Ex-Google CEO: What Artificial Superintelligence Will Actually Look Like w/ Eric Schmidt & Dave Blundin | EP #183

Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends Eric Schmidt is the former CEO of Google. Dave Blundin is the founder of Link Ventures – Offers for my audience: Test what’s going on inside your body at https://qr.diamandis.com/fountainlifepodcast Reverse the age of my skin using the same cream at https://qr.diamandis.com/oneskinpod –- Connect with Eric: X: https://x.com/ericschmidt His latest book: https://a.co/d/fCxDy8P Learn about Dave’s fund: https://www.linkventures.com/xpv-fund Connect with Peter: X Instagram Listen to MOONSHOTS: Apple YouTube – *Recorded on June 5th, 2025 *Views are my own thoughts; not Financial, Medical, or Legal Advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  When do you see what you define as digital superintelligence?

• 0:04Speaker 01

  Within 10 years.

• 0:06Speaker 00

  The AI's ability to generate its own scaffolding is imminent. Pretty much sure that that will be a 2025 thing.

• 0:14Speaker 01

  We certainly don't know what superintelligence will deliver, but we know it's coming.

• 0:19Speaker 00

  And what do people need to know about that?

• 0:21Speaker 01

  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...

• 0:51Peter Diamandis?

  Everybody, welcome to Moonshots. I'm here live with my Moonshot mate, Dave London. We're here in our Santa Monica studios, and we have a special guest today, Eric Schmidt, the author of Genesis. We're talking about China. We're going to talk about digital superintelligence. We'll talk about what people should be thinking about over the 10 years.

• 1:11Speaker 01

  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.

• 1:21Peter Diamandis?

  Incredibly brilliant. All right. Stand by for a conversation with the Eric Schmidt CEO, or past CEO of Google, and extraordinary investor and thinker in this field of AI.

• 1:32Speaker 01

  Let's do it.

• 1:33Peter Diamandis?

  Eric, welcome back to Moonshots.

• 1:35Speaker 01

  It's great to be here with you guys.

• 1:36Peter Diamandis?

  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 them.

• 1:56Speaker 01

  Of course.

• 1:57Peter Diamandis?

  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.

• 2:13Peter Diamandis?

  We'll get into it in more detail, but quick, most important point to make there.

• 2:18Speaker 01

  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.

• 2:34Speaker 00

  Not chips.

• 2:36Speaker 01

  Electricity.

• 2:37Speaker 00

  Really? Okay.

• 2:39Peter Diamandis?

  So that gets me to the next point here, which is a discussion on AI and energy. So we saw recently was Meta 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.

• 3:05Peter Diamandis?

  That private companies are are basically taking over into their own hands what was utility function before.

• 3:14Speaker 01

  Well, just to be cynical, I I'm so glad those companies plan to be around the 20 years it is 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.

• 3:41Peter Diamandis?

  And there have been two in the last, what, 30 years built?

• 3:43Speaker 01

  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.

• 4:03Speaker 00

  Do you think, so if you look at the sort of three year timeline toward AGI, do you think if you started 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?

• 4:22Speaker 01

  A very good question. We don't know what artificial general intelligence will deliver.

• 4:27Speaker 00

  Yeah.

• 4:28Speaker 01

  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.

• 4:59Peter Diamandis?

  Yeah, they've been scaling it, you know, at two or three times the US. US has been flat for how long in terms of energy production?

• 5:06Speaker 01

  From my perspective, 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 about 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

• 5:50Speaker 01

  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 dollars of capital spend per year just to handle the infrastructure. These are huge businesses and huge revenue, which in most places is not there yet.

• 6:10Peter Diamandis?

  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 1,000 times more energy efficient?

• 6:31Speaker 01

  There is a similar amount in 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

• 6:55Speaker 01

  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, um, 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. Yeah. And suck it all up. Higher level code. I have no reason to believe that that's, that that law, Grove, Grove and Gates law has changed. If you look at the gains in like the Blackwell chip or the AS, uh, the, 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.

• 7:40Speaker 01

  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 chat G P D can be understood at, to reasoning and thinking. If you want to look at an OpenAI example, look at OpenAI O three, 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.

