# U.S. China AI Race Strategic Scorecard

**Podcast:** Masters of Scale
**Published:** 2026-10-27

## Transcript

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The China-U.S.
competition is the defining competition of your generation, my generation, of our age.
AI is the single most important thing that's going to happen for the rest of our lives.
Yes, we've got problems.
I think it's important that we continue focusing very hard on this.
I want it to be America.
That's Eric Schmidt.
longtime leader at Google and advisor to the U.S.
government, talking to me live at the Masters of Scale Summit in San Francisco just a few days ago.
Eric Schmidt has singular insights into the critical AI race between the U.S.
and China with high-level contacts both in Silicon Valley and across Chinese government and industry.
This episode is part of a special series of high-impact episodes from the Summit stage that we'll be sharing.
So let's get to it.
I'm Bob Safian, and this is Rapid Response.
We're going to explore one of the highest stakes topics of leadership and responsibility, the AI race between the U.S.
and China.
Our guest has been in rooms with most of us will never access with presidents and generals and tech leaders.
He was the CEO of Google for a decade.
He helped build the U.S.
government's approach to AI and national security.
It's been vocal about what's at stake in the tech competition between the U.S.
and China.
So please welcome investor, advocate, and current CEO of Relativity Space, Eric Schmidt.
There he is.
Hi, Bob.
Nice to see you.
Thank you, Eric.
Always great.
Thanks for doing this.
And it's good to be back.
Yes.
So I want to say that I get whipsawed sometimes about the saber rattling and jingoism that might be happening around China's advancement in AI.
And I'm glad you can help demystify this for us.
You know, the U.S.
had a clear first mover advantage in AI.
There's questions about whether that's going to persist or not.
You've said the AI gap between the U.S.
and China has narrowed to roughly six months.
What's changed most recently?
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, 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-U.S.
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 superintelligence 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.
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, 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 millennium.
Because once these intelligences exist, our children, grandchildren, great children, and so forth and so on, will all be affected in really fundamental ways by the things that are occurring right here, right now.
Some people say that pointing to China's AI advances is sort of an excuse and a rationale to worry about regulating later.
Is there any truth to that?
Do you worry about that at all?
People make up all sorts of stuff.
And, you know, the question, so I've been heavily involved with the China-U.S.
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.
One way to answer your questions is to say, how would a slowdown work?
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.
I mean, China has, in some ways, a structural advantage because the government can dictate AI as a national strategy and everyone...
It sort of can align behind it.
America's advantage, you've kind of argued, is its sort of diversity and decentralization.
These are very different approaches.
Are there things that China's AI community has done that American leaders could learn from?
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.
And, you know, so trust me, it's illegal.
And so U.S.
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.
My friends in China say it's the private sector, that the economics are going to work.
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.
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.
And so...
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.
Yeah.
You mentioned open source, China's leadership in open source models or open weight models.
It's fueled the reach of its AI globally.
For the business leaders in this room, does it make sense for them to build on Chinese open source models or not?
So let's define terms a little bit.
There's something called open source.
I was part of the beginning of 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 spend all this money, then you take the numbers, which there's many, right, 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?
Right.
Why just give that away if you're going to spend that money?
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.
NemoTron, which is the NVIDIA product, has a similar size model.
They're both very powerful.
Google has something called Gemma 4, which is also a very powerful smaller model.
You can download these things.
So it's not completely China versus U.S.
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 Chinese, they'll just arrest you, you know, whatever.
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 the Chinese models, you put what are called guardrails 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 cyberattacks.
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, really worried.
And 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 U.S., they won't do it through that.
They'll do it through their own active cyber war and the same thing, U.S.
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.
And if as a business leader I decide, oh, I'm going to try to use that Chinese model because it's cheaper for me, is that unpatriotic?
Is that short-sighted?
Well, I think American CEOs are patriotic.
to a point, they're also very focused on money.
Yes, they are.
Welcome to capitalism.
And there are many examples where people are using small servers with open source rates 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's 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.
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 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 U.S.
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.
Coming up next, Eric gives us a point-by-point scorecard on who's ahead in the AI race, plus what he calls the no-surprise rule.
We'll be right back.
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Before the break, Eric Schmidt laid out what's at stake in the AI competition between the U.S.
and China.
Now he gives us a scorecard on key elements in the race, talks about what he calls the no surprise rule, and explains why AI development is and isn't a zero-sum game.
Let's jump back in.
So this is complicated.
I'm going to try to simplify it or have you simplify it for us.
I'm not sure if this is going to work.
But, Kerry, can we pull up the scorecard?
So we created a scorecard.
We're going to try to play a game.
So I'm going to ask you.
This is a test that I was not aware of.
I'm going to give you some factors.
Okay.
And you tell us who's got the edge, China or the U.S.
So model performance?
The U.S.
The U.S.
The U.S.
by all the benchmarks is ahead.
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.
Next one, model cost.
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, 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.
All right, so energy and compute, I assume that's China because you'd mentioned that earlier.
I'm supposed to answer.
Oh, sorry, sorry, sorry.
Energy.
Tell me what the answer is.
Okay.
You are correct.
I'm trying to pay attention.
No, no, no.
China had, I think the number was 560 gigawatts was produced last year.
It's an extraordinary number.
