Moonshots with Peter Diamandis - Michael Kratsios on the New Golden Age of American Science | EP #276

Episode Date: August 4, 2026

Peter chats with Michael Kratsios on the White House’s vision for a new golden age of American science, including the Genesis Mission, AI-driven research, and the push to dramatically accelerate scientific productivity. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Michael Kratsios is the White House Director of Science and Technology Policy and Assistant to the President for Science and Technology. He previously served as U.S. Chief Technology Officer and Acting Under Secretary of Defense for Research and Engineering, and later as a managing director at Scale AI. – My companies: Get the blueprint for generative media https://goo.gle/startupgenmedia  Apply to Dave's and my new fund: https://qr.diamandis.com/linkventureslanding   Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Build in the Foundry or get a live demo at: https://voicerun.com Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Micahel: LinkedIn X Instagram Listen to MOONSHOTS: Apple YouTube – *Recorded on July 29, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

I was a kid in the candy store reading the Golden Age report. What you're describing there is a complete fundamental AI-native AI agent up, reimagining of the entire scientific process. And I think it's something that is possible. My sense is... This is the Golden Age of Americans. AI is a technology that is going to impact every agency. Whether you're flying drones, whether you're doing air-powered medical diagnostics, whether you're like in at the SEC and working on financial services, AI is going to impact every single one of views. Do you have sort of a longer-term compelling vision of what you think America could be like?

We as a government need to be opinionated about what the most important things are for the future of our nation. I mean, we're going to putting man back on the moon in 28. We're going to be able build the first elements of a lunar base by 30. We're going to put a nuclear reactor in space by 28. I mean, that's crazy. Okay. Okay. My favorite idea. It falls into the crazy idea. I can't believe Michael actually wrote this down. All right. Welcome to Moonshots, everybody. Today, I'd the pleasure of interviewing a friend, Michael Cracios.

He's the 13th director of the White House Office of Science and Technology Policy. And the science advisor to President Trump, Michael is the principal architect behind three landmark initiatives that are shaping America's acceleration during the singularity. The first is America's AI Action Plan, the administration's roadmap for winning the global AI race. The second is Genesis mission. a Manhattan Project-style effort to accelerate breakthrough discoveries, and then most recently, science and new golden age,

his blueprint for rewarding bold, unconventional ideas and dramatically increasing the rate of scientific discovery. So this is the spot. This is it, yeah. You go up and give a brief. Ladies and gentlemen, I've called you here today to let you know that we have now appreciably approved a trillion-dollar science budget. Congratulations. We're going to be solving every problem on the planet within the next four years of this administration.

There you go. Yeah. Solvers. Well done. One day we'll make that announcement. This interview takes place at the White House, and I'm asking these questions on behalf of myself and my moonshot mates. All right, let's jump in. Enjoy. So, Michael, we are arguably living during the most extraordinary time ever in human history, where science and technology is hyper exponential, and you're in the thick of it, you're in the middle of it, right? Ray Kurzweil predicts we're going to see as much progress in the next decade as we've seen the last century.

And that's like going from the Ford Model T, the starship in the next 10 years. On top of that, we're on the edge of AGI, maybe the next three years, ASI. how does the government process ever keep up with that? You know, we're trying our best. It's something that I think generally governments have struggled with for a long, long time. And I think what we have to do is make sure that areas we are seeing this tremendous growth, that we allow the regulatory system around them to exist in such a fashion that it doesn't get in the way of this progress.

You know, I typically think of technologies kind of in terms. buckets. They're technologies that are either born free or they're born in captivity. So, born free technologies are things like what the internet was in 1990s. And the best thing the government can do in those situations, it's a step back. Get out of the way. Don't jump into it. I think it happened so fast they didn't have a chance to get in the way. Yeah. Well, there was interesting. It was a bill passed in 1996 was essentially like Bill Clinton was behind it. It was kind of bipartisan. And I think it kind of allowed some of this stuff to kind of take take hold. And I think this extends for a lot of technologies. You know, be careful before you start

and before we start regulating. An example of that is in AI. And I think this always comes up when I think about AI regs. You know, the EU AI Act was passed and finalized by the EU Commission before CHATGPT was even invented. So there's no way that what they have today actually applies to the LM of today. But I think the second types of technologies are the ones that we, pay particular attention to, because those are the ones that, you know, we actually have to take action on. And those are technologies that are born in captivity. Think of commercial drone operations. Think of AI-powered medical diagnostics. These are technologies that cannot be commercialized. They will not be, their benefits will not be realized by the American people unless the government affirmatively does

something. And those are places where you have to be really careful, because if you wait too long or you aren't thinking about them, it actually holds up progress. So first, we kind of think of these two two buckets of work and make sure that for technologies of boring captivity, we're thoughtfully approaching, you know, how you can change regular structure to allow that to ultimately be safely deployed to Americans. I'm thinking about the time frame you've got. If we're going to see this kind of extraordinary progress to AGI and ASI in three years, do people here in the White House understand the speed of that change? And I know, and the agencies, I mean, it's dramatically, It's not a little bit faster. It's dramatically faster.

You know, we're trying our best to bring people along to the new pace and velocity of change when it comes technology. And I think the best sort of manifestation of that is our Genesis mission. This is where the president stood up with Secretary of Energy and with me and said, like, look, the most important thing for the nation is to make sure that we're applying this unbelievable technology, got off artificial intelligence to scientific discovery. And it's not just at one agency. It's across all of government. And I think those are the types of actions kind of from the White House level that are the only way you can kind of like really push this down into agencies. But I will say, you know, in most cases, government is not sort of the leading force and the cutting edge of where technology is.

And that's going to be obvious. I think we've seen recently ministers or AI ministers be appointed in various nations. We've seen out of the Emirates, 50% of the government operations being driven to AI. seen in Malaysia, I think it was the president, is like, has an AI representation, be able to speak in all the languages. Yeah. Do you see that potentially happening here in the U.S. in some fashion on the government side? I don't think so. What I've always thought about AI policy, and this was sort of kind of our view of the world, beginning in the first Trump administration, where President Trump signed the first, you know, executive board on official intelligence in history in 2019. So this was years before chat, GPT, and years before it was kind of on the front page of every. newspaper. I think our general view and in how we think about generally AI regulation is that AI is a technology that is going to impact every agency, whether you're flying drones, whether

you're doing AI-powered medical diagnostics, whether you're like in at the SEC and working on financial services, AI is going to impact every single one of use. The idea that you can sort of like centralize that effort in one person and be able to get the right and best policy answer across all those domains, I think it's a tall, tall order that you can be out. I completely agree with you. I guess the question is, will we see AI enter the government in terms of, you know, advising on policymaking or advising in cabinet positions where there's a, you know, sort of an AI instantiation of that member of government to be able to, you know, counsel at the speed that we're seeing? You know, I think I'm not sure where the future hold, but at least in the short term, what I would hope is that all our agencies can actually even start using AI. I mean, I will say, we're sitting in the White House complex today. And I will say, currently, you know, large language models are not allowed for use on our, on our system here because of the Presidential Records Act.

