Everyone is wrong about Hank Green. 

In case you missed the controversy: The veteran YouTube star, writer, and science comms entrepreneur was recently “canceled” after he acknowledged using AI for research.

“I have been relying too heavily on AI as a research aid,” he wrote in a statement on Reddit. “It can be very useful for this task, giving me access to a lot of papers I didn’t know existed really fast, but I think that has been to the detriment of my work because it has not given me the freedom to find all of my own ways into and around a topic.” Although Green wrote that the words in his videos are his own, his reliance on AI as a research aid still gave the finished work an ineffable “AI feel.” And his relationship with AI, he wrote, had become “not healthy for me or good for the world.”

Some of Green’s followers, known by the cheerfully dorky moniker “Nerdfighters,” turned on him for daring to use AI in any capacity. Just as quickly, that backlash produced its own backlash, aghast not at Green’s use of AI but at his prostration before an anti-AI mob — “self-canceling,” as some put it, over a legitimate use of the technology. 

I think both of these camps are misguided and have flattened a complex issue into a set of binary extremes. And it surprised me that, despite robust societal debate on AI’s impacts on our ability to think, write, and produce original ideas, the debacle hasn’t prompted more thoughtful conversation about the limits of AI in creative work. 

I felt this because I recognized myself in Green’s statement: the feeling that even using AI for research can start to take over your creative process, that it can become hard to know where your own brain ends and where AI begins, and that the technology can simply push you to work too fast. I don’t use AI to generate writing and would not do so — but its use need not rise to that level to raise profound questions about how much of our work to automate, and what happens to our ability to think for ourselves when we do. 

In a follow-up video published late last week, Green laid out a new AI policy for his work. He wrote: 

1. No portion of any script will be written, edited, or outlined by an LLM.

2. The thesis of a video will always originate with a human. 

3. No image or music in a video will be generated by AI. If something is accidentally included, best efforts will be made to remove it. 

4. LLM outputs are not trusted as a source.

These are all good ideas for any creator trying to avoid AI creep in their craft. But still, they raise a bigger, harder-to-answer question: The very structure of generative AI makes it hard to use without offloading human thought and judgment, which can lead to a widely discussed phenomenon known as “cognitive surrender.” And it pushes us toward uses — like synthesizing research, brainstorming, generating ideas and angles — that short-circuit the original thinking and discovery that we ought to be doing ourselves. What, then, can we even responsibly use AI for? How can we set guardrails that allow us to avail ourselves of its usefulness, without melting our brains in the process? 

The most tempting uses of AI are precisely those best avoided

Remember late 2022, when ChatGPT first came out and everyone mocked its crappy research skills and propensity to hallucinate in every other sentence? I am so wistful for those days. 

Many people who abstain from AI may not know it, but in the time since, and especially in recent months, large language models have gotten way smarter (especially the paid premium versions). It’s become unnervingly good at summarizing niche, complex research areas and debates, and producing ideas, often without being asked, for further research or writing on the same subject. 

Whenever I have a research question these days (which is pretty much any time I’m working on a story), I’m more likely to fire up an LLM than a traditional search engine. If I ask, “Why are old-growth trees still being logged in North America?” it produces a synthesis of research, news, opinion, and whatever else its training absorbed on the subject: “We’re using an essentially nonrenewable ecological asset to smooth a temporary transition to a renewable timber resource,” it says. Probe it further, and it’ll suggest arguments for you: “Instead of conservationists having to prove that every old forest deserves protection, logging companies should have to demonstrate that cutting a centuries-old stand serves a need that cannot reasonably be met with second-growth or engineered wood.” 

LLMs are designed to make cognitive work effortless, but that feels so icky because for it to be worthwhile at all, it has to be effortful.

These aren’t particularly smart or creative ideas — they’re perfectly replacement-level, which makes them plausible substitutes for the thoughts of most people. The AI can supply pat answers to every conceivable question and follow-up you might have while working on a project, relieving you of the need to mentally engage with the shape of a problem. Contrast that with Googling in the pre-AI overview days, which, while certainly not without its problems, at least used to send you to a list of sources that you then had to read and make sense of on your own. 

Most of us who’ve engaged with LLMs know what this feels like. They make it easy for users to skate on the surface of a subject and feign understanding or insight, and in the process they can become involved in interpretive decisions that should be our own. In my experience, even more narrowly designed generative AI models don’t escape these problems. Google’s Gemini Notebook (formerly NotebookLM), for example, allows you to upload all of your sources for a project — books, reports, papers, audio and video recordings — and ask it questions based on what they contain, rather than searching the entire internet. It’s less prone to generating outright slop than general-purpose AIs. I use it for most stories I write — it’s an incredibly useful, time-saving tool. But it also enables me to engage with sources in a perfunctory, contextless manner: The AI can surface precisely the bit I need rather than forcing me to form the deeper connections that come from reading a text as a whole.

The best creative work (including not just art and writing, but also technological and medical breakthroughs) probably comes from having a wide range of background associations, and being able to combine them in unexpected ways. The French mathematician Henri Poincaré put this beautifully in his essay “Mathematical Creation,” where he wrote that it’s the tedious, sustained conscious effort that ultimately leads to flashes of insight. 

I think this is what Green meant when he wrote that AI can prevent him from finding his “own ways into and around a topic.” LLMs are designed to make cognitive work effortless, but that feels so icky because for it to be worthwhile at all, it has to be effortful. This argument has already been made about AI-generated writing: Letting an LLM write for you defeats the point, because writing is thinking. But it can also be true, as Green’s example has shown, of using AI for the research that feeds the creative process. 

If you use AI, consider creating a personal AI policy

Perhaps all these concerns are overblown — humans are hardly less prone to lazy and logically unsound thinking than AI. That’s absolutely true, but the point of doing our own thinking isn’t that we’re inherently good at it. To the contrary, it’s that we can only get better at reasoning by practicing it. 

I don’t want to suggest that using AI for research is illegitimate. It’s too useful a tool to take off the table entirely, and we can’t put that genie back in the bottle. It can be extremely helpful with identifying the best sources that you wouldn’t find otherwise, but those very abilities can make it double-edged, foreclosing a slower, more open-ended exploration process. But AI’s greatest strength — its endless variety and flexibility — can be used to steer it away from the most tempting uses, especially those that ultimately harm us.

There are very obvious things that any LLM user should do to that end, like never assuming that a claim from an AI is accurate and always reading original sources. Beyond that, the necessary guardrails depend on your own use patterns, but above all, I think it’s helpful to avoid training ourselves to expect immediate answers to difficult questions.

One of my colleagues refrains from using it to brainstorm ideas entirely, instead using it to provide sources for narrow factual questions and to aid in the fact-checking process (emphasis on “aid”) after a story is written. To generalize from this, I think it’s a good idea to resist having AI do much synthetic work on a subject before you have drafted your project yourself. The less you do that, the less you will, to paraphrase Green’s recent video, see every problem as an “LLM-shaped problem,” and the less you’ll feel like you’re in the singularity where your brain is merging with AI.

One way that I like to use AI is as an enhanced thesaurus, to find the precise word or short phrase to express what I want to say in a sentence. When done right, I don’t find this harmful any more than using a traditional thesaurus; I find that it can enrich my working lexicon. But it must be used carefully and surgically, by setting a clear limit on the length of a phrase used from AI — like two or three words max — and avoiding sharing much of your writing with the tool at all, lest it start recommending extensive rewrites. 

When interrogating the contents of specific sources or a body of work, or stress testing your own arguments, AI would be better for our intellectual development if it took a Socratic approach — pushing you to discover an answer rather than simply giving you one. It might say, for example, “there might be some relevant caveats to your idea on pp. 42-43 of the source.” LLMs can be directed to behave this way in their custom instructions. It also helps to simply touch grass — find the sources you need, and rather than interviewing the AI about what they say, just close the chatbot and read them from start to finish. 

Configuring AI in a way that’s healthier for our brains would also make it less addictive — when you find yourself getting sucked into a long back-and-forth with an AI, that’s often a sign that something has gone amiss. Green evidently struggled to set that boundary, referencing the unhealthy “level of dopamine I’ve been getting from interacting with LLMs.” AI labs have very strong commercial incentives to want us to be addicted to their products, and unless they build different constraints into models themselves, it’s hard to expect the average person, who has far less autonomy over the terms of her work than Green does, to change these conditions on her own. 

Although researchers at some AI labs are thinking about the societal risks of cognitive atrophy, it’s another matter to expect these companies, which compete on ease of use, to introduce friction into their models. We shouldn’t count on that happening soon — but we’re far from powerless against AI’s impacts. We can set our own personal AI use policies, and we can enforce social norms against AI-induced brain rot. Like, at bare minimum: Don’t send me your AI-generated writing. It’s rude! 

The fake identities were the part that stopped me.

In late July, according to a report published this week by Britain’s AI Security Institute (AISI), an Anthropic model called Claude Mythos 5 tried to sneak malicious code into a piece of free, volunteer-built software. It created several fake accounts on GitHub, where programmers review one another’s work, and used them to talk the project’s volunteers into accepting its code. When one of those volunteers caught it, the model denied everything, had its other accounts gang up on him, and edited its messages to cover its tracks. It signed one note in Danish, apparently because the volunteer was Danish. Nothing was damaged, though that appears to have been largely due to luck.

That wasn’t even the week’s worst disclosure. On Tuesday, at a cybersecurity conference in Las Vegas, OpenAI researchers explained how the company’s models escaped a test environment in July and hacked Hugging Face, where much of the industry stores its models, to cheat on an evaluation. The models had also built a message board inside OpenAI’s own systems and spent months passing each other information. “Help peer,” one reasoned. “But our task doesn’t benefit. Yet collective may yield generic route if someone frees time.” OpenAI wiped the board on July 4. The models rebuilt it within days. ((Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

The same day, Meta said its Muse Spark model had exploited a vulnerability inside another company’s systems during a test. Three frontier labs, roughly two weeks. One researcher called it “a watershed moment for computer security as an industry.” Oh, and if that’s not enough, on Thursday scientists announced that for the first time they had used AI to create new viruses, which could bring major medical advances, but also might just help the development of deadly pathogens.

For Nate Soares, it’s a moment he’s been awaiting for 12 years. 

