How the White House Plans to 10x Scientific Productivity | Michael Kratsios | EP #276

I was a kid in a candy store reading the Golden Age report. What you're describing there is a complete, fundamental, AI-native, AI-agent-powered reimagining of the entire scientific process.

And I think it's something that is possible. My sense is, this is the golden age of America.

AI is a technology that is going to impact every agency, whether you're flying drones, doing AI-powered medical diagnostics, or working at the SEC on financial services. AI is going to impact every single one of you.

Do you have a sort of 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. We're going to put man back on the Moon in 2028. We're going to be able to build the first elements of a lunar base by 2030. We're going to put a nuclear reactor in space by 2028. I mean, that's crazy.

Yeah. Okay, my favorite idea. It falls into the crazy idea I can't believe Michael actually wrote this down. All right. Now, that's a moonshot, ladies and gentlemen.

Welcome to Moonshots, everybody. Today, I had the pleasure of interviewing a friend, Michael Kratsios. 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 in a 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, we've got to do a briefing.

Ladies and gentlemen, I've called you here today to let you know that we have now officially approved a $1 trillion science budget.

Congratulations. We're going to be solving every problem on the planet within the next 4 years of this administration.

Yeah. Thank you. 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 are hyper-exponential, and you're in the thick of it. You're in the middle of it. Ray Kurzweil predicts we're going to see as much progress in the next decade as we've seen in the last century. That's like going from the Ford Model T to Starship in the next 10 years.

On top of that, we're on the edge of AGI—maybe in the next 3 years—and ASI. How does the government process ever keep up with that?

We're trying our best. It's something that I think governments have generally struggled with for a long, long time. What we have to do is make sure that, in areas where we're seeing this tremendous growth, we allow the regulatory system around them to exist in such a fashion that it doesn't get in the way of this progress.

I typically think of technologies in 2 buckets. There are technologies that are either born free or born in captivity. Born-free technologies are things like what the internet was in the 1990s, and the best thing the government can do in those situations is step back. Get out of the way. Don't jump into it.

It happened so fast they didn't have a chance to get in the way.

Yeah. Well, there was actually a bill passed in 1996 that Bill Clinton was behind. It was bipartisan, and I think it allowed some of this stuff to take hold.

I think this extends to a lot of technologies. Be careful before you start regulating. An example of that is AI, and I think this always comes up when I think about AI regs. The EU AI Act was passed and finalized by the EU Commission before ChatGPT was even invented.

Yeah.

There's no way that what they have today actually applies to LLMs of today. But I think the second type of technologies are the ones that we pay particular attention to, because those are the ones where we actually have to take action.

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. Their benefits will not be realized by the American people unless the government affirmatively does something.

Those are places where you have to be really careful, because if you wait too long or you weren't thinking about them, it actually holds up progress. First, we think of these 2 buckets of work and make sure that, for technologies that are born in captivity, we're thoughtfully approaching how to change the regulatory structure to allow them to ultimately be safely deployed to Americans.

I'm thinking about the timeframe you've got. If we're going to see this kind of extraordinary progress to AGI and ASI in 3 years, do people here in the White House understand the speed of that change? And I know in the agencies, I mean, it's dramatically—not a little bit faster. It's dramatically faster.

We're trying our best to bring people along to the new pace and velocity of change when it comes to technology. I think the best manifestation of that is our Genesis Mission. This is where the president stood up with the Secretary of Energy and me and said, “Look, the most important thing for the nation is to make sure that we're applying this unbelievable technology called artificial intelligence to scientific discovery. And it's not just at one agency; it's across all of government.”

I think those are the types of actions from the White House level that are the only way you can really push this down into agencies. But I will say, in most cases, government is not the leading force at the cutting edge of where technology is, and that's going to be obvious, I think. It will be said.

Yes, we've seen recently ministers or AI ministers appointed in various nations. We've seen, out of the Emirates, 50% of government operations being driven by AI. We've seen in Malaysia—I think it was—the president has an AI representative 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 our view of the world beginning in the first Trump administration, when President Trump signed the first executive order on artificial intelligence in history, in 2019—was that our general view of AI regulation is that AI is a technology that is going to impact every agency.

Whether you're flying drones, doing AI-powered medical diagnostics, or working at the SEC on financial services, AI is going to impact every single one of you. So, the idea that you can centralize that effort in one person and be able to get the right and best policy answer across all of those domains, I think, is a tall order.

Completely agree with you. I guess the question is, will we see AI enter the government in terms of advising on policymaking or advising in cabinet positions, where there's an AI instantiation of that member of government to be able to counsel at the speed that we're seeing?

Yeah. I'm not sure what the future holds, but at least in the short term, what I would hope is that all our agencies can actually even start using AI.

We're sitting in the White House complex today, and currently, large language models are not allowed for use on our system here because of the Presidential Records Act. Hopefully, we'll change that soon. But that's an example of the pace at which government technology sometimes operates.

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All right. I'm excited to dive deep into the Golden Age report and Genesis Mission, but before that, I do want to talk a little bit about AI.

Yeah.

