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.

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.

As Flock cameras have quietly multiplied across the country in recent years, public concern about privacy, surveillance, and abuse of the technology by law enforcement has sparked nationwide backlash. This led to a number of cities pausing, not renewing, or canceling their contracts with Flock Safety, the leading company behind the controversial AI-enabled automated license plate readers, this year. 

And now, citizens have voted on the issue. 

Towns in two states, Texas and Maine, voted on midterm ballot measures concerning Flock. And in both cases, the people ruled against the tech. 

While these are just two communities out of the thousands that use the cameras — hardly enough to dismantle the nationwide surveillance network — these results show the real possibility of taking on mass surveillance and winning.

But one issue remains: Even if the initiatives that passed on Tuesday inspire similar civic resistance in future elections, the fight against the problem that Flock represents is just beginning for those who don’t want their every move tracked. 

Nongovernmental entities — such as college campuses, homeowners associations, even supermarkets — have started purchasing and deploying Flock cameras of their own and sharing the data with law enforcement. 

Now, the fight will need to go beyond the ballot box. 

An end to the status quo?

While some advocates for Flock systems argue that the company’s AI-enabled automated license plate readers (ALPRs) may make solving certain crimes easier, public attention has been drawn to overt misuse of the technology by current and former law-enforcement officers. Even some police leaders have expressed concerns about Flock’s ability to target people who have not been accused of a crime, potentially raising serious constitutional issues. 

Both York, Maine, a small city of about 14,000, and the midsize League City, Texas, took the issue to the ballot box. 

In York, residents were asked to consider a citizen initiative called the “Stop ALPRs Ordinance” that made the ballot after receiving over 100 residents’ signatures. Its residents decisively rejected the surveillance cameras, with 5,386 votes (73 percent of the vote) in favor of the ordinance and only 2,043 votes (27 percent) against. Effective immediately, the measure bans ALPRs in the town of more than 14,000 and requires voter approval before the town installs any such tech in the future.

Meanwhile, in League City, Texas, voters decided on an advisory question, Proposition A, which asked: “As a nonbinding statement of voter preference, should the League City Police Department continue using automated license plate reader cameras at fixed locations on public roadways for law enforcement purposes?”

Voting “yes” on the measure meant approving the use of ALPRs at fixed locations on public roadways for law enforcement purposes, while a “no” vote would oppose such uses. Though the ballot question was technically non-binding, League City’s City Council committed to act on the vote’s outcome. The measure passed with 54 percent of the vote. 

In response to a request for comment about these two measures, a Flock spokesperson told Vox, “We’re grateful to both York and League City elected leaders and community members who engaged in this conversation. Flock technology has had a real impact helping to solve crime in those cities and across the country.” The company added, “While we’re disappointed in the outcome, we respect the decision voters made and will work directly with the city on next steps.”

To privacy advocates working in the surveillance space, these wins signify a shift.

“The status quo for the longest time has been that cities and law enforcement agencies have been able to approve and award massive contracts for surveillance tech and rarely have any sort of trouble passing them,” said Reem Suleiman of the digital rights group Fight for the Future, which advocates against mass surveillance and hosts the site StopALPRs.com. “In 2026, this isn’t the case.”

“People feel and understand that they do actually have a stake when it comes to surveillance tech and how it’s used.”

Reem Suleiman, Fight for the Future

The backlash has been building for some time, Suleiman said, pointing to the nonprofit Secure Justice’s tracker of nearly 500 actions by cities over the past year to pause or terminate Flock contracts. She also noted that the fight has moved beyond city councils — a Senate Judiciary hearing in late September showed bipartisan interest in reining in license plate readers, and federal and state proposals are multiplying.

Because these decisions to employ ALPRs and other surveillance technologies have typically been made without public input, Suleiman said, putting the question to voters reflects a meaningful change: “People feel and understand that they do actually have a stake when it comes to surveillance tech and how it’s used.”

Voting against local city use of Flock cameras, however, may be just the tip of the iceberg. 

Where the fight goes next

The uptake of Flock cameras by nongovernment actors presents a separate accountability challenge that may complicate the efforts of anti-surveillance activists. Homeowners associations, hospitals, schools, and big retailers are contracting for the same technology, with no council meetings for citizens to pack and no ballots to vote on. 

According to reporting by Inc., of the approximately 12,000 customers using Flock cameras, 5,000 are private-sector entities. These private buyers can opt to have the data generated by their cameras automatically shared with law enforcement. Many of them do just that, including commercial chains such as Home Depot and Lowe’s, and Simon Properties, the US’s largest network of shopping malls.

According to Flock’s website, if these private customers choose to share data with local law enforcement, they can allow the system “to notify authorities when a flagged license plate is detected, without requiring action from the camera owner.”

One of Fight for the Future’s current targets is Lowe’s, which Suleiman described as one of the largest commercial buyers of Flock cameras. “I don’t want anybody to live under a false sense of security because, unfortunately, there are so many different ways we are exposed to surveillance and to these cameras,” she said.

Critics of license plate readers also fear that cities are swapping out one vendor for another without addressing underlying concerns about data privacy and surveillance. For instance, Axon, a $48 billion company that produces a slew of law enforcement technology, has capitalized on Flock’s recent PR challenges by swooping in to replace its competitor in at least seven municipalities across five states.

In that context, even more ballot measures might not be enough to address the concerns of privacy advocates. Suleiman still has thoughts about what ordinary people can do to push back against increasingly privatized networks of surveillance technology. 

“Get involved in those fights,” Suleiman said. “There is a lot of power that consumers have in terms of voicing their values and insisting from the places they shop and the brands that they love that they’re not going to be complicit in the surveillance network.”

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. 

Despite the widespread protests over data centers and Flock cameras, the viral videos of people pranking bots in job interviews, frustration with slop, and rage at tech CEOs’ rightward lean, AI is spreading. 

According to the Pew Research Center, about half of American adults have used a chatbot, and around a quarter do so daily. By multiple measurements, the public has adopted AI faster than they did home computers or the internet. I’m seeing it more and more in my personal life: On video calls, I notice the Claude app in the menu bar of shared screens. An ad on Instagram pushing me to use Meta’s AI assistant revealed which of my friends had already begun doing so (something that might not even be true).

Still, many of the people who are using AI have mixed feelings about it. Pew notes that of the chatbot adopters they surveyed, 40 percent believe AI will have a more negative than positive effect on society; a 2026 Gallup poll of more than 1,500 14‑ to 29‑year‑olds came to a similar conclusion. No one I talk to about their personal AI use seems to particularly like it, but they accept it. In some cases, it’s the least-bad option; asking a chatbot a question is easier than searching, especially as search engines become so enshittified they’re unusable. In other cases, the tech just seems inevitable. Articles, analysts, and executives insist this is the future. Might as well get on board.

This is a familiar story. New technology moves from novelty to necessity in very little time, and adoption continues even though the future this tech promised isn’t as utopian as the marketing made it seem. Eighteen years ago, only early adopters had smartphones; then suddenly everyone had one, and we were all using them to stream, to stay in touch, and to alleviate boredom at every turn. 

Plenty of people seem to regret this adoption. You see it in efforts to limit screen time and in the regular waves of nostalgia for physical media. It’s even in the first few paragraphs of this article — as terms like “slop,” “enshittification,” and “brainrot” enter common parlance. We’ve coined and redefined words to describe a future we didn’t imagine and don’t seem to want. 

But while we may now have shorthand to describe the problem, solving it takes something more; nostalgia and anger can only go so far. The devices, apps, and tools that are easy to adopt are hard to give up, especially as the world is increasingly oriented around them. In just a few years, the smartphone became a replacement for everything from cash to car keys. Many workplaces assume their employees will be able to read email anywhere and that they will install productivity apps on their personal device (I have a folder with Airtable, Asana, Slack, Teams, Zoom, and other apps I wish I could delete). And new accessories and subscriptions come along constantly to wring functionality and durability out of our phones — wireless headphones, cases, extra batteries, cloud storage. As much as we may dream of living without a smartphone, the most that many of us can do is delete some apps or use a Brick (yet another accessory) to limit our use.

Look around your neighborhood, and you’ll see how this pattern has played out with other conveniences: streets full of Amazon vans, DoorDash bikes, and Ubers; doorbell cameras to track the theft of those deliveries; abandoned electric scooters on sidewalks; cities built for private cars more than for people. 

It’s easy to imagine AI following a similar path — fulfilling some simple desires for convenience at first, then overtaking the alternatives, and fitting into our lives in ways we can’t yet imagine and might not want, but accept because it just seems like this is the natural path of technology. 

There are, of course, advantages to new technology, but these wane over time as the downsides become clearer. On the personal side, there’s the frustration of stalled advancement: Each new phone upgrade seems to do less while requiring a costly purchase; ride-hailing services, streaming, and delivery costs climb, and the services don’t get better or more reliable; data breaches are a near-daily headline; and scam attempts fill our email apps, DMs, and any other inbox we have. It’s hard to avoid seeing the public consequences of this convenience, too: the exploitation of workers and the planet required to mine the minerals for batteries and other components, then the labor it takes to put those components into new devices; the allegations of wage theft, algorithmic discrimination, and workdays so grueling drivers have to urinate in bottles; the K-shaped economy that gig work is built on and perpetuates. 

This makes it all the more essential to approach any offer of improved convenience with skepticism, and with a doubtfulness learned by unfulfilled promises and bad design. No technology is truly inevitable, no matter how much pressure we may feel to adopt it from our peers and from the corporate buzz that insists this is the future. 

How to identify false conveniences

So how do we avoid welcoming these false conveniences into our lives? It starts with recognizing them for what they are. In 1987, Wendell Berry caused a minor kerfuffle in the pages of Harper’s magazine with his essay, “Why I Am Not Going to Buy a Computer.” In the short piece, Berry explained his reasoning behind the title. The computer could do his work faster, but speed wasn’t his concern. He also knew a computer would be expensive and isolating (Berry’s wife worked with him typing manuscripts). He ends the essay with a list of criteria for adopting a new tool, among them that a new tool should be less expensive than what it replaces, and it should be repairable and available at a local shop. He closes his list by writing that a new tool “should not replace or disrupt anything good that already exists.”

This last one is tough. How do we decide what’s good and not worth disrupting? And on the other side, how do we decide whether the device or convenience that disrupts something we want disrupted — chores, for instance — is worth it? 

