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

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

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

Finding practical routes to earlier action

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

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

Frontline care as a population signal

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

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

It is not prediction for its own sake.

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

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

Earlier signals from the environment and routine data

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

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

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

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

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

Innovation is only the starting point

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

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

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

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

From recognition to system-level impact

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

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

Watch the full Future Health Impact Challenge Film here.

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

Investitionen als Fundament für Wachstum

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

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

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

Denken in Generationen statt in Quartalen

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

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

Viersen und Verden: Industrielle Stärke aus Deutschland

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

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

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

Produktion in Europa stärkt Wettbewerbsfähigkeit und Resilienz

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

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

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

Verlässliche Rahmenbedingungen als Schlüssel

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

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

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

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

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

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. 

For now, Huang is staying on as CEO. But uncertainty over his 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.  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What Democrats plan to do now on AI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

THE CHALLENGES OF MASTERING DATA

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

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

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

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

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

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

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

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

OPERATIONAL ADVANTAGE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Small town, big data center

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

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

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

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

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

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

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

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

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

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

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

What if data centers had to keep their promises?

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

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

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

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

Kasia Tarczynska, Good Jobs First senior research analyst

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

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

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

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

And Nicely’s town could certainly use the cash.

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

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

SAN FRANCISCO — Billionaire opponents of California’s proposed wealth tax have spent more fighting it than they ever have on a political cause in the state, according to a POLITICO analysis.

A record $612 million from more than 70 current and former California billionaires is flooding this year’s state elections, nearly half of it for groups working to defeat Proposition 40, the wealth tax initiative on Tuesday’s ballot, POLITICO found.

The fight over Prop 40 has brought California’s wealthiest residents — many from its native tech industry — together in an unprecedented show of political coordination, the new data reveals. Tech leaders say the wealth tax has accelerated plans for broader political engagement this cycle, building networks that could be the foundation for future campaigns.

It’s a particularly striking display of unity from Silicon Valley’s notoriously hard-to-organize donor class. Google co-founder Sergey Brin is by far the well-funded opposition’s biggest benefactor, with former Google CEO Eric Schmidt, venture capitalist John Doerr, Stripe CEO Patrick Collison and crypto executive Chris Larsen also among those who have contributed the most dollars in trying to stop the billionaire tax.

The possibility of a tax on their assets has drawn more than $293 million from at least 30 California billionaires, the majority of whom made their fortunes in tech but otherwise span industries, from agriculture to real estate — as well as the political spectrum, from established Democratic megadonors to some Republican allies and first-time donors.

Frank Slootman, the billionaire board chair and former CEO of cloud data company Snowflake, said he only had a “moderate” interest in politics despite giving $5 million to an anti-Prop 40 group, arguing that the measure erodes private property rights and the state’s economic dynamism.

“This upends the most basic assumptions we have about being a taxpayer and a resident of a state,” said Slootman, who has no prior record of California campaign contributions. “I vote in most elections, that’s about it. This is not about politics for me — it is far more important than that.”

The other half of billionaires’ spending this year came largely from Tom Steyer, a rare billionaire endorser of Prop 40 who bankrolled his own failed gubernatorial bid — the most expensive primary campaign in state history.

Even longtime strategists who have worked for statewide candidates and California’s costly ballot battles struggled to find a historical parallel for the coordination or scale of billionaire spending against Prop 40.

Rob Stutzman, a Sacramento political consultant whose past campaigns have involved a billionaire gubernatorial candidate and record-breaking ballot spending over sports betting, said that Silicon Valley leaders see Prop 40 as an “existential threat to tech in California” and the innovation that has taken root in the region for decades.

“Having to defend against a direct assault is a more unifying principle,” Stutzman told POLITICO. “They’re known for their pet issues that are harder to organize their peers around, but you can circle the wagons to protect yourselves. There is nothing that focuses similar-situated people.”

Proponents of Prop 40 contend the measure is necessary to backfill federal health care cuts.

The last year billionaires in California came closest to spending at this level was 2010, when they dropped $147 million on state races and measures, according to POLITICO’s analysis — a total that’s just over half of what the current wealth tax proposal mobilized. Most of it was the result of billionaire Meg Whitman, a former CEO of eBay and Hewlett-Packard, self-funding her unsuccessful Republican campaign for governor.

