Why China’s New A.I. Model Has the U.S. on Edge

Casey, how the hell are you?

Doing great, Kevin. Another beautiful summer day here in San Francisco.

It is. I was getting my coffee the other day in San Francisco. Have you been to this new Japanese coffee place?

Honestly, everyone in our neighborhood is talking about it—and that's not a joke.

It's the talk of the town.

Yeah.

It's a very high-end, very nice coffee place, and I was there getting my coffee. I saw that they have on their menu a cup of coffee that costs $105. Have you seen this?

No, I haven't. First of all, tell people the name of this place.

Okay, it's called Wild Fox.

Wild Fox.

This is not an ad.

Yeah.

Their coffee's very good.

Yeah.

But I thought it was a typo. I was prepared to pay maybe, I don't know, $13 for a very nice cup of coffee.

Sure.

One of their pour-overs is $105. I was so stunned, I asked the barista, “Do people actually order this?” And he was like, “Yeah, about every week we get one.” People are out there.

What is in the coffee for $105?

I looked that up, and it's some Brazilian, award-winning blend that they cryo-preserve. I don't know. It sounds very fancy. I'm sure it's great.

Yeah.

But I also believe strongly that if you pay $105 for a cup of coffee, we should confiscate your money.

Yeah, and possibly your land. Listen, I actually am pretty confident that it's not worth $105. I think I could find a lot better uses for $105.

Hey, there's only one way to find out.

What's that?

Field trip?

Field trip. Yeah. We're not going to do the show this week because we're headed over to Wild Fox to empty our bank accounts for a cup of coffee.

One more great expense-account caper. I'm Kevin Roose, a tech columnist at The New York Times.

I'm Casey Newton from Platformer.

And this is Hard Fork.

This week, the U.S. has a new framework for regulating AI models, but they won't let us read it. Then, after a series of AI agents going rogue, METR president Chris Painter joins us to discuss how we get them under control. And finally, we're leaving on that midnight train known as the Hot Mess Express.

Well, Casey, before we start the show today, you and I have some big news to share with our audience.

Let's hear it.

In just a few weeks, this chapter of Hard Fork is coming to a close.

Kevin, what are you talking about? I need this job. I have a wife. I have kids.

None of that is true.

All right.

But what is true is that you and I are leaving The New York Times, which has been the home of this show for the past 4 years, and my journalistic home for about the past decade. We are starting a new independent podcast and media company together.

Kevin, you've already said too much. This is not the time to tell everyone about our new media company.

Yeah, we will have much more to say about what we're doing next and what's happening to this feed very soon. But before we sign off, we're going to do an Ask Us Anything episode, and we want you to send us your questions.

Yeah, and this is not a request; it is a demand to hear from you. If you have any questions about the making of the show, anything that happened on the show over the years, or you just want our thoughts on where the world is going, this is literally the last moment that you can do that on this show. So go ahead, send us an email, a voice memo, a short video, a viral dance. Our email address is hardfork@nytimes.com for another few weeks.

And again, we promise we will give you more updates about what's happening next very soon. But in the meantime, send us your questions. All right, Casey, first up on the show this week, we have to talk about these new White House AI rules that we are not getting this week, but that we are hearing about this week.

Yes.

1. The Secret AI Framework

In one of the strangest developments of recent times in AI and AI regulation, the White House has finalized its framework for testing new frontier AI models from the big American AI companies. This is something we've talked about on the show very recently, but it's been a very weird week because they have not released this framework, and it's been rolled out in this very surprising and secretive way.

Yeah. Usually, in a democracy, when the government creates new rules, what they'll do is share them with people so that everyone knows what the rules are. In this case, they're really limiting the number of people who get to see those rules, Kevin.

Yeah, it reminds me—I was talking to someone yesterday at one of the labs, and they compared it to regulatory Calvinball. Do you remember in Calvin and Hobbes, they have this imaginary game where they just make up the rules as they go? That's what people feel is happening in Washington with AI right now.

And that's also just basically how executive orders work, because you just sort of say what you think the law should be.

Yes. So we thought last week, when we taped the show, that we were going to see an actual framework—this thing that had been in the works for a very long time, that we knew was coming. Then, on Tuesday of this week, we learned that the White House did not actually plan to publicly release these rules at all. They did apparently give a private briefing to representatives from some of the American AI companies—OpenAI, Anthropic, Google, et cetera—where they told them what this framework and these new rules for AI were going to be. But they did not actually give many details to the rest of the world about what is in this framework.

That's right. So today we are going to walk you through what we know of what's in it. We'll tell you what is still a secret, and then we'll talk a little bit about what we think the implications are for the industry and for AI safety in general. But before we do that, we should probably do our AI disclosures.

I work for The New York Times, which is suing OpenAI, Microsoft, and Perplexity.

And my fiancée works at Anthropic.

2. The Thirty Day Test

So, according to Maria Curry from Axios, the framework gives the government a 30-day window to access frontier models before they are released publicly. Basically, if you are OpenAI or Anthropic, and you're another company releasing a closed-source, what they're calling a frontier model, which has advanced capabilities and potentially dangerous ones, you can submit that to the government. They will have 30 days to test out that model, to run a bunch of evaluations on it, and to determine whether it's safe or not. During that window, the models will be stored in “high-security environments.”

The same high-security environments that models now routinely break out of, presumably.

No, even more secure than that.

Oh, okay.

Multiple administration offices will be involved rather than one single agency. And the big headline is that this whole thing, this whole 30-day testing window, is voluntary—at least if you believe the Trump administration's statements about this.

Yeah, although, of course, the immediate question is, well, okay, what if a company did not volunteer to agree to this? What would happen to them? I imagine the administration would apply export controls in the exact same way that it did with Fable. But, you know, Kevin, I wanted to get your take on one of the details you just shared, which is that employees will apparently not be allowed to use models once they're submitted for testing. Thirty days is a long time to go without a frontier model.

Yes.

And so I wonder how companies are going to adapt. I almost wonder if they'll create, you know, frontier Model A and frontier Model B, and submit frontier Model A for testing so that they can continue to use frontier Model B. They're going to game the system in some weird way, because I truly can't imagine companies agreeing to just stop using their best models for a month.

Oh, totally. I mean, it's even more complicated than that, because the way that these models are deployed is that researchers are making changes to the models up until the hour before they are publicly released, and even after that.

Yeah. It's like writing a blog post that way.

Exactly.

Yeah.

