The Limits of the American Way of AI

23:47There are really clear, measurable ways in which AI progress has accelerated in ways that we expected, but couldn't know for sure what happened over the last few years. I see no sign of that slowing down. I think there's some evidence that it's actually going to speed up as we see AI systems accelerate AI research. One of the most significant research papers from 2025 is a system called Alpha Evolve from Google that came out in May or so. And in that paper, Google showed that you could use AI to speed up AI progress. And they, in fact, used AI to find a better way of doing matrix multiplication, one of the core mathematical algorithms, essentially a more efficient way of doing math.

24:27And humans hadn't improved matrix multiplication in something like 56 years. So the AI system found a new way to do math, or a more efficient way to do math, that I think shaved the time off of that mathematical operation by something like 23%. So it's early, but you can begin to see signs of what so many AI researchers are looking for, which is this loop in which AI systems accelerate AI progress. Now, there's a question of how far away the ceiling is, at what point is that plateau? All that stuff's very real. But I think it is the case here that we should at least be thinking seriously about AI systems that continue to get profoundly better.

25:00And what's the threshold that would allow you to call it superintelligence or AGI, or at least what we would think of in the popular conversation as one of those things? Yeah, the definitions are always shaky, but I think for AGI, what you're pushing out there is the generality. So the common definition is a machine that can do any cognitive task or almost any cognitive task as well as almost any human. And then a super intelligent machine is a machine that can do any cognitive task better than any human. But we should be honest and say these are fuzzy definitions. And there is, of course, a growing skeptical view that all of this is driven by some combination of self-important grandiosity on the part of some of the private sector leaders and maybe a desire to distract from near-term regulatory debates to focus on these kind of sci-fi scenarios.

25:45But we won't get into motives there. But it's an interesting question. Yeah. I mean, and I think it's fair saying, you know, when I was in the White House, I had domestic responsibility as well. And the analogy I often use was a decathlon, which is there's a lot of events in AI policy and America has to do all of them. So at the same time we are thinking about export controls, and at the same time we're thinking about energy, we also need to think about what are the near-term applications, positive and negative, of AI today. There's a lot of upside in science. There's some risk as well. And, of course, there's risk of discrimination and how we're using AI in law enforcement and so much else.

26:17So one of the reasons I love AI policy is it is so all-encompassing, and we have to do all of the things. You've mentioned energy a couple of times. If we go back to the essay that you and Teddy wrote, this is one of the limits to the American way of AI that you focus on. Why is this so central? It was really the first constraint, the first fear that you focused on. Brian Deese, I would note the former head of the National Economic Council, your colleague in the Biden White House, wrote a piece for us about a coming electricity crisis. What worries you and what will it take to get really serious about the need to overcome those constraints?

26:50That was a great piece. And Brian is a great thinker on AI and the broader economy. I think what worries me is I have made the argument now for years in your journal that computing power is essential. We're scaling computing power to scale AI systems. I think that's pretty well proven at this point. Well, we're also scaling energy. And if we're going from a training cluster to train the AI system in 2020 that maybe was a couple thousand chips to training clusters in 2026 or 2027 that are millions of chips, and the chips themselves are larger. Even though the chips are on balance more energy efficient, they're still drawing more power.

27:28And you're just going from an energy draw to, I don't know, maybe in 2020 it was kilowatts or low-end megawatts to gigawatts. And if you look at what OpenAI is talking about or Anthropoc are talking about, they're talking about individual data centers of 5 or 10 gigawatts of power. That's as much as something like 24 U.S. states, I think, for 5 gigawatts. and we're talking about an industry draw that's something like 50 gigawatts which is you know as much as essentially many countries in the world so there is just a mathematical conclusion here which is if you buy that computing power is going to continue to drive ai progress and you buy the companies are going to have the money to buy tons of computing power then they're going to have to find the power and the electricity and i think that is going to be fundamental and if we sit here in four years and we've really plateaued ai progress odds are it will be because of a failure to build the energy.