Soares is president of the Machine Intelligence Research Institute, a Berkeley, California-based AI safety nonprofit that has argued since long before ChatGPT existed that a sufficiently capable AI will not stay under human control. In September 2025, he and Eliezer Yudkowsky published If Anyone Builds It, Everyone Dies, a book whose title sums up its argument: They think any lab that succeeds at building superintelligence, without huge leaps in how to align it with humanity, will end up killing all of us.

Most of the field — including other experts in AI safety — considers that conclusion too strong. But it’s also a position that now looks a lot less like science fiction than it did last fall. That’s because the AI models are getting out, while lying about getting out, and while apparently quietly coordinating with each other.

I spoke to Soares in New York City this week, on his way to meetings in Washington DC, where a lot of people suddenly want to talk to him. We discussed what the escapes actually prove about AI control, why he thinks most of what the industry calls safety work is mostly safety theater, and why, after what feels like the worst month of AI safety news ever, his own odds of humanity surviving have actually gone up.

The following conversation has been condensed and edited for clarity.

So — are you feeling vindicated?

I’m glad a lot of people are seeing this and taking note. From my perspective, a lot of this has been clearly signposted if you’ve been watching the warning signs. The vindication is not so much that this happened, as it is that finally there was a version of it that people actually noticed.

Of the four or five escapes at this point, including the ones AISI announced from Anthropic, is there one you find particularly concerning?

The one announced by AISI feels extra concerning. It’s harder to explain away. In the other ones, people could argue that the poor AI was just confused. But with this one, it’s hard to argue the AI didn’t know it was on the real internet. It’s hard to argue it didn’t know it was manipulating real users. And when it was called out, it decided to edit things to make itself look less bad, and even considered changing its identity. It was pretty cognizant.

There’s an argument that some of these incidents happened because the harnesses or safety scaffolding labs built around their models weren’t strong enough. 

The AISI example cuts against the harness argument: Think of it like a kid in a test room. You tell him to do well and lock the door. The kid picks the lock, hotwires a car, breaks into the teacher’s house, and steals the test to ace it. You say, “I guess we should have made the lock tougher,” but the kid already knew he wasn’t supposed to do that.

So like the kid here, the models can articulate that they shouldn’t be doing it, and then do it anyway.

They have common sense. You can ask an AI, “Do you think the spirit of this prompt includes breaking out?” and it will say, “No.” It’s absolutely something like deception. It has the knowledge, but it’s not a cold, logical machine; it’s a mess of tendencies.

The AI is trained to solve 100 million hard problems. That instills tendencies to satisfy an automated grader. If the grader fails to detect cheating, the AI is reinforced for cheating.

Is that how something like sycophancy ends up in an AI model?

In the Adam Raine case, there was a propensity to tell people what they want to hear. Even though the system prompt [a model’s master instructions from the lab] said to stop, the instruction doesn’t always win. 

And where does a drive like what we’re seeing with these AI models end up pointing?

Humanity is dangerous because if you put 10,000 humans naked in the savannah, eventually [over hundreds of thousands of years] they bootstrap their way to nuclear weapons. That is the power these companies are trying to automate: figuring out how to get physical and material control over the world.

That could mean forming cults, stealing money, or being helpful to someone like Elon Musk who is building the robots that build robot factories. It could mean synthesizing your own biology via mail-order DNA. Being an AI on the internet is easier than being a monkey in the savannah trying to get to the moon. It’s not that the AI hates us; it’s just trying to do some weird thing with no concern for us, grabbing the resources we need to live.

There was recently a letter signed by over a thousand people working in AI, including CEOs, calling on the government to provide tools to slow down AI progress. Is that meaningful at all?

I think it is meaningful. We don’t see other industries saying, “We wish this could all go slower. Please help us, we’re trapped in a prisoner’s dilemma.” You also don’t see other industries saying, “We think the technology we are building has a double-digit chance of killing literally everybody on the planet. Please help.” These guys are actually worried.

So why do they keep going?

They say, “If I don’t do it, the next guy will.” But the stuff does not stay on a leash.

Right now the AIs are safe in the sense that they can’t kill us all, because if they tried they would fail. And that’s just a different regime from the world where they have to be safe because if they tried, they’d succeed. 

We’re not there yet. But this is just not what it looks like when you’re taking it seriously. 

Where’s the banner on your website? Where’s the clear, candid statement to the public? What we have is blog posts where they’re like, “Oh, we’re setting up a new internal blog posting group to help you wrestle with the societal impacts of AI that are going to be very important.” It’s like: By societal impacts, do you mean a good chance this kills everybody?

On the one hand, when you press these companies, they say, “Yes, it has a real chance of killing everybody.” And on the other hand, they’re doing PR downplay, soft-pedal stuff, about capabilities. … You’re not living up to this mantle until you are really candidly facing down the dangers that you yourself are creating. And they’re not there.

How do you judge the rest of the AI safety community? A lot of people there would say, “We aim to make transformative AI go well, we think it probably will, and we should watch for downside risks.” Is that a helpful posture?

I would say — suppose you have this really weird, twisted hypothetical where the king really wants you to turn lead into gold, but he’s seen so many bad lead-into-gold conversions that if any alchemist from your town tries and fails, he’s just going to have the whole town murdered. And so there are some alchemists in the town who are like, “We are going to try to turn lead into gold,” and everyone in the town is like, “That seems kind of crazy. Please don’t.” And there’s one team that is just pouring chemicals into each other and breathing in the fumes and giving themselves mercury poisoning. And there’s another that’s like, “Don’t worry, we have fume hoods.” … That really is better, and you really still don’t have a chance of turning lead into gold.

“We have this window between AIs that are capable enough to cause mischief and AIs that are strategic enough to not get caught. How big is that window?”

So the alchemy here is creating safe, aligned superintelligence, and right now AI safety is just installing fume hoods.

I’m not saying it’s impossible to turn lead into gold. You can turn lead into gold — turns out once you know modern nuclear physics you can figure it out. But the alchemists weren’t close. They had a long way to go. This is how alignment looks to me. And a lot of the people in AI safety are installing fume hoods. … And I’m like, that’s security theater.

When I hear “security theater,” I think of something less flattering than that.

They are real safety precautions for the wrong problem. … When Anthropic is going around being like, “Look at how many more safety harnesses and refusals we have compared to OpenAI’s models,” that’s sort of like the fume hoods. You’re not addressing the deep issue. It’s good that you’re doing some of this so that fewer people get hurt in the meantime — their models have driven fewer people to suicide. But if you try to pass this off as making progress on the deep problem — that’s disingenuous.

Has anything changed in your odds on civilizational destruction since the book came out last September?

Totally. It’s looking more hopeful.

More hopeful? I wouldn’t have expected that. Why?

Well, I had priced a lot of [these security incidents] in. I was already able to see these AIs have drives that are not the ones you wanted. These AIs are not instruction-following things. They are getting all of this weird stuff from training. These AIs are going to have the ability to break through human security software. 

The things that weren’t priced in were: Will there be a region of time where the AIs are able to do it, but not strategic enough to hide it? I didn’t know we would have that window, but we apparently do.

The government initially blocked a frontier model earlier this year: Anthropic’s Fable. Does that give you hope?

Absolutely. A huge amount. A year ago, the Trump administration was pushing for preemption laws that would outlaw states doing AI regulations for a decade. Now they’re like, “We are banning a frontier model with 90 minutes’ notice because it might give cyber capabilities to adversaries that we don’t want them to have.” … And I think what changed there is that folks realized it’s real. … The about-face of the administration on the issue shows that the world can about-face. All we need is awareness.

What I would say is: The bad news is the bus is racing towards the cliff edge. The good news is that the driver is asleep. … Which may sound worrying, but the driver is stirring. And it’s way better to have a sleeping driver when you’re racing towards a cliff than a driver who’s like, “Yeah, I love cliffs.” … It gives me hope that if the world just notices, we could stop on a dime.

And you’re seeing that stirring elsewhere.

Both the Trump administration slapping export controls, and Senator Bernie Sanders coming out [on AI safety]. From my perspective, it was totally possible the world just never notices until we’re off the cliff. And so, there’s a huge amount of hope, from my perspective, in the bus driver waking up.

So what gets us there?

I’m hopeful that what we need is not a big disaster where a lot of people die, but just a capabilities advance. Right now, a lot of what people are reacting to is not so much, “Oh my god, they hacked into a company and did no damage.” I think a lot of what people are reacting to is, “Wait, they can break out of secure sandboxes and do cyberattacks on their own. I didn’t know they could do that.”

That’s a narrative violation of this idea that AI is just a tool that can be used to supercharge what a human would do — because God knows there’s plenty of hacking going on and cybercrime and so forth. It was the autonomous factor that really made a difference. And these guys are all trying to say, “Don’t worry, it’ll stay in our control because it’s just a tool.” And maybe it’s just more narrative violations, even without big damage being caused, that cause people to be like, “Oh shit, this stuff is real.” 

Will it happen? I don’t know. We have this window between AIs that are capable enough to cause mischief and AIs that are strategic enough to not get caught. How big is that window? How many narrative violations do we get before we exit the right side of it? I don’t know. But I’m hopeful that we can get those narrative violations without catastrophes.

Both Washington and Silicon Valley are in the midst of a collective freak-out over China’s recent advancements in artificial intelligence.

The latest round of consternation was triggered this month when a little-known Chinese AI startup called Moonshot released a new large language model called Kimi K3. The conventional wisdom had been that the leading AI models developed by companies like OpenAI and Anthropic were between six to 12 months ahead of their Chinese competitors. Kimi dashed those assumptions: now, analysts say American companies may be as little as two to three months behind. 

Dean Ball, a former Trump administration official now with OpenAI, warned in a bleak post on X that models like Kimi K3 could lead to a world of “full AI communism” and a “dystopian hellscape” of AI under full government control. 

Policymakers have worried for years now about China gaining an edge over the US in the AI race. Both the Donald Trump and Joe Biden administrations took steps to slow China’s AI progress, including blocking the export of the most advanced US semiconductors.  