• 8:25Speaker 01

  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, in the sense of intellectual power to gen- generate abundance along the lines that you predicted two decades ago.

• 9:08Peter Diamandis?

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• 9:49Peter Diamandis?

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• 10:08Speaker 00

  Let me throw some numbers at you just to reinforce what you said. We have a couple companies in the lab that are doing voice customer service, voice sales with the new, you know, just as of the last month. And the value of these conversations is $10 to $1,000. And the cost of the compute is, you know, 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.

• 10:44Speaker 01

  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 a large language model can produce the code for your enterprise.

• 11:28Speaker 01

  Now, there's a hundred thousand enterprise software companies, middleware companies that grew up in the last thirty years that I've been working on this that are all now in trouble because that that interstitial connection is no longer needed. It's 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 a use BigQuery or the equivalent from Amazon, which is Redshift, and essentially build that architecture, and it gives you infinite flexibility, and the computer system writes most of the code.

• 12:12Speaker 01

  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. 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. 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 mathematical tasks by computers.

• 12:57Speaker 01

  Now, you said that, 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 are going to 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?

• 13:41Speaker 01

  It's an 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?

• 13:57Speaker 00

  Yeah.

• 13:58Speaker 01

  It's right. It's very exciting.

• 13:59Speaker 00

  I'd love to drill in.

• 14:02Peter Diamandis?

  Okay.

• 14:03Speaker 00

  No, you first.

• 14:03Peter Diamandis?

  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 researcher, the PhD level AIs and

• 14:19Peter Diamandis?

  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 superintelligence in the next few years. Do you buy that timeframe?

• 14:44Speaker 01

  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.

• 15:05Peter Diamandis?

  Sure.

• 15:06Speaker 01

  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 superintelligence, which implies intelligence that beyond the sum of what humans can do. The race to superintelligence, which is incredibly important. Because imagine what a superintelligence 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,

• 15:50Speaker 01

  electricity issues, so forth. We don't even have the language for the deterrence aspects and the proliferation issues of these powerful models.

• 15:58Speaker 00

  Or the imagination. Totally agree. In fact, it's one of the great flaws, actually, in the original conception. 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, that's when we have AGI.

• 16:35Speaker 01

  So, so

• 16:35Speaker 00

  Long before that.

• 16:37Speaker 01

  Yeah, so I think it's worth getting the timeline right.

• 16:39Speaker 00

  Yeah.

• 16:40Speaker 01

  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, in countries, 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 biomedical things, startups, and so forth, and that's the place to watch. So all of that is going to happen.

• 17:25Speaker 01

  The agents are going to happen. This math thing is going to happen. The software thing's 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, they're incomputable. So in other words, you have a, 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, you know, quantum chromodynamics and things like that.

• 18:08Speaker 00

  Yep.

• 18:09Speaker 01

  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?

• 18:20Speaker 01

  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,

• 19:03Speaker 01

  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 powered chips.

• 19:46Speaker 01

  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, right? 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

• 20:26Speaker 01

  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.

• 20:35Speaker 00

  Okay, can I ask you probably one of the toughest questions? I don't know if you saw Marc 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.

• 21:17Speaker 00

  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?

• 21:36Speaker 01

  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? That's, that's, 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

• 22:20Speaker 01

  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.

• 23:05Speaker 01

  So we don't know that.

• 23:08Speaker 01

  To me, the key question to watch is look at DeepSeek. So DeepSeek, 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, DeepSeek comes in and is slightly better than Gemini. And DeepSeek 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.

• 23:41Speaker 01

  What happens? Now, the US people say, well, you know, the DeepSeek 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 spent 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 they're training without us knowing it.

• 24:25Speaker 01

  And by the way, that's a violation of law.

• 24:27Peter Diamandis?

  Oh, really?

• 24:28Speaker 01

  They work, and the whole nuclear information thing is that 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 are replacing it by a new term, which is largely a safety assessment program, which is a fine answer. I think collectively, we in the industry just want the government at the secret and

• 25:11Speaker 01

  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 should have the same thing.

• 25:25Peter Diamandis?

