# Kissinger and the Future of AI ft. Eric Schmidt *Institute of Politics at Harvard Kennedy School* · https://www.youtube.com/watch?v=-m9kY1cpmgw ## Summary Eric Schmidt, former Google CEO, joins Graham Allison at the Harvard Kennedy School's JFK Jr. Forum to discuss Henry Kissinger's legacy and the future of AI. Schmidt describes his friendship with Kissinger, the ## Key points - Eric Schmidt tutored Henry Kissinger on AI late in Kissinger's life, and their book Genesis argues AI is an epochal change because humans have never faced a non-human competitor of similar intelligence. - Schmidt describes the San Francisco consensus: language models, agents, and reasoning will combine into recursive self-improvement, estimated at 2 years in San Francisco and 4 years by Schmidt. - Schmidt reframes Kissinger's 1969 nuclear challenge for the AI era: there is no higher duty than to preserve human agency and human freedom. - China is focused on embedding AI in everything and will outcompete the US on adoption, while the US pursues superintelligence; China adds about a gigawatt of electricity to its grid every day. - China's open-weights strategy means most of the world will likely use free Chinese models, which come with Chinese values and biases embedded. - Schmidt predicts large companies will ultimately not release their largest models because it will be too dangerous to do so. - Schmidt's primary security concern is cyber attacks, since AI that can write code can write attacks; the eventual solution is good AI fighting bad AI. - Schmidt distinguishes superhuman behavior from true superintelligence like Einstein's, and argues the latter remains a very hard boundary. ## Notes ### Kissinger's Legacy and AI - Eric Schmidt tutored Henry Kissinger on AI late in Kissinger's life after Kissinger heard Demis Hassabis speak and immediately grasped the question: "What does it mean to be human in the age of AI?" - Schmidt argues Kissinger's driving goal, after fleeing Nazi Germany and serving in World War II, was to prevent a World War III; historians agree on the goal even if tactics are debated. - Kissinger's undergraduate thesis on Kant was the longest in Harvard's history, prompting the 350-page limit that still applies. - Graham Allison emphasizes Kissinger's strategic acumen: lifting a problem to its full 360-degree strategic challenge and finding points where policy could be impacted. ### The San Francisco Consensus - Schmidt describes a progression: language models, then agents (which can be concatenated into workflows), then reasoning. - The consensus holds these will combine into recursive self-improvement — AI learning on its own. San Francisco estimates 2 years; Schmidt doubles it to 4 years. - Scaling laws have not slowed: Gemini 3 beat OpenAI 5, which beat Claude 4.5, which beat DeepSeek. - Schmidt insists a point will come where someone must say "we went too far" and keep humans in charge — for example, if a computer seeks access to weapons on its own. ### US-China Rivalry - China focuses on embedding AI in every product and will outcompete on adoption; the US pursues superintelligence. - China adds roughly a gigawatt of electricity to its grid every day; the US has built zero new nuclear-scale plants and struggles to provision power. - China pursues open weights and open source, so most of the world may use free Chinese models — which come with Chinese values and biases. - American closed-source models are driven by economics: companies borrowing $50 billion cannot give models away. ### Key Concerns - Cyber attacks: if AI can write code, it can write attacks; the solution to bad AI is good AI fighting bad AI. - Children: society is running a mass experiment with addictive systems; we have no data on children whose best friend is non-human. - Democracy: algorithmic misinformation exploits anchor bias; Schmidt favors free speech but not boosted speech. - Jobs: human dignity involves purpose, and losing jobs is an emotional crisis, not just financial. ### Open Research Questions for Students - What does it mean to be human in the age of AI? - Why is the rivalry only China versus the US? - What does conflict look like in algorithmic warfare? - How would we verify consciousness in AI? ## Transcript [09:58] Mhm. [12:00] >> Good evening and welcome to the John F. Kennedy Jr. Forum at the Institute of Politics. My name is Amo Gyantah and I'm a freshman at the college studying computer science and economics and I'm a member of the JFK Jr. Forum Committee. Before we begin, please note the exit doors which are located both at the park side and the JFK street side of the forum. In the event of an emergency, walk to the exit closest to you and congregate in the JFK park. Please also take a moment now to silence your cell phones. And join me in welcoming Harvard College undergraduate Matteo Cagliardo. [12:47] Good evening everyone. It is my pleasure tonight to welcome you all to the John F. Kennedy Jr. Forum at the Harvard Institute of Politics. My name is Matteo Cagliardo. I'm a junior at the college studying applied math and computer science with economics. I'm a member of the John F. Kennedy Jr. Forum Student Committee. We gather tonight at what is arguably one of the most pivotal moments in human history. Artificial intelligence has evolved from being a futuristic concept from to becoming a fundamental part of our everyday lives. Quietly but profoundly reshaping economies, warfare, and the very fabric of human society. For the first time since the dawn of the nuclear era, global stability may hinge [13:24] not just on military strength or economic weight, but also on competing to secure a competitive advantage on this new technological intelligence. All in all, AI embodies something we have never seen before. A shift in how knowledge is generated. A shift in how decisions are made and ultimately how humanity sees its own place in the world. What will our society look like 20 years from now? And more importantly, how will artificial intelligence have shaped it? Now to comment on these ideas, it is my pleasure to introduce you all to our speakers for the evening. Eric Schmidt served as chairman and CEO of Google between 2001 and 2011. Today he serves as chair and CEO of Relativity Space. He is a founding [14:06] partner at Innovation Endeavors and is a leading voice in AI and national security in the US and around the globe. Graham Allison is the Douglas Dillon Professor of Government at the Harvard Kennedy School where he served as founding dean and director of its Belfer Center for Science and International Affairs. He also served as assistant secretary of defense in the first Clinton administration receiving the Defense Medal for distinguished public service. Now without further ado, please join me in opening up tonight's discussion and welcoming our esteemed guests to the stage. [14:47] So thank you for a generous introduction. It's a extraordinary pleasure to welcome back to the JFK Jr. Forum Eric Schmidt, our colleague and friend, uh for a discussion of Henry Kissinger uh and AI and the future. Uh as you can see here uh this picture of Eric and Henry, uh uh they were best friends. Uh late developing friendship but became very deep friendship in which Eric was uh generously the tutor for a 95-year-old Henry Kissinger who discovered AI hanging around the end of a lecture that he had given uh when uh Demis Hassabis was uh offering to beginning to talk about AI and Henry decided he needed to learn about this. Uh he called me up. I told him, "Henry, forget about it. You know that [15:47] you don't have any background in science and technology. I told him actually you don't know the difference between a chip and a potato chip." Mhm. Uh he said, "That's true but Eric has promised to to to teach me." So we're very pleased to have him here. We had him here last year. This is actually uh maybe it will become an annual tradition. Uh Henry passed 2 years ago last week. Uh so he was 100 years old. Uh to think of such an amazing life over that century. Uh he was a person who made a huge difference in America's national security and the world and made a huge difference in the lives of many many people. Uh some of whom began as his students, some who became as his tutors, uh and many others. So [16:39] uh Eric has been introduced. Uh I would just remind you a couple of things. I would say first that Eric was the chief executive who took Google from a cause to one of the great companies of the world. And