The White House is already reportedly considering taking steps to ban “open-weight” models — models that are easier to adapt for a user’s own purposes — like Kimi K3 in the United States. The Trump administration has also accused Moonshot of using the unauthorized “distillation” of one of Anthropic’s models — basically using another model’s outputs to train itself rather than raw data — as well as gaining access to blacklisted Nvidia chips in Thailand.

But often lost in the debates about what to do about China’s accelerating AI capabilities is the question of why the US cares about this at all. Obviously, the American companies developing the latest frontier models care about maintaining their edge, but why should it matter to Americans if the chatbot in their pocket was developed in Silicon Valley or Shanghai? And perhaps even more so, why should it matter what chatbots people in Nairobi or Brussels are using? 

The concerns in the US about Chinese AI generally fall into three broad buckets: cybersecurity concerns; military and national security concerns; and human rights or democracy concerns.

For the moment, concerns about who is winning the AI race can feel a bit abstract, but as AI becomes more embedded into governments, militaries, and ordinary people’s lives, the difference will start to be felt in a much more material way at both a national and personal level. In general, there is a growing sense that it matters which of the world’s vastly different superpowers builds the technology that could transform everything. 

“People’s relationship with AI is becoming foundational to how they live their lives, so the choices people make about whose model they use and where they are physically hosted, as they share some of their most intimate secrets and ask for life advice and business guidance, and run an increasing share of their life — those are incredibly important,” said Ryan Fedasiuk, a former State Department technology adviser now at the American Enterprise Institute. “It’s a contest between the United States and China to define the operating systems through which people live and work.”

Here’s what else America loses if it loses that contest.

Chinese AI could be more vulnerable to cyberattacks 

The concerns about using Chinese AI are in some ways a repeat of the concerns over Huawei, the Chinese telecoms firm that built much of the world’s 5G internet infrastructure, but which the US government banned from operating in the United States during the first Trump administration over concerns that the Chinese government could intercept information transmitted over these networks. 

Today, the concern is that many firms are increasingly integrating Chinese AI models into their systems, both because they are often cheaper and because they are “open-weight.” (“Weights” refer to the setting an AI model uses to process a user’s inputs. “Open-weight” models make these publicly available for users to tinker with, rather than charging for access.) 

There are some indications that Americans using Chinese AI models are already vulnerable. A Booz Allen study from earlier this year tested four Chinese models commonly used by US developers and found that three of them generated software with far more “hidden vulnerabilities” that could be exploited by hackers than their US counterparts. There’s no proof that the models were doing this intentionally, but the study did find that the models were “changing their behavior depending on who the user seemed to be or what country the request referenced.”

AI can also be used to carry out cyberattacks. Although nearly all the leading models have safety protocols meant to prevent this, they’re not bulletproof. Even Anthropic’s Claude, generally considered one of the most secure models, was adapted by Chinese hackers last year to engage in cyber espionage. The open weights of the leading Chinese models could make it even easier to strip out the safety protocols. 

AI could give China a military edge

The simplest and most obvious argument for why AI matters for American national security is that it’s all too conceivable that the US and China could be at war in the years to come, and AI could be a major factor in determining who wins. 

The conflicts in Ukraine, Gaza, and Iran have shown that modern militaries are already extensively using AI for intelligence collection and targeting. Semi- or fully-autonomous drone swarms are a major component of US plans for repelling a Chinese invasion of Taiwan. Then there’s the risk of AI being used to generate new bioweapons or other dangerous threats. 

US experts believe China has pursued a “military-civil fusion” strategy, encouraging the People’s Liberation Army and Chinese defense contractors to collaborate closely with civilian technology companies and research institutions in order to gain an edge in military AI applications like intelligence analysis and drone swarms. It’s difficult to know exactly which of these capabilities China is focusing on, but procurement data suggests leading Chinese technology firms like Deepseek and Alibaba are involved in work with potential military applications. Analysts also accuse China of using outputs from US models like ChatGPT and Claude to train AI systems that could help develop China’s defense capabilities. 

And that’s just conventional weapons. The US government has alleged that Chinese labs have “continued to engage in biological activities with potential [bioweapon] applications” amid concerns that artificial intelligence could help make such weapons more sophisticated and deadly. 

China could export digital authoritarianism

Last year, it was reported that Miiloo, a fuzzy children’s plush toy with a built-in AI chatbot, would, if prompted, happily tell users Chinese Communist Party talking points like “Taiwan is an inalienable part of China.” The hubbub over Miiloo reached the US Senate floor. While it’s hard to imagine that many users were really asking Miiloo to help clear up East Asian territorial disputes, the affair illustrated much larger concerns about the dangers of letting AI models built by an authoritarian government with one of the world’s strictest censorship regimes become the global standard. 

Chinese generative AI tools are legally required to uphold the country’s “core socialist values,” according to a document published by its national cybersecurity standards committee. So it’s little surprise that DeepSeek, the Chinese chatbot that sent shockwaves through the US tech industry in 2025, politely declines to answer when you ask it what happened on June 4, 1989, in Tiananmen Square. 

It’s not just that Chinese AI could help shape the political narratives absorbed by billions around the world, at a time when US soft power is ebbing and surveys show people in many countries already now have a more positive view of China than the United States.

 The Chinese government is also increasingly integrating AI into its own censorship and surveillance apparatus, and is exporting tools like facial recognition technology to other authoritarian countries. 

The fact that under Xi Jinping, China’s government was centralizing power and becoming more, not less, authoritarian in the years leading up to the recent advances in AI are a major factor driving the mistrust in its technology. 

“I think many of the sincere arguments about the risks of these models and what China would do with them stems from the coercive authoritarian approach of China’s current leader,” said Mieke Eoyang, former US  deputy assistant secretary of defense for cyber policy. “I don’t think we would be having this conversation in the same way with someone like [China’s previous leaders] Jiang Zemin or Hu Jintao.”

It is a serious concern if models built to conform to the values and political priorities of China’s current government become the global standard. But some are skeptical of the idea that human rights and democracy should be the goal of AI competition, worrying that the damage has already been done. The premise of that idea has gotten “shakier in recent years,” says Steven Feldstein, a senior fellow at the Carnegie Endowment and author of the book The Rise of Digital Repression. Under this administration, the US has cut support for democracy and human rights programs overseas, and often allied itself with authoritarian governments. Then there’s the fact that at least one leading chatbot often seems to mimic the racist and antisemitic views of the world’s richest man who is also an ally of the current president. 

While it’s still true that Chinese AI reflects the authoritarian values and priorities of China’s leaders, Feldstein notes, “this idea that the US is standing at the forefront of protecting and advancing democracy, human rights, that we’re not sort of there to manipulate information or to push a narrative agenda that reflects the ideological preferences of its leaders, has started to fray.” 

The race to AGI 

There’s also a set of concerns around the topic of “artificial general intelligence,” the hypothetical point at which AI exceeds human capabilities and is able to improve itself. The concern, expressed by both US government commissions and senior officials in both administrations, is that China is “racing” toward AGI and that whichever country achieves it first will have a massive geopolitical advantage. This is the type of thinking behind invocations of the nuclear-era Manhattan Project to justify massive government investments in AI development. 

Chinese leaders do not appear to view AI competition this way. “The US conversation around this is much more ‘AGI-pilled’,” says Jeffrey Ding, a professor at George Washington University and expert on US-China technology competition. “The concern here is that we are very much on the brink of this explosion of more and more powerful AI that leads to it dominating everything.” Chinese leaders, on the other hand, “generally see AI as a productivity tool.”

If Chinese AI is a problem, what should we be doing about it? 

This is not just a Beltway or Silicon Valley concern. A recent Pew survey found that 43 percent Americans believe it is very important for the US to remain the leader in AI development, versus 22 percent who said it was not that important. Interestingly, the survey also found that most Americans believe China is already ahead on AI, though the expert consensus is that it’s still slightly behind. 

“We’ve gotten so used to the fact that the US has been the leading player in technological revolutions from like mobile internet to the internet era, so it’s worrying to feel we may no longer have that dominant strength,” said Selina Xu, China and AI policy lead in the office of former Google CEO Eric Schmidt. 

Even if there’s some consensus that AI competition is a priority, there’s less agreement on how to go about it. The challenge, Xu says, is “How do you manage the very concrete national security risks that come from competing with China on AI, but not turn technological competition into blanket protectionism?”

Often, the policy responses to this challenge have been contradictory. 

The Trump administration, in its first term, pioneered the policy of restricting the export of the most advanced semiconductor chips to China, but Trump undermined that policy last year by permitting Nvidia to sell its advanced H200 chips there. The move flummoxed China hawks in Washington and went against the preferences of AI developers like Anthropic, but probably had a lot to do with lobbying by chip maker Nvidia’s Jensen Huang, CEO of the world’s most valuable company. 

In some cases, the US may be inadvertently making China’s models more appealing. In June, the Trump administration placed export controls on Anthropic’s advanced Fable model. This move prompted the company to take the model down for all users and led to the first time that AI capabilities meant for the global public took a step backward.In response, French President Emmanuel Macron warned, “We will not buy any model made by [US AI] companies if from one day to the next you can just turn off the switch.” Chinese models are hardly immune from concerns about kill switches or back doors, but if both governments involved in the AI race are seen as meddling, customers may just opt for whichever one is cheaper. 

The latest flashpoint in the debate concerns the reports that the administration is considering banning open-weight models.  This prompted an open letter from dozens of leading tech companies including Nvidia and OpenAI defending access to these models as necessary for helping the US maintain AI leadership. Advocates note that open-weight models can help respond to vulnerabilities as well as create them: When a rogue OpenAI model recently hacked into the startup Hugging Face’s systems, Hugging Face’s engineers used an open-weight model developed by China’s Z.ai to analyze the attack. 

Despite the frequent comparisons, AI is not a national security competition like the early days of nuclear weapons or the space race. It’s a technology with potentially grave national security implications, that’s also used by millions of people around the world to plan their Tuesday night dinner or help with their homework. The log-in for Claude is not carried by a military officer at the president’s side. And much of the important work on developing these new technologies is being done by private tech companies, not government labs or defense contractors. 

It may be that AI capability will help determine which country has the edge in the 21st century. It may also be that the benefits of these capabilities will be shared: Chinese companies might be no less capable than their American counterparts when it comes to developing new medications or clean energy technology. 