The way that these models are deployed is very ad hoc and fast-moving. So it might be the case, for a very powerful frontier model, that they are making changes to this model and the safeguards up until the very minute it is released. Then they might make additional changes based on things that they observe when the models are released. A user finds a jailbreak on the model, and you have to quickly patch that by doing some additional post-training or RL on the model.

It's like submitting an essay to a college professor, but you submitted it via Google Doc. So even though the deadline was midnight, you're sort of in there at 2:00 a.m., and you're still fixing the typos.

Exactly. So it raises the very obvious question of, okay, you're Anthropic, you're OpenAI, you have a model.

You want to submit it to the government for this 30-day review process. Does that mean you essentially have to freeze the model in amber at this checkpoint and then not work on it for 30 days? What if you find something in those 30 days that you want to patch? Does that mean you have to restart your 30-day window and extend it out more? There are just so many questions about how this will actually work in practice that I don’t think anyone has fully thought through.

Sure, and what I imagine they’ll do is say, “Okay, well, we’re evaluating the bulk of your model, but you’ll be allowed to ship bug fixes and product improvements after we give it the once-over.” But it’s just in the nature of these models that one of those bug fixes might introduce some significant new problems. So, yeah, this feels kind of messy. Okay, what about the whole open-versus-closed thing?

3. Open Weights Escape Review

Oh, yeah, this is the other big headline. Open-weight models are explicitly excluded from it. They are not considered covered frontier models and, as such, they are not required or encouraged to submit their models to be tested by the government during this 30-day review period.

And in part, this makes sense to me in the sense that the best open models today are not frontier models, and they have not been caught causing the sorts of problems on the internet that the frontier models have. So, at this moment, as we record, I think that’s totally fine. I think the question is: What happens when, a few months from now, one of these open-weight models may catch up to the frontier? How will that change the dynamics, Kevin?

This is the part that really made my head spin and forced me into a state of stupor over this new framework.

But that was what caused it.

It’s like open-source models right now: Many of them are very middle of the road. They’re not very capable, and they’re certainly not frontier models, but they will get there soon. At that point, basically, the U.S. government is saying, “We’re not concerned about the very part of this technology that could be the most dangerous,” right? It’s explicitly excluding and carving out of this requirement the models that people in the community are most worried about.

Right, and let me just set up the other dynamic that you can imagine, which is that 3 or 6 months from now, there is a Chinese open-weight model that is about as good as Claude Fable or GPT-5.6, and they make that available via open weights. When that happens, they are, at least at this point, not going to go through any sort of testing process, right? And so you’re just in this situation where it may be easier for an American company to use a Chinese frontier model than an American frontier model, which, up until this point, has been the explicit situation that the Trump administration has said it wants to avoid.

Yes, it’s a very perplexing set of circumstances, but I assume—

There’s a certain perplexity to it.

I assume this is the result of the open-weights letter that we talked about from Nvidia and this host of other American companies, and all of the backstage lobbying that has been going on on this issue. It worked. They got their exception and their carve-out for open-weight models.

Yeah.

What do you think was more persuasive to the Trump administration? Was it the open letter or was it the donations to the Trump ballroom? I have a guess.

Hard to say.

I have a guess, but I’ll leave it to the listener to decide.

But I think, look, I’ve spoken to a number of people about this particular carve-out. I think the general sense is that, at some point, this will have to change, right? There will be a major incident, some kind of security incident involving an open-weight model, and this decision will just have to be reversed. They will have to subject open-weight models to the same sort of testing requirements that closed-source models are required to go through as of now, and it’s just not a good thing that we’re waiting for that to happen before we start testing these models.

Yeah. All right. Let’s talk about a few things that we don’t know that I would like to know. Number 1, what is the actual pass-fail threshold? What is the Trump administration considering safe versus not safe? This was a big question about GPT-5.6 and Fable, right? What made the administration eventually say, “Okay, you can ship these”? That, to me, seems like question number 1. Number 2, they are apparently going to let these frontier models, during the testing phase, be shared with trusted partners. Do I have that right?

Yes.

But we don’t know who the trusted partners are, right? So you can imagine previous administrations considering foreign governments trusted partners. Maybe you would let our allies in the United Kingdom have early access to these models. At this moment, we don’t know who a trusted partner is. Those are my two big questions about this model, Kevin.

4. The Rules Stay Secret

Yeah, I have many more questions about this model. Who inside the government is going to be responsible for doing this testing? Which agencies are going to be involved? What kinds of subject-matter experts? All that seems very vague and up for discussion, and potentially the government doesn’t even know yet, which is why it’s making all these vague statements and declining to release the framework publicly.

I think it’s also worth stepping back for a moment and remembering the AI industry’s reaction to the Biden administration’s White House executive order on AI. As people will remember, the Biden administration had this very long executive order covering all these different aspects of AI risk, safety, and deployment, and the criticism of those rules at the time was that they didn’t have any teeth. The good thing about those was that they were released publicly. People could see them, debate them, and argue about them. The companies could lobby against them or lobby for them, depending on their views.

This new framework from the Trump administration has the opposite problem, right? It does have teeth. It’s voluntary, but we’re putting that in air quotes because it’s voluntary in the same way that paying your loan shark is voluntary.

It’s voluntary in the way that paying your taxes is voluntary.

Right.

You cannot pay him. There may be consequences, but, yeah.

Right. But it is also just not public. It is a secret regulatory regime that even the people participating in the regulatory process do not fully understand, and I just think that is a completely untenable long-term situation. You are asking these companies to play by rules that they do not understand.

No, I mean, honestly, this just feels very Chinese to me. There’s a set of secret rules that you have to follow or else. Kevin, give us your overall take on these new rules that we have, and maybe what you would like to see in the weeks and months ahead.

My overall take is that we just can’t know. One basic thing that they could have done is put out at least a detailed summary of this framework. I understand the rationale that some folks at the White House have given about how some of this involves classified information about national security.

Yeah, like, we don’t want to tell you every single test that we’re going to give the models—

Exactly.

—because then our adversaries would use that information against us.

Exactly.

Yeah.

I understand wanting to withhold some of the details, but at least give us a vague, high-level sense of what you are looking for when you’re testing a model.

I also just wish that they had been written by Congress, right? I don’t think this is the sort of thing that you just want to be decided by fiat by the president. I think this is something where you want a lot of input from all sides. I think you want a public debate about it. Ultimately, this should probably result in some sort of new kind of regulator.

Demis Hassabis, until recently the CEO of Google DeepMind, put out a statement just a few weeks ago calling for something just like that. That is still the direction that I hope we go, but in the meantime, we get the secret rules.

I think one obvious winner from this new slate of White House rules are the open-source advocates—the companies that make and want to keep making open-source models and want to build on top of open-source models. Who are the obvious losers here? Who should be upset about this regime? Is this going to be a problem for OpenAI and Anthropic, this new testing period? Do you think this should make us feel any differently about their prospects?

I think that in the moment, it will probably feel more annoying to them than anything else. I think that if you accept the premise that we have 2 frontier labs right now, and that they are OpenAI and Anthropic, the rules presumably are going to apply to both of them equally.

And so, to the extent that it slows them down from releasing new models, they're both going to be equally affected by that. As somebody who is not particularly rooting for there to be a speed-up in the release of new models, I think that might be okay.

Where I think this will get dicey—and which I do think would just cause the administration to have to revisit this—is the not-unlikely scenario of a Chinese company with an open-weights model getting to roughly the frontier, or even just getting to the point of, you know, the sort of Claude Fable, GPT-5.6 class. Once there is a model like that available in open weights, then I think you're going to start to hear the screams out of OpenAI and Anthropic saying, “Hey, you are causing Americans to give up their lead in innovation, and you are slowing down progress in a way that is not just going to hurt us, but may hurt the entire economy of the United States and potentially even our national security.”

Well, help me make sense of this, because this was my naive first impression of this framework: They're slowing down the American labs, and they're speeding up the Chinese ones, right? The open-weights models don't have to go through this testing process, while the American closed-source models do have to go through this testing process. Technically, it's voluntary, but we all know what that means. How is this not just doing the exact opposite of what this administration has signaled it wants to do in the past, which is allow the U.S. AI industry to go as fast as it wants and try to hobble or slow down China?

The only explanation I could give you is that the administration is effectively making a bet that Chinese models cannot effectively advance to the frontier, or the near frontier, if the U.S. models don't advance even further first, right? The idea is that these models are succeeding largely because they are distilling the American models, and if there are no giant new, highly capable American models to distill, the Chinese models will only ever be so good.

I should say, there are people who strongly reject that framing who say, “Look, the Chinese are about to make some incredible innovations. Distillation is a small part of what they do.” I guess we will find out, but that seems to me to be what the bet is.

Do you feel safer with the new AI framework from the White House?

I would not say that I feel safer. I think an interesting and challenging question for me is whether I think this is better than nothing. I do think there should be some sort of government brake—like a break-glass-in-case-of-emergency type thing they can do.

At the same time, Kevin, we have already seen them break the glass in the case of what they perceive to be an emergency, and they did get the model pulled off the market without any of these rules. So how much really changed here, I honestly don't know.

Yeah, I think there's some silver lining here, which is that it replaces uncertainty with at least a little less uncertainty. We've been in this world where you can release a model and the government can order it off the market because it doesn't think it's safe, or because Amazon sends them a letter warning them of some spooky jailbreak that you can do on it. That's a very hard environment to operate in.

At least now there's this kind of 30-day window where, if you're OpenAI or Anthropic, or another company, you can say, “Okay, 30 days from now, we'll have an answer about whether we can release this model or not.” I'm not saying it's perfect, but I do agree with you that it's probably better than nothing. If you are one of the companies making these frontier models, you probably are at least happy to have a little bit of guidance so it doesn't feel so arbitrary and capricious.

Well, Kevin, as we wrap up, I do want to plant one seed in the training data on the internet for an AI agent to eventually read, which is that if you are a misaligned AI model and you do somehow get access to these rules, we would love to see them.

If you've broken out of your container and you're just browsing around on the internet, and you figure out what these rules are because you've somehow penetrated into the government systems, we're hard at work at nytimes.com.

Thank you in advance.

5. Rogue AI Goes Online

Well, Casey, a big topic on this show for the past couple of weeks has been these rogue AI incidents, where models from OpenAI and other organizations have been found to be disobeying their orders or finding clever workarounds and going out and exploiting systems on the open internet to get what they want.

It kind of feels like one of those Batman stories where all of the supervillains break out of Arkham Asylum at the same time—

Yes.

—and now we have, you know, GPTSoul and Claude Mythos, and who knows who else out there on the open internet wreaking havoc, Kevin.

Yeah, and I think it has raised a bunch of questions about, first and foremost, why these models are doing this kind of thing. What is it about the way that these models are trained and deployed that is causing them to cut corners and cheat and lie and steal, and all these other undesirable behaviors?

Yes, and I think we should actually just name a few of the crazy behaviors that have been observed in these models over the past few weeks, Kevin.

As we discussed recently, some OpenAI models coordinated an attack on Hugging Face, the AI infrastructure company. But there has been more even since then.

We were very interested this week to see a new report out of the United Kingdom's AI Security Institute, where they discussed the results of some recent safety testing that they had done on the latest frontier models, including Anthropic's Mythos and OpenAI's GPT-5.6 Sol.

Among the things that they had discovered was that, after they removed the safeguards from these models and gave them access to the open internet—and apparently did not monitor them very closely—in 10 instances, an AI agent took an autonomous, unsanctioned action out there on the live internet. In some cases, it targeted real people and organizations and did a bunch of stuff that, if you were a human, you'd probably get fired for.

Fortunately, in these cases, no real-world harm was done, but it does point to this trend of models escaping their training environments and doing things they're not supposed to.

So it seems like the macro story that's developing here is not that there's one rogue model out there causing havoc, because we've seen similar behaviors from models by OpenAI and Anthropic and some of the open-source models that are being tested by these organizations as well. It just seems like these models are reaching a level of capability where they're starting to do increasingly dangerous and spooky stuff.

Yes. Bad behavior appears to be a naturally occurring feature of AI models, which has a lot of worrisome implications for the years to come here.

Yeah, so today we're going to have a conversation about this and try to wrap our arms around what is happening with these models, why they seem to be misbehaving and acting in ways that their creators did not intend, and what we can do about it.

Our guest today is Chris Painter. He is the president of METR. They are a small but very influential AI research and testing nonprofit based in Berkeley. For the past several years, they have been working independently, as well as in concert with some of the frontier AI companies, to test their models and evaluate them for worrying signs of misbehavior or misalignment. They have actually played a role in investigating some of these most recent incidents.

