About this transcript: This is a full AI-generated transcript of Ex-Google CEO: What Artificial Superintelligence Will Actually Look Like w/ Eric Schmidt & Dave B from Peter H. Diamandis, published June 9, 2026. The transcript contains 14,819 words with timestamps and was generated using Whisper AI.
"When do you see what you define as digital super intelligence? Within 10 years. The AI's ability to generate its own scaffolding is imminent. Pretty much sure that that will be a 2025 thing. We certainly don't know what super intelligence will deliver, but we know it's coming. And what do people..."
[00:00:00] Speaker 1: When do you see what you define as digital super intelligence?
[00:00:04] Speaker 2: Within 10 years.
[00:00:06] Speaker 1: The AI's ability to generate its own scaffolding is imminent. Pretty much sure that that will be a 2025 thing. We certainly don't know what super intelligence will deliver,
[00:00:17] Speaker 2: but we know it's coming. And what do people need to know about that? You're going to have your own polymath. So you're going to have the sum of Einstein and Leonardo da Vinci in the equivalent of your pocket. Agents are going to happen. This math thing is going to happen. The software thing is going to happen. Everything I've talked about is in the positive domain, but there's a negative domain as well. It's likely, in my opinion, that you're going to see...
[00:00:46] Speaker 1: Now that's the Moonshot, ladies and gentlemen.
[00:00:51] Speaker 3: Everybody, welcome to Moonshots. I'm here live with my Moonshot mate, Dave Blunden. We're here in our Santa Monica studios, and we have a special guest today, Eric Schmidt, the author of Genesis. We're going to talk about China. We're going to talk about digital super intelligence. We'll talk about what people should be thinking about over the 10 years.
[00:01:11] Speaker 1: And we're talking about the guy who has more access to more actionable information than probably anyone else you could think of. So it should be pretty exciting.
[00:01:21] Speaker 3: Incredibly brilliant. All right. Stand by for a conversation with the Eric Schmidt, CEO or past CEO of Google and an extraordinary investor and thinker in this field of AI. Let's do it. Eric, welcome back to Moonshots. It's great to be here with you guys. Thank you. It's been a long road since I first met you at Google. I remember our first conversations were fantastic. It's been a crazy month in the world of AI, but I think every month from here is going to be a crazy month. And so I'd love to hit on a number of subjects and get your take on that. Of course. I want to start with probably the most important point that you've made recently that got a lot of traction, a lot of attention, which is that AI is underhyped when the rest of the world is either confused, lost, or think it's not impacting us. We'll get into it in more detail, but quick, most important point to make there.
[00:02:19] Speaker 2: AI is a learning machine and in network effect businesses, when the learning machine learns faster, everything accelerates. It accelerates to its natural limit. The natural limit is electricity. Not chips. Electricity. Really?
[00:02:38] Speaker 3: Okay. So that gets me to the next point here, which is a discussion on AI and energy. So what we saw recently was Meta and recently announcing that they signed a 20 year nuclear contract with, with Constellation Energy. We've seen Google, Microsoft, Amazon, everybody buying basically nuclear capacity right now. That's gotta be weird. That private companies are, are basically taking over into their own hands. What was utility function before?
[00:03:14] Speaker 2: Well, just to be cynical, I, I'm so glad those companies plan to be around the 20 years that it's going to take to get the nuclear power plants built. In my recent testimony, I talked about the, the current expected need for the AI revolution in the United States is 92 gigawatts of more power. For reference, one gigawatt is one big nuclear power station. And there are none essentially being started now.
[00:03:41] Speaker 3: And there've been two in the last, what, 30 years built.
[00:03:43] Speaker 2: There is excitement that there's an SMR, small modular reactor coming in at 300 megawatts, but it won't start till 2030. As important as nuclear, both fission and fusion is, they're not going to arrive in time to get us what we need as a globe to deal with our many problems and the many opportunities that are before us. Yeah.
[00:04:03] Speaker 1: Do you think, uh, so if, if you look at the sort of three-year timeline toward AGI, do you think if you started a, a fusion reactor project today that won't come online for five, six, seven years, is there a probability that the AGI comes up with some other breakthrough, fusion or otherwise that makes it irrelevant before it even gets online?
[00:04:21] Speaker 2: A very good question. We don't know what artificial general intelligence will deliver. Yeah. And we certainly don't know what super intelligence will deliver, but we know it's coming. So first we need to plan for it. And there's lots of issues as well as opportunities for that. But the fact of the matter is that the computing needs that we need now are going to come from traditional energy suppliers in places like the United States and the Arab world and Canada and the Western world. And it's important to note that China has lots of electricity. So if they get the chips, it's going to be one heck of a race.
[00:04:59] Speaker 3: Yeah. They've been scaling it, uh, you know, at two or three times the U.S. The U.S. has been flat for how long in terms of energy production?
[00:05:06] Speaker 2: From my perspective, uh, infinite. In fact, electricity demand declined for a while as has overall energy needs because of conservation of the things. But the data center story is the story of the energy people, right? And you sit there and you go, how could these data centers use so much power? Well, and especially when you think of it, how little power our brains do. Well, these are our best approximation in digital form of how our brains work. But when they start working together, they become super brains. The promise of a super brain with a one gigawatt, for example, data center is so palpable. People are going crazy. And by the way, the economics of these things are unproven. How much revenue do you have to have to have 50 billion in capital? Well, if you depreciate it over three years or four years, you need to have 10 or $15 billion of capital spend per year just to handle the infrastructure. Those are huge businesses and huge revenue, which in most places is not there yet.
[00:06:08] Speaker 3: I'm curious. There's so much capital being invested and deployed right now in SMRs, in nuclear, bringing Three Mile Island back online, in fusion companies. Why isn't there an equal amount of capital going into making the entire chipset and compute just a thousand times more energy efficient?
[00:06:31] Speaker 2: There is a similar amount going in capital. There are many, many startups that are working on non-traditional ways of doing chips. The transformer architecture, which is what is powering things today, has new variants. Every week or so, I get a pitch from a new startup that's going to build inference time, test time, computing, which are simpler, and they're optimized for inference. It looks like the hardware will arrive just as the software needs expand. And by the way, that's always been true. We old timers had a phrase, "Grove giveth, and gates take it away." So Intel would improve the chipsets, right? Way back when. Yeah. And the software people would immediately use it all and suck it all up. Higher level code. I have no reason to believe that that law, Grove and Gates for law, has changed. If you look at the gains in like the Blackwell chip or the 350 chip in AMD, these chips are massive supercomputers. And yet we need, according to the people, have hundreds of thousands of these chips just to make a data center work. That shows you the scale of what this kind of thinking algorithms. Now you sit there and you go, what could these people possibly be doing with all of these chips? I'll give you an example. We went from language to language, which is what ChatGBD can be understood at, to reasoning and thinking. If you want to look at an OpenAI example, look at OpenAI 03, which does forward and back reinforcement learning and planning. Now the cost of doing the forward and back is many orders of magnitude, besides just answering your question for your PhD thesis or your college paper. That planning, the back and forth is computationally very, very expensive. So with the best energy and the best technology today, we are able to show evidence of planning. Many people believe that if you combine planning and very deep memories, you can build human level intelligence. Now, of course, it will be very expensive to start with, but humans are very, very industrious. And furthermore, the great future companies will have AI scientists, that is non-human scientists, AI programmers, that is opposed to human programmers, who will accelerate their impact. So if you think about it, going back to you're the author of the abundance thesis, as best I can tell, Peter. You've talked about this for 20 years. You saw it first. It sure looks like if we get enough electricity, we can generate the power in the sense of intellectual power to generate abundance along
[00:09:06] Speaker 3: the lines that you predicted two decades ago. Every week, I study the 10 major tech metatrends that will transform industries over the decade ahead. I cover trends ranging from humanoid robots, AGI, quantum computing, transport, energy, longevity, and more. No fluff. Only the important stuff that matters, that impacts our lives and our careers. If you want me to share these with you, I write a newsletter twice a week, sending it out as a short two-minute read via email. And if you want to discover the most important metatrends 10 years before anyone else, these reports are for you. Readers include founders and CEOs from the world's most disruptive companies, and entrepreneurs building the world's most disruptive companies. It's not for you if you don't want to be informed of what's coming, why it matters, and how you can benefit from it. To subscribe for free, go to diamandis.com/metatrends. That's diamandis.com/metatrends to gain access to trends 10 plus years before anyone else.
