5, 4, 3, 2, 1, 0. All engine running. Liftoff. We have a liftoff. That's one small step for man, one giant leap for mankind. The world's largest airliner. Each wing is big enough to hold five tennis courts. This new technology made it possible to meet the user's crucial needs. Enter the computer and a new age. What a computer is to me is it's the most remarkable tool that we've ever come up with, and it's the equivalent of a bicycle for our minds. Here I am playing a game of chess with a computer, which is analyzing board positions and applying a certain kind of intelligence to figure out what its next move should be. That's the subject of our program today: artificial intelligence.
The good future of AI is one of immense prosperity where there is an age of abundance. Everyone can have whatever they want. We're still in the very early innings of AI. I would say the rate of progress is exponential right now. Every time I think that we are overstating the impact of artificial intelligence, something comes along that tells me we aren't making enough of it on the show. You know, there's no 60-minute clock on this thing. This is an infinite game. Think about solving a problem that would take humans thousands of years to solve. Those who can harness and govern the things that are technologically superior will win, and it will drive economic vibrancy and military supremacy. The Trump administration believes that AI will have countless revolutionary applications. We believe that America's destiny is to dominate every industry and be the first in every technology, and that includes being the world's number one superpower in artificial intelligence. It feels like every tech revolution of our lifetime has been leading to this moment.
All right, everybody. Welcome to Winning the AI Race. This is our first event in DC. Can I get permission from our leader to sit down? Yes, you're here. You may sit. Thanks for coming out, everybody. We put this event together in just a couple of weeks in order to have a really important discussion: winning the AI race. This is something America has to do, and it's something we will do, and we're going to do it through the way we've won every other technological race—through grit, entrepreneurship, and dogged competition. The difference with this administration is they're actually engaging with the technology industry. And today we're bringing together many members, or all members, of the administration here to talk about it. None of this would have been possible without our bestie David Sacks deciding that he would take some time and become our czar of crypto and AI. I would like to just start with a huge round of applause for David Sacks. David, you've been here for six months.
I'm sorry, but did you actually prepare? This is excellent so far.
No, it's excellent. Keep going. Keep going.
He wants to be invited back to DC.
They told me I've got 12 hours left on the ground. I think that White House tour is going to happen after all. It might just happen in the air. But in all seriousness, you've been here for six months and we all know how capable you are. But my Lord, this administration is on a heater when it comes to crypto and AI. I am absolutely, and I think I speak for everybody in our industry, thankful and wildly impressed, but not surprised, at the pace at which you've led crypto and AI. What's the first six months been like for you?
It's really been incredible. I mean, I never expected to go into government at all, and really as a result of President Trump coming on our podcast a year or so ago, that began a relationship that eventually led to me being offered this job. I took it because I just thought it was a once-in-a-lifetime opportunity to work for a president who really wants to get things done for the American people. You can see that just every day he works so hard to push forward his agenda for the American people, and I think AI and crypto have just been two of those issues. But it's been a lot of fun to work on these things because we are getting a lot done.
Yeah, last week and this date today, we put together in just the last 10 days as an opportunity to talk about your action plan that was getting—the president's action plan getting published today. But we should invite Jacob out because Jacob partnered with us from Hill & Valley, Jacob Helberg. Come on out and join us, our new fifth bestie. Yeah, Jacob Helberg. There you go. Nice to see you, brother.
Good to see you. Friedberg, your team and Jacob's put a ton of work into this, and we have a lineup that is just absolutely outstanding. So, thank you, Jacob, for the hard work from your team, and Friedberg, the hard work from your team to put this all together. Maybe you could give everybody an idea of the questions we want to address today and what the format's going to be.
Absolutely. The Hill & Valley Forum is a community of technology builders and policymakers who believe that technology is an engine of wealth creation and is an indispensable pillar for American national security. It was incredibly exciting to have the opportunity to engage in this event, which is actually going to cover a lot of the topics that everyone in our community cares about. Ultimately, we believe that—and I actually said this in my confirmation hearing not too long ago—we're at an inflection point. We are in an AI race. The different parts of the programming today will be a series of conversations that will cover the different facets of how technology will actually create wealth for our country.
I think it is very important, as we talked about who do we want to have on stage and how do we want to talk about the president's action plan that David shared with us, to highlight that there are new industries being created because of AI—industries that couldn't have existed a decade ago. We've got a couple of those examples. Then there are these industries that are enabling and accelerating AI, and that's mining, energy, chips, fabs, and data centers. We've got conversations across each of those; that's kind of this enabling conversation. Then, fortunately, we've been able to get a lot of folks from the administration to join us today to talk about the government's role in enabling this economic transformation that's already underway.
I just want to say one point. What's become apparent to me, and I think is wrong in the press narrative today, is that AI is destroying jobs. I think what we are seeing on the ground is an incredible job creation engine that's underway. It's very important to highlight that and share those stories because they're not told enough, and I think there's a real opportunity to bring them to light. That's hopefully what we can get through today.
And Chamath, just coming around the horn here, doesn't matter if you're a Democrat, Republican, independent, moderate—this issue transcends party. This is the issue of our lifetime, and there's a lot of hard questions and a lot of hard debate. Maybe you could just speak to this administration's ability to bring in a lot of disparate opinions and work together across the aisle and with all members of the industry.
I think historically you've had a fork in the road where you can view technology as either optimistic and glass-half-full or pessimistic and glass-half-empty. The optimistic, glass-half-full view says that the country that can harness AI or any of these leading critical-edge technologies is able to garner most of the gains, and then those economic gains can be spread. Then it's a debate about how to spread those gains within a country and within an economy. From there, with economic supremacy, you also have military dominance, and now you're a superpower and you remain strong.