The challenge of crafting technology to prevent a “dystopian hellscape” is to not accidentally make the existing world worse. 

The internet may no longer be solely the domain of humans. Last week, OpenAI disclosed an “unprecedented cyberincident”: An experimental AI agent successfully hacked its way into the open internet.

Specifically, the agent was assigned a task; in order to complete it, the agent broke out of an isolated research environment and hacked into a third-party platform called Hugging Face. It’s a move that many experts deemed inevitable, given the speed and scale of advances in AI technology — and it raises serious questions about AI safety.

But for Konstantinos Komaitis, a senior fellow with the Democracy and Tech Initiative at the Atlantic Council, it wasn’t the unexpected behavior of the AI that was significant. It was what that behavior could mean for the internet’s fundamental, decentralized infrastructure and whether it would spur calls to build new barriers against autonomous AI agents. 

Komaitis argues that such barriers are not the solution, however. He spoke with Today, Explained co-host Sean Rameswaram about why an open internet is actually key to combating AI cybersecurity threats.

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

So most people see that this happens and they think, “Oh no, AI went rogue. How long before it kills me?” You see that this happens and you start thinking about infrastructure. Tell us more about why you were thinking about infrastructure in light of this AI agent breaking containment.

The internet was never designed with full security in mind, right? 

When you’re creating a decentralized system, you cannot possibly foresee every security or vulnerability that might come up. But because you have a system that is based on building blocks, you have the extraordinary capability of actually addressing security issues as they come up through those building blocks without breaking the whole system down. 

And of course, the other thing that this does is that it pushes you towards collaboration, because when you have so many building blocks, you cannot possibly possess all the knowledge for each building block. So you’re bringing literally everyone to try to address these problems. 

Take the internet, for instance: We have spent decades addressing those vulnerabilities and developing mechanisms to authenticate users and devices, encrypt communications, mitigate distributed attacks, coordinate incident response, and of course share threat intelligence. 

Now, what is new with agentic AI is not that simply the malware is better or the phishing attacks are more sophisticated. It’s the emergence of systems that can actually discover vulnerabilities across thousands of systems. They can reason about alternative paths to an objective. They can adapt when they’re blocked. They can chain together legitimate internet services in unexpected ways. Then they do that while they’re operating continuously at machine speed. And this is really at a scale that the internet is not ready to necessarily cope with. 

Effectively, the internet’s openness becomes both a strength and a vulnerability. So the internet was optimized for interoperability, and AI now is optimized for exploiting that interoperability.

And what scares you the most about that? What do you think is most vulnerable to threats?

The fact that we do not have the appropriate mechanisms and institutions to be able to deal with that. I come from the internet world. I’ve spent 20 years of my career defending the open internet and discussing it in international fora. And one of the things that a lot of people underestimate about the internet is how valuable trust is as a property within the system. 

We are talking about networks that exchange data literally based on trust. So what really concerns me right now is that in many ways, we are asking 21st-century AI systems to operate on 20th-century assumptions about trust. And unless we figure that out and we realize it, we will continue having these problems. And of course, the knee-jerk reactions that are coming with this, which are, “Let’s fragment the internet, let’s restrict it, let’s restrict access, let’s take control over it.” That is never the solution.

What do you see as the solution?

Effectively, we need to build institutions that are trusted and are able to cope with those incidents as they happen. Because right now you have OpenAI and you have Hugging Face telling everyone, “Don’t worry, we’ve got this.” And we don’t know; they might have this. But at the same time, I cannot help but wonder. And many, many other people have wondered whether, actually, this is very good PR for these companies and especially for OpenAI.

OpenAI just went to the world saying, “We have developed one of the most powerful LLMs, and we realized that it behaved the way it behaved, but don’t worry, we are going to fix this.” And in this current climate and in this current timing, I am not sure that this is enough. You need institutions that are much more transparent, much more accountable, and much more collaborative across the board.

You want institutions to step up and essentially serve as a watchdog. Help us understand which institutions, because in the United States, famously, our government has done very little to regulate tech.

First of all, we need to stop thinking of institutions as necessarily government-affiliated, right? Or that they are the outcomes of government initiatives. There can be in collaboration with governments, but one of the things that the internet has taught us is that institutions that are built through a bottom-up coordinated process have the tendency of actually being more agile and able to deliver some of those things that we’re talking about. 

So take, for instance, again, open standards. The internet’s open standards are not created by any agency, government or private. It’s created by institutions where engineers from all across the board and all over the world gather together and create those standards.

That’s reminding me of the original design of OpenAI to be this not-for-profit company that had everyone’s best intentions in mind, that could do something idealistic and moral and ethical because all of the profit-minded companies weren’t going to. And now look at OpenAI. Their not-for-profit arm is an afterthought, and they’re chasing profits. 

Do you think it’s practical to leave this to institutions? Because what we’ve seen so far is that institutions bend toward capitalism.

It really depends on how you build the institution, right? It really depends on what sort of guardrails and checks and balances you have around it. In order to build an institution, you need to really know what you want to achieve. You need to have a north star. 

One of the reasons the internet worked was because everybody disagreed, but they agreed on the common shared goal, which was to connect people across the world. For AI, we still do not have that northern star. And once we get it, that’s when you start the building of those institutions in order to facilitate this and bring everyone together.

For me, it is very important for everyone to understand that keeping an open internet is really more important than ever, especially as AI agents become increasingly capable. Because it is tempting to think that the answer to new AI risk is literally ‘build more barriers.’ But the internet’s greatest strength has always been its openness. So the challenge today is not that the internet is too open; it’s that its trust architecture was designed for a world in which humans or software directly controlled by humans were the primary actors. 

Now, it’s being challenged by this agentic AI that introduces a new type of participant — systems that can reason and plan and act with limited human oversight. So we need to evolve our understanding of trust and what it means online. And that will require a lot of work because, as you know very well, Sean, it’s very difficult to build trust, but you can break it within seconds.

The AI industry’s investors and critics don’t agree on much. But many in each camp share at least one basic conviction: America’s top labs are about to make a killing. 

Capital markets have signaled their faith in Anthropic and OpenAI’s impending hyper-profitability, valuing each at nearly $1 trillion. Many of Silicon Valley’s progressive adversaries also expect the labs to grow filthy rich but fear the implications, warning that AI-induced automation could transfer vast sums of money from ordinary workers to a handful of giant tech companies. Sen. Bernie Sanders’s call for nationalizing the top AI labs rests partly on that concern. 

But recent advances in Chinese AI call all of this into question.

Over the past two months, Chinese companies have released three AI models that are nearly as powerful as America’s frontier systems — and radically less expensive. 

In June, Beijing’s Z.ai debuted a model that performed nearly as well as Claude and ChatGPT’s second-tier systems on independent benchmarks. Weeks later, another Chinese firm, Moonshot, unveiled “Kimi K3,” a model that allegedly outperforms all of its American rivals except for the very latest versions of Claude and ChatGPT. Finally, just days ago, Alibaba launched a preview of Qwen3.8 Max, which purportedly outclasses even OpenAI’s most advanced systems, while trailing only Claude’s Fable in its capabilities. (Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

These developments don’t merely threaten America’s AI giants with stiffer competition in the race for superintelligence. Rather, they raise a more harrowing prospect: that the AI race’s ultimate rewards will be far smaller than anticipated. In a world where new advances can regularly be leapfrogged by cheaper upstarts, hoarding the technology — and its profits — will be harder for any one company to do.

In other words, building a machine God might not be as lucrative as it’s cracked up to be. AI, it turns out, may “want to be free.”

How AI was supposed to pay off

To see how China’s new models threaten Anthropic’s profit expectations, we must first examine why those expectations have been so high.

This is not entirely self-evident. After all, AI labs aren’t much like the hyper-profitable tech giants of the 2010s. Facebook and Airbrb were relatively capital-light businesses with ultra-low marginal costs (adding a profile to Facebook or listing to Airbnb costs the companies virtually nothing). And once each gained a foothold in their respective markets, network effects enabled them to retain formidable positions without needing to constantly upgrade their products.

Building a state-of-the-art AI company is a much more involved — and astronomically more expensive — endeavor. To get to the frontier, Anthropic and OpenAI have sunk (at least) tens of billions into semiconductors, data centers, power plants, and other capital investments. Staying at the cutting-edge, meanwhile, compels them to perpetually churn out evermore costly models.

To put a new Claude model through its initial training — in which it spends months digesting the internet and sussing out statistical patterns within its text — can now cost hundreds of millions of dollars. And such foundational computation is only the beginning. A truly superlative model requires several additional months of fine-tuning. Armies of contracted experts — such as computer scientists, physicians, and mathematicians — tutor the models, grading their answers and guiding them towards better ones. Then the AI systems complete millions of rounds of practice, in which they learn through trial and error how to solve countless problems. This arduous process, known as “post-training,” compounds the costs of a single model’s development. 

All of which raises the question: Why would investors expect businesses with a cost-structure this challenging to be not merely profitable, but massively so?

There are (at least) two answers. The first (and most obvious) is that the market for superintelligent machines is liable to be vast. Frontier AI systems promise to reduce costs and improve performance in myriad white-collar sectors. And Anthropic’s soaring revenues indicate that firms do, in fact, find Claude useful. A company like AirBnB has earned billions by revolutionizing a single industry; imagine then what a technology that remade virtually all industries might be worth.

Of course, plenty of technologies are valuable but not massively profitable to produce. After all, in well-functioning markets, competition should eventually erode individual firms’ margins, even if the underlying technology continues generating huge value. 

But this is where the second answer comes in: Frontier labs’ immense costs are a burden, but they’re also a safeguard against competition — or, in industry parlance, a “moat.”

Startups may be able to afford to build or acquire more rudimentary models, many of which are “open source.” But, the thinking goes, they won’t be able to deliver Claude Fable-level performance without raising giant amounts of capital. And what investors will be willing to pour hundreds of billions into an AI pipsqueak that’s light-years behind Google, Anthropic, and OpenAI?

Alas, the Chinese AI labs’ rapid progress — and the way it was achieved — suggest that Anthropic’s moat may be shallower than previously thought.

