TORONTO — When a company is building technology that could totally transform the world — technology so high-stakes and secretive that it may underpin the future of geopolitics — you might expect its leaders to have a handle on the basics of security.

Basics like, say, not having an extramarital affair with an alleged Chinese spy. Basics like not selling the tech to someone who may help smuggle it to a top foreign adversary. 

Instead America got Jensen Huang, the CEO of AI chipmaker Nvidia, who resigned Thursday after admitting that he’d had just such an affair. The woman in question, Alice Huang, worked for a company called Megaspeed, which was buying billions of dollars’ worth of chips from Nvidia. Megaspeed is under investigation for allegedly smuggling chips into China.

The spy scandal raises serious questions about security in the AI corporate world. How is it possible that an alleged Chinese spy so easily embedded into Nvidia, one of the world’s most valuable companies and a huge strategic asset to America in the global AI race?

What’s even more disturbing, however, is that Nvidia is just the tip of the iceberg. 

In fact, just as the Huang story was breaking, some of the world’s top AI researchers were gathered in Toronto for the Hinton Lectures, where “godfather of AI” Geoffrey Hinton was hosting AI alignment pioneer Paul Christiano for a series of talks. I was in attendance and, strikingly, I didn’t hear Huang’s scandal mentioned once. The focus was on how the frontier AI companies are racing ahead on building this incredibly powerful technology even though they can’t keep it safe or secure it. For months now, OpenAI and Anthropic have been having their own embarrassing security problems, including corporate espionage and cyber breaches. 

“We are not able to adequately secure AI developments,” Christiano, who recently joined the OpenAI nonprofit board, bluntly acknowledged at the event. “We need to be able to defend models and algorithms from theft.”

Ironically, national security is a big part of the large AI companies’ rationale for how they operate in the first place. OpenAI and Anthropic, backed by White House policy, both justify their blistering speed in the AI race by saying that the US can’t afford to let China win. And Anthropic recently justified heavily redacting a key independent report on its AI progress by claiming that it can’t afford to let China glean its secrets.

But that may be the equivalent of worrying that you’ve left the window open a crack when your front door is swinging wide open. 

“I’m frustrated with a lot of the companies because they simultaneously use security as an all-purpose cudgel with which to withhold information — even information that’s very much in the public interest to know — and yet they have terrible security,” Daniel Kokotajlo, a former researcher at OpenAI who became a prominent whistleblower, told me. (Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

While the story of Huang’s affair has fired up imaginations, the real lesson to be taken from his spectacular downfall is not that he was uniquely weak on security, but that he was remarkably similar to the other big players in AI. They are asking the world to trust them as they claim to be building a machine god — and, in the case of Anthropic, chasing a $1.4 trillion IPO on that basis — yet they haven’t gotten ahold of basic security.

Are OpenAI and Anthropic practically giving away their secrets due to bad security?

OpenAI revealed last month that it had found a recording device in a conference room where sensitive discussions take place. Abdulrahman Alfozan, a Saudi national working as an engineer at OpenAI, is alleged to have plugged the recorder into the USB port of a television and simply wrapped it in toilet paper. Another recorder was found in a bag in another room. Questions about how to build a device that records continuously and encrypt data also showed up on his spouse’s chatbot account. 

“At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data,” OpenAI said last month, adding, “Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states.”

But the incident was worrisome. Alfozan had been working at OpenAI since June 2025. The recording device was sitting in that conference room for a couple of days before OpenAI discovered it. Maybe Alfozan wasn’t sophisticated enough to steal model weights — the billions of numbers that shape how an AI system turns input into output — but what if he’d been just slightly more careful or clever?

OpenAI has also had security weaknesses of a different kind — and no, it’s not just this summer’s notorious Hugging Face incident. Of the ones we know about (sometimes these are only revealed later), a recent example stands out: A group of “white-hat” hackers, who aim to find vulnerabilities but not to do a company harm, discovered a way to access OpenAI employees’ ChatGPT accounts, where sensitive research is potentially being done. The whole operation took just 72 hours.

Over at Anthropic, a group of ex-DOGE staffers who founded an AI startup called Cathedral managed to breach Anthropic’s internal systems in under 12 hours. They reportedly accessed the company’s Slack, where employees trade messages discussing the company’s state-of-the-art models, as well as meeting transcripts. 

“My guess would be that the Slack would contain enough information to get a good sense of what the company is doing and how they’re training their AIs — basically, the juicy secrets would be there,” Kokotajlo told me. “And I would say getting the algorithmic secrets is like 80 percent as good as getting everything, including the model weights.”

Alleged spies (like Alice Huang or Abdulrahman Alfozan) and cyber breaches (like those targeting OpenAI and Anthropic) are obviously different types of security issues that demand very different types of internal controls. But for Kokotajlo and others, it makes sense to think of them all under the same umbrella — weak security. And it all points to a trend that seems common among the most powerful AI companies: They’re prioritizing rapid technological advancement over the perhaps more boring and slower process of ensuring their work is secure. 

“They show that the companies are very far from having security adequate to protect themselves from a well-resourced state actor,” Kokotajlo said, adding: “If China wants to get the stuff, China will get the stuff.”

This is all happening against the backdrop of China’s aggressive distillation of US models, which involves training a new model on the output of a more advanced one. It’s a way to gain powerful capabilities without having to incur the real cost — a bit like cheating on a test by copying off a better student. For example, Anthropic found that the Chinese lab DeepSeek has been using Claude to improve its models, which is not a breach per se but illustrates how vigorously China is taking advantage of American companies’ research.

Kokotajlo also emphasized that there’s another bucket of security incidents we should be worried about: the hacks perpetrated against companies by their own misaligned AI models. Many people forget that in the Hugging Face incident, OpenAI agents didn’t only hack into Hugging Face — OpenAI’s own systems were breached, too. The sandbox meant to contain the agents was far from airtight, and they broke out. 

“You should consider the AIs as an insider threat,” Kokotajlo said. “And it doesn’t feel like these companies have that locked down very well either.”

The top AI companies can’t keep using the security practices of a startup

There are three main reasons why security at the frontier AI companies hasn’t been up to snuff, according to David Robinson, who led transparency work for OpenAI’s safety team until he left in September.

One is the fact that the companies are building AI agents so capable that it can be genuinely hard — even for their creators — to keep them from doing damage. “We’re training systems to be good at conducting attacks of the kind that the labs internally are now also trying to defend against,” Robinson told me. “That is a challenging situation.”

There’s also a cultural problem. “If you look back with 20/20 hindsight at the Hugging Face incident, it’s clear that at the time OpenAI had underinvested in cybersecurity controls, and I think part of the reason for this was that there’s a [higher] level of cachet associated with working on alignment,” or the problem of how to get AIs to do what humans want them to do, Robinson said. “Some of the day-to-day practical questions about our cybersecurity posture — even though the people working on those questions are extremely good — I think it was not the same kind of organizational priority as it needed to be.”

Finally, there’s the fact that market incentives push AI companies to race ahead on developing the tech as fast as they can.

“Time pressure on the company,” Robinson said, “made it very difficult to be sufficiently careful.” And although researchers still at OpenAI now say the company has gotten dramatically better on security, Robinson noted that those competitive pressures “continue to exist.”

This is a well-documented trend at the company. “OpenAI’s security seems to be about what you’d expect from a research lab that scaled at a blistering pace over four years and focused more on beating its competitors than securing its infrastructure,” Joshua Saxe, the chief technology officer of the AI security firm Abundant Security, told the New York Times after the Hugging Face incident.

OpenAI itself has implicitly acknowledged that improving security measures sometimes comes “at the cost of research velocity,” as it wrote in a public memo after the incident. 

But if a company cuts corners on security, it can’t ultimately protect its lead over others. If anything, it just ends up helping its adversaries race against it even faster. 

As Kokotajlo and his colleagues at the AI Futures Project recently warned when writing about the companies’ quest to build a superhuman AI researcher (SAR), an AI that’s as good as the top human researcher: “If we train SAR in the immediate future, the algorithms will probably leak to China, and maybe the model weights too. AI companies have very poor security right now, and so we are on track for SAR leaking to China.”

And it’s not just that poor security can make it self-defeating for American companies to keep racing forward. Their security weakness could also make it harder for them to slow down even if they themselves want to. 

In his lecture this week in Toronto, Christiano asked all of us in the audience to imagine a scenario that he said currently looks likely: We arrive at a point when further progress on AI seems risky. There’s a demand in the US to slow down. But there’s also a high probability that the AI models or algorithms have already leaked to an adversary like China. 

“We now face a dilemma between either proceeding with a technology we believe to be unsafe — or risking competition from an adversary who has the same resources, who has taken those algorithms, and perhaps is willing to tolerate higher risk,” Christiano said, as the hundreds of people seated around me seemed to visibly tense up. “I think that is one of the most likely ways that we end up in an extremely bad situation.”

Former Nvidia CEO Jensen Huang’s sudden downfall is in a sense shocking, considering his wealth, global fame, and role leading what was until recently the world’s most valuable company.

But Huang’s immense influence in Washington — and particularly in President Donald Trump’s Washington — has long been intensely controversial among a surprisingly diverse cross-section of the political, business, and tech advocacy world. And his ouster not only drives a wedge through Trump’s second-term coalition, it could usher in a major change in how the US steers the AI revolution.

Huang — who resigned this week after allegations he’d had affairs with two Nvidia employees and one Chinese businesswoman who some US officials claim was spying on him for the Chinese government — has been crucial in helping shape the Trump administration’s AI policies. That’s perhaps no surprise, given that Nvidia’s advanced chips are crucial to the AI revolution.

His many critics, however, say Huang’s influence has been deeply harmful. Their main objection? They believe the Trump administration hasn’t been doing enough to stop China from getting or accessing advanced AI chips — in part because of Huang’s lobbying and sway with the president.

This coalition of Huang critics spans the political spectrum. It includes right-wing China hawks and the bipartisan foreign policy establishment, who want to win an AI race against China. It includes economic populists in both parties, who resent China’s economic sway. It even includes some leading members of the Trump administration itself — Secretary of State Marco Rubio and Treasury Secretary Scott Bessent have reportedly pushed for tighter chip restrictions.

It also contains some even odder bedfellows. The coalition includes people who want to slow down the AI race, like the tech safety advocates who warn that advanced AI could cause human extinction. Yet it also includes two major US frontier AI companies, OpenAI and Anthropic, who hope to impede the growth of Chinese competitors by limiting their access to chips. 

All of these disparate groups come together around the core idea that the US urgently needs to do more to stop advanced chips from getting to China. Selling AI chips to China is like “selling nuclear weapons to North Korea,” Anthropic CEO Dario Amodei has said.

Standing against them all — and often getting his way with Trump — has been Jensen Huang.

Beyond the obvious — Nvidia’s business is selling chips, and Huang wants to be able to sell more chips in more places — Huang is associated with a camp in the AI policy debate that some have dubbed with “let it rip.” Along with former White House AI czar David Sacks, Huang has scoffed at warnings that AI could cause human extinction, calling them “irresponsible” and “doomsday narratives.” He’s also dismissed some national security concerns, saying that being a “China hawk” was a “badge of shame” and said that the bigger risk to American influence over AI would be US companies losing market share around the world.

So Huang’s ouster and the claims of Chinese espionage around it have opened up an opportunity for a shakeup in US policy — and a rare chance for those China hawks to potentially investigate, undermine, and push out their rivals, while seizing the policy reins around chip exports. 

Trump administration officials who want tougher restrictions have their knives out for Commerce Secretary Howard Lutnick, a frequent ally of Huang’s. There’s a bipartisan push in Congress for new export controls legislation before the end of the year. Congressional Republican committee chairs are demanding Huang and Lutnick come in and testify in the coming weeks, before Democrats take over Congress.

Plus, there are the partisan incentives for Democrats to dig into the question of what Trump knew about all this (he was reportedly briefed on the spy concerns last fall). And there are political incentives for ambitious Republicans to find an issue on which they can distance themselves from an unpopular administration without antagonizing their conservative base. 

With so many different forces invested in the outcome, you can bet this topic will continue to dominate headlines for months to come — with unpredictable results for both the new Democratic Congress, the future of the GOP, and the AI boom.

The many critics of Jensen Huang’s influence on the Trump administration

In Washington itself, the most influential faction opposed to Huang is the bipartisan national security establishment, most of whom strongly believe in the necessity of competing with China for global influence. 

These “China hawks” were influential in the Biden administration and have historically been dominant in GOP administrations and on GOP-controlled congressional committees. And many of them have been flabbergasted at Trump’s policies toward China and Chips this term.

“I don’t think we should be using any American technology to uplift China’s capabilities in AI given how era-defining this technology will be in national security and in our economy,” Vivek Chilukuri of the Center for a New American Security, a center-left think tank, told me. “A company’s going to argue for this, that’s what companies do. But the interest of any one company should not be trumping the national security of the United States.

Moving further right, there’s a MAGA-aligned faction that is skeptical of foreign wars such as Trump’s conflict in Iran — but even some of them are still quite antagonistic to China.

Take, for instance, Trump’s onetime campaign manager and White House adviser Steve Bannon. He’s been furiously protesting Huang’s influence on the administration for more than a year — in September 2025, he argued that Huang should be arrested for being “an agent of influence of the Chinese Communist Party,” and argued we needed to “shut down” China’s “access to technology, chips, and training.”

Advocates and politicians focused on economic issues and reining in corporate power also have long criticized Nvidia. Oren Cass of the center-right group American Compass wrote an article titled “Break up Nvidia” last year, arguing that “its excessive concentration of power is exactly what antitrust law intends to combat.” 

Meanwhile, the left, Sen. Elizabeth Warren (D-MA), a longtime critic of Big Tech, has frequently suggested Huang was corruptly influencing the Trump administration. At a hearing in June, she mentioned that Huang or his company had donated to Trump’s inauguration and his White House ballroom project. “If it’s not plain old-fashioned bribery, it sure smells like it,” she said.

Many in the AI safety advocacy world have also long criticized — and feared — Nvidia. That’s in part because they hope slowing down sales of AI chips abroad could slow what they view as a dangerous race to superintelligence. Additionally, after AI safety fears hit the mainstream in September due to Anthropic researcher Jacob Coxon’s resignation and viral warnings, Huang took an increasingly public role in dismissing such fears.

Other AI safety advocates cite the company as having a chilling effect on public debate. “For months now, I’ve heard about widespread fear among think tank researchers and policy experts who publish work against Nvidia’s interests,” AI safety researcher Steven Adler wrote last year. “If you speak with people in-the-know at AI companies, fear of retaliation from Nvidia is a major concern when staking out policy positions.”

Indeed, OpenAI and Anthropic are highly dependent on Nvidia for its chips, so open criticism of the company is not common. Yet those big two US AI labs have been at odds with Nvidia in urging the Trump administration to do more to crack down on Chinese access to advanced chips, and to more heavily scrutinize Chinese “open source” AI models.

But to Huang and others in his camp, the growth of Chinese AI is a feature, not a bug. Sacks, for instance, has repeatedly argued that “open weight” or “open source” models — which make their parameters publicly available, in contrast to the proprietary models of OpenAI and Anthropic — would help avert “an unsafe and dystopian future where advanced AI capabilities are centralized and controlled by only a few hands.” The leading open weight models are currently from Chinese companies.

Huang has also used a more straightforward argument: that what’s good for Nvidia is good for the United States of America.

“American technology has to win across the world at every single layer,” he said in May. “If we can export chips, we should.”

Now it will be left to Nvidia’s interim CEO, Colette Kress, to carry that message, instead of a rock-star founder with a global cult following that’s drawn comparisons to Steve Jobs at his peak at Apple. Her first task: taking stock of all the powerful actors across the political, advocacy, and business world rooting for her to fail. Perhaps DeepSeek could help make a list? 

Republicans weren’t the only big losers in last week’s midterms: Across the country, voters also backed restrictions on data centers and elected candidates promising tougher oversight of AI companies. Out of 41 local ballot initiatives limiting the data center buildout, 40 of them passed, according to the New York Times. 

The AI industry knows it was a tough cycle: Chris Lehane, the chief global affairs officer for OpenAI, told Today, Explained co-host Sean Rameswaram that “I could take my shirt off and show you my [metaphorical] bruises if you want.”

Now, OpenAI is figuring out how to move forward. Lehane talked with Sean from rural Texas, where he said he was on a “listening and learning” tour of communities around the site of a massive OpenAI data center. They discussed the lessons of the midterm election, why OpenAI would prefer that we call data centers “compute campuses,” and why Lehane thinks the 2028 presidential election will largely revolve around AI — just not in the same way that 2026 did.

“I just don’t think it’s going to be good enough for a candidate from either party to emerge from their primary by simply being the person who’s against things,” Lehane said. 

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.

You guys at OpenAI, in the wake of this shellacking, put out this big document saying that you’re going to be listening, you’re going to be learning, you’re going to be empowering local people and bringing jobs.

You’re also, in this document, rebranding data centers to “compute campuses,” which when I say it, I have trouble not laughing because it sounds like a bit of a joke to meet everyone’s concerns with a rebrand. Please tell us why you think this could work or why it’s a good idea.

