Speaking at the Fortune Brainstorm AI conference this week with editorial director Andrew Nusca, Gordon-Levitt used “The Artist and the Algorithm” session to pose another, deeper question: “Why should the companies building this technology not have to follow any laws? It doesn’t make any sense.”

In a broad-ranging conversation covering specific failures in self-regulation, including instances in which “AI companions” on major platforms reportedly verged into inappropriate territory for children, Gordon-Levitt argued relying on internal company policies rather than external law is insufficient, noting such features were approved by corporate ethicists.

Gordon-Levitt’s criticisms were aimed, in part, at Meta, following the actor’s appearance in a New York Times Opinion video series airing similar claims. Meta spokesperson Andy Stone pushed back hard on X.com at the time, noting Gordon-Levitt’s wife was formerly on the board of Meta rival OpenAI.

Gordon-Levitt argued without government “guardrails,” ethical dilemmas become competitive disadvantages. He explained that if a company attempts to “prioritize the public good” and take the “high road,” they risk being “beat by a competitor who’s taking the low road.” Consequently, he said he believes business incentives alone will inevitably drive companies toward “dark outcomes” unless there is an interplay between the private sector and public law.

‘Synthetic intimacy’ and children

Beyond the lack of regulation, Gordon-Levitt expressed deep concern regarding the psychological impact of AI on children. He compared the algorithms used in AI toys to “slot machines,” saying they use psychological techniques designed to be addictive.

Drawing on conversations with NYU psychologist Jonathan Haidt, Gordon-Levitt warned against “synthetic intimacy.” He argued that while human interaction helps develop neural pathways in young brains, AI chatbots provide a “fake” interaction designed to serve ads rather than foster development.

“To me it’s pretty obvious that you’re going down a very bad path if you’re subjecting them to this synthetic intimacy that these companies are selling,” he said.

Haidt, whose New York Times bestseller The Anxious Generation came recommended from Gordon-Levitt onstage, recently appeared at a Dartmouth-United Nations Development Program symposium on mental health among young people and used the metaphor of tree roots for neurons. Explaining tree-root growth is structured by environments, he brought up a picture of a tree growing around a Civil War–era tombstone. With Gen Z and technology, specifically the smartphone, he said: “Their brains have been growing around their phones very much in the way that this tree grew around this tombstone.” He also discussed the physical manifestations of this adaptation, with children “growing hunched around their phone,” as screen addiction is literally “warping eyeballs,” leading to a global rise in myopia shortsightedness.

The ‘arms race’ narrative

When addressing why regulations have been slow to materialize, Gordon-Levitt pointed to a powerful narrative employed by tech companies: the geopolitical race against China. He described this framing as “storytelling” and “handwaving” designed to bypass safety checks,. Companies often compare the development of AI to the Manhattan Project, arguing slowing down for safety means losing a war for dominance. In fact, The Trump administration’s “Genesis Mission” to encourage AI innovation was unveiled with similar fanfare just weeks ago, in late November.

However, this stance met with pushback from the audience. Stephen Messer of Collectiv[i] argued Gordon-Levitt’s arguments were falling apart quickly in a “room full of AI people.” Privacy previously decimated the U.S. facial recognition industry, he said as an example, allowing China to take a dominant lead within just six months. Gordon-Levitt acknowledged the complexity, admitting “anti-regulation arguments often cherrypick” bad laws to argue against all laws. He maintained that while the U.S. shouldn’t cede ground, “we have to find a good middle ground” rather than having no rules at all.

Gordon-Levitt also criticized the economic model of generative AI, accusing companies of building models on “stolen content and data” while claiming “fair use” to avoid paying creators. He warned a system in which “100% of the economic upside” goes to tech companies and “0%” goes to the humans who created the training data is unsustainable.

Despite his criticisms, Gordon-Levitt clarified he is not a tech pessimist. He said he would absolutely use AI tools if they were “set up ethically” and creators were compensated. However, he concluded without establishing the principle that a person’s digital work belongs to them, the industry is heading down a “pretty dystopian road.”

