The AI-Becker Problem

In the AI Story

The firm still needs experienced workers — needs them more than ever, because judgment has become the bottleneck. But the firm has lost the mechanism by which experienced workers were produced. The pipeline that converted junior labor into senior expertise has been disrupted at its source.

This is not a small adjustment within an otherwise stable system. It is a structural failure in the mechanism by which human capital is produced. And it is perfectly rational. Every firm that replaces an entry-level worker with an AI tool is making the locally optimal decision: the tool is cheaper, faster, more reliable. The collectively irrational outcome — a society that needs experienced judgment and has stopped producing the conditions under which experience is acquired — emerges from a million individually rational choices.

Becker would recognize the structure. It is a coordination failure, the kind of problem markets alone cannot solve because the costs are external to the decision-maker. The firm that eliminates its training pipeline does not bear the cost of the expertise that will not exist in ten years. That cost is borne by the economy as a whole, by the future workers who will lack mentors who were never trained, and by the organizations that will one day discover they need the very capacity they stopped cultivating.

SignalFire's 2025 analysis of 650 million LinkedIn profiles confirms the pattern empirically: new graduate hiring in technology roles declined by twenty-five percent between 2023 and 2025. The New York Federal Reserve reports unemployment among recent college graduates has risen thirty percent since the pandemic, compared to eighteen percent for workers overall. The pipeline is thinning, and the thinning is rational — firms making locally optimal decisions that produce a collectively suboptimal outcome.

Origin

Garicano's formulation draws directly on Becker's foundational work on general and specific human capital, extending the poaching analysis to address the novel conditions AI creates. The problem was named explicitly in 2024 working papers and public lectures at the LSE, gaining traction as the empirical evidence of the thinning pipeline accumulated. It represents one of the clearest applications of Becker's framework to the AI transition.

Key Ideas

The bundling mechanism. Pre-AI, entry-level work combined productive output with on-the-job training, allowing firms to recoup training costs through reduced entry wages.

AI shatters the bundle. When entry-level tasks are automated, the productive output disappears while the training need remains — eliminating the firm's economic rationale for investing in juniors.

The coordination failure. Individual firms rationally eliminate entry-level roles while collectively dismantling the mechanism by which experienced judgment is produced.

The thinning pipeline. Empirically documented decline in entry-level technology hiring confirms the structural prediction that rational individual decisions are producing a systemic failure in human capital production.