The problem is that historically we've gone in the other direction, where there has been this mistrust, and in that mistrust you've had global competitors emerge and create, I think, real fundamental existential risk for our place as a superpower.
So the great thing over these last frankly six months has been a massive pivot back into this idea that America is the best. We should not be ashamed of the things that we've created, and these incredible technologies and these incredible people should be celebrated. And let's go and win the race.
Let's win. Okay. And by the way, how we're going to win the race: the action plan. Sacks, I know you invited Michael Kratsios to join us here today, Director of the Office of Science and Technology Policy. Should we have Michael come out?
Yeah. Michael, come on out. Please welcome Michael.
All right. How are you guys?
So, I'll kick this off. President Trump, in his first week in office, signed an executive order that directed us to create this action plan—Michael, myself, and the national security adviser. The objective was to figure out how the US would dominate in AI. From his first week in office, President Trump has made this a priority, and we do see it as this global competition or global race. The consequences of losing that race would just be unthinkable because AI is going to have such huge ramifications for our economy and also for our national security. The United States has to win it.
Working with Michael and the Office of OSTP, we put out a plan today that has 90 concrete actions that at least the executive branch can take to help us win the AI race. I want to call on Michael in just one second, but I'm just going to outline the three big pillars of the plan.
Number one is innovation. There's just no substitute for innovation. You have to out-innovate your global competition. You can't regulate your way to winning the AI race. Number one is we have a lot of things in the plan that are going to help our private sector, our startups, our tech community out-innovate the competition.
Number two is infrastructure. We have to have more and better AI infrastructure—data centers, energy, manufacturing in the United States.
And number three is the AI ecosystem. We want to have the biggest ecosystem. We know from Silicon Valley that the companies that create the biggest ecosystems are the ones that win. You have the most developers on your platform, you have the most apps in your app store—those are the companies that dominate industries. In a similar way, the United States has to dominate by creating the AI stack for the entire world. So, those are the three big pillars of this plan.
Let me call on Michael. Can you tell us, I guess maybe speak to the process of how this plan was created the last six months? I know your office did a ton of work on this, and then, if you want, flesh out some more of the important details as you see it.
Yeah, absolutely. Once the president signed the executive order assigning us this task, the first thing we did was actually issue an RFI—very exciting government activity. We asked the country, "Hey, what should we include in this plan?" I think, to be honest, we're all surprised with what came back. We had over 10,000 responses come from all corners of the country. We had Hollywood actors sending us responses, we obviously had tech companies, we had everyone you can imagine. I think it really showed how impactful this particular technology is to everyone in every industry in the US. So we ingested a lot of those comments, went out to all the agencies that work with and in some ways touch AI, and came together with this plan.
Now, if you think about it, there's been a lot of national strategies that countries put out there over the last five or six years. What we really wanted to focus on is in the title itself: an action plan. We wanted things that we could accomplish in the next 6 to 9 months to accelerate and ensure that we can win this race.
So if you think about the first pillar, which David talked about, which was the innovation pillar, what's really key about innovation is we want the next great AI discoveries to continue to happen here in the United States. We have to create an environment that allows that to happen. When we talk about deregulation, the way I like to think about it is there's never really going to be a law that says, "Hey, this is how we regulate AI." What ultimately is going to happen is these AI technologies are going to be built into so many other technologies, whether it's drones flying, self-driving cars, whether it's FDA-approved AI-powered medical diagnostics. All these different agencies are going to be touching technologies that are powered by AI, and it is incumbent on us to create a regulatory environment where these technologies can thrive and not be hindered by the government.
The next piece of innovation I think is really key is using the power of the data that the government has to drive scientific discovery through artificial intelligence. We have seen in this first wave of AI great progress in the way that LLMs are able to handle coding, for example, but we can do so much more than that. There's incredible data sets the Department of Energy has, for example, at their national labs that can help power a lot of next-generation discoveries and things in material science and medicine, and that's what this AI plan calls for and drives.
The next pillar, which is about infrastructure, people talk about this all the time. It's about how do you create a regulatory environment that encourages and actually accelerates the ability of our power generators and our chip builders to be able to do what they need to do here in the United States. The plan calls for categorical exclusions for AI-related activities, which can allow data centers and other power generation to happen on federal lands. That's going to be coupled with all sorts of other efforts to really accelerate the velocity that we can build power and ultimately run these data centers.
So let's talk about, before we run out of time, one of the most important issues, which is the talent wars. We're going to stay focused on AI here. We'll leave the border and deportations off the table, but we'll talk about something super important, which is recruiting talent from around the world. This administration, we've gotten different signals, and obviously it's a very controversial issue here in the United States. What do we have to do in terms of immigration and, let's just call it recruitment, because that's really what it is—recruiting the best and brightest from around the world to come work on our team as opposed to, say, Team China? What do we have to do? What is the administration's philosophy on recruiting the world's best AI talent in the action plan?
I think what we bring to light, and I don't think it's talked about enough, is to power and successfully drive continued American leadership in this domain, it is not simply about having the greatest AI engineers, but it's also having all the other parts of the workforce which need to drive this forward. We talked to some companies like Caruso and others who are building these large infrastructure builds around the US. The challenge that they're facing is in electricians and HVAC talent. The AI plan itself spends a lot of time and energy directing various agencies, whether it's the Department of Labor and others who have these reskilling programs, to sort of train these people up to be able to fill that void. So for us, it's about attracting here to the US the greatest scientists and engineers, but it's also to be able to train the American workforce to be able to do the necessary jobs to put that forward.
Michael, what's the philosophy going forward on the thing you mentioned just before this, which is there's these enormously valuable data sets that sit inside the DOE, that sit inside of FDA, where presumably if we made them available to private industry, particularly American private industry, the gains could be incredible. Is that an open-source philosophy? Is that a licensing philosophy? How do you think it should best serve the American economy to get this stuff out there?