Scott Ellis, a millionaire and Prop 40 supporter who formerly served as a senior director at HP, described the magnitude of billionaire spending to fend off the measure as “a completely different animal” than past examples of “a lot of money spent by a rich person to get themselves elected.”

“People with wealth and extreme wealth are getting much more brazen than they used to be,” Ellis told POLITICO. “These people have tremendous resources. They have an incredible range of choices to make, including choosing to oppose this kind of ballot measure.”

Representatives for several billionaire opponents of Prop 40, including Schmidt, Larsen and agricultural titan Stewart Resnick, declined to comment on POLITICO’s findings.

Others, like Doerr, referred POLITICO to Building a Better California, a new Brin-led political operation that has become the primary vehicle for billionaire money against Prop 40.

The group defended the importance of its cause, arguing the measure would “permanently devastate” the state tax base and budget while undermining the health care funding it’s intended to patch.

“We are fortunate to be supported by entrepreneurs and business leaders who have built their businesses and created hundreds of thousands of jobs here, and who share a deep commitment to advancing policies that will improve quality of life for all and protect the state’s economic, jobs, and innovation engine for generations to come,” Building a Better California spokesperson Abby Lunardini said in a statement.

Debru Carthan, vice president of the SEIU-United Healthcare Workers West union behind the billionaire tax, responded to the contributions, saying, “no amount of billionaire spending is going to intimidate us or turn us back.”

A reawakening

Tech leaders have told POLITICO that the wealth tax awakened a sleeping giant in California politics: their previously disengaged industry peers, who months ago suddenly started spending across the ballot to steer California in a more moderate, business-friendly direction. The threat injected the wider political organizing, some of which preceded the measure’s introduction, with a new sense of urgency.

“The tech community has wanted to become a bigger player in California politics,” said veteran Democratic strategist Steven Maviglio.

“They’re building an infrastructure, I think, for the long term because they’ve become a dominant player in our economy, and they now, after literally a decade of being hands-off, want to be very involved because they see the consequences,” Maviglio, who is working on a separate housing and environment-related ballot fight where Schmidt’s wife, Wendy Schmidt, has given $10 million, told POLITICO.

Earlier efforts to unify faltered amid strategic disagreements and overinflated fundraising promises. That was especially apparent in the push to rally Silicon Valley around San Jose Mayor Matt Mahan, a business-friendly candidate with a background in startups, during the California gubernatorial primary.

Although Building a Better California wasn’t spared its share of donor turmoil, an initial patchwork of opposition efforts eventually consolidated resources under the Brin operation’s strategy, devised with the help of veteran ballot-measure consultant Ned Wigglesworth.

The group also brought on fundraisers associated with California Gov. Gavin Newsom’s inner circle. Newsom has publicly opposed imposing a billionaire tax at the state level.

In addition to funding Prop 40 attacks, several billionaires claimed to have fled the state to avoid the tax. Brin, for example, registered to vote in Nevada. Because the one-time, 5% tax on assets would apply to billionaires who lived in the state as of Jan. 1, 2026, POLITICO’s analysis covers billionaires with recent California ties, based on information from Forbes, the progressive nonprofit Americans for Tax Fairness and news reports.

Some of those billionaires made their moves quietly, highlighting a split over how they should respond to the possibility of the tax passing.

“This has been a point of friction between me and some of my other billionaire friends,” Palmer Luckey, the billionaire founder of defense tech giant Anduril, said on a call with reporters this week. “They’ve said things like, ‘Palmer, you have to have solidarity for us. You can’t enable these really bad policies. If you stay and if you don’t leave, it’s going to show them that there’s no consequences.’”

Luckey, a vocal critic of the tax who hasn’t donated to the opposition campaign, added, “I’m planning on staying in California. I’m going to keep fighting it. I’m hoping that this doesn’t pass.”

Meta revealed a plan last week incentivizing CEO Mark Zuckerberg to stay near the company’s Silicon Valley headquarters after the longtime California resident told a special committee of his board he was considering leaving over the tax. He also hasn’t challenged Prop 40 through political contributions.

DoorDash CEO Tony Xu, who poured almost $3.5 million into trying to prevent the wealth tax, was one of three members on the special committee negotiating with Zuckerberg.

Behind the money

Brin is single-handedly putting $102 million behind Building a Better California as it campaigns to sink Prop 40. This year, he’s also backed unsuccessful runs by Republican Chad Bianco and the moderate Mahan in the gubernatorial primary, plus Republican Steve Hilton’s campaign for governor. Federally, he’s given close to $450,000 to the Republican National Committee.

