Matt Stoller didn’t mince words. In his newsletter published today, the antitrust advocate laid out a blunt case: the rush to build ever-larger AI systems rests on systematic violations of law that regulators have chosen not to pursue. The Big Newsletter called it an elite crime spree. Documents unsealed in the New York Times copyright suit against OpenAI show the company circumvented paywalls to scrape content. When informed of the hack, President Greg Brockman replied, “ah nice.”
A Microsoft director of applied science went further. He described training big models on copyrighted material as “the largest theft of labor in human history.” Those words come from court filings uncovered by Jason Kint. They suggest possible breaches of the Computer Fraud and Abuse Act. Yet enforcement remains absent.
Short. Direct. The pattern repeats across the industry.
While tech executives warn of future existential risks, present-day harms multiply. Voice clones mimic grandchildren in distress. Deepfakes pose as executives demanding urgent wire transfers. Synthetic identities slip past verification systems at scale. Losses have reached hundreds of millions in the United States alone. And the technology that enables this arrives cheaper and more convincing each quarter.
The FBI’s Internet Crime Complaint Center tracked the shift. In 2025 it received more than 22,000 complaints tied to AI tools. Reported losses hit $893 million. Investment scams accounted for $632 million of that total. Older Americans bore a heavy share, losing $352 million. Yahoo News reported the figures yesterday.
But those numbers capture only what victims recognize and report. Many scams now arrive polished. A few seconds of audio from social media suffices for convincing voice clones. AI writing tools generate error-free phishing messages that once betrayed their origins through awkward phrasing. The Washington Post explained the change this week. Scams aren’t new. Their cost and quality have transformed.
Organized networks recycle the same fraudulent assets across targets.
Shufti’s 2026 Identity Fraud Report, released last week, paints an even darker picture. Deepfake document fraud made up 80 percent of AI-enabled attacks in the first half of the year. Synthetic identities followed at 12 percent. The firm projects a 495 percent rise in AI-powered identity fraud for 2026 compared with 2025. Organized rings reuse the same forged documents and devices. One network linked 70 identities through just 13 devices. Digital Watch Observatory covered the report on September 11.
TRM Labs tracked parallel growth in crypto-related crime. Its 2026 AI-in-Crime Adoption Index climbed to 54 from 28 in 2024. Deepfake-scam losses in early 2026 already exceeded the entire previous year by 263 percent. Deloitte forecasts U.S. generative-AI-driven fraud losses will reach $40 billion by 2027, up from $12.3 billion in 2023. The firm published its analysis this month. TRM Labs detailed the index on September 17.
And. The same tools power romance scams that once required weeks of patient grooming. Incode’s Agentic Fraud Report documented 66 incidents, 44 of them confirmed AI cases. Autonomous agents now handle target research, conversation maintenance and fund extraction with minimal human oversight. Global fraud losses reached $579 billion in 2025 according to Nasdaq Verafin data cited in the report. Incode released its findings today.
Real people feel the impact. A Georgia man lost his car and cash after months of communication with an AI-generated persona posing as a sheriff’s deputy. Police arrested 25-year-old Caleb Mills on charges including identity fraud and impersonation. He allegedly used stolen photos, fake accounts and voice-changing tools. Cybernews reported the case on September 9.
Another victim, Kris Kolakosis, handed over $400,000 in a catfishing scheme built on AI-generated profiles and conversations. He met “Eliza” on Facebook. The interaction felt genuine until the requests for money began. Newsgram told his story in July.
Corporations face sophisticated variants. In one documented case, fraudsters used a cloned executive voice during a video call to authorize a large transfer. Such business email compromise schemes involving AI generated $30 million in reported FBI losses for 2025. The bureau has warned repeatedly about deepfakes impersonating government officials, including videos of senior FBI agents directing victims to fake recovery sites.
But what about the foundation? The training data itself.
Stoller argues the entire edifice depends on mass copyright infringement and unauthorized access. Hyperscalers scrape the internet without permission. They circumvent technical protections. When caught, responses range from indifference to celebration. Existing laws already prohibit these acts. The Computer Fraud and Abuse Act. Copyright statutes. Antitrust rules against illegal monopolies. Yet prosecutors rarely charge the powerful.
Sam Bankman-Fried once served as a major early backer of Anthropic. Meta faced accusations of facilitating mass sex trafficking on its platforms. Financial maneuvers fund the enormous data centers required. Each element adds to a picture of an industry operating beyond normal legal constraints.
Enforcement lags for a reason. Political pressure favors new regulations over application of old ones. Safety standards modeled on the FDA gain traction among some lawmakers and even certain AI firms. Bernie Sanders calls for oversight. Anthropic, OpenAI and Google support versions of it. Stoller counters that such measures miss the point. The problem isn’t lack of rules. It’s selective blindness to violations by elites.
Recent actions show tentative pushback. The Manhattan District Attorney seized 12 domains selling AI-generated non-consensual deepfake pornography in mid-September. The sites allegedly turned photos of 1,200 real people into explicit videos without consent. Victims included actors, politicians and influencers. Manhattan DA’s Office announced the seizures on September 14.
Still, these represent surface-level responses. The deeper architecture remains untouched. Organized crime networks in Southeast Asia use American AI models like ChatGPT and Gemini to automate multilingual scam operations. One tool set generated tens of millions in illicit profits. AP and FRONTLINE documented the supply chain in June. Scammers pay for specialized software built on U.S. tech. The profits flow back through crypto wallets.
Projections point higher. Incode estimates AI could accelerate global scam losses toward $1 trillion annually if trends continue. Shufti sees fraud rings scaling through reusable synthetic assets. TRM Labs notes the industrialization of deepfake production across dozens of countries.
So the panic over future AI dangers feels oddly timed. Current capabilities already extract value at unprecedented scale. They steal creative labor. They defraud individuals and businesses. They erode trust in voice, image and video. All while the architects claim to race toward beneficial artificial general intelligence.
Critics like Stoller demand something simpler. Enforce the laws on the books. Treat the “largest theft of labor in human history” as a crime worth prosecuting. Apply antitrust standards to break illegal monopolies. Pursue clear violations of computer fraud statutes.
Without that shift, new regulations risk becoming theater. They may slow responsible developers while sophisticated operators continue unchecked. The crime spree, elite in its participants and global in its victims, rolls on. Billions in losses accumulate. And the public grows weary of promises that tomorrow’s safeguards will fix today’s thefts.
The evidence sits in court documents. FBI reports. Industry analyses released this month. The pattern is clear. AI doesn’t just enable fraud. In many corners it was built on it.
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