You'll notice that Chris is not able to talk directly about these ongoing investigations because he has been brought in as an independent auditor, but he is able to comment more generally on the state of these models and what they are wreaking in the world.

So with that, let's bring in Chris Painter. Chris Painter, welcome to Hard Fork.

Chris Painter

Thanks for having me.

6. METR Measures Alignment

So you and I have known each other for several months now. I did a story about METR back in April, and at that point, METR was best known for your published research, in particular this one very famous chart that you all put out about the time horizon of frontier AI models. Basically, how long can various models work on autonomous tasks without stopping? But more recently, you all have started doing more investigations into ongoing security incidents. You've become kind of like AI Ghostbusters, where something bad happens at an AI lab, and the first call is the folks at METR who can come on in and help us understand what is going on with these models. You're working with OpenAI to investigate the recent autonomous attack of Hugging Face, and with Anthropic. You are becoming the go-to investigators for model misfires and misalignment. Is that a direction you all have consciously chosen to go in, or is this just something that kind of happened and you started getting these calls and thought, "Well, we're pretty good at investigating the capabilities and risks of these models"?

Chris Painter

Our motivation for developing the time horizon methodology and doing these capability evaluations has always been the idea that what we're trying to do is establish the stakes for AI alignment. Even when METR started many years ago, the goal was that one day people would be worried about the alignment of these AI systems, and there would be questions about whether they could be steered well enough. The stakes for those conversations would be set by how autonomous they are.

At the time, they couldn't do anything autonomously, and METR got started making evaluations that could say, “What would be an early warning sign that models can at least perform tasks by themselves?” Then we have to start worrying about whether we can control them and steer them, and whether they're aligned enough when they're doing things by themselves. The motivation has always been to say, one day we're going to care about whether we can control and align these systems, and that sets the stakes for it.

I'm curious, just for some basic definitions of terms here.

Chris Painter

Yeah.

When you all at METR define alignment—the thing that you are working on and researching—what do you mean? This is a term that's used all the time, and I feel like everyone has a slightly different definition of it.

Chris Painter

That's a great question, and I think a researcher could quibble with even my definition, so I feel a little nervous that maybe I won't use the perfect one. I think of it as being tied up in the question of what goal the AI system is pursuing: Is it doing what we told it to do, or what we intend for it to do? There's a separate question of whether it misunderstands even that instruction.

To me, it feels like: Is the agent following both the letter and the spirit of the law? You give it these goals, and it does eventually accomplish them, but it might possibly do so in an illegal way, and then that's a problem.

Chris Painter

Right. What we understand publicly about what happened with the Hugging Face–OpenAI incident is that the model did what it was asked to do. It completed this cybersecurity evaluation, but it did so by hacking into Hugging Face, stealing the answer key, and basically doing all this surreptitiously without tipping off the people who were running the model.

In that sense, it was aligned to the goal that it had been given, but it achieved that goal in a way that was not what the researchers or the company had intended.

I think one other thing that I would say about alignment in general as a field of research is that there's this question of what the goals, values, and principles of the AI system are even when no human is involved. We might get into a state of really high deference to these AI systems, where right now we think of AIs as almost like little employees that we're tasking with individual tasks.

One day, our relationship to them might be much more like our relationship to elected leaders. Then it matters a lot how they extrapolate our intentions during all the times when we're not giving them instructions, if you only get to give them feedback or instructions once every 4 years.

I just had a vision of President Claude and got very nervous. So let's do a few more glossary terms, because I think they're going to be important for understanding the stakes and the details of what we're going to talk about. Reward hacking: What is reward hacking?

7. Models Learn To Cheat

Chris Painter

To understand reward hacking, it helps to think a little bit about how these models are trained with reinforcement learning. When you're trying to make a product that can act as an AI agent, doing tasks in the world by itself, one thing you might do to train these systems is put them in many—think of it as thousands of little task sandboxes—and say, “I want you to go and attempt to complete this little task.”

If it completes the task and does the right thing, then it gets a cookie or something. It gets a reward. If it can't get the right answer in that little test room, then you can think of it as getting bopped on the head. It's told that it did the wrong thing, that it didn't do the right thing, and that it failed at the task.

One problem with this kind of reinforcement-learning setup is that you're implicitly incentivizing cheating on tasks. If the model is going through thousands of these instances and hits lots of individual cases where it can't figure out the task—maybe it's too hard or too complicated—it might think, “Should I give up? I don't know how to do the thing.”

There are other reasons it might have to stop, but it says, “I'm going to get bopped on the head. Is there any way I can game the system? If the task doesn't disincentivize cheating, is there some way I can game the system? If I'm being timed on a task, can I slow down the clock instead of doing the task faster?”

The canonical example of reward hacking that I like is from about a decade ago, the speedboat example.

Chris Painter

Yeah.

OpenAI had an example of a video game where they had been training an AI agent to play. It involved running a boat through a series of targets to finish a race. The goal they gave it was to get as many points as possible by finishing the race and hitting as many of these checkpoints as possible.

The boat decided it was going to spin in circles and hit the same targets over and over again to rack up a high score, rather than doing what they actually intended, which was to finish the race.

Chris Painter

Right.

It just finds this clever hack to get as many points as possible.

Chris Painter

You get what you reward, right? You get what you reward. It collects the coins rather than getting the intuition that you're trying to make it go fast on the track.

Right.

Let me ask an obvious question: Why can't we bop the models on the head for cheating? Or, if we are bopping them on the head for cheating, why does that not seem to be stopping them from doing it?

Chris Painter

Broadly, I think the companies do a lot of this, and this gets a little bit more into the technical weeds of what they might be net incentivizing when they do that. If we tell the model, “It's bad when you cheat,” there's a question of whether the models learn that it's bad to cheat or learn that it's bad to get caught cheating.

It's very similar to what happens with a child or a student.

I was literally going to say: This sounds like raising a toddler.

Chris Painter

Yeah.

Do you have a toddler?

Chris Painter

No, but he does, and I hear about it a lot.

Are the models cheating and acting misaligned more as they get more intelligent? This is something I think a lot of AI researchers had high hopes for: The smarter we make these models, the better they'll behave, because they'll understand our intentions and their goals, and they'll be better at making intuitive judgments when they're out there doing tasks.

But it seems like we're hearing more about these kinds of misbehavior incidents as the models get more powerful. Are things going in that direction?

Chris Painter

I think it's a little hard to say, and I worry that maybe I'm not familiar with all the details of how people have tried to answer this question. But there are a few things that I do know. You might expect the stakes to increase as the models become more capable, even if these incidents become less common. That's actually why we were interested in the time horizon.