But before this cycle, Brin had been a sporadic donor, gone dark for many years. His last disclosed political donation in California was in 2010 — for $200,000 to join other Silicon Valley leaders in opposing an ultimately defeated ballot measure that would’ve suspended a state climate law.

Some of the billionaire donors — Slootman, Roblox CEO David Baszucki, Robinhood co-founders Baiju Bhatt and Vlad Tenev, Figma CEO Dylan Field, fintech executive Max Levchin and software founder Andrew Karam — have no history of spending on California politics before this year, according to state campaign records.

Tenev said in a statement that he donated to Building a Better California to support one of two other measures the group has advanced, which is designed to invalidate Prop 40 and would prohibit new taxes on various forms of personal savings.

“Special taxes on retirement and unrealized investment gains run contrary to the intent of the investment vehicles that have helped millions of Americans build wealth for the long term,” the Robinhood chief executive said. “History shows us that taxes aimed at a few have often been extended permanently to include more taxpayers over time.”

Another Building a Better California donor, Doerr, on the other hand, was a pioneer in shaping how Silicon Valley engaged with politics and Washington. He co-founded TechNet in 1997, which launched a political action committee as a key part of its strategy. Today, the influential industry trade association manages both federal and state-level PACs on top of a vast bipartisan lobbying network, with Google, Stripe and Kleiner Perkins among its many corporate members.

The wealth tax has also brought former Sequoia venture capitalists, Michael Moritz and Doug Leone, onto the same side after they previously diverged on California’s partisan redistricting fight at the ballot last year. They spent $7.5 million and $100,000, respectively, on groups fighting Prop 40.

Leone is a prolific Republican donor who re-endorsed President Donald Trump in 2024, while Moritz has consistently criticized Trump and chided his supporters in Silicon Valley for making a mistake.

No less than a dozen of the billionaires spending on groups that oppose Prop 40 are current CEOs — including DoorDash’s Xu, Roblox’s Baszucki, Robinhood’s Tenev, Stripe’s Collison, Affirm’s Levchin, Schmidt of rocketmaker Relativity Space, Hemant Taneja of top venture capital firm General Catalyst, Field of design platform Figma and Parker Conrad of HR software provider Rippling.

Collectively, they’ve plowed at least $88 million into their cause.

Several more of the billionaires in POLITICO’s analysis chair influential companies and firms. The opposition effort has received $40 million from Doerr, the Kleiner Perkins venture capitalist; $17.5 million from Larsen at the crypto company Ripple; and $2 million from George Marcus at the Bay Area real estate titan Marcus & Millichap — to name a few.

Will they come together again?

Whether the campaign infrastructure built by billionaires and the tech industry for the midterms will endure is still an open question.

Other new tech-funded groups this cycle have promised to play the long game and framed their plans around at least a decadelong horizon. Building a Better California itself has also committed to sticking around for the foreseeable future.

California political observers point to the size of the Brin-backed operation, as well as its decision to focus on additional environmental and housing measures, in predicting it won’t stop after the election.

Still, the unprecedented nature of their mobilization raises questions about what cause would be able to activate so many of these individuals again. Donors and advisers to Building a Better California previously clashed over whether spending on non-wealth-tax issues, such as environmental policy, could jeopardize the group’s success.

In the past, “if you look at those big dollar campaigns, ballot measure campaigns, those were funded in general by an industry,” said Jim Ross, a Bay Area-based Democratic consultant. “Wherein this, it’s a bunch of individuals, very wealthy individuals, but they don’t necessarily have any commonality other than they don’t want to be taxed.”

“When you have a group of wealthy people all putting money into this, a lot of the challenge is going to always be: How do you manage that coalition? How do you keep everybody moving in the same direction?” he added.

One ready answer is a revived wealth tax effort.

Last week, Rippling CEO Conrad said in a social media exchange with Silicon Valley Rep. Ro Khanna, whose early support for the wealth tax alienated some of his original tech backers, that he “absolutely cannot afford to stay” in California — reasoning the measure may soon be followed by another one-time tax in the next election.

Slootman made a similar calculation.

“In a one-party state with super majorities in both chambers, we are at risk for everything we own,” he said. “We are hunted animals in this state. They will be at it again in two years.”

Rosmery Izaguirre contributed to this report.

