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The a16z Show
Beyond P(doom): Marc Andreessen - Betting on America
01:04:31
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 ha...
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- [00:01]
A
We could have a revolution in education, we could have far better education at far lower cost, 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 to 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 sectors eat the entire economy, which is what's happening, right, which is healthcare, education, housing, law, government are eating the entire economy. Artificial intelligence is often described as a technology story. Marc Andreessen sees it as something bigger. In this conversation with CSIS's Naveen Girishankar, 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, us, 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.
- [01:37]
B
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 dentist 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. Naveen I'm Naveen Girishankar and today I'm in conversation with Marc Andreessen, co founder and general partner of Andreessen Horowitz and a member of the President's Council of Advisors on science and technology. PCast. Welcome to betting on America. Marc Andreessen, what a privilege to have you on Betting on America. Thank you for doing this.
- [03:14]
A
Good morning. It's great to be here. Great to be with you.
- [03:16]
B
You know, you've been an innovator, a technologist, an investor, and importantly, which everybody knows, but importantly, such a hu 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, like give us your picture on what this looks like when the boom actually arrives. What will life look like?
- [04:00]
A
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. There's kind of that view. And then of course, it's also very easy to get the dystopian view of doom and death and destruction. And many people are out selling books based on that idea. I think maybe I tend to have a little bit more of a nuanced view, which is 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, over the preceding 80 years in terms of, you know, 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, you know, it may well be a catalyst for more of those. And so I would put myself, you know, I'm a, I call myself an optimist, not a utopian. And then, you know, some days I, you know, when I, when I take a look at like what's happen of fields like healthcare, 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 that needs to happen. And I think we'll probably touch on a bunch of that today.
- [05:35]
B
Yeah, let's, let's take a couple. One thing you've said which I find resonates 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.
- [06:21]
A
Yeah, you probably know there's actually been some research studies in this so far that kind of frame the question consistent with what you just said, which is basically there are many fields in which there are sort of superstars who are hyper productive and then there's the sort of rank and file people who are at kind of average levels of productivity. And so there's this question of. Right. Does AI basically cause the superstars to become 1000x superstars and kind of cause the power law curve to spike way up, you know, for the outliers and. Or does it cause the media and performer to, you know, to become much better? Right. You know, the good. To become very good. And at least so far in the research, interestingly, the answer is yes to both, you know, which, which is. It does both. And the 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, on the distributional effects, you know, which are important. But I. The dominant thing is I think everybody gets better. Having said that, the other side of that is the way I describe what you said is, yeah, you now have 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 AI actually can't be your lawyer because it can't get admitted to the bar. It can't be your doctor because it can't be admitted to the. 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 you can't get licensed as a cpa by the way. It can't be your teacher because, you know, as, you know, like, you know, K through 12 teachers are, you know, government sponsored monopoly and you know, you can't, you can't get it. I can't get a credit as a teacher. So, 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.
- [08:24]
B
And 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?
- [08:45]
A
Right, yeah, that's right. Right, right, exactly. If you, yeah, if you remove variables, then you max out the impact of the remaining variables, you know, it's 100% correct. And so, yeah, so I mean, like, you know, you know, there's as, as you well know there, you 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 in The K through 12 system is going to be the government protection of your job, which, which by the way is the direction that, that that field has been going for, for 50 years anyway. And so it'll, it'll just blow it out all the way. Right. So, So K through 12 teachers become a purely political function, which of course, you know, to a purely political.
- [09:27]
B
Or maybe they teach something else. Right. Like, I mean.
- [09:30]
A
No, they don't. No, not at all. No, no, no, they don't. They won't change at all. They don't have to. They're completely protected.
- [09:35]
B
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 they. 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?
- [10:02]
A
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 in other words, Somehow K through 12 is a free market system like everything else or 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.
- [10:21]
B
I've heard of it. Yeah, yeah, yeah.
- [10:23]
A
So it's a case study for what you're describing. So it's, 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, cash pay thing, you know, with parents. It's obviously, you know, it's expensive. So, you know, most, you know, it's out of, out of reach, obviously of most kids, most parents. But it is a, 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 Limont, who's one of the actually like, like a real software legend, you know, in the technology field, you know, from the, 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. 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 there's 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, 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, you know, 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, you know, as they can master the material. The teachers are there, but the teachers are there to assist in that process for that, for that two hours. And so the teachers are there when a student's having trouble with something or something's confusing the other six hours of the day. The teachers are primary. But that's not academic instruction, the way you're used to thinking 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.