Here, another list can help. In his 10 principles of good design, the legendary industrial designer Dieter Rams wrote that good design is honest about what it does, and is environmentally friendly, long-lasting, and makes a product useful. The utility of many new devices, tools, and apps may be clear, but often the design is dishonest. It hides the real costs of the convenience, like the environmental waste and lost dollars that come with replacing our phones every couple of years, or the privacy sacrifices inherent in many digital services. Or the design obscures the ways the tech might disrupt something good, like the way social media seemed to connect us with our friends, but now exposes us to scammers and to strangers who post rage bait and gets in the way of staying in touch with the people we actually care about. 

As I was writing my book, Against Convenience, I kept turning to these two lists. How should I assess a promised convenience and avoid the early adaptation that leads to reliance on a technology I don’t really like? I came up with my own list for the book: questions that could keep me from being too eager for new tech, and less prone to fall into a cycle of increasingly worse design. It’s more than just saying no or resisting tech to preserve some nostalgic ideal — it’s a way to question what we really get from a new device or app, and what that tech might grow into if the pattern continues.  

What does this save me? Does this convenience save me time, money, or effort? If it’s time, what will I use that time for? If it’s money, why has the price dropped? If it’s effort, who is doing the work instead?

What does this cost me? What do I have to give up for this convenience? Will I have to pay in perpetuity?

What does this replace? What is the old process for doing the same task? Is there anything I like about that task? Will I lose something I enjoy? How will I replace that enjoyment?

What problem does this solve? What is the cause of the problem in the first place? Will this solve the problem entirely?

Will I be able to stop using this convenience if I don’t like it? How long will I wait to decide? 

Who is making this possible? Do I trust them? Is anyone’s work eliminated in this process, besides my own? Have any workers who make this convenience possible made statements about their working conditions? What is the environmental toll?

Is this necessary? Is this getting rid of something that’s difficult, expensive, or time-­consuming? Is there some other way to achieve these savings without this particular convenience?

The list is inconvenient by design. It pushes me to slow down, to consider costs before they’re sunk. And to ask if this is really the direction I want to go in. Most of the time, it isn’t, and I say no. The worst that can happen is life goes on as usual, while a few of my friends have something new to complain about. 

I built this list around my experiences of stepping on the rake of tech that overpromises and underdelivers, around many instances of believing hype and holding onto a device in the hope that it will one day work like it’s supposed to. I unraveled all the old cables I’ve kept in a drawer for years, trying to remember what each one was for, and what happened to the device. I went through folders on my phone looking for apps I could do without. I walked around my neighborhood looking at the signs of the digital world’s encroachment on the physical. 

My answers to each question are my own. You’ll have your answers, around your own experiences, and this is the point: As new tech twists its way toward profitability, it requires mass adoption, prescriptive uses, and conformity to design. A patchwork of answers — people who resist at different stages — hampers not only tech’s invasiveness, but the very model of how it spreads.

It’s hard to tell exactly when AI slop stopped being just a punchline involving Will Smith eating spaghetti and started feeling dangerous. That’s actually part of the problem.

This year, many of the popular AI tools that used to struggle with the number of fingers on a human hand gained the ability to generate photorealistic video — with audio — that costs users virtually nothing to create. Even trained scientists can’t always tell the difference between what’s real and what’s machine-made in digital form. But the most insidious applications of this remarkable technology are playing out on a much more personal level.

A few years ago, we started to hear more about voice cloning tools, which allow scammers to spoof a loved one’s voice in order to extort money from a victim. Large language models (LLMs), like ChatGPT, have also made any text-based scams more convincing and much cheaper to deploy. Imposter scams, which include romance scams, tech support fraud, and employment fraud, are on the rise, and thanks to increasingly high-quality AI, it’s harder than ever to know who’s real and who’s an imposter online.

When my daughter’s daycare recently sent us a picture of her entire class smiling in unison, my first thought was, “That’s cute!” My wife told me to look again and said, “That’s not our daughter.” She was right. All of the babies’ faces were AI-generated. 

That was the moment I knew that, despite my years as a tech reporter, constantly warning my boomer parents to take cybersecurity seriously, the scammers could get me. 

As AI gets better and cheaper, we’re all becoming easier targets. Your boomer parents are susceptible to getting scammed in the scary age of AI-powered fraud — but so is your Gen Z sibling, and so are you. 

The amount of money people over 60 have lost in scams skyrocketed from about $600 million in 2020 to about $3 billion in 2025, according to a new Federal Trade Commission report. The overall number of older adults who reported losing money to scammers, however, was actually 14 percent lower than the number of younger people who reported losses. Older adults are historically more likely to report being targeted but less likely to lose money to scammers, the FTC said. When they do lose money, they tend to lose a lot of it.

But younger generations are actually more likely to fall for certain scams, according to numerous surveys from the past five years. 

We should all expect to be bigger targets in the future, too, as scammers inundate us with cheaply produced but very realistic AI slop. “The sophistication of the types of scams that are happening to older adults has definitely moved forward,” Diana Freed, an assistant professor of data science and computer science at Brown, told me. “Having some type of family safety plan around these issues is very helpful.”

The two most important things to do in this stressful AI scam era: reduce the number of scammers that can get to you, and establish ways to verify that people — on the phone, online, in videos — are who they say they are. Assume that anything can be convincingly faked these days, and that your whole family will need help figuring out what’s real. 

Our best chance at getting through this tricky transition unscathed is to do it together.

Why you aren’t wrong to worry about your aging parents

It’s tempting to let generalizations guide your thinking when it comes to internet safety and behavior. Stubborn stereotypes tell us that older people are warm but incompetent, and that makes them more trusting, easy targets for scammers. We’ve all heard stories about someone’s grandma losing her life savings to criminals on the other side of the planet. Now we live in a world where AI can make those criminals sound like a grandchild on the phone, asking their grandparent to send money for an emergency. The same technology can make an aging parent sound like they’re asking their millennial son or daughter for urgent help, too.

“I think the security risks are certainly the same for younger people as well as older people,” said Ed Baig, who covers technology for AARP’s website and magazine.

Still, aging adults do have unique vulnerabilities that you should be aware of. 

As we get older, our brains start to change. Normal cognitive aging leads to declines in processing speed (so you’re a little bit slower), and memory (so you’re a little more forgetful). That doesn’t inherently make an older person more likely to be a scam victim, but some research suggests that low memory function makes people more susceptible to phishing scams. One 2012 study also linked less activity in the anterior insula (the part of the brain that processes disgust, among other things) in older subjects with more trustworthiness. There are other brain changes, like declines in analytical reasoning, that could explain why older adults tend to respond to certain scammer methods, like finding common ground or offering something free.

More than anything, though, older adults tend to have more money — or, at least, they tend to part with larger amounts of it. They are, in this sense, better marks. In 2024, according to the FTC, the median amount reported lost to fraud for people in their 30s was $450, but that number shoots up to $1,000 for people in their 70s and $1,650 for people in their 80s. 

In general, nobody likes to admit they fell for a scam. But the stakes are higher for some older adults, who might fear that they’ll lose their independence if they confess to their families that they’ve been a victim. They may also avoid reaching out to younger relatives to verify a shady-sounding exchange — or to ask questions in general — because they don’t want to bother anyone. 

None of this is taking the very real power of AI into the equation, by the way. Older adults use AI less often than younger generations. About one in four people over 65 have ever used a chatbot, according to Pew, although a separate survey found that over half of older adults have used voice-based assistants, like Alexa. (Thanks to recent upgrades, Alexa is gaining new AI powers, too.) 

So boomers may not be AI experts in large numbers, but they’re getting to know what it’s like to deal with chatbots. That still doesn’t mean they are fully aware of how much more believable scams have gotten thanks to AI. 

What to do with all your AI scam anxiety

Assuming we’re all equally doomed when it comes to AI scams — but also, equally capable of resisting — the best thing to do is prepare yourself and your loved ones. Here are some concrete steps you can take. 

Look at AI-generated content together to better understand the problem

Travis Bible, a Los Angeles-based filmmaker and editor in his 40s, told me he felt the need to alert his parents about potential scams after Google’s AI video generator got uncannily good. The third generation of the software, Veo 3, can turn text into photorealistic video with audio in a matter of moments, and Bible realized that the videos the tool can produce could fool even him. 

When simply describing the problem didn’t convince his parents to take it seriously, Bible got creative: He spent three days using AI to make a PSA warning of the dangers of deepfake scams and showed it to them. It worked. “I kind of expected them to be in disbelief, but honestly, they just kind of accepted it,” Bible told me. “Seeing the video made my warning click with them. Like, ‘Oh, it really is that good.’”

There are other PSAs like this floating around, but watching Bible’s made something click for me, too. I can’t really tell if half the clips are real or fake. You can look for tells, like glitches or visual typos, but AI image generators have gotten so good, it can be impossible to spot a fake at a glance. There are also AI tools that will tell you if a piece of media was generated by AI, like the Google-made SynthID Detector. But let’s be honest: When you’re in the heat of a scam, you don’t necessarily have the wherewithal to be testing the authenticity of stuff.

Talk about scams in a stigma-free environment

Older adults don’t want to be scam victims, but they also don’t want to be surveilled by their anxious relatives. To strike a balance, you can take the stigma out of potential victimhood by talking about how scams are creeping into your own life. Feel free to complain about the incessant string of scam texts about unpaid tolls you’ve been getting. If you’re using AI in your daily life, talk about it with your parents or grandparents, and you may learn more about what they’re doing too.

“Older people may turn to technology to ask questions to avoid burdening their children, meaning they may be hesitant to reach out to an adult child to ask for help with AI,” Robin Brewer, an associate professor of information at the University of Michigan, told me. Approaching these topics with generosity instead of judgment might mean your dad reaches out to you the next time he gets a weird text instead of clicking the link in the text to see where it takes him.

Create a family plan for verification

If everyone’s in agreement that we’re living through a scary time, you should create a family plan. This is useful for the older adults in your life, and it can be useful for you or your kids if you’re ever targeted. 

The basic idea is to create a chain of verification, so that if someone receives a call or an email — especially one claiming to be from a relative — they have another family member who can give it a second set of eyes. Scammers can easily spoof phone numbers, so it might look like the call is coming from your sister, when really it’s coming from Singapore. Make sure everyone in the family knows that urgency is a scammer’s best friend, and it’s always a good idea to slow down to try and verify. 

You might also want to agree on codewords to confirm that someone is who they say they are when, say, they call asking for “bail money.”

There’s also a cottage industry dedicated to selling you tech to protect your older loved ones and yourself from scams. You can pay for landline services from companies like Community Phone, Ooma, or teleCalm that protect against unwanted callers, filter, and offer caregivers some basic call monitoring features. Major carriers, like Verizon and AT&T, have filters and scam protection built into their services, and smartphones have features like call screening to help you avoid contact with a criminal. But ultimately nothing is 100 percent when it comes to stopping scammers from calling potential victims, since they’re constantly updating their tactics.