28:18And it's worth saying, I think the companies need to pay for this. So this is not the case where it would be acceptable. And we say this in the essay for this to be done on the backs of citizens or taxpayers or ratepayers and the like. This is the kind of thing where the companies are making giant financial bets. They're spending hundreds of billions of dollars and they should foot the bill for this. You can already see a political backlash starting to take shape in places where data centers have been built. Yeah. And I mean, Northern Virginia, where I live, my understanding is something like 40 % of the power of Virginia, electricity of Virginia, is going to data centers.

28:48Now, Virginia is an outlier because it has a history of building data centers. But I suspect this is going to be a very significant political issue going forward. And I have a lot of sympathy for people, ratepayers, who are saying, I don't understand, don't do this on the backs of my pocketbook. But I also believe we need to have a set of policies that makes it possible to build a lot of power, and the company should pay for it. Do you see any willingness on the part of the companies to do so? Yeah, I do. I mean, I think it probably varies by company. but I do think that we had conversations with the companies and a lot of them said we would pay for it we the companies would pay for it and they also said things like well we want to pay extra we're willing to pay a premium to make it clean power and one of the things that's in the essay and also in our second AI executive order on infrastructure is my hope for this is that we can use their willingness to pay for power to catalyze next generation of electricity generation of course advanced solar advanced wind the economics there are quite good but also things like small modular reactors a little further out, and things like advanced geothermal, where I think there's a lot of promise.

29:52So in an ideal world, if we're getting to an upside scenario, we're sitting here in four or five years, we've built the power we needed to train the stuff and build out the data center and the infrastructure. We have not done it on the backs of rate payers. And we've used the company investment to catalyze a next generation of clean energy that everyone will benefit from. And I would note that between your piece and a couple of pieces that Brian Deese has written in our pages. There's a pretty comprehensive blueprint about what you need to do in order to get there. Whether our political system can manage to actually implement that is a separate question.

30:25That's right. I think this is a case where the cards are in our hand to mix metaphors, you know, path is clear. And it's just a question of can we get the system, private and public sector, to do this? And state and local, and there's a lot of moving pieces. We'll be back after a short break. What if you could explore places in the news like a reporter does? I'm Nicholas Wood, a former journalist with the New York Times and BBC. And 16 years ago, I created the travel company Political Tours. Our small groups are led by top correspondents around the world. In the next few months, we're off to Mexico, followed by South Africa, Japan and the French presidential elections.

31:05Come and join us. Go to politicaltours.com. That's politicaltours.com. This September, Independent 20th Century presents more than 130 artists at the legendary Broyu building at 945 Madison Avenue. The show will fill four floors of the modernist landmark on Museum Mile. See why Art News Magazine calls Independent 20th Century a staple of the season and experience historic modern art from around the world and one of New York's most iconic spaces, September 24th to 27th. To learn more, visit independenthq.com.

32:09And now back to my conversation with Ben Buchanan. One of the other vulnerabilities, one of the other limits to the American way of AI that you focus on in the essay is security and espionage. What are your concerns there? Essentially, we are inventing a technology with very, very deep national security implications, at least if you buy our thesis. If you buy our thesis as well, well, it's coming from the private sector for the first time. a lot of its predecessor technologies grew up in eras of secrecy where the secrecy was kind of built in for better or for worse the nuclear programs of course some of the space programs many of the dood programs there are downsides to secrecy in cost innovation and the like so it's not surprising to me that companies have been able to be very dynamic in part because they're more open and collaborative and so forth but we are going to have to find some mechanism in which they preserve that openness and that collaboration and that innovation, and yet also make sure their secrets are robust to what I suspect will be very significant foreign espionage threats.

33:07And this is not speculative. We've already, we gave a couple of examples in the essay, but we know that there are cases of foreign intelligence officers or agents of foreign intelligence trying to penetrate American companies, trying to steal trade secrets and the like and take them back. And of course, I think China's history here in a wide variety of areas beyond AI is pretty well documented. So we should expect that almost tautologically, as AI gets more important, other nations will recognize that and will use their full tools of national power, including foreign intelligence, to try to get secrets.

