The vibes were strong at Code with Claude, Anthropic’s two-day event for software developers in London that kicked off on May 19, the same day as Google’s I/O in Palo Alto. (A coincidence, not a flex, Anthropic staffers assured me.)
“Who here has shipped a pull request in the last week that was completely written by Claude?” Jeremy Hadfield, an engineer at Anthropic, asked from the main stage. Almost half the people in the packed room—many sitting with laptops on their knees, coding or prompting as they watched the talks—raised their hands.
Pull requests are fixes or updates to existing software that are submitted for review before they go live. They are the bread and butter of software development, the chunks of code that most professional developers spend their lives writing—or did until now.
“Who here has shipped a pull request that was completely written by Claude where they did not read the code at all?” Hadfield asked next. Nervous laughter. Most of the hands stayed up.
It’s not news that LLM-powered tools like Anthropic’s Claude Code and OpenAI’s Codex have upended the way software gets made. Top tech companies now like to boast of how little code their developers write by hand. (“Most software at Anthropic is now written by Claude,” Hadfield said. “Claude has written most of the code in Claude Code.”) OpenAI, Google, and Microsoft make similar claims. Many others wish they could.
Even so, it is striking how normal this new paradigm already seems, and how fast it has set in. This was the second year that Anthropic has put on developer events, which also run in San Francisco and Tokyo. This time last year, the company had just released Claude 4. It could code, kind of. But with Anthropic’s latest string of updates—especially Claude 4.6 and then 4.7, released in February and April—Claude Code is a tool that more and more developers seem happy to hand their work off to.
Let Claude cook.
ANTHROPIC (GRAPHIC) / WILL DOUGLAS HEAVEN (PHOTO)
Anthropic says its goal is to push automation as far as it will go. Instead of using AI to generate code and then having humans clean it up and fix the mistakes, it wants Claude to check and correct its own work. “The default isn’t ‘I’m going to prompt Claude’—the default is now ‘I’m going to have Claude prompt itself,’” Boris Cherny, who heads Claude Code, said in the opening keynote.
If all goes well, human developers shouldn’t even see the error messages when something doesn’t work. That will all be handled by Claude, which will test and tweak, test and tweak, until everything runs as it should. As Ravi Trivedi, an engineer at Anthropic, put it in another talk: “The key principle is getting out of Claude’s way. We like to say: ‘Let it cook.’”
Trivedi presented a new feature in Claude Code, announced two weeks ago, which Anthropic calls dreaming. Claude Code agents write notes to themselves, recording and saving useful information about specific tasks. When another coding agent later starts to work on the same code, it can use the notes to get up to speed faster and learn from any errors that previous agents may have made.
Dreaming is a system that Claude Code uses to read through all these notes and consolidate the information they contain, spotting patterns and common issues across different tasks. In theory, dreaming should help Claude Code learn about a particular code base and get better and better at working on it.
Success stories
Code with Claude is an event aimed at developers. As well as product showcases and hands-on workshops from Anthropic, there were how-tos from a range of companies that had reshaped their software development teams around Claude Code, including Spotify and Delivery Hero as well as Lovable, Base44, and Monday.com—three startups vibe-coding apps that help people vibe-code apps.
There were no signs of unease at Code with Claude. Everybody I met wanted in.
And yet outside the conference there have been a number of reports that many coders are starting to question this bright new future. Some gripe in online forums like Reddit and Hacker News that AI coding tools are being pushed by managers chasing productivity gains, when in practice the technology makes software development harder because of all the extra code developers now have to review. “The only people I’ve heard saying that generated code is fine are those who don’t read it,” a user called pron posted on Hacker News last week.
I sat down with Claude engineering lead Katelyn Lesse and Claude product lead Angela Jiang and asked them what they made of the concerns that a sudden flood of code generated (and shipped) without proper human oversight was kicking serious security and maintenance problems down the road.
“All of the old software development best practices still apply. They’ve applied this entire time,” said Lesse. “I think there are a lot of people and teams that may have lost sight of them in this moment.”
And yet as Anthropic and others push for greater automation and tools like Claude Code improve, the temptation increases to offload more and more tasks, including oversight. Lesse told me that some of the technical managers at Anthropic are exhausted by keeping up with all the code their teams now produce. “Part of things happening so much more quickly is just managing your time,” she said.
“I think that right now Claude is probably as good as a midlevel engineer at writing code,” she added. You still need expert engineers to design a system and troubleshoot harder problems, she said, “But over time we want Claude to get better and better at all different types of engineering.”
Jiang agreed: “I think the absolute end state we’re trying to get to is Claude basically being able to build itself.”
Storytelling is core to humanity’s DNA, stemming from our impulse to express ideals, warnings, hopes, and experiences. Technology has always been woven through the medium and the distribution: from early humans’ innovation of natural pigments and charcoals for cave paintings to literal representation by the camera.
The landscape of storytelling continues to shift under our feet. Social and streaming platforms have multiplied, audiences have fragmented, and our demand for fresh, unique media is insatiable. A recent McKinsey podcast cites that we are watching upwards of 12 hours of video content daily, often on multiple devices and multiple platforms.
All this content is expensive to produce: With a baseline budget of $150M, a Hollywood feature runs $1M per minute of finished film; prestige streaming content is in the hundreds of thousands per minute. And since consumers want to engage with authentic, original material, every company is now effectively a media company. That means we all face the same pressure: more content, with the same time and budget constraints.
There is no longer a question whether to use AI for content; the math doesn’t work any other way. What leaders need to focus on now is how to adapt responsibly, protect brand integrity, uplift team creativity, and build customer trust.
A few things worth holding onto as this era accelerates:
AI amplifies what’s already there, both good and bad. Weak strategy stays weak.
Responsible adoption means knowing what’s in your tools and models. Provenance and transparency are the foundation, not the finish line.
Scale without taste is just noise. Investing in your team’s judgment is what makes more content matter.
Fundamentals of great storytelling have not changed. Regardless of format or channel, what makes audiences lean in are still characters, arc, ingenuity, and surprise.
The permanent sprint
Creative teams are trapped on the endless hamster wheel of production, and it’s not slowing down. According to Adobe research, content demand will grow 5x over the next two years. Social content shelf life is now measured in hours, not weeks. Keeping fresh work in the pipeline is a permanent sprint, requiring teams to rethink how creative production functions.
The first move is freeing creative teams by having AI absorb the repetitive work so they have space for the strategic creative decisions that require human ingenuity. In a recent study from Adobe, 94% of creatives report that AI helps them produce content faster, saving an average of 17 hours per week. That recovered time is not a productivity metric; it is renewed creative capacity.
As a use case, Nestlé offers a useful blueprint. Its teams operate across 180 countries with a portfolio of iconic brands including Nescafé, KitKat, and Purina. Using Adobe Firefly Custom Models embedded in existing content workflows allows teams to generate assets in a brand-informed style without disrupting creative flow. At Nestlé, workflow cycle times dropped 50%. “With Firefly Custom Models, we can react at the speed of culture. It’s the closest thing we’ve had to magic.” says Wael Jabi, global strategic comms lead for KitKat.
As we move into the agentic era, the possibilities expand further. Adobe’s Creative Agent thinks in systems, not tasks, orchestrating across workflows, apps, and processes to close the gap between idea and execution, and get teams out of the production cycles that consume their productivity.
Build for your brand, not every brand
A company’s brand is how the world recognizes and connects with them. And it’s more than a collection of assets—it is dynamic, subjective, and expressed in thousands of micro-decisions made every day by the people who know it best. As production scales, keeping everything tuned to the brand gets more challenging. Off-the-shelf AI cannot replicate the level of nuance creative teams bring to content, and there’s a real cost to getting it wrong; diluting a brand in market with almost-right output is not an acceptable option. Customer trust is fragile.
