t be valuable. Cornelius Vanderbilt’s New York Central ended up becoming very valuable, as did the Pennsylvania Railroad. Google and Facebook have converted eyeballs to cashflow better than anyone has ever converted anything to cashflow. Uber ended up turning top line growth into market dominance and turning that into $10 billion in 2025 free cash flow.

The question is always: can the thing generate returns?

For tokens, the question is: what is your Return on Tokens (ROT)?

Return on Tokens

When you invest in a new machine, you expect it to generate a return. When you hire an employee, you expect them to generate a return. Business is the process of making investments big and small and expecting them to create more value than they cost.

Tokens need to be held to the same standard.

Return on Tokens = (Value of Output - Cost of Tokens) / Cost of Tokens x 100

There are two ways, then, to increase your ROT. You can create more valuable things with them, or you can spend less on them. Ideally, you spend less to create more value.

The first thing that companies are focused on, because it is easier to measure than output value, is spending less.

Now that the spell has been broken, cooler heads are proudly discussing “routing” as a means to lowering the cost. Use Anthropic and OpenAI’s best models for the really big brain stuff, but do most of the work with cheap Chinese open source models. Coinbase CEO Brian Armstrong’s recent tweet is a good example of this logic:

You can see this in the OpenRouter AI Model Rankings. The move to Chinese models actually showed up in lockstep with consumption-based pricing, although this is a self-selecting group of users that were already thinking about routing tasks to the right models. The rest are scrambling to do the same now.

It’s a good start, but Agents spending tokens, American or Chinese, to figure everything out from scratch is not endgame, either.

Because you know what’s cheaper than Chinese models? Code.

Code, good old fashioned deterministic code, is not only cheap, it is a better fit for most economically valuable work. We have learned this lesson.

In the past, companies hired humans to do all manner of repetitive tasks. Before “computers” were digital, they were humans.

About half a century ago, we began the process of taking repetitive tasks that humans did, like calculating the trajectory of a missile or the profits of a business, and handing them over to software. Code ran more reliably than even the most reliable human computer. It made no mistakes. It answered instantly. Enter the same numbers and same formulae in the same cells in an Excel spreadsheet anywhere in the world, at any time, and it spit out the same number.

Then we got Agents, and we forgot the lesson. We decided that we needed to throw these pseudo-humans at everything, because everyone else was. Agents are great at some things, but they’re not the right shape for a lot of others.

It’s no wonder companies aren’t getting a positive ROT on their tokens. All the dashboards have been dashboarded, and now they’re sending Agents to do software’s job.

There is an argument to be made that a lot of companies aren’t getting a positive Return on Tokens because they don’t know how to use them yet, or because they haven’t re-architected their companies to be AI-native yet. This is one of the reasons, perhaps, that both Anthropic and OpenAI have launched consulting subsidiaries to help companies better deploy tokens. And there are certainly examples of startups built during the AI era that seem to be using tokens to great effect, which shows up in their own supersonic revenue growth. If the Old Economy can’t generate a ROT, well, this is creative destruction baby.

And while it’s certainly true that not all companies are deploying AI equally well, we believe that there are more fundamental structural reasons for the negative ROT: Agents are the wrong architecture for most work.

Agents Aren’t It (For Most Work) Either

There are three structural reasons for Agents’ negative ROT:

The Agentic architecture can’t do long-running work at the nines of quality that real economic work requires.

Agents improvise. They’re spawned fresh onto repetitive tasks like every day is their first day on the job, which hurts consistent accuracy. For new features, prototypes, or dashboards, 80% accuracy is fine. For the real repetitive work on which the economy runs, like fraud detection or underwriting decisions, 80% accuracy is 0% usable.

Engineers don’t know what to build because they don’t do the work.

Most of the process-driven work we’re describing exists as a combination of written rules, which Agents can ingest, and then like 3,000 tacit rules and sub-rules that live in people’s heads, in offices around the country, far away from the engineers’ San Francisco desks. AI can only evolve what it can touch, which is why it’s been great at coding but has largely failed to do useful things in the enterprise.

The original sin is that there are no goals.

If people have no goals then the Agent has no goals, and then the thing achieves no end. Without a goal to hill-climb against, code (whether written by humans or generated by Agents) decays into slop in the limit because there’s no purifying force to evaluate what’s good and bad.

One of the beautiful things about Agents, from a laziness perspective, particularly when you are being encouraged to spend a lot of tokens, is that you can just set them loose without knowing exactly what it is that you’re solving for. They can go spin on a vague instruction for a while, bring something back that’s decent but not perfect, and then go out and spin some more.

This process drives more token spend without delivering any value, which is a fast track to negative ROT.

People are searching for new things for Agents to do assuming that AI will do for everything else what it’s done for code. But it doesn’t have to.

There is a surprising amount of work that is best done with plain old code. The challenge has been that, until recently, there were not enough engineers to turn everything every business does into code, and then update it as things changed. There are now. AI makes writing code trivial, so if we can get the knowledge out of people’s heads, we can turn businesses into code.

AI is a Compiler, Not a Runtime

Basically, software works in two steps, thinking and doing.

First, thinking: you take the goals and requirements for what a piece of software should do and compile it into code that a computer can run.

Then, doing (and doing and doing and doing): every time it needs to do the thing, the code runs cheaply and predictably (or deterministically).

While computer science has a precise definition of a compiler, you can also think of a software company or a software engineer as a compiler. They take the goals and requirements and turn it into code. Then customers buy the code they built, and run it over and over again. This is the beauty of the zero marginal cost software business, and it’s why companies can sell software that took millions of dollars to develop for $20 per month and still generate mouthwatering margins.

The way that most people think about (and use) Agents today is that they replace both the software company and the software. That is the wrong way to think about it.

Agents should replace the software company; they should take goals and requirements in English (or whatever language), and turn it into code that runs over and over and over, deterministically.

Thinking is expensive but happens rarely. Doing is cheap and happens forever.

Agents should do the thinking, code should do the doing.

For most economic work, you want to use humans to figure out the rules, use AI to turn the rules into code, and then run that code forever at near-zero token cost, only bringing the AI back in when the rules change.

Why would you use a prompt to add two numbers? Just write a line of Python, dawg.

The current Thinking-Doing Ratio (TDR) in AI implementations is roughly 1000:1, which is not surprising. San Francisco is a Thinking town. Anthropic’s hats say, simply, “thinking.”

Silicon Valley built AI assuming work is mostly thinking, but work is mostly doing.

Chat is the rare exception where you genuinely don’t know what comes next. So maybe customer support chat continues to churn through thinking tokens (although even customer support Agents kick complex problems to humans). Almost nothing else in a business looks like constant improvisation.

So we use Agents for Doing, but Agents are the BlackBerry of doing. They are not where most work will get done inside of companies in five years’ time. It will get done in the deterministic code that they write.

The Agents’ role is in compiling into code, not into running and doing work day to day. Which means that it’s more like CapEx than OpEx. Everything you think is gonna be AI running is just going to be code running.

Everyone thinks the thing that is going to change in the world is that AI is going to become a person, but the real change is that a business is going to become a piece of software.

That’s the world we’re building at Poetic.

Turning Businesses Into Software That Evolves

We are building the antidote to tokenmaxxing: software that tokenminns itself.

Poetic is a new class of software: adaptive like AI, reliable like code.

We use AI as the compiler. We learn everything that a business does by taking in all of the processes that are written down, then going on-site in Nebraska or Providence or wherever the work is done, sitting on people’s shoulders, and asking “What did you just do?” “Why did you do that?” hundreds of times to learn the thousands of hidden tacit rules on which every company runs. Then we turn it into code.

The code is the runtime. When the world stays the same, this code runs the exact same steps every time. When the world changes, it learns, regenerates, tests itself against the objective, and then runs the new code until the world changes again.

The result is 100x less token usage and nines of accuracy on complex tasks. Put differently, each token you spend does 100x more, and it does it right.

The value of the output increases, because Poetic does something that your business actually needs to do, over and over. And the cost of tokens is lower, because Poetic only uses tokens when the world changes. Combined, Poetic generates a clear, measurable Return on Tokens.

We are doing it today, for companies like AIG, SoFi, and Chime. AIG CEO Peter Zaffino said that Poetic has already “achieved 99%+ quality outcomes on multi-hour processes - delivering real enterprise value.”

These companies’ leaders believe what we believe: that every business will have to be re-founded as a software business. The story of the next decade is the beginning of those new businesses. Some will be truly new, built from scratch. Others will be businesses that have existed for hundreds of years, brave enough to reinvent themselves.

Everyone talks about the fact that it took reconfiguring factories around electricity to benefit from electricity, and follows that with the AI equivalent of “so the new businesses that are built to throw a ton of electricity at the problem will win.” What you really need to do is refactor the businesses into code.

Doing that takes a ground game, going deep into the guts of companies, wherever they are, to understand how they work and migrate their logic into programs. We need people to get out there into Minnesota to be like, what the hell do you guys do all day?

Most of our team are engineers, a lot of them ex-Palantir, who spend weeks at a time on-site with customers, learning from them, getting into the nittiest of gritty details.

The term gets a bad rap, but relative to engineers who spend all day at a desk prompting Claude, they are the most Social Engineers. Engineers who understand people, business, and AI will rule the world. If that sounds like you, come join us at Poetic.

It’s hard work, but the biggest mistake of the AI era so far has been believing that anything worth doing could be easy.

This is worth doing. It’s what I’ve wanted to do for as long as I can remember, because all of the institutions that run our world, every business, every government, does so much stuff that doesn’t make sense, operates way more slowly than it should, and gums up the works.

Our goal is to discover the perfect process for every business - the plan, the set of steps that is ideal for achieving your goals. This will evolve as the world evolves.

The role of AI is not “Agentic,” improvising as it goes. It is an evolutionary force: changing, testing, evaluating what plan is most successful and sticking to it until a better one emerges.

In the end, this is what a business is. A piece of living, evolving software reconfiguring, testing, evaluating itself, hurtling towards ideal form at the fastest clip imaginable. Humans exist to define what good looks like, not how to get there. Shaping behavior, directing behavior.

Through running in production, the system gains a record of what happened. What happened, step by step, for every dispute, underwriting case, insurance claim. Thus, every process change becomes testable. The answer to “what if” is known after minutes of backtesting. Run both scenarios in shadow, compare outcomes, decide which is better.

When impact is entirely known, there is little risk - you know exactly what would have happened. Change simply becomes a choice. Then the choice becomes: which outcome is ideal? The process lead, the person responsible for making sure the process achieves the goals above it, simply makes choices.

To make a change, you have to know what the impact of the change is. It’s easy to generate code, hard to know what happens if you run it. After months of running, you’ll be able to ask questions like “What if we approved every dispute under $25?” and know in minutes.

The hill-climb towards the most beautiful process will then begin. Experts experiment, asking what-if questions. Now that humans are no longer bottlenecks, they can begin searching.

This is endgame.

Every business is not just a piece of software; it’s a piece of software constantly editing, testing, evaluating changes. Evolving at the highest frame-rate possible, climbing towards its most correct form. All energy is spent evolving, figuring out the ideal form of the rules.

We don’t use tokens to run the business. We use tokens to turn the business into code and evolve it. We tokenminn to ROTmaxx.

Over billions of years, we have evolved from ocean slime, through trial and error, into fish, lizards, voles, monkeys, and humans.

We don’t want to have to wait billions of years for businesses to evolve into their diverse and ideal forms, and Agents won’t build them. You cannot build the butterfly.

Beautiful, ideal, complex things can only emerge through evolution. I want to speed it up and see how far we can go.

Thanks to Markie for dropping her knowledge and to Adam and Ben for introducing us!

That’s all for today! We’ll be back in your inbox on Friday with a Weekly Dose.

Thanks for reading,

Packy

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Hi friends 👋,

Happy Monday!

J. Storrs Hall first asked “Where is my flying car?” in 2005. Sixteen years later, in 2021, Stripe Press was able to republish his cult classic because the question remained open. Five years later still, in 2026, a year in which computers write most of the code themselves and medicines seem poised to cure everything that ails us, Hall is still waiting along with the rest of the world.

There are many reasons we don’t yet have flying cars, but there are even more to suggest that now may be the time to build them.

About a year and a half ago, I started to notice Tsung Xu tweeting about a VTOL (vertical takeoff and landing) plane he was building from scratch. Then he kept making progress.

I knew and respected Tsung from his writing on energy and materials - I linked to his work way back in September 2022 - and thought it was cool that he was playing around with drones, but I didn’t think much more of it, until he reached out to ask if I wanted to learn more about the company he was building, Vight.

On that call, we nerded out about the myriad why nows for flying cars, what a practical flying car might look like, and what it would take to make them affordable enough to impact humans’ daily life. I angel invested in Vight, and have gotten a front-row seat to the progress Tsung and team have been making towards delivering Hall a satisfactory answer.

Then, when I wrote that “I have a hunch that drones, EVs, and EVTOLs should expand local frontiers” in April’s Scarce Assets, I asked Tsung - the rare entrepreneur who I knew as a writer first - to write us an essay expanding on how he’s planning to expand the radius of daily life.

The goal for these essays is to understand the bets that people building at the frontier are making with their careers. They are one person’s view, by definition, because I think that the beliefs that makes talented people go all-in on something are the best raw material I can give you for shaping your own beliefs on the future. And the future that Tsung is betting on is one that I want: one where we finally get flying cars.

Let’s get to it.

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Expanding the Radius of Daily Life

It’s hard to fully imagine how drastically flying cars will change our lives, because we don’t have flying cars yet.

When you think of flying cars, you might think of cars with wings that let you fly right over the traffic from your current home to your current office, or of air taxis that shuttle you between Manhattan and JFK. You think about traveling faster between the places you already go.

That undersells the promise of flying cars. What flying cars do is expand the radius of daily life.

In his 1934 tome Technics and Civilization, Lewis Mumford credits the mathematician Bertrand Russell for discovering what would come to be known as Marchetti’s Constant:

Mr. Bertrand Russell has noted that each improvement in locomotion has increased the area over which people are compelled to move: so that a person who would have had to spend half an hour to walk to work a century ago must still spend half an hour to reach his destination, because the contrivance that would have enabled him to save time had he remained in his original situation now—by driving him to a more distant residential area—effectually cancels out the gain.

As Cesare Marchetti detailed in his 1994 paper Anthropological Invariants in Travel Behavior, “humans are ‘naked apes’ in many dimensions of their behavior involving territory and personal contacts.” It is an anthropological invariant rooted in territorial behavior that when humans access a faster mode of transportation, they use it to travel further instead of traveling less. We are born to spread.

We like to commute about 30 minutes each way, or about an hour a day. Give us a faster vehicle, and we don’t pocket all the time we save. We spend it to reach more of the map.

The ability to fly point-to-point further in 30 minutes than you can drive today promises more optionality: more places where you can live, work, and visit, more often. This might mean living where you can have more land, or by the water, or in a community that fits your values, and still working where the opportunities are. At scale, it might even mean reshaping where economic opportunity exists. In the future, the choice will be yours, and it will happen sooner than you might think.

That is the future I’m working on at Vight. For a number of reasons that we’ll discuss, I believe that now is the time to build the flying car we were promised.

To get there, the experience we build needs Tesla supervised-FSD levels of intuitiveness, flying you the vast majority of the time. It needs to be safe. It needs to be mass manufactured, eventually, like a Model T.

Most importantly, it needs to be fast and offer freedom, to be able to fly to most places cars can drive to, and some places that cars can not.

That is the point of technology, to push out the efficient frontier and eliminate trade-offs. And if we want to explore the physical world nearly as easily, and much more magically, than we explore the digital one, we will need to eliminate the trade-off between speed and freedom.

Speed or freedom: we don’t have both (yet)

For all of our cumulative efforts, we have yet to devise a mode of transportation that is freer than walking. You start wherever you are, and stop wherever you want. You can duck into alleys or buildings, climb up or down stairs, or simply stop and wonder, wherever you’d like.

Walking, though, is slow, so humans have invented myriad modes of transportation that can get us from point A to point B in less time, at the expense of point-to-point freedom.

Bicycles are faster, but they can’t reach quite everywhere you can on foot. A car is faster than either, but they can only take you as far as the edge (or endpoint) of the road network.

There’s another class of vehicles, hub-to-hub vehicles, that offer less point-to-point freedom, but require less work from the traveler and, in the case of commercial airplanes, go much faster in transit. Each requires you to get somewhere, at an appointed time. Riding a bus requires first going to a bus stop, a train leaves from the train station, and an airplane takes off from an airport after some unknown but now-almost-guaranteed delay. Using any of these means real friction getting to and from the hub, and once at your destination airport, you still need another mode of transportation to cover the “last mile” to your destination.

Still, these are the marvels we know. The car has been the best form of point-to-point freedom for the last 120 years in America and other wealthier countries. Aircraft win on raw speed over any surface, land or water. But cars and aircraft each solve only one axis of the problem.

You can see the split in the history of transportation: one lineage optimized for point-to-point freedom, the other for speed between hubs.

The future we want requires both: aircraft speed with car-like point-to-point freedom.

To understand why flying cars are the answer to this particular trade-off, we will traverse the history and near-future of transportation technologies. We are in the midst of a revolution in these technologies across many fronts - from autonomous vehicles to supersonic planes to air taxis - each of which will change the way that we move ourselves and our things. We’ll also find that while each is awesome at certain things, they don’t eliminate the specific trade-off we’re after in the way that flying cars uniquely do.

Flying cars, of course, come with their own challenges, which we’ll discuss, but we believe that expanding peoples’ worlds is worth the fight.

Because where we’re going, we don’t need roads.

The limitations of cars and roads

Thanks to the incredible progress made by Waymo and Tesla, people are starting to get rightly excited about self-driving cars. “Always ten years away,” they’re here now. Just last week, Tesla opened up its unsupervised Robotaxis to serve the entire Austin Metro area.

Self-driving cars will serve a growing portion of our driving miles in the decades to come, and their growth will save many lives and give us time to do other things. I’m personally excited for when they start redesigning self-driving cars as moving lounges instead of traditional cars. But until and unless they replace almost all of the human-driven cars on the road and learn to communicate losslessly with each other, they don’t solve the speed-freedom trade-off, because they’re still cars, and they still need roads.

Cars have been incredibly useful in helping us get to places further away and faster, while protecting us from the elements, but they’re fundamentally a 120-year-old technology that depends on road infrastructure. Roads present chokepoints, are jammed with traffic, and slow us down.

Despite the fact that our cars are much more capable than the Ford Model T, cars in 2026 don’t take us from point A to B much faster in urban or suburban areas than Tin Lizzie did. During rush hour, cars crawl at 17-24 mph in LA and 11-15 mph in New York City. A smaller city like Austin does not fare much better at 14-33 mph.

Adding more lanes to the highway famously doesn’t help much. Traffic is the canonical example of induced demand: add more lanes, and you encourage more people to drive, and the traffic is just as bad. Worse, even, because it’s not the highway itself that’s a chokepoint, but the on and off ramps. Off the highway, traffic lights gum up the works. Even if Autobahn-like unlimited highway speeds were approved in your city, you’re still idling at the next traffic light once you get off during rush hour, and the highway itself will still block up as a result. All that assumes the highway will be built, which is probably too much to ask if you’re living in California, for example.

There’s also the physics problem. Drag increases with the square of velocity. By increasing your speed from 70 mph to 200 mph, you are effectively increasing your drag by ~8x. Power scales with drag x velocity, so about 23x more power is needed at 200 mph vs 70 mph. To go really fast, the range of your car, whether gas or electric, is going to take a massive hit. Driving that fast will massively increase the wear and tear on your tires and other components. Making 200 mph normal, efficient, safe road travel is a completely different problem than making cars drive that fast.

The capabilities of the infrastructure gate the capabilities of the vehicle. You could be driving a Corolla or a Bugatti and it would not matter in cities during peak hour.

That said, roads and later highways did expand the capabilities of cars. The infrastructure answered the question “Where can cars go?”. Carmakers could then invest a century in making them easier to drive and driving down costs to make them usable by more people. Automating the ignition, automatic transmission, and manual then adaptive cruise control were all examples of this. With self-driving today, the easiest to use car is the one that does not need to be driven.

A car’s fundamental job is to get you from A to B. By that standard, a Model Y with supervised FSD or Waymo does that job better than a car that requires you to still drive it. In fact, many people I know primarily drive a Tesla with HW 4 because of FSD. It’s a functional utility, but such a step change in ease of use that they can’t live without it. The best version of a car is no longer the most expensive purist vehicle but the one that gets the most people from A to B with the least effort.

Still, AVs make cars easier to use but don’t solve the challenges with the limitations of roads.

AVs are really convenient, but in the context of our speed-freedom matrix, they’re not any faster. In fact, hailing Waymo is slower than just getting in your car and driving. This is especially true in rush hour. Fundamentally, they don’t expand the radius of daily life.

No matter how much intelligence the vehicle has, the infrastructure layer is the limiting factor in point-to-point speed.

So… go above the infrastructure, right?

The limitations of today’s personal aircraft

Unfortunately, personal aircraft like the Cessna 172 have not changed much in the last 50 years, either.

The biggest upgrade on base models was shifting from “six pack” steam gauges to “glass cockpits,” which effectively offer digitized flight displays for pilots and passengers. The 172 is still one of the best selling GA aircraft by units. Newer 172s give better situational awareness for pilots, but they still require a human to be in-the-loop flying the aircraft with a control stick, throttle and rudder pedals. At $400,000, a new 172 is not even close to affordable, let alone easy to fly.

Personal aircraft are also limited by runway dependence and operating friction. There will only ever be a limited number of people who will purchase a vehicle that is usually stored far away from where their home is.

To fly, the pilot needs to drive to the GA airport, go through a manual pre-flight checklist that could take 15-30 minutes, and crank the engine, perhaps multiple times. Many engines still use a carburetor to mix air and fuel for combustion, components phased out by the automotive industry since the late 1980s.

Personal aircraft never could offer the same point-to-point freedom as cars did. The open skies were gated by sparse airports. Aircraft had speed, but airports and runways answered “Where can it go?” with “To and from an airport.” So the industry optimized for capability without reducing pilot workload; it could not optimize for everyday usability. The result was better aircraft, but not aircraft that more people could use.

Imagine how many people would drive cars if they were not parked at home, but in parking garages miles away. This is the challenge of any hub-to-hub vehicle, minus the scheduling issues in this case, because you can take off and land when you want, and it is the fundamental limitation of personal aircraft.

GA aircraft sales: booms, busts and runway dependence

That limitation is one of the reasons sales of general aviation (GA) aircraft sales have crashed since the 1970s. There are many, but if people were clamoring to fly 172s, I suspect the market would have figured out the rest.

Americans did use to buy GA aircraft, back when they were more affordable. Higher volumes enabled lower prices, and lower prices enabled lower volumes. In the late 1970s, US shipments of GA aircraft were close to 20,000 units per year:

Without looking hard, you can see that shipments fell off a cliff beginning in the late 1970s, falling nearly 90% to 2,000 units in 1985. To understand why “this time might be different” for flying cars, we should understand what’s in the black box on GA aircraft.

First, as always, money. The second 1970s oil shock and the early 1980s recession, combined with high interest rates, made new purchases, especially financed ones, unappealing. At the same time, in an echo of the nuclear industry around the same time, aircraft OEMs had expected that the boom times would continue, and so they overproduced. By the 1980s, technology improvements compared to the decade prior were minimal, there was a glut of used supply practically as capable as new vehicles, and tax incentives pushed people to buy used.

Normally, people blame the lawyers. J. Storrs Hall, too, assigns them a good deal of the blame:

The 1970s brought an increase in product liability, a major social change that was nominally aimed at safety. Scare stories worked on juries just as well as on the reading public, and juries would vote enormous awards for accidents that didn’t have any reasonable connection to malfeasance on the part of a manufacturer . . . This led directly to the collapse of the general aviation industry. Over the course of the 1970s and 80s, it was strangled by the explosion of product-liability lawsuits.

Again, as with nuclear and regulators, that read confuses the timing. Product liability lawsuits locked in the collapse in piston engine aircraft sales that was already underway. Brian Potter wrote a great analysis on this, Planes, Claims, and Automobiles, which includes this quote from John Baker, the former president of the Aircraft Owners and Pilots Association (AOPA):

Product liability judgments are not the cause of the new aircraft shortage in which we find ourselves. It is merely a symptom. The cause of the problem was clearly some unbelievable bad business decisions by the manufacturers 15 to 20 years ago, which is compounded by some lousy products. If the industry was annually producing the 20–25,000 quality products at an affordable price that the marketplace would absorb, then the per-unit product liability insurance costs would not be significantly greater than they were in the mid-70s.

John Baker, 1988 letter, emphasis mine.

So fix demand. Simple, right?

It’s worth a quick look at the very left of the chart above. The peak shipments ever were actually not in the 1970s, but in 1946, at 35,000 units. As pilots retired, there was a subsidy for flight training and most of the flight hours were from instructional flights. When the new pilots were trained and subsidies went away, the demand did, too.

Market gluts are not sustainable drivers of demand. For that, you need new technologies that actually improve the product in material ways. For that, though, you need demand. It’s a vicious circle when it works against you.

The lack of technological improvements was a major reason the GA aircraft industry never recovered after the 1970s. New aircraft engine programs cost around $100 million, and no one wanted to take the risk of investing in better technology in such a small market. Even when companies promised new compelling concepts in GA, the aircraft still ran into the same problems of runway dependence and difficulty to fly.

What about GA helicopters? They can take off and land vertically (VTOL), but lose out on ease of flying and safety perceptions. There is a high level of pilot skill required to learn to fly a helicopter safely, as well as the ongoing maintenance of those skills. In order to remove much of that pilot workload, you’d need to purchase stability augmentation and autopilot systems. But your autopilot system will still cost around $50k+ on top of an already expensive $500k helicopter like an R44. Because they’re complex machines and prone to mechanical failure, helicopters also place a high maintenance and operational burden with manual pre-flight and post-flight checks to ensure safety.

(Packy note: Kobe Bryant died in a helicopter crash on my birthday and Puja and I once watched a helicopter crash into New York’s East River, so I don’t plan to ever fly in a helicopter.)

Helicopters are getting upgraded, though. Skyryse takes existing aircraft and removes all mechanical controls, installs a digital fly-by-wire system, and replaces the cyclic, collective, and tail rotor pedals with a single control inceptor (like a specialized joystick) and a pair of intuitive touchscreens. It makes helicopters much easier to fly, and adds autonomy to the mix. At the moment, however, Skyryse’s system costs ~$400k more than a SAAS upgrade on an R44, and is being used on more expensive craft, like the Blackhawk Helicopter.

Helicopters are cool, but they, too, don’t technologically solve the trade-off for regular people.

Today, though, we finally have new technology: eVTOLs.

What about Air Taxis?

Going from a metro airport to downtown, air taxis can be faster than taking a car, but they are still hub-based. They have about as much point-to-point freedom as taking a bus, in that you still need another mode of transportation to get to and from the vertiports they plan on using. Once you’re there, though, air taxis go further, faster than buses can.

Air taxis use VTOL technology, which stands for vertical takeoff and landing. As the mouthful of a name suggests, this technology makes precise takeoff and landing possible. Air taxis demonstrated that electric VTOLs (eVTOLs) can fly, but they are limited by the constraints of the takeoff and landing hubs they must operate from.

These hubs, the takeoff and landing infrastructure for air taxis, are called vertiports. With commercial services, they are in many ways like a train station. People need a place to wait for their ride. Trains are awesome, and they expand the world too, so we’re excited for the possibilities that air taxis open up. Adding new options on the hub-to-hub frontier is good, because places that are currently inaccessible for urban workers will become accessible.

It’s just that we don’t think they’re the end game, and they face their own challenges.

Charging and maintenance logistics are tricky, for example. Where should the vehicles be kept when charging? There’s a lot of complexity on the ground to be managed. Plus, it’s challenging to get permits for vertiports, especially when sited in dense urban areas, and it becomes more challenging when you need to establish a network of them for any of them to be useful.

Air taxi companies have also been constrained by the regulatory pathways available to them. The regulatory burden is a large reason why US-based air taxi companies have been in development for 8 to over 15 years but are not yet in commercial operations, although that is showing signs of thawing slowly.

The air taxi model likely works better in dense cities, and particularly in places like China, where it’s much easier to stand up urban vertiports. Even with pilotless operations, though, Chinese air taxi company EHang’s cost structure is high. Focused on tourism as their first wedge, 298 RMB ($45) per person would “basically cover flight costs”for two people for up to ~20 minute flights, as per a recent earnings call.

These costs should come down with scale. Part of the challenge is that EHang’s fleet is still fewer than 10 aircraft. But ground ops, low utilization, and vertiport overhead add layers of economic difficulty. The extra friction associated with putting landing pads on buildings in crowded cities - even in China, let alone America, makes it much harder for air taxis to come down the learning curve.

That said, American companies like Joby and Archer are making real progress.

Joby has logged over a thousand test flights across three countries, completed Stage 4 of FAA Type Certification (the furthest any eVTOL has gotten in the US) and is now in Stage 5, the final phase, with a type certificate targeted for late 2026. They’ve demonstrated JFK-to-Manhattan routes and been selected for the FAA’s eVTOL Integration Pilot Program across 13 states.

Archer closed Phase 3 of its own certification process in April, is flight testing Midnight nearly every day at Hawthorne Airport in LA, and is building out a Stellantis-backed manufacturing facility in Georgia targeting 650 units per year by 2030.

Both companies have proven that eVTOL aircraft can fly reliably in real-world conditions, navigate complex certification processes, and attract serious capital and partnerships: Delta and Toyota for Joby, United and Stellantis for Archer. That work has de-risked the core technology for the entire category, including us. And in the short-term, they will offer transit options that we don’t and unlock routes that we won’t. We are targeting different initial markets, and we want both to succeed.

In the future, cities will be dotted with vertiports, but I believe that can only happen meaningfully once the unit economics close and operational friction can be removed. Then, demand will merit the investment necessary to make vertiports work.

Air taxis will replace and improve on some hub-to-hub transport modes. They will not deliver full point-to-point freedom, though, which is what we are attempting to achieve. We want to give people their own flying cars.

In that pursuit, there are several key lessons we’ve learned from air taxi companies:

• Regulations are destiny and constrain the GTM pathway,

• Slow certification pathways drag iteration loops, and

• eVTOL flight dynamics are hard but have been proven solvable.

Many people conflate air taxis and eVTOLs, whereas air taxis are just one application of electric VTOL technology.

That’s why I use “flying cars” in this essay, even though the aerospace industry says eVTOL. eVTOL describes the technology. Flying car describes the category we’re focused on: aircraft speed with car-like point-to-point freedom. Calling this an eVTOL is a little like calling an automobile a horseless carriage. Technically understandable, but not how normal people think about the thing once it becomes truly useful.

Extreme speeds are coming, but not for daily travel

It’s worth noting some other ways we’ll move faster and further in the future. There are a few archetypes that are being developed today that focus on extreme speed, hub-to-hub.

In ten to twenty years we could be taking supersonic or hypersonic aircraft, leveraging the very low drag in the upper atmosphere to fly at Mach 2 or higher across the Pacific, across the Atlantic, or anywhere on the planet.

Suborbital rockets like Starship would make these trips less comfortable, but would take you from any launch site to any landing site at up to Mach 25. Again, these are hub-to-hub options, and the hub, for the foreseeable future, is a launch pad somewhere far enough from people to launch rockets from. They optimize for speed but at the cost of point-to-point freedom of the vehicle.

As a result, you need very powerful and large vehicles that don’t make sense for point-to-point use. These are better suited for moving groups of passengers very quickly over very large distances. It’s a logical but radical extension of the hub model.

The future of long-distance travel will be supersonic and maybe suborbital, and we can’t wait, but these journeys will fall outside of Marchetti’s constant.

For fast, point-to-point travel that expands where we can go in 30 minutes or an hour, there’s one option left.

Flying cars offer speed and point-to-point freedom

It should be pretty clear by now that no widely used or anticipated vehicle category offers both speed and point-to-point freedom. The tradeoff we’ve faced for as long as we’ve built tools to augment how far and fast we can travel still exists.

Flying cars will break the trade-off.

Let’s define a flying car. Most people think of a flying car as a car that flies and can travel on roads. I think it’s simpler than that.

A flying car is really a vehicle that flies point-to-point with car-like convenience.

People will disagree and say, “That’s not a flying car. It can’t use roads.” As we talked about earlier, roads are an infrastructure constraint. Flying cars will not need to be roadable to be useful.

They have VTOL capabilities, allowing for takeoff and landing anywhere there’s a small pad. That means vertiports, eventually, but it also means right in your backyard.

At Vight, our first vehicle platform will cruise at about 110 mph, and future platforms will target close to 300 mph. Plus (and this is also very important), they will dramatically reduce the workload required to pilot an aircraft. This trifecta has never been possible before.

Flying cars fill the empty quadrant in our 2x2:

VTOL changes the answer to “Where can it go?”. Software-defined flight changes the answer to “How hard is it to operate?”. Put them together, and personal aviation can finally make flying dramatically easier and more accessible. Cars spent a hundred years turning a vehicle that already had endpoint freedom into something easier, cheaper, safer, and eventually intelligent. Flying cars start with the benefit of that entire software and autonomy stack being mature.

That is why we can now target a step change in the ease of flying a personal aircraft. For most of aviation history, that was not possible. You could add better instruments, better autopilots, or better training, but the aircraft was still fundamentally a pilot-workload machine tied to runways and airports. A practical flying car will use intelligence to absorb more of the workload, while also removing the runway as the core endpoint constraint.

So why not just make the runway shorter? Couldn’t STOLs (Short Take Off and Landing) be the answer for faster point-to-point travel?

After all, short takeoffs with an STOL bush plane means you can take off with as little as 50 feet of runway.

The problem is they are still dependent on a runway. Yes, runways can be shorter, but STOLs still need clear approaches, clearance over obstacles like trees, dependence on wind directions and maintenance of the surfaces. If you put all of these together, that ends up requiring about ~5x more land relative to a personal-use eVTOL. In addition, pilots need to be more skilled and have less margin for error on takeoff and landing.

Flying cars, done right, can offer a different experience.

The pilot experience for a flying car

You tap your phone, set your destination, and start the automated preflight checklist. The battery starts pre-conditioning and the onboard system runs safety and other checks for you, all in the background. You walk outside to your Vight aircraft on your pad. You take a quick walk around the vehicle, hop in, press a button on the single control stick, and take off.

Once off the ground, you will be able to use the advanced autonomous assist features. Tap to confirm transitioning from hover to forward flight, and when approaching your destination, tap again to confirm the landing zone. At any time during the flight, you can direct the aircraft using the control stick. It should be a nearly pedestrian, boring experience. When flying with the autonomous capabilities we will have for our first-gen aircraft, it will be as simple as that. The experience is far closer to that of just getting in your Tesla and driving with FSD than it is to that of piloting a personal aircraft.

We can only leverage this breakthrough if safety and redundancy are built into the design from day one. Our aircraft will use redundant motors, battery packs, flight computers, sensors, and other safety-critical components, with the system designed so that no single failure should lead to loss of the aircraft. It will intelligently handle situations like a motor or single battery pack failure as a pilot-assist feature, while keeping the pilot in control. We’re sizing the propulsion system to handle weather like wind gusts at up to 10,000 ft above sea level. And in the extremely unlikely case of a complete loss of power, the vehicle will deploy a whole-aircraft parachute.

Just as critically, we’re designing to dramatically reduce pilot workload, one of the major contributors to the pilot-related causes that dominate GA aircraft accidents. In AOPA’s 2023 non-commercial accident data, pilot-related causes accounted for two thirds of aircraft fatal accidents and almost 80% of helicopter fatal accidents. Simplifying controls, having guardrails during flight, and intelligent pilot-assist features help avoid the primary cause of accidents from happening.

The design breakthrough with flying cars is that it can operate like both an eVTOL and a fixed wing aircraft, depending on which phase of the flight you’re in.

The takeoff and landing phases are vertical or near vertical, which gives the precise endpoint capabilities we’ve been talking about. During the transition phase, the wings tilt forward, shifting the lift production from the rotors to the wings. Once the aircraft transitions, it climbs on its wings, at a forward airspeed of over 100 mph and much higher efficiency than in hover. It then stays wingborne for the vast majority of the flight, like an aircraft, before transitioning back to hover for landing.

For eVTOLs, cruise is several times more efficient than hover, so maximizing time flying like an aircraft is important. Not to scale.

This is possible because a multicopter’s core flight control system is already software-defined. The pilot doesn’t have to directly handle aircraft stability, whether the vehicle is a 250 gram drone or a 1200 kg eVTOL. The onboard flight controller does that continuously, adjusting RPM for each rotor hundreds of times per second to maintain stable hover. Without that stabilization layer, a multirotor would be effectively unflyable by a human using a stick alone.

Our platform’s autonomy and VTOL capabilities enable dramatically lower pilot workload, while still maintaining control that mimics how a great pilot would fly a helicopter. A helicopter is very hard to fly, and especially to hover, without simplified flight controls, and the flight controls in helicopters today are not simple.

This lack of easy-to-use controls was one of the reasons GA aircraft and helicopters weren’t able to reach the mass market. Flying is very skill-intensive.

With supervised autonomy, however, we can have guard rails through the whole flight envelope, as well as auto-takeoff, auto-landing, and intelligent sensing. On our first generation platform, these are all sophisticated pilot-assist functionality so the pilot is always in control. That’s only possible when a pilot-defined system becomes software defined.

Beyond that, the Vight flying experience will simplify things like radio and air traffic control (ATC), or getting updated altimeter readings (i.e. for barometric altitude) while coming into landing. Today, even small cockpit tasks are manual for piloting a small aircraft. A pilot may still get the local altimeter setting by listening to an ATIS/AWOS broadcast, copying down the pressure value, and setting it in the barometric altimeter before departure or arrival. It’s not hard, but it is an example of many GA-specific microtasks that have to happen correctly. This is why pilots lean on checklists, but to make flight as simple and normal as driving, they should not have to.

For Vight, our customers will still need a private pilot’s license (PPL) that is required to fly any GA aircraft. That said, we’ll standardize and accelerate this experience, thanks to MOSAIC, which we’ll discuss more shortly. Customers will do either a week-long intensive course or finish over a few weekends. They will learn on site about the nuances of our simplified approach to aviating, navigating and communicating (A-N-C). Because of the pilot workload required to A-N-C, attaining a PPL has traditionally taken 60 to 80 hours. With simplified controls, we will target the 40 hours minimum of flight time required to attain a PPL. This is similar to the amount of hours required to become a competent driver, with states like CA, NY and FL all requiring 50 hours of driving experience for teenagers before granting a license.

We’ll give our pilots the confidence to know what to do in a low-probability failure event, like a motor failure or battery pack failure. They’ll know how to land safely and how to deploy the whole vehicle parachute in a failsafe scenario.

That experience sounds simple, as it needs to be. But the technology to deliver this experience wasn’t feasible until now.

Why flying cars now?

To have car-like convenience, we need to build VTOL aircraft that are easy to fly, with supervised autonomous capabilities from the start. They will also need to be fast, have useful range and be low cost to purchase, operate and maintain.

This requires the vehicle have several key underlying technologies and principles:

• Software-defined systems and autonomous capabilities

• Electric powertrains

• Designed for higher volumes and standardized manufacturing

• A regulatory path

Let’s walk through each of these.

Software-defined flight closes the sim-to-real loop

The most important technology for a flying robot is not actually the vehicle itself. It’s the development process for closing the gap from simulation to reality for how the vehicle flies and performs.

That loop has historically been slow for novel aircraft. High-fidelity models were expensive, specialized software was locked behind costly licenses, and compute was slow and expensive. Closing the gap between simulation and flight test took time.

The best hardware companies turned this loop into an advantage. In their first version of their Roadster, Tesla used a Matlab simulation to model the whole vehicle. In the early days of NVIDIA, when running out of cash, they bet on emulation software to test the RIVA 128 GPU design virtually. This allowed the GPU to be designed in only six months instead of 18 months. SpaceX made reusable rockets possible through tight integration of simulation, flight software, sensors, actuators, and control loops.

Flying cars need the same type of iterative cycle. The faster we close the sim-to-real gap, the faster the aircraft becomes safer, easier to fly, and more capable.

Agentic engineering accelerates this further. Agents in the loop in experiments maintain requirements and do other grunt work. Engineers still make the design decisions and integrate learnings from results to close the loop. We’ve been agentic-native from our inception at Vight. All engineering, compliance and ops are in repos that give the latest frontier models access to do much of the grunt work.

Autonomy reduces and eventually removes pilot workload

Aerial autonomy is not easy, but it has a different shape than road autonomy. A car in a city has to reason about pedestrians, cyclists, lane markings, traffic lights, construction, and other vehicles. Once an aircraft is in the air, the environment is more structured and less cluttered.

The enabling stack has also gotten much better. Cameras, inertial sensors, GPS, LiDAR, onboard compute, and vision models have all improved dramatically while costs have fallen. Tesla’s FSD system is one example of how far camera-based perception has come, but aviation will likely use a more redundant sensor stack. Zipline has made similar strides in their platform’s autonomous capabilities. The components needed for supervised autonomy are mature and standardized and the autonomy problems are hard but solvable.

We will also be able to use synthetic training data before we have large-scale fleet data. Today, Tesla trains a world model on fleet video, generates future camera and sensor data from a given scene and action, and then creates adversarial corner cases that are hard to collect naturally. We aim to apply this to Vight. Rare events, sensor failures, and emergency procedures can be simulated repeatedly before they happen in the real world.

The electric stack makes VTOL practical

We’ve seen remarkable improvements in many standardized components, especially in the last few decades. Performance continues to improve as technologies keep coming down their cost curves.

Batteries

As I’ve written about previously, lithium ion cell and pack costs per kilowatt-hour have come down dramatically since the first commercial cells that Sony used in 1991. They’ve become almost 99% cheaper at a compounding 12.5% annual rate, as deployed volumes have risen about a million fold.

Specific energy and power (both per unit mass) have both increased substantially in that time. Energy density is the biggest lever on range. They are up 3x-5x since 1991 depending on the cell. Improved packaging has increased pack level energy. Power density is actually just as important for VTOL capabilities, as it’s the higher power discharge rates in hover and takeoff that are critical for our use cases. There, 2.5 kW/kg is commercially available at volume and at a low premium over lower power cells.

