Transcription
When people see that, they may take up arms against it because it'll be so frightening to humans. Now, I think this will occur within a decade.
So, Eric Schmidt just gave another controversial interview. In his hourlong interview, he warned that AGI is arriving very soon and it's going to change everything for humans. He warned that most humans will lose their jobs within the next 3 to 6 years. Let's listen.
I'm actually quite convinced that this is the beginning of a new epic. Henry, who was my best friend and we miss very dearly, compared it to the enlightenment that during the enlightenment we learned how to go, we as humans learned from going from direct face faith in God to using our reasoning skills. So now we have the arrival of a new nonhuman intelligence which is likely to have better reasoning skills than humans can have generally known as AGI and essentially super intelligence. This is going to happen really really fast and I think people are not prepared for it.
So my when I talk to governments what I tell them is one this is not chachi is great but that was two years ago everything's changed again. Second, you're not prepared for it. And two, you better get organized around it. The good and the bad.
>> Okay. So, in the following part, Dr. Schmidt gives a timeline for AGI's arrival. Pretty interesting, actually.
>> There's a group of people that I work with. They're they are all in San Francisco and they've all basically convinced themselves that in the next two to four years, the average is three years, the entire world will change. And here's roughly how they make their argument. And I I call it a consensus because a consensus is it's a it's true that it's a consensus but it's not necessarily true that the consensus is true. Right? So you have to follow that reasoning.
So uh the scenario goes like this. First you have language to language LLM's chachi is the best one. They did a great job. Uh the other folks get started and they're catching up in various forms in that department. Meanwhile you have the development of reasoning. uh the the the adoption of memory inside these systems. And so the agentic revolution which everybody understands here what that means because many of you are in the middle of the agentic revolution can be understood as language in memory and language language out.
So typical example is I'll use an example uh I have a house in California. I want to build another one. I have an agent that finds the the lot. I have another agent that is that works on what the rules are. another agent that works on designing the building, another selects the the contractor, and at least in America, you have an agent that then sues the contractor when the house doesn't work. Right? That's sort of how. And why would I give you such a stupid example? I just gave you a workflow example that's true of every business, every government, and every group human activity.
So when you understand that the agentic revolution and the reasoning revolution together really changed the way we operate as humans, then you understand why the San Francisco consensus is so powerful. Um the reasoning revolution goes something like this. Take a look at the the uh I'll use 03 from Chat GBD4. Uh there are others as well. Watch it go forward and backward, forward and backward and it's reasoning and it will blow your mind away. Especially when you ask it a question that you have no idea what the question is about and you watch it reason.
Uh in the Google case, Google now has a math model that is at the 90 percentile of math graduate students. If you look at the other ranking systems, it's fair to say that we now have systems now that are 90% of all of the high order uh graduate school skills, you know, math, physics, and so forth and so on. That tells you that when this thing happens, it will happen at a big scale.
So the consensus is that this marches forward and that there's a moment when what is called recursive self-improvement the system begins to learn on itself where it goes forward at a rate that is impossible for us to understand. It becomes combinatorial in a way that we as humans do not understand cominatorics. This is both incredibly exciting and also very worrisome. And by the way the consensus is three years. My own view is at six, but who knows?
>> Okay, so in the upcoming part, Eric is going to talk about the difference between AGI and ASI and how big ASI is going to be. Watch this.
>> The shortterm answer is there are people who are now looking at how do you control a super intelligence with a dumb intelligence, right? And there's math that I did not understand which says that to some degree you can contain it, but we don't know. Um, but I think this the first point to make is that recursive self-improvement or recursive intelligence is where the system is learning itself and you're beginning to see agentic solutions that are doing self-learning.
Um, I expect by the end of this year, so 6 months, you'll you'll start using systems that are learning as you're using them. It's the natural progression. One of the reasons to think that the hardware investments that you're seeing, which are insanely large, make sense, is that that those reasoning models are infinitely more expensive than a Google search, many many thousands of times more electricity queries and so forth and so on. And so you the recursive stuff has already started.