[00:10:06] Speaker 1: Let me throw some numbers at you just to reinforce what you said. You know, we have a couple companies in the lab that are doing voice customer service, voice sales, just as of the last month. And the value of these conversations is $10 to $1,000. And the cost of the compute is maybe two, three concurrent GPUs is optimal. It's like 10, 20 cents. And so they would buy massively more compute to improve the quality of the conversation. There aren't even close to enough. We count about 10 million concurrent phone calls that should move to AI in the next year or so.
[00:10:44] Speaker 2: And my view of that is that's a good tactical solution and a great business. Let's look at other examples of tactical solutions that are great businesses. And I obviously have a conflict of interest talking about Google because I love it so much. So with that as in mind, look at the Google strength in GCP now, Google's cloud product, where they have a completely fully served enterprise offering for essentially automating your company with AI. And the remarkable thing, and this is to me is shocking, is you can, in an enterprise, write the tasks that you want and then using something called the model context protocol, you can connect your databases to that. And the large language model can produce the code for your enterprise. Now there's a hundred thousand enterprise software companies, middleware companies that grew up in the last 30 years that I've been working on this that are all now in trouble because that, that interstitial connection is no longer needed. Was their business. And, and, and of course they'll have to change as well. The good news for them is enterprises make these changes very slowly. If you built a brand new enterprise architecture for ERP and MRP, you would be highly tempted to not use any of the ERP or MRP suppliers, but instead use open source libraries build, essentially use big query or the equivalent from Amazon, which is red redshift and essentially build that architecture. And it gives you infinite flexibility and the computer system writes most of the code. Now programmers don't go away at the moment. It's pretty clear that junior programmers go away. The sort of journeyman, if you will, of the stereotype, because these systems aren't good enough yet to automatically write all the code. They need very senior computer scientists, computer engineers who are watching it. That will eventually go away. Yeah. One of the things to say about productivity, and I call this the San Francisco consensus, because it's, it's largely the view of people who operate in San Francisco, goes something like this. Uh, we're just about to the point where we can do two things that are shocking. The first is we can replace most programming tasks by computers, and we can replace both most math, mathematical tasks by computers. Now you sit there and you go, why? Well, if you think about programming and math, they have limited language sets compared to human language. So close, they're simpler computationally and they're scale free. You can just do it and do it and do it with more electricity. You don't need data. You don't need real world input. You don't need telemetry. You don't need sensors. So it's likely, in my opinion, that you're going to see world-class mathematicians emerge in the next one year that are AI based and world-class programmers that can appear within the next one or two years. When those things are deployed at scale, remember math and programming are the basis of kind of everything, right? It's an accelerate, accelerant for physics, chemistry, biology, material science. So going back to things like climate change, can you imagine if we, and this goes back to your original argument, Peter, imagine if we can accelerate the discoveries of the new materials that allow us to deal with a carbonized world? Yeah. It's right. It's very exciting.
[00:13:59] Speaker 3: I'd love to drill in after you. I just want to hit this because it's important. The potential for there to be, I don't want to use the word PhD level, you know, other than thinking in the terms of research, the PhD level AIs and that can basically attack any problem and solve it and solve math, if you would, and physics. This idea of an AI, you know, intelligence explosion. Leopold put that at like 26, 27 heading towards digital super intelligence in the next few years.
[00:14:42] Speaker 2: Do you buy that time frame? So again, I consider that to be the San Francisco consensus. I think the dates are probably off by one and a half or two times, which is pretty close. So a reasonable prediction is that we're going to have specialized savants in every field within five years. That's pretty much in the bag as far as I'm concerned. Sure. And here's why. You have this amount of humans and then you add a million AI scientists to do something. Your slope goes like this, your rate of improvement. We should get there. The real question is once you have all these savants, do they unify? Do they ultimately become a superhuman? The term we're using is super intelligence, which implies intelligence that beyond the sum of what humans can do, the race to super intelligence, which is incredibly important. Because imagine what a super intelligence could do that we ourselves cannot imagine, right? It's so much smarter than we. And it has huge proliferation issues, competitive issues, China versus the US issues, electricity issues, so forth. We don't even have the language for the deterrence aspects and the proliferation issues of these powerful models.
[00:15:58] Speaker 1: Or the imagination. Totally agree. In fact, it's one of the great flaws actually in the original conception. You remember Singularity University and Ray Kurzweil's books and everything. And we kind of drew this curve of rat level intelligence, then cat, then monkey, and then it hits human, and then it goes super intelligent. But it's now really obvious when you talk to one of these multilingual models that's explaining physics to you, that it's already hugely super intelligent within its savant category. And so Dennis Hassabis keeps redefining AGI day as well, when it can discover relativity the same way Einstein did with data that was available up until that date.
[00:16:33] Speaker 2: That's when we have AGI, but long before that. Yeah. So I think it's worth getting the timeline right. Yeah. So the following things are baked in. You're going to have an agentic revolution where agents are connected to solve business processes, government processes, and so forth. They will be adopted most quickly in companies that have a lot of money and a lot of time latency issues at stake. It will be adopted most slowly in places like government, which do not have an incentive for innovation and fundamentally are job programs and redistribution of income kind of programs. So call it what you will. The important thing is that there will be a tip of the spear in places like financial services, certain kind of bio biomedical things, startups and so forth. And that's the place to watch. So all of that is going to happen. The agents are going to happen. This math thing is going to happen. The softer thing is going to happen. We can debate the rate at which the biological revolution will occur, but everyone agrees that it's right after that we're very close to these major biological understandings. In physics, you're limited by data, but you can generate it synthetically. There are groups, which I'm funding, which are generating physics, essentially models that can approximate algorithms that cannot be, they're incomputable. So in other words, you have essentially a foundation model that can answer the question good enough for the purposes of doing physics without having to spend a million years doing the computation of quantum chromodynamics and things like that. All of that's going to happen. The next questions have to do with what is the point in which this becomes a national emergency? And it goes something like this. Everything I've talked about is in the positive domain, but there's a negative domain as well. The ability for biological attacks, obviously cyber attacks. Imagine a cyber attack that we as humans cannot conceive of, which means there's no defense for it because no one ever thought about it, right? These are real issues. A biological attack, you take a virus. I won't obviously go into the details. You take a virus that's bad and you make it undetectable by some changes in its structure, which again, I won't go into the details. We released a whole report at the national level on this issue. So at some point, the government, and it doesn't appear to understand this now is going to have to say, this is very big because it affects national security, national economic strengths, and so forth. Now, China clearly understands this and China is putting an enormous amount of money into this. We have slowed them down by virtue of our chips controls, but they've found clever ways around this. There are also proliferation issues. Many of the chips that they're not supposed to have, they seem to be able to get, and more importantly, as I mentioned, the algorithms are changing and instead of having these expensive foundation models by themselves, you have continuous updating, which is called test time training. That continuous updating appears to be capable of being done with lesser power chips. So there are so many questions that I think we don't know. We don't know the role of open source, because remember, open source means open weights, which means everyone can use it. A fair reading of this is that every country that's not in the West will end up using open source because they'll perceive it as cheaper, which transfers leadership in open source from America to China. That's a big deal if that occurs. How much longer do the chip bans, if you will, hold and how long before China can answer? What are the effects of the current government's policies of getting rid of foreigners and foreign investment? What happens with the Arab data centers, assuming they work? And I'm generally supportive of them. If those things are then misused to help train Chinese models, the list just goes on and on. We just don't know.
