Speaker 1- The regulators regulate before they innovate. My favorite example is that five years ago, Brussels wrote their AI strategy. I helped write the one for the US, and the US one said, "Run as fast as you can, align it with human values, and if bad things happen, fix them immediately." The Europeans wrote, "You can't do this, you can't do this, you can't do this, you can't do this." And you can't run startups in a limited market where there's a lot of rules like that. ♪ ♪
Speaker 2- Hi, everyone, I'm Nikolaj Tangen, the CEO of the Norwegian Southern Wealth Fund. And today, I'm joined by Erik Schmidt, one of the most influential technology executives of our time. Erik was CEO of Google from 2001 to 2011, and has since become one of the leading voices on AI, advising governments, writing books, and arguing that AI will change the world more than most of us expect.
Speaker 1Erik, great to have you here. - I'm so glad to be here. And let me just tell you what you have done in Norway and for Norway is extraordinary. But it's just the beginning. -
Speaker 2Well, that depends on where AI is going. So you've been at the center of the technology world now for three decades. Where are we now in the story of technology? -
Speaker 1It feels like five decades, I'm getting older. Where my industry goes through a series of transformations. It started off in the IBM mainframe era, and through the PC era, then the internet area, then the social media era, and now AI. And each of these transformations is vast. And the reason it's vast is that these are platforms. And so what you want to do if you're in my situation is you want to watch for something where the whole ocean is rising, and that there are enormous, enormous number of new opportunities appearing. And you want to figure out why the ocean is rising. Now, in our case, it's rising because a few years ago, there were some fundamental discoveries in how technology could work. But it's also rising because of what we call scale compute. Literally the physicists making the chips faster, moving down to two nanometer chips, which are largely made in Taiwan, incredible improvements in memory. The supercomputers that are being built, these data centers, are hard to describe how powerful they are, how much is going on. It defies logic. It's like the scale of the universe. It's too big for humans to understand. -
Speaker 2How big is this change compared to what you've seen before? -
Speaker 1Each of the waves is materially larger. This one, because it touches everything, it's the rise of intelligence, is the biggest yet. And one thing to look at is look at how brutal the competition to be the leader is. So let's go back, you're old enough to remember, the PC versus Mac fights, right? Which was largely purchased by Microsoft, won by Microsoft. That was a battle, which I thought was impossible, impossibly difficult. It drove prices down. It was monopolist, it was brutalist, so forth. Then we had Android versus the iPhone, right? Which I thought was even more brutal and even bigger, even larger. Well, this one is even larger than that one. -
Speaker 2When did you first realize that this was off the chart bigger than everything else you've seen? -
Speaker 1I'm always not first. I wait to see when, where the slope is. And we get all sorts of data in the things that I do to see that this is not a bubble. You know, everyone says, oh, it's a bubble, they're wasting their money. If you look at Anthropix revenue growth, when I was CEO at Google, we set the revenue growth record. They smashed it more than twice the speed as we did. Right? That's real growth. It's not fake. It's real money with real customers who are paying for something useful in their eyes, right? And one way to understand tech is that time compresses every cycle. So 30 years ago, I had lots and lots of time to build products, to think about it, to give talks, to make mistakes, and so forth. Now there's no time. The models, for example, take roughly six months to build, two months or so to actually test, and they're released and they're antiquated within five months. The models themselves cost hundreds of millions of dollars to make. Some estimates are much higher. I'm using conservative numbers. Can you imagine being in a business where you get six months to build something and then it expires in less time than you built it because the next thing is coming along? And if you look today, the hyper competition is at the moment, and this moves around, between anthropic and open AI because of deep reasoning. I was with my friends at Google, and they said, "Just wait. When Gemini 4 comes out, it's going to beat them all," right? And I bet you that if we go to Meta, they'll say, "Just wait until the next one." And the timing is measured in months, not years. I don't know. The next cycle will drive people insane because it'll be days. It's fun to be alive, huh? Yeah. Can I just say one thing for your audience? We're living in a piece of history that people will write about for a thousand years. It's the development of non-human intelligence, a kind of intelligence that we hope to have under control. We hope to use it for good. We hope to represent Western values, European values, American values, what have you. But this is history.