But hopefully we'll change that soon. But that's an example of, I think, I think the pace at which sometimes government tech operates. This episode is sponsored by Google for startups. Think about this for a second. You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's startup technical guide for generative media gives you a complete blueprint for deploying Google DeepMinds models in production, images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below. All right. I'm excited to dive deep in the Golden Age

Report and Genesis mission, but before that, I do want to talk a bit about AI. Yeah. You and I both know that AI is the engine fundamentally. It's going to uplift every American, every aspect of Americans' lives. And I think we both feel that very deeply. The challenge, of course, right now is that three-quarters of Americans fear AI. Those are the numbers right now. Seventy-one percent Americans oppose data centers near their homes, which is a larger percentage than object to nuclear power plants in their neighborhood, which is insane. So I guess, So, you know, one of my missions is helping people see the optimistic future, you know, reduce fear. So I guess the question is, how does the administration get out in front of this?

You know, why is there so much resistance? How do we demystify AI? What's the conversation going on around those concerns? Yeah. AI has a massive PR problem. Yeah. And I think the AI companies realize that. I think everyone that's sort of adjacent to orthogonal to AI,

industry realizes that and it's a problem. I mean, you and I know the, and do deeply believe in our core of the, of what great things, you know, AI can do, but, and bring to everyday American lives. But, you know, this, you know, I, I think back, first off of like, how did, how did we get here? And, and I think back actually to, to the first, um, AI safety summit that was held by the UK government in, in Bletchley Park two years ago under, under way back, way back. Yeah, this is, I mean, it was it two, three years ago. It's all sort of blurring. I think it was maybe, the year after Chachib D came out. And I think what was so fascinating about that when I attended it, almost the entire concept

of it was wrapped around fear associated with AI. What could go wrong? And we collectively, as the smartest people in the world and the most important government leaders must come together to make sure that these harms don't impact the world around us. And I think that's an example that the narrative for so long coming from government prior to President Trump has been so fixated on the negative impacts of this technology, like, of course, people are skeptical. It's the only thing they're hearing from politicians and from people in

industry is that, oh, there's going to be a bunch of job losses and everything is dangerous, and maybe there's going to be biarisk. Like, I think those are the things that people get worked up, and it shouldn't surprise us that the PR is bad. So I think for us, you ask kind of a positive take on it. I think we try to look for areas where Americans actually connect with AI in a positive sense. And I think by far what we have seen, it's in the health care domain. Sure. And that's where if we can actually show the impact in changing the way that individuals, individuals themselves get health care, their families get health care.

I mean, those are the places where I think you can start, you can start to push back a little bit on that narrative. Is there any effort to try and change the narrative from a, I guess, from a centralized sense, right? It kills me that the narrative in China is the flip of that, or 80% are pro-AI. And I just want to get out and like shut from the rooftops. Like you have to understand what's going on. Where does that responsibility lie? Probably in the AI labs, sure. But does the administration have a place in that as well?

I think the government can serve as a convener to bring to the surface all the great stories that are going on around this industry. And I think sometimes we think about AI in a little too narrow of a sense. AI is having this dramatic impact in manufacturing. in hiring across the country, in this huge buildouts that are happening for all, for everything that's sort of supporting the AI industry. These are great stories. I mean, I just heard from Jensen was here and he was talking about kind of the supply chain that supports Nvidia.

He said that the order that he's put into Corning for the chips that he's going to be building in the future is the largest sort of order in the history of Corning, and they're going to be producing more than ever have in history just because of his order. Yeah, it's lifting up the entire GDP of the nation in an extraordinary fashion. And those stories should be told. And factories are being built. People are being hired. Stuff is being produced.

And I think those are the things that I think can connect with Americans. One of the biggest fears is AI-related job loss. Yeah. And AI-related loss of incoming jobs. And, you know, I report on this on moonshots every week or so as part of the conversations we're having. And it's confusing because there's a lot of people. people who are putting forward data, yes, there's job loss, yes, there's these layoffs, and others who are feeling like, no, we're going to see more job creation like we've seen

with every technology so far. Do you have a sense of this? Do you have a sense of what you believe is? I mean, personally, I believe kind of in the long term, I'm very optimistic about the impact that AI is going to have on jobs. I think we've been thinking about this problem or I have since the first Trump administration where kind of the most of the narrative around AI was around automation. and there was fears about all this automation-related job loss. And if you even fast forward through that period, that was never, never realized, and only employment has increased.

But I do think it's something we have to think about. And I think we've put in some programs that start to hit at this problem. One of the things that I think that even you mentioned your problem is, the data around what is actually happening is not very good. And I think we're trying to launch an initiative at our Department of Labor that was actually called for in the AI Action Plan. we want to start collecting better data on the impact that AI is having on the labor for. So you can collect data from players that typically don't submit their data department.

And through there, you can actually start making the right decisions on where to do better reskilling, retraining, and where to sort of do more targeted labor assistance programs. I remember one of the conversations we had in Miami over lunch at the FI Summit was the idea of sort of a parachute program where you incentivize companies, if they are doing an AI-related layoff to give the employees. employees are letting go a AI upskilling so that they get trained into that environment. Do you think something like that might materialize? You know, I think I think every company will think about it differently. I do think opportunities like that are interesting.

You know, I'm still trying to wrap my head around what it, what an AI-related job loss even really means. I think, like, a lot of folks these days, like when they're doing a layoff that they would have done anyway, just like to assign it or blame it to AI because it plays better in the And their stock price goes up if they are, you know, producing. you more revenue with fewer people. Precisely, yeah. Yeah. Yeah.

You know, so on the notion of fear, because it's one of the things I'm always trying to quench and address, right? Because fear is an awful place to face the future from, especially at the speed of change. And people don't understand AI enough to understand its implications on their lives. We just launched this year something called the Future Vision XPRIZ. So it's the world's largest film competition for creators to create a film that shows a hopeful, positive vision of the future, where technology and AI is working together. Because part of the challenge for me is that, you know, films like Terminator, Ex Machina,

Black Mirror, majority of all the sci-fi films out there are dystopian. And if that's what we're teaching the average American, like, this is what happens when you have robots and AI. So I guess my question along those lines are, do you have sort of, of a longer-term compelling vision of what you think America could be like on the back of this AI revolution beyond just better, you know, better health care from AI, which is low-hanging fruit agreed. Of course, yeah. Is that those, does that future visioning happen here?

