a16z Podcast - Beyond P(doom): Marc Andreessen - Betting on America

Episode Date: June 29, 2026

Marc Andreessen joins CSIS's Navin Girishankar for a wide-ranging conversation on artificial intelligence, productivity growth, industrial policy, and America's technological future. Andreessen argues that while AI has already begun reshaping the economy, the largest impacts are still ahead. He explores how AI could dramatically expand access to expertise, improve productivity, and transform industries ranging from healthcare and education to law and software development. At the same time, he warns that many of the biggest barriers to progress are not technological but institutional, driven by regulation, policy choices, and infrastructure constraints. The discussion also covers the global AI race, U.S.-China competition, export controls, data centers, energy, reindustrialization, defense technology, and the role of government in fostering innovation. Along the way, Andreessen shares his views on technological progress, national competitiveness, and why he believes America still has an opportunity to lead the next wave of economic growth.   Resources: Follow Marc Andreessen on X: https://x.com/pmarca Follow Navin Girishankar on X: https://x.com/ngirishankar Follow CSIS on X: https://x.com/CSIS Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

We could have a revolution education. We could have far better education, and far lower costs. We could have a revolution in healthcare. There's all kinds of things that are possible now that weren't possible before. We could be in a world here within a decade where robots are building all the houses

at far cheaper prices than today. Technology is a lever that could cause all those things happen. It is really remarkable that China has decided that open source AI is something that is good and that they want to exist and that they want to propagate. We're in a weird state of the world where the supposedly totalitarian regime is trying to open up the technology

and the supposedly democratic governance system is trying to restrict and control the technology. We live in this bifurcated economy where we've decided that some sectors are going to be subject to technological change and price declines and productivity growth and some sectors are not. As the prices for the blue sectors collapse, deflation,

and as the prices for the red sectors inflate dramatically, what happens mathematically, right, is that the red sector is eat the entire economy, which is what's happening, right? Which is health care, education, housing, law, government, are eating the entire economy. Artificial intelligence is often described as a technology story. Mark Andresen sees it as something bigger.

In this conversation with CSIS's Navin Ghirishankar, Mark argues that AI has the potential to expand access to intelligence itself, putting world-class expertise into the hands of billions of people. But realizing that potential will depend on more than just better models. The discussion explores productivity growth, infrastructure, regulation, industrial policy, U.S.-China competition, and the question of whether America's institutions can adapt quickly enough to take advantage of one of the most important technological shifts in history. Exponential growth is seductive, starting slowly and virtually unnoticeably,

but beyond the knee of the curve, it turns explosive and profoundly transformative. Those are the words of futurist and author Ray Kurzweil. He argues that two world wars, the Cold War, and every major economic upheaval of the last century, failed to make the slightest dent in the pace of technological progress. The disruptions are real, but the curve inevitably wins out. That's the accelerationist thesis. Now, even if we were to accept that society will always yield to technological progress, that is a prediction, not a policy.

And predictions, however accurate on the trend, tell us nothing about the transition itself, who wins and loses, whether institutions can absorb the shock, and what government and the private sector must each do to ensure that the gains are broad and the losses are survivable. That is the question before us today. Not whether AI transforms the world.

It's already doing that, but which policies are needed to ensure that the benefits are broad and that the risks are managed, risks like labor displacement, the concentration of power, geopolitical rivalry, and importantly, physical infrastructure gaps. I'm Navin Gyr Shankar, and today I'm in conversational.

with Mark Andresen, co-founder and general partner of Andresen Horowitz, and a member of the President's Council of Advisors on Science and Technology, P-CAST. Welcome to Beating on America. Mark Andresen, what a privilege to have you on betting on America. Thank you for doing this. Good morning. It's great to be here. Great to be with you. You know, you've been a innovator, a technologist, an investor, and importantly,

which everybody knows, but importantly, such a huge contributor to the public debate, on AI and technology. And so we wanted to make sure we had the opportunity to speak with you. There are a lot of questions around policy that are very pertinent. You speak eloquently about modern alchemy,

turning sand into thought. I love that metaphor. And you talk about the AI boom and that it's actually not quite here yet. It's coming. So help us, give us your picture on what this looks like when the boom actually arrives.

What will live? life look like? Yeah, so, I mean, so I think there are a lot of questions. I think there are a lot of open questions around that. I think that, I mean, the big thing I always kind of point out, like, I think it's very easy to find people who have a utopian view,

you know, basically where, you know, we're off to the races. Productivity growth, you know, goes to 10% or 20% or 30%, economic growth follows, you know, material prosperity is everywhere. Every, you know, every field is transformed. AI solves every problem. You know, there's kind of that view.

And then, of course, it's also very easy to get the dystopian view of, you know, doom and death and destruction. And many people are out, you know, selling books, you know, based on that idea. I think maybe I tend to have a little bit more of a nuanced view, which is, you know, we have the potential for something resembling the utopian view, but we have a set of policy choices that are between us and that. And, you know, many of these policy choices are choices that have been made over the preceding 80 years. in terms of really sharply restricting the ability for technology to actually affect the economy and day-to-day life in many, many ways. And, you know, AI does not make any of those go away.

And in fact, it may well be a catalyst for more of those. And so I would put myself, you know, I call myself an optimist, not a utopian. And then, you know, some days, you know, when I take a look at what's happening, it feels like health care or education, housing, law, and others, you know, I maybe even become a little bit pessimistic. And so anyway, like, I think this is like an actual complex, nuanced conversation needs to happen. And I think we'll probably touch on a bunch of that today.

Yeah, let's take a couple. One thing you've said, which I find resonate strongly, is that AI is going to be your new, brilliant, genius friend. Whether it's a private tutor for your kids or whether it's your financial advisor, your legal advisor, it's kind of a companion and tailored to your needs. And that this is something that we're all beginning to experience, all of us who are doing this. And you've said that intelligence, in a sense, is the real differentiator in human history with respect to progress.

And that AI, I guess my question is, does this become the great equalizer on intelligence? Or is it a magnifier of the differences? Let's start there, because I think it's an important question. Yeah, you probably know. There's actually been some research studies in this so far that kind of, you know, frame the question, consistent with what you just said, which is basically, you know,

there are many fields, you know, in which there's, you know, there are sort of superstars who are like hyperproductive and then there's sort of rank and file, you know, people who are kind of, you know, average levels of productivity. And so there's this question of, right, is AI an excel,

does AI basically cause the superstars to become, you know, a thousand X superstars and, you know, kind of caused the power lock curve just like way up, you know, for the outliers? And or does it cause the median performer to, you know, to become much better, right? You know, good to become very good. And at least so far in the research, interestingly,

the answer is yes to both. You know, which is it does both. And a way to think about it is, you know, this should make a superstar lawyer as an example or, by the way, Hollywood screenwriter or computer programmer, you know, far better, but it should also raise, you know, raise the average. You know, there will be a huge amount of focus,

obviously, you know, politically on the distributional effects, you know, which are important. But I think that the dominant thing is I think everybody gets better. You know, having said that, the other side of that is, you know, the way I described what you said is, yeah, you now have, you know, the world's best doctor in your pocket, you have the world's best lawyer in your pocket, you have the world's best accountant in your pocket, you have the world's best teacher in your pocket. But that's immediately where, again, I run up against the kind of real world and political policy constraints, which is, A, I actually can't be your lawyer because it can't get admitted to the bar. Yeah. It can't be your doctor because it can't, you know, be admitted to the, you know, it can't actually be a doctor. it can't, you know, by the way, for example, it can't submit for reimbursement on insurance.