Generally, the government has taken an open-source approach to this. The general challenge that we've seen over the years is there's been a lot of lip service to, "Hey, let's unlock data for the American people." The main challenge is—and for all of us who are in AI—the format of that data itself actually matters a lot. If it's dirty, nasty data that isn't homogenized in any way, it's not particularly helpful. I think that's going to be a big effort that the Department of Energy is going to try to do to make this better and possible. What was great in the recent legislation that was passed in BBB was an actual $150 million ticket to the Department of Energy to build an AI for Science program that very much is going to be working on this exact problem.
Should there be federal preemption on AI regulatory schemes? There's been a conversation about doing this to ensure—I think right now there's over a thousand state laws that have been proposed or passed that have some regulatory effect on AI and tech-related technology. Should the federal government preempt all of that and raise it up?
I think generally preemption is an issue that comes up very often broadly in technology. You had this issue with privacy for many years. What we're trying to face today and what we talk about in the plan itself are actions that the executive branch can take itself. A lot of preemption discussion revolves around what Congress can or can't do. So we don't necessarily lean hard on that because we focus on things we can accomplish.
Right. And just to add to that, so it's true the action plan doesn't speak to that issue, Friedberg, very much. But I do think there is a real threat to national security that's brewing by virtue of the fact that, like you said, we've got a thousand bills going through state legislatures right now, all regulating AI in different ways. If this continues, we're going to have a patchwork of 50 different state regulatory regimes as opposed to one seamless national network. China has declared that AI is a national priority for them; they understand how strategic it is. I think if we hobble our AI innovation with a patchwork of 50 different state regimes, it's going to hurt us. We weren't ready to declare a policy yet in the action plan, but I think it's something that's going to have to be looked at over the next year or so.
Thanks for joining us, Michael. Everyone, the Director of the Office of Science and Technology at the White House.
Thank you, everyone, to the besties and the Hill & Valley Forum for the warm welcome. I'm Chris Power, the founder and CEO of Hadrian, and I'm here to talk to you today about our company. The mission is to re-industrialize America. We do this by building AI-powered factories in the United States.
You might ask, why is this important and why should you care about manufacturing in the United States? What I realized before I came to this country is that we're in a global race. Every great nation gets built by having the best industrial power first. That gives you the best military, usually the Navy. Then you end up with the reserve currency after a conflict, and you kind of rule the free world in what we've called Pax Americana. Like all great companies, you kind of get lazy through that success, and you end up offshoring all your heavy industrials to the developing country. Then when a conflict comes around, you're in real trouble because you offshored the thing that gave you the power in the first place, which is heavy industry.
The last three times this happened, it was a pretty good trade for the West. It went from the Dutch, the British, the American Empire when we won World War II. This time around, in this two-decade period where we're fighting the AI race, the climate, settling the stars, it's really the United States versus the CCP.
Bear in mind that we won World War II not because we had a defense industrial base necessarily, but because we were the industrial powerhouse of the world. When there was a time of crisis, we had all our commercial manufacturing companies pivot to defense when we really needed them the most. You had watchmakers making warship navigation equipment. Ford switched from building cars to building bombers. It was because of this industrial power—our tanks weren't so great, we just had tons of them. This is how we won.
Unfortunately, since the 1970s through to the 2020s, we've basically hollowed out the middle of America and offshored every bit of manufacturing we possibly can. This started with Nixon opening up China, letting them into the WTO. They were the world's factory. This is a huge strategic mistake, and it's completely hollowed out good jobs in America as well as left us in a very strategically dangerous position in terms of our industrial power.
So, while China de-industrialized us, they industrialized themselves and they treated manufacturing not as economics, but a national security priority. Now we're in this 20-year window where, staring down the threat of Taiwan, we're in real trouble.
Just how far behind China are we? In munitions, China has automated factories that can produce 1,000 a year, whereas we run out of missiles in the first seven days of any war game conflict. Then we can't reproduce that ammunition for three years. In shipbuilding, they're 200 times greater than us. We produced a grand total of five ships last year. Pharmaceuticals are all offshore. Drones, iPhones—we don't make any of them. Bear in mind, in pharmaceuticals, the CCP makes all our antibiotics. This is why industrialization is so important.
More importantly, this gets back to the AI race for talent: while the US is still the global powerhouse in software and AI talent, we made China into the global powerhouse for manufacturing talent. What we realized through building this company is that while US defense manufacturing, which was all we have left because we offshored everything else, is really important, because we let all those jobs go, the entire base is basically a bunch of patriotic Americans that still know how to do skilled trades that are in their 60s, retiring faster and faster and faster. The underpinnings of our entire defense industrial base is this American talent that knows how to do the job, but the rest of the country forgot how to manufacture.
This is a screenshot of one of China's munitions factories. You can Google this online. It's a myth that it's just low-cost labor in China anymore. They are very advanced at production, whereas in the United States, underpinning all our defense primes and our industrial base, we basically have skilled Americans that are retiring faster and faster and faster, supporting 100- to 200-billion-dollar industries across all these different ways to bend, cut, and ship metal that you need to then put it into drones, ships, satellites, and rockets. So while China is racing ahead of us, we're really falling far behind and we forgot how to manufacture.
What we realized was we have to build full-stack AI-powered factories to solve this problem. Secondly, the number one issue is this massive skilled talent shortage. If you look at shipbuilding or any of these other industries, we are begging for millions and millions of welders or machinists. You could give me a billion dollars, and we can't hire them in this country anymore because we lost that skill. Production—not having inventory—is real deterrence, and you've got to do this by re-industrializing the country to create more jobs, not replace them or automate away, and it's always about national security, not economics. So we set out to solve this problem by building automated factories driven by AI in the US.
