Fed’s Daly warns regulatory hurdles curb AI productivity gains

Federal Reserve Bank of San Francisco President Mary C. Daly told a May 29 audience that the United States’ productivity boost from artificial intelligence is being throttled by a patchwork of state regulations and corporate inertia, rather than by a lack of technology itself [1].

Key takeaways

• State‑level AI regulations create compliance costs that hit startups harder than large firms [1].
• Daly sees “green shoots” of productivity improvement in sectors such as call centers, software development, and financial services [1].
• She argues that merely adopting AI tools is insufficient; firms must reorganize processes to capture broader gains [1].
• The Fed president does not anticipate mass unemployment or large‑scale worker displacement from AI, describing the technology as a net productivity driver [2].
• Daly calls for a micro‑level, business‑focused data approach—mirroring Alan Greenspan’s 1990s IT strategy—to better gauge AI’s economic impact [1].

Regulatory friction and the productivity paradox

Daly highlighted a “productivity paradox” reminiscent of the 1980s PC boom, where heavy investment did not translate into immediate aggregate gains. She noted that today’s AI spending shows a similar lag: while companies pour money into AI infrastructure, measurable productivity improvements remain modest at the national level and are concentrated in a few sectors—call centers, software development, and financial services [1]. The chief obstacle, she said, is not technological scarcity but regulatory fragmentation. State‑by‑state AI rules impose uneven compliance burdens, disproportionately affecting startups that lack the resources of larger corporations [1].

To address this, Daly advocated for a Fed approach akin to Alan Greenspan’s methodology during the 1990s information‑technology surge. Greenspan had urged the Fed to look beyond aggregate statistics and examine micro‑level business data. Daly wants the same granular outreach, expanding contacts with firms to collect detailed evidence of AI’s impact on productivity and employment [1]. This follows a February 17 speech to the Silicon Valley Leadership Group and San Jose State University, where she first called for more ground‑level data collection [1].

Outlook on jobs and policy

Despite the regulatory headwinds, Daly expressed confidence that AI will not trigger widespread job loss. She said she does not see evidence of mass unemployment or large‑scale displacement from AI, countering more pessimistic forecasts about rapid automation [2]. Instead, she pointed to early signs of efficiency gains across sectors, describing them as “green shoots” of productivity growth that could eventually support higher hiring and wage growth without sparking inflationary pressure [2].