[00:20:35] Speaker 1: Okay. Can I ask you probably one of the toughest questions? I don't know if you saw Mark Andreessen. He went and talked to the Biden administration, past administration, and said, "How are we going to deal with exactly what you just talked about, chemical and biological and radiological and nuclear risks from big foundation models being operated by foreign countries?" And the Biden answer was, you know, we're going to keep it into the three or four big companies like Google and we'll just regulate them. And Mark was like, that is a surefire way to lose the race with China because all innovation comes from a startup that you didn't anticipate or, you know, it's just the American history and you're cutting off the entrepreneur from participating in this. So as of right now with the open source models, the entrepreneurs are in great shape. But if you think about the models getting crazy smart a year from now, how are we going to have the balance between startups actually being able to work with the best technology, but proliferation, not percolating to every country in the world?
[00:21:36] Speaker 2: Again, a set of unknown questions. And anybody who knows the answer to these things is not telling the full truth. The doctrine in the Biden administration was called 10 to the 26 flops. It was a point that was a consensus above which the models were powerful enough to cause some damage. So the theory was that if you stayed below 10 to the 26, you didn't need to be regulated. But if you were above that, you needed to be regulated. And the proposal and the Biden administration was to regulate both the open source and the closed source. Okay. Those are the summary. That of course has been ended by the Trump administration. They have not yet produced their own thinking in this area. They're very concerned about China and it getting forward. So they'll come out with something from my perspective, the core questions are the following. Will the Chinese be able to use, even with chip restrictions, will they use architectural changes that will allow them to build models as powerful as ours? And let's assume they're government funded. That's the first question. The next question is, how will you raise $50 billion for your data center if your product is open source? Yeah. In the American model, part of the reason these models are closed is that the business people and the lawyers correctly are saying, I've got to sell this thing because I've got to pay for my capital. These are not free goods. And the US government correctly is not giving $50 billion to these companies. So we don't know that. To me, the key question to watch is look at DeepSeq. So DeepSeq, a week or so ago, Gemini 2.5 Pro got to the top of the leaderboards in intelligence. Great achievement for my friends at Gemini. A week later, DeepSeq comes in and is slightly better than Gemini. And DeepSeq is trained on the existing hardware that's in China, which includes stuff that's been pilfered and some of the Ascend, it's called the Ascend Huawei chips and a few others. What happens now? The US people say, well, the DeepSeq people cheated and they cheated by doing a technique called distillation, where you take a large model and you ask it 10,000 questions, you get its answers, then you use that as your training material. Yep. So the US companies will have to figure out a way to make sure that their proprietary information that they've sent so much money on does not get leaked into these open source things. I just don't know. With respect to nuclear, biological, chemical, and so forth issues, the US companies are doing a really good job of looking for that. There's a great concern, for example, that nuclear information would leak into these models as their training without us knowing it. And by the way, that's a violation of law. Oh, really? They work, and the whole nuclear information thing is there's no free speech in that world for good reasons. And there's no free use and copyright and all that kind of stuff. It's illegal to do it. And so they're doing a really, really good job of making sure that that does not happen. They also put in very significant tests for biological information and certain kinds of cyber attacks. What happens there? Their incentive is their incentive to continue, especially if it's not if it's not required by law. The government has just gotten rid of the safety institutes that were in place in Biden and replacing it by a new term, which is largely a safety assessment program, which is a financer. I think collectively we in the industry just want the government at the secret and top secret level to have people who are really studying what China and others are doing. You can be sure that China really has very smart people studying what we're doing. We at the secret and top secret level
[00:25:24] Speaker 3: should have the same thing. Have you read the AI 27 paper? I have. And so for those listening who haven't read it, it's a future vision of the AI in the US and China racing towards AI. And at some point, the story splits into a we're going to slow down and work on alignment or we're going full out and you know, spoiler alert and the race to infinity humanity vanishes. So the right outcome will
[00:25:56] Speaker 2: ultimately be some form of deterrence and mutually assert destruction. I wrote a paper with two other authors, Dan Hendricks and Alex Wang, where we named it mutual AI malfunction. And the idea was go something like this. You're the United States. I'm China. You're ahead of me. At some point, you cross a line, you know, you Peter cross a line and I China go, this is unacceptable. At some point, it becomes the amount of compute and amount of it's, it's something you're doing where it affects my sovereignty. It's not just words and yelling and an occasional shooting down a jet. It's, it's a real threat to the identity of my, my country, my economic, what have you. Under this scenario, I would be highly tempted to do a cyber attack to slow you down. Okay. In mutually assert malfunction, if you will, we have to engineer it so that you have the ability to then do the same thing to me. And that causes both of us to be careful not to trigger the other. That's what mutual asserted destruction is. That's our best formulation right now. We also recommend in our work, and I think it's very strong that the government require that we know where all the chips are. And remember, the chips can tell you where they are because they're computers. Yeah. And it would be easy to add a little crypto thing, which would say, yeah, here I am. And this is what I'm doing. So, so knowing where the chips are, knowing where the training runs are, and knowing what these fault lines are, are very important. Now, there are a whole bunch of assumptions in this scenario that I described. The first is that there was enough electricity. The second is that there was enough power. The third is the Chinese had enough electricity, which they do, and enough computing resources, which they may or may not have. Or may in the future have. I mean, in the future have. And also, I'm asserting that everyone arrives at this eventual state of super intelligence at a roughly the same time. Again, these are debatable points. But the most interesting scenario is we're saying it's 1938. The letter has come, you know, from Einstein to the president and we're having a conversation and we're saying, well, how does this end? Okay. So if you were so brilliant in 38, what you would have said is this ultimately ends with us having a bomb, the other guys having a bomb, and then we're going to have one heck of a negotiation to try to make sure that we don't end up destroying each other. And I think the same conversation needs to get started now, well before the Chernobyl events, well before the buildups.
[00:28:38] Speaker 1: Can we just take that one more step and don't answer if you don't want to, but if it was 1947, 1948, so before the Cold War really took off, and you say, well, that's similar to where we are with China right now. We have a competitive lead, but it may or may not be fragile. What would you do differently in 1947, 1940, or what would Kissinger do different in 1947, 1948, 1949 than what we did do?
[00:29:03] Speaker 2: You know, I wrote two books with Dr. Kissinger and I miss him very much. He was my closest friend. And Henry was very much a realist in the sense that when you look at his history in roughly 36, 38, he and his, I guess 37, 38, his family were Jewish, were forced to emigrate from Germany because of the Nazis. And he watched the entire world that he'd grown up with as a boy be destroyed by the Nazis and by Hitler. And then he saw the conflagration that occurred as a result. And I can tell you that whether you like him or not, he spent the rest of his life trying to prevent that from happening again. So we are today safe because people like Henry saw the world fall apart. So I think from my perspective, we should be very careful in our language and our strategy to not start that process. Henry's view on China was different from other China scholars. His view was in China was that we shouldn't poke the bear, that we shouldn't talk about Taiwan too much. And we let China deal with our own problems, which were very significant. But he was worried that we or China in a small way would start World War III in the same way that World War I was started. You remember that World War I started with essentially a small geopolitical event, which was quickly escalated for political reasons on all sides. And then the rest was a horrific war, the war to end all wars at the time. So we have to be very, very careful when we have these conversations not to isolate each other. Henry started a number of what are called track two dialogues, which I'm part of one of them, to try to make sure we're talking to each other. And so somebody who's a hardcore person would say, well, you know, we're Americans and we're better and so forth. Well, I can tell you having spent lots of time on this. The Chinese are very smart, very careful, capable, very much a peer. And if you're confused about that, again, look at the arrival of DeepSeq. A year ago, I said they were two years behind. I was clearly wrong. With enough money and enough power, they're in the game.