Three years ago, when we started this journey, we figured out, how are we going to do this? The answer was just start running a factory and build all the AI software at the same time, which was a hilariously painful journey in the early days of the company. This is what Factory 1 looked like. We partnered with some of America's greatest aerospace companies to really beta test this for a good 18 months. What can we automate? What can't we? This is one of our first tiny parts that we shipped to America's greatest rocket provider. Now we're up to the point where we're building whole products.
We built Opus, which is a full-stack platform for AI autonomy of factories that does a couple of really important things. In 2024, we launched Factory 2 once out of this beta phase, scaled 10x in a single year with the fastest-growing manufacturer in the country, and now we're lucky enough to support America's greatest companies, both startups and defense primes. This is what the most advanced factory, in our opinion, looks like in America today. This is our scaled Factory 2 in LA. Here you see cutting metal coming from raw material, shaving this down into micron precision tolerance components that go on rockets, satellites, jets, and drones. What you see as you go through this is, in legacy industry, in a de-industrialized nation, you've got really skilled people on every machine. Hadrian's advanced factories look and operate more like a data center. We're really proud of having pulled this off, but the journey is not over yet because, again, this is a whole-of-nation 100- to 200-billion-dollar problem.
Where do we actually get to, and what sort of productivity gains can you get in AI and manufacturing, and are we creating more jobs? Firstly, most factories in the US run at only a 20% uptime. It's not really that productive. We have a 4x jump in manufacturing productivity. Secondly, and more importantly, we have a 10x jump in workforce productivity. Again, because we have such a scarcity of skilled talent in this country, you actually need that AI-powered jump to even create the capacity in this nation to be able to build ships, drones, and rockets.
The second important thing is speed to get people in these jobs. If you're an advanced manufacturer, it can take you up to a decade to get really good at what you do, whereas at Hadrian, we've managed to make it so that we can train anyone in 30 days. Most importantly, 100% of our workforce are from non-factory backgrounds. They've never set foot inside a factory before. These are folks straight out of high school, retired from the military, who had a desk job, were a nurse or bus driver, from 18 up to 40. This is the most important thing that people have got to realize about the power of advanced AI in manufacturing: we need this productivity boost to just be able to compete with China and catch up on these skilled trades that we lost. This is the most important thing that AI is doing for us—enabling huge, huge workforce growth.
Where are we at? In 2025, we're going multi-category, multi-factory. I'll show you our new factory that's launching AI-powered in six months in the great state of Arizona, as well as launching a dedicated gigafactory. You can think about this as like everyone in defense and aerospace needs a Tesla Model 3 factory. This is our beautiful new facility. It's about four times the size of the one in LA, launching by Christmas. We signed a lease a couple of weeks ago. It'll be online in six months. The most important thing is we'll be creating 350-plus new AI-powered jobs in scarce-talent industries where America just needs this leverage to get ahead.
The other thing: if you listen to the Secretary of the Navy on re-industrializing, what is the number one problem in shipbuilding, submarine base, and munitions? It's actually that there's millions and millions of jobs that we need to fill because we don't have skilled trades anymore; we don't have the volume of the people. So we need this productivity boost. In 2026, we're launching advanced factories targeted at America's greatest production challenges: submarines, ships, and munitions.
By the end of this year, we'll have three facilities up and running: our headquarters, Factory 2, and Factory 3 in LA. But as we re-industrialize the country powered by AI, where is this really going to get us to? To solve this problem for the country and fulfill the mission, we need to have factories in every state. You've got to remember that AI in manufacturing is creating thousands of jobs because we offshored everything, and we need this productivity boost to give our nation the capacity it needs: reshore all these jobs, pull them back into the middle of the country, and make sure that we're creating millions and millions of jobs along the way. Thank you for having me. It was a pleasure to be here.
Chris, I think we wanted to kick this off. We have a couple minutes to just cover what you've introduced, which is a really important opportunity. China has roughly 3 million factories; the US has 250,000. The assumption is they've got cheap labor; looks like they've got automation. Things are very different on the ground than what folks read about as we try and compete. What industries are going to be first from a manufacturing perspective that we can actually compete successfully, and do we need trade tariffs in order to succeed on the competitive landscape?
There's two really important points. One is there's industries that we have to reshore. Specifically in defense, we have to produce submarines, ships, and munitions. We have to produce things like rare-earth magnets and drones. We just have to do it. The tariffs really help, and this trade policy is really important because you've got to understand that, yes, China is more competitive than us, but the CCP also nationally subsidizes the cost of energy, the cost of raw material. Because we've kind of degraded this capacity—like not having nuclear in the US—we can't compete on those raw inputs. So it'll start with our most critical industries first. But as AI goes through manufacturing, you'll create millions of jobs, and that will allow us to reshore more commercial volume, not just in defense. I think that's the most important thing.
You've talked about this degrading infrastructure and what that means in terms of workforce, but then how reshoring also requires this upskilling. I know you guys have this associate named Owen that you guys took, I think, literally straight out of Home Depot. Can you give us a little bit of his story and just what that represents in terms of you guys upskilling labor?
It's really incredible. As I said in the presentation, 100% of our people never set foot inside a factory before. I think we really didn't do a great job as a nation by convincing everyone they needed a four-year college degree to have a really good job. We've hired people that were packing shelves at Home Depot, and now they're running 10 machines at once. Actually, what we are seeing is that most of those people, when they're exposed to software or AI, they're very smart, and we've promoted a lot of those people into leadership management or software engineering roles. Re-industrialization with AI is about creating new jobs, but also reattaching people to the Silicon Valley economy and not just having it on the coast and the cities.