Starting with a bespoke AI model built with Adobe Firefly Foundry addresses this directly. Firefly Foundry starts with a commercially safe base model and trains further on a company’s IP, making it possible to produce content that genuinely reflects the team’s vision.
And to ensure that Firefly Foundry models truly represent the creatives at the helm, Adobe has partnered with film studios like Wonder Studios, Promise.ai, and B5 Studios, and the “big three” talent agencies CAA, UTA, and WME to deeply understand what it means (and what it takes) to build an IP-immersive model that keeps creatives at the center as these film studios and talent agencies scale their visions. These brand ecosystems can accelerate nearly every phase of the production process, from ideation and storyboarding to production and promotion, all while preserving artistry and authorship. And to power the next generation of creativity and content, Adobe has recently announced a strategic partnership with NVIDIA, delivering best-in-class creative control along with enterprise-grade, commercially safe content at scale.
Generic AI gives teams a starting point. But a model trained on a brand’s own IP gets them to the finish line, while still leaving room for the creative calls that matter most.
When agents become the audience
AI is not only reshaping how we create; it is reshaping how customers find and engage with brands entirely. According to Adobe Digital Insights, AI-powered shopping has surged 4,700%. Agentic web traffic is up 7,851% year over year. Yet, most businesses still have significant gaps in AI-led brand visibility. If content is invisible to AI agents, then a brand is invisible to customers.
Major League Baseball is ahead of this curve. Using Adobe LLM Optimizer, the league monitors how its content surfaces across AI interfaces and makes real-time adjustments to maintain visibility. As fans search for tickets, stats, or game-day experiences, the league ensures its brand shows up wherever that search is happening. And with Adobe’s recent acquisition of Semrush, brand visibility goes even further.
The agentic web created an entirely new content surface that did not exist two years ago, and this exponential proliferation of content illustrates precisely why scaled, on-brand content production has become a strategic imperative. A well-built agentic foundation offers full visibility into (and control over) every piece of content, from production to performance.
How to prepare for AI integration
Here are a few steps to get started:
Audit before automation. Content supply chains usually include duplicated processes, unclear ownership, and assets living in many different places. Before AI can accelerate anything, develop a clear map of how content moves through the organization today: who creates it, who approves it, where it lives, and where it breaks down. AI applied to a broken process just breaks it faster.
Walk through workflows. Resist the urge to overhaul everything at once. Start with production tasks that are high-volume, low-stakes, and well-defined: asset resizing, localization, and background generation. Use those wins to build internal confidence before expanding into more complex creative territory.
Build responsible governance from the start. Governance added as an afterthought becomes a bottleneck. Building it in from the beginning creates a competitive advantage that lets teams move fast with confidence. And this means clear policies on model training, content provenance, human review thresholds, and communicating AI use to customers. The brands that earn lasting trust will treat transparency as a feature, not a footnote.
This content was produced by Adobe. It was not written by MIT Technology Review’s editorial staff.
AI companies want to build systems that understand the external world and overcome the limitations of LLMs. Recent developments have brought world models to the forefront of the AI discussion.
Watch a conversation with editor in chief Mat Honan, senior AI editor Will Douglas Heaven, and AI reporter Grace Huckins exploring how AI might enter the physical world.
Elon Musk lost his suit against OpenAI, in which he alleged CEO Sam Altman and President Greg Brockman had deceived him over the company’s non-profit status.
Watch as AI reporter and attorney Michelle Kim, who covered the trial for MIT Technology Review, joins in conversation with editor in chief Mat Honan to go behind the scenes of the trial and the implications for the AI race.
Optimizing the “human as a weapons system”: Anduril is building smart glasses that let soldiers order drone strikes through eye-tracking and voice commands.
Two different bets: The company is pursuing both a $159 million Army contract and a self-funded helmet-headset combo called EagleEye. The latter is something the military never asked for, but Anduril is confident the Army will eventually prefer it.
The attention problem: Soldiers already drowning in information could reject the technology if it demands more mental bandwidth than it saves. And a smart glasses system tasked with identifying threats and recommending strikes would introduce massive new risks of mistakes.
A high bar, years away: The system must survive dust, explosions, and limited connectivity. The Army won’t even put a prototype into production until 2028, if it picks one at all.
The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it’s prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands.
Quay Barnett, who leads the efforts as a vice president at Anduril following a career in the Army’s Special Operations Command, says his fundamental goal is to optimize “the human as a weapons system.” The vision is undoubtedly cyborg-inspired: Barnett wants drones and soldiers to see together, share information seamlessly, and make decisions as one.
Anduril actually has two such projects in the works. The first is the Army’s Soldier Born Mission Command, or SBMC, for which the company won a $159 million prototyping contract last year to work with Meta on augmented-reality glasses to attach to existing military helmets. But Anduril has also embarked on a self-funded side quest, announced in October, to design its own helmet and headset combo called EagleEye. This is something the military has not asked for, but Anduril insists it will prefer it and purchase it in the end.
So far, both systems are years away. The Army isn’t expected to move its top choice for the SBMC program into production until 2028, if it picks one at all (the previous lead for the effort, Microsoft, was set to receive a $22 billion production contract that was ultimately cancelled when the glasses didn’t prove viable). But Barnett told MIT Technology Review about where both Anduril’s prototypes are headed.
Depending on the situation, the glasses for either prototype will overlay certain information onto a soldier’s field of view. This might be as simple as a compass or as complex as an entire map of the area, information about where nearby drones are flying, or AI-driven recognition of a target like a truck.
The soldier would then speak to the interface in plain language—for example, to order an evacuation for someone who’s been injured or to plan a route taking into account which areas are off limits. A large language model—Anduril is in tests with Google’s Gemini, Meta’s Llama, and even Anthropic’s Claude, despite the company’s conflict with the Pentagon—will be used to help translate a soldier’s speech into commands the software can follow. And the engine for it all will be Anduril’s software Lattice, which incorporates data from lots of different military hardware into one picture. The Army announced in March that it would spend $20 billion to integrate Lattice with essentially its entire infrastructure.
Barnett’s team is designing the headset to carry out multi-step tasks. A soldier might send a drone to surveil an area and instruct it to come back once it’s found something that looks like an artillery unit; then the system would recommend courses of action, like sending a nearby drone to strike, that would have to be approved by the normal chain of command. Leading the system through this, if all goes to plan, might not even require speech; the soldier could instead communicate through tracked eye movements and subtle taps.
That’s the idea, anyway. It’s worked on early prototypes, Barnett says, but there aren’t yet versions ready for the Army to test at scale. The component parts began arriving in March. Because of federal military contracting rules, these parts—unlike Meta’s commercial smart glasses—required new supply chains that don’t rely on Chinese companies.
It’s a lot for soldiers already bogged down in information overload, says Jonathan Wong, a former US Marine who works as a senior policy researcher at RAND on Army efforts to buy new tech. Both smart glasses projects aim to create a clean interface that presents only the right information at the right time. But it’s a product that soldiers will reject if it costs more of their attention than it saves. “How much mental bandwidth do you have to be both aware of your surroundings and to operate this technology in a way that makes you and your whole unit better?” he says.
Wong recalls that as a platoon commander, for example, he had a radio that operated on three different channels at once. “The moment that two people were on different channels talking at the same time, I immediately couldn’t comprehend anything that either one of them was trying to tell me, and I was probably not aware of my own surroundings,” he says. “I think there are limits to what you can take in.”