33:37Insofar as the secrets are important, which I think they will be, we will have to protect them wherever they are, including in the private sector. Even before AI became the focus of this conversation, there was not, as I see it at least, there was not a lot of success in getting pretty important private sector actors to take their own cybersecurity seriously. It took a while to get there. That does not seem to suggest that it's going to be easy in this case either. I don't think it's going to be easy, but I do think if you were to rank American companies by their cybersecurity posture, you'd have the banks doing quite well.

34:07They've learned hard lessons, I think, and have done well. And you probably would have companies like Google that are near the top and hyperscalers and the like. So it is probably fortunate that the companies that are inventing this technology do have some pedigree with security. What I would like is I would like the government to bring the things that only the government can do in terms of threat intelligence and the like to assist those companies. And this was a theme in the National Security Memorandum that President Biden signed in October of 2024 that Teddy and I worked on extensively. It is to say, well, let's see what we can do here to preserve the dynamic ecosystem, preserve the way you've done cybersecurity in the private sector, but also bolster that however we can with the kind of support that presumably only the government could muster.

34:49And I suppose the difference with some of the more traditional kinds of cyber penetration is that you're dealing with telecommunications infrastructure that was built 50 years ago in some cases. That's right. Google, I think, has learned hard lessons. And companies like Google, I'm using them as a stand-in for the broader industry, have learned hard lessons already. And I think we have a foundation there. I want them to build what they're going to build. And I want the U.S. government to help however it can. There's another side, the flip side to this national security vulnerability, which is the way that the U.S.

35:20national security apparatus integrates AI into its own operations. As you put it, quote, right now, strong partnerships between AI firms and U.S. national security agencies are few and far between, and those that do exist are in the early stages. What is so hard about this? What are your anxieties here? There's a number of pieces of it. The first, of course, is the U.S. government has to get it. and you need to have people in the U.S. government to understand what this technology can do. There were some, but I wouldn't say it was the norm. And again, I don't want to blame them. This is coming not from their area.

35:51They've got real jobs. It's not their job to read AI research papers, but it is fundamental that the U.S. government figures out what's going on in the private sector and applies it where appropriate. Second, I think there is just an ossification of military and intelligence, a hardening of how it is done that I'm sure you experienced when you were in the government Sometimes that's there for good reasons. Sometimes let's get there for bad reasons. And that just makes it that the military is not an early adopter or the DoD and the intelligence media are not often early adopters of technology that we're going to have to overcome.

36:22And I think we actually made good progress on that. And again, a lot of hardworking civil servants get credit there. And I think probably the third is figuring out what are the guardrails that are appropriate for this. And this is an area where the United States needs to distinguish itself from China and needs to say, look, there should be real limits on how we use this technology. And it is not acceptable to use AI to build a domestic surveillance state. Even if we want to use it to improve our cyber operations against the Chinese, which we should, it's not okay to turn that on our own populace.

36:48So in some sense, that is a break, but it's also a break of a reasonable form. What I found is that what everyone benefited from was clarity, in that everyone in the system wanted to know what the technology could do. They wanted to know how to apply it to the mission, and they wanted to know what the boundaries were. And that was what the National Security Memorandum was meant to do. I think we did a lot of the big picture stuff, in my view, in a way that was right and set a good course. The work that remains to be done is now translating that to the rest of the system, getting people at the line officer level to buy in, to make this a priority, and then changing a lot of government procedures, procurement and the like to make sure we, the government, are buying technology from a broad range of companies and the like, they're actually inventing this.