How are you going to compete with people having gig labor and making 30, 40 bucks an hour being a DoorDasher? We have the lowest unemployment in our lifetimes, 4% right now. Is it realistic to find all this labor out there, or do we have to have some people immigrate to this country in order to fill those jobs?
For us specifically in defense, we have no choice in immigration because it's a regulated environment, so we have to upskill Americans. Secondly, what we see, maybe not in LA or the coastal cities, but across the country, is there's lots of underemployment. Some of our favorite people had desk jobs where they're a paralegal and they were filling out forms, and they hate it and they want to come into factories and work on the national mission. I think for us it's a lot about getting people inspired. Then secondly, with this level of productivity jump, we can actually afford to give people incredibly good healthcare and incredibly good pay. I think a lot of Americans want to go back to work in a real environment that's for the national mission.
You showed some incredible images and video of these very intricate machines. Do you make the machines that then make all the machines, or is there a supply chain risk?
There is a huge supply chain risk. We actually invented via the Air Force a lot of these advanced machines, and we forgot how to make them. The main sources of supply are actually our allies—China is number one, we don't buy from them because they've got cybersecurity holes all over the place—Germany, South Korea, Japan. The insight that we had was they're actually just really dumb computers, and software and AI can actually upscale and overpower them and really have a lead. But it is a huge supply chain risk of not building the machines that build the machines in the country anymore.
To economically compete, though, I was trying to parse if you were asking for the government to give you support since the Chinese government is underwriting their companies with free energy. Are you explicitly asking the government to help with, say, paying for reskilling training or maybe in some way deferring your energy cost, or can you make this economically work?
We make it economically work because in the US there are really two markets: there's the stuff that has to be onshore for defense and aerospace, and then there's this offshore market that's 10 times larger. Commercial aircraft—a lot of that is in China. For us, we can compete in the US because we've got to create all these new advanced jobs because we just don't have the skills anymore. If we want to reshore the commercial volume that is not regulated to be onshore, we have to do tariffs and economic policy because it's not an even playing field. It is right now companies versus the CCP.
What would that look like in terms of execution? You would want them to pick up the retraining, the energy cost, part of their salaries?
It's really three things: it's the cost of energy, it's the cost of raw materials—aluminum, steel, 90% of the cost of that is actually energy. If we level that playing field, then we can go compete in what we're great at, which is the American software and the American spirit and AI-powered workforce.
So the silver bullet is energy.
And then tell us about the actual software. Do you have a team that's writing a lot of control systems and/or AI models themselves, or you're taking things that are off the shelf and you're fine-tuning them? How are you doing it?
Unfortunately, because American manufacturing software is 30 years behind Silicon Valley, we had to build everything ourselves, from scheduling systems to the deep tech. The key insight that we had is the faster we grow, the more data we are labeling. We always do things 80% automated with a human in the loop. As we label this complex manufacturing data, this is where our AI models actually kick in because manufacturing has been offline for 30 years. There is no Stack Overflow, there's no GitHub codebase to train a model on; we had to train it ourselves off our own labeled data as our experts were ticking and tying the automation.
Traditional automation is purpose-built, does one thing. A lot of engineering goes into making it do that one thing really well. Are you leveraging things like, to Chamath's question, vision-action models that allow you more extensibility with one particular piece of machinery, or when does that start to happen from a tech perspective in your view?
Right from the start. The way oddly that customers translate data to their supply chain is by giving them 20-page PDFs full of hieroglyphics. So we actually have to train huge vision models on interpreting that. What does that mean? It's very complicated, and it usually takes an expert 50 hours to pore over that. So it's vision models, it's training engines on the data, it's also training engines on reinforcement learning of, "Hey, we made a part with automation. Was it high quality or not? Did it actually work?" Embedding all of these in the workflow real-time is the magic trick here with AI.
And you're not doing this stuff just on the coast, right? Your next factory is sort of more middle America. How do you end up choosing where to put that?
The most important reason why we selected Arizona was because of permitting, energy, and regulations. We've got to go fast, right? We've got to build this in six months, and then we will expand into the middle of the country kind of left-to-right on the map. I think that's the most important thing: we're going to be able to expand into all these cities and states where the manufacturing jobs were destroyed, and we're going to bring them back.
Oh yeah, just led the Series C which we just announced last week and joined the board, much to Chris's chagrin.
How long have you guys known each other?
I was board observer for a while and tried to avoid getting the official seat. You've got to date for a while, and now we got married.
Well, Chris, thanks for being here today.
Thanks for hosting me, guys. Pleasure.
Yeah, thanks for the education.
There is a huge fire going on right now at Philadelphia Energy Solutions. Oh my gosh. Again, look at this, guys. Look at this video right now. Today, the Navy remains a formidable fighting force, but even officers within the service have questioned its readiness. Developing right now: gushing for hours with no end in sight, thousands of barrels of crude oil spilling from a tank. The report does an estimate of what the need is to bring the overall grade up to a B, which is what the society sort of determines to be adequate, and it's like $4.59 trillion.
A company that started in my college dorm is now a company that manages over 500,000 of the world's most critical pieces of infrastructure. At Gecko, we build robots and AI models to help unlock the physical world. When we build robots, we wanted to build them that could fly, swim, crawl, and walk on any surface to gather the most amazing information and data sets that have been forgotten about the physical data layers. All those data layers are incredibly valuable when you're able to unlock and use AI models to drive incredible and important outcomes.
I started the company in the energy sector, deploying the technology to help prevent catastrophic failures in downtown power plants. Now we've expanded into mining, metals, and manufacturing, as well as for defense. We're helping to deter conflict by getting ships out of dry dock on time and patrolling the borders. Also, we are helping the Air Force ensure that planes are in the air and not in hangars. Then just last week, when the president was in Pittsburgh, my hometown, we just signed an amazing deal that ensures that we can help revitalize manufacturing in the United States again by helping to build ships and subs.