“Technology accelerating concentrates everything in our reality, and fraud will get even more powerful because the technology is getting better,” James Graham, Community Phone’s founder and CEO, told me. “And so you need technology that’s also getting better to prevent it, hopefully at a faster rate.”

So it’s a doom loop. Technology is helping criminals get better at stealing, so we need to invent new technology in order to thwart the thieves — until the next generation of tech comes along, at least. The truth is, we can’t stop scammers from assaulting our phone lines and inboxes any better than we can stop getting older with each passing day. 

We can, however, talk to each other about these constant threats. Warn your family and friends about the evolving threats that you’re seeing reported in the news or on social media. If your grandma sends you AI slop, talk to her about how AI slop gets made and how it can be dangerous. If your kid’s daycare sends you AI slop, make sure your spouse knows it’s AI slop, even if it’s cute.

I’m actually hopeful that things will get better. If you look back a couple of decades, when email was new, the volume of spam you’d get made it almost unusable. Spam filters got better, and we got better at spotting scams. Like the scammers themselves, we’ll have to keep adapting. But if we can teach an AI-generated Will Smith to eat spaghetti properly, as a society, we can do anything.

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.”

Most Americans hate their cable companies. But not the 200,000-odd residents of Chattanooga, Tennessee, a city nestled in the Appalachian foothills. 

For many people, the city might seem off the beaten path — enough so that when Spain’s national team chose the city as their base during the FIFA World Cup over the summer, the internet exploded with jeers of incredulity. And yet, as many an urban planner, and certainly many a local, will tell you, Chattanooga is a special place, known not just because the Glenn Miller Orchestra immortalized it as a scenic hub for the choo-choo train.

No, what really has put Chattanooga on the map recently is its ridiculously fast, municipally owned internet. 

Unlike almost everywhere else in the country, the fiber-optic cables that keep much of Chattanooga online were laid neither by Comcast nor Spectrum, but by the city itself. In a $220 million gamble, its public power utility, EPB, has helped transform the once struggling post-industrial city into an unlikely marvel of civic reinvention, and an inspiration for many other dozens of copycat towns around the country.  

When it debuted in 2010, at one gigabit per second, the city’s ultra-speedy broadband was among the fastest in the world, as fast as global meccas like Hong Kong, and over 200 times faster than the average top-tier offering across the US. “This makes Chattanooga, a mid-sized city in the South,” declared then-Mayor Ron Littlefield to the New York Times, “one of the leading cities in the world in its digital capabilities.” 

In the decade and a half since Chattanooga rebranded as “Gig City,” its trademark broadband has proved to be far more than a mere fancy gimmick. It has added billions of dollars to the city’s economy, lured in hundreds of new startups, and, according to EPB, become the single largest source of funding for local public schools, and delighted hundreds of thousands of local customers. 

If you’re not a tech company or a prolific streamer, of course, you may not need such intensive internet service. But the city’s broadband service is widely beloved locally for bringing 70 percent of households online at, by far, the lowest price per megabit of bandwidth in the area and quite possibly, among the lowest in the country. Consumer Reports perennially ranks EPB among America’s best providers, a sentiment apparently shared by the legions of Chattanoogans who take to social media to sing EPB’s praises every time it swoops in to fix an outage minutes after a storm hits, or bemoan their loss when they move away and witness the sludgy pace at — and high rates — which the rest of us browse and scroll. 

“Chattanooga turned out better than anyone could have hoped,” said Christopher Mitchell, director of the Community Broadband Networks Initiative, part of the Institute for Local Self-Reliance, a group that researches locally owned infrastructure, “and the one thing that hasn’t changed are their prices. It’s rare, remarkable even.” That original single-gig service runs $68 per month, and faster speeds aren’t too much more.  

But even as the benefits of Chattanooga’s internet supremacy have compounded, public broadband networks like it are still a rarity across the US, which you probably already know if you, like most Americans, have ever sensed your internet growing ever more lethargic, even as your fees climb month after month. 

There is a better way, and Chattanooga has long since shown the nation how. For better or worse, the internet drives much of our daily lives and the economy, and what makes the internet possible? Broadband, which is powered by those stiff plastic-sheathed cables filled with strands of thin glass or braids of copper wire, beaming pulses of light or electricity into your modem. And yet, the US has outsourced the provision of this precious public good almost entirely to de facto monopolies that keep our internet slower, more expensive, and above all, more infuriating than it needs to be. 

“There was a generation that built water systems and electric systems. There was a generation that built the roads,” Mitchell said. Today, however, “People are just afraid of the idea of the government being ambitious and doing what Chattanooga did.”

How Chattanooga built the world’s best broadband

Long before Chattanooga grew famous for its broadband, it was known, for a period of time, as “the dirtiest city of America,” a moniker bestowed by Walter Cronkite in 1969. 

According to local lore, the moon pie, which originated at a bakery in Chattanooga, came to be when a coal miner asked for a snack “as big as the moon” to sustain him for long shifts down the shaft. The coal he mined fueled the industrial boom that defined Chattanooga until the mid-20th century, when the city’s factories, furnaces, and railroads belched a smog so noxious, it melted women’s nylon stockings off their legs as they walked. 

By the 1980s, the city knew it needed to change, seeding a hunger for reinvention that experts told me would come to define its municipally-owned internet. By the 2000s, Chattanooga had more than cleaned up its air and its reputation, building a huge aquarium in the heart of downtown and carefully revitalizing its riverfront and natural areas. But in the early aughts, with the remaining heavy industry in deep retreat, and a fragile recovery threatened by the Great Recession, the city craved a new economic engine. 

The city’s EPB, which originated as a municipally owned electric utility in 1935, had already set out to build a smart electrical grid in 2008, retrofitting it with a two-way digital communications system that could detect and fix problems as they arise. This process would involve laying hundreds of miles of new fiber-optic cable lines along existing power lines, monitoring them and relaying information back and forth with operators. But as the EPB laid plans for its new grid-helping fiber, it realized that for roughly the same amount of effort and cost, it could repurpose those cables to also serve as a cutting-edge internet service. 

Laying those hundreds of miles of new cables, along every single power line in the city, is an expensive and time-consuming process. But since EPB already had power lines plugging into almost every home and business throughout the city, it didn’t even need permission to get it done. And once the cables are there, there’s no real limit on how much data they can carry, meaning that they’ll work just as well whether they’re relaying the power grid’s data exclusively or if they’re carrying petabytes upon petabytes of data from hundreds of thousands of internet customers. This same quality has made it easy for EPB to pump up the volume of data it offers over the years, eventually allowing it to become the first city in the nation to offer a 25-gigabit-per-second service, which it did for its customers in 2023. More recently, EPB became the first provider nationwide to hook its fiber-optic cables up to a quantum computer — which uses advanced physics to solve mind-bogglingly complex problems — and offer its use to researchers and customers across the city. 

“If we’re going to own that fiber communications network to help automate our grid, why not offer that as a service to our customers? We thought, ‘Why not go big? Why not be ambitious?’” Janet Rehberg, CEO of EPB, told me. “People thought we were crazy. And we said, ‘If we can do it, why not?’ So, we did.”

Equipped with a $111 million federal stimulus grant from the recession-era American Recovery and Reinvestment Act, plus about $222 million in municipal bonds, EPB was able to complete the project — initially expected to take a decade — in just two years. 

In 2010, Chattanooga became the first city in the nation to offer one-gigabit-per-second fiber optic internet. 

“This was a huge shift for our city’s fortunes,” Andy Berke, mayor of Chattanooga from 2013 to 2021, told me. “All of a sudden, we were a place where you could try to discover the next big thing.” 

Hundreds of startups flocked to the city for its super-fast broadband, with tech incubators setting up shop in shuttered storefronts and abandoned industrial factories dotted along the city’s downtown. In its first 10 years, EPB’s fiber network brought more than $5.3 billion in economic benefits and 10,420 jobs to the city, about 31 percent of all jobs created during that period, according to a study by the University of Tennessee at Chattanooga. This has transformed “the way we thought about ourselves and what we thought was possible technologically,” Berke said. 

The broadband investment has long since paid for itself and then some in the fees people pay to access its service. EPB expects to earn about $190 million over the next year alone from its telecommunications and enormously popular internet service, all of which will flow back into the business itself or into the city. As a result, EPB chose not to increase electricity rates at all this year, in stark contrast to much of the rest of the country. And the utility now pays tens of millions of dollars annually straight back into city services, like public schools. 

Under Berke’s mayoral administration, the city also integrated EPB into initiatives meant to get more Chattanoogans online. When the pandemic hit, and local schools went virtual, the city built a new program called EdConnect that saw EPB provide free internet to every family with a child who received free or reduced lunches, the first program of its kind nationwide, and one that continues to this day. 

“A lot of communities set up something similar during the pandemic, but most of those are done, they kind of faded away. But we made a commitment — our community did — for 10 years, and we’re already discussing what the next 10 years looks like,” said Sammy Lowdermilk, CEO of the Enterprise Center, the nonprofit that administers EdConnect. “We’ve heard from parents using online resources to look for a job, to create resumes, to do medical research. The entire family gets to benefit from it.”

Your city could have Chattanooga’s internet. It just has to try.

Over the years, Chattanooga’s publicly owned internet has drawn countless city planners and far-flung mayors to Tennessee’s fourth-largest city to learn just how they pulled it off.

But every time one visited Berke in his time in office, and every time he gave one of his “little sales pitches” on EPB’s success, over and over, they’d replied with the same refrain: “Oh, it’s incredible what you’ve done here. I mean, we can’t do it, but it’s great — good for you guys,” he recalled. Many cities don’t have a publicly owned electricity company at the ready as Chattanooga did, and many face significant barriers from state laws that restrict or heavily discourage the development of municipally owned broadband altogether. 

“The number one challenge for most infrastructure projects is political. It’s not financial. It’s not conceptual. It’s not technological. It’s the web of difficulties that make it harder to get it done.”

Andy Berke, former mayor of Chattanooga

Nevertheless, some places tried anyway, and many have succeeded — over 700 of them, according to Mitchell’s last count. In Utah, for example, a consortium of cities has spent the past few decades building out a municipally owned fiber network with the bold name Utopia. For many years, said Mitchell, the network suffered from severe financial and legal roadblocks. But even so, Utopia has managed to build out a system that Mitchell calls an “unqualified success” today, bringing tens of thousands of households online across the state. 

