Fed Official Jeff Schmid Questions If AI Financial Risk Has Become Too Big to Fail

Fed's Schmid says finances around AI buildout merit watching

Aug 4 (Reuters) - Federal Reserve Bank of Kansas City President Jeff Schmid said on Tuesday the financial situation involved in building out the artificial intelligence sector bears watching. "We have to ⁠correlate what's happening in AI, just from a pure scale standpoint, to some of the other experiences we've had that could, in fact, create a systemic problem," ⁠Schmid said at a conference at his bank. "I would argue that there's some signs...that ⁠we have to really start to talk about that on ⁠a macro level...is this industry becoming another too ⁠big to fail?," he added. Reporting by Michael S. Derby; Editing by Jacqueline Wong Our Standards: The Thomson Reuters Trust Principles., opens new tab

A Fed official is asking whether AI is becoming 'too big to fail'

Kansas City Fed president Jeff Schmid says the scale of AI investment now warrants watching at a macro level, borrowing a phrase last heard in the 2008 crisis. A senior Federal Reserve official has put an uncomfortable question on the table. Speaking this week, Kansas City Fed president Jeff Schmid said the finances around the AI buildout now merit close watching. Jeff Schmid said there were signs the industry had grown large enough that policymakers should start discussing it at a macro level, and asked whether AI was becoming another sector that was too big to fail. That phrase carries weight. It was the language of the 2008 financial crisis, when banks had grown so central that governments felt forced to rescue them, and hearing it applied to AI is a notable escalation in official tone. Schmid's worry is about scale and correlation. He suggested the current wave of AI spending should be compared with earlier booms, warning that concentration on this scale can turn a single sector's troubles into an economy-wide problem. The numbers behind the anxiety are staggering. Big Tech is now carrying nearly $2.4 trillion in AI spending commitments, a figure that dwarfs most previous corporate investment cycles and leaves little slack if demand disappoints. It is not only the amount but how it is financed. The Bank for International Settlements has warned that an AI bust could hit credit markets as hard as 2008, precisely because so much of the buildout runs through debt and circular deals. Those interlocking arrangements are what unsettle regulators. When chipmakers, cloud providers and model developers invest in one another, a stumble at any one node can ripple outward, and the exposure is harder to see from the outside. Markets have started to notice. Nvidia's wave of $750bn in AI deals pushed its credit default swaps to record levels, a sign that even lenders to the sector's strongest name are pricing in more risk. The comparison to past bubbles is not exact. On some measures the AI boom looks like the dot-com era, with valuations and concentration above 2000 levels, though the leading companies today are genuinely profitable in a way many 1999 darlings were not. That is the knot facing the Fed. Real earnings make the boom look sturdier than a mania, yet the spending is so large and so leveraged that even a healthy sector could transmit shocks if sentiment turns. Schmid's comments also touch monetary policy. If AI investment keeps driving demand for power, chips and construction, it complicates the central bank's reading of inflation and its decisions on interest rates. The spending is also straining the companies themselves. Big Tech's AI outlays are now catching up with its free cash flow, forcing firms that once funded everything internally to lean harder on debt and outside capital. That shift is what pulls the Fed into the story. As financing moves from corporate balance sheets into credit markets and private lenders, the risk spreads to institutions the central bank is charged with watching. Schmid is not alone in his unease. A growing chorus of officials and analysts has begun comparing the AI cycle to past manias, even as few are willing to call the top of a boom that keeps producing real revenue. The practical worry is concentration. A handful of enormous firms now account for a large share of market gains and capital spending, so a stumble by any one of them would be felt far beyond the technology sector. None of this amounts to a prediction of collapse. A Fed president musing about systemic risk is doing his job, and flagging a concern early is meant to prevent trouble rather than forecast it. But the vocabulary matters. Once officials reach for the language of too big to fail, they are signalling that AI has moved from a market story to a question about the stability of the whole system.