I mean, we think about it more in the terms of national missions, which I think we'll kind of talk a little bit about in Golden Age. And I think what my general take is, you know, We as a government need to be opinionated about what the most important things are for the future of our nation. And there was an era beginning with a Manhattan project going through Apollo that we had big ideas. We had bold things. And national pride around them. Exactly.

And it motivated young people to go into science. And we were looking at a North Star of something that a lot of people thought couldn't be done, but we did it. And I think there are places where we can do more of that. An example of that is all of the space-related efforts that you, I think, talked to Administrator Isaacson and Jared about very recently. I mean, we're going to putting man back on the moon in 28. We're going to be able to build the first elements of a lunar base by 30. We're going to put a nuclear reactor in space by 28. I mean, that's crazy.

If you told someone we're going to put a nuclear reactor in space that has enough propulsion power to send folks to Mars, and next a year and a half, that's nuts. But we're going to do it because we're Americans and we can accomplish that. That's so special. And I think that plus some of the other national missions can kind of do that spiring. We'll get to them later, but name a few of those big, bold Manhattan projects, Apollo programs. Yeah, I think the other one, it doesn't have quite a quick, it's sort of the Genesis mission, which is our AI for science mission. We're essentially want to double the productivity of the entire scientific enterprise in the United States or the next decade. And that's going to have an incredible amount of sort of like fallout results there.

The third one that the president directed through an executive order is to create a, scientifically relevant quantum computer by the end of his term. And this is finally saying, like, we appreciate and we, I think it's amazing that this incredible basic research has been done in quantum for so long, but we're going to put a stake in the ground and we're going to say we're going to build this machine. And have it do something. And have it do something. Yes, yes. Do you have a sense of, in the quantum world? Because quantum supremacy and all of these, all these terms have been as loose as AGI at ASI. What do you hope the first functional capable quantum computer is able to do? What industries are you impacting most with that?

I think to me, sorry to keep going back to health, I think pharmaceuticals is where I'm most excited about. I do honestly believe that the types of calculation you can run on those for the particular molecules and things that you want to apply towards, toward drugs is going to be, it's going to be pre-transformational. And kind of the last one, which is outside of our term, which I think is important for us to keep thinking about, is fusion energy. People have been saying it's 10 years away. They've been saying it's 50 years away forever. It's always. some many years away, but we've, part of Energy put out its latest iteration of kind of our national strategy a few months ago. And I think we're, I don't think any time in history,

have we had more private sector investment in this particular energy source than today? Yeah, my last count, there were 37 venture-backed fusion companies, which is crazy. It's nuts. It's nuts. So I want to take a moment, because it's timely, to talk about the conversation that's been sort of dominating X and the AI sphere in D.C., which is the open source versus close source activity, right? So Jensen comes out with the Open Secure AI Alliance. Where is the policy today? How important is it for American companies to develop top-tier open-weight models?

It's very important. As administration, we believe that the U.S. must lead the world both enclosed and open-source models in that the best thing for the country is that we have a vibrant ecosystem on both sides of that coin that's able to support any type of customer that wants to work on AI or use AI. You know, right now, I think our open source ecosystem is one where we could be doing better. It's, I think we have a couple of like very well-known startups that are extraordinarily well-funded that are kind of pursuing getting close to the frontier. And Miramaradhi's release was amazing.

Yes, terrific. I think we're waiting on reflection for their model later this year, from what I understand. But I think the ecosystem and the tooling around open source continues to get much, much better. And the U.S. has continued to be sort of the default sort of harnesses and tools around open source. But to me, I think, and we expressed this kind of, I think, on the first page of our Aaction plan from last year, the U.S. has to lead on open source. And right now, look, I'll be honest, the Chinese have very well-performing open-source models. And if you're an American entrepreneur and you're cash strapped and you're trying to bootstrap your company and get started, you know, I can't blame you for using the cheapest model out there. And at this moment, it's Chinese. But I think over time, you know, I think we'll be able to cultivate a pretty vibrant ecosystem here. Yeah, that's what America does. Yeah.

You know, the Financial Times reported that Beijing is out in the world exporting open source, you know, basically. as an instrument of influence and going to the Global South and providing them access to, you know, infrastructure and capabilities. One of your stated goals in the AI Action Plan, which I love is the world should build on America's AI and tech stack. And I think fundamentally because, I mean, a lot of people are going to use the frontier AI, but the majority of the world, the vast majority of the world is going to use the, you know, on-prem open.

source models. How does America think about that? Maybe this is not necessarily your realm, but I love your thoughts on that. No, we think about this a lot. To operationalize the, well, you mentioned the AI Action Plan, we launched something called the American AI exports program. And the vision behind it was, look, we have the best AI stack in the world. We have the very best chips in companies like Nvidia and AMD and many other new entrants. We have the best models that we all know about. We have the best applications. And if you, you have you, you're a customer around the world, if you're a government or if you're a someone in the private sector, if it's looking to build AI, there is nothing better in the world in the American

stack. And we launch this program at Commerce, but it's now become a whole of government effort, where we put out an RFP and we said, great American companies come back to us with what an American stack looks like and submit those proposals to us. We're now looking at those proposals and ultimate we're going to create essentially like turnkey American AI stack options for the world. Then we'll back those with the government financing organizations that can make them economically viable for a lot of countries around the world. So organizations like the Export Import Bank and the Development Finance Corporation can provide financing to make these more economical. So a complete package.

A complete package. And I think that's something that a lot of governments and a lot of folks around the world want. Like for them to be able to have to choose seven different vendors to set up their stack, that's probably not what they're looking for. The other appeal that I think the U.S. has right now is everyone wants our check. We have the best chips by far. And because of- Yeah, so much out of AI wants your chips. It wants our chips, yeah.

And sort of between the export control we have in place and other factors that are going on, particularly on EUV, you know, our lead over where the best Chinese chip is continues to increase year over year. So I think from a, from a kind of a basis standpoint, kind of the foundation of the stack, we'll continue to have the best product. Now, I will say, I mean, I think this product, this AI export program, I think kind of was born because of a lot of a product, was born because of a lot of the frustration I had in the first administration with Huawei and our inability as the United States to counter some of the actions that the Chinese were taking on telecom because, you know, they had a good enough telecom stack that was run by Huawei and they had it super subsidized by the PRC and they went out and kind of proliferated it pretty quickly. I think we're in a moment we want to avoid that.

And, you know, to us, I think we have a pretty vibrant, you know, open source ecosystem Marcel, but I think our sort of open source solutions plus our coast force, in addition to the chips we have and the great American applications that everyone wants to use, I think we'll make our stack more competitive. Do you think the restrictions on Nvidia's chips to China in retrospect might have been a mistake? I mean, one of the things that we've seen in the past, we saw this in the launch vehicle industry in the satellite defense industry, that when we start restricting our exports, It simply cultivates the competition to build their own capabilities and they come back and compete with us.