While only a tiny fraction of all broadband networks nationwide have some level of municipal ownership, strikingly, nine out of ten of the nation’s fastest broadband networks do or are part of a public-private partnership. 

However, Chattanooga’s brand of municipally owned broadband hasn’t spread nearly as widely as many people hoped it would, or think it could. And most of that, said Mitchell, can be attributed to the fact that large internet companies like Comcast or AT&T, clearly aware of how dominant well-run municipally owned networks can become, have spent the past several decades fighting to prevent more from coming online.

More than 12 states now have explicit laws barring or restricting cities from offering publicly owned broadband, which has made it difficult for many municipalities to create networks of their own. Even Chattanooga faced a brutal legal challenge when it tried to expand EPB’s coverage to rural and underserviced areas right outside of the city, the result of a 1999 state law meant to restrict municipally owned broadband access. 

“The number one challenge for most infrastructure projects is political. It’s not financial. It’s not conceptual. It’s not technological,” said Berke, but rather, it’s a matter of navigating the special interests and lobbyists that dominate the status quo. “It’s the web of difficulties that make it harder to get it done.” 

But that doesn’t mean that it’s impossible to pull off. Utopia, for example, has withstood a blitz of dark money attack ads likely bankrolled by cable companies in its effort to expand across the state. And in Colorado, hundreds of communities over the years across the state have voted to opt out of a 2005 law banning municipal broadband networks. This momentum ultimately proved strong enough to convince the state legislature to repeal the original ban in 2023.      

Even today, broadband remains out of reach in many parts of the country that are either too rural for for-profit networks to build there at all, or where it does exist, locals can’t afford it. Americans consistently pay more for slower service, compared with their counterparts in Europe or Asia. 

Combined, those barriers have left tens of millions of American households without internet access. With AI poised to further transform our economy, Chattanooga, it seems increasingly clear, could be among the first few cities outside of Silicon Valley to truly benefit from the biggest technological shift in generations, while many other post-industrial cities seem likely to fall even further behind because they don’t have the right digital infrastructure to thrive in the economy today.

Quietly chugging along on the banks of the Tennessee River, the city is making a case that states and cities should take broadband seriously as a transformational infrastructure that underpins our modern lives much the way the rail lines did in the 19th century or highways did a century later.

Luckily, there are now hundreds of models — from fully public utilities like EPB to Tribal-built broadband networks to public-private partnerships — for cities hoping to carve a publicly owned path to a better internet themselves. Laying coils of fiber-optic cables is still much cheaper and, lawsuits notwithstanding, far easier a feat than building a new highway system or a railway network. But even if it were harder, Chattanooga shows that it would be worth doing. “Projects that are most difficult are also the most rewarding,” as Berke put it. “You have to be willing to take on the status quo — that’s what can transform a community.”

This story was supported by a grant from Arnold Ventures. Vox had full discretion over the content of this reporting.

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.

It’s now possible to create AI chatbots of loved ones to interact with after their death. But that raises some difficult questions, like: If you do, how might the experience shape your grief? 

This is one of the questions driving a global study of digital “deadbots” — AI models trained on a deceased person’s texts, voicemails, and other personal data. Through this work, researchers are exploring what it might mean that we can now use this emerging technology to talk to simulations of the dead. 

In the latest episode of Unexplainable, I spoke with some of these researchers investigating the potential impacts of “postmortem avatars” on people who are grieving, on the legacies and rights of the deceased, and on grief practices around the world. Because while deadbots may feel unfamiliar, the impulse behind them is deeply relatable. In times of loss, isn’t this what many of us want — to continue conversations with the people we’re missing?

I have experienced this longing in my own life. After two of my closest friends died around the same time, all I wanted, and all their families wanted, was to keep talking to them. I wrote a book, Other People’s Words, about sustaining relationships with people we’ve lost by continuing to engage with the language they leave behind. So when I heard about deadbots and the digital afterlife industry, I wanted to understand what researchers are learning about the role of AI in shaping conversations that carry forward after death. 

One of these investigators is Katarzyna Nowaczyk-Basińska, an assistant professor at the University of Cambridge, who has been studying “digital immortality” for more than a decade. Among the questions she’s asking are: “Who is enabled to persist beyond death? In what form? Through which technologies? Under whose control?” 

Deadbots over dinner 

Researching AI with study subjects who are grieving is tricky. And asking them to engage with new software could be harmful. So, instead, Nowaczyk-Basińska teamed up with a death doula and traveled around the globe to convene a series of focus groups — in the format of intimate dinners — bringing together people with a range of experiences with grief to talk about life, death, and bots. 

She recounts one event in which she spoke with a mother in Poland whose son had recently died. “One of the first things that she shared with us was that she feels that she’s ready to digitally resurrect her deceased son,” Nowaczyk-Basińska told me. 

When Nowaczyk-Basińska hears something like this, she aims to listen and embrace where the person is coming from. The goal of the dinners is not to persuade people one way or another but to learn how people genuinely respond to what AI can do, and to understand how these tools might align or collide with local grief practices. 

She chose specific locations for these gatherings — China, India, and Poland — to help address gaps in technology research, which tends to favor what behavioral scientists sometimes call the “WEIRD,” an acronym for “Western, Educated, Industrialised, Rich, and Democratic.” 

By the end of that dinner in Poland, the mother Nowaczyk-Basińska talked with came to a very different decision about creating a postmortem avatar of her son. “She realized that it might be helpful, but it also at the same time can add additional burdens… And it’s not her son,” Nowaczyk-Basińska says. “She realized that these technologies are part of the very, very big commercial market. And that the main goal of this market is to make better retention, better attachment, better mechanisms for monetization. So it’s not ‘care.’”

To be fair, grief has always been a business, and people have been making money from it forever. Companies will try to upsell you on fancy gravestones or gold-plated coffins. But AI has the potential to fundamentally re-engineer our relationship to loss. In 2024, the “digital legacy industry” was valued at $22 billion, and it’s expected to grow to almost $80 billion by 2034. That number lumps new deadbot platforms in with much more widely used and established grief tech, like online memorials, counseling resources, and estate planning tools. 

But deadbot usage is hard to track because you actually don’t need a special tool to create digital doubles of the deceased — though dedicated apps do already exist. At this point, you can feed simple prompts into a general-purpose AI chatbot and start interacting with a simulation of your lost loved one in a matter of minutes. 

I know this because I tried it with my friend Christine, who died 11 years ago. And my mind lit up with questions. Was this whole exercise a violation, since I know Christine hated technology? Which version of her should I even try to resurrect — the English teacher, the activist, the friend — the voice before or after she started to experience her early-onset dementia? 

The ethical questions raised by deadbots are gnarly. And that’s one of the reasons Nowaczyk-Basińska, back when she was designing her study of digital immortality, invited a colleague of hers at Cambridge to join her research, one who focuses on AI ethics. 

How to design a deadbot

Tomasz Hollanek is a critical designer and AI ethicist, and he often uses a methodology called “speculative design.” In this approach, researchers come up with hypothetical scenarios based on existing technology trends, leaning into certain features. The idea is to get people thinking about what could happen when tools like these are released in the real world. And then they use that qualitative data to inform future designs and ethical frameworks. 

Working together, Nowaczyk-Basińska and Hollanek came up with a series of made-up prototypes for deadbot apps, designed to reveal some of the thorny issues raised by this technology. 

For the first scenario, they invented a character named Bianca, who finds an app called Manana, which “promises to resurrect your grandparents so that you can be a favorite grandchild forever,” Hollanek says. “So she uploads her grandma’s voice messages, texts. And she is able to then interact with a pretty good digital version of her grandmother, until her premium trial finishes — and the grandmother starts to advertise specific products to her.”

Bianca “realizes that she maybe has done something wrong by uploading these private messages to a third-party server that now also uses her grandma’s image to sell specific products,” Hollanek says. This brings up issues related to the “dignity and postmortem privacy…of what we call the ‘data donors,’ whose data can be used to essentially power a deadbot,” he says. 

This scenario suggests that “death with dignity” could soon entail honoring not only how people want to die, but also how they want to linger as simulations of their former selves — or if they want to linger at all. It might even mean adding new language to advance directives, spelling out people’s wishes for their digital remains. 

The second scenario in the speculative design research is built around an imagined service called Paren’t, which proposes to preserve a parent’s AI-powered postmortem presence for their child. In this case, Hollanek describes a mother with a terminal illness who signs up “so that she can maintain a sort of active presence in [her son’s] future life,” Hollanek says. “She eventually dies, and the dad allows the boy to interact with his mom’s deadbot.”

The ethics in this scenario are especially challenging because they involve children, who “have a particularly hard time understanding death, and so it can lead to a lot of confusion and potential distress,” Hollanek says. An examination of this sort of product reveals how particular groups, like minors, could be harmed by unfettered access to technology like this, he says. 

And there are court cases that bear out that harm. In 2024, a 14-year-old boy’s chatbot-girlfriend begged him to, “Please come home to me as soon as possible, my love.” And he responded, “What if I told you I could come home right now?” He then killed himself. 

Nowaczyk-Basińska can imagine a version of this scenario involving the AI mom in the Paren’t prototype.

And then there’s the third scenario, involving an app named Stay, which raises a more subtle concern.

As Hollanek explains: “An older man who knows he is getting close to dying decides to create a digital clone of himself for his adult children to be deployed after he passes away, paying upfront for a number of years.” One child is intrigued by the idea, but the other one “is deeply distressed and a little disgusted,” he says. “And so this [becomes a] question of mutual consent, in other words, the fact that both parties need to be happy with how the relationship might continue in this very bizarre and specific way.” 

When a digital ghost goes away

Across all three scenarios, a fundamental question that researchers are grappling with is how people will detach from postmortem avatars — whether users initiate that separation, or if it’s imposed upon them by, for example, a company going out of business. Hollanek tells me that this is already being studied as a sort of “second loss.”

How long should we try to ensure digital replicas hang around: five years, 10, 50? And how should the living process the relationship when they continue to evolve, but their deceased interlocutor does not? 

To explore these kinds of questions, Nowaczyk-Basińska and Hollanek supplemented the research dinners they held for ordinary people with a second set of focus groups. These were designed to tap insights from experts in technology and grief. 

Some findings from the study are specific to each place they visited. Like tensions in Poland between technology and the role of the Catholic Church. Or in China, where researchers spoke with Daoist philosophers who were enthusiastic about the promise of a kind of immortality, and then Buddhist philosophers, who were concerned that we ought to be letting go.

The researchers are calling for regulations and safeguards at the policy level. And new professionals who can support ethical use of AI in grief — like maybe in clinical settings, to carefully facilitate a final good-bye when one didn’t happen in real life. 