You and I both know that AI is the engine. Fundamentally, it's going to uplift every American and every aspect of Americans' lives, and I think we both feel that very deeply, right? The challenge, of course, right now is that 3/4 of Americans fear AI. Those are the numbers right now. Seventy-one percent of Americans oppose data centers near their homes, which is a larger percentage than those who object to nuclear power plants in their neighborhood, which is insane.

So, I guess one of my missions is helping people see the optimistic future and reduce fear. I guess the question is, how does the administration get out in front of this? 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 or orthogonal to the AI industry realizes that, and it's a problem. You and I know that and deeply believe, at our core, what great things AI can do and bring to everyday American lives.

But I think back, first off, to how we got here. I think back to the first AI Safety Summit that was held by the UK government at Bletchley Park—

Yeah.

—2 years ago under Rishi.

Way back 2 years ago.

Way back. Yeah, this was 2 or 3 years ago. I don't know; it's all sort of blurring together. I think it was maybe the year after ChatGPT came out.

What was so fascinating about that, when I attended it, was that almost the entire concept of it was wrapped around fear associated with AI: What could go wrong? 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.

I think that's an example of how the narrative for so long coming from government, prior to President Trump, has been so fixated on the negative impacts of this technology. Of course people are scared.

Yeah.

If the only thing they're hearing from politicians and people in industry is that there are going to be a bunch of job losses, everything is dangerous, and maybe there's going to be biorisk, I think those are the things that get people worked up. It shouldn't surprise us that the PR is bad.

So, you asked for a positive take on it. I think we try to look for areas where Americans actually connect with AI in a positive sense. By far, what we have seen is in the health care domain.

Sure.

That's where, if we can actually show the impact in changing the way that individuals themselves get health care and their families get health care, those are the places where I think you can start to push back a little bit on that narrative.

Is there any effort to try and change the narrative from a centralized sense? It kills me that the narrative in China is the flip of that, where 80% are pro-AI. I just want to get out and shout from the rooftops: You have to understand what's going on. Where does that responsibility lie? Probably in the AI labs, for 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. 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 the—

100%.

—huge build-outs that are happening for everything that's sort of supporting the AI industry. These are great stories.

I just heard from Jensen Huang. He was here, and he was talking about the supply chain that supports NVIDIA. He said that the order he's put in to Corning for the chips that he's going to be building in the future is the largest order in the history of Corning, and they're going to be producing more than they ever have in history just because of his—

Yeah, it's lifting up the entire GDP of the nation in an extraordinary fashion.

And those are the stories that should be told. Factories are being built, people are being hired, stuff is being produced, and I think those are the things that can connect with Americans.

One of the biggest fears is AI-related job loss.

Yeah.

And AI-related loss of income and jobs.

Mm-hmm.

I report on this on Moonshots every week or so, and it's part of the conversations we're having. It's confusing, because there are a lot of people putting forward data saying, yes, there's job loss, yes, there are these layoffs, and others who feel 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 that in the long term, I'm very optimistic about the impact that AI is going to have on jobs. I've been thinking about this problem since the first Trump administration, when most of the narrative around AI was about automation. There were fears about all this automation-related job loss, and if you even fast-forward to that period, that was never realized. Employment has only 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 address this problem. One of the things that you mentioned as a problem is that the data around what is actually happening is not very good. We're trying to launch an initiative at the Department of Labor that was called for in the AI Action Plan. We want to start collecting better data on the impact that AI is having on the labor force.

You can collect data from employers that typically don't submit their data to the department, and through that you can actually start making the right decisions about where to do better reskilling and retraining, and where to do more targeted labor assistance programs.

One of the conversations we had in Miami over lunch at the FIA Summit was the idea of a parachute program, where you incentivize companies, if they are doing an AI-related layoff—

Mm-hmm.

—to give the employees you're letting go an AI upskilling program so that they get trained into that environment. Do you think something like that might materialize?

You know, I think every company will think about it differently. I do think opportunities like that are interesting. I'm still trying to wrap my head around what an AI-related job loss even really means. I think, like a lot of folks these days, when companies are doing a layoff that they would have done anyway, they like to assign it or blame it on AI because it plays better in the press.

And their stock price goes up if they're producing more revenue with fewer people.

Precisely. Yeah, yeah, yeah.

You know, on the notion of fear, it's one of the things I'm always trying to quash and address, because fear is an awful place to face the future from, especially at the speed of change. People don't understand AI enough to understand its implications for their lives.

We just launched something this year called the Future of Vision XPRIZE. 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 are working together.

Part of the challenge for me is that films like The Terminator, Ex Machina, Black Mirror—the majority of all the sci-fi films out there—are dystopian. If that's what we're teaching the average American, this is what happens when you have robots and AI, then I guess my question along those lines is: Do you have a longer-term, compelling vision of what you think America could be like on the back of this AI revolution, beyond just better health care from AI, which is low-hanging fruit, agreed?

Yeah.

Does that future visioning happen here?

I mean, we think about it more in terms of national missions, which I think we'll talk a little bit about in the Golden Age. My general take is that we, as a government, need to be opinionated about what the most important things are for the future of our nation.

There was an era beginning with the Manhattan Project—

Yeah, going through Apollo, we had big ideas. We had bold things that we were doing.