that's a pretty amazing thing. Uh secondly, early on he had identified AI as an arena of the future. And at Google essentially bought up all the super talent in the world that he could find including uh uh DeepMind uh which was actually the company that then brought to Google uh Demis Hassabis who got the Nobel Prize last year for his work at Google on uh protein. Mustafa Suleyman who's now running uh uh Microsoft's consumer AI was another part of the acquisition and many many many many others. [17:43] So the other thing to say about Eric about Eric and why it's such a pleasure to have him here is that as one tries to make any sense of all the claims that are being made about AI, most of the people who talk loudest are talking their own book. So when I listen to Sam Altman or to folks at Anthropic or with folks even Mustafa now at Microsoft, they have to talk their companies >> holding my book. Uh well, wait a second. They have to talk their companies, you know, business, their future and they are chasing what they believe is the biggest pot of gold at the end of the rainbow that there's ever been. So it's pretty hard to tell what they actually think and what they're saying. Eric has kind of graduated to be the [18:30] statesman in this domain having had a big stake in it but now having stand standing back and I particularly after his work with Henry, basically trying to say things as as clearly as he can and as clearly as he can see them. So I take him to be a much more valuable source of clues about what's happening than listening to most of the people who are talking their own books. So let's start with Henry and then we're going to go to AI and then we'll go to questions of the audience. So uh Eric uh you uh spoke beautifully at at Henry's memorial about how much impact Henry had on your own life and even on the you know, the questions that you were asking, the things that you think that matter. So tell us a little [19:27] bit about more what Henry meant in this relationship and also for people that wouldn't have the opportunity to to know him, what what how might they capture a little bit of this magic? You know, when I when I first met and thank you Graham as always, it's great to be here. Um I I met Henry when he was in his early 80s and normally men in their early 80s become a little off. And I was struck by how brilliant he was. And so I thought to myself, you know, he he served in World War II, he got the Bronze Star, and you know, emigrated or escaped from Germany cuz they were a Jewish family. And um came to fought in the war, came on the GI Bill to Harvard uh undergraduate and graduate, and ultimately was a professor here. [20:17] And I tried to figure out he must have been really smart when he was in your chairs, not that the building existed at the time. So here's a quote when he was an undergraduate at Harvard. Quote, "In the life of every person there comes a point when he realizes that out of all of the seemingly limitless possibilities of his youth, he has in fact become one actuality. No longer is life a broad plane with forests and mountains and beckoning all around, but it becomes apparent that one's journey across the meadows has indeed followed a regular path. That one can no longer go this way or that, but that the direction is set and the limits are defined." That's what he wrote when he was your age. Um when he was an undergraduate here, he [21:08] holds the record for the longest undergraduate thesis in the uh in the college. After he submitted his undergraduate thesis on Kant and the meaning of the world, they instituted a new rule which still applies to you that your thesis can be no more than 350 pages. FYI. It's true. >> So he was clearly an unusually brilliant polymath. And uh my own explanation for Henry, aside from you know, I really cared deeply about him, was that watching his family, and in particular his father, and he we talked a lot about this, um see the destruction of the world around him in Germany as the Nazis took over. And when they fled, and he saw the damage to his father and his father's identity and so forth, Henry after the [21:56] war decided that he would do everything he can could to avoid a future war. Now you can quibble with or argue very much with the things he did, but you cannot argue that his that his goal was not what I just said. The the data supports it, the historians all agree. You can disagree with the specific tactics, but at the end of the day what he sought is he sought a a a world that did not have a World War III. And think about the issues that he faced in the '50s and '60s. He used to tell me about this small group, and he always thought that the most interesting policy things happen with small groups, and this was a collaboration between MIT and Harvard and RAND, uh which in the early 1950s dev- de- um [22:42] um invented mutual assured destruction. And I said, "Henry, well who was in this group?" He says, "Oh, Oppenheimer." And I'm like, you know, give me a break. Right? All the most famous people. So uh he was lucky uh to be born at the right time. He was clearly one of the smartest people at alive at the time. He was lucky to get out of Germany before his family and he were killed. He was lucky to be part of the GI Bill. Um and he had all sorts of interesting stories. My favorite was one day I told him, "You have to go to the doctor." You know, he's old. And he goes, "Okay." And I said, "I want you to go to the Mayo Clinic." And I said, "Okay." Now he's a New Yorker. And the people in Mayo were like, you [23:21] know, Midwestern, soft-spoken, pleasant, nice, not like the intense New Yorker types. He goes to the Mayo Clinic, comes back, and he said, "I loved it." I said, "Why would you like the Mayo Clinic in the middle of Rochester, Minnesota?" He said, "It reminded me of the war." I said, "What?" And he goes, "Well, he was in a uh he didn't speak English when he came to the country, and he worked as a um uh in a um shaving brush factory where they made shaving brushes, and he was drafted as everyone expected, and he went to war, and he went with a Wisconsin-based American group. So he indexed his identity, could hardly speak English, and and nobody could understand his English anyway, he indexed to that group. So he had all [24:03] these sort of touching little stories. I have lots of stories like that about how he became an American, and of course became an American citizen. And you know the rest of the history. Well, I would say a fantastic person. I had the good fortune to enroll in the course as a student. >> What was that like? >> 1965, God help us, uh taught by Henry, uh and ever thereafter was part of his entourage, and I think the thing that struck me most about him was this uh and the strategic acumen. That is, the ability to take a problem and lift it up to its whole 360° of strategic challenge, and then find the points at which the policy process could be impacted. And uh I wrote about uh for his 100th birthday, which Richard [25:06] asked us to write a little, you know, something that Eric wrote something, I wrote something, I said, "Really in thinking about Henry, uh he was not about, if you think about, he was often criticized for his realism or ruthless realism or realpolitik, but if you look and look and see what he was doing, uh it wasn't raw real- realpolitik uh just for the advancement of the interests of one country. It was always about the construction of a viable order to prevent catastrophic war. He had lived through the experience of the Holocaust and the most deadly war in history. He foresaw the prospect of a nuclear war that could actually extinguish life on Earth. He was caught up in the Cold War, the fiercest rivalry that we'd seen to that [26:05] point between the US and the Soviet Union, and all the time he was trying to reach beyond that to some way to survive for both the US and for for uh for fellow human beings. So that's I think and that's how he kind of backed into the AI issue, which for him was another generation of a somewhat analogous problem. Well, he had written his undergraduate thesis on Kant and the meaning of meaning, I think. And um he was very when he went to hear Demos speak, um it sort of he immediately got it. And he immediately said to himself, "What does this mean to be human?" And we are today grappling with the question that he foresaw 20 years ago when we when we first started working on this, "What does it mean to be human in [27:02] the