  Have you read the AI 27 paper?

• 25:28Speaker 01

  I have.

• 25:29Peter Diamandis?

  And so for those listening who haven't read it, it's a, it's a future vision of the AI and 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, in the race to infinity, humanity vanishes.

• 25:54Speaker 01

  So the right outcome will ultimately be some form of deterrence and mutually assured 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-

• 26:25Peter Diamandis?

  Terms of amount of compute and amount of-

• 26:27Speaker 01

  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 assured 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 assured destruction is. That's our best formulation right now.

• 27:10Speaker 01

  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. 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 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.

• 27:49Speaker 00

  Or may in the future have.

• 27:50Speaker 01

  And may in the future have. And also, I'm asserting that everyone arrives at this eventual state of superintelligence 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 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,

• 28:34Speaker 01

  well before the Chernobyl events, well before the buildups.

• 28:38Speaker 00

  Can I 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, 1948? Or what would Kissinger do different in 1947, 1948, 1949 than what we did do?

• 29:04Speaker 01

  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 thirty-six, thirty-eight, he and his, I guess, thirty-seven, thirty-eight, his family were Jewish, were forced to immigrate 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.

• 29:46Speaker 01

  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 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 her 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.

• 30:32Speaker 01

  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 capable, very much a peer. And if you're confused about that, again, look at the arrival of DeepSeek. 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.

• 31:16Speaker 00

  Let me actually drill in just a little bit more on that, too, because I think one of the reasons Deepsea 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, that nobody anticipated.

• 31:50Speaker 01

  And CBRN, remember, is chemical, biological, radiological, and nuclear.

• 31:54Speaker 00

  Mm-hmm.

• 31:57Speaker 01

  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

• 32:39Speaker 01

  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.

• 33:25Speaker 01

  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 is trained on a single cluster.

• 34:08Speaker 01

  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, right? If that is the future, then we're okay, because we'll be able to know where they are.

• 34:28Peter Diamandis?

  Yeah.

• 34:29Speaker 01

  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.

• 34:40Peter Diamandis?

  Which is my, which is my greatest concern, right? China and the US are rational actors.

• 34:46Speaker 01

  Yeah.

• 34:46Peter Diamandis?

  The terrorist who has access to this. And I don't want to go all negative on this, on this podcast. It's, it's an important thing to wake people up to the deep thinking you've done on this. My concern is, is the terrorist who gains access? And are we spending enough time and energy? And are we training enough models to watch them?

• 35:10Speaker 01

  So the, first, the companies are doing this. There are, there's a body of work happening now, which can be understood as follows.

• 35:21Speaker 01

  You have a super intelligent model. Can you build a model that's not as smart as the student it's studying? Now, 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 a 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.

• 36:06Speaker 01

  One, and this is to discover completely new schools of thought.

• 36:09Peter Diamandis?

  Which is what's the most exciting thing in the next years.

• 36:12Speaker 01

  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.

• 36:55Speaker 01

  So one thing to watch for is algorithmically, when can they do that? This is generally known as the non-stationarity problem 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.

• 37:23Speaker 00

  Peter and I were over at OpenAI yesterday, 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 green field opportunity, it won't, 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

• 38:08Speaker 00

  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 their point of view.

• 38:25Speaker 01

  So recursive self-improvement is the general term for the computer continuing to learn?

• 38:32Speaker 00

  Yeah.

• 38:33Speaker 01

  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.

• 38:46Speaker 01

  When does the system have the ability to generate its own objective and its own question? It does not have that today.

• 38:53Speaker 00

  Yep.

• 38:54Speaker 01

  That's another sign. Another sign would be that the system decides to exfiltrate itself, and it takes steps to get it, get itself away from the command, the control and command system. That has not happened yet.

• 39:10Peter Diamandis?

  Jim, and I hasn't called you yet and said, hi, Eric.

• 39:13Speaker 01

  But, but there, there are theoreticians who believe that the, that the systems will ultimately choose that as a reward function, because they're programmed to, you know, to continue to learn. Another one is access to weapons, right, and lying to get it. So these are tripwires, right? All of, each, each of which is a tripwire 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 US government is not focused on these issues. They're focused on other things, economic opportunity, growth, so forth, it's all good. But somebody's going to get focused on this, and somebody's going to pay attention to it, and it will ultimately be a problem.