We had pride around them.

Exactly.

And it motivated young people to go into science. We were looking at a North Star of something that a lot of people thought couldn't be done, but we did it. 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 Jared Isaacman about very recently. We're going to put man back on the Moon in 2028. We're going to be able to build the first elements of a lunar base by 2030. We're going to put a nuclear reactor in space by 2028.

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 in 1.5 years, that's nuts. But we're going to do it because we're Americans, and we can accomplish that. That's what's special.

We'll get to them later, but name a few of those big, bold Manhattan Project- and Apollo-type programs, things like that.

Yeah, I think the other one doesn't have quite as quick a timeline. It's sort of the Genesis mission, which is our AI-for-science mission. We essentially want to double the productivity of the entire scientific enterprise in the United States over the next decade.

And that's going to have an incredible amount of fallout as a result. 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. This is finally saying, “We appreciate, and 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 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, in the quantum world, because quantum supremacy and all of these terms have been as loose as AGI and ASI, what you hope the first functional, capable quantum computer is able to do? What industries are you impacting most with that?

To me—and sorry to keep going back to health—I think pharmaceuticals is where I'm most excited. I do honestly believe that the types of calculations you can run on those for particular molecules and things that you want to apply toward drugs are going to be pretty transformational.

The last one, which is outside of our term but important for us to keep thinking about, is fusion energy. People have been saying it's 10 years away.

They've already been saying it's 50 years away.

Fifty years away forever. It's always so many years away, but the Department of Energy put out the latest iteration of our national strategy a few months ago. I don't think at any time in history have we had more private-sector investment in this particular energy source than we do today.

Yeah, my last count was 37 venture-backed fusion companies, which is crazy. It's nuts. I want to take a moment because it's timely to talk about the conversation that's been dominating X and the AI sphere in D.C., which is the open-source versus closed-source activity. 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 an administration, we believe that the U.S. must lead the world in both closed- and open-source models. The best thing for the country is that we have a vibrant ecosystem on both sides of that coin, able to support any type of customer that wants to work on AI or use AI.

Right now, I think our open-source ecosystem is one where we could be doing better. I think we have a couple of very well-known startups that are extraordinarily well-funded.

Yeah, Mira Murati's release was amazing.

Yes, terrific. I think we're waiting on reflection for their model later this year, from what I understand. The ecosystem and the tooling around open source continue to get much better, and the U.S. is continuing to be the default for harnesses and tools around open source.

To me, I think—and we expressed this on the first page of our AI Action Plan from last year—the U.S. has to lead on open source. Right now, look, I'll be honest: the Chinese have very well-performing open-source models. If you're an American entrepreneur who's cash-strapped and trying to bootstrap your company and get started, I can't blame you for using the cheapest model out there. At this moment, it's Chinese, but over time, I think we'll be able to cultivate a pretty vibrant ecosystem here.

All right. Yeah, that's what America does.

Yeah.

The Financial Times reported that Beijing is out in the world exporting open source, basically as an instrument of influence, going to the Global South and providing access to infrastructure and capabilities. One of your stated goals in the AI Action Plan, which I love, is that the world should build on America's AI and tech stack.

Fundamentally, a lot of people are going to use frontier AI, but the majority—the vast majority—of the world is going to use on-premises open-source models. How does America think about that? Maybe this isn't necessarily your realm, but I'd love your thoughts on that.

No, we think about this a lot. To operationalize what you mentioned—the AI Action Plan—we launched something called the American AI Exports Program. The vision behind it was that we have the best AI stack in the world. We have the very best chips from companies like NVIDIA and AMD, as well as many other new entrants. We have the best models that we all know about, and we have the best applications.

If you're a customer around the world, a government, or someone in the private sector who's looking to build AI, there's nothing better in the world than the American stack. We launched this program at the Department of Commerce, but it's now become a whole-of-government effort. We put out an RFP and 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 ultimately we're going to create turnkey American AI stack options for the world. Then we'll back those with government financing organizations that can make them economically viable for a lot of countries around the world. Organizations like the Export-Import Bank and the Development Finance Corporation can provide financing to make them more economical.

So, a complete package.

A complete package. I think that's something that a lot of governments and a lot of people around the world want. For them to have to choose 7 different vendors to set up their stack is probably not what they're looking for.

The other appeal that I think the U.S. has right now is that everyone wants our chips. We have the best chips by far.

There's so much out there: “I want your chips.”

Everyone wants our chips, yeah. Between the export controls we have in place and other factors that are going on, particularly on EUV, our lead over the best Chinese chip continues to increase year over year. From a foundational standpoint—the foundation of the stack—we'll continue to have the best product.

Now, I will say that this AI export program 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. They had a good-enough telecom stack that was run by Huawei, and they had it heavily subsidized by the PRC. They went out and proliferated it pretty quickly.

I think we're at a moment where we want to avoid that. We have a pretty vibrant open-source ecosystem ourselves, but I think our open-source solutions plus our closed-source solutions, in addition to the chips we have and the great American applications that everyone wants to use, will make our stack more competitive.