Yeah. I don't think so. I think that was probably one of the savviest decisions that we made as administration. And I think it was probably one of the most impactful in being able to throttle their ability to make competitive models to ours. My personal belief is that it's been a priority of Xi Jinping to have competitive, to have a competitive semiconductor. semiconductor industry for a long time. It started certainly in Trump One, and that's why the EUV lithography export controls are so critical. I would say that is probably one of the most impactful export controls in the history of the United States if that hadn't been done in

2019. And because of that, their ability to create their own sort of competitor to our leading edge chip has really succeeded. This episode is brought to you by Blitzy, Autonomous Software Development with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform,

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Let's talk about robots one second. You know, I love robots. I've got my robots on order. We've got a number of great U.S.-based humanoid robot companies. But you can count them on your two hands compared to China with 150 plus humanoid robot companies. And it feels like they've been really aggressively supporting them, funding them, using them in national events. creating centers for robotic advancement and so forth. When do we start doing that?

I think we have to do more of it. I believe that sort of one sort of manifestation of AI that is going to be critical of future success of the country is robotics. As you probably saw, we took some pretty dramatic action around humanoid robots just this week, where we essentially limited the importation of any non-U.S. humanoid robot that hasn't already been shipped going forward. And I think that shows how important it is to us for the U.S. to lead in this domain. Homegrown industry. Homegrown industry. And we have to start sort of building the supply chain to support this. I mean, we believe the administration that these robots are critically important for the future of the country, for advanced manufacturing,

for so many other things. And we have to sort of build that supply chain muscle to be able to have the supply chain security in the future. Do you see capital going to these companies, to accelerate that capability? I do. I do. And I think the best example of that is we took a similar action around UAS or drones in, I think in December of last year. And if you look at the numbers of the investment that went into essentially sort of drone supply chain since that action has been pretty, pretty dramatic. And I think that that is kind of what we hope to see here. And I think this industry needs a bit of a push because in just like so many other places,

I think that the Chinese are certain subsidizing and dumping on on robots. So I want to return for a second before we head to the new golden age and Genesis mission to the speed of change. So a mutual friend, Elon, friend of the pod, you know, when I interviewed him a few months ago, he's like, we're going to see double-digit growth in the GDP in the next 18, 24 months, triple-digit growth in five years. And then he was on The Economist. I don't know if you saw the clips saying we're going to be by 2036, basically post-capitalist.

We're going to have anything or anything you could possibly want will be delivered by AI and robotics. Now, honestly, I think Elon is the most brilliant engineer on the planet. I think most everybody agrees on that. And his predictions are always directionally correct. The timing may be off a little bit. But the speed of change that he's projecting sort of breaks every system. And I'm just wondering, you know, A. could you imagine that speed of change?

And, you know, are, is the appropriate members of cabinet thinking about what, what the economy looks like in that situation? You know, it's hard for me even to kind of wrap my head around kind of that velocity of change. But, you know, maybe I don't quite ascribe to that particular speed, but I do think things are changing. And I think our cabinet and the leadership in the White House recognizes that and is, is getting in front of it. I think, as you probably saw, Secretary Bessent was very involved in some of the post-methoes activities that happened in the USG. And I think that's a great example of saying,

look, like, if we have a super capable cyber model that in the hands of the wrong actor could pose a risk to our systemically important financial institutions, we have to approach this seriously and quickly. And we addressed it. And I think that's the kind of action that I think you see a lot of a leaders in administration taking when it comes to these, comes to these rapid changes. All right. Let's turn to Genesis and Golden Age. In your paper, you outline a series of challenges that need to be solved in the age of AI. And I'd like to hit on each one one at a time. Yeah. Because they're really important. And just again, as I was saying before we started here, I was a kid in the candy store reading the Golden Age report. It was like, you went much further

than I expected is sort of naming ideas, and we'll get to those. So the first point you make is that scientific productivity has been declining despite larger budgets. Irum's law, right? Moore's law spelled backwards. That's sort of like the discovery per unit dollar has dropped. Why? What's going on here?

We have better tools. Yeah. To me, I think we have, been unable to or just don't unable to

to change the way that we conduct science. And I think this goes back to kind of one of the main sort of reasons

why we wrote this report. The president wrote me a letter after I was confirmed and essentially kind of like charged us

with how do we revitalize the science the science enterprise. And we went back and kind of thought about it and kind of the data that you talked about

kind of this declining productivity was kind of one of the first things that we looked at. And we asked ourselves, you know, like, why, like, why is it? Like, we have better technology than we ever have. Our budgets are more than we ever have. And I think it's most probably relevant in the, in the biomedical field where the NIH budget now is ballooned to almost like $45 billion, yet the cost of drugs is more expensive than ever. Yeah. And kind of list goes on. And I think one, one conclusion we had was like, we just are not experimenting enough in the way that we conduct science. We're doing the same thing over and over again and just putting more money towards it and believing that the outcomes are going to get better.

And I think one of the sort of the main sort of core the thesis of the whole Golden Age report is we have to be more experimental and more ambitious about testing out new ideas. And why I found it so particularly relevant and shocking in the world of sciences, you would think that in science, people would, those people would be the most interested and the most excited to try different ways of doing science. Yet, funny enough, that community doesn't want anything to change. They want the system to be exactly the way it was 30 years ago. Yeah, the way I say that if you're an expert in something and there's a revolutionary breakthrough, you're no longer the expert at it.

So there's a disincentive for doing that. Could it be regulatory bloat? Could it be, you know, legal bloat? Could it be just the paperwork that's developed over time? It is. I think it's all the above. So some things that we talk about in the report are around research burdens. So I think there was a very well-known study that the National Academies put out a few years ago that

essentially said something like 45% of the time that a researcher spends is to do the administrative for it associated with their grant. And that is one of the most depressing statistics I can think of. These scientists are the crown jewels of our country. We want them to be doing their work 100% of the time. Same thing in healthcare. Yes. Physicians are spending all their time filling out paperwork.

Yeah. And I think that the regulatory stuff does kind of matter too. And I think one example that very close to my heart and has been one of my pet, projects for a long time is how do we bring back supersonic flight to to america and an example of that is it's kind of a kind of a regulatory issue where in the u.s there has essentially been a speed limit for flights over land so essentially there was if you're flying over mac one it's just not allowed anymore because the sonic boom and the concerns right and the reality is i think research has shown and i think boom supersonic a company um showed that they're able to fly over mock one

without creating a sonic boom. So our regulations, the way it stands, disincentivize them forever trying to fly over Mach 1 because they'll never be able to. So, again, due to the president and executive order that he signed, we are now changing that rule to create a noise limit rather than a speed limit. Yeah, I saw that. If you can keep it quiet, fly, just fly.

