AI and the Market for Signals

Abstract

Early-career work both signals a worker's ability and builds long-run skills. We introduce an AI industry into a career signaling model. The industry sells tokens, which workers convert into output indistinguishable from their own. We compare three regimes: no token trade, purchases only, and open trade with side sales. With no token trade, market learns every type, signalling incentives create a rat race and workers work above the full information level. With token purchases only, lower types buy output but the market still learns every type. Buying by lower types raises the signal higher types must post, so the high types can work harder than before AI. Buyers work less with AI, even below the full-information level. With side sales and abundant AI, the signal no longer depends on effort and every type under-invests in skill. Workers gain from token trade only when welfare improves, never the reverse, and the difference is the AI industry's profit. In education, where output has no value, every worker loses in the separating equilibrium, but with valuable output and cheap tokens everyone gains. Token taxes, wage taxes, and watermarking have ambiguous effects on workers' payoffs and welfare, and their effects depend on whether workers can sell output on the side.