Too big to pause: Could an AI slowdown crash the economy?

The artificial intelligence industry in the United States has powerful forces pulling it in completely opposite directions.

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 and limits on developers in democratic countries, and eventual international coordination, with countries including China.

OpenAI’s Sam Altman, Google DeepMind co-founder Demis Hassabis and xAI founder Elon Musk have endorsed his approach. While Amodei explicitly says pacing would allow model training and technical progress to continue, some politicians want to impose a harder brake. Senator Bernie Sanders and Representative Greg Casar’s “Ban Artificial Superintelligence Act”, introduced Sept. 23, would permanently prohibit superintelligence and pause advanced AI development until federal safety rules are established.

Source: Donald Trump

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, rather than 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.

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 large bets?

A huge amount of money is currently pouring into the AI build out. SoftBank launched another $10 billion and €1 billion ($1.15 billion) bond sale recently to fund its OpenAI investment. It would be Asia-Pacific and Japan’s largest non-financial corporate bond deal and among this year’s 20 largest globally, according to Reuters. SoftBank had already invested about $54.6 billion in OpenAI by the end of July.

Data suggests that the pace of AI investment is growing at such a rate, that it is having a significant impact on the broader US economy. A January St. Louis Fed analysis 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 result in economic disaster in the US — but it would certainly have an impact. It could lead to a change in market expectations, cause companies to cancel infrastructure plans, see investors reprice AI assets and lenders to withdraw financing.

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, expecting a 35% equity shock plus capex retrenchment produces a US recession.

The glass half full view

The more optimistic view is that we haven’t even started to tap the full potential of the AI technology that already exists. David Minarsch, the CEO of AI phone agent service Valory and founding member of AI agent system Olas, tells Magazine that slowing frontier AI capability progress would still see gains 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’s regardless of a model development slow down. “It’s going to keep growing, and quite a lot of opportunities may be created,” he concluded.

The glass half empty view

While not talking about the slowdown 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. Those effects would then echo through the economy by weighing down on 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 offered 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 paper estimated that over investment in AI infrastructure is at roughly 1.5 times the socially efficient level — highlighting debt and circular equity 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.”