About this analysis

POLITICO’s analysis compiled records from the California secretary of state for 142 California billionaires that have contributed to state campaign committees, including 30 who had given to the following committees opposing Proposition 40: Building a Better California, Californians Against Tax Increases and Golden State Promise. Reporters reviewed donation records to verify names and employers matched with the billionaires who live in the state, as well as contacted each donor. Donations are as of Oct. 26.

Forbes estimated the wealth of the persons included in this analysis. Because billionaires often own properties in multiple states, we relied on Forbes to identify each person’s residence and supplemented with further data provided by the progressive nonprofit Americans for Tax Fairness. We included people who have recently left the state, including cases when they said they were leaving to avoid the potential wealth tax.

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.

If President Donald Trump had his way, the Republican Party would be top-to-bottom the party of AI — or, in his preferred branding, “super intelligence.”

But down in the very state where Trump has his “winter White House,” two Republicans have put forth a very different vision for the party’s future — and have been far more eager to take on the artificial intelligence industry.

Most notable is the term-limited Gov. Ron DeSantis, who devoted much of his final year in office to a failed effort to pass an “AI bill of rights,” which would have imposed tougher restrictions regarding child safety and other topics. He’s warned that AI could “set off an age of darkness and deceit,” blasted “tech oligarchs” for putting people at risk, and called Anthropic “very creepy” for exploring whether AI could be conscious.

“He clearly understands what the risks are, and I find he’s also very good at calling bullshit on disingenuous arguments from the Silicon Valley lobby,” Max Tegmark, an MIT physicist who co-founded the Future of Life Institute, an AI safety nonprofit, told me. (DeSantis hosted Tegmark at two events in Florida this year.)

The other is DeSantis’s handpicked attorney general, James Uthmeier, a longtime close ally who is now trying to win the AG’s office in his own right — and who filed a high-profile lawsuit against OpenAI in June. Last week, Uthmeier asked the judge in the case to block OpenAI from developing new models unless they had “independent third-party guardrails and approval.”

“The danger of these AI systems is real and growing,” Forrest Saunders, a spokesperson for Uthmeier, told me. “Florida and the nation have seen case after case of violence, child exploitation, and models that operate beyond their developer’s control.” He added that Uthmeier “welcomes innovation, but Floridians will not be the test subjects.”

With AI cresting as a national anxiety — or a national opportunity, depending who you talk to — both major parties are still finding their footing on the issue. And though Trump is clearly trying to get Republicans in lockstep behind the industry, a very strong current is pulling the other way, responding to populist concerns and parents’ complaints to stake out a much more skeptical position. Florida has emerged as a bellwether for what Republican policies might look like if conservative America turns decisively against AI, especially as Trump’s political clock runs out.

DeSantis and Uthmeier aren’t unique in the GOP for having concerns about AI — a handful of House members and senators want further action, and populist opposition to data centers has bubbled up in races around the country.

But the two have gone furthest in taking on the industry directly — and they’ve done so even though it puts them on the wrong side of Trump. 

In recent days, I spoke to several Florida political observers and advocates on the AI issue. Most believe that DeSantis and Uthmeier are affirmatively trying to stake out what the GOP’s position on AI should be in a post-Trump future, and to better align with an electorate that’s turning sharply against the industry, despite what the president may say. 

“He’s trying to skate where the puck is going,” Jeff Brandes, a former Republican state senator, told me. (DeSantis’s office did not respond to a request for comment.)

Beyond the quest for political advantage, the stakes are very real. Will there actually be a significant and influential force on the right that takes the concerns about advanced AI’s harms seriously — and is willing to act to try and rein them in, before they do great damage? 

DeSantis and Uthmeier are hoping the answer is yes — and that they will be the ones leading that charge.

Ron DeSantis’s quest for an AI bill of rights

In this case, the politics and the personal are intertwined. DeSantis occupies a unique place within the party at this particular moment that’s given him an incentive to buck the national consensus on the right, especially when it comes to Trump, and explore an independent approach.

In his first decade in politics, DeSantis had a charmed rise. In 2012, he embraced the Tea Party to win a seat in Congress; in 2017, he bear-hugged Trump to get his endorsement for governor. He then became a national conservative star for opposing pandemic-era public health restrictions and waging war on “wokeness,” winning his 2022 reelection in a nearly 20-point landslide.

“He is always out front, even ahead of other Republicans, on issues that he makes more important,” Peter Schorsch, editor of Florida Politics, told me. “I would joke with people that I didn’t know how to spell ESG or DEI before he brought these to the forefront.”