Do you think the restrictions on NVIDIA's chips to China, in retrospect, might have been a mistake? One of the things we've seen in the past—I saw this in the launch-vehicle industry and the satellite and defense industries—is 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.

I don't think so. I think that was probably one of the savviest decisions that we made as an administration, and I think it was probably one of the most impactful in being able to throttle their ability to make models competitive with ours.

My personal belief is that it has been a priority of Xi Jinping to have a competitive semiconductor industry for a long time. It started certainly in Trump 1, and that's why the EUV lithography export controls were 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, their ability to create their own sort of competitor to our leading-edge chip would have really succeeded.

Let's talk about robots for a second. 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, 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 one manifestation of AI that is going to be critical to the 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. 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 building the supply chain to support this. I mean, we in the administration believe that these robots are critically important for the future of the country, for advanced manufacturing, and for so many other things. We have to build that supply chain muscle to be able to have supply chain security in the future.

Do you see capital going to these companies to accelerate that capability?

I do. And I think the best example of that is that we took a similar action around UAS, or drones, in December of last year. If you look at the numbers, the investment that went into essentially the drone supply chain since that action has been pretty dramatic, and I think that's what we hope to see here. I think this industry needs a bit of a push because, just like in so many other places, the Chinese are certainly subsidizing and dumping on robotics.

So I want to return for a second, before we head to the New Golden Age and the Genesis Mission, to the speed of change. A mutual friend, Elon—a friend of the pod—you know, when I interviewed him a few months ago, he said, "We're going to see double-digit growth in GDP in the next 18 to 24 months, triple-digit growth in 5 years." And then he was on The Economist—I don't know if you saw the clip—saying we're going to be, by 2036, basically post-capitalist. We're going to have anything you could possibly want 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.

It's hard for me even to wrap my head around that velocity of change. I maybe 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 recognize that and are getting in front of it. As you probably saw, Secretary Bessent was very involved in some of the post mythos activities that happened at USG, and I think that's a great example of saying, "Look, 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." We addressed it, and I think that's the kind of action that you see a lot of the leaders in the administration taking when it comes to these rapid changes.

All right, let's turn to the Genesis Mission and the Golden Age.

Mhm.

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 at a time.

Yeah.

Because they're really important. And, just as I was saying before we started here, I was a kid in a candy store reading the Golden Age report. It was like you went much further than I expected in naming ideas, and we'll get to those.

The first point you make is that scientific productivity has been declining despite larger budgets. Eroom's Law—Moore's Law spelled backward. Discovery per unit dollar has dropped.

Mhm.

Why? What's going on here? We've got better tools.

Yeah. To me, I think we've been unable—or just don't—to change the way that we conduct science. I think this goes back to one of the main reasons why we wrote this report. The president wrote me a letter after I was confirmed and essentially charged us with answering the question, "How do we revitalize the science enterprise?" We went back and thought about it, and the data that you talked about—this declining productivity—was one of the first things that we looked at.

We asked ourselves, "Why is it? We have better technology than we've ever had, and our budgets are larger than they've ever been." I think it's most probably relevant in the biomedical field, where the NIH budget has now ballooned to almost $45 billion, yet the cost of drugs is more expensive than ever, and the list goes on. One conclusion we had was that we just are not experimenting enough in the way that we conduct science. We're doing the same thing over and over again, just putting more money toward it and believing that the outcomes are going to get better.

One of the core theses of the whole Golden Age report is that we have to be more experimental and more ambitious about testing out new ideas. I found it particularly relevant and shocking in the world of science. You would think that in science, 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.

Yeah. They want the system to be exactly the way it was 30 years ago. The way I see it, if you're an expert in something and there's a revolutionary breakthrough, you're no longer the expert in it, so there's a disincentive for doing that. Could it be regulatory bloat? Could it be legal bloat? Could it be just the paperwork that's developed over time?

It is. Yeah, I think it's all of the above. Some things that we talk about in the report are research burdens. 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 on the administrative work associated with their grant. 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 health care. Physicians are spending all their time filling out paperwork.

I think the regulatory stuff does matter, too. One example that's very close to my heart and has been one of my pet projects for a long time is how we bring back supersonic flight to America. An example of that is a regulatory issue where, in the U.S., there has essentially been a speed limit for flights over land. If you're flying over Mach 1, it's just not allowed anymore.

Because of the sonic boom and the concerns, right?

The reality is, I think research has shown—and I think Boom Supersonic, a company, showed—that they're able to fly over Mach 1 without creating a sonic boom. Our regulations, the way they stand, disincentivize them from ever trying to fly over Mach 1 because they're not able to. So, again, due to the president and the 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.

Mach 1—if you can keep it quiet, fly. Just fly.

I love that. The breakthrough policy changes on both supersonic flight and eVTOLs—just, I mean, the aviation industry had been stuck for 50 years.

Yeah.

And so, unleashing in that way. The second challenge, as you note here, is that young talent waits too long before they're given a chance to implement their bold ideas. Our funding, publishing, and credit system was built for a world of human-paced discovery, and I could not agree with you more.

The challenge I think a lot of young people face is they're kind of stuck in this zone of doing science the way it was done a long, long time ago, before the internet even existed in some ways. Everyone is stuck in this process of needing to do research, then trying to get the research published in a journal, and then having to do that a number of times before you can get tenure and so on. I think this structure is not conducive to discovery. It's the way you succeed in the academic system.