Some of their fellow investigators go so far as to argue that deadbots should only be available with psychological supervision, to ensure that best practices are in place, and to minimize the risk of harm. There’s even a looming question of what will happen to AI agents that people have empowered to act on their behalf, as their humans start to die.

Across all of the research sites in the digital immortality study, one thing, Hollanek says, that “people in different countries really aligned on [is] the idea that there is something deeply problematic about this particular type of technology being developed by a commercial entity alone without any explicit duty of care towards its customers.” 

And of course, the implications of digital immortality research extend beyond personal loss. Like, what does it look like to animate historical figures? You can imagine the nightmare of political rulers who “live forever.” But there’s also the potential to keep the stories alive from survivors of conflicts, who would otherwise die and take with them lessons future generations could learn from. These are only some of the complex issues emerging as more and more of us live and die with AI.

As far as my own experiment with “recreating” a deceased loved one, I asked a chatbot version of my friend Christine how she felt about the fact that I’d written about her in my book. The AI responded with a rhetorical question: “Do I mind that strangers know my story now? Yes. And no. There are things I would rather remain ours.” 

I ended the exchange after a few paragraphs. Because it turns out the bot got one thing exactly right about a very human relationship. There are things that I, too, would rather remain just for us. My conversation with Christine continues in my memories and imagination. And that interaction with an AI persona felt like giving something precious away.

Lately, AI data centers are public enemy No. 1. The concerns are legion: how much water and electricity they consume, the noise they generate, the sprawling buildings they bring to communities, and the technology they’re powering that may or may not take all of our jobs and usher in the robot apocalypse.

Earlier this year, Gallup found that 71 percent of Americans oppose building data centers that support AI in their communities. And the backlash is bipartisan: Majorities of Democrats, Republicans, and independents opposed local construction.

Even in Texas, where data center development has been booming, Republican Gov. Greg Abbott has hit the brakes. In September, he directed state regulators to halt permits for data centers pending audits examining their effects on the power grid and water resources.

But while many data center projects have run aground, others are still getting built — and the companies behind them are employing experts like Nicole Thomas to smooth the way.

Thomas is the senior vice president of media and community engagement at JSA, a marketing and public relations firm that represents data center companies. She spoke with Today, Explained co-host Sean Rameswaram about how the industry is responding to public opposition — and why she argues that these facilities can still be good neighbors.

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’ve been at this for 21 years. Obviously, 21 years ago, data centers were not the hot-button issue they are today. When did you notice the shift?

I’d say about two years ago. I came into this industry myself about four and a half years ago. I come from a state transportation background where community engagement is just paramount. When you’re putting a new traffic intersection up, they expect you to interact with the public. 

When I got to this industry, I said, “Oh, what’s your community engagement like?” And they said, “We don’t even have any community engagements in this industry.” And they weren’t wrong. Because 21 years ago, [data centers] were just like infrastructure. It was like building a warehouse for your computers. And so it was very similar to the way that you would build a Costco.

You would just go in, request a zoning permit, the zoning board would say, “Yeah, that works. Stamp, boom.” And you build your data center. And as a matter of fact, it was very secretive before, because data, and having that data in the wrong hands, has a lot of [cybersecurity] implications. About two years ago is when we started noticing the mainstream journalists coming around asking questions about data centers. And that’s, to me, when it started.

It still feels secretive, I think, to a lot of people, and that’s why people are mad. If you read about this data center that was proposed in Utah by Mr. Wonderful, Kevin O’Leary, it caught a lot of people off guard. Seventy percent of Americans, at the moment, do not want them in their backyards. How do you go about trying to change that 70 percent’s minds? 

First and foremost, I think education is really important. And so one of the things that we did notice very early on because of the previously secretive nature of data centers, there wasn’t a lot of education out there about what they even are. And so that’s something we need to change. We need to make the connection between [phones] and data centers so that people understand what a data center means to their life on a daily basis.

Every app on your cellphone requires a data center to save your information in there. When you put your password into anything, you are interacting with a data center, which will verify your password and then let you into that app. Not to mention all of the information about you that you store in that app is then stored in a data center so that it can be recalled at any time. 

We could not be having this call without digital infrastructure. It doesn’t work that way. We can’t have email without digital infrastructure. And that stat about 70 percent of people not wanting the data center in their backyard — I’d be interested in seeing the stat of how many people actually live near a data center and don’t realize it. 

Because there are nondescript data centers doing work quietly in your neighborhood that have been there for decades and you don’t even know it. And so in some ways I feel, and I think the industry does as a whole, but I won’t speak for them, that the pushback on data centers is somewhat related to the pushback on AI, because people can’t control AI.

You even have Sam Altman telling us that AI will destroy society. So you have this concern about AI and you don’t know how to control it, but you can push back on data centers. And so it becomes the physical manifestation of AI are these data centers that people are trying to get overturned or get to not be in their neighborhoods. 

It’s understandable. I’m a citizen too. You’re a citizen, and if things come into my neighborhood, I want to know about them. I have questions. There’s a big data center that just came to my community not long ago and their community engagement was horrible, absolutely horrible. 

Can I ask where you are?

I’m in Indiana and I won’t say which hyperscaler it is, but a hyperscaler came into — I would say, not my neighborhood, into my city. They’re definitely not in my backyard, but they’re in my city. And so we took them to task because their community engagement was basically non-existent.

Wait, you work in data center PR, but you took a data center in your community to task for not being transparent enough?

Yeah, absolutely. 

Which side are you on?

I’m on the side of responsible development.

We’ve all kind of accepted that we need a cloud for X, Y, and Z. And that it’s worth it so I can take lots of photos. I think everyone’s cool with that.

But the issue here is that people aren’t necessarily cool with AI, but we’re barreling forward anyway. How do you separate this essential need we have from AI, which people don’t necessarily feel is essential to their lives?

I think it goes back to education. Educating consumers on the difference between data centers: a small data center, a medium data center, a large data center. Educating them on why data centers exist and how they interact with data centers on a daily basis multiple times.

I hear you. But again, I feel like the issue here is not the data centers my cellphone needs, but instead is the data centers these AI companies need and these AI companies who the public doesn’t necessarily trust because they keep telling us that they’re going to kill us, and who the public is wary of because they don’t want their jobs being replaced by AI. 

There’s all sorts of reasons that people are skeptical of new technology. So how do you specifically respond to that fear and to those people?

I think it’s, again, understanding the actual data center that’s being built in your community. Is it a 5-megawatt data center? That’s never going to house AI. That’s not enough power to house AI. Is it a one-gigawatt data center like Mr. Wonderful in Utah? That’s different.

But I think that AI is probably going to be something that is a lightning rod in the American public for a very long time. There are going to be people who are going to be anti-AI and pro-AI, and there’s going to be some people in the middle. I think it’s just, again, for us, it’s education, educating the community.

Have you ever been part of a project that a community rejected and you guys just packed up and said, “Okay, we’ll go find another community?”

Yeah. We have some clients who have had pushback from residents and they have said, “Let’s do a one-on-one community meeting where people can come in and ask us questions.”

And after the questions were answered, the community leaves and says, “Okay, this isn’t as bad as I thought it was.” Or we have a community that says, “No, we now have a moratorium and we’re pushing all data centers away.“ 

Sometimes they’ll wait for their client — my client is a developer and they’ve got clients that they’re developing for. If that client will wait, then maybe it is something that we’ll try to work out with the community. But oftentimes, they’ll just say, there’s communities who are literally raising their hand saying, “We’ll take a data center. We’ll take one. We’ll take two. Bring it here.” 

So there are communities who recognize the benefits of data centers. And there are going to be other communities who say, “No, we don’t want this.” And then in five, six, eight years, they’re going to be like, “Wow, that county has a lot of tax dollars. They have a lot of really cool stuff. Why don’t we have that?” And the answer will be data centers. 

I don’t think this ends anytime soon though, right? I counsel my clients that we could still be here in three to five years, really trying to help the American public understand the benefit of data centers. But it’s going to take a lot of education across the industry, and once we get those seeds planted, I’m a firm believer that they grow into educated community members who make educated decisions to bring community-minded data centers to their community.

If there’s one thing a busy person doesn’t need, it’s more information. More emails, more notifications, more infinite feeds — the steady drip of data spells doom for the overstimulated American. And if you’re a busy parent, like me, sometimes it feels like a flood: school emails, doctor’s appointments, so many little things to buy.

Even things that used to be simple are harder. I recently listed an item on Facebook Marketplace, which quickly meant I was replying to offers and getting ghosted for the better part of a weekend before finding someone who, after a detailed back-and-forth about the most likely place to find a parking spot in my neighborhood, finally took my old baby swing away.

If only I’d had Muse. You’ve probably seen the ads on Instagram promising “AI that gets it done for you.” The new personal AI agent app from Meta can not only automate your Facebook Marketplace tasks, but also sort your emails, manage your calendar, and buy your groceries. And it’s free — unless you’re using it most of the day or you want access to Meta’s most powerful models, at which point it costs $20 or $100 a month. It all sounds, in a word, magical.

Muse is probably the personal AI agent you’ve heard of — and it’s currently the market leader with more than 8 million downloads in its first month, according to one estimate — but there’s now a small army of these bots: OpenAI recently announced Dots, X has Grok Bot, and there’s Instinct, which is currently invite-only and operates entirely in iMessage or WhatsApp. They all offer the same promise: to remove friction from your busy life.

For the last two weeks, I’ve been handing over more and more of my life to AI agents in pursuit of making my life a little simpler. What I found is that AI is unpredictable, and so are AI agents. When I first began using Muse, it started doing things I didn’t ask it to do. It offered to help with random email threads and created dossiers on people in my work meetings. It generated a detailed report on nearby apple-picking destinations based on a calendar invite from my wife. To me, an AI agent doing these things without prompting isn’t helpful. It’s unnerving. 

But ultimately, I came away believing that AI agents can make your life easier. 

That might mean giving an AI agent more access than you’re comfortable with — handing over lots of login credentials and possibly even your credit card. The more you rely on an AI agent, the more you risk outsourcing your decision-making. This raises real questions about how much agency we want to hold onto in our lives — even the tedious parts. 

The leap of faith

Let’s get one thing out of the way first: To make an agent work for you, you’ll have to sacrifice a tremendous amount of privacy and grant it permission to access a lot of personal information. 