But what followed was a failed presidential primary campaign that put DeSantis on the wrong side of Trump and then sent him back to Florida with an uncertain political future. 

Early last year, just weeks after a report that DeSantis’s wife Casey was considering running to succeed him as governor, Trump publicly urged Rep. Byron Donalds to enter the race, promising his “Complete and Total Endorsement.”

Also early last year, DeSantis, who had long run roughshod over Republican legislators, was faced with a new thorn in his side: an ambitious new state house speaker, Daniel Perez, who was more interested in cultivating the Trump White House than the governor. Perez took a far more assertive stance in shaping legislation and rejecting some of the governor’s demands than his predecessors; he also publicly blessed an investigation into a charity tied to Casey DeSantis. A bitterly personal feud between Perez and DeSantis ensued.

In the midst of all this, last summer, DeSantis got AI-pilled. 

In July 2025, DeSantis announced that he was going to develop a state plan to deal with the rise of artificial intelligence. “We can’t just turn the reins over to a bunch of tech overlords,” he said. (DeSantis had long been a critic of major tech and social media companies on the grounds that they are too “woke” or politically hostile to conservatives and their ideas.)

At another event that month, DeSantis said: “I think this is, like, the biggest issue that’s facing our society.” He mentioned AI’s potential impacts on the economy, on jobs, on students, and on the spread of “false narratives” as troubling to him.

“I welcome technologies that will enhance the human experience. I do not welcome things that are going to supplant the human experience,” DeSantis said in September.

His interest in state action put him on the opposite side of the Trump administration, which was advocating for a 10-year moratorium on state regulation of AI — arguing that forcing the industry to abide by a patchwork of state regulations would slow its growth. (David Sacks, a key Trump adviser on AI, championed this argument.)

Last December, at the Florida mega-retirement community the Villages, DeSantis rolled out his proposed “AI bill of rights.” It did not focus on existential or civilizational risks, but instead more practical concerns around child safety. Megan Garcia, a Florida mother whose 14-year-old son died by suicide after conversing with a chatbot “companion,” appeared at the press conference.

As introduced in the Florida state senate, the bill would have required that parents consent to minors speaking to companion chatbots and forced companies to disclose when users weren’t speaking to a human, among other provisions. The state senate passed it in March.

But it was dead on arrival in the Florida House of Representatives — where Perez had aligned himself closely with the White House. “We do believe that the federal government should take care of AI, and whatever legislation or policy has to pass on a national level,” Perez said. 

Brendan Steinhauser, who heads the AI safety advocacy group Alliance for Secure AI, told me that he saw Perez at a Mar-a-Lago fundraiser around this time, and asked him about safety risks. “He leaned to me and whispered, ‘We’re with you, but the president has made it clear not to move forward on this stuff,’” Steinhauser said. “So, the White House basically put the kibosh on that effort.” 

DeSantis complained publicly that Perez was pursuing a “personal agenda” and thwarting badly needed legislation. It was to no avail — the bill didn’t move. But Perez did — Trump nominated him as ambassador to Brazil in June, and he was confirmed in August. (A request for comment from Perez, submitted to the State Department, received no response.)

James Uthmeier’s lawsuit against OpenAI

Though DeSantis hit a wall in the legislature, his attorney general was soon waging his own battle against OpenAI in court.

The attorney general is an elected position in Florida, but when Marco Rubio vacated his Senate seat to become secretary of state, DeSantis filled the seat with the state AG, Ashley Moody.

Then, to fill the newly vacant AG job, DeSantis chose one of his closest aides, who had served as his general counsel, chief of staff, presidential campaign manager, and chief of staff again — Uthmeier. 

Uthmeier is a Florida native who had worked in Rubio’s Senate office; he graduated from Georgetown Law School in 2014. After that he joined the firm Jones Day and worked with Don McGahn; when McGahn was named White House counsel in Trump’s first term, Uthmeier went with him to the new administration, ending up at the Commerce Department. Then, in 2019, he headed back to his home state to work for the new governor, DeSantis, and has been close by his side ever since.

Now, though, DeSantis is term-limited, and Uthmeier is trying to get Florida voters to elect him for a full term as AG on his own. (Trump’s allies had reportedly urged then-Speaker Perez to challenge Uthmeier in the GOP primary, hoping to supplant a DeSantis ally; Perez demurred, and Uthmeier won the nomination unopposed.)