• 39:58Peter Diamandis?

  A quick aside, you've 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 Cap, and Bob Hurrey, founded Fountain Life, a one-stop center to help people understand what's going on inside their bodies before it's too late,

• 40:40Peter Diamandis?

  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.

• 40:53Speaker 00

  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, and ones not in Brussels probably, ones maybe in Dubai.

• 41:08Speaker 01

  Or, you know, Israel.

• 41:11Speaker 00

  Israel, okay, there you go.

• 41:12Speaker 01

  Somewhere like that.

• 41:13Speaker 00

  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, a little box about this size. And it's just as intelligent, you know, it's, and that's one of the beautiful things about it.

• 41:46Speaker 01

  This is called stealing the weights.

• 41:47Speaker 00

  Stealing the weights, exactly. And the new, new thing is that that weight file with, if you have an innovation in inference time speed. And you say, oh, same weights, no difference. Distill it or or just quantize it or whatever, but I made it 100 times faster. Now it's actually far more intelligent than what you exported from the data center. And so the

• 42:11Speaker 01

  But all of these are examples of the proliferation problem.

• 42:14Speaker 00

  Yeah.

• 42:15Speaker 01

  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, 100, 1,000, a million, a billion, at different levels of complexity. So the system that's on your future phone may be, you know, three orders of magnitude, four order of magnitude

• 42:56Speaker 01

  smaller than the one at the very tippy top, but it will be very, very powerful.

• 43:01Speaker 00

  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?

• 43:34Speaker 01

  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.

• 43:53Speaker 00

  Yeah.

• 43:53Speaker 01

  Right?

• 43:54Speaker 00

  Yeah.

• 43:54Speaker 01

  And that's why they need gigawatts of data centers and so forth. And their argument is that that flexibility of intelligence that we that they are seeing will continue. Dario wrote a piece called

• 44:08Speaker 01

  Basically about machines. And he argued that there are

• 44:12Peter Diamandis?

  Machines of the of Grace.

• 44:15Speaker 01

  Machines of amazing grace.

• 44:16Peter Diamandis?

  Amazing grace.

• 44:17Speaker 01

  And 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 this, the second and third one beginning. That's why I I'm sure our audience would be frustrated. Why, why do we not know? I'm just, we don't know. Right? It's too new, it's too powerful.

• 44:57Speaker 01

  And at the moment, all of these businesses are incredibly highly valued through 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.

• 45:21Peter Diamandis?

  Eric, shifting 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.

• 45:31Peter Diamandis?

  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, you know, 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?

• 46:02Speaker 01

  Let's posit that in thirty or forty years, there'll be a very different employment robotic human interaction.

• 46:12Peter Diamandis?

  Or the definition of do we need to work at all?

• 46:15Speaker 01

  The definition of work, the definition of identity, let's just posit that. And let's also posit that it will take twenty or thirty years for those things to work through the economy of our world. Now, in California and other cities in America, you can get on a Waymo taxi. Waymo is 2025. The original work was done in the late 90s. The original challenge at Stanford was done, I believe, in 2004.

• 46:43Peter Diamandis?

  The DOPRA Grand Challenge, it was 2004. Sebastian Thrun won.

• 46:47Speaker 01

  So more than twenty 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 robot's going to decide to slap you. You just doesn't, you know, society's not going to allow that sort of thing. It's just not, it's not going to, it's not going to allow it.

• 47:16Speaker 01

  So in the shorter term, five or 10 years, I'm going to argue that this is positive for jobs in the following way.

• 47:25Peter Diamandis?

  Okay.

• 47:28Speaker 01

  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 in cars and 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 or one, and typically not a human or robot, but an arm. 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 welder, who's now operating the arm, has a higher

• 48:11Speaker 01

  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.

• 48:55Speaker 01

  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

• 49:39Speaker 01

  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 of 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. And the typical, simple example would be all those people who work in, in Amazon distribution centers, in Amazon trucks, those jobs didn't exist until Amazon was created, right?