Ideally, Barnett says, smart glasses can ease that information overload. Anduril’s approach is to get creative with ways the user can access necessary information quickly. Voice commands and eye tracking are a piece of that strategy. But even if it’s all technically feasible, it might take years of field testing to know if the system is actually useful for soldiers, Wong says.
Such a system would mark a major escalation in how closely soldiers rely on imperfect AI systems. While computer vision models used to identify objects have long been employed by militaries, and chatbots have recently entered decision-making during the war in Iran, these technologies have not yet made their way to most frontline soldiers. A smart glasses system tasked with identifying threats and recommending strikes would introduce massive new risks of errors.
Anduril is not the only one competing to develop smart goggles for combat. Rivet, which specializes in wearable sensors for the military, received a $195 million prototyping contract the same time, and in March the Israeli defense-tech company Elbit received its own $120 million contract. This all comes after Microsoft lost its role leading the Army’s smart glasses effort, following a Pentagon audit that found the Army wasn’t properly testing the glasses, a mistake that could have wasted $22 billion.
For both Anduril’s prototypes, the company is testing a new system for digital night vision, which uses electronic sensors and algorithms to boost low levels of light. It’s been a promised technology for decades but has tended to work too slowly for practical use and produce grainy images. Anduril says it has found improvements over previous prototypes through techniques rooted in both new generative AI and older machine learning.
Much of the other hardware for both projects is being built by Meta, including the displays and the waveguides that send visuals to the user’s eye without blocking the view. That might be a surprise to anyone who knows the backstory: In 2017, Facebook (now Meta) ousted Anduril founder Palmer Luckey following an internal conflict involving his support for Donald Trump. The two are now back in the augmented-reality business together, while Mark Zuckerberg has also adopted a friendlier posture toward the second Trump administration.
For the Army initiative, this suite of smart glasses, night vision, and sensors will be attached to the helmets and other gear soldiers already wear, with a separate battery pack. The EagleEye version will instead incorporate the tech into the helmet itself. Even if the Army doesn’t prefer EagleEye in the end, Barnett says, Anduril will attempt to sell the system to foreign militaries.
Multiple challenges must still be overcome. Unlike Meta’s Ray-Ban glasses, the prototypes have to operate in an environment full of dust, explosions, and smoke. Adding the computing power and battery life they need also means more weight for soldiers already carrying upwards of 100 pounds. Then the technology has to work in environments without ubiquitous 5G cell connections; powerful computer vision and AI models will need to run locally on the device.
For the Army to want to buy it at scale, “it’s got to work, and it’s got to be pretty seamless,” Wong says. “It’s a high bar.”
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
When Google opens its doors tomorrow for its annual developer conference, I/O, it will do so as a clear third place in the foundation model race. A year ago, at Google I/O 2025, the situation looked very different: The company was still riding high from the launch of Gemini 2.5 Pro that March, and distinguishing among the top-tier large language models often felt like a subjective splitting of hairs.
But a foundation model’s reputation these days rests largely on its coding capabilities, and for months Google’s coding tools have been outgunned by Anthropic’s Claude Code and OpenAI’s Codex. Those systems are so dramatically superior to Google’s own offerings that the company has reportedly had to allow some engineers at DeepMind, its AI division, to use Claude for their work—lest they fall farther behind.
So when I arrive at the conference in Mountain View, California tomorrow, I’ll certainly be on the lookout for any efforts Google is making to claw its way back into frontrunner position. But I’m also eager to see new developments in areas where Google shapes the cutting edge, such as AI for science. The company’s moves there might receive less attention, but they will be no less consequential.
Here are three things I’ll be paying particular attention to over the next two days.
An attempted coding comeback
Google is taking its AI coding crisis seriously. According to reporting from The Information, there’s a new AI coding team at DeepMind. And the Los Angeles Times has reported that John Jumper, who shared a 2024 Nobel Prize in chemistry with DeepMind CEO Demis Hassabis for their work on the protein structure prediction software AlphaFold, is lending his talents to the efforts. I would be surprised if we don’t see a major new coding release at I/O, perhaps in the form of an update to the company’s Antigravity agentic coding platform.
That said, we shouldn’t expect anything transformative here. Googlers have access to models and products that are substantially ahead of those released to the public, yet they were still reportedly fighting over who got access to Claude Code last month. Unless the company has made astonishing progress since then, Google probably won’t make it back to the coding frontier in the next two days.
Science and health
Coding might be Google DeepMind’s weakness, but science is its conspicuous strength. It is the only frontier AI company to have earned a Nobel Prize. And as LLMs have come to dominate the AI-for-science landscape, Google has only solidified its lead. Last year, the company released multiple scientific AI tools, including the AI co-scientist, which formulates hypotheses and research plans in response to user questions and has been described as an “oracle” by one Stanford scientist, and AlphaEvolve, a system that iteratively discovers new solutions for mathematical and computational problems. If any new scientific tools are announced at I/O, they’ll be worth noting.
I’ll also be paying close attention to any moves Google makes in health and medicine. Google is doing some of the best research out there on LLM-basedhealth tools, but OpenAI has defined the health AI conversation since the release of ChatGPT Health in January. Google has announced that it will be making its AI-powered Health Coach publicly available tomorrow, but promotional material suggests that the tool is geared more toward providing advice on topics such as fitness and diet than to addressing users’ medical concerns. Is this another area where Google has fallen behind, or is the company exercising appropriate caution in a high-stakes domain?
The drama
While Google fans congregate down in Mountain View, roughly 30 miles north in Oakland the Elon Musk v. Sam Altman trial will be wrapping up. The past few months have seen more than their fair share of AI CEO drama—before the trial, the animosity between Altman and Anthropic CEO Dario Amodei took center stage as Anthropic and OpenAI worked to negotiate deals with the US Department of Defense. But DeepMind’s Hassabis has, for the most part, steered clear of such drama. He effectively presents himself as a Nobel Prize-winning nerd, and if he has written screeds about any of his peers, they haven’t been leaked to the press or appeared in legal discovery.
That’s not to say that Google is controversy free. Last month, a group of 600 employees, many of whom work for DeepMind, sent a letter to CEO Sundar Pichai protesting an impending DoD deal. Google signed that deal the next day. Hassabis, Pichai, and all the other big names will surely do their best to skirt these and other touchy subjects while on stage, but controversies will worm their way in regardless. It will be interesting to see whether Google can maintain its veneer of neutrality.
On Monday, the jury in Musk v. Altman dealt Elon Musk a major blow—reaching a unanimous advisory verdict that he had sued OpenAI too late and, as a result, his claims are barred by the applicable statutes of limitations. US District Judge Yvonne Gonzalez Rogers immediately accepted it.
Musk announced on X that he will be appealing the decision. “The judge & jury never actually ruled on the merits of the case, just on a calendar technicality,” he wrote.
OpenAI was cofounded by Musk and a group of researchers in 2015 as a nonprofit with a mission to develop AI for the benefit of humanity, unconstrained by a need to generate financial returns. Musk donated $38 million to the company during its early days, allegedly on the basis that OpenAI CEO Sam Altman and president Greg Brockman had promised to keep the company a nonprofit committed to the mission.
Musk brought two claims against OpenAI. First, he argued that Altman and Brockman breached the charitable trust he created through his donations by breaking their promise to keep the company a nonprofit and creating a for-profit subsidiary that ballooned over the years. Second, he argued that Altman and Brockman unjustly enriched themselves at Musk’s expense. He sued OpenAI in 2024.
Musk asked the court to unwind a 2025 restructuring that converted OpenAI’s for-profit subsidiary into a public benefit corporation and to remove Altman and Brockman from their roles.
