ChatGPT Comes Under Investigation by Federal Trade Commission

WASHINGTON—The Federal Trade Commission is investigating whether OpenAI’s ChatGPT has harmed people by publishing false information about them, posing a potential legal threat to the popular app that can generate eerily humanlike content using artificial intelligence.

In a civil subpoena to the company made public Thursday, the FTC says its investigation of ChatGPT focuses on whether OpenAI has “engaged in unfair or deceptive practices relating to risks of harm to consumers, including reputational harm.”

One question asks the company to “describe in detail the extent to which you have taken steps to address or mitigate risks that your large language model products could generate statements about real individuals that are false, misleading or disparaging.”

The new FTC investigation under Chair Lina Khan marks a significant escalation of the federal government’s role in policing the emerging technology.

Khan, who appeared before the House Judiciary Committee on Thursday, said the agency is concerned that ChatGPT and other AI-driven apps have no checks on the data they can mine.

“We’ve heard about reports where people’s sensitive information is showing up in response to an inquiry from somebody else,” Khan said. “We’ve heard about libel, defamatory statements, flatly untrue things that are emerging. That’s the type of fraud and deception that we are concerned about.”

For critics of the FTC, the probe represented another venture into uncharted territory for an agency that has suffered recent legal setbacks in its antitrust enforcement efforts.

“When ChatGPT says something wrong about somebody and might have caused damage to their reputation, is that a matter for the FTC’s jurisdiction? I don’t think that’s clear at all,” said Adam Kovacevich, founder of Chamber of Progress, an industry trade group.

Such matters “are more in the realm of speech and it becomes speech regulation, which is beyond their authority,” he said.

OpenAI didn’t respond to requests for comment.

Marc Rotenberg, who heads a group that filed an FTC complaint over ChatGPT in March, said it might be unclear whether the FTC has jurisdiction over defamation. But “misleading advertising is clearly within the FTC’s purview,” said Rotenberg, president of the Center for AI and Digital Policy. “And disinformation relating to commercial practices is already, according to the FTC, an area within its authority.”

Rotenberg’s group filed a complaint with the FTC in March concerning ChatGPT, terming it “biased, deceptive and a risk to privacy and public safety,” and arguing that it satisfies none of the FTC’s guidelines for AI use.

The FTC has broad authority to police unfair and deceptive business practices that can harm consumers, as well as unfair competition, but critics say Khan has sometimes pushed its authority too far—as illustrated by a federal judge’s decision this week to dismiss the FTC’s attempt to block Microsoft’s acquisition of Activision Blizzard.

At the House committee hearing Thursday, Khan came under fire for her agency’s investigation of Twitter’s privacy protections for consumers. Republicans say the probe was driven by progressives angry over Elon Musk’s takeover of Twitter and his loosening of content moderation policies. And Twitter asked a federal court Thursday to terminate a 2022 settlement it agreed to with the FTC over alleged privacy violations, saying it had been subject to a “burdensome and vexatious enforcement investigation.”

Khan responded that the agency was only interested in protecting the privacy of users and that “we are doing everything to make sure Twitter is complying with the order.”

In its civil subpoena to OpenAI, the FTC asked the company detailed questions about its data-security practices. It cited a 2020 incident in which the company disclosed a bug that allowed users to see information about other users’ chats and some payment-related information.

Other topics covered by the FTC subpoena include the company’s marketing efforts, its practices for training AI models, and its handling of users’ personal information. The FTC inquiry was reported earlier by the Washington Post.

The Biden administration has begun examining whether checks need to be placed on artificial-intelligence tools such as ChatGPT. In a first step toward potential regulation, the Commerce Department in April put out a formal public request for comment on what it called accountability measures.

The White House’s Office of Science Technology Policy is also working to develop strategies to address both the benefits of AI, such as the possibility of using it to expand access to government services, as well as harms such as increased hacking capabilities, discriminatory decisions by AI systems, and the potential for AI-generated content to disrupt elections.

Lawmakers in both parties—led by Senate Majority Leader Chuck Schumer (D., N.Y.)—also have made regulating artificial intelligence a priority for the current Congress.

In addition to concerns about potential reputational risks, lawmakers say they worry that AI tools can be abused to manipulate voters with disinformation, discriminate against minority groups, commit sophisticated financial crimes, displace millions of workers or create other harms. Lawmakers have been especially concerned about the risks of so-called deepfake videos that falsely depict real people taking embarrassing actions or making embarrassing statements.

But new legislation or other measures are likely months away, if not longer. And lawmakers must worry that any significant action they take will risk slowing the pace of U.S. innovation, in what is shaping up as a vital competition with China to dominate the markets for AI tools.

Even ChatGPT’s creators have urged more government oversight of AI development.

In a hearing before Congress in May, OpenAI Chief Executive Sam Altman called on Congress to create licensing and safety standards for advanced artificial-intelligence systems, as lawmakers begin a bipartisan push toward regulating the powerful new tools available to consumers.

“We understand that people are anxious about how it can change the way we live. We are, too,” Sam Altman said of AI technology at the Senate subcommittee hearing. “If this technology goes wrong, it can go quite wrong.”

Altman has been traveling the world talking about both the promise and perils of AI, including meeting with heads of state including French President Emmanuel Macron and Indian Prime Minister Narendra Modi.

A sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.

The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.

The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.

The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.

This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.

The debt behind the AI build-out

Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.

Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.

JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.

The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.

That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.

A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.

When interest rates rise, those assumptions become less forgiving.

Inflation changes the calculation

September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.

The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.

CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.

Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.

Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.

The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.

China adds another source of pressure

Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.

Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.

The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.

That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.

A reset rather than the end of the boom

The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.

What has changed is the market’s willingness to overlook the price and financing structure of that growth.

The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.

More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.

For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.

Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.

The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.

Fact box

Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026

Philadelphia Semiconductor Index: Down approximately 11.4 per cent

Micron Technology: Down approximately 14.6 per cent

Samsung Electronics: Down approximately 18 per cent

SK Hynix: Down approximately 24 per cent

US September inflation: 3.7 per cent annually

Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion