The United States' artificial intelligence industry is being pulled by powerful forces in two completely opposite directions. Some of the industry's most prominent leaders are jointly calling for the pace of development to slow, while US President Donald Trump wants to go full steam ahead to beat China and has announced a so-called “Super Intelligence Force” headed by former SEC Chairman Jay Clayton. Meanwhile, Goldman Sachs expects the five major US-based AI hyperscalers to tip $800 billion into the AI buildout this year. With the US stock market and GDP growth increasingly dependent on the health of the AI industry, could a slowdown in the pace of AI development tank the economy? The question has sharpened in recent months, spilling out of op-eds and conference stages and into legislation, bond markets and central bank warnings.

Who wants a slowdown, and who doesn't?

Anthropic CEO Dario Amodei wants to “pace the frontier,” slowing advances in the most powerful AI models so that safety research can catch up. His September proposal combines independent evaluators inside labs, shared safety standards, limits on developers in democratic countries, and eventual international coordination that would include China.

OpenAI's Sam Altman publicly endorsed the approach, as did Google DeepMind co-founder Demis Hassabis, while The Guardian reported that Altman and xAI founder Elon Musk have backed the calls to put brakes on AI development. While Amodei explicitly says pacing would allow model training and technical progress to continue, some politicians want to impose a harder brake. The “Ban Artificial Superintelligence Act,” introduced Sept. 23 by Senator Bernie Sanders and Representative Greg Casar, would permanently prohibit superintelligence and pause advanced AI development until federal safety rules are established, according to a statement from Sanders' office. For now, the bill remains a proposal rather than law, and whether it gains traction in Congress is one of the unresolved questions hanging over the debate.

Senator Elizabeth Warren has also called for an immediate pause in advanced AI development. European Commission President Ursula von der Leyen has voiced support for pacing frontier AI research.

Trump, by contrast, opposes a slowdown, arguing that restrictions would benefit China. “Don't kill the Golden Goose!” he warned on Truth Social. Meta's Mark Zuckerberg favors letting each lab determine its own safe pace, citing competition and liability as incentives. Nvidia's Jensen Huang similarly urges rapid development while explicitly supporting company-specific pauses when products are unsafe or control is uncertain — their objection is to a coordinated slowdown, not to every form of restraint.

On Sept. 29, Trump and leading AI executives signed a voluntary safety accord centered on internal controls, independent audits and oversight. The agreement establishes safety commitments without imposing a collective development pause, leaving the actual pace of development with the companies themselves. But with global anxiety around the technology growing, the next high-profile AI safety incident could renew the push toward a slowdown.

Will a slowdown take down the big bets?

Enormous sums are pouring into the AI buildout. SoftBank recently launched another $10 billion and €1 billion ($1.15 billion) bond sale to fund its OpenAI investment — according to Reuters, it would be Asia-Pacific's and Japan's largest non-financial corporate bond deal and among the 20 largest globally this year. SoftBank had already invested about $54.6 billion in OpenAI by the end of July.

Data suggest AI investment is growing at such a pace that it is having a significant impact on the broader US economy. A January analysis by the St. Louis Fed estimated that broad AI-related investment accounted for 39% of real GDP growth during the first nine months of 2025:

“Together, the AI categories contributed 0.97 percentage points to real GDP growth in the first three quarters of 2025 [...] Through the third quarter of 2025, these categories made up 39% (36% excluding data centers) of total GDP growth versus 28% in 2000.”

An AI slowdown would not automatically produce economic disaster in the US — but it would certainly have an impact. It could change market expectations, cause companies to cancel infrastructure plans, lead investors to reprice AI assets, and push lenders to withdraw financing. That is also why the debate matters far beyond the tech industry: the same capital that funds data centers and chips now runs through GDP figures, equity valuations and credit markets.

In April, the IMF estimated that an AI-investment reversal would bring a 20% decline in US equity markets and tighter credit, with US GDP 1.5% below baseline and world output 1.2% lower. A scenario released this month by major credit rating agency Fitch is even harsher, concluding that a 35% equity shock combined with capex retrenchment — a pullback in the data-center and infrastructure spending at the heart of the buildout — would entail a US recession.

The glass-half-full view

The more optimistic view holds that the full potential of the AI technology that already exists has not even begun to be tapped. David Minarsch, CEO of AI phone agent service Valory and a founding member of AI agent system Olas, tells Magazine that slowing frontier AI capability progress would still see significant productivity gains made through agentic system development.

“There's ample evidence that AI adoption is severely lagging across many industries and even within software engineering, lagging across different types of businesses and organisations,” he explains. “Even with a complete halt of training new models, the dissemination of existing models through the economy would continue, yielding the associated gains.”

Shiv Shankar, founder and CEO of AI computing platform Boundless, agrees. He tells Magazine that “as people find more and more use cases, at least for the short to medium term, meaning the next couple of years, we only see inference demand going vertical.” That would hold regardless of a model development slowdown. Inference — running already-trained models for end users — is a different activity from the frontier training that slowdown proposals would pace. “It's going to keep growing, and quite a lot of opportunities may be created,” he concluded.

The glass-half-empty view

While not speaking to the slowdown debate specifically, the International Monetary Fund (IMF) warned in January that weaker AI-productivity expectations could see reduced investment, trigger a market correction and erode household wealth — effects that would then echo through the economy by weighing down consumption and further investment:

“Risks to the outlook remain tilted to the downside. Reevaluation of productivity growth expectations about AI could lead to a decline in investment and trigger an abrupt financial market correction, spreading from AI-linked companies to other segments and eroding household wealth.”

Bank for International Settlements (BIS) Administrator Pablo Hernández de Cos explained earlier this month that “should the returns to AI disappoint, a pullback in investment could turn today's capital expenditure boom into a bust.” He added that history offers some instructive parallels:

“The canal mania of the 1830s, the British railway mania of the 1840s, the electrification boom of the 1920s and the dotcom surge of the late 1990s were all based on important technological breakthroughs. All drew in more capital than eventual returns could justify. In each of these cases, the eventual correction that followed had economy-wide implications.”

A July BIS working paper estimated that overinvestment in AI infrastructure stands at roughly 1.5 times the socially efficient level — highlighting debt and circular equity arrangements that make a bust and broader economic contagion more likely:

“The AI race generates significant over-investment, exceeding the socially efficient level by around 50% under a conservative baseline. Larger booms end in more disruptive busts.”

The Union Bank of Switzerland argued last week that “pacing does not necessarily imply lower capex,” announcing that the bank retains its “2027 AI industry capex forecast of USD $1.2 trillion, a rise of 33% from our estimate of USD $900 billion this year.” Concrete signposts in the coming months should help clarify the debate: whether proposed legislative pauses gain any traction in Congress, whether actual spending tracks the forecasts of Goldman Sachs and UBS, and whether the new safety accord's audits and controls hold as the technology advances.

So perhaps a slowdown could, in fact, alleviate some of this reported overinvestment? Only time will tell.