Batteries are the best baseline energy source for flying cars. They simplify their development and operations, requiring only a standard port, like the SAE J3400 in the US, for up to ~20 kW home and destination charging. We will extend the range on future platforms, using a series hybrid architecture with a piston or turbine generator. In a hybrid architecture, engines can be sized for efficiency in forward flight, and provide additional power on takeoff and landing.

Even in a hybrid powertrain, batteries are still required to provide fast power transients. Hovering in wind, maintaining control margins, and responding to motor commands require near-instantaneous power changes. Engines and generators would take seconds at best to respond, and would be too slow to handle those rapid transients.

Electric motors and power electronics

Electric motors have also gotten dramatically better and cheaper.

Specific power and torque have been improving consistently for permanent magnet synchronous motors (PMSM) for decades now. They are highly efficient at a wide range of rpm, and can be direct drive for less moving parts and complexity. High end commercial PMSM motors like Joby’s have more power per kg than all but the most exotic turboshaft engines.

DC electric motors like PMSM have surged in specific power, from hundreds of W/kg to now high thousands of W/kg, with most of the gains coming in the past decade or so.

High torque at low rpm is also a driving constraint for direct drive motor performance. Fast adjustments in motor rpm for maintaining hover stability require high torque motors to spin up and down rapidly. That’s no longer a problem, as the best commercial PMSM electric motors today are extremely high torque, with specific torque around 50 Nm / kg.

There’s still a ton of headroom here to design more optimized motors, partly because there have been so few motors built specifically for VTOL aircraft. Few iterations have yet happened on designs to continue improving them. There is even a motor designer I know who has used workstations in his garage (and more recently on AWS) to design motors in simulation that match the performance of best-in-class commercial motors, at a fraction of the cost. Expect motors to continue getting better while continuing to get cheaper.

Power electronics have improved alongside the motors. Inverters convert battery DC into precisely controlled three-phase current for the motors, which means thrust can be commanded almost instantly and very precisely. Silicon carbide power electronics in inverters enable high voltage and efficiency in smaller form factors. That means inverters are becoming an integrated part of the propulsion system rather than a mass penalty. Plus, they continue to get cheaper as they scale.

Embedded systems

The whole electric flight stack has been rapidly maturing. Sensors have become cheap and precise enough that drones and flying cars can measure their attitude, position and health in real-time. Embedded computers have become powerful enough to make that understanding actionable for control and navigation. Both the sensors and computer have also become much lighter, even as their performance has improved. This is the reason an iPhone or drone has better sensing and computation than aircraft had access to for most of aviation history.

This might surprise most, but the embedded compute supply chain is one layer of the Electric Stack not dependent on China. OEMs like ST Micro and TI operate global manufacturing networks. That said, commodity parts can’t simply be dropped into a certified aircraft, but the playing field has certainly changed. Redundant and reliable embedded systems that are low cost and lightweight are now practical.

The small aircraft cost premium can collapse by an order of magnitude

No laws of physics say that flying cars should be much more expensive than automotive at scale. But today, there’s more than an order of magnitude delta between the cost structure of a Cessna vs that of a Tesla.

Low volumes explain much of that difference. The chart below (log scale on both axes) shows vehicle price adjusted by weight against annual unit volumes. It’s a good-enough proxy for cost structure differences between the vehicles. Robinson sells around 100 R22 helicopters per year, Cessna around 200 172s. These aircraft designs are fundamentally unchanged except for optional glass cockpits, touchscreen flight displays. Contrast that with a Ferrari 296 GTB with far higher performance engines, and they are still about 2x the cost per pound.

Look at the clear pattern of the falling cost/lb for different types of vehicles. Performance cars at low volumes cost more than a Model 3 or Corolla at much higher volumes. The Nighthawk is a low-volume, high-cost premium boat, vs the Bayliner M17. The pattern holds across structural materials as well, including composites. For example, see the Ferrari 296 vs BMW i3 cost/lb and unit volumes, with the i3 having a composite monocoque structure but at a much lower cost/lb.

It’s very, very hard to make things at low cost when only a hundred are made per year.

Product value is upstream of manufacturing volume. As we discussed above, the job for a car of going from A to B is often best done by one that drives itself. Software-defined intelligence is the logical conclusion for cars that are easy to use. Once a vehicle clears the baseline for acceleration, highway speed, range, and safety, reducing the driver’s burden becomes one of the most important forms of capability. Runway dependence and high pilot workload kept aircraft optimized around skilled performance. Ease of use, and the technology that unlocks it, could not become the primary dimension of aircraft design.

So the question is, why do we make so few personal aircraft?

We sprinkled clues throughout, but it’s effectively a high Idiot Index, Elon’s phrase for ratio of finished product cost over input material cost. Personal aircraft have had a high Idiot Index because of the skill required to fly them, their dependence on runways, and a high certification burden. It’s also a result of the low demand and glacial speed of technological progress in most of the GA industry for the last 50 years. You can’t approach the cost of materials if you’re not iterating.

The opportunity is to shrink the cost delta to only a double digit premium over cars. We can do that with two seat electric aircraft designed from the start for high-rate manufacturing, integrating low cost components into redundant systems, and low cost fabrication and assembly.

Composites have gotten cheaper and more reliable at scale. The higher-rate composite processes are becoming increasingly mature, though not widely used in aerospace. The problem for GA aircraft has been that they don’t have enough volume to justify processes like light RTM.

With volume, we can justify building out the flying car supply chain. The industrial base underpinning the tech stack for flying cars has not been decided yet.

The critical components like the high-performance electric motors and scaled-volume composite fabrication that we need are not concentrated in one country. This was an important reason for starting Vight now. We want to do final assembly, and vertically integrate increasingly more aspects of the full technology stack over time in the US: the software, compute, powertrain, structure and more.

The hard part is now execution, not invention

Building flying cars is definitely not easy. But we’re no longer waiting for miracle leaps in technology like batteries. The biggest risk is now integrating a system that offers a compelling product that is easy to use.

The first wave of eVTOL companies forced a lot of the unknowns into the open. Learnings like how to handle complex transition aerodynamic interactions, redundancy of distributed electric propulsion, flight-test discipline. Those are still hard problems. But they are now engineering problems. They do not require a new battery chemistry, a new motor physics, or a new theory of flight.

One non-technical thing that has blocked the future of flying cars has been the regulatory pathway. For flying cars to be feasible today, even with all of the improvements on the tech side, we’d need a new one.

Is there now a regulatory pathway for such a product to exist? Yes indeed.

MOSAIC, the new regulatory path

For decades, personal flying cars were trapped between two regulatory paths: too small to be useful, or too expensive and slow to certify. The FAA’s MOSAIC ruling opens up another, better path.

MOSAIC stands for Modernization of Special Airworthiness Certification. It’s a regulatory path that is a performance-based approach that is designed to enhance safety without overburdening the engineering and testing required. It also opens up more useful VTOL electric aircraft for private use.

MOSAIC is a huge upgrade to the Light Sport Aircraft (LSA) category that had initially been introduced in 2004 to make safe personal flying more accessible and lower the certification burden. Since then, the FAA has seen that light-sport aircraft (LSA) have become as safe as other general aviation aircraft in recent years, giving them the confidence to expand the type and capabilities of LSAs, while intending to make them even safer. The FAA also wanted to encourage people to fly fewer amateur-built aircraft and more factory-built LSAs under MOSAIC.

For the future of flying cars, that unlocks all these capabilities that we’ve been talking about.

MOSAIC makes critical changes to unlocking the high point-to-point freedom future at high speeds. It allows, for the first time, VTOL-capable general aviation aircraft to be certified without having to go down the very expensive 21.17(b) path that air taxis had to follow, or be constrained into a small form factor like a Jetson or Pivotal ultralight.

It unlocks building VTOL-capable platforms, initially with two seats with up to 250 knot (287 mph) cruise speed, that can be flown in any airspace you can fly a GA personal aircraft today. There’s no restriction on max takeoff weight.

So why don’t air taxis just try to do this? Because their aircraft, companies, and certification programs were built around a different product. A 21.17(b) air taxi is a commercial aircraft for an operator network. A MOSAIC aircraft has to be designed from the start around the personal-aircraft pathway to be viable.

This is why the first market really matters, and why we’re taking a different approach.

The initial opportunity

Flying cars won’t start on downtown rooftops at first. The better initial opportunity is with owners of private properties who already pay for vehicles that expand what they can reach. Examples of such vehicles include utility terrain vehicles (UTVs) and powerboats.

An acreage sized property becomes a home base from which our customers can take off and land. A second property, a trailhead, winery, golf course, or resort become potential destinations. Vight aircraft will be intuitive and easy to fly, right from your backyard.

Many of our early backers want the vehicle for personal use. They’re tech-forward, excited by a vehicle that expands their possibilities for leisure and work at hard-to-reach locations. In addition, helicopter pilots already fly routes like this, albeit with significant workload. With the owner’s permission and the right local approvals, they can take off and land without needing runways.

To have real point-to-point freedom, our vehicles need to operate near where customers live, and eventually work. That makes noise a core design requirement. The first wave of eVTOL companies has already shown good progress, with aircraft that are dramatically quieter than helicopters in both hover and cruise. They got there by optimizing the aircraft as an integrated system, and the propeller blade design in particular. This is one of the places where Vight can leverage the hard-won learnings of what came before.

It’s easy to forget the leisure vehicle market is already large. Boats and RVs are not the same product, but they prove that people spend serious money on leisure-focused vehicles. The US powerboat market is about a 200,000-unit-per-year market, and about $15 billion. The RV market is also tens of billions of dollars and ships over 300,000 units per year. In the US alone, there are more than 10 million registered powerboats and RVs. Those numbers are more than twice as large globally, but the US is by far the biggest market for these vehicles.

This is how large the leisure-only use case for human-piloted VTOLs is already, without considering the flying or autonomous capabilities of our platform on day one and as we continue executing.

Another compelling early market is for flight clubs and schools. Today, flight clubs are member-run non profit organizations that let pilots share the cost of owning and using aircraft. The limiting factor is they are runway-dependent. Runway-independent aircraft allow for flying experiences to move closer to the places people already travel for leisure, work, and community.

This is a small market compared to our vision, to be sure, but a necessary one. We will take whatever ravenous demand exists in the early days, because scale will be how we win. Once we’ve ramped up to about a thousand units per year for the first platform, we’ll have the ability to scale rapidly down the cost curve.

We are targeting a purchase price of $200k-$250k at those volumes. We’ll continue to obsess about simplifying and automating the experience, improving speed and range, and shrinking the on-ground footprint. Of course, we don’t intend to keep the purchase price out of reach for most people for too long. By our fourth generation platform, we are targeting a sub $100k price point, and in partnership with the FAA, we aim to make the flying experience as effortless as taking a Waymo is today.

The master plan for new software-defined hardware has been codified for a while. The Tesla Roadster is the classic vehicle example of this, with about 1,000 units of the Roadster per year at similar inflation-adjusted prices to what we’re targeting. Henry Ford shipped multiple vehicles before the Model T became a breakout success, even more so in rural than urban areas.

As personal transport became more capable, people spent more on it. Cars became a better investment as highways were built out and vehicles became safer and easier to use. The Model T entered production in 1908 and over the next century, transportation rose from a negligible share of household spending to over 15%.

I saw this pattern up close. My dad immigrated to Australia when I was three. In his first year, my dad would walk for hours looking for work. His first car was a beat-up $500 manual early 80s Ford Meteor. His second was a $2,000 Holden Camira, an automatic but it still ran on leaded gas. He washed that car proudly. A year later, though, he bought a brand-new 1994 Toyota Camry.

That is what better transportation does. It stops being just an expense and starts becoming a bigger part of daily life.

With each vehicle generation, we’ll leverage our learnings to build more capable, more intelligent platforms that cost less and unlock significantly more demand. Then, we’ll continue down the cost curve, opening up even more demand.

This is the use case that’s eluded aviation forever. But once we have vehicles shipped to paying customers, Vight can then convert learnings from those early flights into an operating flywheel.

The operating flywheel

We have a long way to go to get from where we are today to our full vision, but our bet is that operating from private property compounds.

Takeoff and landing sites are owned or controlled by our customers, avoiding the need for public vertiports with high buildout cost and permitting friction. Even more importantly, private properties unlock high point-to-point freedom, enabling flights to happen where our customers will need them. More flights happen, across more geographies and uses.

Every aircraft and every flight teaches us how the vehicle behaves and how customers actually use it. It also helps speed up iterations on improving maintenance, site operations, and what makes the experience feel safe and simple.

Those lessons feed five loops at once:

1. Aircraft model loop

2. Customer experience loop

3. Endpoint/destination loop

4. Cost loop

5. Autonomy and services loop

1. Aircraft model loop

For development, the impact implications are massive as we can feed that back into both our simulation model, closing the gap on sim-to-real and better predicting how our platforms will fly before they are in the air.

2. Customer experience loop

For customers, it means we can improve the flying and maintenance experience through the vehicle life cycle. This includes over-the-air updates for non-safety critical features, including autonomous pilot-assist, and predictive maintenance, such as swapping battery packs near end of usable life.

3. Endpoint/destination loop

For endpoints (i.e. destinations), each site gives us a clearer operational playbook for what works. Property and flight club owners can use that playbook to activate more sites, especially at places like resorts, clubs, private communities, and industrial parks.

Indeed, fly-in communities already exist where residents can access by plane or helicopter. There are a few hundred in the US, and about 30,000 people live in them. The largest, Spruce Creek Fly-In, has about 5,000 residents in about 1,500 homes spread over about 1,300 acres. Others are spread all over the country, with even some that occupy small islands like North Captiva Island in Florida. Some are suburban, like Lakeway in Austin, and others are remote, like Alpine Park in Wyoming or Mountain Air in North Carolina.

Over time we’ll see shared endpoints emerge for approved users. These will not need the infrastructure required for an urban vertiport or runways. We’ll also be able to piggyback on any existing vertiport build-out by other companies, as we won’t gain an advantage by owning and building out this infrastructure. That said, we will help with standardized operating playbooks to make setting up infrastructure as easy as possible for property owners. We expect many land owners to be motivated to set up infrastructure that enhances the usage of their properties, and want to make this as seamless and profitable for them as possible.

4. Cost loop

We’ll also come down the cost curve by making better platforms at lower cost. Cruise speed, range, intelligence, noise reduction, and hybrid powertrains will all improve vehicle capabilities, increasing demand to keep pulling costs down. The goal is to drive down the unit costs toward a car-like cost structure, with only a double-digit premium at scale, as we discussed in the cost premium section above.

5. Autonomy/services loop

Over time, more pilots utilize the network. This accelerates the operating flywheel and lets us double down on future platforms and more advanced autonomy.

As our fleet compounds flight hours and we continue to engage with the FAA towards end-to-end unsupervised autonomy. We’ll standardize and automate maintenance, servicing and site ops, accelerating the advantage that will deepen with more flight hours. Our advantage will be the real world data lead that accelerates our ability to validate our autonomy models in sim. Simulation will help scale that evidence, handling every edge case required for safe, reliable truly autonomous flight and streamlined operations, starting with smaller pilot programs for fully autonomous operations. Once proven, that enables a network of autonomous flying robotaxis that do not require vehicle ownership.

When people picture flying cars at scale, they usually imagine cities filled with aircraft moving between rooftops. It’s true that they will increasingly fly to business campuses, industrial parks, and eventually more urban corridors, and this will drive real demand. But I don’t think that is the biggest unlock.

The biggest unlock is actually expanding the map: making places that are currently too far, too remote, or too inconvenient feel close enough to become part of daily life.

Expanding the radius of daily life

The biggest impact of flying cars will be expanding the radius of daily life. The promise is optionality: more places where you can live, work and visit.

No one can predict the future, but also, this future will not happen without highly motivated people aligned on the goal. We think this future could happen in three phases:

• Existing acreages and remote destinations become more valuable.

• More people move into areas that most benefit from flying cars.

• Cities lose their monopoly on proximity (but also benefit in this future).

Being able to travel point-to-point at 120 mph expands where we can go on a daily basis. There’s no limitation from traffic or roads that have to follow the contours of the land. What was once a weekend trip becomes a quick hop. As we upgrade cruise speeds to approach 300 mph and beyond, our daily radius will continue to expand:

Marchetti’s Constant has proven so reliable throughout human history and in so many regions that he called it an “invariant.” The hard part is expanding where people can get within 30-60 minutes. If you pull that off, it seems inevitable that people will take advantage of the speed to spread out, and when they do, we can expect some interesting second- and third-order effects.

Existing acreages and remote destinations become more valuable. Over several product generations, when you can compress a 200 mile / 3-4 hour drive into a 45 minute flight, the land inside the larger circles becomes a compelling real estate play (not investment advice). Packy was one of the first people I spoke to who understood how this expands the local frontier, as he wrote about in Scarce Assets.

More people move into areas that most benefit from flying cars. Before cars and highways, suburbia was not a thing. Suburbs shaped where many people live today, defined by cars as the primary mode of transport in almost every American city. They made access to downtown areas closer for people living further away from them.

Autonomous and fast long-range eVTOLs will also enable a new pattern of living. It will be easier to live on a ranch, a forest cabin, or new developments like CA Forever. Mountain or island homes that were too disconnected to be practical will become normal places to live, work, and build. That’s only possible when the people, activities and amenities take a fraction of the time to reach.

People in remote areas already have access to Starlink. They have battery backup and can be off-grid with solar if they choose to. Small modular electrolyzers and direct air-capture will enable on-site synthetic hydrocarbon production. Drone delivery services will be normal. Autonomous trucks will move heavier and bulkier supplies to wherever people want to live.

The hinterlands become more valuable, more compelling, more usable. Regional economies can become more connected without needing to route everything through the same city center. Flying cars will decentralize economic opportunity.

Cities lose their monopoly on proximity (but also benefit in this future). Cities will not just disappear. They benefit too, mostly through services and shared endpoints. Flying car garages in cities might look like the automated car parking warehouses that have existed for decades. Ground robots or automated tugs will move aircraft between landing pads, charging bays, maintenance areas, and dense storage slots, the same way automated parking systems already move cars through compact garages.

But cities stop being the default hubs for opportunity. As more people, goods, energy, data and eventually manufacturing organize around new endpoints, the frontier of daily life moves outward. The suburbs give way to more distributed communities and an incredibly exciting future on our planet.

If this all sounds too utopian for you, this vision will take decades and multiple product generations. The flying car layer of this future is very hard. We’ll need to make the experience safe, affordable, quiet enough, and easy enough to operate. We’ll need to earn the trust of our customers, property owners, insurers, local authorities, and the FAA. Future platforms with unsupervised autonomous capabilities will require many iterations of our operational flywheel, and ongoing engagement with regulators. But it’s a good quest and, as we’ve shown, it’s possible. Now we just need to do it.

This is just the beginning

It is too narrow to think of flying cars only as vehicles. They are autonomous robotics platforms operating in open physical space. Many robotics categories will automate tasks inside the world we already inhabit: homes, warehouses, factories, sidewalks.

Flying cars do something different. They change the shape of the world people can inhabit. That is why the better analogy is the car and the highway network. They changed where people lived, what land was valuable, how regions grew, and what freedom on the open road felt like.

Flying cars will help usher in the next paradigm for lived environments. Cars and highways enabled suburban life for tens, if not hundreds, of millions of families in developed countries. Flying cars will unlock new ways of living in new locations for just as many, if not more, people in the decades ahead. They will unlock freedom in the open skies.

Growing up, each new transportation mode changed the size of my world. My first bike gave me suburban reach in Adelaide, Australia, letting me visit my friends on weekends without needing a parent driver. My first car expanded that reach. Both made more places feel reachable and made my life feel less bounded. While the specific places are different, this growing sense of freedom is a universal experience.

That freedom, the expansion of what is possible in daily life, is the promise of flying cars.

Nothing worth doing is easy. Making life incredible on Earth will be extraordinarily hard. But if we’re locked in on the mission and we execute well, the path is becoming clear. We can do this.

If you want to help make this future real, come build it with us.

Thanks to Tsung for writing and teaching me about flying cars.

That’s all for today. We’ll be back in your inbox this week.

Thanks for reading,

Packy

Hi friends 👋 ,

Happy Friday and welcome back to our 196th Weekly Dose of Optimism!

A new American nuclear reactor has gone critical. Dan and I are doing a Hyrox in a few hours. New York City is hot and buzzing. What a week for the optimists.

Let’s get to it.

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(1) Antares Goes Critical

Big day for the U S of A, and for our friends at Antares.

Last night, Antares announced that its Mark-0 low power reactor was brought to criticality at Idaho National Lab with a self-sustaining fission reaction. In doing so, it became the first novel reactor design to undergo a fueled test in over 50 years.

“We are now the first reactor to meet the intent of President Trump’s May 2025 EO 14301,” CEO Jordan Bramble said, “which calls for three reactors to meet this milestone before America’s 250th birthday on July 4, 2026.”

This is a massive milestone, and one that seemed implausible even a year ago. It’s also just the beginning - more reactors will go critical in the coming month, and Antares itself is moving from testing to producing electricity. Or as Jordan put it, “We’ve made neutrons. Next up: electrons.”

Two and a half years ago, Julia DeWahl and I started a podcast called Age of Miracles to try to understand why America no longer made this miracle technology, and what it might take to change that. We talked to most of the founders in the space, some of whom are racing to criticality alongside Antares. Turns out, Julia, the co-founder of Antares, was the one who pulled it off, alongside Jordan and the rest of the team. It is an incredible accomplishment to go from “Why aren’t we doing this?” to doing it - going critical - in under three years. I’m very proud of Julia for making it happen, and excited for the country now that we’re producing new nuclear in America again. 🇺🇸

(2) New Limit Raises $435M to Bring First Aging Reprogramming Medicine into Human Trials & Cure Hangovers

for

There are no approved treatments for alcohol-related liver disease. It kills 30,000 Americans a year, about 40% of the population has some degree of fatty liver, and nothing.

Until now. NewLimit, the epigenetic reprogramming company co-founded by Coinbase CEO Brian Armstrong and computational biologist Jacob Kimmel, just closed a $435 million Series C led by Founders Fund with Thrive, Greenoaks, and others at a $3.1 billion valuation, more than triple what the company was worth a year ago. For good reason - NewLimit is going into human trials with the first-ever test of an age-reprogramming medicine in human subjects, starting in 2027 in Australia, after success in mice.

NewLimit fed old mice alcohol as their sole calorie source for 11 days and then gave them a binge dose. The untreated old mice flipped onto their backs and stayed sedated for 8 to 12 hours, like me if I have a couple drinks now. Young mice on the same protocol just popped up like it was nothing. But when old mice got a single dose of NewLimit’s therapy, RNA encoding specific transcription factors, delivered via lipid nanoparticles, they stopped passing out. They were as good as young.

As Kimmel told Ashlee Vance on Core Memory, “It is just binary. They’re either passed out or they’re not.” The therapy didn’t help them metabolize alcohol faster. It restored the liver cells’ intrinsic ability to withstand stress and regenerate, as if they were young again.

The discovery engine behind those transcription factor combos is an AI model called Ambrosia that ingests published gene-function literature, protein sequences, and DNA binding motifs, then trains on roughly 10,000 real lab experiments. It explains more than half the variation in results and can be run in reverse: you specify the cell state you want, and Ambrosia proposes the combination most likely to produce it. As frontier LLMs and protein-folding models improve, the embeddings Ambrosia feeds on get better for free.

NewLimit initially expected a decade-plus timeline to reach the clinic. They’re now five years ahead of schedule. The first indication will be steatotic liver disease delivered via a 20-minute IV infusion, with the long-term goal of a subcutaneous pen for broader GLP-1-like use.

Beyond the liver, they’re engineering nanoparticles to target blood vessel lining (with a focus on chronic kidney disease), T-cells (autoimmune conditions), and eventually the blood-brain barrier.

The sooner they get through their roadmap, the longer we all live.

(3) Top AI CEOs Call for Law Protecting Against Biological Weapons

Amrith Ramkumar for WSJ

AI for bio will be used for a lot of good. We need to prevent it from being used for bad.

The leaders of the big AI labs don’t agree on much these days. Over the past week, for example, OpenAI CEO Sam Altman said that he doesn’t like Anthropic’s telling everyone they’re going to lose their jobs in one interview and that AI budgeting has become a huge issue for some companies, which would hurt both OpenAI and Anthropic but which I think he’s probably OK saying out loud because Anthropic is the one that just filed an S-1. “Sam Altman. You could parachute him into an island full of cannibals and come back in 5 years and he’d be the king.”

One thing they do agree on, though, is that people shouldn’t be able to use their products to make bioweapons. Sam, Anthropic CEO Dario Amodei, and Google DeepMind CEO Demis Hassabis are among the signatories on a new letter urging Congress to pass laws that would require companies that sell synthetic DNA and RNA to screen their customers and block any combinations that could be dangerous.

Per the WSJ, “Trump previously revoked a Biden-era executive order that resulted in a gene synthesis screening framework. The White House last year said it would replace the Biden framework with its own screening guidelines but hasn’t yet published a replacement policy.”

Congress should probably go ahead and just do this. “AI is going to kill us all” is better left an Anthropic marketing tactic than a real scenario.

(4) Helion Raises $465M to Accelerate Commercial Fusion Development

On Age of Miracles, one of the people Julia and I interviewed was Helion CEO David Kirtley. Coming in, we’d heard from people in fusion that Helion was brash, and more “Silicon Valley” than others. Its SpaceX-like cadence of building new generations while still testing older ones was aggressive.

We both came out of the interview incredibly impressed, and believing he might just pull it off.

This week, Helion got one step closer. It announced a $465 million Series G led by Thrive Capital (big week), bringing its total funding to $1.5 billion and valuing the company at $15.5 billion.

In February, its Polaris became the first privately developed fusion machine to demonstrate measurable deuterium-tritium fusion and hit plasma temperatures of 150 million degrees Celsius, or ten times the heat of the core of the sun. It’s also the first private fusion machine to operate with D-T fuel, after becoming the first company to receive regulatory approval to possess and use tritium for fusion energy production. Meanwhile, Orion, Helion’s 50-megawatt facility in Malaga, Washington and first commercial fusion power plant, is already under construction, with a contract to sell electricity to Microsoft starting in 2028.

Helion will use the money to scale manufacturing from Polaris, which proved the physics, to Orion, which is an actual power plant that aims to produce electricity that customers actually use using the same energy-generation process used by the sun.

It’s fusion, and it’s really hard, and there’s lots to prove, but the funding will help the company take a swing at the goal Kirtley laid our in our conversation: to make “generators per day rather than generators every few years” by 2030. Ab sole.

(5) 80-year-old Alzheimer’s Patient Showed Recovery with Mushrooms

Frontiers in Neuroscience

An octogenarian Japanese-American woman with a 10-year history of Alzheimer's disease, including five years of near-total functional decline, monosyllabic speech, chronic urinary incontinence, flat affect, and dependence in virtually all daily activities, was given a single 5-gram dose of psilocybin mushrooms.

Nineteen hours later, after fits of intense sweating, she woke up and “the patient spontaneously initiated autobiographical conversation lasting several hours.” Over the following days and weeks, her urinary continence came back after five-plus years, and she began walking independently, dressing herself, sustaining eye contact, retrieving contextual memory contextual, and engaging emotionally.

A second session a month later produced spontaneous humor, vivid emotional imagery, and increased agility. She told her caregivers, unprompted: “It is pleasant to come here.”

This was a single case and not a clinical trial. The authors are careful to note that causality can’t be established, and the improvements were transient. Whether or not it was the mushrooms, what’s incredible is that, even after 5 years, her capacity for all of those things was still in there. As the researchers put it, “Residual functional capacity may persist in advanced Alzheimer's disease and may become transiently accessible under specific neuromodulatory conditions.”

My grandmother had Alzheimer’s, and me and my loved ones getting it is one of my biggest fears, so any good news about this terrible disease is welcome, and I want to believe. If taking some mushrooms helps fight Alzheimer’s, sign me up for the clinical trials.

EXTRA DOSES: No Science Breakthroughs this week (back next), but we have a lecture on the grid & batteries, MAFIA, Hoffman, and Resonant Computing below…

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Hi friends 👋,

Happy Tuesday! Welcome to a Deep Dive I’ve been excited to write since The Electric Slide, about a company I invested in to build one layer of the Electric Stack - motors and actuators - in America.

If you’ve heard of Westmag, it might be because they make really great hats. Everyone loves the Westmag hats. I’m wearing one right now, which co-founder David Hansen gave me right off his head. This hat, I think, in a decade or two, will be a collector’s item.

I think that because over the past year, in stealth, Westmag has been making a lot more than hats. It’s been making motors and actuators, and it’s been making them to scale, so that in a decade or two, when we sit on the grass looking up at drone-decorated skies while robots do our chores, those drones and robots will be driven by Westmag motors and actuators.

Let’s get to it.

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America Spins on Westmag

If you drive out to South San Francisco, past and away from the city’s “Stop Hiring Humans” billboards and into the industrial part, you will find a low-slung facility from which David Hansen and Jordan Sanders plan to sling American-made motors and actuators to the American companies making drones, robots, and eventually, anything that moves under the power of electrons.

It is called Westmag, a portmanteau of Western and Magnetics, which pretty much sums up the mission. Electric motors are spinning magnets, and if America is to participate in the coming electric revolution, we are going to need to make them in the West.

This is the kind of statement you read on Twitter, at varying degrees of jingoism, as in “Get those Chinese motors out of my yard.” But it isn’t self-evident that we need Western motors, or, therefore, Westmag. The promise of globalization was cheaply-made Chinese motors powering expensively-designed American products for the benefit of all mankind.

Certainly, we want to design our own robots and drones. Intellectual Property is what America does best. We probably want to manufacture them here, or in a friendly country, too. Even if you don’t believe the China hawks’ predictions that there will be a hot Great Power Conflict within the decade, it is better to err on the side of not putting a heavy, software-controlled, and easily-bugged Chinese hunk of metal inside every American home, or, eventually, putting every American human inside a flying, software-controlled, and easily-bugged Chinese hunk of metal to fly through the sky.

But where do you draw the line?

Should you make every magnet that goes inside every motor in the USA? Should you mine the rare earths that make the magnets here, mine them elsewhere and refine them here, or import them? The rare earths are commodities, after all, and impossible to bug or remotely control with known technology. And even assuming that you want to make everything here, is there a company to be built doing so? Who in their right mind would want to compete with China’s massive scale and subsidy advantages?

American motors seem to violate both David Ricardo’s concept of Comparative Advantage and Michael Porter’s Five Forces. And yet, Westmag exists, and I invested in the company, alongside a16z, Founders Fund, Lux Capital, NFDG, Menlo Ventures, and my Electric Slide co-author, Sam D’Amico.

In that essay, I wrote, “I even just invested in a stealth company making electric motors.” Westmag is that company. We also shared the reason America wants a company like Westmag, and many similar ones in all areas of the Electric Stack, to exist in America:

Manufacturing and design are inextricably linked. When you make things, you learn how to make them better. You learn which parts of the underlying stack need to be improved, improve them, and make better products. This is a theme that comes up over and over again in our Electric Stack story.

In the Electric Era, maintaining design leadership without manufacturing leadership is not a coherent strategic position, and one that gets less coherent the better you believe AI will get.

Elon has solved this by building practically everything inside of his own companies. There is a parallel American ecosystem emerging to serve the growing number of electric companies not in the Elonverse. Westmag will be the winning motor company in this ecosystem, and will, in turn, enable its customers to win.

In a time of experimentation and innovation, like the one drones and robots are in today, if you place component manufacturing near product manufacturing, the whole machine spins faster. This is particularly true when, to say nothing of geopolitics and everything of commerce, American companies are producing at volumes at which they are not Chinese component suppliers’ top priority.

But that is half the story, a geopolitical imperative, not a corporate strategic one.

The other half of the story is why a specific company like Westmag should exist, and how it can generate persistent differential returns in a market in which practically zero-margin Chinese alternatives exist. While we will tell the whole thing, that’s the half we’ll focus on today.

It’s a story about Westmag specifically, and motors and actuators specifically, but it’s also the story of how to ride the Red, White, and Blue premium for just as long as you need to without counting on it long-term in order to kickstart demand and ride that growing volume down the cost/performance curve until, without subsidies and without patriotic premia, you can use scale and proximity and flexibility and speed to compete on overall system cost and win.

This, to be sure, is a scale game, and that’s not the type of game we’ve played well recently. As David said, “Motors are capped at 100% efficiency, so most improvements are incremental. So you can go from 89% to 91%, but it turns out that doesn’t matter at all if you don’t make it and get it adopted at scale.” Previous attempts to compete in motors were focused on those efficiency gains or theoretical breakthroughs at the expense of the ability to quickly reach meaningful adoption and scale, he continued:

Actually building a lot of it is the only way to get good at building it. China’s strength, which we are replicating, is building a lot of things and then improving it along the way. Bespoke low volume doesn’t create a manufacturing powerhouse with compounding advantages.

Westmag plans to build a manufacturing powerhouse by focusing on scale. As Jordan put it:

We are first focused on scale: scaling what works now and what is in demand now, while in parallel innovating on, and through scaling that, we will drive this virtuous feedback loop of innovations in how we manufacture and how we design motors, both for manufacturability but also for performance. You only get good at the stuff if you build a lot of it. And then you only win the market if you can actually get it out to the market in large numbers.

So this is a story about why and how to build electric motors in America for economic reasons, how to build them at scale, and how to win. And it’s a story about what it will mean for the rest of America’s electric ecosystem, alongside which Westmag is growing up, if it does.

It begins with what electric motors are, why they’re important, and where they’re made today.

Electric Motor Primer

In The Electric Slide, we used the electric motor as the vehicle for understanding the whole Electric Stack, because the motor is where everything comes together. The batteries supply power to the electromagnets that create rotating fields that pull the Neodymium magnets around and around, coordinated by the embedded compute that takes in data from sensors and tells the power electronics how to flip the current thousands of times per second to create the smooth rotation that turns electrical power into mechanical power.

Specifically, in a brushless DC motor, which power drones and the actuators inside humanoid robots, the stator, the part that holds still, is a ring of copper coils wound around teeth of laminated electrical steel, and the rotor, the part that spins, is studded with permanent neodymium magnets.

The controller fires the stator coils in a precise rotating sequence. Three phases switch on and off thousands of times per second, so that the magnetic field appears to whirl smoothly around the stator. The permanent magnets in the rotor chase that rotating field and never quite catch it, and that perpetual chase is what spins the shaft. As we wrote:

The magnetic force is doing the spinning. Everything else is about getting the magnets in the right place with the right polarity; nature does the rest.

An electric motor simply directs electromagnetic forces that want to move towards equilibrium.

The “brushless” part is an upgrade from older motors, which used carbon brushes to physically scrape current onto a rotating commutator, a mechanical hack that worked well enough but wore out, sparked, and capped achievable speeds. If you swap the brushes for electronics, you get a motor that is quieter, more efficient, more controllable, capable of tens of thousands of RPM, and good for a billion rotations before anything wears out. Which is why it has become the motor of choice for almost everything that needs to move precisely under software control, which is an increasingly large number of things.

If you want a deeper understanding of how electric motors work, check out our explainer, How Electric Motors Work, or watch this video:

For our purposes, what you need to know is that any electric product that moves is basically a bunch of actuators, of which motors are a subset, converting some form of energy into physical motion, wrapped in bodies that allow them to understand the world around them and move certain ways in response. The bill-of-materials (BOM) for a humanoid robot, for example, is roughly 50% actuators with motors and magnets at their core, which act as their joints.

“Motors are simple,” Jordan told me, someone to whom motors are not simple, when I visited the proto-motor factory. “They’re just magnets and copper wire wrapped around some electrical steel.”

From a materials perspective, motors are simple. Making motors, however, turning those materials into a precision component at scale, is much more complex.

A brushless DC motor is a bundle of compounding tolerance requirements. The electrical steel arrives as thin sheets that have to be stamped into laminations, coated to prevent eddy currents, and stacked into a stator with the layers aligned to within a hair. Copper wire, sometimes thinner than a human hair itself, has to be wound around the stator teeth at a precise tension, in a precise pattern, with as much copper crammed into the available slot area as physically possible.

Then there are the magnets. Neodymium magnets get pressed into the rotor at orientations specified to a fraction of a degree, and magnetized in place by fixtures that fire enormous bursts of current through coils to align the magnetic domains. Increasingly, you can get blocks of neo magnets in the US, but what you get is a slab of metal that isn’t yet magnetized or cut to the right shape. To turn block into a usable motor magnet you have to cut it (small-motor magnets are curved, and the curve is not easy), shape it, coat it, and magnetize it. No one does this in America today, so if you want to do it right and quick (i.e. if you don’t want to send the American magnet block to China or Malaysia and back), you probably need to do it yourself. If you get the orientation slightly wrong, your field ends up lumpy. If your field is lumpy, your motor cogs, vibrates, and wastes energy.

Stack those tolerances on top of each other (lamination, winding, magnet orientation, rotor balance, bearing fit, etc…) and you start to see why motor manufacturing is mostly a process problem, not a materials problem. The materials are simple. The process is annoyingly precise.

That is why, as much as their total dominance of the rare earth magnet supply chain or (no longer) cheap labor, China is so good at this. Over the past thirty years, they have made a lot of motors, and in the process, they have written a library of institutional knowledge. The winding machines in Shenzhen have been refined by a thousand small revisions. Chinese line workers know what a properly-wound stator feels like in their hands. Their test rigs have been calibrated against millions of motors.

This knowledge compounds for you, just like the tolerances compound against you, and it’s only if the knowledge wins that you can produce a lot of motors, cheaply and reliably. For the past three decades, China has been doing all of the compounding.

China Spins Up Motor Production

Like every layer of the Electric Stack, electric motors were invented in the West and Japan. Michael Faraday of cage fame built the proto-motor in London in 1821. A decade later, in Princeton, Joseph Henry discovered that you could make incredibly powerful electromagnets by wrapping insulated wire around iron cores.

Forty years later, in 1871, Zénobe Gramme built the first commercially successful generator, using electromagnets for the field magnets, powered by some of the current it generated itself in a process called self-excitation. In the 1950s and 1960s, American researchers began replacing mechanical commutators with electronic switching, and in the 1960s and 1970s, Japanese firms like Yaskawa, Panasonic, Sony, and later Mabuchi aggressively productized compact permanent‑magnet motors as transistors and then MOSFETs got cheaper. These were the first truly mass‑manufactured, consumer‑scale BLDC motors.

In 1983, in a story that you need to read if you haven’t, Sumitomo’s Masato Sagawa (Japan) and GM’s John Croat (US) independently discover Nd₂Fe₁₄B, the modern neo magnet, and presented their findings at the same conference in Pittsburgh.

Sumitomo perfected sintered high‑performance blocks, while GM’s Magnequench division perfects bonded molded magnets, both of which were used in neodymium magnets’ alpha product: 3.5” hard-disk drives (HDD), which relied on two small electric motors, voice coil motors and spindle motors.

As 3.5” HDDs overtook 5.25” HDDs, neo magnets swept the market.

And as neo magnets scaled, they and the motors they powered got cheaper, unlocking new use cases, more scale, better price-for-performance, and therefore more use cases, and more scale, in a virtuous cycle that we are still riding today. You can read the full story in The Electric Slide.

Today, however, the products that run on electric motors don’t run on American-made electric motors or neo magnets.

In 1995, GM, under financial pressure, sold 80% of Magnequench for $70M to a “US‑led” consortium that was, in reality, two PRC‑controlled companies led by Deng Xiaoping’s sons‑in‑law. CFIUS approved the deal on the condition of a 5‑year pledge to keep production in the US. Long before the five-year pledge expired, Magnequench’s Chinese owners had already cloned the Indiana lines in Tianjin. By 2003, the US plant shut down.

In parallel, as part of Xiaoping’s long-term plan, China came to dominate rare earth mining and manufacturing, including the mining and manufacturing of neodymium. By the early 2000s, having undercut them on price and environmental standards, China forced the US’ only big rare earth mine, Mountain Pass, into bankruptcy, and came to control the full rare‑earth → NdFeB magnet chain that is essential for high-performance BLDC rotors.

But supply without demand does not an industrial superpower make. Enter Shenzhen.

Shenzhen was a small fishing village of about 30,000 people across the border from Hong Kong before Xiaoping, as part of his “reform and opening” policy, designated it the opening country’s first Special Economic Zone in May 1980.

Throughout the 1980s and into the 1990s, China’s quasi-capitalist city exploded. Cheap labor poured in from across China to fill the demand for hands: Shenzhen had quickly become a hub for assembly and low-end manufacturing. Soon, dozens of contract manufacturers were making toys, watches, and all manner of cheap electronic devices.

BYD started in Shenzhen, to execute an arbitrage: reverse engineer Japanese battery manufacturing processes and replace all of the expensive automation with Shenzhen’s cheap, abundant labor. Over time, Wang Chuanfu’s company built out Shenzhen’s battery supply chain while becoming the world leader in electric vehicles, one feeding the other. If you make the components, you can make better products.

Johnson Electric, founded in Hong Kong in 1959 by Wang Seng Liang and his wife, set out to manufacture miniature DC motors specifically for the booming Hong Kong toy industry. When Xiaoping opened the Pearl River Delta to ~capitalism, Johnson moved production across the border, like many of Hong Kong’s electronics companies. By the 1990s, more than 80% of Hong Kong’s factories had moved to the mainland, mostly into the Pearl River Delta. Per the (surprisingly shitty) Porter’s Five Forces website entry for Johnson Electric, “the 1980s show a shift toward application‑specific motion solutions as automation and automotive electrification rose.” By the 1990s and early 2000s, “Johnson Electric established mainland China production and verticalized stamping, molding, and magnetics to protect margins; it diversified from brushed motors into BLDC, stepper, linear actuators and subassemblies.”

Thanks in part to the competence that Johnson Electric had built in motor manufacturing, Shenzhen became the epicenter of the RC hobby industry, which refined brushless motors specifically. The next big thing will start out looking like a toy.

So by the time that Frank Wang used the proceeds from selling flight-control parts to universities and Chinese power companies to move to Shenzhen and start Da-Jiang Innovations, or DJI, the city was already the place where almost every component a flying camera needs was already being made within a couple hours’ drive of his apartment, by people who had been making them for years. RC hobbyists were refining the brushless motors, and the speed controllers to drive them. BYD had already been making lithium cells and packs for a decade. Camera modules were widely available hand-me-downs from the smartphone industry that had turned Shenzhen into the best place on Earth to turn a bare Sony sensor into a working, calibrated eye for a few dollars. Gimbals, plastics, PCBs, radios, and GPS units were available on a quick stroll through Huaqiangbei, the city’s electronics district, where Wang could pull from some 30 billion components crammed into a single square mile, get a custom PCB turned around in 90 minutes, and go from sketch to prototype in a few days.