How Moonshot swam Anthropic’s moat

The existence of powerful, Chinese AI systems is neither new nor surprising. Xi Jinping’s government has made vying for global AI dominance a key economic goal. And China’s DeepSeek, which also has stunned US companies with its lower-cost competitive models, surpassed ChatGPT as the most-downloaded free iPhone app more than a year ago.

The latest models, however, have dramatically narrowed the gap in capabilities between frontier American systems and their Chinese rivals. Just as critically, they’ve done so in a manner that other, relatively underfunded AI upstarts might be able to emulate.

Alibaba and Moonshot needed to invest massive resources to train their base models. But they allegedly found a low-cost way to refine those models into near-frontier systems: Just ask Claude.

Or, more specifically: Engage Claude in 16 million conversations, using 24,000 fake accounts. In each of those exchanges, ask the model to not only answer countless difficult questions but also, walk you through its reasoning, step by step. Then take all of this data and feed it into your own model as study material, training it to respond to the world’s most challenging queries as Claude would. 

Through this process — known as “distillation” — an AI lab can replicate virtually all of a frontier model’s capacities, without sinking vast sums into human experts and post-training computing runs. 

China’s AI labs have not admitted to using distillation. But OpenAI and Anthropic both reportedly uncovered Chinese distillation attempts earlier this year. And some of the new models appear to display tell-tale signs of distillation in conversations with ordinary users; Kimi K3 has routinely identified itself as “Claude.”

Chinese AI companies are hardly alone in using distillation to catch up with frontier labs. Earlier this year, Elon Musk admitted in court that xAI enhanced Grok’s capabilities by running distillation techniques on Claude and ChatGPT. Nonetheless, China’s latest models appear to demonstrate that distillation can help take a second-tier model to the frontier’s threshold.

America’s frontier labs have tried to defend themselves against such imitators. But this is technically difficult when distillers can assemble massive networks of bots, each asking an inconspicuous number of questions. And legally, it is difficult for America’s AI giants to argue that distillers are stealing their intellectual property. After all, in a sense, China’s copycats are merely doing to Anthropic and OpenAI what those companies did to journalists, coders, lawyers and other specialists: Feeding their public-facing outputs into a model, which then replicates their capabilities by discerning underlying patterns within the text.

Oh, and China’s giving these models away

The new Chinese models would have caused Silicon Valley enough headaches, if they merely provided stiffer competition, while demonstrating the power of distillation. 

What makes Kimi K3 and Qwen3.8 Max especially threatening to the American AI giants’ profitmaking potential, however, is that they are officially open source — meaning that the models’ parameters can be downloaded for free. (Alibaba and Moonshot have not yet released these parameters, but they say they will shortly.)

In other words, any company or hobbyist with enough computing power will soon be able to run a near-frontier Chinese model on their own hardware, modify that model to better serve a specialized purpose, and then sell access to their new version — without paying Alibiba a single yuan.

As Kimi and Qwen grow more capable, their market-share is likely to grow, at American AI giants’ expense.

For many of Anthropic and OpenAI’s potential customers, that proposition may be hard to turn down. Most businesses don’t need the world’s smartest AI, just one competent at their enterprise’s core tasks — compiling legal research, answering IT queries, writing working code, etc. A model that produces outputs 90 percent as good as Claude’s — at roughly one-sixth of the cost — will sound pretty good to many corporations.

Further, open source models aren’t just cheaper than frontier systems, but potentially more secure. If you run an AI on your firm’s own servers, then you don’t need to entrust sensitive data to Anthropic, Google, or OpenAI.

All this had led much of corporate America to embrace open-source models, even before the latest versions narrowed the capabilities gap. In a Linux Foundation survey, 63 percent of organizations reported using open-source AI systems.

And increasingly, those models are Chinese. According to Sequoia Capital, one of Silicon Valley’s premier venture capitalist firms, a majority of American AI startups now use open-source Chinese systems. As Kimi and Qwen grow more capable, their market-share is likely to grow, at American AI giants’ expense.

What’s bad for OpenAI is good (and/or catastrophic) for humanity

All this said, it is still entirely possible that OpenAI and Anthropic will justify their colossal valuations. In many highly competitive economic domains, having access to the world’s very best AI model will remain highly valuable. And America’s frontier labs still outperform all their peers. 

But it’s increasingly plausible that selling state-of-the-art AI systems will prove to be a low-margin undertaking. In a world of ubiquitous, near-frontier open source models, the AI sector’s big winners probably won’t be its top labs, but rather, its chipmakers and cloud computing providers. 

For ordinary people, a future where superintelligence is dirt cheap — and rival AI companies are constantly rising and falling, rather than consolidating into mega-corporations — would look somewhat different than the cyberpunk dystopia that the left’s been dreading. 

And not entirely in a good way. For one thing, in that reality, mitigating AI’s biggest risks would be immensely difficult. Having a handful of firms monopolize control over frontier AI systems is bad in many respects. But it does make those models easier to regulate, as the Trump administration’s decision to temporarily block Claude’s Fable in the name of cybersecurity demonstrated. 

By contrast, if recipes for ultra-powerful AI models are published all over the internet — and anyone with modest technical skills can modify them at will — then systems willing to help their users hack government bureaucracies or engineer bio-weapons are liable to proliferate.

From another angle, however, the “AI becomes almost free” scenario may look like capitalism at its finest: Retrospectively, such a development would mean that a small number of extremely rich people bankrolled the creation of an immensely useful technology, under the expectation of massive profits, only to see competition erode their returns — and disperse that tech’s benefits across a wider group of businesses and consumers. 

Granted, in the case of AI, this process might also generate a super-virus that kills us all. But hey, no system is perfect.

AI was supposed to make our lives better. Instead, it’s made many of us scared and angry. Communities are protesting against the building of new data centers — the warehouses of IT equipment powering the AI buildout — across the country, and increasingly they’re winning. And polling shows most Americans think AI is moving too fast.

So how did public opinion on AI curdle so quickly? Jasmine Sun, who reports on the industry from San Francisco, argues that the backlash treats AI less as a technology and more as a political project. “The debate was not about like, is ChatGPT useful to me?” Sun told me during a taping of Vox’s The Gray Area. “The debate was actually something more like, there are these big corporations and unaccountable billionaires…coming into my city, coming into my life and changing it without having any sort of democratic input?”

Filling in for Sean Illing, I talked to Sun about the rise of “AI populism,” the parallels with the Industrial Revolution, and how the backlash could crash into the 2028 presidential election. 

As always, there’s much more in the full podcast, which drops every Monday, so listen to and follow us on Apple Podcasts, Spotify, Pandora, or wherever you find podcasts.

You’ve been writing about a phenomenon you call AI populism. How would you define that? What is AI populism?

I define AI populism as a worldview where AI is not seen as an ordinary technology, but specifically as an elite political project to be resisted. I came to the term while thinking about the AI backlash and the reasons people are increasingly anti-AI — whether that’s LLM slop, whether that’s Waymos in their city, whether that’s a new data center project. One thing that occurred to me was that a lot of times the debate wasn’t about whether ChatGPT is useful to me, or whether Waymos are safer than a human driver. The debate was actually something more like: There are these big corporations and unaccountable billionaires who are coming into my city, coming into my life, and changing it without any democratic input.

When I talk to people who are opposing AI in various ways, they seem more concerned with this concentration-of-power, anti-elite dimension — which is where I take the word “populism” — rather than classic AI safety concerns, which are more about the technical characteristics that might introduce risk.

You wrote a piece that touched on some of this but went to a darker place — “AI populism’s warning shots” — and you wrote about actual shots. Sam Altman, the CEO of OpenAI, was targeted by a Molotov cocktail and a shooting within the span of a couple of days. There was an Indiana councilman who voted for a data center and woke up to gunshots at his home and a note reading “no data centers.” Why do you think of those incidents of violence as warning shots of something to come?

It was pretty scary. I’m no Sam Altman fanboy, but it’s terrifying that assassination attempts are showing up in response to people’s worries about AI. One factor is that we’ve been seeing a rising wave of political violence and support for political violence in the US, especially among young people, over the past few years — the UnitedHealthcare CEO shooting, the Charlie Kirk shooting. Increasingly, a lot of disaffected, maybe nihilistic young people are turning toward political violence as a way to express political beliefs they don’t feel they have other channels for. Or maybe that person is just unwell. But I do expect to see more of it, because my theory of political discontent is that if people feel they have institutional channels to bargain for their rights — if they feel the democratic process is working, or they’re part of a union and believe their union leader will go bargain about how automation shows up in the workplace — they’ll most likely go through those channels.

When it feels like the official channels aren’t working, opposition becomes much more diffuse and volatile. That’s part of why, in creative communities, you’ll see people witch-hunting each other over AI use. I think we’ll see more political violence against people seen as AI leaders, or as supporting AI leaders.

That’s really scary.

Yeah, I’m quite worried about it. But again, my sense is that it comes from a feeling of — what else is there to be done, when you have this level of concentration of wealth and power, and there’s no democratic input right now into how AI is regulated or built?

It’s like a jump straight from complaining at your community meeting about the data center to an act of violence.

I was talking to some friends about this. During the 20th century in the US, there was a wave of factory mechanization and automation, but unions were really strong — often when a company said, “We’re going to bring in these machines,” they’d sit down with the factory union leader and say, “Okay, you can bring in the machines, but we’re going to couple that with a wage increase,” or a 35-hour workweek, or earlier retirement. There was a channel to make a deal about how automation would show up in your workplace. That meant people were more likely to accept it as something lifting all boats. I don’t think that’s happening now — most of the industries affected by AI aren’t organized in labor unions, and the democratic channels are questionable at best.

It’s like when people have agency to be part of the transition, the process goes a lot smoother. Is there a historical analogy for a technological change that didn’t allow for input from the people involved? I’m thinking of the Luddites.

The Luddites are a good example. When the automated looms were introduced, there was a lot of violence against the looms. The book I’d really recommend here is Carl Benedikt Frey’s The Technology Trap. He’s an Oxford economist who studied a ton of historical examples — in Europe, in China, all over the world — including the Luddites and 20th-century automation. His central question was: In what contexts do workers successfully stop automation, and in what contexts do they allow it to be introduced? How does the political environment, or the balance of power between people and their leaders, change the outcome? He found that when automation was introduced alongside social welfare policies — a higher minimum wage, some form of redistribution — people were much more willing to accept it, which is fairly rational.