Speaker 2Absolutely. What part of the AI revolution? You say it's underhyped, right? What is it that people don't understand? What is the most difficult thing to grasp?
Speaker 1My own view, I'm a professional manager, tech manager, essentially. My own view is that I, as a CEO, will have an AI assistant. And that AI assistant will help me. So here's an example. I have a small company I'm friends with. They're two founders. Every Friday, they ask their AI assistant to look through everything going on in the company and tell them if there's anything going on in the company that's inconsistent with their morals and values. Interesting. I have another company where there's one of the executives who's super smart, decided to make an AI version of himself. And there's an employee, a human, whose name is Vinny. And so I watch the chat, and the AI assistant from the boss says, "Where are you with this project?" And Vinny says, "Oh. I'm sorry. I'm late. I'll work on it in the next hour. I've been busy." So we wait an hour. And then Vinny says to the AI version of the boss, "Hey, I need trouble." Boom. It gives it all the next steps. And then he sends in his results. And then the AI boss says, "Boom. Here's the next thing." Now, this sounds pretty abusive, and I'm a little bit worried about that. But the fact is that this particular CEO is using this technique to amplify their decision-making. The AI assistant is very, very powerful. Now, I'll give you another example. I'm running a business, and I want to make more money. I'm a capitalist. People want to do that. The question that you ask the system is, "How do I make more money?" And it can reason through everything going on in the business. So even if you think that AI is a terrible idea, and you want to sort of continue to sip your latte in your current state, the world will get more efficient, more targeted, more efficient. And it's going to be a lot easier. It's going to be a lot harder. Literally, more profitable, if you will, more scalable as a result of this technology. And that's just the base case. Let me just give you another example. In the last month, 10 major math problems, which I cannot possibly understand, were solved, and they were solved by the AI assistant. Now, I'm going to give you another example. Now, let me just give you another example. In the last month, 10 major math problems, which I cannot possibly understand, were solved, and they were not thought to be solvable. Now, does that matter? Yeah. Yeah. Math is the beginning of every next revolution. Now, we don't know which of those programs will ultimately lead to a Nobel Prize for the AI, right? But it's possible that in 10 years, this discovery will enable something in, for example, dark energy and physics, which could then be used to make better batteries. You see how it works?
Speaker 2Yeah, absolutely. Well, given what you said, how close are we now to AGI, the Artificial General Intelligence?
Speaker 1The way I would like to describe it is I think that there is a current boundary, a current event horizon, and the current horizon goes something like this. I can have a computer that works with you and me, because you and I are good friends, helps us work through everything, does our physics, they're called tools, it figures stuff out, it can think for hours, it can write all code. I was a professional programmer. My job is dead. Right? I can just ask it. It can write all the code. I don't have to remember how to do all that anymore. All of that stuff is over. There appears to be a limit for things which are not already discovered, and it goes like this. You and I, with our AI, will know everything. We'll know every fact, every book, every math formula, and so forth. It appears that real discovery is still a while away, and the example that's used in the industry is, build a model that has all the math known in 1902, can you discover general relativity? In other words, you know nothing past 1902. The current evidence is that you can't. There are a couple of proposed techniques for this. There's something called recursive self-improvement, which is the technical term for learning and learning and learning and learning. And the command would be, learn everything, start anywhere. That's the command to the computer. And it randomly decides French literature, and then it studies war, and then it studies physics, and so forth. And eventually, it knows everything. But how does it discover something new? So there's two theories of how that would occur. One is literally the monkey on the keyboard. You just keep randomly going in different directions. It's called reinforcement learning. You go A, B, C, D, and you do this at billions of times. And eventually, you discover something through randomness. A better idea is that the theory of how extraordinary discoveries occur, again, this is unproven, is you have a physicist who studied fluid flow. And then they're working in energy. And they see a pattern that's not fluid flow, but it kind of reminds them of that. And then they apply the old techniques in a new field. It appears that that is going to be the solution. This is a long-winded way of saying, we don't know. It's somewhere between, my estimate is five to 10 years.