- [12:04]
B
Right.
- [12:04]
A
Or learn how to do, you know, whatever it is, you know, that you, you know, 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.
- [12:21]
B
Yeah.
- [12:22]
A
Now the challenge is Alpha School is a private system. Of course the existing US educational system is going to do everything possible to marginalize or destroy it. The existing government K12 system will not do any of what I just described and fundamentally very little will change.
- [12:37]
B
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.
- [12:49]
A
I appreciate your optimistic framing. My observation of institutions is that they don't want to change, they have no intention of changing.
- [12:56]
B
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.
- [13:06]
A
Oh yeah, 100%. Look, we could have a revolution in education. We could have far better education at far lower cost. We could have a revolution in healthcare. There's 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 could open and then self driving cars open up entire areas of geography in the country for housing. Much better housing at much lower cost. Yeah. The revolution of government services. You can imagine the government literally becoming state of the art if you look at what the National Design Studio, for example is doing right now. And federal government trying to make government services as compelling and easy to use as private sector consumer offerings. Yeah. Yes. The sort of modern alchemy of AI technology is a lever that could cause all those things to happen. I just observing the behavior of all the, of every single institution that I just referenced, they all seem 100% opposed to that.
- [14:06]
B
So 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 meso 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 get your thoughts on that.
- [15:03]
A
The, the thing with the modern economy, the thing with the modern, you know, industrialized economy is the productivity, productivity growth or by the way, productivity decline. It varies dramatically by sector. Yeah. And so, so, so there's no longer an economy wide concept of productivity growth that, that makes any sense. Well, 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, 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, consumer electronics, television sets, as an example, software, entertainment, content, 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 healthcare, education, housing. And then I would add to that law and government, which are often sort of excluded from the economic analysis. But I would argue, I would put those basically as like, five red sectors. The red sectors are characterized by rapidly rising prices, rapidly rising prices, rapidly rising spend, zero or negative productivity growth, and almost no technical technological innovation to speak of. And then, of course, the other 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, oligopolies, licensing restrictions, inability to fundamentally compete. And then because of the spiraling upward prices, there's subsidization of demand. Right, right. You see this with housing, housing policy all the time now, which is like, well, it's too expensive to buy houses, so therefore we're going to subsidize, you know, home buying. Well, if you subsidize a market in which you've restricted supply, you just cause prices to rise further. Right, right. Which is why those sectors have this upward spiral.
- [17:21]
B
Yeah.
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A
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.
- [17:30]
B
Right.
- [17:31]
A
And then. And mechanically, what happens as the prices for the blue sectors collapse? Deflation. And as the prices for the red sectors inflate dramatically? What happens mathematically is that the red sectors eat the entire economy, which is what's happening, 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 basic, basically, state of affairs since 1970. The change actually, of course, started 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. 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 activity 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, basically another way to think about it is it's just going to be trapped. 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 on healthcare and education and real estate. That's where all the money is going to go. Yeah, and by the way, everybody seems fine with this. This is sort of the state of, sort of, I don't know, this is like my state of sort of disassociative 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, why didn't we get the pay? Like where's the payoff? Where's the economic growth? Why didn't we get it? And of course the answer is we didn't want it because we'd rather have, you know, we'd rather have healthcare, education work the way that they do today.
- [19:37]
B
Yeah. And I think that. So I. Now I understand your skepticism about institutional reform.
- [19:44]
A
Yes, exactly.
- [19:46]
B
And I'm going to come back to that again. But let's just. So we're, 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.
- [20:29]
A
Yeah, well, so look, so on the supply side, so on the supply side, basically the state of affairs right now is basically 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 at every single layer of the supply chain. And so by the way, it starts at the very bottom with energy, where there's bottleneck and energy production, for reasons that you well understand, then there's bottleneck on literally physical facilities, physical plants.
- [21:03]
B
Right.
