Melanie Hart

That's a really great question. And there are three ways that a lot of people in Washington and elsewhere are looking at how to measure who's ahead in AI capability. One, Kori mentioned frontier models.

Who's doing the things that no one has done before with AI? So, ChatGPT has had moments where they've done that, for example. DeepSeek has also had moments where they've done that out of China.

But overall, when it comes to frontier models, who's first? The US tends to be first out of the gate. And I think we're looking at that too much.

I agree with Kori, if I understood you correctly. There's too much focus on that in the United States sometimes. Thinking about whoever spends the billions and billions of dollars to do the first model before anyone else will maintain leadership is not necessarily the case.

The second metric to look at and where people are is compute. How much data can you crunch to power these models? And some of that is to do with chips.

The United States and its allies have the lead on the most advanced AI chips. China's about five to seven years behind the US and its allies on the advanced AI chips. And we're hearing from Chinese companies and the Chinese military that that is constraining what they can do with some of the AI deployment.

But another aspect of compute is electricity and data center infrastructure. And that's where we're really starting to run into barriers in the United States. I live in Virginia where our electricity bills have shot up because there are, I think, something like 1,500 new data centers being built in Virginia.

And now American citizens are starting to protest them because there's water pollution. They're raising our electricity bills. People don't want to live next to a data center.

So I think we're going to see some shift in that balance of power and compute because China can roll out nuclear projects much faster than we can. They don't really care if you want to live next to a data center or not. In China, the data center will go wherever Beijing wants to go.

So compute is the second. But the third, I think, is going to be the most important, and that's deployment and distribution. Who uses the AI that you come up with in a lab?

And that's where China is really focusing its efforts. And this is where China has the opportunity to really shape the way that AI is deployed globally. Here in the United States, you know, ChatGPT and Claude and these different models are at the frontier in what they can do, but they're also relatively expensive.

What China's doing is deploying DeepSeek and finding ways to do an almost as good capability, but a lot cheaper. You don't need as good chips. You don't need as much electric power.

They're doing open-weight, locally deployed models. That means you can just take it and install it on your own computer and tinker with it and change the way that the model works. You can own it a little bit more, whereas with ChatGPT, it's kind of like doing a Google search.

Everything lives within OpenAI and ChatGPT. China's experimenting with smaller, cheaper, easier-to-deploy solutions that are probably going to be more like the kind of solution that Global South nations in particular are going to want, because they're going to want to be able to have their data in-country or in-company and not give it to OpenAI. They're going to want to know, how can I deploy AI at the lowest cost with the easiest, lowest-end chips, without consuming too much electric power?

Here in the United States, a lot of leading American universities can't get access to the most advanced chips to experiment with in the lab, because OpenAI and some of these other companies are buying them all up. Deployment is probably going to be most critical for who shapes the global landscape. That's an area where right now I think Washington is paying too much attention to, let's be ahead on frontier.

Let's be first, as Kori put it. Let's maintain our chip edge. Both of those are important, but if nobody buys what you're selling, or nobody can afford to buy what you're selling, then that's where we really lose the game to China.

Then just four areas that I'll be watching really closely in terms of US-China competition. The first one is China likes to turn a cost advantage into dependencies. We've seen with 5G telecom that Huawei was always the cheapest.

Once you bought Huawei 2G, you could only go for Huawei for 3G. You could only go for Huawei for 4G. They aren't compatible with other systems, so they tend to lock in their customers to the Chinese ecosystem.

There's a lot of risk there with AI that maybe you go for DeepSeek or another China alternative because it's cheap today. Then you find, well, the only chips you can use are Huawei chips. The only data center you can use is a Chinese data center.

Then that opens you up for risk. A second factor that I'm really watching is information shaping. My team here at the Atlantic Council did the same search in DeepSeek, China's AI model, and in Western AI models, and the information is just different.

The Chinese government shapes what you hear and know about the world. I'm sure Audrey is more aware than I, you're definitely going to get different information about Taiwan, for example, but also about human rights and how well the Chinese government performs. There is a big risk if the entire planet is getting their AI search information from DeepSeek because when it came to general basic internet search, China's alternative to Google was Baidu.

It's horrible. Nobody ever wanted to use Baidu because it is such a mess. It is so difficult to use.

We were in China a couple months ago and I tried to use Baidu just to get a restaurant address and it was giving me search results dated in 2003 for no clear reason whatsoever. It's just very messy. China never took off on internet search, but it could on AI-powered internet search.

That would really shape the information that the global public has access to. Then just one other point, China, we really are watching closely. How is China working to export AI surveillance models?

We know that in previous years, there have been issues with China exporting surveillance networks that would be, for example, a network of cameras that you can deploy around a city. Now they're looking at new models that can predict in advance what kind of person, who is going to be a possible dissident in the future, who's going to be a political agitator in the future, using AI models to look at who are their friends, to listen to their telephone conversations, to review their emails, to use geolocation and see where do they go around the city, what internet websites did they look at.

China is experimenting with AI for a forward looking surveillance program, not just tracking what people do now or in the past, but what kind of person are you and should we be worried about you in the future. I have huge concerns about that being exported to other countries as well. That's it for me.

Can I ask a question? I think this is an important point. So if you go to the DeepSeek website, for example, and look into their pricing, it is fascinating.

They describe their pricing in direct relation to OpenAI. And they say, like other models who require a monthly subscription minimum, we only charge by the search or by the token. So OpenAI...