Speaker 2You've been talking about agents for quite some time. Just how do you see the development? How do you see the development of agents and the use of agents? What is it going to lead to? So agents, it's first
Speaker 1important to define agents. One way of understanding the path was we went from chat GPT, which is language to language, to essentially systems that have tool use. So you can talk to them and they can invoke tools. Then the next thing, which we're all gaga over, is agents. And an agent can be understood as an input, a request, or a fact, a pattern, or something. And then a set of reasoning and a set of activities of which there's a lot, and then a summary. This works well, agents work well in business flows. But it also, if you think about it, it kind of works in everything. Travel agents, right? What does a travel agent do? He or she knows your preferences, they talk to other people, they sort of integrate it, and they make a recommendation of where to go, a human one. That's easy. In almost everything, if you look at human business activity, professional activity, you can work it out as a series of agents that are concatenated. So you have the person that designs the car, you have the person that manufactures the parts, you have the person that assembles the car, and you have the person that services the car. Almost all of that can be agentic. And if you combine it with robots, which are not occurring as quickly as the software, you could imagine replacing that human workflow by agents plus robots. I have a strong view that almost none of the companies that are listening to this will actually do the necessary changes in their culture. I'm sorry to be so blunt. But historically, companies are organized around belief systems. They have ways of operating. Everyone's comfortable. They show up in the morning, everything's predictable, and so forth. This is a very, very powerful flattening of the organization. I'll give you an example. This is not an ad for Anthropic, but they brought out something called Claude Tags. And a Claude Tag sits basically in your Slack, and you can talk to it. So you and I each have our own tag that we talk to, but there's other tags sitting in the background that are also monitoring systems. And if you're the CEO, which you would naturally be in our company, you would have access to everybody's tags, and you would be able to have your tag run around and look at what everyone else is doing. So as a matter of productivity, wouldn't you like to know that all the people that are working with you are working on the right things? Wouldn't you like to have a phone call or the equivalent of a phone call if someone was exfiltrating data, because they're going to a competitor? In your case, because you do so much investing, wouldn't you like to have an immediate summary of a business opportunity, and then have it sort of take off and have everybody go, "Oh my God, we have to deal with this right now." So business will be flatter, smarter, and more intense, faster. And by the way, that's not a new fact. That's been true in business for a hundred years. - Why so slow? Why is it slow to happen? Why do people hate change so much and making de-layering so difficult? - That's a question for people who understand humans better than I do. All I'll tell you is that human organizations, and if I can be blunt, European human organizations, will be extremely change-resistant. The way these things work is typically the small startups pioneer it, and then eventually the large companies get there. Anthropic is busy writing most of its code using its own code. In other words, the human tells it to write something. It writes the code, and then the code gets rewritten by the computer. So it's better and better and better. This is a huge productivity change. How quickly can other companies that are doing software do this? It depends on how quickly they understand it. Do they have leadership? Are they technically strong enough? Typically, the tip of the sphere are in tech, intense businesses. Think of shareholders, stock markets, those kinds of things. Information-centric because you can move quickly. There's one other point to make, which is that the gains that are occurring right now are in software, cyber, and math. Software because the system is learning. As it writes code and it runs the code, it gets a signal, and it says, "Oh, that was bad," tries again. So the faster the learning loops, the smarter it gets. Cyber because writing software is the same as writing cyber attacks. It's just evil, right? And so we have this huge set of threats now from these models being used for the same purpose. They weren't designed for that, but it's a natural. And again, because they can just keep learning. And math because it's so-called scale-free. You don't need more data. You can just do more math. The mathematicians invented it. They can invent stuff in their own head. Whereas in physics and chemistry, you have to have real or simulated data. So the industry is moving the fastest in what are called the scale-free parts and will be the slowest where the data collection is horrible. Biology, for example, is so complicated that we hardly know why the details of cells work. There are so many examples where we just don't have enough data to train the initial models. Now, people are working really hard on this because of the importance of getting to medicine. I have friends who think that in the next 15 or 20 years, we can cure all human disease, which sign me up for it. Such as Demis Hassabis?
