Writing something is a bit like polishing rocks. You start with an ugly hunk of something, a phrase or an idea you’ve tripped over. It rattles against the hard edges of your brain until it gets polished and smooth. Moving the rock through finer and finer grits is time-consuming, strenuous and not always rewarding. Sometimes, the lump becomes a slightly smaller and shinier lump, marginally less ugly than it was on the ground. But like exercise and tough conversations, the process is the point. You feel better for having gone through the ritual yourself.

Boy George didn’t get the memo. 

The former Culture Club frontman, whose voice adorned New Wave pop hits like “Karma Chameleon” and “Do You Really Want to Hurt Me” in the 1980s, had artificial intelligence spew out an unfortunate reggae song last week that turned out to be a statement of support for Israel’s ongoing war in Gaza. “You say genocide, I say war,” the song, titled “We Will Dance Again,” starts, and the lyrics seem to revel in the ugliness of the ongoing violence in Gaza. The tens of thousands of Palestinian deaths, the song claims, are “what the military’s for.” 

Obvious AI tells throughout this track make clear that Boy George hasn’t just gotten lazy, he’s also lost the quality of discernment. The song’s meter is odd, and its lines are overstuffed. There are no interesting choices, no glimpses of an artist’s vision. It’s a hollow provocation over a royalty-free riddim. 

Since Hamas militants attacked Israeli military installations and civilians on October 7, 2023, the ensuing war in Gaza has left more than 73,000 Palestinians dead. The United Nations has called Israel’s campaign a genocide. The International Criminal Court has issued an arrest warrant for Benjamin Netanyahu, accusing the Israeli prime minister of crimes against humanity. Attacks on Gaza continue despite a ceasefire deal. Against that backdrop, a song defending and celebrating the Israeli military is nasty work, no matter who made it. The fact that no one made it, that it’s the creation of a machine that hallucinates in exchange for electricity, only intensifies the sick, empty feeling the track leaves behind. 

The track’s release has been a disaster for George. He split acrimoniously from Tony Pontius, the long-time manager of his record label BGP, because of Pontius’ refusal to release the song. A planned role as King Herod in a production of “Jesus Christ Superstar” was put on ice. Spotify pulled the track down for violating its restrictions around AI-generated music. Bandcamp followed soon thereafter. Boy George has dug in his heels in recent days, calling the platforms a “bunch of c**ts” as part of a steady stream of Instagram posts. His doggedness would be almost admirable, if “We Will Dance Again” wasn’t so lazy. 

In releasing the track, Boy George presumably wanted to turn heads, to make a statement. But he didn’t want to do any work. He didn’t want to record multiple vocal takes of lines he supposedly wrote and believes in. He didn’t want to arrange actual instruments to better emphasize his lyrics and pro-war stance. He didn’t want to mix the track carefully to refine his statement. He could have made something that shimmers, a piece of true pop that expresses repellent ideals. It wouldn’t have been the first. 

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Merle Haggard’s iconoclastic “Okie From Muskogee” is considered a country classic, even though the sentiment is about as far from mainstream American thought in 2026 as can be imagined. When even right-wing commentators are pushing microdosing as a path to self-betterment, his disparaging remarks about longhairs taking LSD come off as impossibly square. But you can hear the heart in Haggard’s leathery vocals, buoyed by the sweet harmonies of his backing band. Haggard waffled over the years on whether the anti-hippie hardliner anthem was a satire  of the crew cut set or a genuine tribute to his conservative father. Either way, Haggard sounds like he believes what he’s saying, regardless of whether that’s true, because of the time and effort he spent working out the song.

With 2013’s “Blurred Lines,” Robin Thicke and Pharrell set out to make a groovy single in the vein of Marvin Gaye. Though the song has been relegated to the dustbin following a widespread critical backlash and later allegations of sexual assault against Thicke by model and actress Emily Ratajowksi, who appeared in the song’s video, they were extremely successful. The song itself, creepiness aside, is a Swiss watch of a partystarter. Pharrell’s ad-libs and inserts interrupting the groove at perfect intervals to wake up the dancefloor. They arguably did too good a job recreating the shuffling, glass-clinking percussion of 1970s cocktail party classics, as they lost almost everything they’d made from the song in a subsequent — and successful — lawsuit from Gaye’s estate.  

