Federal Reserve’s Daly cites regulatory barriers to AI-driven productivity growth

San Francisco Fed president warns that state-level AI regulations and organizational inertia are throttling the economic payoff from massive AI investments.

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Add us on Google by Editorial Team May. 29, 2026

San Francisco Federal Reserve President Mary C. Daly has a message for anyone expecting AI to turbocharge the economy overnight: not so fast. In remarks delivered on May 29, Daly argued that state-level regulatory barriers are actively suppressing the productivity gains that companies hoped to unlock through artificial intelligence.

The productivity paradox, again

Economists have a name for this pattern. It’s called the productivity paradox, and it’s happened before. In the 1980s, businesses poured money into personal computers, but aggregate productivity barely budged for years. The gains only materialized in the 1990s, after companies fundamentally restructured how they operated.

Daly argues that simply adopting AI technologies is insufficient. Companies need to reorganize their business processes to capture broader benefits.

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Despite substantial financial commitments to AI infrastructure and development, the aggregate productivity effects remain modest at the national level. The gains that do exist are concentrated in a handful of sectors: call centers, software development, and financial services.

State regulations and the startup squeeze

Daly singled out state-level AI regulations as a key near-term challenge to scaling productivity gains. The patchwork of rules across different jurisdictions creates compliance costs that don’t hit all companies equally.

Regulatory costs disproportionately affect startups compared to larger firms.

The Greenspan playbook

Daly advocated for a Fed approach reminiscent of Alan Greenspan’s methodology during the 1990s IT boom. Back then, Greenspan resisted the consensus view that productivity gains were illusory. He pushed the Fed to look at micro-level data and business-level evidence rather than relying solely on aggregate statistics.

Daly wants to apply the same framework to AI. She’s pushing for expanded business outreach and granular analysis to better understand AI’s true economic impact. This echoes comments she made earlier, during a February 17 speech at a gathering organized by the Silicon Valley Leadership Group and San Jose State University, where she first articulated the need for more detailed, ground-level data collection.

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