Mary Daly argues AI is neither utopia nor doom: it is a powerful tool whose economic outcome depends on how businesses, workers, and policymakers choose to use it. She says the Fed is already adopting AI carefully, but the real productivity gains will come only when firms redesign business processes, not just speed up existing tasks.
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This interview centers on Mary Daly’s view that AI should be understood as a tool whose economic impact is not predetermined. She repeatedly rejects the extremes of “abundance and utopia” on one side and “existential crisis and risk” on the other, arguing that the future is shaped by choices made by businesses, workers, and institutions. Her core thesis is that the important question is not whether AI exists, but whether organizations use it to simply do work faster and cheaper or to fundamentally change what they do and create new revenue and capabilities. Daly says she is already seeing signs that firms are moving past cost-cutting uses and beginning to ask what they can now do that was previously impossible. …
Near term, AI is not the market-moving macro variable Daly is focused on; the immediate setup still revolves around inflation prints, tariffs, oil, and labor data. AI infrastructure can create local bottlenecks, but that looks more like a pocket risk than a broad macro shock.
Over the next few months, watch for whether firms move from pilot AI use to genuine operating-model changes; that is the confirmation Daly needs for a productivity story. If that does not appear in the data, AI stays a narrative theme rather than a macro driver.
Longer term, Daly’s view implies AI could become a general-purpose productivity engine that lowers costs and raises potential growth, much like electrification. The structural question is whether the labor market adapts fast enough to avoid distributional damage while the aggregate gains materialize.
AI is neither a utopia nor an existential crisis; its outcome depends on how people choose to use it.
Direct thesis statement against extreme narratives.
Firms are moving from asking how AI can make work faster to asking how it can create new revenue and new business models.
She cites CEOs shifting from cost reduction to revenue expansion.
The San Francisco Fed initially restricted AI use for confidentiality reasons, then built a safe sandbox and now has widespread internal adoption.
Describes operational rollout and governance at the Fed.
Why haven't we seen AI productivity gains show up in the data yet?
Productivity growth is 'everywhere except in the data' — a famous phrase by Robert Solow. Gains show up in particular firms and sectors but haven't reached aggregate scale yet. She compares it to electrification: the real productivity boom came not from putting electric motors on old steam-powered lines, but from business process change, which firms are only now beginning to explore with AI.
Is the wall of money coming into AI public companies and near-record-high assets a cause for concern?
The Federal Reserve System is always watching financial stability issues and releases quantitative surveillance summaries twice a year. She asks what the value of the work behind these investments is, and finds it hard to say these technologies aren't valuable — people see them in their personal lives and businesses. She cautions against extreme views that AI will save everything or destroy everything, noting the hard work is all in the middle.
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