age of AI?" What does it mean to be a child to an adult, to be a leader? What does it mean for economics? What does it mean for jobs? You know, all of that. But his core argument was that this is an epochal change in the sense that it's like the um uh the various major changes that we've had in sort of reasoning, the scientific revolution and so forth, cuz we as humans have never had a competitor that is not human but of similar or greater level of intelligence. And it is unpredictable what we human will do. He used to say that what would happen is in magic, when people don't understand things, they either decide that it's a new religion or they take up arms. And so he would say, "Well, are we going [27:50] to take up arms to AI or we going to make it a new religion?" And I said, "I hope it's a religion." Um cuz of course I benefit from the religion, I guess. So in the book that was published this time last year called Genesis, which was the work that Eric and Henry and Craig Bundy were still doing up to the point at which he uh passed, he here's a a passage that clearly Henry wrote. It says, quote, it talking about the US and China, "If each wishes to maximize its unilateral position in the AI space, then the conditions would be such for a contest between rival military forces and intelligence agencies, the likes of which humanity has never faced before. Today, in the weeks and months and days leading up to the arrival of the first [28:44] superintelligence, a security dilemma of existential nature awaits us." So you've thought about this superintelligence as an existential threat beyond what what we can imagine? Say more, yeah. Well, um there is something which I call the San Francisco consensus. And the reason I call it the San Francisco consensus is everyone in San Francisco believes this. It may or may not be true, but if you go to San Francisco, trust me, that's what they're doing besides the usual San Francisco recreational behaviors. And um the the basic build goes that we are we're we're we've gone through a language revolution. You understand that, you know, language to language chat GPT, everybody here knows what that [29:33] is. Agents, which are now on the horizon, and these agents allow you to essentially automate tasks. And the key thing to understand about agents is they can be concatenated. So you can, you you do do this and this and this and this and this and then they all get piped together. Why am I talking about agents? Because I've just described workflow. And that's what businesses do, what it's universities do, it's what governments do, so forth and so on. And then the next one is reasoning. Now, reasoning is higher-order function in in humans and the reasoning revolution is just beginning. Um as of today, it is technically correct to say that the scaling laws that operate in AI have not slowed down yet. The scaling law basically says that [30:19] if you put more data and more electricity and more chips, you get this emergent behavior one after the other. And you can see that, for example, with Gemini 3 which just came out, which beat uh OpenAI 5 which just came out, which beat um Claude 4.5 which just came out, which beat Deep Seek cuz they copied it anyway, and blah blah blah. You know, so so it's >> In case you didn't notice, Gemini 3 is from Google. Yes. I'm proud to say that we're back in charge um until the next one. It's it's very very competitive and what's happening is that these massive data center build-outs uh which are, by the way, one of the key drivers of the American economy are helping not only lift our economy, but they're also building building this kind [31:03] of stuff. And it's a whole new world compared to my whole experience. So, the question is what happens over time? So, you have language, agents, and reasoning. Well, isn't that what we do? Right? We do stuff, we communicate, and we do actions. So, the San Francisco consensus is that at some point that stuff comes together and you get what is called technically recursive self-improvement. And recursive self-improvement is when it's learning on its own. This is not true today. Today, when you set up one of these huge data centers, you know what they look like, um you have to tell them what to learn. But the belief is that this is coming. And there's lots of evidence that this is coming. The ability for computers to [31:47] write programs, to generate mathematical conjectures, to discover new facts looks like it's very very close. Many people believe that there will be new math design, new mathematicians, AI mathematicians to love in the next year. So, we collectively as an industry believe that this is going to happen soon. If you ask the San Francisco people, they'll say 2 years. Which is really soon. If you ask me, I double that to 4 years, which is really soon. Right? So, it's happening. It's happening very quickly. I want and Henry certainly wanted it. We wanted it to be built with American values and human values. There is a point, in my view, and we talked a lot about this, where somebody's going to have to raise [32:29] their hand and say, we just went too far. There's too much danger here. We don't want to give that agency to the computer. We want humans to be in charge of it. It is not agreed to where that point is, but our book spends a lot of time talking about where that might be. Another example would be the you discover that the computer has decided on its own to get access to weapons. That that's clearly a Like everyone would agree that's not a good idea, right? It's bad enough that humans have access to weapons. Imagine if the computer has it. And what are the criteria by which And you can think of many other such examples. Fundamen- fundamentally about human agency. We also spend a lot of time talking about [33:05] the effect on children. Um we're running a mass experiment on human development by deploying these systems that are incredibly addictive. Um what whether it's on iPad or phone or, you know, whatever for young people who may not be certainly not adults, may not have their own identity, and they can really be manipulated. So, what does it mean to be a child whose best friend is non-human? Aside from maybe becoming like a super nerd. But I don't know, what does it mean? We we we have no data. We don't know what it means to young young boys and girls, to their development, to their ability to associate. Will they ultimately rebel and say, I only love people, I hate computers? Like, you know, kids rebel. [33:45] We just don't know. So, can you take the next slide? I'm sorry, I have the clicker, but I left it. [33:57] Can I Can you guys make the next slide go up? Yeah, thank you. So, here's the so-called Kissinger challenge. This is Henry 1969. So, Nixon was elected in 1968. Became president January 1969. Appointed Henry, who was a professor here at Harvard, as his national security advisor. And Henry writes, as you'll see here, that anyone coming to office in the late '60s could not fail to be awed by the unprecedented dimensions of the challenge of peace. And then down to the bottom line, there could be no higher duty than to prevent the catastro- catastrophe of nuclear war. So, that became for him a defining challenge in the Cold War with nuclear weapons, arsenals developing, but quote, no higher duty than to prevent. [35:03] So, how might this apply currently as we try to think about the US, China, and AI? Well, let me say that I think there's no higher duty than to preserve human agency and human freedom. Right? The things that we value collectively the most. And I think that's going to be a central challenge for all of you when you graduate. Um all of you will be facing these issues and they're complicated and they're subtle. Um imagine if the internet had been invented by China and it didn't have the kind of openness that uh the internet has today. It was under the Chinese internet. Um it looks like China is pursuing a different strategy than what I'm talking about. In my most recent visit to China, my the way I operated, I ask engineers [35:50] technical questions cuz they don't lie to you. Whereas kind of everyone else, I'm not so sure. And I eventually figured out what the Chinese are doing is they're really really focused on applying AI in their businesses. And they're going to outcompete with us. We We will lose to China for their incredible adoption of AI in every product. Because they're just relentless and they work so hard. Called 996, 9:00 