Right. Right, which is a key function of doctors and hospitals today. It can't be your CPA. Like, it can't get licensed as CPA. By the way, it can't be your teacher because, you know, as you know, K through 12 teachers are a, you know, government sponsor monopoly. And, you know, you can't get a, I can't get a credit as a teacher. So we're going to be in this world in which the software is going to be much better

than almost anybody you deal with in any of those professions. And yet those professions, as far as I can tell, are going to stay completely untouched. Yeah, you know, it's another way of saying, if intelligence becomes less of the binding constraint on the margin, then what becomes the binding constraint? Is it the way we interact with each other? Is it our values?

Is it how our institutions function? Doesn't that, it shines a light on our weaknesses in that realm, right? Yeah, that's right, right. Exactly. If you, yeah, if you remove variables, then you max out the impact of the remaining variables. So, you know, it's 100% correct. And so, yeah, so, I mean, like, you know, there's, as, as you well know, there's already, like,

extensive politics around things like, you know, K through 12, you know, teacher unions as an example, like, you know, if everybody in the world has the world's best teacher in their pocket, then, you know, all of a sudden, the entire point of being a teacher on the K-12 system is going to be the government protection of your job, which, which, by the way, is the direction that that field has been going in for 50 years anyway. And so it'll just blow it out all the way, right? So K through 12 teachers become a purely political function, which of course, you know, in two large extent they already are.

Purely political or maybe they teach something else, right? Like I mean... No, they don't. No, not at all. No, no, no. They won't change at all. They don't have to. They're completely protected. Right.

You're making a political economy point. Fully appreciate the point. I'm just saying that ideally, if more and more of that function is taken over by AI or supported by AI, then what, if anything, do teachers do? there's an opportunity for them to do other things, right? Teach other things, like perhaps more interpersonal skills or values

or I don't know what it is, but it's not the thing that the AI is doing, right? Yeah, so look, if we didn't have government, let's hypothesize the world where we don't have the government protections and controls, right? So it's somehow, you know, K-12 is a free market system like everything else, or, you know, like people want to imagine it could be. So you may know there is a school that is doing what you described. There's a private school system called Alpha School. or if you've heard of it yeah yeah yeah so it's a case study for what you're describing so it's a

private school so it's outside the you know the public outside the public system it's a you know completely paid you know it's a cash pay thing you know parents it's obviously you know it's expensive so you know most you know it's out of reach obviously of most kids most parents but it is it is a model of what you're saying and i'll just describe it for a moment so the guy who built alpha schools this guy joe lemont who's one of the actually like a like a real software legend you know in the technology field you know from the 90s a really brilliant guy, and he spent the last, I don't know, 15 years or something. And I think he's put like a billion dollars of his own money into it. Like, he's very committed to this.

And so he's built this new school system, which is, by the way, which is in-person schools, which he's building all over the country, you know, kind of as fast as he can. And the model is that the academics are, so there's classrooms and their teachers, you know, just like an existing school, but the day is very different. So there's two hours in the morning of actual academic instruction, which is run by AI. And so it's AI-mediated, you know, sort of computer-based instruction. The point of that being that the AI is already a better teacher than most human teachers, and then specifically the AI could be in a one-to-one relationship with each student.

And so each student stays in what's called the zone of proximal development, which is they're sort of proceeding as fast as they can master the material. The teachers are there, but the teachers are there to assist in that process for that two hours. And so the teachers are there when a student's having trouble with something or something's confusing. And the other six hours of the day, the teachers are primary, but that's not academic instruction the way you're used to think about in the classroom. It's all project-based work and activity-based work. And so it's students coming up together to, you know, whatever,

to have a, you know, community garden and learn how to take care of plants or to, you know, learn how to start a small business, right? Or learn how to do, you know, whatever it is, you know, that you have, you know, long projects on, you know, like, model UN or whatever the version of that is today that, you know, people do for, like, learning about government. And so to your point, like that the teachers are hands on with the kids working on all of these kinds of things that in a normal classroom you never get to. Now, the challenge is Alpha School is a private system.

Of course, the existing U.S. educational system is going to do everything possible to marginalize or destroy it. Yeah. The existing government K-12 system will not do any of what I just described. And, yeah, and fundamentally, you know, very little will change. But what's really interesting is the story you're telling is how technology is providing the motivation and the driver for institutional change, or at least the impetus for institutional change. I appreciate your optimistic framing. My observation of institutions is they don't want to change.

They have no attention to changing. I agree. And it's a hard thing to make them change for sure. Yes. Yeah. But the opportunity is provided by technology to do something that has not been done before. Oh, yeah, 100%. Look, we could have a revolution in education. We could have far better education, and far lower costs. We could have a revolution in health care. There's a lot of all kinds of things that are possible now that weren't possible before. By the way, housing construction, I mean, you know, we could be in a world here within a decade where robots are building all the houses at far cheaper prices than today.

You know, you could open up. And then self-driving cars open up entire areas of geography in the country for, you know, for housing, you know, much better housing at much lower cost. Yeah, you could, I mean, but the revolution in government services, you can imagine the government, you know, literally, you know, becoming, you know, state of the art, you know, if you look at what the National Design Studio, for example, is doing right now in federal government trying to, you know, make government services as,

as compelling and easy to use as private sector consumer offerings. Yeah, like, yes, the sort of modern alchemy of AI is a, technology is a lever that could cause all those things happen. I just, just observing the behavior of all the, of every single institution that I just referenced, they all seem 100% opposed to that. I fully appreciate it. In fact, I want to come back to the question of public sector reform

through this conversation. But I want to just put out the notion that it's not just the age of AI. It could be the age for institutional reformers. And it's something for us to consider. But I want to come back to regulatory constraints in a second. One more question for you. How do you see the productivity boom playing out?

Because I've heard you talk about it and give me a second here. I've heard you talk about that upward sloping curve to the right in terms of productivity, enhancements in the aggregate. And again, that resonates strongly. Let's see how it plays out through different sectors. But that's the macro story.