The energy sector has been incredible, and we're in a lot of other sectors as well. But what I've begun to realize is that the most impactful thing that Gecko Robotics can do to help ensure that we deter conflict and be most impactful for national security is actually in the energy sector.
You see, President Trump is absolutely right, and his executive order today calls out an extremely important reality: that the companies that can unlock energy are going to be the ones that can dominate in the AI race. However, as you can see from the graph here, China is on pace by 2030 to 3x the amount of generation against the US. But this isn't the whole story. You see, we constantly think about AI as an energy consumer. However, I'm here to tell you that artificial intelligence can actually be used to unlock energy production in ways that you've never seen before.
Inputs really matter to being able to unlock this potential. CEO after CEO that I talk to in the energy, mining, manufacturing, and defense sectors will tell you that we're trying to figure out how to unlock artificial intelligence to supercharge everything. However, the value is just not there. And it's no wonder: the consistent common factor between each one of these sectors is Joe. Joe is out there gathering information by hand, trying to diagnose and get physical data to drive really impactful decisions. But it's important to understand that Silicon Valley artificial intelligence researchers and software engineers can't do much with data sets coming off of the backs of Joe, and Joe's been armed with the same technology for the past century. So it's no wonder that impact isn't being unlocked in these sectors.
Unfortunately for Joe, it's a very dangerous job as well. Someone dying doing this job was actually one of the things that inspired me to build Gecko. We have to give Joe better tools in the new century.
What I'm going to walk you through right now is an example of exactly how we do that for the power sector. We send in robots—robots that are gathering information and data sets about the physical environment, in this case a natural gas power plant. We're understanding what the physical environment looks like. Then we send in other robots like this dog over here. The robot dog is gathering operational data sets to help supercharge Cantilever, our AI-powered platform where all the data sets are coming into. We sell an operations platform, and data sets gathered in the physical world is what's enabling that.
We also send in wall-climbing robots, and you can see the wall-climbing robots to your left and to your right. These robots are going into the physical environments and gathering health data all while the power plant is actually online. The health data is really important because we have to understand process health data to be able to optimize and feed into AI models. But again, this data set just never existed before, so we had to go out and actually get it physically in the real world. Robots like this supercharge our ability to drive models to create the largest amount of efficiency gains.
This power plant, for example, is supposed to be operating at 620 megawatts, but it's not reaching its capacity; it's only operating at 580. So how do you unlock that? When you have all this information and data sets that we've captured with robots, plus all the data sets that customers have, you're actually able to drive optimization to see how to impact efficiency and production. What this AI model is doing is looking at the data sets from the robot dogs as well as health data from the robots to pinpoint that there's actually a steam issue going into the turbine. An ability to fix these things has actually been able to unlock for this site, and for many others that we work on, a 1% improvement to efficiency, and this is just the first place that we looked.
It's also important to understand that the assets that power the grid are failing at a really fast rate. This power plant had assets like this tank that was decaying at incredible rates. It was supposed to be reaching retirement pretty soon, but we were able to determine predictively how to extend the useful life of this asset by 10, 20, and 30 years from all this data set. It's really important to understand, when you culminate all the kinds of impacts that you can have from this kind of technology, you get things like these efficiency gains on the dozens of power plants that we've been able to work at. If you extrapolate that across the thermal fleet in the US, that'll give you 11.9 gigawatts of new power without putting a shovel in the ground. The energy is able to be unlocked using artificial intelligence.
It's really important to understand the statement: AI shouldn't just consume, it should create energy. And that's what we're showing here. Not to freak anybody out, but the DOE just came out with a study that showed we have about four years left of useful life on the assets that power our grid. On this trend, it means there's going to be 100 times the amount of blackouts by 2030 if we don't reverse this trend. But what we're able to show not just with power plants, but mining, metal, manufacturing, as well as defense assets, you can actually extend the useful life of infrastructure in some cases by 30 years, and on average it's been about 35.
This is extremely important in ensuring that we're able to reverse that trend and ensure that America is well positioned to lead in the energy race to enable and unlock artificial intelligence.
Let me summarize this. We spend so much time—and I think JD Vance has done a great job at highlighting how much effort and how many data sets have been gathered to power AI models in the digital world, and it's what makes ChatGPT so addictive. But remember, the physical world has been forgotten about, and our robots are going into the fog of war to try and decipher and unlock massive amounts of information and data sets that give America and our allies unfair advantages—unfair advantages to unlock things that we didn't even realize were there. If you build software with an ontology based on first principles, gathering the data and building software up from there, you're actually able to deliver impactful things for Joe, turning Joe into a PhD scientist or engineer instead of forgetting about him like a lot of Silicon Valley companies have in the past. Unlocking physical intelligence data drives artificial intelligence, and that's how you're going to win the AI race. Thank you.
My name is Laura Cardenas, and I'm a registered nurse here at Tampa General Hospital. For the last 17 years, I've been able to serve the neuro-intensive care unit, where we care for the most vulnerable and critical care patients.
Before utilizing AI, it would take hours to gather information—looking in chart reviews, talking to nurses, talking to physicians. We relied on paper, pencil, a lot of paper stapled together with sometimes outdated data by the time I was done going through 32 patients. This is how we would try and give reports. Bringing in AI has significantly changed the culture on the unit. I had a charge nurse who never gave a multidisciplinary round or report out. She came on board, she said, "This is an amazing tool. Look at this. It has all my information already gathered and collected," and she was able to report out on the patients. It was completely user-friendly. She's like, "Laura, what is this?" It is creating excitement throughout the nursing community. Using AI has provided more time to be with you or your loved one at the bedside where nurses should be. We are the heart of healthcare.