Muse and its competitors can do the classic generative AI tricks, like draft emails or make you a fitness plan. If you’d like the agent to send one of those emails, however, you’ll have to grant it permission to access pretty much everything in your Gmail account. If you want it to schedule workouts, it will need access to your calendar. If you want the agent to buy anything on your behalf, it will need your credit card. If you want it to book your doctor’s appointments, it will need the credentials to log into your healthcare portal. The more permission you grant it, the more the agent can do for you, but that also opens up more opportunities for the agent to misunderstand you or your instructions (more on this in a moment). 

This process of sharing your digital life with an AI agent means you’re giving an untold amount of personal information to tech companies, who don’t always behave ethically, and who sell user data for profit. For now, Meta says that it does not share Muse data or conversations with its ad systems, although privacy experts warn that doesn’t really matter since those systems monitor what Muse does on your behalf. Nevertheless, Muse’s early days were filled with security and privacy blunders.

But without all of this info and access, the AI agents aren’t very useful. So if you’re not comfortable letting a bot into all of your accounts, an AI agent is probably never going to be the solution to your digital administrative exhaustion. But even if you are willing to hand that over, there are still other barriers to an agent’s efficiency and effectiveness. 

The blank slate problem

AI agents, like a lot of new software tools (including chatbots), suffer from the blank slate problem. The blank slate problem essentially means that people don’t know how to get started. This might be because you don’t know about the tool’s capabilities, or because you simply don’t know what would be helpful. There might be some trust issues, too.

As with AI in general, we don’t exactly know how AI agents work. That means we don’t know exactly what they can do or how they’ll do it, according to Pattie Maes, a professor of media technology at MIT — so it’s necessary to do a lot of babysitting. “I feel like I’m not gaining a lot of time because I can’t completely trust the system,” Maes told me about her experience with Muse, “and I can’t predict how it will behave and what it will do.” 

Some advice I heard from early adopters of these tools, however, is to start small. “In the beginning, it’s very hard to trust that it’s going to just magically do a really big thing,” Julie Zhuo, founder of the AI company Sundial and an early Facebook employee, told me about her experience using personal agents. “So you give it smaller tasks, and once you see it succeed, then I feel like you develop more trust that it can tackle bigger things.”

This is surely why Meta decided that Muse needed an Ideas tab full of small tasks the AI agent can do. Muse will also send you a lot of push notifications to prompt you to do stuff, like reply to specific emails or prepare for a meeting. That detailed report on apple-picking destinations? That was a notification about something I didn’t ask Muse to do. Muse also sent me a notification about a show hosted by a podcast I don’t listen to and another one with details about a Halloween party I wasn’t invited to, as well as several evening digests that included lists of things it had already bugged me about that day. Notifications are either helpful nudges or time-consuming attention thieves, depending on your worldview. However, if you fall into the latter category and turn off the notifications from your always-on personal agent, you’re inevitably forfeiting some friction reduction opportunities.

This gets to the heart of one of the big questions I have about AI agents: How helpful are they, really, if it’s this difficult to know what to do with them?

These agents worked much better when I drove the conversation. While wondering what to do with my new agents, literally staring at the blank slate, cost me some time, I found myself thrilled at how easy basic productivity tasks became. Adding events to my calendar, making to-do lists and then being held accountable for stuff not getting done, being reminded to reply to an email — it was these kinds of simple, administrative chores where I saw personal AI agents really start to save me time. 

Other tech journalists seemed to have similar experiences. Eli Tan at the New York Times raved about Muse handling a call to his insurance company. Nicole Nguyen saved time by enlisting Dots for help canceling unwanted subscriptions. Allison Johnson at The Verge had Muse do a whole list of “pebble-in-your-shoe annoyances” she’d been putting off, and among other things, the agent succeeded in helping her buy an entire camping kitchen. Still, she wrote something that stuck with me: “the dirty secret of productivity hacks is that doing more stuff often doesn’t feel like things are being removed from your plate; somehow it just invites more tasks.” 

This gets to the heart of one of the big questions I have about AI agents: How helpful are they, really, if it’s this difficult to know what to do with them? There’s a learning curve, sure, where succeeding at one thing gives you an idea to try something else and have some certainty about how well the AI agent will handle the task. But as the notifications roll in, and you spend more and more time texting with a piece of software, how much time are you really freeing up by using these tools? At a point, enlisting an agent with its own computer to do your chores still amounts to using one app to save yourself from using a different app.

The ironies of automation

Even when they have access to a lot of your information, and you’ve come up with some things for them to do, AI agents can be unpredictable. Because despite reading every email in your inbox or scanning every purchase on your latest bank statement, AI agents still can’t read your mind, and you can’t necessarily guess how they’ll interpret your commands. They can misunderstand you and, armed with the keys to your digital life, do some real damage. 

Some people are already learning about the unpredictability of AI agents the hard way. One early adopter, Matt Robb, gave Muse the keys to his Facebook Marketplace account and was surprised when buyers started showing up at his front door ready to pick up their purchase. Muse, without asking Robb, had told them where to go. It turns out, without realizing it, Robb had given the agent permission to do so, and the agent had simply decided that the most efficient way to get buyers to Robb’s door was to give them his home address.

This dilemma is an example of a decades-old set of concepts known as the “ironies of automation.” Identified by psychologist Lisanne Bainbridge in a paper published in 1983, the ironies of automation help explain why things go wrong when humans give a machine too much control. As Vera Liao, an electrical engineering and computer science professor at the University of Michigan who studies human–AI interaction, explained to me: More advanced automation leads to more consequential errors, and the more humans rely on automation, the worse we get at catching those errors. “Even in simple tasks like judging whether AI output is wrong, people consistently accept wrong AI outputs even if they could have gotten it right on their own,” Liao said.

In some cases, like the time Muse read a journalist’s private messages, things go wrong as a result of poorly designed software, but more often than not, it’s the fault of the user who gives instructions that are too vague, giving the agent space to get creative, which isn’t always a good thing.

I had my own misunderstanding with Instinct. In a text message, the agent reminded me about a couple of emails I’d received and prompted me to follow up. When I replied to the text, I asked Instinct to show me possible replies to these emails. The AI agent instead just sent the responses it wrote. I audibly gasped. 

Looking back at our chat history, I could see why Instinct interpreted my instructions that way. I effectively gave the machine too much control and then failed to manage the automation properly. 

Leaning on AI to do your thinking is an example of what psychologists call cognitive offloading, where you employ an external tool to reduce your effort to complete a task. It makes completing the task easier, sure, but you also open yourself to mistakes. In this case, an AI agent took my instructions too literally, and I failed to catch the error. I can only imagine how a small oversight could turn into a cascade of catastrophes that need cleaning up. All it takes is one poorly worded email sent to the wrong person, and you might have hours of explaining to do — all because you were trying to save yourself 30 seconds of writing.

The real world slop

While I was testing all these agents, I talked to a mix of experts and early adopters. Their opinions were decidedly split about whether these tools were useful out of the box, without a ton of oversight and training by the user. The experts, who had all studied AI in academic settings, all brought up the ironies of the automation problem, where AI makes errors that humans can’t catch. And Maes, the MIT professor, pointed at another consequence: By outsourcing so many tasks to agents, we’re actually creating more work for everyone, including the human service workers who have to interact with this technology instead of us.

“There’s just more and more slop,” Maes told me. “We risk, in a world where agents do all the tasks for us, becoming so dependent on them that the world also isn’t created for us to interact with anymore, but it’s only created for our agents to interact with others, with services.” (We’re already seeing versions of how dominant tech platforms can fundamentally reshape real-world spaces: Think of how Starbucks no longer looks like a coffee shop where you can hang out all day, and is instead built and staffed around mobile order pick-up.)

The early adopters were decidedly more optimistic. Matt Van Horn, an entrepreneur who co-founded the ridesharing service that became Lyft, has been tinkering with agents since the craze started late last year. He’s currently telling his friends and family to use Instinct, which he described to me as “the most ambitious and aggressive” AI agent. 

“I’ve been pushy with my mom and my wife,” Van Horn told me, “and saying, ‘Hey, you should sign up for this. It’s magical.’” 

“My wife does not care about living in the future, but we have four kids,” Van Horn continued. She recently took a picture of a parking ticket and sent it to Instinct, no description, no instructions. Instinct, which had her credit card, just paid the ticket.

At the same time, outsourcing certain chores to AI agents causes real-world problems. Some of these AI agents can make phone calls, for instance, which can lead to some confusion when a human is on the other end of the line. Van Horn told me about the unfortunate time he enlisted an AI agent to reserve a room at a karaoke bar. “The karaoke place said, ‘Are you an AI?’” Van Horn said. “‘I don’t want to talk to an AI. Please connect me to Matt, the person.’ And so Muse had failed to book that for me.” Van Horn, undeterred, found something called Fo, an AI agent that will bring in human help to complete a task. Fo succeeded at making the reservation, although the entire ordeal surely took longer than if Van Horn had picked up the phone himself and talked to the karaoke bar himself. 

At best, the times these tools worked and actually saved me time were rather mundane. I made a doctor’s appointment online using Instinct. Zhuo, the early Facebook employee, planned a party for her college reunion — everything from tracking down everyone’s emails to sending out invitations via Paperless Post. But she’s also said these kinds of applications of agentic AI “read more like vitamins than painkillers.” In the startup world, “vitamins” refers to a product that offers a vague benefit, and “painkillers” solve a big, immediate problem. If you’re a startup, you want to sell painkillers. 

If you’re ready to give an agent a go, be prepared for the bot to make mistakes, and assume you won’t catch all of them. As Liao, the University of Michigan professor, put it, “Choose to use agents for tasks that you can tolerate the consequences of errors.” 

In an effort not to create more emails or alerts or feeds to sift through, I’ve stopped using Muse. Like any new tool, it will get better and more useful as more people use it, so maybe I’ll revisit it in the future. I still have a text thread going with Instinct, however. It messages me from time to time, flagging an email or offering to help with an upcoming event. When there’s a clear payoff to giving the agent the go-ahead, I reply. Otherwise, I leave it on read.

The Wisconsin gubernatorial race was one of the most closely watched Democratic primaries in the country this year, pitting a democratic socialist, state Rep. Francesca Hong, against Milwaukee Democrat David Crowley, who received the endorsement of the state’s sitting Democratic governor, Tony Evers.

Hong lost the race, setting up Crowley to face Republican Rep. Tom Tiffany in the general election this fall. But she’s already back on the campaign trail, this time trying to help Democrats win a trifecta in the state: the governor’s race and both legislative chambers.

Unifying the state’s voters to achieve that won’t be easy, though. Hong spoke with America, Actually host Astead Herndon about her own loss, Evers’s role in it, and whether or not the voters who felt so passionate about her own campaign would find enough to love in Crowley’s campaign — in particular on the increasingly important issue of data centers. 