in the morning, 9:00 in the evening, 6 days a week. Illegal, by the way, in China. Illegal in America, right? But nevertheless practiced. Um they're coming. Um they do not appear to be as focused on superintelligence and the path that I was describing as the San Francisco consensus. Of course, that could change. [36:34] So, it appears that the two are pursuing different paths. One of the questions for you all and uh at the graduate level, even undergraduates given it's Harvard, is start figuring out what happens when these divergent paths hit roadblocks. Right? Cuz they're both have it. Example would be in America, we have essentially produced no new electricity because it's very hard to provision electricity. China has infinite electricity because of their incredible investment in renewables and so forth. They built something like 120 gigawatts of new renewable energy in the last 5 years. It's some number like that. >> no. Wait. Wait. Wait. Wait. Every day in China, Thursday, Saturday, Monday, every day uh another gigawatt of electricity has [37:21] added to the grid. Wow. That's pretty extreme. >> Every day. So, my point is And here And here, by the way, just to give you an example, how big is a nuclear power plant? About 1.5 gigawatts. So, that's the scale of the electricity revolution that's going on in China, again using those numbers. How many of those kinds of plants have we built in America? Zero. Uh and we're certainly losing the renewables race to China for the reasons that everybody here knows. So, they have a lot of power, right? We don't. We have a lot of chips, they don't. Um it sets up the competition, right? And each will pursue different ways. Um one of the technical questions is something called diffusion, where what you do is you take a very powerful [38:07] model, we'll use Gemini 3, the top one at the moment, um and you ask it 10,000 questions and you take the answers, and then the system can learn from the questions and answers enough to mimic, without the expense of doing the big training, the big model. So, again, thinking through the strategies that China will pursue and the the strategies that are available for America um are is probably pretty important. What's interesting is both countries are relying on the private sector to do this. You would have thought in Henry's period that you would have used the government. But in fact, our government cannot move this quickly, the compensation systems, and so forth. It turns out it's probably true for the Chinese as well. I don't [38:49] know at the at the security level if this is true, but I've not found any large, weird, Manhattan-type projects within China, although there's plenty of people in the private companies that are working hard on national security. So, on the AI topic in general, tell us Just give us a minute or two of what is the upside, the things that excite you the most that are 1 year, 2 year, 3 years that are the line of sight to. Well, the first question is, why is this madness occurring? It must be a bubble and it's going to crash. No, it's not a bubble. If if anything, it's underhyped because you're fundamentally automating businesses. And the reason people are spending this enormous amount of money is to automate [39:33] the boring parts of what they view as the boring parts of their business. So, whether it's billing or accounting or product design or delivery or inventory or management or whatever, people are automating those. Um and there's an awful lot there. Think about medicine, think about climate change, engineering, new science, it's extraordinary. [39:57] So, what excites you the most of the ones that you see that you have that you have a line of sight to that the rest of us probably do or not seeing yet? Sorry, I have to Sorry. have a cough. I apologize. We can all see in our own imagination what we're thinking of and then we'll see what Eric says. Yes. Well, when I started when I was in high school, I was an early programmer. And I delighted in writing code. When I went to college and graduate school, that's all I ever wanted to do. I didn't I ignored all these history things and things like this. I was the definition of a nerd at the time. And everything that I did in my 20s, which got me to where I am, has now been completely automated. Every aspect of the programming that I [40:47] did, every aspect of the design is now done by computers. I recently had it write a whole program for me and I'm sitting there watching it define the classes and the detail of the interactions and so forth as it's generating it got like, holy crap, you know, the end of me. And I think watching I've been doing programming for 55 years. So, to see something start and then end in front of your own life and you're still alive is really profound, if I might say. Now, computer science is not going away. The computer scientist will be at least until the computer scientist gets replaced, will be supervising this. But the ability to generate code at the power that these systems can do is revolutionary. It means that each and [41:34] every one of you has a supercomputer and a superprogrammer in your pocket. Now, nobody here is a terrorist, right? But using it's always easier to use negative examples. There's plenty of I'll use a stereotype, young men living in the basement, their mothers give them food and they sit there on the equivalent of crypto for chan, you know, paranoia, whatever. What pick your pick your poison. They all have the ability to use these tools to build incredibly powerful systems. Right? Cyber attacks, other things. No whatever they care about. Um there was there's some evidence that I think it's Mengioni, the fellow who killed the insurance insurance executive, um was into some of this and there's some people were looking at some of his [42:19] writings. Of course, he's in jail now, but that he was somehow influenced with that. Now, I'm not proving causality here, but it's an example of some of the darkest recesses of humanity. You give those people these kinds of tools, we have to be ready. Now, the industry is well aware of this and we're working on it. It's very important that the defensive systems be capable. The eventual solution to AI, by the way, bad AI is AI fighting good AI fighting bad AI and that's how it will resolve itself. Okay, can we do the next slide, please? I want to ask you about the US China rivalry in AI as you see it. So, this is apologies if the slide's not clear enough, but what it suggests is that if we take the series of indices, [43:04] uh if you look at the performance gap in January of 204, it was significantly larger than it is today. And then the uh What do we make of this and what do we make of the likely future? So, the chart is correct, but the people who are influenced by this claim that it's not going to be true for long because the reasoning revolution requires so many chips and so much of the magic that the San Francisco people have invented, using that as a sort of moniker, that the gap will widen. My own view is that the gap will widen, but for different reasons. I think that the Chinese focus is largely, as I mentioned, on embedding AI in everything. Smart toasters, cars, so forth and so on. They're moving much more quickly than [44:00] our their robots. I think that the vast majority of humanoid robots will be Chinese AI powered and Chinese AI manufactured simply because they know how to drive the cost of things down. Their supply chains are incredible, their cost management, they work so hard, blah blah blah, all that kind of stuff. So, my guess is that that it's true that the gap will probably get larger, but the real question is will you as a consumer ultimately have a a better experience as a consumer with a Chinese product than a US product? And the answer is from a fit and finish, probably the Chinese product and that's of concern. So, let me drill a little further on this one. So, what there is a half dozen questions about which people are making [44:42] bets and you've thought about them quite deeply. So, one is are we going to bet on computer chips or stacks or brains? Another one is are we going to bet closed or open? Yep. Another one is are we going to bet um working hard on AGI or diffusion and applications? So, if you go across the spectrum there, uh if I look at the Chinese piece, uh certainly the guys at Deep Seek think 200 people uh who just have brains can have a reasoning machine that costs 1/1000 of the cost of Open AI. And