Am I getting to the heart of this follow-up question correctly? OpenAI is cheaper for users to run. So, for example, a company based in South Africa or Colombia or anywhere in the world, if they're comparing some enterprise options between DeepSeek and Claude and OpenAI, everything that I'm seeing is that the DeepSeek version is going to be currently much cheaper for them.

And I think that's something that we need to fix.

Yeah, so first of all, I'm thrilled to hear that example. I think the US needs to learn from Taiwan on this. You know, the US is really behind Europe on data protections, for example, and I think really behind Taiwan on helping people manage propaganda flooding from all kinds of actors.

Just two comments. First, I think I agree with Emmanuel, his earlier comment on transparency behind these models is going to be really important, because you cannot trust a model's decision-making if you aren't sure what are the factors it's using to make the decision. And if folks, I highly recommend the book Empire of AI , which really gets into some of the details about the kind of human decisions that go into some of the model design and what some of the challenges are there.

The US needs to do a better job still on transparency and having open, clear models so that people can trust them more. And just an example, again, I live in Virginia, and Virginia is one of the states here in the United States that are having huge political battles on how to draw our districts. So if you have one state, where do you draw the lines to decide what parts of the state vote together to elect a senator or to elect a representative?

And everyone's fighting over doing crazy lines and crazy shapes to try to get, how do we get the most Republicans or how do we get the most Democrats? As a Virginian, I would love a neutral AI version that would just say, okay, here's a clear kind of neutral option that everyone can look at, but that's really hard to do if you don't know the assumptions that went into it. And then just an example on AI for good, I'm doing a lot of work in the biotech space, and a lot of our new medicines, medicines that are being discovered to treat diseases that we previously didn't have treatments for are coming from mapping of the genome of not only humans, but other species as well.

So here in the United States, a lot of people are talking about GLP-1 medication, that emerged from mapping the genome of the Gila monster, which is a kind of lizard. And there are just so many species of fish and mammals and amphibians and creatures on the planet, and we haven't fully mapped all of those genomes, and AI-empowered science is helping us to do that much faster. So we now increasingly have the capabilities to map genomes and then test how that might translate into medicines in human bodies at a speed and scale that we never had before.

And China has companies like XtalPi that are building huge factories that are nothing but robots doing biological testing to get data points to feed into an AI model that's doing this mapping to try to come up with novel treatments for disease. And as an American, if I have a rare disease or a hard to treat disease, and a Chinese company uses these models to come up for medicine, to create a medicine that works for me, that I can't get anywhere else, to me that's great, that's a net positive. I will definitely want access to that.

So I think the more we are empowering science around the planet to come up with new information faster, that's a great thing.

Yeah. So just a few comments. On the supply chain question, the United States and China are currently the AI leaders, depending on how you look at the metrics.

I'll say that they're the two AI leaders. And it will be interesting in that there are areas where we can collaborate and areas where we just can't. So on critical minerals, on rare earths, China has made very clear that they will cut us off from those supplies as a way to control U.S. decision making. So you will see a growing decoupling between the U.S. and China in critical minerals. We just have to have alternative supply. We cannot depend on China for that.

And when it comes to the higher end chips, there's very clear evidence that the Chinese military is trying to get ahold of those to run AI in ways that in our view undermines our national security. And we have to have export controls there. What I find really interesting is that there's also areas where we can collaborate, but doing so safely is going to require new mechanisms.

And I agree totally with Emmanuel that on data, cross-border movement of data in a way that is safe and secure and benefits both sides equally is really going to be the big key to unlock what we can actually do with AI. And there's tremendous potential there with China, but also tremendous risk. So I was really interested to hear about the French-India framework.

I think maybe that could be a model for the U.S. and Europe and China and others, right? Because we will have to have a kind of independent standards and mechanism for how we can exchange data. It can't sit in any one country.

I'm doubtful that the UN could be the solution, but hey, welcome some ideas on that front. On the question about AI increasing inequality between nations, I totally agree with Kori's comment. This is going to be the biggest upheaval since the industrial revolution, and we still don't fully understand it.

But I think the economic losses where countries are not deploying AI are going to be real and serious. And just to give an example, last fall we went to a car and automobile manufacturing factory in China, the Xiaomi factory that uses AI plus robotics to make one electric vehicle in 76 seconds. And next week, we'll go to California to visit a Ford factory that's using similar AI and robotic innovation to make a Ford truck rapidly and at a price comparable with what the Chinese companies are reaching.

So you see the U.S. and China in this race. How can we use the in-factory AI models with robotics to manufacture higher quality faster with fewer people? If you're not deploying the same models, you're not going to be able to compete.

You won't be able to compete on scale, on cost, or on quality. What does that mean for Mexico or for another country that's an automobile manufacturer? You may wind up with just two different worlds, and you can't be in that higher level of competition without the most advanced AI and robotics, which are expensive.

So I think a lot of countries, including the United States, including China, are going to have to figure out what's our new economic plan, because some of what we were doing before may not work going forward. And then just finally to the question about what can governments do to push models to be more transparent, looking at the U.S. political system and sitting here in Washington, I don't think Washington can do this effectively, because our political leaders are always so far behind the companies in understanding what the AI is doing, much less trying to regulate the models. I think a lot of this, at least in the U.S. system and probably globally as well, will be based on market choice. So companies and citizens are going to want the more transparent option.

And if they have choice, then transparency is ultimately going to win out at the end of the day.

Thank you.

這是一個非常棒的問題。華府及其他地方的許多人主要透過三種方式來衡量誰在 AI 能力上領先。第一，正如 Kori 提到的前沿模型。