• 50:22Speaker 01

  The number one shortage in jobs right now in America are truck drivers. 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, right? Going back to education, it's really a crime that our industry has not invented the following product. The product that I wanted 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. 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?

• 51:03Speaker 01

  The investment in the humans of the world is the best return always. Knowledge and capability is always the right answer.

• 51:12Speaker 00

  Let me try and get, 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, no more than three years and maybe two years. And the, 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, you're really screwing over that employee.

• 51:56Speaker 01

  So, 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 fifty people working in this other way and we'll do something else with the other people.

• 52:35Speaker 00

  And when you say big companies, we're thinking Google, Meta, we're not thinking, you know, big bank hasn't done anything.

• 52:40Speaker 01

  I'm thinking about big banks. When I, 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.

• 53:26Speaker 01

  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 pull down that was invented in Xerox PARC, right? Fifty years ago, why am I still stuck in that paradigm? I just want it to work.

• 53:47Peter Diamandis?

  Kids in high school and college now, any different recommendations for where they go?

• 53:53Speaker 01

  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 fifteen 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. If I were

• 54:10Speaker 00

  They're digital natives.

• 54:11Speaker 01

  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.

• 54:41Speaker 00

  Purpose driven.

• 54:42Speaker 01

  Yeah. Any form of solution that you find interesting. Most, 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, 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?

• 55:26Speaker 00

  Yeah.

• 55:26Speaker 01

  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 not that less carbon? You see my point?

• 55:41Speaker 00

  Yeah, I've noticed, uh, because I have kids exactly that, 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. Like if I try to use this thing, it's going to crash. I'm going to be, it's going to be torture.

• 56:05Speaker 01

  What's also interesting is that in my career, I used to give these speeches about the internet, which I enjoyed, uh, 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.

• 56:33Peter Diamandis?

  Yeah.

• 56:34Speaker 01

  And indeed, the reward system in the world has now caused us to not even be able to fly in peace.

• 56:39Peter Diamandis?

  Yeah.

• 56:40Speaker 01

  Right? Drive in peace, take a train in peace.

• 56:42Peter Diamandis?

  Starlink is everywhere.

• 56:43Speaker 01

  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.

• 56:57Peter Diamandis?

  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.

• 57:39Peter Diamandis?

  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 film, 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.

• 58:16Speaker 01

  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 from it 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 the video is created by by a computer, those they're going to be humans that are producing it and directing it for reasons. My best example in Hollywood is that, let's let's use the example and I was at a studio where they were showing me this.

• 59:01Speaker 01

  They had, they happened to have an actor who was recreating William Shatner's movies, movements, a young man. And they had licensed the likeness from, you know, 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. They, the whole, 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, you know, scary movies, instead of having the makeup person, they just add the makeup digitally.

• 59:47Speaker 01

  So who wins? The costs are lower. The movies are made quicker. In theory, the movies are better, right? Cause 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, 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, or the relative income, because in Hollywood,

• 1:00:29Speaker 01

  they essentially have their own accounting and they essentially allocate all the revenue to all the key producing people. The, 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 poor, the lowest quality jobs, the most dangerous jobs. There, 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.

• 1:01:03Peter Diamandis?

  Okay. I saw a study recently out of Stanford that documented AI being much more persuasive than the best humans.

• 1:01:13Speaker 01

  Yes.

• 1:01:14Peter Diamandis?

  Uh, that set off some alarms. It also set off some interesting thoughts on the future of advertising. Any particular thoughts about that?

• 1:01:23Speaker 01

  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

• 1:02:05Speaker 01

  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?

• 1:02:20Speaker 01

  We've all grown up in democracies where there's a sort of a consensus around trust and is 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? 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?

• 1:03:04Speaker 00

  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 gonna completely open their eyes to what this is gonna do.

• 1:03:14Speaker 01

  Well, remember that voice casting was solved a few years ago, and that you can cast anyone else's voice onto your own. And that has all sorts of problems.

• 1:03:24Speaker 00

  Have you seen an avatar yet of somebody that you love that's passed away, or Henry Kissinger or anything?

