Efforts to Rein In AI Tap Lesson From Social Media: Don’t Wait Until It’s Too Late
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Efforts to Rein In AI Tap Lesson From Social Media: Don’t Wait Until It’s Too Late

Activists and officials race to shape rules and public understanding of new artificial intelligence tools

By DEEPA SEETHARAMAN
Tue, Jul 18, 2023 8:38amGrey Clock 4 min

Social media was more than a decade old before efforts to curb its ill effects began in earnest. With artificial intelligence, lawmakers, activists and executives aren’t waiting that long.

Over the past several months, award-winning scientists, White House officials and tech CEOs have called for guardrails around generative AI tools such as ChatGPT—the chatbot launched last year by Microsoft-backed startup OpenAI. Among those at the table are many veterans of the continuing battle to make social media safer.

Those advocates view the AI debate as a fresh chance to influence how companies make and market their products and to shape public expectations of the technology. They aim to move faster to shape the AI landscape and learn from errors in the fight over social media.

“We missed the window on social media,” said Jim Steyer, chief executive of Common Sense Media, a child internet-safety organisation that has for years criticised social-media platforms over issues including privacy and harmful content. “It was late—very late—and the ground rules had already been set and industry just did whatever it wanted to do.”

Activists and executives alike are pushing out a range of projects and proposals to shape understanding and regulation to address issues including AI’s potential for manipulation, misinformation and bias.

Common Sense is developing an independent AI ratings and reviews system that will assess AI products such as ChatGPT on their handling of private data, suitability for children and other factors. The nonprofit plans to launch the system this fall and spend between $5 million and $10 million a year on top of its $25 million budget to fund the project.

Other internet advocacy groups including the Mozilla Foundation are also building their own open-source AI tools and investing in startups that say they are building responsible AI systems. Some firms initially focused on social media are now trying to sell services to AI companies to help their chatbots avoid churning out misinformation and other harmful content.

Tech companies are racing to influence regulation, discussing it with global governments that are both wary of AI and eager to capitalise on its opportunities. In early May, President Biden met with the chief executives of companies including OpenAI, Microsoft and Google at the White House. OpenAI CEO Sam Altman has spent weeks meeting with lawmakers and other leaders globally to discuss AI’s risks and his company’s idea of safe regulation.

Altman and Microsoft President Brad Smith have both argued for a new regulatory agency that would license large AI systems. Tesla CEO Elon Musk, who on Wednesday announced the official launch of his new AI startup, said in May that the government should convene an independent oversight committee, potentially including industry executives, to create rules that ensure AI is developed safely.

The Federal Trade Commission also is taking a hard look at AI. It is investigating whether OpenAI has “engaged in unfair or deceptive practices” stemming from false information published by ChatGPT, according to a civil subpoena made public this past week. Altman said OpenAI is confident that it follows the law and “of course we will work with the FTC.”

Looming large over all this activity is the growing feeling among many activists and lawmakers that years of efforts to regulate or otherwise change social-media companies including Facebook parent Meta Platforms, Twitter and TikTok were unsatisfactory. Facebook was founded in 2004 and Twitter in 2006, but widespread discussion about regulation didn’t really take off until after discoveries of Russian interference and other issues in the 2016 U.S. election.

“Congress failed to meet the moment on social media,” Democratic Sen. Richard Blumenthal said during a congressional hearing on AI in May. “Now we have the obligation to do it on AI before the threats and the risks become real.”

Though social-media executives in recent years called for more regulation, no new U.S. federal laws have been set that require companies to protect users’ privacy and data or that update the nearly three-decade-old rules for how platforms police content. In part that is because of disagreements among lawmakers over whether companies should do more to moderate what is said on their platforms or whether they already have overstepped into stifling free speech.

Some of the activists who are veterans of those battles say two major lessons from this era are that the companies can’t be trusted to self-regulate and that the federal government is too gridlocked to pass meaningful legislation. “There’s a massive void,” Steyer of Common Sense Media said.

Yet he and others say they are encouraged by the willingness of AI companies to discuss major issues.

“We’re seeing some of the people from trust and safety teams from social media are now at AI companies,” said L. Gordon Crovitz, co-founder of NewsGuard, a company that tracks and rates news sites. Crovitz, former publisher of The Wall Street Journal, says these people seem much more empowered in their current roles. “The body language is ‘we’ve been freed.’”

Large language models such as GPT-4 are trained on anything that can be scraped from the internet, but the data contain large chunks of hate speech, misinformation and other harmful content. So these models are further refined after their initial training to weed out some of that bad content in a process called fine-tuning.

NewsGuard has been talking to AI companies about licensing its data—which Crovitz calls a “catalog of all the important false narratives that are out there”—for fine-tuning and to bolster AI models’ guardrails against producing just those types of misinformation and false narratives.

Ravi Iyer, a former product manager for Meta, is now at the University of Southern California’s Marshall School of Business and developing a poll that tracks how people experience AI systems. He hopes the poll will influence how AI companies design and deploy their products.

“We need to know that’s a choice platforms can make and reward them for not making the wrong choices,” Iyer said.

The Mozilla Foundation, a nonprofit that builds the Firefox internet browser, said it is building open-sourced models as alternatives to large private AI models. “We need to build alternatives and not just advocate for them,” Mark Surman, Mozilla’s president, said.

Steyer described the AI ratings system being built at Common Sense as the most ambitious in the nonprofit’s history. Tracy Pizzo Frey, a consultant who previously worked for Google and is helping craft the system, said there is no set way to evaluate the safety of AI tools.

So far, Common Sense is looking at seven factors, including how transparent companies are about what their systems can do and where they still have shortcomings. The nonprofit may factor in how much information companies provide about their training data, which companies including OpenAI view as competitive secrets.

Frey said Common Sense won’t ask for proprietary data but needs information that helps parents and educators make informed decisions about the use of AI. “There are no rules around what transparency looks like,” Frey said.



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The AI Boom Faces Its First Serious Debt Test

Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.

By Ruba Jaajaa
Mon, Oct 19, 2026 4 min

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

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