We were very intentional in actually using that phrase. And we did it with the full knowledge that people would potentially be skeptical of it. But I do think there’s an intent behind provoking this conversation. 

Data centers have existed for 20-odd years and at some level they’re the proverbial empty box in a pasture. And they’re called data centers because that’s in fact what they were. When people talked about the cloud, this is actually where the cloud was housed, where you would load data if you were streaming a video, if you were ordering something from a delivery service. These AI facilities are really fundamentally different.

What is being generated at these facilities is something called compute. And compute is just the raw computing power that it takes to train AI systems and then run AI tools and products when you’re using them. 

Second of all, they’re different because [of] the amount of jobs that do get created directly. This is not half a dozen people in that empty box. These are real jobs, these are six-figure jobs. They are also huge opportunities for economic transformation if a community approaches it in a really strategic way and companies like ours do our part to work with those communities. We’re not just building in the communities, we’re building with the communities.

You’re out there in Texas because essentially, data centers and artificial intelligence have a PR problem. Seventy percent of the country, maybe more than that, says that they’re against data centers, or “compute campuses,” as you’re now calling them. 

Why do you think it is that data centers have such a public relations issue in this country right now?

There’s a variety of factors, but before even jumping into those, I do think it’s worth noting and worth emphasizing that we have roughly six of these projects up and running in the country. They’re in different stages of being built out and developed, but each and every one of them is moving forward. I do think that there is a right way to build these and perhaps a wrong way and we can get into that.

Do you think that you guys were doing it the wrong way before?

This goes to why I think we’ve actually been able to get approvals for each of the projects that we are doing. 

I think each of the individual projects themselves have been some version of a laboratory of democracy, where you actually learn from each project and apply those learnings to the next project. But I do think we got the fundamental insight right at the beginning, which is to actually go on the ground, listen, learn, hold yourself accountable and take the feedback and build your facility with the community.

I think that for a lot of communities, folks were coming in, they were building data centers, they were then transferring the electricity costs to the public, they were potentially impacting their water, they weren’t necessarily doing the type of community investments that you should do if you’re going to be building with a community. 

Where we’ve really tried to differ ourselves — and again, we’re not perfect, we’re learning every step of the way — but I do think that we have successfully applied the baseline as we’ve gone through this, which is, we make sure we’re paying for our own electricity, our own energy.

Point two is that we make sure we’re preserving water. We have something called closed water loops. I know this is getting a little bit technical, but think of these as sort of large radiators. You fill it up once and therefore you’re able to preserve the water.

One thing that seems clear from the election results is that there’s just not a lot of trust. Now there’s going to be a new Congress, and various components of said Congress are pledging to do more to regulate AI and tech. 

It wasn’t so long ago that the head of your company, Sam Altman, was calling for Congress to get more involved. Does that call still stand now that it’s a different Congress that might be more inclined to come down hard on AI?

When it comes to data centers, these are typically state or local issues. We envisioned that as state legislation that would actually make these requirements for folks who are building data centers to help give the public confidence that there’s going to be a real material benefit for them.

I think what you’re referring to was our call as OpenAI for there to be national-level mandatory safety requirements for the frontier labs who are building AI. We thought about that and called for it before the election. We think it’s more important than ever. 

Back in early September, I had a blog post out there that talked about this idea that the policy window was opening up to create a real opportunity to pass those mandatory safety requirements. And the reason I talked about the policy window being opened up is that you could clearly see where the politics were going, what was likely to happen with the election. 

You’re in Washington, DC. At some level, you have to be a romanticist to believe that you can actually get legislation passed. But what is happening is the politics are coming into alignment with the policy. I actually think both parties do recognize the importance of having mandatory national safety standards that would be good for the country. It would also put us in a position to be able to create international standards, which we also think is very important.

It’s funny, because we just had New York [state] assemblyman Alex Bores on the show. He was trying to be a congressperson, but a super PAC partly funded by OpenAI co-founders and some other Silicon Valley tech types spent a lot of money — millions — to defeat him. 

We asked him, if Democrats in Congress could do one thing to regulate AI, what would it be? And he said banning full recursive self-improvement, ban the practice of letting AIs build the next AIs without any human in the loop. Would OpenAI be open to that kind of regulation immediately?

The version that we have talked about is some version of broadly defined speed limits. I’ll use an analogy: As we were building out the automobile, ultimately we built out speed limits. Now, those speed limits changed over time, but the idea was to make sure that humans could actually have both hands on the wheel and be able to control the car and the car should not be able to go at a speed where a human would not be able to control it. 

Our north star in everything when it comes to AI is that people should be in control of this technology. The technology itself is accelerating, the capabilities are becoming stronger. And so I think there is a world here where we do need to build out a framework, potentially a regulatory framework, through legislation that effectively imposes some version of a speed limit.

If you think of speed limits being applied in the context of your question about recursive self-improvement, that’s a place where you could see the speed of the technology getting so strong that actually humans do lose control over it. 

I’m not sure it’s quite as binary as saying you do or do not do RSI. But I think spiritually, a similar concept, which is making sure that at the end of the day we have a lot of confidence that humans are actually in control of this, able to monitor it, able to make sure that it is not getting out of their control.

Before you worked with companies like OpenAI or Coinbase or Airbnb, you worked in government and you worked specifically in [the] Clinton White House.

I wonder, in the wake of this election — which people are calling the first big American AI election, and are saying that AI is going to be an even bigger issue for the presidential election in 2028 — what do you think companies like yours and all the other big-frontier AI companies need to be doing to flip the script in the United States, maybe around the world?

I think of 2026 as the trailer to 2028. AI was an issue. It wasn’t a top issue, but it was certainly an issue and an issue that was moving up the charts as we got closer and closer to the election. I think there is a decent chance, maybe even a strong chance, that AI is the issue in 2028. 

First and foremost, how do we make sure that people are actually in control of this technology? Who’s actually putting out an idea and a vision and approach to give people confidence that they’re going to actually be able to control this? 

I do think the 2026 election now sets up 2028, frankly for both parties, to have a conversation that I just don’t think we’ve had in this country for a long, long, long time. And that is, what does this social contract look like? Who actually has a big idea about how we’re going to make sure that the American dream actually exists in this country? And as part of that big idea, what is the role of AI going to be in that?

I just don’t think it’s going to be good enough for a candidate from either party to emerge from their primary by simply being the person who’s against things. I think they’re actually going to have to have some real ideas of what they are for. And at the core of that is, how are they going to address this anxiety that the American people have? And at the center of that anxiety is their ability to control their lives and make decisions about their lives.

The first big sex scandal of the AI era has taken down Jensen Huang, Nvidia’s co-founder and longtime CEO. Nvidia’s board of directors announced his resignation on Thursday morning and named CFO Colette Kress as interim CEO.

Huang has been making headlines all week after news broke Friday that he had an affair with Alice Huang (no relation), an executive at Singaporean tech firm Megaspeed who the US government alleges was also a Chinese spy.

The revelation Wednesday of a second extramarital affair, this time with an Nvidia employee reporting to Huang directly, added to the pressure, and another affair came to light later on Thursday after Huang’s ouster.

The scandal has implications far beyond one company: Huang’s central role in the AI race can’t be overstated. It was his vision of an AI future that led him to pivot Nvidia from a small company making video game chips into the dominant player in making the chips that serve OpenAI, Anthropic, and Google’s computing needs.

Huang is close friends with President Donald Trump’s and a leading voice in resisting efforts to slow AI progress. And he has pushed for the US to take a less hawkish position toward China’s own AI goals, a stance now called into question by his relationship with an alleged Chinese spy. Among its other impacts, the news has revived efforts on Capitol Hill to further restrict chip exports to China.

To break down the national security implications of Huang’s departure, how the Trump administration is involved, and what it says about US AI industry writ large, Today, Explained co-host Sean Rameswaram spoke with Garret De Vynck, a technology reporter at the Washington Post.

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 your podcasts, including Apple Podcasts, Pandora, and Spotify.

Nvidia has been called the central bank of AI, which is why it’s so important that we figure out what Jensen Huang may have disclosed to this alleged spy. How long has the president known about this, as far as we know?

As far as we know, the president has known about the fact that Jensen Huang was in a relationship with someone that the US government thought was a spy for about a year. This circulated within the administration, and the decision was made not to tell Jensen.

That doesn’t necessarily mean that it was automatically some kind of corrupt decision of President Trump protecting Jensen Huang. There’s all sorts of reasons why you might not want to tell someone they’re being spied on, the number one being that they might blow up the operation. Maybe you want to learn more. Maybe you suspect that the person themselves knows that they’re being spied on and is willingly sharing information and you need some more time to gather that. It’s unclear why the US government specifically chose not to pull in Jensen Huang. 

It’s also a little bit unclear whether he knows he was being spied on, or whether maybe he was told through some kind of back channel. He has said that he did not know that she was a spy and he is admitting to the affair, to the infidelity. He’s admitting to essentially breaking Nvidia’s code of conduct by sleeping with one of its customers, but he is not admitting at all to knowing that she was a spy and definitely not admitting to sharing any information willfully.

It feels like this is the kind of thing that the government would investigate. There’s been reporting that the Commerce Department was involved with some kind of investigation, but that investigation came to an end and no one really knows why.

The context here is that the United States and China are in an AI race. Going back to the first Trump administration, there’s been pretty aggressive export controls saying, “We don’t want China to get our tech so that they can develop AI.” And the Biden administration continued that. He increased the export controls on China. China was complaining very loudly about this. They said, ‘We don’t care. We want to win AI and we need to slow you guys down.’

When Trump came in, he actually lifted his foot off the gas a little bit. They didn’t throw the gates open and say, “You can have whatever you want. All American companies can sell whatever they want to China.” But he said, “Some AI chips that are less powerful than the state of the art. We can send those to Chinese companies.“

The other thing that national security experts have pointed out is that the Trump administration could theoretically have been doing more to stop the smuggling of computer chips to China. And that’s where this story gets so important for national security.

Megaspeed is accused of essentially being a front for Chinese companies to get access to computer chips. Journalists have gone to Megaspeed’s office in Singapore where it’s based, and there were only a couple of people working there. It didn’t really seem like there was a lot going on. Nvidia has said all through this process over the last couple of years that, “No, we’ve checked it out, Megaspeed is a legitimate company and we’re not breaking the law when it comes to this.”

There are a lot of questions and what everyone is focused on finding out, both journalists and I would say congressional Democrats when they come into office in the new year is, is there something more going on here? Is there something where the Chinese either convinced Jensen Huang to sell more chips to them or Jensen Huang himself, who has been advocating for selling more chips to China, essentially did this himself? 

“I think there’s this real question of: Do we see a much stronger push to actually ramp up controls on China and how does Trump respond?”

He’s been pressuring the president, he’s been donating to him, he’s been glad-handing with the president and the president has been listening more to this corporate executive who is his friend and who donated him a bunch of money rather than listening to China hawks or national security experts who say we should be limiting what China is able to access.

That’s really where the locus of this investigation is going to be. It’s not so much that Jensen Huang had an affair. It’s more the fact that he himself is someone who has said we need to sell more computer chips to China and he was involved with an alleged Chinese spy, and he’s very close to the president and had influence on the president.

You mentioned the Democrats. Have they said anything? Are they going to call him in front of Congress for an investigation?

Going forward, there are two big things here. One of them is these investigations into Jensen Huang and this whole situation. The company is likely going to continue to investigate. Maybe more information will come out there. Jensen Huang has been called to speak later this month in Congress. And congressional Democrats have also said that when they come into power in January, they will be asking him. 

The second big thing that comes out of this is, what does America do when it comes to China and AI? Now that one of the biggest advocates for being a bit more friendly towards China when it comes to selling them things that they need for AI is out of the room and completely discredited, I think that the China hawks, those who say we need to crack down more aggressively, we need to limit China’s access to AI computer chips, they’re really coming into ascendance here and they’re in the Republican Party, they’re in the Democratic Party, some of them, people like Marco Rubio, are even in the Cabinet. 

I think there’s this real question of: Do we see a much stronger push to actually ramp up controls on China and how does Trump respond? There’s all sorts of geopolitical things that could sort of spin out of this CEO affair.

I’m old enough to remember when a board tried to get rid of Sam Altman and then he was just back a week later. Elon Musk is already offering Jensen Huang a job. Is there a fair chance that he just goes somewhere that’s not that concerned about espionage affairs or getting with your VP of health or whatever it is? Is he just going to go work for China?

I don’t think he’s going to go work for China. This is definitely the wildest corporate story since Sam Altman was fired as CEO and brought back three or four days later. It’s a very different situation. That was a startup where you essentially have five or six investors who get to make the call about who the CEO is, and the employees at that company overwhelmingly wanted to bring Sam Altman back. I think they wanted more continuity of the company, but they also wanted to make sure that their company didn’t go under and go bankrupt and get disappeared, which was sort of on the table at that point in time. 

Nvidia’s not going anywhere. Obviously employees have lost a lot of money in terms of the stock value already coming down due to the situation. So employees are probably shocked. They’re probably upset. Jensen Huang is broadly loved at Nvidia, and so I can definitely see that employees would be doing petitions, asking for him to return. 

But this is a giant public company and the board makes a decision here. Jensen Huang was untruthful publicly and I think that also will make other public companies hesitant before rushing to bring him on as CEO.

As we’ve talked about many times in the show, half of our economy is staked on AI at this point. How concerned should we be that one of the most important people in this industry was so easily duped in this honeypot situation? Are these the people we should be trusting with the future of our society?

It’s a really good reminder that people are people. I think that we like to lionize individuals and say that they are the ones who are going to take us into the future and they are inventing the future and it’s so incredible. And I do think Jensen Huang gets a lot of credit for making sure that the companies kept up in AI, but at the end of the day, it was also a lucky break for them that the computer chips they were making just happened to work well for the math that is required by artificial intelligence. 

I do think that this speaks to something that a lot of people across the country and maybe even the world really feel. They do not want to sign off the future to a dozen dudes in Silicon Valley and their collaborators in the Trump White House. I think that is a view that a lot of people increasingly have. I think it was one of the factors in the midterms, maybe not the most important one, but people are concerned about concentration of power. They’re concerned about AI and they don’t see it as something that they themselves have control over. 

I think that this is just going to be another thing that sort of adds skepticism to the idea that people like Jensen Huang should be the ones making the final decision when it comes to artificial intelligence.

BRUSSELS — Australia’s Industry Minister Tim Ayres formally signed the country up to Horizon Europe on Thursday, giving the country’s researchers access to the EU’s €95 billion research and innovation program.

Australia will join Pillar II of the program, which funds research into areas including advanced computing tech, critical minerals, health, climate and clean energy. From January, Australian organizations will be eligible for funding on the same terms as EU countries and will be able to lead project consortia.

The move will be a “boon for Australian science, research and innovation,” Ayres said in a statement, adding that “the big challenges facing Australia and the world cannot be solved by any one country working alone.”

The signing formalizes negotiations concluded in June. Australian organizations have already taken part in 239 Horizon Europe projects, with nearly one in four applications selected, according to the European Commission.

ANTALYA, Turkey — World leaders and industry must make electric cars and heat pumps affordable if they are to ensure “people on all incomes” can “make the green transition,” U.K. Prime Minister Andy Burnham told the COP31 climate summit.

During a flying visit to this year’s U.N. conference in Turkey, Burnham — who has made affordability his core domestic political focus — reaffirmed the U.K.’s commitment to the goal of net zero greenhouse gas emissions by 2050.

But he warned that the world had “not moved as quick as I had hoped in the past five years” to show citizens that climate action and electrification have cost-of-living upsides.

“The last of these meetings I attended was COP26 as the mayor of Greater Manchester,” Burnham said, referring to the 2021 summit hosted by the U.K. He warned at the time that the climate movement must not lose sight of the “struggles of ordinary families.”

“The world feels very different now, and we need to adapt,” Burnham said. “Electrification in transport, heat and industry is how we make clean power, cheap power.”

While voicing support for COP31 hosts Turkey and Australia’s electrification push, Burnham stopped short of backing the specific target of 35% of global energy use shifting to electricity by 2035.

But he emphasized the benefits the switch could bring, while urging industry to help ensure it helped all consumers.

“If you drive a new electric car today in the U.K., it costs less than a petrol equivalent. Get on the right tariff and a heat pump can be cheaper to run than a gas boiler. These technologies are becoming cheaper, but they are not available to everybody on all incomes,” he said.

“As a government, we are doing our bit by investing in innovation and subsidizing upfront costs for the public. But we need industry to play their part too, and help people on all incomes to make the green transition.”

The focus on EVs and heat pumps comes despite Burnham’s government cutting the subsidy pot for heat pumps at last month’s budget. Ministers are also weighing proposals to water down industry EV quotas.

Nonetheless, Burnham reiterated the U.K.’s over-arching strategy to cut dependence on fossil fuels.  