[00:31:15] Speaker 1: Yeah. Let me actually drill in just a little bit more on that too, because I think one of the reasons DeepSeq caught up so quickly is because it turned out that inference time generates a lot of IQ. And I don't think anyone saw that coming. And inference time is a lot easier to catch up on. And also, if you take one of our big open source models and distill it and then make it a specialist, like you were saying a minute ago, and then you put a ton of inference time compute behind it, it's a massive advantage and also a massive leak of capability within CBRN, for example,
[00:31:48] Speaker 2: that nobody anticipated. And CBRN, remember, is chemical, biological, radiological, and nuclear. Let me rephrase what you said. If the structure of the world in five to 10 years is 10 models, and I'll make some numbers up, five in the United States, three in China, two elsewhere, and those models are data centers that are multi gigawatts, they will be all nationalized in some way. In China, they will be owned by the government. The stakes are too high. In my military work one day, I visited a place where we keep our plutonium. And we keep our plutonium in a base that's inside of another base with even more machine guns and even more specialized because the plutonium is so interesting and obviously very dangerous. And I believe it's the only one or two facilities that we have in America. So in that scenario, these data centers will have the equivalent of guards and machine guns because they're so important. Now, is that a stable geopolitical system? Absolutely. You know where they are, president of one country can call the other, they can have a conversation, you know, they can agree on what they agree on and so forth. But let's say it is not true. Let's say that the technology improves, again unknown, to the point where the kind of technologies that I'm describing are implementable on the equivalent of a small server. Then you have a humongous data center proliferation problem. And that's where the open source issue is so important because those servers, which will be proliferate throughout the world, will all be on open source. We have no control regime for that. Now, I'm in favor of open source. As you mentioned earlier with Marc Andreessen, that open competition and so forth tends to allow people to run ahead. In defense of the proprietary companies, collectively, they believe, as best I can tell, that the open source models can't scale fast enough because they need this heavyweight training. If you look, I'll give an example of Grok, who's trained on a single cluster. It was built by NVIDIA in 20 days or so forth in Memphis, Tennessee, of 200,000 GPUs. GPU is about $50,000. You can say it's about a $10 billion supercomputer in one building that does one thing. If that is the future, then we're okay because we'll be able to know where they are. If, in fact, the arrival of intelligence is ultimately a distributed problem, then we're going to have lots of problems with terrorism, bad actors, North Korea, poorly funded countries.
[00:34:41] Speaker 3: Which is my greatest concern, right? China and the US are rational actors. The terrorist who has access to this. And I don't want to go all negative on this podcast. It's an important thing to wake people up to the deep thinking you've done on this. My concern is the terrorist who gains access. And are we spending enough time and energy? And are we training enough
[00:35:07] Speaker 2: models to watch them? So, first, the companies are doing this. There's a body of work happening now, which can be understood as follows. You have a super intelligent model. Can you build a model that's not as smart as the student it's studying? You know, there is a professor that's watching the student, but the student is smarter than the professor. Is it possible to watch what it does? It appears that we can. It appears that there's a way. Even if you have this rogue, incredible thing, we can watch it and understand what it's doing and thereby control it. Another example of where we don't know is that it's very clear that these savant models will proceed. There's no question about that. The question is, how do we get the Einsteins? So, there are two possibilities. One, and this is to discover completely new schools of thought. Which is what's the most exciting thing in the next few years? Yeah. And in our book Genesis, Henry and I and Craig talk about the importance of polymaths in history. In fact, the first chapter is on polymaths. What happens when we have millions and millions of polymaths? Very, very interesting. Okay. Now, it looks like the great discoveries, the greatest scientists and people in our history had the following property. They were experts in something and they looked at a different problem and they saw a pattern in one area of thinking that they could apply to a completely unrelated field. And they were able to do so and make a huge breakthrough. The models today are not able to do that. So, one thing to watch for is algorithmically, when can they do that? This is generally known as the non-stationarity problem. Yeah. Because of the reward functions in these models are fairly straightforward. You know, beat the human, beat the question, and so forth. But when the rules keep changing, is it possible to say the old rule can be applied to a new rule to discover something new? And again, the research is underway. We won't know for years. Peter and I were over at OpenAI yesterday,
[00:37:25] Speaker 1: actually, and we were talking to many people, but Noam Brown in particular. And I said the word of the year is scaffolding. And he said, yeah, maybe the word of the month is scaffolding. I was like, well, okay, what did I step on there? He said, look, you know, right now, if you try to get the AI to discover relativity or, you know, just some greenfield opportunity, it won't do it. If you set up a framework, kind of like a lattice, like a trellis, the vine will grow on the trellis beautifully. But you have to lay out those pathways and breadcrumbs. He was saying, the AI's ability to generate its own scaffolding is imminent. That doesn't make it completely self-improving. It's not, it's not Pandora's box, but it's also much deeper down the path of create an entire breakthrough in physics or create an entire feature length movie or, you know, these, these prompts that require 20 hours of consecutive inference time compute, pretty much sure that that will be a 2025 thing, at least from, from
[00:38:23] Speaker 2: their point of view. So, uh, recursive self-improvement is the general term for the computer continuing to learn. Yeah. We've already crossed that in the sense that these systems are now running and learning things and they're learning from the way they own, they think within limited functions. When does the system have the ability to generate its own objective and its own question? Does not have that today. Yeah. That's another sign. Another sign would be that the system decides to, uh, exfiltrate itself and it takes steps to get it, get itself away from the command or the control and command system. Um, that has not happened yet. Jim and I
[00:39:10] Speaker 3: hasn't called you yet and said, hi, Eric. But there, there are theoreticians who believe that the,
[00:39:15] Speaker 2: that the, that the systems will ultimately choose that as a reward function because they're programmed to, you know, to continue to learn. Uh, another one is access to weapons, right? And lying to get it. So these are trip wires, right? All of each, each of which is a trip wire that we're, we're watching. And again, each of these could be the beginning of a mini Chernobyl event that would become part of consciousness. I think at the moment, the U S government is not focused on these issues. They're focused on other things, economic opportunity, growth, and so forth. It's all good, but somebody's going to get focused on this and somebody is going to pay attention to it. And it will ultimately be a
[00:39:58] Speaker 3: problem. A quick aside. You probably heard me speaking about fountain life before, and you're probably wishing, Peter, would you please stop talking about fountain life? And the answer is no, I won't because genuinely we're living through a healthcare crisis. You may not know this, but 70% of heart attacks have no precedent, no pain, no shortness of breath. And half of those people with a heart attack never wake up. You don't feel cancer until stage three or stage four until it's too late, but we have all the technology required to detect and prevent these diseases early at scale. That's why a group of us, including Tony Robbins, Bill Kapp, and Bob Hurri founded fountain life, a one-stop center to help people understand what's going on inside their bodies before it's too late and to gain access to the therapeutics to give them decades of extra health span. Learn more about what's going on inside your body from fountain life. Go to fountainlife.com/peter and tell them Peter sent you. Okay, back to the episode.
[00:40:53] Speaker 1: Can I clean up one kind of common misconception there? Because I think it's a really important one. In the movie version of AI, you described, hey, maybe there are 10 big AIs and five are in the US, three are in China and two are, one's not in Brussels probably, one's maybe in Dubai. Or, you know, Israel. Israel. Okay, there you go.
[00:41:12] Speaker 2: Somewhere like that.
[00:41:13] Speaker 1: Yeah. In the movie version of this, if it goes rogue, you know, the SWAT team comes in, they blow it up and it's solved. But the actual real world is when you're using one of these huge data centers to create a super intelligent AI, the training process is 10E26, 10E28, you know, or more flops. But then the final brain can be ported and run on four GPUs, eight GPUs. So a little box about this size. And it's just as intelligent, you know, it's, it's, it's, and that's one of the beautiful things about it is you can clone it. This is called stealing the weights. Stealing the weights, exactly. And the new, new thing is that that weight file with, if you have an innovation and inference time speed and you say, oh, same weights, no difference, distill it or, or just quantize it or whatever, but I made it a hundred times faster. Now it's actually far more intelligent than what you exported from the data center. And so the,
[00:42:11] Speaker 2: but all of these are examples of the proliferation problem. And I'm not convinced that we will hold these things in the 10 places. And, and here's why. Let's assume you have the 10, which is possible. They will have subsets of models that are smaller, but nearly as intelligent. And so the tree of knowledge of systems that have knowledge is not going to be 10 and then zero. It's going to be 10, a hundred, a thousand, a million, a billion at different levels of complexity. So the system that's on your future phone, maybe, you know, three orders of magnitude forward or magnitude smaller than the one at the very tippy top, but it will be very, very powerful.