• 1:03:30Speaker 01

  Well, we created, we actually created one with the permission of his family.

• 1:03:34Speaker 00

  Did you start crying instantly?

• 1:03:35Speaker 01

  It's very emotional. It's very emotional because, you know, it brings back, I mean, it's a real human, you know, it's a real memory, a real voice, and I think we're gonna 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, and it will know what we knew at the time, and you can ask it a question. So can you imagine asking Einstein, going back to Einstein, what did you really think about, you know, this other guy? You know, did you actually like him, or were you just being polite with him with letters? Right? And in all those sort of famous contests that we study as students, can you imagine

• 1:04:17Speaker 01

  being able to ask the, you know, the people, you know, with today's retrospective, what did you really think?

• 1:04:24Speaker 00

  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, oh. But it's so, it's so, 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.

• 1:05:09Speaker 00

  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.

• 1:05:22Speaker 01

  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, Barnes and Noble bookstore.

• 1:05:36Speaker 00

  It was big.

• 1:05:37Speaker 01

  Oh my God, my old home. 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.

• 1:06:11Speaker 00

  Yeah, you got four pings of it going at the same time.

• 1:06:14Speaker 01

  And so this is again, a change and it's a more fundamental change to attention. I've been working, I work with a lot of 20 some things 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. You can't think deeply as a researcher with this thing buzzing. And remember that, that part of the, the industry's goal was to fully monetize your attention.

• 1:06:46Speaker 00

  Yeah.

• 1:06:46Speaker 01

  Right. We, 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 ads, 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.

• 1:07:30Speaker 00

  Yeah, it's 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 wifi on the plane is working, time went by. So my first experience with technology that went the other direction.

• 1:08:04Speaker 01

  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 some, an area I don't understand very well. It's 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.

• 1:08:44Speaker 00

  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. It's getting a little ratty.

• 1:08:57Speaker 01

  You're the only person besides me who did the same.

• 1:09:00Speaker 00

  Tech forum.

• 1:09:01Speaker 01

  I read it 500 times because I had to.

• 1:09:03Speaker 00

  Because you had to.

• 1:09:04Speaker 01

  It was legally required.

• 1:09:06Speaker 00

  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 was 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, they're, 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?

• 1:09:47Speaker 01

  No, but you have, you have this with Netflix. There was this whole discussion about would, 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 don't afford, cannot afford it.

• 1:10:18Speaker 00

  Yeah, yeah.

• 1:10:19Speaker 01

  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's 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. I said, 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, wow, computer science program in a nation does not have textbooks.

• 1:10:51Speaker 00

  Yeah. Well, let me, uh...

• 1:10:52Peter Diamandis?

  Let me jump in a couple of things here. Eric, in, in the next few years, what moats actually exist for startups as AI is coming in and disrupting? Do you have a list?

• 1:11:09Speaker 01

  Yes. I'll give you a simple answer.

• 1:11:11Peter Diamandis?

  And what do you look for in the companies that you're investing in?

• 1:11:14Speaker 01

  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 of things.

• 1:11:34Peter Diamandis?

  Hardware's hard, yeah.

• 1:11:34Speaker 01

  Hardware is hard for those reasons. In software, it's pretty clear to me, it's going to be really simple. These 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. So I'll give you a simple learning loop. That as you get more people, the more people click and you learn from their click. They they they express their preferences. So let's say I invent a whole new consumer thing. So 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.

• 1:12:19Speaker 01

  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 I'm my learning advantage. By the time my competitor figures out what I've done is too great.

• 1:12:42Speaker 00

  Yeah.

• 1:12:42Speaker 01

  Now, how close can my my 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.

• 1:13:14Speaker 00

  You said 10 meta Google size companies. Do you think there are 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?

• 1:13:33Speaker 01

  I think I'm largely speaking about consumer scale because that's where the real growth is.

• 1:13:40Speaker 01

  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.

• 1:13:56Speaker 00

  Yeah.

• 1:13:57Speaker 01

  Educational systems are largely regulated and run by the unions over. 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. You do have-

• 1:14:32Speaker 00

  Do you think brand would be a moat?

• 1:14:33Speaker 01

  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.