Cheap labor had attracted BYD, Johnson Electric, and countless other manufacturers to Shenzhen, but it was the components and expertise they’d built with that labor that made the city an ideal place to start a drone company in 2006.

But precisely because components were so widely available, a hundred copycats could pull them off the same shelves. So DJI started vertically integrating. It built the flight controller first, because that was the part Wang understood best, and the cost fell from several thousand dollars in the mid-2000s to a few hundred by 2012. Then they developed the gimbal in-house and shrunk until it cost a tenth of the professional rig it replaced. Then the camera. Then, eventually, the propulsion system itself, the motor and ESC and propeller, designed as a single matched unit.

Today, DJI makes something like 70-80% of the drones in the world, and it is, by itself, supplying itself, the largest drone motor manufacturer in the world by a wide margin. It got so good at making motors that it’s even started selling its Avinox e-bike motors to other companies.

Thanks in large part to DJI, but also to the ecosystem it grew up in and Xiaoping’s foresight, to the fact that someone buying a motor can also buy a battery and power electronics and custom-made PCBs right next door, China absolutely dominates the world’s production of drone motors.

That same expertise allows them to dominate the world’s production of robot actuators, each of which has a drone motor at its core.

Which means, along with all of those batteries and power electronics and custom-made PCBs, that China absolutely dominates the world’s production of drones and robots.

Because if you want to build products on the Electric Stack, it is critical to have fast-turn components available nearby.

Why America Needs Its Own Motor Company

On its face, it is not bad that China dominates drone motor and actuator production. In a frictionless utopia, it would be great.

A company in one country (say, America) would design a drone or a robot or anything that moves, they would send the specs for the components they need to a bunch of other countries (like, for example, China) where they could be made best-for-the-cost, those countries would make the components and ship them near-instantly back to the buyer, the buyer would assemble those components into a finished drone or robot or whatever, and it would sell them to customers around the world, each of whom would benefit from a product made in the most efficient way possible.

Sure, America could design and manufacture here, and there was a time in the 1970s when we were better at both designing and manufacturing than China, but if we’re better at designing than manufacturing, and if design captures more of the value, we can design here and manufacture in China, which has a comparative advantage in manufacturing.

Comparative advantage is a concept coined by the economist David Ricardo in 1817 to explain why countries engage in international trade even when one country’s workers are more efficient at producing every single good than workers in other countries. When Nobel Laureate Paul Samuelson was challenged to “name me one proposition in all of the social sciences which is both true and non-trivial” by the mathematician Stanislaw Ulam, he thought for a few years and came back with comparative advantage: “That it is logically true need not be argued before a mathematician; that it is not trivial is attested by the thousands of important and intelligent men who have never been able to grasp the doctrine for themselves or to believe it after it was explained to them.”

This is the logic that policymakers and economists used to justify globalization and the World Trade Organization, and it fueled China’s rise.

From the reform era through WTO accession in 2001 and into the 2000s, China was labor-abundant and capital-and-skill-scarce, so it specialized in labor-intensive assembly while the US specialized in the capital-, IP-, and skill-intensive ends. For a while there, it worked as planned. Americans were smiling all the way to the bank.

The Smiling Curve depicts how “value added varies across the different stages of bringing a product on to the market in an IT-related manufacturing industry,” and therefore, where value is captured.

This was the dream. China would sit low in the middle while America captured value on both sides. The iPhone is a canonical example. Kraemer, Linden, and Dedrick’s 2010 teardown found that Apple captured 58.5% of the value of the iPhone 4/3G in profits, while China’s labor earned just 1.8% of the value.

But China, as we’ve seen, didn’t plan to stay on the bottom lip, and it didn’t, because doing the assembly taught it the adjacent capabilities. Assembling electronics pulled it into making components, then the tooling and machines that make components, then design itself. Serving as the world’s factory was a thirty-year education that pulled China up the smile curve and across the product space into denser, more complex nodes. Over time, the country’s edge became capability instead of labor cost.

Today, in a specific set of sectors, China holds an absolute advantage, with the lowest cost and highest capability and most complete stack, all at once, such that capital and capability rationally flow toward China rather than away. The advantage rests on scale economies (largest volume → furthest down the learning curve → lowest unit cost), agglomeration (the Shenzhen/Pearl River cluster where all of the inputs, tools, and skill sit within a short radius), accumulated process knowledge, and vertical integration.

This is the situation we described in The Electric Slide. China doesn’t hold an advantage in everything, but it certainly does hold the advantage in the Electric Stack.

“Today, China produces 75% of lithium-ion batteries globally and manufactures 90% of the neodymium magnets that make motors spin. In power electronics and embedded compute, it’s rapidly gaining ground.” As a result, the world’s leading drone company (DJI), electric vehicle company (BYD), and humanoid robotics (Unitree, although the market is still very small) are all Chinese. As the WSJ reported, even Tesla is turning to China for Optimus’ actuators.

Read statically, at a moment in time in the late 20th Century, comparative advantage correctly identified assembly as the low-margin thing for America to offload. Read dynamically, however, low-margin assembly was the tuition that China paid to climb up the value ladder into absolute advantage over America in a category that I believe will define the future.

Again, there is a way to read all of this as “China Bad,” which isn’t particularly interesting. Certainly, if China is currently America’s greatest adversary and largest geopolitical threat, it is not ideal that they produce the magnets, motors, and batteries on which our drones, future humanoid soldiers, and all manner of electric vehicles run. It is in America’s defense interest to incentivize the production and consumption of American components by American companies, and it is doing that, as we will discuss.

But America’s goal should not simply be to survive militarily, but to thrive economically through the Electric Era, when almost everything we combust fuel to power today, and many things that aren’t currently possible, will need to be rebuilt on the Electric Stack.

And to do that, we will need to manufacture key components here, right next to the companies that make the products that use them because maintaining design leadership without manufacturing leadership is not a coherent strategic position.

In 2010, former Intel CEO and Silicon Valley legend Andy Grove wrote an editorial for Bloomberg Businessweek titled How to Make an American Job.

In it, he argued that it was unsustainable for Silicon Valley to pay a small number of Americans increasingly high compensation while hollowing out manufacturing jobs, and correctly predicted that China’s lithium-ion battery dominance would lead to EV dominance.

There’s more at stake than exported jobs. With some technologies, both scaling and innovation take place overseas.

What microprocessors are to computing, batteries are to electric vehicles. Unlike with microprocessors, the U.S. share of lithium-ion battery production is tiny.

That’s a problem. A new industry needs an effective ecosystem in which technology knowhow accumulates, experience builds on experience, and close relationships develop between supplier and customer. The U.S. lost its lead in batteries 30 years ago when it stopped making consumer electronics devices. Whoever made batteries then gained the exposure and relationships needed to supply batteries for the more demanding PC laptop market, and then after that, for the even more demanding automobile market. U.S. companies did not participate in the first phase and consequently were not in the running for all that followed. I doubt they will ever catch up.

Grove disagrees with the then-(and maybe still-) popular idea “that as long as ‘knowledge work’ stays in the U.S., it doesn’t matter what happens to factory jobs… Not only did we lose an untold number of jobs, we broke the chain of experience that is so important in technological evolution. As happened with batteries, abandoning today’s ‘commodity’ manufacturing can lock you out of tomorrow’s emerging industry.”

Sixteen years on, it’s safe to say that Andy Grove was right.

“Without scaling, we don’t just lose jobs — we lose our hold on new technologies,” he wrote, long before LLMs — so put away your Pangrams. “Losing the ability to scale will ultimately damage our capacity to innovate.”

What I like about Grove’s analysis, apart from his predictions proving correct, is that it was written at a time when more Americans viewed China’s ascendency as a positive than a negative.

It rests on economic and industrial rather than geopolitical logic; it is offensive, not defensive. And he is arguing that to innovate in America, you need to manufacture the key components here, too.

To make great American drones and robots, for example, you need to manufacture motors and actuators in America, too.

The next question is: given the intense competition, is there a way to build a profitable drone motor and actuator company in America?

David and Jordan Decide to Start a Motor Company

It is de rigueur to care about drone motors, and to want to make them in America, but David Hansen has been obsessed with motors and the Chinese manufacture thereof for longer than some would-be motor mavens have been alive, and he has the tweets to prove it.

An archaeological dig through his account finds that he began reply-guying to tweets on IEEE electric motor-related articles in November 2018 with his own YouTube rabbit hole discoveries on BLDCs.

That’s the same year that David went to China for the first time, to walk Huaqiangbei for himself and begin sourcing for the self-balancing, AI-copiloted e-bike company he would cofound in Seattle in 2018, Weel.

Weel custom designed and built its motors and actuators by hand, due to the fact that there wasn’t yet an off-the-shelf Chinese actuator that worked for their needs. Most small component suppliers didn’t even have English-language websites; it just wasn’t worth it, they had all the demand they could get from customers who spoke Mandarin.

But something you learn about David is that he wants to get to the center of things, to speak to the suppliers’ suppliers’ suppliers directly, to see what he can buy from as close to the source as possible, and to learn how the source does what it does. Despite his best efforts, he was basically stonewalled, until COVID-19 happened and the world shut down.

All of a sudden, there wasn’t too much demand to waste time talking to the persistent American with the small orders anymore, and there was plenty of time to waste:

I learned the Chinese supply chain in 2020 and 2021. When China shut down for COVID, the suppliers were all stuck at home, downloaded WhatsApp, and started replying more to Western companies. Over 2020 you suddenly saw them building English websites — small suppliers with a dozen people who’d never have bothered before, since they already had the channels. A lot of factories went direct that year, just as a means of survival.

So 2020 and 2021 is when I started buying a lot of stuff direct from China: magnets, stators, other parts you couldn’t buy from the factory as easily before. Everybody’s stuck at home in both countries, talking over the internet and buying stuff.

A quick Twitter search verifies the timing, because around 2020 is when David, aka @boxcardavid, started becoming the motor guy on Twitter.

(Lore: David’s handle is @boxcardavid because he lived in this train car for most of his 20’s. If you’re trying to make motors or actuators, this is who you’re competing with.)

You could tell the man liked motors back in the COVID times, but he really let his motorhead flag fly starting in 2024, when he started figuring out what to do after Weel. Teardowns, comparisons, technical debates, knockoffs, factories. If you were one of the small handful of Americans who cared about brushless motors before they were cool, you probably followed David.

That same search for the next thing took David on a tour of the American companies whose products relied on motors and actuators. “David was the motor guy on Twitter, traveling around everywhere,” Jordan said. “Everyone said it was a clear problem. A lot of folks wanted to hire him as Head of Hardware or Head of Motors to solve it, but it was unclear what that role would mean. There was a desire, but not necessarily the urgency to reduce their reliance on Chinese suppliers.”

Chinese motors were just too cheap and too high quality, and besides, it didn’t make much sense for any given company, each of which had low volumes and other things to worry about, to spin up their own in-house motor and actuator assembly lines.

From all of these conversations, David was starting to realize that someone might have to build the American brushless motor company. His plan then, in November 2024, was to try to push someone else to do it.

Then our friend Sam D’Amico told him, “do it,” and David said, “k,” and two days later, Western Magnetics Company, a C Corporation named by Sam in a DM, was incorporated.

The company that may represent the West’s best hope to compete in drones and robotics began the way that most great companies begin: with an “lol”.

Incorporation docs are not a company, however, and David kept traveling, trying to figure out what everyone who needed motors and actuators was doing to get them. In early 2025, his odyssey took him to Georgia to see Jordan, who had been an early investor and advisor to Weel and was now Chief Commercial Officer at Slip Robotics, a company that builds robots that load and unload trucks.

“Everyone has motor supply and performance issues,” Jordan told me. “Having worked in robotics for a decade, I’d seen and been part of companies that thought about producing their own motors in-house. Then you look into it and realize, oh man, it’s actually pretty expensive and hard. It just didn’t make sense for one company to do it in-house just to fill their own demand.”

But, David and Jordan put their brains together and thought, it might make sense for one company to aggregate demand and build all of the motors and actuators for the rest of them as a vertically integrated horizontal play. To do all of the things that you’d have to do to get really good at making high-quality motors and actuators, like going as far as needed upstream into the supply chain and building automation and, most importantly, getting to scale to drive all sorts of efficiencies.

“Any competent engineer with focus can build a prototype actuator or a motor,” David told me. “You can build a bad motor really easily, and an okay motor without much practice and using open-sourced designs. Same with actuators. The design has literally been open-sourced since 2018, thanks to the great Ben Katz, who I really want to work with one day.” (Ben, if you’re reading this, go work with David and Jordan.)

“How to do it is known: you can order stuff from China, hand assemble it, and make it happen pretty easily,” he went on. “But doing it at scale is almost unrelated.” So scale is what they’d do.

David and Jordan teamed up to found Westmag, a company that was born to scale by growing up alongside the nascent American drone and robot industries, aggregating their demand, and providing them with the motors and actuators they’d need to make their products move, with the consistency, responsiveness, and respect that those companies were simply too small to get from their Chinese suppliers, on tight timelines, and eventually, with scale, at a similar price.

Their timing couldn’t have been better, although they didn’t know it at the time.

Soon after they decided to start Westmag, before they’d made a single hat, let alone a motor, the US Government would sanction T-Motor, China’s largest drone motor supplier, sending drone companies scrambling for a Western second source, and a little later, robotics would become the topic du jour in San Francisco and in the White House, which had no interest in letting China get another 10+ year head start in one of the future’s most important industries.

None of that was part of the plan. That part was just pure luck.

“What’s that phrase?” David searched his brain. “You can’t count on luck, but luck counts, and I definitely count on it. It’s turned out great. Good to be lucky.”

It’s not that Westmag wouldn’t have worked without the new government-mandated necessity of its products, it’s just David and Jordan expected it to take a lot longer. “Our bet was that this stuff would happen in five to ten years,” David said, “but the wind shifted last year, in the middle of us doing all this, with the government, regulators, and customers.”

And truly, I don’t want to leave you hanging here, and the stories about this stuff are some of the best inside baseball in the piece, including T-Motor’s hasty launch of a sub-brand “Lig Power” (as in, we strongly suspect, Ligma, which, if so, nice work, China…) to get around sanctions, and I promise that I will tell them to you, and that they make Westmag’s position in the market stronger than it would have been otherwise at this point, but the whole point of this essay is that Westmag is not a geopolitical bet, that it stands on its own industrial and economic logic, and that, sanctions or no, Great Power Competition or no, Westmag’s strategy is a smart and well-supported one that will require a Herculean grind to pull off but is certainly possible to pull off, and if it does, answers the question “is there a way to build a profitable drone motor and actuator company in America?” in the affirmative, so we have to pause the storytelling for a moment here, in early 2025, before Westmag knew that it would become more necessary, sooner than it expected, to unpack the strategy.

Westmag’s Strategy: Going Vertical to Go Horizontal

When I wrote the Vertical Integrator series, I had a whole section that I ended up cutting on Porter’s Five Forces and why I don’t normally like the position of doing something really hard in order to earn the right to sell components into a handful of large, powerful, and slow-moving incumbents. Maybe, if there are only a couple of key buyers, they’ll hammer you on price and force you to build to spec. Almost certainly, they’ll move on a procurement timeline so slow it will bleed you dry.

Plus, given where we are, at the dawn of a new techno-economic paradigm, in what Carlota Perez calls an Installation Period, book says vertically integrate: “the Installation Period favors Vertical Integrators and the Deployment Period favors modularized suppliers.” If you’ve created a magical new technology, use it as the core of a new system that competes directly with them. In Power in the Age of Intelligence, I summarized my thinking in a question:

If your technology is so good, why aren’t you using it to compete?

Which is to say, in a very simple reading of the situation, Westmag is not the kind of company I’ve been looking to back.

But the fun thing about all of this, and why I find business strategy so endlessly fascinating, is that there are exceptions to every rule, and understanding where to apply the rule or where to grant an exception requires a deep dive into the specific details of the case at hand.

Take Westmag’s key thesis at the beginning, which Jordan described as: “at this moment, maybe these industries are taking off enough that you can actually aggregate this demand and jump to scale.”

There are no large, powerful American incumbents in drones or humanoid robotics. There is a fragmented market of younger companies that are too small to have much buyer power. Each, as Jordan realized, is too small or stretched too thin to spin up meaningful motor or actuator manufacturing themselves, which means there’s an opportunity. It also means that the normal pitfalls of trying to sell to large, powerful incumbents don’t apply: startups can move fast, and for now, no individual company has enough power (or alternatives) to exert pricing power.

The “for now” part is important, because the plan requires that some of the companies Westmag serves get very big. The key is, Westmag will ride to scale alongside them, and aggregate demand among many of them, so that by the time any drone or robot company gets really big, Westmag will have achieved a scale that none of them can match alone.

Scale, they realized early, is everything in this game, and getting to scale first means that Westmag will be able to do all of the hard things required to actually do this well.

Specifically, it will vertically integrate its own supply chain where needed in ways that are deeply impractical for any single drone or robotics company to do.

Two examples, both of which Jordan and David said were the things that kept them up at night early on but no longer do (as much).

First, as discussed, while there are American companies like MP Materials and Vulcan Elements that can provide neo magnet block today, there is a gap in western suppliers that can cut, coat, and magnetize the magnets to the requirements needed for drone motors. Today, the block gets sent to Malaysia or China for cutting and coating, and back to the customer in the US. Leaving aside the added time or export restrictions, this is an issue: “magnets are fragile and like to stick to each other, so they’re difficult to buy the further away you are,” David explained. “That worried us for a long time. We’re less worried now because we know that world, we know how magnets flow around the world and how to get them.”

Westmag is exploring, by itself or with partners, opening up capacity to cut, coat, and magnetize its own magnets in America. This will be annoying, and come with high upfront costs, but it will give Westmag greater control over its supply chain, lower costs, simpler logistics, and the ability to get to a wider product mix.

Second, Westmag will buy electrical steel from Japan (they are clear that the material supply chain will be US and allied countries, not just US), but it will stamp and powder-coat them here to make their own stators.

Doing those things only makes sense if you’re planning to get to scale, but if you can do them, they allow you to build out a broader catalog more quickly, which helps get more scale, because you can serve a larger number of customers.

In motors and actuators, broadening the catalog to serve a high mix means making “smaller circles and bigger circles,” which sounds simple, but it really helps if you control your design and inputs.

“Planning out the product catalog,” David explained:

We realized that if we make our own stators, it’s not too hard to build a different stator size the next day. But if we have to design everything, send it out, and have someone else stamp it, you’ve got built-in cost and friction. That’s another huge reason to move upstream: it’s what enables higher mix.

On the actuator side, if you want to build a different actuator, controlling the motor matters, because the core of an actuator is the drone motor. If you don’t control that, your constraint is whatever motors you have available. I feel like I should say something smart after that, but it’s just true.

A couple minutes later though, he found something smart to say and came back. “Something else on the actuator side, since you control the motors…”

The design-cycle time for actuators right now, for almost anyone in the US, is in the months, because you rely on overseas suppliers to order pretty much everything. We saw the same thing with drone motors: you don’t just start making American motors that are better than China’s. To get good, you have to make a bunch. Same with actuators. So cutting the iteration time on actuators is super important: if you can only redesign four times a year, how far can you get? We think it’s key to making them reliable.

Moving fast and breaking things, along with scale economies, will bring other advantages, namely, process power.

“You can’t easily pull process knowledge out without literally pulling the people out of the neighborhood,” David acknowledged, “but a way to get it is to build and break much, much faster.”

You can start to see how all of this fits together into a strategy.

First things first, you need to get to scale.

That means, Jordan said, “Going to high-volume customers and asking, ‘What do you have? What are you using now?’ and benchmarking our initial SKUs against what they need in order to get larger offtake agreements against an aggressive manufacturing ramp, which justify making larger buys and investments of input materials.” Quite literally, the plan is to not reinvent the wheel, but to make the wheel that people need right now so that they can get enough wheel orders that it makes sense to make bigger input material orders and even to integrate upstream.

Then, if you can integrate upstream, you can spin your whole process faster.

If you can stamp your own stators, you can offer a larger motor on short-notice, which means cost efficiencies plus more demand and larger orders. Plus, if you control motors of a bunch of different sizes, you can iterate on actuator designs more quickly, and Tasmanian Devil your way into process power in months that would otherwise take years. You can then automate the parts of the process that are amenable to it, and start to gain small advantages over Chinese suppliers.

Eventually, riding drone motor volume demand that exists right now, and using it to fuel its parallel work on actuators, each of which has a drone motor at its core, you get to “high volume, high mix” offering on par with the Chinese, but on the same continent as its customers and more responsive to them.

To get there, they’ll need to operate like a fab in the near-term, a bit like a TSMC for motors and actuators.

Being a fab - serving the demand that customers have today - is how you get volume, and you can use that order to standardize, so that your processes get easier, your margins get fatter, and you become the platform on top of which anyone, from a scaling drone manufacturer to a pre-seed robotics team to a hobbyist high schooler, can build all manner of electric things that fly and roll and grab.

The North Star is essentially the T-Motor catalog with a “Buy Now” button and fast shipping.

All of this is hard but not impossible, and most of it requires getting to scale first and dominating the space. This doesn’t work as well if the drone motor and actuator supplier landscape becomes really fragmented, because it could mean no one gets to the scale required to offer great quality at a great price.

To that end, Westmag has some advantages.

While there are a number of motor startups popping up now that the space is hot, it really helps that Westmag has been building out its production capacity, relationships, know-how, and supply chain since before it was cool. It has a head start.

It also has the right investors. In August 2025, Westmag raised its $11 million Seed Round from a16z, Founders Fund, Lux Capital, NFDG, Menlo Ventures and a group of angels including SendCutSend’s Jim Belosic, Sam, and me. These firms are among the most likely to back something like this over the many years and hundreds of millions, or billions, of dollars it will take to win. Their portfolios are also full of the companies that will be Westmag’s first customers if they can deliver.

As I wrote in Vertical Integrators Part IV, “Among startups, I expect we’ll see much less competition. The companies that show an early ability to execute against a big and credible enough vision will attract the top talent and the limited pool of investors willing to back such hard-to-underwrite companies, sucking the air out of the room for would-be challengers.” In an industry that requires scale more than raw innovation, this is a feature.

Still, the longer it takes to get to scale, the more opportunity there is for new entrants to come in and fragment the market.

Which is why it helps that Westmag got a little lucky.

Westmag Becomes Urgently Needed

Ok we’re back.

When David was running around talking to drone companies in 2024, most of them weren’t urgently trying to get out of China. They knew cerebrally they needed to diversify their supplier base as they scaled, as any company does, and probably would have preferred to diversify into a country other than China, but they were hooked on China’s cheap prices and availability. It just didn’t make sense to spend the reps figuring it out only to get more expensive and possibly lower quality motors, so at most, they’d diversify to two or three companies in China just in case T-Motor, the gold standard, had an issue.

All of that changed on January 15, 2025, five days before President Trump’s second inauguration and a couple of months after David had incorporated Westmag, when the Treasury’s Office of Foreign Assets Control (OFAC) sanctioned Jiangxi Xintuo / T-Motor by name for sending more than $9 million worth of items to Russian companies, including entities involved in Russian UAV production.

“There was a bit of a delayed reaction,” David remembered. They followed the rules and stopped buying, but they figured that some sort of alternative would pop up, and they had some inventory stored up in the meantime.

And there were workarounds. T-Motor unsubtly spun up “Lig Power” - like, if you go to T-Motor’s website and click “North America,” it just takes you to Lig Power, which is not sanctioned but also probably not your best long-term bet.

It wasn’t until April or May, when their motor shelves started to go bare, that they began to freak out. So while there had been companies taking China seriously - Skydio had already been sanctioned by China, and Neros was already serious about the risk - it was May 2025 when the American drone industry as a whole started thinking urgently about dual-sourcing.

Then Jordan left Slip and the duo really got to work, realizing they’d need to scale up faster than previously anticipated, which was both a blessing and a challenge. And the blessings kept coming.

In December 2025, the Federal Communications Commission (FCC) gave Westmag an early Christmas present when it added Uncrewed Aerial Systems (UAS) and UAS Critical Components Produced Abroad to its Covered List. Already-approved systems were grandfathered in, and it didn’t make using drones or motors you’d already bought illegal, but the Covered List meant that the FCC wouldn’t authorize new foreign-made drone components, and the list of components was broad: data transmission devices, communications systems, flight controllers, ground-control stations/controllers, navigation systems, sensors/cameras, batteries/BMS, motors, and associated software.

The FCC’s move was divisive in the industry. On the one hand, it makes sense: we probably don’t want to rely on Chinese motors for drones that may be used in a war with China. On the other hand, it included foreign countries beyond China (although there are exemptions for some allied suppliers) in an effort to support a Buy American agenda, while kneecapping American drone companies’ ability to produce.

In either case, it meant that American drone companies need to dual-source and find reliable domestic suppliers pronto.

That was the drone side. From the beginning, the plan was: there is a drone industry today, with fast-growing order volume today, so start with the drone motors, and use them to ramp production volume. Then, eventually, since every robot actuator has a drone motor inside of it, use the motor to expand into the potentially much larger robot actuator business over time, as that industry ramps.

Each drone has 4 motors, and Ukraine will need 28 million drone motors for 7 million drones this year; each humanoid robot has 20-40 actuators, and by some estimates, we will have 10 million of them in a decade, and three billion by 2060. That puts actuator demand in the tens to low hundreds of billions.

Westmag would have been golden riding drone motors down the learning curve, but the gifts of urgent demand just kept on coming.

In late 2024 and early 2025, when David was talking to robotics companies, he kept hearing that they just couldn’t get the changes they wanted made, that orders were inconsistent, and that Americans were simply at the bottom of the priority list.

You’d order 100 actuators, it’d come in two shipments, and on the second shipment they’d move the connector location or type, change the firmware, or completely change the mechanicals inside.

Jordan and David like to tell the story of a friend of theirs who’s a famous robotics guy over here who found a bug in the firmware for one of these Chinese actuators and sent them a bug fix, and the Chinese manufacturer just didn’t care.

Still, while they were casually looking for other paths, their #1 question back then was, “What is this going to cost?” China wins that question 100 times out of 100 today, especially at lower volume. They wanted to spec their own actuator designs to work with their specific robots, but you could find a contract manufacturer in China who was willing to do that pretty easily. As long as it was roughly to spec and cheap for the quality, they didn’t really care where it came from, so they stayed.

That started to change late last year. Something was in the air.

People began to realize that with AI taking off, robots would be the next big thing, and that, like AI, robots would be hardware constrained. Specifically, they’d need a lot more actuators than we can currently produce.

Quickly, the discussion went from “How much do my actuators cost?” to “Where are we going to get all of the actuators we need in one, two, five years? Can or should all the actuators come from China?”

David is friends with the head of hardware at this one robotics company, and every time they talked last year, the conversation was always about cost. Now, “every text conversation is about it not being in China, having more than one supplier, and planning for if China cuts us off.”

This is the definition of sovereignty in the future as David and Jordan see it: controlling your own robot supply chain.

The US Government seems to be coming around to this definition. Across government and industry, there is a growing belief that while we are about a decade behind in drones and their supply chains, and are taking actions to play catch-up, robotics is so nascent that we can try to be competitive from the jump. People are starting to think about what we can do to be more proactive in the robotics supply chain, which means eating more of the Electric Stack alongside our allies.

Because as we wrote of China’s bet in The Electric Slide, “for intelligence to truly matter, it needs energy and action.” Or as Aaron Slodov put it more pictorially:

Having the smartest computers doesn’t really matter if they can’t act on the physical world. To act on the physical world, they need bodies (whether humanoid, arms, or vehicles). And to build bodies, you need to control enough of the components to build them well.

God Bless the Red, White, and Blue Premium

Westmag’s initial bet was that if you aggregated demand from the nascent American drone and robotics industries, and made it easier for them to iterate quickly and grow, you could grow with them. It was a true bet, because both drone and robotics companies cared about cost above all else.

And Westmag could get to cost parity, with enough volume and time, but how do you solve that initial chicken and egg? How do you get the early orders that give you the volume to bring your price down to competitive levels?

For motors and actuators, as with many industries, from chips to solar panels, the initial push down the learning curve has come from Defense demand.

With the government’s actions, and the industry’s waking up to the fragility of relying on China for its components, price has become a secondary concern for a certain buyer, particularly one selling into Defense. The DoW is willing to pay more for American drones with American components, which means that the companies making American drones can afford to pay more for American motors. This is industrial policy through Defense demand like the type we discussed in Thank God for Data Centers.

This is referred to as the Red, White, and Blue Premium. It is not a long-term strategy. But it is a hell of a bootstrap and jumpstart.

“DC and Defense are near-term feral markets,” Sam D’Amico told me on a call last week, “but long-term, that’s actually a small part of the TAM. Robot actuators and consumer and commercial demand are. All of the electric cars, appliances, computers… brushless motors are in everything. And if you’re long robotics, you’re long actuators.”

Strategically, the RWB Premium is less about the actual price, and more about the fact that today, it’s where the volume is. Everything else flows from that.

Westmag has begun signing contracts with customers focused on Defense-related applications. In just the last few weeks, it has signed offtake agreements for hundreds of thousands of motors, and is in late-stage discussions with many other drone and robotics companies for similar deals. Commercial demand will be important to getting to scale, but it’s not where the highest volume demand is today. As Sam has pointed out many times, American demand is our greatest advantage.

The government could also potentially get more directly involved in lowering Westmag’s prices and increasing its volume, through subsidies, loans, and offtake agreements. There is precedent: it did deals with American neo magnet manufacturers MP Materials and Vulcan Elements last year, giving both access to cheap capital to fund CapEx, and offtake agreements to support scaled manufacturing. Even if the private market won’t buy the magnets (which is unlikely), the government set a price floor.

In either case, the trick is not to rest on Defense-related contracts, but to use them to fund the things that will make Westmag commercially competitive, potentially as a second-source and then as a primary source for growing drone and robotics companies.

That means doing the things that scale allows you to do, quickly.

As discussed, it will mean moving further upstream into magnet cutting and stator stamping. It will mean deeper partnerships with suppliers in the ecosystem, from electrical steel producers in Japan to machine parts manufacturers in Tennessee.

It will also mean expanding into a larger manufacturing facility in the Bay Area this year. While there are good reasons to spread its upstream facilities throughout the country, it makes sense to manufacture right next to the drone and robotics companies that will be its biggest customers, as Shenzhen has proven.

Speaking of things that Shenzhen has proven, in the beginning, it will mean making the motors the exact same way customers expect. That means designs optimized for Asian supply chains, for now: CNC-ing parts with complex shapes out of aluminum, very little design-for-manufacturing, manual assembly.

Over time, however, Westmag plans to adapt its manufacturing to modern American processes. As volume grows, designing a new way of making motors begins to make sense.

Westmag will design for manufacturing and, given that much of its initial costs come from labor, automation. It will redesign the same motor over time so it uses simpler machined parts and more processes that scale with capital instead of labor, like stamping, die-casting, molding, and near-net-shape parts, and so the geometry is something a machine, not a person, can make and assemble.

This is the other reason to build in San Francisco: it’s where the automation and robotics engineers are.

Working with these engineers, it will design the motor and the factory together, because as David put it, “The design of the factory floor and the design of the motor go hand-in-hand.”

One of China’s gifts is that all of its suppliers are in the same neighborhood, but that also means that it doesn’t make sense for any one of them to put everything under one roof. Westmag doesn’t have that luxury, so it will have to.

Surprisingly, Chinese suppliers still do a lot of this stuff manually, like running wires by hand. There aren’t many automated lines. There isn’t really even one “line,” they’ve discovered:

The “factory” in China isn’t usually in one place. There’s a dense neighborhood of specialized subcomponent shops connected by couriers. There are automated machines, but they’re at one address; a part gets built there, a courier takes it an hour later to another location a few blocks away where it is joined with another part, then a couple hours later that goes via courier somewhere else.

So part of what we’re doing, and will continue to do, is to vertically integrate, bring more processes and machines in house, and connect them. Essentially: connect everything by conveyors, not couriers.

The more it can do in-house, in this Installation Period for American motors and actuators, the faster it can spin, and therefore the faster its customers can, too.

The one place it won’t integrate is downstream, into end products like a full drone or robot, because the point of Westmag is to make affordable, reliable, fast, high-quality motors and actuators on top of which every other American electric product can build.

Without knowing exactly where the peculiarities of the supply chain will take it, it’s hard to know when Westmag will get to cost parity with China, but “it’s not crazy five-to-ten-year math at insane scale to be competitive. It’s classic industrial logic. The Idiot Index denominator on these things (the cost of the raw materials) is very low,” David said, “so even when they’re selling them for $20, there’s room for us to grow, scale, and lower the Index.”

That is a high bar, the unit-to-unit cost comparison, a hard-but-achievable-but-probably-not-necessary one to clear, because the real value if Westmag succeeds, and its raison d’être even if the US and China become best friends, is that if you can make critical components near the customers that use them and supply them reliably, the whole innovation machine spins faster.

Spinning America Faster

“A new industry needs an effective ecosystem in which technology knowhow accumulates, experience builds on experience, and close relationships develop between supplier and customer,” Andy Grove wrote, and time has proven him right.

China has built this way and is now winning the Electric Stack; America has not, and is not.

Westmag’s key insight is that while China has an early lead, the race is just beginning. Both drones and robotics are very new industries.

As much as drones dominate the conversation today, thanks to the war in Ukraine, and as important as people think they will continue to be, I expect that they’ll be even bigger. For large industries, Defense is usually a very small initial market in retrospect. Packages and people will fly through the air faster, cheaper, and more efficiently than they crawl through terrestrial traffic today.

Robotics is even more nascent, with unit volumes in the thousands, and live debates as to which form factors and model architectures will win out. What everyone seems to agree on is that robotics is going to get much bigger.

OpenAI is getting back to its roots and back into robots…

Jensen Huang is getting excited about robots…

And just this week, The Wall Street Journal shared PitchBook data showing that venture investment in physical AI and robotics is on pace to pass an already-record 2025 in the first half of 2026 alone.

Wall Street estimates vary, but they all project unit volumes in the millions over the next decade, and an installed base in the billions in the decades beyond. We are currently operating in bars that will be barely visible above the x-axis looking backward.

The question is: who’s going to make all of the drones and robots, and therefore, whose economy will most benefit from their growth?

If you had had to bet, back when Nvidia was founded as a gaming graphics chip company in 1993, whether it, Intel, or AMD would be the biggest thirty years hence, you would have gotten ludicrously good odds on Nvidia. The chip market seemed set, even though, as we now know, it was barely in its infancy.

“Real men have fabs!”, former AMD CEO Jerry Sanders declared about chip manufacturing in the 1980s, and were he operating today, he might say the same thing about motors and actuators. DJI and Unitree make the most drones and robots, respectively, today, and both leveraged the know-how from their ecosystems to vertically integrate down to the motors and actuators, respectively.

But in 1987, a former Texas Instruments engineer named Morris Chang launched TSMC, which offered to fab everyone else’s chips for them. Nvidia, founded six years later, was born fabless and free to focus on architecture, software, developer ecosystem, cadence, and market selection. These are the things that have compounded into Nvidia’s moat over time.

Nvidia is now the largest company in the world by market cap, and it still fabs with TSMC. The second and third largest companies, Google and Apple, do too.

TSMC, by vertically integrating the hard, CapEx-intensive, process knowledge-driven work of fabbing chips and serving horizontally, made it possible for an entire ecosystem to flourish. It’s grown up alongside that ecosystem, and even now that the companies it’s enabled are cash-rich enough to build their own fabs, the compounding that their collective business has paid for would make it almost impossible to catch up. So even at scale, TSMC and its customers keep winning together.

A bet on Westmag is that we are at the same place in the Electric Age today that we were in the Information Age then.

Westmag’s hope is that by vertically integrating the hard, CapEx-intensive, process knowledge-driven work of making motors and actuators and serving horizontally, it will make it possible for the American drone and robotics ecosystem to flourish.

Because the company is still small, like the industries it serves, Westmag can afford to give American drone and robot companies the time of day. Because it’s vertically integrated, it can be responsive to their needs and spin up prototypes in days instead of months. Because of that, and because it’s in their backyard, it can shorten the iteration loop, so that its customers can iterate their way to better drones and robots than are made anywhere else in the world.

The real big bet on Westmag is that it’s not too late. That if you build a machine that marries American-style innovation with scaled manufacturing, the way we used to, and spin it really fast, slope will outrace intercept and the future might be built in America again after all.

Thanks to Jordan, David, Sam, and many others for helping me get smarter on motors.

That’s all for today. We’ll be back in your inbox Friday with another Weekly Dose.

Thanks for reading,

Packy

Hi friends 👋,

Happy Friday, and welcome to our 195th Weekly Dose of Optimism.

Man, just when I thought it was going to be hard to top last week’s Dose, we have an LDL cholesterol-fighting gene therapy, evidence that GLP-1s slow cancer, supersonic flight, and nanotech, and a Moon Base. We also have the coolest Science Breakthroughs roundup yet. Even a massive Blue Origin explosion can’t slow us down.

If you get through this and want even more optimism injected into your veins, check out this week’s essay:

Let’s get to it.

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(1) Eli Lily Does it Again, Now with LDL Cholesterol Gene Therapy

For those keeping score at home, this is the second week in a row that we’ve led off with Eli Lily. Last week, their Reta Phase 3 trials showed astonishingly good results, and this week, they published the results of a phase 1, open-label, single-ascending-dose study on the VERVE-102 gene therapy targeting PCSK9, which is responsible for LDL cholesterol, which is in turn responsible for a lot of heart disease and deaths.

Now this is just a phase 1, and there were only 35 people involved, but a single dose gene therapy reduced PCSK9 levels “from 51% at the 0.3-mg-per-kilogram dose to 88% at the 1.0-mg-per-kilogram dose,” and showed corresponding reductions in the LDL cholesterol level “from 9% at the 0.3-mg-per-kilogram dose to 62% at the 1.0-mg-per-kilogram dose.”

This is a big deal! Globally, about 4.4 million deaths a year are attributable to high LDL cholesterol, roughly 7.8% of all deaths. Cardiovascular disease causes roughly 18.6 million deaths a year worldwide, so high LDL accounts for somewhere around a quarter of those.

Now, we might be able to knock out 4.4 million deaths a year with a shot.

PCSK9 inhibitors are not new. On a recent Invest Like the Best episode, Braidwell Managing Partner Alex Karnal called PCSK9 medicines “pretty much a free lunch”:

The headline here is PCSK9 medicines are amazing, because what they do is they today can lower our bad cholesterol, that LDL cholesterol, by 50%. And we now have outcome studies in patients at different degrees of having high cholesterol, having significant protection from ever developing and having a heart attack or a stroke. And so in patients that have previously had a heart attack or stroke, we can reduce that risk by over 20% in the future. And for people that are at high risk of having a heart attack or a stroke, the medicines that are approved and on the market today can lower that risk by about 25%.

With previous PCSK9 medicines, people would have to stay on them forever to continue to eat that free lunch. Now, if Lily’s early results hold, they will get all of the benefits in a one-and-done shot. Essentially, the drug edits your gene to be like the “population of people in the world that have a genetic mutation that conveys a massive advantage. They have a mutation in their PCSK9 gene, which means they don’t produce the PCSK9 protein.”

We are still a few years away from this miracle drug hitting the market, and there are still trials to be done, but this is a really big deal, not only because it’s an assault on LDL cholesterol and therefore heart disease, but also because it’s another example of our ability to see an advantageous mutation in a certain population, make a drug to mimic it, and knock more and more diseases off the list.

Plus, as the WSJ reported, a number of pharma companies are developing drugs to attack LDL’s cousin, Lp(a), or lipoprotein(a). Lp(a) is almost entirely genetic and currently untreatable. About 90% of a person’s Lp(a) level is fixed at birth, diet and exercise don’t lower it, and statins can actually raise it. So unlike LDL, which we have many tools for, there’s essentially no approved therapy for high Lp(a) today. Roughly one in five people globally have dangerously high levels, and that’s bad. Lp(a) hardens arteries and promotes clotting, which is why it’s so noxious in driving cardiovascular events.

So far, science has been powerless against Lp(a), both in treating it and in proving that lowering a person's Lp(a) will actually reduce heart attacks and strokes. Earlier drugs lowered the particle moderately but failed to reduce events in studies. The current bet from Novartis, Amgen, and, you guessed it, Eli Lilly is that new technologies can cut Lp(a) far more dramatically, with late-stage trials underway and Novartis's first Phase 3 results (pelacarsen) expected this year. Human genetics suggests it should work, but they don't know for sure.

Heart disease is the leading cause of death in the US, and now we have even more data points suggesting that you should really figure out how to stay alive for the next few years, because thereafter, you may be able to stay alive for a very long time. “Medicine keeps getting better and better.”

(2) Weight-Loss Drugs May Have Surprising Side Effect: Stalling Cancer

Xavier Martinez for The Wall Street Journal

You thought we were done with Eli Lily? We’re not done with Eli Lily.

Just as the ink was drying on last week’s Dose, The Wall Street Journal published an article about GLP-1s’ cancer-fighting abilities. “A suite of four new studies suggest that people taking so-called GLP-1 drugs like Novo Nordisk’s Ozempic and Eli Lilly’s Mounjaro saw reductions in tumor progression, lower overall chance of death and less risk of developing breast cancer.”

In lung cancer patients, the rate of progression to advanced disease was cut roughly in half—10% in GLP-1 users versus 22% in the comparison group. Breast cancer patients showed a similar pattern, with progression rates of 10% versus 20%. Colorectal and liver cancers also showed statistically significant reductions.

These studies are from places like UT MD Anderson, University of Pennsylvania, and Cleveland Clinic, and while researchers don’t yet understand the mechanism, the research points to yet another miracle for this miracle drug.

I’m not even sure what to say at this point, so say it with me… get fucked, cancer.

(3) Hermeus Quarterhorse Mk 2.1 Completes First Supersonic Flight

In Riding the Leopard, I wrote, “I would like to live in the future in which we have spaceships and abundant energy and supersonic planes, the future in which car crashes and cancer are a thing of the past.”

