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

27:59Again, 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? So if you were so brilliant in 38, what you would have said is this ultimately ends with us having a bomb, the other guy is having a bomb, and they were 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 build ups.

28:38Just 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 1947 when it was in 1949 than what we did? Yeah, I wrote two books with Dr. Kissinger and I miss him very much. He was my closest friend. 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.

29:28And 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 in China was that we shouldn't poke the bear, that we shouldn't talk about Taiwan too much.

30:10And 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 and 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 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.

30:49And 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 appear. 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. Let me actually drill in just a little bit more on that too, because I think one of the reasons DeepSeek 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.

31:27And 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 as specialists, 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. And CBRN, remember, is chemical, biological, radiological and nuclear. Let me rephrase what you said. If the structure of the world in five to ten years is ten 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.

32:18They 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 it's already dangerous. And I believe it's the only one or two facilities that we have in America. So in that scenario, So these data centers will have the equivalent of guards and machine guns because they're so important. Now is that a stable geopolitical system?

33:01Absolutely. 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 that 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.

33:40Now I'm in favor of open source, as you mentioned earlier with Mark and Jason, 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 a given example of GROC is trained on a single cluster, it was built by NVIDIA in 20 days or so forth in as Tennessee of 200 ,000 GPUs. GPU is about $50 ,000. You can say it's about a $10 billion supercomputer in one building. It does one thing. If that is the future, then we're okay because we'll be able to know where they are.

34:29If in fact, the arrival of intelligence is ultimately a distributed problem, then we're which is my greatest concern, right? China and the US are rational actors. Yeah. The terrorists 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 terrorists who gains access. And are we spending enough time and energy and are we training enough models to watch them? So, the first the companies are doing this. There's a body of work happening now, which can be understood as follows.

35:20You have a super intelligent model. Can you build a model that's not as smart as the student it's studying? There's 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't. 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 the of where 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 Einstein's?

36:03So there are two possibilities. One, and this is to discover completely new schools of thought. Which is what's the most exciting thing in a magic. 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. it. Okay? Now, it looks like the great discoveries, the greatest scientist and people in our history had the following property. They were experts in something and they looked at 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.

36:52The 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 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 at OpenAI yesterday actually and we were talking to many people but no and brown in particular.

37:29And 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, right now if you try to get the AI to discover relativity or just some green field 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. We 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 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 these prompts that require 20 hours of consecutive inference time compute.

38:19pretty much sure that that will be a 2025 thing, at least from from their point of view. So, 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. That's another sign. Another sign would be that the system decides to exfiltrate itself. It takes steps to get itself away from the control and command system.

39:09That has not happened yet. Jim and I hasn't called you yet and said, hi Eric. But there are theoreticians who believe that the systems will ultimately choose that as a reward function because they're programmed to continue to learn. Another one is access to weapons and lying to get it. These are trip wires, each of which is a trip wire that we're watching. and again, each of these could be the beginning of a mini -tronobile 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, you can have 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.

39:59A quick aside, you probably heard me speaking about fountain life before And you're probably wishing, Peter, would you please stop talking about found life? And the answer is no, I wouldn't, because genuinely we're living through a healthcare crisis. You may not know this, but 70 % of heart attacks have no preceding, no pain, no shortness and breath, and half of those people with a heart attack never wake up. You don't feel cancer until stage 3 or stage 4, 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 Hourry, founded Found 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.

40:44Learn more about what's going on inside your body from Found Life. Go to foundlife .com slash peter and tell them peter sent you. Okay, back to the episode. 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 AI's in five or in the US, three in China and two or one's not in Brussels, probably, one's maybe in Dubai. Or Israel. Israel, okay, there you go. Somewhere like that. Yeah. In the movie version of this, if it goes rogue, the SWAT team comes in, they blow it up and it's salt.

41:21But 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 10, e26, 20, 28, or more flops. But then the final brain can be ported and run on four GPUs, a GPU. So the box about this size. And it's just as intelligent. And that's one of the beautiful things about it. It is called stealing the weights. Stealing the weights. Exactly. And the new new thing is that that weight file, if you have an innovation and inference time speed. And you say, oh, same weights, no difference, distill it or just quantize it or whatever. But I made it 100 times faster.

42:04Now it's actually far more intelligent than what you exported from the data center. And so the, 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 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, a thousand, a million, a billion, at different levels of complexity. So the system that's on your future phone maybe three orders of magnitude, four order magnitude, smaller than the one at the very tippy top, but it will be very, very powerful.

43:02You know, to exactly what you're talking about, there's some great research going on at MIT. It'll probably move to Stanford just a minute. It always does, but it's great research going on at MIT. And 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 parameters set to be a specialist without losing general learning? So the people who are opposed to that view, and again we don't know, would say the following.

43:39If 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. Daria wrote a piece called, basically about machines. And he argued that there are machines of the media of grace, machines of amazing grace as in grocery. And he argued that there are three scaling laws playing.

44:21The 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 will be frustrated. Why do we not know? I'm just, 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.

45:05The uses of them, I mentioned earlier, going back to Google. the ability to refactor your entire workflow in a business. It's a very big deal. There's a lot of money to be made there for all the companies involved. We will see. Eric, shifting the topic, one of the concerns that people have in the near term and people have been ringing, you 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 Gerry has gone on TV shows now and speaking to significant white collar job loss?

45:53We'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. Let's pause it that in 30 or 40 years there'll be a very different employment, robotic, human interaction. Or the definition of, of, do we need to work at all? The definition of work, the definition of identity. Let's just pause it that. And let's also pause it that it will take 20 or 30 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.

46:40The original challenge at Stanford was done I believe in 2004. Dr. Grant Schaulier, what's 2004? That's right. One. 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 are going to decide to slap you. You just doesn't, you know, society's not going to allow that. So you want to? It's just not, it's not going to, it's not going to allow it.

47:16So in the shorter term, five or 10 years, I'm going to argue that this is positive for jobs in the following way. Okay. 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 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 and typically not a humanoid robot but an arm. So the world dominated by arms that are intelligent and so forth will automate those functions.

48:01What 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 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, it's sort of accelerant, they can get a higher paying job.

48:42So 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 birth rate is now down to 2 .0. The rest of the world is choosing not to have children. If you look at Korea, it's down to 0 .7 children per two parents.

49:24China 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 following. And what happens is people have talked about this for 20 years. 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.

50:03Now, there are plenty of people who lose their jobs, but there's an awful lot of people who have new jobs. And the typical example would be all those people who work in Amazon distribution centers and Amazon trucks, those jobs didn't exist until Amazon was created. it. The number one shortage in jobs right now in America are truck drivers. Why? Truck driving is a lonely, hard, low -paying, low status of good people job. They don't want it. They want a better paying job. 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.

50:56Right? That can all be done on phones now. You 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. Knowledge and capability is always the right answer. You're trying to get your opinion on this because you're so influential with... So I've got about 1 ,000 people in the companies around 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 in loyal.

51:28But 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. So, we're 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 they typically don't.

52:15They 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. And when you say big companies were thinking, Google and Meta, we're not thinking, you know, big bank hasn't done anything. Well, 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.

52:51You 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. Give you another example. There's this whole industry people who work on regulated user interfaces or one or 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.

53:26So 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 Park, right? 50 years ago. Why am I still stuck in that paradigm? I just wanted to work. Kids in high school and college now, any different recommendations for where they go? 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.

54:06It all makes sense to them and we're in their way. If I were... 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. That's just how life works, I'm sorry to say. So we have wisdom. They have intelligence they went, 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. 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.

54:52So 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. 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? Why don't you figure out a way to use these powerful models to come up with new materials?

55:30Right 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. Yeah, I've noticed 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. 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.

56:04What's also interesting is 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 out 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 but not even be able to fly in peace, right?

56:41Drive 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. 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 One Skin OS 1 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.

57:20I 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. out. Alright, back to the episode. Good. I. O was amazing. I mean, just hats off to the tire team there. The O3 was shocking. And we're saying 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 a with a genic AI. When we're going to see an individual be able to compete with a Hollywood studio and should they be worried about their assets?

58:16Well, 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 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 this scenario where a lot of the video is created by a computer, there are going to be humans that are producing and directing it for reasons.

58:53My 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 lightness 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. Schattener gets more revenue. The whole movie genre works. That's a good wit thing. Another example is that nowadays they use green screens rather than sets.

59:38And 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. To everybody who 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 gonna be just fine.