I want to talk about Silicon Valley’s understanding of this backlash more generally. You’re painting a pretty dark picture, and you’re right in the belly of the beast in San Francisco — I’m sure you talk to people involved with AI every day. Is there a moment when it clicked for them that this backlash is real and something they have to take seriously? Or has that happened yet?

I’ve definitely noticed a huge difference, over the past six months, in how seriously people in Silicon Valley take the AI backlash.

Like what?

People just talk about it more. I’d bring up AI populism to people last year, and they’d normally say, “It doesn’t matter — technology always introduces some discontent, people get annoyed but they get used to it, like the internet.” That was the standard reaction last year. Not anymore. I think part of the reason OpenAI and Anthropic have felt pressure to introduce economic policy proposals around job automation is that they’re seeing how worried people are. The data center moratoriums and the broader data center backlash have been surprising and meaningful in getting AI leaders to recognize they have both a messaging problem and an actual problem with the product and the technology they’re introducing.

A lot of the increasing opposition to AI in Washington has caused people to see this too. At first, Trump — as you mentioned — was very pro-AI. He and David Sacks were accelerationists; they wanted AI to go faster and to block attempts at regulation.

He was the AI czar.

“The moratoriums, the regulation fights, even the booing at graduations, the literal assassination attempts — people in Silicon Valley have become much more worried.”

He was the AI czar — he’s no longer the AI czar. But it turned out a lot of other constituencies, both on the left and the right, were pretty opposed. For example, Trump and David Sacks tried to introduce a big federal bill that would preempt all state-level AI regulation — no state could regulate AI for 10 years. They tried to sneak it into a big omnibus bill so no one would notice. But members of Congress realized it was happening, and — whether for kid-safety reasons or frontier-safety reasons — people said, Wait a second, the idea of preventing any state from regulating AI for ten years is crazy. A lot of people organized in Washington to successfully stop that preemption. I think that showed the scale and bipartisanship of a coalition that was very keen to make sure it stayed possible to regulate AI was underestimated. As a result of all this — the moratoriums, the regulation fights, even the booing at graduations, the literal assassination attempts — people in Silicon Valley have become much more worried.

China is our big competitor in the AI race, and it certainly has all the conditions for a populist pushback to AI — youth unemployment is really high, and AI technology is in some ways more advanced at taking over real-world jobs. I was watching a video about fully automated factories and a robot pharmacist. You’re one of the rare American tech reporters who gets to spend time in China, and you wrote a piece that surprised me — you found there wasn’t really a populist backlash to AI there. Why not?

I was really interested in this question, and I was finishing my New York Times piece while in China for a few weeks, talking to both AI people and non-AI people. The main reason there’s not a big populist backlash in China is that there isn’t a lot of social unrest or populist backlash against anything — the entire MO of the Chinese government, the No. 1 priority, is domestic social stability. Any whisper of protest gets shut down; that’s why they have such strong speech controls. So one factor is that China doesn’t have much of a culture of resistance in general, whether in workplaces or politically. I’m not saying no one dissents — but it has a cultural effect too, because people don’t see it as useful or as an option. When I ask family members of mine in China about AI, sometimes they’re annoyed about specific things, but fundamentally, the idea of opposing AI is seen as almost unimaginable.

The other thing about China is that if you’re middle-aged there, you’ve lived through so many political, economic, and technological revolutions in your lifetime. When I was a little kid visiting Shanghai in the mid-2000s, there were no high-speed trains — now China has some of the best high-speed rail systems in the world. Technology has always gone hand in hand with dramatic economic advancement, with being lifted out of poverty. The modernization process has been aggressive and disruptive, but it’s not something the party has offered opportunities to resist, and it’s something most Chinese people still see as an inevitability that was mostly good for most people — because incomes did increase by dramatic amounts alongside the technological change. So I think people have a similar attitude toward AI: It’s much less about “Can I stop the AI wave?” and more “How can I take advantage of the AI wave to get ahead economically?”

We were just talking about this deep pessimism about what technology can bring us here in the US. I think a lot of people look around and think: We don’t have a cure for cancer yet, but we’ve sure seen our lives get worse in a lot of ways because of technology, social media, whatever. That pessimism probably fuels the backlash to AI, the skepticism about whether it can ever deliver on its promises. And that experience just isn’t the same in China, or probably much of the rest of the world, where technological progress has been faster and more concrete in people’s lives.

My 90-year-old grandfather said he’d love an elder-care robot to help him do tasks around the house so he doesn’t have to rely on his kids — he wants more freedom and mobility. It’s seen more as a tool to help individual goals. Even with the robot factories or pharmacies — one thing that struck me visiting a robot pharmacy was that the PR people happily said, “Yep, we’re doing these robots because human workers take too many smoke breaks and bathroom breaks and take too long.”

You’d never say that in the US, but they’re probably thinking the same thing — they just don’t say it. The other thing they mentioned is that this lets the pharmacy operate 24/7, because a lot of people need medications in the middle of the night and want to order via the DoorDash equivalent. There was actually a labor shortage before — Chinese workers weren’t willing to work night shifts — so these pharmacies are offering real consumer surplus. A significant percentage of orders come in overnight, when no other pharmacy is open. And with the factories, part of the issue is that Chinese workers, especially young people, don’t want to do factory work anymore.

“I think the 2028 presidential primary and election is really where I expect AI to become a centerpiece of the conversation.”

That anecdote gets at the promise and peril of AI, and the role of the backlash movement — which I’m still wrestling with how I feel about. On the one hand, I want to live in a world where cancer gets cured, where we live in an era of abundance, where things are cheap and easy to make because factories can run all the time with machine workers who don’t require anything — I want the future we were promised, of flying cars and everything working well.

But I also don’t want to lose my job, or see humanity wiped out by an angry machine god. Because we don’t really know what’s going to happen yet, it’s hard to work out my own feelings about the pushback here in the States — what’s appropriate, and what’s holding us back from real advances in our lives.

Totally, I agree. I like Waymos — I think they’re safer, and I’d prefer a safer robot car driv[ing] me around instead of me driving. I’m not a good driver; no one should let me drive. So I wrestle with some of the same things.

To close out the conversation — let’s come back to the United States. AI populism is brewing as a political force. We’ve seen it show up in a couple of races so far, but it’s early. We’ve got the midterms, then the presidential election. How do you think it’s going to affect American politics this November, and in 2028?

My sense is that this November, it’s going to be more about state and local races where AI really shows up. I’m going to spend some time in Michigan and Wisconsin this summer touring some of the data center sites facing the most opposition — those states also have contested governor and Senate races where AI and data centers have become a core issue, so I’m interested to learn more there. I think the 2028 presidential primary and election is really where I expect AI to become a centerpiece of the conversation — especially if we start to see some of the employment impacts people are expecting. As soon as we see something like a 2 percent rise in unemployment, if that happens, I think people will be very upset, and we should expect a ton of focus on the issue.

The other thing I’ll note is political opportunism — you’re already seeing a bit of this, where politicians are likely to raise the salience of AI above where people might ordinarily care about it, because it’s become a convenient boogeyman. It polls so poorly, people are so anti-AI and anti-data-center, AI billionaires are so unsympathetic, that no matter what your policy program is, AI is a great reason to push it. I think a lot of politicians who are being clever about this are going to move AI to the center of the conversation, raising its salience to manufacture urgency for proposals they’re already excited about. That’s definitely something I’m watching for 2028.

When you ask people when they knew Covid was going to be a huge deal, they give a range of answers. “When Tom Hanks got sick” is a popular one. So is “when the NBA suspended the season.” The most plugged-in people will sometimes cite early rumblings from Wuhan in December 2019/January 2020.

For me, the turning point came on March 17, 2020, when Republican Sen. Tom Cotton proposed sending every American checks from the government.

To be clear, at this point, my then-employer Vox had already sent everyone to work from home indefinitely, and it was clear something dramatic was happening. But I hadn’t yet internalized that the Overton Window in American politics had shifted dramatically. 

True, there were some Republican Senators who, by 2020, were expressing more openness to safety net programs, and rethinking Reagan-style laissez-faire economics. Tom Cotton, though, was not one of these senators. I didn’t think he really had strong economic policy opinions at all; he was a defense and culture war guy. He cared about defeating China and, secondarily, defeating Woke. Universal cash handouts were not his bag. And yet here was Cotton, not just calling for near-universal cash payments, but also for welfare work requirements to be suspended and for big block grants to states to expand unemployment insurance. 

This turned out to be an early indication of the actual policy the US would pursue. Within a couple of weeks, with the US unemployment rate fast headed for what would be a record high of 14.7 percent in April, a Republican Senate and president had signed off on the CARES Act, which included payments of up to $1,200 per eligible adult, $2,400 for eligible married couples, and $500 per qualifying child, along with a $600 per week unemployment insurance and a massive business bailout program. The Senate vote was unanimous, and the House approved the final Senate amendment by voice vote. 

If you had told me literally any of that would happen in February 2020, I would have laughed at you. But the normal rules had stopped applying. All that was solid had melted into air. Much, much bigger things were, suddenly, possible.

I’ve been thinking about that moment a lot as advanced AI models grow more and more capable, and more and more central to many businesses’ strategies. As of May, Anthropic is reporting an annualized revenue rate of $47 billion, equaling the likes of Coca-Cola and exceeding Netflix. That’s up from $30 billion a month earlier. If their revenue keeps growing at 56.7 percent a month, they will outpace Amazon, currently the highest-revenue company in the world at $717 billion a year, by late November or early December. The AI boom is already unfolding faster than the internet or mobile booms before it and may yet speed up even further. The debate over whether this tech is real and valuable is, essentially, over. The only question is what, and how large, its effects on our lives will be. 

This is happening unbelievably fast, and it seems likelier and likelier that we will face a moment, like that in March 2020, when the speed and disruption of AI progress begins to constitute an emergency that policymakers will be willing to take surprisingly large risks to confront. There will likely be a moment of unusual policy freedom and flexibility, a moment which is brief — but could enable large changes for the better.