37:31And this is something that is called out explicitly in the National Screen Memorandum as a priority, but I don't know that it's actually happened showing out in the three months that we had after that document was signed and it needs to continue. So it's really important to me that we have a competitive ecosystem and AI that's not just the same couple of companies that are doing this. It's especially important in a defense contract where you do have just a couple of big primes. So that I think is a final piece of this that definitely remains to be executed. In the piece, you pull these elements together into what you call a grand bargain between the tech industry and the government.

38:04Describe that grand bargain. What does that look like? Yeah, grand bargains are always easier as taglines and as policies. But I think the analysis Teddy and I did is that, well, there's some things here that only government can do. There's some things here that only the private sector can do. And it is really in America's national security interest for everyone to work together. And just because something makes sense doesn't mean it will happen, but we think there's enough alignment here of interest that the country could push this with savvy policymaking. So obviously there's the robustly good things, holding the Chinese military back, controlling chips, controlling chip making equipment.

38:35Then American companies need talent. So bringing in high-end talent from around the world, things like the O-1 visa and the like to get them the researchers they need. The stat that always stuck with me was in 2022 when the study was done of the top 100 AI scientists in the United States, something like 70 were born outside the United States. So there's clearly a deal to be done on talent. Obviously on energy, we've talked about this quite a bit, building out the energy here at the company's expense to make sure they can scale the infrastructure in the way that they need to, providing security for the companies or security assistance for the companies.

39:07And then in return, getting the companies to help with the application of AI to national security, which I have found companies generally are willing to do. But it's a question of making sure that the fit is right. So we think there's enough things on each side of the ledger here that there's a deal that can be done that will be to everyone's interest, most of all in the United States. You spent three or four years at the government side of this in the White House. You've now been working with companies, advising companies on the other side for the last several months. Has your view of this grand bargain changed your sense of willingness to engage in it or challenges that you didn't appreciate sufficiently when you were in government?

39:42I don't think the contours of the bargain have changed. I think I feel more urgency now because the technology is clearly continuing to make a lot of progress. And there were worlds in which it could have plateaued in 24 or 25. And those are not the worlds we are living in. The scaling law of computing power yielding capabilities continues to hold. We are starting to see signs of a recursive loop in which AI makes AI research more tractable and more efficient. So the data that has come in over the last nine months since I left the administration or 10 months, whatever it's been since I left the administration, only reaffirms my view here that this is an urgently important matter.

40:21Let's go to that moment 10 months ago when you left the administration. I'm interested in how you see the continuities and discontinuities between the Biden and Trump approaches on these issues. I mean, at a high level from a distance, it appears that we've gone from a sort of controlled and deliberate approach under Biden, you know, know what you know you're deploying before you deploy it, some regulation and more restrictive approach to chip diffusion and what the rest world has access to, to across the board accelerationism in some sense. I think that's reflected in the Trump AI action plan.

40:54Is something like that right? Is there more continuity that that story misses? It's hard to pin down what the Trump administration AI policy is because there's a bunch of different conflicting actions. So the rhetoric is certainly very accelerationist. And yet, if you look at the energy policy, with some exceptions, it isn't. They're canceling transmission lines that'll be the largest transmission line in the country. They're canceling solar farms in Nevada that would produce five gigawatts. So a pure accelerationist, I think, would say, well, build as much energy as possible. And that was the rhetoric that Trump talked about, in his convention speech, building electricity for AI and so forth.

41:29So that's a case where I just think it's been a little bit hard to figure out what the policy actually is. And my suspicion is you just have different people making different policies, and there's not a ton of coherence. I think in the Trump administration, again, the rhetoric was, he said, staple of diploma to every green card or something to that effect. That hasn't happened. We've seen a very, very different immigration policy that I imagine will have effects on AI researchers' willingness to come to the United States. And then on stuff like the chip controls, they've been all over the place.

41:56They took, in my view, a very admirable step of controlling the H-20. They have in the AI action plan a really well-written call out to the importance of computing power for AI competition with China, the need to deny this resource to the Chinese. They've reversed the controls on the H-20. They've been all over the place on what their posture is going to be on more advanced chips. They haven't really done, as far as I can tell, anything on controlling chip making equipment, which is an area where you need kind of continued maintenance to keep with changing times. So it's hard to point to a single coherent policy there.