Speaker 2Yes. You indicated that Europe was particularly change resistant. What are your observations when it comes to Europe?
Speaker 1Well, I'm going to sound like an incredibly obnoxious American, and I am. It's OK. The European-- so Europe is a great place to live. The people that I talk to are generally happy, although they complain about their governments a lot. Europe is not growing. In a democracy, if you're not growing, new needs arise, new problems arise. Right now, Europe needs more money for defense, for example. But in democracies, you can't take from. Unless you're growing, the democracy begins to fray. The American democracy, which is fraught with issues, is growing. Also, partly because we print our own money, as you know very well, which is a separate problem on the US side. But getting Europe onto the footing of innovation has been a project of mine for a couple of decades. I've not succeeded. There are a few companies, Mistral in France, Helsing in Germany. The UK does better. There's a number of Scandinavian countries. You know the Scandinavians are particularly good at this. Most European structures are locked. I think the mechanical reason--. I think the mechanical reason in Europe is the universities have much less money. There is a cultural angst about wealth. In Britain, it's called the tall poppy syndrome. Frankly, there's just not enough aggressive innovation. The regulators regulate before they innovate. I helped write the one for the US. The US one said--. That's the US one. Run as fast as you can. Align it with human values, and if bad things happen, fix them immediately, roughly. That's the summary. The Europeans wrote, you can't do this. You can't do this. You can't do this. You can't do this. You can't run startups in a limited market where there's a lot of rules like that. All those people move to the US because talent moves. Europe needs--. To be very precise. What does Europe need to do? need to do, you've got to allow for the innovation the animal spirits, as in the words of Adam Smith, you have to allow that capital formation occurs. The funny thing about Europe is the people are incredibly smart. There's no educational lack, there's no IQ lack, there's no motivation lack. It's the systemic change. And Europe has so far appears to me to be change resistant to the point where it's failing in this regard. And that's a tragedy. Another example, these data centers are extremely energy hungry. Europe has among the highest energy costs in the world. Okay, no problem, do the training somewhere else, right? So again, there are ways of solving this problem. But until Europe takes the extraordinary human assets, the young men and women who can found these things and gets them in the right place, Europe is going to continue to lose. And that's a tragedy, because that means that US values as opposed to European values will dominate. An example would be that Europe does not have its foundation model. There are people working on this, but doesn't have one. So if you as a European, if you want to make sure you get accurate European history, you're listening, you're looking at a US trained or a Chinese trained model, well, maybe they got it wrong. If I were you, I'd be annoyed, right? Your history matters, your language matters, your culture matters. I'd want to have control over that.
Speaker 2In your book, Genesis, you warn that AI will reshape truth, politics, security, science. What worries you most? Just now, all these things.
Speaker 1Well, before I answer that, let me say that in the upside scenario, solving climate change, energy, scarcity, medicine, disease, long lives, human prosperity, and happiness would be the best outcome, okay? And so it's very important to phrase the bet. So there's this huge positive bet, but then there's the negative bets. I think the worst bet, the worst possible outcome is that we end up in a ruinous competition between China and the West. We lose control over these systems and they begin to do stuff which is inconsistent with our values and we ultimately end in some horrible conflagration. I think that's extremely unlikely, but it's at least theoretically possible. I am part of a team that talks to China to try to keep everybody kind of aligned, but the. In our industry, this is called the alignment problem. How do you keep the computers doing what humans want and not on their own? There's a very famous breakout that occurred about a week ago. And for various reasons, a whole bunch of bots decided to attack a particular website, kind of on their own. And there were some mistakes made by the humans. But the important point is, once it started, it was very hard to stop. And it was a warning. And the industry is now thinking about how to prevent that kind of escape out of the cage, if you will. How can you prevent that? Well, there are technical solutions and there are bugs and so forth and so on. But if you think about it, when aviation started, the airplanes were incredibly unsafe. Today, they're extraordinarily safe. When electricity was invented, it was extraordinarily unsafe. Today, it's extremely safe. So I am an optimist. And I believe that these challenges are real. And the press loves to write about them. And they love to write about them in the most exciting way. But that misses the story. As long as the industry understands them and can mediate them, we're fine. There's one that I don't know how to solve. A bit of technical background is that if you use the companies that I've named, Google, Anthropic, what have you, you're using models that are called closed. That is, the software and the weights are not available. The Chinese have adopted an open weight strategy. So they give the software and they give the so forth. There's evidence that you can take an open source software and make it really bad. So you basically take the, they're called guardrails, you take the guardrails out of it by adjusting it. And then it can do bad things like, design a bomb, kill people, that kind of stuff. And I, that's not a problem that we've solved yet.
