Every market prices scarcity. The AI transition has restructured what is scarce in the knowledge economy, and the restructuring is so thorough that the price signals — the signals that every career decision, every hiring choice, every educational investment responds to — have shifted in ways most institutions have not yet registered. Before AI, execution was scarce. The capacity to write working code, draft a competent legal brief, build a financial model, design a functional interface — these were the skills that commanded premium salaries. The scarcity was maintained by the difficulty of acquisition: years of training, practice, and the specific friction through which deep expertise is deposited. The investment was large; the market rewarded it because the output was scarce.
AI has made execution abundant. Not universally, and not perfectly, but abundantly enough that the scarcity premium on competent execution is falling across every domain where the work can be described in natural language. Competent code is abundant. Competent prose is abundant. Competent analysis, design, summarization, translation — all abundant, available to anyone with a subscription and the capacity to describe what they want.
Becker's framework generates the prediction: when the supply of execution increases and the demand remains constant, the price of execution falls. The workers whose human capital consisted primarily of execution capacity experience a return reduction. But the scarcity has not disappeared — it has migrated.
The historical pattern provides the answer. When VisiCalc made calculation cheap, the scarce resource became judgment about what to calculate. When LexisNexis made legal research cheap, the scarce resource became legal strategy. When diagnostic imaging was automated, the scarce resource became clinical interpretation. In each case, automation of execution migrated scarcity to judgment. The market repriced accordingly.
The constellation the AI market is now rewarding includes: the capacity to identify which problems are worth solving (a function of values, empathy, and market understanding no current AI can originate); the capacity to evaluate whether a solution serves its intended users well (a function of taste, itself the product of deep engagement with domain and users); the capacity to integrate across domains (seeing that a technical decision has ethical implications, that a design choice has business consequences); and the capacity to make decisions under genuine uncertainty — not uncertainty that can be resolved by gathering more data, but the irreducible uncertainty that characterizes every meaningful choice.
The migration of scarcity from execution to judgment is a recurring pattern in technological history, but its acceleration under AI and its recognition as a structural rather than sectoral shift is characteristic of the post-2022 economic analysis. The pattern is implicit in Becker's framework and made explicit in work by Autor, Brynjolfsson, and Goldin on how technology interacts with skill complementarity and substitution.
Scarcity migrates, not disappears. Automation of any particular cognitive task shifts the locus of scarcity upward to whatever the automated task does not replace.
Judgment as the new bottleneck. When execution is abundant, the binding constraint becomes the capacity to decide what is worth executing — and the market prices this capacity accordingly.
Judgment is not a single skill. It is a constellation of capacities — problem identification, solution evaluation, cross-domain integration, decision under uncertainty — that existing educational institutions were not designed to produce.
The institutional lag. The market has repriced; the institutions that produce human capital have not yet reorganized, creating a gap whose welfare cost grows with every month of delay.