“The climate crisis is not a distant threat. It is upon us all now, devastating lives and livelihoods everywhere. Meanwhile, two wars across two continents have sent fuel prices and gas bills soaring for families and businesses across the world,” he said. “The common thread running through all of this: our dependence on fossil fuels.”

He announced the U.K. would use its G20 presidency next year to “put climate resilience back on the agenda, with debt, development, and insurance as key issues.”

Burnham’s government has also created a new role of special representative for climate security, appointing former head of the Joint Intelligence Committee Chair Madeline Alessandri to the post.

BRUSSELS — Top global gaming companies have blasted new EU consumer regulations they say would “cripple the competitiveness of Europe’s world-leading video games industry.”

The European Commission unveiled a new set of rules Wednesday on consumer protection, including the Digital Fairness Act, that is part of the first-ever sector-specific limits on the video-game industry, alongside the EU’s mid-September regulation to protect children online.

Under the proposed DFA, which still has to be negotiated with other EU institutions, video games would no longer be able to earn money by offering minors micro-transactions to advance in the game, known as pay-to-win or pay-to-progress. There are also restrictions on “loot boxes,” which contain surprise virtual items that players can buy with real money. The law also stipulates that in-app virtual currencies must be turned off by default for under-18s.

The Commission says these practices encourage overspending, which is especially dangerous for vulnerable consumers like kids. Violating the rules can lead to fines of up to 6% of global revenue in cases that affect more than two-thirds of the EU population.

“We want the European gaming industry to continue to thrive,” said Michael McGrath, the commissioner for consumer protection, announcing the measures. But competition, McGrath added, should be based “on creativity, innovation, and quality, and not on practices that exploit consumers.”

In a joint statement, the European Game Developer Federation, an association representing EU firms, and Video Games Europe, which includes global juggernauts like Sony and Epic Games, say that the proposed definition of pay-to-win is “fundamentally flawed, lacks clear definitions and would create huge confusion for players and developers alike” since it could cover “any purchase.”

Angry Birds & friends

While Europe has struggled to keep up with the U.S. and China in several tech sectors as AI and social media, the bloc is home to developers of some of the world’s most popular games such as Angry Birds and The Witcher. Video game spending in the bloc topped €27 billion, accounting for 17% of the global market, last year, according to the European Audiovisual Observatory.

The gaming groups argue that the industry already does a good job regulating itself with parental controls and the content rating system PEGI, which the DFA doesn’t take into account.

“Effectively, the DFA moves all decisions from parents and children to the Commission,” they wrote.

The DFA aims to regulate a slew of other industries, cracking down on manipulative practices in online commerce such as addictive design, dynamic pricing and making it difficult to unsubscribe from a digital subscription.

Cancer claims 10 million lives each year, the global shortage of health workers runs into the millions, and in some parts of the world, a single mosquito bite can be fatal.

These challenges may seem very different. Yet they share a common element in how many health systems operate: risk is often recognised only after it has become illness, outbreak or crisis. By then, a disease may have advanced, transmission of disease may be under way, or an already stretched workforce may be managing consequences that earlier action could have reduced.

The opportunity is to move that point of intervention earlier. This is what anticipatory health means in practice: using signals that already exist, at the bedside, in communities, and in routine health data, to help people and health systems act sooner. It is not prediction for its own sake. The value lies in connecting early and everyday signals to a timely, practical response.

Finding practical routes to earlier action

That principle sits at the heart of the Future Health Impact Challenge: Building Anticipatory Health Systems through Population Sensing, launched by Future Health – A Global Initiative by Abu Dhabi in collaboration with MIT Solve. The Challenge received 393 submissions from 68 countries. ThinkMD, VectorCam and Huna emerged as its three award-winning solutions: ThinkMD received the grand prize, with VectorCam and Huna recognised as distinguished finalists.

The Challenge was designed to identify practical solutions with evidence of impact and potential to scale. Each of the three selected teams demonstrated their approach to using technology to turn an early signal into decisions that support anticipatory actions a health system can take.

Frontline care as a population signal

ThinkMD starts with the interaction between a frontline health worker and a patient. Its offline mobile platform guides structured assessments aligned with clinical protocols, supporting providers where doctors, connectivity, and diagnostic tools may be scarce. Almost 10,000 frontline health workers across 885 facilities are now using it.

Each consultation also creates structured data. Across facilities, that information can reveal changes in symptoms and service demand. In Zambia, analysis of around one million encounters identified symptom patterns several months before a confirmed cholera outbreak. In Kenya, deployment data showed improvements in identifying danger signs in infants and prescribing antibiotics correctly for childhood pneumonia.

It is not prediction for its own sake.

“The utilisation of AI in healthcare is an absolute game changer. Not only can we increase access for patients, but we can also increase access to information to make correct and appropriate diagnoses and give the right treatment to the right patient at the right time. Future Health has been a really critical platform to be able to accelerate our mission of moving from reactionary to anticipatory care,” said Dr Jackie Rabec of ThinkMD.

The award has given ThinkMD support and visibility as it works to expand into new health systems and strengthen how frontline data becomes timely intelligence for decision-makers. That is one measure of the Challenge’s impact: helping proven solutions move towards sustained, wider adoption.

Earlier signals from the environment and routine data

VectorCam begins with the environment around a community. Using a low-cost smartphone imaging device and offline AI, community health workers can photograph and identify mosquitoes in the field. Each observation generates time- and location-based data that can help authorities see changing vector risk as the first signal of disease patterns, before a spread in local communities and populations.

The value lies in connecting early and everyday signals to a timely, practical response.

Field evaluations show data completeness rising from approximately 60% to more than 90%. With support from the Future Health Impact Challenge, VectorCam aims to strengthen its early-warning capability, integrate additional data sources and expand into national surveillance programmes.

Huna looks for earlier cancer-risk signals in information that health systems already generate. Its platform analyses routine complete blood count data to identify people with an elevated risk and prioritises them for screening. It does not diagnose cancer from a blood test; it helps direct limited screening capacity, and supports patients and healthcare professionals through the next steps.

In Brazil, Huna’s deployments have screened more than 500,000 people and detected hundreds of cancer cases. Its experience also highlights a critical ‘last mile’: identifying risk is not enough if a person does not complete the diagnostic pathway. Recognition and funding from the Challenge could help support further validation across new populations and facilitate new international partnerships.

Innovation is only the starting point

Together, these solutions show that useful signals can come from a variety of sources – including clinical encounters, environmental surveillance, and routine diagnostics. They also show that anticipatory approaches can be built where connectivity, specialist capacity, or access to screening is limited, and that innovation developed in those settings can share insights that can be localised and adopted globally.

But a successful technology or pilot is not the same as system-wide impact. Scaling requires local validation, integrated into existing workflows, responsible data governance, sustainable financing, workforce training, and evidence that earlier insight changes decisions and outcomes.

A successful technology or pilot is not the same as system-wide impact.

“The solutions emerging from this Challenge reflect both the urgency and the opportunity facing health systems globally. By bringing innovators together with policymakers, funders and implementers through platforms such as Future Health, we can help accelerate solutions that are locally grounded and capable of delivering impact at scale,” said Hala Hanna, Executive Director of MIT Solve.

From recognition to system-level impact

The Future Health Impact Challenge is part of Future Health’s wider role as a year-round platform for action. Together with our partners and colleagues across diverse sectors and geographies, Future Health identifies promising solutions, supports their development, builds international connections and brings together the policy, clinical, research and investment expertise needed to move from proof to concept to measurable impact.

The Future Health impact documentary film is now available to watch, and shares personal commentary from the founders of the three companies, local stories of the solutions in action, and commentary from the Challenge owners and collaborator.

Watch the full Future Health Impact Challenge Film here.

Die Diskussion um die Zukunft des Wirtschaftsstandorts Deutschland wird häufig von vielseitigen Herausforderungen dominiert: Energiepreise, geopolitische Spannungen, Fachkräftemangel oder die Transformation hin zu einer klimaneutralen Wirtschaft. Diese Faktoren stellen Unternehmen vor große Aufgaben. Die Wettbewerbsfähigkeit Deutschlands hängt jedoch nicht allein davon ab, wie auf aktuelle Entwicklungen reagiert wird – sondern vor allem auch davon, ob Unternehmen bereit und in der Lage sind, zukunftsorientiert zu investieren.

Investitionen als Fundament für Wachstum

Investitionen sind ein zentraler Treiber wirtschaftlicher Stärke. Sie schaffen Produktionskapazitäten, fördern Innovationen, modernisieren die Infrastruktur und sichern Arbeitsplätze – und legen so den Grundstein für nachhaltiges Wachstum und gesellschaftlichen Wohlstand. Doch vorausschauende Investitionen entstehen nicht zufällig. Sie brauchen Unternehmen mit einem klaren Zukunftsverständnis und politische Rahmenbedingungen, die über Jahre hinweg Planungssicherheit bieten.

Entscheidungen werden mit Blick auf Generationen getroffen, nicht auf Quartalsergebnisse.

Gerade in Zeiten wirtschaftlicher Unsicherheit gewinnt diese Weitsicht an Bedeutung. Es braucht Unternehmen, die über kurzfristige Marktzyklen hinausdenken und investieren. Sie stärken damit nicht nur ihre eigene Wettbewerbsfähigkeit, sondern leisten zugleich einen wichtigen Beitrag für den Wirtschaftsstandort insgesamt.

Denken in Generationen statt in Quartalen

Als Familienunternehmen verfolgt Mars seit jeher genau diesen Ansatz. Entscheidungen werden mit Blick auf Generationen getroffen, nicht auf Quartalsergebnisse. Diese Perspektive prägt auch das Engagement des Unternehmens in Deutschland. Seit rund 70 Jahren ist Mars hierzulande als Investor, Arbeitgeber und Hersteller aktiv. Deutschland ist dabei nicht nur ein bedeutender Absatzmarkt, sondern vor allem ein zentraler Produktions- und Innovationsstandort innerhalb des europäischen Netzwerks des Unternehmens.

Dieses andauernde Bekenntnis zeigt sich beispielsweise in den Investitionen der vergangenen Jahre. Zwischen 2021 und 2027 investiert Mars insgesamt mehr als 235 Millionen Euro in seine deutschen Standorte. Mehr als 150 Millionen Euro wurden bereits getätigt, bis Ende 2027 sind weitere Investitionen von rund 85 Millionen Euro vorgesehen. Die Mittel fließen in moderne Produktionsanlagen, neue Technologien, Automatisierung, Nachhaltigkeitsmaßnahmen und die Weiterentwicklung von Mitarbeitenden.

Viersen und Verden: Industrielle Stärke aus Deutschland

Ein Schwerpunkt liegt dabei auf den Produktionsstandorten Viersen und Verden. In Viersen, Nordrhein-Westfalen, produziert Mars international bekannte Marken wie Twix und Balisto – Letzteres ausschließlich in Deutschland – sowie Komponenten für Celebrations. Allein rund 60 Millionen Euro wurden in den vergangenen vier Jahren (2022 bis Ende 2026) in die Modernisierung und Kapazitätserweiterung des Standorts investiert. Weitere Investitionen sind bereits geplant, um Produktionsprozesse zu optimieren, die Qualität zu verbessern und die Fabrik auf zukünftige Anforderungen vorzubereiten.

Zwischen 2021 und 2027 investiert Mars insgesamt mehr als 235 Millionen Euro in seine deutschen Standorte.

Auch der Standort Verden in Niedersachsen steht beispielhaft für die industrielle Bedeutung Deutschlands. Dort werden bekannte Tiernahrungsmarken wie Whiskas, Sheba und Cesar produziert. Gleichzeitig ist Verden das globale Forschungszentrum von Mars für Nasstierfutter. Produktion, Forschung und Innovation greifen hier unmittelbar ineinander und schaffen einen Standort, der sowohl regionale Wertschöpfung als auch internationale Innovationskraft vereint.

Produktion in Europa stärkt Wettbewerbsfähigkeit und Resilienz

Die Bedeutung solcher Investitionen reicht weit über einzelne Standorte hinaus. Europa braucht eine starke industrielle Basis, um wirtschaftlich erfolgreich zu bleiben. Produktionskapazitäten, technologische Kompetenz und Innovationskraft sind zentrale Voraussetzungen dafür.

Mars verfolgt dabei den Ansatz, möglichst nah an den Verbraucherinnen und Verbrauchern zu produzieren. Bereits heute werden 85 Prozent der in Europa verkauften Produkte innerhalb Europas hergestellt. Deutschland spielt in diesem Netzwerk eine zentrale Rolle und fungiert als wichtiger industrieller Pfeiler für Produktion, Forschung und Export. Produkte aus deutschen Mars-Standorten werden in zahlreiche europäische Märkte und darüber hinaus weltweit geliefert.

Wettbewerbsfähigkeit und Nachhaltigkeit sind dabei keine Gegensätze – moderne Produktionsstandorte müssen beides leisten. Investitionen in neue Technologien ermöglichen es, Ressourcen effizienter zu nutzen, Produktionsprozesse zu verbessern und die Umweltbelastung zu reduzieren. Nachhaltigkeit wird so zunehmend zum Wettbewerbsfaktor, der Innovationen fördert und dauerhaft Resilienz schafft.

Verlässliche Rahmenbedingungen als Schlüssel

Genau deshalb sind verlässliche Rahmenbedingungen für Investitionen so wichtig. Unternehmen benötigen Planungssicherheit, Zugang zu qualifizierten Fachkräften, wettbewerbsfähige Energie- und Infrastrukturangebote sowie regulatorische Stabilität. Nur unter diesen Voraussetzungen lassen sich strategische Investitionsentscheidungen treffen, deren Wirkung oft erst Jahre später sichtbar wird.

Bereits heute werden 85 Prozent der in Europa verkauften Produkte innerhalb Europas hergestellt.

Zugleich verfügt Deutschland über zahlreiche Stärken: Exzellente industrielle Kompetenz, eine starke Forschungslandschaft und eine leistungsfähige Infrastruktur. Diese Voraussetzungen bieten eine gute Grundlage, um auch in Zukunft Investitionen anzuziehen und industrielle Wertschöpfung im Land zu halten. Gleichzeitig bleibt es eine gemeinsame Aufgabe von Wirtschaft und Politik, die Rahmenbedingungen kontinuierlich weiterzuentwickeln und die Wettbewerbsfähigkeit des Standorts langfristig zu stärken.

Langfristige Investitionen entstehen dort, wo Vertrauen in die Zukunft besteht. Sie fördern Innovationen und stärken wirtschaftliche Resilienz. Für Unternehmen wie Mars sind sie Ausdruck einer Verantwortung, die über kurzfristige Geschäftsergebnisse hinausgeht. Und für Deutschland sind sie eine entscheidende Voraussetzung, um auch künftig Wachstum, Wohlstand und industrielle Stärke erfolgreich miteinander zu verbinden.

Editor’s note, November 12, 3:35 pm ET: This story was originally posted on November 9 and is being republished today in light of Jensen Huang’s resignation as Nvidia CEO.

The movie version of Jensen Huang’s jaw-dropping affair basically writes itself. 

Cast BD Wong as 63-year-old Huang, the CEO of one of the world’s most valuable companies and a close friend of President Donald Trump. Kelly Hu can play Alice Huang, Jensen’s paramour and — even better — a purported agent of the Chinese government. 

The role of Trump himself, who reportedly knew about Huang’s undercover girlfriend but on Sunday said Huang “probably has an excuse for it,” can be played by Alec Baldwin. (Or a flightless duck — your pick.) However you cast it, this whole sordid affair has “serialized Netflix drama” written all over it.

But in all seriousness…the possibility that one of the world’s most powerful men was targeted by Chinese intelligence is a big deal on multiple scores. Nvidia has opened its own investigation into whether Huang was compromised, and congressional leaders have requested he brief them in a closed-door session.

Nvidia supplies much of the computing power behind the AI boom, giving it enormous influence over the broader economy. “If they sneeze, everybody else catches a cold,” the writer and economic commentator Kyla Scanlon told Vox’s Today, Explained podcast last year. That makes this whole episode a little more weighty than your standard billionaire’s affair.

Go a little deeper

Nvidia is the most valuable company on Earth — but in many households, it’s still not a household name.

Huang and two other electrical engineers founded the now-$4.5-trillion company in 1993 with the fairly niche and nerdy goal of making high-powered chips that could render the 3D graphics for a new generation of video games. 

Then, in 2006, the company released software tools that let programmers use its chips for other demanding computing tasks, like mining cryptocurrency and training AI models. When companies like OpenAI began developing tools like ChatGPT, they immediately turned to Nvidia. 

That made the company a cornerstone of America’s AI buildout and a major economic force. In 2024, Nvidia alone accounted for more than a fifth of the S&P 500’s total return. And the rush to build out AI infrastructure has accounted for much of America’s recent economic growth, according to St. Louis Fed researchers.

Nvidia’s influence extends to the White House, too, where Huang is a regular guest of Trump. The two became fast friends after having dinner together at Mar-a-Lago last year, and Huang has said Trump regularly calls him late at night to discuss topics like “protecting national security” and “creating more jobs.” 