- [21:03]
A
So the big data center controversy, 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. 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 for power generation. Cooling systems are sold out, the big H vac systems that you need, big water cooling systems. And then inside the data center, Nvidia, the GPUs and the chips that go in are in very tight constraint. Memory chips. The price of memory chips are exploding right now. And the companies that make memory chips, their stocks are exploding because they're shortage memory chips. 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 high semiconductors themselves are becoming bottlenecks. Yeah. And so there's physical constraints actually at every layer. And that's important for several reasons. One is that it actually means that the AI products and services that you have access to today as a consumer or as a 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 them, to be able to train more advanced models. And so you're getting worse versions of the products. And then this is going to hit pricing. And we've been in this world for the last five years where the price per token of intelligence has been hyper deflating because the algorithms have been 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 in intelligence are going to stop. And in fact it may be that actually intelligence is going to start getting
- [23:03]
B
more expensive because of 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 identified, 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 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 build out, doesn't it?
- [23:39]
A
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, re industrialization question because, you know, just as an example, one of the things we haven't touched on yet is Taiwan. Right, right. Because I'm not laughing because it's funny because it's very serious and it's kind of amazing how serious it is, which is we are completely dependent on Taiwanese fabs for the chips right now to a degree that I think is actually bad for Taiwan because the fact that Taiwan is so central for the making of advanced ah just makes them an even bigger prize where the Chinese government decided to move. So I think Taiwan sort of amazingly is like, Taiwan's almost like too important for its own good right now.
- [24:26]
B
Right.
- [24:28]
A
And then there's all this strategic kind of aspects which is if the Chinese do ultimately 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 reindust, you know, as, you know, there's this need to re. Industrialize. Several reasons, not least of which is national security. And so then you get 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 financial transactions, trade. And there are people, many people who have high moral dudgeon about that as being somehow very, very bad. But we have many internal, as we've been discussing, we have many internal constraints on trade, 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 tariffs is a huge crisis, but somehow all of our internal taxes and restrictions,
- [25:29]
B
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?
- [25:48]
A
Yeah, but like 99% of the practical restrictions and constraints are not the tariffs. 99% are on the things we do to ourselves inside our own country.
- [25:56]
B
Fair. Fair point behind the bo.
- [25:58]
A
So I would just. I would. Yes. So it would just every. Whatever. What I'm reacting to is not you. I'm reacting to, you know, five, you know, whatever 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 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 far more constrained and controlled world. Yeah.
- [26:26]
B
And we should be upset about both. It's. 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. I mean, you tell me, is it good for investors?
- [26:41]
A
Well, you know, I'm just saying 99 of the issue. 99% of the issues are internal.
- [26:45]
B
Yeah,
- [26:49]
A
it's almost entire, like the internal. I'm sure you're tracking this. What's happening literally in the US Right now, county by county, with the ability to build data centers is, like, profoundly destructive.
- [27:00]
B
No? True. Yeah.
- [27:01]
A
And that's entirely domestic. And a large number of politicians are, like, feeding that hysteria as much as they possibly can.
- [27:08]
B
Right.
- [27:09]
A
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, it's just like this kind of hyper paranoia about building data centers and the consequence of data on this. I'll just give you an example. This completely fake meme about water use use, 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. That factor is, like, so much a bigger factor holding us back than anything involving external trade. So it's like external trade is this thing that's easy to talk about. It's all of our internal issues that are much, much more important.
- [27:46]
B
That's a very fair point. Let's. Let's just talk about. 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, is a separate question. I look at this and I pose it as a question for you. Is the use of those export controls 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.
- [28:45]
A
Yeah, so I think there's a whole bunch of, you know, very complicated topics. There's a whole bunch of factors. I, I would start with a very high level kind of view on this though, which is we have, as is often the case with anything, you know, complicated in the real world, there, you know, there are multiple contradictory goals, you know, that, that we would like to be able to solve them all at the same time, but it's hard because they, because, because they conflict. And so let's just start with the US versus China part because I think that drives a lot of this. Yeah. Because I think if China didn't exist, I think we'd be having a different discussion. We'd be having a different and simpler discussion because it would just be about us. To start with, AI right now is a two horse race. Like it's US 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 nother thing we could talk about. You know, they've taken every bad idea that we have and, you know, kind of maxed it out to 11 and so that, you know, they're maybe becoming case study of what not to do. But be that as it may, it's basically a two horse race now. It's US versus China. So to start with, we have two contradictory goals. One of which is we want to make sure that the US wins the global technology race. 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.