This is the benefit of industrial policy in an authoritarian country.
If they need energy, they've built an enormous amount of it, largely renewable.
All right.
Let's try something like leadership and talent.
I'd give that one to the U.S.
Okay.
And we are doing some really stupid things in our country.
Not that that's a surprise to this audience.
Taking the smartest people and arresting them and kicking them out of the country is like a bad strategy.
Denying them high-stakes visas to come to the country.
My favorite are that we take these graduate students, typically from China, but others, we give them a PhD and then we kick them out.
That's really smart, right?
That's really smart.
So my favorite there is the guy who is leading the quantum program for China was such a person, wanted to stay in America, and we kicked him out.
And by the way, do you know who our quantum adversary is?
China and this guy.
So QED, stupid.
All right, let's try.
Sorry.
All right, I've got four more for you.
Let's see.
Military and defense AI.
The Chinese model is opaque to me.
I work on military matters.
I've done a lot of work in the Pentagon and in Ukraine and helping the U.S.
in the Iran War and things like that.
There are studies which indicate that the Chinese models have been used by the Russians, for example, but not in an extensive way.
You have to assume that China will...
should there be a war, which is to be avoided, trust me, that China will immediately use the dominance that they have in consumer drones and other kinds of things, and they'll move more quickly than the West.
That's a challenge for the West.
But so who gets the point?
I'd say it's either zero-zero or slightly U.S.
Slightly U.S.
Carrie, you get to decide whether you want to give that point to the U.S.
or not.
Safety controls.
Oh, look at that.
Clearly an American.
That is a patriot.
Safety controls.
The U.S.
has a better safety control system.
There have been a series of meetings, which I've been part of, where the various labs have agreed to exchange safety practices and so forth.
It's kind of obvious.
I think that as a general rule, we should expect the government will regulate AI.
Not that I'm welcoming it.
But the AI is so pervasive in society that there are going to be bad outcomes, and those bad outcomes will cause regulation.
And safety is the most obvious one, human alignment and so forth.
So I'd give that one to the U.S.
To the U.S.
Okay.
Speed of deployment?
I'd say China.
China.
And I'll tell you why.
We are the ones inventing the frontier.
They're the ones doing deployment.
And I'm going there in the next month, and I was there a few months ago.
And the rate at which AI is being deployed into everything, digital, mechanical, and so forth, is astounding.
And they love to show you their demo.
And there's no equivalent product in America.
But as you're building hardware, I encourage you to go to China to look and understand because they're beating us in this area.
That's clearly China.
All right.
And global influence?
If you measure it in terms of impact over 10 years, it will be China.
It'll be China.
And the reason, I'm sorry to say, and here's why.
What's the difference between a free product and an expensive product?
More users for the free product, right?
The vast majority of countries do not have the economics, they do not have the wealth to really support the kind of costs and the token costs and other things that we impose, which I understand, I'm not arguing.
It's pretty clear to me that...
eventually the majority of the world is going to be using Chinese models.
That has huge geopolitical implications.
The average geopolitical person I talk to has not figured this out yet.
It's obvious if you think about it.
Free, cheap, good enough, expensive, powerful, reserved for elites, people who are wealthy, important companies, great research institutions.
So by our scorecard, at least in this rough way...
But she awarded one of them to the U.S.
Well, I just wanted to ask you, are we missing anything?
Is there any scenario where the U.S.
and China both win?
Or is it a zero-sum game?
I don't think AI is a zero-sum game.
China's not our enemy.
And I've worked in markets where two companies, although they're competitors, fundamentally create a whole new market.
The best possible scenario, which of course is not possible with the current administration, is you'd have some rational conversation between the U.S.
elites and the Chinese elites saying, how are we going to manage this?
So let's agree to some things.
So for example, a no surprise rule.
In my Pentagon work, I learned that when you launch a missile, You notify all the other countries, and they train their satellites on your missile launch, which is two reasons.
One, they make sure that you did what you said, and second, they get to train their missile launch watcher.
It's an example of an accommodation to keep everyone safe.
You don't want random missile launches and not know where they're going.
Makes perfect sense.
These decisions were made 50 years ago.
So there's a list that I think is quite rational.
for collaboration in order to achieve human flourishing.
And let me finish by saying that AI is the single most important thing that's going to happen for the rest of our lives.
AI is the underpinning of scientific progress, which is what I'm working on.
That scientific progress creates wealth, wealth creates opportunity, blah, blah, blah, well-being for society.
The future for our children and grandchildren is very, very bright.
because of what we're doing in AI.
Yes, we've got problems.
I think it's important that we continue focusing very hard on this.
The innovations are coming faster than even I can keep up, and I've been doing this a long time.
And second, I want it to be America.
And for the folks in this room, is there some message that they should take away?
I mean, is there something they should do differently in order to make it be America?
Start by using it.
I love these people come up and they talk to me about AI and I ask them, have you actually been using it?
And they said, no, no, no.
Maybe they're smarter than I am.
You got to use this stuff.
What really did me in was when I started having it do long chain, 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 changed the way I thought about my life, the way I work, the way I invest, the way I run companies.
It's very obvious, for example, that the finance function, I'll use an example, lots of business people here.
Finance is relatively easy for an AI system.
Why don't you have the AI produce the financial reports and you and the CFO can have a conversation?
Very interesting.
What about?
manufacturing inventory?
What about security?
What about cash forecasts?
Stuff like that.
And you sit there and you go, oh, that's boring.
No, 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.
Well, Eric, this has been fascinating.
Well, you're the best, Bob.
You know that.
Thank you.
Thank you so much.
Thank you very much.
Thank you.
Thank you.
Super.
Thank you.
Eric is both optimistic about AI and cautious about how missteps could derail things, whether economically or geopolitically.
This balancing act was a central theme at the Masters of Scale Summit.
One we'll be returning to in subsequent episodes in our special summit series.
We'll talk about managing our careers and our lives in a time of chaotic change, subversive brands, challenging convention, how to avoid being corrupted by greed and more.
So stay tuned.
I'm Bob Safian.
Thanks for listening.
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