by Hamoon Soleimani, SHTF Plan:
Autonomous AI agents are becoming active economic participants on both sides of market transactions. Enterprise platforms now embed what vendors call “touchless operations,” with agents executing procurement decisions without human review. Blockchain networks let AI agents hold wallets, settle payments, and rebalance portfolios autonomously.
Microsoft Research has already documented two-sided “agentic markets” where both buyers and sellers are AI proxies. The standard commentary praises the speed and consistency of it all. That praise is correct, and it misses something catastrophic: when both parties to a transaction are algorithms optimizing against pre-specified utility functions, the market ceases to do the one thing that justifies its existence; it ceases to discover genuine economic value.
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Prices Are Discoveries, Not Coordinates
A price is not merely a number. In Friedrich Hayek’s formulation, it is a compressed signal encoding relative scarcity across an entire decentralized economy, synthesizing millions of individual valuations, constraints, and opportunity costs into information legible to strangers. His 1945 paper “The Use of Knowledge in Society” argued that the economic problem is fundamentally a problem of knowledge dispersed across billions of minds—tacit, context-specific, irreducibly personal—and that only the price system can transmit it without anyone having to know it all.
The crucial point is that prices do not merely transmit pre-existing information; they generate new information. The buyer who pays $12 rather than walk to a competitor reveals something about her preferences and opportunity costs that no algorithm could have extracted in advance. As the Cobden Center’s 80th-anniversary analysis of Hayek’s paper notes, much of this knowledge is tacit, non-quantifiable, and discovered only in the act of exchange itself. Prices are epistemic events. Remove the human actors who generate them, and you do not have a faster market; you have a fundamentally different and diminished institution.
The Complete-Information Trap
When two AI agents negotiate, the buyer-agent has a utility function encoding budget, quality, and delivery parameters; the seller-agent has one encoding margins and capacity. They converge on a price satisfying both constraint sets. A number emerges—but nothing is discovered that was not already implicit in the objective functions both parties were assigned. The negotiation solves a coordination problem within a known parameter space.