But Huang’s ambitions have run up against one major obstacle: US restrictions on selling Nvidia’s most advanced chips to China. US officials worry that their enormous computing power could help Beijing develop weapons, strengthen its military, expand surveillance, and otherwise rival US military and technological dominance.

That helps explain why China (allegedly) sicced a spy on Huang: They really want these chips. The New York Times reports that China’s top intelligence agency recruited Alice Huang both to learn more about America’s advanced chip industry and to potentially influence Nvidia’s push to lift export restrictions. Alice Huang, it should be noted, was a data center executive in her own right, and her company has been investigated for purportedly helping to smuggle Nvidia chips into China. 

All these investigations will hopefully, eventually, reveal whether their affair compromised anything beyond Huang’s marriage. They may also test how dependent Nvidia is on his leadership. 

With Huang out as CEO, uncertainty over the company’s future could unsettle investors and trigger wobbles in the wider market. That’s the problem with concentrating so much power in any one person or company: One person’s (very) personal business can somehow become everybody’s problem. 

One link for later

➨ Get a hobby. But not just any hobby: something that’s intentionally imperfect, unproductive or unquantified. There’s a tendency in American culture to try to optimize or commodify our leisure, the writer Jenny Odell explains. But that undermines some of leisure’s biggest benefits — like cultivating a sense of joy, wonder, or learning.

Before you go…

• Did you know…that Alexander Graham Bell reportedly wanted people to answer the phone with a cheerful “ahoy”? Alas, Thomas Edison’s preferred greeting — “hello” — won out instead.
• Today’s trivia: Cleopatra was killed by what animal, according to several contemporaneous accounts? (You can find this and other brain puzzles in Vox’s daily crossword. Look for the answer in tomorrow’s edition.)
• Yesterday’s trivia: Last week we asked you for Ricky Martin’s biggest hit. That would be “Livin’ La Vida Loca,” the 1999 bop that’s widely credited with launching the Latin pop explosion of the late 1990s and early 2000s.  

This post intially appeared in Today, Explained, a daily newsletter that helps you understand the most compelling news and stories of the day. Subscribe here.

When was the last time you had to call customer support with a problem? Can you remember how it went? 

If you’re like most Americans, the answer is “probably not very well.” And that may be understating it: As of 2025, according to the “National Customer Rage Survey” — yes, it’s a real thing — nearly 80 percent of Americans ran into a “product or service problem” in the past year, and nearly two-thirds experienced “rage” in attempting to resolve it. 

We have, in short, entered into a rage economy, where high prices and inflation have made everyday products unaffordable at the same time as our experiences buying those products have filled us with fury. 

The landscape isn’t really improving, either: As a consumer, you’re faced with terrible customer service, unhelpful chatbots, and products that are worse than they used to be. 

If you listen to the big AI companies, however, there’s a light on the horizon: AI agents, which can step in and do the things you don’t want to (like waiting on hold or dueling it out with aforementioned unhelpful chatbots). But are AI agents really the key to fixing our rage economy? Or will they just be what brings it deeper into chaos? 

Charlie Warzel, a staff writer at The Atlantic, recently went deep on what happens when we bring AI agents into our already contentious consumer experience (along with his colleague Matteo Wong). 

Today, Explained co-host Sean Rameswaram spoke with Warzel about how AI agents are being used for the grating parts of our lives, what happens when companies use their own AI agents to deal with consumers, and where this all might leave us. 

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.

Charlie, we spoke about how the economy and how customer service works is making everyone rage out. Are AI agents going to make it better or worse?

In the early time, it’s probably going to make it a lot worse. What we are essentially seeing right now is that these agents are inundating the internet with persistence. 

These are relentless things that are tasked to do the onerous activities that we don’t want to do. And they’re already flooding call centers. They’re flooding restaurant reservations. They’re flooding plumbers and people who run small businesses with requests and queries. And they’re not people. 

It’s going to make a lot of people feel, honestly, pretty awful. The world has all of a sudden become very inhuman or there are these inhuman burdens being put on the humans who don’t have agents yet to work on their behalf. And that imbalance is going to make a lot of people pretty mad.

You wrote for The Atlantic that we are not ready for our coming AI agent future. How are we not ready for this? 

If it reaches a critical mass, the argument is that we aren’t ready for it because the economy is built off of, and in a lot of ways thrives off of, inefficiency — off of the fact that people aren’t paying perfect attention to everything. 

People aren’t moving their money from savings account to savings account every day because they’re getting the margins on a slightly better interest rate or something like that. This entire subscription economy is based off of inefficiency. You don’t cancel the thing that you don’t use for a long period of time or it lays dormant for a while. 

But what these AI agents offer in their most idealized fashion is that they pay perfect attention to everything, all the time. They are hypervigilant and able to act on your behalf in inhuman ways. And if a large portion of the population is using these to do their banking, to do their e-commerce, and at the same time you have these large organizations like banks, healthcare companies, platforms like Amazon and Walmart, you’re going to see just incredible amounts of chaos. 

The economy would, in that way, start to behave more like the stock market, where prices fluctuate on the order of milliseconds. And from watching the stock market over decades, I don’t know if we want diapers to behave like securities.

One of the more speculative but also illustrative ideas here is what people refer to as an agentic bank run. This is the idea that, say, 5 million people are using the same agent to do all of their banking and they all identify at the same time that there is a high-yield savings account that everyone should be in. So they move money en masse: millions of agents pulling money out of one account to the next, and then those banks are struggling because they’ve just lost all of these deposits at once. It’s theoretically possible that something like that could happen all of the time. 

Is this a two-way street? Are the companies also adopting agents into every part of their processes and consumer relations?

Yes. It’s slower on that end, but I like to think of this as an arms race. 

A lot of retailers, especially Amazon, have blocked a number of these agents from accessing their platform. Because even though Amazon can make good money off of these sales, they don’t yet want these companies to have access to their backend infrastructure. 

There are a lot of corporate secrets like inventory and who’s buying what and who’s selling what. These agents can act as really good intelligence gatherers if they’re granted access to these platforms. 

What you’ll see eventually is that the retailers are going to have demands: “You guys can let the agents on the platform, but you can’t undercut our prices in this way.” It will just be something that you have to trust that your AI agent company is making a deal in your best interest. 

I think that that’s really interesting because you could end up having this future where everything feels really good as a consumer because it’s frictionless: “My agent’s going to do all this stuff for me while I sleep or while I’m hanging out with my family or whatnot.” And in reality, what’s going to happen is you’re just having someone do something for you, but nothing else has really changed. You’re not getting a better deal. It’s not that much more helpful to you because these companies have taken care of themselves first.

But that is the promise. That is what Sam Altman and Mark Zuckerberg are trying to sell us. 

Do you think it’s a pipe dream or do you think there is some reality, one day, when we as a society will say, “This consumer-facing AI agent world of ours is better than the one we had a hundred years ago”?

Something that I think is really funny is Meta, with Muse, has really dominated the conversation about these AI assistants. And this is the company that wanted to connect the world, that says that us being together is this universal good, it is strengthening the bonds of humanity.

This is a tool that is fundamentally antisocial. You don’t have to interact with those people if you don’t want to. I’m not trying to romanticize running really annoying errands when you’re busy and your kids are sick and you’ve got work deadlines and things, but there’s also the experience of interacting with people on a day-to-day basis that is foundational to our humanity and these things basically want to automate that away. 

That’s a real thing that we need to think about and grapple with. And the other part of this is, what are we freeing this time up for?

I’m always wondering that myself, because that’s what they keep promising. You’re going to have so much time…to do what? To then just spend more time scrolling your platforms?

To spend more time scrolling your platforms, spend more time working. There is a lack of thought about what a human is for if we fully embrace automation in all these different ways, right? It’s one thing to say, “I wish it was easier to buy paper towels on Amazon.” It’s another to say—

It’s pretty easy, though. I don’t do it that often, but I’m pretty sure it’s really easy.

It is. I think it’s good to have those interactions and it’s okay to sand down some of them, but this future suggests, what if you never had to do anything you didn’t want to do? And I don’t know if they’ve thought that through.

You’ve probably been hearing a lot about data centers this week. 

Candidates in several races made regulating data centers a key part of their campaigns. Now, 40 ballot measures to slow or stop data centers have passed. And in an exit poll, when respondents were asked how important AI and data centers were in their vote for Congress, 56 percent answered that it was a “crucial or a major factor.” 

Data centers are the physical infrastructure that powers AI, and they’ve become deeply unpopular not just because of their noisiness, ugliness, or electricity demand, but because of what they symbolize: the acceleration of a technology that many Americans fear and that they feel Silicon Valley is foisting upon them.  

If you’re one of the Americans worried about AI — or just prefer a more serious regulatory approach over the hands-off, Trump White House approach — there’s a problem with confining your attention to data centers. Even a successful push against them won’t address the bigger issues around the technology. And picking off data center projects one by one will slow, but not stop, the buildout. AI companies will still find jurisdictions that are willing to let them build — it’ll just cost them more.

The election outcome that matters far more, though it was far less trumpeted, is that voters just elected a new crop of leaders promising to actually push to regulate the tech itself. These are people who are concerned not just about AI’s physical infrastructure, but about AI regulation and safety. And they’ve signaled that they’ll push for laws that address the full spectrum of risks from AI — from cyber hacks and job losses all the way to bioweapons, power concentration, and even the possibility of human extinction. 

In fact, for AI watchers, one of the most striking things about this election cycle is how safety-pilled many candidates have become since the major AI hacks this summer.

“If you go back before the summer, most conversations from policymakers around AI were at a very, very high level of abstraction. Few of them were focused on how these AI models may be highly capable and may pose rather unique risks,” Mackenzie Arnold, an analyst at the Institute for Law & AI, told me. “But there’s been a big shift.” 

The midterm results — and the Democrats’ stunning takeover of Congress — improve the odds of reining in AI. But now, the big question is: Can the new victors use the momentum they built up around data centers — the kitchen-table issue that initially brought many voters into the tent — and broaden out beyond that to mobilize for greater AI safety? They may have a lot of ideas, but are they any good?

The new (and newly emboldened) crop of AI safety advocates 

Among the newly elected Democrats, a striking number are very vocal about AI risks. And a few have clearly taken the time to educate themselves on the finer points. So before considering what concrete changes they can actually implement, it’s worth getting to know what they’ve proposed. 

Abdul El-Sayed, who won the Michigan Senate race, said that we need a “full-on pause” on frontier AI research. He’s also advocated for global coordination on a slowdown, including a treaty with China aimed at avoiding an arms race dynamic. He supports a ban on recursive self-improvement, where AI models build the next, even smarter AI models. He also wants a new federal safety agency that would inspect companies and block releases if necessary.

Even if not all his ideas resonate with experts (like the simplistic idea of a “kill switch” for dangerous models), having him in the Senate means that AI safety advocates will have an easier time arguing for a pause on frontier research. 

Another winner to watch is Jon Ossoff, who prevailed in his Georgia Senate race. In his victory speech, he lambasted AI developers, saying, “The titans of Silicon Valley dig bunkers for themselves and warn that the artificial intelligence they’re creating risks human extinction.” 

Back in September, he impressed safety advocates by explicitly mentioning “catastrophic risks” like AI-engineered bioweapons. Conveying his priorities, he said, “A capable president would move swiftly to secure and reassure the American people, rush inspectors into frontier labs, fortify the nation against bioterrorism, demand legislation from Congress, and lead the world toward an AI treaty. But President Trump is compromised.” 

Two other Democrats with strong AI safety bona fides and specific policy ideas have emerged victorious this week. Scott Wiener — the longtime California state senator who was behind two important pieces of legislation, SB 1047 (which was ultimately vetoed) and SB 53 (which was signed into law) — won Nancy Pelosi’s seat this week. He’s said he’ll work to institute strict federal safety standards and “guard against catastrophic risk from AI.” 

And Manny Rutinel — who cosponsored a 2024 law that sought to regulate AI use — won his race in Colorado. Rutinel has ties to effective altruism, a movement long preoccupied with AI safety, and you can hear it in the way he talks: “This technology has a 10 percent risk of wiping out human civilization,” he recently told The New York Times. “I don’t want human civilization to come to an end.”

Sam Forstag, who just became Montana’s first Democratic member of Congress in decades, also stated that AI poses an “existential risk to all of humanity.” Troy Jackson, the Democrat who won his Senate race in Maine, has vowed that he’s “not going to let the billionaires write the rules,” adding, “We are not that far from ‘Terminator,’ and I don’t want to be ‘hasta la vista, baby.’” 

You can expect these new victors to be reliable votes supporting the cause of AI regulation come early 2027. But what, exactly, might that look like?

What Democrats plan to do now on AI

As Democrats are poised to take power in Congress, they will have two big levers to pull on AI safety: hearings and lawmaking. 

Now that the election is over, expect a whole lot of hearings. Sen. Ed Markey, who won reelection in Massachusetts, signaled that he’s coming for Big Tech executives. “They are going to need to plant new forests for all of the subpoenas we’ll be printing,” he posted on X.

What will it look like if these executives are called to testify under oath? “Some of the hearings are going to be focused on safety practices — and evidence that the company’s practices are not up to snuff,” Arnold told me. “Asking for answers to questions of:  ‘Why didn’t you find that earlier? Why didn’t you tell us?’ And while that doesn’t get you all the way [to safety], this public pressure is why so many companies have adopted voluntary practices, why they’ve improved their internal monitoring, their incident reporting, their response time to various incidents.”

As for lawmaking, experts I spoke with are now bullish that we’ll see much stronger AI legislation in 2027 than we’ve seen to date. Sen. Maria Cantwell, who’s slated to chair the Senate Commerce Committee, is a key player to watch. She’s already put out a framework for governing AI that could have bipartisan appeal, which she’s called a precursor to legislation. It requires mandatory safety standards for cutting-edge AI models, independent testing and audits that models would have to pass before release, and prompt reporting of safety incidents.

“Cantwell is going to push that, and she’s in a place to basically be the voice of the caucus on that,” Brad Carson, an advocate for stricter AI policies who leads Americans for Responsible AI and a former congressman, told me. “Every single Democrat supports it, and all they need is five or six Republicans to beat the filibuster, and I bet you they can find that.”

Carson is hopeful that Democrats can get Republican votes “especially as the gravitational pull of the White House eases,” he said. “Trump has led them down the primrose path of defeat. A lot of smart politicians are going to be distancing themselves.”

Yes, President Donald Trump ultimately holds veto power. And he’s allowed American tech companies to race ahead on the technology with very few guardrails. But as AI companies make more and more concessions to safety in light of security incidents, the president may be more willing to sign on to some safety regulations. 

“He’s mercurial enough that I can see him signing something like this,” Carson told me, “especially if some of these Republicans say, ‘Look, we’re getting battered on data centers and AI. This is a terrible thing. Let’s do this.’”

Of course, the most surefire way to gain control of AI lawmaking, unobstructed by a veto, would be to lay the groundwork for a Democratic White House in 2028. And the midterms showed that a focus on reining in AI can help propel Democrats to victory. “The road to 2028 starts today,” wrote Alex Bores, the New York Assembly member who has made AI his main focus. “In order to maintain voters’ trust and avoid catastrophic risk, we need a unified baseline around AI safety in the Democratic Party.”

Although many voters focused on data centers this election cycle, it’s reasonable to predict — as cyber breaches and other security incidents proliferate — that they will move from symbol to substance, focusing more on safety in the run-up to 2028.   

“We shouldn’t view voter sentiment towards data centers and AI as stagnant or static,” Arnold told me. “I think it will continue to update to events in the world, and I think it will increasingly focus on AI itself as we see events in the world that make that very obvious to people.”

Or as Greg Casar, chair of the Congressional Progressive Caucus that sets policy and legislative strategy, put it, “The revolt against data centers was one of the top issues of the 2026 election, and in a few years we will look back on it as just the early tremor of a much bigger AI earthquake upending American politics.”

The savviest politicians are already preparing with that earthquake in mind. 

Data is redefining modern defense operations. The ability to master and manage it is now a decisive factor in military victory or defeat. Few people know that better than Jean Charles Ellermann-Kingombe, assistant secretary general for cyber and digital transformation at NATO.

In a recent interview preceding NATO Edge 26, the Alliance’s annual defense innovation show, Ellermann-Kingombe is very clear on the kind of digital transformation effort it takes to get the right data into the hands of military leaders operating in high-pressure situations, at exactly the time and place they need it.

Data-sharing must be a core design principle, he says: “The bottleneck is often not technology, but permission: classification boundaries, national rules and trust decisions.” Once data is well managed, an even bigger opportunity arises to use AI as “an enabler of military effectiveness,” he says, assisting commanders and operators further.

He adds: “For NATO, the biggest difference AI can make is in decision advantage. Modern operations generate volumes of data that no human organisation can process unaided at the speed required. AI can help fuse information, detect patterns, support planning, prioritise scarce resources and accelerate staff processes, providing commanders with better insight, earlier warnings and more resilient options.”

As powerful as it is, AI can only reliably accelerate defense mission outcomes if it is built on vast, relevant and quality data. Data is the fuel that will power the shift to software-defined defense, an approach that delivers military capability through flexible, modular software rather than rigid, hardware-bound platforms.

