Eric Schmidt scores US vs. China: Who’s winning the AI race? | Masters of Scale Summit 2026

What really did me in was when I started having it do long chain of thought, deep, deep reasoning, and I went, "Oh my god, it has arrived." Now, I don't know that I understood exactly what it was, but I knew it had arrived. It's transformative because once your mindset is in that, then you'll have another idea and another idea and another idea. That's why this is such a precious moment to be alive.

There he is. Hi, Bob. Nice to see you.

And it's good to be back.

The biggest change in the last year that's notable for everyone here is that the cost of software has gone from very expensive to almost nothing. And when something falls precipitously in price, you get an awful lot more of it. So when I think about the next few years, all I think about is an enormous amount of software and what will it do, who will it do it, how will it work together, how will agents work, and so forth.

China thinks the same way and my own view is that the China-US competition is the defining competition of your generation, my generation, of our age. And the reason is that both are trying very much to develop reasoning and intelligence systems that are superhuman. And that doesn't mean like us. It means that they have superhuman or super intelligence capabilities that are beyond human capabilities already. When you look at the Waymos that are running outside, they all have better vision than human drivers, especially because they don't drink, you know, you sort of get the idea, right? So, in the race, I was part of the group that put the restrictions on China some years ago of chips and I assumed that they would work for about 10 years. A year ago or two years ago, it looked like China was a year or two behind. Now, it looks like they're six months behind. And there are many, many studies which show this. And how have they done this? After all, didn't we restrict their access to chips? We did, but they're so smart. They figured out a way to do the kind of algorithms that we develop and knowledge that we developed with essentially one-tenth of the energy and some factors less of performance. Now, there's a huge debate in the industry as to whether that gap will get slower or bigger, but at the moment, it's fair to say that it's a two-horse race. And that to me is the defining one. The race for intelligence is the race of literally a millennium because once these intelligences exist, our children, grandchildren, great children, and so forth and so on, we'll all be affected in really fundamental ways by the things that are occurring right here, right now in the Bay Area.

People make up all sorts of stuff. [laughter] And you know the question, so I've been heavily involved with the China-US discussions. Dr. Kissinger and I set them up before he died in his last visit to see President Xi, which I did with him. And the Chinese understand what the race is about. They understand the dangers of AI. And they're not stopping. So, one way to answer your questions is to say, how would a slowdown work?

Okay. So, how many frontier labs are there in America? Five, six, seven, some number like that. How many are there in China? Five, six, seven. So you have a 12-body problem. On the Chinese side, presumably the government can order them to do what they want. In America, you'd have to have some kind of voluntary agreement. So now it's a two-body problem. How are we going to convince the Chinese to slow down given they already think that they're behind? Not happening. We have to come up with other ways to ensure that this technology is safe while advancing it ahead of them.

Well, I'll give you a simple framing. China has infinite energy, most of it renewable, something we said we were going to do in our country 10 years ago and we failed to do. They don't have interconnection queue weights that go on seven years. They don't have the kind of debates. They're building the data centers and they're building them massively. Furthermore, even with a much more restricted financial market, the Chinese have been able to fund the startups. DeepSeek is now seeking funding. It's illegal for us to invest in DeepSeek. I know because I tried. [laughter] And you know, so trust me, it's illegal. And so US law, I might add. So DeepSeek just did a huge raise. The funding looks like it's going to show up. Now, in China, you never know if it's really the government or really the private sector.

There's a complex question involving open source, which we can talk about. But fundamentally, they're on their way. They just don't have the chips. In the United States, we've got everything we need. We've got the smart people. We've got all the controversy. We've got incredibly deep funding markets. Where in the world could you get $50 billion on a text message? I mean, it just doesn't exist except thank God that the American financial industry is financing our hobbies here. You know, we'll see. What we don't have is the energy and we don't have the data centers. And the correct answer for this is build your own power systems. One of the great things about America, there's an old quote from de Tocqueville about America. This is 250 years ago. America is a great country because of the optimism of its people, the abundance of the land, and the absence of a king 250 years ago. And at least you can score the three. We've got a lot of land [laughter] and so lots of empty land. Put these data centers in the empty land. It's good for the local economy and so forth and build the power plants yourself. And that's what we have to do. Current estimate is we need somewhere between 60 and 80 gigawatts of power to get through 2030-2031. We'll see if we can get that financed. One gigawatt is about $50 billion in cost. By the way, one gigawatt is about one nuclear power plant. The last time I checked, we weren't building them at a very high rate.