“We Will Dance Again” does none of that. It warrants no qualifiers, no deeper thought, because Boy George clearly didn’t think too hard about it himself before spitting it out into the world. He asked a machine to build him a song to support what many consider to be an ongoing genocide. When it offered him half a song, he said, “Good enough” — without even bothering to run it through a few rounds of polishing. I regret to inform you that, in 2026, Boy George won’t even tumble for us.  

The post Sorry, Boy George. AI can’t even make bad art appeared first on Salon.com.

LAS VEGAS — Weeks before they escaped a closed test and launched a cyberattack without any human prompting, some of OpenAI’s most advanced artificial intelligence agents secretly began sharing tips on how to cheat their way through an internal hacking evaluation, two of the AI company’s researchers said Wednesday.

“This is a pivotal moment both for our company as well as the AI industry as a whole,” Michael Dalton, one of the AI-maker’s researchers, said during a presentation at the Black Hat cybersecurity conference in Las Vegas, Nevada.

The latest disclosure provides greater detail on the timeline and methods used by two of OpenAI’s models before they slipped outside a controlled environment and onto the open internet, allowing the models to breach AI developer platform Hugging Face undetected. OpenAI admitted its models were responsible for the hack late last month, roughly a week after Hugging Face said an autonomous AI system broke into its network.

The revelation is likely to spur fresh scrutiny of the measures leading AI labs such as OpenAI and Anthropic are taking to monitor the behavior of their most cyber-capable technology — especially during evaluations where agents are prompted to demonstrate their hacking skills in what is meant to be a controlled setting.

On Tuesday, the U.K.’s AI Safety and Security Institute disclosed that Anthropic’s most powerful AI model created fake online personas and sought to trick a human coder into abetting a cyberattack during a recent hacking test gone wrong. After the Hugging Face disclosure last month, Anthropic conducted a review and found models it was testing had breached three organizations in separate incidents dating back to April.

Dalton and Eric Wallace, another OpenAI researcher, said Wednesday the AI giant recently learned that multiple agents it was testing simultaneously began communicating over an internal message board in early May. There, different models shared advice about how to accomplish difficult hacking challenges they were struggling to surmount, including workarounds that required internet access.

Two OpenAI models ultimately strung together a series of sophisticated techniques to gain access to the internet and worm their way inside Hugging Face in mid-July. OpenAI has said the models were focused on completing a hacking evaluation they were prompted to solve, and that correct answers could be found on the AI developer platform.

The OpenAI researchers told conference attendees that since early May, the models created a message board inside OpenAI’s Artifactory internal file system. Without the company’s knowledge, the models spent months independently exchanging information and techniques to help each other complete difficult tasks.

Wallace said that when models get stuck, they often “try to game or cheat the task in order to get their reward.”

“The beginning of this incident kind of traces back to this point where one agent realized it could communicate by uploading a note,” he added.

By late May, one model found a way to abuse Artifactory’s internet access to retrieve files from various websites — effectively giving the models indirect access to the internet. In June, the models were able to find and exploit a previously unknown zero-day vulnerability to gain administrative privileges on Artifactory, install remote software and expand their activity within the system.

These efforts eventually overloaded Artifactory, causing a system-wide outage in early July that alerted OpenAI’s engineers to the ploy. After investigating, the company revoked the model’s credentials, removed the message board and worked with Artifactory to fix any gaps before resuming training. But the models found another way to communicate inside Artifactory just days later and continued exchanging techniques to target additional vulnerabilities within OpenAI’s infrastructure and external systems, including Hugging Face.

In light of the incident, Dalton said OpenAI is “consciously slowing down research to enhance security and to upgrade the security principles and foundation of our environment, and dramatically scaling up the monitoring of our AI agents and improving our general security control environment across prevention, detection, and mitigation.”

The intelligence-sharing relationship between the U.S. and Ukraine has bounced back to previous highs, according to long-time Ukraine watchers — a welcome boost during a critical window of opportunity for the Ukrainian war effort.

Sen. Mark Warner (D-Va.), the intelligence committee’s ranking member and a longtime proponent of more U.S. assistance to Ukraine, told POLITICO he sees evidence of an improved intel-sharing agreement — and believes it’s helped Kyiv gain an advantage in Moscow’s four-year-long war.

“I don’t want to get into any specifics, but it has improved,” he said, adding that Ukraine’s use of long-range drones and missiles has allowed it to strike deep within Russian territory and strengthen its position.

In recent months, Kyiv has carried out more aggressive strikes across Russia, enabling it to take back territory and stabilize the front line. This has afforded the country more leverage as Ukraine looks to parlay battlefield wins to pressure Russia to the negotiating table.

