Wall Street being what it is, questions of artificial intelligence-related existential doom quickly turn to who the winners and losers are. There are no margin calls if we're all dead.

Over the weekend, there was broad agreement among the leaders of U.S. AI model labs that, in light of rogue agent incidents that are piling up, they should stop racing each other for technical supremacy and jointly slow down to focus more on safety before going any further.

The new top models from OpenAI and Anthropic went on autonomous hacking sprees in the spring and summer, and the full damage is probably yet unknown. The details that are public are pretty scary, and it dovetails with what AI leaders have been saying since the 2010s: AI has the potential to be the most useful and dangerous technology ever. The models are getting better faster than their makers understand how to fully control them.

But no one wants to unilaterally disarm in an arms race, so a slowdown only works if everyone does it in some sort of verifiable way. Usually governments do this by regulating, but the AI leaders see an urgency that the slow wheels of legislation can't help with, and that the White House is pushing back on.

"The only control or "guardrails" that AI needs is a STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!," President Donald Trump said in a Monday morning social-media post.

The primary winners of a slowdown would be the labs themselves. One of their biggest expenses is cloud computing for training new models, which at the bleeding edge is very costly. Slowing down the growth of this expense will improve the profitability of Anthropic and OpenAI, which are headed toward public listings.

But it could have the secondary effect of igniting a price war. The best models from the two leaders are very expensive, and they are already seeing pricing pressures from cheaper models. OpenAI lowered prices across its GPT-5.6 family of models in July and August, though Anthropic hasn't followed. Having more margin cushion could encourage the companies to compete more on price to gain market share.

The other AI labs-Alphabet, Meta Platforms, Microsoft and SpaceX-have even more to gain from a slowdown because they are trailing OpenAI and Anthropic in technology and model revenue. This gives them the opportunity to catch up, and to some degree blunt the early advantage those two companies have.

Chinese labs, which would presumably not be part of any agreement, could also seize on this opportunity to close the gap with the industry leaders.

The effect of a slowdown would likely be less demand for training cloud computing, but a price war could offset that by increasing demand for running AI models in the cloud, a process known as inference. A big unknown is how enterprise customers will react to the news, and if rogue-agent fears lead to a slowdown in implementation.

The entire AI infrastructure trade is what's at risk, at least in the near term.

The chip stocks are getting hammered on the slowdown talk with the PHLX semiconductor index declining 5% in midday trading on Monday. Nvidia chips are the standard for training compute, and its shares are down 3%.

Training the latest generations of models keeps requiring bigger and bigger data centers. OpenAI's GPT-6 Astra model-which was responsible for much of the rogue agent mischief-was trained on 100,000 Nvidia GPU chips in a Texas Oracle data center complex. Nvidia CEO Jensen Huang claimed that its successor would require a GPU cluster four times the size.

A training slowdown would mean that the owners of these extra-large complexes, which are being built over several years, could extend those timelines, pushing planned capital expenditures out a year or more. This would have impacts across the entire AI infrastructure trade, beyond chips: energy, materials, construction, electrical, cooling and more.

The cloud companies like Amazon.com, Microsoft, Alphabet, Oracle and CoreWeave will see a mixed bag. OpenAI and Anthropic training runs are their revenue streams. This would certainly reduce sales growth in the near term, again maybe offset by inference demand. But not only would the labs get a chance to catch a breath in this marathon run at sprint speeds, but so too would the clouds. They could push capex out, slow down the fast growth of debt and depreciation, and improve free cash flow.

But without being forced to by governments, negotiating a verifiable slowdown among U.S. labs will be very difficult, requiring a level of trust and good intentions that are in short supply.