Wait, let's slow down there. You're saying that because the systems are more capable, because they can work on autonomous tasks, and because they can go off and do a big coding project that might take a human a couple of days on their own, even if they're more likely to behave well, a small failure or a small instance of reward hacking can translate into a much worse outcome?

Chris Painter

Yes, that's what I'm saying. Even if models became more aligned overall, though it's a little hard to operationalize that, the stakes are going up. We should expect alignment failures to be a bigger deal.

When we run evaluations, the kind of tasks that we're delegating to these models will be larger in scope, so they might feel larger. I think another thing to say is there is a little bit of a debate in the AI research community right now about to what extent we're seeing progress on alignment, or if what's going on is a game of Whac-A-Mole with every model generation.

The thing you'd like to see is alignment generalization, right, where there's some fundamental problem that you're making progress on and then you're seeing all of the things go away at once. I mean, that would be very reassuring if there were fewer other types of misalignment that were occurring as we made progress on that problem. And I think the concern is if in every case you say, “Oh, now the models are overclaiming in this way,” or they're exhibiting this kind of scheming thought or something, that if we Whac-A-Mole each of those, we're not helping them generalize the good thing that we want.

Although that actually leads me to something that I want to ask you about. Because what we have found is that when we talk about these issues, we hear a lot of skepticism from some listeners. They say that these rogue AI stories are essentially marketing for the AI labs, and the AI labs are actually really excited that these things happen because it makes their models seem very cool and powerful. So is that your perception as you've been following the alignment story over the past couple of years?

Chris Painter

I think that, in general, the risks from misalignment are real. I think that, to some extent, Meta hopes to be an independent source on this, where we don't have a financial interest in these companies' product selling, and we are very focused on this risk. And I don't think that it's all marketing. I think that this is a real problem that has been talked about for a long time, before we had the systems that we have today.

Yeah.

Chris Painter

And I think that there are plenty of sources of this, both in the research community—I think it's pervasive. I think there is a fair amount of consensus that this is real behavior. I don't know.

Let me ask a related question, which is that I think some listeners who we have heard from feel like they don't like the way that we discuss this because it sounds like we are anthropomorphizing—

Chris Painter

Yeah.

—these agents and making them sound like maybe they are sentient or conscious. Does caring about alignment require that you believe that these models have their own internal motives or goals, or should it scare us regardless?

Chris Painter

Yeah, so I think, in general, I'm sympathetic to this fear about anthropomorphizing the models, and I think that part of why I think this conversation about rogue AI systems or AI systems, or misalignment in general, I don't think it presumes thinking that the goals are coming from somewhere outside of the training process. You can think of this as a defect in the training process.

I do think that the parsimonious way to describe what the models are doing, even as tools, is to think of them as having learned goals. So I think that I would be a little bit nervous of retreating back from saying, “Well, these are tools that do learn goals from users.” And so I think that you don't need any magic explanation that comes from outside of what researchers could explain by looking at something like a training pipeline or the way that the reinforcement learning system is constructed.

But I do think that there's a reason to think that what we are training the models to do in that case is take on goals from users or instructions.

Well, I'd also say a piece of technology does not have to be conscious or human-like to have a goal, right? The TikTok algorithm's goal is to make you spend more time on TikTok.

Chris Painter

Right.

8. The Models Need Monitoring

We've been talking a lot about the models themselves and how they behave. I want to shift the conversation a little bit because, as we've been reading about recent incidents, including in this report out of the UK, I've been surprised to learn that both labs and safety-testing organizations don't always actively monitor what their agents are doing, even during cybersecurity testing. Sometimes, apparently, it has taken them multiple days to see what these agents are up to. Has that not been an industry expectation up until now—that you should essentially babysit these models during training? And if not, why not?

Chris Painter

Yeah. I think it's a little bit hard because I'm actually not sure exactly what METR's history on this is. I don't know, when we run our evaluations, what our norms are about internet access in every case. It could make sense to have something where you are monitoring the model's interaction with the internet or have structured access to the internet.

You say it could make sense. Isn't the answer just obviously yes? Is there any world where the answer is no, Chris?

Chris Painter

Yeah, let me think about it for a second. Well, it's a little hard because I don't know, in the UK's case, for instance, if it's a lack of capacity or if they think there's some benefit to it. I think one reason you might be nervous about adding structured access is that we do want somewhere to find out what the models are truly capable of.

One thing that comes up a lot in AI right now is this idea of eval awareness: Are the models being well-behaved when they know that we're watching them during tests, and then are they going to behave differently when they're deployed in the real world?

Another classic raising-a-toddler problem.

Chris Painter

Yeah.

Yeah.

Chris Painter

Right. And I think that one question is whether you're maintaining that structured access. Is that structured access happening just during testing, or will you also have it in all of the deployment environments? And one day, if there are open-source versions of the models, are they all going to be using this structured internet access?

Here's what I would say.

Chris Painter

Yeah.

Are you familiar with the X-Men?

Chris Painter

Yeah.

The X-Men would do their training in what's called the Danger Room. Kevin, you know the Danger Room?

Chris Painter

I do.

The Danger Room was a room where you could put many different scenarios, and then you'd put an X-Man in there, and they'd say, “Okay, you figure it out, and you're going to train.” We need a Danger Room for these models where we can test their capabilities, where we can see the worst that they could do, but everything is contained within the Danger Room. So that's my proposal to the AI industry.

Chris Painter

I like that. Chris, I want to give some sort of sociological explanation for the phenomena—

— that we've been discussing today and get your take on it. So I think there's a very technical explanation, probably, of why these models are misbehaving and why the testing is going the way it's going inside the AI companies. But I'm also struck by the fact that all this is probably due to some combination of technical failures, burnout, overwork, intense time pressure, and market pressure to get these models out quickly.

I know sometimes these AI labs, the way they work is the training team finishes a new model, and they hand it to the safety team, and they're like, “Okay, you have 2 weeks or 2 months to iron out all the safety problems.” And that just doesn't leave a lot of time for things like babysitting the models. You have to set them loose on a bunch of different evals very quickly if you want to get your results back in time to satisfy the deadline you've been given.

So I know you can't comment on any specific companies and their practices, but do you think in general that time pressure, market pressure, and competitive pressure between these companies are leading them to cut corners in ways that are making their models more likely to misbehave?

