## meta-science units
ence, 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. Like let's do some fast track grants and let's see what are the types that work, what are the ones that aren't, and then update and the next the next um iteration of them is even better. And we as a government just don't do that. We never do meta-science. So one of the things we call in the in the in in Golden Age is to launch meta-science units and those have been announced at at places like NIH and NSF already. >> Nice. Okay, my favorite idea. >> Yeah. >> Uh it falls into the crazy idea, I can't believe Michael actually wrote this down. Uh is you describe the use of prediction models, crowd-sourced 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 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 ex- exchange data, hypotheses, compute, and funding through microtransactions, while smart contracts release payments as milestones are met. Prediction markets could guide grant makers, bounty markets could direct resource towards unsolved problems, and reput

## fast tracks
ave 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, you know, 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 are some ideas that need 5 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 who will be making the great breakthroughs. >> Uh the next idea you put forward, which I love, is fast tracks. 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. Um and we've seen this historically around times of crisis. And I think this really came to the fore in um during COVID. And Tyler Cowen and others came together and and pooled some capital and actually did this on the side themselves. We're making grant decision in a matter of like hours for people who were submitting to to to work on problems during the COVID. But there's no reason it should be restricted only in times of crisis. There's lots of ideas that if we're able to to answer them, then they 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, whatever it might be, to write their grants. And I'm expecting that grant reviews will also u

## prediction models
d to run the experiment. Like let's do some fast track grants and let's see what are the types that work, what are the ones that aren't, and then update and the next the next um iteration of them is even better. And we as a government just don't do that. We never do meta-science. So one of the things we call in the in the in in Golden Age is to launch meta-science units and those have been announced at at places like NIH and NSF already. >> Nice. Okay, my favorite idea. >> Yeah. >> Uh it falls into the crazy idea, I can't believe Michael actually wrote this down. Uh is you describe the use of prediction models, crowd-sourced 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 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 ex- exchange data, hypotheses, compute, and funding through microtransactions, while smart contracts release payments as milestones are met. Prediction markets could guide grant makers, 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 deci

## bounty markets
 prediction models, crowd-sourced 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 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 ex- exchange data, hypotheses, compute, and funding through microtransactions, while smart contracts release payments as milestones are met. Prediction markets could guide grant makers, 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. I Look, I This opportunity gave This report gave us an opportunity to kind of dream big of where we could end up going. And I think it's something that is possible. And I think it really is. And I think what what we try to get at there in the report, 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 a

## decentralized autonomous organizations
ants and let's see what are the types that work, what are the ones that aren't, and then update and the next the next um iteration of them is even better. And we as a government just don't do that. We never do meta-science. So one of the things we call in the in the in in Golden Age is to launch meta-science units and those have been announced at at places like NIH and NSF already. >> Nice. Okay, my favorite idea. >> Yeah. >> Uh it falls into the crazy idea, I can't believe Michael actually wrote this down. Uh is you describe the use of prediction models, crowd-sourced 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 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 ex- exchange data, hypotheses, compute, and funding through microtransactions, while smart contracts release payments as milestones are met. Prediction markets could guide grant makers, 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 des

## four-year PhDs
vate sector and uh and only about 30% is funded by by the federal government. >> Yeah, companies can take a 10-year horizon if they need to. >> Yeah, and I think what we see and you have this flip with the 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 there's 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 made 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'll 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 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 prizes. >> Mhm. >> Uh so your report leans into prize challenges, advanced market commitments, pay for results, not proposals, right? And you cite the $10 million Ansari X Prize in there. Thank you. I appreciate it. So I spent my, you know, 30 years of my life focused on incentive prizes. And uh it's my sort of my home turf. Most agencies haven't experienced this area, haven't le

## Genesis mission
o 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 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 trillion dollar science budget. >> [laughter] >> Congratulations. We're going to be solving every problem on the planet within the next four 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 hu

## golden age
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 um >> This is the golden age of America. >> [music] >> 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 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 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

## AI native
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 um >> This is the golden age of America. >> [music] >> 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 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 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. >> Yeah. Okay, my favorite idea. It falls into the crazy 

## rewarding bold
y, 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 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 trillion dollar science budget. >> [laughter] >> Congratulations. We're going to be solving every problem on the planet within the next four 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 is is hyper exponential. Uh, and you're in the thick of it. You're in the middle of it. All right. Uh, Ray Kurzweil pr

## Endless Frontier
st look at the at the the OpenAI Foundation itself, it's almost still a quarter trillion dollars in today's valuation. Um so, to me I think we have an opportunity for 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, Marc Benioff, all, you know, funding science directly. >> They're doing 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 1950 when Endless Frontier was written by by Vannevar Bush that kicked all this off, you know, 70% of um 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 uh and only about 30% is funded by by the federal government. >> Yeah, companies can take a 10-year horizon if they need to. >> Yeah, and I think what we see and you have this flip with the 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 there's 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 made today about four-year PhDs, this idea that we want to get more PhDs out faster and

## Vannevar Bush
undation itself, it's almost still a quarter trillion dollars in today's valuation. Um so, to me I think we have an opportunity for 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, Marc Benioff, all, you know, funding science directly. >> They're doing 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 1950 when Endless Frontier was written by by Vannevar Bush that kicked all this off, you know, 70% of um 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 uh and only about 30% is funded by by the federal government. >> Yeah, companies can take a 10-year horizon if they need to. >> Yeah, and I think what we see and you have this flip with the 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 there's 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 made today about four-year PhDs, this idea that we want to get more PhDs out faster and actually have their experience 

## President wrote me a letter
 of naming ideas and we'll we'll get to those. Um so the first point you make is that scientific productivity has been declining despite larger budgets. I I I Eroom's law, right? Moore's law spelled backwards. That's sort of like the discovery per unit dollar has has dropped. >> Mhm. >> Why? What's going on here? We got better tools. >> Yeah, I I to me I think we have um been uh unable to or um or or just don't don't unable to to change the way that we conduct science. And I think this goes back to kind of one of the one of the main um sort of reasons why why we wrote this report. The the 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 we 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 one of the first things that we looked at. And we asked ourselves, you know, like what why like what 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 the NIH budget now has ballooned to almost like $45 billion yet the cost of drugs is more expensive than ever 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