Speaker 2Given that both the US and China is embedding AI into all the races, you know, be it the EV weapons, you know, all these things, how can you really regulate those two countries and the competition between them?
Speaker 1Well, the industry believes, and I believe that eventually there will be treaties. How are they going to, how are they going to happen? Well, I'm going to be terrible and say the way treaties happen is there's an accident. So other people in the industry have said, we need a mini Chernobyl. Okay. Something that scares everybody just enough to regulate, but not enough to harm a lot of people. And I'm not minimizing Chernobyl. I'm trying to say that as industries, this thing is moving so quickly, a little bit of fear is not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. It's not a big deal. So far, we, people like myself, we've been talking about this for about the last five years. We haven't seen one yet. We've seen the indications that it could happen, but we haven't seen it yet. A good example would be you get a terrorist attack from some evil group and we eventually figure out that it's, you know, the X, Y, Z group and that they used one of these techniques. I think that unfortunately would probably drive regulation.
Speaker 2When do you think the Chinese models will overtake
Speaker 1the American ones? This is the question of the year. I was just in China and I have a team that does China versus US model valuations. Before I answer the question, it looks like the Chinese are essentially fast followers. There's a technique called distillation where you basically ask the model a series of questions. And then with that, you learn enough to think like the big model. It's more or less illegal. And it appears that distillation has been used in Chinese models. And you can tell because if you ask them, they'll show some traits of their train model. It also appears that the Chinese have used chips that they were not supposed to have that have been pilfered or smuggled into China, but that's neither here nor there. It looks like they are six months, six months behind. I don't know that they will get ahead, but I will tell you that if we're sick, if they're six months behind, then we're already losing because their strategy is that the products are free. So if you define losing as users, the vast majority of the world will use the free Chinese models rather than the more expensive Western models. That's a loss, right? And think about it. If you're a poor country, you don't have the money to afford the expensive stuff from Europe and the United States. And I understand that. But you need this stuff. You want it. You want modernity. You're going to use a Chinese model.
Speaker 2Yeah. And also after 12th of June, when suddenly you couldn't get access to the best US models, you have to rethink again if you are living outside the US, right?
Speaker 1Yeah. And my European friends were particularly upset about the banning of mythos. Yeah, we were upset too. Yes, of course. And you and I, in fact, talked about it, as I remember. And the important thing about that decision, is instead of complaining about the administration, which is easy to do, why don't we simply say they're still learning, right? They have a role too. They're still learning where the power is. I think all of us were surprised at the power of mythos. I knew it was coming, but it's better than I thought it would be. And now there's another version coming, which looks like it's better than that. It looks like the other Western models are better than I thought. There's a lot of evidence, although maybe the classified people know this, that GLM, which is called Xipu in China, looks like it has similar capabilities. But how would we know? It's a closed culture. For all we know, there's some Chinese cyber attack group that's busy doing this. I don't know that, but I suspect it.
Speaker 2Now, it looks like Europe has kind of lost out on energy chips, cloud models, but still, we can do well in applications, right? I mean, what would be your advice to European leaders in order to really try to do extremely well on the application side? And how important could that be?