Warsh, a former Fed governor, now has a 47% probability, up from 39% on Sunday and just 11% on Dec. 3. Hassett, director of the National Economic Council, has fallen to 41%, down from 51% on Sunday and 81% on Dec. 3.

A report from CNBC saying Hassett’s candidacy was running into pushback from people close to President Donald Trump seemed to put Warsh on top. The resistance stems from concerns Hassett is too close to Trump.

That followed Trump’s comment late Friday, when he told The Wall Street Journal Warsh was at the top of his list, though he added “the two Kevins are great.”

According to the Journal, Trump met Warsh on Wednesday at the White House and pressed him on whether he could be trusted to back rate cuts. 

The report surprised Wall Street, which had overwhelming odds on Hassett as the favorite, lifting Warsh’s odds from the cellar.

But even prior to the Journal story, there have been rumblings in the finance world Hassett wasn’t their preferred choice to be Fed chair.

At a private conference for asset managers on Thursday, JPMorgan Chase CEO Jamie Dimon signaled support for Warsh and predicted Hassett was likelier to support Trump on more rate cuts, sources told the Financial Times.

And in a separate report earlier this month, the FT said bond investors shared their concerns about Hassett with the Treasury Department in November, saying they’re worried he would cut rates aggressively in order to please Trump.

Trump has said he will nominate a Fed chair in early 2026, with Jerome Powell’s term due to expire in May. 

For his part, Hassett appeared to put some distance between himself and Trump during an appearance on CBS’ Face the Nation on Sunday.

When asked if Trump’s voice would have equal weighting to the voting members on the rate-setting Federal Open Market Committee, Hassett replied, “no, he would have no weight.”

“His opinion matters if it’s good, if it’s based on data,” he explained. “And then if you go to the committee and you say, ‘well the president made this argument, and that’s a really sound argument, I think. What do you think?’ If they reject it, then they’ll vote in a different way.”

But that begs the question: What happens to all those older-generation chips?

The AI stock boom has lost a lot of momentum in recent weeks due, in part, to worries that so-called hyperscalers aren’t correctly accounting for the depreciation in the hoard of chips they’ve purchased to power chatbots.

Michael Burry—the investor of Big Short fame who famously predicted the 2008 housing collapse—sounded the alarm last month when he warned AI-era profits are built on “one of the most common frauds in the modern era,” namely stretching the depreciation schedule. He estimated Big Tech will understate depreciation by $176 billion between 2026 and 2028.

But according to a note last week from Alpine Macro, chip depreciation fears are overstated for three reasons.

First, analysts pointed out software advances that accompany next-generation chips can also level up older-generation processors. For example, software can improve the performance of Nvidia’s five-year-old A100 chip by two to three times compared to its initial version.

Second, Alpine said the need for older chips remains strong amid rising demand for inference, meaning when a chatbot responds to queries. In fact, inference demand will significantly outpace demand for AI training in the coming years.

“For inference, the latest hardware helps but is often not essential, so chip quantity can substitute for cutting-edge quality,” analysts wrote, adding Google is still running seven- to eight-year-old TPUs at full utilization.

Third, China continues to demonstrate “insatiable” demand for AI chips as its supply “lags the U.S. by several generations in quality and severalfold in quantity.” And even though Beijing has banned some U.S. chips, the black market will continue to serve China’s shortfalls.

Meanwhile, not all chips used in AI belong to hyperscalers. Even graphics processors contained in everyday gaming consoles could work.

A note last week from Yardeni Research pointed to “distributed AI,” which draws on unused chips in homes, crypto-mining servers, offices, universities, and data centers to act as global virtual networks.

While distributed AI can be slower than a cluster of chips housed in the same data center, its network architecture can be more resilient if a computer or a group of them fails, Yardeni added.

“Though we are unable to ascertain how many GPUs were being linked in this manner, Distributed AI is certainly an interesting area worth watching, particularly given that billions are being spent to build new, large data centers,” the note said.