Does platform pricing of inspection tokens encourage excess search?

The same paper ("Subsidizing Sequential Search," arXiv 2605.28985) extends its baseline subsidized-search model to "AI–mediated platforms that can create and price 'inspection tokens.'" In the baseline model, the per-unit price a firm pays to subsidize a buyer's inspection cost is treated as a fixed primitive; the extension instead lets a platform choose that price to maximize its own profit. The paper's finding: "the platform's optimal linear pricing leads to excessive inspection relative to the social optimum. While this distortion does not reduce consumer welfare, it reallocates surplus from sellers to the platform and consumers."

The mechanism runs through the pooling band described in How do seller subsidies sort quality in search markets?: the model's own comparative statics show the upper cutoff marking that pooling band "is strictly increasing in both p and n" — a higher token price pushes more firms into the pool rather than letting them separate. Because pooled firms all signal the same (full) subsidy, the consumer cannot distinguish among them and ends up inspecting more of them than finer sorting would warrant. In the paper's words, "if a platform can mint and sell inspection tokens, its problem reduces to a linear–cost mechanism. Yet profit motives may distort attention. By thickening the top pool, a platform can steer consumers to inspect too much, raising revenue but lowering social efficiency." Consumer welfare does not fall because the subsidies themselves still flow to the consumer upon inspection; it is firms that pay the marked-up token price, so the cost of the added inspection lands on seller surplus and on total efficiency, while the platform's own take rises.

This is the same divergence-of-interest shape as Why do consumers delay adopting agentic search?, but inverted: that model finds a platform can prefer agentic search before it is privately optimal for the consumer, producing too little switching from the consumer's standpoint, while this one finds a platform's optimal pricing produces too much inspection relative to the social optimum, with consumers left no worse off. Read together, both models treat the platform's revenue objective, not the consumer's welfare, as the thing actually being optimized when an AI-mediated layer sits between buyers and sellers — the direction of the resulting distortion depends on which side of the transaction the platform's fee structure falls on.

This is a theoretical extension (Section 6 of the paper) of the baseline equilibrium to a case where the token price is chosen by a profit-maximizing platform rather than given exogenously; the paper proves the comparative-statics direction — more pooling, more inspection, unchanged or higher consumer surplus, lower total welfare — within its own Perfect Bayesian equilibrium framework, but the excerpt names no existing marketplace that actually prices inspection tokens this way, and offers no empirical test of the distortion's size. The implication the model supports, at that strength, is that a profit-maximizing AI-mediated discovery platform with the power to price access to buyer attention has an incentive to engineer over-inspection rather than under-inspection, and that the resulting welfare loss would show up in seller margins and total efficiency rather than in any consumer-facing metric — making the distortion easy to miss if only consumer-side outcomes are tracked.