now there's six other little dragons coming along from the same from the same space. So, that one makes me worry. On the closed versus open, if I remember our last conversation, you had pretty much concluded that open is going to [45:42] beat closed, but all of our companies are mostly closed. So, what about that? And then thirdly, uh maybe if there's this AGI breakthrough, all the rest of this other stuff won't matter, but if the if the diffusion and application is already working in these other arenas, so just take us another step on the on the rivalry. So, diffusion refer backward diffusion refers to essentially learning from one model by the by many pairs and learning from it, as we discussed. My own opinion is that which is not I'm not sure of, but I think so, that the large companies will ultimately not release their largest models. It'll be too dangerous to do so that they'll subset them. That's what I would do. Now, I assume that they'll come to that [46:34] decision. I think the most interesting question is open versus closed. For the those of you who don't have a background in this, open source open weights, open source is what I did for decades. Um when you use any form of computer, much of the software that you're using was developed by open source, which meant that the source was published and people would collectively advance it. And there was a whole movement around this, which I was part of. So, I'm relentlessly open source in my view. Um the major companies are closed source largely for economic reasons. Um basically, if you borrow 50 billion dollars from the financial market, they do want a return and saying to them, oh, by the way, we're going to give all the [47:16] models away and you won't get your return probably isn't a very good legal or financial strategy. So, it So, the American model seems to have emerged as as closed. Strangely, the Chinese model is completely open open weights open source. Why? I don't know. Um one possible explanation is that the Chinese government has figured out that they were losing in closed source because they couldn't get the hardware and open source, because it's free, gives them proliferation. So, one of the consequences of open source open weights is the vast majority of humans on the planet will use Chinese models. Why? Cuz they're free, right? And most countries cannot afford the computing and the data centers and so [48:01] forth. They're just going to take the Chinese models for free and embed them. Now, is that an issue? Absolutely, because it comes with Chinese values, Chinese training, Chinese biases and so forth. We would prefer to have it be American. Um we will see. There is a there's a couple of open source projects in America that I am a supporter of, but they can't raise the 10 billion dollars that are needed from the public markets to get to where they want. So, they are gems, but they're not at the scale. I've argued that the US government should help fund them. I've argued that philanthropists should help fund them, but I don't really know. Okay, that's very helpful. So, one last question and then we're going to uh [48:45] take questions from the audience. If you were picking two or three questions that a graduate student or an undergraduate interested in this space, AI and geopolitics, should be thinking about. Give us two or three questions that we might have somebody that has an answer to or part of an answer to the next time you come. So, so a couple well, again, we're dealing with some of the smartest people in the world here. So, the first question is what does it mean to be human in the age of AI? That is a question that you can write many PhD theses on. So, basically, study history, study philosophy, study how people work, study economics and then figure out what this new technology is going to do. We always in in my [49:26] industry, because we didn't take those classes, we always ignore those things, right? You guys are capable of answering these questions in ways and if we get out of line, maybe you can use that to call to call us out. The second question has to do with this China-US rivalry. Why China versus US? The only two countries that are going to matter. And the reason is you need an enormous amount of money and an enormous number of people. And as much as I like Europe, Europe is not organized, doesn't have enough people, doesn't have enough money to do it. India is not organized enough yet to do it, although they're trying. And most of the other countries don't have enough money, don't have enough of the talent, [50:03] don't have the right universities and so forth. And then the third question has to do much more with conflict. In a world where you have terrorists who have access to AI and you have governments that have access to AI, what does conflict look like? What does a terrorist attack look like against a major power? Obviously, I'm not endorsing that, it's a terrible thing. How do we defend against it? Same thing for China versus US. But what about Russia versus Ukraine? What about Europe versus whatever? Trying to understand how conflict plays out in an algorithmic warfare where the AI is driving everything is a very, very fertile area for research and new ideas. And it's just starting. So, good topics for somebody to work on [50:50] in the interim. Let's start here. Please introduce yourself and a short question. Hi, my name is Teresa. I'm a second year student here at the Kennedy School and I'm in Professor Allison's national security class. We had a class talk about cybersecurity and most of the tech that underpins US homeland security is all in private hands. So, what governance governance model do you think will actually help work to coordinate government and private companies to during especially during like a AI type of emergency? And given we talked a lot about like US-China rivalry and despite the technology and investment rivalry, there's also a big difference in governance model. So, how do you think the two governance model [51:37] will lead in terms of there's a homeland security cyber crisis? Well, first place, I think in under the Trump administration, you're not going to see much regulation of AI. That's pretty clear. In China, it appears that they're allowing the the companies to do whatever they want. Although they have the laws about various things, they don't seem to be implementing them. So, it looks like it's an all-out business conflict. The biggest concern I have is cyber attacks. If you can write code the way that I've seen these things write, and every company in my world is now using programmers and AI programmers together. It's extraordinary. It's happened very quickly. Look at Claude code, for example, the most recent one which is by [52:19] far at the top. It's from Anthropic. And others coming. Gemini, of course, claims it has a competitor, but at the moment Claude code is a bit better, it looks like. If you can write code, you can also write cyber attacks because the objective function is easy. Just keep writing code until you break something. And if you have enough hardware and enough energy, you can just keep doing that. So, I think there's going to be a many, many more cyber attacks. And that doesn't mean from governments, it could also be from terrorists and and bad organizations. And I think getting organized for that would be my primary concerns. Please. Hi. Thank you for coming. I'm David Wideman. I'm MPP2 here. You You mentioned the strength of current [53:03] closed-source models. You can tell me if I'm wrong, but I think open-source models are about a half generation behind the current closed-source models. Perhaps it's a little dangerous to characterize open-source versus closed-source as an America versus China issue. And my my fear is that the Silicon Valley people that you're talking about are seeking regulatory capture and that's why they want to fearmonger around open-source models. What would it take if we wanted to establish a moratorium on closed-source models if that's something that we seek? What would it take in order to do establish this moratorium worldwide? I think I just don't agree with your question, so I'm sorry. I I don't see the open-source leadership [53:46] in America. And I see nothing but open-source leadership in China. I think those are the facts. So, I don't think there's regulatory capture on the open-source side. And I think again under the Trump administration, it's highly unlikely that you're going to