[00:43:01] Speaker 1: You know, to exactly what you're talking about, there's some great research going on at MIT. It'll probably move to Stanford just to be fair, but it always does, but it's great research going on at MIT on if you have one of these huge models and it's been trained on movies, it's been trained on Swahili. A lot of the parameters aren't useful for this savant use case, but the general knowledge and intuition is. So what's the optimal balance between narrowing the training data and narrowing the parameter set to be a specialist without losing general, you know, learning.
[00:43:34] Speaker 2: So the people who are opposed to that view, and again, we don't know, would say the following. If you take a general purpose model and you specialize it through fine tuning, it also becomes more brittle. Their view is that what you do is you just make bigger and bigger and bigger models because they're in the big model camp. Yeah. And that's why they need gigawatts of data centers and so forth, and their argument is that that flexibility of intelligence that they are seeing will continue. Dario wrote a piece called basically about machines, and he argued that there are... Machines of grace. Machines of amazing grace. He argued that there are three scaling laws playing. The first one is what you know of, which is foundation model growth. We're still on that. The second one is a test time training law. And the third one is a reinforcement learning training law. Training laws are where if you just put more hardware and more data, they just get smarter in a predictable way. We're just at the beginning, in his view, of the second and third one beginning. That's why I'm sure our audience would be frustrated. Why do we not know? We don't know, right? It's too new. It's too powerful. And at the moment, all of these businesses are incredibly highly valued. They're growing incredibly quickly. The uses of them, I mentioned earlier, going back to Google, the ability to refactor your entire workflow in a business is a very big deal. That's a lot of money to be made there for all the companies involved. We will see.
[00:45:20] Speaker 3: Eric, shifting to the topic, one of the concerns that people have in the near term and people have been, you know, ringing the alarm bells is on jobs. I'm wondering where you come out on this and flipping that forward to education. How do we educate our kids today in high school and college? And what's your advice? So on the first thing, do you believe that as Dario has gone on TV shows now and speaking to significant white collar job loss, we're seeing obviously a multitude of different drivers and robots coming in. How do you think about the job market over the next five years?
[00:46:02] Speaker 2: Well, let's posit that in 30 or 40 years, there'll be a very different employment, robotic, human interaction.
[00:46:12] Speaker 3: Or the definition of, do we need to work at all?
[00:46:15] Speaker 2: The definition of work, the definition of identity. Let's just posit that. Uh, and let's also posit that it will take 20 or 30 years for those things to work through the economy of our world. Um, now in California and other cities in America, you can get on a Waymo taxi. Um, Waymo is 2025. The original work was done in the late nineties. The original challenge at Stanford was done, I believe in 2004. The Doppler Grand Challenge, it was 2004. 2004.
[00:46:45] Speaker 3: When Sebastian threw and won. That's right.
[00:46:47] Speaker 2: So, so more than 20 years from a visible demonstration to our ability to use it in daily life. Why? It's hard, it's deep tech, it's regulated and all of that. I think that's going to be true, especially in robots that are interacting with humans. They're going to get regulated. You're not going to be wandering around and the robots going to decide to slap you. It just doesn't, you know, society is not going to allow that sort of thing. Because you want to. It's just not, it's not going to, it's not going to allow it. So in the shorter term, five or 10 years, I'm going to argue that this is positive for jobs in the following way. Okay. Um, if you look at the history of automation and economic growth, automation starts with the lowest status and most dangerous jobs and then works up the chain. So if you think about assembly lines and cars and, you know, furnaces and all these sort of very, very dangerous jobs that our forefathers did, they don't do them anymore. They're done by robotic solutions of one another, and typically not a humanoid robot, but an arm. So the, so the world dominated by arms that are intelligent and so forth will automate those functions. What happens to the people? Well, it turns out that the person who was working with the, the welder who's now operating the arm has a higher wage and the company has higher profits because it's producing more widgets. So the company makes more money and the person makes more money, right? In that sense. Now you sit there and say, well, that's not true because humans don't want to be retrained. Ah, but in the vision that we're talking about, every single person will have a human, a computer assistant that's very intelligent that helps them perform. And you take a person of normal intelligence or knowledge and you add a, you know, sort of accelerant, they can get a higher paying job. So you sit there and you go, well, why are there more jobs? There should be less jobs. That's not how economics works. Economics expands because the opportunities expands, profits expands, wealth expands and so forth. So there's plenty of dislocation, but in aggregate, are there more people employed or fewer? The answer is more people with higher paying jobs. Is that true in India as well? It will be. And you picked India because India has a positive demographic outlook, although their, their birth rate is now down to 2.0. Ah, that's good. The, the, the rest of the world is choosing not to have children. If you look at Korea, it's now down to 0.7 children per two parents. Yeah. China is down to one child per two parents. It's evaporating. Now, what happens in those situations? They completely automate everything because it's the only way to increase national priority. So the most likely scenario, at least in the next decade, is it's a national emergency to use more AI in the workplace to give people better paying jobs and create more productivity in the United States because our birth rate has been falling. And, and what happens is people have talked about this for 20 years. If you, if you have this conversation and you ignore demographics, which is negative for humans and economic growth, which occurs naturally because capital investment, then you miss the whole story. Now, there are plenty of people who lose their jobs, but there's an awful lot of people who have new jobs. Mm-hmm. And the typical example, simple example would be all those people who work in, in Amazon distribution centers and Amazon trucks, those jobs didn't exist until Amazon was created, right? The number one shortage in jobs right now in America are truck drivers. Yes. Why? Truck driving is a lonely, hard, low paying, right? Low status of good people job. They don't want it. They want a better paying job. Yeah. Right? Going back to education, it's really a crime that our industry has not invented the following product. The product that I want it to build is a product that teaches every single human who wants to be taught in their language, in a gamified way, the stuff they need to know to be a great citizen in their country. Mm-hmm. Right? That can all be done on phones now. It can all be learned and you can all learn how to do it. And why do we not have that product? Right? The investment in the humans of the world is the best return always. Yeah. And knowledge and capability is always the right answer.
[00:51:12] Speaker 1: Let me try and get your opinion on this because you're so influential with, so I've got about a thousand people in the companies where I'm the controlling shareholder. And I've been trying to tell them exactly what you just articulated, where a lot of these people have been in the company for 10, 15 years. They're incredibly capable and loyal, but they've learned a specific white collar skill. They worked really hard to learn the skill. And the AI is coming within no more than three years and maybe two years. And the opportunity to retrain and have continuity is right now. But if they delay, which everyone seems to be just, let's wait and see. And what I'm trying to tell them is if you wait and see, you're really screwing over that employee.
[00:51:56] Speaker 2: So we are in wild agreement that this is going to happen. And the winners, we are the ones who act now. What's interesting is when you look at innovation history, the biggest companies who you would think of are the slowest, because they have economic resources that the little companies typically don't, they tend to eventually get there. Right? So watch what the big companies do. Are there CFOs and the people who measure things carefully, who are very, very intelligent. They say, I'm done with that thousand engineering team that doesn't do very much. I want 50 people working in this other way and we'll do something else with the other people.
[00:52:35] Speaker 1: And when you say big companies, we're thinking Google, Meta. We're not thinking, you know, big bank hasn't done anything.
[00:52:39] Speaker 2: I'm thinking about big banks. When I talk to CEOs, and I know a lot of them in traditional industries, what I counsel them is, you already have people in the company who know what to do. You just don't know who they are. So call a review of the best ideas to apply AI in our business. And inevitably, the first ones are boring. Improve customer service, improve call centers, and so forth. But then somebody says, you know, we could increase revenue if we built this product. I'll give you another example. There's this whole industry of people who work on regulated user interfaces of one another. I think user interfaces are largely going to go away. Because if you think about it, the agents speak English, typically, or other languages. You can talk to them. You can say what you want. The UI can be generated. So I can say, generate me a set of buttons that allows me to solve this problem. And it's generated for you. Why do I have to be stuck in what is called the WIMP interface? Windows icons, menus, and pulldown that was invented in Xerox PARC, right? 50 years ago. Why am I still stuck in that paradigm? I just want it to work.
[00:53:47] Speaker 3: Kids in high school and college now, any different recommendations for where they go?