OpenAI argued that the time for Musk to sue the company had run out before he brought the case. The statute of limitations on the breach of charitable trust claim is three years, while the statute of limitations on the unjust enrichment claim is two years. This means that Musk should have discovered, or had reason to discover, Altman and Brockman’s alleged breach of charitable trust no earlier than 2021 and their alleged unjust enrichment no earlier than 2022.
While Musk argued he discovered that Altman and Brockman had broken their promise only in 2022, OpenAI claimed that Musk had reason to think this well before 2021.
Musk told the jury that he has gone through “three phases” in his beliefs about OpenAI: In phase one, he was “enthusiastically supportive” of the company. In phase two, “I started to lose confidence that they were telling me the truth,” he said. In phase three, “I’m sure they’re looting the nonprofit.”
Here’s a deeper dive into a timeline of the events as testified in the trial. You can read my dispatches from all three weeks of the trial here and here and here.
2017: Musk proposes creating a for-profit subsidiary
In 2017, two years after OpenAI was founded, Musk and the other cofounders tried to create a for-profit subsidiary to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. They fought a bitter power battle over who would get to control the entity. Musk also proposed merging OpenAI with his electric-car company, Tesla.
During the trial, OpenAI’s lawyers pressed Musk on these discussions, suggesting that Musk knew in 2017 about Altman and Brockman’s plans to pivot the company—even participating in such plans—and had reason to sue then.
“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” Musk told the jury, “as long as the tail didn’t wag the dog.”
2019: OpenAI creates a for-profit subsidiary with capped profits
In 2019, OpenAI created a for-profit subsidiary, under which employees and investors would receive a capped return on their investment. At the same time, the company secured a $1 billion investment from Microsoft. OpenAI argued that Musk again had reason to sue the company then.
But Musk testified that he didn’t think the move was violating the nonprofit’s mission. “If you’ve got a capped-profit situation, it hasn’t violated the nonprofit’s goal,” Musk told the jury earlier in the trial. “There was no basis for me to file a lawsuit at that time.”
2020: Microsoft snags an exclusive license
In 2020, when Microsoft secured an exclusive license to OpenAI’s GPT-3 model, Musk posted on X: “This does seem like the opposite of open. OpenAI is essentially captured by Microsoft.” OpenAI once again argued that Musk had reason to sue then.
But Musk testified that after reading the post, Altman reassured him that “OpenAI was staying on the mission as a nonprofit.” Musk said although he was skeptical, he still had no reason to sue the company at that point.
2022: Microsoft prepares to invest $10 billion in OpenAI
It was only in 2022, Musk testified, that he discovered OpenAI had abandoned its nonprofit mission. At that time, Microsoft was preparing to invest $10 billion in OpenAI—a deal that closed in 2023.
“I was disturbed to see OpenAI with a $20B valuation,” Musk texted Altman after reading the news. “This is a bait and switch.”
Musk told the jury this was the moment that made him realize “the for-profit is the tail wagging the dog.” He thought Microsoft would give $10 billion only if it expected “a very big financial return.” He argued that this was the point he realized “OpenAI had become, for all intents and purposes, a for-profit company with a $20 billion valuation.”
“The 2023 deal was different,” Steven Molo, one of Musk’s lawyers, hammered home during his closing argument.
The jury sides with OpenAI
It was up to the jury to decide whether the evidence supported Musk’s claim that he first realized in 2023 that OpenAI was no longer a nonprofit committed to its mission. In the verdict announced today, they found Musk did in fact have reason to think that he was being misled by Altman and Brockman before 2021. They did not address whether he was in fact misled.
Courts often decide cases on procedural grounds like statutes of limitations when they can, because it can be the cleaner way to resolve a case than to grapple with its merits.
Musk has said he will appeal the decision to the Ninth Circuit Court of Appeals, a federal appellate court that reviews decisions from district courts in California and other states.
In a dimly lit bedroom, a frightened young woman is thrown onto a bed by a tall, muscular man. He grabs her hand, and flame-like vines crawl across her body, fusing with her flesh. She levitates, then drops. A dragon-shaped tattoo appears across her chest.
“Two months,” the man says. “Give me an heir, or I will eat you.”
The scene is from Carrying the Dragon King’s Baby, one of the many hundreds of short dramas that appear on apps like DramaWave and ReelShort. There’s just something about this one that isn’t quite right. The lighting may be glossy and cinematic, but the show has an odd visual texture like something between a movie and a video game cutscene.
That’s because Carrying the Dragon King’s Baby is part of a new trend for making these shows entirely with AI: no actors, camera operators, cinematographers, or CGI specialists required.
China’s short drama industry has boomed since its launch, in 2018. These ultrashort, melodramatic, and often smutty shows are designed for smartphone viewing, with episodes often running just one or two minutes long: Viewers can finish an entire series in as little as 30 minutes to an hour. The films are made for endless scrolling, packed with emotional confrontations and melodramatic plot twists. The trend’s growth is driven by apps that bombard TikTok, Instagram, and Facebook with cliffhanger-heavy ads designed to lure viewers into buying subscriptions. In 2024, China’s short drama market reached roughly $6.9 billion in revenue, surpassing the country’s annual box office earnings for the first time.
Since 2022, Chinese short drama companies have aggressively expanded overseas, translating existing hits and producing localized series featuring local actors. Globally, short drama apps have approached a billion cumulative downloads. The United States is the biggest market outside of China, providing around 50% of the revenue, according to research firm DataEye.
Now the industry is reinventing itself. Chinese short drama companies—already masters of low-budget, algorithmically optimized entertainment—are embracing generative AI to produce content faster and cheaper than ever. An average of 470 AI-generated short dramas were released every day in January, according to DataEye. Short-drama companies like Kunlun Tech are ramping up AI productions, shrinking film crews, and reorganizing the labor pipeline from the ground up. For some studios, AI has moved from being a supporting tool to providing the backbone of production itself.
Infinite stories, infinite tropes
Short dramas are already famously low-budget. But AI has made them dramatically cheaper to mass-produce, helping to accelerate the entire process—and save money. Production timelines have collapsed. Conceptualization, script writing, casting, shooting, and editing used to take three to four months. With AI, the process can now take less than a month, says Tang Tang, vice president at short-drama platform FlexTV. Producing a short drama in North America once cost roughly $200,000, but AI can cut that cost by 80% to 90%, according to Tang.
After expanding into the US market, Chinese short drama companies largely followed the same playbook they used in China: Buy traffic aggressively on TikTok, Facebook, and YouTube; offer a handful of free episodes; then charge viewers to unlock the rest inside the companies’ apps. Decisions about what to produce next are often driven less by creative instinct than by performance data. “We look at what themes, plotlines, and writers resonate with audiences, then quickly adjust,” says Tang.
The industry operates at a relentless pace. “Everyone expects quick returns,” Tang says. “In China, if a series doesn’t break even within a month, the industry considers it a failure.”
As a result, screenwriters who spoke with MIT Technology Review said platforms often categorize projects using highly specific keywords that encompass everything from genre and setting to plot structure, such as “campus romance,” “gang rivalry,” “enemies to lovers,” or “rags to riches.” Recently, one of the most popular genres has been “reborn revenge,” a fantasy trope in which a wronged protagonist is miraculously reborn and given a chance to change their fate.
“You kind of have to keep the emotional intensity extremely high throughout the show, using the same plot devices over and over again: sudden deaths, betrayals, physical violence, huge confrontations,” says Phoenix Zhu, a freelance short drama screenwriter based in Suzhou. “It’s common to sacrifice narrative logic for shock value, because otherwise people are more likely to scroll away.”