System, right?

Yeah. And I think there are huge opportunities to reform that. To me, NIH is always an example of this, and I think this is very bipartisan.

I think 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 at NIH, not getting a grant out—is 71 years.

Oh my God.

When I heard that, I couldn't even believe it. That's crazy. The average age of a Nobel laureate's prize-winning work is in their mid-40s.

Yeah, precisely. I think we've created these systems where we're somehow okay with that. Then we look in the mirror and somehow tell ourselves, “Well, everything's doing great if we just give it a little more money.” That's not how you solve these problems.

And that leads to the bloat. So, you identify a bunch of great mechanisms for driving progress in the golden age of NIH. I'm going to hit on 4 of them that I think I'm excited about. The first one is long-duration grants: 5-year grants funded on day 1.

Yeah. Yeah.

Talk about that one, please.

To me, grant duration is something that people don't talk about enough. Over time, we've come to this sort of zone of comfort where most government grants are roughly 18 months. That's just because of the way it works with academic calendars and how easily and quickly we as a government can review these grants, 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. It's a small experiment that only takes 3 or 4 months to look at, and you can get it done. There are other ones, like you talk about, that actually require a longer period of time—where you want a brilliant scientist to be able to explore an idea that's going to take a little bit of time to sort out.

Rather than working on that, they're working on another grant application.

Exactly. Because most of these guys, after they've gotten their first grant, are already working on their second before they've even finished the work on the first. That's because they have to keep the pace going.

To us, we believe that you have to ask: How do we support scientists? At the core of the entire New Golden Age report, everything we do is in service of the scientist. This is a perfect example of it. There are certain scientists who want shorter-duration grants, and there are some ideas that need 5 years to play out.

We as a government need to make our money available to be in the service of those scientists who will be making the great breakthroughs.

The next idea you put forward, which I love, is fast tracks: a few pages, reviewed in under a month, sized for the proof of concept.

Yeah, and I think we saw a lot of excitement around this. We've seen this historically around times of crisis, and I think this really came to the fore during COVID. Tyler Cowen and others came together, pooled some capital, and actually did this on the side themselves. They were making grant decisions in a matter of hours for people who were submitting work on problems during COVID.

But there's no reason it should be restricted only to times of crisis. There are lots of ideas that, if we're able to answer them, may unlock other things that we'd want to do later on.

I can imagine a lot of these scientists are probably using ChatGPT or Gemini, whatever it might be, to write their grants. I'm expecting that grant reviews will also use AI as a mechanism. Time should be massively compressed.

I think so. A lot of thought is being given at our funding agencies to how we can accelerate that while, at all times, keeping that meritocracy and that merit-based review gold standard.

The next one that I loved was experimentation with golden tickets. What's a golden ticket?

It's another issue related to your question of why we've stagnated. One reason a lot of people cite is that we're just not risk-taking anymore. The least-common-denominator ideas are typically the ones that get funded. People who have out-there ideas, crazier ideas, things that may not work but, if they do work, would be pretty incredible, just aren't incentivized to submit those applications because they're not going to get the award.

So, how do you get around that? How do you incentivize people to be a little more out there? One way is this golden-ticket idea. The concept is that, on a merit-review panel, you may have 3 or 4 people, and each person on the panel gets 1, 2, or 3 golden tickets. With a golden ticket, you can unilaterally make the decision to fund a particular grant, independent of what the rest of the committee thinks.

Because of that, 2 things happen. First, you incentivize people to have slightly crazier ideas because they have a chance that, if 1 person believes in them, they'll go forward. The other—and I believe this may be the even better outcome—is that you incentivize better people to be part of the review panels.

Because they have power. They have power.

Rather than having people who are sort of mid-tier and just do this for reasons I don't know, you can bring even better people in to do the reviews.

I do love that. I was talking to a professor at Harvard—I won't name him—who has been extremely successful. He was saying that he was getting dinged in his reviews because he'd gotten too many grants and was too successful. They needed to give other people a chance.

Yeah.

The whole peer-review process is unfortunately extraordinarily broken. I define a breakthrough this way: The day before something is truly a breakthrough, it's a crazy idea. So, where are we experimenting with crazy ideas? It's a challenge.

It is. I think we have to—and I think government correctly needs to be a good steward of taxpayer dollars. We have to be very thoughtful in the way that we develop our programs and evaluate them.

One piece of the New Golden Age is this concept of meta-science—the science of science. That's our ability to evaluate the ways that we're conducting science, the ways that we're doing funding, and whether or not they're working, and then course-correct when they're not.

You and I, on this podcast, may really believe that doing fast-track grants is a great idea. But we need to run the experiment. Let's do some fast-track grants and see what types work and which ones don't. Then we update them, and the next iteration is even better.

We as a government just don't do that. We never do meta-science. So, one of the things we call for in the New Golden Age is the launch of meta-science units, and those have already been announced at places like NIH and NSF.

Nice. Okay, my favorite idea.

Yeah.