The general consensus there are different definitions of AGI is that AGI means it's generally intelligent. Now generally intelligent means that you get up there in the morning and you have free will. Uh I think largely invented here in France and you have free will and you basically have this notion of I get up I do what I want I learn what I want I seek what I want. That kind of strategic intelligence is not yet present in these systems. The consensus is that occurs my own dates are between you know four to six years relatively soon.
The step after that is when the system, the general term there is super intelligence. It's where the system is smarter than the sum of all humans. And the test for for super intelligence is pretty simple. It can prove something that we know to be true, but we cannot understand the proof. We humans, no human can understand it. Even all of us together cannot understand it, but we know it's true. Henry would say, "What is that? Is that magic?" He also would say, and we said in the book, that when people see that, they may take up arms against it because it'll be so frightening to humans. Now, I think this will occur within a decade.
Whoa. So, Eric Schmidt actually thinks that ASI will occur within the next 10 years. That's crazy considering the fact that ASI will be smarter than the sum of all humans. A number of us wrote a a paper on super intelligence where we speculated how do you contain super intelligence? I do a lot of national security work and how do you deal with the fact that your competitor might be ahead of you? Well, in network effect businesses, which is what I've lived in for my whole life, didn't have a name when I started, but there understand that all the most valuable companies in the world are essentially network effect businesses because network effects work.
What happens when you've got two countries where one is ahead of the other and I'll give you a simple example. This is you and me. We have our little tech startup. We have a thousand people so forth. And then one day you and I decide that we're going to have AI researchers. That is computers doing AI research. And how many do we have? Well, we have a million because we we have enough money. And all they need is electricity. You know, they don't need pizza. They don't sleep. Whatever. So now your slope of innovation goes like that. this in a network effect business. This is likely to produce slopes of gains at this level. The opponent may decide that once you get there, they'll never catch up and then you can do really bad things to them. Then you have an issue essentially a race condition of preeemption.
So these are just the beginnings of these ideas. But if you believe that AI is this powerful, then every aspect of human experience will be touched by it, including national security, politicians, democracies, and so forth. I spend lots of time in conversations with people who say, will democracy uh survive AGI, right? These are really the core questions about humanity that are being raised by the arrival of this intelligence. Pretty wild interview so far.
So, in the upcoming part, Dr. Schmidt talks about the computational limits of building modern AI.
So if if you ask most of the executives in the industry, they will say the following. They'll say that we're in a period of overbuilding. They'll say that there will be over capacity in two or three years. And when you ask them, they'll say, "But I'll be fine and the other guys are going to lose all their money." So that's a classic bubble, right? What would mean what would it be? What would need to be true for that not to be a bubble? If you believe the San Francisco consensus and you believe that reasoning, which is done through inference and through these back and forth um essentially reinforcement learning chains, if you believe that those are going to be the defining aspects of humanity, then it's underhyped and we need even more. I know that sounds insane. Right. But that's the basis of the reasoning.
I personally don't know. Um I've looked at this pretty hard because you have these massive data centers and Nvidia is quite happy to sell them all the chips, you know, and uh the real estate developers are used to borrowing money to build these things. I've never seen a situation where hardware capacity was not taken up by software. There was an old saying when I was young that was grove giveth and gates taketh away, right? Referring to Intel. So you the old-timers will remember that the Intel chips would get better, but your computer never got faster. And we used to say, why does it not get faster? Well, because in this case, Microsoft would add all these extra features. So I think it's it's unlikely based on my experience that this is a bubble. It's much more likely that you're seeing an whole new industrial structure.