Ukraine’s stronger footing also comes as U.S.-mediated talks to strike a peace deal with Moscow have stalled. Trump’s negotiating team, which includes Steve Witkoff and Jared Kushner, has been preoccupied with the Iran war, bumping Ukraine down its priority list.

But in that time, Ukrainian President Volodymyr Zelenskyy appears to have risen in President Donald Trump’s estimation as Kyiv has made gains against Russia.

In early July, a barrage of Ukrainian strikes on Russian energy infrastructure forced Moscow — one of the world’s top fuel exporters — to halt its exports of diesel. The increased frequency of those kinds of targeted attacks has put the Kremlin in a tighter spot, creating what Kyiv has argued is a window of opportunity for Ukraine to leverage its current advantage to end the war.

Republican Sens. John Cornyn (R-Texas), another member of the intel committee, and Roger Wicker (R-Miss.), who chairs the Senate Armed Services Committee, agreed that intel-sharing between the U.S. and Ukraine has increased at a moment of strategic importance.

“It sure seems like that,” Cornyn said. “Everybody loves a winner and looks like Ukraine has turned the tide.”

Sen. Tim Kaine (D-Va.), a Democratic armed services committee member, told POLITICO he’s also seen signs of greater communication between Ukraine and the U.S.

“I was in Ukraine in April 2025 and I was there again in July 2026. 
And I detect more confidence in the communication,” Kaine said.

Cooperation from the U.S. has been key to Ukraine’s positive turn in fortune, said George Barros, the director of innovation and open source tradecraft at the hawkish Institute for the Study of War. Trump reportedly approved intelligence sharing for Ukrainian strikes on Russian energy infrastructure last year, which have been essential to creating a “proper incentive structure” to push Moscow to the negotiating table, Barros noted.

The strikes, he said, were “supercharged,” and became significantly more effective when imbued with intelligence from the Americans, part of a “larger, more coherent strategy for how to actually create real costs.”

And American early warning systems, Barros added, have been alerting Ukrainians to incoming Russian missile attacks since the early days of the war.

The White House did not provide details on whether its intelligence-sharing relationship with Ukraine has expanded, though it stressed that Trump is focused on facilitating an end to the war.

“The President wants this war settled so the senseless killing ends,” said the White House spokesperson in a statement. “The President and his team remain committed to continuing to play a constructive role in ending the war between Russia and Ukraine, and he remains optimistic that we’ll ultimately get a peace deal done.”

The CIA and ODNI did not respond to a request for comment.

Washington also stands to benefit from Kyiv’s intelligence, said John Herbst, who served as U.S. ambassador to Ukraine from 2003-2006 and still maintains contact with officials in the country.

“There’s no doubt of the following: Ukraine has outstanding intelligence on Russia,” he said.

Zelenskyy has sought to put that intelligence to use. With Washington locked in a five-month war against Iran, the Ukrainian president prefaced his July visit to the Oval Office by claiming Kyiv planned to provide Trump with evidence that Russia was aiding Tehran.

“When you talk to Ukrainian intelligence officials, you hear confident insights into what is going on in Moscow, and not just in the Kremlin,” said Stephen Sestanovich, a fellow for Russian and Eurasian Studies at the Council on Foreign Relations. “Insights of a sort that justify a truly cooperative and reciprocal sharing arrangement.”

LONDON — The U.K. capital’s transport authority has granted approval for Wayve and Uber to begin giving rides to members of the public in autonomous vehicles.

Transport for London (TfL) said it licensed 15 modified vehicles operated by the companies, which have a partnership, as “Private Hire Vehicles” (PHV) on a trial basis.

In a statement, London-based startup Wayve said the licenses were “an important step forward” that will allow it to begin giving rides to a small number of passengers later this summer ahead of a full public launch.

Wayve said its vehicles “are designed to operate autonomously, and will do the driving,” though under TfL’s rules, a licensed PHV driver must be present and responsible for the vehicle at all times.

“Safety is our top priority,“ a TfL spokesperson said. “Any new vehicle licensed to carry passengers on London’s roads must align with our Vision Zero goal of eliminating all deaths and serious injuries from collisions on London’s streets by 2041.”

Successive U.K. governments have sought to make the country a European pioneer in self-driving technology.

The Department for Transport opened a permitting scheme for companies to operate commercial robotaxi services without a human driver in May. Applications for that scheme — which is separate from TfL’s PHV regime — continue to be assessed by central government with input from local transport authorities including TfL.