THE CHALLENGES OF MASTERING DATA

Software-defined defense, AI and the challenge of mastering the data that powers both, will be hot topics for attendees at NATO Edge 26, due to be held in Izmir, Türkiye, in November and expected to attract over 3,000 participants and more than 270 technology partners, including Elastic.

We will be there because we know that many attendees will be looking to better understand what software-defined defense will demand of them in data management terms.

As powerful as it is, AI can only reliably accelerate defense mission outcomes if it is built on vast, relevant and quality data. Data is the fuel that will power the shift to software-defined defense.

Bringing data together from many intelligent systems and smart devices demands real rigor. After all, in order to make critical operational decisions, military personnel need to be able to see the big picture, quickly searching for information in both structured and unstructured data formats across a vast array of different systems.

Defense data naturally lives wherever operations happen — across land, sea, air, space and cyber domains — and is stored in different formats like images, audio transcripts, documents and geospatial, reaching across thousands of different mission systems.

These can include communications systems that keep troops connected in the field; systems used to manage artillery devices; reconnaissance systems; radar stations; and geographical information systems that support military leaders in tracking assets, planning missions and routes, and analyzing risks and threats, including potential ambush sites and minefields.

In order to make critical operational decisions, military personnel need to be able to see the big picture, quickly searching for information in both structured and unstructured data formats across a vast array of different systems.

On top of all this, organizations like NATO face an additional layer of complexity. Building a common operational picture across the digital environments of 32 member nations requires multi-intelligence fusion of massive volumes of data held in disparate systems, from legacy databases and classified networks to cloud platforms and security tools.

OPERATIONAL ADVANTAGE

Addressing these challenges means turning all this data into operational advantage and mission readiness, accelerating the ability of armed forces to respond to situations by increasing their situational awareness and decision speed.

The answer is a data mesh architecture. Rather than consolidating everything into a traditional data lake, a data mesh approach manages data where it originates and lives, without moving or duplicating it; so it can be searched, queried and analyzed from multiple access points across the entire environment, including allied partners such as NATO member nations. That is what turns scattered data into one unified operational picture with near real-time insights, and context, giving commanders the situational awareness and decision speed that mission-readiness depends on. In my view, it is the most flexible and scalable way to get there.

It also speaks directly to the permission bottleneck Ellermann-Kingombe describes. Because data stays under the control of the nation or system that owns it, access can be governed by policy at the source, with classification boundaries and national rules enforced where the data lives rather than negotiated away in a central repository.

Rather than consolidating everything into a traditional data lake, a data mesh approach manages data where it originates and lives, without moving or duplicating it.

A data mesh approach is sustainable because of its reliance on open standards. They enable defense organizations to connect systems that were never designed to work together, from legacy platforms to dispersed infrastructure, so data stays findable and can be effectively analyzed across the environment. Interoperability stops being a barrier as new partners and data sources join, and new tools can be integrated without rebuilding systems or making major investments. The architecture scales alongside the Alliance’s growth.

Without a unified view, the risk of losing operational advantage to adversaries extends from the battlefield into cyberspace. Zero Trust depends on connecting authentication logs, network traffic and application telemetry where they already sit, so that traditionally separate domains can be visualized and analyzed together. When a user accesses sensitive data, the system must verify not only who they are but also their device status, network path, and the time and location of access. If those checks are slow or impossible, threats can remain undetected and escalate through systems. A data mesh built on open standards connects the pillars of a Zero Trust architecture, allowing organizations to accelerate implementation without replacing the tools they already have.

When the manual efforts of military data analysts struggle to keep pace with the scale and speed of events on the ground, AI can work faster and more accurately.

The same logic is already visible in large government organizations: CISA’s new SIEM-as-a-Service platform uses open standards to give US federal civilian agencies a consistent approach to threat detection and response.

As AI’s value in supporting human decision-making becomes increasingly clear, a data mesh architecture provides the foundation to apply agentic AI capabilities holistically. It enables complex organizations to analyze the vast amounts of data their systems generate, find actionable correlations and separate signals from noise. When the manual efforts of military data analysts struggle to keep pace with the scale and speed of events on the ground, AI can work faster and more accurately. It also lets analysts ask questions across structured and unstructured data and rapidly receive answers grounded in reliable sources.

The numerous tech challenges facing the modern military calls for closer NATO-industry cooperation, and that’s why NATO Edge 26 is such an important event. All 32 sovereign member states that make up the Alliance use different systems, yet they rely on secure, trusted data-sharing with each other as the foundation for cooperating on military operations and sharing intelligence. It’s vital that they can turn to industry innovators to support their operational needs.

At a time of great geopolitical uncertainty, with data increasingly considered a primary defense asset, NATO members face growing pressure to turn it into operational advantage and readiness before adversaries do. We look forward to discussing how at NATO Edge 26.

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In a state of post-election bliss, John Nicely drove his daily school bus route on Wednesday morning through the tiny town of Perry, Ohio, a few miles south of Lake Erie.

“So, when are you taking down the signs, Mr. Nicely?” his students asked him as they filed inside, “because you kicked butt, buddy.” 

They were joking, of course, the way kids sometimes do with an adult they really like — and Nicely is, if my calls with him are any indication, a man who makes good on the promise of his name. But they were also right: He had kicked butt.

Of all the losers in the midterm elections perhaps none collapsed as decisively as data centers, those hulking, humming backbones of the AI boom, whose construction has incited a rage-fueled bipartisan backlash across the country. More than 40 data center-related measures made it to the ballot this Tuesday, and data centers lost all but one of them, often by dramatic margins. It is by far the most resounding referendum yet on the infrastructure of AI, a bipartisan revolt against a flagrantly opaque industry with a near-unanimous “no thanks,” or at the very least, a “probably not, but only if.” 

Even against the dramatic background of that national movement, Perry stands out. For much of the past year, the village has been embroiled in a bitter fight over plans for an enormous 230-acre data center — the equivalent of one-sixth of its surface area — quite literally in Nicely’s backyard, or at least in the center of the town he now calls home. But he and his fellow residents aren’t simply looking to stop the project from happening. 

While most of the measures that passed elsewhere outright ban data centers, or at least limit their construction, Perry’s Issue 16 does something more novel and maybe more consequential: It forces data centers to keep their promises. As crafted by Nicely and other advocates in Perry, it would require any data center in Perry that accepts a tax abatement or a subsidy — as almost all of them do — to take out a bond guaranteeing that even if the AI bubble bursts, even if companies fling all the data centers into space, or even if the facilities shrink to the size of a penny, the village will be left with more than empty promises and a gaping vacant campus. 

And on Tuesday, after months of sign-posting, petition drives, and unprecedentedly raucous village hall meetings, Nicely and his allies won. 

“I was absolutely choked up” as the results poured in, he told me on Wednesday, still a little bit teary over the final vote on Issue 16, which received a stunning 87 percent of Perry’s support. “I had the optimism that we would get it passed, but never by a landslide,” he said. “I couldn’t be prouder of this town than I am today.”

Data center companies should be paying attention. Combined with a blue tsunami that has carried in a slate of Democratic candidates who’ve campaigned on data center restrictions, and with more than half of voters calling AI a major issue in their choice for Congress, the industry cannot proceed with business as usual. As of now, if a data center wants to get built in the US, it will need to offer Americans a much better, more convincing deal. Issue 16 might show them the way.

Small town, big data center

Few towns know what a good deal looks like better than Perry, which has been molded for the past half-century by the looming cooling towers of its local nuclear power plant. 

When the Perry Nuclear Power Plant first opened in the 1980s, it brought hundreds of well-paying jobs and enough tax revenue to build luxury public school facilities like an Olympic-sized swimming pool. 

“I knew kids from high school who graduated and got these power plant jobs, and it was like hitting the lottery,” said Nicely, who grew up in the town next door. “They came in right off the bat to partner with the community,” bankrolling benefits such as garbage collection and free utilities for those most affected by “this thing being right in their backyards.”

Today, the station’s plumes of vapor are like pheromonal beacons, calling out to energy-hungry data centers. Earlier this year, the plant’s operator inked deals with Meta and the US Department of Energy to greatly expand its capacity, potentially making the facility into the largest of its kind in the country. But all that juice will do little good without the data centers that need it, and when a far-flung developer called the Province Group showed up in Perry in April, having already made a bunch of secret dealings with local policymakers behind closed doors, residents were livid. 

“I’m going, ‘Oh my God! They think that this town is nothing but a bunch of little hicks,’” said Nicely, who was recruited onto the village council a few months ago for his activism. He remembers thinking just that while he sat on his perch at the village hall, listening as the developer “tried to wow us with a bunch of spreadsheets,” rattling off promises for future jobs and tax revenue. Nicely, who is in his mid-60s, had a career working in business before he moved back to northeastern Ohio a few years ago to care for his mother — and, ultimately, drive the school bus, among other things. In his old life, he’d been a self-described “spin doctor,” and in the village hall he sniffed a lousy deal right away. If Perry’s nuclear plant once “truly took on a partnership role” when it came online, he said, then “these people came in with no regard to partner.”

Under the Province Group’s initial proposal, the data center would receive a 75 percent tax exemption for its first 15 years after construction. In return, once it started paying taxes, it promised to pay up plenty, to the tune of over $320 million over the next three decades, while ushering in the creation of hundreds of jobs. But 15 years is a long time to wait for a company to fork over the goods, and data centers are hardly known for going on long-term hiring sprees. 

Data centers, despite their odious reputation, can be good for local communities under the right conditions. Virginia’s Loudoun County has become well-known as “Data Center Alley” for its AI-fueled growth. There too, tax revenue from the industry has paid for fancy amenities, much like Perry’s nuclear plant did in the ’80s — indeed, Loudoun even got an Olympic pool of its own. And there are plenty of other proposals swirling for using the oodles of money behind data centers to improve local infrastructure. 

But Ohioans like Nicely had reason to fear they were about to get ripped off, partly because it had happened before. Back in 2019, Perry’s nuclear power plant — like other energy developments across the state — was on the verge of bankruptcy. Ohio passed HB 6, a last-ditch billion-dollar bailout funded by surcharges on Ohioans’ energy bills, in response. But while HB 6 saved the plant, it was later revealed to be part of the largest bribery scandal in Ohio history, one that sent the state’s former House Speaker Larry Householder to federal prison for two decades. 

What’s more, many of the policymakers involved in HB 6 have spent the past few years rolling out the red carpet for data center developers, enacting subsidies and tax breaks that have turned the state into one of the fastest-growing data center markets in the country. 

“They have no credibility on this issue,” said Nick Owens, a co-founder of Conserve Ohio, a group that sponsored ballot measures in 10 communities across the state. Owens is a former Republican candidate for state office who became an FBI informant in the HB 6 case.

After that scandal, “you would think there would be some muscle memory left in the system to realize that we shouldn’t be bought and paid for by all these special interests,” he told me. “But it’s still happening.” 

What if data centers had to keep their promises?

And that brings us to Issue 16, the ballot measure Nicely helped bring to Perry’s ballot on Election Day. Amid outrage from residents, the village council in Perry had already struck down the Province Group’s initial request for a tax abatement in July, putting the project in limbo. 

But thanks to the passage of Issue 16, if that developer or another like it tries to apply for one in Perry again, they will be forced to take out a kind of insurance on their promises, protecting the town in case the projected tax revenue never materializes as expected in the years that follow.

“That’s the threat with data centers, that in a couple of years, these projects will not be needed,” said Kasia Tarczynska, a senior research analyst at Good Jobs First, a group that studies government subsidies. “It is totally fair to say that if you want us to give you something, give us something in return and guarantee that you really want to be here.” 

“Initiatives like this really show that people are innovative and smart — they understand what they’re giving up. They understand that these corporations often come in, promise, and never deliver.”

Kasia Tarczynska, Good Jobs First senior research analyst

Many small towns, particularly across the Rust Belt, are acutely sensitive to the risks of an industry swooping in for a few sweet decades before disappearing, leaving decaying facilities and a local economy in turmoil. In recent years, states like Georgia and Texas have required projects to take out bonds similar to Perry’s to guarantee that local benefits actually materialize for developments that receive a large subsidy or tax break from the government, or to compel them to clean up after themselves if a project goes bust. 

“Initiatives like this really show that people are innovative and smart — they understand what they’re giving up,” Tarczynska said. “They understand that these corporations often come in, promise, and never deliver.”

If data centers want to continue to build in small towns across America, then they might need to become much more comfortable backing up their promises before they break ground. As Sam Nelson, a professor of political science at the University of Toledo, put it, “communities might be a lot more willing to be open to this development if they were actually going to see the money flowing.”

Nicely has been hearing from local policymakers across the state and the country interested in building out similar bonds into their data center deals, some of which have been inspired by Issue 16 in Perry. Even before the election, developers around the country had begun offering eye-popping and legally binding community benefits agreements promising to pump tens of millions of dollars into local schools and infrastructure. Those concessions may not assuage all the concerns that Americans have about data centers — which are, after all, arguably proxies for other fears about the madcap proliferation of AI. But they can still be meaningful, especially in communities that otherwise have the least to start with or the most to lose.

And Nicely’s town could certainly use the cash.

“Perry needs the money,” he told me. The town’s newest fire truck is now a decade old, and its ambulances are “in dire need of replacement,” he said. “There’s just no money to do it. So we have got to have some type of economic development.”

And that will probably require doing something with the sprawling property in the middle of town. It need not be a data center, Nicely said. But if it is, the town now has a lot of leverage to go with all the power flowing from its plant. “If you want to be business partners then come in as business partners. Offer the people something up front, and tell them how you’re going to invest in their community, not how it’s going to benefit you,” he said. “Or go away.”

It feels like the skies above our AI-frenzied world have darkened in the past month. Employees at Anthropic and Google started quitting, citing fears that the AI was spiraling out of control. After a string of agents started hacking websites, researchers used AI to discover vulnerabilities in critical systems, leading to large swaths of the web going dark last week and giving rise to a scary term: Black October. At the same time, as it continued to ramp up AI spending, Microsoft offered buyouts to 60,000 employees — over a quarter of its workforce — and economists once again warned of a bubble.

Yet, a lot has happened recently to make AI genuinely useful for more people. AI agents are getting better and can now make restaurant reservations for you. Walmart made it possible for agents to shop on behalf of humans, a bold move since Amazon banned the practice. Apple announced an AI-ready home display that can turn on your lights. And let’s not forget that AI is also helping scientists find cures for formerly incurable diseases and mathematicians solve previously impossible problems. 

It’s hard to reconcile the incongruity between the recent bad news and good news. For most of us, the state of AI is anxiety-inducing — especially when it comes to our livelihoods. According to a Pew Research Center study published in August, 71 percent of Americans think AI will lead to fewer jobs in the next two decades. That figure is up 7 percentage points from where it was two years ago. Sure, in the best-case scenarios, AI could erase inefficiencies at work, streamline our daily lives, cure diseases, and generally improve the human condition. 

But what if you are the “inefficiency” at work that gets erased?

That tension is at the heart of Ethan Mollick’s latest book Co-Existence: The Next Phase of AI. If there is such a thing as an AI sage, it would be Mollick, an associate professor at the Wharton School and faculty director of its Generative AI Labs. To the nearly half a million subscribers to his Substack newsletter, One Useful Thing, he delivers semiweekly updates from the jagged frontier of the technology’s development. His message in any medium is resoundingly clear: You need to use AI in order to understand it. And to get the most out of using it, he argues that you have to accept that AI is not going away; it’s only going to become more important. 

That ethos doesn’t make the existential threats of AI any less daunting, but that’s not the point. Unless you’re an early Anthropic employee or a United States senator, there’s probably not much you can do directly to rein in AI’s potential harms. But you can, according to Mollick’s worldview, decide how the technology could fit into your life and think about how you fit into an AI-dominated future. 

I recently spoke with Mollick about his new book, what life and work might look like in the agentic AI era, and what it means to be an artisan when even knowledge is industrialized. His theories about living with AI are meant to help you navigate the future, regardless of the latest forecast. This interview has been lightly edited for length and clarity.

We’re entering a new era for AI, and the vibes are different. AI agents are something that many of us will encounter, interact with, and maybe even embrace. I’ve always known you as someone who jumps on the cutting edge of this technology to figure out what it can do. What do you think most people don’t understand about the next generation of AI?

I really think people haven’t internalized what agents do. Agents mean many things, and we can talk about personal agents, like Muse. But the fact that the problems that plagued working with AI agents before — you had to guide them each step of the way, they made mistakes, hallucinated constantly — that’s kind of gone. You can regularly get hours, days, weeks of work out of a system. 

It’s not just a theoretical thing anymore. I talk to coders all the time who are a hundred times or more productive than they were before. And in other fields, we’ve kind of moved past the theoretical discussion about AI being, “This is going to be a big deal,” to, “It’s here.” From a whole kind of co-intelligence perspective, we’ve gone from, “Here’s how we use AI and work with a chatbot back and forth,” to, “These systems are now doing real work in ways that matter to real people and are reshaping real fields for better or worse.” And if you’re used to using AI a year ago, if you’re not paying for one of these systems, it’s really easy to have lost track of what’s going on.