I love that. I mean, the breakthrough, you know, the policy changes on both supersonic flight and on EVTALs, just to, I mean, the aviation industry had been stuck for 50 years. And so unleashing in that way. The second challenges you note here is young talent waiting too long before they're given a chance to implement their bold ideas that our funding, publishing, and credit system was built for a world of human-paced discovery. And I could not agree with you more. Yeah. The challenge I think a lot of young people face is they're kind of stuck in this zone of the way science was done long, long time ago, before the Internet even existed.

some ways. And everyone is kind of stuck through this process of needing to do research and then trying to get the research published in a journal. And then you have to do that a number of time before you can get tenure and so on. And I think this structure tends to, I think, not be the way that is conducive to discover. It's the way to like how you succeed in a, in sort of a academic study. That structured system, right? Yeah. And I think there's huge opportunities to kind of reform that. To me, I think NIH is always an example of this. And I think this is very bipartisan. A statistic I heard today, which was shocking, is that the median age of an intramural NIH scientist. So this is a scientist at NIH who's doing research in NIH, not getting a grant out, is 71.

Oh, my God. When I heard, I couldn't even believe it. That is crazy. And, you know, the average age of a Nobel laureate's prize winning work is in their mid-20s. Yeah. Precisely. And I think we've created these systems where we're somehow okay with that. And then we look in the mirror and then somehow we tell us.

ourselves, well, everything's doing great if we just give it a little more money. And it's like, that's not how you solve these problems. And that leads to the bloat. So you identified a bunch of great mechanisms for driving progress in the Golden Age. I'm going to hit on four of them that I think I'm excited about. The first one is long duration grants. Five-year grants funded on day one. Yeah. Yeah. Talk about that one for this. So to me, I think grant duration is something that people don't talk about enough. Again, over time, we've come to this sort of like zone of comfort where most government grants are roughly in the 18-month period. That's just because the way it works with academic calendars and just how easy or quickly weaken as a government be able to review these grants

and adjudicate them and give them out. But that's not the pace of scientific discovery. There are certain scientific endeavors that are quick and short, and it's a small experiment that only takes three or four months to look at and you can get done. And there are other ones like you talk about that actually require a longer period of time, that you want a brilliant scientist to be able to explore an idea, which is going to take a little bit of time to sort out. And focus on that versus another grant application. Exactly, because most of these guys, after they've gotten their first grant, they're already working on their second before we even finished the work on the first, because that's the

have to keep the pace going. So to us, we believe that you have to, and this is, again, how do we support scientists? at the core of the entire new golden age report is everything we do is in service of the scientist. And this is a perfect example of it. It's like there are certain scientists that want shorter duration grants and there's some ideas that need five years to play out. And we as a government need to make our money available to be able to be in the service of those scientists are going to be making the great photos. The next idea you put forward, which I love, is fast cracks, a few pages, reviewed under a month, you know, sized for the proof of concept. Yeah, and I think we saw this and a lot of excitement around this, and we've seen this historically around times of crisis.

And I think this really came to the floor during COVID. And Tyler Cowan and others came together and pulled some capital and actually did this on the side themselves. We're making grant decision in a matter of like hours for people who are submitting to work on problems running to COVID. But there's no reason it should be restricted only in times of crisis. There are lots of ideas that if we're able to answer them, then it may unlock. other things that we'd want to do later on. And I can imagine, I mean, a lot of these scientists are probably using chat GPT or Gemini, or what it might be, to write their grants.

And I'm expecting that grant reviews will also use AI as a mechanism. I mean, time should be massively compressed. I think so. And I think a lot of thought is being taken at a lot of our funding agencies about how we can kind of accelerate that, while sort of at all times keep kind of that meritocracy and that merit-based review cold standard. Next one that I loved was experimentation with golden tickets. So what's a golden ticket?

Yeah, I think another issue of like back to your question of like why we stagnate it. I mean, one reason a lot of people say is like we're just not risk taking anymore. We're sort of like the least common denominator ideas or the ones typically get funded. And people who have kind of out their ideas, crazier ideas, things that may not work. But if they do work, it's pretty incredible. They're just non-incentivized to even submit that application because they're not going to get not going to get the award. So the question is like, how do you get around that? How do you get people, how do you incentivize people to be a little more out there?

And one is this golden ticket idea. And the concept here is that on a merit review panel, you may have three or four people. Each person on the panel gets one, two, three golden tickets. And with a golden ticket, you unilaterally can make the decision to fund a particular grant, independent of what the rest of the committee thinks of. And because of that, you get two things happen. One, you incentivize people to have a little crazier ideas because you have a chance that if one person believes in you, you'll go forward.

The other is, I believe, and I think this made me the even better outcome, is you incentivize better people to be part of the review panels. Because they have power. Because you get a golden ticket. So rather than having these people that are sort of like, you know, I don't know, mid-tier people who just do this, you know, because I don't know what reasons, you kind of can bring even better people in to kind of do the reviews. I do love that.

You know, it's interesting. I was talking to a professor at Harvard. I won't name him who has been extremely successful. And he was saying that he was getting dinged in his reviews because he had gotten too many grants and was too successful and they needed to give other people a chance. The whole peer review process is something that is unfortunately extraordinarily broken. You know, I define a break.

The day before something is truly a breakthrough. It's a crazy idea. Right? So where are we experimenting with crazy ideas? It's a challenge. It is. And I think we have to do. And I think government correctly, I think, needs to be a good steward of taxpayer dollars.

And we have to be very thoughtful in the way that we develop our programs and we evaluate them. And I think one piece of golden age is this concept of meta science, the science of science, our ability to evaluate the ways that we're conducting science, the ways that we're doing funding, and evaluating whether or not they're working and then course correcting when they're not. Like you and I in this podcast may really believe that doing fast-track grants is a really great idea.

But we need to run the experiment. Let's do some fast-track grants and let's see what are the types that work, what are the ones that are, and then update. And the next iteration of them is even better. And we as a government just don't do that. We never do medicine. So one of the things we call in golden age

is to launch metascience units and those have been announced at at places like NIH and NSFRA. Nice. Okay, my favorite idea. Yeah. It falls into the crazy idea. I can't believe Michael actually wrote this down. Is you describe the use of prediction models, crowdsourced intelligence, and decentralized autonomous organizations to fund scientific research directly. I'm going to read a paraphrase from your report because I think it's very powerful. Imagine the scientific marketplace where funders post bounties for breakthroughs. A.I. agents identify

promising leads, higher autonomous labs, and verify cryptographically signed results. Agents exchange data, hypotheses, compute, and funding through microtransactions, while smart contracts release payments as milestones are met. Prediction markets could guide grantmakers, bounty markets could direct resource towards unsolved problems, and reputation systems could identify reliable agents. The system would operate continuously at machine speed, replacing slow institutional coordination with market incentives, while human experts remain essential for judgment and biggest results in deciding which scientific questions and breakthroughs matter most.