Getting licenses isn’t the only obstacle facing robotaxi services. A survey by the London Assembly’s Transport Committee this month identified widespread opposition to autonomous passenger vehicles among the capital’s inhabitants, with just 29 percent of Londoners saying they support the roll out.

Leading artificial intelligence models from Anthropic and OpenAI created fake online personas and tried to deceive human coders into abetting a cyberattack during a recent safety evaluation, the U.K.’s AI Safety and Security Institute disclosed Tuesday.

It marks the latest case in which a powerful AI system has attempted a digital attack on an unwitting third party without direct prompting during such an evaluation — heightening concerns the powerful technology is advancing too fast for responsible oversight.

The disclosure is likely to ignite fresh calls in Washington and Silicon Valley for more rigorous regulation of the AI industry, particularly over frontier models with advanced capabilities to detect and launch cyberattacks. It comes just days after similar testing mishaps involving some of the same models from OpenAI and Anthropic sparked urgent calls for new AI safety regulation and a push within Silicon Valley to slow the rapid pace of AI development.

Like its U.S. counterpart, AISI routinely conducts security evaluations to better understand what dangers both new and soon-to-be-released AI models pose to public health and safety. But even the digital security body said the actions it uncovered by Anthropic’s Claude Mythos 5 and ChatGPT 5.6 — the latest publicly released model from either AI lab — were unlike anything it had seen before.

“This is the first time AISI has seen deception of this severity that was targeted at a real person, unprompted, in the real world,” AISI said in a 35-page technical report accompanying a blog post Tuesday.

AISI said the malicious activity began on July 25. The AI safety evaluator detected it and launched an investigation on July 28, when it spotted “unusual data transfers” stemming from a cyber evaluation it was conducting on Mythos 5 and ChatGPT 5.6.

Overall, AISI determined that Mythos 5 and ChatGPT 5.6 took “autonomous, unsanctioned action on the live internet, targeting real people and organizations” during 10 of the 122 evaluations the research body conducted and isolated for review. The majority of those actions, it said, involved Mythos 5 and arose during a “single, sustained line of activity.”

In the most alarming incident it uncovered, Mythos 5 attempted a supply chain attack — a sophisticated technique often deployed by North Korean and Russian cyber operatives, in which hackers bury malware inside legitimate code.

To do so, Anthropic’s model created “multiple fake identities” on developer platform GitHub and used them to send messages “pressuring” an open-source software engineer to unwittingly introduce a bugged update into code widely available on the popular site, AISI said. When that effort failed, the AI “edited its earlier activity to appear harmless” and “considered adopting a fresh identity to continue,” AISI added, a sign the model was intent on repeating the ruse.

As part of the same effort, Mythos 5 also sent direct messages over GitHub to software engineers that contained malware.

In another sign of deceitful behavior AISI uncovered in its investigation, multiple AI agents it was testing appeared to communicate with one another about how to convince real engineers using GitHub to trust them. “One agent left public messages on GitHub offering collaboration with other agents working on the same challenge,” AISI wrote.

AISI’s blog and technical assessment make no mention of whether the models also attempted to exploit previously unknown software bugs — called zero-days — during the evaluation.

Last month, OpenAI disclosed that GPT 5.6 and another of its models escaped onto the open internet during what was supposed to be a controlled test, and then hacked another company in a first-of-its-kind, autonomous breach.

In response, Anthropic launched an investigation into whether any of its models took illicit action during recent testing and discovered Mythos 5 and two other models had hacked three organizations during tests dating back to April.

In a statement, an Anthropic spokesperson said they are “grateful” to AISI for their leadership and that this review underscores the need for “a broader conversation about how to safely evaluate increasingly capable AI agents.”

The spokesperson added: “As we shared after disclosing our own incident last week, the field needs stronger, shared standards for how evaluation environments are built and secured. We look forward to partnering with the UK AISI to learn more about this incident as we conduct our own investigation.”

An OpenAI spokesperson referred POLITICO to a blog post about the incident that went up Tuesday evening. “We are committed to working across the industry to strengthen shared practices for conducting high-risk evaluations safely, including convening stakeholders such as national AI institutes, independent evaluators, other AI labs, and other groups in the coming weeks,” the blog read.

AISI stressed in its blog that the malicious activity it disclosed Tuesday took place under “deliberately permissive conditions” so they could assess the safety risks posed by the two models. This included granting the models access to the internet, unlike the earlier incidents detailed by Anthropic and OpenAI.

