Martin Beraja is an Associate Professor in the Economic Analysis & Policy Group at UC Berkeley Haas School of Business. His research studies technological innovation and business cycles, with a recent focus on the macroeconomics of artificial intelligence—including its impact on institutions and property rights, firm dynamics and productivity, and worker adjustment. Professor Beraja received a National Science Foundation CAREER Award in 2023, the NSF's most prestigious award for early-career faculty.
Abstract
Organizations learn over time. They build organizational capital and form beliefs about their fundamentals. Motivated by recent advances in AI, we study organizational learning technologies that accelerate both processes. We show that, in a large class of models of firm dynamics, the value of organizational learning technologies (VOLT) is governed by two simple statistics: the relative size and lifespan of mature firms. In the United States, VOLT is on the order of one GDP — implying that organizational learning technologies like AI have the potential to double aggregate output. Much of VOLT reflects increases in average firm lifespans rather than productivity. Across industries, VOLT varies widely and is orthogonal to existing AI exposure measures. Overall, our results point to faster organizational learning as a meaningful and distinct channel of AI’s transformative potential, beyond production automation and scientific discovery.
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