What you're describing there is a complete fundamental AI-native AI agent up, reimagining of the entire scientific process. Yeah. Look, this report gave us an opportunity to kind of dream big of where we can end up going. And I think it's something that is possible. And I think it really is. And I think what we try to get at there in the report or I do is, is I think incentives aren't always that easily aligned in the current system we have today. And over time, we have technical solutions to be able to bring those incentives together and to do the information sharing at a pace and at a speed, which will allow all things you just mentioned to come true.

I mean, having Dow's involved, having, you know, agents run the cycle, you know, arguably millions of times faster than a human would, if not even faster, sounds like a mechanism that could make Elon's 2036 prediction happen. Actually, come true. Well, look, my hope is that this inspires some folks. I think one thing that I've observed, and I think you have too, is the level of philanthropic scientific capital is greater today than it's ever been in human history. If you even just look at the OpenAI Foundation itself, it's almost a quarter trillion dollars in today's valuation. So to me, I think we have an opportunity for really smart people to try to push the envelope

to try some of this stuff. And you've got folks like Yuri Milner, Eric Schmidt, Mark Banyoff, all funding science directly. They're doing really incredible work. And I think when we think about, you know, and I think kind of one of the underlying or main premises of the whole Golden Age report is that the science ecosystem has changed. In 1951, Endless Frontier was written by Van Ver Bouch that kicked all this off. You know, 70% of R&D was done by the federal government and 30 was done by the private sector. And that has like flipped entirely today.

The majority is done in the private sector and only about 30% is funded by the federal government. Companies can take a tenure horizon if they need to. Yeah, and I think what we see, and you have this flip with private sector being more involved, you also have philanthropy playing a bigger role. So now if you look at all the sort of pieces on the chessboard, people, you can bring all those people together to drive scientific discovery in a way which you could never imagine in a system designed in 1960. And when we think about sort of, take for example an announcement we make, today about four-year PhDs, this idea that we want to get more PhDs out faster and actually have

their experience during their PhD program, prepare them for a job in industry, not only in academia. So the idea that you have a private sector company that is paired with an academic institution to help a student pursue a four-year PhD, I mean, that's amazing. And that's something that is reflective of today's reality, not something you'd imagine in sort of 1960. All right. The last mechanism I want to hit on is the use of incentive process. So your report leans into prize challenges, advanced market commitments, pay for results, not proposals, right?

And you cite the $10 million and sorry XPRIZE in there. Thank you. I appreciate it. So I've spent my, you know, 30 years of my life focused on incentive prices. And it's sort of my home turf. Most agencies haven't experienced this area, haven't learned how to use it. So XPRIZE right now, we've launched $600 million of prizes and we've driven. about $30 billion of R&D as a result of those prizes. How could XPRIZ help your agencies?

To me, I think it goes back to partnership. If they're, if they're a big national scientific endeavors that we need to pursue or problems need to solve, I think there's opportunities to try to pull money together and make the prize even bigger and draw people to kind of solve these big challenges. Do you imagine that the government would put out like billion dollar incentive prizes or are these going to be small? Do you imagine on your grand challenges, you know, I'm talking to Elon about this. I said let's launch 10, one billion dollar prizes that focus every grad student, every company, like these are the important things we need to achieve. You know, I think that the government spends about 200 billion a year in in R&D. If you think about it,

about 45, as I said, is kind of sits at NIH with biomedical. I think having a billion dollars may be a lot for a single prize for the government budget to swallow, but I think, you know, in the range of 100 million is certainly doable if the, if the project is big enough, for sure. Speaking of large prizes, our largest XPRIZE right now, it's the $101 million X-Prize health span. It's a prize. We have 830 teams competing, and the goal is reverse. your functional age by 20 years, give you cognitive abilities you had 20 years younger, muscular immune system capabilities you had 20 years younger. I have, you know, I fundamentally believe in there are a number of incredible scientists like David St. Clair and George Church and others

who agree that if you wanted to impact the U.S. economy the most in a positive fashion, you would tackle aging. Yeah. That if you could enable people to have, And it's really health span versus aging. You know, today in the United States, the average life expectancy is about 78, 79. The average health expectancy is 63. So the last 16 years of your life, you're in poor health. If you could move that needle up, give people an extra decade of health, an extra decade of productivity, they would transform everything.

Yeah. Is that is so, I found longevity lacking from your report, just, you know, You hit a lot of other great things. Well, now that you mentioned it, we probably should have included it. I think it's something that's critically important, and I think it ties to just general health of Americans. And we should find ways to work together on this and figure out what programs and NIH on their places can kind of accelerate that. A lot of folks believe aging is a disease that can be at least slowed if not cured. We had Dario say that he could imagine doubling human lifespan.

in the next five to 10 years. And Demas talking about curing all diseases within the next decade. And those are impactful. That is huge. I mean, I remember when I went to, the first time Demas sort of told me, you know, cure all diseases in 10 years. I wasn't sure if he was kidding or serious. He's dead serious.

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I find biotech missing from that. Is there room for that to come on? There is certainly room for it, and one car you may have even been explicitly listed. I will say, I think a lot of the biotech work has been nested under the Genesis Mission, so the first national initiative around AI for Science. I think we announced about $5 billion worth of Genesis Mission grants just last week at the Genesis Mission Summit, And so many of them relate to biotech. It was a pretty important.

That was a question, my dear friend, Alex, AWG wanted me to ask. You know, my entire moonshot mates Dave and Alex and Saleem are jealous. I'm here alone with you. We've got to have them out next time. I wave a load of you guys. Interesting. Some states and cities are far more pro-tech than others, right? You've got states and cities, and we just created a map.

I'll show you after this that Max put together looking at this across the U.S. Sort of, I call them singularity zones, right, where they're pro-autonomous vehicles, pro-drones, pro-nuclear, pro-data centers, and so forth. Do you imagine that you could see some type of regions of volunteering to be innovation hubs where the regulations are sufficiently relaxed so that they can accelerate research. I mean, we see China doing this. I see other places in the world. Chile did this as well.