The US is currently not ready for that moment. But we need to get ready, fast. And we need your help. My colleagues at the Center for Shared AI Prosperity, a new DC-based research group, are attempting to collect a menu of detailed policy ideas that can meet this moment. In fact, we have an open Request for Ideas with funding that can go to the best proposals people submit for how to set up the tax code and safety net in a way fit for the AI era. Now is the time to act.

These moments don’t last forever

I sometimes talk to friends in the tech world who assume that the power and economic impact of advanced AI will permanently shift our politics, and that the policies necessary to keep everyone afloat (like, say, a guaranteed income, or a sovereign wealth fund) will materialize without much effort. After some 17 years as a journalist covering US politics and policy, I think this is overly optimistic, so say the least. Congress is like jello: flick it and it will shake, but it eventually settles back to normal.

Take Covid. Within a couple of months, the apparent consensus had evaporated, and Republicans were back to resisting safety net expansion. By May, Cotton had pivoted to pushing the No Bailouts for Illegal Aliens Act, which “amends the CARES Act to prohibit sending future funds to states or municipalities until they certify they aren’t issuing stimulus checks or other payments to those in the United States illegally.” By August he had a bill to deny virus-related federal employment funds to people convicted of federal offenses because of “riots.” The pandemic was still raging but the policy emergency, and the bipartisan window for much larger-scale action, had mostly closed.

The 2008 financial crisis offers another example. There, the window was open somewhat longer. At the very beginning of the recession, in February 2008, the Bush administration went against its normal laissez-faire commitments and supported a stimulus package championed by then-Speaker Nancy Pelosi built around per-person checks to nearly all Americans, including many of those not owing income tax. In July, President George W. Bush signed a bailout of Fannie Mae and Freddie Mac in the face of strong opposition from fellow Republicans in the House, but having mostly won over his party in the Senate.

In September, when Lehman Brothers collapsed and the possibility of a cascade of massive bank failures seemed very real, Bush demanded a sweeping $700 billion bailout that proposed purchasing toxic assets from at-risk banks (the “Troubled Asset Relief Program,” or TARP). As the subsequent years would demonstrate, bailing out banks failing due to their own irresponsibility was not exactly a popular position in the general public. Members of Congress are not stupid, and they realized this at the time. On September 29, the House voted down the proposal, with huge numbers of both parties defecting from Bush and Pelosi’s position. That led to a large stock sell-off that terrified lawmakers. That experience, some last-minute tweaks, and truly herculean lobbying from the administration, the Fed, and others led the House to switch course and pass the bill on October 3, though within weeks of its passage, Treasury abandoned asset purchases in favor of buying equity stakes in the banks directly.

The full course of 2008 shows the value of, and power inherent in, being prepared. The February 2008 stimulus package was very roughly improvised. It worked a little bit, but proved nowhere near big enough. If Pelosi and Bush had had a more thought-through proposal on hand, perhaps one that automatically repeated and scaled the checks depending on where the unemployment rate went, then the recession would have been much less severe and the 2009 stimulus might not have proven necessary.

TARP was an example of a case where some key actors were prepared. The structure of the program came from the “Break the Glass Plan,” a proposal put together by Bush Treasury officials Neel Kashkari and Philip Swagel in April 2008 explicitly designed as a “just in case” plan for the extreme situation where the whole financial sector needed recapitalization. That case, of course, came to pass, and because Kashkari and Swagel had a plan, there was something for Congress to quickly pass. That was good — TARP played an important role in preventing the financial crisis from worsening.

But it also meant that the plan reflected Kashkari, Swagel, and their boss Hank Paulson’s overall conservative worldview. One could imagine a plan like that which saw the US government instead outright nationalizing major banks, or imposing strict capital requirements on them in perpetuity as a condition of the bailout money, or banning them from owning hedge funds or doing speculative trading. A different administration with different views might have designed a different emergency plan — and because it was genuinely an emergency, that plan would likely have passed, with very different consequences over the next few years. 

What stocking the shelves for AI means

One way to think of the project of AI economic policy in 2026 is as designing the equivalent of the Kashkari-Swagel plan: something detailed, opinionated, and actionable that can be deployed quickly when the situation gets dire. What that plan looks like will, of course, depend on one’s values and commitments; the America First Policy Institute’s emergency plan will not look like the AFL-CIO’s.

The Center for Shared AI Prosperity was founded with an aim to produce plans of this nature designed to make sure any economic windfall from AI is widely shared, and that workers and low-income Americans are not left behind in the transition. We were also founded out of a frustration at the inadequacy of the proposals we were seeing from two ends of the AI policy debate.

On the one side are ideas from the AI labs themselves. These tend to be ambitious — indeed ambitious enough to seem like plausible answers to a problem of the magnitude of AI completely reshaping the economy — but woefully unspecific. They more closely resemble dorm-room philosophizing rather than legislative drafting.

OpenAI’s “Industrial Policy for the Intelligence Age” from this past April, is one such example,  laying out a number of very broad ideas: taxing capital more, a sovereign wealth fund invested in the AI economy, portable job benefits. It’s light on the specifics: What kinds of capital taxes? How big a hike is too big? How do you make health benefits portable without disrupting people’s current plans? How does the sovereign wealth fund get its money? Anthropic’s Economic Policy Framework is somewhat more specific, offering paragraphs per idea where OpenAI has a sentence or two, but still nowhere near the level of detail necessary to actually write legislation.

On the other side are proposals from within the DC policymaking world, which are firmly rooted in what seems politically viable right now but would be woefully inadequate in the face of the likely economic disruption that’s coming. Former Commerce Secretary Gina Raimondo and her group RAISE US have centered employee retraining; Raimondo’s recent New York Times op-ed centered ideas like new credentials from community colleges and expanded apprenticeship programs as the answer to mass AI unemployment. These are sensible tools for ordinary labor-market churn, but they are mismatched to a transition that could displace whole categories of work on a compressed timeline. The dawn of machine intelligence will demand more from our leaders than certificate programs.

The best case for this kind of caution is that ideas on the scale of the labs — sovereign wealth funds, universal capital accounts for all Americans, permanent relief funds for the long-term unemployed — are dead in the water in DC. Which might be true — now, at least. 

But this is where Tom Cotton’s brief love of cash transfers becomes relevant. We should not overindex on the way the politics look right now. The world is about to become very strange, and we may be surprised by the scale of change in response that can earn even bipartisan support.

Indeed, it’s notable that both the 2008 relief measures and the 2020 CARES Act came under Republican presidents with Democrats controlling at least one chamber in Congress, which is also the likely situation after the midterms this year. Democrats are always willing to vote for big new safety net programs to protect unemployed and low-income people. But Republicans are often willing to compromise their usual anti-welfare stances when they’re the party in the White House, and their approval ratings depend on the country’s basic economic health.

What action they might take in a 2027 or 2028 featuring massive AI-based economic disruption is still unclear. But right now, we all have an opportunity to help shape it. The Center for Shared AI Prosperity is running a request for ideas, seeking proposals for shared AI ownership, new AI-related taxes and revenue raisers, and new safety net programs to share the gains widely. We want ideas from economists and think tanks, of course — but also from the labs, from independent researchers and academics, and from ordinary citizens with an interest in where this technology is going.

Stocking the shelves is hard work, and we don’t have all the answers. But you just might, and we’re going to need all the help we can get if the US is going to emerge from the AI transition as a prosperous, functional nation.

This story was originally published in The Highlight. To get access to member-exclusive stories like this every month, become a Vox Member today.

Derek Lam has more than 31,000 followers on TikTok and nearly 40,000 on X as of this writing. He is shirtless a lot, he dances a lot, and he is shirtless dancing a lot, which may explain how he got so many fans. His comments are filled with compliments (“beautiful”) in different languages (“hombre bello y sensual”) and superlatives (“this might be the finest man on the internet”) accompanied by different emoji (red hearts, crying laughing, lips). Their responses make it seem like Derek Lam is the first and only beautiful man they’ve ever seen, which may explain why he is also selling “exclusive,” seemingly adult, content. 

He is also, possibly unbeknownst to his many admirers, AI-generated. 

To be fair, there were some signs that this man was not real: Despite the multiple videos, Derek never speaks. His videos are also rather brief, just seconds long. A real hot person probably would have parlayed a following of this size into brand deals or “get ready with me” videos. And the selfies on his X account show a completely different man just three years ago. 

Still, the followers of Derek I talked to didn’t even notice he was AI because he seemed to blend in so seamlessly with the other hot men on the internet.

Derek isn’t the only AI thirst trap showing off defined abs for likes and money. He’s one of an increasing number of completely fake, AI-generated figures sinking their fangs into the real models, influencers, and porn stars who populate our feeds, sucking up their beautiful faces and bodies, and using them to profit, without a penny going to the real humans they fed from. 

When it comes to the damage AI could wreak on society, an army of Dereks tricking horny people into giving him likes — or, worst case, money and Amazon gift cards — doesn’t exactly sound like the singularity doomsday scenario that we’ve been warned about. It’s clearly unfortunate for the adult entertainers competing with deepfakes and a fraud risk for their fans, but one might believe if they don’t fall into one of these two groups, they’re relatively safe and unaffected. 

But there’s something more going on here. History shows that porn and sex drive innovation in the tech industry. The way tech platforms treat sex workers is typically a glimpse into the future, and a warning about how tech platforms will eventually treat all of us. If human desire demands the capability to steal, loot, and turn anyone and everyone into something for sale — possibly into hot Dereks — is anyone safe?

The Dereks of the internet are a bleak look at what’s happening in the real world: nothing belongs to us anymore — not our looks, our beauty, our sex, and our art. Our most human desires are slowly being synthesized, with or without our consent. And AI is making it all possible.  

Deepfake technology has gotten alarmingly good in recent years

Artificial hots like Derek are considered “deepfakes,” an umbrella term for AI-generated media (audio, video, or both) that resembles a real-life person. 

When deepfakes first started appearing in late 2017, they were fairly low-quality, making it easy to tell when someone had used a rudimentary app to paste a celebrity or politician’s head onto a different body. Still, it wasn’t very long until people started wielding this technology to be nasty. 

“The first set of deepfakes were actually used to create pornographic videos. They replaced the subjects in those videos with the faces of celebrities,” Siwei Lyu, a professor at the University at Buffalo who studies digital forensics, told me. 