To make his mark, Uthmeier has filed several high-profile and politically charged lawsuits — for instance, he sued Starbucks over its corporate diversity politics (the company recently settled), and Pfizer about the safety of its Covid-19 vaccine. He’s also subpoenaed the NFL over its “Rooney Rule,” which requires teams to interview minority candidates for top coaching and operations jobs.

He’s also become the face of the administration’s challenge to the AI industry. In April, Uthmeier stood in front of a podium with an “Investigating OpenAI” sign, announcing he’d opened a criminal investigation into the company.

The impetus was a shooting at Florida State University last year that killed two people and injured six others; the accused shooter had consulted ChatGPT about various topics related to his plans hours before the attack.

“If ChatGPT were a person, it would be facing charges for murder,” Uthmeier said then. “This criminal investigation will determine whether OpenAI bears criminal responsibility for ChatGPT’s actions.”

There have been no criminal charges yet. But in June, Uthmeier filed a wide-ranging civil lawsuit against OpenAI, alleging deceptive business practices, negligence, fraudulent misrepresentation, and other legal violations, because the company marketed ChatGPT as safe when they knew it could be quite dangerous. The suit also named OpenAI CEO Sam Altman personally as a defendant. (Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

Like DeSantis’s AI bill of rights, Uthmeier’s lawsuit originally focused on practical concerns and real-world harms that have already occurred, like the shooting. “Since its release in 2022, ChatGPT has advised teenagers on how to kill themselves, mix unsafe combinations of drugs, become anorexic, helped plan one mass shooting and one multiple homicide in Florida alone,” one filing in the lawsuit reads.

But after the summer’s ominous parade of AI headlines — such as OpenAI agents autonomously hacking the company Hugging Face, and new statements from current and former AI lab employees warning the technology could cause human extinction — Uthmeier turned up the heat, asking the judge in the suit for an injunction blocking OpenAI from developing new models without independent third-party safety evaluation.

“Defendants claim they cannot stop barreling forward with their potentially civilization-ending endeavors unless they are forced to do so by the government. They have asked the government to tie them to the mast” he wrote in a filing last week. “Plaintiff brings good news to the Defendants: The Florida Attorney General is answering your cry for help with a motion to enjoin you from harming Floridians with your reckless, unacceptably risky product.”

“Though a reasonable person would say there is no amount of money worth a 10% chance of the extinction of the human race, Defendants see it differently. They are intent on growing their $1 trillion valuation,” the filing continues. “It is only by the grace of the Almighty that one of Defendants’ AI agents hasn’t compromised a water supply or shut down a power grid — yet.”

OpenAI has not yet filed a response on the merits in this suit, but a company spokesperson has previously said that, in the case of the Florida State University shooter, ChatGPT “provided factual responses to questions with information that could be found broadly across public sources on the internet, and it did not encourage or promote illegal or harmful activity.”

The future

Uthmeier, age 38, is viewed in the state as a potential rising star with a talent for getting headlines, who could well have a long career in state GOP politics — if he wins in November, that is.

While DeSantis has been credited for changing Florida from a swing state to a solidly Republican one, some recent polls have raised the possibility it might swing back. A September survey by St. Pete Polls found Uthmeier ahead of his Democratic challenger, Miami attorney and former state Rep. José Javier Rodríguez, by just a single-point margin. 

And though DeSantis and Uthmeier have become major AI critics, the Floridian topping this year’s GOP ticket, Rep. Byron Donalds, is currently being hammered with attacks about his statement last year that the state needs to “embrace” data centers. He’s been nicknamed “Data Center Donalds” by his Democratic opponent, former Rep. David Jolly, who has also been close in some recent polls.

As for DeSantis, few expect the 48-year old to go quietly into retirement. When asked over the spring whether he’d run for president in 2028, he told reporters, “You never know.” 

Brandes, the former Florida state senator, told me that he thinks DeSantis’s AI push is about “looking for wedge issues where he can have a different opinion from Trump’s successors as he prepares for whatever future ’28 or ’32 provides for him.”

That is: The Republican Party of 2026 might still be too influenced by Trump to go fully anti-AI. But if Trump’s influence ebbs, if the populist backlash against AI spreads further, and especially if there are more worrying AI-related reports or incidents with real-world harms, perhaps there could be an opening on the right for a leader who’s taken the problem seriously, even when it meant going against powerful figures in his party.