The national conversation about AI in the US is full of fear: fear about AI’s potential to end humanity, fear of data centers moving in, fear that government officials will do absolutely nothing even as the industry pleads for regulation, you name it.

But the AI conversation looks remarkably different across the Pacific.

In China, driven by public enthusiasm and a state-backed push for AI dominance, the country is in the midst of a large-scale AI integration into all facets of its society, driven by Chinese leader Xi Jinping’s support for the technology.

“He’s super AI-pilled,“ said Kyle Chan, an expert on China’s technology development and industrial policy at the Brookings Institution. “When you look at his speeches, when you look at Chinese policy documents, everything is about rolling AI out to as many domains as possible: manufacturing, services, education, healthcare, pharmaceuticals, and of course the military. Everything is about trying to integrate AI.”

Beneath this push for a high-speed AI adoption across Chinese society lies a regulatory framework designed to safeguard political stability and keep boundaries on the kind of information AI models are able to share.

Chan joined Vox’s Today, Explained podcast to speak with co-host Sean Rameswaram about the different AI anxieties in the US and China, and whether joint regulations on AI safety are possible while the two countries are engaged in an AI arms race.

Below is an excerpt of their conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify.

One thing that really struck me about the way the Chinese government is approaching AI is that China has this explicit goal, right?

China has some concrete plans to boost AI adoption through the country, including its recent AI-plus initiative. And their goal is to try to boost the penetration of AI in so-called terminal devices, everything from computers to smartphones, to 90 percent or more by 2030. 

It’s going really fast and it is, in part, a state mobilization effort. There are state-owned companies, there are government agencies that are trying to integrate AI, but also there’s just an enthusiasm from the general population.

If you look at surveys like the latest Gallup poll, people in China are much more enthusiastic and positive about how AI might help them and help the country. It’s quite a striking contrast with what you see in the US in terms of attitudes towards AI.

Because people here are worried about AI as an existential threat. People here are worried about AI taking their jobs, people here are worried about AI data centers in their backyards. Is that not the vibe in China?

Not so much. There is some concern about AI and jobs. But in general, China’s not worried about AI wiping out humanity. You really don’t hear that from the Chinese policymakers. There’s no talk about a P(doom), the probability that AI will wipe out humanity. So this is just a different approach to AI risk in general.

And why not the existential risk? 

You know, one factor, I think, is sci-fi and the culture. 

Huh. We’ve watched too many movies?

It seems like every major sci-fi book or film involves machines trying to kill us all, and I think that that idea has become very prevalent. 

When you imagine a future with AI, that is one of the primary futures that people imagine, versus in China, I think a lot of the attitude towards technology in general, not just AI, is about using this edge to keep up and to become more competitive. 

For a lot of people in China, technology was something that they saw transform their lives in real time. And Chinese leaders think it’s the key to China’s future modernization and transformation to a global superpower.

Let’s talk about what concerns there are.

One of the biggest concerns that Chinese political leaders have when it comes to AI is political and social instability. They don’t want AI-generated content to act the way that social media or the internet broadly could, which is to disseminate information, maybe deepfakes, maybe viral news that could threaten the [Chinese Communist Party’s] hold on power.

For AI-generated content, it was about making sure that the models didn’t say the wrong things about sensitive topics. A lot of this is about social and political risk rather than about existential technological risk per se. 

From pretty early on, China has been developing a model registration system where basically AI services have to be vetted in advance and go through a battery of tests where they’re asked sensitive political questions and they’re supposed to either not answer them or say the “right thing” before they are officially registered. And once they’re registered, then they can be released to the public. So it’s a pretty tight regulatory framework for controlling things like political content.

We know that there’s a high unemployment rate in China, especially among young people, which is also a great concern of the government. Are people worried that this bullish approach to AI could affect employment numbers?

Yes and no. On the one hand, China is actually facing this demographic problem with a shrinking labor force. Fewer people want to go into, say, factory jobs, but China still wants to make everything the world wants to buy. 

They see AI as a partial solution to that, a way to automate things like manufacturing. On the other hand, though, there are growing concerns in China about job loss. And you see also some recent court cases in China where they basically are telling employers, you cannot use AI as a reason to fire workers. 

But I think overall, what they’re trying to do is they’re trying to find this balance between the diffusion of the technology and trying to prevent any kind of large-scale protest movement that might arise from sudden mass unemployment of, say, factory workers or food delivery drivers.

And President Xi was of course here in Washington last week. From the limited photos we have of the state dinner or of President Trump and President Xi on the tarmac, everything looks so chummy, even though we are so clearly in a cold war with China over AI. The president’s always talking about how he wants to win. 