What about the mezzo and micro story? Because while it's upward sloping to the right, it could be pretty bumpy along the way, and there could be winners and losers, and I just wanted to get your thoughts on that. The thing with the modern economy, the thing with a modern industrialized economy is the productivity growth,

or, by the way, productivity decline, it varies dramatically by sector. Yeah. And so there's no longer an economy-wide concept of productivity growth that makes any sense. You have to disaggregate by sector. And what you find in the charts, basically the chart that I always use

is sort of separates between the sort of red sectors and blue sectors. So the blue sectors are sectors in which there's very rapid productivity growth. There's very rapid price declines. And there's very rapid technological innovation. And these are sectors, you could say, like television sets, you know,

consumer electronics television sets as an example, software, entertainment content, you know, basically toys, by the way, fall in this category, where you have this sort of hyper deflation of prices over time because of really rapid productivity growth, technological advances. But then you have the red sectors. The red sectors are the sectors in which you have either zero or probably negative productivity growth. You probably have productivity declines happening. those sectors are specifically health care, education, housing,

and then I would add to that law and government, which are often sort of excluded from the economic analyses, but I would argue, I would put those basically as like five. The red sectors are characterized by rapidly rising prices, rapidly rising prices, rapidly rising spend, zero or negative productivity growth, and almost no technological innovation to speak of. And then, of course, the other point,

Part of it is the red sectors are sectors in which there's heavy government regulation. And then that government regulation from an economic standpoint takes the form of two mutually reinforcing factors, which is restrictions on supply. So those are sectors of the economy in which there are cartels, monopolies, oligopolis, you know, licensing restrictions, inability to fundamentally, you know, compete. And then because of the spiraling upward prices, there's subsidization of demand. Right. Right. You see this with housing policy all the time now, which is like, well, it's too expensive to buy houses,

so therefore we're going to subsidize home buying. Well, if you subsidize a market in which you've restricted supply, you just cause prices to rise further. Right. Which is why those sectors have this upward spiral. And so we live in this bifurcated economy where we've decided that some sectors are going to be subject to technological change and price declines and productivity growth,

and some sectors are not. And then mechanically what happens as the prices for the blue sectors collapse, deflation, and as the prices for the red sector is inflate dramatically, what happens mathematically, right, is that the red sector is eat the entire economy, which is what's happening, right? Which is health care, education, housing, law, government are eating the entire economy. And so a modern Western economy consists increasingly of the sectors that are not affected by technology. And this is very important because this is the world that we've been living our entire lives. Everything I just described has been for sure the basically state of

affairs just 1970. The change actually, of course, started in. in the 1930s when the federal government became much stronger. The consequence of this is if you go back 100 years, productivity growth was running two or even three times higher than it is today. Right. And so we think that we live in an era of rapid technological change. There's endless books and magazine articles and news stories about how we live in an area of incredible technological change.

We think the computer revolution has been this huge change. We think the Internet's been this huge change. We think AI is going to be this huge change. And if you look at the economic statistics, the result is super low productivity growth and super low economic growth. And so this is the problem, right, this is the problem is you can have the best technology

in the world that could bend these curves. And if the policy setup in those industries prevents that from happening. But there's another way to think about it is it's just going to be true. We're just going to take all of the monetary gains that we get from AI, and we're just going to spend them all in healthcare and education.

So interesting. And real estate, right? Like, that's where all the money is going to go. Yeah. And by the way, Everybody seems fine with this. Like, you know, this is sort of the state of sort of, I don't know, this is like my state of sort of disassociative, you know, living, which is like everybody seems totally fine with this. Like everybody keeps talking as if there's going to be a big technological revolution.

And the technology is changing fast. But the actual impact of it is going to be much, much less than people think. And I think 20 years from now, we'll look back and we'll say, well, wow. Like, why didn't we get the pay? Like, where's the economic growth? Why didn't we get it? And then, of course, the answer is we didn't want it because we'd rather have, you know, we'd rather have health care education and housing, work the way that they do today.

Yeah, and I think that, so now I understand your skepticism about institutional reform. Yes, exactly. And I'm going to come back to that again. But let's just, so we're in the early innings of this. There are many different potential constraints. And I think you're pointing to the fact they're ultimately policy and regulatory. But there is an infrastructure, AI infrastructure, build out that's happening that has some constraints,

whether it's on energy, labor, others, permitting, I should say. And then there are these constraints on particular sectors, even as AI, you seek to flow AI through those sectors. What are the big, big constraints? Like the top two or three we should be thinking about when it comes to policy and regs? Yeah, well, so look, on the supply side, so on the supply side, you know, basically what the state,

The state of affairs right now is basically at every single component that goes into the stack of infrastructure and technology and capabilities that are needed to field AI. There's basically a bottleneck on every single, at every single layer of the supply chain. Right. And so, by the way, it starts at the very bottom with energy, you know, where there's bottle like energy production,

you know, for reasons that you well understand. Then there's bottleneck on literally physical facilities, physical plants, right? So, you know, the big data, center controversy, right? Right. And the whole thing on that. There's, by the way, there's constraints on all the physical infrastructure that go into building data centers.

For example, turbines are sold out, I think, for four years. Yeah. Like, you can't buy turbines. You can't buy transformers. I know of one hyperscaler that's actually milling its own turbine blades to try to get new turbines into, for power generation. Cooling systems are sold out. You know, the big, the big AX systems that you need,

big water cooling systems. And then inside the data center, you know, and, you know, Nvidia, you know, the GPUs and the chips that go in are in very tight constraint. Memory chips, you know, the price of memory chips are exploding right now. And the companies that make memory chips, their stocks are exploding because they're shortage of memory chips. Right. And then you even go deeper,

you even go backwards into the raw materials. The actual raw materials, like the rare earth materials that go into, like high and semiconductors themselves are becoming bottlenecks. Yeah. And so there's physical constraints actually at every layer. And that's important, you know, for several reasons. One is that it actually means that the AI products and services that you have access to today as a consumer of the business

are actually not as capable as they could be if the supply chain was more liberated. So you're actually getting dumber versions of the AI today than you could get if chips were more plentiful. Because they're constrained. They literally, these companies don't have enough chips and power and data center space to be able to train more advanced models. And so you're getting worse versions of the products, and then, you know, this is going to hit pricing.