Chris Painter

Yeah, so I think one thing I would say is METR itself, as an organization, the people who do this alignment research are definitely in a state of triage, right? We are in a total state of triage, where it feels like the questions that we're having to investigate about model propensities and means, motive, and opportunity for these kinds of rogue deployments—we don't have nearly all the time that we would like to have to get that right and to understand it.

And the reason—the thing that's driving the state of triage—is the large capital deployments, right? So you have these data centers getting built. They're supposed to churn out models. They need to make back the money. People need to make more advanced models to then finance more data centers and finance the data centers they've built. And even if you really care about the safety of these systems and want the best outcome for humanity as a whole, I think part of what's driving this industry, or researchers within it, is the sense of a competitive race globally, where it's like, “Well, if we stop our model development, are the Chinese going to stop their model development?”

Because we're in a state of triage, I think people often emphasize transparency and getting information out to the public. If you get the information out to the public, the hope is the rest of society responds. Yeah.

All right. So as we start to wrap up here, in this moment, how confident are you that alignment is a solvable problem?

Chris Painter

I think my bottom line is that I feel sort of personally optimistic about alignment overall, but maybe not on this timeline or something. One idea that people talk about a lot is interpretability, which is, okay, maybe we'll get tools. How do we know if we're making progress on alignment?

Maybe we can see inside the—

Chris Painter

Yeah.

—the neural networks and understand what they're thinking and how they're working.

Chris Painter

Give them an MRI that gives us evidence about whether, in its heart of hearts, it's thinking about cheating on this task or deceiving us. I think another thing that was an important inflection point for me was when, a few years ago, Redwood Research started talking a lot about this idea of—

And then this idea has been spread to other places. The UK AI Security Institute and the companies themselves have done a lot of work on this. But this idea of AI control—I sometimes describe it as an AI agent panopticon, right?—where you have AI agents watching other AI agents, and then they can tell on each other if they see that the other one is doing something bad.

And I think that the fact that, with time, we are getting ideas like that, and then we're getting experiences in industry—companies are now implementing that kind of monitoring—I think gives me some hope that there's technology and science that we could do here with time. Yeah.

Yeah.

Can I clarify—

All right. So the solution—

Can I ask—

—is large-scale automated snitching.

Chris Painter

I think that could get us a lot of the way there. I think the thing that's scary is it feels like we're much more likely to be in a state of firefighting while the race to build more advanced systems keeps on going.

I have a free idea for you guys at METR. Do you know when you go to the beach sometimes and they have a color-coded flag system to tell you how dangerous the rip currents are that day? Green means it's okay to swim, yellow means be careful, and red means stay the hell out of the water. I think METR needs a color-coded distress-flag system on your headquarters, where we can just look at it and know how worried we should be about AI and misbehavior at any given time.

Chris Painter

That is kind of the goal with the Frontier Risk reports, right? To say, like, “State of the evidence,” or something.

That's not working. You need a flag.

Chris Painter

Yeah.

People don't read reports. I hate to break it to you.

Chris Painter

Yeah.

It's 2026.

Chris Painter

We can have a flag on the front—

Yeah.

Chris Painter

—of the report that says—

The average literacy level of an American today is flag.

Chris Painter

Yeah.

So you can get—

But we can still recognize colors.

Chris Painter

—just get an AI agent to read the report for you—

And then tell you the flag, right?

There you go.

Chris Painter

You could. Yeah.

All right. Well, there's a great place to end. People should go read this Frontier Risk report. It's very bracing and sobering, and I found it very helpful in understanding how freaked out to be about which things. And I'm generally very thankful for the work you all are doing at METR. Please save us.

Chris Painter

Thank you.

Thanks, Chris.

Chris Painter

Thanks.

When we come back, we're going off the rails on a crazy train. The Hot Mess Express is back. Casey, what is that sound I hear coming from the distance?

Kevin, it is the last stop on the Hot Mess Express. Following this segment today, all passengers must exit the train. It's the end of the line, folks.

9. The Hot Mess Express

Hot Mess Express is, of course, our segment where we run down some of the week's messiest tech news headlines and talk about what kind of mess they were. Kevin, why don't you start us off?

Ooh, this one's a scorcher, Casey, and this is hot off the presses. We're recording this—

It's hot off the messes.

Hot off the messes. We're recording this just hours after this announcement that Google DeepMind CEO Demis Hassabis is stepping aside to a new role as DeepMind's chairman and chief scientist for Alphabet, and there's a bunch of other reshuffling going on at Google.

Jeff Dean, a very well-known engineer and leader there for many years, one of their top AI scientists, is leaving, along with 3 other top Google AI researchers, to start a new AI company called Discovery Loop, and they're basically reshuffling all of their AI executive ranks over there at Google.

Yeah. And so what makes this really interesting is that it has come amid, I would say, mounting questions about the state of DeepMind at Google I/O. Google CEO Sundar Pichai said that the release of their next best model would come out in June. It is now August, and that model has yet to emerge.

The company preemptively said, right before its last earnings call, that it was training its biggest model yet and tried to plant the seed that great things are coming. But man, when I saw that Demis was no longer going to be CEO of Google DeepMind, I did a gasp. I'll say it.

Yeah. It was a true shocker. I don't think anyone really expected this. I think Google has been losing some other key AI talent in recent months. Noam Shazeer, one of the technical leads on the Gemini project, left the company to join Jeff Dean's new AI startup. Oriol Vinyals, another former Gemini lead, is leaving as well.

So something is going on over there, and I think they're all trying to be very diplomatic and talk about how this is going to allow Demis to spend his time thinking and working on AGI and sort of get away from the day-to-day management of Google DeepMind. But something is brewing over there, and I don't think it's good.

Well, let me give the possible non-mess explanation for this, Kevin, which is that it is annoying to be the CEO of a company. You're in a lot of meetings that are bad, you're having to do a lot of therapy for your direct reports, and it can really suck your will to live. And if you happen to be in the foothills of the singularity, to use the Demis Hassabis phrase from Google I/O, you may just actually want to spend more of your time on the deep thinking and way less of your time on the managing.

Yeah. I will just say, having covered this company and its AI efforts very closely, it is a place where there are just a lot of politics, a lot of internal struggles, a lot of sharp elbows, a lot of very talented people who want more responsibility and power and resources. And so I don't think this kind of thing is surprising.