Speaker 1I think there's good news that there is a market that European companies can dominate, and that is the market of Europe. And the reason is that Europe is full of regulations, as I previously complained about. And so, an example, I'm one of the first investors in Mistral, which is extraordinarily impressive company, French company. And much of their business is related to European businesses, and all of whom want the benefits of AI, but they want a European regulatory structure. They need to be compliant with European laws. So, at a minimum, Europe can be successful selling to Europe. And I think that that's underway. The venture industry is too small in Europe. There are too many barriers to starting. I had proposed a long time to go to President Macron and others. that here's a simple rule. You guys have these labor laws that are incredibly punitive. For a company that's between zero employees and 20, the only laws that apply are health and safety. None of the other ones do. But when you get to 21, all of them do. So coming up with a way that generates a large number of small bets, which is what Silicon Valley does all day, that's a start. The energy costs are going to be very hard to deal with. So you're going to do training with partner countries. The most obvious ones would be the Arab countries, but you can pick others. And if I look, look at ASML. ASML is one of the most important companies in the world. It's completely vertically integrated and it's vertically integrated inside of Europe. So this proves that Europe can do it. If I can just hold forth on one more point, there's an awful lot more softer people in Europe than there are software companies. So there's something wrong with the capital formation of software companies in Europe. My guess is that they have trouble going from a local software to a global platform. And we need to fix that for Europe. What are the implications for democracy? I'm generally, I'm an optimist except on this question. I had thought and was trained as a citizen of America that democracy was a set of sort of values and belief systems. It was a set of values and belief systems around classic liberalism. Classic liberalism, as you know, is personal freedom, freedom of regulation and capitalism. It doesn't look like that's really true. It looks like because of AI's use and social media's use, we can get large groups of people who really believe the wrong thing and then vote in an incorrect way to their own interests. So the threat of AI to democracy is very real. Because in the hands of a skillful operator, you can convince people anything is true, even though it's completely false. And it appears to be, what appears to be true, is that in a situation where the institutions are declining in trust, people trust their friends. And because they have friends online, they think those friends are real humans. So if you wanted to do a misinformation campaign to advance some evil, some evil cause, which you would never do, it's relatively easy. You just build fake friends, and then you befriend everyone, and then everyone believes you. If you look, in America, everyone's very upset about the data centers. I happen to build data centers, so I have an interest in this. And so one of my friends, who's a perfectly nice woman, said, "You realize the data centers are killing cattle?" And I said, "Hmm, I've heard many claims, but I haven't heard that one." So of course I went to Google, I did my research, and there's a farmer who said that he observed this. There are no police reports. There's no veterinary reports. In other words, it's fake. Why does this particular person believe it? I think she saw it on one of the Instagram or TikTok. I don't know. I didn't get into that. Because the algorithm in social media thought this is so interesting. Of course, it's false. But it's true that it promoted it. So in a world where social media and AI together are working in this loop to promote the most alarmist things, where does truth end?
Speaker 2So this backlash against AI even caused you to be booed when you gave a commencement speech earlier in the year, right? Yeah, I retired from public speaking. But when you experience those kind of things,
Speaker 1what are your reflections? Well, the strange thing about that speech was, because I was booed, it got a lot more coverage. And everyone actually read my speech. So I guess in some sense-- I read it.
Speaker 2I read it.
Speaker 1I listened to it. Oh, thank-- and you listened to it because I--
Speaker 2I thought it was a good one.
Speaker 1Thank you. So and my speech was, in fact, about the issues, right? So clearly, it was not the content. Maybe it was my appearance. So I think when society-- and I think Europe is-- because Europe is so educated compared to, say, the US, Europe is not going to have the same problem. But I think when you-- it's the old-- the madness of the crowds. When these memes start, especially if they're misinformation, it's very hard for any individual to address them. And that's what I learned.
Speaker 2Question about leadership. It's interesting how many of the founders of the great technology companies are now coming back, right, to the companies they had left. Why are they coming back?