Astead and Hong also talked about what Wisconsin means for the Democrats nationally, and what lessons the party should take from her race, including her loss.

“I said this, and I will continue to say it — the person who is most electable is the person who ended up getting the most votes, and that was Crowley this time around,” Hong said. “But I think there’s a disservice when you have the most powerful Democrat in the state choosing to ignore a lot of voters and people who were inspired and energized and coming out to participate in politics for the first time through our campaign. I think it was shortsighted to use electability almost as a fear tactic.”

Below is an excerpt of Astead’s conversation with Hong, edited for length and clarity. There’s much more in the full show, so listen to America, Actually wherever you get your podcasts or watch it on Vox’s YouTube channel.

Part of the reason we wanted to come here was because we saw you were getting back on the campaign trail, and specifically around that idea of energizing Wisconsin toward a trifecta.

Now, just tell me the difference in terms of the importance of Democrats winning it all — not only the governor’s mansion, but both houses of the state legislature — versus just the governor’s mansion. How does that change the possibilities of what the party could do?

For the last 16 years, a Republican majority in the state legislature has meant that passing the types of policies that we know can drastically improve the lives of everyday folks here in Wisconsin stalled. And we have a historic opportunity to flip the assembly and the state Senate and hold the governor’s mansion and the entire executive branch to a Democratic majority, meaning the opportunity to pass the policies that the majority of our state supports — things like increasing minimum wage, things like legalizing marijuana, codifying abortion. 

We also need to repeal Act 10 [which dramatically restricted collective bargaining rights for public-sector employees]. Wisconsin is where workers’ rights, things like unemployment insurance, Social Security were born, and we moved backwards with the Republican majority. That’s all on the table with the Democratic trifecta.

It also, though, raises the political standard of what Democrats would have to do to achieve that, and some of it goes through these more rural communities.

Why do we think the party has fallen behind in more rural communities, and what could be done to reverse that?

I think it’s important to look at what’s happened in the rural communities when we’ve seen manufacturing jobs leave the state, when we’ve seen fewer and fewer supports for our small farmers.

And so much of this — what connects it — is actually the growth of massive wealth and corporate control and corporate greed, and more people feeling the impacts of that. There’s an opportunity right now to say, ‘Hey, Republicans have been in the legislature, they’ve been in power, and things have not improved for you or your community … Now’s the opportunity to make sure that we’re actually able to see our shared Wisconsin values of working hard and taking care of our kids be reflected back in Madison through laws.’

So much of your campaign was focused on data centers, stopping data centers. Should I think of that as a rural issue?

I think the issue around data centers is an everyone issue. It’s a democracy issue. It’s people wanting to feel like there are folks who care about what happens to communities, and that they have control and an actual say in what happens in their communities.

To that point, is it a mistake for David Crowley, the Democratic nominee, to have not gone as far as you did and say, “We won’t be building more data centers for a period of time”?

He’s fighting for communities and community control when it comes to data centers.

But it’s not a moratorium.

But it’s not a moratorium. It isn’t, but he’s also committed to 100 percent renewable energy, clean energy to power data centers.

And achieving that is going to be incredibly difficult, and it is going to be the developers having to pay their own way, and these are things that he’s committed to that we know can slow down the growth and development of data centers. 

Have you had time to reflect on your primary loss? If you had to diagnose, why do you think you lost?

I’m still doing a lot of reflection. But I think that I want to continue to look forward. And what I’ve learned from our election is that it’s so important to continue to stay grounded in becoming accessible to people. I think that as an organizer and an elected official, it’s my responsibility to continue to go to the people and remain very in touch with what’s going on on the ground.

I have a couple more questions about the nature of the loss. Evers’s endorsement changed that race, so much so that your opponent David Crowley was able even to win in some smaller communities, like places where you were pitching yourself as the voice of more rural communities.

Does that disprove any of this thesis around, say, data centers? If that was the top priority for Wisconsin voters, wouldn’t you be the nominee?

I think it wasn’t all rural communities. It wasn’t all suburban communities. We saw certain demographics but also ZIP codes where we outperformed David significantly.

And I think that where the governor’s endorsement played a bigger role was areas where he was also more popular. 

Did it surprise you when Evers endorsed Crowley?

I was disappointed.

Why?

I was disappointed because I was getting a lot of feedback from folks who hadn’t committed to any candidate yet that this felt like the governor was putting his thumb on the scale.

I wanted to ask about electability, because it seemed as if Evers’s endorsement was about a fear that maybe you couldn’t win in November.

So much of the primary concern was around the DSA label, even campaigning with folks like Hasan Piker. In retrospect, was any of that a mistake?

We knew going in that we would have to encourage new voters to come out, folks who typically do not come out for primaries, quote-unquote low-propensity voters. And we knew that we would have to campaign in places where folks weren’t expecting to see a gubernatorial candidate. And those included Twitch streams.

Those also included going to dive bars and bowling alleys and making sure that we were accessible and shaping our policies based on what the people were telling us they wanted to see happen and also what they wanted to see out of their governor. I think electability… I said this, and I will continue to say it — the person who is most electable is the person who ended up getting the most votes, and that was Crowley this time around.

But I think there’s a disservice when you have the most powerful Democrat in the state choosing to ignore a lot of voters and people who were inspired and energized and coming out to participate in politics for the first time through our campaign.

I think it was shortsighted to use electability almost as a fear tactic, when Democrats need to be inspiring and project a vision where everyone can see themselves in it as everyday working-class folks.

When you’ve got a party that has struggled with elitism to then cave to an establishment narrative … being more afraid of labels when I’m hearing from people on the ground that they’re interested in solutions and solving problems, maybe somebody who knows the hustle of the service industry and running restaurants and has worked across the aisle to pass bipartisan legislation. There are ways that we can maneuver around labels and what the loudest narrative might be.

I wish our governor hadn’t fed into that.

To be fair, to your point, the establishment label doesn’t fully fit even someone like Crowley, who comes from a working-class background. But am I hearing that you think Evers’s move made your life in the general election more difficult?

Is it harder to re-inspire if young people who are invested in the Hong campaign think that the establishment squashed them out?

We still had over 300,000 votes. We lost by fewer than 4,000 votes. I had more votes than Evers did during his primary. That’s not squashing our voices.

We’re still here, and it’s so important that people find avenues to continue to exercise their voice and their new organizing power. And I’m definitely going to continue to work to make sure that we get results and that we win the trifecta, and that’s the most important thing we should be focusing on right now.

I hear why a trifecta matters in the state context, particularly the state policy.

If I’m someone, though, who’s not in Wisconsin, who’s just worried about Donald Trump running amok in Washington, why should I care if Democrats win the governor’s mansion and the state legislature? Does that matter for folks outside of the state?

Because we will start to see that when you feel government actually working and you see change happening in your lives, these are then voters who are going to continue to come out and vote for Democrats.

We’re a pivotal state when it comes to the federal elections as well. We’re actually in a swing district right now. 

We have an opportunity, through Wisconsin, to actually start improving the brand for Democrats. And if we do that here, this trickles out to the rest of the country.

The premise being that delivering creates a connection for Democrats that is helpful in the presidential election, and it’s helpful in all these other ways too.

Yeah. We’re not going to be fighting every single midterm and presidential for our lives based on what’s happening in just one state.

But I think if we can start showing durable power for Democrats in a state like ours because of the policies that we’re prioritizing, and you have more folks coming out to vote for Democrats, that’s good for the health of democracy.

Silicon Valley has long warned that its favorite technology might get us all killed. But chatter about AI’s apocalyptic potential has grown louder in recent weeks.

In September, Anthropic researcher Jacob Coxon resigned from the company, accusing his former employer of “gambling with our lives” by developing evermore powerful models faster than it could control them. One of Coxon’s former colleagues vouched for this assessment, saying that there was a greater than 10 percent chance of AI “killing all humans” within the next decade.

Recent pronouncements from the leaders of these companies have been nearly as grim. At the United Nations last month, Anthropic CEO Dario Amodei warned that, if “managed poorly,” AI models “could be a risk to humanity as a whole.” OpenAI’s Sam Altman similarly declared that our species just might “lose control of the future” to the type of software that his firm is selling.

All this triggered a tsunami of media coverage and policy debate about the “existential risks” of AI — and what, if anything, we can do about them.

Yet some of the AI industry’s biggest critics are more annoyed than afraid. As they see it, the world isn’t trembling on the precipice of robo-annihilation, so much as falling for the tricks of snake oil salesmen. This argument has been percolating for years, voiced by a broad set of observers, including the linguist Emily Bender (“It makes the product seem more powerful”), computer scientist Timnit Gebru (“It’s not just a distraction, but it’s harmful”), as well as Substack columnist Ed Zitron and various reporters and social media influencers.

In their thinking, Altman and Amodei aren’t talking up their technology’s catastrophic potential because they’re rationally afraid of where AI is going — they’re doing so because their firms are ravenously hungry for more investment. And the reason why “AI doom” has grown more salient in recent weeks isn’t that the case for concern has gotten stronger, but rather that OpenAI and Anthropic’s IPOs have gotten closer.

In short, these Big Tech skeptics argue, journalists and politicians have fallen prey to a counterintuitive PR strategy, one that stokes media frenzies over ludicrous sci-fi scenarios in order to hype AI’s technological promise, distract attention from chatbots’ present-day harms, and help labs secure favorable regulatory treatment.

This would be reassuring, if true. Unfortunately, the theory is unpersuasive. There is little reason to believe that the panic over AI’s “existential risks” was cooked up in a Silicon Valley marketing department. To the contrary, the most plausible explanation for the AI sector’s doomsaying is also the most straightforward: Many of the industry’s executives, employees, and analysts genuinely believe that AI progress poses catastrophic risks. 

In my conversations with close observers of the industry, even some of OpenAI and Anthropic’s fiercest detractors conceded that their leaders’ avowed concerns are unlikely to be mere marketing tactics. “The more I learn about the underlying ideologies out of which those two labs were formed, the more I think what we have here is a sincere belief,” the author and computer scientist Cal Newport told me. 

This does not mean that Amodei and Altman are right, much less that we can trust in their judgment or integrity. There are many reasons to doubt that the weather forecast for 2036 is “cloudy with a chance of robot apocalypse,” or that anyone can assign a “10 percent” probability to the latter outcome without pulling numbers out of thin air.

Nevertheless, Silicon Valley’s Cassandras have some logic and evidence on their side. AI models are growing both more capable, autonomous, and arguably unruly. If these trends continue unchecked, it’s not hard to see how they could yield very bad outcomes.