- [30:11]
B
Yeah.
- [30:11]
A
Which, which today sounds crazy, but that actually was the ultimate resolution of the first Cold War with the Soviet Union, which was, you know, as, you know, like The, The Cold War with the Soviet Union ended because the Soviets, they gave up. They gave up because just becoming part of the west or as best that they could do that was a better outcome than trying to run their own parallel system.
- [30:28]
B
Right?
- [30:28]
A
And so, like, I, I think we have a vision of sort of global technology supremacy that says the entire world runs an American AI, including ultimately, including, including ultimately China. To do that, what do we have to do? We have to export, right? We have to take our technology and we have to make it available to the world. We have another goal, which is, as far as I can tell, just as important, which is AI is a extremely disruptive new technology. It has profound, not just economic implications, but also national security implications, also, by the way, competitiveness implications. And with that goal, 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. Right. And this goes straight to topics like, for example, chip export controls, which of course were in place even before the Mythos issue. But right away there you can see these are directly contradictory goals. And I think what. Right. And I think what you have, what you, I think what you have in, in. In the, in the, in the, in the US Government is I think you have extremely well meaning people who have the country's best interests at heart, some of whom have the first goal as a primary goal, some of whom have the second goal as a primary goal. And those, and those goals are, are, are, are, are. Are. Are exactly contradictory with each other.
- [31:53]
B
Yeah.
- [31:53]
A
And so I, I think that's actually the, the, the underlying kind of logical question that you have to have. Then the other example that directly on your Mythos point, I would say is another example of sort of diametrically imposed 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 cyber systems and they are better at defending cyber systems than human beings are. And so you have these models, they're a threat of disruption. And of course, this is where the current US Government's very worried about disruption of the financial system. Mythos models being used by bad guys, criminals or terrorists or foreign governments to, for example, break into and really wreck US Banks or US Stock market or whatever, which is, 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. 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 US you need to fix all the security holes and all the systems and have new kinds of AI, cyber defenses and everything. 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 banks, for example, aren't subject to cyber attack, the more you want to deploy it. And so anyway, so I just. A lot of people, when they engage on these issues, it's sort of the questions, people's motives. I think in this case you've got, you've got, in both cases you've got these directional contradictory motives and you have to go straight to the underlying conversation of which is actually the most important goal before you can figure out what the right policies are.
- [33:43]
B
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 a lot of times the same person is trying to balance those two objectives in government. Having served in government, I recognize many people 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 one becomes more important than the other. And you describe the like, really challenging situation that we're in.
- [34:26]
A
Yeah, so my view, my, my normal view on these things is basically, it's sometimes called the technological imperative, which is basically this idea of like, you, you don't uninvent new techn.
- [34:35]
B
Right.
- [34:36]
A
Once a new technology exists, it exists and it's, and it's, it's, it's going to make its way into the world like it is, 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 like 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, like, 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, same thing for steam power and the same thing for electricity and the same thing, right, the same thing for, you know, what is it, the haberwash process. And you just go right down the list of all these innovations and 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, if I were 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's not, at some point they're just going to say, this isn't even worth competing with us. This is a complete waste of time. And so I would come out very strongly on the side of you want maximum export, right? You want to just basically turbocharge exports. You want the US 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 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, you know, they have a, you know, I think they have very reasonable arguments for what they're doing. But, but, but I would go, I would go in that direction. And then, and then on things like Mythos, the direction I would go in is 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 ex 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, you know, banks and all these other government agencies are getting hacked all the time even without AI. And so, and then AI, AI hacking is going to be, is going to be much more effective. And so I think you need to get the defenses we need to focus on the defenses. We need to get the defenses in place. And the way to get the defenses in place is we need to use these new advanced AI models. We need to put them in the hands of all companies as fast as possible to be able to basically earmark up and have AI defenses against AI hacking and non AI hacking, for example. This is also how you solve the ransomware crisis, 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 rare already. Again, I think that's a very good faith argument. And I think that people are arguing that, you know, are doing so out of a good. Out of a good place. You know, I, I guess I would say this. The winds 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 we're going to be living in. As one, in which probably the opposite arguments are going to prevail.