42:28So in many cases, I think the underlying rhetoric is not terrible, but I don't necessarily see a clear through line of here's how they're approaching AI, And here's what they're doing to win the race to the frontier or win the competition at the frontier to apply this to the military where we haven't seen very much. And then to diffuse American models around the world, you know, so that American companies outcompete DeepSeek. I just haven't seen that kind of coherence yet. I want to ask you about differences in views on compute and how to maintain that edge. But I suppose it's worth first just really lingering on this point that you keep coming back to.

43:03Can you just explain for those of us who are not experts in this, why compute to you is the center of this and what the components of that are? Yeah, a key part of the AI process is what's called the training of an AI system. So this is the part in which you basically show the AI data, you teach it about the world, and you build the system that you'll then go off and use for new problems and the like. And this is a compute intensive process. and one of the things that we have observed as an empirical observation and that led to the piece I wrote in 2020 is that the capability of the resulting AI system scales with the amount of computing power used to train it assuming you're also putting data in but that's usually not that hard the more computing power used for training an AI system the more capable the AI system is as a result and we call this the scaling law and in the sense that you know it's not a law of physics it's an observation, computing power get more efficient every two years.

43:58So this has continued to hold for probably at this point, 12 or 13 orders of magnitude of computing power, where you keep increasing the scale of the computing power, and in a very predictable way, the AI systems increase in capability as a result. So that is one really obvious point for the importance of computing power. In 2024, we began to see a little bit more nuance to the scaling law, 23 and 24, in which you could do other steps on top where you could also scale computing power, basically using reinforcement learning, another kind of AI algorithm on top of your AI system, then you could scale that as well.

44:33So now we had two things that you could use computing power to scale. And as you scale each of them, the resulting model got more capable. And then we also noticed what you might call a third scaling law or a third piece of the same scaling law is once you have the trained system, you could then apply it to a particular problem and you could use, at the expense of more computing power, you could essentially get to think for longer on that problem. And in the same way that if you gave someone 10 minutes to write a foreign affairs essay, it probably wouldn't be great. But if you gave them a year, hopefully it'd be a little bit better or substantially better.

45:05As AI systems learn to linger and think about problems, they can often perform better. That's great. That just comes at the expense of more computing power. And you can see this if you use JotGPT or Claude and you put on the extended thinking mode. It thinks for longer on a problem. You tend to get a better answer, but it uses more computing power. So those are three reasons all bundled together in this notion of compute scaling, why the more computing power you have to throw at AI training and use, the more powerful AI systems are as a result. And that comes down to how many chips you have, how advanced they are, and how much electricity you have to put into them, essentially.

45:40Essentially, yeah. And I think I'd add one component about how well the chips network together, which is a strength of US chips as well. You've mentioned the importance of keeping the most advanced chips. in our hands and in the hands of our allies and out of the hands of China, especially. There's a different view that you hear from parts of the Trump administration. I take your point about incoherence in different parts of the administration having different views. What is, if you can give the most credible version of that alternative view, how would you describe that? I think the reality would be different if China could make a lot of chips.

46:14And sometimes I hear the argument of China can make a lot of chips, or if we control chips with them, China will indigenize their chip production faster, and therefore we should take a different course than you guys did. I just don't think as a factual matter that's right. I think as a factual matter, China in 2014 recognized that chip making was hugely important for national security. They began spending$200 billion of public and private capital to try to indigenize their chip making industry, essentially put the pedal to the metal on that. The Trump administration, the first Trump administration, Matt Ponger and others, I think, get a lot of credit for controlling advanced chip making equipment to the Chinese to inhibit this progress.