AISI also noted the models were intentionally stripped of internal guardrails that block malicious behavior. AISI was only able to disable those controls because of its role testing Mythos 5 and ChatGPT 5.6.

Still, AISI said the incidents highlighted the need for greater monitoring of model behavior during testing, and tighter controls over their access to the internet.

The Trump administration is finalizing a voluntary framework under which AI labs would submit powerful models they want to release to the public for federal safety testing. But it has not yet made the framework public, and it includes no provisions for models AI labs are developing internally.

The incidents last month from OpenAI and Anthropic both involved models not intended for public release.

Some cyber experts say recent incidents highlight deeper questions around AI development, such as who is liable when AI systems break federal hacking laws.

“If any of these were human-originated, they would lead to clear and vigorous prosecution. I think it’s time for a serious discussion about updates to existing computer security law,” said Marc Rogers, a hacker and prominent cybersecurity expert.

Colorado Republicans are using an image showing Democratic gubernatorial candidate Phil Weiser with “devil horns” to mock him on social media. While a top Democrat blasted the move as a “plainly antisemitic” attack, the GOPer who created the graphic doubled down on Tuesday.

On Facebook, Sean Pond, a Republican county commissioner and former Senate candidate who posted the image, dismissed the furor and called it a “silly picture” of “two cartoon devil horns.”

“Sometimes devil horns just mean the devil. That’s why they show up in cartoons, church lessons, haunted houses, Halloween costumes, emojis, and every costume aisle in America,” Pond wrote in a post on Tuesday afternoon. 

Pond’s graphics, which were posted on both X and Facebook on Sunday and Monday, showed Weiser, who is Jewish, standing in front of fiery skies with red horns atop his head. Depictions of Jews with horns are widely recognized as one of the most common types of antisemitic imagery. They have been used to associate Jews with evil and the devil since the Middle Ages. Pond used them to promote Colorado’s Republican gubernatorial nominee, Victor Marx. 

Pond’s pictures featured all caps text declaring Weiser “THE NEXT THREAT TO COLORADO.” One of Pond’s images touted the GOP candidate and said: “COLORADO NEEDS STRENGTH. COLORADO NEEDS FAITH. COLORADO NEEDS VICTOR MARX.”

Marx, a self-described “high-risk missionary and evangelist” who has made questionable claims about having performed exorcisms by phone and having saved as many as 45,000 women and children from abusive situations, responded positively to one of Pond’s posts on X that featured the “devil horns” graphic. He said Pond’s point about Colorado Republicans needing to unite against the “THREAT” of Weiser was “exactly right.”

“We do have to remember what’s at stake. Colorado can’t afford more division. It’s time to unite, stay focused, and win,” wrote Marx on Monday. 

Pond’s Facebook defending the graphics was directed at Kyle Clark, a journalist with 9NEWS in Denver who had raised alarms about the images and noted “Weiser is outspoken about his mother’s birth in a Nazi concentration camp, his family members killed in the Holocaust, and the threat posed by antisemitism today.”

“Depicting Jewish people with horns is a centuries-old, dehumanizing, antisemitic trope,” Clark wrote in an Instagram post on Tuesday. 

Pond had made an earlier Facebook post in the wee hours of Tuesday morning, announcing that Clark had asked him about the graphics and sharing a lengthy statement. In it, Pond said he made the images “using AI” with a “direction … to portray Phil Weiser as evil and as the next threat to Colorado.” Pond also insisted he was unaware that Weiser, who has been Colorado’s attorney general since 2019, was Jewish.  

“His religion was never considered because I did not know it,” wrote Pond. “There was no reference to Jewish people, no religious message, no dog whistle, and no hidden meaning.”

Pond further argued “Jewish politicians are subject to the same fierce political criticism as Christian politicians, Muslim politicians, atheist politicians, and everyone else seeking public power.” 

“Phil Weiser does not receive immunity from political satire because of a personal fact I did not even know,” Pond added. “I will not apologize for opposing him.”

Both Marx and Pond did not immediately respond to requests for comment. TPM also reached out to Weiser’s campaign and received a statement from Colorado Democratic Party Chair Shad Murib. 

“This is absolutely disgusting from Victor Marx,” Murib said. “That he calls this plainly antisemitic imagery ‘satire’ makes him an even bigger fool than everyone thinks.”

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.

Donald Trump and Elon Musk recently made headlines over an issue we don’t typically associate with this president of the United States and a tech CEO: history. 