And we've been in this world for the last like five years where the price per token of intelligence has been hyper deflating because the algorithms are getting so much better. But that is rapidly running up against these physical constraints of being unable to build new data centers. And so I think the price declines and intelligence are going to stop. And in fact, it may be that actually intelligence is going to start getting more. expensive because of those constraints. Such a great insight, such a great insight. But let me ask you something there because I could think on that full list of problems

you identify, there are several things that could be done, either at the federal level or the state level, whether it's permitting, whether it's constraints on energy, so on and so forth, maybe even labor. But here's one that's sticky. And we're looking, we look at this often in our institution here at CSIS is the tariff agenda because the tariff agenda cuts against some of what we need to do on the data center buildout, doesn't it? Yeah. Yeah, so tariffs, I mean, look, there's sort of the, you know, there's sort of the, you know, I don't know, whatever, the classical kind of economic view of tariffs,

you know, sort of a form of taxation. And then, you know, and then you get into the, you know, question of, you know, reindustrialization question. Because by the, you know, just as an example, one of the things we haven't touched on yet is Taiwan. Right. Right. Because, because I'm not laughing because it's very, it's very serious. it's kind of amazing how serious it is, which is, you know, we are completely dependent on Taiwan, Taiwanese tabs for the chips right now.

Right. To a degree that I think is actually bad for Taiwan. Right. Right, because the fact that Taiwan is so central for the making of advanced aid chips makes them an even bigger prize, you know, where the Chinese government decided to move. So I think Taiwan, Taiwan sort of amazingly is like,

Taiwan's almost like too important for its own good right now. Right. And so, and then there's all the strategic kind of aspects, which is if the Chinese do ultimately move, move in Taiwan, it's like, okay, are we going to be able to get chips? Are we going to be able to build anything? You know, and so there is this need to reindustrialize. 100% several reasons, not least to which is natural security.

Yeah. And so then you get, you know, the industrial policy debate. But I will tell you that the other thing about the tariff thing, which I find fairly amazing in the discussion is, you know, a tariff, it's really funny. A tariff is, of course, it's a tax on international, you know, financial transactions, trade. And there are people, you know, many people who have, you know, high moral dudgeon about, you know, about that as being somehow, you know, very, very

bad. But we have many internal, you know, as we've been discussed, we have many internal constraints on trade. You know, we have many internal taxes and many internal restrictions. And I find a lot of the arguments on this whole thing sort of suggest that, like, tariffs is a huge crisis, but somehow all of our internal taxes and restrictions on trade are kind of something. It's both.

I'm just saying that. If we're trying to solve the problem you're talking about, which is the data centers, the physical infrastructure is now going to become a constraint on AI. Don't you want to remove all the obstacles to it? That means the permitting stuff, but also it means tariffs, right? Yeah, but like 99% of the practical restrictions,

the practical restrictions and constraints are not the tariffs. 99% are on the things we do to ourselves inside our own country. Fair, fair point behind the border? Yeah, so I would just, yes. So it would just, every, whatever I'm reacting to is not you. I'm right I'm reacting to, you know, five, you know, four years right now of sort of this kind of, you know, hysterical kind of frenzy in the press

and among the pundit class on the tariff topic from people who would think it's a great idea to have all equivalent taxes and restrictions on internal trade. So it's like the only thing that people get upset about in the public discussion on this is trade with foreigners. Like trade domestically is former constrained and controlled. Yeah, and we should be upset about both. I'm just saying like there are two dimensions to it.

there's the cost dimension, and then there's the volatility, the erratic nature in which these things have been implemented. None of which is good for it. I mean, you tell me, is it good for investors? No, I'm just saying 99% of the issues are internal. Yeah. Fair point. It's almost entire, like, the internal,

what's happening literally in the U.S. right now, county by county with the ability to build data centers is, like, profoundly destructive. No, true. Yeah. And that's like, that's entirely domestic. And a large number of politicians are, like, feeding that hysteria as much as they possibly can't. Right.

And a lot of our leading public figures and a lot of intellectuals and a lot of the press and a lot of the analysts and the rest of it is just like this kind of hyper paranoia about building data centers and the consequence of data. I was giving an example, this completely fake meme about water use. Right. Which is just like factually not true, which is just like running wild through the public discussion that somehow these data centers are like basically destroying all the water. which is like this completely insane idea. Like that factor is like so much a bigger factor holding us back than anything involving internal trade. And so it's like external trade is the thing that's easy to talk about.

It's all of our internal issues that are like much, much more important. That's a very fair point. Let's just talk about models for a second. So, you know, the mythos case and the export controls that were put on it, it's really interesting because whether or not the Commerce Department has the legal authority to do it as a separate question, I look at this and I pose it as a question for you, is the use of those export control is really just a reflection of some weaknesses around our approach to safety and governance? Because the EO that was issued by the White House just right before that was quite reasonable and reasonable approach. I would say quite a well-thought-out approach.

But then obviously crisis hits and then this export control is put in place. How do you assess that whole thing? Because that's like as far as models are concerned, separate from leading edge chips, that's an important question that we would have to answer as well, no? Yeah, so I think there's a whole bunch of, you know, very complicated topic.

There's a whole bunch of factors. I would start with a very high-level kind of view on this, though, which is we have, is often the case with anything complicated in the real world. There are multiple contradictory goals. You know, we would like to be able to solve them all at the same time, but it's hard because they conflict. And so let's just start with the U.S. versus China part,

because I think that drives a lot of this. Okay, yeah, yeah. Because I think if China didn't exist, I think we'd be having a different and simpler discussion because it would just be about us. Right. To start with, AI right now is a two-horse race. Like, it's U.S. and China, you know, effectively there's no other player.

By the way, there could be other players, specifically in Europe, they've decided to make everything illegal. So they've suicidally taken themselves out of the race, which is a whole other thing we can talk about. They've taken every bad idea that we have and kind of maxed it out to 11. And so they're maybe becoming a case study of what not to do, but be that as it may, it's basically a two-horse race now. It's U.S. versus China.

So to start with, we have two contradictory goals. One of which is we want to make sure that the U.S. wins the global technology race. Right? So we want to make sure that when we wake up in a decade, the world is running on American AI and not on Chinese AI, right? And in fact, ideally, what we would like to do

is live in a world in which China itself is running on American AI. Which today sounds crazy, but that actually was the ultimate resolution of the first Cold War with the Soviet Union, which is, you know, as you know, the Cold War with the Soviet Union ended because the Soviets ended because just becoming part of the West

as best that they could do that was a better outcome than trying to run their own parallel system. Right. And so, like, I think we have a vision of sort of global technology supremacy that says the entire world runs an American AI, including ultimately China. To do that, what do we have to do? We have to export. Yeah. Right? We have to take our technology and we have to make it available to the world. Yeah. We have another goal, which is, as far as I can tell, just as important, which is AI is a, you know, extremely disruptive new technology.

It has profound, not just economic. implications, but also national security implications. Also, by the way, competitiveness implications, right? Right. And with that goal, we need to, you know, we need to control and restrict and constrain and maybe even hoard AI to ourselves, right?

We need to make sure that the AI is this magic technology that only we have and we need to make sure other people don't get it. And we have to absolutely make sure that China doesn't get it. Yeah. Right. And, you know, and this goes straight to topics like, you know, for example, chip export controls, right? which, of course, you know, we're in place even even before the mythos issue.