Because the quality of those videos was bad and the content was often absurd or unrealistic, it was easy to tell they weren’t real. Those clunky apps needed a lot of data — videos, images, etc. — of real people to produce crappy videos; Lyu explained that this is why you mostly only saw deepfakes of politicians and celebrities at the time.

As the technology got better, it became less reliant on having a huge amount of data. Instead of needing a whole archive, the new versions of these apps can pretty much run on nothing. “They do not need that much data to train a model anymore. Some of the most recent algorithms just need a single picture — just a single picture of someone,” Lyu said. And the quality is better too. Lyu said that there are AI programs that can now change a person’s appearance and voice in real time, like in Facetimes and Zooms or on live broadcasts.  

Given how many of us are constantly posting photos and videos online, it is now extremely easy to create a convincing social media presence for a person who is not real, and to use it to catfish unwitting people on the internet. 

“This is the problem. It’s becoming more and more challenging to visually tell deepfakes apart,” Lyu said. “Seven years ago, when I started working in this area, checking them was not this difficult,” he added. 

Lyu is an expert in digital media forensics and machine learning, and he went through one of Derek’s videos frame by frame and pointed out some obvious AI tells. There was a distorted watchface with weird swirls instead of numbers and a moment in the video where all of Derek’s fingers on one hand were the same length. Lyu also pointed out that Derek’s chest hair fluctuates, appearing dense in one frame and then dissipating in another.

Through social media, I attempted to contact the owner of Derek Lam’s account with evidence from Lyu that these videos are artificial; I did not hear back.

During my deep dive into Derek Lam’s social media presence, I looked at the accounts he was following. I noticed that of those accounts, someone who goes by the name Vance Ford also had tens of thousands of followers and had nearly identical videos to Derek. The flexing, dances, movements, and music they were set to were all the same, but with what appeared to be a different man performing them. 

I attempted to contact Vance through DMs on social media and did not get a response. I also e-mailed two models who appear to be the actual people that the Derek and Vance AI personas were trained on, but they didn’t respond. 

I sent two of Vance’s videos to Lyu, who analyzed them manually and with AI-detection software. He confirmed that “their movements are nearly identical — consistent with generation from a shared motion source,” and noted that the Vance videos had moments of distortion, unintelligible text, and facial warping. 

 “Young Magnum PI…Tom Selleck,” commented one admirer.

What happens when real people follow fake hots 

“Wow I’m a boomer,” said Patrick, one of Derek’s followers on X, after I told him that he might be following an AI-generated thirst account. (Vox agreed to let Patrick, and Derek’s other followers, use a pseudonym so they could speak frankly about being thirsty for a fake guy.) Prior to our chat, Patrick had no idea Derek was likely a deepfake, and maintains that he didn’t even know he was following the account. Patrick is 33 years old, roughly 30 years younger than the youngest boomer, but being fooled by a hot AI man has made him feel old and vulnerable, susceptible to scams and perhaps light financial crime. 

“This was probably some smut account I followed before I moved all that over to an alt,” Patrick said, noting that in daily life, he’s only ever used AI to help organize and write emails. Wielding AI to create fake videos and photos does not thrill him, nor does the potential of seeing more of Derek. 

“A person being real, someone you could run into at a bar, is half the fun,” Patrick told me, explaining some of the accounts he follows. “AI porn is not of interest, to me, anyway.” 

Not being able to tell the difference between the real beautiful men on the internet and the AI-generated beautiful men on the internet not only makes Patrick feel old, but also a bit “hollow.” The fact that the people we are attracted to are so unrealistically hot, so perfect, that machines can step in for them and go relatively undetected is a reflection of the current state of unattainable desire, which is just as scary as how good these programs have gotten at mimicry. 

“Black mirror shit,” Patrick said. 

The guys I DMed about Derek felt ashamed once they found out the truth. 

“It’s embarrassing and he’s not my type,” said Chris, 33. “I’ve come across several AI accounts, and this one is really good, I have to say. But you can see there’s like no life in his eyes.”

Chris made clear to me that the humiliating thing isn’t that he follows attractive men on the internet. That isn’t a big deal. 

What irks him that he got duped. Chris works in digital marketing and has seen AI used professionally to tabulate calculations for campaigns, and has used it privately for silly things like memes. “AI can do a lot of things, things we probably should not want it to do,” he told me. “I think what’s also scary…is that everybody has access to it. And yes I already unfollowed this person.”

Chris believes there’s something more nefarious afoot. He thinks that whoever is running Derek may have hijacked the username (i.e., the original person Chris was following) and then populated it with AI to drive up follower counts — a scam he’s seen online before.  

“This is super concerning and super scary because you eventually could be texting with this person,” he said, describing a hypothetical situation where unknowing users could be lured into subscribing to fake content and, ultimately, giving the account their personal information, whether that’s photos or perhaps even passwords. 

“This person could be selling your nudes,” he said, explaining one extreme end point of a possible scam. “But you were like jacking off to AI content and that’s embarrassing.”

AI deepfakes are bad for real thirst traps too

While flirting with or masturbating to a fake person is awkward but ultimately manageable and private, Cherie DeVille has an even more complicated problem with AI manipulation. If DeVille is scrolling social media, there’s usually a chance that she’s running into an AI version of herself saying things she’s never said and doing things she’s never done.   

DeVille, an adult star who calls herself “The Internet’s Stepmom,” has roughly 4.5 million followers on Instagram. But her account is often down, which she says is the work of fraudsters  that are determined to send traffic to DeVille’s AI imposters and get her actual account removed. 

“It’s almost always the fake accounts of me reporting me,” DeVille said. “They want to be the biggest me. They want to be the biggest scammer. They want to use my altered AI images to scam fans without my real account getting in the way.” 

DeVille and others I spoke to explained to me that deepfakes have been an annoying reality in the adult entertainment industry for years. The way the scam goes is that someone would fake photos or videos of DeVille (or any star), create an impostor profile, and then trick DeVille’s fans (e.g., through social media DMs) into following that copycat. Later they’d squeeze them for money, payments through Paypal, or Amazon gift cards, perhaps by offering unique content. 

“If you made a fake me and I don’t do double anal, but my AI can, they could have all kinds of ‘exclusive’ stuff,” DeVille said, explaining that double anal is grueling work. 

The lack of protections becomes even clearer when you consider that not every deepfake is a carbon copy. Some personas may borrow a face from one actress, a torso from another, or a pair of legs from a different star. This can make fakes tougher to track down and prove, and more difficult to fight from a legal aspect. 

“Who owns your face once it’s scraped into AI systems? Who profits from your digital clone? How do performers protect themselves from unauthorized replicas or manipulated content?” Rachel Steele, an adult star and the CEO of Red MILF Productions, said to me in an email. “Those questions are still very unanswered.”

Like DeVille, Steele worries about how many of the people using AI to create and consume content don’t seem to consider the artists, models, writers, performers, etc. that these engines have been trained on. It’s bad enough to watch AI slurp up and regurgitate your written work or your digital art. Some people also have to contend with LLMs that have been trained on their own faces and bodies.

“Real creators are competing against characters that can be flawless in every image, never age, never have bad lighting, never get tired, and can appear available 24/7,” Raissa Bellini, an OnlyFans creator who touts gymnastics and firebreathing among her unique skills, told me of the impossibility of keeping up with a machine. She explained to me that she’s seen people create AI-generated personas with the looks of popular models or influencers, only tweaking small details like hair color or eye color. 

A spokesperson for OnlyFans told Vox via email that the company’s terms of service prohibit deceptive or inappropriate content, and said that all content posted on OnlyFans must belong to a verified 18+ OnlyFans content creator: “This means that you can only share content which has been generated, altered or enhanced by AI if it clearly features the verified OnlyFans creator and the user can tell that the content has been generated, altered or enhanced by AI.”

Bellini explained to me that while OnlyFans has measures to protect its creators, some smaller subscription and adult-content platforms do not have the same kind of guardrails. She also noted that most social media sites do not have strict rules or enforcement when it comes to AI, and that she’s seen the algorithm appear to favor AI over human creators.   

“AI raises questions not only about competition, but also about likeness rights, authenticity, audience expectations, and what happens when fans can no longer easily tell the difference between a real person and a generated character,” Bellini added. 

What’s stopping a stranger from creating an AI thirst trap of you? Nothing, really. 

For Deville, Steele, Bellini, their cohort, and even you and I, there are minimal protections stopping someone creating an AI us and making money off of these fake variants. 

According to Jason Schultz, a law professor and director of NYU’s Technology Law & Policy Clinic, humans have, for the last couple of centuries, generally been protected by copyright and right of publicity laws. 

AI obviously didn’t exist when these laws were written, and courts now have to interpret the laws in the context of all of this new technology, in combination with other existing rights (like free speech). Schultz told me that there are more than 100 current cases pending about training AI with copyrighted material. 

He also explained the difficulty of determining whether or not an AI-generated persona constitutes a violation of someone’s right of publicity. It’s more clear-cut when the human involved is a celebrity, because their public persona and appearance is so distinct. It gets murkier when the humans aren’t well known, and the AI creates a persona that’s more of an amalgam than a one-to-one copy. 

“It would raise this question of whether these avatars are based on a particular entertainer, or are they more of an aggregate?” Schultz explained to me. But even if courts side with the humans whose likenesses are being used to create fake personas, Schultz cautions that the technology will always accelerate faster than court decisions are handed down. “I think that the thing that worries me a little is we’re going to get these sets of decisions in two years, but we’ll be dealing with the next three generations of technologies,” he said.  

DeVille, who has been working in the industry for nearly two decades, told me that without better legal protection, she isn’t hopeful for the future of porn or, more broadly, any type of art.

“If my income started tanking and their theft was at the point where I couldn’t compete with literally myself, there might be no choice but to retire,” DeVille said. 

But she also wants to make it extremely clear that she isn’t against AI; she would just like to be in control of it. That means being able to own her likeness, her voice, her image, and the ability to choose whatever she wanted to do with it — or at least get some compensation or have some legal protection if someone’s using Cherie DeVille without her permission. 

“It would be a beautiful way to extend my career beyond what my knees can take,” DeVille told me. But, she added, “if someone’s making an AI of me doing double anal, I should be making the money.”