Those simple tropes have made the format particularly compatible with AI-generated production. Earlier this year, FlexTV halted all traditionally shot productions and shifted entirely to AI-generated dramas. Kunlun Tech, the parent company of drama apps DramaWave and FreeReels, began producing AI-generated short dramas in 2025 and now offers more than 1,000 AI titles on its platforms. StoReels, another popular short drama company targeting a global audience, has said it aims to produce 100 AI-generated dramas per month.
“People’s attention spans are getting shorter, and serialized drama naturally has to get shorter,” says Han “Daniel” Fang, the CEO of Kunlun Tech. Fang told MIT Technology Review that the company is not going to stop investing in traditionally shot short dramas with real actors. But the company is expanding AI-generated productions and gradually increasing their share on its platforms as a low-cost way to experiment with new genres, themes, and ideas. “We want to bring the amount of AI work to 20% of the platform,” Fang says.
The format is also rapidly growing overseas. Research firm Omdia estimates that the global microdrama market reached $11 billion in 2025 and will grow to $14 billion by the end of 2026. The United States is expected to generate $1.5 billion in revenue in that market this year.
“No one comes to short dramas expecting high art,” says investor Shangguan Hong, former partner of Legend Capital. “The short-drama industry already stands out from traditional TV and filmmaking by being real-time and data-driven. AI only furthers that logic. In a sense, short drama is perfectly compatible with AI.”
Inside the content machine
The industry’s AI revolution is already changing the type of roles required to make short dramas.
Phoenix Zhu graduated from college in 2024 with a degree in philosophy. After months of rejections from traditional media and film studios, she eventually found work writing scripts for short dramas. “It was a very difficult job market for young people,” Zhu says. “I couldn’t afford to be picky about what I wrote.”
To support herself, Zhu worked a string of part-time jobs, including as a barista, a flower seller, and an event coordinator, while taking freelance writing gigs online for advertising and education companies. In April 2025, she sold her first short-drama script for around 20,000 yuan (approximately $2,945). More commissions followed, and she thought her career was finally beginning to pick up.
Then AI arrived. Two projects already in the contract stage were abruptly canceled, Zhu says. Rates across the industry began falling. The raises she expected as she gained more experience never materialized.
Still, writers like Zhu have been among the less disrupted workers in the industry. Many production roles on traditional filming sets have disappeared almost entirely from AI-generated productions.
“We could shrink the production team down to around 10 people,” says Tang, vice president at FlexTV. Like many companies in the industry, FlexTV relies primarily on Chinese writers and production teams, even for shows featuring non-Chinese characters and targeting overseas audiences. The reason is not just lower costs, Tang says, but also that Chinese writers better understand the pacing and narrative rhythm of short dramas.
Instead of camera crews, lighting technicians, makeup artists, and visual effects teams, AI productions now rely on smaller groups consisting largely of producers, writers, AI directors, and “AI asset curators.”
An AI asset curator translates scripts into prompts and generates reference images of characters, costumes, and scenes for AI video models to follow. MIT Technology Review found hundreds of job listings for the role on Chinese job sites, many requiring little prior industry experience beyond familiarity with AI tools.
“The technology has improved enormously just in the past few months,” says Hanzhong Bai, an AI short-drama producer based in Beijing. Bai says it is common for AI asset curators to use prompts like “combine the faces of these celebrities I like” when generating characters. Studios typically use a mix of tools, including Google’s image-generation model Nano Banana, ByteDance’s Seedance, and Kuaishou’s Kling.
For producers like Bai, AI also makes it economically viable to produce genres that were previously too expensive for short dramas, especially fantasy series requiring elaborate visual effects, costumes, or makeup. “We’ll see many more dragon and mermaid shows for exactly this reason,” Bai says.
The compressed production cycle has also changed the writing process itself. Writers once had two to three months to finish a script. Now, Zhu says, platforms often expect delivery within a month. Scripts can also be rougher and more flexible, since scenes, visuals, and even plot details can be changed later through prompts.
As a result, writers increasingly have to write for AI models as much as for human audiences. Zhu says she now has to describe scenes with far greater visual specificity, effectively taking on responsibilities once handled by cinematographers or visual effects teams.
“Before AI, writing ‘He gave her a cold stare’ might have been enough,” Zhu says. “Now I might need to write, ‘Cold beams of light shot out from his eyes.’”
Fang of Kunlun Tech believes the future quality of AI-generated short dramas is ultimately a numbers game. “Good ideas and good writing still stand out,” Fang says. “The quality [of AI short drama] will improve simply because more people with strong ideas will be able to make their shows.”
In the final week of the Musk v. Altman trial, lawyers traded blows over Elon Musk’s and OpenAI CEO Sam Altman’s credibility. Altman was grilled on his alleged history of lying and self-dealing involving companies that do business with OpenAI. But he fired back, painting Musk as a power-seeker who wanted to control the development of artificial general intelligence (AGI)—powerful AI that can compete with humans on most cognitive tasks.
As evidence of their commitment to AI safety, OpenAI brought out a golden trophy of a donkey’s ass that was gifted to an employee after he was called a “jackass” for standing up to Musk’s plans to race toward AGI.
Lawyers for both sides also presented their closing arguments, floating unflattering mugshot-style photos of Musk and Altman next to each other on a giant screen. Musk’s lawyer Steven Molo argued that Altman and OpenAI president Greg Brockman broke their promise to use money Musk donated to maintain OpenAI as a nonprofit that develops AI for the benefit of humanity. Instead, they created a for-profit subsidiary that made them extraordinarily wealthy.
OpenAI’s lawyer Sarah Eddy argued that Altman and Brockman never promised to keep OpenAI a nonprofit. She added that even though it’s been restructured, OpenAI remains a nonprofit dedicated to developing AI safely.
She claimed that Musk sued too late—and that his real motive is to sabotage a competitor to his own AI company, xAI, which he launched in 2023.
Musk is asking the court to unwind the 2025 restructuring that converted OpenAI’s for-profit subsidiary into a public benefit corporation and to remove Altman and Brockman from their roles. He is also seeking as much as $134 billion in damages from OpenAI and Microsoft, to be awarded to OpenAI’s nonprofit.
The jury will begin deliberating on Monday and deliver an advisory verdict as soon as next week. The jury verdict is not binding on the judge, who will decide the case.
If the judge rules in Musk’s favor, it could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.
Musk the power-seeker, Altman the liar.
In the first week of the trial, Musk said he was suing to save OpenAI’s mission to build AI safely for the benefit of humanity. This week, Altman denied Musk was a paladin of AI safety and painted him as a power-seeker who wanted to control OpenAI.
Altman told the jury that in 2017, when Musk and other cofounders were discussing creating a for-profit arm, they asked Musk what would happen to his control over such an entity if he died. “Maybe the control of OpenAI should pass to my children,” Musk said, according to Altman.
Musk’s lawyer shot back, grilling Altman on his alleged history of lying. He pointed out that OpenAI’s former executives Ilya Sutskever and Mira Murati, and former board members Helen Toner and Tasha McCauley, all testified that Altman had lied to them. In 2023, Altman was briefly fired as CEO over the alleged behavior.
Molo also pressed Altman about his personal investments in startups that do business with OpenAI. Altman testified that he tried to steer OpenAI to buying power from the nuclear energy company Helion Energy, a third of which he owns.
(Last Friday, the US House oversight committee launched an investigation into Altman’s potential conflicts of interest. Attorneys general from more than a half-dozen states called for the Securities and Exchange Commission to review them.)
