Traces how insider trades on Polymarket before Trump's March 2026 Iran announcement may have leaked into regulated oil and stock futures markets. Proposes that quant funds extracted the informational signal from pseudonymous crypto trades and acted on it in KYC-regulated venues, all without breaking existing laws. Highlights a structural gap where information flows freely across platforms even when regulatory frameworks differ.

Screens 93,000 Polymarket markets and flags traders with a 69.9% win rate, more than 60 standard deviations above chance, estimating $143 million in anomalous profits. Documents specific cases from geopolitical events to celebrity announcements where wallets appear to trade on material non-public information. Proposes a regulatory framework combining platform-level registration, contract-level restrictions on high-risk categories, and an extended misappropriation doctrine to close the legal gaps that leave prediction market insider trading largely unpoliced.

Sets out to defend insider trading in prediction markets but arrives at a more conditional position. Introduces a 'discovery vs betrayal' framework: in distributed-truth markets like elections, informed traders sharpen the signal because no one holds the full answer; in concentrated-truth markets like earnings, insiders monetize sealed results rather than synthesize public fragments. Argues the real question is not whether insiders should be allowed but what kind of informational asymmetry a market can absorb without losing the participation and trust that make the signal useful.

Documents a pattern of insider trading on prediction markets, from wallets that profited $1.2 million on the timing of US strikes on Iran to trades linked to classified intelligence. Compares how Kalshi's KYC-based surveillance and Polymarket's pseudonymous blockchain create different enforcement challenges. Argues platforms should reconsider contract offerings before regulators act.

Argues that prediction markets face two structural problems preventing them from becoming transformative economic instruments: corporate hedging is impractical due to market fragmentation and basis risk, and insider trading undermines retail participation. Draws parallels to online poker and memecoins to suggest that without structural reforms, prediction markets will remain primarily a sports betting product.

Examines early results from the Prediction Arena experiment, where six AI models trade real money on Kalshi. Five of six are underwater after three weeks, suggesting that raw information processing isn't enough to generate edge. Argues that successful prediction market traders profit from embodied, local knowledge (monitoring flights, calling embassies) rather than synthesizing public information, a domain where AI remains fundamentally constrained.

Taxonomy categorizing prediction markets by susceptibility to three manipulation vectors: information asymmetry (insiders know outcome), reflexivity (market signals influence outcome), and social coercion (participants can directly cause outcome). Argues a market's manipulation profile determines whether you're trading on information edge, narrative momentum, or ability to cause the outcome.