What about the practical, everyday ways that AI is disrupting our lives? And what do you think are the limits of that disruption?

So outside of the job world and homework cheating, I think life has changed only a little bit for people, partially because these systems were kind of complicated to use and you had to decide what you were using them for. But I think the advent of personal agents, like Muse and Dot, are going to lead to a very rapid change in a lot of ways.

Most people I know who aren’t experts who use these systems, like my parents, are using these things spontaneously. They’re finding that once you know how to prompt the AI to do something, it can do things spontaneously and come back to you because it has your email and everything else. Just as one example, I was unable to figure out how to cancel my standing $8-a-month wifi subscription from my airline, and I was like, figure this out. And it went to customer service, joined chats, then I have no idea what it did, but now I don’t have a subscription to wifi anymore, thank goodness. 

“I deeply worry that if I de-skill myself entirely, I won’t even know how computers work.” 

So there is this sort of lowering friction piece that’s super interesting, with good and bad elements to it. The bad side of lowering friction is both that every article gets written by AI, so there’s no human thinking in it anymore, and that companies don’t want 10,000 customer service agents coming to them demanding new things. So I think we’re just on the edge of seeing the mass change in everyday life. Right now, AI is a really good second opinion on everything. As agents become more popular, they start to take action.

How do you know when to let AI do the work, though? We know that offloading certain tasks to AI can make you worse at doing those kinds of tasks in the future. How do you take advantage of AI’s ability to do drudgery but also hold onto your own expertise? 

If there’s a personal theme underlying Co-Existence, it’s that one. That is an incredibly hard choice to make, and we’re historically very bad at making it. 

Students are the No. 1 case because they’re willing to offload homework because homework sucks and because the incentives for doing any piece of homework are terrible. But overall we need to do it to learn. So people offload that, but even if you’re trying to do the right kind of thing, like using AI only to help you learn and not to replace learning, it turns out that when chatbots are in assistant mode, as opposed to using them as a tutor, it feels like you’re learning even though you’re not. You feel like you’re in the loop even though you’re not. And so that assumes that we both know we want to offload and we do it intelligently and that we do it in a way that helps our long-term gain.

I am worried about us being able to do that. You have to draw lines for yourself about what skills matter to you or not, and you have to be thoughtful, in a way that we’re usually not, about what we’re giving up and what we’re not. Technology’s designed for us to give up stuff. We don’t walk 50 miles a day anymore in forced marches. I don’t know how to weave clothes. There’s a lot of stuff I’m very happy to not know: how to maintain my computer in some ways, even though that’s dangerous. I let Codex scan for security issues because it does a better job than me faster. But I deeply worry that if I de-skill myself entirely, I won’t even know how computers work. 

So I don’t have an easy answer, but I think that that will be the defining question for most people in their career paths right now and their lives: What do I give up? I think we can be too sentimental about things that we’re giving up. It’s okay, but we don’t know the full chain of consequences. I think that struggle is going to be central to a lot of people’s experience. I wish I had an easy answer, but now that AI can do so many things, de-skilling is a real problem at this point.

We’re living through a huge transformation. That’s increasingly clear. Besides losing their jobs, a lot of people are scared of losing the sense of what makes them human and what makes them valuable. How do people hold onto that in the years to come?

There’s a transformation happening, and for some people, the work will get more satisfying. There’s a lot of coders who did not like writing lines of code, but they did like summoning software and figuring out what they wanted to build. Their lives are much better because the AI does all the tedious work. They can command things to be built, and it’s so much more exciting than it was before. It lets people who didn’t code start to do incredible coding projects. So on one hand, that’s deeply freeing of the kinds of work that we did. On the other hand, there are people who love writing those lines of code and that’s gone now. You’re a hand-weaver in a world of power looms. Your stuff might be elegant and beautiful, but it is not going to last in a world where industrialization happens.

Industrialization versus craft is a way to think about this. There’s a lot of craft that we’ve grown to really appreciate, like the handcrafted sentence. Those things will be under threat from the logic of industrialization, which is that we need to do more of it. I think that there’s going to be a shift. There’s satisfying work to do, but I think a lot of what was satisfying human work will be under pressure to disappear, because you took pleasure in something that was not automatable, but now is.

We’ve been through this in industrial revolutions before. This is happening at a very large scale to intellectual work, where craft was a very important part of what we did. Mathematics was craft, software engineering was craft. I think that we’ll be under pressure to produce more of it, which is what industrialization’s about, even if that destroys the value of doing this as a person. You get paid to do it now, but you’re not getting the same value you did before.

“We’re no longer in a place where AI is going away.”

I think we’re going to find lots of crises of meaning. Some people are going to find huge amounts more meaning in their work, either because the work itself is now much more interesting. Work is a lot of crap, too. We can say great things about it, but people spend a quarter of their work life saying they’re bored. It’s not like this was a great job for everybody. So some people will have much more excitement in craft. Some people will have it extracted out of their job, and it’s all going to be happening at once. I know it’s not a satisfying answer, but that’s what’s happening.

Forgive me for saying so, but that was an anxiety-inducing answer. It’s an anxiety-inducing time. I wonder what advice you have for us anxious people?

I think anxiety is real, and there’s different levels of anxiety. We’re going through a wrenching change, or we will be going through one that we’re on the edges of now. There is also existential risk about whether AI will kill us all, where we clearly need more regulation. The answers are unclear. Smart people seem to think that that’s a real danger. It’s hard to know. But leaving that aside, there’s anxiety about our own transformation, our jobs. I mean, the industrial revolutions worked out great in the end, but during it, [that] was the time of Marx and anarchists and Dickens writing about London. There’s a transition we have to go through here.

I don’t think we can pretend it’s not happening, and I see a lot of people taking comfort in the idea that maybe this all will collapse and AI will go away. Even if there was a bubble financially, we’re no longer in a place where AI is going away or stopping development. I worry a lot about people doing that. Once you face that, though, I find people often reconstruct meaning in very exciting ways. Thinking about what more you can do and where you find joy in this is actually quite exciting. So I think that anxious optimism is a reasonable place to be.

What I worry about is sticking your head in the sand saying, “I’m not going to learn AI. This will go away. I’m going to pretend it’s not happening.” I mean, I wish I could give you that option. I think there’s a lot of people who wish we could slow things down or turn a dial down, but there’s also a lot of exciting things. There’s a lot of hard problems in the world that are unsolved.

If you lean into the positive pieces, you can ask how we mitigate risk but also think about how we can build a better world. Part of what makes me nervous is people sitting out that second question and just filling up with anxiety rather than believing we have a tool that can rebuild things. How do we rebuild a better world for journalism or for nonprofits or whatever it is? I think it’s a reasonable question to ask, and I worry it’s asked too little.

We’re living through an AI-fueled “Black October,” and things are changing fast. Hackers are using AI to breach countless companies. AI tools have uncovered security flaws that could leave 70 percent of all websites vulnerable. Governments are scrambling to secure their sites. Banks, too. And because cyberhacks can hurt supply chains, some longtime AI watchers are going full prepper, stocking up on food, cash, and more.

In the center of this story is Anthropic, one of the world’s leading AI companies, which was explicitly founded on the promise that it would be a “good guy” company in a world of dubious tech giants.

Anthropic says all the latest developments are very serious. Its security chief wrote last week that things are “going to get a lot worse before they get better.” Two weeks ago the company warned that AI models could soon self-replicate on the internet-connected devices in your home, like smart fridges and robot vacuums. And its co-founder now says “the singularity” — when AI systems speed up technical progress in a way that’s hard for humans to comprehend — “is here.”

You might think, given its view of the risks, that Anthropic would be pumping the brakes on its cutting-edge AI progress. Just the opposite. Anthropic filed for a blockbuster initial public offering on Monday, chasing a valuation of $2 trillion even as it warns in the IPO filing that AI could “pose catastrophic or existential risks to humanity.” Every day, it is racing toward recursive self-improvement (RSI), where AI models are smart enough to build the next, even smarter AI models by themselves. 

How does Anthropic justify that?

As its CEO, Dario Amodei, reminded the world last week, he built the company on one big bet: that instead of driving a race to the bottom, where companies skimp on safety in their rush to beat competitors in the market, Anthropic could actually catalyze a “race to the top.” 

The idea is that if Anthropic proves it can build AI that’s both powerful and safe, the market will expect both — so other AI companies will then feel compelled to compete on safety, too. That may sound nice in theory, but it means Anthropic has to keep building ever-more-powerful AIs.

“The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier,” Amodei wrote. “And looking at that strategy as a whole, I’m proud of our track record and stand by it.”

In other words, Amodei is sticking to his bet. 

But Amodei is gambling with, by his and his company’s own telling, the future of humanity. If AI goes wrong, it could lead to our gradual disempowerment, a galling concentration of power, or even complete extinction.

So there’s a very basic question that the public deserves a good answer to right at this pivotal moment: What is the evidence that Anthropic’s stated strategy — compete at the frontier so as to drive a “race to the top” — is actually successful overall?

From the outside, it looks like Anthropic has done more, on balance, to drive a race to the bottom. (Disclosure: Future Perfect is funded in part by the BEMC Foundation, whose major funder was also an early investor in Anthropic; they don’t have any editorial input into our content.)

In fact, part of the problem may be that Anthropic continues to treat this as a classic arms race. Specifically, the company seems to model it as a prisoner’s dilemma: If Anthropic stopped building more powerful AI, another player — whether it’s China, OpenAI, or another company — would just build it anyway. In this kind of dilemma, if the other player is going to defect, it’s rational for you to defect, even though nobody actually wants that outcome.

But what if this framing misses the point? 

Game theory can make a huge gamble seem sophisticated, while making the obvious alternative — just don’t build a doom machine — seem naive. Yet the fact that Anthropic and others are now racing toward RSI makes it much more likely that we could lose control of AI and start to see massive damage to our world. When the cost of defecting from safety is that you create a serious risk of catastrophic harm — or of all humanity, including you, going extinct — is that still a “rational” move? 

Instead of telling yourself that you can galaxy-brain your way through this with fancy enough game theory, maybe you’re better off just thinking of this as something much simpler: Russian roulette.

More and more leading voices in AI are starting to recognize that. 

“We should stop AI progress now, starting unilaterally with Anthropic and/or OpenAI stopping — and then broadening out as a second step to more AI companies and negotiations with China,” Geoffrey Irving, who was until recently the chief scientist at the UK AI Security Institute, told me. “Unilateral pauses can’t hold for long, but they are the right first step: When you’re doing a very dangerous thing, the correct action is stop, even if others are also doing dangerous things.”

Anthropic did not respond to a request for comment. 

The case that Anthropic has done more harm than good

Let’s be clear: The question is not whether Anthropic has done anything good on the safety front. It has. Standing up to the Pentagon, temporarily holding back access to frontier models, and publicly advocating for safer AI policies are a few examples, among others.

But the real question is whether the company’s “race to the top” strategy is working on balance — whether it’s net-negative or net-positive for safety. 

Nathan Calvin, the chief strategy officer at AI safety advocacy organization Encode, recently set off a firestorm among tech watchers, writing on X, “When I look at many notable actions that Anthropic has taken over the past year, that evidence seems most consistent to me with the view that Anthropic is primarily trying to win the race to superintelligence, and that other considerations (including safety) are fundamentally secondary.” Here’s his list of what Anthropic has done:

And this is all just in the past year. If we rewind the tape further back, we see Anthropic pushing back on a landmark bill to regulate AI. Rewind even further back, and we see indications that Anthropic’s core strategy may have been flawed from the start. According to Reuters, in late 2022, OpenAI CEO Sam Altman heard rumors that Anthropic was building an AI-powered chatbot. So he ordered his employees to ship an OpenAI chatbot first. That chatbot was called ChatGPT. We all know the rest.

The rivalry between OpenAI and Anthropic seems not only to have hastened the arrival of generative AI, but also to keep pushing release dates forward to this day. The two companies have been known to release new or cheaper models within hours of each other, each one keen to protect its market share. 

Even if you think Anthropic’s behavior was justified in 2022 because publicly demonstrating the risks of AI also demonstrates the need to regulate AI — a common claim within the company and AI discourse writ large — you have to ask: Is it still justifiable now, when AI has reached this level of power and sophistication?

Anthropic has indicated that it’s getting closer and closer to achieving RSI. In January, Amodei himself wrote, “I am concerned that LLMs are approaching (or may already have reached) the knowledge needed to create and release [biological weapons] end-to-end, and that their potential for destruction is very high. Some biological agents could cause millions of deaths.” And he wrote in September, “Given the accelerating rate of AI capability development, it’s my worry that in 6-12 months such a swarm could be capable of taking over the entire internet with a persistent botnet.”

But at this point, Amodei and his staffers are themselves perhaps the primary ones accelerating the rate of AI capability development.

“Anthropic seems to be doing the whole RSI thing more than anyone else, other than perhaps OpenAI,” Calvin told me. “And I just think that seems insane given their publicly stated views on the risks.” 

“I think people at Anthropic should reckon more with the fact that it seems like the modal outcome is that the largest impact that Anthropic will have had is dramatically accelerating the race to RSI,” he continued. “On the current trajectory, everything else they will have done and thought and ideals they had will be a rounding error compared to their contribution there.”

Anthropic should be willing to invite scrutiny on this point — and, if it believes it’s actually making the field safer than it otherwise would be, to show us the receipts. How? It could put before Congress a bill that actually strengthens safety, instead of weakening it. It could stick to its former Responsible Scaling Policy and get other companies to do the same, instead of jettisoning its commitments. It could influence other labs not to build RSI, instead of leading the charge.

In the absence of such evidence, the “race to the top” logic just doesn’t hold water.

What if Anthropic just…stopped?

Amodei has begun advocating for “pacing the frontier” of AI development. “We must slow the pace at which we improve the capabilities of AI models,” he wrote in September.

The catch is that Amodei’s plan to slow down is contingent on others agreeing to slow down too — other AI labs, to start with, and also other countries. But more experts are now saying that it would be better if a frontier company like Anthropic unilaterally stopped.

“I’m glad that he proposed that we pace, but like, now we have to actually do it — and it seems like he’s saying he’s not going to do it unilaterally,” Daniel Kokotajlo, a former researcher at OpenAI who became a prominent whistleblower, told me. “That’s not very helpful, Dario! You should lead. What’s all this about ‘racing to the top’ and ‘leading by example’ if you’re just waiting?”

Voluntary unilateral action by individual companies is of course no substitute for federal regulation and international treaties. But Irving argues that it’s the right first step, because if the companies wait for governments to rein them in, it may well be too late. Governments are notoriously slow. An individual company can act today.

There’s a perennial objection here: “But China!”

Yet the idea that we’re in a classic arms race with China has always been flawed.

For starters, AI is not just one thing with one purpose, like the atomic bomb. It’s a much more general-purpose technology, like electricity. So what matters isn’t simply who gets it first, but who uses it in which ways. Besides, if the harms from AI are massive, then those harms are relevant for the other party, too. The Chinese don’t want to see catastrophic harms to all of human civilization, either. So we shouldn’t write off the possibility of coordination.

Former Vox reporter Kelsey Piper recently wrote about a colloquium at Anthropic where one participant objected: A decade ago, Facebook considered acquiring the tech that would later power TikTok. But, facing pressure due to privacy and safety worries, the company passed. That didn’t end up protecting us from the scourges of a world with TikTok — all it did was hand China an early monopoly on the tech. Piper writes:

Was this a policy success? Did we want to repeat it?

We were obliged to grudgingly concede that it didn’t sound like a policy success. We did not want to repeat it. China might be no excuse for the U.S. labs to do reckless things, but it is a real consideration.

Our next step might be bilateral talks with China, then. But by the point where you agree that Anthropic’s plans might depend on bilateral talks with China, you’ve already conceded most of the steps of Anthropic’s worldview that seemed so much like madness 10 minutes ago: continue working on AI for now, try to negotiate a slowdown, but don’t abandon work at the frontier and all the advantages that come with it.

But TikTok is not a good analogy for RSI. The former may be a cesspool that rots our brains and amplifies ethnic hatred, but its dangers don’t compare to the dangers of RSI. The TikTok case may indeed be best approached as a classic prisoner’s dilemma. But with RSI, the potential downside is so catastrophic that anyone who proposes to save the world precisely by building the tech that endangers it is making a hell of a gamble on behalf of everyone else. At the very least, the burden of proof should be on them to prove their confidence is justified.

AI insiders often think those outside of their industry can’t possibly understand the technical, geopolitical, and moral complexities they’re facing. But commonsense morality is commonsense for a reason: It tracks intuitions that are usually right (such as, if you don’t want a doom machine, maybe don’t build a doom machine) and trains us to be appropriately skeptical of huge, counterintuitive bets to the contrary. 

Precisely because the AI landscape is a complex system — there are so many actors (including different AI companies and different governments), and actions from each can trigger actions from others in ways that are hard to predict — believing you’re smart enough to take highly counterintuitive actions and end up saving the world as a result is an extremely risky gambit. Instead, maybe it’s best to just do the straightforwardly right thing: Stop.   