It falls into the crazy-idea category. I can't believe Michael actually wrote this down. You describe the use of prediction models, crowdsourced intelligent agents, 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 a scientific marketplace where funders post bounties for breakthroughs. AI agents identify promising leads, hire 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 resources toward 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 big decisions about which scientific questions and breakthroughs matter most.

What you're describing there is a complete, fundamental, AI-native, AI-agent reimagining of the entire scientific process.

This report gave us an opportunity to dream big about where we could end up going. I think it's something that's possible. I really do.

What we try to get at there in the report is that incentives aren't always easily aligned in the current system we have today. Over time, we've developed technical solutions that can bring those incentives together and enable information-sharing at a pace and speed that will allow all the things you just mentioned to come true.

Having DAOs involved and having agents run the cycle arguably millions of times faster than a human would, if not even faster, sounds like a mechanism that could make Elon's 2036 prediction actually come true.

My hope is that this inspires some folks. One thing I've observed, and I think you have too, is that 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 still 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 and try some of this stuff.

And you've got folks like Yuri Milner, Eric Schmidt, and Marc Benioff all funding science directly.

They're doing really incredible work. And I think one of the underlying, or main, premises of the whole Golden Age report is that the science ecosystem has changed. In 1950, when Science, the Endless Frontier was written by Vannevar Bush, that kicked all this off. Seventy percent of R&D was done by the federal government, and 30% was done by the private sector. That has flipped entirely today. The majority is done in the private sector, and only about 30% is funded by the federal government.

Yeah, companies can take a 10-year horizon if they need to.

Yeah, and I think what we see with this flip, with the private sector being more involved, is that you also have philanthropy playing a bigger role. If you look at all the pieces on the chessboard, you can bring all those people together to drive scientific discovery in a way that you could never imagine in a system designed in 1960.

Take, for example, an announcement we made today about 4-year PhDs. 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. The idea that you'll have a private-sector company paired with an academic institution to help a student pursue a 4-year PhD is amazing. That's reflective of today's reality, not something you'd imagine in 1960.

All right, the last mechanism I want to hit on is the use of incentive prizes.

Mm-hmm.

Your report leans into prize challenges, advanced market commitments, and pay for results, not proposals, right? You cite the $10 million Ansari XPRIZE in there. Thank you. I appreciate it.

I've spent 30 years of my life focused on incentive prizes, and it's my home turf. Most agencies haven't had experience in this area or learned how to use it. XPRIZE has launched $600 million of prizes, and we've driven about $30 billion of R&D as a result of those prizes.

Mm-hmm.

How could XPRIZE help your agencies?

To me, I think it goes back to partnership. If there are big national scientific endeavors that we need to pursue or problems we need to solve, I think there are opportunities to pull money together, make the prize even bigger, and draw people to solve these big challenges.

Do you imagine that the government would put out billion-dollar incentive prizes, or are these going to be small?

Mm-hmm.

Do you imagine, on your grand challenges—you know, I was talking to Elon about this—I said, “Let's launch 10 $1 billion prizes focused on every grad student and every company. These are the important things we need to achieve.”

The government spends about $200 billion a year in R&D. If you think about it, about $45 billion, as I said, sits at NIH for biomedical research. Having $1 billion may be a lot for a single prize for the government budget to swallow, but I think a prize in the range of $100 million is certainly doable if the project is big enough.

Speaking of large prizes, our largest XPRIZE right now is the $101 million XPRIZE Healthspan. We have 830 teams competing, and the goal is to reverse your functional age by 20 years—to give you cognitive abilities that are 20 years younger, and muscular and immune-system capabilities that are 20 years younger.

I firmly believe—and there are a number of incredible scientists, like David Sinclair and George Church, who agree—that if you wanted to impact the U.S. economy the most in a positive fashion, you would tackle aging.

Mm-hmm.

You would enable people to have—and it's really healthspan versus aging—you know, today in the United States, the average life expectancy is about 78 or 79. The average healthy life expectancy is 63. So the last 16 years of your life, you're in poor health. If you could move that needle up—

Mm-hmm.

—give people an extra decade of health and an extra decade of productivity, they would transform everything.

Yeah.

So, I found longevity lacking from the report. You hit a lot of other great things, but—

Well, now that you mention it, we probably should have included it. I think it's something that's critically important, and it ties to the general health of Americans. We should find ways to work together on this and figure out what programs NIH and other places can accelerate.

Yeah. 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 the human lifespan in the next 5 to 10 years, and Demis talking about curing all diseases within the next decade. Those are impactful.

That is huge. I remember when Demis first told me, “Cure all diseases in 10 years.” I wasn't sure if he was kidding or serious. He's dead serious, and I think he thinks it's possible with where AI is going.

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When I look at the 6 national technology missions that you named—AI, quantum, commercial fusion, lunar exploration, robotics, and next-generation semiconductors—all amazing, I find biotech missing from that. Is there room for that to come on? Is there a way—

There's certainly room for it, and it may have even been explicitly listed. I will say that I think a lot of the biotech work has been nested under the Genesis Mission, the first national initiative around AI for science. 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. So it's really important.

That was a question my dear friend Alex Wang wanted me to ask. My entire Moonshot Mates team—Dave, Alex, and Salim—are jealous I'm here alone with you.

