Training in the Age of AI: A Theory of Career Viability

Luis Garicano and Luis Rayo

No 20634, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: Across the economy, juniors pay for training by doing menial tasks. AI now performs an increasing share of that work, putting the bargain at risk. We introduce AI into a dynamic career model with an automation threshold and possible complementarity for experts. The expertise leverage ratio, measuring the output of a fully-trained graduate relative to that of a novice who has just enough knowledge to outperform AI, governs the overall impact of the technology. Our central result is that careers are guaranteed viable, in the sense that they are at least as profitable as they were before the arrival of AI, when this ratio is above a critical threshold, specifically Euler's number e; in this case, training has a fixed duration and the training path is not at risk. Below the threshold, the senior's saleable knowledge shrinks and training compresses; in this case, advances in AI threaten wholesale career collapse.

Date: 2025-09
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