Federal Reserve deploys AI tools to monitor financial stability

Cook’s speech is one of the clearest statements yet from a major central bank that AI is now a macro‑critical technology. She frames AI as both a driver of productivity and capital expenditure—citing more than a trillion dollars in data‑center plans—and a new source of inflation pressure via chips, energy and specialized labor.([federalreserve.gov](https://www.federalreserve.gov/newsevents/speech/cook20260527a.htm)) At the same time, she warns that AI‑driven trading, sector disruption and highly leveraged infrastructure build‑outs could generate correlated risks and funding strains that traditional stress‑testing might miss.([federalreserve.gov](https://www.federalreserve.gov/newsevents/speech/cook20260527a.htm))

Equally important for the race to AGI is how the Fed is using AI itself. Cook describes small, cost‑efficient models distilled for large‑scale text classification, LLM experiments on herd behavior, and an agentic‑AI “sprint” that systematically maps network risks and runs many stability scenarios that humans struggle to do in time.([federalreserve.gov](https://www.federalreserve.gov/newsevents/speech/cook20260527a.htm)) This is a glimpse of a future in which supervisors and macro‑prudential policy are partially AI‑augmented. For advanced AI labs and infrastructure providers, the message is clear: their systems are already treated as systemically relevant, with regulatory attention on AI‑specific shocks, cyber‑enabled instability, and AI‑financed leverage. That scrutiny may not slow capability progress directly, but it will shape capital costs, disclosure expectations and acceptable deployment patterns in finance.