- [37:59]
B
Yeah. If I might just offer a couple of reflection on that. That was a great rundown, couple of reflections on that. One is that when it comes to chips, for example, fully accept your point that over time, like 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, people would you agree with that point?
- [38:29]
A
So I think that's true, but also I think something else is true, which is if you deny them chips, you incent them to create their own chips,
- [38:36]
B
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 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.
- [39:19]
A
Yeah, I mean, you know, you can try, you know, this is all the discussions around industrial policy. Right. You can try. You know, I'll just give you my background here. So my first commercial product I ever built was, and took to market was the Netscape browser in 1994. It was export controlled. It was classified by ITAR as a munition. It was in the same classification category as a Tomahawk missile. It was explained to us by our lawyers in no uncertain terms that we could not possibly let this outside the U.S. right. And by the way, when it started, you'll enjoy this. Actually, when it started, the encryption was such a sensitive topic in the 1990s that we were actually, actually export control, not just on strong encryption, but also on weak encryption. We couldn't even ship browsers or server software, a web server software that had weak encryption. And it took years to get the government to basically come to grips with the idea that if we were not allowed to do that, 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 such 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, you know, they had very good arguments. I mean, you know, they, they come in and, you know, I don't know if you've probably been through this yourself. They, you know, they come in and they show you like, okay, here, here are the bad guys. Yeah, here what the bad guys are doing. Like, here's the dangers, here's the threats, here's the stuff that your, your encryption is gonna, you know, is gonna cover up and make it harder for us to prosecute or catch.
- [40:44]
B
Yeah.
- [40:44]
A
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 US technology loses because encryption was going to happen. Right. I used to own a T shirt. I probably still have it somewhere. I used to own a T shirt. Remember the RSA algorithm was the key encryption 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 probably. These are very complicated, hard to read code, but there are 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, right. It was actually illegal. Like, in theory, I never, by the way, I never tested this. But in theory, if I had worn that T shirt and board an international flight, I could have been put in jail.
- [41:34]
B
Wow.
- [41:34]
A
Okay, so, so, so there's that.
- [41:37]
B
What a great story.
- [41:39]
A
Yeah, yeah, yeah. It took years. It took years. It took years to work through that. Right? And so it is kind of amazing. Okay, so then on, on AI, like AI is math. Like at the end of the day, it's math. Like it, it, it's, it's actually, by the way, it's actually remarkable. It's actually quite straightfor math. It's basically linear algebra and then it's a handful of algorithms with names like gradient 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 Methos level or whatever, that math looks hard and expensive for about six months. And then somebody figures out a way to run it on a picture PC. 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 centers get built to being something that is an open source, by the way. Open source from the us, open source from China, or open source, in theory, from anywhere in the world. 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, 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 Krishna case and 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 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 all the computers in your house. Right. Including your kid's laptop. Right. And 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 AI that's too powerful, you know, then there need to be some set of consequences to it. Yeah, right. And then of course, the very next thing is, well, that needs to be a global regime. Right. And in fact, you actually 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, you know, like a UN with teeth that like, controls global use of software.
- [44:08]
B
Yeah.
- [44:09]
A
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 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 unapproved 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, do the dance. I think in practice the, you know, the, the sort of downstream effects get to be quite, quite scary.
- [44:35]
B
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, running 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. Do you agree with that?
- [45:39]
A
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, it's the opposite, we're in like, opposite world from what you would think.
- [46:09]
B
Well, but that might just be a reflection of where they are in the, in the race. Right. So they're also restricting critical minerals in a pretty, a coercive way. So I think that the minute the, 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.
- [46:34]
A
Oh, I, I mean, I would take it a step further. I think it's a deliberate strategy. 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.
- [46:45]
B
Right.
- [46:46]
A
Which is that the Chinese are 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 American industry. Like it's like a turbo dumping strategy. Right, right. So flood the market with, with, with basically, you know, with basically free AI to prevent the American companies from being able to make money on it. But, but, so I totally agree with what you're saying. I just think it's, it is fairly amazing, at least for now, that they're the proponents of free and open AI. Yeah, go ahead.
- [47:25]
B
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. We need to stay. 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. The civil military fusion risk, which is very real there.
- [48:28]
A
Meaning that if I understand properly, you're saying that the Chinese. The Chinese take American AI, they use it to build better military weapons.