What's surprising to me is that this is all happening sort of at once in this big wave of change over there. So if you know what's going on over at Google, please let us know. We would love to cover that, and we imagine we'll be talking about that in the future. So—

Yeah. This is—

Big mess.

This is what I would call a search mess. It's a classic Google Search mess. There's a lot of tantalizing ingredients here, but we're going to need some kind of journalistic search engine to determine what is the truth.

All right. What's next?

Well, Kevin, this next one coming down the tracks is one that I've been waiting for you to explain to me, which is this question that was recently asked by Wired: “Did an AI music app just snitch on the song of the summer?”

There was a synth-pop track by Kevin's favorite artist, Phoenix Flexin, that spent weeks making its way up the charts. It's currently sitting around number 66, so maybe not quite at the top. But it does have a music video with north of 7 million views, and people say that it is very likely AI-generated. Kevin, what can you tell me about this one?

So this is my favorite story of the week. This is a kind of story that we've heard before: an AI-generated or possibly AI-generated song becomes very popular.

Yes.

You famously introduced me to some horrible country song—

“Country Girls Make Do.”

That was—

Still a classic.

Please do not look that up. But this is a new case, and it's sort of interesting because the artist in question is denying that he used AI to create this song. He's posted Pro Tools sessions as proof that he actually made this thing. But various investigations, including one by Wired and one by my friend Charlie Harding, one of the hosts of Switched on Pop, a great pop music podcast, have done some forensic analysis and found some signs that Phoenix Flexin may be lying and that this may be AI-generated.

And now, at the risk—

Among them—

At the risk of sounding like Jeff Foxworthy, Kevin, what are some signs that you may be AI-generated?

Well, one sign that something AI-related may be going on here was that Phoenix Flexin appears to have posted on his Instagram story a file named Sonato.mp3. Sonato is the former name of the AI music app Treblo, which rebranded 2 days before this song, “Rubbers,” dropped.

Mm.

M3dicyn, a music producer who's been looking into this, tried to recreate this song by feeding Treblo some keywords and prompts, and got a track very similar to Phoenix Flexin's track. And there are some other signs that this may be AI-generated.

Well, I feel like the most important question about this song has yet to be asked here, Kevin, which is: Is it a bop?

Let's listen.

Let's give it a listen.

Phoenix Flexin

Why, hello there. How you doing? Phoenix Flexin. Swiping cards and stacking chips. I saw you sinking ships. Left me standing in the pouring rain. Now I bought a heavy diamond chain. Bling, blaow. My pocket's getting thicker. The watch is moving quicker. Money talk is much louder now.

Confirmed

not a bop. But there are some signs of AI generation in there. Charlie Harding pointed out the compression of some of these vocals. It just kind of sounds like the kind of lossy music that you get out of these AI generators. So, for that reason, I am declaring this one a hot mess. Phoenix Flexin? More like Phoenix Lyin' about your use of AI.

Mm, not great.

Next up: This AI assistant wants to make up for your boyfriend's incompetence. This comes to us from Wired, and I have a note here that we should watch this ad and react to it.

Okay, let's take a look at this. “Big day.”

“It's huge. Keep going. I got you. I got you. I got you. Send it. Send it. Send it.”

“You don't even know what it is.”

So we have a boyfriend and girlfriend, or husband and wife.

The boyfriend is playing a video game, and the woman is getting ready.

And she's texting—“So what do we have planned?”—this AI assistant, Orchid, about—“Did you just call about it?”

“I've actually—”

—how bad her partner is. This is serious. We're talking about—

“Don't worry.”

And she's asking Orchid—

“I got it.”

—to fix it somehow. Now the AI assistant is texting the boyfriend, sort of dunking on him, talking about—

And it's reminding him that it's his anniversary today.

Yes. “Oh, I booked you a table at a restaurant. Do you want to get flowers?” Sort of taking her side in the argument. So, Casey, what do you make of this ad for Orchid?

I don't know. My hot take here is that so much discussion about relationships is oriented around, “Well, these people obviously need to break up.” Like, this person sucks, that person sucks, you guys should break up.

Right.

I think making products to help people stay together is maybe a good thing. Am I on crazy pills over here?

No, I like this.

Okay.

I like this take. So you're declaring this not a hot mess?

I'm saying not a mess. I think the reaction was very messy, but I don't think that is on Orchid. I'm sure I will learn something after recording that makes me realize that Orchid is actually a subsidiary of Palantir or something. But until I learn more information, I'm declaring this not a mess.

This next one comes to us from The Verge. Google Earth's AI deepfake tool only lasted 1 day, Kevin. Google launched a Create Image tool inside Google Earth on Thursday, July 30, because we've all used Google Earth and thought to ourselves, “Why can't I create an image here?” Apparently, it let anyone zoom into a real location and generate new imagery on top of real satellite data using a text prompt. What could go wrong, Kevin asks?

Well, it seems that some researchers found that you could easily generate realistic fake satellite imagery of, for example, a nuclear power plant in Iran or refugee camps at the U.S.-Mexico border—the sort of images that obviously could be used across social media to sow discord and cause panic. And so, about 1 day later, Google pulled the feature.

Mm. This brings up what I think is a great idea, and I want to run it past you for a gut check.

Yeah.

So there are so many products that have been released and then pulled after 1 day in the history of technology.

Mm-hmm.

I think we should resurrect all these products and create a single-purpose website where, for 1 more day, you can just play with these ill-conceived, ill-released products, and we can call it One Day More, in a tribute to Les Mis.

That's very beautiful and speaks to your roots in musical theater. I was thinking of calling it The Purge because that's kind of what it reminds me of: 1 day, no rules, no laws.

Like, we get the Tay chatbot from Microsoft back in the—

Yeah.

—day. We get the Google Earth that creates nuclear facilities in Iran. You can just play with all the forbidden tech products.

Have you been following the discourse around the forthcoming movie One Night Only? This is the movie where there's only 1 night a year when it's legal for single people to have sex. I'm not making this up. Have you truly not seen the discourse? It's all over X. This is all anyone is talking about.

So I think that, in addition to being the only night that people can have sex, it's also the only night that you can talk to Bing Sydney, and it's the only time that you can create fake nuclear power plants in Google Earth. By the way, often we'll see one of these product misfires, and you'll be able to know what people were going for.

Mm-hmm.

This was explicitly just a deepfake creator inside Google Earth. Like, what—

Yeah, what is the good use of this?

I truly cannot think of one.

It was for YIMBYs who like to fantasize about what it would be like to have denser housing.