And so, but right away there, you can see, like, these are directly contradictory goals. And I think what, right, and I think what you have, what you, I think what you have in the, in the, in the, in the, in the, in the, in the, in the, in the, in the, in the, in the, in the, in the, are, in the, are exactly, in the, in the, are exactly contradictory with each other. And so I think that's actually the underlying kind of logical question that you have to have. Then the other example of that directly on your mythos point, I would say, is another example of sort of diametrically opposed goals, which is now you have a level of capability with this technology, starting with the current models and the next set of models like Mythos, where they are better than human at both attacking cybersystems,

and they are better at defending cyber systems that human beings are. And so you have these models, they're a threat of disruption. And of course, this is where the current U.S. government's very worried about disruption of the financial system, mythos models being used by bad guys, criminals, or terrorists or foreign governments to, you know,

for example, break into and really wreck U.S. banks or U.S. stock market or whatever, which is, like, I think, a very legitimate concern. But you also have this diametrically opposed thing where the same tool that's good at penetrating is also very good at defending. Right. And so the other thing you need to do

is you need to get those tools in the hands of every existing company and business everywhere in the West, everywhere in the U.S., and you need to fix all the security holes and all the systems and have new kinds of AI cyber defenses and everything. Yeah. But again, here, you can see this thing

where these are directly contradictory because the more scared you are of it, legitimately scared you are of it, worried about it, the more you want to restrict it, but the more you want to actually use it as a prophylactic to make sure that all of our, you know, banks, for example, aren't subject to cyber attack, the more you want to deploy it.

Yeah. And so, anyway, so I just, you know, a lot of people, when they engage on these issues, it's sort of, you know, they question people's motives. I think in this place, in this case, you've got it in both cases, you've got these, like, directly contradictory motives and you have to go straight to the underlying conversation of, like, which is actually the most important goal before you can figure out what the right policies are? 100%.

And by the way, you describe two groups of people, one that holds the kind of innovation. goal, the other one that holds the safety goal, I would say, like, a lot of times the same person is trying to balance those two objectives in government. Having served in government, I recognize many people like struggle and wrestle with that. So, I mean, I'm going to ask you because in a way, you're now on the President's Council of Advisors for Science and Technology. What would you advise them to do? Because how do you weight these goals? Because I can imagine at any given point in time, and one becomes more important than the other. And you describe the, like, really challenging situation that we're in.

Yeah, so my view, my normal view on these things is basically, it's sometimes called the technological imperative, which is basically this idea of, like, you don't uninvent new technologies. Right. Like, once a new technology exists, it exists, and it's going to make its way into the world. Like, it is going to have a way of making its way out. And, you know, there may be physical constraints or whatever that prevent it from being fully realized everywhere,

but fundamentally, you know, I don't know, once the process for making steel, like, was a known thing, you know, it was inevitable that all military equipment was going to get made out of steel. And by the way, and so was all civilian equipment going to get made out of steel, and that was going to happen. And the same thing, same thing for steam power and the same thing for electricity and the same thing, right?

The same thing for, you know, was the Haber walk process. And you just go right down the list of all these innovations and, you know, the computer chip. And they were going to happen. And they may happen faster or slower, but they're going to happen. And so if you're going to live in that future world, in my view, you want to be as strong and powerful and dominant as you can possibly be when those things do happen, right?

You want to win. And to me, victory, like if I were, you know, king for a day, victory would be, like I said, you would set a vision. You would say, we're going to have a world in which the entire world is going to run an American AI. And American AI is going to be so good and proliferated so broadly and going to be so universal in the world that even China is not, at some point they're just going to say this isn't even worth competing with us. Like this is a complete waste of time. And so I would come out very strongly on the side of you kind of want, you want maximum export, right? You want to just like basically turbocharge exports.

You want the U.S. government working hand in hand with the companies to figure out optimal policies to make sure that American AI at the software level, chip level and so forth, you know, basically proliferates and runs the entire world. Now, having said that, I think the people who are arguing, for example, for chip export controls against that are doing so in completely good faith. And I think they have a, you know, I think they have very reasonable arguments for what they're doing. But but I would go on that direction. And then the, and then on things like Mentos, the direction I would go.

And as I would say, look, the whole reason why we're worried about like cyber exploitation of systems is, so this is actually very important. So AI hacking does not create new security vulnerabilities that don't already exist. AI hacking exploits existing security vulnerabilities that already exist. And those security vulnerabilities are subject to being exploited both by AI, but also by non-AI hackers. And of course, banks and all these other government agencies are getting hacked all the time, even without AI.

And then AI hacking is going to be much more effective. And so I think you need to get the defenses. We need to focus on the defenses. When you get the defenses in place and the way to get the defenses in place is we need to use these new advanced AI models. You need to put them in the hands of all companies as fast as possible to be able to basically armor up

and have AI defenses against AI hacking and non-AI hacking, right? Like, for example, this is also how you solve the ransomware crisis, right, which is you need to go fix all the systems in the hospital so that they can't be held hostage by ransomware. And so again, I would err there on the side of proliferation. I would say we have to get mythos or equivalent model capability into everybody's hands as fast as possible so that we can do the defenses.

But again, I think the people who say, no, that's irresponsible because that's putting this sort of cyber weapon in people's hands before the defenses are ready. Again, I think that's a very good faith argument. And I think that people are arguing that are doing so out of a good place. You know, I guess I would say this. The wins are going against me on both of those topics. And so it feels like I'm not going to be king for a day.

And so it feels like from all to Greek. We're going to be living in as one in which probably the opposite arguments are going to prevail. Yeah. If I might just offer a couple of reflection on that, that was a great rundown. A couple of reflections on that. One is that when it comes to chips, for example, fully accept your point that over time diffusion is going to happen,

you can't prevent it. But whether you can change the timetable is an open question, especially when it comes to chips. And that timetable can be critical depending on where you are in your competition with China, for example. Would you agree with that point?

So I think that's true, but also I think something else is true, which is if you deny them chits, you incent them to create their own chips. And you see that happening already. Yeah, we do see that happening. already, yeah. And they can start creating ecosystems that prevent us from entering them.

They can advance faster than us. And then now we're not close to where innovation is happening. All of those things are true. But at the same time, taking that off the table is a real challenge. Like I find it very difficult to say we're going to seem unilaterally not use this instrument if we can use it. I think the challenge is, or the question is, how do you use it where it really hits the mark? rather than sort of taking a buckshot approach and using export controls all over the place for every problem,

which is really what I think sometimes we over-index on that. Yeah, I mean, you can try. You know, this is all the discussions around the doctor policy, right? You can try. You know, I'll just give you my background here. So my first commercial product I ever built was the, and took the market was the NASK browser in 1994. It was expert control.