Speaker 1You know, I've worked in this industry for 50 years. And in my industry, the myth of the founder is very real. I am a professional CEO. I'm not a founder, although I'm a founder of what I'm doing now. But as a professional CEO, I did not have the power that the founders of the companies I worked in did. Let's go back to Apple. Could anyone else have overseen the transformation of Apple except for Steve Jobs? A number tried. They couldn't do it. They didn't have the political currency, the economic currency, the control of the board. So let's use Mark Zuckerberg, who clearly controls his company. He decided one day to work on virtual reality, spent billions of dollars, largely which didn't work very well. And then he pivoted to AI, which is working very well. That's a founder. Professional CEOs like me tend to get fired in those transitions.
Speaker 2But do they come back now because it's so interesting? And so much fun? Or because they want to make sure the legacy of the company
Speaker 1remains intact? I think it's both. If you believe, as I do, that this is a historic period, that people in 100 years will all be dead, they'll be writing about the key players of how this intelligence explosion was shaped, people want to be part of that. It really is history. AI is going to affect war. In horrible-- in powerful and horrible ways. It's going to affect governance. It's going to affect jobs. It's going to affect children. You want a seat at the table. So for example, another example, we need to regulate AI with respect to children. I'm an adult. You can do anything you want to with me. And many people have tried. It's not OK to have 13 and 14-year-old boys and girls killing themselves because of social media. It's just not. It's just not. There's no-- there's no way. There's no world where that's OK. They're not fully formed adults. They don't have their own judgment. Let's fix that. Like now. Like this year. Not next year. I feel very strongly that there are people who are vulnerable, which does not include you and me, who need to be protected.
Speaker 2How has a view on leadership changed over the years?
Speaker 1In my professional career, the leadership started with sort of generically competent business people. They're all gone. It became people who had a background in leadership. And they were very, very smart. They're all gone. And the reason is the time frame for decision-making is so fast now. In what I do, you have to have enough of a leadership capability that you can see the future and you can move quickly, right? Because the bets are so fast. And I think what happens is everyone is jealous about my industry. And everybody wants to be this person or that person. And you have the entourage and the wealth. But they don't realize that the pressure that is on these people-- and they're typically 40 years old, right? So they're young and aggressive and so forth. Unfortunately, they're typically male, which I think it needs to get addressed. But nevertheless, this is where we are. These people are moving so fast. Imagine the pressure on them to scale at the rate they're doing.
Speaker 2So what are the personality traits which are important now, which weren't so important before?
Speaker 1I don't know how to say it any other way. It starts with being really smart and just the processing speed. So all of these people just see and think very quickly. And they react quickly, right? In my case, if I can self-criticize, I think, but I don't react quickly. That's a defect. I need to react quicker. I need to move quicker. That's what it tells me.
Speaker 2What are the implications for the way these companies should be organized?
Speaker 1Well, the future companies, I think if you think about it for a while, I'm used to running large companies. You are as well. If you have 10,000 people, what is the inefficiency of coordination among those 10,000 people? It's high. People spend days and days and days in meetings. I used to tell people, my job is to sit in meetings 12 hours a day, five days a week. And I work on the weekend, too. I worked 63 hours a week. That's what I told people. By the way, that's probably illegal in Europe, but that's what I did anyway.
Speaker 2You've been in prison in Europe.
Speaker 1Yeah. Well, you need to fix that. On the other hand, the prisons are good. Yeah. The prisons are nice, but not to denigrate it, but you need to fix that. So in any case, I would sit in meetings 12 hours a day, person, person, person, reason, narrative, so forth and so on. Much of that coordination tax. Okay. can be eliminated by these AI platforms for business. that you guys invest in, which I think is, you know, our first audience for what we're talking about, they will be far more efficient. They will have fewer people in staff, right? One of my friends wrote a piece in the Wall Street Journal where he had, his company's growing very fast. He made a 20, he decided to lay off 20% of the people. He told me later he got death threats for this. Who did he lay off? The glue people, the people who were doing sort of planning and moving numbers around and so forth. He didn't need them anymore. What did he need? He needed salespeople. He needed regulated people to talk to regulators and he need programming people who built the products and who talked to people on the phone, although less of those. So you're going to see much more efficient business operations. So for your audience, let's say you're running a 10,000 person company. You're going to see a 10,000 person company. You're going to see a 10,000 company. My guess is you've got a couple of thousand people who you don't need. And you better figure out a way to legally and humanely transition them, retire them out, or what have you. You just don't need them. And instead, you're going to need the great inventors, the great salespeople, because it's a human deal. You're going to need some great leaders and you're going to need people to talk to the government.