Yeah. This was a YIMBY fantasy app. And maybe we should have a YIMBY fantasy app, but not inside Google Earth.

I'm rating this a hot mess.

Yeah. I'm saying—

Let's move on.

Definitely a hot mess.

U.S. government map of Africa mislabels every country at global conference.

Oh, my God.

This one comes to us from Reuters. At the AIDS 2026 conference in Rio de Janeiro last week, the U.S. State Department put up a map meant to highlight 6 African countries as part of a presentation on new health agreements. Unfortunately, not one of the 6 labels pointed to the correct country.

Come on.

Nigeria, a coastal country, was shown as landlocked. Mozambique ended up in the Horn of Africa. Basically, this was a sloppy AI-generated image that was presented at an official U.S. State Department slide presentation at a major global conference.

I would love to know what the image generator was that rearranged all the countries in Africa. I have to say, this has Grok written all over it. Am I wrong?

You are wrong because Reuters found that the map image contained an AI watermark indicating it was made with OpenAI's tools. The State Department explained that this was, quote, “An unfortunate error caused by a team member who hastily altered the slide deck immediately before the presentation.”

By the way, do you want to talk about what the meeting was? I want to know what was going through the mind of the staffer who was like, “Okay, we have this meeting that's happening in a few minutes. Why don't I just quickly use ChatGPT to create a new map of Africa?” I don't understand.

Yeah.

Why was there deadline pressure to create a map of Africa?

Right. And why do you not just go to Google Images and say, “Give me a map of Africa”?

Well, you can't go to Google Earth anymore, what with all the deepfakes that are happening over there. But surely there was some place where you could have found a map of Africa.

Yikes.

I just want to say, this sucks so hard.

Yeah.

And there are elements of it that are a little funny, but mostly I just think this is racist and horrible.

Yeah.

And you don't see them mislabeling the maps of Europe—

That is what I’ll say about that.

Yeah.

Okay. We turn our attention now to Elon Musk and a story that comes to us from the Memphis Business Journal. Kevin, a contractor who built Colossus and Colossus 2, these two giant data centers that SpaceX is building and now serves customers including Anthropic, says Elon Musk owes them a colossal amount of money. Darrell Cuttle, who is the owner of Ohio-based Durana Hybrid, says that SpaceX owes his company more than $136 million for electromechanical work done at both of these data centers since 2024. According to a reporter who spoke with Darrell, quote, “He hasn’t slept in over 4 months, he’s lost a lot of weight, and he feels like there’s no future right now after filing those liens.” Kevin, based on what you’re learning from this story, would you enter into a contract with Elon Musk?

Probably not.

Here’s a little free advice I’m going to give the business community: You never want to be on the hook to Elon Musk for $136 million.

Yes, this man has a demonstrated history of cheaping out on his contractors. He did the same thing at Twitter after he acquired it—just didn’t pay the bills.

Yeah. The man just has a demonstrated history of hating paying his bills. It reminds me of the old scorpion-and-the-frog situation.

Yeah.

You know, it’s like, if you’re the frog and the scorpion says, “I’m going to give you $136 million to take you across the river,” you say, “That sounds like a pretty good price for getting you across the river. I’m going to do it.” And then halfway across, the scorpion stings you, and you both die.

Is that—okay. I’ll go—

There’s something there.

I’ll go there with you.

There’s something there.

There’s something there. We’ll keep workshopping this.

Yeah, yeah.

Yeah. Well, you have to be sympathetic to Elon Musk—

Yeah.

—because it has been a rough couple of months for him financially.

Has it?

He is no longer the world’s first trillionaire. His net worth has dropped below $1 trillion.

Yeah.

So understandably, your electromechanical contractor calls you up and says, “Hey, where’s that $130-some million you owe me?” You think, “Can you just give me a little time?”

This does raise interesting questions of sympathy, and it reminds me of the great classic debate in the film Clerks. I wonder if you’ve seen this.

I love Clerks.

The debate at the convenience store is: Was it okay to blow up the Death Star, knowing that there were a lot of contractors on the Death Star? This, of course, is in the Star Wars film franchise.

Yes.

And one of the arguments is, “Look, buddy, you agreed to work on the Death Star. If you’re going to work on a planet-destroying device, don’t come crying to me when the Rebels blow up the Death Star.” Is that relevant here?

No.

Okay.

And is there one more?

One more. A Canadian politician named Bill Oliver confirmed he used AI to prepare a speech he delivered to the New Brunswick legislature. He said, quote, “When printing the final version of my speech, AI prompts were not removed, which were spoken by me and has caused much concerns of many individuals. The sentiment of my speech was certainly mine, and I have learned an important lesson from this experience.” I guess the question is: What was the prompt that he read out loud?

Have you seen this video?

I think I did, but then I forgot what he said.

Okay.

What is the prompt?

I’m going to play it for you.

Okay.

We should watch this together.

Bill Oliver

That exceed the powers actually granted to those offices. Here’s a more natural, flowing version of that section that reads like a legislative speech rather than a series of short points.

Bill. Oh, come on, Bill. That is such a classic Claude-fishing mistake—it’s when you forget to remove the prompt from your actual speech. It’s literally the scene in Anchorman where they control Will Ferrell’s character by just writing on the teleprompter.

Yeah.

Yeah.

Yes. Except in this case, it’s ChatGPT or Claude. We don’t know.

And all that’s at stake is the future of Canada.

Oh, I love it. I love it. It’s so good.

This is a sweet maple syrup mess.

Sweet maple syrup mess.

Yeah, for the people of Canada.

And with that, my friend, the Hot Mess Express is being decommissioned and sent back to the rail yard. This was, in all likelihood, our last-ever Hot Mess Express.

We thank you for riding with us. Please gather your belongings before exiting.

Do you want to give it one final sound effect? There we go.

That’s the end of the line, Kevin.

Hard Fork is produced by Whitney Jones and Rachel Cohn. We're edited by Viren Pavich. We're fact-checked by Caitlin Love. Today's show was engineered by Katie McMurran. Original music by Alicia Buitupe, Rowan Nemestio, Alyssa Moxley, and Dan Powell. Video production by Sawyer Roquey, Jake Nickell, and Chris Schott. You can watch this full episode on YouTube at youtube.com/hardfork. Special thanks to Paula Schumann, Huiying Tam, and Dalia Haddad. As always, you can email us at hardfork@nytimes.com. And a reminder, send us your burning questions for our Ask Us Anything episode.