Meet the world’s top AI-pilled economists
Academic economists have lagged in studying AI’s economic impact despite its transformative potential, with only a fraction of NBER papers focusing on AI compared to COVID-19 research. A group of 'AI-pilled economists' outside universities, including figures like Tyler Cowen and Susan Athey, is leading analysis, but many studies rely on abstract models or flawed assumptions about AI’s immediate effects on labor markets.
Artificial intelligence has reshaped industries and created trillions in market value, yet academic economists remain slow to analyze its economic impact. While COVID-19 research dominated NBER working papers in 2020, AI-related studies have grown only modestly since ChatGPT’s 2023 launch. Even in 2024, COVID-focused papers outnumbered AI ones, and this year’s NBER conferences will likely feature more healthcare discussions than AI. Some economists, like Stanford’s Susan Athey and MIT’s Daron Acemoglu, are leading AI research, but their work lacks mainstream recognition compared to COVID-era studies. Acemoglu’s 2024 paper on AI-driven economic growth, though widely cited, assumes limited productivity gains, a critique echoed by George Mason’s Tyler Cowen, who argues the model underestimates AI’s disruptive potential. Empirical studies often rely on flawed assumptions, such as a 2024 paper by Stanford’s Erik Brynjolfsson attributing youth unemployment drops to AI—despite ChatGPT’s early limitations. Academic economists may struggle due to AI’s gradual, data-hidden effects, unlike COVID’s immediate, visible impact. The gap between academic research and industry engagement is widening, with one top economist admitting few colleagues interact with AI labs like Anthropic or OpenAI. Meanwhile, a network of 'AI-pilled economists'—practitioners outside universities—is driving much of the field forward, highlighting a shift in economic analysis toward real-world AI applications.
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