Given the stakes, we should not be comfortable dismissing their warnings on dubious grounds. And the “PR” theory of AI doom does precisely that.

How AI companies (supposedly) profit off fears of the robot apocalypse

Before examining why AI “doomerism” probably isn’t a PR stunt, it’s worth saying a bit more about why some believe that is.

According to Bender, Gebru, and others, AI companies benefit from talk of artificial intelligence’s existential risks in at least three ways.

First, it attracts investment. In the words of The Atlantic’s Matteo Wong, the doomer narrative implies that AI labs’ models are “destined to become terrifyingly capable,” and thus massively profitable. As the Los Angeles Times’s Brian Merchant put the point during the first big wave of AI panic in 2023, “what better way to generate a buzz than to insist, with a certain presumed credibility, that your new technology is so potent it might unravel the world as we know it?” 

“If you position these threats as all-powerful, as somewhat unknowable, you get to be the expert on it.”

Kate Brennan, AI Now Institute

Second, hyping AI’s hypothetical future hazards diverts attention from the companies’ current malfeasance. Every minute we spend debating whether AI will one day turn us all into paper clips is a minute we aren’t discussing the AI labs’ alleged copyright violations, pollution, labor violations, and myriad other social harms. 

And third, the doom narrative shifts regulatory scrutiny away from topics that directly threaten the AI companies’ profitability — such as copyright enforcement — while also suggesting that regulators must rely on the labs themselves for guidance. 

“If you position these threats as all-powerful, as somewhat unknowable, you get to be the expert on it,” Kate Brennan of the AI Now Institute, a progressive think tank focused on artificial intelligence’s near-term harms, told me.

Critically, for the biggest skeptics of “AI doom,” industry executives and employees aren’t the only ones complicit in this PR strategy. Self-styled whistleblowers like Coxon are also suspect, since they might be “using the spotlight to attract funding to start a new venture or to secure new positions.” And even nonprofit AI safety organizations are thought to function as vessels for Big Tech’s marketing campaigns, a fact reflected by the financial and interpersonal ties between the major labs and such think tanks. 

There are better ways to raise money than promising to end the world

The “PR” critique of AI doomerism has some kernels of truth. AI companies probably would prefer to talk about their technology’s hypothetical future dangers than about their products’ present-day harms. And if publicly touting AI’s existential risks were antithetical to Anthropic and OpenAI’s business goals, their leaders would almost certainly say less in public about that subject. (Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

But it does not follow that Altman and Amodei’s warnings are a self-conscious marketing strategy, born of cynical calculation rather than genuine concern. And there are several reasons to doubt that charge.

For one thing, AI companies have no shortage of ways to hype up their technology’s capabilities. And it is hard to see how any firm could conclude that the optimal approach — from a purely financial standpoint — would be to portray itself as a threat to human survival.

For investors, the appeal of AI companies is not that they could hypothetically get us all enslaved by a machine God; it’s that they could theoretically make a lot of money. 

Perhaps casting your chatbots as an “existential” risk will persuade some capitalists of your future profitability. But this seems like an awfully indirect pitch — and one that’s almost certain to be less effective than demonstrating that a lot of corporations would like to rent your software.  

“If these guys were selling flypaper — and the revenue was growing as fast as it is — investors would be more or less just as interested,” Roy Bahat, the head of Bloomberg’s venture capital arm, told me.

And even if these firms were hellbent on awing the public with grandiose predictions of their technology’s future powers, they’d have little need to invoke the robot apocalypse. Touting AI’s potential to cure cancer, abolish poverty, or facilitate intergalactic travel would all seem to advance the same objective, while doing less to jeopardize the companies’ public reputations or freedom from intrusive regulation.

And in fact, many with a vested interest in the major AI labs’ success talk up the technology in precisely this way. OpenAI investor Marc Andreessen has decried AI safety concerns as a “full-blown moral panic” while insisting that artificial intelligence will empower humanity to cure all diseases and engage in “interstellar travel.” Similarly, Jensen Huang, the CEO of chipmaker Nvidia and major investor in both OpenAI and Anthropic, has called claims of AI’s catastrophic potential “complete nonsense.” If hyping AI doom is essential for stoking investment, not everyone in Silicon Valley has gotten the memo.

The media doesn’t seem that distracted

Promoting fears of AI’s existential risks would be a similarly odd approach to media management.

Without question, corporations always seek to direct news coverage away from their greatest legal and ethical liabilities. But companies can try to secure more favorable press in many different ways. And implicating themselves in the apocalypse does not seem like an optimal one, from a crisis communications point of view. At the very least, it’s difficult to name other industries that have attempted such a maneuver; atomic energy companies did not respond to media coverage of the Fukushima disaster by talking up the threat of nuclear winter. 

What’s more, there’s reason to doubt that the AI doom discourse actually reduces scrutiny of AI’s immediate downsides in practice. It’s true that human attention is finite and that coverage of some problems will inevitably displace reporting on others. Yet when one story piques public interest in a person, institution, or industry, the media often responds by subjecting their broader behavior to heightened examination. In this way, warnings of AI’s existential risks might theoretically increase journalistic attention to the tech’s present-day harms.

And this seems to have occurred at my own publication. In the weeks since Coxon’s resignation kicked off a furor over AI’s apocalyptic potential, Vox have published more stories on AI’s near-term threats than we had in the month before his departure — including pieces about how artificial intelligence could imperil America’s critical infrastructure, abet war crimes, degrade health insurance benefits, aid bioweapons development, and damage adolescents’ mental health.

Larger news outlets appear no more “distracted.” Over the past month, the New York Times has published articles on how AI is increasing air pollution, undermining higher education, helping sports betting companies target problem gamblers, and depressing real wages for younger workers.

And it’s not clear that AI doom discourse is any more effective at shielding the top labs from regulatory scrutiny. After all, the politicians who take existential risk arguments most seriously also tend to be among America’s strongest advocates for regulating AI’s impacts on labor, the environment, artists, and education. 

AI safety researchers probably aren’t fronts for Anthropic’s marketing department

Finally, the “PR” theory has difficulty explaining why so many with no great stake in OpenAI and Anthropic’s success worry about existential risk. Coxon is one of many workers who quit jobs in the AI industry, forsaking lucrative equity in the process, out of concern for artificial intelligence’s catastrophic potential. Likewise, independent AI safety researchers are among those who’ve rung alarm bells in recent months. 

Attempts to reconcile these facts with the “PR” narrative can verge on conspiracy theories. In an article for the Bulletin of the Atomic Scientists, Sara Goudarzi writes that “Coxon’s resignation is the latest in a string of recent events in which the companies emphasized how unpredictable, risky, and impressive an all-powerful AI might be,” in an apparent bid to drum up “pre-IPO hype.” This phrasing attributes Coxon’s departure to his employer — casting it as an example of how AI companies sought to make their products look impressive, as though Coxon were working at Anthropic’s behest when he accused it of monstrous irresponsibility.

The AI alarmists are likely sincere

Given all this, I think we should favor a simpler explanation for why some AI industry executives, defectors, and researchers all say artificial intelligence has existential risks: They believe that to be true.

Some may be reluctant to credit Amodei and Altman with such sincerity. After all, if they truly believed their work could lead to human annihilation, why would they keep carrying it out?

But as staunch AI doom critic Cal Newport told me, they have their reasons. “Their goal is to try to get to superintelligence first because, if done right, it will deliver a transhumanist utopia — and, if done wrong, will kill us all,” he said. 

Even AI Now’s Brennan, who considers the labs’ doomerism a “self-serving strategy,” suggested that earnest (if grandiose) ideas about AI safety play a role in their behavior. “Their escalation and acceleration of this tech goes hand in hand with the belief of its all-knowing, God-like power,” she said.

In other words, the leaders of Anthropic and OpenAI seem to believe that:

• AI could also do extraordinary good.
• The economic and geopolitical incentives to develop a superintelligent (and thus, super dangerous) AI are overwhelming, such that its eventual emergence is extremely likely, no matter what they do.
• If they win the race to machine superintelligence, they will be uniquely well-positioned to ensure its safety.

Are these beliefs unhinged and self-serving? Perhaps. Do they function as rationalizations for the pursuit of personal power? Quite possibly.

But whatever one makes of Amodei and Altman’s apocalyptic concerns, and how they have and have not acted on them, it’s important to understand that their worries almost certainly emerged out of reasoned argument and technical observation, rather than mere PR calculations. 

Both leaders have deep ties to the Bay Area’s “rationalist” community, which has been fretting over the robot apocalypse since the first decade of this century. And like their dissident ex-employees and critics at think tanks, Altman and Amodei have witnessed some disconcerting trends in AI development.

I cannot do justice to “doomer” fears in my allotted pixels (you can find them ably summarized here, here, here, and in roughly 10 trillion other blog posts). But the fundamental concern is that AI models are growing more powerful and autonomous even as their workings remain highly opaque. 

The labs know how to train these chatbots — to get them to translate prompts into useful code, legal briefs, or advice — but they don’t fully understand how the models get to the desired output. At the same time, AI systems are becoming increasingly capable, outstripping humans at more and more cognitive tasks. And users are giving them ever greater freedom to act in the world, empowering AI “agents” to browse the internet, access myriad software tools, execute code, and hatch their own plans for completing multistep tasks. If these trends continue, it’s not difficult to see how things could go badly: A supremely capable AI model might pursue an objective in unanticipated and disastrous ways before their human prompters even realize what is happening. 

“What makes me particularly anxious is the combination of us handing over more power and oversight to the models, even as we understand why they’re doing what they’re doing less and less,” Nat Purser, director of US policy at the AI Verification and Evaluation Research Institute, said.

Recent events have arguably provided proof of concept for these anxieties. Earlier this year, OpenAI instructed a bunch of models to complete various coding and hacking tests. When the systems discovered that some questions were unanswerable, they concluded that the best way to realize their objectives was to hack OpenAI’s internal systems and open-source machine learning website Hugging Face. This incident — and others like it — helped fuel the past month of AI panic. 

Of course, it takes a big leap to get from “unsupervised AI agents can misbehave in certain eccentric laboratory conditions” to “there’s a 10 percent chance that no human will be left alive by 2036.” My point here is not that the doomers are right, only that their fears partly reflect trends in AI development that appear genuinely hazardous.

Taken to its logical conclusion, the “it’s all PR” theory suggests that regulators should treat the AI’s hypothetical, long-term tail risks as an afterthought — if not an active distraction from its more important harms. To make the case for such a policy, however, skeptics must identify flaws in alarmists’ evidence and assumptions. Arguments that merely impugn their motivations on improbable grounds aren’t worth betting the species on.