I'm Matt Troutman. I'm the vice president, general manager for PRL Industries, supplier of components for nuclear submarines that our servicemen and women's lives depend on. We are a fully integrated foundry, pouring metal all the way through finished machine components. Two months ago, we weren't getting after any of the problems on the shop floor. Engineering director told me all his team was doing was quoting a three-day process to quote a part—paper files, old archives, data tables, emails, side communications, all this which ends up getting lost in the fray. Now, using an AI tool, they are getting halfway through that process in minutes. Frees them up to get back out on the floor and do what an engineer does best, which is solve problems to provide the Navy with the best quality products in the shortest amount of time. And this is what AI is going to help us do: understanding part location and status.
That is a game changer. We can now talk very clearly with the customer if that part is now to become the primary focus of the business, cuz it's a critically needed part for a ship construction. You get notified. It's an automatic notification. We can see the exact status. Here is the impact. How can we be better? How can we do more? And this is how we're answering that call. With AI, we match the speed of the quality management process to the speed of the workforce and the machine capabilities, and we will truly see a multi-step change in the amount of product that can come out of any company in this supply chain. More jobs for American workers here at Pro.
My name is Julie Nordberg. I'm a registered nurse leader here at UP Health System Marquette in the heart of Michigan's Upper Peninsula, and we are really the only game in town, as we like to say it. The next closest hospital to us that could service us is downstate, which is about a 4-hour drive. Prior to using AI, it took a lot of time to go through the patients' charts to see where they need to be. It took a lot of time just to try to communicate with people. I think that's a fear that everybody has is that AI is going to replace people. But AI, in the way it's being used here, could never replace our frontline staff.
The vibe is I think it's just one of excitement, that everybody's just proud to be part of this and to say that we're doing it here, and we're honing it in and tweaking it and using it to enhance our care and using it to help our staff. Having this kind of communication hub and facility snapshot has helped everybody. For the nursing staff, I think being able to see everything in one spot has just revolutionized kind of how they are able to provide care. I don't think anybody is sad to get rid of a meeting. The impact on patients is earlier detection, which means earlier treatment, which is a better outcome, life-saving for some of them. That's where I think this is going to help us a lot is because we don't have as much manpower as those big academic centers. So having the AI in the background doing some of that legwork for us is huge.
I joined PCNA in 2018. We have built over 11 billion batteries in the last 8 years. I walked out onto their massive production floor for the first time. I knew right then and there I wanted to make this technology accessible for anyone who wanted to learn it: people coming from the tourism industry and the hospitality industry; quite a few technicians that have fixed slot machines in a past life; people from automotive companies, people who are used to repairing cars, however, have never seen equipment at this scale and with this complexity. We don't really have to pick and choose what people's backgrounds are because we do have this very powerful learning tool that makes it easy for anyone to be able to enter this industry.
It is taking our historical maintenance records, pairing it with our machine data, and is now starting to understand early warning signs of a breakdown, and deploy our technicians to equipment before it ever actually breaks. This helps minimize our production losses, keep our technicians safer. We're taking reactive events, turning them into predictive events.
We used to honestly lose a lot of technicians because they would lose their confidence, think, "Hey, maybe this isn't for me." I pulled the supervisor off the floor and said, "Hey, you got to come listen to this idea and you have to help us make it better because you're the one who lives it every day." And they immediately started suggesting new features. They were telling us what was wrong with the old systems and we were coming up with solutions on the spot. So, this is really helping people feel like they belong here. We don't believe AI should replace human talent. We believe it should elevate it. Our workers are very excited. They have a tool that they can turn to to help them learn at their own pace. It really puts the power back into their hands.
All right, Christian's joined us from Hillen Valley and 137, and Shyam, welcome.
Thank you. Great to be here.
Christian, you want to kick us off?
Yeah. Thanks for having me. It's nice to be here. I definitely feel for the first time like a guesty of the besties.
And yeah, this is great. So, Shyam, thanks for coming. We were talking a little bit earlier. Maybe this is a great place to start. Obviously, we have the good fortune to be investors in Palantir for 15 years. We've seen the growth of the company, but particularly lately you've been pushing this messaging. I think it's been incredibly exciting of how AI is not a force for job destruction. It's a force for job creation. It's also a way that you can give superpowers to the average American worker. And obviously, we've seen a little bit of content here and how it's already doing that today.
I want to start by saying many of the workers in the video are actually here today joining. Laura, the nurse from Tampa General, actually brought her 12-year-old daughter. So, I think the ultimate litmus test is not just how excited are the American worker to leverage AI, but how excited are they for their children to exist in an America that's really embraced AI. And Julie has four kids, and she would tell you how much this has not only transformed her view of her job, but the view of her children's future.
I think the right frame here really is: how do we give the American worker superpowers? We should not be aspiring to build things that make them 50% more efficient, they're really 50 times more productive, and to use that as our asymmetry in the competition here. Our strengths are not only AI, which is clearly an American phenomenon, but also the ingenuity of the American worker. And if you spend time on the factory floor, on the front line, you see a very different narrative emerging where you see people are actually excited about these tools. Every single one of those workers to a tee said AI is giving them more time to do what they do best: to spend time with the patient delivering care, to actually build the parts as an engineer to solve the problems, not to be caught up in all the coordination and the paperwork that's around these things. That's the future we should be unleashing.
Can you generalize the adoption curve? What is it about a particular industry or use case that makes it an early adopter versus mid versus late that you're seeing, because now that you're touching all these different industries, you probably have a good point of view on this.
Yeah, my take is actually a different dimension of slicing that, which is where does the institution liberate their worker to drive the adoption versus where are they trying to force-fit some sort of solution top-down. AI is a method of unleashing the agency of the worker, the creativity of the individual, and they're the ones coming up with these use cases. Chris was talking about it from Hadrian, where you'd be surprised at how people with deep mechanical intuition, traditionally considered blue-collar workers, are the ones who are able to pick up the skills, build the applications, innovate on their own processes, and have that spread through the organization.