see significant regulation of the closed-source companies. But I think the closed-source decision is largely driven by economics, not policy. If you see my point. Literally, you just can't I mean, think about the cost of these things. We're talking about $10 billion, $20 billion. How would you raise that without it? And I think Eric, this goes back to before making our list of questions, I would add add to it. How does the financing considerations [54:29] impact the strategic choices? Because if it if it just happens to be an accident of the financial structure, that wouldn't necessarily reflect the national interest. That would just reflect the capital markets as they are. I think that it's important to acknowledge that America has the most extraordinary capital market financial system in the world by far. I think the numbers are 60% of the volume and 90% of of the value dollar and so forth is in US dollars. And so countries that are not dollar denominated do fear the power of that financial market. And we see it in the ability to raise money. When I was in China, it was quite clear in talking to my friends, they don't have access to the depth of the [55:17] financial market. They literally cannot get the money. The venture financing that was in China is cut by a factor of five since three years ago. Now, there are many reasons for that, not just our world and not just the US. But without that access to capital, it's very hard to to do these large models with these companies and training. Now, you can imagine everything I'm saying changing if the underlying algorithms change. There are people who are working on new non-transformer models which are less economically expensive. There are many people who have commented on the energy efficiency of human brains versus the cost of these data centers. And trust me, our brains are complicated, but they're not energy [55:58] they're not energy expensive. So, so again, there there are there might be a breakthrough that would change the calculus that we're discussing. Go ahead. Please. Thank you, Dr. Schmidt. My name is Fatjon Semanajaku. I'm an MC/MPA student here at Harvard Kennedy School. Originally from Albania. In the age of AI, you and Dr. Kissinger describe a world where AI systems begin to interpret reality for us. If human strategic judgment becomes shaped, even subtly, by machine-generated frames, what becomes the anchor of responsibility in global affairs? In other words, when an AI-influenced decision produces a real-world consequences, who holds the moral burden? The human who acted, the institution that deployed the system, or [56:51] the algorithm that shaped the perception? And how should democracies redesign their institutions before this ambiguity becomes a geopolitical vulnerability? A very well-phrased point. So, I worry that the future of democracy is uncertain simply because I'll use an American example. We believe in free speech and I'm certainly in favor of absolute free speech in America. I'm not in favor of boosted speech or algorithmic speech. So, where is the line, right? If if I say something that's wrong and then the algorithm, because my claim is outrageous, decides to spread it everywhere, is that appropriate in a democracy? And you can imagine, and again, I'm not making a partisan thing. I think any any any part of the [57:39] political system can use this for whatever effect. The ability to generate misinformation that people really believe is so simple now that I hope the answer is better education and critical thinking among human beings. But you could imagine if I were evil, which I hope I'm not, and I I sat down and I started to flood everyone with my particular unique messages, that I could overwhelm your belief in truth by simply relentless copying and repetition. We know that there's somebody called essentially anchor bias. If you hear it first, then you judge So, if I managed to get the message to you first that the building is on fire, which it's not, so I'm not committing a crime. If I managed to get that first, you then [58:21] anchor from that point. You see the danger of this. It's very real. I don't think and my my answer to your question is that every every democracy will face this. And I think democracies will make different decisions based on cultural values and their understanding of the threat. You won't see a single answer from democracies. And by the way, the way authoritarian countries solve this problem is they just ban it. Right. So, a lot of this stuff is illegal in authoritarian countries because they have the ability to suppress speech. And obviously, we're we're all in favor of free speech. Please. Good. Yes, ma'am. Yes, thank you very much for for coming here this evening. So, I'm called Elena Kraine and I'm a fellow at the Belfast [59:02] Center. So, I'm wondering firstly, could you comment on the role of humans such as you and me in the very long term with regards to AI? What's our what is our role become? And also, we often frame, you know, because of the value systems, the US-China debate as a competition. But is there is there value to thinking about that in terms of a collaboration? And maybe more interestingly, you know, you mentioned Europe being maybe somewhat disorganized, but they also have enormous strength and enormous talent like Mistral in France and so on. Is there something to do there with um collaboration? Um so so a couple comments on Europe, um I was the first investor in Mistral, so I'm a big Mistral fan. Mistral cannot raise the money [59:46] that their US competitors. They have the same problem and they're working on solutions to that. Um with respect to US and China, the um I started because of Henry, I I spent about 5 years looking at Chinese peer competition. And I thought that it would be possible to get closer to China. And I eventually through his work and others discovered that the Chinese were more afraid of competing partnering with us than we were with them. So it takes two to tango, right? And I think it's highly unlikely. I think you're going to see in your lifetime um hopefully understanding that we have to coexist, but it's highly unlikely that the two systems will become best buddies for the obvious reasons. I think on this this other question [1:00:37] about what is what are humans useful for uh in the very long term. Um it's very very clear that humans are social animals and want to be around other humans. It's also very clear that there are some things that we need, medical care and so forth, which will be delivered by humans. I think that that it's a rough way to say this is most other functions will be doable by computers. Will we allow them? That's a question for your research. Right? Is where is that line? My my old example was the following. This is back when we were building Waymo at Google. So the thought experiment is New York City is nothing but Waymo and competitor automobile cars. And the engineers at Google and the other companies have figured out how to [1:01:26] completely optimize the traffic so that the the streets which don't change, you have the absolutely mathematically true highest loading per street ever and it's seamless. And then you have somebody with an emergency, a pregnant woman, you know, whatever who has to get to the hospital and needs an exception. Is there an exception button in the car which says you have to violate all the rules because there's something uh Now if there is not an exception button in the car, then that's dominance of humans by a computer. And humans will revolt. If I go back to the pickaxe versus religion, humans will fight that. That will be seen as oppression by the government, in this case, in the governor of New York City, [1:02:08] whatever metaphor you want, and the computer companies themselves. If the system, on the other hand, is adaptable to human needs and says, "Ah, we have an actual, you know, medical emergency and not somebody who's high on drugs or, you know, whatever who's just goofing around or some kid who's, you know, playing with the buttons." Um and it reasons and it says, "Oh my god, I've got to do everything I can to get the the person to the hospital." Then it'll be more accepted. So a lot of this depends on whether it integrates with our human experience and needs. Does it limit our freedom or does it increase freedom? Uh I've come to the view, I'm old enough now to believe that preserving our freedom, our freedom of thought, of [1:02:48] motion, of assembly, of