So let's define terms a little bit. There's something called open source. I was part of the beginning of the open source movement back when I was at Berkeley and very proud of that. It was a long time ago. And my code is still open source and still available for you if you want to look at very old code. And then there's something called open weights where the training benefits and you know you spend all this money then you take the numbers of which there's many, right, trillions, trillions of numbers and then you make those available. China's strategy is largely open source and open weights. And America's strategy is largely closed source, closed weights. On the American side, it makes perfect sense. These things cost $100 million to $500 million for one model. Right.

Well, this is what the lawyers say. Now, I spent 50 years arguing with lawyers that you should give things away because it creates a platform. So the open source movement basically is create a platform that people build on top of it. The closed source movement exemplified by Microsoft and basically Bill and Steve way back when. Closed source focus on the end user do a good job or not and so forth and that was the test. Open source has largely won in America as the foundation for what we work on. But the large models are largely closed for good reasons. I'm not criticizing them. There are a couple of examples. One is called Reflection AI that's just released a 500 billion parameter model. Neotron, which is the Nvidia product, has a similar size model. They're both very powerful. Google has something called Gemma 4 which is also very powerful, a smaller model. You can download these things. So it's not completely China versus US. China chose open source open weight because it was the most reasonable strategy for them. They were behind. They needed to get users quickly and they're willing to take the thieving risk if you will and it looks like it's working because at least they're getting funded. So the contest of open source open weights versus closed source closed weights is the defining question. If you talk to the people here in San Francisco who are in closed source closed weight companies, they'll all explain to you that their strategy is better because they'll stay ahead. If you talk to the Chinese people, they'll say their strategy is better because it's going to stay ahead. So we have a real competition here.

There are some issues and I want to highlight one that I believe is today unsolvable and we spent a lot of time working on it. If you think about it, the closed source weights have the property that it's controlled by a company. The company is controlled by the laws of that country. The CEO is subject to the law. In China, they'll just arrest you, you know, whatever. So there's real control over what those labs are doing. If you release an open source weights model, open weights open source model such as a Chinese model, you put what are called guard rails in it and there's a technique called obliteration which allows you to basically remove those restraints and then you get the raw model.

This is a problem because those raw models can be used for cyber attacks and it looks like remember what I said about the beginning, the cost of software coming to zero. The rate at which software capabilities are expanding in the next few months is so extraordinary that the technical people here in San Francisco and elsewhere are really worried. This is the source of this hey can we slow down kind of optimism which is very difficult to do. So I don't fully understand how we're going to navigate through this open source open weight stuff when it gets into the hands of terrorists.

You can be sure China does not want this stuff to be misused against themselves. After all, they're a one party state and if they're going to attack the US, they won't do it through that. They'll do it through their own active cyber war. And the same thing, US versus China. Although I'm not suggesting that we should do that. This is a hard problem and many people believe that there will be some kind of a cyber event in the next 12 months that will illustrate this point.

Well, I think American CEOs are patriotic to a point. They're also very focused on money.

Welcome to capitalism. And there are many examples where people are using small servers with open source weights to solve some problem because the token cost is lower. The token costs are about three to five times cheaper if you run your own and so forth. They're not as functional. So one model for what's going to happen which worries everything, worries me, right, is that the frontier models will dominate the things that we care about here. These incredible achievements in math and chemistry and so forth. I mean you know we have Armageddon for mathematicians, you know it was like all of a sudden their whole world which has not changed in 50 years since you know Gödel's hypothesis basically like everything was overthrown in one month, right? They have an existential crisis that's going to occur because of the frontier models. For most of us we don't need that kind of math to solve the problem of financial accounting, it just isn't that complicated, trust me. And so the idea is that many people will use these cheaper open weights models for little things. Is that okay? It's likely to be legal in the US. Is it a good idea? Depends on what your view of Chinese threats and espionage threats and so forth and so on is. In China, of course, they'll use the open weight models.

Okay.

The US.

The US. The US by all the benchmarks is ahead.

And one of the ways to understand it is that every lab has the one that they've released and then a next one. The indications from the labs are that the next ones are incredibly performant, that we're still in the scaling law, and there's amazing things coming. US.

China, because as I mentioned, the token cost is at least three times cheaper. In many ways, that's actually because they were forced to be cheaper. We didn't give them the powerful chips and so they found algorithmic improvements, super supervised fine-tuning, other kinds of things that are very, very impressive. And the interesting thing about their inventions is the moment the Chinese invent something in open source, we copy it in America. Thank you, China. [laughter]

I'm supposed to answer.

Okay. [laughter] You are correct.