I wasn’t expecting that future to come quite so soon. While GLP-1s go to work on cancer, Hermeus became “the world’s first privately developed, unmanned supersonic jet and the fastest unmanned aircraft flying today” when its Quarterhorse Mk 2.1 went supersonic at Mach 1.21 in an unmanned test flight. Huge congrats to AJ and the Hermeus team. I got chills watching the video.

Now, they’ll have more fuel to go even faster, more often. Yesterday, they announced that the Defense Innovation Unit expanded its contract by $159M to $219M to tackle high-Mach flight and payload release.

I may have dreamed too small. Hermeus is now gunning for hypersonic flight.

(4) Atomically precise mechanosynthesis of carbon structures on hydrogenated Si(100) by inverted-mode STM

One thing that I forgot to mention in my vision for the future, but that I would like to add now and would very much like to see, is nanotechnology.

At the end of Where Is My Flying Car?, J. Storrs Hall paints a picture of the future we could have with abundant energy: flying cars, utility fog, the Weather Machine, a “space pier,” and maybe most tantalizingly, atomically precise manufacturing (nanotech), nanofactories and self-replicating machines that build objects atom by atom, collapsing the cost of physical goods the way semiconductors collapsed the cost of computation.

Nanotech is the vision Richard Feynman laid out in Plenty of Room at the Bottom, the one Eric Drexler theorized in Nanosystems, the one Hall imagines in Where Is My Flying Car?, and the one Neil Stephenson painted in The Diamond Age. If you can manipulate atoms one-by-one, you can build anything you can dream up.

Until now, though, it’s remained a dream. Drexler’s theory of positional mechanosynthesis, alongside Ralph Merkle and Robert Freitas, was attacked, most famously in the Drexler–Smalley debate, where Smalley's "fat fingers / sticky fingers" objection held that you couldn't do controlled positional chemistry at that scale.

Well, we can tell Smalley exactly where to stick his fat fingers now.

On Tuesday, Merkle, Freitas, and a number of researchers published a paper demonstrating simultaneous spatial and chemical control over the mechanosynthetic fabrication of carbon structures. Concretely, using a technique they call inverted-mode STM, carbon dimer (C₂) units are donated from surface-deposited molecules onto pre-patterned reactive sites on a hydrogen-passivated Si(100) surface. They show three escalating things: single-site C₂ donation, spatially patterned multi-site donation, and the stepwise assembly of polyyne structures through successive C–C bond formation. Their framing is that this establishes controlled mechanosynthetic donation as a foundational capability for programmable atomically precise fabrication.

For roughly forty years, Mechanosynthesis— using mechanical positional control to drive site-specific chemistry, building structures atom by atom — lived almost entirely in theory and computational chemistry. The canonical proposed primitive was a "dimer placement tool" that deposits C₂ units onto a workpiece to grow diamondoid structures. That is almost exactly what this paper demonstrates experimentally!

There is still a massive gap between this work and Hall’s nanofactory. They've built short carbon chains, one dimer at a time, with an STM tip, which is a primitive. The chasm to the vision is throughput and dimensionality: a single tip placing dimers serially is astronomically slow versus the massively parallel, self-replicating systems APM actually requires, and going from 1D polyyne chains to 3D diamondoid objects is its own huge challenge.

But come on! This thing that people said was impossible was just demonstrated to be possible! I am going to spend the weekend dreaming up all of the things I want to order from the nanofactory, like my own APM island a la The Diamond Age.

In the meantime, if you want to get smart on nanotech, I recommend Hall’s primer for the Abundance Institute and Jacob Rintamaki’s A Technical Review of Nanosytems.

(5) MOON BASE

On Tuesday, NASA Administrator Jared Isaacman and the NASA team held a press conference at its HQ in Washington to provide updates on the Moon Base program, a long-term lunar exploration and infrastructure initiative under the Artemis program aimed at enabling sustained human presence and expanded scientific and commercial activity at the lunar South Pole.

It also launched a Moon Base Website, complete with the hype video above and a timeline for the Moon Base. Because we are establishing a Base, on the Moon.

Isaacman & Co laid out the plan for three initial Moon Base missions. Moon Base I, targeted for no earlier than fall 2026, will use Blue Origin’s privately funded Blue Moon Mark 1 Endurance lander to deliver NASA science payloads, including the Stereo Cameras for Lunar Plume-Surface Studies instrument and a Laser Retroreflective Array, to the Shackleton Connecting Ridge. Moon Base II, planned for later in 2026, will use Astrobotic’s Griffin lander to deliver more than 500 kilograms (over 1,100 pounds) of cargo, including Astrolab’s FLIP rover, to mature lunar terrain vehicle mobility, autonomous operations, and logistics. Moon Base III will prioritize scientific payloads to expand understanding of the lunar surface.

Then, eventually, we’ll have people living on the Moon, which is a harsh mistress but a potentially perfect launchpad for humanity’s mission to Mars. What a universe.

EXTRA DOSES: Science Breakthroughs, Telescope Ranchers, Encyclical

Quick note: I think this might be the coolest Science Breakthroughs yet, with entries on the genetic architecture of complex traits, hallmarks of aging and mortality, homing pigeons relying on superparamagnetic macrophages for navigation, and armadillo-inspired morphing skeletons for robots. Science Breakthroughs is a roundup of the bleeding edge, the stuff that’s even earlier in its development than what we cover in the Dose.

I think it’s worth the subscription alone, and in general, I’m trying to share a lot more value with not boring world subscribers, including recent pieces like Riding the Leopard, Cowboy Space Corporation Case Study, and Thank God for Data Centers. I hope you’ll subscribe and join us.

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Hi friends 👋,

Happy Wednesday!

A couple weeks ago, I asked if you wanted me to start sharing more off-the-cuff notes with not boring world subscribers, and the response was great, so we’re back. It’s a Wednesday afternoon, not my normal send time, but these are meant to be less formal and more, “I noticed something interesting, here are my quick thoughts.” This one happens to be a little longer than it is quick, but it’s one I wanted to get out for two reasons:

1. People hate AI Data Centers, and I think they’re wrong, even if they don’t like AI.

2. Because I keep hearing, reading, and seeing that AI Data Centers are funding new technologies before they’ve come down the learning curve, which might be a providentially big boon to Reindustrialization and all of the hard, physical things we want to see in the world.

It’s pretty beautiful that gaming chips that evolved from Apollo-funded integrated circuits are creating a product with so much demand that their houses can pay for all sorts of novel technologies, like the Apollo Program did.

Let’s get to it.

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Thank God for Data Centers

There exists a vast pool of technologies that are potentially superior to those we employ today, but which require scale and learning curves to reach their potential.

Advanced nuclear reactors are one such technology - they are more expensive than alternatives today, but manufactured at scale, and benefiting from the learning curves required to get there, may become cheaper than other generation technologies. The cost physics are on their side, and nuclear is reliable, safe, firm, and clean.

The challenge with these technologies, in normal times, is that there is little economic incentive for the buyers who would enable the scale to stick their necks out. Natural gas is cheap and abundant, and it’s not that bad for the environment, compared to coal and oil at least, and the environment is someone else’s problem, anyway. And so, in normal times, we remain stuck in local maxima without the demand to push towards global ones.

Historically, these stalemates have cracked in a couple of main ways: Alpha Products and extraeconomic Buyers of Capabilities. These are essentially the same mechanism at different scales and with different motivations.

In The Electric Slide, we discussed the role that Alpha Products played in providing the initial demand that eventually brought each layer of the Electric Stack down their respective cost-performance curves. For lithium-ion batteries, the alpha product was the Sony Handycam. For neodymium magnets and motors, it was the 3.5” hard‑disk drive. For power electronics (IGBTs / inverters), it was the variable‑frequency drive (VFD) for industrial motors. For microcontrollers (MCUs), it was the calculator. Etc.

For each of these, the new technology was advantageous enough in a specific way to the end product that it was worth paying higher costs or sacrificing on other capabilities to capture those benefits.

Alpha Products, however, typically support components that are not multi-hundred million or multi-billion dollar projects in their own right.

The role of the extraeconomic Buyer of Capabilities in the development of new technologies is even better-understood. This is the DoD or NASA, mainly, buying technologies to confer a specific advantage, almost irrespective of their cost. This category of customer cares less about price than about capability.

Theirs is an important role because it gives new technologies the opportunity to get to scale, come down the learning curve, and ultimately compete in the much larger commercial market.

For a while now, but particularly over the past couple of weeks, I’ve heard some version of the same story over and over again:

“We are still going after our long-term mission, but to fund it, we’re planning to sell to data centers.”

Today, Data Centers are increasingly serving as Buyers of Capabilities, acting as something between a government and a commercial buyer. The Data Center is the meta-Alpha Product. If you can sell them something they need, fast, they have an almost bottomless bid.

This is true for obvious things like GPUs, inference chips, and DRAM, but it’s also true for companies that you wouldn’t typically associate with AI data centers, like supersonic turbines, enhanced geothermal, modular construction, high-voltage direct current grids, solid-state transformers, silicon photonics, optical fiber, lasers, batteries, and nuclear.

Many of these technologies have the potential to be better and cheaper than the incumbent technologies they aim to replace, but they have been too expensive and unproven to compete. With backlogs in all of the traditional inputs to Data Centers, however, developers are willing to pay up for new technologies that can deliver fast, which gives them the opportunity to scale up and cost down.

For these technologies, Data Centers act as a third type of Buyer of Capabilities, a commercial analog operating on DoD-style procurement logic but commercial timescales.

Given the size of the budgets, the relative smallness of any one input’s cost relative to the overall project cost and revenue opportunity, and the speed with which Data Centers are making decisions and putting down deposits, Data Centers may meaningfully increase the odds of success of hard tech companies and Vertical Integrators more than the market realizes.

Far from being the villains they are painted as (often using misinformation and largely due to their association with deeply unpopular AI), Data Centers may be the greatest accelerant of American Reindustrialization and a built-world future that benefits all people that we’ve ever seen.

They offer dilution-free capital (real revenue on a negative working capital cycle) to fund the big vision, and more importantly, the opportunity to get to scale and down the learning curve years earlier than would otherwise have been possible. This both accelerates timelines of things that might have worked, but more slowly, and makes companies that might otherwise have died in the Valley of Death viable.

Whatever your feelings are on AI, the furor towards Data Centers is misplaced. Hell, whether or not you think we’re in an AI Bubble barely matters here. In five years, this could all fall apart, and the world will be much better off. Data Centers are funding the future where no one else will.

This isn’t the first time that people have gotten mad that something that seems frivolous is sucking up so many resources. The immediate stuff - like how much money is being spent or power is being consumed - is an easy target, while the long-term benefits are hard to see.

We Choose to Go to the Moon Not Because it is Popular

With the benefit of hindsight and distance, the Apollo mission has become one of America’s proudest accomplishments. At the time, though, not everyone loved JFK taking us to the Moon. There were too many problems on Earth to be solved to waste all that time, money, and smarts on a lunar boondoggle.

In May 1961, Gallup asked Americans, “It has been estimated that it would cost the United States 40 billion dollars-or an average of about $225 per person-to send a man to the moon. Would you like to see this amount spent for this purpose, or not?” 58% of respondents said that they would not, another 9% had no opinion, and only 33% supported the mission.

President John F. Kennedy gave his canonical “We choose to go to the Moon” speech not because it was popular, but because it was unpopular and he needed to rally support.

It didn’t work that well. In a 1964 Gallup poll that asked “Do you think the United States should go all out to beat the Russians in a manned-flight to the moon–or don’t you think this is too important?”, 66% of respondents said that they did not think it was too important, and another 8% said “Don’t know,” which amounts to the same thing. In the summer of 1965, “one-third of the nation favored cutting the space budget, while only 16% wanted to increase it.”

Even a decade after NASA pulled off the near-impossible, in 1979, only 41% of Americans told an NBC/AP poll that the benefits of the space program outweighed the costs. By 1995, that had risen to 47%, by 1999 it hit 55%, and by the 50th anniversary of the Moon Landing, in 2019, support had reached 64%.

One explanation is that, not having to pay the costs ourselves but getting to bask in the memory of victory, today’s Americans can of course look back and say it was worth it.

Another, though, is that over time, the real benefits, not at all obvious at the time, have become more clear.

Apollo’s critics were ultimately proven wrong, both because beating the Soviets to the Moon was awesome…

… and more directly because the absurdity of the task’s ambition coupled with the bottomlessness of its budget that they complained about were exactly the conditions needed to create terrestrially-useful innovations that would otherwise have taken much longer, or never been invented at all.

While the role that the Apollo Program played in inventing new technologies is probably overblown, it accelerated, scaled, and de-risked a lot of things that otherwise may not have gotten to sufficient scale or cost to impact our lives. Some of the technologies and products for which they served this role include:

• Fireproof fabrics and flame-retardant materials (developed after the Apollo 1 fire), which made their way into firefighter suits and racing gear

• Improved freeze-drying processes for food preservation

• Mylar-based reflective insulation, which became the emergency space blanket

• Advances in composite materials and ablative coatings

• CAT and MRI imaging benefited from digital image processing techniques developed to enhance lunar photographs at JPL

• Implantable cardiac pacemakers improved from bidirectional telemetry developed for astronaut biosensors

• Cool suits (liquid-cooled garments) used for MS patients, burn victims, and racing drivers came directly from the spacesuit undergarment

• Kidney dialysis machines used a chemical process developed to remove toxins from astronauts’ water

• Black & Decker developed the technology to make cordless power tools for collecting lunar samples

• NASA developed Memory foam (Temper Foam) for crash protection in seats

• Water filtration using silver ions, based on the system that purified the Apollo crew’s water

• Scratch-resistant lens coatings, derived from coatings developed for astronaut visors

• Improved smoke detectors (the modern ionization-type was refined for Skylab, which leveraged Apollo hardware)

Even cleanroom protocols for handling lunar samples spread into pharmaceuticals and, relevant to today’s discussion, semiconductor manufacturing.

That’s just one program, albeit a very large one. If you zoom out to include the technologies that received early support from the DoD, the list includes pretty much everything that defines modern life.

The internet and TCP/IP directly, and Ethernet indirectly. Satellite communications, GPS, the inertial navigation systems that run in our phones and cars, and radio navigation. The mouse, windowing interfaces, and hypertext; the work that Doug Engelbart showed off in his “Mother of All Demos” was ARPA-funded. Public-key cryptography was invented for UK SigInt, Grace Hopper developed COBOL on the Navy’s dime, and DARPA funded the first AI labs at MIT, Stanford, CMU, and Stanford Research Institute for decades. The chips AI runs on, GPUs, and the CUDA software behind them, owe something to DARPA-funded parallel computing research. Siri’s lineage, for better or worse, through DARPA’s CALO speech recognition program at SRI. There are the more obviously military products, like the jet engine and the planes jet engines power, stealth coatings, night vision, radar and synthetic aperture radar, Lidar, ultrasound, drones, and infrared and thermal imaging. There are materials, like composites, including carbon fiber, titanium alloys, and advanced ceramics, all of which scaled through defense procurement. LEDs were funded through early signaling work, and digital photography found a buyer in early spy planes. In medicine, we have the DoD to thank for EpiPens, tourniquets, hemostatic agents like QuikClot, prosthetics, blood banking, and plasma storage. Penicillin was first mass-produced in a wartime crash program. And energy? Civilian nuclear power descended directly from Admiral Rickover’s Naval reactor program, lithium-ion battery research had defense funding, and solar photovoltaic cells were pulled forward by satellites’ unique power needs.

Military procurement is the closest thing that the United States has to a national industrial policy, and its worked. The military has funded the development of practically every general purpose technology of the past century, before the commercial market took those de-risked, cost-downed technologies and figured out how to bring them to the masses.

Of all of these, the cleanest case study and the one that maps most directly onto what’s happening in 2026, is the integrated circuit.

Minuteman, Apollo, and Moore’s Law

Fairchild Semiconductor was founded in 1957 as a transistor company, and its first big customer was the military.

Specifically, facing a threat from larger Soviet boosters that could launch intercontinental ballistic missiles (ICBMs) that could fit vacuum tubes, the US military, with its smaller boosters, had to invest in miniaturization, which meant transistors. Between 1958 and 1960, Fairchild’s revenue grew from $500k to $21 million, largely on the back of the Minuteman I program, for which it produced custom designs. Challenge was, as the number of transistors grew, the electronics industry ran into the “tyranny of numbers”: now that they could, engineers wanted to design circuits with thousands of components, all of which had to be wired together by hand.

So in Dallas, Texas Instruments’ Jack Kilby came up with his “monolithic idea.” “He realized that,” Carl Leonard writes, “instead of connecting separate components, an entire electronic assembly could be made as one unit from one semiconducting material by overlaying it with various impurities to replicate individual electronic components, such as resistors, capacitors, and transistors.” He had invented the integrated circuit (IC).

Three months later, in Mountain View, Fairchild co-founder Bob Noyce arrived at a similar idea from a different angle. Starting from the planar process, invented by Jean Hoerni in early 1959, Noyce realized that you could build transistors flat on a silicon wafer, with all the connections on the top surface, protected by a layer of silicon oxide. He wrote in his notebook, “In many applications now it would be desirable to make multiple devices on a single piece of silicon in order to be able to make interconnections between devices as part of the manufacturing process, and thus reduce size, weight, etc., as well as cost per active element,” and filed a patent that year.

While there are differences between the ICs they developed (Kilby’s used Geranium and Noyce’s silicon, for example), the two are credited as the co-inventor of the IC. What matters for our story is 1) the IC may not have been developed without demand from the Air Force, Army, and Navy, which were each spending real money on parallel attempts to solve it, and 2) Fairchild was now in the IC business.

In 1961, after starting at $1,000 per chip on tiny pilot runs the year before, Fairchild brought the integrated circuit to market at $120 per chip. The challenge was, no one really needed an integrated circuit, not enough to pay that price. Any electronics firm could wire together discrete transistors to do the same thing for a fraction of the price. As Britannica puts it, “a buyer had to have a serious space constraint to justify purchasing ICs.” Fortunately for Fairchild, NASA had a serious space constraint.

In early 1962, MIT’s Instrumentation Lab, which was responsible for the Apollo Guidance Computer (AGC), placed a test order for 100 ICs at $43.50 per unit. Meanwhile, getting ahead of volume and hoping to use scale to rev aerospace demand, Noyce cut the price of the IC from $120 to $15, an 87.5% drop, while still charging NASA and MIT premium prices to fund scale. “Noyce slashed prices, too, gambling that this would drastically expand the civilian market for chips, Chris Miller wrote in Chip War. “In the mid-1960s, Fairchild chips that previously sold for $20 were cut to $2. At times Fairchild even sold products below manufacturing cost, hoping to convince more customers to try them.”

Meanwhile, the government funded the gap. That November, MIT decided to go with Fairchild’s IC, or more specifically, its Micrologic computer made up of ICs. By 1963, MIT was consuming 60% of US IC production for the AGC; other military and aerospace buyers made up the rest. In a 1964 article for IEEE, Noyce wrote, “Military and space applications accounted for essentially the entire integrated circuits market last year, and will use over 95 per cent of the integrated circuits produced this year.”

In 1965, the year that Gordon Moore wrote the Cramming More Components onto Integrated Circuits paper that birthed Moore’s Law, ICs had reached cost parity with discrete components, at around $10, and were beginning to beat them. It was around this time that the Minuteman II program, which now used Texas Instruments ICs, became the technology’s largest buyer. That meant two things – 1) there were multiple large buyers of ICs and 2) there was competition – which combined caused Noyce to cut prices again, down to $2, and again, to $1. The cash coming in from Apollo allowed him to attack the commercial market with lower prices.

And it worked. By the end of the decade, America beat the Russians to the Moon, and Burroughs released the B2500 computer, the first to use ICs.

From that point forward, Moore’s Law has largely been driven by demand from the much larger and faster-moving commercial market. But the IC would not have come down the cost curve so quickly – and Moore’s Law, that self-fulfilling prophecy, may never have been coined or executed against – without the early space and military demand. It is to these Buyers of Capabilities that we owe the computer-powered world we inhabit today.

Something similar might be happening with Data Centers today.

Data Center Demand

There is a sentiment floating around that we can’t build hard things in America anymore, but by any measure, modern AI data centers are hard assets to build and operate, and we are building a lot of very big ones. Western hyperscalers, labs, and neoclouds will spend something like$750 billion this year and more than $1 trillion next year building them. Goldman estimates that AI CapEx will take $7.6 trillion of capital between 2026 and 2031 across Compute, “Data Centers,” and Power.

This is an enormous amount of CapEx, absolutely, historically, any-way-you-slice-itly. With US GDP around $32 trillion, this year’s spend represents 2.4% of GDP. Assuming GDP grows by an aggressive 3% next year, AI CapEx will account for 3.1% of GDP. For context, the Manhattan Project reached 0.4% of GDP, the late-90s telecom bubble reached 1.2%, and even the Apollo Program only hit 0.4%. To find a more GDP-dominating project, you’d need to look to one of the two World Wars, the New Deal, or the Railroad Boom.

For a little more context, the $15 billion per year that Anthropic will pay SpaceX for the use of two of its Colossus data centers is roughly 60% of NASA’s entire annual budget.

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Hi friends 👋,

Happy Friday and welcome to our 194th Weekly Dose of Optimism. I’m emailing you from beautiful Utah, where I attended the Abundance Institute Gala last night and got to talk nuclear with Oklo’s Jake DeWitte and General Matter’s Scott Nolan, and where I’ll be spending today at the state’s Operation Gigawatt Conference.

It’s hard not to feel the optimism in the mountain air. When we started writing the Dose a few years back, nuclear was a controversial dream. Today, we’re heading into the Memorial Day Weekend that kicks off the summer during which multiple new reactors will go critical for the first time.

Plus, you’re about to read one of the most action-packed Doses in Dose history.

Things are moving fast, and mostly for the better. Happy Memorial Day Weekend, y’all.

Today’s Weekly Dose if brought to you by… Lightwork

It turns out, my house has been making me worse at writing. Sorry about that. I have it under control now.

Lightwork came by my place recently and tested our air and water quality, light sources, likelihood of mold, electromagnetic exposure (turns out electromagnetism does run the world), and household products. It was an awesome experience. I learned a lot, including that my home’s air quality and lighting were undermining my energy levels, our shower water was full of VOCs whose vapor I was inhaling, and a whole lot more.

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(1) Retatrutide Stuns In Phase 3 Obesity Trial

Eli Lily

Yesterday morning, Eli Lilly reported topline results from TRIUMPH-1, the Phase 3 trial for retatrutide in adults with obesity and at least one weight-related comorbidity. Here’s a summary of the results from Superpower founder and peptide enthusiast Max Marchione:

• 28.3% bodyweight lost on 12mg over 80 weeks

• 70.3 pounds on avg. or 31.9 kg

• 45.3% of patients hit 30%+ weight loss (this is bariatric surgery territory)

• 30.3% weight loss (85 lbs) at 104 weeks in higher-BMI patients

• 65.3% of 12mg patients dropped below the obesity BMI threshold

• 19% loss on 4mg over 80 weeks (47.2 lbs) with fewer dropouts than placebo (4.1% vs 4.9%)

• significant drops in blood pressure, triglycerides, non-HDL cholesterol, waist circumference, and hsCRP

• no cardiac or liver signals

These are bariatric surgery results in a shot, with a bunch of freebies thrown in.

People have been excited about reta on social media for a while, but it’s still gray market, so this is a key step towards getting it to the general public, and just an astonishing set of results. People are losing 28.3% of their bodyweight, or 70 pounds, in less than two years!

For context, Zepbound (tirzepatide) tops out around 20-22% weight loss in its big trials, and the previous-generation Wegovy (semaglutide) at around 15%.

Retatrutide is the first triple hormone receptor agonist (GIP + GLP-1 + glucagon). We previewed this back in Dose #175, in our piece on grey-market peptides and Gen3 GLP-1/GIP/glucagon agonists. The author of the essay we covered, Not for Human Consumption, called these drugs “the holy grail of weight loss medications” before any of them had a Phase 3 readout. Now one of them does.

This is Lebron-level. Unbelievably hyped out of the gate, and then exceeds the hype.

As with all of these miracle drugs, there are positive side effects beyond weight loss. Retatrutide reduced osteoarthritis knee pain by an average of 75.8%, with more than 1 in 8 patients reporting they were completely free of knee pain at the end of the trial. LDL cholesterol dropped 20%. 72% of prediabetic participants returned to normal blood sugar levels. Seven more Phase 3 readouts are expected this year, including diabetes, sleep apnea, chronic back pain, and cardiorenal outcomes.

Get ready to get hot and healthy. It’s Reta Summer (in 2027).

(2) Colossal Biosciences Hatches Chick From Artificial Eggs

“Before we named the stars, before we mapped the atom, we asked ourselves an impossible question: which came first, the chicken or the egg?”

On Monday, Ben Lamm's Colossal Biosciences announced that it has hatched 26 healthy chicks from a fully artificial egg, an oval printed shell coated in an oxygen-permeable membrane that lets an embryo develop from poured-in yolk to pipping bird, without a biological eggshell. Researchers crack a freshly laid fertilized egg, pour the contents into the artificial shell (Colossal adds ground-up calcium back in, because the embryo eats the shell as it grows), and watch through the top window as eyes form, vasculature spreads, and, 18 days later, a chick taps its way out.

Real scientists will tell you (and MIT Tech Review's Antonio Regalado dutifully did) that growing birds outside the shell isn't new. A Japanese group hatched quail in 1998. Katsuya Obara hatched chickens under transparent plastic film in 2024. Colossal's marketing is, per Regalado, "pure Hollywood." Which is kind of fair but too clucking cynical.

Colossal’s real engineering advance is the membrane, which lets the embryo pull enough oxygen from ambient air that the system doesn't need supplemental gas. Previous shell-less methods did, and chicks tended to die. I like the version in which the chicks live, Regalado.

While this experiment was on a chicken, we have plenty of those. Colossal likes to de-extinct. Its stated goal is the 12-foot, 500-pound South Island giant moa, which laid four-liter eggs no living bird is large enough to surrogate. Colossal staff are reportedly already calling the prototype scaled-up version the "salad spinner." More immediately, the artificial egg is a tool for genetic rescue of endangered birds and, as per our discussion in Dose #186 with not boring capital portfolio company Neion Bio, a substrate for transgenic chickens producing therapeutic proteins in egg whites at a fraction of mammalian cell culture costs.

This is the first member of what Colossal calls its "exogenous development" family. Up next: artificial wombs for marsupials.

With this chicken breakthrough, it feels like humans have crossed a road.

(3) SpaceX Launches S-1

For the entirety of its existence, human civilization has lived on a single celestial body: Earth. The current paradigm, in which human civilization is confined to one planet, exposes humanity to existential threats that are unpredictable and uncontrollable on a planetary scale. By moving beyond the only home we have ever known, we ensure species-level redundancy and that the light of consciousness will not be tied to a single planet subject to the inevitable hazards of a harsh and vast universe. We do not want humans to have the same fate as dinosaurs.

On Wednesday, SpaeX dropped what may have been the most highly-anticipated S-1 in the history of the planet (until Anthropic files later this year). You can read it here.

Financial highlights include:

• Revenue: $4.69B (run-rate ~$19B)

• Operating loss: $(1.94B), but Adjusted EBITDA of +$1.13B

• Connectivity (Starlink) prints cash: $3.26B revenue, $1.19B operating income in just the first quarter. Its revenue is growing ~50% per year, and profitability is growing faster.

• AI is losing money for now…: $818M revenue, $(2.47B) operating loss in Q1. Capex on AI alone was $7.7B in Q1, more than Space ($1.05B) and Connectivity ($1.33B) combined, by far.

• But has the monster TAM: AI infra, consumer subscriptions, digital advertising, and enterprise applications - the AI segment - has a company-estimated TAM of $26.5 trillion, which seems small when you consider the size of the universe and the potential to spread Enterprise Applications throughout it.

  

SpaceX also disclosed that it’s doing a second compute deal with Anthropic, in which Dario & Co. will pay SpaceX $1.25 billion per month for Colossus 2, which is nearly as much as entire SpaceX makes now. For now, it’s selling compute on earth, but per the S-1, it plans to begin deploying orbital compute in 2028 and scale it to 100 GW of compute launched annually, which would make Colossus seem like a pale blue dot.

Starlink, which had been the bull case until AI, is at 9,600 satellites, 10.3 million subscribers across 164 countries, and now accounts for ~75% of all active maneuverable satellites in orbit.

There is no one operating at a higher level than Elon and the SpaceX team, across everything from terrestrial data centers to rockets to satellites to, soon, Terafab. After more than 20 years, it’s awesome to see the company go public (and generate some much-needed DPI for the venture and seven-layer-SPV ecosystems). Is the price a little high? Maybe, yeah, sure. But what would you pay to own a Scarce Asset that catches Statue of Liberty-sized rockets with chopsticks…

We’ll find out in June.

In the meanting, after scrapping the launch of Starship v3 last night, SpaceX is giving it another go today. What a way to launch summer.

(4) GPT 5.5 Pro Makes Breakthrough on Erdős Planar Distance Problem

Take n dots on a piece of paper. How many pairs of dots can sit exactly one unit apart from each other?

Paul Erdős posed this in 1946, conjectured the answer grows just barely faster than n, and offered cash for a resolution. For 80 years, the best constructions anyone could find were boring square grids, and most of combinatorial geometry treated Erdős's upper bound as essentially right.

This week, an internal general-purpose OpenAI reasoning model produced a proof that disproves it. The model isn’t math-specific, it just thought for a long time and burned an estimated ~$1,000 max worth of tokens, which is pretty affordable for an 80 year old unsolved problem! It found that there's an infinite family of point arrangements that beat the grid, polynomially. Erdős was wrong, and his “favorite problem” is settled.

How’d it pull it off? Instead of more clever geometry, it pulled in algebraic number theory (specifically, infinite class field towers and Golod–Shafarevich theory, of course, why didn’t I think of that) to construct number systems with the right symmetries, then read the geometric configurations off of those. No human had tried this connection. Thomas Bloom thinks it will unlock other long-stuck problems in discrete geometry.

And, importantly, it’s verified. Last October, OpenAI claimed GPT-5 had solved ten Erdős problems; on inspection, it had just surfaced answers from the literature. This time, it got external review by Fields Medalist Tim Gowers and Will Sawin at Princeton, with a companion paper. Gowers's verdict: "if a human had written the paper and submitted it to the Annals of Mathematics, I would have recommended acceptance without any hesitation." It also got endorsements from Noga Alon, Melanie Wood, Arul Shankar. Shankar said the models “are capable of having original ingenious ideas, and then carrying them out to fruition.” The full proof is available as a public PDF.

Ten months ago, frontier models were at IMO-gold level. Really smart high schoolers, basically. Now, they’re doing Annals-worthy novel work.

I’ve been more skeptical about AI than I normally am about new technologies, and less excited than the average person in tech. Per the evidence that keeps coming out, I’ve been wrong. On the business side and the capabilities side, the pace has been truly remarkable, and I can’t wait to see what problems they’ll solve for us in the years ahead.

(5) SendCutSend Raises $110M at $1B Valuation

Jim Belosic (SendCutSend)

Jim Belosic started SendCutSend in 2018 because he couldn't get custom metal parts fast enough for the cars and motorcycles he was restoring. Eight years later, bootstrapped to this point on credit cards, savings, a PayPal loan, and bank-financed machines, the Reno laser-cutting and bending shop is doing roughly $200 million in revenue, growing 100% year over year, and turning into one of the most important on-demand manufacturers in America.

Austin Vernon's February essay Speed Can Reindustrialize America is the best piece on why the company is so impressive and potentially important. Vernon's argument is that the US is great at high-volume manufacturing and terrible at low-volume, short-lead-time custom work because soft costs (quoting, programming, billing, all the white-collar overhead) swamp the actual fabrication cost on small orders. SendCutSend's whole company is an attack on soft costs: instant quotes, one-click buying, software-routed production. It’s generating ~$275K revenue per employee, and the market would support a hell of a lot of employees.

Interestingly, Patrick Collison was the one person Vernon thanked for feedback, right above the “Funding Opportunities Apendix.”

And I guess Collison took it literally, because he reached out to Belosic about investing, and although SCS had never taken outside capital, he decided to take money from the Collisons, Sequoia (Andrew Reed), and Paradigm (Matt Huang) to go bigger, faster.

Jim and SCS are easy to root for and have become a cult classic over the past few years. Now, the cat’s out of the bag, the money is in the bank, and IT’S TIME TO BUILD.

In other great news for building stuff here, Amca raised $300M at $1B+ from Caffeinated and crew. Amca builds and modernize factories to design and produce critical aerospace and defense components that are supply-constrained and often sole-sourced.

Jai Malik’s eighteen-month-old El Segundo startup already runs factories in California, Iowa, and New York, supplies components for the F-35, and uses its RAPID software platform to deliver parts to BAE, Airbus, Textron, Honeywell, and GE Aerospace 67% faster than the legacy defense supply chain. Now, it can make more of the critical components on which the country runs.

Extra Doses: Science Breakthroughs, Anthropic, Valar Atomics, Quantum Investment, Grindslop

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I saw a great case study on the Great Differentiation this week, so put together some quick thoughts off the dome, as I continue to study how companies tell their stories in new and interesting ways. Let me know what you think.

Cowboy Space Corp Differentiation Case Study

Earlier this week, I saw a headline that a company called Cowboy Space Corporation had raised over $200 million to build rockets whose upper stages are just foldable data centers. My first thought was who would be dumb enough to compete with Elon in launch at this point and my second was that this had to be the peak.

Then, I saw that it was Robinhood co-founder Baiju Bhatt’s company, Aetherflux, rebranded and expanded, and I got a little more interested, because at least they’d been planning to do energy in space since before data centers were cool, and because turning Robinhood money into sci-fi energy and compute moonshots is exactly how you should billionaire. This is Choose Good Quests.

I wrote about it in the Dose yesterday, because if nothing else, it’s bold.

But in that process, I watched the videos. There’s the one I included in the Dose, which was pretty cool because it had a tumbleweed, and some cowboy-adjacent music, and because the product itself - the upper stage that unfurls into a data center powered by a bunch of solar panels - is arresting, and because calling space “The High Frontier” is both cool and relevant for a company now called Cowboy Space Corporation.

If it were just for that video, though, I wouldn’t be writing this note. This is the one that made my fingers do the keyboard two-step:

It’s so fucking weird when you watch it, right? Baiju, who I’ve never met and I’m not sure watching this if he’s really like this or if this is a bit, is just talking into the camera while he slaps cowboy hats on his team’s head.

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Hi friends 👋 ,

Happy Friday and welcome back to our 193rd Weekly Dose of Optimism.

The sun is shining, the birds are chirping, Demis is going to solve all disease, Varda is making space drugs, pancreatic cancer keeps getting punched in the mouth, we’re turning whole rocket upper stages into space data centers, and the IPO window is OPEN. Y’all mind if the optimists have another good week?

Let’s get to it.

Today’s Weekly Dose is Brought to you by… SimpleClosure

We celebrate ambitious companies every week in the Dose, but ambitious also means long odds, and not every ambitious company ends the same way. Some shut down. Some sell assets. Some get acquired. Some simply reach the point where winding down is the right move.

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(1) Isomorphic Labs Raises $2.1 Billion to Solve All Disease

I recently read Sebastian Mallaby’s book on Demis Hassabis and DeepMind, The Infinity Machine, after having already watched the documentary on DeepMind’s development of AlphaFold, The Thinking Game, and a big takeaway for me is that you don’t want to bet against Sir Demis.

In addition to DeepMind, now part of Google, Demis spun out a bio-focused company called Isomorphic Labs whose mission, right there on the homepage, is to “Solve All Disease.” As of today, they have $2.1 billion more in the anti-disease arsenal thanks to a Series B led by Thrive Capital with participation from Google vehicles Alphabet, GV, and CapitalG, and sovereign vehicles, MGX, Temasek, and the UK’s Sovereign AI Fund. It is the second largest biotech round ever, after Jeff Bezos and Yuri Milner’s AltosLabs came out with $3 billion in 2022.

Back in Dose #180, we covered IsoDDE, Isomorphic Labs’ unified AI drug design engine that crushed AlphaFold 3 on the hardest protein-ligand benchmarks and, in one of the more striking results, rediscovered cereblon’s second binding pocket from sequence alone, an achievement it took human researchers 15 years to confirm experimentally. Our take then was that the engine was real but the proof would come when AI-designed molecules actually entered humans. The money will go towards that, with the first of their wholly-owned drug candidates expected to enter human trials at the end of this year, and to expand the pipeline, continue to improve IsoDDE, and keep hiring the very best talent.

While there are a number of companies promising to solve drug discovery with AI, Isomorphic Labs seems to have the track record, talent, and war chest to do it.

Nobel Laureate Sir Demis Hassabis has a history of planning to do things that sounded crazy at the time and then doing them, and the craziest part is that if he does it again this time, the thing that sounds crazy that he will actually pull off is solving all disease. God Save the Queen, and all of us as well.

(2) Varda and United Therapeutics Testing Space Drugs

Sana Pashankar for Bloomberg

Isomorphic seems promising for Earth Drugs. But what about Space Drugs?

Way back in June 2023, almost three years ago, I wrote a Deep Dive on not boring capital portfolio company Varda in which we discussed its plans to manufacture drugs in space, where the lack of gravity lets you do things you can’t on earth, and send them back down to earth, where the people who need them are.

After three years of launches and re-entries with defense customers, the company is igniting stage 2 of the plan: start testing space drug manufacturing.

This week, it announced a partnership with $25B publicly-traded pharmaceutical company, United Therapuetics, to “explore the use of microgravity in creating drugs for chronic diseases in a deal that could prove out the value of in-space development for the pharmaceutical industry.”

United makes drugs to treat cancer and high blood pressure in the lungs, and it’s expected that this partnership will start by testing molecular samples of drugs intended to treat lung diseases specifically.

That’s one big step for Space Drugs, and one potentially giant leap for mankind.

(3) Pancreatic Cancer Just Met Its Match

for

Back on earth… it’s been a bad few weeks for pancreatic cancer, which means it’s been a great few weeks for humans. We led off Dose #190 with a pancreatic cancer mRNA vaccine, Dose #191 with pancreatic cancer-detecting AI, and now, Ruxandra Teslo drops a banger on a different pancreatic cancer fighting drug, Revolution Medicines’ daraxonrasib.

Last month, Revolution shared daraxonrasib’s Phase 3 results. The oral, once-daily pill roughly doubled median survival for metastatic pancreatic cancer, pushing it to 13.2 months. More importantly, it represents a new class of drugs that use a “molecular glue” strategy to hit targets that don’t offer the usual drug-binding pocket. Daraxonrasib targets RAS, a cancer-driving protein found in roughly 25% of human cancers and more than 90% of pancreatic cancers, long considered “undruggable.” This isn’t a cure, but it is a crack, an early sign of the undruggable becoming druggable.

Go read Ruxandra’s excellent essay. Get fucked, cancer.

(4) AI Chipmaker Cerebras Climbs 68% After Year’s Biggest IPO

Bloomberg

Do you feel that breeze? That’s the IPO window, baby. It’s wide open.

Yesterday, fast inference chipmaker Cerebras had a very successful IPO, pricing well above initial range at $185, and then closing 68% above that. Bill Gurley has got to be conflicted… on the one hand, the man hates when a company leaves money on the table in an IPO. On the other, his old firm, Benchmark, is going to make an extraordinary ~$5 billion+ at the $311 opening day closing price.

Fervo, the geothermal company whose S-1 we covered recently, also IPO’d this week. Its shares popped 33% on Day 1 from its $27 price, and then jumped another 11% today.

Look, things are a little frothy. I’m not buying Cerebras at $311. But we love to see tech companies win, but I’m also just psyched to see some liquidity flow back to LPs to flow back into venture to fund the next batch of companies whose IPOs we’ll cover right here in the Dose in ~8-10 years.

Plus…

In older recent tech IPO news, Figma defied the SaaSpocalypse / Anthropic design plugin narrative by crushing its first quarter financial results. The stock popped 8%. Not dead.

(5) Cowboy Space Corp. Gets in the Saddle

Speaking of AI infra exuberance, Robinhood co-founder Baiju Bhatt’s space-based energy company, Aetherflux, has rebranded to Cowboy Space Corp, a company that will, of course, be doing orbital data centers.

On the one hand, it’s a little 2026 Mad Libs. On the other, though, it kind of rocks.

Choose Good Quests ✅ - Baiju is Robinhooding himself, stealing his own gains from the fintech app and giving them to the space company. It’s what we want out of our billionaires.

Also, the idea is pretty interesting. Instead of an upper stage that holds and releases a data center payload, the upper stage is the data center payload. That means that Cowboy has to build its own rockets, in competition with SpaceX, but is focusing fully on orbital data centers by making them half of the rocket.

Maybe this is a sign that we’re nearing a peak. Maybe it’s exactly what we need to provide AI with all of the compute it will need. I don’t know.

I think we’re at the point in the cycle where we just sit back, enjoy, crack open a High Life, and root for these crazy Cowboys to wrangle their space GPUs for the benefit of all mankind. Giddy up baby. Yeehaw.

Extra Dose for not boring world subscribers down below. We got…

Science Breakthroughs, Vacuum Energy, Starship v3, Magic Words

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Hi friends 👋 ,

Happy Wednesday! Last week, I went out to LA to give a talk at my friend Grant Gittlin’s event, The Mountain. He asked me to do something different and weird, and I took the opportunity to pull a bunch of the ideas I’ve written in essays like Means and Meaning, The Company as a Machine for Doing Stuff, The Return of Magic, Most Human Wins, and others into one cohesive ~philosophy. It was a good excuse to think about the meaning of life.

This is the talk. It was written to be spoken, so it may seem an oddly-written essay, but I hope it’s useful nonetheless, if for no other reason than that it makes you pause and wonder at the insane gift and responsibility we’ve all been given to experience and create in the world from our own unique points-of-view.

I was very nervous giving this talk to a room full of 80 people, but once I got through that, and a bunch of people told me that I should share it more broadly, I decided why not send it to 265,556 of my closest friends. I do think a lot of people are wondering what it is we’re here to do when machines can do more and more.

This is my best attempt yet, in what will be a lifetime full of attempts, to answer that question and then try to live the answer.

Let’s get to it.

Note: I’m sharing the first half with everyone and the whole thing with not boring world subscribers. Join us.

Riding the Leopard

Transcript of a Talk Given at The Mountain on May 6, 2026

Thanks for inviting me, Grant, and to all of you for letting me fill your experience and attention for a little while.