Was there tension in his visit to Washington last week? Did you notice any?

The weird thing is that just between these two men, there seems to be this kind of romance where Trump really fawns over Xi Jinping. 

I think he has tremendous respect, even admiration, for Xi Jinping’s grip on power and control over the government apparatus in general. 

What is happening under the surface, though, is continuing tensions and a deep competitive structural relationship. That involves especially competition in AI. AI has emerged as a new fault line of competition between the US and China, one that could come to define the relationship going forward. 

I think that on the one hand, they’re talking about cooperation on things like AI safety and AI risk. On the other hand, there’s a whole bunch of issues where the US wants to lead. China wants to also get ahead. And when it comes to things like distillation or cyber capabilities, it’s as zero-sum as it’s ever been.

Who’s winning?

It depends on what you’re looking at and how you define winning. If you’re talking about the best models, for sure the US. And virtually everyone in China would agree, right? In fact, Anthropic’s Claude is very popular in China, even though technically Anthropic bans its use in China.

But aren’t their open-source models kind of popular here?

Yes, exactly. Chinese open-source models are getting more popular in the US, especially at a time when some of the capabilities are so incredible that most normal people don’t really need them, and they also don’t want to pay for the most advanced and most expensive models. Ultimately it’s this weird sort of cooperation and competition at the same time.

American models are popular in China. Chinese models are popular in America. We’re at each other’s throats, but quietly, politely.

Does the cold war make it all but certain that safety isn’t going to really be a chief priority for either country until something terrible happens?

The fierce level of competition and the very, very deep distrust between the two countries makes any kind of cooperation on AI safety very, very difficult. Even just talking to each other, having the US and China establish an official AI dialogue, which apparently they’ve agreed to, is seen as a huge first step, even though they’re just talking. 

There’s a long, long road to go before we can even think about something like an arms control treaty or a broader international agreement about AI and AI safety. But I think that’s what some people, especially in Silicon Valley, are trying to jump to.

If there’s one thing that Americans can unite behind, it’s anxieties over artificial intelligence.

According to a recent NBC News/Decision Desk poll, 70 percent of adults say they are personally more worried than excited about the technology; it’s basically underwater in every area of society with one notable exception: its use in scientific research and medicine.

According to Dr. Dhruv Khullar, a physician at Weill Cornell Medicine and writer at the New Yorker, that optimism is well-founded.

Khullar uses AI tools in his own practice as a physician and writes about their advancements in the medical field. He says that “right now, at least, there’s a generally optimistic view of how AI will change healthcare going forward.”

AI in medicine is nothing new. In 2024, the Nobel Prize in Chemistry was given to researchers who developed an AI model that enabled breakthroughs in predicting protein’s complex structures. As it becomes more ubiquitous, it’s also emerging as a resource for doctors to get second opinions and as a tool to take notes on patient-doctor interactions.

On top of that, Khullar says, it might help unlock new medicines to treat diseases. 

Khullar joined Today, Explained co-host Sean Rameswaram to discuss the ways that AI is transforming drug discovery and research, as well as what it still can’t do. They also discuss how to think about AI’s potential in healthcare at the same time as top AI researchers are increasingly warning about the technology’s risk.

Below is an excerpt of the conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify.

You’ve been thinking about lifesaving AI innovation. Has AI already started saving lives?

I think it has. This is the fastest I’ve ever seen healthcare take up a new technology. Part of that might be that the healthcare system is so messed up that there’s an appetite for some type of change. It’s unaffordable, it’s inaccessible, it’s inconvenient, the quality is uneven. 

There’s at least a hope that AI is going to help with all of these things, and I think it’s already starting to make its way into the healthcare system. Now it’s our responsibility to figure out how to make the most of it without also promulgating some of the downsides.

For those who are unaware, tell us how it’s working its way into the system.

It’s helpful to categorize it into a few buckets, at least for healthcare. 

The most rapid uptake, and I think the place that it’s already starting to make a difference, is on the administrative side. Many people who have gone to a doctor recently might have noticed that there’s an AI scribe that’s taking notes now. And people seem to really like it, at least so far. Doctors are able to look their patients in the eye in the way that they weren’t able to when they were poring over their computers and just typing what the patient was telling them. 

The second big area is patient navigation. When is your next scan? When’s your next appointment? Do I need to take this medication on an empty stomach or not? There’s a huge opportunity for people, let’s say, who are diagnosed with a serious illness like cancer or heart failure to navigate the system more seamlessly. 

The third big area that I’m really excited about is drug discovery. AI is making a huge dent in the early parts of drug discovery. 