gathering, you know, of all of the things, right? Uh are really really important. If they impinge on our freedom, then they will be fought and I will be leading that fight. Please. Namaste, sir. My name is Ilma Afroz. I'm from India. You mentioned India in your conversation. I'm lucky to be Professor Allison's student and to be learning from him. My question is how we can bring together US and India. India has got some excellent talented people. How we can bring both our countries together in order uh to create a world where democracies flourish and it's a win-win partnership for both our countries. Um I strongly agree. I've spent lots of time in India and the literally because of the IITs and the [1:03:39] quality of the people, the um the depth of talent in India is extraordinary. However, the depth of computing is not. Um last year we did a calculation, there were only about a thousand GPUs in the entire country for 1 billion people. So I and others have organized to try to to fix that. Um so I was alarmed with the most recent trade war, uh which I think has set uh India and the United States back and that needs to get fixed. I don't understand the trade war and I just my position is that's hurting us. India is our natural partner, right? It's a democracy, it's a messy democracy, but we are too. Um w- in in Silicon Valley, most of the people I work with are of um essentially South Asian origin with [1:04:28] India or some of the other countries. So at least in Silicon Valley, the Indians are flourishing and I want the tightest possible integration with them. Thank you. Please. Hi, my name's Josh. I'm an MPP2 here at the Kennedy School. Earlier, you outlined two national strategies earlier. There was pushing the frontier or pursuing adoption, but ideally you have both, so thinking about adoption in the US, what are some of the major barriers to enterprise adoption right now? Does that vary by industry and what should government do to help address those barriers if anything? Um the government usually doesn't do a very good job on that. Um the industry believes that there is something called a technology overhang, [1:05:13] that we, your friendly industry, have produced more tools than you are using. Now that that is our belief, whether it's true or not, you can discuss. Um and a lot of it seems to be readiness to adopt the technology, uh the fact that a lot of the stuff is software, most companies don't have very good software people. I'm sorry to say that. Um the the changes internally that are required. Um I personally believe that this technology adoption thing is just a temporary problem and that as new CEOs come in and winners emerge, the competitive pressure, which are very high in America, will cause that adoption. Um you don't see very much adoption in regulated industries because regulation is used as a excuse [1:05:57] to not innovate, but at least in the hard innovative industries, I think this will get solved. But the general answer is I would not have the government do very much because I don't think it would help. Um what would help is just having every business understand that if they want to make money, which is the capitalist vision, they need to use AI much more profoundly. Remember, you can target your customers, you can serve them, you can understand them, you can talk to them and so forth, all with AI. Um and a lot of this there's a lot of negative issues with this. For example, it may lead to layoffs in companies. There's lots of examples where low-end jobs are being replaced by computers and that's obviously a job loss, that's a [1:06:34] societal problem, but from an adoption perspective, the answer is just time. Please. Hi, my name's Ayush. I'm a third-year PhD student in computer science doing AI research. Um my question is regarding um you were mentioning China's sort of intense focus on automating AI in business. I guess in the Valley it seems like, you know, every day there's a new startup that comes around like there's the big seed targeting some automation for workflow and we we also sort of have the the 9-9-6 fad. Um what would you identify as like a key distinguisher in China's AI um automating business that you think the US needs to catch up on or at least in the the Silicon Valley startups now that is What is your PhD topic? I work on [1:07:17] reasoning for large language models. Perfect. Right? So what is your time frame for AGI? Uh I'd give it like six to seven years. Yeah, you see, not San Francisco consensus, East Coast consensus. Good. We will see. You may be right. Um I think that the It has to do, I think, with the grandeur of the dreams. When I'm in China, I don't hear the same rhetoric as I do in California. In California, it's two years, the world will change, no one is ready, this is happening so fast and so forth and so on. It's this sort of rhetoric that drives and it's self-reproducing, it's a belief system, it's like a religion. Now it always takes longer than than uh the dreamers say. I don't hear that in China. It it's it's it's noticeably different. [1:08:11] So for example, with reasoning, you know what DeepSeek did with R3, they did a fantastic job, they invented a new way of doing supervised fine-tuning. I mean, it's really really clever stuff. You just don't hear it at a national scale. And by the way, DeepSeek is a national champion for China. Right? They literally now are, you know, they they're they're on the list of famous companies, they're getting a enormous amount of money. Uh last time I met with them, they said, "We've solved our hardware problem." which was code for the government is just going to give them a lot of chips. Right? Welcome to a communist country. Okay, so this gentle lady here, please. Hi, thank you so much for being here. I'm Sonia. [1:08:48] I'm a joint degree student with the MBA and uh and the policy school. Um this might be a slightly more out there question and and open-ended, but you talk a lot about what it means to be human and I think a lot of that is consciousness. Um and I'm curious when I think about theories of consciousness, which obviously is very ill-defined, AI is hitting more and more of those and so it it like if anything, we need a revolution in how we think about consciousness, but I'm curious how you think about consciousness in AI and if you do think that there's a possibility that AI is or will become conscious, what that looks like, what model welfare looks like in that case. Well, let me ask let me ask you a simple question. [1:09:29] So, we have I'm going to give Graham credit for being conscious. And in return, he appears to be conscious. And in return, Graham is going to give it to me. So, for purposes of argument, the computer is on the table. And we ask the computer, "Are you conscious?" And it says, "Yes." And so, you, being the smart graduate student here at Harvard, um come up with a series of questions. It answers every question correctly. How do we know? How could you know that the computer is conscious? How would you understand its internal reasoning state? Now, you could instrument the way its reasoning works, which people are now doing. What they're doing is they're watching what are called supernodes within the um within the weight [1:10:13] structure to watch how it's actually making its decisions. So, maybe you could discover consciousness by inspection, but that's speculation. So, I sat down with a bunch of neuroscientists cuz I didn't know the answer to this question. I said, "How does it How does consciousness evolve?" And their theory, which is just a theory, was that consciousness evolves when you have dissimilar systems that are working together and growing, and they develop an awareness of the other. Right? And that human consciousness evolved because we needed to understand that we were something. Right? That the the the development of the id, the identity, and so forth and so on was necessary in order to command the system. [1:10:56] Now, there's no way to prove that. So, the answer is I don't know, but this is a good another it's a fourth question to ask this audience to work on. And and there's two questions. The first, how does it work? And the second is, how would you verify it? Thank you. I have the unfortunate responsibility to say that we've come to the witching hour. We there Graham, this is so much fun. I love this. Well, you want to stay for a while more? Five more minutes. This is good. I love this. >> no. We'll stay forever. I'm