What a week to get to talk to a room of technology people. Sierra just raised at $15 billion. Anthropic crossed a $44 billion run rate, and launched a new company with some huge funds that has $1.5 billion to deploy AI in big companies. OpenAI did the same thing but with $4 billion. Long Lake bought AmEx global travel for $6.3 billion.

All of which raises an important question: who gives a shit?

I mean that: why do we care?

Things are moving so fast that it’s worth thinking about what it is that we’re doing here.

Last night, a woman who reads my newsletter reached out over Substack DM. She said she had been diagnosed with Stage IV cancer (she’s in remission now!), so she had been confronted with a question we’d all be facing: what happens to human purpose when AI removes scarcity (or in her case, the need to care about being productive at all)? To answer it, she analyzed more than 200 sci-fi books. Across all of those books, by far the most common thing left to solve for post-scarcity is meaning. 59% of books were about the search for meaning. Identity was next, at just 17%.

Assume that companies will keep getting bigger and bigger, and growing faster and faster, and who cares… the thing we’ll be left solving for is meaning.

Luckily, that’s what I’d been planning to talk to you about.

When Grant asked me to talk and I asked Grant what he wanted me to talk about, he said, basically, “Whatever you want, the weirder the better. The line I really love from your recent essays is ‘You have the right to the work only but never to its fruits. Let not the fruits of action be your motive, nor let your attachment be to inaction.’”

The funny thing about that line, which comes from the Bhagavad Gita, is that when I wrote about it, Venkatesh Rao, who is smarter, better read, and more Indian than me, replied, “This interpretation of the Gita verse is a bit of a stretch.”

After going back and forth in the comments, I think Venkatesh and I actually agree, but I am warning you that I am going to do a little bit more Indian-text-stretching and leaping in this talk to build a framework that I think is potentially useful and give it some ancient gravitas.

There is this category of questions I’ve been wrestling with, which I think a lot of people have been wrestling with recently, which is something along the lines of:

If new technology is so great, why are so many people unhappy?

If we have means our ancestors couldn’t have dreamed of, why is there a meaning crisis?

What is technology for, anyway? What are we doing here?

In his 1978 essay collection, The Unheard Cry for Meaning, Holocaust survivor Viktor Frankl wrote, “The truth is that as the struggle for survival has subsided, the question has emerged: survival for what? Ever more people today have the means to live but no meaning to live for.”

Frankl, who wrote Man’s Search for Meaning about his experience finding meaning in the concentration camp, in the worst situation imaginable, writes three decades later about the lack of meaning in what could historically be described as the best situation imaginable.

This weird inverse relationship between material and spiritual wealth shows up over and over again.

“Again I tell you,” preached Jesus, “it is easier for a camel to go through the eye of a needle than for a rich person to enter the kingdom of God.”

The greater our means, the harder it might be to find meaning. The more we accomplish, the sooner we ask, “Cool, now what?”

It seems that we are in the middle of creating more technological, financial, and material wealth than we have at any other point in human history, thanks in part to some of the people in this room.

In order to avoid being the proverbial car-catching dogs, we’d better get prepared for what lies on the other side of all of those means. The world’s spiritual traditions were formed in times of scarcity. How are we supposed to handle all of this abundance? Is abundance bad, actually?

I’m going to propose a framework, drawing loosely on a broad set of sources including, but not limited to, the world’s major religions and a guy who took a lot of acid.

Here’s what I propose:

The meaning of life is to increase the range and depth of experience in the universe.

OK, so for the first stretch.

In the Upanishads, there is this concept of “Neti, neti,” or “Not this, not this.” It is a process of understanding what we are not so we can better understand what we are.

When you sit in silence and make yourself observe yourself, you notice things like:

“I am not my thoughts, because I can observe them come and go.”

“I am not my emotions, because I can watch them rise and fall.”

“I am not my bodily sensations, because I can feel them changing.”

The idea is to strip all of that away so that you know, gnostically, in your bones, that “you” are a process, an undefinable part of an incomprehensible whole. Which doesn’t mean that you are nothing.

What’s left is awareness, and at any moment, awareness is aware of something, experiencing something. You are awareness itself, and if you’ll allow me to stretch one step further, you are what you experience.

This probably isn’t a coincidence. Experiencing things might be the whole reason we’re here.

A few years ago, I started going down this rabbit hole that sounds insane when you list it out, from Dan Simmons’ sci-fi series Hyperion Cantos to Jesuit scholar Pierre Teilhard de Chardin’s The Phenomenon of Man to The Telepathy Tapes to The Perennial Philosophy, Aldous Huxley’s book arguing that mystics in every tradition – Christian, Hindu, Buddhist, Sufi, Taoist – have peered deeply into the nature of reality and come back saying basically the same thing.

The same thing is: thou art that.

This is a phrase that comes from the Upanishads, too. The idea is that the innermost self in you (Atman) and the ultimate reality of the universe (Brahman) are not separate things but the same thing.

If I am not my thoughts, not my emotions, not my body, what am I? Thou art that.

You are god, the universe, whatever, looking at itself through a particular set of eyes, experiencing itself through a particular consciousness.

If this is true, and it’s at least weird that so many of the world’s religions came to the same conclusion!, it means that every unique experience a human has is the universe experiencing itself in a way it couldn’t have otherwise.

This idea is both woo-sounding and load-bearing for the rest of the talk, so I’m going to reinforce it a little bit through some good ol’ fashioned appeal to authority.

Alan Watts said that “‘You’ is the universe looking at itself from billions of points of view, points that come and go so that the vision is forever new.”

The 13th Century Sufi Poet Rumi said, “You are not a drop in the ocean, you are the entire ocean in a drop.”

The Jesuit de Chardin said something similar. He held the heretical belief that evolution had a direction, towards more complexity, consciousness, and interiority. He asked, “What is the worth of human works if not to establish, in and by means of each one of us, an absolutely original center in which the universe reflects itself in a unique and inimitable way?”

I can see that you want to get weirder. Let’s get weirder.

Chris Bache, a Professor Emeritus in Philosophy and Religion at Youngstown State University, conducted 73 high-dose LSD sessions on himself over the course of twenty years, going deeper and deeper, often painfully, to the point of tears and vomit, each time. From his later sessions, when he was getting really deep, he came back with the knowledge that “the essence of the individual is the essence of Totality, that Atman is Brahman.”

He portrays humans as incarnated aspects of a Creative Intelligence whose purpose is to awaken within physical existence, exercise controlled creativity, and thereby participate in the universe’s self-emergence.

Too weird? Let’s re-anchor on someone more traditionally credible.

Alfred North Whitehead, the mathematician who wrote Principia Mathematica with Bertrand Russell, spent his 60s building a metaphysics from scratch. “With the becoming and perishing of each actual occasion,” he wrote of moments, “a perspective on the universe is achieved that has never existed before.” In Whitehead’s metaphysics, the universe is not made of things, but of, and I quote, “drops of experience.”

So far, we have religious mystics of all traditions, a philosophical entertainer, a psychonaut, and a mathematician staring deep into the universe and reporting back essentially the same thing.

Why, though? If we are slivers of the universe experiencing itself, to what end?

Because through our imperfect experience, an infinite and perfect universe can know itself and create itself.

A couple of years ago, a podcast called The Telepathy Tapes rocketed to #1 on the podcast charts. The host, Ky Dickens, had heard stories of non-verbal autistic kids who were able to communicate telepathically, and on the podcast, she interviewed a lot of them and their parents and ran experiments to test their abilities.

The thing that got me was a conversation with one of the kids, named Asher, who said, through a verbal woman named Jess, that he visits a place called the Realms to access information on any topic, which sounds crazy except for the fact that the kids know things about people they’ve never met that they couldn’t possibly know, and understand languages they were never taught and which their parents don’t speak.

Explaining the Realms, Asher said:

It’s all parts of our soul exploring consciousness in different ways, and we’re basically building up huge amounts of information.

Every new thought we have, every new idea we have is new information. And that way, we’re expanding the cosmos because each new piece of information that we come up with then goes into this library of his and becomes available for everybody else. But we have to get this knowledge through direct experience in 3D life.

This is something that comes up again and again, too: that God or the universe, omniscient and omnipotent as it is, needs imperfect beings to experience things to sample the full range. There are no stakes if you know how things will play out, no sacrifice if you can just fix it.

There’s this great book by William Egginton called The Rigor of Angels: Borges, Heisenberg, Kant, and the Ultimate Nature of Reality, in which the author examines the ultimate nature of reality from the perspective of the author, the quantum physicists, and the philosopher. All three came to the same conclusion:

To observe anything, you need distinctions — between here and there, before and after, this and that. Without those gaps, there’s nothing to observe. A timeless, spaceless, perfect unity would be unobservable. Perfection can’t know itself. Only finite, imperfect conditions make experience possible.

Our job, then, cosmically, is to be limited, imperfect things having experiences on behalf of something too perfect and limitless to be able to.

Which was kind of the point of Jesus, right? Christianity’s deepest claim - that God became man, the infinite became finite - is the same claim the other traditions are making, from a different angle.

Jesus even had his own version of thou art that: “The Kingdom of God is within you.”

In the Gospel of Thomas, discovered at Nag Hammadi in 1945, Jesus says, “If you bring forth what is within you, what you have will save you. If you have nothing within you, what you do not have within you will kill you.” Interpretations range from the gnostic - if you recognize and bring forth the divine light within, you will return to source - to the psychological.

This quote is famous today because Jung’s followers use it to explain the concepts of Shadow and Individuation. “If you bring forth what is within you…” refers to Individuation, or the process of taking the hidden parts of your personality, your talents, your repressed emotions, your Shadow, and bringing them into the light of consciousness. By becoming fully and uniquely you, you are saved from living a fake life.

“The privilege of a lifetime,” Joseph Campbell said, “is being who you are.” You’ve probably heard that one before. It comes from In the Field, the opening of Reflections on the Art of Living: A Joseph Campbell Companion, something that I highly recommend everyone read. What I forgot until I looked it up while writing this talk was what he said later in that essay.

Where there is a way or path,

it is someone else’s path.

You are not on your own path.

If you follow someone else’s way,

you are not going to realize

your potential.

Eternity

is a dimension

of here and now.

The divine lives within you.

And later still:

The goal of the hero trip

down to the jewel point

is to find those levels in the psyche

that open, open, open,

and finally open to the mystery

of your Self

being Buddha consciousness

or the Christ.

That’s the journey.

It’s all about finding

that still point in your mind

where commitment drops away.

The separateness

apparent in the world

is secondary.

Beyond that world of opposites

is an unseen (but experienced)

unity and identity in us all.

And then:

You must return

with the bliss

and integrate it.

The return is seeing

the radiance everywhere.

The goal is to live

with godlike composure

on the full rush of energy,

like Dionysus riding the leopard,

without being torn to pieces.

Which is one of the coolest lines I’ve ever read, and if you take just one thing from that talk, I hope it is that: to live with godlike composure on the full rush of energy, like Dionysus riding the leopard, without being torn to pieces.

This is a man who spent his entire life studying the stories and myths that humans across space and time have told ourselves to understand ourselves, and he came back reporting the same thing.

Thou art that. Thou must be the specific version of that you were put here to be.

Every one of these people is saying the same thing: you are a piece of the universe experiencing itself. And every one of them is saying, in different words, that this imposes an obligation on you: to be the fullest, strangest, most irreducible version of yourself. All of this so that each of us might create and experience the world differently than anyone else could.

In other words, differentiation is a moral obligation.

It is also a mathematical one, and here I will bring this room full of technologists back to familiar ground.

In his 1948 “A Mathematical Theory of Communication,” Claude Shannon, the founder of information theory and the originator of the word ‘bit,’ proved mathematically that information is surprise.

The more predictable a message is, the less information it contains. A universe full of identical people producing identical experiences would contain no information at all.

An omniscient god cannot be surprised.

It’s only through the surprising, the differentiated, the irreducibly specific, that any new information enters the system. Shannon was in no way intentionally writing about the meaning of life, but if you take his math seriously, and mix it in with the mystics and philosophers’ message, the universe learns something through you only to the extent that you are the unexpected thing only you could be.

Forty years later, John Wheeler, the physicist, re-enchanted Shannon’s concept of the bit when he proposed the now-famous “It from Bit” at a conference in Tokyo. In Wheeler’s Participatory Universe, the universe doesn’t exist until it’s observed by a conscious mind. Matter (It) emerges from information (bit).

Wheeler wasn’t a kook. He was a giant. Richard Feynman, his most famous student, said “Some people think Wheeler’s gone crazy in his later years, but he’s always been crazy.” Feynman credited Wheeler’s insights as the foundation of the Quantum Electrodynamics that won him the Nobel Prize.

CalTech’s Kip Thorne, another Nobel Laureate Wheeler protege and the physics consultant on Interstellar, said that “Wheeler was the greatest master of metaphors and names I have ever known.” He came up with Black Hole and Wormhole and Quantum Foam. Thorne called him “the last of the giants.”

And Wheeler argued that humans are co-creators of the universe, through the surprising actions we take and the things we experience.

OK, so having stretched and mixed a bit, where are we?

1. You are not your job, your outfit, your emotions, your body. You are awareness. You are a little slice of the universe experiencing itself.

2. Christians, Buddhists, Hindus, psychonauts, non-verbal autistic children, and myths from around the world that the meaning of life is to expand the range and depth of experience in the universe in order to expand and co-create it.

3. You are here to experience and create in a way that only you can, and for many of you in this room, that means experiencing and creating in a way that expands humanity’s capacity for experience and creation. Differentiation is a moral obligation.

If some of this sounds familiar, it might be because it sounds very similar to how we train AI models. Humans take actions and create information, from which the model learns and grows. Labs are willing to pay a ton of money for new information; feeding it the same old stuff actually degrades performance.

So where does that leave us, the humans in the room?

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Hi friends 👋,

Happy Friday and welcome back to our 192nd Weekly Dose of Optimism!

I’m out in California getting optimism injected straight into my veins with a bunch of company visits from the future, on many of which more to come.

Plus, we finally got some UFO disclosure.

I am feeling the sci-fi, and it’s real early here, so…

Let’s get to it.

Today’s Weekly Dose is Brought to you by… SimpleClosure

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BREAKING: UAP Disclosure

Department of War

The American people can now access the federal government’s declassified UAP files instantly. The latest UAP videos, photos, and original source documents from across the entire United States government are all in one place – no clearance required.

It is early here in SF and I haven’t had time to go through these or even see the internet sentiment on what we got here, but the DoW has released the first batch of UAP encounter photos, files, and videos. They’re all on this website, and there will be more to come.

The truth is out there. Happy Disclosure Day.

(1) GENE-26.5: Advancing Robotic Manipulation to Human Level

Genesis AI is getting handsy. This week, the company released GENE-26.5, the first public model in their GENE family, meant to solve dexterity.

It showed robots cooking a four-minute, 20+ subtask meal that includes one-handed egg cracking and bimanual knife work; pipetting liquid into a centrifuge tube and screwing on a 1cm cap; solving a Rubik's Cube bimanually (which Genesis claims is a first for a general-purpose system without mechanical fixtures, building on OpenAI's 2019 single-handed milestone); making a smoothie from raw ingredients; doing wire harnessing (the "holy grail" of automotive manufacturing); and, separately, playing piano (Rush E, naturally) as a stress test for their control stack.

The model is one of four pieces in Genesis’ stack that make the demo possible:

1. A robotics-native foundation model trained jointly across language, vision, proprioception, tactile, and action, using flow matching over a joint distribution of trajectories. Genesis says they've collected over 200,000 hours of multimodal data with partners.

2. A data-collection glove with EMF-based finger tracking and dense tactile sensing across the hand, designed to be worn during real work on the assembly line, in the lab bench, in the kitchen, on the keys, etc… so collection doesn't change the behavior being collected.

3. A 1:1 human-scale robotic hand ("Genesis Hand 1.0") with 20 active back-drivable degrees of freedom and soft material across the palm and fingers. (For now, GENE-26.5 itself runs on an existing dexterous platform, reported to be China’s Wuji Tech; Hand 1.0 is the next step in the hardware roadmap.)

4. A custom control stack and simulator. They threw out the vendor controller on their bimanual arms and rewrote it. End-to-end latency dropped from ~80ms to as low as 3ms, and tracking error on a 15cm circle dropped from ~20mm to ~2mm. The simulator (Genesis World) lets them run 2,700 robot-hours of evaluation per chart point without touching real hardware.

The argument is that all four pieces have to be co-designed, because compromises in any one of them propagate everywhere else. Vertical integration for the win.

The reason they believe robots haven't generalized like LLMs isn't that the models aren't smart enough, but that the data has been a fraction of a percent of what humans naturally generate every day, captured through interfaces that distort the very behavior they're trying to record, which is what Evan Beard and I argued in Many Small Steps for Robots, One Giant Leap for Mankind. The glove is their bet on fixing that and the hand is a bet that you can match the human end-effector closely enough that mapping is "near-lossless." Together, they make the case that the full stack is the thing.

The company claims that most of the demo tasks required less than one hour of task-specific robot data, or under 200 episodes for skills shorter than 20 seconds. So train the general model, and then efficiently post-train on very small amounts for each specific task.

The Genesis team frames GENE-26.5 as “an early but important step.” While there are still open questions about how the system would operate in the real-world, on tasks that weren’t set up specifically for the demo, this seems like one of many small steps on the path to Rosies in every home. Beep boop baby.

(2) Neuralink Building Surgical Brain Robot

Big week for Elon. Doing battle in court with Open AI. Selling Colossus 1’s 300MW of compute to Anthropic. And, on the side, building a surgical robot that can operate on any region of the brain. What did you get done this week?

We're building a surgical robot capable of reaching any brain region. The goal: a generalized neural interface to help solve any condition that originates in the brain.

The new robot is a generational upgrade on the R1 that's been used since 2024. It moves through five axes with 10-micron precision, places an electrode thread in 1.5 seconds (vs. 17 seconds on the prior generation, an 11x speedup), uses eight optical coherence tomography cameras to map vasculature in 3D in real time, pierces the dura mater directly without removing it, and can reach roughly 99% of brain structures, not just the cortex.

“Any brain region” expands what Neuralink can fix (and ultimately enhance). Cortex-only means paralysis. Cortex-plus-deep-regions means epilepsy (disrupted electrical activity), Parkinson's (motor circuits in the basal ganglia), depression and PTSD (mood and memory circuits), and the long tail of “conditions that originate in the brain,” which, depending on who you believe, is… everything.

Neuralink is already dramatically improving its patients lives, but if you combine what Neuralink is up to with what Genesis is up to, the future starts looking a lot like the future.

(3) Magrathea Raises to Make Magnesium from Seawater

Way way back in Dose #79, we talked about Magrathea Metals’ $28 million partnership with the DoD to produce carbon neutral magnesium from seawater and brines. Now, Magrathea has raised $24 million to produce clean, carbon-neutral magnesium in the west.

Zero primary magnesium is produced in North America. Zilch. Nada. The continent's last major producer, US Magnesium on the Great Salt Lake, went bankrupt and shut down in 2025 after Utah denied the permits. Roughly 85% of the world's magnesium now comes from China, mostly via the Pidgeon process, the coal-fired ferrosilicon reduction of dolomite ore. Both China-dominated and dirty.

Magnesium is the "gateway metal" for the alloys that make airplanes, EVs, and the Blackhawk helicopter (which apparently needs ~400 lbs of the stuff per unit). Magnesium glycinate also helps me sleep at night. The Department of Energy, Department of War, and Department of the Interior all officially classify it as a critical mineral, and as with many other critical minerals, we import nearly all of it, mostly from a strategic competitor.

Magrathea can produce here by extracting magnesium electrolytically from seawater and waste brines instead of digging up ore. The technical innovation, according to CEO Alex Grant, isn't really the electrolysis (which has been around since the 1940s), but the dry-down step that turns wet magnesium chloride into pure, anhydrous MgCl₂ that an electrolyzer can crack. The company says its facilities will cost ~50% less per ton of capacity than competing approaches, and they've already locked in $500M+ per year in future metal sales via MOUs and binding agreements (including one major automaker).

Engineering is underway for Commercial Phase 1 at TETRA's Evergreen brine site in southwest Arkansas and a pilot plant in Oakland is already running. Mag-nificent.

(4) Aalo Atomics Receives DOE DSA Approval for Aalo-X

The Department of Energy’s Idaho Operations Office has approved Aalo Atomics’s Documented Safety Analysis (DSA) for the Aalo-X Critical Test Reactor. The DSA serves as the authoritative safety basis for DOE nuclear facilities, documenting analysis of potential hazards to workers, the public, and the environment, and is equivalent to an NRC license for commercial reactors operating under DOE jurisdiction. Go time.

I’m a small personal investor in Aalo, and we’ve been tracking its rapid progress here in the Dose for a while (#58, #135, #157, #175), and now they have DOE approval, pending Readiness Review, the final phase, where DOE verifies that the people, the building, and the procedures are all cleared to operate as documented.

Aalo is targeting zero-power criticality by July 4, 2026, the date set by President Trump's May 2025 executive order to coincide with America’s 250th birthday, which directed DOE to get at least three test reactors critical on DOE land by Independence Day. Aalo broke ground at INL last August, completed assembly in March, signed a 5%-LEU fuel deal with Urenco, contracted Baker Hughes for the steam turbine, and is now staring down the regulatory finish line.

Aalo-X is a 10MWe demonstration. Aalo’s commercial product, the Aalo Pod, will be a 50MWe block of five sodium-cooled, factory-built reactors purpose-designed for sitting next to a hyperscale data center. We will start seeing new, critical advanced reactors online within months for the first time in a long time.

God Bless America and welcome to the Electronaissance.

(5) Google DeepMind Invests in and Partners with EVE Online Maker

Cecilia D'Anastasio for Bloomberg

In World Models, Pim DeWitte argued that games are the best path to useful world models. Google DeepMind, which has a long lineage of using games to train and show off its world models (Atari, chess, Go, etc…) seems to still agree.

EVE Online, the 23-year-old single-shard sci-fi MMO famous for player-run wars, scams, central-bank-grade economic intrigue, and protracted heists with real-money damages in the tens of millions of dollars, is becoming a research environment for Google DeepMind.As part of the deal, DeepMind takes a minority stake (low millions) in Fenris Creations, the rebrand of CCP Games which just bought itself back from Korean parent Pearl Abyss for $120M, less than half what Pearl Abyss paid for it in 2018.

DeepMind will work on isolated EVE servers to study coordination, deception, and "long-term planning and continual learning," all things, per DeepMind's Adrian Bolton, that AI hasn't fully mastered.

EVE creator and Fenris CEO Hilmar Veigar Pétursson said: "We jokingly say that the final boss for AI in games would obviously be Eve Online. Eve is giving insights about our own society and the human condition." EVE has hired actual central bankers to manage its virtual economy. Researchers already study its emergent politics. It is the closest thing humans have ever built to a synthetic society at scale, with a corpus two decades deep. Pim also tweeted that it is the fuel for the Von Neumann probes.

If GDM (what a lab!) can dominate EVE like it dominated Go, Google may have found an even better business than search ads (solving the economy).

EXTRA DOSE: Science Breakthroughs, Panthalassa Raises, Magical Methane, Ramp, Johnson’s Balls

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Hi friends 👋,

Happy Friday and welcome to our 191st Weekly Dose of Optimism!

Packed Dose today - we had to double-up a few related stories to fit it all. We have everything from cancer-sniffing dogs to space-based energy lasers. Plus, the Sixers crushed the Celtics last night to force a Game 7 and it’s a perfect 50 and sunny heading to 62 here in New York City heading into the first May weekend of 2026… my god, life is good.

Let’s get to it.

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(1) Dogs That Can Sniff Out Cancer

Tigerfeathers on Dognosis

Last year, waiting in the taco line at the Progress Conference, I started talking to the guy in front of me, Akash Kulgod, who told me that he was working on using dogs to detect cancer. His company is called Dognosis.

The idea is that dogs have an exquisite sense of smell but don’t speak human, so they train teams of beagles, labradors, and shelter dogs to sniff disease signatures in human breath samples (collected by having the patient breathe normally into a face mask for 10 minutes) while wearing EEG helmets, sensor suits, and being recorded on video so AI can read the dog's perceptual judgment as a digital signal.

I love dogs and I hate cancer, so I thought it was an awesome idea, but woof did it seem like a longshot.

Well, last week, the Journal of Clinical Oncology published the results of their Phase 2 study, covering 3,275 participants across six hospitals in Karnataka, the largest breath-based multi-cancer detection study ever run. Each sample was assessed independently by at least three dogs, and a Bayesian fusion model combined their judgments. And the results are…

Hold up. For context here, the leading blood-based Multi-Cancer Early Detection (MCED) test on the market, GRAIL's Galleri, runs ~$500–$1,000 per test and posts overall sensitivity of ~51.5% with stage-dependent performance ranging from 16.3% at Stage I to 90.1% at Stage IV. Meaning, today's gold standard MCED catches about 1 in 6 Stage I cancers. Early stage cancer detection is really hard, but it’s also where you have the best chance of just knocking it out.

Dognosis scored a 90.8% on sensitivity and 91.3% on specificity across seven cancer types (oral, breast, esophageal, cervical, lung, colorectal, prostate), which is excellent, but the insanely great part is that the sensitivity for early-stage (Stage I-II) disease held at 90.6%, showing basically no degradation versus late-stage detection. That is huge. Catching cancer early is the best cure we have.

In other words, today's leading blood test catches about 1 in 6 Stage I cancers; Dognosis catches 9 in 10. Galleri is a little more specific (fewer false positives) but Dognosis is meant to be radically cheaper, which is important in places like India where 80% of cancers are caught at Stage III or IV and roughly 1% of the population has ever been screened, and which is important to get more early screening generally. After the cheap test, we can figure out how to lower false positives at subsequent, more expensive stages.

The next step is real-world programs across multiple Indian states, with a U.S. study to follow.

This is an incredibly cool company showing very promising early results, made in India, meant for the world. For much, much more, our friend Rahul Sanghi at Tigerfeathers did a super in-depth interview with Akash that you should read.

Woof woof, woof. (Get fucked, cancer.)

(1b) Mayo Clinic AI helps detect pancreatic cancer up to 3 years before diagnosis

Man all the homies hate cancer and are figuring out how to detect it early.

Last week, we led with the six-year follow-up on BioNTech and Genentech's personalized mRNA pancreatic cancer vaccine, writing, “it’s sometimes called the ‘silent killer’ because symptoms don’t show up until very late, and by the time they do, only about 1 in 10 patients has a tumor that’s still operable.”

Early detection would help enormously, and it turns out, AI might be able to help. In a paper published in Gut, Mayo researchers showed that an AI model called REDMOD, run on routine abdominal CT scans that had already been read as normal, can detect pancreatic cancer up to three years before clinical diagnosis.

REDMOD showed 73% sensitivity vs. 39% for specialist radiologists looking at the exact same scans, at 88% specificity, with a median lead time of about 16 months. For cancers that eventually showed up more than two years later, REDMOD flagged 68% of them while the radiologists got 23%. The model is now being moved into a prospective trial called AI-PACED to test it in real care pathways for high-risk patients (e.g. people with new-onset diabetes, which is sometimes an early sign of pancreatic cancer).

Woof woof, woof.

(2) Zuck Commits $500M to Bio & Space Energy Lasers

Yesterday, in Scarce Assets, I wrote about Zuck’s purchase of the most expensive home in Miami history at $170 million, beating the previous, pathetically low record by $50 million. Later that afternoon, rumors emerged that Zuck was one of the people interested in buying the Seattle Seahawks from Paul Allen’s estate for ~$6B or more. Super Bowl winning NFL teams in two of your rivals’ backyard are very Macro Scarce Assets indeed. The man is worth $211.6 billion. He lost $17.8B yesterday alone, or three Seattle Seahawks. Excited to see Zuck talk sports with Theo Von.

But Zuck is spending on some pretty civilizationally useful stuff, too.

On Wednesday, Axios reported that he and his wife Priscilla Chan were committing $500 million out of Biohub, their bio non-profit whose mission is “to cure or prevent all disease,” for its Virtual Biology Initiative. More concretely, the money will go to more compute and more data, which is where money goes these days.

Alex Rives, Biohub’s chief, said they hope to amass a dataset an order of magnitude larger than the current one billion cell datasets, as a step on the “path to building accurate predictive models of the cell.” $400M will go to Biohub’s own work generating massive multi-modal proteomic, genomic, transcriptomic, cellular, and tissue-level datasets, and towards developing the next generation of imaging tools (cryo-ET, advanced microscopy) needed to capture them. The remaining $100M will fund external grants to a who’s who of bio: Allen, Arc, Broad, Sanger, the Human Cell Atlas, the Human Protein Atlas. NVIDIA will be the the technology partner.

To understand why we might want a virtual cell, check out ’s What Are Virtual Cells?

But what do you get for the guy who has record-setting real estate, an NFL team, and a virtual cell?

Space lasers, baby. On Monday, Meta announced that “it signed the first capacity reservation agreement with Overview to receive up to 1 gigawatt of power from the company’s spacecraft.” Overview is Overview Energy, whose founder Marc Berte I spoke with just last week.

The company’s game plan is to put satellites in geosynchronous orbit that beam power to existing solar farms on Earth, so electricity can be delivered wherever it’s most needed without being tied to one location. Unlike Reflect Orbital, which plans to send mirrors up to space to reflect the sunlight back to earth’s solar panels directly, Overview plans to capture the solar energy in space, convert it to laser light, then beam those photons down to existing solar panels, which turn them back into electricity.

I understand that it sounds insane and sci-fi, but I came away from my call with Marc thinking it’s much more near-term than I expected, and Meta’s deal supports that belief.

To get into the nitty gritty, read our friend Rob L'Heureux’s Making Space Lasers Boring in Arena Magazine.

Zuck future so bright he gotta wear shades1.

(3) Rainmaker Proves It Can Make it Rain

Augustus Doricko

I’ve probably covered Rainmaker in the Dose more than any other non-portfolio company, because being able to control the elements for humanity’s benefit is awesome, and because they keep putting out good news with good videos.

This time, CEO Augustus Doricko announced that “Rainmaker is the first company in history to routinely, unambiguously, modify the weather. Last quarter, we produced >143MM gallons of unambiguously man-made precipitation.”

While others have made it rain, Rainmaker is the first to use radar and satellite data to validate that they were actually responsible for the rain, and just how much rain they produced.

Make it rain. Count the drops.

(3b) Skydio Commits $3.5 Billion to U.S. Drone Manufacturing Expansion

Rainmaker makes it rain by sending drones into clouds. Zipline uses drones for delivery. The military uses drones. Law enforcement uses drones. Infrastructure owners use drones. Drones are everywhere, most of them are made in China, and there will be a whole lot more of them in the future than there are now.

To that end, Skydio announced that it plans to invest $3.5B over the next five years to expand American drone manufacturing, including spending over $1 billion with U.S. suppliers. The headline program, SkyForge, is a fifth manufacturing facility five times larger than the existing four (Skydio outgrew four facilities in eight years), with select suppliers invited to co-locate inside it.

We’re gonna need a lot of drones. Skydio is the best American bet. My friend Molly O’Shea went to Skydio HQ to see how they do it.

(4) Colossal Announces the De-Extinction of the Bluebuck Antelope

“More than 200 years ago, humans erased an iconic species.”

“They’re yet another example of a species that has gone extinct by interacting with us.”

“We are excited to announce the de-extinction of… Hippotragus Leucophaeus.”

Colossol Biosciences, of Wooly Mammoth and Direwolf fame, plans to bring back the Bluebuck. Just because we can absolutely means we should.

(5) How American Dads Became the Parents Their Fathers Never Were

and

What’s the point of all of this time-saving and life-extending technology if we can’t use all of that time and life on the important stuff. Luckily, we (dads) seem to be, according to Derek Thompson and Aziz Sunderji, who ran the time-use data on three generations of American dads.

In 1965, the typical married father spent ~30 minutes a day actively engaged with his kids. Today, Millennial dads spend more than 80. That's roughly triple the Boomer baseline and nearly quadruple the Silent Generation. Mothers' time with kids went up, too. The new childcare hours are not coming out of the mom column.

The piece walks through four explanations, none of which fully covers it on its own. The mass entry of women into the workforce is the obvious one but the timing doesn't quite work. Dads also seem to like it. There’s Rameys' "Rug Rat Race," where intensive parenting is partly status competition, partly anxiety about a kid's path through college and the labor market. And as Marc Dunkelman has written, the decline of community and weak ties in America has pushed leisure indoors, which means dads are physically around more, which means they're parenting more.

One that they don’t really talk about but feels right is technology. I got to spend way more of Dev and Maya’s first few years with them because I was able to work from home thanks to Substack and Zoom. While it’s bad that we’re distracted by phones when we’re with our kids, phones also mean that we’re still reachable at the playground.

The essay’s subtitle captures my own experience pretty well: “The new American dad is more present and more exhausted—but also, more satisfied with life.” Also this: “new parents often have short-term loss of brain volume.” Also this:

I wouldn’t trade it for anything in the world.

EXTRA DOSE: Science Breakthroughs (including a 5-in-1 GLP-1 successor), Earth AI, PTJ, Nicholas Thompson

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Hi friends 👋,

Happy Thursday!

A few weeks ago in Not Boring Capital’s quarterly LP update, I wrote a small essay on scarce assets in a period of abundance. Then Thrive bought the Giants and HOF bought Bugatti and Marc Andreessen pointed out that when one thing becomes abundant, another becomes scarce, and I figured it was a good time to dive a little deeper on what is going to be a supercycle in scarce assets of all shapes and sizes (and a bear market for the easily replicable).

It is also, unofficially, the latest installment in my ongoing plea that you just be different and more you.

Let’s get to it.

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Scarce Assets

The playwright S.N. Behrman, in a short study of the Gilded Age English art dealer Joseph Duveen titled “Duveen,” quipped that “Duveen … noticed that Europe had plenty of art and America had plenty of money.”

Duveen made his fortune and his legend balancing that imbalance. “Joseph Duveen sold hundreds of Old Masters, for soaring prices, to American multimillionaires between the early years of the twentieth century and 1939, when he died, at the age of sixty-nine,” wrote Peter Schjeldahl in The New Yorker. He sold a Rembrandt to Carnegie partner Henry Clay Frick in 1906 for $225,000, which was absurd at the time, and the absurdity only grew. The hundreds of millions, in today’s dollars, of Frick’s money that Duveen spent on the Old Masters formed the basis for The Frick Collection.

Not to be outdone, and the whole point was not to be outdone, the Pittsburgh banker and US Treasury Secretary Andrew Mellon purchased, through Duveen, Raphael’s Cowper Madonna for $970,000 (~$21 million today) in 1929, and then, in 1936, a collection of 42 Italian Renaissance paintings and sculptures for $21 million, or half a billion today. These became core to the National Gallery of Art.

Duveen was an absolute animal, who practically spied on clients and potential clients in order to understand their psychology, their likes and dislikes, and even their trash. Per The New Yorker, “(A Duveen employee crowed that, during the years that Mellon was Treasury Secretary, ‘the contents of his wastebasket reached the train to New York in the time it took the Secretary to walk home from the office.’)”

Duveen knew the value of understanding what was priceless to someone who could afford anything with a price tag. For the newly rich of the Gilded Age, as for the wealthy of any era, that was status via scarcity, and the thing that was high-status and scarce for American millionaires back then was connection to the heritage of the old country.

Humans are funny, and the Robber Barons’ clans had the money to be funnier than most. Alva Vanderbilt, the wife of Cornelius Vanderbilt’s grandson William, was the funniest of them all.

Despite the Vanderbilts’ gargantuan railroad and shipping fortune – the Commodore had more money than the US Treasury when he died in 1877, something like 5% of all the money in the country per The First Tycoon – they lacked social standing in New York. Caroline Schermerhorn Astor and Ward McAllister’s “Four Hundred,” instituted as a response to the meteoric rise in multimillionaires after the Civil War, defined who counted as Society. The Vanderbilts were not on the list.

So Alva did what one does. She built a French chateau on Fifth Avenue that dwarfed the street’s older townhouses, and threw the party of the century: the 1883 Vanderbilt Costume Ball. The point was to get on Astor’s list, so Alva supposedly withheld an invitation from Carrie Astor because Mrs. Astor had never called on the Vanderbilt home. To get her daughter invited, Mrs. Astor had to leave her visiting card at 660 Fifth Avenue, which meant officially recognizing the Vanderbilts. The Astors’ invitation arrived the next day.

The party cost $250,000, or $6 million in today’s dollars, but as The Museum of the City of New York writes, “as of March 27, 1883 the Vanderbilts were at the top of a new New York society that was not just limited to 400 people.”

New York City, evidently, wasn’t enough for Alva, nor for many of America’s multimillionaire class. In 1895, Alva married her daughter, Consuela Vanderbilt, to the Duke of Marlborough. Her money, his title.

By 1915, Titled Americans reported that there were 454 “Dollar Princesses,” or American heiresses who had married into European aristocracy.

Titles were scarce. They weren’t making more of them. And therefore, they were desirable to a rising class of people who could buy almost anything.

As Duveen might have noted, Europe had plenty of titles and America had plenty of money.

Not so fast, says Duveen biographer Meryle Secrest, who thought Behrman’s line was overly simplistic. Whether art or titles, she writes, “Nothing would have persuaded titled Europeans, and particularly the British landed aristocracy, to part with their family heirlooms had there not been a catastrophic change of fortune in the final years of the nineteenth century.”

Why were the Europeans suddenly much poorer than they were classy? What was the catastrophe?

“Cheap food from the United States and elsewhere had wrecked the profits of European agriculture.”

The Europeans were done in by abundance, so they had to sell their Scarce Assets, which became more valuable, thanks to the wealth generated by that very same abundance. Pip pip.

Humans fucking love scarce things. Always have. The more we win from abundance, the more we want to roll the winnings into scarce things. Scarce is special.

If we are entering a period of untold abundance, expect a roaring bull market in Scarce Assets.

Macro Scarce Assets

Which is what we are seeing.

Last Friday, Josh Kushner announced Thrive Eternal, the firm’s permanent capital holding company that will concentrate in a small handful of “Iconic franchises and cultural institutions rooted in tradition, identity, and shared experience,” starting with the San Francisco Giants.

Later that same day, HOF Capital announced that it’s leading a consortium to acquire Porsche’s stakes in Bugatti Rimac and Rimac Group.

They are not making any more 143-year-old baseball teams in the technology capital of the world, nor are they making any more 117-year-old French hypercar houses.

There is an obvious story here that, despite its obviousness, is worth spelling out.

As wealth grows and concentrates, demand for a limited pool of Scarce Assets dramatically outstrips supply.

Since the turn of the millennium, global GDP has more than doubled from $78.69T to $174.28T (in 2021 Dollars), while the number of San Francisco MLB teams and Bugatti-makers has stayed flat.

GDP growth undersells the situation, though, because the composition of the growth is more relevant to our conversation. If the $100T increase in GDP was split evenly across the world’s 8.3 billion people, each person’s extra $12,048 wouldn’t mean squat for the price of the San Francisco Giants. People would be able to spend less of their money on food and more on clothes or vacations, but they wouldn’t be able to buy baseball teams.

More relevant is the fact that the wealth of the world’s Top 100 wealthiest people has grown 10x in nominal terms, from $895 billion in 2000 to $7.2 trillion today. Adjusted for inflation, the world’s richest 100 people are more than 4x wealthier today than they were a quarter century ago, and their wealth has grown twice as fast as the global economy.

Bill Gates was the world’s richest person at the turn of the millennium, with $60 billion. Today, Elon Musk and Larry Page have more money than all of 2000’s billionaires combined. The amount of money that Cambricon CEO Chen Tianshi made yesterday would rank him 60th on the 2000 list. You probably haven’t even heard of Cambricon.

Plus, more of the world’s savings sit inside of professionally managed pools of capital whose mandate is to preserve purchasing power, compound over long periods, and find assets that can’t easily be printed, copied, or competed away. Pension assets have more than tripled since the early 2000s, reaching roughly $70 trillion by the end of 2024. Private markets, a rounding error at roughly $600 billion to under $1 trillion in 2000, are now a $13 trillion to $15 trillion asset class. Sovereign wealth funds, which held roughly $1 trillion in 2000, now control something like $12 trillion to $15 trillion.

So, a much higher and more concentrated numerator (more cash) chasing a ~flat denominator (Scarce Assets).

Compounding the issue, these assets are often taken off the market altogether, shrinking the pool. Frick’s art is in The Frick Collection. Mellon’s is in The National Gallery. Thrive named its new vehicle Eternal.

So, a much higher and more concentrated numerator (more cash) chasing a decreasing denominator (Scarce Assets).

And boom goes the dynamite.

This dynamic not new. All of that concentrated capital has been putting upward pressure on major sports franchise prices for a while. Check out this NBA team value data visualization by Koba Khitalishvili.

It’s interesting to note that even within this small pool, the team that has become the most valuable ($11B) and whose value has increased the most (2,344%) is the one in San Francisco, where top-end wealth has increased the most over the time period.

Per Sportico, the total value of NFL teams grew from $190B to $228B between 2024 and 2025, and perhaps unsurprisingly, the top 10 teams’ value grew 24.3% compared to 17.8% for the bottom 22. There are levels to this, scarcity within scarcity.

I could go on. I will go on. People love wealth porn.

Last year, Vlad Doronin set the Miami-Dade record with the $120 million sale of his Star Island home, pricemogging Ken Griffin’s $107 million 2022 purchase.

That record didn’t last long. In March, Meta CEO Mark Zuckerberg purchased 7 Indian Creek, on the even more exclusive Indian Creek Island, nicknamed “Billionaires’ Bunker,” for $170 million.

Coincidentally, Zuck purchased the home from L.A.-based cosmetic surgeon Dr. Aaron Rollins. I say coincidentally, because in a previous draft of this essay, I wrote “thanks to Instagram-face, full lips and smooth skin aren’t what they used to be.” It is only fitting that someone making previously scarce “beauty” abundant would put the fruits of that commoditization to work in Scarce Assets.

Is $170 million a good price for the neighborhood? How does it comp on a per square foot basis? Who gives a shit? You are asking the wrong questions. $170 million is like one AI researcher. It is less than 0.1% of Zuck’s wealth. It is about one-third of what Zuck and Priscilla committed to create better AI simulations of the human body yesterday, while I was in the middle of writing this paragraph. There is only one Indian Creek Island, and it has only so many lots, especially when you consider how many Jeff Bezos has taken off the market (three).