[00:53:52] Speaker 2: When you spend any time in a high school, or I was at a conference yesterday where we had a drone challenge, and you watch the 15-year-olds, they're going to be fine. They're just going to be fine. It all makes sense to them, and we're in their way. They're digital natives. But they're more than digital natives. They get it. They understand the speed. It's natural to them. They're also, frankly, faster and smarter than we are, right? That's just how life works, I'm sorry to say. So we have wisdom. They have intelligence. They win, right? So in their case, I used to think the right answer was to go into biology. I now actually think going into the application of intelligence to whatever you're interested in is the best thing you can do as a young person. Purpose-driven. Yeah. Any form of solution that you find interesting. Most kids get into it for gaming reasons or something, and they learn how to program very young. So they're quite familiar with this. I work at a particular university with undergraduates, and they're already doing different algorithms for reinforcement learning as sophomores. This shows you how fast this is happening at their level. They're going to be just fine. They're responding to the economic signals, but they're also responding to their purpose, right? So an example would be you care about climate, which I certainly do. If you're a young person, why don't you figure out a way to simplify the climate science to use simple foundation models to answer these core questions? Yeah. Why don't you figure out a way to use these powerful models to come up with new materials, right, that allow us, again, to address the carbon challenge? And why don't you work on energy systems to have better and more efficient energy sources that are less carbon? You see my point. Yeah.
[00:55:41] Speaker 1: You know, I've noticed, because I have kids exactly that era, and there's a very clear step function change, largely attributable, I think, to Google and Apple, that they have the assumption that things will work. And if you go just a couple of years older during the WIMP era, like you described it, which I'll attribute more to Microsoft, the assumption is nothing will ever work. If I try to use this thing, it's going to crash.
[00:56:04] Speaker 2: What's also interesting was that in my career, I used to give these speeches about the internet, which I enjoyed, where I said, you know, the great thing about the internet is it has, there's an off button, and you can turn off your odd button, and you can actually have dinner with your family, and then you can turn it on after dinner. This is no longer possible. So the distinction between the real world and the digital world has become confusing. But no one, none of us are offline for any significant period of time. And indeed, the reward system in the world has now caused us to not even be able to fly in peace. Right? Drive in peace. Take a train in peace. Starlink is everywhere. Right. And that ubiquitous connectivity has some negative impact in terms of psychological stress, loss of emotional, physical health, and so forth. But the benefit of that productivity is without question.
[00:56:57] Speaker 3: Every day, I get the strangest compliment. Someone will stop me and say, "Peter, you have such nice skin. Honestly, I never thought I'd hear that from anyone." And honestly, I can't take the full credit. All I do is use something called OneSkin OS1 twice a day, every day. The company is built by four brilliant PhD women who identified a peptide that effectively reverses the age of your skin. I love it. And again, I use this twice a day, every day. You can go to oneskin.co and write "Peter at checkout" for a discount on the same product I use. That's oneskin.co, and use the code "Peter" at checkout. All right. Back to the episode. Google I/O was amazing. I mean, just hats off to the entire team there. VO3 was shocking. And we're sitting here eight miles from Hollywood. And I'm just wondering your thoughts on the impact this will have. You know, we're going to see the one-person feature film, like we're seeing potentially one-person unicorns in the future with the Genic AI. Are we going to see an individual be able to compete with a Hollywood studio? And should they be worried about their assets?
[00:58:15] Speaker 2: Well, they should always be worried because of intellectual property issues and so forth. I think blockbusters are likely to still be put together by people with an awful lot of help by AI. I don't think that goes away. If you look at what we can do with generating long-form video, it's very expensive to do long-term video, although that will come down. And also, there's an occasional extra leg or extra clock or whatever. It's not perfect yet. And that requires human editing. So even in the scenario where a lot of the video is created by a computer, they're going to be humans that are producing it and directing it for reasons. My best example in Hollywood is that... Let's use the example. And I was at a studio where they were showing me this. They happened to have an actor who was recreating William Shatner's movies, movements, a young man. And they had licensed the likeness from William Shatner, who's now older. And they put his head on this person's body and it was seamless. Well, that's pretty impressive. That's more revenue for everyone. The unknown actor becomes a bit more famous. Mr. Shatner gets more revenue. The whole movie genre works. That's a good thing. Another example is that nowadays they use green screens rather than sets. And furthermore, in the alien department, when you have scary movies, instead of having the makeup person, they just add the makeup digitally. So who wins? The costs are lower. The movies are made quicker. In theory, the movies are better because you have more choices. So everybody wins. Who loses? Well, there was somebody who built that set. And that set isn't needed anymore. That's a carpenter and a very talented person who now has to go get a job in the carpentry business. So again, I think people get confused. If I look at the digital transformation of entertainment subject to intellectual property being held, which is always a question, it's going to be just fine, right? There's still going to be blockbusters. The cost will go down, not up, or the relative income, because in Hollywood, they essentially have their own accounting and they essentially allocate all the revenue to all the key producing people. The allocation will shift to the people who are the most creative. That's a normal process. Remember we said earlier that automation gets rid of the lowest quality jobs, the most dangerous jobs. The jobs that are sort of straightforward are probably automated, but they're really creative jobs. Another example, the script writers, you're still going to have script writers, but they're going to have an awful lot of help from AI to write even better scripts. That's not bad.
[01:01:03] Speaker ?: Okay.
[01:01:04] Speaker 3: I saw a study recently out of Stanford that documented AI being much more persuasive than the best humans. Yes. That set off some alarms. It also set off some interesting thoughts on the future of advertising. Any particular thoughts about that?
[01:01:22] Speaker 2: So we know the following. We know that if the system knows you well enough, it can learn to convince you of anything. So what that means in an unregulated environment is that the systems will know you better and better. They'll get better at pitching you. And if you're not savvy, if you're not smart, you could be easily manipulated. We also know that the computer is better than humans trying to do the same thing. So none of this surprises me. The real question, and I'll ask this in as a question, is in the presence of unregulated misinformation engines, of which there will be many, advertisers, politicians, just criminal people, people trying to evade responsibility. There's all sorts of people who have free speech. When they have free speech, which includes the ability to use misinformation to their advantage, what happens to democracy? We've all grown up in democracies where there's a sort of a consensus around trust and there's an elite that more or less administers the trust vectors and so forth. There's a set of shared values. Do those shared values go away? In our book about Genesis, we talk about this as a deeper problem. What does it mean to be human when you're interacting mostly with these digital things, especially if the digital things have their own scenarios? What does it mean to be? My favorite example is that you have a son or a grandson or a child or a grandchild and you give them a bear and the bear has a personality and the child grows up, but the bear grows up too. So who regulates what the bear talks to the kid?
[01:03:04] Speaker 1: Most people haven't actually experienced the super, super empathetic voice that can be any inflection you want. When they see that, which will be in the next probably two months, they're going to completely open their eyes to what this is going to do.
[01:03:14] Speaker 2: Well, remember that voice casting was solved a few years ago and that you can cast anyone else's voice onto your own. Yeah. And that has all sorts of problems.
[01:03:24] Speaker 1: Have you seen an avatar yet of somebody that you love that's passed away or Henry Kissinger or anything like that? Well, we created, we actually created one with the permission of his family. Did you start crying instantly?
[01:03:35] Speaker 2: It's very emotional. Yeah. It's very emotional because, you know, it brings back, I mean, it's a real human. Yeah. You know, it's a real memory, a real voice. And I think we're going to see more of that. Now, one obvious thing that will happen is at some point in the future when we naturally die, our digital essence will live in the cloud. Yeah. And it will know what we knew at the time and you can ask it a question. Yeah. So can you imagine asking Einstein, going back to Einstein, what did you really think about, you know, this other guy? Yeah. You know, did you actually like him or were you just being polite with him with letters? Yeah. Right. And in all those sort of famous contests that we study as students, can you imagine being able to ask the, you know, the people? Yeah. Today, you know, with today's retrospective, what did you really think?