The last thing that I’m excited about and that I use every day when I’m in the hospital is [AI] as a second opinion. Now instead of having to get a consult or turn to a textbook, AI can be a very, very helpful clinical decision support. It’s not to say that I never consult someone, of course, but that first pass — I’m not sure what’s going on. What are some recent trials that might influence my decision here? Is there something I’m missing? Is there a test that I should be ordering that I’m not ordering? — all those types of things, they start to help with.

The biggest chunk of this positive view of AI and medicine is drug discovery. Tell me more about how that’s going so far and how it might be going in a year, or five, or 10.

I don’t want to leave this conversation and say that we’ve solved the cure for cancer because of AI, but I do want people to know that at least for the early stages of drug discovery — figuring out the very basic steps of is this an interesting molecule? Does it have potential biological applications? — AI is already very helpful for that. 

They’re trying to take, basically, what is an infinite number of drug-like molecules that are theoretically possible to be drugs and they’re trying to match that to some disease that’s going on inside a human. And human biology is incredibly complex as well. There’s tens of thousands of genes, hundreds of thousands of proteins, trillions of cells, and you have to match a potential molecule to a potential target within a human. 

In the past, a researcher might take years to study a disease or a biological pathway and figure out, okay, this protein seems to be involved. And actually, it might not be involved, it might be involved, it might be disrupted, but not be the causative agent. There’s a lot of uncertainty there, so now AI can take enormous amounts of data, and it can rank various potential targets that seem to be the most likely to be causing a particular disease and give you that short short list of potential targets that may have taken months or years in the past.

So now you know what to attack, but you have to figure out what to attack it with. So you need a molecule, you need to generate a molecule that’s going to fit into that target or disrupt that target in some way. 

Now again, AI can scour vast chemical spaces and give you a list of the things that seem to be most likely to be able to disrupt that target. And then it can actually help you generate that molecule in some cases. 

So now we have the target. Now we have something that we’re going to attack the target with. And then you have to do something called lead optimization, and that’s the third thing. Lead optimization means you have this lead and you have to optimize it.

“I would make the counterargument that healthcare is ripe for disruption and you don’t even need the frontier frontier models to make it a lot better with AI.”

Now we have to figure out, of all the molecules that seem to be good at potentially affecting this target, which ones are able to be absorbed in the body, which ones are going to get to the right tissue. AI models can predict the most likely molecule to give you that Goldilocks set of properties that’s needed for a safe and effective drug.

As we all know from anyone who’s asked AI to write an email for them or Googled a question, these tools have a certain degree of certitude, even though these tools can just be flat out wrong about stuff. Are you guys worried about that in the field?

Absolutely. And so this is why I think the narrative around AI just replacing scientists or doctors or other workers is incorrect. Because you still need a lot of judgment. 

You need to be able to adjudicate the output of these models to figure out what is most promising and what is potentially dangerous. That is going to require wet labs and scientists and reasoning based on prior experience and understanding the context. There’s all sorts of issues around, can you manufacture some of the drugs that are being proposed by these models? So just because it’s set, it dreams something up doesn’t mean you can actually make that thing in the real world.

Some of the drugs that it proposes might actually be toxic in certain ways that it didn’t predict. And of course, then you have to take this thing into clinical trials. You have to recruit l people who are willing to put this medication in their bodies. You have to find out who might benefit, you have to put it through the regulatory process. 

That’s why I said that the first part of drug discovery, where you’re trying to figure out how do we get the right drug candidates, what seems most exciting to test further, that’s going to be really accelerated. That whole second half where we actually have to figure out if it works in human biology, that is still going to be in some respects an analog process.

It doesn’t sound like you’re terribly scared that we are like ceding control of our hospitals, of our research facilities, to AI. This is very much in the sort of assistant bucket?

For now it is. And I think one of the challenges is to maintain our agency as these models become more and more sophisticated. As you lean more on these machines, if you’re a doctor, inevitably some of the skills, the critical thinking, the reasoning that we put into coming up with the diagnosis that we honed over the course of years, that can start to atrophy. 

These are the types of things that we still need to sort through as we’re implementing more and more AI into the healthcare system.

For the people who just say, shut it all down, this is too dangerous, the risk is far too great, we don’t actually need this, this isn’t doing anything for society, would you make a counterargument?

I would make the counterargument that healthcare is ripe for disruption and you don’t even need the frontier frontier models to make it a lot better with AI.

Even if we wanted to “slow the pace of the frontier,” fine. But there are models from a year or two years ago that could be very helpful in the healthcare setting. When we’re talking about slowing the pace of the frontier and the most sophisticated and potentially dangerous models, fine. 

But if we’re talking about shutting down AI and not using it in biotechnology or not using it in scientific research or not using it in clinical care delivery, that’s where I would push back pretty hard.