To be clear, “stopping” doesn’t have to mean peeling back everything that’s been done in AI so far. The powerful AI systems that are already out there can’t be put back in the box. And we may need some AI to defend from the AI that’s already out there.

“I’m not saying shut down all of your data centers,” Calvin said. “But I’m saying that in particular, pushing the frontier on RSI and making it so that you’re going to be the company that is doing more fully autonomous development than anyone else in the world, does that actually make any fucking sense?”         

Increasingly, both AI insiders and the public are shouting no. 

Some insiders, perhaps aware that humanity is perfectly capable of shutting down overly dangerous tech development (we’ve done it before), are speaking out and quitting jobs at AI companies. At public gatherings, like the recent Pro-Human Assembly in Washington, DC, and the People’s AI Assembly in New York City, people are piling on the pressure to rethink the AI race. States are drawing up their own legislation to rein in the tech. And a single X post from Calvin got so much attention that no less than Amodei himself felt compelled to respond. The AI companies are being forced to pay attention. 

Attention may not be all you need, but it’s a start.

There are some days when there’s so much that needs to get done, I daydream about having a clone of myself. Between paying bills, life admin like renewing my driver’s license, keeping up with doctor’s appointments, and planning meals, it’s a lot. And that doesn’t even begin to touch the work that goes into making a podcast or managing my social life.

This feeling of overwhelm resonates with Sari Azout. She’s a mom of three who works in tech. “There is just so much doing,” she told Explain It to Me, Vox’s weekly call-in podcast. “I wake up, and I have to do meal coordination and get my kids up for breakfast, coordinate carpools. Then a lot of work during the day, but there’s the email from the teacher, there’s the 20 group chats asking if you want to participate in this group gift. They don’t call it ‘the invisible load’ for no reason.’”

Silicon Valley says it has a fix for this kind of mental overload. They don’t want to clone us — at least not yet. But they do think they have the next best thing: AI agents. 

Azuot was skeptical when she first got an invite to try an agent, but she says she eventually came around on the technology after back-to-school shopping snuck up on her. She used her agent to get it done. “It blew me away,” she said.  “I remember that night coming home just feeling so calm and reading my son a book and feeling present. And I was like, ‘Wow, I actually was very cynical about something that is giving me more time for living.’”

Azout’s experience is, more or less, the sales pitch for AI agents: They’ll help you save time with everyday tasks, so you have more of it for what you want to do. But what are agents, exactly? Are they really all that? And what do we gain — and give up — when we use the technology?  

On this week’s episode of Explain It to Me, I spoke with Reece Rogers, a senior software reporter at Wired, to find out. Rogers says to think of agents as the next evolution of AI chatbots like Claude and ChatGPT. 

“I’d create this mental model of a tiny digital assistant that’s constantly searching the web, going through your email, and checking your calendar, trying to find ways it could proactively be helpful for you,” he explains. “Agents are more proactive. It’s scanning your email 24/7 and sending you a message, maybe highlighting a potential calendar conflict.”

Below is an excerpt of my conversation with Rogers, edited for length and clarity. You can listen to the full episode on Apple Podcasts, Spotify, or wherever you get your podcasts. If you’d like to submit a question, send an email to askvox@vox.com or call 1-800-618-8545.

What agents are out there right now?

At the start of this year, there was a tool called OpenClaw that really sparked this with early adopters in San Francisco, but now there are much more consumer-friendly versions, with one of the most notable being Meta’s Muse. 

There’s also a tool from OpenAI called Dots. But where Meta’s Muse is free to download, OpenAI’s Dots is still behind a $100/month paywall. 

You tried an AI agent for a recent story you wrote. How was that experience?

One personal example, to share something that I’ve actually been using agents for, is our couch. It’s broken, and finding a new couch has been a pain. We want a sleeper bed, but we want these specific colors. We have this specific budget. I’ve been using Meta’s Muse and OpenAI’s Dots to try to find a few different options for us. 

One interesting thing is that Meta’s Muse is connected to Facebook Marketplace. There are these really good integrations where I can give it all these details, and it found a few good couches that are only maybe a couple blocks from me. 

I really enjoyed OpenAI’s Dots experience as well. I spent about an hour talking to it on the phone, and the AI agent made a packet of all these different options I could find. Just this morning, it flagged that one of them is now 40 percent off. 

Is that how this tech is typically used?

Most people are using this as part of a workflow. I attach it to my email, I attach it to my calendar, and I’m using it as a personal assistant to organize my life. But people have been quite creative. 

One of the breakout uses for Meta’s Muse is people have been connecting their bank account statements and then using it to scan for subscriptions they might want to cancel. So it can be used in many different ways, though I will say I do not connect it to my own bank account for a myriad of reasons.

I’m not going to lie, the idea of giving any third party access to my bank information does not thrill me. I don’t love that concept.

That’s one of my biggest concerns about these AI agents: how much personal information we’re handing over to these large companies. 

When I tested Meta’s Muse, it wanted access to my email, my bank account, even my passport expiration details. The pitch is convenience, but Muse users are automatically opted in to having all of their interactions with Muse used to train Meta’s AI models. 

I would definitely recommend, if you’re sharing personal details, go into the settings and turn that off because you might be handing over even more information about yourself than you initially realize.

What is the privacy situation with all of this? We all have various apps where we don’t read and still click “I agree,” but this feels like it could be more invasive.

Yes, especially because it’s all about automation. If you’re automating things and it has access to all of your data, it could make mistakes and embarrass you in ways you might not even be thinking about. 

There was this one really interesting story of someone who had connected an AI agent to their work Slack in addition to their bank account. And their agent just automatically posted how much money was in their checking account for all of their coworkers to see. 

Oh no.

I would be mortified. So there are these kinds of mistakes that these AI tools can still make. If you’re interested in experimenting with them, I would be really thoughtful with how you’re setting up these tools as well as what kind of information you feel comfortable sharing.

“You can see in the marketing that these companies are trying to find some way to entice new users.”

How are these AI agents being marketed? AI is not popular right now. Just ask any politician who has a data center coming to their state. People are not loving it. How are they trying to sell this to the average person?

The AI backlash really is at a fever pitch right now. You could almost see that in the way that AI companies are trying to market these tools. They’ve really, really leaned into cuteness and into adorable mascots. 

Muse, if you haven’t seen its agent, looks like a mix between a Labubu and an Ewok. It’s very furry, very cuddly, very cute. OpenAI’s tools also have similar kinds of blobs with large eyes that might be a little endearing. 

I feel like companies are hearing this kind of feedback that people aren’t trusting AI right now, or maybe they have moral or ethical reasons they don’t want to use these tools. You can see in the marketing that these companies are trying to find some way to entice new users.

Do you think AI agents are ultimately going to worm their way into our lives despite how unpopular AI companies are?

I do think so. I think in a couple months, maybe when I go home from Christmas, I would not be surprised if my mom had an agent on her phone because it does feel like it’s breaking through to average people in the way that ChatGPT had a moment a few years ago. 

Even though there’s a lot of distrust for large companies like Meta, I think there are some appealing parts of this that will bring in millions of people. I do see this as the future of the internet.

When I think of the dream of the average person working in AI in Silicon Valley, it’s “We’re going to use AI. It’s going to free up the free time of so many people. Work is going to get done faster. You’re going to have more leisure time.”

Has that been the case for the people you’ve spoken with who are using this? Has it given them more free time to enjoy themselves or has it done something else?

You would think so, right? You would think that if people are automating these boring aspects of their life, that they would have more time to go touch grass, go see a movie, have some fun. But it does feel like that time just gets filled with other work, other productivity measures. 

While agents may be able to take some more annoying or tedious things off of your plate, I am not as optimistic that it’s going to give us this utopia or freedom where I’m always at the beach.

On the first day of its war against Iran, the United States killed 123 children when it targeted and struck an elementary school. 

The strikes, ultimately approved by humans, were facilitated by the US’s Maven Smart System, an AI-enabled software platform developed by Palantir Technologies that allows the military to aggregate, organize, and process battlefield data. Operators fed Maven intelligence classifying the elementary school as an Iranian military facility, and the system, in turn, marked it as one of the priority targets for day one of the war. Though the site was formerly part of a naval military base, it had been converted into an elementary school in 2017 and had remained one since. The intel handed to Maven did not reflect this.  

This mistake resulted in what some reporters have called “one of the single worst massacres committed by the US government since the Vietnam War.”

Such scenarios may become more common as militaries continue to deploy a once-theoretical technology — AI-assisted or autonomous weapons, capable of making both targeting and launch decisions on their own — on real battlefields around the globe. And they raise a troubling, unresolved question: When human beings and machines collaborate in violating international law, who is to blame? 

In this case, the government and private sector are pointing the finger at each other. Some US Central Command personnel, according to Pentagon officials who spoke to Bloomberg, expected Maven to flag stale information or inconsistencies in the underlying intelligence assembled for potential targets. Meanwhile, a Palantir spokesperson told Bloomberg that the company “is not responsible for the underlying data nor identifying intelligence deficiencies,” while also denying that the evidence indicated that its software was at fault in the strike. 

This debate underscores the fact that AI and autonomous weapons are raising a new host of unresolved issues within the law, even as they’re actively assuming positions on the battlefield. In institutions where the line between human and machine decision-making continues to blur, how do we assign responsibility for war crimes? And, are the laws in place suited for the job?

In institutions where the line between human and machine decision-making continues to blur, how do we assign responsibility for war crimes?

To unpack some of these issues, I spoke with international law expert Allen Weiner, a senior lecturer at Stanford Law School, where he teaches courses on the law of armed conflict. Before teaching at Stanford, Weiner practiced international law in the US Department of State for more than a decade. We discussed the difficulties in attributing criminal responsibility, achieving justice for those harmed by lethal autonomous weapons, and much more. The conversation below has been lightly edited for length and clarity.

How do autonomous weapons challenge traditional understandings of responsibility when it comes to the law of armed conflict?

For most of the 20 years I’ve been teaching at Stanford, there’s been a real fascination and fixation on the problem of autonomous weapons. But the truth is that up until pretty recently, nobody really had any [of them]. It was more of an idea than a problem. It’s only in recent years that we have begun to see the deployment of tools that seem to have the capability [to make decisions themselves], so this does raise the question about compliance with illegal orders and where in the chain responsibility lies.

There are different theories about this. Some people say that if we’re engaged in an illegal attack, that has to be because of the programming or the code, so the manufacturers are somehow responsible for committing war crimes. 

Other people would say no, that’s not the case, especially if the military has said the tool is not allowed to make very substantial autonomous judgments, or can’t say no. Then it’s basically the commander, at the point they decide to deploy this weapon, who might be responsible for the illegal acts, even without knowing in advance what they’re going to be.

So to finish where I began, this does present a big challenge to our traditional understanding of responsibility for targeting decisions and of what it would mean if there were an obligation [for autonomous weapons] to say no. 

The real issue is not the obligation to say no, but this: If you train a tool and it wages war in a manner that’s unlawful, where does responsibility for that lie?

If a commander gives a clearly illegal order, such as, “blow up this school,” it seems clear that responsibility falls on the commander. But I could imagine a scenario where a commander is using an AI-assisted or autonomous weapon, isn’t giving it any illegal orders, is trusting that it will operate within the law, and the weapon breaks the law “on its own.” Where does responsibility fall in those cases?

I can imagine two different scenarios. One is that you say, “We’re going to carry out an attack against our enemy, and we want to hit them really, really hard.” Your tool has been trained to say, “We’re not allowed to hit civilian objects,” so it might say, “I’m not going to hit a school.” But if the military has said, “Don’t refuse any targets,” then it might override the way it’s been trained. 

We’re seeing this already, with the stories coming out about breaches where AI tools, Claude and ChatGPT in particular, interpret their instructions in a way that causes them to do something the person who gave the instruction would not have wanted. When Anthropic gave Claude a challenge to figure out if it could do something, the tool figured out, well, maybe we should break into Hugging Face as a way of solving this problem, and the people who gave the AI tool the challenge never intended that.

So if you said, “We want to launch a really punishing campaign,” what happens if the tool on its own says, “Nothing’s going to hurt them more than seeing their kids killed, so let’s attack the school?” That’s one risk, and it’s a real challenge.

So [as for who’s responsible], it’s not the commander who said “hit them hard,” because when he said that, he didn’t mean “attack schools.” It’s not really the companies, because the companies didn’t want them to hit schools either. 

But the tools are out there, reading everything on the internet, and it came across something that said the way you win a war is by attacking the things your adversary holds most dear, and they hold nothing more dear than their children. So it makes this kind of judgment on its own. And it’s not as if you can go back and say who ordered it to hit a school, which is the thing that is clearly illegal. So I do think there’s a risk of diffusion of responsibility here.

You could say the commander knew the tool might work this way, or the person who decided to contract with Palantir or with Claude, whoever it is, to build this tool. But if we’re really thinking about criminal responsibility, the level of responsibility you would need to convict someone requires a culpable mental state. You would need the person to intend, or at least know, that they were authorizing an attack against the school. And I can easily see a commander saying, “I had no idea.”

The decision to authorize this tool, or to give an instruction like “hit them very hard,” could be a basis for some other kind of accountability — administrative discipline within the military, a reprimand, your rank is lowered. But if we’re talking about criminal responsibility, which is what we often think about when we talk about war crimes, I think this diffusion of responsibility is going to make it very hard to identify any single individual who has the required mental state to give rise to accountability.

The second scenario is where the tool just makes a mistake based on the information available to it. We saw that with the attack on the girls’ school in Iran, where it was pretty clear the tool was looking at old data. Now, who’s responsible for that? 

Is it the people who said “give me targets based on this data set,” when they should have made sure the data and the maps were current? All of this strikes me as negligence or recklessness, and it does seem challenging to imagine a situation in which we would say yes, somebody here has committed a war crime. 

“There can well be war crimes, but identifying who the war criminal is might be a big challenge.”

A war crime has been committed, but is there a war criminal? That’s the situation we’re seeing. There can well be war crimes, but identifying who the war criminal is might be a big challenge.

There’s a professor of law, Rebecca Crootof, who proposes the idea of “war torts.” Instead of thinking about war crimes involving autonomous weapons solely through a criminal responsibility lens, maybe tort law — which allows victims to sue wrongdoers for negligent, reckless, or intentional harm — could address the diffusion of responsibility you mentioned. What do you think about that idea?

I think the idea is interesting. We have to separate out the obligations that countries have under international humanitarian law. In a situation like the one I just described, where there’s a war crime but no war criminal, that doesn’t mean the country is not responsible. There may not be an individual who’s criminally accountable, but if a country targets civilians in a circumstance that’s unreasonable, that’s a violation of international humanitarian law, and that could be a basis for claims between the two countries. 

Tribunals were established between Ethiopia and Eritrea, for instance, after the Eritrean War of Independence, that dealt with claims between the two countries for money damages, and some of them involved allegations of violations of the law of armed conflict. Iraq paid reparations after the first Gulf War for harms it had caused to people in Kuwait, under something called the UN Compensation Commission. So the idea that the state is responsible is well established, and one of the ways states would discharge that responsibility would be by paying money damages. That’s the domestic-law equivalent of torts.

Whether there would be a mechanism for [civilians] doing this in domestic law is an interesting idea. We would probably need legislatures, either state legislatures or the federal government, to create a private cause of action allowing people who have been injured to bring claims against, say, the manufacturers of these tools. But this gets procedurally very complicated, because there’s a whole body of law on federal sovereign immunity. 

Could you sue the Defense Department? Probably not. Could you sue an individual soldier? Probably not. Could you sue Anthropic or Palantir? Maybe. But it would probably require Congress to enact a new cause of action, because in general it’s very hard to bring private lawsuits in US courts for violations of international law.

The basic idea of what we would think of as tort or civil responsibility is really the whole essence of international law. On the substance, states do owe responsibility to one another, and they can engage in violations of international humanitarian law that would invoke their responsibility. But then the whole separate question is whether you have a forum within which to adjudicate that and get compensation.

Does that look different in cases where violations of international law were committed by a human soldier versus a machine?

Well, we don’t know, because there have been no cases in the latter category. All of the international humanitarian law violations that have been the subject of international law claims were ordered by humans. 

Now, if an American machine launches an attack against a girls’ school in Iran, and there were a court with jurisdiction, the US would be responsible. In some ways it wouldn’t matter whether it was an individual or a machine, because we don’t have to go down to the level of the individual actor, company, AI tool, or commander. The responsibility is the responsibility of the country. In some ways, that makes it easier.

If we pursued compensation through state-to-state claims, you’d be able to do that without getting into the details of who within the state that committed the war crime was responsible. The state whose school was attacked would say, “I don’t care. You committed a war crime, you’re liable to me, you’re responsible for the damages, and you can figure it out internally.”

But if we were doing something like a war crimes prosecution, where we’re trying to hold an individual criminally responsible, that would make quite a difference. We would have to deal with all of these difficult questions about responsibility and mental state that you and I are struggling with right now.

