Analyzes Polymarket's short-duration crypto up/down markets, which now account for 16% of monthly volume but generate ~40% of platform fees. Finds professional algo traders (19 addresses) extract consistent profits through paired trading and maker execution while 69% of retail traders lose money. Argues prediction markets are becoming a game of volatility rather than forecasting.

Argues that leverage solves, rather than amplifies, prediction market problems. In a 1x market, only whales can move prices because the barrier is capital, not insight; at 10x, thousands of smaller traders can collectively contest a mispricing. Addresses gap risk (binary resolution makes platform-native margin dangerous) by describing a temporal arbitrage approach where leveraged markets close before event resolution. Also proposes a vault-based yield layer where LPs earn returns from trading activity rather than directional outcome exposure.

Proposes a two-stage screener that uses Granger causality to find lead-lag pairs across Kalshi Economics markets, then passes candidates through an LLM that checks whether the proposed direction has a plausible economic transmission mechanism based on event descriptions. The LLM re-ranker barely moves the win rate (51.4% to 54.5%), but it dramatically shrinks the downside — average losing trade drops from $649 to $347 by filtering out statistically fragile links that look good in backtests but break in practice.

Frames prediction markets as crypto's first truly native financial primitive, one that couldn't scale on traditional finance rails due to regulatory chokepoints. Traces the historical pattern where financial innovations move from 'gambling' to infrastructure, and argues that margin and derivatives layers are the missing pieces that will unlock institutional capital. Highlights the unique properties of prediction market positions: time-bounded decay and binary convergence to truth, which create a distinct trading mechanic the author calls temporal arbitrage.