2026-06-12 20:58:11
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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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.
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.
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.
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)
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:
2026-06-11 02:02:16
Welcome to the 192 newly Not Boring people who have joined us since Monday! Join 269,285 smart, curious folks by subscribing here:
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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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 - 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)?
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.
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.
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.
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
2026-06-08 21:04:26
Welcome to the 575 newly Not Boring people who have joined us since our last essay! Join 269,093smart, curious folks by subscribing here:
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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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.
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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:
Aircraft model loop
Customer experience loop
Endpoint/destination loop
Cost loop
Autonomy and services 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.
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.
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.
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.
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.
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.
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
2026-06-05 20:53:49
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.
When it comes to implementing AI within your company, most orgs struggle with a knowledge problem. You can plug in all the AI tools you want, but if your internal info is scattered, outdated, or contradictory, you just get the wrong answers faster.
Guru fixes that.
It’s a central, governed knowledge layer that sits underneath your tools and actually makes them useful. Teams like Spotify and Brex use Guru to:
Keep company knowledge in one place (and actually maintained)
Verify information so it stays accurate over time
Power AI tools with trusted, up-to-date context
Deliver answers with sources, not guesses
Fix the input once with Guru, and it improves every output, across every tool your team uses. If you’re investing in AI, Guru can help make it actually work.
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. 🇺🇸
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.
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.
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.
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…
2026-06-02 23:34:10
Welcome to the 730 newly Not Boring people who have joined us since our last essay! Join 268,518 smart, curious folks by subscribing here:
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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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.
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.
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.
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?
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.
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.
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.
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.
“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
2026-05-29 20:32:20
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.
Hiring globally doesn’t have to be complicated
Hiring globally can unlock growth, but local laws, payroll, and compliance often slow startups down.
Deel’s free guide breaks down what an Employer of Record (EOR) is, how startups use EORs to hire internationally without opening entities, and when it makes sense to use one so you can scale with confidence.
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.”
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.
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.
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.
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.
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.