Take the money from The Everything Store (abundance). Roll it into Indian Creek real estate (scarcity).

And then, of course, there is art. Once used to launder class from Europe to nouveau-riche America, it is now playing the same role in the Gulf.

In November 2025, Gustav Klimt’s Portrait of Elisabeth Lederer sold at Sotheby’s for $236.4 million.

It was the highest price ever paid for modern art at auction, and the second-highest price paid at auction for any art ever after the November 2017 Christie’s sale of Leonardo da Vinci’s Salvator Mundi for $450.3 million.

While both buyers were anonymous and neither has been confirmed, the strong rumor is that Saudi Crown Prince Mohammed bin Salman (MBS) bought Salvator Mundi, and those in the know believe either MBS or Abu Dhabi purchased the Klimt. If the latter, it would reportedly be to anchor the collection at the Frank Gehry-designed Guggenheim Abu Dhabi.

Not just any country gets a Gehry-designed Guggenheim, you know.

Is Portrait of Elisabeth Lederer “worth” $236.4 million? You are missing the point. Who gives a shit? Is it even one of Klimt’s top 10 works? Artnet didn’t think so in January 2025, before the piece came to auction, but who cares. Klimts don’t go up for auction every day, and exchanging abundant dollars for scarce Klimts is a trade you do every day and twice on Sunday.

These things are like some hyperVeblen Goods - not only are they more desirable the higher the price, the high price is the entire point.

I am having a lot of fun writing this and I would love to keep going, but I have the rest of the essay to get to.

Everything we’ve covered so far fits into a bucket I’d call Macro Scarce Assets.

What I mean by that is that if the top-end of the wealth distribution keeps getting richer, the prices of these assets will keep going up. There is global competition for them. The buyers discussed in this section include multi-billionaires from the United States of America, Russia, and the Middle East.

If we want to include Asia, we might throw the $600 million wedding Indian industrialist Mukesh Ambani threw for his son Anant in 2024, the closest thing to the Vanderbilt Costume Ball this decade.

If you have the money and access, and you believe that we are embarking on the Singularity, I recommend that you buy as many Klimts, NFL Franchises, and Yellowstone Club homes as you can get your hands on.

I don’t though, sadly, so I’ve been spending a lot of time thinking about how a little guy like me might participate in the abundance → scarcity trade, and I’ve come to the conclusion that it’s everywhere.

Positional Scarcity

Alex Danco, in one of my favorite essays ever, wrote: “In conditions of abundance, relative position matters a great deal.”

He then grouped the different flavors of positional scarcity into categories:

What we’ve called Macro Scarce Assets mostly fit in the Prestige piece of the chart, including where it overlaps with Access to become Proximity and Curation to become Legitimacy.

Again, the useful way to think about Macro Scarce Assets is that as long as the rich get richer, they will get more valuable. “Relative position” here refers to the relative position of the asset owners versus each other based on the Scarce Assets they are able to accumulate.

But there is a different kind of scarcity that exists in relation to other assets, and moves as those assets’ relative abundance and scarcity changes. These are more specific. They are Micro Scarce Assets.

Like, when the Printing Press makes printed text cheap, handwritten text gets more valuable.

Federico da Montefeltro

In the 15th century, long before the Europeans were reduced to selling either their titles or their scarce things, Federico da Montefeltro, the Duke of Urbino, built one of the world’s greatest libraries.

Close readers will appreciate the century: it’s the same one in which Johannes Gutenberg invented the printing press. If you were a Duke looking to fill a library, I mean, what a gift!

Except that Federico da Montefeltro, the Duke of Urbino, refused to taint his shelves with even a single printed book.

His bookseller Vespasiano da Bisticci wrote, “In this library all the books as superlatively good, and written with the pen, and had there been one printed volume it would have been ashamed in such company. They were beautifully illuminated and written on parchment.”

Printed text became abundant and cheap, which made handwritten works more scarce and valuable.

The Duke attempted to do two things at once: first, as da Bisticci writes, “to do what no one had done for a thousand years or more; that is, to create the finest library since ancient times,” and second, to do it all by hand. “It is now fourteen or more years ago since he began the library,” fawned da Bisticci, “and he always employed, in Urbino, in Florence and in other places, thirty or forty scribes in his service.”

After his death, da Montefeltro’s successor dukes, the della Rovere, continued the handwritten-only tradition. “They continued to collect codices, even to have printed books copied by hand (a Borgesian touch), since only codices could enter this hallowed hall, and by the time the Library went to Rome there were 1,760 volumes.” This detail comes from Roderick Conway Morris’ coverage of the 2007 Federico da Montefeltro and His Library exhibition at the … Morgan Library.

The Morgan Library, which itself holds a Bezosian trio of Gutenberg Bibles, was built on the same impulse to collect the irreproducible in a time of abundance that motivated the Duke, and the same desire to import heritage that drove Mellon, Frick, Vanderbilt, and the other Gilded Age Industrialists. Human nature is remarkably consistent.

The da Montefeltro Library was doing something different than the Morgan Library or the Frick Collection. It was specific. When printing made text abundant and cheap, it made handwritten codices scarce and valuable.

Micro Scarce Assets

Marc Andreessen spent part of his weekend doing what he does best: memeing an idea into the mainstream through repetition. In this case, “When something becomes abundant and cheap, another thing becomes scarce and valuable.”

Macro Scarce Assets are structurally scarce: their supply is fixed or shrinking while global wealth rises. Micro Scarce Assets are relationally scarce: they become valuable because something adjacent becomes abundant. Some things can be both.

If Macro Scarce Assets are like blowing up a balloon, it just keeps getting bigger, then Micro Scarce Assets are like squeezing a balloon, the air has to move from one place to another.

Clayton Christensen nailed the Micro mechanism down most tightly in his Law of Conservation of Attractive Profits. From The Innovator’s Solution via Stratechery: “The law states that when modularity and commoditization cause attractive profits to disappear at one stage in the value chain, the opportunity to earn attractive profits with proprietary products will usually emerge at an adjacent stage.”

When one particular thing becomes abundant and cheap, this other specific thing becomes scarce and valuable.

It is an important lesson to keep in mind, one of those ideas that’s as close to a law of business physics as business gets. This is why Joel Spolsky observed that “Smart companies try to commoditize their products’ complements.” Make the thing next to you more abundant and cheaper, so you become scarcer and more valuable.

As a non-but-aspiring-billionaire, the hunt for potential Micro Scarce Assets is where I spend most of my time. These you can catch before they actually become scarce, when they’re not priced scarce.

This was basically the theme of Power in the Age of Intelligence, the hunt for companies that could use technology to capture a scarce position, expand outward from there, eat the industry, and become Scarce Assets themselves.

Real estate is one of the places this is easiest to see. The supply of land on the Earth is basically fixed, so you’re betting that demand for your particular piece of it will increase, and you can do things to make that happen.

If you’re an individual, you might build a very nice house on your land. If you’re a developer, you might put in shops and restaurants and a walkable Main Street. If you’re Japan’s JR-East, you build trains through and to the land, and then build apartments, hotels, and shops on the land, so that you can physically get more people there and convince them to stay.

If you’re Proto-Town, you buy a bunch of land 30 minutes outside of Austin, make it incredibly fast and easy to build things there, and attract a group of tenants / residents who can also build most of what the town needs, from houses to power.

On Friday, the same day that Thrive announced Eternal and HOF announced Bugatti, Proto-Town had its coming out party with that Core Memory video and a WSJ article. This is Micro Scarcity at work: as capital for hard tech and ideas about what to build become abundant, a place you’re actually allowed to build fast becomes scarce and valuable.

Land near fast-growing cities is structurally scarce, but it is more valuable to a group like Proto-Town than it would be to someone who just wanted to buy and hold, because Proto-Town can add value.

The right Scarce Asset to the right buyer basically has a scarcity-set floor and an abundance-uncapped upside.

Take the Giants purchase.

Assume for a moment that AI researchers like sports, and imagine Thrive letting its portfolio companies use its newly-acquired baseball team to recruit those researchers: owner’s box, batting practice with the team, whatever. All hypothetical. The value of a top-end researcher, as set by Zuck, is somewhere in the hundreds of millions of dollars. How do you price a small edge in recruiting? Or in capital raising? Or, if Thrive is able to beat out a rival firm for a deal on the margin because it owns the Giants, and that deal ends up returning $5 billion, did they basically get paid to own the Giants?

The more uncapped your upside, the more valuable the right Scarce Asset is to you.

If this sounds silly, it was the logic for OpenAI’s acquisition of TBPN for a reported “low hundreds of millions.”

Obviously, OpenAI was not buying TBPN’s $30 million in revenue, because it stopped ads immediately. OpenAI bought a universally-beloved show hosted by two of the most commercially creative people in tech, tech media’s Scarce Asset.

The way Scarce Assets work, I doubt OpenAI had a list of ten tech media platforms with some valuation-math-based number it would pay for each. At 900 million weekly active users, ChatGPT has wider distribution than any media property in the world. What it didn’t have, which TBPN did, was likeability. OpenAI should be willing to pay almost anything for that.

More dumb math: at OpenAI’s $852 billion valuation, a 1% lower chance that the company is regulated out of existence is worth $8.52 billion. A 1% bump in its eventual public market price is worth, give or take, $8.52 billion. If there’s even a 1-3% chance TBPN helps deliver either of those 1% swings, it’s worth low hundreds of millions to OpenAI.

If that sounds silly, consider the Elon Musk Family of Companies.

A few weeks ago, I was walking around Washington Square Park with a friend and we were talking about whether Anduril would suffer the fate of recent tech IPOs like Figma and Navan if and when they go to market. My bet is that it wouldn’t, because it is a Scarce Asset.

Like the asset itself, the equity, is scarce, in a way that most post-IPO tech stocks are not.

There’s this thing I’ve noticed for a while but never put into words or numbers, that startups are incredibly sexy while they’re in the private markets, and then become pretty boring within months of IPO.

This is separate from the idea that the private markets overvalue startups and the public markets look at them clear-eyed and set the rational price. I am talking about an aesthetic thing, the butterflies you get looking at a top startup versus the ick you get looking at the same exact company on the public markets a few months later.

Look at the performance of the US venture-backed non-bio companies that have gone public since 2025. The chart shows their performance versus the Nasdaq Composite since Figma’s IPO day on July 31, 2025. Every single one of them has underperformed the Nasdaq over that period.

And there are a lot of good reasons for that. Maybe these companies were overvalued in the private markets. Maybe they popped too hard at IPO and their prices have floated back to reality. Maybe their earnings lag their narratives. Maybe they’ve gotten caught up in the broader SaaSpocalypse. There are a million potential reasons, but one that I haven’t heard but do believe is that they go from being relatively scarce in the private markets to undifferentiated, as assets, in the public markets.

Like, could you explain what Figure Technologies does that’s different from what Coinbase or Robinhood or eToro does? Given a whole entire universe of stocks you could own, why would you buy Navan in particular? Would you rather own Figma as your bet that software will grow, or would it make more sense to buy the basket of software companies and forget it?

In the private markets, a great startup is sexy. It’s a narrative, a secret, a status symbol, a thing you had to win your way into. Once it goes public, it becomes a ticker sandwiched between thousands of other tickers in your brokerage account, sortable by revenue multiple, gross margin, growth rate, blah blah blah. It will come up as one of 248 of companies in a screener for “software companies with 50%+ margins growing 30%+” or whatever. Even if the company hasn’t changed at all, the asset has.

The asset gets compressed down to the boring stuff. It gets Tickered.

This sounds dumb. I get that. But we are so overwhelmed by the abundance of everything that if something wants our attention, it needs to rip it from us.

I’m not commenting on any of these companies in particular, mind you, just trying to illustrate the point that as public companies with tickers, they feel less differentiated. There are other things you could put in your portfolio that could fill a similar role. There are many ways to bet on software.

If you want to bet on space, though? Well, you’re going to have to buy the SpaceX IPO.

SpaceX the stock is a Scarce Asset because SpaceX the company does something rare and valuable, something that cannot easily be copied (and Elon constantly reminds people of that through his owned platform). The micro-scarce leads to the macro-scarce.

This is why, while those other companies struggle in the public markets, with a combined market cap of ~$125 billion across the twelve of them, SpaceX plans to go public at a market cap between $1.75 trillion and $2 trillion. This, despite the fact that those 12 companies combined to do roughly double SpaceX’s $15 billion in revenue last year.

The scarcity of the business and the scarcity of the asset feed back on each other.

Because the business does something rare, valuable, and defensible, and because Elon Musk knows how to use his platform to make it seem even more rare, valuable, and defensible, investors treat the stock as a Scarce Asset. Because investors treat the stock as a Scarce Asset, the company has a lower cost of capital with which to do things that make it more rare, valuable, and defensible.

And then, because the company is worth more, it can do things that have the potential to make it more valuable in reality, like ~acquiring Cursor for $60 billion. As Kevin Kwok explains, the logic for the deal is tight. It would have been harder to pull off if SpaceX were worth ~10x revenue like the other recently-public companies.

Scarce Assets have outsized value today even relative to the actual economic value their moats provide.

I think Anduril is much closer to a SpaceX than it is to a Navan. To bet on the modernization of defense, you can’t buy the Nasdaq or the DJIA or Boeing. Anduril stock, I argued, will be a Scarce Asset.

After I had that conversation with my friend, Primary’s Jason Shulman tweeted this:

It was cool to see Vertical Integrators called out as its own category, and as I wrote in Part IV of the Vertical Integrator Series, the potential to become scarce from a competitive perspective is why I like them so much: “The biggest advantage may be this: because it’s so difficult, and because the advantages you accumulate by doing a million hard things well form barriers, there will be very little serious competition awaiting on the other side of the Great Filter.”

That also means the winning Vertical Integrators have the potential to be Scarce Assets, meaning that not only will their business benefit from micro-scarcity, but they will be able to more easily attract capital thanks to macro-scarcity.

One of the things that’s interesting about Vertical Integrators is that while they share an overall approach, each one can be a unique and scarce asset. Dandy and Flock, for example, are both Vertical Integrators, but Dandy is attacking the dentistry supply chain and Flock is supplying law enforcement with better data. Each can be a Scarce Asset - the best company in its category, and the best way to bet on that category - in a way that almost no pure software company can.

This is another reason I think more value will accrue to a smaller number of companies that win their category. It’s soft and squishy and irrational, but so are we.

Way back in COVID times, in October 2020, to be precise, when I was sleep deprived with three-week-old Dev, I wrote this piece called Software is Eating the Markets that both holds up and doesn’t. A lot of the specific examples look very COVID in hindsight, but I think the core idea, that retail investors, like angel investors, pay for more than just future cash flows when they buy an asset, holds up.

It may not just be retail investors, either. There is so much demand for certain private companies’ secondaries and so little for others among family offices and institutional allocators that startup shares feel like Veblen Goods: there is more demand the higher the price goes. Check out Setter’s list of “the most sought-after venture-backed companies in the global secondaries market.”

This, I think, is how a market behaves when it views certain companies’ equity as Scarce Assets as opposed to purely financial instruments. If you tell someone at a party that you invested in Anthropic or SpaceX, chances are they will know what you’re talking about and perhaps even think that it’s cool! If you buy them now, even if you aren’t going to make 100x, you’ll get to live through the thrill of an IPO that everyone’s talking about with skin in the game.

People are status-seeking monkeys, Eugene Wei wrote, and still we keep underestimating it. This status-seeking shows up even in things that should be purely spreadsheet-based, like late stage investments, and even in hard-headed industrialists like Henry Clay Frick. We value what is scarce.

As more things that were traditionally scarce become abundant, we’re going to keep seeing this playing out across… everything.

Thanks to GLP-1s, skinny isn’t what it used to be. So what will be scarce when you can take a shot or a pill to look fit? As popular as marathons and fitness competitions have become, I bet they become even more popular. You can’t fake a sub-2 hour marathon.

Thanks to AI, generic knowledge has become free and abundant. Meanwhile, prices for top-end rare physical books have jumped. Per the delightful Rare Book HUB, the cutoff to make the year’s Top 500 sales jumped from $81,250 in 2023 to roughly $120,000 in 2024, while the number of lots selling for more than $1 million rose from 12 to 29. Interestingly, the median price has decreased a bit, while the top got hot.

If we are successful in creating the abundant near-future that has been promised, the competition for Scarce Assets will continue to intensify.

There are some Scarce Assets that only a lot of money can buy, like sports teams and rare art. I bet we will see more tech billionaires, or tech companies, buy F1 teams if and as they become available.

But there are many more to be built than there are to be bought.

Unprecedented abundance will create unprecedented demand for things that cannot be made abundant.

That might mean local things: really unique restaurants, theaters, hotels. It might mean new towns altogether. I have a hunch that drones, EVs, and EVTOLs should expand local frontiers. For an enterprising new settler, it might make sense to start gobbling up cheap, buildable land where it doesn’t currently make much sense, or open up new frontiers beyond the land. You might get it wrong, you probably will. No one said scarce was easy.

It might mean personality-driven media, like TBPN, but it certainly won’t mean something that looks like TBPN. People will always want to outdo other people, but we will also like and trust each other more than we like and trust the machines.

You know what, I’m going to stop listing things.

The scarce and valuable thing you create will not come from someone else’s list.

This is the temptation, and the trap. The trap of this era of abundance will be believing that you can do great things easily, that you can create things other people truly value at the press of a button. You can’t.

All of this sounds so obvious when you write it out that I almost didn’t want to hit publish. More money buy scarce things dur dur. When all same do different har har.

But this is how almost no one is acting. People get the abundance button and they start mashing it, like those rats in that experiment, believing that the thing that is now easy for them is only easy for them, that there are no second-order effects, that the world is a static place. That maybe, hey, just for me, greatness will be easy.

Whoops. The millisecond something becomes easy, the value shoots elsewhere. Scarce is hard and hard is scarce, forever and ever, amen.

That’s all for today. We’ll be back in your inbox tomorrow with another Weekly Dose.

Thanks for reading,

Packy

Hi friends 👋,

Happy Friday and welcome to our 190th Weekly Dose of Optimism!

Busy week: cures for cancer, autonomous everything, Sixers actually stole a win off the Celtics, lots of Extra Doses. No time to waste…

Let’s get to it.

Today’s Not Boring is brought to you by… Silicon Valley Bank

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(1) Pancreatic cancer mRNA vaccine shows lasting results in early trial

Kaitlin Sullivan, Marina Kopf and Anne Thompson for NBC News

Pancreatic cancer is often a death sentence. It kills about 87% of the people it’s diagnosed in within five years. Worse, it’s sometimes called the “silent killer” because symptoms don’t show up until very late, and by the time they do, only about 1 in 10 patients has a tumor that’s still operable. For the rest, there are no good options. And for a long time, oncologists thought the disease was biologically incapable of mounting a meaningful immune response at all. Pancreatic cancer was supposed to be the cancer that immunotherapy couldn’t touch.

This week at the AACR annual meeting in San Diego, Dr. Vinod Balachandran of Memorial Sloan Kettering presented six-year follow-up data from a Phase 1 trial of a personalized mRNA vaccine for pancreatic cancer that strongly suggests otherwise.

The trial enrolled 16 patients with early-stage, operable pancreatic cancer. After surgery, each got a custom-built mRNA vaccine (autogene cevumeran, developed by BioNTech and Genentech) alongside the checkpoint inhibitor atezolizumab and standard chemotherapy. Each patient’s tumor was shipped to BioNTech in Germany, sequenced to identify up to 20 mutations most likely to look foreign to the immune system, and then turned into a one-of-one vaccine that was shipped back to New York and infused into the patient.

Eight of the sixteen patients mounted a T-cell response to the vaccine. Six years out, seven of those eight responders are still alive (87.5%), versus two of the eight non-responders (25%). One of those seven — Donna Gustafson, who was the first person in the world to receive one of these vaccines in February 2020 — just hiked Mt. Etna for her 50th wedding anniversary. The immunologic data are equally encouraging: 85% of the T-cell clones primed by the vaccine persisted into memory phase, and the memory T cells were still functional even after chemotherapy, producing cytokines and attacking cancer cells on re-exposure to the original neoantigens.

Of course, there are caveats. This was a 16-person Phase 1. There was no randomization. Half the patients didn’t respond at all, and we don’t know why yet. Operable pancreatic cancer is already the most survivable version of the disease, and a subgroup of patients always lives longer than expected. Phase 2, now running globally under Genentech and BioNTech, will tell us a lot more.

With those out of the way, this is awesome, at the very least for these seven people and their families, and hopefully for a lot more. The belief for years has been that pancreatic tumors were an immune desert because it has too few mutations to flag and too dense a stromal wall to penetrate. This trial suggests the wall can be climbed, if you give the immune system the right map: a personalized neoantigen set, manufactured in weeks, and delivered as mRNA.

As Elliot wrote in Going Founder Mode on Cancer, personalized cancer therapeutics hold a ton of promise, and they’re both expensive and complicated for wide-scale usage today. That said, these are exactly the kind of thing that will get radically cheaper and faster to produce as the underlying biotech stack matures.

If this holds up at scale, it’s a twofer for our anti-cancer canon. One: pancreatic cancer is not immunologically untouchable. Two: the personalized mRNA playbook that Moderna/Merck have been running in melanoma is generalizable, and generalizing fast.

As we like to say here at the Dose: get fucked, cancer.

(2) Cursor and SpaceX: In Search of a Complete Loop

Kevin Kwok

Speaking of two-fers… SpaceX basically purchasing a call option on Cursor for $10 billion is the kind of story we would have covered in the Dose anyway, and we get to throw in the first Kevin Kwok essay in nearly a year for free.

On Wednesday, SpaceX and Cursor announced that the newly combined SpaceX / xAI was basically buying a $10 billion option to buy Cursor later this year for $60 billion. SpaceX will most likely buy Cursor after its planned IPO for $60 billion. If it doesn’t, it will pay Cursor a $10 billion breakup fee / fee for the work they did together this year.

It sounds a little confusing, which is why I was so happy to see Kevin explain the deal, both from a deal mechanics perspective and the strategic perspective.

On the deal mechanics side, he expects to see more deals like this.

On the strategy side, the deal makes a lot of sense. Cursor has a great coding product and model, but not enough compute to compete with Anthropic and OpenAI. SpaceXAI is really good at building out compute, but pretty bad at making coding models. Put the two together, and you might have something that can compete with the leading labs in a use case that they all agree is incredibly important to win.

In short, the world’s greatest rocket company bought a product that can write code as it prepares to colonize the Moon and then Mars, obviously.

(3) Fervo Energy Files S-1 for IPO

M&A. IPOs. Oh my. We are back.

Last week, we led the Dose with Quaise breaking ground on the world’s first commercial superhot geothermal plant. Superhot is a drilling problem, and Quaise is looking to solve it.

This week, the company doing the most to solve geothermal’s economics problem at commercial scale filed to go public. On Friday, Tim Latimer and Jack Norbeck’s Fervo Energy filed an S-1 with the SEC to list on Nasdaq under the ticker FRVO, with J.P. Morgan, BofA, RBC, and Barclays leading.

It’s great to see a new clean power generation source hit the public company milestone, with a real, traditional IPO and all, because it will hopefully mean more money for the space. But it’s also fun because it gives us a better look into what Fervo is up to.

Fervo is operating or developing 3.65 gigawatts of geothermal capacity, a number that, if fully built, would nearly double the total installed geothermal capacity in the United States. Its flagship Cape Station project in Beaver County, Utah, breaks ground to first power later this year; 500 MW is under construction at the site and Fervo has permits in hand for another 1.5 GW on the same piece of land. There’s also a 150 MW site in Nevada under a Google + NV Energy deal, targeting 2030.

The economics are even more interesting, because they give us a firmer sense of where geothermal sits today and where it’s headed. Fervo says Cape Station will deliver carbon-free 24/7 power at $7,000 per kilowatt of installed capacity, which is in the same ballpark as advanced nuclear. The company’s goal is to cut that to $3,000/kW, which would beat natural gas on an unsubsidized basis for always-on power. That would be a massive unlock.

Fervo is able to pull this off by bringing well-worn horizontal drilling and completion techniques over from shale (Latimer was a drilling engineer at BHP before founding Fervo in 2017) to geothermal.

To which we say, drill baby drill and let’s get this frackin’ thing public.

(4) Humble unstealths with a cabless autonomous electric truck & $24M

Ana-Maria Stanciuc for TheNextWeb

On Tuesday, a San Francisco startup called Humble came out of stealth with $24 million in seed funding and a design that is so obvious once you’ve seen it that it makes you wonder why no one had done this before. Basically, they got rid of the cab where human drivers sit, because these trucks won’t have human drivers.

Every existing autonomous trucking company, including Aurora, Kodiak, Waabi, and Einride, retrofits driverless tech onto a tractor-trailer architecture designed around a human sitting in front of a steering wheel. As founder Eyal Cohen put it, “Trucks were never designed to be autonomous.”

Humble built one that was. Remove the cab, and you get 360-degree sensor coverage, a significantly lighter vehicle, and a geometry you can optimize for intermodal containers. The Humble Hauler is built for 40- and 53-foot shipping containers and goes dock-to-dock, unloading at the destination rather than handing off to a human driver at a yard near city limits like Aurora does. Eclipse led the seed with participation from Energy Impact Partners.

Cohen, Humble’s CEO, has been on this problem for a while, and on hardware even longer. He previously worked at Apple, Uber ATG, and Waabi, and co-founded Spark AI (acquired by John Deere in 2023). That experience helped him get to a prototype in under 6 months.

This is an idea that keeps popping up as we talk about autonomy: when you design around the actual constraint instead of the inherited one, you end up with a different machine. Kalanick’s Atoms is explicitly anti-humanoid because most jobs want wheels, not legs. Tesla’s Cybercab has no steering wheel, because they won’t have human drivers. Humble is making an analogous bet with trucks.

Better, faster, cheaper. Ship it.

(5) Medra Launches Robotic Bio Lab

for & Michelle Lee on X

Here in the Dose, we’ve written a lot about the software side of the AI-for-bio boom: IsoDDE can suggest drug candidates against previously undruggable pockets, Evo 2 can design entirely new genomes, Boltz and AlphaFold have collapsed structure prediction, and Chai is cranking out antibody candidates at a pace that would have sounded insane five years ago. It’s all fantastic and futuristic.

Having said that… a designed molecule is not a validated molecule. Every one of those candidates still has to be synthesized and tested by a physical lab staffed by physical humans who have to sleep and take weekends. Or, I guess that’s an assumption, certainly these candidates need to be synthesized and tested in a physical lab, but…

This morning, Michelle Lee’s company, Medra, formally opened the doors on a 38,000 square foot warehouse in San Francisco where roughly a hundred robotic arms are running biology experiments simultaneously, 24/7, linked by a small courier robot that ferries pipette tips and sealed plates between stations like an extremely focused junior scientist. A pair of scientists could run maybe a dozen antibody-binding experiments in a day. Medra’s arms run hundreds at a time, don’t clock out, and get better at it every week. We love a good robot arm.

Lab automation has been overpromised for two decades. What’s different about Medra is that it doesn’t require the lab to buy new robot-native instruments. Only about 5% of bench equipment in biology has APIs that legacy automation can plug into; the other 95% (centrifuges you open by hand, pipettes you tilt and time, etc…) was built for humans. Medra’s robots use computer vision and manipulation models to operate the same instruments a human would. Lee says the combination can push the share of bio-tech tasks that can be automated from 5% to 75%.

There’s a second layer on top of the hardware that is maybe even more interesting. Every arm and bench is instrumented with cameras and nine kinds of sensors, so the system logs the exact pipette angle, the exact depth of insertion, the exact timing between reagent additions. The AI scientist on top reads the results, proposes protocol changes, and rewrites the protocol itself. Lee describes one customer whose antibodies weren’t binding at all (the readout came back at zero); the AI scientist narrowed the cause to two hypotheses, proposed adding a vortexing step, and watched binding jump to over 70%.

Lee’s frame for what she’s building is TSMC for biology. TSMC is what makes it possible for chip designers to exist without owning a fab. Medra wants to be what makes it possible for drug discovery companies to run experiments without owning a wet lab.

Just yesterday, in The Great Blue Frontier, Will and I wrote that AI for bio is data-limited, and suggested the 91% of sea creatures we’ve yet to discover as one source. Madra is another. Lee believes that with more throughput, we “cure cancer, Alzheimer’s, infectious disease.” This, she thinks, is also how we keep American bio competitive with China by speeding ourselves up.

All sounds great. Get to work, robots.

EXTRA DOSE: Science Breakthroughs, Oceans, Flipbook, Tokyo & NYC Subways, and a Boots Manifesto

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Hi friends 👋 ,

Growing up, I sailed a little sailboat called a Sunfish. I was OK, not great, which is a shame, because had I been a little more competitive, I would have raced against the next town over, whose team included a young Taylor Swift.

When I turned 16, I became a beach lifeguard, which is pretty much the greatest job ever for a 16-year-old, and I held onto it for the next four summers. I wasn’t a great swimmer or a great rower, but I was a good enough runner to make up for it so they kept me around. Over those summers, I fell in love with Wes Anderson’s The Life Aquatic with Steve Zissou, which was based, red knit cap and all, on the life of Jacques Cousteau.

All of which is to say, while I spent a lot of time by the ocean, I had a tourist’s relationship with it. I enjoyed it, swam in it, sailed in it, and then went back to the real world, where classes and eventually a real economy awaited.

This, Will O’Brien argues, is how we’ve all handled the ocean until now. We come, we explore or exploit, and we get back to dry land. This, Will thinks, is no longer how we will handle the ocean. With Ulysses, The Ocean Company of which he is co-founder and President, Will plans to help build the infrastructure that would allow us to treat the ocean as a permanent fixture of the economy, and potentially even as a new home for humanity. The way our grandparents and JFK thought we would.

Since meeting Will two and a half years ago, he’s become one of my favorite people in tech to talk to about everything from Irish omnipresence to religion to aliens to vertical integration. I’ve been asking him to put his gift of gab to paper with me for a while, and now that the company has successfully raised $46M from a group of investors led by a16z American Dynamism, he finally had the time to oblige.

Note: not boring capital is not an investor in Ulysses, but I am a huge fan.

In this co-written essay, Will tells a history of the future of the ocean that I’d never heard, before making the case that the ocean is the last great frontier and one of the greatest economic opportunities available to humanity. It’s an adventure, and in the words of Steve Zissou, “Anyone who wants to tag along is more than welcome.”

So throw on the most underrated soundtrack of the 2000s…

And let’s get to it.

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The Great Blue Frontier

With Ulysses President Will O’Brien

The Death of the Ocean Dream

At 4 AM on February 17, 1969, Berry Cannon was lowered 610 feet below the surface of the Pacific in a steel personnel transfer capsule to make repairs to the Navy’s “yellow submarine,” Sealab 3. At that depth, normal air becomes toxic to humans and the nitrogen can send you delirious, so Cannon was breathing helium instead. Helium keeps your mind clear, which was helpful for Cannon. He had been awake for twenty straight hours. Unfortunately, helium also strips heat from your body six times faster than air does. This was a problem. That particular morning, the capsule’s heater was broken.

No matter. Cannon, one of history’s greatest maritime frontiersmen, was running on amphetamines and conviction. When the capsule reached the seafloor, he dropped through the hatch into open water and began swimming through pitch darkness toward a leaking underwater habitat. This habitat, the U.S. Navy believed, would be humanity’s first permanent foothold on the ocean floor.

It was a routine fix as far as deep sea habitat fixes go. Except for one fact: Cannon’s rebreather’s CO₂ scrubber canister was empty of Baralyme. He didn’t know that. Nobody did. On a TV monitor topside, Captain George Bond watched Cannon swim gracefully—and then he watched Cannon’s body suddenly jackknife.

“Any time you see rapid motion in a diver,” Bond would say later, “you know he’s in trouble.”

Berry Cannon was dead. And with him died America’s plans to conquer the ocean.

Five months later, Neil Armstrong walked on the Moon, fulfilling one of JFK’s most ambitious promises. It’s been over half a century, and we all know Neil Armstrong’s name.

But nobody remembers the man who died trying to live on the seafloor. Nor do we remember the ocean dream he represented.

I grew up in County Cork, on the south coast of Ireland, and spent every summer at Garrettstown jumping off the pier or the cliffs, bodyboarding and pulling crabs out of rock pools, or heading out in the boat with my dad to catch mackerel. I loved the ocean the way you love something before you understand it. The textures, the cold, the endlessness of it. As a kid, I loved explorers too. Steve Irwin, especially. He wasn’t an ocean guy, but he had that renegade explorer energy, that willingness to just get in there and figure it out. I think I always assumed someone was figuring the ocean out, that there was a Steve Irwin of the Deep. That someone was down there, mapping it, understanding it, and protecting it.

As I got older, I realized nobody was.

So I’ve made the oceans my life’s work. In just the past few months, my office has rotated between Washington D.C., San Francisco, Los Angeles, London, Western Australia, Maine, Virginia, Florida, New Orleans, and San Diego. I would do this for free; I might even pay to do it. But as I’ve been living out my childhood dream, I’ve found that the ocean economy is something like a sunken treasure that’s been waiting on the bottom of the sea for anyone intrepid enough to go grab it.

Today, I’m going to let you in on the secret I’ve discovered down there: Earth’s largest domain and last great frontier is also its grandest economic opportunity.

The Two Frontiers

In the 1960s, America was a country dreaming at full volume. You could taste optimism in the air — and in the sea.

Most of us alive today never learned how important ocean exploration was to the future our grandparents imagined. Americans were fantasizing about ocean exploration in the same way they were about going to the Moon. They were as captivated by The Great Blue Frontier as they were by the Space Race.

At the 1964 New York World’s Fair, GM’s Futurama II ride carried more than 26 million visitors past a vision of the near future. Alongside the lunar base and the Antarctic weather station, guests saw ocean-floor oil rigs, submarine trains hauling minerals to shore, and Hotel Atlantis, a sub-oceanic resort around which vacationers explored in various personal oxygenated craft.

The ocean stood right beside the Moon in the American imagination, and it promised just as much: food and living space for a rapidly growing country, minerals and energy for a booming industry, and the upper hand in the Cold War.

President John F. Kennedy saw the problem clearly. “We know less of the oceans at our feet, where we came from, than we do of the sky above our heads,” he told the National Academy of Sciences in 1963.

His administration had been trying to close that gap from the start. In a March 1961 letter to Congress, he called for “a national effort in oceanography,” warning that “knowledge of the oceans is more than a matter of curiosity — our very survival may hinge upon it.” Kennedy’s FY1962 budget request nearly doubled federal oceanography spending, funded ten new research vessels, and expanded shore facilities fivefold. Two years later, he sent Congress a ten-year, $2 billion plan called Oceanography: Science for Survival, and directed the Navy to begin SEALAB — an underwater habitat program explicitly conceived as the ocean floor’s answer to the Space Race.

President Kennedy placed the ocean alongside space as the twin frontiers of American ambition and coequal national priorities, and his ambition was matched by action.

While Kennedy persuaded Americans about the ocean’s importance as a governmental priority, French naval officer, explorer, and filmmaker Jacques Cousteau charmed them in their living rooms.

Cousteau had co-invented the modern scuba regulator and was one of the most famous people in the world. He built Conshelf, a series of underwater habitats where teams of divers lived for weeks at a time on the seabed. Conshelf II, built in the Red Sea in 1963, was essentially an underwater village: a starfish-shaped structure at a depth of 33 feet with bedrooms, a kitchen, hot showers, and a television.

A parrot named Claude served as the carbon dioxide detector. If Claude fell off his perch, the air was bad. You couldn’t whistle because of the helium. Matches wouldn’t light, although the crew still managed to light cigarettes. Sparkling wine went flat under pressure. Fried food was forbidden because greasy fumes couldn’t be scrubbed from the air.

The documentary about this village, World Without Sun, won the Academy Award. The Undersea World of Jacques Cousteau ran on prime-time American television from 1966 to 1976. The ocean was mainstream culture, Academy Award-winning cinema, and prime-time television. It had become a fixture of the popular imagination.

Simultaneously, the U.S. Navy was building permanent undersea infrastructure with the same seriousness that the Army had when it built forts across the American West. Its program, SEALAB, consisted of three progressively deeper underwater habitats that were designed to prove that humans could live and work on the ocean floor for extended periods. We were going to settle the ocean. On SEALAB II, astronaut Scott Carpenter, one of the original Mercury Seven, spent 30 consecutive days underwater, becoming the only astronaut-aquanaut in history.

By the late 1960s, more than 60 underwater habitats dotted the world’s seabeds: Hydrolab, Helgoland, Tektite, Aquabulle, Hippocampe, dozens more.

Kids wanted to be aquanauts the way they wanted to be astronauts. The ocean was a national obsession on par with the Moon.

Then, in roughly five years, it all died.

SEALAB was suspended immediately after Cannon’s death. The Navy, which could invest in the future during peacetime, was pulled into the Vietnam War. Even Jacques Cousteau, the man who had done more than anyone alive to prove humans could live underwater, the man who had unmatched cultural authority on anything to do with the ocean, pivoted from exploration to activism. He founded the Cousteau Society in 1973 to protect and preserve the oceans instead of exploring and settling them.

By the 1992 Rio Earth Summit, he was dubbed “Captain Planet.” America, in some sense, always followed Cousteau’s lead on the ocean. When he explored, we wanted to explore. When he said “protect,” oceanic policy and funding priorities fell in line. The regulations that followed, including the Marine Mammal Protection Act, UNCLOS, the IWC whaling moratorium, and the High Seas Treaty, were individually important protections, responding to real ecological crises that demanded our attention. But their cumulative effect, compounded by Cousteau’s cultural reframing, led to a collective shift in how we considered the ocean as a domain to build in. Within a generation, any proposal for persistent ocean activity faced a default presumption of harm.

In the 1960s, America set its sights on two frontiers. By the 2010s, we had all but stagnated on those hopes. We landed on the Moon, went back a few times, and then decided to keep our eyes on the ground. We gave up on settling the ocean altogether.

But we are going back to the Moon, this time to settle it. Just this month, the Artemis II astronauts orbited the Moon for the first time since 1972 in an important step towards settlement. NASA Administrator Jared Isaacman has laid out a plan to begin building a permanent base in 2028, a mission made possible by progress in the commercial space sector over the past two decades. We are going to be an interplanetary species! The Moon should be a state, experts are saying.

It’s time that we settle the ocean too.

This forgotten frontier is no less economically or geopolitically important than space; in fact, it is both larger and more urgently strategic. Through our forgetfulness, the ocean has become a wild and lawless domain whose potential to benefit humanity lies dormant, one which is being abused by those who care nothing for protection or preservation.

What Happens to a Frontier Forgotten

In 2015, a wooden fishing boat washed ashore on the coast of Japan carrying the skeletal remains of its crew. Then another arrived. And another. Over the next several years, hundreds of these “ghost boats” drifted onto Japanese beaches, small wooden vessels with many carrying only bones and tattered North Korean flags.

Investigators eventually pieced together the story, which turned out to be more tragic than supernatural. 900 Chinese distant-water fishing vessels had moved into North Korean waters in violation of UN sanctions (sanctions that China itself had signed and ignored), strip-fishing the stocks and pushing local fishermen further and further offshore in boats not built to take on the open ocean. Those fishermen starved at sea, and their boats drifted east until Japan’s coastline caught them. Nobody stopped the Chinese fleet, and nobody rescued the fishermen, because there was nobody out there to stop or rescue.

A 2024 study in Nature found that three out of four industrial fishing vessels are invisible to public tracking, which is wild in a modern society that tracks everything.

Consider the pandemonium that broke out when MH370 went missing, because planes, unlike fishing vessels, don’t just go missing. Last weekend, I spoke with a man who was in a terrible car accident on a Canadian highway in 1994; his right leg and hip were crushed, but he lived because a helicopter arrived to whisk him to the hospital within five minutes. In the air and on land, we track our people and our critical assets with stunning accuracy.

But the ocean doesn’t operate like that, in large part because we don’t have the infrastructure to make monitoring possible. So we’ve kind of given up. Two-thirds of the ocean falls outside any country’s jurisdiction, and there is no police force or coast guard with global reach. There is a reason that so many ocean movies involve protagonists lost at sea, hoping that someone chances upon them.

As a result, we often find boats doing bad things, too late. Fleets of hundreds of Chinese vessels have been caught operating illegally off the coast of Ecuador, right at the edge of the Galápagos Marine Reserve, one of the most ecologically sensitive places on Earth. In 2017, Ecuador intercepted a single vessel, the Fu Yuan Lu Leng 999, which was carrying 6,000 sharks, many of them endangered species, fished from Galápagos waters. Workers on distant-water fishing vessels are held for years, passports confiscated, in conditions that meet every definition of slavery.

There is no way of knowing how many illegal vessels go undetected, because so much of the ocean remains unmonitored, and we have less of a clue the further beneath the surface we go. While humanity has mapped 100% of the surface of Mars, a planet with no known life 140 million miles and seven months (if you time it just right) away, we have mapped only 27% of the ocean floor with modern sonar, and much of that is coarse, low resolution.

“If you just look at the size of the ocean,” Bob Lazar (yes, that Bob Lazar) recently told Jesse Michels, “you can hide an entire civilization down there. Especially if they’re immune to the effect of the ocean. You just gotta be deep. We’ll never find ‘em.”

While we can neither confirm nor deny the existence of subsea civilizations (except for maybe Atlantis), the fact is there is certainly enough space and little enough visibility to get away with it. The ocean is unfathomably large, and we haven’t even begun to fathom what’s happening all those fathoms below.

This, as President Kennedy warned in 1963, is more than a matter of curiosity. Our survival, or at least our flourishing, may hinge upon it.

Sixty-three years later, we have failed to heed his words, and to capitalize on the opportunity. The ocean covers 70% of our planet, carries 99% of our internet traffic and 80% of our trade. It contains more critical minerals than all known land reserves. And we still know critically little about any of it.

A full 91% of ocean species remain unknown to science. When researchers sequence DNA from deep-sea sediments, they can’t match it to any known organism by species or taxonomic group. The largest library of biological information on earth is functionally unread. Chances are, that library contains a well-stocked pharmacy.

Marine organisms have already given us Ziconotide (a painkiller 1,000x more potent than morphine, from cone snail venom) and Trabectedin (a cancer drug, from a sea squirt). The alien-looking jellyfish Aequorea victoria provided researchers with the green fluorescent protein (GFP) that won them the 2008 Nobel Prize in Chemistry, which now “enables scientists to track, amongst other things, how cancer tumours form new blood vessels, how Alzheimer’s disease kills brain neurons and how HIV infected cells produce new viruses.”

