AI tops the risks to US financial stability in Federal Reserve survey of market contacts

Nov 15, 2026
Respondents to the Fed's November survey cited AI stock valuations, AI-enabled cyberattacks and complex AI financing, and AI mentions rose sharply since spring.

AI was the most frequently cited risk to US financial stability in a survey of market contacts published in the Federal Reserve's November Financial Stability Report on November 13. Staff at the Federal Reserve Bank of New York collected the views of 23 contacts at broker-dealers, banks, investment funds and advisory firms in September and October. Mentions of AI, higher long-term interest rates and cyberattacks rose the most compared with the spring survey.

Respondents named three areas of concern. The first is a sharp correction in AI-related stocks, driven by uncertain returns on large capital spending; many said stretched valuations, circular financing arrangements and heavy reliance on debt to fund the AI buildout could amplify losses and spill over into business investment and household consumption. The second is cybersecurity, from attacks by sophisticated state actors or damage caused by frontier AI models themselves, as advances in AI lower the barrier to sophisticated attacks. The third is complex forms of AI-related financing.

Cyberattacks and fiscal debt sustainability were seen as the risks with the most severe potential consequences. The report says the survey summarises respondents' views and does not represent the views of the Federal Reserve Board or the New York Fed.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Finance AI Weekly, Cybersecurity AI Weekly or Daily AI Brief.

Also, consider following us on social media:

Free newsletter

Finance AI Weekly

Weekly newsletter about AI in finance. Covers AI-driven trading, fintech innovations, and data analytics transforming markets

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more
Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.