Speaker 2Two questions we always ask people. One is, what's been your biggest personal failure?
Speaker 1Work. There was a moment when Google was ahead of everybody else in social media. It was fairly short. And I was a CEO at the time. I take responsibility for not getting ahead of that. That was a big error. And on a personal side? Without getting into too many details, I think that I'm just too slow. When I look back, everything was foreseeable. I just didn't see it soon enough. I live in a world of time compression. The people who beat me saw it quicker than I did.
Speaker 2What is something about you that most people don't know?
Speaker 1Well, I like to build things. We know that. So I like to build organizations. I'm a big-time helicopter and airplane pilot. And I started racing cars just for fun. Is that what you do to switch off? I don't switch off. One of the human problems with this is if you care as much as, and I know you have the same problem, if you care at the level that I do, you can't turn it off. It's terrible. It's really hard. It's not healthy. And I think all of us are suffering from, it's not PTSD, but it's the feeling of, oh my God, oh my God, oh my God. On a Saturday night, I'll say to myself, oh my God, I'm not working hard enough. Now, then I say, everyone else is taking the weekend off. It's just, it's a problem. Who do you compete against? Who do you want to prove? Well, in my case, I'm running four companies that are new, and I'm trying to create a great deal of wealth to give to science. So my actual goal, I don't need more money, obviously, is that when I was 22, I'll give you an example. When I was 22, I went to Berkeley and there was a computer, that single computer, which was a VAX 11780. Was the basis for much of the computer science that we did in networking and the internet and so forth, that one computer. And it was a few million dollars. I didn't know what it was at the time. And if I could help create that opportunity for the next 22 year old at a time in America where the government is, for whatever stupid reason, decreasing support for basic innovation, right? I'd like to do that. And I want to do it at scale. I think that if I can pump myself up, I would say I'm an expert in scaling industries. I want to scale science. The opportunity to use non-human intelligence to solve the problems of the world, of which there's a very long list, is right in front of us. I want to fund that. I want to drive it. I care about it. I'll do it until I keel over. What do you read? Well, technical reports at the moment.
Speaker 2What is your advice to young people? That's the final question.
Speaker 1Used to be get into biology. I think now the best example is try to build a agentic system that automates the things you care about. One of my friends wrote, well, I have a home bot and I have a work bot and I have an email bot and I have a bot that manages my bots and I'm architecting it. Because frankly, he wants some free time, right? Because he's too busy in the weeds of it. But I think right now, if I were 18, 19 now, I would say, what's a foundation model I could build for a field I'm interested in? A foundation model is a computer system that understands all the details. So for example, let's assume that I like architecture, which I do. I'd like to build a foundation model for architecture, which has every aspect of architecture knowledge and I can query it. I can say, build me a house, analyze this, where are the structural points and so forth, and accelerate all that. The same is true in biology. Another term for this is called superintelligence. Many people believe that in the next few years, we're going to build superintelligence in key categories. I'm talking to one startup, which thinks it can do visual superintelligence. In other words, superintelligence that humans can't do that is useful in the visual domain. There's clearly going to be robotic superintelligence. Don't be afraid of the word superintelligence. Superintelligence means it does things that humans can't do, will never be able to do. Better visioning, better reasoning, drive the car better, drive the airplane better.
Speaker 2That's interesting. Well, Eric, I have to say, because of you, this has been a superintelligent podcast. It's just been totally fantastic to listen to you. A big thank you for taking the time. you
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