Presumably, there is more where that came from inside the other 91% of sea creatures we have yet to discover. If AI for bio is data-limited, we have a library full of it in the ocean.

There is much for a knowledge-loving people to discover down below, so… let’s just go find out what else is down there. Let’s send people and instruments and start intervening.

Alas, there is nothing to intervene with, because we never built anything that could intervene, and little more to listen with, because we let our ears rot.

Back in the 1950s, back when the nation had aquatic ambitions and a Cold War foe against which to sharpen them, the US Navy built a classified network of undersea hydrophone arrays called the Sound Surveillance System, or SOSUS. By exploiting the deep sound channel, or SOFAR channel, SOSUS could track Soviet submarines across enormous swaths of the Pacific and Atlantic Oceans. Individual listening stations could pick up a submarine across entire ocean basins thousands of miles away.

Turns out, SOSUS could track whales, too. When it was partially declassified in the 1990s, marine biologists realized the Navy had been accidentally collecting the richest dataset on ocean biology ever assembled. They’d captured volcanic eruptions, seismic events, whalesong, species migrations, and, in 1997, the “Bloop,” a mysterious sound that has still not been fully explained…

But the Cold War ended in the ‘90s, and by then, our ocean dreams were already long dead. The US government declassified SOSUS, celebrated its scientific contributions, then cut its funding. Some assets were folded into another program, IUSS, a bunch of the hydrophone arrays were put into standby status, and stations at places like Bermuda, Adak, and Keflavik were shut down.

Today, our greatest effort at ocean observation is a network of 4,000 robotic floats called Argo, the 27-year-old “crown jewel of ocean observing systems.” Each of the 4,000 floats monitors an area larger than Portugal, surfacing once every ten days to transmit a single temperature reading. They are deployed like this…

Or, like this...

The entire program is tax payer-funded. And despite running on just six cents per American per year, those 4,000 floats now generate more subsurface ocean data every month than the entire rest of the observing network combined. Argo is great, but that is much more an indictment of the state of our ocean awareness than a celebration of the system.

Plus, Argo can only observe, just like SOSUS could only listen. If a float detects a chemical anomaly, a biological collapse, or a dangerous trend, there is nothing and no one for miles around to respond, in much the same way a thermometer can tell you that you have a temperature but can’t do a damn thing about it.

The question is, in a time of technological wonder, why the ocean remains dark.

The Ocean is Pre-Industrial

Frontiers are tamed when there’s money to be made from taming them and the technology to tame them is viable.

President Thomas Jefferson sent Lewis and Clark west to wrest the fur trade from the Canadians and establish commercial routes. The Forty-Niners went to find gold. These were risky and potentially highly profitable expeditions, and when they paid off, America built the railroad to connect the West with civilization.

The railroad meant that normal people, not just bold or desperate explorers, could establish their homes, families, and businesses in the West, and it made new industries possible: ranching, mining, commercial agriculture, towns, and cities. The railroad industrialized the Western Front.

Something similar happened in space, a domain accessible only to national governments and a handful of intrepid telecommunications pioneers in search of vast riches before SpaceX collapsed launch costs. Before SpaceX, the biggest satellite constellations were measured in dozens. Today, SpaceX has more than 10,000 Starlinks in orbit; people make fun of Jeff Bezos for flying “only” a couple hundred satellites.

Reusable rockets are the space railroad, and on them fly hundreds of businesses seeking to get to space and stay. SpaceX industrialized space, and the effects of that industrialization are just beginning to be felt.

For all of the exploration and trade we’ve done out there, the ocean has never had this moment. We’ve fished it, laid cables across its floor, drilled rigs into it, plopped wind turbines in it, raced around the globe on it, and shipped goods across it, but nobody has ever cracked the economics of operating within it, broadly and persistently, as a domain. The ocean still runs on an 1800s-era expedition model that puts expensive humans on expensive ships for expensive campaigns, then brings them back to dry land.

There’s a word for a frontier that hasn’t had this moment yet. The ocean is pre-industrial.

To industrialize a frontier means to transform it from a place with industries in it, exploring and opportunistically extracting, to a place built to support industries persistently and economically, at scale.

An industrialized frontier is a platform, not a series of projects. Each new piece of infrastructure makes the next one cheaper and unlocks activities that weren’t possible before. An industrialized frontier compounds.

The railroad industrialized the West and SpaceX is doing the same for space. Nobody has industrialized the ocean yet.

If we want to gain dominion over Earth’s oceans and steward them properly, we need to make it a place where industry can thrive.

Creating more industry in the ocean would decrease crime, increase our understanding of our home planet, accelerate scientific discovery, grow existing maritime industries and enable new ones altogether, unlock resources that are literally sitting on the floor, and, counterintuitively, incentivize our stewardship of it.

A Note On Industry and the Environment

Industrialization has dirty connotations. As you read the word, you might be picturing smokestacks, or worse, the soot-darkened faces of pre-pubescent factory workers.

I’d argue that the industrialization of new frontiers has been one of the most powerful engines of human progress. So I want to tell you how I think about industrializing the ocean while protecting it, because a desire to protect it, to restore it, is where I began this journey.

When we started Ulysses, we set out to help restore the oceans by using underwater robots to replant seagrass more efficiently. Seagrass meadows cover less than 0.2% of the ocean floor but store up to 18% of the ocean’s carbon, support roughly a fifth of the world’s fisheries, and have been declining at about 7% a year since the 1990s. This means we’ve lost roughly a third of all seagrass globally in the past few decades. Restoring these meadows is an important part of bringing back the vitality of the oceans.

That work is underway in Florida, Virginia, Western Australia, and in the Great Barrier Reef. As we waded into the ocean, however, we realized a few things.

First, we realized that the infrastructure we’d need to do restoration well didn’t exist. We’d have to vertically integrate and build most of it ourselves. Soon, we realized that the infrastructure we had to build was exactly what everyone else working in the ocean needed too. We’ll talk about that below.

Second, there are big differences between 19th-century industrialization and modern oceanic industrialization, the biggest of which is the difference between combustion and electric machines. Unlike those factories, or even modern oceanfaring vessels, our vehicles won’t emit smoke or smog. They run on electrons and don’t emit exhaust or greenhouse gases during operation. We will discuss this, too.

Third, we realized that when we expanded our economic ambitions, we also increased our environmental ones. I’ve just shown you what a pre-industrial ocean looks like. There is slavery, overfishing, the extinction of entire species, and ignorance of endless pots of gold. A pre-industrial ocean doesn’t mean an untouched ocean; it just means that the pirates are the ones doing the touching. Commerce brings law and order, if only selfishly, but that law and order has positive externalities. You are far less likely to be murdered in a Western Saloon today than you would have been in 1826. We believe that by industrializing the ocean, we are incentivizing its protection and restoration.

The ocean is too precious to leave to the pirates.

How to Industrialize a Frontier

So if we want to industrialize the ocean, how do we do it? There seem to be four stages that frontiers go through on the path to industrialization.

Discovery → Expedition → Access Breakthrough → Industrialization

Discovery proves the frontier is real and reachable. Expedition explores it, maps it, and finds the resources worth pursuing, but it does so episodically and expensively (or illegally). The fruits of expedition incentivize the development of the access breakthrough: a technology that dramatically collapses the cost of reaching and operating at the frontier, making entirely new categories of economic activity viable for the first time. That triggers industrialization: the frontier integrates into the broader economy, and industries emerge that couldn’t have existed at the old cost structure.

The American West went through all four stages. The early explorers proved it was there. For sixty years, wagon trains, fur traders, and the Oregon Trail explored it episodically. Then, in 1869, the Transcontinental Railroad met at Promontory Summit, Utah, collapsing the cost of access, and what followed was the industrialization of half a continent. The Homestead Act alone distributed 270 million acres, an area larger than France, Germany, Italy, and Spain combined. Western mines produced the copper that wired America’s cities, the gold and silver that backed its currency, and the timber and beef that fed its industrial workforce. Over the century that followed, the frontier compounded into trillions of dollars of real economic value and provided the physical substrate of the American century.

Space has followed the same arc. Sputnik proved it was reachable. Apollo and the Shuttle explored it at $1.5 billion per launch. Then SpaceX spotted that rocket materials cost about 2% of the selling price, vertically integrated them, built reusable boosters, and cut costs 20x. The space economy has tripled over the past two decades, reaching $613 billion in 2024, and is projected to hit $1.8 trillion by 2035.

Both of these examples are gross oversimplifications, but what I want to establish is that there is economic activity on the frontier even before industrialization. Risk-seekers are captivated by the promise of the untamed. But true market creation occurs when early economic activity incentivizes and funds access infrastructure, and access infrastructure facilitates normal business creation. Industrialization moves frontiers from limited, risky, low-volume, potentially high ROI trades to higher volume, safer, lower-individual-ROI but higher-overall-output activities.

Today, the ocean’s pre-industrial economy is already enormous. We spend $2.6 trillion a year across shipping, offshore oil, fishing, subsea cables, and coastal ports. That is an order of magnitude larger than the space economy was when SpaceX was founded.

But the economics of the ocean severely limit where we can operate, and therefore the size of the ocean economy.

We ship across the surface because the surface is the cheapest layer to traverse, and the only one where our GPS and WiFi work. We drill oil from expensive fixed platforms because oil is valuable enough to pay for the helicopters and the two-week crew rotations. We fish from the top of the water column because that is where our tools reach, and as a result, we decimate the stock.

In short, we are limited to the easiest-to-access parts of the ocean, unless an extremely valuable and fairly predictable commodity can justify deeper exploration, just as the fur trade did during America’s westward expansion.

To the extent that we can decrease the cost of access, we can increase the size of the ocean economy. We need the ocean’s railroad. It is surprising that, if $150 billion of activity was enough to trigger a railroad for space, $2.6 trillion of pre-industrial economic activity didn’t trigger one for the ocean long ago.

Why have we not built the ocean’s railroad?

The Ocean Fights Back

Two factors must be simultaneously present for industrialization to occur: suitable technology and urgent demand.

Humans have had boats for a very long time. As early as 3000 BC, the Austronesian people migrated throughout the islands of the Indo-Pacific using sailboats like this one.

But the ocean has never made traversing its surface easy. During the Age of Sail, an estimated 3-5% of merchant fleet ships were lost per year. Rather than being retired, most wooden ships ended their careers by being wrecked, foundering, or being lost at sea. Over time, we got better at making sturdier boats. But even still, the 1990s’ most popular movie was about the sinking of the “Great Unsinkable” Titanic.

Stewart Brand recently published Maintenance with Stripe Press. He chose to open the book with the story of the 1968 Golden Globe Race, the first solo, non-stop circumnavigation of the earth by sailboat. The race’s sponsor, The Sunday Times, charged no entry fee and laid down almost no rules. Sailors simply had to leave from a British port, travel around the globe, and come back to it without stopping.

Nine sailors entered the race, and historians mainly focus on three of them.

Donald Crowhurst, a brilliant inventor with arguably the most technologically advanced boat in the race, never made it past the Atlantic. His electronics failed, his hull leaked, and the isolation broke him. He falsified his logbook, drifted in circles, and eventually stepped off the back of his trimaran into the sea. His body was never found.

Bernard Moitessier, the most gifted sailor in the fleet, abandoned the race despite being far in the lead. Months alone in the Southern Ocean convinced him he’d rather keep sailing to Tahiti than return to civilization.

Robin Knox-Johnston spent 312 days at sea, and most of them were spent not sailing but repairing. Where the others designed their sailboats for technological superiority or speed, Knox-Johnston designed his to be fixed:

To prepare SUHAILI for a ten-month passage, most of it in the world’s roughest waters, he packed into his small boat all the ‘materials and tools’ he could imagine he might need – specialized wrenches for every exotic nut on the boat; ditto for screwdrivers; a sailmaker’s bag full of needles, sewing palms, and twine; a bosun’s bag with every kind of shackle, thimble, and marlinspike for managing all his steel wire rope; a spare bilge pump and extra rubber pipe; 12 yards of canvas; caulking chisels and cotton; plenty of oil, glue, and Stockholm tar; spare parts for everything mechanical; and medical supplies for repairing himself.

It was the right move, because he spent most of his 312 days on the water fixing. And yet… “I realized I was thoroughly enjoying myself,” he said later. Still, the enjoyment was the enjoyment of a man who understands that the ocean is in a permanent state of war with anything humans put in it.

Knox-Johnston won the race, “and the prize of £5,000 – which he gifted to Donald Crowhurst’s bereaved wife and young children.”

Typically, the story is told as a story of different flavors of the human spirit, and it is certainly about that. But Brand chooses to tell it as a story about maintenance. The universe tends towards entropy, but it does so particularly quickly on the open sea. There is no environment on earth that so persistently attacks those who dare challenge it.

Which is to say that the ocean is the hardest frontier on Earth. It fights you every day that you’re in it. The ocean doesn’t necessarily want to fight you. It is simply a matter of the ocean’s constitution.

Saltwater is one of the most corrosive environments on earth, eating through steel, degrading composites, and attacking every electronic component it reaches. Since water flows happily through cracks, it ultimately reaches almost everything. The North Sea, one of the most developed ocean regions on Earth, is so punishing that corrosion alone accounts for roughly 60% of maintenance costs on production platforms, and lifetime operating spend routinely exceeds the original construction cost. Offshore wind has struggled to meet expectations because maintenance costs 2-3x more than onshore wind, components break down more frequently, and turbine performance degrades by an average of 4.5% per year. The global cost of marine corrosion runs to $50 to $80 billion a year, and that’s just for the structures we’ve bothered to build.

Then there’s biofouling (think barnacles): the moment you put something in the water, organisms begin to colonize it. Within weeks, without active maintenance, a clean sensor is blind, and a clean hull is dragging tons of extra weight. The offshore oil industry spends billions per year fighting exactly this. Everyone who chooses to operate on the ocean must become a Robin Knox-Johnston.

Below the surface, the battle only intensifies. Pressure increases by one atmosphere every ten meters of depth. At the average seafloor, you’re looking at 370 atmospheres. Everything on a subsea vessel must be engineered for forces that have no analog on land or in space, and which vary continuously with depth. Space is extreme, but the vacuum is a single, well-understood engineering problem. In the ocean, every meter deeper you go changes the engineering envelope.

We have thus far described fair weather conditions, but the ocean is a stormy place. Storms routinely destroy purpose-built infrastructure: Hurricanes Katrina and Rita alone destroyed 113 offshore platforms and ruptured 457 pipelines.

In each one of these cases, unless you are right there with your vessel, you’re unlikely to even know when things go wrong, because the ocean is a communications desert. GPS doesn’t work underwater at all. Radio and optical signals penetrate about 20 meters before the water absorbs them. Acoustic signals can travel further, kilometers in some cases, but at painfully low bandwidth, about a million times slower than surface 5G. To get around these constraints, you can tether a vehicle to a surface ship with a cable, but that limits your range and requires an expensive crewed vessel overhead, at which point, you’re still stuck in the expedition model.

The ocean is a domain that degrades everything you put in it, crushes anything you send deep, destroys what you bolt to the surface, and isolates whatever survives.

This is before we even address the immense scale of the ocean. So even if you solve every engineering problem, you still face the question of how to cover a domain that dwarfs anything we’ve ever tried to operate in while fighting corrosion, biofouling, pressure, storms, and the comms desert. None of these is impossible to overcome, but every one of them is a tax. A tax currently paid in steel thickness, redundant systems, $20,000/day ship time, mobilisation windows, insurance premiums, and in the engineers you have to send offshore to fix what can’t be fixed from shore.

And on top of that, the industry that grew up around this domain never got what aerospace and automotive got: scale, software, modern supply chains. Maritime is still largely a cottage business of bespoke parts and hand-built systems. So you pay the tax twice: once to the physics, and again to the pre-modern industry that evolved to serve the physics.

We call it the Ocean Tax: the compounding cost the sea, and the industry that grew around it, extracts from anyone who tries to do anything in it. Every existing ocean company is, at heart, a machine for paying the Ocean Tax.

So the natural question is, how do we get around it?

What It Would Take to Industrialize the Ocean

The good news about the ocean being such a challenging environment is that every problem that the ocean throws at you doubles as a design requirement. If we solve for the challenges, we know what kind of system to build.

The ocean is vast. Instead of a few expensive, exquisite vessels, you need a lot of cheap ones.

This is the same trend that we’ve seen in space, going from a few billion-dollar communications satellites to thousands of small, cheap ones. The latter means that the network can cover more area more reliably than single satellites can, and inevitable damage to a single satellite doesn’t knock it out. It is resilient. We are seeing the same trend in defense, with the move towards high-volume, attritable drones instead of a few exquisite platforms.

Like space, it is hard to comprehend just how much room there is to cover in the ocean. The 361 million square surface kilometer number hides just how big it is, because it also goes deep. Industrialization requires coverage of the X, Y, and Z axes. The ocean is more than two miles deep, on average, and its deepest trenches are so deep that Mount Everest could be dropped into the Challenger Deep with 2km of clearance above it. Its total volume is something like 1.3 billion cubic kilometers, which is so large that I’m not even sure what to tell you, other than to say that the number of vehicles we put in it each year stands absolutely no chance.

Today, the entire global autonomous underwater vehicle (AUV) market produces roughly 1,000 vehicles per year, and the incumbents’ AUVs cost on average $500k to $5 million each. For scale, a thousand units across the entire ocean is equivalent to just 27 vehicles across the entire United States. Imagine trying to patrol the entirety of the United States with just 27 vehicles. And that is just the surface comparison! These vehicles are precious, and are treated as such, which is no way to build an economy.

We think the answer to making ocean infrastructure possible is manufacturing at radically lower costs. At Ulysses, we build AUVs for as little as $50,000 per unit, a 10x to 100x reduction over the most commonly sold incumbent models. At that price, we and our customers can think in terms of fleets rather than individual vehicles, and fleets, we believe, are the unit of infrastructure the ocean demands.

The ocean demands you operate at the surface and subsea as a single integrated system.

How do you design a vessel meant to operate on the surface and in the deep ocean, when the two are almost entirely separate domains, with radically different physics? Well, you don’t.

You design a system that handles both, consisting of separate vehicles optimized for each. The surface is where GPS and Starlink work, and doesn’t face the same pressure challenges as lower down, so that’s where communications, fuel, power, and logistics live. But most of the work is below the surface, where the undersea cables, seafloor, and marine ecosystems lie. You need vehicles that can go where the work is, and you need surface platforms that can deploy, recover, recharge, and connect them to the rest of the world.

A fully autonomous system is a much cheaper system, because the cheapest AUV in the world is still expensive if it’s hand launched and recovered, and requires a crewed ship to operate it. There’s a joke in the maritime industry that “unmanned” systems are only unmanned in the sense that the people aren’t on or in them, but right beside them while they operate. As long as humans have to babysit autonomous vehicles, they’re not really autonomous, and it will be impossible to achieve scale.

We’ve designed the system at Ulysses with those requirements in mind. Our underwater vehicle Mako goes deep and does the work. Our autonomous surface craft and mothership, Leviathan, equipped with our autonomous launch, recovery, and recharge platform, Kraken, remains on the surface and serves as the fleet’s connective tissue: deploying Makos, recovering them, recharging their batteries, and relaying their data to onshore operators via satellite. Leviathan is a working asset in its own right, a node in a domain awareness network that can carry its own sensors and serve its own missions. Together, linked by Kraken, they form a single integrated system that can persist at sea without retreating to port.

The underwater environment demands autonomy.

Because of the underwater communications desert we described earlier, you cannot supervise an underwater vehicle in real time the way you would a drone in the air. The bandwidth simply isn’t there, and the latency would make remote control dangerous and unreliable.

AUVs need to think for themselves. Because we’re talking about large fleets of small, inexpensive vehicles, there is no room in either the cost structure or the vehicle itself for a human pilot. Each AUV needs to navigate without GPS, make decisions based on what they see, avoid obstacles, adapt to changing conditions, and execute complex missions with only periodic check-ins.

This is as much a compute problem as it is a software one. At Ulysses, we pack over 100 times more onboard compute than anyone else in our underwater form factor, data-center-class GPUs running inside an AUV. Since we can’t talk to our vehicles, they need to be smart enough not to need us.

Industrializing the ocean demands both observation and action.

Most ocean technology stops at sensors, which are valuable in their own right. However, sensors are not by themselves industrial infrastructure. To industrialize a domain, you have to be able to act on what you observe.

Consider a vehicle that can only observe. It would swim around, notice that a pipeline was in need of repairs, ascend to the surface, and send a message back to a team of humans, who would then schedule and weather permitting, get on an expensive vessel, travel out to the site of the damage, and send a human or ROV (remote operated vehicle) below to make the necessary repairs. That process costs an unnecessary amount of time and money. Conversely, a platform that can both observe and act would be able to both inspect and repair the pipeline, quickly and cheaply. More persistent monitoring would also mean that each individual repair is likely to be simpler.

Here, modern robotics is key. The fleet of machines we send into the ocean must be able to manipulate, intervene, and repair, which means that they need arms, tools, and the dexterity to do meaningful work at depth. If they can, you’ve turned a sensor network into an industrial presence. This is the hardest part of the stack to get right, but given our heritage in sea grass planting, it’s the part that we started with.

The ocean demands a fully integrated system.

You may have noticed, reading this section, that each requirement builds on the others. If you want low costs, for example, vehicles need to be autonomous and they need to be able to act. As soon as you introduce humans back into the equation, coverage and persistence drop while costs rise. If you want vehicles that are truly autonomous, persistent, and can act at depth, you need surface vehicles and subsea vehicles that work together to do what each can do best.

We think this is how you industrialize the ocean, and we believe that the solution has to look more like a network of Starlinks than a railroad: a distributed network of coordinated assets, surface and subsea, each playing a different role. Together, they collectively give you persistent coverage of the hardest domain on earth and the ability to do something about what you find.

This is not a novel insight. Mariners have understood it in theory for at least a few decades. But there have been two challenges: it wasn’t possible to build, because the underlying technologies weren’t ready, and it wasn’t easy to fund, because the urgent demand for such capabilities didn’t exist.

Within the past five years, both of these aspects have changed.

Why Now?

There is perhaps no more important question that a technology startup can answer than “why now?” I argued earlier that every frontier becomes industrialized when supply-side technology and demand-side urgency converge. For the ocean, that convergence is happening right now.

Supply Side: The Ocean Stack

In Cable Caballero, Packy coined “Curve Convergence“: if multiple component technologies improve on exponential curves as industry architectures remain frozen around outdated assumptions, it’s possible to create the opportunity for disruption with a better product.

The ocean is experiencing its Curve Convergence right now. Four exogenous technology curves all crossed critical thresholds in the same window, roughly 2020 to 2025. Each crossing has removed a specific barrier that previously made persistent ocean work impossible, and together they enable a total rewrite of how mankind can work at sea.

Connectivity

Before Starlink, communicating at sea meant paying over $5,000 a month for slow, unreliable satellite connections with multi-second latency. That meant any “autonomous” ocean vehicle was really just pre-programmed and running a script with no ability to adapt in real time.

Starlink Maritime changed this completely: it offers roughly $250 a month for 100+ Mbps with sub-70-millisecond latency, or a 100x improvement in price-performance in about three years.

Starlink gives you high-bandwidth, high-fidelity visibility into what your fleet is doing, at a cost that scales. Before, you might have gotten a trickle of compressed data over a satellite phone. Now, if one of your vehicles surfaces or relays data through a surface platform, you’ll get full sensor feeds, video, vehicle health, and mission statuses. The full picture will be streamed in real time to an operator who might be sitting in Dublin or San Francisco. The cost also helps: $250 a month instead of $5,000 or more has meant that connectivity can be placed on every surface asset in a fleet, making it possible to coordinate hundreds of vehicles across an ocean basin. Underwater, the vehicles can think for themselves, and when they’re back above ground, you can see everything they saw.

Energy

Energy is one of the most critical binding constraints of ocean work. Vehicles need to stay on or under the water for days, weeks, or indefinitely, and that requires generating, storing, and efficiently using energy in ways that simply weren’t possible a decade ago. Improvements have come on those three fronts simultaneously.

On generation, solar costs have fallen 75% in a decade, and that drop has reached the water. Solar- and wind-powered surface vehicles have already stayed at sea indefinitely on harvested energy alone. Below the surface and beyond solar, wave energy is reaching viability, achieving capacity factors around 90% (compared to 30–40% for offshore wind and 25% for solar) because waves run round the clock.

On storage, battery energy density has doubled in a decade, from 150 Wh/kg in 2015 to over 300 Wh/kg today, while pack costs have collapsed from $1,191 per kWh in 2010 to $115 per kWh in 2024. As Not Boring readers will understand intimately, increased battery density is important for everything from drones to AVs, but nowhere is it more important than it is underwater: every increment of energy density directly extends mission duration. Five years ago, a subsea vehicle on the best available batteries could run four-hour sprints before it needed to be recovered and recharged. Today, double the energy density, dramatically cheaper packs (which means you can afford to carry more total capacity), and more efficient motors and drive systems combine to make multi-day deployments possible.

All of these improvements compound. Each one matters, but it’s the combination, more energy per kilogram, cheaper per kilowatt-hour, less energy consumed per kilometer, that transforms the operating model from sprint-and-retrieve to deploy-and-sustain.

Compute and AI

The vast majority of underwater work today is still performed by remotely operated vehicles (ROVs), tethered to a surface vessel by an umbilical cable and piloted in real time by a human operator watching a screen. The ROV goes where the pilot tells it, sees what the pilot looks at, and does what the pilot commands. It’s capable and proven, but it means every subsea operation requires a crewed ship overhead, which is why day rates for subsea work start at six figures.

Truly Autonomous Underwater Vehicles exist, but their capabilities have been narrow. They can run pre-programmed survey routes and collect data (sonar mapping, pipeline inspection, environmental monitoring), but that’s essentially read-only work. When it comes to acting on what they find, making a decision, adjusting course, manipulating something physical, the vehicle surfaces and a human takes over. The ocean has been a domain where machines can look but not think, and certainly not act.

That’s changing fast as the AI-driven terrestrial compute bonanza reaches the water. Onboard processing power has increased by orders of magnitude in the past five years, and the cost of edge inference is plummeting. Vehicles can now run real-time object detection, navigate around unexpected obstacles, and make mission-critical decisions without surfacing for instructions. The trend is moving from read-only to read-write: not just gathering data but interpreting it and acting on it autonomously. A thousand vehicles can’t each have their own human pilot. They need to think for themselves, and for the first time, they can.

Motors and Sensors

An underwater vehicle is, at its core, a collection of motors, thrusters, actuators, sensors, seals, and structural components held together inside a pressure housing. Until recently, every one of those parts was bespoke, sourced from specialist defense suppliers who charged accordingly. This is one of the reasons that incumbent AUVs built by legacy defense contractors with artisanal production methods cost so much.

What has changed recently is that adjacent industries built new supply chains for us. The commercial drone industry commoditized motors, ESCs, and flight controllers. The EV industry drove down the cost of power electronics, drive systems, and battery management. Consumer electronics made high-performance sensors and embedded systems cheap and abundant. Packy has written about this phenomenon as the modern electric tech stack: the convergence of commodity components that lets you build sophisticated electromechanical systems at a fraction of what they used to cost.

A new generation of ocean vehicles, built on these supply chains, can be produced for hundreds of times less than incumbent vehicles.

That cost collapse makes the fleet model possible. We couldn’t possibly cover 361 million square kilometers of ocean with million-dollar vehicles, but we can with fifty-thousand-dollar ones. Thanks to these curves, we can make cheap vehicles useful enough that we need a lot of them, and by building more of them, we will make them even cheaper and more useful.

All four of these curves had to converge for ocean industrialization to become possible, and finally, they have. It’s the convergence of what I call the Ocean Stack that makes the phase transition possible.1

These curves are a thing of beauty, and we are fortunate to be alive at a time when so many of them are converging at once. I view it as our role to combine the Ocean Stack into useful products that are better, faster, cheaper, and entirely different in capability than previous generations of hardware.

Demand Side: Everything, Everywhere, All at Once

No matter how good the technology, in order to reach the scale that both affordability and industrialization require, the supply needs to be met by demand, which it currently is, from all angles. What makes right now different from every previous decade is that the demand drivers are independent of each other. Any one of them would justify building ocean infrastructure, but all of them are accelerating simultaneously.

Undersea Cables

Ninety-nine percent of intercontinental internet traffic travels through submarine fiber-optic cables. Over a million kilometers of cable sit vulnerable on the ocean floor with essentially zero persistent surveillance. Now, there is simultaneously more demand for new cables and greater threats to them, natural and deliberate.

First, AI is driving an explosion of new builds: Meta announced Project Waterworth in 2025, a 50,000-kilometer cable spanning five continents --- the longest in history.

Amazon, Microsoft, and Google are all commissioning major new transoceanic routes. Investment in subsea cables is expected to nearly double between 2022 and 2027, with tech giants now controlling roughly half the market.

Second, those cables break 100 to 200 times per year due to accidental damage --- trawlers, anchors, earthquakes, and sharks --- and each break requires expensive, slow, ship-based repairs with no persistent monitoring in between.

Third, and most urgently: deliberate sabotage is escalating. In November 2024, two Baltic Sea cables were severed simultaneously --- the Lithuania-Sweden interconnect and the 1,200-kilometer C-Lion1 linking Finland to Germany. A month later, on Christmas Day, a Russian shadow-fleet tanker dragged its anchor through the Estlink 2 power cable and four telecom cables, cutting Estonia’s cross-border electricity capacity by two-thirds. NATO launched Operation Baltic Sentry in January 2025, sending frigates, patrol aircraft, and naval drones because no other assets were available to detect or deter these attacks.

Just two weeks ago, the UK accused Russia of running covert submarine operations over its cables and pipelines. Addressing Vladimir Putin, UK Defence Secretary John Healey said, “We see you. We see your activity over our cables and our pipelines, and you should know that any attempt to damage them will not be tolerated and will have serious consequences.”

In short, more cables are being built, more value is flowing through them, and more bad actors are learning what sharks have known since humans started laying subsea cables: that, Healey’s warning aside, they’re basically unwatched and undefended.

Today, subsea cable monitoring and repair is a ~$3B market expected to double by the early 2030s. That, however, underestimates the size of the opportunity, because the market is constrained by vessel supply, not demand. There are something like 60 specialized repair vessels in operation worldwide, and proactive monitoring is still in its infancy, as are novel approaches to using the cables themselves as sensors in a method called Distributed Acoustic Sensing (or DAS). Lower the cost of supply, and we will expand the market.

Critical Minerals

The Clarion-Clipperton Zone in the Pacific alone contains more cobalt than all known land reserves, along with polymetallic nodules rich in nickel, manganese, and copper.

Providentially, these are the exact minerals that EV batteries, wind turbines, and AI hardware are consuming at accelerating rates. Regulation, along with cost and technology, has kept these from being mined, but the regulatory dam seems to be cracking. After years of stalled negotiations at the International Seabed Authority, The Metals Company forced the issue in 2025 by filing the first-ever application for a commercial deep-sea mining license, bypassing the ISA entirely via a US regulatory pathway established in 1980. Whether you think deep-sea mining is the answer or not, the pressure to access these resources is intensifying, and you cannot extract, survey, or even responsibly monitor extraction without ocean infrastructure that does not yet exist.

It is not worth trying to pin down real numbers here, because the field is so nascent and the numbers so large. The Metals Company estimates the NPV of its first two projects to be $24 billion. The U.S. Geological Survey study on polymetallic nodules projects that if deep-ocean mining follows the evolution of offshore petroleum production, about 35–45% of the demand for critical metals will come from deep-ocean mines by 2065. And there is an estimated $233 trillion worth of value in polymetallic nodules worldwide, although, as with asteroid mining, these reserves will be difficult to access and, if accessed at scale, should dramatically decrease prices. For our purposes, it is safe to say that there is a lot of value down there.

Defense

When we started discussing writing this piece, maritime defense was already an important topic. We’ve all seen the chart comparing China’s shipbuilding capacity to the United States’. The ability to manufacture ships, traditional or autonomous, big or small, will be a deciding factor in any conflict between China and the U.S. Ideally, this ability will also be instrumental in deterring conflict in the first place.

Over the past couple of weeks, however, the question of maritime defense has become urgent. I’m writing this in April 2026, as the IRGC and United States are locked in a conflict over whether to reopen the recently closed Strait of Hormuz, the passage through which a fifth of the world’s oil supply normally flows. When Iran first closed the Strait, tanker traffic dropped 70%. On the surface, ships passing through the Strait have been struck by missiles, and below the surface, Iran has planted mines. In a dark confirmation of everything we have been arguing in this piece, Iran was unable to comply with President Trump’s demands to allow more ships to pass through the Strait, because it is unable to find the mines it just recently put there.

In the Red Sea, Houthi attacks forced global shipping to reroute around Africa, adding 11,000 nautical miles and 10 to 15 days per voyage, at a cost of roughly $1 million per ship. Container rates on the Shanghai-to-Europe route quadrupled. A trillion dollars worth of goods were disrupted in the first six months of the Houthi effort alone.

Meanwhile, China is building submarine-sized autonomous underwater drones with ranges exceeding 18,000 kilometers, developing cable-cutting tools that operate at a depth of 4,000 meters, and constructing an “Underwater Great Wall” of seabed sensors across the Indo-Pacific.

The post-war international order was fundamentally underwritten by the United States’ ability to make the ocean safe for trade. That either has broken or is breaking, depending on who you ask.

The United States is aware of it and is putting resources behind maritime superiority in the modern paradigm. The FY2026 Pentagon budget allocates $13.4 billion for autonomous systems. This is the first time autonomy has been its own budget line. The Navy alone gets $5.3 billion, a 70% year-over-year increase.

This is another truth about frontier industrialization. Every frontier in history was industrialized when powerful competition demanded it.

America pushed West to fulfill our Manifest Destiny. We raced Russia to the Moon to achieve superiority in the Cold War. The drive to industrialize the ocean is now fueled by China, Iran, and a world waking up to the fact that America is no longer capable of guaranteeing the maritime safety that has underpinned global trade since World War II.

Beyond the specific demand drivers, though, there is something more fundamental driving the Oceanic Renaissance.

If you look at the sweep of human history, there is an inexorable, consistent drive to pursue the frontier. We make the most of everything. We find everything. Technology, capitalism, human ingenuity, creativity, and sheer stubbornness combine to produce extraordinary outcomes from the resources available to us. Every frontier that could be industrialized eventually was.

The ocean is the last frontier. The technology is finally ready at the same time as demand is coming from every direction at once. This is going to happen. The only question is who builds it and how fast.

The New Ocean

One thing is certain: the incumbents cannot lead this transition.

Other than the water, the ocean industry looks like any other that’s dominated by sclerotic incumbents too content with the world that was. You’ll even recognize many of the names.

Maritime defense primes include old faithfuls like Lockheed Martin, RTX, L3Harris, Leidos, and Thales. They sell everything from combat systems to sonar suites to legacy AUVs, often in a cost-plus model. Shipbuilders, like HII, General Dynamics Electric Boat, BAE Systems Maritime, Kongsberg, and the big three Korean yards, turn out hulls on multi-year contracts, which are cost-plus as well. Then there are companies that you’ve probably never heard of, the subsea service primes like Fugro, DOF, Oceaneering, Subsea 7, and ASN, which run thousands of campaign-based vessels for oil majors and telcos.

Between the ocean’s incumbents, hundreds of thousands of people generate tens of billions of dollars a year, every cent of it organized around the assumption that the ocean is expensive and will stay that way. You can forgive them for operating as if nothing will change, because that’s how their customers buy.

This is textbook Innovator’s Dilemma.

Christensen’s point is that incumbents fail by doing everything right for their best customers. Here, ocean incumbents’ best customers (navies, oil majors, telcos, shipping companies) are conservative, risk-averse, and demanding. They want proven, crewed, campaign-based services with mil-spec redundancy and rigorous SLAs. Autonomy-first, priced-for-scale platforms don’t meet those requirements yet. So the incumbents, rationally, keep investing in the fleets they already own in order to protect their margins and serve their best customers better.

By the time the new cohort is good enough to matter, the incumbents will have a decade of sunk cost in crewed vessels, a specialist workforce that has never shipped an autonomy stack, a mil-spec supplier network that cannot pivot, and an entire organization whose every incentive points at the existing business. The incumbents never lose because they’re stupid, or even because they don’t listen to the market. In fact, they listen to the current market too closely.

Once again, space is the case study. For fifty years, launch was owned by a small cohort of cost-plus primes – Lockheed, Boeing, and Northrop Grumman – whose economics depended on expendable rockets, government contracts, and scarcity. Then a new generation showed up, looked at the same physics, and decided that if you vertically integrated the supply chain, built for reusability, and priced for scale instead of scarcity, everything changed. That cohort, led by SpaceX and including Rocket Lab, Astranis, Planet Labs, Relativity, Varda, K2, and far too many to list, has a name now: New Space. As the cost of a kilogram to orbit fell by more than an order of magnitude, entire industries, including Earth observation, satellite-IoT, constellation broadband, and even space drugs, space-laser energy, and orbital data centers became possible and potentially profitable.

The same thing is happening in the ocean right now.

A new generation of companies is rebuilding the ocean from first principles. They operate less like modern versions of the rusty old ocean incumbents, and more like New Space companies that happen to work in salt water instead of vacuum.

While each of these companies is different, they tend to share some common characteristics:

• Vertically integrated instead of subcontracted.

• Autonomy-native instead of crewed-first with autonomy bolted on.

• Software-defined instead of hardware-frozen.

• Built on commercial supply chains (drone motors, EV battery packs, Jetson-class compute) instead of custom mil-spec.

• Priced for scale instead of for scarcity.

We call this cohort the New Ocean.

As with any market map, there are a number of ways that we could slice and dice our fellow ocean startups, but the simplest is to group them into three buckets: infrastructure, platforms and vehicles, and vertical applications.

At the foundation are the Infrastructure companies that enable the Ocean Stack. These are the companies that are bending the curves on which the rest of us ride.

Starlink put a high-bandwidth, low-latency pipe to every square meter of ocean for the price of a weekly Uber habit. Panthalassa will generate persistent electricity at sea from wave energy, a marine renewable that runs through the night at much higher capacity factors than solar or wind.

Karpowership already operates forty floating power stations delivering 7.5 gigawatts across multiple continents, proving that energy supply at sea can be mobile and matched to demand.

Armada is deploying full compute clusters at sea, bringing the data center to the data with nearly free cooling from the ocean itself. Kraken Robotics builds the synthetic-aperture sonar that turns a cheap AUV into a survey-grade mapping platform. Each new capability that one of the Infrastructure players provides is a capability that the rest of the Ocean Stack can snap in and use.

In the middle of the stack are the Platforms, or Vehicles. These companies, including Ulysses, take advantage of the infrastructure layer and enable vertical applications. Collectively, these are the railroads of the ocean’s industrialization.

Above water, Saildrone has sailed more than two million autonomous nautical miles across the world’s oceans on solar and wind power alone. They are proof that persistent surface presence without humans or fossil fuel is an operational reality.

Saronic is rebuilding American shipbuilding around autonomous surface vessels for defense, attacking the same capacity gap that keeps the US Navy dependent on decades-old hulls. Blue Water Autonomy is designing 190-foot Liberty Class uncrewed naval ships from a blank sheet. The ships would be larger than any USV afloat today, built for the open ocean rather than the coastline.

Regent is electrifying coastal transport with all-electric seagliders that skim above the water faster than a ferry and don’t need an airport. Poseidon Aerospace is flying autonomous cargo aircraft to move freight across ocean basins at a fraction of the cost of a container ship and a fraction of the time of a plane.

Navier is building electric hydrofoiling boats that use 75% less energy than traditional hulls. Arc is rethinking how boats are built entirely: electric-first, hull-up, designed for modern manufacturing instead of inherited from the combustion era. In addition to its electric sport boat (which is incredibly fun, fast, and quiet), Arc is now moving into commercial tugboats on the same electric platform.

Beneath the surface, Anduril’s Dive division is producing the Dive-LD large-displacement AUV and leading Australia’s Ghost Shark program, bringing a defense-tech mindset to underwater warfare.

Across the sensor layer, Andrenam is weaving low-cost AI-powered sonar buoys into persistent maritime domain awareness meshes to string together a distributed listening grid from surface to seabed. Sofar Ocean is doing something similar for ocean observation, operating the largest privately-owned network of ocean sensors on Earth and turning real-time wave, weather, and current data into a platform the rest of the stack can build on.

With modern Infrastructure and Platforms, new Vertical Applications become economical.

The Metals Company and Impossible Metals are making the case that the ocean floor holds the cleanest path to the critical minerals the energy transition demands. TMC is filing the first-ever commercial deep-sea mining license, while Impossible builds robots that selectively pick nodules off the seabed rather than vacuuming it.

Shinkei Systems is using robotics and computer vision to automate the ancient Japanese ike jime harvesting method. They put their machines right on fishing boats to instantly, humanely, and precisely kill fish, eliminating the stress response that degrades most of the world’s wild-caught seafood and tripling its shelf life.

A market map of the New Ocean today looks platform-heavy, because that’s what new frontiers looks like at this stage. The platforms always show up early. But even among the platforms today, we think something is still missing. Each company on this map is solving a critical slice, but to truly industrialize the ocean — to turn it from a domain of campaigns and expeditions into one of systematic, repeatable, economically productive work at scale — you need all of those pieces operating as one integrated system: persistent surface and subsea vehicles, edge autonomy, real-time communications, and the ability to act. You need the whole stack in one.

That’s our thesis at Ulysses, and that is what we are building.

We’ve always believed that the ocean was Earth’s most mismanaged resource, and if it had better tools, it would be transformative for society. We looked at how SpaceX had approached space — the bet on vertical integration, the bet on pricing for scale instead of scarcity, the refusal to accept that the physics of the domain justified the legacy cost base — and decided the ocean needed someone willing to take the same approach. Our bet is that if the whole stack is built together, as one autonomous system, you can dramatically collapse the cost of working in the ocean.

My co-founders and I think about the opportunity through a single governing metric: the cost of ocean work, which we define as the cost of active operation over one square kilometer of ocean for one month ($/km²-month).

We believe the ocean is a constrained economy, severely limited by the tools available to work in it. Every constrained economy has a governing metric, the one number that, if you move it, everything else follows. For space, it was $/kg to orbit. For the ocean, it is the cost of ocean work.

