This week, San Francisco Fed President Mary Daly gave a speech at the Silicon Valley Leadership Group that deserves attention. The topic was AI and productivity, but the real substance was a story about Alan Greenspan in the mid-1990s, and what that story means for how we should think about technology and economic data right now. The speech is worth reading in full (it is available on the San Francisco Fed's website), but the core of it is a historical parallel that I think cuts against a lot of the conventional wisdom in financial commentary.
Here is the setup. In 1995 and 1996, businesses were pouring money into information technology. PCs, networking, inventory management systems, GPS for trucking fleets. Investment was surging. But the official productivity numbers were flat. Standard macro models said the economy was overheating, the labor market was too tight, and the Fed should raise rates. Greenspan looked at the same economy and reached a different conclusion. He talked to executives. He walked factory floors. He saw firms not just buying computers but reorganizing their operations around them. Wholesale firms were using inventory management to cut warehouse stockpiling. Manufacturers were doing mass customization with computer-aided design. Trucking companies were eliminating deadhead hauling with GPS. Greenspan argued that official productivity data was lagging reality, and that the economy could grow faster than the models suggested without triggering inflation. The FOMC stayed patient. The roaring 1990s followed.
Daly's point is that we may be in a similar moment with AI. Businesses are investing. Use cases are multiplying. Firms in the Twelfth District (the Fed's western region, which includes Silicon Valley, Seattle, and the broader West Coast tech ecosystem) are reporting real savings from AI in consumer research, back-office operations, product development, and more. But the aggregate productivity statistics have not moved. Most macro studies find little evidence of a significant AI effect on economy-wide productivity growth. Daly cites Daron Acemoglu and a recent NBER working paper by Yotzov et al. surveying over 5,000 firm executives, both finding minimal impact so far.
The question Daly poses is whether GenAI is a sufficient catalyst to change the nature of production and business, or whether it is still at the stage of replacing a steam-powered motor with an electric one while leaving the factory floor unchanged. Her answer is honest, that no one knows yet. But her implication is clear: "we won't find all the answers in the aggregate data on productivity, the labor market, or inflation," she says. "Seeing developments before they fully emerge requires digging deeper, relying on disaggregated information that foreshadows transformation." The lesson from the 1990s is that aggregate data will be the last place to see transformation, not the first.
I find this compelling, and I think it highlights a bias that runs through a lot of financial journalism. Robert Solow's 1987 line about seeing the computer age everywhere except in the productivity statistics is one of the most quoted observations in economics. It is clever, it is pithy, and for decades it has been the default frame for skeptics. The Economist ran a piece in late 2024 titled "There will be no immediate productivity boost from AI." Carl Benedikt Frey argued in the Financial Times that "AI alone cannot solve the productivity puzzle," noting that even Microsoft's Satya Nadella acknowledges AI has yet to have its transformative moment. Apollo's chief economist Torsten Slok recently told CNBC that AI is "everywhere except in the incoming macroeconomic data," consciously echoing Solow. Robert Gordon at Northwestern has built a career arguing that the great innovations are behind us and that nothing since indoor plumbing will move the needle the way electrification did.
There is something to this skepticism. Rigor matters. Demanding evidence before declaring a revolution is exactly what serious people should do. But I think the skepticism has an undercurrent that is worth naming. In financial circles, there is a professional incentive to be the person who sees through the hype. Calling a bubble, puncturing enthusiasm, being the grown-up in the room, these are status moves. And they are coupled, I think, to a cultural distance between the people who build technology and the people who trade on it. The nerds are excited, so the sophisticated response is to be unimpressed. This is not analysis. It is posture.
What Daly's speech illustrates is that Greenspan's innovation in the 1990s was not a model or a formula. It was a practice. He looked at data that contradicted his priors and investigated rather than dismissed. He listened to people doing the work instead of relying solely on the aggregate statistics that his own institution produced. That is harder than running a regression, and it is harder than writing a clever headline about the Solow paradox.
Erik Brynjolfsson at Stanford recently pointed to revised 2025 data suggesting U.S. productivity growth may have roughly doubled to 2.7%, nearly twice the prior decade's average, while job creation was minimal. If that holds up, it is the pattern you would expect from technology-driven productivity gains. Fewer workers producing more output. Brynjolfsson calls it the transition from the investment phase to the "harvest phase." It is early, and the data will be revised, but the signal is consistent with what Daly is describing.
The practical takeaway is simple. If Greenspan had listened to the macro models and the skeptics in 1996, the Fed would have raised rates and may have choked off one of the strongest periods of broad-based growth in modern American history. He did not, because he looked past the aggregates. Daly is asking whether we are willing to do the same thing now. The answer will matter for interest rates, for labor markets, and for whether the current wave of AI investment produces widely shared prosperity or just another round of capital returns to the firms that got there first. That is not a question for the nerds or the traders. It is a question for everyone.