During his closing statement, Molo put Altman’s credibility on the stand again. “Imagine that you’re on a hike, and you come upon one of those wooden bridges that you see on a trail, and it’s over a gorge,” he said. “A woman standing by the entry to the bridge says, ‘Don’t worry—the bridge is built on Sam Altman’s version of the truth.’ Would you walk across that bridge?”
Altman, who sat behind his lawyers, looked up uneasily every time his name was mentioned.
During her closing argument, Eddy fired back. Musk “never cared about the nonprofit structure,” she said. “What he cared about was winning.”
Musk, though, was absent. Despite the judge’s order that he remain available, he flew to China with President Trump.
Did Altman promise to keep OpenAI a nonprofit?
During her closing argument, Eddy argued that no testimony or evidence showed any conditions on Musk’s donations, or any promises made by Altman and Brockman to keep the company a nonprofit. “No commitments or promises were made. No restrictions were placed on Mr. Musk’s donations,” she said.
Eddy added that it was evident Musk wasn’t truly committed to keeping OpenAI a nonprofit. She noted that in 2017, he tried to create a for-profit subsidiary and fought a bitter battle with Altman and Brockman to have control over it.
“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” Musk told the jury earlier in the trial, “as long as the tail didn’t wag the dog.”
Eddy then argued that Musk sued too late, filing in 2024 after the statutes of limitations on his claims ran out. In 2019, OpenAI created a for-profit subsidiary, under which employees and investors received a capped return on their investment.
But Musk testified that he discovered OpenAI had abandoned its nonprofit mission only in 2022, when Microsoft was preparing to invest $10 billion in OpenAI—a deal that closed in 2023. “I was disturbed to see OpenAI with a $20B valuation,” he texted Altman after reading the news. “This is a bait and switch.”
Musk told the jury that the $20 billion valuation made him realize “the for-profit is the tail wagging the dog.”
“The 2023 deal was different,” Molo hammered home during his closing argument.
Is OpenAI still a nonprofit committed to its mission?
A central question raised in the last week of trial was whether OpenAI remains a nonprofit committed to developing AGI safely for the benefit of humanity. Eddy, the OpenAI lawyer, argued that the nonprofit still controls the for-profit and seeks to “help AGI turn out well for humanity.” “The OpenAI nonprofit is the best-resourced nonprofit in the world,” thanks to the for-profit, she added.
Molo countered that while the OpenAI’s nonprofit nominally controls the company, it does not do so in practice. OpenAI’s nonprofit and for-profit are controlled by the same people—seven of the nonprofit’s eight board members are on the for-profit’s board. The nonprofit hired employees only a month before the trial started and does work only in grant-making rather than AI research.
Molo played a video interview of Altman saying that the nonprofit board’s failure to fire him in 2023 was “its own kind of governance failure.”
“We’re left with this nonprofit that doesn’t have any voice,” Jill Horwitz, a law professor at Northwestern University who studies nonprofits, told MIT Technology Review. “It doesn’t have much money, and OpenAI doesn’t think it has any obligation to fund it. It barely has a staff,” she says. “It’s unclear how on earth the nonprofit is supposed to exercise its duties and control the entire company.”
Civil society groups and policymakers have spoken out against OpenAI’s restructuring over the years. So has Musk, although his own stake in the AI race makes him a dubious champion for the public interest.
“The public interest in the nonprofit loses, no matter who wins or loses this trial,” says Horwitz.
Jackass for AI safety
Despite US District Judge Yvonne Gonzalez Rogers’s warning during the first week that this trial was not about AI safety, the issue stole the show again. Throughout the trial, the lawyers from both sides traded barbs over the safety track records of ChatGPT (which has allegedly caused teen suicides) and Grok (which has flooded X with porn).
On the last day of testimony, OpenAI’s lawyer Bradley Wilson handed the judge a small golden trophy of a donkey’s ass, inscribed: “Never stop being a jackass for safety.”
The trophy belonged to Joshua Achiam, OpenAI’s chief futurist. He testified that he’d warned, when Musk announced in 2018 that he was leaving OpenAI to race toward building AGI, that speed could compromise safety. Musk snapped and called him a “jackass,” said Achiam. His colleagues, including Dario Amodei, now CEO of Anthropic, gave him the trophy to enshrine the diss.
“I don’t want it,” said the judge. The shenanigans spilled out into the street too. In front of the Oakland courthouse, a protester paraded around wearing a costume of Musk holding a bag of ketamine and driving a Cybertruck. Another held a photo of Sam Altman and a poster reading, “Stop AGI or we’re all gonna die.”
When Jennifer got a job doing research for a nonprofit in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see if the tech would pull up the porn videos she’d made more than 10 years before, when she was in her early 20s. It did in fact return some of that content, and also something alarming that she’d never seen before: one of her old videos, but with someone else’s face on her body.
“At first, I thought it was just a different person,” says Jennifer, who is being identified by a pseudonym to protect her privacy.
But then she recognized a distinctly garish background from a video she’d shot around 2013, and she realized: “Somebody used me in a deepfake.”
Eerily, the facial recognition tech had identified her because the image still contained some of Jennifer’s features—her cheekbones, her brow, the shape of her chin. “It’s like I’m wearing somebody else’s face like a mask,” she says.
“It’s like I’m wearing somebody else’s face like a mask.”
Conversations about sexualized deepfakes—which fall under the umbrella of nonconsensual intimate imagery, or NCII—most often center on the people whose faces are featured doing something they didn’t really do or on bodies that aren’t really theirs. These are often popular celebrities, though over the past few years more people (mostly women and sometimes youths) have been targeted, sparking alarm, fear, and even legislation. But these discussions and societal responses usually are not concerned with the bodies the faces are attached to in these images and videos.
As Jennifer, now 37 and a psychotherapist working in New York City, says: “There’s never any discussion about Whose body is this?”
For years, the answerhas generally been adult content creators. Deepfakes in fact earned their name back in November 2017, when someone with the Reddit username “deepfakes” uploaded videos showing faces of stars like Scarlett Johansson and Gal Gadot pasted onto porn actors’ bodies. The nonconsensual use of their bodies “happens all the time” in deepfakes, says Corey Silverstein, an attorney specializing in the adult industry.
But more recently, as generative AI has improved, and as “nudify” apps have begun to proliferate, the issue has grown far more complicated—and, arguably, more dangerous for creators’ futures.
Porn actors’ bodies aren’t necessarily being taken directly from sexual images and videos anymore, or at least not in an identifiable way. Instead, they are inevitably being used as training data to inform how new AI-generated bodies look, move, and perform. This threatens the livelihood and rights of porn actors as their work is used to train AI nudes that in turn could take away their business. And that’s not all: Advancements in AI have also made it possible for people to wholly re-create these performers’ likenesses without their consent, and the AI copycats may do things the performers wouldn’t do in real life. This could mean their digital doubles are participating in certain sex acts that they haven’t agreed to do, or even perpetrating scams against fans.
Adult content creators are already marginalized by a society that largely fails to protect their safety and rights, and these developments put them in an even more vulnerable position. After Jennifer found the deepfake featuring her body, she posted on social media about the psychological effects: “I’ve never seen anyone ask whether that might be traumatic for the person whose body was used without consent too. IT IS!” Several other creators I spoke with shared the mental toll that comes with knowing their bodies have been used nonconsensually, as well as the fear that they’ll suffer financially as other people pirate their work. Silverstein says he hears from adult actors every day who “are concerned that their content is being exploited via AI, and they’re trying to figure out how to protect it.”