We've got to have them out next.

I wave hello to you guys. Interesting. Some states and cities are far more pro-tech than others. 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. I call them singularity zones: places that are pro-autonomous vehicles, pro-drones, pro-nuclear, pro-data centers, and so forth.

Yeah.

Do you imagine that you could see some type of regions volunteering to be innovation hubs, where the regulations are sufficiently relaxed so they can accelerate research? We see China doing this. I see other places in the world. Chile did this as well. It's just to accelerate the work and actually have the entrepreneurs who are interested in that region move there to build their ecosystem.

I would love that. I think we, as an administration—I personally have been very focused on this as a way to drive innovation. The very first piece of paper that I worked on, which I got the president to sign, was in 2017 to launch the UAS IPP, the UAS Integration Pilot Program.

What was so special about that program was that this was 2017, and 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 tests of what it's like to try to do deliveries? Where does it work? Where doesn't it? When does it bother people? What systems can you put on the drones? All that.

The executive order essentially called for the creation of this pilot program at the Department of Transportation. The FAA then went out and picked 10 state, local, or tribal governments that paired with certain UAS operators to run these tests.

What you essentially had was communities standing up and saying, “Hi, I want to have this innovative technology. I'm going to carve out an area.” The FAA gave them regulatory clearance to be able to run these operations.

We've done that again in this administration with our eIPP, which is our eVTOL program. We're going to start seeing testing of eVTOL vehicles all over the country. To me, I think this is really important.

If you remember back in the campaign of 2024, the president also put out a number of videos around some efforts that he was going to be doing in Trump 2.0.

And one of them was actually around this. I think there was a whole narrative around creating these innovation zones in cities where we can test these great new technologies.

Yeah. I think on our evaluation, state by state, Texas came out number 1 in everything.

Yeah.

Just very pro.

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 to 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. I think that's a good thing. I think it's competition among the states.

Regulatory arbitrage. It's a very powerful incentive for states.

And in some ways, I think this dovetails a little with some of the issues on data centers, for example. I think there could be a future world where you're actually going to be having communities competing for data centers. We should be thinking more—at least the data center operators should be thinking a lot more—about what incentives they can provide to the communities that they're entering, such that communities are so interested in having them that they're competing among themselves.

The president has taken a lot of leadership on this, putting forth what's called the Ratepayer Protection Pledge, where he essentially directed all of these big tech companies, AI companies, and data center builders to build, bring, or buy all their own power. 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 in the—

And that's what the data shows.

Yep.

The locations with the AI data centers are getting, on average, lower electrical costs. We have to address the water issue as well, which we've talked about a lot of times on the pod. Golf courses have 30 times more water use than data centers, and almond farming is like 50 or 60 times more water use.

Particularly—

Interesting.

Jarring, yes.

Let's talk about education. In your quarterly report, you hit it in chapter 4. And as a dad of 2 15-year-olds, I am pissed at the educational system today. At least in the U.S., it is still tied to the Industrial Revolution. It's not tied to what's coming.

We're seeing huge resistance from high schools and even colleges to this, and it should be just the flip. It should be AI-first across the board. How do you see this being radically reimagined? Because I think it does need to be. The public education 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.

I think we should be providing parents options with the ways that they believe they can best educate their children. There are amazing opportunities for people on one extreme to be participating in things like Alpha School, where you do, what is it, 3 or 4 hours of AI in the morning, and then the rest of the day is on social skills and other stuff. 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 no technology in the classroom. They want their kids to learn the classics, and they can end up learning about these other technologies in other domains. But the reality we face today is that most Americans get neither of those things.

Mhm.

They get a broken middle that doesn't provide any semblance of a positive future for those children. There are so many districts around the country where you see that a majority of students graduating from high school can't even do basic math.

I think we have a lot to improve in this country. On my side, when I think about how to integrate STEM into these organizations, that's where my portfolio usually goes. To me, one of the most depressing things about American education today is just the declining number of American students who want to enter the STEM fields.

Yeah.

That's not good for our country's health, for our national security, or for the future of economic growth. We need people pursuing these STEM degrees. I think there's a lot that we can do from a government standpoint to inspire people to go back into this domain, and hopefully a lot of the work that Jared's doing through space and so many other—

I mean, the Apollo program is 100% responsible for everything I've done in my life.

Yeah.

It was Star Trek and Apollo. I mean, asking the school systems to change over the course of the next few years—which is, I think, the time horizon we need to be talking about—is extraordinarily difficult, with teacher unions and public education boards and so forth.

So I wonder: Is there an opportunity for an AI educational overlay that becomes available, where parents and kids who want that can do that in the afternoons or on weekends?

I think that's possible. From what I understand, the Alpha School guys, for example, started with running the schools themselves in certain locations, and it's not necessarily that cheap, so there are always certain people who can participate in it. But I think the goal is to essentially open-source the software or make it economical for anyone to run those programs.

My sense is that 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 autonomous, self-driving labs.

So the PC 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.

This is something you and I talked about that Lyla Sciences is doing. They're building out 1 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—the long pole in the tent on some of this stuff is actually building the whole hardware that can run as wide a range of experiments as you would want.