- [48:35]
B
Yes, because they have deep fusion across their commercial and their military system sectors. That's their policy.
- [48:41]
A
Oh, yeah, yeah, for sure. Yeah. I mean, look, I think that's a real. I mean, 100. I think that they absolutely would do that, by the way. I think they're likely doing that today.
- [48:50]
B
Right.
- [48:51]
A
This is, 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, where they go.
- [49:01]
B
Yeah.
- [49:01]
A
And by the way, like, do. Here's. Here's a. Quiet. Here's a question. Do we think the Chinese already have mythos? All Mythos is, is it's a set of numbers on a hard drive.
- [49:13]
B
Yeah.
- [49:14]
A
It's a file.
- [49:15]
B
Yes.
- [49:16]
A
Like, how incompetent is the MSS if they haven't already figured out a way to 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 systems 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 would question whether the controls can actually hold. Like, how. Here, 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 a government background would possibly find to be even remotely acceptable.
- [49:52]
B
Yeah.
- [49:53]
A
Like, they, they all have. They all employ, like, large numbers of Chinese nationals. They all employ large numbers of, frankly, Chinese Americans with. With relatives in mainland China.
- [50:02]
B
Right.
- [50:02]
A
You know, who are subject to, you know, to exploitation. They, you know, they run open and collaborative R and D environments. They don't have internal, you know, they don't have internal stove 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 US Citizens work on its systems. So, anyway, so first of all, it's likely that the Chinese have everything that we're describing anyway, A, and then B, yeah, 100%, like, 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 trade off, 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 US 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? But again, I would say, look, I think it's a completely legitimate question observation because I think there are real trade offs 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.
- [51:36]
B
Yeah, I mean, I think this is the moment we're in, right? Like we, the, 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. You know, I think we need a significant public sector reform effort. I put out a piece in 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 techforce 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? Now?
- [52:32]
A
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 Gabbia, who's, you know, one of the great Silicon Valley founders, co founder of Airbnb you know, he'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 Doge. A lot of the really sharp table Doge people are still in government. And I think having, Having. Having pretty big impact. And so, you know, I think. 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 what are the levels of the antibodies that come out, you know, whenever there's any suggestion of reform? And, you know, and as you know, the antibodies are extremely strong and vigorous. Everybody who tries to.
- [53:17]
B
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, cheese. And the holes are not where you need those holes to be. Right.
- [53:31]
A
You know, I would love to. I would like you. I would love to see other approaches to reform that work.
- [53:37]
B
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. Needs in this era of AI, which we're far from thinking. We're. It's. It's. We're not there yet. Right.
- [54:04]
A
And I also add, and I say this hopefully on an optimistic note, this doesn't have to be a partisan issue.
- [54:10]
B
Yeah.
- [54:10]
A
What you're saying. And, and in fact, you. You may remember, you know, there was actually the Clinton Gore administration in the 1990s had a big effort in this direction.
- [54:17]
B
Yes.
- [54:17]
A
Called. Called rego. Reinventing Government.
- [54:19]
B
Right, Reinventing government. Yeah.
- [54:22]
A
And. And Al Gore in particular put, Put a lot of time and effort into it and, you know, got. Got. Got some ways down the field. And so, like, I, I've been like, yes, one. One could imagine. One could. One can certainly imagine what you're describing. I think you're 100% right. That it would. That we needed. And it would be great. Having said, I would just say the people who have tried it. The people who have tried to do it with whatever method are. Have a lot of scar tissue. And so it's. Yes, it's a.
- [54:46]
B
It's.
- [54:47]
A
Yeah. I was going to say this, it's never been harder.
- [54:49]
B
Yeah, I hear that. I want to, I want to ask you. There's a lot of talk about what the, what policy do we need for AI? What about AI for public policy? What's your view on that?
- [55:01]
A
How so?
- [55:02]
B
Well, I just think that there are a lot of policies that we put that we, 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 healthcare or housing or whatever it is. So, I mean, AI for policy and the policy evaluation and design.
- [55:34]
A
Yeah, I think that's a great idea. I think the current tools are actually quite, quite good at this. I think optimistically you could say that this is kind of happening in the field, in the academic field of economics.
- [55:43]
B
Right.
