Perhaps oddly, there now is a symmetry of interests for U.S. frontier-model developers, the financial community and politicians to call for slowing the pace of AI development and the massive amount of spending by hyperscalers.
It is perhaps not simply coincidence that calls for slowing development (and presumably spending rates) are happening at the same time as scrutiny over capex levels is high, hyperscalers are sacrificing profit margin and midterm elections are approaching.
The financial community does not like the uncertainty or magnitude of AI infrastructure capital investment. Firms would rather achieve their former substantial profit margins instead of enduring a period of low to negative cash flow.
Politicians want to appear to voters to be “doing something” about AI before midterm elections, while AI developers, aware that government policy powerfully shapes their business prospects, might want to “play ball” with legislators to avoid punitive policies.
Frontier-model companies have an economic incentive to participate in policymaking, not merely to oppose regulation, in other words, as well as having incentives to rein in AI capex to mollify investors and the financial markets; preserve credit ratings and reduce financial risks.
The point: for different reasons, frontier-model developers, financial markets and lawmakers all have reasons to prefer some moderation of AI spending.
The big unresolved question is whether such coordination is possible without simply handing a strategic advantage to whoever refuses to participate, particularly China. That's the problem that makes this much harder than simply saying, "Everyone should spend less."
Ironically, though, AI safety regulation could function partly as an investment-coordination mechanism.
A slower, more predictable frontier-development schedule reduces the incentive for every participant to make defensive investments against the possibility that its competitors will move faster. And that arguably reduces the danger of overinvestment; reduces risk; allows politicians to be seen as proactive; helps hyperscalers boost profit margins and cash flow and alleviates some “creative” financing methods that are not transparent.
So perhaps “safety” becomes the enabler for a more-measured pace of frontier-model development that also reduces capex demands. That might also help prevent some amount of overinvestment danger.
S&P Global’s new credit outlook for hyperscalers resembles the famous quip about one-handed economists: strong credit ratings and ability to finance artificial intelligence infrastructure on one hand, and risk from less transparency about payback from those investments.
“We assume the top six U.S. hyperscalers will spend more than $7 trillion on data centers and AI-related capex from 2025 through to 2030, the ratings agency says.
Aside from the obvious issue of revenues emerging quickly enough to justify the spending, there is business risk from unexpected failures in the complex financing ecosystem, which “increases the chances that the failure of an unrated entity could dent the credit standing of a highly rated firm,” the agency says.
To be sure, “the question isn't just whether AI creates value, it's whether that value arrives fast enough, and in sufficient quantity, to justify the current pace of investment.”
Perhaps ironically, concerns about model “safety” are raised just as developers, financiers, investors and politicians also are looking for more moderation.
Paradoxically, that might also help alleviate the danger of unexpected financial damage were an interlocked ecosystem surprised by a sudden collapse of any key partner’s business.
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