[01:04:24] Speaker 1: I know that the education example you gave earlier is so much more compelling when you're talking to Isaac Newton or Albert Einstein instead of just the, oh. You talk about, you know, it's so, it's so. This is coming back to the VO3 and the movies when one of the first companies we incubated out of MIT, Course Advisor, we sold it to Don Graham in the Washington Post. And then, so I was working for him for a year after that. And the conception was, here's the internet, here's the newspaper. Let's move the newspaper onto the internet. We'll call it WashingtonPost.com. And if you look at where it ended up, you know, today with Meta, TikTok, YouTube, didn't end up anything like the newspaper, it was the internet. So now here's VO3, here are movies. You can definitely make a long form movie much more cheaply. But I just had this experience of somebody that I know is a complete, this director will try and make a tearjerker by leading me down a two hour long path. But I can get you to that same emotional state in about five minutes if it's personalized to you.
[01:05:22] Speaker 2: Well, one of the things that's happened because of the addictive nature of the internet is we've lost sort of the deep state of reading. So I was walking around and I saw a Borders, sorry, a Barnes and Noble bookstore. It was big. Oh my God, my old home is back. And I went in and I felt good. But it's a very fond memory. But the fact of the matter is that people's attention spans are shorter. They consume things quicker. One of the things interesting about sports is the sports highlights business is a huge business. Licensed clips around highlights because it's more efficient than watching the whole game. So I suspect that if you're with your buddies and you want to be drinking and so forth, you put the game on, that's fine. But if you're a busy person and you're busy with whatever you're busy of and you want to know what happened with your favorite team, the highlights are good enough. Yeah, you got four panes of it going at the same time too. And so this is again a change and it's a more fundamental change to attention. I work with a lot of 20-somethings in research. And one of the questions I had is how do they do research in the presence of all of these stimulations? And I can answer the question definitively. They turn off their phone. Yeah. You can't think deeply as a researcher with this thing buzzing. And remember that part of the industry's goal was to fully monetize your attention. Yeah. Right? Aside from sleeping, and we're working on having you have less sleep, I guess, from stress, we have essentially tried to monetize all of your waking hours with something, some form of ad, some form of entertainment, some form of subscription. That is completely antithetical to the way humans traditionally work with respect to long, thoughtful examination of principles, the time that it takes to be a good human being. These are in conflict right now. There are various attempts at this. So, you know, my favorite are these digital apps that make you relax. Okay. So the correct thing to do to relax is to turn off your phone, right? And then relax in a traditional way for, you know, 70,000 human years of existence.
[01:07:30] Speaker 1: Yeah. I had an incredible experience. I'm doing the flight from MIT to Stanford all the time. And, you know, like you said, attention spans are getting shorter and shorter and shorter. The TikTok extreme, you know, the clips are so short. This particular flight was my first time brainstorming with Gemini for six hours straight. And I completely lost track of time. And I'm trying to figure out, it's a circuit design, a chip design for inference time compute. And it's so good at brainstorming with me and bringing back data. And as long as the Wi-Fi on the plane is working, time went by. So my first experience with technology that went the other direction.
[01:08:04] Speaker 2: But notice that you also were not responding to texts and annoyances. You weren't reading ads. You were deep inside of a system for which you paid a subscription. So if you look at the deep research stuff, one of the questions I have when you do a deep research analysis, I was looking at factory automation for something. Where is the boundary of factory automation versus human automation? It's an area I don't understand very well. It's a very, very deep technical set of problems. I didn't understand it. It took 12 minutes or so to generate this paper. 12 minutes of these supercomputers is an enormous amount of time. What is it doing? And the answer, of course, the product is fantastic.
[01:08:44] Speaker 1: Yeah. You know, to Peter's question earlier too, I keep the Google IPO prospectus in my bathroom up in Vermont. It's 2004. I've read it probably 500 times. But I don't know if you remember. Is your bathroom read it? It's getting a little ratty actually at this stage.
[01:08:57] Speaker 2: You're the only person besides me who did the same thing. Tech form, right? I read it 500 times because I had to. Because you had to. Well, it was back in the day. It was legally required.
[01:09:06] Speaker 1: Well, I still read it because of the misconceptions. It's just so it's such a great learning experience. But even before the IPO, if you think back, you know, there's this big debate about will it be ad revenue? Will it be subscription revenue? Will it be paid inclusion? Will the ads be visible? And all this confusion about how you're going to make money with this thing. Now the internet moved to almost entirely ad revenue. But if you look at the AI models there, you know, you got your $20 now $200 subscription and people are signing up like crazy. So you know, it's ultra, ultra convincing. Is that going to be a form of ad revenue where it convinces you to buy something? Or no, is it going to be subscription revenue where people pay a lot more and there's no advertising at all?
[01:09:47] Speaker 2: No, but you have this with Netflix. There was this whole discussion about how would you fund movies through ads? And the answer is you don't. You have a subscription. And the Netflix people looked at having free movies without a subscription. And advertising supported and the math didn't work. I think both will be tried. I think the fact of the matter is deep research, at least at the moment, is going to be chosen by well-to-do or professional tasks. You are capable of spending that $200 a month. A lot of people cannot afford it. Yeah, yeah. And that free service, remember, is the thing that is the stepping stone for that young person, man or woman, who just needs that access. My favorite story there is that when I was at Google and I went to Kenya. And Kenya is a great country and I was with this computer science professor and he said, I love Google. And I said, well, I love Google too. And he goes, well, I really love Google. And I said, well, I really love Google too. And I said, why do you really love Google? He said, because we don't have textbooks. And I thought, top computer science program in a nation does not have textbooks. Yeah.
[01:10:51] Speaker 3: Well, let me jump in a couple of things here. Eric, in the next few years, what moats actually exist for startups as AI is coming in and disrupting? Do you have a list? Yes, I'll give you a simple answer. And what do you look for in the companies that you're investing in?
[01:11:14] Speaker 2: So first, in the deep tech hardware stuff, there's going to be patents, patents, filings, inventions, you know, the hard stuff. Those things are much slower than the software industry in terms of growth. And they're just as important, you know, power systems, all those robotic systems we've been waiting for a long time. They're just, it's just slower for all sorts. Hardware is hard. Hardware is hard for those reasons. In software, it's pretty clear to me, it's going to be really simple. Software is typically a network effect business where the fastest mover wins. The fastest mover is the fastest learner in an AI system. So what I look for is a company where they have a loop. Ideally, they have a couple of learning loops. I'll give you a simple learning loop. As you get more people, the more people click and you learn from their click. They express their preferences. So let's say I invent a whole new consumer thing, which I don't have an idea right now for it, but imagine I did. And furthermore, I said that I don't know anything about how consumers behave, but I'm going to launch this thing. The moment people start using it, I'm going to learn from them. And I'll have instantaneous learning to get smarter about what they want. So I start from nothing. If my learning slope is this, I'm essentially unstoppable. I'm unstoppable. Because my learning advantage, by the time my competitor figures out what I've done, is too great. Yeah. Now, how close can my competitor be and still lose? The answer is a few months. Because the slopes are exponential. And so it's likely to me that there will be another 10 fantastic Google scale, meta scale companies. They'll all be founded on this principle of learning loops. And when I say learning loops, I mean in the core product solving the current problem as fast as you can. If you cannot define the learning loop, you're going to be beaten by a company that can define it.
[01:13:13] Speaker 1: And you said 10 meta Google-sized companies. Do you think there will also be a thousand? Like if you look at the enterprise software business, the Oracle on down, PeopleSoft, whatever. Thousands of those? Or will they all consolidate into those 10 that are domain-dominant learning loop companies?
[01:13:33] Speaker 2: I think I'm largely speaking about consumer scale because that's where the real growth is. The problem with learning loops is if your customer is not ready for you, you can only learn at a certain rate. So it's probably the case that the government is not interested in learning. And therefore, there's no growth in learning loop serving the government. I'm sorry to say. That needs to get fixed. Yeah. Educational systems are largely regulated and run by the unions and so forth. They're not interested in innovation. They're not going to be doing any learning. I'm sorry to say. That has to get fixed. So the ones where there's a very fast feedback signal are the ones to watch. Another example. It's pretty obvious that you can build a whole new stock trading company where you learn. If you get the algorithms right, you learn faster than everyone else. And scale matters. So in the presence of scale and fast learning loops, that's the moat. Now, I don't know that there's many others that you do have. Do you think brand would be a moat? A brand matters, but less so. What's interesting is people seem to be perfectly willing now to move from one thing to the other, at least in the digital world. And there's a whole new set of brands that have emerged that everyone is using that are, you know, the next generations that I haven't even heard of.