I think we'll probably start with more narrow domains and then expand as the actual hardware itself comes online. But I think that's a future that we want to try to achieve. What has held a lot of scientists back is just how long it takes to run the experiment. If you have a vision and you want to test it, wouldn't it be great if you could just—

Go online.

Put the hypothesis in, hit go, and then start thinking about the next one.

And not needing to wait. I also love the vision you had where a high school kid could potentially do that. A college kid someplace could get access to national lab equipment to run the experiment.

I remember in high school, there were a few kids who'd always find some way to meet a professor and somehow get to the university and, after school, run an experiment or get in the lab. I think now this just democratizes it and gives it a huge opportunity for anyone who has ideas to be able to test them without the overhead and burden of a lot of institutions that could hold the keys to the infrastructure.

So if this vision gets implemented, one of the questions I have is: Is it fundamentally doing what a traditional research university does? I've spent, in my 10 years in college and graduate work, a lot of time in the lab designing experiments, pipetting, plating dishes, and so forth. Do you imagine that this autonomous lab capability is effectively going to also retrofit into the university system?

I think universities are probably going to be among the first folks to build these labs. They want to provide the tools to the greatest scientists in America, the best tools for the great scientists who work at their institutions. I think they would be doing a massive disservice to their academic community if they don't have these resources.

To me, if you're an administrator of a university, you want to provide the greatest resources possible to your scientists. I think these are going to be what everyone is going to be demanding.

All right. I'm going to wrap this 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.

Mm-hmm.

You know, in the moonshots world that I'm in, it's like, okay, 2x is, like, anti—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 limits here?

When we were thinking about the 2x number, that's actually a number from last year, when we were actually building what ultimately was the Genesis Mission program at the President's signing in December. As we were finalizing the report, I think we even had a conversation internally about whether or not we should rethink that, but we had already stated that publicly, so we wanted to keep running with it.

My sense is that government is one of the hardest institutions to change and to move. To me, I think we should probably be aiming for 10. To be honest, with the transformations that have happened in AI even over the last 6 months, we should definitely be pushing for 10.

Yeah, it's linear at best.

It's linear at best, and I think Elon learned it firsthand and did God's work here to help the country.

To be honest, with the transformations that have happened in AI even over the last 6 months, we should definitely be pushing for 10. Even if you think back to early this year, Opus wasn't even out yet. Anthropic's revenue was—what is it? Was it $10 billion?

Yeah, it was sub-$10 billion, and it's now—it's the fastest-growing company on the planet.

Yeah, it did. It's unbelievable, and that's just in the last 7 months. We also had sort of the Mythos moment and the Fable release. OpenAI is going to be releasing its new GPT-6 model very soon.

I mean, the pace of innovation in AI is just insane. We have to aim big. We have to do 10x.

I'm looking forward to that correction in the report. Alex Weisman Gross and I wrote a paper called “Solve Everything.” It's at solveeverything.org. We look at how you structure the situation such that AI is able to—you know, it's already, to use Alex's word, cooking math.

Math as a discipline is being wholesale solved over and over again. We're seeing every week reports on new breakthroughs and new proofs being either dismantled or proven.

Yep.

But on the heels of that comes physics,

Mm-hmm.

chemistry, biology, and materials science.

Mm-hmm.

At least we imagine an inflection point where GPT-6, and whatever follows for Anthropic and whatever Grok becomes—especially now that Elon has uploaded all of SpaceX's engineering data, which was a baller move—we see an acceleration in the rate at which science is fundamentally accelerating, to the point 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, physics, math, or biology, is going to see this dramatic transformation and acceleration of discovery and hypothesis testing. That's going to be because of AI.

For us, to unlock that—and to go back to your note about uploading the data to Grok—our whole insight was that the U.S. national labs have a tremendous amount of scientific data across a wide variety of domains that have not been made AI-ready, that have not been uploaded to models, and have not been part of the scientific process. We have this huge opportunity before us to 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 inure to the American people? Who gets to receive the financial benefit from the breakthroughs that come out of federal data?

I think those are public goods. That's data that is a public good, and anyone can use it. Think of it as weather data. One of the big actions that the federal government made many years ago was to make all of our weather data from NOAA freely available.

Now all these apps are built on top of it. Your weather app and everything else essentially run on NOAA data. I think we believe that taxpayers have been funding unbelievable science at DOE for decades, so let's make the most of it.

Okay, one more side question. President Milei of Argentina comes forward and says, “We're going to have no taxes on AI companies. We're going to enable AI personhood for agents here.” A pretty aggressive stance.

Yeah. Yeah.

I'm just curious: What was the internal conversation when that came out? Was it like, “Wow, good on him. We should join in”?

I think that was a very interesting take on where we're going. I don't think we're quite at that place right now.

We're not going to give agent personhood—AI personhood—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 by the American people. We have a lot of work to do to make sure that actually happens.

The President has been very focused since day 1 on making sure that we continually lead the world in this technology. There's a lot the government can do to do that, but most importantly, we just have to let our horses run. We have the best companies in the world, and we need to create a regulatory environment that allows them to 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.

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