Yeah. It was classified by ITAR as ammunition. It was in the same classification category as a Tomahawk missile. Yeah. It was explained to us by our lawyers and know on certain terms that we could not possibly let us outside the U.S. Right. And by the way, when it started, you'll enjoy this.

Actually, when it started, encryption was such a sensitive topic of the 1990s that we were actually expert controlled not just on strong encryption, but also weak encryption. We couldn't even ship browsers or server software, a web server software that had weak encryption. And it took, you know, it took years to get the government to basically come to grips with the idea that if we were not allowed to do that, you were going to, what was happening, of course,

is what you'd expect, which is the growth of web software companies in many other countries that were not under, that were not under strict constraints. And so, and again, the people who argued, you know, we had long arguments with a lot of folks, including in the intelligence community and others. And, you know, they had very good arguments. I mean, you know, they come in and, you know,

I don't know, you've probably been through this yourself. they come in and they show you like, okay, here are the bad guys. Here are what the bad guys are doing. Like, here's the dangers, here's the threats, here's the stuff that your encryption is going to cover up and make it harder for us to prosecute or catch. And like I think those are all legitimate, legitimate points. Having said that, you know, again, back to the core argument is do you really want to live in a world in which that means that U.S. technology loses?

Because encryption was going to happen. Right. Right. And they're like, I used to own a T-shirt. I probably still have it somewhere. I used to own a T-shirt. I remember the RSA algorithm was like the key in Christian algorithm of that era. And there was an implementation of the RSA algorithm, which was just math.

There was an implementation of it in four lines of code. It was a sort of very complicated, hard to read code, but there were four lines of code. And I had a t-shirt that had the four lines of code on it. And, of course, the joke, which wasn't a joke, was that t-shirt was ammunition. Like, it was actually illegal. By the way, I never tested this. but in theory, if I had worn that T-shirt and born in the National Flight, I could have been put in Jeff.

Wow, yeah. So there's that. What a great story. Yeah, yeah, yeah. It took years. It took years to work through that, right? And so it is kind of amazing. Okay, so then on AI, like, AI is math.

Like, at the end of the day, it's math. Like, it's actually, by the way, it's actually remarkable. It's actually quite straightforward and simple math. It's basically linear algebra, and then it's a handful of algorithms with things like radiant descent, reinforcement learning. It's math, and you've probably been watching this, or I know your organization's been tracking this, which is the version of the math

that implements a model at whatever, GPT 5.0 or 5.5 level or a mythos level or whatever, like that math looks hard and expensive for about six months. And then somebody figures out a way to run it on a PC. Yeah. They figure out a way to shrink it down and basically run it on a piece of consumer hardware. Increasingly, by the way, these things just run on your cell phone.

And the lag time, between the new version of the AI, the new capability being something rare and special that you can control, because you can control where the data news get built to being something that is in open source. By the way, open source from the U.S., open source from China,

or open source, you know, in theory, from anywhere in the world. You know, any academic institution could do this now. You create the open source version, and then you have a version that can run on a PC or can run on a phone. And so this goes back to, like, in theory, you can calibrate

who gets access to what and when, and in theory, you can kind of do this dance. Like in practice, you do find yourself, in both in Christian case, in the AI case, you find yourself trying to control the propagation of math, which is an extremely difficult thing. And then by the way, there's another kind of dimension on this that I would put out there, which is if you really want to make sure the powerful AI doesn't proliferate, and if you talk to the people who are very worried about this,

they will say this with complete seriousness. Like you have to start to watch what people do on all computer systems. Yeah. Right? You have to start to watch what happens on every chip. Right. And so, so, so what, like the policy recommendations that ultimately flow out of this line of thought include things like putting a software agent on every chip on every computer everywhere in the world, including every, all the computers in your house. Yeah. Right. Including your kid's laptop, right? And you put it, you put an agent on that. And that agent reports back to the government, like what that computer is being used for. And if it's used to run, you know, AI is too powerful, you know, then there need to be some set of consequences to it. Yeah. Right. And then, and then, and then, and then, and then, and then, and then, and then, and then, of course, the very next thing is, well, that needs to be a global regime. Right. And in fact, you have to, you have to, you have to.

you hear this from a lot of people in the industry, they're like, well, we need a global governance regime. It's like, well, what does that mean? Well, it means like a UN with teeth that, like, controls global use of software. Yeah. And you find yourself walking down this kind, in my view, my view, you find yourself walking down this kind of

1984 Orwellian totalitarian, you know, playbook where a big brother is watching what happens on everybody's, on everybody's computers, like all the time, and then stepping in when you're running on approved software. And so, again, it's just like, you know, in theory, you can kind of, play this game, you know, you can kind of do the dance. I think in practice, you know, the sort of downstream effects get to be quite scary.

Such a great rundown of so many different issues and how they're connected. I would just say that you make a very compelling case, like focus on innovation and innovating faster. That's really the only long-term way of staying ahead and remove the obstacles to doing that. Because trying to, I mean, trying to apply an export control on a model is like, exceedingly difficult. I don't know how you would implement that and enforce it effectively.

But the second thing I would say is just look at what is happening in the PRC, which is a real commitment to diffusion and a real commitment to using AI in various realms of the economy. And I wonder whether our challenge now, especially coming on the heels of a, you know, on the run-up to an election,

skepticism about AI. And the fears about it are like the overwhelming thing, and I think it might be getting in the way of our staying ahead in the tech race and getting all kinds of economic benefits from that. You agree with that? Yeah, I agree for sure.

And by the way, I'd start just to stay on the geopolitics for a moment. It is really remarkable that China has decided that open source AI is something that is good and that they want to exist and that they want to propagate. Like, we're in a weird state, we're in a weird state of the world where the supposedly totalitarian regime is trying to open up the technology

and the supposedly democratic governance system is trying to restrict and control the technology. Like it's the opposite. We're in like opposite world from what you would think. But that might just be a reflection of where they are in the race, right? So they're also restricting critical minerals

in a pretty coercive way. So I think that the minute if the balance, God forbid, the balance were to shift, then I can't imagine that they would be committed to open AI models just because of the goodness of their heart, right? So it might be just a reflection of where we are, no? Oh, I mean, I would take it a step further. I think it's a deliberate strategy.

I mean, I really agree with what you just said, which is I think it's a deliberate strategy. I think the Chinese, and by the way, I think the US government believes this very specifically, which is that the Chinese are very deliberately, the Chinese CCP is very deliberately encouraging or mandating its companies to create AI open source. and to advance it as fast as possible

precisely to prevent the success of the American industry. It's like a turbo dumping strategy. Oh, yeah, okay. Right. So flood the market with basically free AI to prevent the American companies for being able to make money on it.