So one way to think about this is the discovery of LLMs and foundation models which we're properly going to give credit to the team at OpenAI at the time is a historically important discovery. it began right to give them some credit although in fairness quite a few of those people used to work for me uh we won't bring that up but we'll give them credit that historical event is a very big deal right and I say that with great respect for what they in particular Sam and Daario and and uh Meera and the team did
>> so in the upcoming part Eric Schmidt calls AI a scalefree field meaning there's no limit to how much AI can advance
>> So when you build these systems, you're limited by data, right? And we've sucked up all the data, right? That's what everybody thinks. It's not really true. The way these systems will scale is if they have the the fastest scaling things will be called scale-free. So what's an example of a scale-free field? Math. Right? As you know, mathematicians with the whiteboards or the chalkboards or whatever, they just make stuff up all day. None of which is comprehensible to me. Uh, and they do it over and over again. And they're perfectly happy inventing completely new structures which have no basis in fact because that's the beauty of mathematics and we know how powerful mathematics is.
So it makes perfect sense that using a protocol called lean, which is an interchange protocol for defining proofs, you can have one system that generates conjectures and another system that proves it and that's a scale-free solution software. And by the way, notice that the number of verbs in math, literally the number of tokens is much more limited than in language. And that's important for serving and performance architectures, which we can discuss later. Uh let's look at software. What do software programmers do? Um, they write code that looks an awful lot like everybody else's. So, you have this explosion. You mentioned cursor. There are competitors as well. Um, anthropic of course on its own is is probably the underlying technical leader in that space with the others competing. The competition is healthy. Um, software can be understood as eventually software people will say what they want and the computer will write the code. And as a person who spent my undergraduate and graduate and my PhDs on language design and computer OS architectures to see my entire validation be destroyed in my lifetime is a little concerning. I was talking to one person I said what language do you program in? And he goes why does it matter? It's a new world.
So software and math remember and software is the twin to cyber. So it will also on the negative side be possible to generate an enormous number of cyber attacks because you can generate the software you can generate the attacks against the software because you can you can do it repetitively until you find the buffer overflows and so on and so on. So the combination of all of that is amazing right those two scale free will be followed by things which need data biology for example we don't have enough biology data chemistry we don't have enough chemistry data physics we don't have enough physics data but it's coming and that's the basis for this power explosion and infrastructure that's how we solve climate change that's how we you know we we do these extraordinary things in medicine all because of that.
>> I think you once said silicon is strategy. How does the cheap race um change global AI power dynamics and how China is catching up?
>> Well, it's very interesting. Um I was in a meeting earlier with with some folks who are leading open source here. Uh it's called commit and they're here at the conference and they were telling me that on a math basis there's more open source in Europe than there is in the US. So that was really interesting because I'm historically an open source person. The structure that's emerging is very strange in the United States because of the enormous amount of capital that companies are not typically doing open source. They're buying these incredible data centers offering these incredibly powerful services. And in China they're doing the exact opposite with Deep Seek and others. They're doing uh essentially open source open weights and presumably the Chinese government is funding it.
Now what's the consequence of that? It's very interesting. If if you have two models, open source and closed source, the open source will get much more adoption, especially in all the countries that we don't go to. There's like a hundred countries that you never visit. Um they don't have enough economics. They have their own problems and so forth. So we can end up in a situation where America and the west are ahead but the majority of the AI use is actually not from US western models but rather from open source Chinese models. I don't think people have quite figured out the geopolitical issues that are part of that. Maybe the last one before an amazing founder will join us on stage.
So you led Google during the mobile shifts. What mistakes uh were made during the mobile wave that companies should today take into consideration and try to avoid in their new road map.
>> I think that I can tell you as a CEO every mistake I made was fundamentally one of time. When I look back at what we did at Google, we obviously did very well. Android has 90% market share, but we didn't move fast enough. We didn't take it to its logical extreme. Um, and I think other companies, Uber being an obvious example, combined the GPS and so forth and make it happen. If you look at WhatsApp, um, as a sort of communications mechanism, I did not fully understand that your phone number would become your your real unique ID in the world. It's now obvious, but it wasn't obvious to me at the time. But in each case, it was fundamentally an error of time. My message to you is if you're going to do what I'm the areas that we're discussing, do it now and move very very fast. This market has so many players. There's so much money at stake that you will be bypassed if you spend too much time worrying about anything other than building incredible products. Thank you, Eric. Thank you all.