• 1:14:51Speaker 00

  Within those learning loops, you think domain-specific synthetic data is a big advantage?

• 1:14:57Speaker 01

  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, right? 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.

• 1:15:25Speaker 00

  So what about the capital for the learning loop? Because, do you know Daniela Ruse, who runs CCL MIT? So Daniela 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.

• 1:15:36Speaker 01

  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, right? For the entire campus and all of research. Yeah. 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.

• 1:16:17Speaker 01

  When I was young, I was on a National Science Foundation scholarship for, and by the way, I made $15,000 a year. The return to the nation of my $15,000 has been very good, based on the taxes that I pay and the jobs that we have created. So core question, so glad you said that. So creating, 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.

• 1:16:53Peter Diamandis?

  I want to, I want to take of, I want to take us in a direction of, of wrapping up on superintelligence and the book.

• 1:17:03Peter Diamandis?

  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. 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

• 1:17:31Peter Diamandis?

  that you say rogue actors and stabilization and such.

• 1:17:36Peter Diamandis?

  One of the biggest concerns I have is the diminishment of human purpose.

• 1:17:42Peter Diamandis?

  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, Dad.

• 1:17:50Speaker 01

  Low attention span.

• 1:17:51Peter Diamandis?

  You, 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.

• 1:18:15Speaker 01

  We're very, in the book, and my own personal view, is it's very important that human agency be protected. 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.

• 1:18:42Peter Diamandis?

  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.

• 1:19:00Speaker 01

  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.

• 1:19:13Peter Diamandis?

  Sure.

• 1:19:13Speaker 01

  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, you know, trying to get your, trying to get your, your money, create opportunities, deceive you, what have you. So I think human purpose will remain, because humans need purpose.

• 1:19:52Peter Diamandis?

  That's the point.

• 1:19:54Speaker 01

  And, you know, 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, is 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.

• 1:20:38Speaker 00

  So 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 has such a force multiplier that you can create virtually anything, what are you creating and why? And that becomes the challenge.

• 1:21:10Speaker 01

  If you look at Wittgenstein and the sort of theories of all of this stuff, it is all fundamentally, 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 figured it out, it's taken care of for you.

• 1:21:41Speaker 00

  That's beautiful.

• 1:21:42Speaker 01

  That provides purpose.

• 1:21:43Speaker 00

  Yeah.

• 1:21:44Speaker 01

  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.

• 1:21:57Speaker 00

  Yeah.

• 1:21:59Peter Diamandis?

  Take me forward, when do you see what you define as digital superintelligence?

• 1:22:06Speaker 01

  Within 10 years.

• 1:22:07Peter Diamandis?

  Within 10 years. 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 a employer, as a CEO?

• 1:22:23Speaker 01

  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.

• 1:22:36Speaker 01

  So you're gonna 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. 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

• 1:23:20Speaker 01

  the daily struggle they have. That is a great human goal.

• 1:23:22Peter Diamandis?

  That's the goal. That's the goal we should have. Does GDP still have meaning in that world?

• 1:23:28Speaker 01

  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

• 1:24:11Speaker 01

  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, right? Those are problems that we're gonna face. Those are the new problems of the age.

• 1:24:36Peter Diamandis?

  I love that. Eric, so grateful for your time.

• 1:24:41Speaker 01

  Thank you. Thank you both. I love your show.

• 1:24:43Peter Diamandis?

  Yeah, thank you, buddy.

• 1:24:45Speaker 01

  Thank you. Thank you, guys.

• 1:24:46Peter Diamandis?

  If you could have had a 10-year head start on the dot-com boom back in the 2000s, would you have taken it? Every week, I track the major tech meta-trends. These are massive, game-changing shifts that will play out over the decade ahead. From humanoid robotics to AGI, quantum computing, energy breakthroughs, and longevity, I cut through the noise and deliver only what matters to our lives and our careers. I send out a meta-trend newsletter twice a week as a quick, two-minute read over email. It's entirely free. These insights are read by founders, CEOs, and investors behind some of the world's most disruptive companies. Why? Because acting early is everything. This is for you if you want to see the future before it arrives and profit from it.

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