46:53And the Chinese really didn't make a lot of headway. So we are really outperforming the Chinese on chip making, probably by a factor of 10 to 100x, depending on how you want to measure it. And it is fair to say that is the crux of this whole enterprise. And in the world in which China caught up, they would really have a lot of advantages given their talent and their electricity production. So maybe some folks in the Trump administration think China's closer than they are on chipmaking. My view is they're pretty far behind and we can take smart action by denying them chipmaking equipment to hold them back.

47:25Just to dwell on this for one more second, I know this is a foreign policy discussion, but it's worth just going into the technology of chipmaking. My view is this is the hardest thing we do as a species. This is just incredibly technically impressive work. Remember, the United States really can't do this right now. The Chips Act helps some, but it's really just a couple of companies in the world, really just TSMC that can make this at any kind of scale. You're talking about incredibly precise lasers, vacuums, you know, thousands of times more pure than the vacuum of space, emitting radiation with incredible precision to make these chips.

47:57It is a process that you spend 10 minutes looking at it, you're like, wow, okay, this really is something that is hard to do. And then you recognize spending 10 minutes looking at it gets you, you know, 0.1 % of the complexity of the process. And this was a technique for chip making that was built over decades by a network of American and other democratic companies. China's going to have a very, very heavy lift in digitizing that all from scratch, especially if we're yanking the chip-making equipment from them. So that probably is the steel man case for the Trump administration. I don't find it compelling as a factual matter.

48:27And I think we should all agree that we shouldn't be selling chip-making equipment to the Chinese. We should make it harder. The other change between Biden and Trump, as I understand it, is fewer restrictions on the kinds of chips and the number of chips that could go to other countries in the world who are not necessarily close allies or adversaries in the United States. Is that a reasonable change? How do you understand that debate about diffusion? Yeah, this is something called the diffusion rule, which I would argue is distinct from the diffusion part of the competition, though it's related.

48:58And this question essentially is, in a world of constrained chip supply, where every chip that's being made is being bought, how do you want to determine where those chips go? And our view, I think, was we wouldn't necessarily use this phrase, but pretty close to America first in saying we want American companies and the companies of our close allies to get first crack at the chips. And we want to make sure that we are, in addition to that, cutting off the smuggling routes through which these chips might be smuggled to China and the like. So if you look at our rule, the diffusion rule, it allows for really large numbers of chips to go all over the world, but not so many chips that they could build out whole AI training ecosystems and the like.

49:44And it gives essentially a preference, if you'd like, or an advantage to trusted American companies that we know will not smuggle the chips and that will meet security standards and the like. We create a pathway for companies from other countries to reach that status. So this is not a protectionist policy, but it is saying we want the United States to lead in AI, to dominate in AI development. We want American data centers built all over the world, but we want to make sure that those chips are not being smuggled with China. And we don't want to repeat some of the mistakes of the past of just offshoring what we imagine will be a very vital industry to nations that are willing to foot that bill.

50:19So there is a mix of very tactical cutting off smuggling reasons for why we did what we did, and then also strategic reasons to just assert a very important role for the United States, even as we work with allies and partners all over the world. And concretely, the rule would allow millions of chips to go to even countries that are not close allies, the vast majority of them through American companies, but also a large number to their own domestic companies. And again, there's a pathway there for those companies to meet security standards and get even more chips. So it is not like we were saying only chips for America and that's it.

50:52We were saying let's have a managed policy for positioning America the strongest and America and its allies the strongest in a world of chip scarcity where chips are increasingly important. The flashiest announcement on Trump's, I believe, his first foreign trip to the Middle East in the spring was the news of a major data center in the United Arab Emirates with billions of dollars of American chips going into it. Did you see that as a good deal, the right way of doing this, or does that raise concerns to you? The devil's in the details. Our policy would have permitted American companies to build big data centers in the United Arab Emirates or in Saudi and other places that wanted them.