[01:14:50] Speaker 1: Within those learning loops, you think domain-specific synthetic data is a big advantage?
[01:14:57] Speaker 2: Well, the answer is whatever it causes faster learning. There are applications where you have enough training data from humans. There are applications where you have to generate the training data from what the humans are doing. So you could imagine a situation where you had a learning loop where there's no humans involved, where it's monitoring something, some sensors. But because you learn faster on those sensors, you get so smart, you can't be replaced by another sensor management company. That's the way to think about it.
[01:15:26] Speaker 1: What about the capital for the learning loop? Do you know Daniella Roos who runs C-Sale MIT? So Daniella and I are really good friends. We've been talking to our governor, Maura Healy, who's one of the best governors in the world.
[01:15:36] Speaker 2: Yes, I agree. So there's a problem in our academic systems where the big companies have all the hardware because they have all the money, and the universities do not have the money for even reasonable-sized data centers. I was with one university where after lots of meetings, they agreed to spend $50 million on a data center, which generates less than 1,000 GPUs for the entire campus and all of research. And that doesn't even include the terabytes of storage and so forth. So I and others are working on this as a philanthropic matter. The government is going to have to come in with more money for universities for this kind of stuff. That is among the best investment. When I was young, I was on a National Science Foundation scholarship. And by the way, I made $15,000 a year. The return to the nation of that $15,000 has been very good, shall we say, based on the taxes that I pay and the jobs that we have created. So core question. So glad you said that. So creating an ecosystem for the next generation to have the access to the systems is important. It's not obvious to me that they need billions of dollars. It's pretty obvious to me that they need a million dollars, two million dollars. Yeah. That's the goal. Yeah.
[01:16:53] Speaker 3: I want to take us in a direction of wrapping up on superintelligence and the book. We didn't finish the timeline on superintelligence. And I think it's important to give people a sense of how quickly the self-referential learning can get and how rapidly we can get to something, you know, a thousand times, a million, a billion times more capable than a human. And on the flip side of that, Eric, when I look at my greatest concerns, when we get through this five to seven year period of, let's just say, rogue actors and stabilization and such, one of the biggest concerns I have is the diminishment of human purpose. You know, you wrote in the book and I've listened to it, I haven't read it physically. And my kids say, you don't read anymore. Low attention span. You listen to books, you don't read. But you said the real risk is not terminator, it's drift. You argue that AI wouldn't destroy humanity violently, but might slowly erode human values, autonomy and judgment if left unregulated, misunderstood. So it's really a Wally-like future versus a Star Trek boldly go out there.
[01:18:15] Speaker 2: We're very, in the book and my own personal view, is it's very important that human agency be protected. Yeah. Human agency means the ability to get up in the day and do what you want subject to the law, right? And it's perfectly possible that these digital devices can create a form of a virtual prison where you don't feel that you as a human can do what you want, right? That is to be avoided.
[01:18:41] Speaker 3: I'm not worried about that case. I'm more worried about the case that if you want to do something, it's just so much easier to ask your robot or your AI to do it for you. The human spirit that wants to overcome a challenge. I mean, unchallenged life is so critical.
[01:19:00] Speaker 2: But there will be always new challenges. When I was a boy, one of the things that I did is I would repair my father's car, right? I don't do that anymore. When I was a boy, I used to mow the lawn. I don't do that anymore. Sure. Right? So there are plenty of examples of things that we used to do that we don't need to do anymore, but there'll be plenty of things. Just remember the complexity of the world that I'm describing is not a simple world. Just managing the world around you is going to be a full-time and purposeful job, partly because there will be so many people fighting for misinformation and for your attention. And there's obviously lots of competition and so forth. There's lots of things to worry about. Plus, you have all of the people trying to get your money, create opportunities, deceive you, what have you. So I think human purpose will remain because humans need purpose. Yeah, that's the point. And there's lots of literature that the people who have what we would consider to be low-paying, worthless jobs enjoy going to work. So the challenge is not to get rid of their job. It's to make their job more productive using AI tools. They're still going to go to work. And to be very clear, this notion that we're all going to be sitting around doing poetry, is not happening, right? In the future, there'll be lawyers. They'll use tools to have even more complex lawsuits against each other, right? There will be evil people who will use these tools to create even more evil problems. There will be good people who will be trying to deter the evil people. The tools change, but the structure of humanity, the way we work together is not going to change.
[01:20:38] Speaker 1: Peter and I were on Mike Saylor's yacht a couple of months ago, and I was complaining that the curriculum is completely broken in all these schools. But what I meant was we should be teaching AI. And he said, yeah, they should be teaching aesthetics. And I looked at him like, what the hell are you talking about? He said, no. In the age of AI, which is imminent, look at everything around you. Whether it's good or bad, enjoyable, not enjoyable, it's all about designing aesthetics. When the AI is such a force multiplier that you can create virtually anything, what are you creating and why, when that becomes the challenge.
[01:21:10] Speaker 2: If you look at Wittgenstein and sort of the theories of all of this stuff, we're having a conversation that America has about tasks and outcomes. It's our culture. But there are other aspects of human life, meaning, thinking, reasoning. We're not going to stop doing that. So imagine if your purpose in life in the future is to figure out what's going on. And to be successful, just figuring that out is sufficient. Because once you've figured it out, it's taken care of for you. That's beautiful. Right. That provides purpose. Yeah. It's pretty clear that robots will take over an awful lot of mechanical or manual work. And for people who like to, you know, I like to repair the car. I don't do it anymore. I miss it. But I have other things to do with my time. Purposeful. Yeah.
[01:21:59] Speaker 3: Take me forward. When do you see what you define as digital superintelligence?
[01:22:06] Speaker 2: Within 10 years. Within 10 years.
[01:22:07] Speaker 3: And what do people need to know about that? What do people need to understand and sort of prepare themselves for, either from as a parent or as an employee or as a CEO?
[01:22:23] Speaker 2: One way to think about it is that when digital superintelligence finally arrives and is generally available and generally safe, you're going to have your own polymath. So you're going to have the sum of Einstein and Leonardo da Vinci in the equivalent of your pocket. I think thinking about how you would use that gift is interesting. And, of course, evil people will become more evil. But the vast majority of people are good. Yes. They're well-meaning, right? So going back to your abundance argument, there are people who've studied the notion of productivity increases and they believe that you can get, we'll see, to 30% year-over-year economic growth through abundance and so forth. That's a very wealthy world. That's a world of much less disease, many more choices, much more fun, if you will, right? Just taking all those poor people and lifting them out of the daily struggle they have, that is a great human goal. That's the goal. Let's focus on that.
[01:23:24] Speaker 3: That's the goal we should have. Does GDP still have meaning in that world?
[01:23:28] Speaker 2: If you include services, it does. One of the things about manufacturing, and everyone's focused on trade deficits and they don't understand, the vast majority of modern economies are service economies, not manufacturing economies. And if you look at the percentage of farming, it was roughly 98% to roughly 2% or 3% in America over 100 years. If you look at manufacturing, the heydays in the 30s and 40s and 50s, those percentages are now down well lower than 10%. It's not because we don't buy stuff. It's because the stuff is automated. You need fewer people. There's plenty of people working in other jobs. So again, look at the totality of the society. Is it healthy? If you look in China, it's easy to complain about them. They have now deflation. They have a term where people are, it's called laying down, where they stay at home. They don't participate in the workforce, which is counter to their traditional culture. If you look at reproduction rates, these countries that are essentially having no children, that's not a good thing. Those are problems that we're going to face. Those are the new problems of the age.
[01:24:35] Speaker 3: I love that. Eric, so grateful for your time.
[01:24:41] Speaker 2: Thank you. Thank you both. I love your show. Thank you, buddy. Thank you. Thank you, guys.
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