One law professor and expert in violence against women calls these creators the “forgotten victims” of NCII deepfakes. And several of the people I spoke with worry that as the US develops a legal framework to combat nonconsensual sexual content online, adult actors are only at risk of further injury; instead of helping them, the crackdown on deepfakes may provide a loophole through which their content and careers could be stripped from the internet altogether.
How deepfakes cause “embodied harms”
During his preteen years in the 1970s, Spike Irons, now a porn actor and president of the adult content platform XChatFans, was “in love” with Farrah Fawcett. Though Fawcett did not pose nude, Jones managed to get his hands on what looked like pictures of her naked. “People were cutting out faces and pasting them on bodies,” Irons says. “Deepfakes, before AI, had been going around for quite a while. They just weren’t as prolific.”
The early public internet was rife with websites capitalizing on the idea that you could use technology to “see” celebrities naked. “People would just use Microsoft Paint,” says Silverstein, the attorney. It was a simple way to mash up celebrities’ faces with porn.
People later used software like Adobe After Effects or FakeApp, which was designed to swap two individuals’ faces in images or videos. None of these programs required serious expertise to alter content, so there was a low barrier to entry. That, plus the wealth of porn performers’ videos online, helped make face-swap deepfakes that used real bodies prevalent by the 2010s. When, later in the decade, deepfakes of Gal Gadot and Emma Watson caused something of a broader panic, their faces were allegedly swapped onto the bodies of the porn actors Pepper XO and Mary Moody, respectively.
But it wasn’t just high-profile actors like them whose bodies were being used. Jennifer was “a very minor performer,” she says. “If it happened to me, I feel like it could happen to anybody who’s shot porn.” Since he started his practice in 2006, Silverstein says, “numerous clients” have reached out to report “This is my body on so-and-so.”
Both people whose faces appear in NCII deepfakes and those whose bodies are used this way can feel serious distress. Experts call this type of damage “embodied harms,” says Anne Craanen, who researches gender-based violence at the UK’s Institute for Strategic Dialogue, an organization that analyzes extremist content, disinformation, and online threats.
The term reflects the fact that even though the content exists in the virtual realm, it can cause physiological effects, including body dysmorphia. The face-swapped entity occupies the uncanny valley, distorting self-perception. After discovering their faces in sexual deepfakes, many people feel silenced, experts told me; they may “self-censor,” as Craanen puts it, and step back from public-facing life. Allison Mahoney, an attorney who works with abuse survivors, says that people whose faces appear in NCII can experience depression, anxiety, and suicidal ideation: “I’ve had multiple clients tell me that they don’t sleep at night, that they’re losing their hair.”
Independent creators aren’t just “having sex on camera.” For someone to rip off their work “for their own entertainment or financial gain fucking sucks.”
Though the impact on people whose bodies are used hasn’t been discussed or studied as often, Jennifer says that “it’s just a really terrible feeling, knowing that you are part of somebody else’s abuse.” She sees it as akin to “a new form of sexual violence.”
The uncertainty that comes with not being aware of what your body is doing online can be highly unsettling. Like Jennifer, many adult actors don’t really know what’s out there. But some devoted followers know the actors’ bodies well—often recognizing tattoos, scars, or birthmarks—and “very quickly they bring [deepfakes] to the adult performer’s attention,” says Silverstein. Or performers will stumble upon the content by chance; some 20 years ago, for instance, the first such client to tell Silverstein her body was being used in a deepfake happened to be searching Nicole Kidman online when she found that one of the results showed Kidman’s face on her porn. “She was devastated, obviously, because they took her body,” he says, “and they were monetizing it.”
Otherwise, this imagery may be found by an organization like Takedown Piracy, one of several copyright enforcement companies serving adult content creators. US copyright violations can be challenging to prove if someone’s body lacks distinguishing features, says Reba Rocket, Takedown Piracy’s chief operating and marketing officer. But Rocket says her team has added digital fingerprinting technology to clients’ material to help flag and remove problematic videos, often finding them before clients realize they’re online.
By capturing “tens of thousands of tiny little visual data points” from videos, digital fingerprinting creates unique corresponding files that can be used to identify them, Rocket says—kind of like an invisible watermark. The prints remain even if pirates alter the videos or replace performers’ faces. Takedown Piracy has digitally fingerprinted more than half a billion videos and the organization has gotten 130 million copyrighted videos taken down from Google alone (though, of those videos, Rocket hasn’t tracked how many of these specifically include someone else’s face on a performer’s body).
Besides copyright, a range of legal tools can be used to try to combat NCII, says Eric Goldman, a law professor at Santa Clara University. For example, victims can claim invasion of privacy. But using these tools isn’t particularly straightforward, and they may not even apply when it comes to someone’s body. If there aren’t, for instance, unique markers indicating that a body in a deepfake belongs to the person who says it does, US law “doesn’t really treat [this content] as invasion of privacy,” Goldman says, “because we don’t know who to attribute it to.”
In a 2018 study that reviewed “judicial resolution” of cases involving NCII, Goldman found that one successful way plaintiffs were able to win cases was to assert “intentional affliction of emotional distress.” But again, that hinges on the ability to clearly identify the person in the content. Relevant statutes, he adds, might also require “intent to harm the individual,” which may be hard to show for people whose bodies alone are featured.
“AI girls will do whatever you want”
In the last few years, Silverstein says, it’s become less and less common to see the bodies of real adult content creators in deepfakes, at least in a way that makes them clearly identifiable.
Sometimes the bodies have been manipulated using AI or simpler editing tools. This can be as basic as erasing a birthmark or changing the size of a body part—minor edits that make it impossible to identify someone’s image beyond a reasonable doubt, so even porn actors who can tell that an altered image used their body as a base won’t get very far in the legal realm. “A lot of people are like, That looks like my body,” says Silverstein, but when he asks them how, they’ll reply, It just does.
At the same time, other users are now creating NCII with wholly AI-generated bodies. In “nudify” apps, anyone with a minimal grasp of technology can upload a photo of someone’s clothed body and have it replaced with a fake naked one. “So [much] of this content being created is just someone’s face on an AI body,” Silverstein says.
Such apps have drawn a ton of attention recently, in incidents from Grok’s “nudifying” minors to Meta’s running ads for—and then suing—the nudify app Crushmate. But there’s been relatively little attention paid to the content being used to train them. They almost certainly draw on the more than 10,000 terabytes of online porn, and performers have virtually zero recourse.
One reason is that creators aren’t able to demonstrate with any certainty that their content is being used to train AI models like those used by nudify apps. “These things are all a black box,” says Hany Farid, a professor at the University of California, Berkeley, who specializes in digital forensics. But “given the ubiquity” of adult content, he adds, it’s a “reasonable assumption” that online porn is being used in AI training.
“It’s just not at all difficult to come up with pornographic data sets on the internet,” says Stephen Casper, a computer science PhD student at MIT who researches deepfakes. What’s more, he says, plenty of shadowy online communities provide “user guides” on how to use this data to train AI, and in particular programs that generate nudes.
It’s not certain whether this activity falls within the US legal definition of “fair use”—an issue that’s currently being litigated in several lawsuits from other types of content creators—but Casper argues that even if it does, it’s ethically murky for porn created by consenting adults 10 years ago to wind up in those training data sets. When people “have their stuff used in a way that doesn’t respect or reflect reasonable expectations that they had at that time about what they were creating and how it would be used,” he says, there’s “a legitimate sense in which it’s kind of … nonconsensual.”
