An academic paper confirms it: the bottleneck is judgment, and it's getting more valuable

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Matteo Collina, maintainer of Node.js, Fastify, and other major projects, analyzes an academic paper on the economics of AI and connects its findings to his hands-on experience. The paper introduces three key concepts: the Red Queen Effect (AI model value is relative, forcing constant reinvestment), the Structural Jevons Paradox (cheaper AI inference leads to more complex and widespread usage, not less), and the Wrapper Trap (thin application layers on top of foundation models lose value as models improve). Collina argues that human judgment — the ability to evaluate correctness, understand real business needs, and apply domain expertise — is the scarce resource that grows more valuable as AI handles more implementation work. He also flags the data flywheel dynamic as a risk for open source ecosystems. The core takeaway: implementation is becoming commoditized, but judgment is becoming the economic bottleneck the entire expanding software market depends on.

The Economics of Judgment