1:00:20Right? 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 most dangerous jobs. The jobs that are sort of straightforward are probably automated. 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.

1:01:01That's not bad. Okay. I saw a study recently at a Stanford that documented AI being much more persuasive than the best humans. That's it awesome alarms. It also set off some interesting thoughts on the future of advertising. Any particular thoughts about that? 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.

1:01:50So, 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 is an elite that more or less administers the trust factors and so forth is a set of shared values.

1:02:31Do those shared values go away? In our book about Genesis, us, 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 is a personality, and the child grows up, but the bear grows up too. So who regulates what the bear talks to the kid? Most people haven't actually experienced the super, super empathetic voice that can be any inflection you want.

1:03:09When they see that, which will be in the next probably two months, they're going to completely open their eyes with the same thing. 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. Have you seen an avatar yet of somebody that you love that's passed away or Henry Kissinger or anything as a creative, we actually created one with the permission of his family. Did you start crying instantly? It's very emotional. It's very emotional because it brings back, I mean, it's a real human, it's a real memory, a real voice, and I think we're gonna see more of that.

1:03:46Now 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 the Sothegrai? Did you actually like him, or were you just being polite with him with letters? And in all those famous contests that we study as students, can you imagine being able to ask the people? with today's retrospective, what did you really think? I know, 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...

1:04:31No. You talked about it. It's so coming back to the VO3 and the movies, one of the first companies we incubated out of MIT, of 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 washingimpost .com. And if you look at where it ended up, you know, today with Meta, TickTock, YouTube, didn't end up anything like the newspaper moves the internet. So now here's VO3, here are movies. You can definitely make a long -form movie much more cheaply.

1:05:08But 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. 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. It was still a big. I have it. Oh my God. My old home. Did you know it was back? And I went in and I felt good. But it's a very fun memory.

1:05:43But the fact of the matter is that people's attention spans are shorter. You know, they consume things quicker. One of the things interesting about sports is the sports highlights business. It's 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 have 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 up and you want to know what happened with your favorite team, the highlights are good enough. Yeah, you have four pains of it going at the same time too.

1:06:14And so this is again a change and it's a more fundamental change to attention. Mm -hmm. I've been working, 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 simulations? And I can answer the question definitively. They turn off their phone. Yeah. Mm -hmm. You can't think deeply as a researcher with this thing buzzing. And remember that part of the the industry's goal was to fully monetize your attention. Yeah. Right. We we sent aside from sleeping and we're working on having you have less sleep, I guess, from stress.

1:06:52We 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. They're various attempts at this. So my favorite are these digital apps that make you relax. So the correct thing to do to relax is to turn off your phone, right? And then relax in a traditional way for 70 ,000 human years of existence. Yeah, an incredible experience.

1:07:31I'm doing the flight from MIT to Stanford all the time. And like you said, attention spans are getting shorter and shorter and shorter. The TikTok extreme, the clips are so short. This particular flight was my first time brain storming 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 brain storming with me and bringing back data. And since the Wi -Fi and the plane is working, time went by. So that my first experience with technology that went the other direction.

1:08:04But 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. Whereas 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.

1:08:40What is it doing? Right. And the answer, of course, the product is fantastic. Yeah. You know, to Peter's question earlier too, I keep the Google IPO Perspectus 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 old radia. The only person besides me who did the same. Techfall. I rented 500 times because I had to. It was legally required. Well, I still read it because because of the misconceptions. It's just so it's such a great learning experience. But even before the IPO, if you think back, 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.

1:09:23Now, the internet moved almost entirely ad revenue. But if you look at the eye models, you've got your $20, now $200 subscription and people are signing up like crazy. So it's ultra -ultra -convencing. Is that going to be a form of ad revenue where it convinces you to buy something or knows it going to be subscription revenue where people pay a lot more and there's no advertising at all? 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.

1:10:04I think both will be tried. I think the fact of the matter is deep research, at least at the moment, is gonna 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. 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. I was with this computer science professor and he said, I love Google and I said, I love Google too. He goes, well, I really love Google.

1:10:40I said, I really love Google too. I said, why do you really love Google? He said, because we don't have textbooks. books. And I thought, wow. Top computer science program in a nation does not have textbooks. Yeah. Well, let me jump in a couple of things here. Eric, in the next few years, what modes 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? So first, the deep tech hardware stuff, there's going to be patents, patents, filings, inventions, you know, the hard stuff.