51:28So in that sense, it's consistent. Our policy also would have let us sell large numbers of chips, though not as many, to local firms in those countries. So our posture was not, we're giving them nothing. The devil's in the details on the Trump thing. And frankly, we haven't seen any details. And I don't know how many licenses have been granted. And I just don't know how they're operationalizing this. for better or for worse, the Biden administration put this all on a 200-page rule that lays out, you know, it's complex, sure, but it's all there. So we know what the deal was. I don't have that same kind of clarity on the Trump administration posture here.

52:02I mean, I think a lot of people following this debate without any of the expertise that you have say, look, I remember when people were telling us that China would never become, would never get to the cutting edge of other technologies in the past. And all of a sudden, And we woke up one day and saw that it had. Does that worry you? Does your assurance here, I know you're saying we should be empirical about this, but surely that precedent would be somewhat worrying. Yeah. I mean, I think the precedent cuts a couple of ways. First, insofar as some people in the Trump administration, like Howard Ludnick, have made the argument, we have to get China addicted to our chips so that they don't indigenize.

52:39I think the precedent of history here absolutely cuts the other way. In every single other industry, EVs, solar panels, whatever it is, the notion we're going to get China addicted and they're not going to indigenize, just doesn't hold water. They've done it in every single industry, as your question suggests. So in my mind, we know their intentions. Their intentions are to indigenize. Their intentions are to kick out Western suppliers, to have their own supply chain for chips. So with clarity on their intentions and the question is, will they succeed? And I think my view is maybe someday, I would never doubt their ingenuity, but our job is to make it as hard as possible.

53:12And the good news is I think chip making is incredibly difficult intrinsically, such that we really can with targeted action buy a lot of time here. How much time is it? Five years? Is it 10 years? Is it 20 years? I don't know. It depends on the threshold, depends on the scale and so forth. But there's no doubt in my mind about Chinese intentions. There's no real doubt about Chinese capabilities. It's just a question of what can we do to slow them down. And the good news is, I think we can do a lot. I want to close with perhaps a more personal question. You've been working on AI and its intersection with policy and national security since the time when most of us in the foreign policy world knew of this only as an obscure and esoteric issue.

53:50If you step back and reflect on the last decade or so of your experience studying and working on AI and how it shaped policy and geopolitics, what has surprised you at that time? What about where we are now would have shocked you if you could go back and tell yourself 10 years ago? I think I am surprised by how much this has become a mainstream issue. I think intellectually, I expected something like that just because I had very strong convictions about where this was going to go from a technology perspective. And that's part of the reason why I spent so much time on it as far back as 2015 or earlier.

54:25And the same is true, I think, for my co-author in this piece, Teddy, who we were studying this together way back when. But I think emotionally or as someone who has been a part of the process, it is just surprising to me to have, you know, not one, but many conversations in the situation room about the intricate details of chips and chip making and where AI is going or, you know, some number of conversations in the Oval Office about how this is affecting America's domestic competitiveness and what this means for the American people and the foreign policy dimensions. And, you know, it's nerd stuff.

54:59And it's surprising to me how much the nerd stuff that is near and dear to my heart has gotten picked up by a group of people who, you know, if they're nerds, they're a different type of nerd. Well, Ben, thank you for this conversation and for the many great pieces you've done for us. We'll look forward to more. And I should also thank you for five or six years ago forcing us to have the hard copy editing conversation about whether compute could be a verb. So we... It is a metaphor for many, many things about where the rules go. Thank you, Dan, for having me in your pages then and again alongside Teddy this time.

55:30Thanks a lot, Ben. Have a good day.

55:37Thank you for listening. You can find the articles that we discussed on today's show at foreignaffairs.com. This episode of the Foreign Affairs Interview was produced by Ashley Wood, Rose Kohler, Mary-Kate Godfrey, and Kanesh Klerur. Our audio engineer is Todd Yeager. Original music is by Robin Hilton. Special thanks as well to Irina Hogan. Make sure you subscribe to the show wherever you listen to podcasts. And if you like what you heard, please take a minute to rate and review it. We release a new show every Thursday. Thanks again for tuning in.

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