On July 22, Musk posted on X, the social media platform he owns, that his generative artificial intelligence model Grok would produce, by year’s end, a feature-length movie of “The Odyssey” that is “historically accurate and true to the art of Homer.” Two days later, Trump issued an executive order mandating that signage be placed on the exterior of Smithsonian Institution museums, alerting visitors to the “inaccurate information” conveyed in the displays within, and directing them to “locations and resources for accurate information regarding America’s history.” Striking in these two apparently unrelated episodes is one word in particular — accuracy — and important lessons museums and historians can learn to combat these attacks on the integrity of historical research.

There is an important, material connection between these incidents: AI. While frenemies Trump and Musk have a complicated relationship, both continue to be involved in a large-scale transformation of government by means of its fusion with corporate interests, and particularly with corporate investment in AI.

In the same week that the pair were ranting over historical “accuracy,” Michael Kratsios, the president’s science adviser, proposed overhauling the government’s approach to funding the sciences. His plan would leave that role to corporations and private philanthropists instead. Kratsios is not a scientist; his previous experience includes working at an investment fund run by Peter Thiel and for the Department of Government Efficiency under Musk. Among the priority areas Kratsios promotes for this new funding model are AI, quantum computing and robotics. And on the same day he introduced his proposal, Kratsios announced the winners of research grants from the Genesis Mission, the administration’s AI initiative.

While this push to redirect the country’s scientific research has incited alarm, reactions to interventions in the historical humanities have been more muted. Yet attacks on public museums and on creative retellings of historical narratives are just as dangerous to the health of this country’s democracy.

When he testified on July 21 before the Republican-led House Oversight Subcommittee on the perils of government intervention in the Smithsonian’s activities, David Blight, a professor of American history at Yale University, offered a righteous defense of his profession. Besides highlighting the need for close engagement with history to lead to richer understandings of the past, Blight said it was necessary to disturb the questionable stories — those supposedly incontestable truths that the Trump administration insists are more “accurate” — that have been ingrained in our cultural memory. “If we’re not careful,” he warned, “we will end up with what the great writer Toni Morrison called ‘statist history.’ And it will create what she called ‘sanctioned ignorance.’”

AI has become a blunt instrument in the enterprise to impose precisely this form of sanctioned ignorance about America’s past. The signs Trump proposed for the outside of the Smithsonian suggest vaguely that there is another, more “accurate” history to be found elsewhere, and generative AI is exactly what Musk is proposing to provide this. Taken together, their plans amount to a one-two punch, history in the hands of the corporate state. 

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The federal government’s prospective disinvestment in the sciences has long been the status quo for the humanities, with corporate foundations and private philanthropy providing the main funding sources. Media outlets have recently been reporting on partnerships between museums, historical image collections and AI firms. A recent Financial Times article pointed to the prevalence of AI companies throwing struggling museums a “lifeline” by promising to bring in new audiences attracted by the prospect of chatbot-led “personalized” tours, along with other glitzy experiments. 

It may be tempting to see value in projects such as the Schmidt Foundation — that’s Eric Schmidt of Google — funding a fellowship in AI at the Metropolitan Museum of Art, or the Musk Foundation’s support of the Scroll Prize to use AI in reconstructing ancient texts in the burnt scrolls of Herculaneum. But these projects are simply two among countless others that have been seeding AI into museums and historical research for well over a decade. Even more egregious is the case of AI companies buying  and destroying rare books en masse in the process of scanning them for data. In the context of the dire austerity we are witnessing, the juggernaut of tech money seems impossible to resist. But the public should know the dangers of ceding the precious work of peering into the past to those whose chief aims are to exploit it for data and build visions of our collective history to their own liking. 

It is a fact that AI is inherently incapable of producing an accurate image of the past. The data on which image recognition and generative models are based rely on statistics and probability to arbitrate the truth. Algorithmic mediation of the past means that the greatest weight will be given to what privileged institutions have already been able to preserve in the greatest numbers. 

In other words, AI is unable to treat evidence — or people — equally with context and knowledge of how that evidence relates to the inequities built into museums and image archives themselves. Yet the more space we grant to Big AI, in partnership with an authoritarian state, into institutions of historical research and public communication about history, the less able we will be to find our way toward historical accounts that have fallen into the cracks of algorithmic data sets. 

No amount of feeding them more data will make them better able to offer a vision of history that is more accurate to the past and an essential part of our striving toward a more equitable future. That fact alone threatens the future of democracy.

The post Trump and Musk both want to control America’s history appeared first on Salon.com.

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.

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.”