Just to accelerate the work and actually have the entrepreneurs who are interested in that region move there to build their system. I would love that. I think we as an administration, I personally have been very, very focused on this as a way to drive innovation. The very first piece of paper that I worked on that I got the president's assigned was in 2017 to launch the UAS IPP, which was the Innovation Pilot Program, and integration program. And what was so special about that program was essentially, this was 2017, commercial drone delivery wasn't really a thing yet. It was kind of a dream that people wanted. But how do we

accelerate that? How do we get people to actually start running the tests of like, what does it like to try to do deliveries? Where does it work? Whereas it doesn't? When does it bother people? What, like, systems you didn't place them on the drones, all that. And the EEO essentially called for the creation of this pilot program at Department of Transportation. And FAA then went out and picked 10 state, local, or tribal governments that paired with certain UAS operators to run these tests. Well, you essentially had, you had communities stand up and say, hi, I want to have this innovative technology. I'm going to carve out. And the FAA gave them sort of like regulatory clearance to be able to run these operations.

And we've done that again in this administration with our EIPP, which is our EVTAL program. We're going to start seeing testing of EVTAL GEOPLE all over the country. So to me, I think this is really important. And if you remember back in the campaign of 2016, sorry, of 2026, sorry, 2024, the president also put out a number of videos around some efforts that he was going to be doing in Trump, too. And one of them was actually around this. I think there was a whole narrative around creating these sort of innovation zones and cities where we can test these great new, great new technologies. I think on our evaluation state by state, Texas came out number one in everything.

Yeah. Just very growth. It's very much an American, it's very much core to us as a country, this idea of federalism in a way where states can choose to make decisions and people will vote with their feet and companies will vote with their feet and go to places where they're most comfortable. I think back sort of this Elon conversation, I think we saw where a lot of the self-driving vehicles that were being tested in California up and left and went to Arizona when the rules changed. And I think it has a good thing. I think competition among the states.

Regulatory arbitrage. It's a very powerful incentive for states. And I think some ways, you know, and I think this dovetails a little with some of the issues on data centers, for example, where I think there could be a future world where you're actually going to be having communities competing for data centers. And we should be thinking more, at least a data center operator, should be thinking a lot more about what incentives they can provide to the communities that they're entering in,

such that communities are so interested in having them that they're community amongst themselves. And the president has taken a lot of leadership on this, putting forth what's called the Rapeer Protection Pledge, where essentially directed all these big tech companies and AI companies and data center builders to build, bring or buy all their own power. And you're not going to go into any community

unless you cover all the costs associated with it. And if anything, you're going to lower the costs of electricity. And that's been, and that's what the data shows. Yeah. The locations with AI data centers are getting, on average, lower electro cost. We have to address the water issue as well, which, you know, we've talked about a lot of times on the pod. You know, golf courses have 30 times more water use in data centers. And like almond farming is like 50 or 60 times more water use.

That one is particularly, uh, interesting. Jarring, yes. Let's talk about education. It's quarter report. You hit it on Chapter 4. And as a dad at 2.15-year-olds, I am pissed at the educational system today. It is, at least in the U.S., still tied to the Industrial Revolution. It's not tied for what's coming.

And we're seeing huge resistance of high schools and even colleges to use this. and it should be just the flip. We should be, it should be AI first across the board. You know, how do you see this being radically reimagined? Because I think it does need, you know, the public educational system is not serving our kids' future for the world that is racing at us to use Elon's term like a supersonic tsunami. To me on education, I think it's something that we think a lot about for a lot of tech domains. We always have to put, in my opinion, the parents first. Sure.

And I think we should be providing parents' options with the ways that they believe they can best educate their children. You know, there's amazing opportunities for people on one extreme to be participating in things like Alpha School, where, you know, you do, what is it, three hours or four hours of AI in the morning, and then the rest of the day is on sort of social skills. and other stuff. And for some sets of families, that works amazingly. And people get really hands-on with this technology. I think there are other parents that prefer to have sort of like no technology in the classroom and they want their kids to learn the classics

and they can end up learning about these other technologies kind of in other domains. I think what, but reality we face today is that most Americans get neither of those things. They kind of get a broken middle that doesn't provide any semblance of a positive future for those children. There's so many districts around the country

where you see that a majority of students graduating from high school can't even do basic math. So I think we have a lot to improve in this country. And I think on my side, when I think about how do you integrate STEM into these organizations, that's where my sort of portfolio usually goes. To me, one of the most sort of depressing things

about American education today is just the declining number of American students that want to enter the STEM fields. And that's not good. for our country's health, for our national security, for the future of our economic growth. Like, we need people to pursuing these STEM degrees. And I think there's a lot that we can do from sort of a government standpoint to inspire people to go back into this domain. And hopefully all the work that Jared's doing through space and so many other things.

Yeah, I mean, the Apollo program, you know, is 100% responsible for everything I've done in my life. Yeah. It was Star Trek and Apollo. I mean, so, but asking the school systems to change over the course of the next few years, which is, I think, the time rise to be talking about is extraordinarily difficult with teacher unions and public education boards and so forth. So, you know, I wonder, is there an opportunity for sort of an AI educational overlay that becomes available where parents and kids who want that can do that in the afternoons or weekends?

I think that's possible. I mean, from what I understand that the Alpha School guys, for example, obviously, they started with, like, running the schools themselves in certain locations. And, you know, it's not necessarily that cheap. So there's only certain people that can participate in. But I think the goal is to be able to essentially like open source of software or make it economical for anyone to kind of run those programs. So in my sense is a lot of that stuff is happening. And hopefully there'll be lots of options for parents who want to pursue that. Okay. All right. Another fun topic, which I love is the autonomous self-driven. labs. So the piece of revision is, and let me quote this, AI proposes the hypothesis, robots run the experiment, the model reads the results and designs the next one, 24-7, no humans in the loop. I mean, you're basically running closed-loop science at machine speeds. Yeah, yeah. So this is something you and I talk about that Lila Sciences is doing. They're building out a million square feet, you're looking at having this retrofit into the federal labs. We believe that we should be building these and being able to use federal dollars to test

experiments on these labs. To me, I think, and maybe you even know better, kind of a long pull in the tent on some of this stuff is actually building the hardware that can run sort of a wide range of experiments as you would want. So I think we'll probably start more narrow domains and then expand as the actual hardware come itself. But I think that's a future that we want to try to achieve. I mean, I think that what has held a lot of scientists back is just how long it takes to run the experiment. And if you have a vision and you want to test it, wouldn't be great if you could just go online, go online, put the hypothesis in and hit go, and then start thinking about the next one and not needing to wait. And I love also the vision

you had where, you know, a high school kid could potentially do that. A college kid someplace. could get access on national lab equipment to run the experiment. I remember in high school, you know, there were a few kids who'd always found some way to, like, meet a professor and somehow get to the university and after school run an experiment or get in the lab. And I think now this just democratizes it. And it gives it a huge opportunity for anyone that has ideas to be able to test them without the overhead and the burden of a lot of the institutions that could hold the keys to the infrastructure. So if this vision gets implemented, one of the questions I have. is it is fundamentally doing what a traditional research university does. So I've spent in my 10 years in college and graduate work a lot of time in the lab, designing

experiments, pipeting, plating dishes, and so forth. And the idea that it could be done, so do you imagine that this autonomous lab capability is effectively going to also retrofit into the university system? I think universities are going to be probably one of the first folks to build these labs. I think they want to provide the tools to the greatest America. They want to provide the best tools to the great scientists at work at their institutions. And I think they would be doing a massive disservice to their academic community. If they don't have these as resources, to me, I think if you're an, if you're an administrator

or university, you want to provide the greatest resources possible to your scientists. And I think these are going to be what everyone's going to be demanding. All right. I'm going to wrap us up with this question. And I want to go deep on it. In your report, Genesis talks about doubling productivity. Which I guess historically might have been thought of as ambitious. You know, in the moonshots world that I'm in, it's like, okay, 2x is like anti, like, you know, how do we 10x it?