1:11:21Those 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 have been waiting for a long time. They're just slower. For all sorts of hardwares. 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 they'll give you a simple learning loop.

1:12:01As 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.

1:12:42Now 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, Metascale companies that'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 you can't. If you cannot define the learning loop, you're going to be beaten by a company that can define it. And you said 10 metagougal sites companies, do you think there will also be a thousand, like if you look at the enterprise software business, the oracle on down, people soft, whatever thousands of those or whether they all consolidate into those 10 that are domain dominant learning loop companies.

1:13:33I 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. Education 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 we have to get that as to get fixed.

1:14:07So 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 Now, I don't know that there's many others that you do have. 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 the next generations that I haven't even heard of.

1:14:51Within those learning loops, you think domain specific synthetic data is a big advantage? 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 based by another sensor management company. That's the way to think about that.

1:15:25So what about the capital for the learning loop? Because do you know Daniela Russo runs the C7? So Daniel and I are really good friends. We've been talking to our governor, more a Healy who's one of the best governors in the world. I agree. So there's a problem in our academic systems where the big companies have all the hard work 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 a thousand GPUs. For the entire campus and all of research.

1:16:02And 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 for and by the way I mean $15 ,000 a year The return to the nation of my that $15 ,000 has been very good You know, based on the taxes that I pay and the jobs that we have created. So core question So glad you 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.

1:16:47Mm -hmm. It's pretty obvious to me that they need a million dollars, two million dollars. Yeah, that's the goal. So I want to take a, I want to take us in a direction of wrapping up on super intelligence and the book. We didn't finish the timeline on super intelligence. And I think it's important to give people a sense of how quickly the self -ferential learning can get and how rapidly we can get to something, you know, a thousand times, a million, a billion in 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, uh, that you say rogue actors and stabilization and such, uh, one of the biggest concerns I have is the diminishment of human purpose.

1:17:42You know, you wrote in the book and I've listened to it, I haven't read it physically, and my kid say you don't read anymore. You listen to books, you don't read. But you said the real risk is not Terminator, it's drift. You argue that AI won't destroy humanity violently, but might slowly erode human values, autonomy and judgment if left unregulated, misunderstood. doing. So it's really a wally -like future versus a Star Trek boldly go out there. We're very in the book and my own personal view is it's very important that human agency be protected. Human agency means to be able to get up in the day and do what you want to subit through the law, right?

1:18:30And 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. 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. 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. I don't do that anymore. When I was a boy, I used to mow the lawn.

1:19:11I don't do that anymore. Sure. 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 to see you, what have you.

1:19:47So I think human purpose will remain because humans need purpose. Now 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, is to make their job more productive using AI tools. They're still going to go to work. 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.

1:20:29There 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. Peter and I were on Mike Sailor's yacht a couple months ago and I was complaining that the curriculum is completely broken in all his schools. But what I meant was we should be teaching AI and he said, yeah, they should be teaching aesthetics. 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.

1:21:04When the AI has such a force multiplier that you can create virtually anything, what are you creating and why when that becomes the challenge? If you look at Vidconstein and the sort of theories of all of this stuff, It is all fun. 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 gonna 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 figure it out, it's taken care of for you.

1:21:41That's beautiful. Right. That provides purpose. 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 touch. Yeah. Take me forward. When do you see what you define as digital superintelligence within 10 years? Within 10 years. And what do people need to know about that? So, what do people need to understand and sort of prepare themselves for either from as a parent or as a employee or as a CEO? 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.

1:22:35So 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 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?

1:23:17Just taking all those poor people and lifting them out of the daily struggle of him, that is a great human goal. That's the goal that's the goal we should have. Does GDP still have meaning in that world? 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 or 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 hay days and the 30s and 40s and 50s, those percentages are now down well lower than 10%.

1:23:55It'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. Right, those are problems that we're gonna face.

1:24:33Those are the new problems of the age. I love that. Eric, so grateful for your time. Thank you, thank you both. I love your show. Yeah, thank you buddy. Thank you. Okay, thank you guys. If you could have had a 10 year head start on the dot com boom back in the 2000s, would you have taken it? And every week I track the major tech metatrends. 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 metatrend newsletter twice a week as a quick two -minute read over email.

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