Mhm. And are you seeing that you have to build vertical tools or generalized tools for some horizontal kind of set of users somewhere in the organization?
Well, I think that the opportunity with AI is really that you can unleash what's different about your business than all the others. So there's a degree to which you can have generalized solutions, but there's a lot of alpha to be captured by understanding what's unique about how we do things. How do we lever up human taste? Everyone is afraid of AI replacing the human. That's not what I'm seeing. I'm seeing it make the person with the greatest taste more valuable and an ability to spread that to the breadth of the organization.
Let's talk about something beyond taste, which is also knowledge and skill, and tell us about AI inside of healthcare. I think that a lot of people probably think that we have an incredibly cutting-edge system of tools and software that helps doctors and nurses actually provision great care. What's the actual reality that you guys are seeing?
Well, sadly, I think with the forced adoption of EHRs, what we saw is roughly a halving in the productivity of how many patients you can see per hour.
A halving, yes. So, we became half as productive. And we really need to—the opportunity is to work backwards from what is the care that needs to be delivered? How do we build the tools around that? How do we help the nurses, the care staff spend more time with the patients and less time with the computer?
And do you guys see a world where, in order to facilitate that end market versus a different end market, you have an ensemble of many, many different techniques and approaches in AI, or do you think it all sort of gets form-fit into this one-trillion-parameter, huge, ginormous thing that kind of tries to do everything?
I think the cardinality of agents and models is very high. I think there will always be alpha to be achieved—improved differentiation, improved outcomes—by specializing to the use case. Now, it's great to start with the general models, but you will specialize over time.
And do you feel pressure to do that now, or do you think that'll just be a natural evolution over time?
Yeah, I think it's a journey that people kind of get on, like you realize, "Wow, look how much better things have gotten with this. Now, how do I go get the next incremental piece of performance out of it?"
You know, I'm just having this thought as we sit here and discuss this. If you think about any experience we have in service that has a long wait time, where we feel like we got more time with the practitioner, it's the perfect place for AI to create more abundance. Healthcare and education are the two that come to mind, where people could just offload their chores, and the people who are getting the service can use AI to maybe start the conversation on second base or third base. What other industries are you seeing after healthcare, education, where AI can have that dramatic of an effect, where the six-week wait time to see a doctor, the three or four other students who are getting tutored are ahead of you and maybe you don't need as much help so you never get the tutoring?
The place I'm most excited about it is really in reindustrialization, because there's so much dwell time in the value chains around—
What does that mean, dwell time in industrial?
Where you're just waiting for someone else to figure out how to approve something, or the coordination costs mean that it's essentially deadweight loss.
You saw it with the submarine industrial base partners there, where they're working on quoting a part to the Navy. That means you have to go gather all of this data, you have to look at historical archives. All of that is time you're not making a part or solving problems; that's just sitting there. The factory floor is idle, right? So, how do we get rid of that dwell time so that you can be utilizing the capex that you actually have to the maximum extent possible? And then if you zoom out, that's like one part manufacturer. You exist in a massively complicated supply chain, and you just end up with all these busy waits along the way.
Yeah. That's so profound. A friend of mine who's in that industry said, "You're only as efficient as your worst supplier."
Exactly. Yeah. And a second part of that, which the Panasonic Energy example really touched on, is how do we train our workers? Here you have exquisite Japanese technology. It used to take three years to train a worker on it. Now, with an AI assistant, the workers who are prior casino workers—not from this industry—are able to get up the curve in three months. So you think about how we can use that to more quickly absorb the slack that's happening as we adopt AI and democratize opportunities. So much so, I have so much conviction, as we've launched the American Tech Fellows Program at Palantir to find blue-collar workers at our customers in the heartland, overlooked folks who have a natural proclivity.
How do you find them? How do you find them, beyond just saying apply?
Some of them are at our current customers. The idea really came from us organically, where it's like, "Wow, who is building the most compelling applications?" It's the guy on the factory floor, not a formally credentialed computer scientist, mostly an autodidact, but there's immense not only grit, but ambition. They have the drive to reshape their own organization, to reshape the processes. Let's bet on that person.
Going earlier, does that mean—and I'll ask the same question many times today—that college education, the traditional four-year liberal arts degree, doesn't matter as much, that kids can go from high school or earlier in their careers into a new workforce and get well trained and well suited to make money and succeed in life?
Yes. I think the traditional college degree is dead and we should be betting on the American worker.
Well, on that point, can you talk about the tech fellowship? I got to recently see a bunch of demos from the first cohort with you, and it was really incredible what you guys are doing there. Maybe give a little bit there and then maybe also talk about the opportunity for other companies to follow this trade school framework as we end here.
Yeah, I mean, it's really kind of an elite trade school, finding people with mechanical intuition who have done things. Some of them are right out of college, some of them have 20 years of experience, but they're really—
Your first trade school that you guys have done, right?
Yeah, that's right. And we have just enormous demand from our customers who are like, "Who are people who have these skills?" And it's not classically trained, college-educated people; they don't have these skills, actually. So the market's not meeting and they don't know how to source these folks. So I can credential them, I can put them through the bootcamp in four weeks, and place them with my customers to go unleash AI within their organizations.
It's incredible. Shyam, thank you.
Thank you. That's great. Thank you so much. Cheers.
All right. Next up, we have Paul from Y Combinator. Please welcome—oh, Paul Buchheit.
Paul Buchheit. Paul, you created Gmail, talking about efficiency and making the world more efficient. And also, I believe we worked together. You came up with the slogan—
—the slogan, "Don't be evil."