That cost is unreasonably enormous today: covering a meaningful area of ocean with incumbent methods (crewed vessels, campaign-based operations, six-figure day rates) costs millions of dollars per month. If we can collapse it, we’ll make it economically viable to monitor cables, survey mineral deposits, manage fisheries, patrol shipping lanes, and do a hundred things nobody has thought of yet, at a scale that was previously impossible.

The ocean economy today is $2.6 trillion, built almost entirely on transit, extraction, and coastal adjacency. Imagine what it becomes when you can actually afford to work in the ocean, persistently, at scale. We are very early on the cost curve.

We collapse the cost of ocean work in two ways: on OpEx and on CapEx.

On OpEx, we integrate surface and subsea vehicles into a single autonomous system, removing the crewed surface vessel that drives the majority of incumbent operating costs. Our surface platform, Leviathan, deploys, recovers, and recharges our subsea vehicles via our autonomous launch, recovery, and recharge platform, Kraken, without returning to port and without requiring a crew of 80 to 150 people on board.

On CapEx, we design and manufacture the vehicles ourselves. Our underwater vehicle, Mako, is a modular AUV that starts at $50,000 per unit, compared to $500k to $5 million for incumbent AUVs. It carries more onboard compute than any small or medium AUV on the market, and we build for read-write, not read-only: these vehicles are designed for robotics from the start, not just data capture. The modular design means we and our customers can reconfigure the same vehicle for entirely different missions in minutes.

We’re deeply vertically integrated because we have to be. The existing supply chains and component manufacturers for ocean hardware are slow, expensive, and not built for scale. So we design and build many of our sensors in-house, we do our own manufacturing, we do our own PCB assembly, we develop all our own software from the autonomy stack down to the middleware, and we’re progressively bringing more complex components (navigation systems, advanced sensors, motors) under our own roof. And we believe the best way to capture the value of collapsing the cost of ocean work is to sell the work itself: we operate these vehicles for customers as a service, selling outcomes rather than hardware.

Build the fleet, deploy the fleet, sell the coverage.

The demand thus far has validated our thesis. Just this month, a major commercial customer requested a network of 10,000 vehicles operated as a service. Another requested at least 1,000. We have never seen numbers like this before, and they keep getting bigger. I fully expect this year to be the year demand outstrips our ability to supply, and our challenges shift from market creation to manufacturing and scaling.

Scale, after all, is what it will take to truly industrialize the ocean.

What an Industrialized Ocean Looks Like

We’ve drawn many parallels between the ocean, the American West, and space in this essay. We expect that there will continue to be many similarities between those stories and the one that plays out at sea. Permanent infrastructure will replace episodic missions, and it will enable new types of businesses to operate at high volume and affordable prices. Governance will replace lawlessness, because those businesses will have assets they need to protect.

Space and the ocean share the same incumbent problem. Their incumbents are incentivized to keep things the way they are, which shows up as a high cost-physics gap, or what Elon Musk calls the “Idiot Index.” Prices are too high for all but the most profitable or profit-agnostic use cases. The same strategy to beat the incumbents applies to both, as well: vertically integrate and collapse the supply chain, and sell what used to be expensive hardware as an affordable service.

But the ocean is unique. Where space inspires sci-fi, the ocean inspires poetry. And the ocean has structural advantages that space doesn’t.

The ocean economy is already seventeen times larger than the space economy was at its inflection point. The ocean contains physical resources (minerals, energy, protein) that we have not yet been able to access (or find) in space. And the regulatory environment is the most permissive of any frontier: international waters begin 12 nautical miles from shore, with no FAA equivalent beyond that. The ocean is where modern cowboys roam.

The exciting part about operating among the cowboys is that it’s hard to predict exactly how things are going to play out. But I have a few thoughts and predictions.

Permanent autonomous infrastructure will replace episodic campaigns. Fleets of autonomous vehicles will operate continuously across ocean regions, persisting there as Starlink satellites do in orbit. Docking and charging stations will create underwater and surface logistics networks. Energy nodes will harvest wave and solar power. Compute clusters will float at sea. The ocean’s infrastructure will be networked instead of physically connected, but it will be no less permanent.

Resource management will move from blind to precise. Fishing will evolve from blind harvesting to precision management, replete with real-time stock assessment, targeted harvesting, and sustainable yields at scale. Mineral extraction on the seafloor will become viable with proper monitoring. We will harvest from the ocean itself at an industrial scale while stewarding its resources. The resources have been there since long before we were, but it’s only now that humans are cracking the economics of managing and accessing them with technology, as we once did with agriculture to great success in the Green Revolution.

Governance will follow infrastructure and economy. Sunlight is the best disinfectant, they say, and they haven’t even seen what sunlight does to a surface covered with salt water. The era of lawlessness will end through better visibility. The Bad Guys can’t fish illegally if the good guys can track every vessel, can’t cut cables if the routes are patrolled, and can’t dump waste if the water chemistry is monitored. The Wild West tamed down when the railroad brought people and their economic interests. More generally, lawlessness ends when persistent infrastructure brings visibility and an economy produces something people are incentivized to protect.

Infrastructure will beget infrastructure. Autonomous vehicles will create demand for energy nodes, which will enable longer missions. Longer missions will generate more data, which will create demand for communications networks. Each layer will make the next layer viable and necessary at greater scale.

Commercial activity will overtake military spend. Defense is always the early customer for frontier infrastructure. Before the Gold Rush transformed it into a commercial hub, San Francisco began as a Catholic mission and a military fort, the Presidio; today the Bay Area generates $1.4 trillion in GDP and would rank as the world’s sixteenth-largest economy if it were a country. Just as military forts preceded commercial towns in the West, military satellites dotted the skies before telecommunications satellites. The military cares more about capabilities than cost and can therefore bootstrap markets before the economics pencil out. But as always, the commercial market will dwarf defense in time via cable monitoring, offshore energy, fisheries, shipping, and a number of unknown but inevitable use cases that always emerge when costs come down.

New industries will emerge that nobody can predict today. I’m cheating a bit here, because this happens every time. The most valuable businesses that emerge from frontier industrialization are often the ones that people figure out once they have time to get their hands dirty with new capabilities. It’s not a coincidence that a disproportionate number of the richest people who have ever lived (Vanderbilt, Carnegie, Rockefeller, Musk) built the access infrastructure at exactly the right moment and then created new markets around the new capabilities they brought into the world.

That said, we can imagine some specific opportunities, including but not limited to: managed fisheries at scale; critical mineral extraction with proper environmental monitoring; persistent maritime domain awareness sold as a subscription; autonomous shipping and smarter logistics; ocean energy at industrial scale; offshore compute powered by ocean energy and cooled by seawater; an undersea acoustic and optical communications network (the subsea equivalent of Starlink), connecting every vehicle and sensor in the water; a persistent ocean observation layer (the subsea equivalent of Planet Labs) continuously mapping the ocean in three dimensions; systematic scientific exploration of the 99.9% of the deep seafloor that nobody has ever studied.

There are an estimated three million shipwrecks on the ocean floor, most of them never found, collectively carrying billions of dollars in treasure and untold secrets about the human story. And we may finally realize our parents’ and grandparents’ dreams of permanent ocean habitats (seasteading, seriously: I think it could actually work in the next decade with the right supporting logistics, communications, and energy infrastructure). A new Atlantis is at hand.

The scale of what opens up when you collapse the cost of persistent ocean presence is difficult to overstate. The ocean economy today is built on transit, extraction, and coastal adjacency, and it’s already worth trillions. Imagine what happens when you add persistent infrastructure, domain-wide monitoring, in-water energy, autonomous logistics, undersea communications, and a continuous observation layer covering 70% of the planet. The new economy will dwarf the old one. We just can’t see most of it yet, and that’s exactly what you’d expect at this stage.

We return to the question at the heart of my mission. What does industrialization mean for the health of the ocean itself?

There is a widespread assumption that economic activity in the ocean and ecological health are in tension and that industrialization means exploitation. I believe the opposite is closer to the truth.

Counterintuitively, with all of this economic activity, we will get conservation that actually works, practically for free.

The technologies that industrialize the ocean are the same technologies that make real conservation possible for the first time.

Consider the state of ocean conservation today. Marine protected areas cover roughly 8% of the ocean on paper. In practice, many of them are “paper parks,” with no enforcement and no monitoring. We protect the ocean by drawing lines on maps and hoping people respect them. I know this because I have worked in marine protected areas on both coasts of Australia and the US. I can tell you that it doesn’t work for the same reason that protecting the Amazon didn’t work until Brazil launched real-time deforestation alerts in 2004, after which Amazon deforestation fell 70%. Visibility is what made the difference. You can’t conserve what you can’t see.

Persistent ocean infrastructure gives you that visibility, as well as the ability to intervene. We won’t know what’s possible until we experiment with the right equipment and measurement.

Consider the story of Russ George.

In 2012, George, a Californian entrepreneur, tried one of the boldest and most controversial experiments in the history of ocean science. He decided to implement the work theorized by the controversial oceanographer John Martin, the father of an idea called “ocean iron fertilization”. A few decades earlier, Martin stood up at a scientific conference and declared: “Give me half a tanker of iron, and I’ll give you an Ice Age.”

Martin’s hypothesis was that iron fertilizes ocean plankton, which multiplies and absorbs enormous quantities of CO₂. This then feeds the base of the marine food chain, which, if done at scale, might be able to alter the planet’s climate and restore fish stocks. He died before he could test it properly.

In 2012, Russ George picked up his work and convinced a First Nations village on Haida Gwaii, a remote archipelago off the coast of British Columbia, to invest $2.5 million in the experiment. He loaded 100 tonnes of iron sulfate onto a fishing boat and dumped it into the Pacific about 200 miles offshore.

And by George, he did it. NASA satellites confirmed a plankton bloom covering 35,000 square kilometers — an area so large that it was visible from space. The following year, the pink salmon run returned at 226 million fish, the largest run in Canadian history.

The scientific establishment lost its mind. Researchers disputed the causal link. Environment Canada executed search warrants on his office. The Canadian government investigated George for violating international dumping conventions. The UN imposed a moratorium on ocean fertilization.

None of his opponents argued that the idea was wrong, nor even that it didn’t work. The problem was that nobody could verify what had actually happened. There was no persistent monitoring to measure the bloom’s full effects, no way to track downstream consequences across time and space, and no ability to adjust the intervention as conditions evolved.

George had a hypothesis and a budget, but he was intervening blind. One rogue entrepreneur with a fishing boat had produced what looked like one of the most successful ecological interventions in history, and nobody could prove it, so nobody has reproduced it.

Now imagine that same experiment in a fully instrumented ocean. Autonomous vehicles monitoring the bloom in real time across its full extent. Sensors tracking nutrient levels, oxygen, pH, and biodiversity indicators continuously. The ability to see unintended consequences developing and respond before they cascade. Real-time data flowing to scientists and regulators who can assess the intervention as it unfolds, not years later from fragmentary satellite data. Iron fertilization is just one example. Targeted reef restoration, invasive species removal, pollution response, managed rewilding: these are the kinds of active stewardship that become possible when you can actually see what’s happening in the ocean and respond to it. The problem was never the idea of active ocean management. The problem was attempting it without the infrastructure to close the feedback loop.

The same sense-intervene-measure-adjust feedback loop that George’s experiment lacked is exactly what persistent infrastructure provides.

The dual-use nature of ocean infrastructure could make all of this work. The same vehicles monitoring subsea cables will collect environmental data. The same platforms enabling offshore energy will measure ocean chemistry. The same communications networks connecting autonomous fleets will transmit scientific data. It is inefficient to build one system for commerce and another for conservation when you can run both on the same system.

Economic growth and biodiversity health can and should be genuinely symbiotic. The fishing industry benefits from healthy stocks, which require monitoring. The offshore energy industry benefits from accurate ocean data. The insurance industry benefits from better climate models. Every commercial use case generates data that improves our understanding of the ocean, and better understanding leads to better stewardship. We still need to respect the ocean as a living system, and there will be hard decisions about where and how to operate, but we need to have the ability to decide. The virtuous cycle only works if the infrastructure is in the water.

I am not naive about this. There are real risks. Poorly managed extraction can damage fragile ecosystems. Increased activity in previously untouched areas brings the possibility of contamination. Geopolitical competition over ocean resources could intensify rather than stabilize. The fact that international waters have no FAA equivalent is an opportunity, but it is also a governance gap that will need to be filled as activity scales. Getting this right requires exactly the kind of persistent monitoring and real-time visibility that ocean infrastructure provides. The best argument for building the infrastructure is that without it, the ocean’s resources will continue to be exploited blindly and unsustainably by those who can already afford to be there.

When you fix the cost of doing persistent work in the ocean, you fix everything downstream of it. Conservation, commerce, defense, science, and discovery are all bottlenecked by the same constraint. If we remove it, we can unblock all of them.

We Must Return to the Great Blue Frontier

Berry Cannon died at 610 feet, breathing helium in the dark, because somebody forgot to fill a scrubber. That was 1969. That was the best we could do back then.

We can do much better today. The technology exists to build persistent, autonomous infrastructure across the ocean without sending human beings into the abyss. But the point of this essay is not that we can finally do it safely. The point is convincing you that we have to do it at all.

We have the means now to conquer the Great Blue Frontier and do what our grandparents dreamed of. Frontiers matter. Conquering them matters. Not just economically, though the economic case is extraordinary, but for what they do to us and more broadly, the soul of civilization. Their pursuit lights a fire in the heart of men that leads to civilisational greatness.

Every great era of advancement has corresponded to an active frontier. The Renaissance had the Age of Exploration. The twentieth century had aviation, then space. When there is somewhere new to go, the best minds build toward it. When there isn’t, that energy turns inward. It optimizes. It litigates. It fights over what already exists. I think we’ve all felt that. The creeping sense that the great adventures are behind us. That the best we can do is make what we have a little more efficient.

Civilization is a lot like sharks in this regard. Many species of shark (e.g., Great Whites, Makos, Whale sharks) die when they stop swimming. They need the forward motion to oxygenate their gills. Stop, and they suffocate. Humans are the same. Without forward motion, we stagnate, and stagnation is a kind of death.

The ocean is the answer to this stagnation. It has been there the whole time, covering 70% of the planet, largely unknown, barely touched, immensely rich. We mapped its surface and called it quits. We drilled through it and called it industry. We fished from it and called it the economy. But we never truly entered it, and we certainly never built the infrastructure to stay, steward, and leverage its full potential

The last great frontier on Earth isn’t behind us. It’s below us.

I grew up jumping off the pier at Garrettstown, pulling crabs out of rock pools, heading out in the boat with my dad to catch mackerel. I loved the ocean before I understood it, before I could have told you anything about technology curves or governing metrics or the cost of persistent presence. I just knew there was something down there worth knowing. I still believe that. And now we can finally go find out.

Come build it with us. There is no greater adventure than settling a frontier.

Will O’Brien is the co-founder and President of Ulysses Maritime Technologies, the ocean company, building the fleets of autonomous underwater and surface vehicles to solve the most critical tasks in the ocean.

Thanks to Will for sharing his knowledge, to Badal for my favorite cover art yet, and to all of the oceanic explorers buried beneath the waves.

That’s all for today. We’ll be back in your inbox tomorrow with another Weekly Dose.

Thanks for reading,

Packy

Hi friends 👋,

Happy Friday and welcome back to our 189th Weekly Dose of Optimism.

There are a couple of big themes this week: energy, and non-invasive, non-molecular control of the human body and brain. If we’d published this week’s Dose back in, say, 1995, I worry that a few readers would drop dead from shock. It’s hard to recognize you’re in a sci-fi novel when you’re living through it, but if you zoom out a little bit, it sure looks like we are.

I’d highly recommend the Extra Doses this week, for Friedberg contextualizing everything happening and why it’s good, and for one of the craziest stories we’ve covered in Science Breakthroughs on gene control in mice using electromagnetic fields. Plus, Science Breakthroughs now have one-sentence plain English summaries thanks to a request from Chris B.

Let’s get to it.

Today’s Weekly Dose is brought to you by… Create

Funnily enough, I’m writing this as I sip my morning Creatine + Electrolytes. It’s something of a morning ritual in our house. The kids wake up and come downstairs, Maya asks if we can make creatine, we open two packets, she pours them in, Maya and Dev each take a small sip, and then I down the rest. Dan is making me do a Hyrox with him, so C+E is a lifesaver.

I’m apparently not alone. Since Dan and Sienna launched Creatine + Electrolytes two weeks ago, the product is already run-rating $15 million. And no wonder.

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They have all of the well-studied benefits of creatine, plus the hydration, muscle function, recovery, nerve signaling, heart rhythm, and pH balance benefits of electrolytes. Before they put it all in a little packet, I was dumping salt in my creatine water. This is better, and it tastes so good that my kids demand sips.

So if you want a great-tasting extra kick for your muscles and your brain, and you want to support long-time Dose writer and my brother Dan, get 30% off C+E here:

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(1) Quaise Energy Introduces World’s First Superhot Geothermal Plant

Beneath our feet sits essentially infinite, always-on, carbon-free power. Estimates are that there 63 terawatts, or roughly eight times all the electricity humanity currently generates, would become accessible by tapping just 1% of the world’s superhot rock resources. It’s just really far down there. The hottest, densest heat sits two to twelve miles down, past the point where conventional drill bits work.

This week, MIT spinout Quaise Energy announced Project Obsidian: the world’s first commercial superhot geothermal power plant, being built in Central Oregon near the Newberry Volcano. Phase I will be 50 MW, Phase II will scale to 250 MW, and Phase III will be more than a gigawatt. Quaise is targeting commercial operations in 2030.

The reason this can exist now is a new way to drill. Quaise uses a gyrotron, originally developed for fusion research at MIT’s Plasma Science and Fusion Center, to produce millimeter-wave energy that ablates rock by vaporizing it with no mechanical contact. Last year, they drilled through 100+ meters of granite in Central Texas in the first field demonstration of the technology. This year, they’re targeting a kilometer, then eventually, 10-12 miles. At full depth, a single superhot well would produce 5-10x more power than a conventional geothermal well.

Both the superhot rock and the drill that can reach it are what make this different from Fervo, a company we’ve covered previously in the Dose, and the other EGS players working closer to the surface. Conventional EGS taps warm rock. Quaise is going for the really hot 300–500°C rocks, which provide enough pressure to produce supercritical water, which makes gigawatt-per-well plausible.

We have been waiting for this. When Julia and I talked to Eli Dourado about geothermal on Age of Miracles, he said the reason we couldn’t just count on geothermal for all of our electricity needs was heat. “Geothermal doesn’t get super hot,” Eli said.

If Quaise pulls this off, that may no longer be true. Drill baby drill.

(2) Panthalassa Makes Wave-Powered Nodes for Offshore AI DCs

and Garth Sheldon-Coulson on

A year or so ago, I had coffee with Lowercarbon Capital’s Ryan Orbuch, and I asked him what the coolest companies in his portfolio were. The first one he brought up was Panthalassa, which I remember him describing as building massive structures in novel geometries to pull energy from the ocean’s motion (turns out both the size of the boat and the motion of the ocean matter).

Panthalassa, based out of Portland of all places, has been working on an 80-meter-tall, 20-meter-wide, lollipop-shaped self-driving floating power plant that lives on the open ocean miles and miles offshore and generates electricity from waves, which it will first use, of course, to power GPUs. The lollipops will have just one moving part. Waves force water through internal channels, the channels pressurize the water, the pressurized water spins a turbine, and the turbine spins a generator.

CEO Garth Sheldon-Coulson told Ashlee Vance that they chose offshore waves because it’s one of three potential tens-of-terawatt-scale energy sources (the other two are solar and nuclear (fission and fusion), and there’s a potential fourth in the next story). When he and co-founder Brian Moffat dug in, they realized it also had advantages over other renewable sources: capacity factor up to 90%, against 30-40% for offshore wind and 25% for solar and a target cost around $1,500 per kilowatt. It can also self-propel to wherever the wave resource is best at a given time at 1-1.5 knots. It all sounds insane, but results from sea trials of their Ocean-2 prototype in Puget Sound matched simulation almost exactly.

If this works, we’re going to need to figure out a lot more things to do with all our power.

(3) Vital Lyfe Opens Up Access for Pre-Orders

One of which may be to desalinate the very water in which Panthalassa swims.

Look, I love putting Creatine + Electrolytes in my water more than the next guy, but there are plenty of times when you want to get the salt (and other stuff) out of your water to make it safe to drink. For those times, there is now Vital Lyfe’s Access.

Jon Criss (13 years at SpaceX, Lead Integration and Test Engineer on Dragon, Product Manager on Starlink) and Andrew Harner (9 years at SpaceX, Stanford aero) co-founded Vital Lyfe to build what they call a personal water-making system. This week, pre-orders went live for Access: a 25-pound box that turns seawater into drinking water. Put a hose in the ocean, press start, and collect six gallons of clean water per hour. Freshwater sources get you 12-13 gallons.

Access is $749, with an $8 deposit to reserve, and deliveries begin later this year.

Desalination has always worked. Israel runs its whole country on it. The problem is that it has required municipal-scale infrastructure like billion-dollar plants, dedicated power, and pipelines, which means that the 2+ billion people who lack reliable access to clean water can't get it, because it depends on the kind of infrastructure their governments haven't built and probably won't.

Vital Lyfe's bet is the same bet that made Starlink work: if centralized infrastructure can't reach everyone, decentralize the infrastructure. Make the unit small enough that an individual, an NGO, a fishing village, a disaster response team can just… have one. In March, they field-tested Access in Tierra Bomba, Colombia, with local NGO Amigos Del Mar. They opened a 37,000-square-foot manufacturing facility in Torrance the same month. Criss told press the production line is designed to manufacture more desalination units in a single month than currently exist worldwide.

Drink up.

(4) This Beanie Is Designed to Read Your Thoughts

Humans have been wearing hats for at least ~30,000 years. We’ve been thinking even longer than that. What if, Sabi asks, we could just think while wearing a hat that reads our brain, and use that to interact with machines?

Yesterday, Sabi, backed by Khosla, Accel, Initialized, and OpenAI VP of Science Kevin Weil, came out of stealth with a BCI that decodes the words you think into text on a screen, but instead of needing brain surgery, the BCI comes in a hat.

The device uses 70,000 to 100,000 EEG sensors, versus a dozen to a few hundred for most consumer EEGs, all in a baseball cap or beanie. The bet is that a ton of sensors can compensate for the skull’s signal dampening, the problem that most BCI companies compensate for by going under the skull. “If you're going to have a billion people use BCI for access to their computers every day,” Vinod Khosla said in a WIRED interview, “it can't be invasive.”

Sabi is also building a Brain Foundation Model to decode the brain’s signals and turn it into speech. They’re targeting 30 words per minute, but the plan is to improve the speed as the hats learn about their wearers’ brains.

Sabi expects to begin shipping at the end of the year, so now is the time to start meditating and controlling your thoughts before your hat starts telling everyone around you what you really think about them.

(4b) Prophetic Successfully Demonstrates Increased Lucidity in Dreams

Speaking of non-invasive brain devices… we’ve written about Prophetic, a company that plans to use Transcranial Ultrasound to induce lucid dreams, a few times here in the Dose, for the simple reason that it’s one of the coolest companies imaginable. It is also an expression of a belief I have that as the machines get more capable, we will see a wave of technologies built to unlock the superpowers latent in our human bodies and minds.

Good news for the humans: Prophetic has successfully demonstrated the ability to increase lucidity in dreams. “By sending safe ultrasonic energy through the forehead and into the prefrontal cortex,” the company tweeted, “we are able to activate the Central Executive Network aka Frontoparietal Network.” Test subjects reported greater dream recall, dream vividness and continuousness, clearer dream thinking, and the ability to make deliberate choices that changed what happened inside of the dreams.

Lucid dreams are wild, with benefits (studied to varying degrees) around nightmare reduction (including PTSD-related ones), motor skill practice (you can practice throwing darts in a lucid dream and get better at actually throwing darts), emotional processing and fear exposure, and creative problem solving. Nikola Tesla wrote about his deliberate conscious dream-journeys in his excellent and short autobiography My Inventions, writing “Every night, when alone, I would start on my journeys – see new places, cities, and countries; live there, meet people and make friendships.” Christopher Nolan created Inception on concepts from the lucid dreaming literature, and said in interviews that he drew on his own dream experiences for the film.

Aside from the practical benefits, lucid dreaming just seems magical, like VR that the brain you always have with you can generate. In lucid dreams, people can fly, interact with dream characters, and generate worlds that defy the laws of physics. The world needs more magic.

(5) Salk Institute Gets $41.3M from ARPA-H to Advance Sonogenetics

Ultrasound is making a lot of noise this week. Salk Institute neuroscientist Sreekanth “Shrek” Chalasani received up to $41.3 million from ARPA-H to turn the technology he invented, sonogenetics, into a clinical therapy.

Where Prophetic is using ultrasound to stimulate brain regions we already know respond to acoustic energy, sonogenetics flips the approach: you engineer specific cells to express an ultrasound-sensitive protein, then use a focused ultrasound pulse to turn just those cells on or off. It’s like optogenetics (using light to control neurons), except ultrasound goes through skin and bone without needing fiber-optic implants. It’s also more precise than a drug, because only the tagged cells respond.

Chalasani found the first ultrasound-sensitive protein in C. elegans in 2015, coined the term "sonogenetics," and spent the past decade building the field on seed funding from the Jacobs family at Salk. The ARPA-H award pulls together a team built for this kind of translation: Nobel laureate Ardem Patapoutian at Scripps engineering the next generation of ultrasound-sensitive proteins, Aravind Asokan at Duke working on delivery vectors (Go Duke), Xuanhe Zhao at MIT on wearable ultrasound hardware, plus collaborators at UC San Diego, St. Boniface/Manitoba, and the California Medical Innovations Institute. Salk spinout SonoNeu (co-founded with Venkat Reddy at General Inception) will manage the path to FDA.

Their first target is peripheral neuropathies, including diabetic neuropathy. Longer-term, they want to build a platform for on-demand, location-specific control of cell activity in both the peripheral and central nervous system.

One of the things I find most exciting about biology today is how many of the big frontier ideas are about programming living systems with something other than small molecules: Michael Levin on bioelectrics, Prophetic on ultrasound at the circuit level, Chalasani on ultrasound at the cell level. Welcome to the Body’s Electric Age.

EXTRA DOSES: Science Breakthroughs, Friedberg on Modern Wisdom

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Hi friends 👋,

Happy Friday and welcome to our 188th Weekly Dose of Optimism!

I keep saying this, and then it gets cold again, but I think this is the one: it’s a beautiful morning here in New York City, spring is springing, and our fellow humans keep pumping out optimism-worthy work.

This week, for example, we have new funding for AI-assisted Alzheimer’s research and, in Science Breakthroughs, a peptide that degrades the amyloid-β linked with the disease. That, and much much more, in this week’s Dose.

Let’s get to it.

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(1) OpenAI Foundation Commits $100M to Cure Alzheimer’s

My grandmother had Alzheimer’s. One of my biggest fears in life is that me and the people I love will get it; it’s one of the reasons I started writing, to help me remember. Unfortunately, since my grandmother had it, we’ve made no progress on defeating Alzheimer’s. Measured by age-standardized mortality rate, while we’ve made strides against cardiovascular disease, cancer, and infectious diseases, Alzeheimer’s has only gotten worse. It is a complex disease with hundreds of environmental and genetic risk factors interacting across cell types over decades, and as such, treating it has been a sad game of whack-a-mole.

Maybe AI can help. This week, the OpenAI Foundation, the nonprofit parent of OpenAI, now sitting on a $25 billion life sciences and resilience commitment, announced it’s putting $100M+ into six research institutions to throw modern AI at the problem. Jacob Trefethen, who ran half a billion dollars of science grantmaking at Coefficient Giving before joining OpenAI, is leading the effort. The grants span AI-assisted drug design, biomarker discovery, mapping disease pathways, and personalizing treatment.

The most interesting grantee is Arc Institute, whose progress we’ve covered many times in the dose, which announced a parallel partnership focused on what co-founder Patrick Hsu calls an “AI lab-in-the-loop” approach. The idea is to systematically perturb human brain organoids guided by patient data, measure what happens, feed the results back into AI models, and iteratively build a causal map of the disease, the actual chain of cause and effect from genetics to protein misfolding to synaptic collapse. Arc executive director Silvana Konermann describes the goal as finding “perturbations that can click and drag a cell from a diseased state back into a healthy one.” The other grantees include David Baker’s Institute for Protein Design and EvE Bio.

NIH funds Alzheimer’s, and has for decades, and we are where we are. What Arc and the OpenAI Foundation are doing is what private capital and motivated foundations can do that institutional science usually can’t: pick a hard problem, fund a full-stack experimental-and-AI engine, and run the loop fast enough that we might actually get somewhere by the time it matters to my family.

I would very much like to forget about Alzheimer’s once and for all.

(2) Information to Atoms

Substrate x Google DeepMind

One big lab I have been consistently impressed with is Google DeepMind, and those wacky geniuses are at it again. This week, the American semiconductor foundry startup Substrate, whose launched we kicked off with in Dose #168, shared that GDM’s AlphaEvolve rewrote a chunk of its lithography software and made it nearly seven times faster while cutting compute costs by 97%.

Substrate is the James Proud-founded startup that came out of stealth last October with $100M and a crazily ambitious mission: build a U.S. foundry that uses X-ray lithography to compete with ASML's EUV machines by patterning features tens of atoms wide, on American soil, at lower cost than the Dutch monopoly. AlphaEvolve is DeepMind’s evolutionary coding agent, the latest entry in the Alpha lineage that runs from AlphaGo to AlphaGoZero to AlphaZero to MuZero to AlphaFold to AlphaChip.

This week, Substrate pointed AlphaEvolve at its computational lithography stack, the software that simulates how trillions of photons interact with photoresist to print a chip layer. In just a few weeks, AlphaEvolve explored thousands of algorithmic variations and landed on lossless compression tricks and lower-precision representations that cut memory by 74%, sped up runtime 6.8x, and dropped the Google Cloud TPU bill by 97%. Now, Substrate’s tool can print metal-one layers (the hardest, most defect-prone layer in a chip) at a 24nm pitch in a single exposure, with bidirectional 2D patterns and sharp 90-degree corners. That’s 2nm-node territory, the bleeding edge. Without the speedup, the same patterns would have required multi-patterning: two or three exposures, which means more defects, more cost, and more time.

This is fun because: a) we are rooting for American ASML and b) it’s a combination of the stuff we’ve talked about in the last couple of co-written essays: World Models talked about the evolution of the Alpha line of models, which learn causality instead of rules, and Electromagnetism Secretly Runs the World talked about using AI to design better chips. It’s also an example of America’s best hope in the electric competition with China: using new technologies to find new, better, and cheaper ways to build the things we don’t build today. I suspect we’ll be hearing more from Substrate - it would be insane to compete with ASML at their own game, so they’re creating a new one.

(2a) Arena Physica Releases Heaviside

Speaking of EM modeling… A couple of weeks ago, in Electromagnetism Secretly Runs the World, Arena Physica CEO Pratap Ranade and I mentioned the new models and blog posts they would be releasing soon. Soon has come. Its new model, Heaviside, is its first EM foundation model, and you can play with it in the RF Studio.

(3) Syncere Unveils Lume Lamp That Folds Your Laundry

I have spent a lot of time thinking about robotics recently, and I’ve got to admit, I did not think of a lamp that folds your laundry. This is the future that Beauty and the Beast and The Brave Little Toaster promised.

Lume is a pair of six-foot bedside floor lamps with hidden six-axis robotic arms, cameras, and on-device AI processors. Toss your clean laundry on the bed, the lamps quietly extend their arms and fold it in under two hours for a full load. You don’t have to tell it to do anything, it just watches the bed and figures out when to get to work. (There’s a Lume sitting in the factory somewhere just praying Bryan Johnson orders it.)

The graveyard of laundry-folding robots is packed. Foldimate made you feed clothes in one at a time and quickly… folded. Laundroid was a $16,000 closet-sized monster that bankrupted its parent company in 2019. Laundry is so hard that every robotics company makes demos of their bots figuring out how to do it in a controlled environment, and people go wild, because folding laundry is decidedly a thing we want the robots to do for us.

The why now? is the perception stack. Vision-language models (VLMs) are now good enough that a small startup can train a thing to look at a balled-up t-shirt on a comforter, figure out what it is, grab it, and fold it.

It’s also a constrained problem. It doesn’t promise to do everything; it gets paid to do one thing well, and learn. Maybe it, like Standard Bots, will be able to take small steps across the spectrum. And you gotta buy a lamp anyway.

I want to wait to see the reviews before I drop $2,499 on two lamps, but if you want to be an early adopter, be our guest.

(4) Humans Travel Farther From Earth Than Ever Before

Jared Isaacman for NASA

Last week, we covered the launch of the Artemis II mission, which planned to take North Americans back to the Moon for the first time in more than half a century, and farther from the home planet than we’ve ever traveled (that we know of). As the crew rounded the Moon on Monday, NASA Administrator Jared Isaacman tweeted:

On the far side of the Moon, 252,756 miles away, Reid, Victor, Christina, and Jeremy have now traveled farther from Earth than any humans in history and now begin their journey home. Before they left, they said they hoped this mission would be forgotten, but it will be remembered as the moment people started to believe that America can once again do the near-impossible and change the world.

It’s rare for us to do back-to-back coverage of the same story, but unprecedented accomplishments call for unprecedented measures. Instead of writing about it, I’d just ask you to watch Astronaut Victor Glover’s Easter message back home:

(5) Primer Launches New Brand with The Orator

Primer is one of the not boring capital portfolio companies I’m most proud to be involved with. I’ve written about them here and talked to the CEO Ryan Delk on moving from software into running schools here. They take kids seriously, and exist to give more kids access to excellent education.

This week, they released a video narrated by one of its students and featuring others that highlights both its approach to education, and an optimistic vision for how we can all meet the future: “The next 200 years will look nothing like the last. We will face problems we can’t yet imagine, and possibilities we can barely dream of. We won’t just need answers. We’ll need resilience, flexibility, courage, mastery.”

Amen.

EXTRA DOSE: Science Breakthroughs, Hermeus, Pace Fellowship

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Hey friends 👋,

Happy Friday and Happy Easter Weekend!

We’re going back to the Moon. OpenAI bought TBPN for a lot of money. Somos is going to blanket the globe in fast internet. And a whole lot more good news where that came from.

Let’s get to it.

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(1) NASA’s Artemis II Crew Launches to the Moon

NASA

For the first time in 53 years, 5 months, and 20 days, human beings are on their way to the Moon.

Last night at 6:35 p.m. EDT, NASA’s Space Launch System (SLS), standing 322 feet tall, weighing 5.7 million pounds, and generating 8.8 million pounds of thrust (17% more than the Saturn V), lifted off from Pad 39B at Kennedy Space Center carrying four astronauts aboard the Orion spacecraft (which the crew named Integrity). Commander Reid Wiseman, Pilot Victor Glover, and Mission Specialists Christina Koch and Jeremy Hansen are now on an approximately 10-day trip around the Moon and back. No one has done this since Apollo 17 splashed down in December 1972.

Integrity will spend about a day in a high Earth orbit stretching 46,000 miles out, the crew will manually fly the capsule to test its handling, and then a six-minute translunar injection burn will send them on a free-return trajectory using the Moon’s gravity to slingshot them around the far side and back home. On Monday, April 6, the four of them will become the first humans to lay eyes on parts of the lunar far side, reaching a record distance of 252,000 miles from Earth. They’ll splash down in the Pacific on April 10.

Is getting to the Moon more expensive than it should be? Hell yeah, roughly $4.2 billion per SLS flight by the Inspector General’s math. Did it take way longer than planned? Also hell yeah. But four people are about to travel further from our home planet than any human has traveled before, in the first of a series of missions designed to get us back to the Moon to stay.

I can’t really sum up how I feel any better than this kid did:

(2) OpenAI Acquires TBPN

Katie Deighton for WSJ

Speaking of going to the f*cking moon… OpenAI, which just closed a $122 billion round at an $852 billion post-money valuation, has spent some of its treasure to acquire tech media’s crown jewel, TBPN. The FT reported that the deal is in the “low hundreds of millions of dollars.”

Huge congrats to Jordi, John, Dylan, Ben, and the whole TBPN crew.

This is an incredibly smart acquisition for OpenAI. I’ve been saying that the frontier AI labs have completely botched their narrative, and this is about as good of a way to get to work fixing it as I could imagine.

A lot of the commentary so far has been about OpenAI acquiring the media property, which will certainly be helpful, but the big get is Jordi & John. They’re simultaneously highly generative creative geniuses and normal guys. I expect that we’ll see their impact on the way the company communicates to non-San Franciscans pretty quickly, and I hope they’ll have a hand in shaping where AI goes more broadly.

In the meantime, it’s just great to see the good guys get a W.

(3) Google DeepMind Releases Gemma 4 Open Source Model

Completely unrelatedly, I’ve told you guys how awesome I think Google DeepMind is, right? Been saying it for years. What a great lab. Could obviously use a little help telling their story, but wow. What a family of models. AlphaFold! Gemini! AlphaProof! AlphaGo! AlphaZero! Nano Banana! Genie! SIMA! DolphinGemma! I could go on and on.

Yesterday, the good folks at GDM just released a family of AI models that can see images, watch video, understand speech, reason through competition-level math, and write code, all open source. You can download them for free, modify them, and ship them in a commercial product under the Apache 2.0 license.

GDM released Gemma 4 in four sizes: a 31B dense model, a 26B mixture-of-experts model, and two edge models (E2B and E4B) designed to run on phones and laptops. None of them is quite up to par with the best frontier models, including GDM’s Gemini, but since they can run locally on your hardware, it means you get the tokens they produce basically for free.

Yesterday, in Bad Analogies, I wrote that OpenAI and Anthropic were burning a ton of money to compete in an incredibly competitive field, and that Google DeepMind was Zeus in this battle of the gods. Gemma is another lightning bolt. The more things people can do with free tokens, the less they’ll spend on tokens, even if the most advanced capabilities will always come at a price. Of course, closed model customers are paying for more than tokens: reliability, ease-of-use, etc… but it’s fun to see the competition drive down prices for consumers.

Jokes aside, I continue to be very impressed by GDM. The lab came up more than any other in my research for both World Models and Many Small Steps for Robots; they’ve done more to push the frontier in more directions than any lab in the world. With Gemma 4, Demis the Menace strikes again.

(4) Solar cells just did the “impossible” with this 130% breakthrough

Kyushu University via Science Daily

Three weeks back, we led off the Dose with a story on Swift Solar buying a gigawatt-scale solar factory to produce 30-40% efficiency silicon-perovskite tandem solar cells.

This week, researchers at Kyushu University in Japan announced cells with a seemingly impossible 130% efficiency.

There’s a number in solar energy called the Shockley-Queisser limit. It says that a single-junction silicon solar cell can convert, at most, about 33% of the sunlight hitting it into electricity. Silicon-perovskite tandems can up that to 45%. In either case, the rest, especially the high-energy photons, gets wasted as heat. Shockley-Queisser been the ceiling since 1961.

Until now! A team led by Associate Professor Yoichi Sasaki at Kyushu University in Japan, collaborating with Katja Heinze’s group at Johannes Gutenberg University Mainz in Germany, just published a paper in the Journal of the American Chemical Society showing that they paired tetracene-based molecules with a molybdenum-based metal complex (a “spin-flip” emitter) and achieved a quantum yield of roughly 130%. That means for every photon absorbed, the system produced 1.3 energy carriers. More out than in, which seems like it shouldn’t happen.

The trick is a process called singlet fission. When a high-energy photon hits the tetracene, instead of creating one excited electron and dumping the rest as heat, the molecule splits that energy into two lower-energy “triplet” excitons, a little bit like nuclear fission (don’t yell at me real physicists). The problem has always been that a competing process called Förster resonance energy transfer tends to steal the energy back before fission can finish.

Sasaki’s team solved this with the molybdenum complex, which selectively accepts only the multiplied triplet excitons by flipping the spin of its electrons during near-infrared emission. If molybdenum sounds familiar, it’s because it’s the hard-to-pronounce mineral Earth AI found in its first discovery, and because it’s come up a couple of times in recent American Alchemy episodes.

There is an asterisk, for now: the process was demonstrated in solution, not in a working solar panel. It’s a proof of concept. The team’s next step is integrating the tetracene and molybdenum materials together in a solid-state system.

But the result proved that the 100% quantum yield barrier isn’t a physical law, which means the engineers can get to work sorting out the details. If singlet fission can be made to work in commercial cells, we’re going to start putting a lot more photons to good use.

(5) Somos Internet Raises $40M Series B

Alex Konrad for Upstarts Media

In November, I released my longest company Deep Dive ever: Cable Caballero, the story of Forrest Heath III and the vertically integrated internet company he and his band of pirates are building down in Medellín, Colombia.

Explaining how he built what is now the best-performing internet in the region, and maybe the world, with its own fiber network, switches, routers, and ISP, Forrest said:

It’s been this never-ending game of doing something janky, getting credibility, doing crazier stuff, getting more resources, getting smarter people so that we can fix the things that were messed up in the janky past iteration, then gaining credibility to get more resources to get cooler people to do crazier stuff. It’s like this self-sustaining fission process.

Now, Somos has a lot more resources to hire more smart people and do even crazier stuff! This week, the company announced that they raised a $40 million Series B co-led by Bracket Capital and Ribbit Capital. Following on to not boring capital’s original investment was a no-brainer.

Somos is growing fast in Colombia, and it announced that it’s getting ready to expand into Mexico. On TBPN, Forrest even hinted at Somos Caracas…

I think this company is going to build a ton of better, faster, cheaper infrastructure in the coming decades, and they have both the fuel and the backers to do it. If you’ve been looking for a sign to put your stuff in a bag and move to Colombia, this is it.

Vamos Somos!

EXTRA DOSE: Science Breakthroughs, Electric Delivery, Bangalter

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Hi friends 👋,

Happy Thursday! I wasn’t planning on sending an essay this week. I wanted to give you a little time to breathe after World Models and Electromagnetism.

But then I saw someone make a bad point on the internet, and it was a particular bad point that has always bugged me, so I whipped up a short and sweet rebuttal. In short: not every company that burns a lot of money is the next Amazon. (a bit of a strawman, I admit, except this analogy keeps popping up!)

Let’s get to it.

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Bad Analogies