sorry. It's just I was pro- I I promised your I promised your schedulers that I would get you out of here. So, please. Short questions and short answers. Uh thanks, Eric. Troop here from Harvard [1:11:36] Business School, a second-year student there. Also a background in international relations before my MBA. Um you Are you convincingly about the need for an IAEA or International Atomic Energy Agency equivalent for AI? So, my question is, how does the State Department, how does the Pentagon, and similar agencies need to configure themselves to prepare for this the coming era of superintelligence? And my second, slightly risky question is, um if you are looking for someone to help you this summer or this winter to work on this topic, I'd be very happy to. I love people with courage. Um so, the IAEA question. So, there's a group of people who are very close to me who concluded that the only solution to [1:12:16] the problems that I'm answering is to create the equivalent of CERN. And the idea was that we would get together, all of us, including China, you know, everybody, and we put it all in here. Take all the best minds, and we'd all work on this. And um as a result, we would m- m- build this huge future because the benefits to humanity of this are so great. Start thinking about every disease eliminated, solutions to real hard problems. The We have Megan here. Energy issues, and she's the expert. All of this kind of stuff. I think it's pretty you probably say it's probably pretty unlikely that's going to happen. So, then the next group says, "Well, then we need an a an IAEA, which is essentially a mandatory [1:13:04] inspection group for nuclear." And what in that scenario, what happens is you'd have to go, and this group would visit the data centers and the algorithms of every company in every country. Now, that occurred, remember, the Vienna Group occurred after 15 years of negotiation, including people like Henry, and after two nuclear bombs were dropped on Hiroshima and Nagasaki, which we can all agree was hor- horrific. We've not had such an example. There are people that I know who say the following, and they don't say this with evil intent. They say that we won't we will have an event that will force this IAEA thing, and we hope that it is a Chernobyl-level event. And I said, "What does that mean?" And [1:13:53] they said, "Not that many people died." Okay? As opposed to a nuclear attack. So, there's a a strain of thinking in my world that is consistent with that. But I think uh first, I think nobody knows, and it's unlikely that the countries will um put up with that uh until there's a real crisis. I used to say that in I don't know, 20 years after the climate is being destroyed and, you know, the Earth is melting and sea level rise, there's going to be a there's going to there's going to be a meeting of all the powers where people say, "Well, we really screwed this up. We now have to fix it." So, I think there is precedent for a global challenge and a resolution, but it's very messy. It's not done rationally. It's not like [1:14:46] you and I agree. There has to be some crisis and some political dynamic, and it can be quite severe. It's an opportunity for the next generation. Let's do this very quick. Do we take the three questions? Just do your short questions. We'll let Eric wrap up, please. Yes. Hi. Uh thank you for your extra time. Uh my name is Kevin. I'm a second-year at the law school. Um my question is about automation and jobs. Um a couple months ago, Sam Altman went on an interview saying that insinuating that if something could be automated, maybe it wasn't a real job to begin with. Uh I want to get your thoughts on that statement, and if you agree with the premise that if something can be automated, should it? Um and relatedly, [1:15:24] if you disagree, what are ways that we as a society can decide what we should and shouldn't automate? >> Sure. Please have it. Um good night. My name is Griselda. I'm a middle mid-career MPA and a former Googler, so I'm very happy to see you. Um my question is regarding algorithm diplomacy. Like Dr. Kissinger used to say that constructive um ambiguity and the human pause is really necessary, but then AI is binary. Are we getting to a towards a world where de-escalation is computationally impossible? And does this push for new algorithm diplomacy that we could push harder? Ben. Uh thank you. My name is Kanishk. I'm from Harvard Business School. Uh my question is about ethical superintelligence. Uh first, do you Why [1:16:06] do you not believe that if America develops superintelligence, other nations will follow and be able to copy it, like China did with Deep Seek? And then second of all, if we're trying to embody it with ethics, then if someone else comes up with an unethical superintelligence, isn't that going to be strictly more free and more capable than a constrained superintelligence model that America tries to develop? You It's uh one word about your question again? Uh yeah, I wanted to ask what your thoughts were on Sam Altman's quote. Yeah. >> So, um one of the things that people at this level forget is that human dignity involves purpose. And an awful lot of jobs provide purpose to an awful lot of people. And the loss [1:16:47] of those jobs is a major crisis, not financially, but emotionally, in terms of their meaning. So, in order for us to get through this, we're going to have to address that. Right? We're going to have to actually do the right thing. And the right thing will be some combination of better tools and so forth. I'm not as worried about the jobs issue because we're producing fewer humans, which I view as a major crisis. Um collectively, you all are having fewer children than my generation, which is having fewer children than my parents' generation, and so forth. And we need more humans. Um partly I'm a businessman, so you need more customers. But the important point is, we need more humans. And if we have less humans, then [1:17:29] there's going to be open jobs and no people to fill them. AI can help take people who are not ready for a job and get them. One word from yours, aside from being a a future Googler? No, the algorithm diplomacy and information and computation. So, in your premise of your question, you described the algorithm as as binary. And so, if you think about Kissinger in 1971, when he came up with strategic ambiguity, do you think that a computer in one or two years will be able to invent strategic diplomacy ambiguity, given that it's already been done before? I think the answer is yes. So, I think that that um the algorithms are getting so intelligent that as long as it's a concept that's been around in [1:18:15] the past, it can probably figure out a way to apply it. So, I don't agree with you that it's as binary as your question thing. And your question was >> Uh superintelligence. Superintelligence. Um I I think it's the same answers, right? It Superintelligence in one form or another is going to apply. My own view, which is maybe different from the consensus, is that we're going to develop brilliant AI physicists, brilliant AI biologists, brilliant AI chemists, brilliant AI writers, brilliant AI historians. Um but that that the concept and and there'll be systems that can drive them. It's not at all obvious that if you were Einstein in 1902 with that amount of math available to you, that you would [1:19:07] have had the brilliance to invent um special relativity using the algorithms of today. In the industry, there is a view that that's the next really hard problem. There are various theories about it. Uh one answer is you could just do uh, repetition. You could just keep asking questions, you know, the monkey at the keyboard kind of thing, and eventually you do it. Another way is that you could give the optimization function to be curiosity, and if you just wait long enough, it'll discover special relativity. But that's not how Einstein did it. Einstein sat with his little pen and paper at the age of 18 or 17 with a lamp and figured it out. We're not there yet. My own view is that's going to be a [1:19:48] very hard boundary. So, in other words, we're going to get to superhuman behavior, which is not the same thing as superintelligence, which I would say Einstein and Kissinger and so forth really were. Graham, it's such a privilege to be here with >> It's such a pleasure to have you here, and we look forward to having you back. Let's say thank you very much. Thank you all.