- [55:44]
A
In a way that's sort of analogous or maybe even directly on point, which is my sense of economics is a sort of shifting from. Call it the post World War II method of sort of physics, of, you know, kind of a physics of economics where everything is formulas.
- [55:58]
B
Yeah.
- [55:59]
A
To a, you know, the, the newer generation economists work much more with data.
- [56:03]
B
Yes.
- [56:04]
A
You know, they gather large data sets 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, an argument about, you know, whatever, you know, kind of, you know, concepts or formulas, formulas, you know, instead you, you go, 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 want, you know, who legitimately want to do what you're describing, I think the, the new tools are quite good at that.
- [56:30]
B
Yeah. I would love to see if like the GAO 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 and we could save a lot of money, a lot of time. Hopefully. Hopefully. I got to end on one thing because you are, have been such a great investment leader over the years and American dynamism in some, many ways is quite inspiring about, you know, with respect to re. Industrialization 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, 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.
- [57:19]
A
Yeah, so I think there's a lot. So here I'm reasonably optimistic. I think there's a lot going on and it happens on a number of fronts. So one that's just very specific is re. Industrializing on the defense side. Yeah. And so as your organization has studied at length, you know, there are very real issues about the physical supply chain that goes into the US Military and national security. And so they're, they're, you know, we are intensely proud of our companies that are, 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, you know, really, really helping, you know, get new defense companies, you know, into business in a critical mass. By the way, the, I, I won't, I won't weigh in specifically on the politics of it, but the, you know, the proposed expansion of the defense budget, you know, at least the promise is that a lot of that money will go to these, to these new approaches and in a lot of cases, new vendors. And so I think that's, as you well know, like there was an explicit policy decision made in the 1990s to shrink the number of defense vendors in the US 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, that's been kind of a bootstrap. I would describe, 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.
- [58:42]
B
Yeah.
- [58:43]
A
And 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 Luckey and the team in Andrew and these other companies. And, you know, there's startups, you know, many of whom are backing, but there are startups doing new nuclear, you know, nuclear fission reactors for the first time in decades. There are startups doing, we have multiple companies going after rare earth, you know, mineral discovery, extraction processes. 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 where there's both a lot of good and bad things happening, but there's a new industrial, I don't know, even manufacturing ecosystem, entrepreneurial cluster in and around Los Angeles, around El Segundo and Thorn. And these play, you know, where SpaceX was born and so forth and where Andrew 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 la. So I think that's a possibility. Like look this way, the entrepreneurs all want to do it. The money by the way is lined up, the money is available to do it. That 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 nonpartisan issue which is, you know, I think Democrats are at least as interested in reindustrialization as Republicans, at least logically because you know, you want, it's the old thing, you know, you want jobs, right? You want jobs and all these communities that 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 more good new jobs. And so optimistically this could be a
- [60:37]
B
bipartisan effort and I would say even the pre. We can. There's a, 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 in, in, in 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, from an investor's perspective is, 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.
- [61:20]
A
So look, so I don't think, 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 trade off, at least for what we do. There's not a case that there's a direct trade off of like financial investing versus versus the larger goals, which is what you do. 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 our companies that have a view, for example, of American manufacturing, they're not doing it because they're making some explicit dollars and cents trade off of should we invest in the US versus here versus there. They're setting a North Star goal of wanting to do something specific. And then they're basically saying, what's the way to turn that into a mission that then attracts the smartest people in the field, that attracts people who are the most ambitious about undertaking new programs. You infuse the company with Patriot, 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 and 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 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 these are the kinds of companies you're building. You're not building companies that just default to Chinese 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 probably at this point dozens of companies that are doing what I just described phenomenal.
- [62:52]
B
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. Thank you for contributing to the national debate.
- [63:05]
A
Mark.
- [63:05]
B
It's really fantastic.
- [63:06]
A
Good. Thank you so much for having me. I really appreciate it.
- [63:09]
B
Thank you for listening to today's conversation with Marc Andreessen. You can find this episode and more on CSIS.org, youTube or wherever you get your podcasts. This is Naveen Girishankar reminding you that everyone has a role to play in winning the tech race.
- [63:26]
A
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 X16Z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include paid promotional advertisements, other company references, and individuals unaffiliated with A16Z. Such advertisements, companies and individuals are not endorsed by AH Capital Management, LLC, A16Z or any of its affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy. Sam.