Yeah. Is it 2x because it's politically acceptable? Yeah. Is it 10x? Is it 2x because you don't think 10x can happen? Yeah. Where do you see the limits here? Yeah, when we were thinking about the 2X number, that's actually a number from last year,

when we were actually building what ultimately was building the program at the president signed as a Genesis mission in December. And it's funny you say that because I've been sort of noodily on that actually myself for quite a months now. And as we were finalized in the report, I think we had even a conversation internally about whether or not we should rethink that, but we sort of already stated that publicly so wanted to keep running with it. My sense is, you know, government is one of the hardest institutions to change and to move. Yeah, it's linear at best. It's linear at best. And I think, you know, Elon learned it firsthand and did and did God's work here to help help the country.

To me, I think we should probably be aiming for 10. And to be honest, with the transformations that have happened in AI, even over the last six months, we should definitely be, definitely be pushing for 10. And I think even if you think back to sort of early this year, you know, Opus wasn't even out yet, you know, Anthropic revenue was it, what is it, was it 10 billion? Yeah, it was a sub-10, and now it's a post. Exactly. Fastest growing company on the planet.

Yeah, it's unbelievable. And that's just in the last seven months. We also had sort of the mythos moment. sort of the fable release. Open AI is going to be releasing their new six model very soon. I mean, the pace of innovation on AI is just insane. And I think we got it. We got to aim big.

We got to do 10X. I'm looking forward to that correctional report. Alex Weiser Gross and I wrote a paper called Solve Everything. It said, Solve Everything.org. We look at how do you structure the situation such that AI is able to You know, it's already to use Alex's word, cooking math, right? Math as a discipline is being wholesale solved over and over again. We're seeing every week reporting on new breakthroughs, new proofs being either dismantled or proven.

But on the heels of that comes physics, chemistry, biology, material sciences. And at least we imagine an inflection point, right, where, you know, GPT6 and whatever follow. was for anthropic and whatever GROC becomes, especially now that Elon's, you know, uploaded all of SpaceX's engineering data, which was a, you know, a baller move. We see an acceleration in the rate at which science is fundamentally, you know, sort of,

I don't want to say other than accelerated, is that it's stunning to people. That's what I'm excited about. That is why we did the Genesis mission. We believe that every scientific domain, whether you're in chemistry or physics or math or biology, you're going to see this dramatic transformation and acceleration of discovery and hypothesis testing. And that's going to be because of AI. And for us, to unlock that, and back to your question about, back to your note about uploading the data to GROC, our whole insight was that the U.S. national labs

have a tremendous amount of scientific data across a wide variety of domains that has not been made AI ready, that have not been uploaded to models, and have not been part of a scientific process. And we have this huge opportunity before us to actually bring all that great data from 70 years of scientific discovery in the U.S. into an AI model to be able to accelerate discovery. By the way, when that data gets brought in, let's say wholesale health data from NIH and such, and breakthroughs occur, Does the value of that breakthrough in order to the American people, who gets to get the financial benefit from the breakthroughs that come out of federal data? So I think those are public goods. That's data that is a public good and anyone can use it. Think of it kind of as like as weather data.

One of the big sort of actions that the federal government made many years ago was to make all of our weather data from NOAA freely available. So now all these apps are built on top of it, your weather app or everything else essentially runs on NOAA data. And I think we believe that, you know, taxpayers have been funding unbelievable science at DOE for decades. And let's make the most of it. Okay. One more side question. President Milley, Argentina comes forward and says we're going to no taxes on AI companies. We're going to enable AI personhood for agents here.

A pretty aggressive stance. Yeah. I'm just curious. What was the internal conversation when that came out? Like, wow, good on him. We should join in. I think that was a very interesting take on where we're going. I think I don't think we're quite at that place right now. We're not going to give AI person of it in the U.S. yet. I don't think so. I think for us, what we want to focus on is making sure that the benefits of AI are actually realized with American people. And we have a lot of work to do to make sure that that actually happens.

And the president has been very focused since day one to make sure that we continue to lead the world in this technology. And there's a lot the government can do to do that. But most importantly, we just have to kind of let our horses run. We have the best companies in the world. And we need to create a regulatory environment that allows them to just keep making the best breakthroughs in the world. Amazing. Michael, thank you so much. Thank you.

This was so fun. Thanks for your work. Really brilliant. Welcome to the health section of moonshots brought to you by Fountain Life. You know, my mission is to help you use the latest technologies, including AI, to not just do your work at home, teach your kids, but to help you live a long and healthy life. I'm here today with an extraordinary physician, the chief medical officer of Fountain Life, Dr. Don Musaylum Don. Let's talk about cancer. You know, I know from the member database that we have at Fountain, are members who come in who think they're healthy.

It turns out 3.3% of them have a cancer in their body they don't know about. That's right. You know, the majority of cancers that we screen for, those aren't the ones that are necessarily taking the lives when found at a late stage. We know that when cancer is found early, the chances for cure are much higher. We know it's much easier to treat a cancer when found early versus when found late. What we're finding in our members is over 3.3% were found to have these cancers that were otherwise wouldn't have been found or detected. Yeah, you know, it's interesting. People, you don't feel the cancer until stage three or stage four. And if you don't know what's going inside your body, it's like driving your car with your eyes closed. And you can know. And so when members come through found how do they detect cancers?

So we're doing full body MRI and we also do early cancer detection screening. This is very, very important. And these are not typical tools used in the conventional care setting when it comes to prevention. This is a hard thing because currently these are not studies that insurance would yet. be covering, but the goal is to collect these numbers, do the research, and work hard to democratize wellness. Yeah. So at the end of the day, you can know what's going on inside your body. It's your obligation to know. So check out FountainLife. You can go to fountenlife.com slash Peter to get access to the latest technology to help you detect cancer at the very beginning at stage one when it is curable before it gets to stage three or stage four in your world of hurt.