So I totally agree with what you're saying. I just think it is fairly amazing, at least for now, that they are the proponents of free and open. Let me take a... Let me take 30 seconds. I got to ask you something because a lot of the people

who argue for the pro-innovation stance on national, let's just call it, economic competitiveness and national security, right? The argument you've made. It resonates with me. But invariably people who hold that position, when you ask them, what about deep civil-military fusion in China

and the risks that a broader set of commercial technologies are dual use, I don't get a strong answer for them. Give me your strong answer to that. Because it's true. They have a policy of deep civil military fusion. My question is that your argument that the only way to get ahead of it

is to out-innovate and have them use American AI. Like, we need to stay ahead. But in doing that, this would be my counter-argument with others who say, just open the doors and just let us trade, let us kind of export American technology to the Chinese. You know, what about the civil military fusion risk, which is very real there?

Meaning that, if I understand properly, you're saying that the Chinese take American AI, they use it to build better military weapons. Yes, because they have deep fusion across their commercial and their military sectors. That's their policy. Oh, yeah, yeah, for sure. Yeah, I mean, look, I think that's a real,

I mean, 100%. I think they absolutely would do that, by the way. I think they're likely doing that today. Right. This is the other thing, which is, are we actually successfully embargoing chips? Like, you know, there's a lot of chips in the world. It's hard to control where they go. Yeah.

And by the way, like, here's a question. Do we think the Chinese already have mythos? Yeah. Like, all mythos is, it's a set of numbers on a hard drive. Yeah. It's a file. Yes. Like, how incompetent is the MSS if they haven't already figured out a way to die?

download that file. And by the way, any data center that runs an AI model has a copy of that file. Like that is how the system work. It's a giant matrix of numbers. And so I would say to start with what you're describing is probably already happening. A. B,

because, you know, we have to, you know, we have to, you know, we have a question whether the controls can actually hold. Like how, another way to put it is, there are no American AI companies that have anything resembling counterintelligence or any security control system that you would, that anybody with

the government background would possibly find to be even remotely acceptable. Yeah. Like they all have, they all employ like large numbers of Chinese nationals. They all employ large numbers of, frankly, Chinese Americans with relatives in mainland China. Right. You know, who are subject to, you know, to exploitation. They, you know, they run open and collaborative R&D environments. They don't have internal, you know, they don't have internal, you know, they don't have internal stope piping.

They don't have classification. They don't have counterintelligence. By the way, it's actually illegal for American AI companies to not employ Chinese engineers under civil rights law. Right? So even if you try to control for that, you can't. Like, it's not allowed. Right? I mean, SpaceX got prosecuted by the previous administration's Justice Department for not hiring enough refugees as a federal military contractor that's only allowed to, in theory, that's only allowed to have U.S. citizens work on its systems. So anyway, so, anyway, so first of all, it's likely the Chinese have everything that we're describing anyway. A. And then B,

yeah, yeah, if they get free and unalloyed access to everything, then, yeah, they're going to use it. But again, you're back to the question of Threatoff, which is, okay, if they're not using the American technology to do that, then they're building domestic technology to do it? And then do you really want to live in the world in which their domestic technology is their military technology? Like, wouldn't it be better for a national security standpoint if the U.S. government always knew that they could go talk to any American technology company for anything happening anywhere in the world, as opposed to having black box companies in mainland China that they have no access to and no way into?

Yeah. And again, but again, I would say, look, I think it's a completely legitimate question of observation, because I think there are real tradeoffs. And if American AI wins all over the world, then, yeah, American AI will be the basis of everybody's military systems. And yes, that could lead to faster advances in enemy military systems. And I think that's a completely real question. Yeah. I mean, I think this is the moment we're in, right?

Like, we, the economic policies, the national security policies of the last 75 years, 80 years, really are not crafted for the moment that we're in. And this raises a question for me. I think we need a significant public sector reform effort. I put out a peace and foreign affairs saying, America needs economic warriors. And it was kind of a... The title was the title,

but the main argument was like, we need to retool government to do the things that it has to do in this moment. And that means not only efficiencies, but also new capabilities that we do not currently have. the administration deserves some credit for doing that with tech force and other things like that. But we're far from that. And I just wanted to get your thoughts.

I think you were pretty optimistic about what Doge could do and some of these other things. But where do you think we are now? Yeah, so I think there's a bunch of people like in this administration who are trying very hard. And I just mentioned earlier the National Design Studio, Joe Gabia, who's one of the great Silicon Valley founders, co-founder of Airbnb. You know, who's literally in the White House trying to do what you're describing. I think he and his team are doing great work. By the way, a lot of the Doge people, a lot of the really sharp-capable Doge people are still in government, and I think having pretty big impact.

And so, you know, I think there are examples of that. You know, having said that, again, the main issue is not sort of, you know, what's possible. The main issue is do these institutions want to be reformed? And one of the levels of the antibodies that come out, you know, whenever there's any suggestion to reform. And, you know, as you know, the antibodies are extremely strong and vigorous. everybody who tries to change these things. I totally agree with that.

But there are better and worse ways of doing reform, don't you think? Like, I mean, I think that some people would argue that the Doge effort has left some bureaus like Swiss cheese and the holes are not where you need those holes to be, right? You know, I would love to say, like you, I would love to see other approaches to reform that works. Yeah. And I think that's the moment that we're in, Mark. Like I feel that we need American institutional reformers like par excellence who know how to do this, who can face the interests that are going to resist against it,

but also are imaginative in terms of thinking about the capabilities that government needs in this era of AI, which we're far from thinking. We're not there yet, right? And I also add, and I say this hopefully on an optimistic note, this doesn't have to be a partisan issue. Yeah. what you're saying. And in fact, you may remember, you know, there was actually the Clinton Gore administration in the 1990s had a big effort in this direction.

Yes. Called, called Rego, reinventing government. Right. Renaming government. Yeah. And Al Gore in particular put a lot of time and effort to do it and, you know, got some ways down the field. And so, like, yes, one could, one could, one could, one could certainly imagine what you're describing. I think you're 100% right that it would, that it would, that we needed and it would be great. having said, I would just say

the people who have tried to do it with whatever method have a lot of start tissue and so it's, yes, it's a, yeah, I was going to say this, it's never been harder. Yeah, I hear that. I want to ask you,

there's a lot of talk about what policy do we need for AI. What about AI for public policy? What's your view on that? How so? Well, I just think that there are a lot of policies that different politicians promote,

but we don't know if they're effective or not. And I wonder if we have an opportunity now to really accelerate evaluation in real time of what's working and what's not working, so it improves the quality of the debate on what policies we should undertake, whether it's in health care or housing or whatever it is. So, I mean, AI for policy

and policy evaluation and design. Yeah, I think, I think that's a great idea. I think the current tools are actually quite good at this. I think optimistically, you can say that this is kind of happening in the field, in the academic field of economics. Right. In a way that's sort of analogous, or maybe even directly on point,