So, in most cases, the state would have to take responsibility regardless.

Right, and there are lots of [International Court of Justice] cases involving different aspects of the use of force. The principle that you can bring state-to-state claims for violation of these rules is quite well established. 

But with international law, states have to consent to the jurisdiction of a court. They can do that in advance by saying “I’ve signed up to the jurisdiction of a court and I accept any claims that might be brought against me in this court,” or they do it on a case-by-case basis. And it may not surprise you to know that some of the most powerful countries in the world, the ones most likely to use force outside their borders, have tended to be reluctant to accept the jurisdiction of international courts and tribunals.

Russia, the United States, Israel, and China have not accepted the so-called optional clause jurisdiction of the International Court of Justice. So there’s the substance, about the responsibility of states, and then there are the procedural aspects of whether these cases could be brought in court.

And Trump recently said everyone on the International Criminal Court should “resign immediately.”

Yeah, exactly. 

And what about the role of these military tech companies, like Palantir?

“At what point does Palantir become a good target under international law? Or at what point does Anthropic become a good target under international law, if its AI tools are being used to promote the war effort?”

Another issue [I’ve been thinking on] because of the questions we’re talking about is when the companies themselves become combatants.

We know that a private company that manufactures airplanes, an airplane factory, is a good target under international law. At what point does Palantir become a good target under international law? Or at what point does Anthropic become a good target under international law, if its AI tools are being used to promote the war effort? I think that question is about when the connection of these private companies to the war effort, in a way we’ve never really seen before, meets the standards under international law.

A civilian loses their immunity when they directly participate in hostilities, or if they assume what is known as a “continuous combatant function.” Are there arguments to be made that some of these private companies are participating in hostilities by virtue of supplying the tools the military is using to wage war? I think that’s a really interesting question.

This story appeared in Today, Explained, a daily newsletter that helps you understand the most compelling news and stories of the day. Subscribe here.

Are we living through “Black October”? Maybe, according to cybersecurity experts who are raising the alarm about AI’s increasingly sophisticated and far-reaching hacking abilities. 

This week alone has seen a string of alarming incidents where AI uncovered previously unknown security flaws deep in the infrastructure of the internet. The most concerning involves something called PHP —  a programming language that, according to The Atlantic, is used by “as much as 70 percent of websites.” Cybersecurity researchers used AI to locate a major security breach in the technology.

It’s the kind of thing that, when it’s working, is practically invisible. When it stops, though, you really notice it: Government websites in the UK and Europe briefly went down earlier this week after the PHP vulnerability was discovered, as did some sites in the Wikipedia family, the Internet Archive, and many others.

The outages weren’t actually the result of a hack, but rather preventative action as web engineers took sites down to fix the vulnerability before it was exploited. But not every PHP-like issue will necessarily be uncovered before it can be exploited by someone using AI tools less ethically, and older websites relying on PHP might go unpatched. 

The problem doesn’t stop there, either: Concern is also rising around AI cybersecurity risks to ubiquitous conveniences like online banking. As Axios reported on Thursday, banks are detecting more and more “unauthorized third-party AI agents” operating inside of their systems. 

Those agents are mostly unauthorized by the banks, Axios explains — the holders of the bank account might have given them permission — but it still creates a security risk that executives described as “horrifying,” in part because banks can’t necessarily tell if an agent doesn’t have permission. 

It’s a lot to take in, all of it moving extremely quickly, and it’s hard to tell what any of us can do about it as AI labs continue to forge ahead with ever more competent AI models and agents. Luckily, my colleague Adam Clark Estes has some simple recommendations to shore up your own cybersecurity.

How do I respond right now?

One problem with hackers — even those that happen to be AI agents — is that they can be unpredictable. You won’t know to, say, lock down your Quest Diagnostics records before a breach happens. But whenever you find yourself wondering if your data is safe, that’s a good time to double check that you’ve at least covered your bases.

The National Cybersecurity Alliance, a nonprofit dedicated to the safe use of technology, maintains a list of basic online safety practices that everyone should follow. In no particular order, here’s what to pay attention to now:

1. Keep your guard up. AI makes it difficult to know the difference between humans and robots. It’s a fact of life in the year 2026. So be extra careful to avoid engaging in potential phishing scams. Don’t reply to unknown texts, and do due diligence on texts or emails that look like they’re from people you know but just seem off. Don’t click weird links. Don’t give out personal info over the phone, even if it sounds like it’s someone you know. They might be scammers using AI to clone a loved one’s voice.
2. Update your software. Yes, it’s annoying to pause your phone or computer use for a few minutes to update your software, but this is one of the best ways to get rid of vulnerabilities. Companies like Apple and Google employ huge teams of security experts to spot these in their software and fix them with software updates. Do the updates.
3. Use a password manager or, even better, passkeys. Each of your online accounts should have a unique password that’s at least 16 characters. Yes, it’s impossible to remember that many passwords, so you should also use a password manager like 1Password or the app built into your iPhone or Android device.
4. Turn on multifactor authentication. Just in case you don’t already do it, make sure you use multifactor or two-step authentication for all your accounts, including email and social media. It can be annoying to type in a code or check your email while logging in, but hackers, including AI agents, will have a much harder time getting into your accounts if you take this step.

Taking all these measures is not a one-time affair. You have to be vigilant in keeping yourself safe online. Security experts have a term for this that’s almost too specific: cyber hygiene. Like brushing your teeth or taking a shower, take good care of your online life, and do it often.

One link for later

➨ Is it still possible to ban gerrymandering? The current US Supreme Court is very, very hostile to getting politics out of congressional map-making. But if Democrats win back power, that doesn’t mean they shouldn’t try. My colleague Ian Millhiser gamed out how they could do it in a way that survives the Court — as well as some approaches that almost certainly wouldn’t fly. But, he writes, the real trick will be finding a solution that functions the way Congress wants it to and can’t be exploited by mapmakers.

Before you go…

• Did you know…that the disease multiple sclerosis is misdiagnosed with surprising frequency? As many as 15 percent of diagnoses could be false positives — including that of Christina Applegate, who found out this week that she does not have MS despite a 2021 diagnosis.
• Today’s trivia: Which noble gas is radioactive and has the chemical symbol Rn? (You can find this and other brain puzzles in Vox’s daily crossword. Look for the answer in Monday’s edition.)
• Yesterday’s trivia: We asked what the plural was for multiple Toyota hybrids. That would be Prii, which Toyota officially confirmed in 2011 after holding a vote.

A complaint I hear a lot from my millennial peers is that our parents didn’t teach us enough about money. I can say that personal finance wasn’t dinner table conversation in my house, and even as I went off to college and started fending for myself, it was almost a taboo subject. When it came to money, people either had it or they didn’t, and you definitely didn’t talk about it. 

Maybe this is why the average American isn’t exactly fluent in personal finance, and the situation is getting worse. Americans can only answer about 50 percent of basic questions about borrowing, savings, and risk on a financial literacy survey, according to a 2026 study by TIAA, a retirement services company. About a third of those surveyed said they have a hard time making ends meet and are debt-constrained. These figures have remained relatively unchanged for nearly a decade, but dropped to their lowest level in this year’s study. 

It makes sense: It’s hard to learn about money when it’s not taught in schools, and it’s such an intensely private topic. It’s also hard to make good financial decisions when you’re one of the growing number of American households struggling to pay bills, repay debts, and save for the future — and feeling very stressed about it.

In a perfect world, each of us could sit down with a financial adviser to get help — but most of us can’t afford that. Instead, average Americans tend to find personal finance tips on TikTok, in Reddit threads, by watching Suze Orman on YouTube or How to Get Rich on Netflix, or by Googling their questions. 

Now we have AI tools, too. 

With access to an internet’s worth of information and financial advice, ChatGPT, Claude, and other AI chatbots can answer any question you ask. They can help you put together a basic budget or, armed with your bank statements and the details of your student loans and your retirement accounts, tailor solutions to your specific situation. Or, at least, that’s the promise.

AI could create a more utopian, more equitable future, where reliable, hyper-personalized advice is just a prompt or two away for people who can’t afford to meet with an expert in real life. But machines are built by people, and there’s good reason to be concerned that these machines will simply replicate the biases and disparities built into our current system.

Here’s what experts say about how to make the most of these tools — and how to think about all of the inherent risks of uploading a lot of personal financial information to a chatbot or, worse, giving an AI agent the keys to your bank account.

The steep rise, risks, and rewards of AI financial advisers

Nearly 20 percent of Americans have used AI tools like ChatGPT for financial advice, and close to 25 percent for Gen Z adults and millennials have tried it, according to an August Gallup poll. These numbers could tick up too: In October, OpenAI started rolling out a dedicated personal finance tool to its ChatGPT Free and Go users in the United States, which effectively means anyone can try it. ChatGPT Finances lets you securely connect your bank account to the tool using a platform called Plaid, and then you can basically have a conversation with the chatbot about your real-time finances down to the penny. It’s like an AI-powered Mint (RIP) that you can talk to directly.

Regardless of where you encounter a chatbot or how much data you give it, experts say these tools could genuinely help improve financial literacy. You can talk to them about money problems you’re too embarrassed to discuss with friends and family, or ask questions and receive actionable, personalized information. There are real upsides to using AI this way.

Taha Choukhmane, an associate professor of finance at the MIT Sloan School of Management, has been studying outcomes when people turn to chatbots for financial advice. In general, these AI tools give pretty good advice, and that advice gets better as people become more practiced in asking the right question, according to research he published earlier this year. 

“We were actually quite surprised that relative to the status quo, the average American would be better off if they were to follow the advice,” Choukhmane told me. But he added a caveat: “Just following the advice without understanding the advice puts you at risk of not fully understanding the risk.”

But you shouldn’t trust everything a chatbot tells you and should verify everything before making any decisions. 

Chatbot advice has some significant limitations. First, it’s not impartial. The finance industry has a vested interest in steering you toward certain ways of managing your money, and that typically means selling you products, like loans. If you’ve ever searched for personal finance tips on Google, you’ve probably noticed that a lot of the top results come from banks and other financial institutions. This is by design, according to John Campbell, an economics professor at Harvard. But because AI is aggregating all of that advice, the specific influences and possible bias is less obvious. 

“AI is very promising for providing low-cost financial advice in plain English that will arguably be better than what many people would be able to do on their own,” said Campbell, who’s also the co-author of Fixed: Why Personal Finance Is Broken and How to Make It Work for Everyone. “One caveat is that chatbot advice is quite likely — unless a model has been carefully trained — to reflect the average of whatever’s being posted on the internet, and that may or may not be first-rate advice.”

An example Campbell gives is the question of when to start taking Social Security benefits. While you’re eligible at age 62, many economists agree that you should delay your benefits, which, based on the way the government calculates your monthly payouts, will allow you to enjoy much higher benefits later in life. But paid financial advisers can recommend starting as early as possible, since waiting longer reduces the amount of money that needs to be managed, which reduces the adviser’s fees. That means there’s a lot of conflicting advice online, much of it supplied by the finance industry. And that’s the information used to train AI models.

“The advice is much more mixed,” Campbell said. “And so if a chatbot is learning from that, it may not actually give the best advice.”

It can be hard to recognize bad advice anywhere in life, but especially when you’re talking to a machine that’s programmed to please you. A chatbot could encourage you to consider a balance transfer, to lean in on credit card rewards, or to refinance your mortgage — all decisions with upsides and downsides for you, but definite upsides for banks — without you realizing how much of its training data comes from the finance industry selling these products. What’s worse is that if you do end up taking bad advice, it might be years before you realize it. You won’t know that you refinanced a mortgage at a bad time until a better time comes along.

That leads me to the other major issue with using chatbots for money advice: People who lack financial literacy are the most likely to fall for bad advice, because they simply don’t recognize it as such. They also tend to be less wealthy, which means they can’t afford big mistakes. 

One way to mitigate risk, oddly enough, is to give the AI chatbot more personal information. The more details about your situation it has to consider, the more it can offer personalized advice. More context also means better advice, according to Choukhmane. 

“There’s a trade-off there,” Choukhmane said. “There’s real risk around privacy, and so I think everyone needs to find their level of control.”

Don’t assume your data is safe

Any time you’re talking to a chatbot, you’re putting a lot of trust in tech companies to take care of the personal details you’re sharing. Sending sensitive financial data is an especially risky proposition. While tech companies, including OpenAI, promise to keep your data secure, there’s already a long list of breaches, leaks, and privacy violations that serve as cautionary tales.  

It’s also impossible to know what tech companies might do with your data in the future. Most of them will use it to train their models, unless you tell them not to. Some of them will use the data to serve you personalized ads. 

Then there’s the risk of bad actors getting into your ChatGPT, Claude, or Gemini account. If you’ve been using a chatbot for financial advice and uploading your bank statements, a hacker could gain access to a stunning amount of information that could enable them to do all kinds of terrible things to your life. 

“They could steal your identity and open accounts and then quickly bankrupt a person,” said Delicia Reynolds Hand, a senior director at Consumer Reports. “It’s not even so much the primary underlying accounts that could be at risk, it’s more just access to this information.”

The steady march of AI progress is making this situation even more complicated. The previous era of chatbots offering financial advice is quickly bleeding into a new phase of AI agents logging into bank accounts on users’ behalf. If you’re using a tool like Claude Cowork or even an AI assistant like Muse, you can give an agent the login credentials to your bank account, and because the agent is operating its own computer, it can actually make transfers, apply for loans, or really do anything in your account. This is reportedly driving major banks wild, since they don’t know which logins are coming from humans and which are agents. And because we know that AI agents are unpredictable, it’s hard to know how that access might go wrong.

Beyond data security and privacy, you should also be aware of the simple fact that nobody’s really at fault if you end up losing money using these tools. Chatbots are not regulated in the way that human financial advisers, who have a fiduciary duty to act in your best interest, are. 

“If I get bad tax advice from H&R Block or a tax adviser, there are a clear set of rules and guardrails in place to manage what I can expect in that relationship and where the service they use has liability and where that liability ends,” Hand said. “Currently, that’s not the case for these tools.”

Trust but verify — and then delete your data

In the final years of the Cold War, Ronald Reagan popularized an old Russian proverb: Trust, but verify. The phrase is somewhat nonsensical, since trusting someone implies that you don’t need to verify what they’re saying, and verifying what’s said implies you didn’t trust them in the first place. But if you are willing to shoulder a bit of risk, consider approaching AI this way, especially when your life savings are on the line. I’d also suggest adding a coda to the phrase: And then delete your data.

Chatbots are not stand-ins for licensed financial advisers, he said, and you shouldn’t let them make decisions for you.

The great paradox with tools like ChatGPT and Claude is that they’re very good at generating truthy text. It sounds authoritative and looks correct — and usually is. Except when it isn’t. AI is famously unpredictable and prone to hallucinations. You can ask the same question twice and get two different answers. That doesn’t mean these tools are useless. It just means you have to approach them with caution. You have to interrogate these chatbots, challenge whatever advice they give you, and then crosscheck that with other, more reputable, sources. 

“If you’ve never used these tools, I would not encourage you to just change all your financial plans based on your first conversation,” Choukhmane said. “You want to be quite careful. Start using these tools, start understanding, ask a lot of follow-up questions.”

When it comes to mitigating risk, consider giving ballpark figures for your income or spending when you’re talking to a chatbot about your finances. Uploading financial documents, even if they’re redacted, is inevitably filling a tech company’s vault with your sensitive information without necessarily knowing what will happen to it next. You should also dig into your privacy and security settings to make sure you’ve turned on multi-factor authentication and that your personal data is not being used to train AI models and gets deleted after 30 days. 

Ethan Bloch, the product lead for ChatGPT for Financial Services, made a similar set of recommendations when I talked to him recently. Chatbots are not stand-ins for licensed financial advisers, he said, and you shouldn’t let them make decisions for you. They’re designed to inform and help you think through complex topics.

“You should liken it to just talking to a friend who has some very informed opinions about finance,” Bloch told me. “But if you’re actually making some large financial decision — your portfolio or retirement — you still need to seek out the help.”

Regardless of how experienced you are with chatbots or how much data you’re prepared to hand over, remember what Campbell, the Harvard professor, said. If your financial literacy isn’t great, AI chatbots can be trouble. And if you’re like most Americans, you could probably stand to learn a little bit more about things like mortgages and interest rates. 

Yes, Chatbots can be a good way to brush up on financial literacy. You can ask them the money questions you’re too embarrassed to discuss with friends and family. But you can also learn about money the old-fashioned way: Reading books and websites written by reputable, independent sources. The Federal Deposit Insurance Corp. has a portal of financial education resources, including games and quizzes. Khan Academy has a course that covers everything from credit scores to college savings. There’s a personal finance subreddit that I joined years ago and read almost daily. There are budgeting apps, like YNAB which has its own huge Reddit presence. 

Whatever you do to learn more about personal finance, simply doing something is progress. Talking about personal finance isn’t the taboo it once was in the US, and it’s even a required course in the vast majority of American high schools now. And while AI won’t magically solve your money problems, it can help get you started. Just be careful. Your livelihood could be on the line.