Adult performers who started working years ago couldn’t possibly have consented to AI anything; Jennifer calls AI-related risks “retroactively placed.” Contracts that porn actors signed before AI, adds Silverstein, might provide that “the publisher could do anything with the content using technology that now exists or here and after will be discovered.” That felt more innocuous when producers were talking about the shift from VHS to DVD, because that didn’t change the content itself, just the way it was conveyed. It’s a far different prospect for someone to use your content to train a program to create new content … content that could replace your work altogether.
Of course, this all affects creators’ bottom line—not unlike the way Google’s AI overviews affect revenue for online publishers who’ve stopped getting clicks when people are content with just reading AI-generated summaries. Performers’ “concern is … it’s another way to pirate [their] content,” says Rocket.
After all, independent creators aren’t just “having sex on camera,” as the adult content creator Allie Eve Knox puts it. They’re paying for filming equipment and location rentals, and then spending hours editing and marketing. For someone to then rip off and distort that content “for their own entertainment or financial gain,” she says, “fucking sucks.”
KIM HOECKELE
Tanya Tate, a longtime adult content creator, tells me about another highly unsettling AI-created situation: She was recently chatting with a fan on Mynx, a sexting app, when he asked her if she knew him. She told him no, and “his eyes just started watering,” Tate says. He was upset because he thought she did know him. Turns out he’d sent $20,000 to a scammer who’d used an AI-generated deepfake of Tate to seduce him.
Several men, Tate subsequently learned, had been scammed by an AI version of her, and some of them began blaming her for their losses and posting false statements about her online. When she reported one particularly aggressive harasser to the police, they told her he was exercising his “freedom of speech,” she says. Rocket, too, is familiar with situations where AI is used to take advantage of fans. “The actual content creator will get nasty emails from these people who’ve been scammed,” she says.
Other porn actors say they fear that their likenesses have been used without consent to do other things they wouldn’t do. One, Octavia Red, tells me she doesn’t do anal scenes, “but I’m sure there’s tons of deepfake anal videos of me that I didn’t consent to.” That could cost her, she fears, if viewers choose to watch those videos instead of subscribing to her websites. And it could cause fans to develop false expectations about what kind of porn she’ll create.
“I saw one AI creator saying, ‘Well, AI girls will do whatever you want. They don’t say no,’” says Rocket. “That horrifies me … especially if they’re training those AI models on real people. I don’t think they understand the damage to mental health or reputation that that can create. And once it’s on the internet, it’s there forever.”
Efforts to “scrub adult content from the internet”
As AI technology improves, it’s increasingly difficult for people to discern any type of real video from the best AI-generated ones on their own. In one 2025 study, UC Berkeley’s Farid found that participants correctly identified AI-generated voices about 60% of the time (not much better than random chance), while advances like false heartbeats make AI-generated humans tougher than ever to spot.
Nevertheless, most lawyers and legal experts I spoke with said copyright laws are still adult performers’ best bet in the US legal system, at least for getting their face-swapped content taken down. For his clients, Silverstein says, he tries to figure out the content’s origins and then issue takedown requests under the Digital Millennium Copyright Act, a 1998 law that adapted copyright law for the internet era. “Even recently, I had a performer who has an insanely well-known tattoo,” he says, and with a DMCA subpoena he managed to identify the poster of the content, who voluntarily removed it.
But this way of working is becoming increasingly rare.
These days it’s nearly “impossible,” Silverstein says, to determine who produced a deepfake, because many platforms that host pirated content operate facelessly. They’re also often based in places that “don’t really care about US law when it comes to copyrights,” says Rocket—places like Russia, the Seychelles, and the Netherlands.
While governments in the EU, the UK, and Australia have said they will ban or restrict access to nudify apps, it’s not an easily executed proposition. As Craanen notes, when app stores remove these services, they often simply reappear under different names, providing the same services. And social platforms where people share NCII deepfakes, argues Rocket, are slacking in getting them removed. “It’s endless, and it’s ridiculous, because places like Twitter and Facebook have the same technology we do,” Rocket says. “They can identify something as an infringement instantly, but they choose not to.”
(An Apple spokesperson, Adam Dema, said in an email that “’nudification’ apps are against our guidelines” in the app store, and it has “proactively rejected many of these apps and removed many others,” flagging a reporting portal for users. A Google spokesperson emailed, “Google Play does not allow apps that contain sexual content,” noting that the company takes “proactive steps to detect and remove apps with harmful content” and has suspended hundreds of apps for violating its policy. A Meta spokesperson shared a blog post about actions that company has taken against nudify apps but did not respond to follow-up questions about copyrighted material. X did not respond to a request for comment.)
As porn performers are forced to navigate AI-related threats, the only current federal law to address deepfakes may not help them much—and could even make matters worse. The Take It Down Act, which became US law last year, criminalizes publishing NCII and requires websites to remove it within 48 hours. But, as Farid notes, people could weaponize the measure by reporting porn that was made legally and with consent and claiming that it’s NCII. This could result in the content’s removal, which would hurt the performers who made it. Santa Clara’s Goldman points to Project 2025, the Heritage Foundation’s policy blueprint for the second Trump administration, which aims to wipe porn from the web. The Take It Down Act, he argues, “allows for the coordinated effort to scrub adult content from the internet.”
US lawmakers have a history of hurting sex workers in their attempts to regulate explicit content online. State-level age verification laws are an example; visitors can pretty easily get around these measures, but they can still result in reduced revenue for adult performers (because of lower traffic to those sites and the high price of age-checking services they have to purchase).
“They’re always doing something to fuck with the porn industry, but not in a way that actually helps sex workers,” says Jennifer. “If they do something, they’re taking away your income again—as opposed to something like giving you more rights to your image, [which] would be tremendously helpful.”
But as generative AI plays an increasingly large role in NCII deepfakes, the types of images to which adult performers have rights moves deeper into a gray area. Can actors lay claim to AI images likely trained on their bodies? How about AI-generated videos that impersonate them, like the one that tricked Tanya Tate’s fan?
The biggest challenge will be creating “legitimate, effective laws that will absolutely protect content creators from abusing their likeness to train and create AI,” Rocket says. “Absent that, we’re just going to have to keep pulling content down from the internet that’s fake.”
In the meantime, a few porn actors tell me, they’re trying to take advantage of copyright laws that weren’t really made for them; they’ve signed with platforms that host their AI-generated duplicates, with whom fans pay to chat, in part so they’ll have contracts that protect ownership of their AI likenesses. When I spoke with the actor Kiki Daire in September 2025 for a story on adult creators’ “AI twins,” she said she “own[ed] her AI” because she’d signed a contract with Spicey AI, a site that hosted AI duplicates of adult performers. If another company or person created her AI-generated likeness, she added, “I have a leg to stand on, as far as being able to shut that down.”
Even this, though, is not a sure thing; Spicey AI, for instance, shut down several months after I spoke with Daire, so it’s unlikely that her contract would hold. And when I spoke in October with Rachael Cavalli, another adult actor who had signed with an AI duplicate site in hopes it’d help protect her AI image, she admitted, “I don’t have time to sit around and look for companies that have used my image or turned something into a video that I didn’t actually do … it’s a lot of work.” In other words, having rights to your AI image on paper doesn’t make it easier to track down all the potentially infinite breaches of those rights online.
If she’d known what she knows about technology today, Jennifer says, she doesn’t think she would have done porn. The risks have increased too much, and too unpredictably. She now does in-person sex work; it’s “not necessarily safer,” she says, “but it’s a different risk profile that I feel more equipped to manage.”
Plus, she figures AI is unlikely to replace in-person sex workers the way it could porn actors: “I don’t think there’s going to be stripper robots.”
Jessica Klein is a Philadelphia-based freelance journalist covering intimate partner violence, cryptocurrency, and other topics.