which is, you know, my sense of like academic economics is sort of shifting from, call it the post-World War II method of sort of physics, you know, kind of physics of economics where everything is formulas. Yeah. To a, you know, the newer generation economists work much more with data. Yes. you know, they gather large datasets and process the data sets. And so, yeah, one could imagine a similar kind of change of analysis

where instead of, you know, kind of having an argument about hypotheticals and argument about, you know, whatever, you know, concepts or formulas, you know, instead you go get the data and you analyze the data. And of course, you know, AI is very good at that. And so, yeah, so for people who, you know, who legitimately want to do what you're describing, I think the new tools are quite good at that. Yeah. I would love to see if, like, the GAO and, you know,

and CBO and others really jump into this in a significant way because they could really help us understand what's working and what's not. We could save a lot of money and a lot of time, hopefully. I got to end on one thing because you have been such a great investment leader over the years. And American dynamism in many ways is quite inspiring with respect to reindustrialization and investing in sectors that VC has largely forgotten or not even looked at in the past. And we, some people say we are in the midst of an industrial renaissance.

I wanted to get your perspective on that. You know, and by the way, that effort kind of spans multiple administrations. And I wanted to get your thoughts on where we are there. Yeah. So I think there's a lot. So I'm reasonably optimistic. I think there's a lot going on. It happens on a number of fronts.

So one that's just very specific is reindustrializing, reindustrializing on the defense side. Yeah. And so, you know, your organization has studied at length. You know, there are very real issues about the physical supply chain that goes into the U.S. military and national security. And so they're, you know, we are intensely proud of our companies that are in that space. And, you know, I would say the current administration has been incredibly supportive of those efforts and is working very aggressively with young companies, you know, really for the first time in, you know, I don't know, 40 years or 80 years, you know, really, really helping, you know, get new defense companies, you know, into business into critical mass. By the way, I won't, I won't weigh in specifically on the politics of it, but the proposed expansion of the defense budget, you know, at least the promises that a lot of that money will go to these new approaches and in a lot of cases, new vendors.

And so I think that's a, you know, as you well know, like that, there was an explicit policy decision made in the 1990s to shrink the number of defense vendors in the U.S. And for the first time, we have a strategy to actually expand that, create more competition, and advance the technology faster. So that's very helpful. And then that's been kind of a bootstrap, I would distinguish. described, like, those are like early wins in a way that then leads a lot of entrepreneurs

in my world to think, like, well, maybe we can do this for other categories of manufacturing. Yeah. And of course, you know, Elon, of course was a, you know, has been an incredible leader there, but there are many, many other founders that are inspired by Elon, inspired by Palmer Lucky and the team in Anderrol and these other companies. And, you know, there's startups, you know, many of whom we're backing, but their startups doing new nuclear, you know, nuclear efficient reactors for the first time in decades, their startups doing, we have multiple companies going after rare earth, you know, mineral discovery extraction processing. There's energy.

We've actually backed a company, by the way. I mentioned electrical transformers are sold out. We backed a new generation electrical transformer company that's building electrical transformers in the U.S. And so, yeah, so I think there's optimism there. By the way, I would say even in California, you know, where there's all kinds of, you know, both a lot of good and bad things happening. But, you know, there's like a new industrial, I don't know,

even like manufacturing ecosystem, entrepreneur cluster in and around Los Angeles. Yeah. You know, around El Segundo and Hawthorne and these play, you know, where SpaceX was born and so forth. And where Andrewl is based. And so, you know, like, optimistically,

we maybe get actually two Silicon Valleys in California. We get kind of software AI Silicon Valley up north and we get, like, defense and industrial Silicon Valley around L.A. Yeah. So I think that's a possibility. Like, like, look at this way. The entrepreneurs all want to do it. The money, by the way, is lined up.

the money is available to do it. At least this government really wants this to happen and is doing everything that it can to foster it. By the way, again, I would hope this is the kind of thing that becomes a non-partisan issue, which is, you know, I think Democrats are at least as interested in reindustrialization as Republicans. And it's logically because, you know, you want,

you know, you want jobs, right? You want jobs. And all these communities have gotten hollowed out, you know, and in many cases have gone sharply to the right as a result, like you actually want reindustrialization because you know, you want those people to have good new jobs. And so optimistically, this could be a bipartisan effort.

Yeah, and I would say even the previous, we can, there's a debate on what tools are the best tools, but the previous administration made efforts around chips and you talked about chip making that were important and similarly under the IRA. I would just say, you know, across both administrations, this is a huge priority across parties, I would say. The thing that I find interesting from an investor's perspective is that are we in a moment

where you could pursue financial objectives as an investor and non-financial objectives around, say, national security or national interest without giving up returns? And it seems like you're saying we are in that moment. So look, so I don't think it's the case that like this, I don't think it's the case that there's like a direct tradeoff, at least for what we do. There's not a case that there's a direct tradeoff of like financial investing versus the larger goals, which is what you do in our world is you organize the entire purpose of the company around the larger goals. And then if you execute on the larger goals, the financial results follow.

And so I think that like our companies that have a view, for example, of American manufacturing, like they're not doing it because they're making like some explicit dollars and cents tradeoff which should we invest in the U.S. versus here versus there. They're setting a North Star goal of wanting to do something specific. And then they're basically saying like, what's the way to turn that into a mission that then attracts the smartest people in the field, right, that attracts people who are the most ambitious about undertaking your programs. You know, you infuse the company with patriotism. You get a completely different kind of energy than you get if you're just outsourcing everything to China.

You get, by the way, co-located R&D happening with manufacturing, which is actually what everybody actually wants when they're trying to manufacture anything complicated. You then bring customers into this, and the customers have their own incentives to want to buy, you know, more American-produced goods. So what you do in our world is you create the strategy, first and then you line up the financial plan behind that. And so from that standpoint, you basically just set out, like, these are the kinds of companies you're building.

You're not building companies that just defaulted Chinese, you know, contract manufacturing. Like, that's not anywhere in the DNA of the company. And then you see if you can actually build a superior model with a new approach. And I think we have, you know, probably at this point, dozens of companies that are doing what I just described. Phenomenal. Well, thank you so much for spending all this time today. And we'll be watching what you're doing and also what you keep saying about these

things, including in your role at PCAS. So thank you for contributing to the national debate, Mark. It's really fantastic. Good. Thank you so much for having me. I really appreciate it. Thank you for listening to today's conversation with Mark Andresen. You can find this episode and more on CSIS.org, YouTube, or wherever you get your

podcast. This is Navin Ganeshankar reminding you that everyone has a role to play in winning the tech race. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure. to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X, a A16Z, and subscribe to our substack at A16Z.substack.com. Thanks again for listening, and I'll see you in the

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