A clear-eyed diagnosis of why decision markets (futarchy) haven’t taken off despite the elegant theory. Identifies two structural problems: thin markets that can’t attract enough informed traders for idiosyncratic decisions, and a conditional-futures architecture where token-price-as-KPI is too noisy to incentivize rational trading. Covers MetaDAO and Combinator as current implementations grappling with these issues.

Follow-up to functionSPACE's V1 discretisation analysis, splitting Polymarket's 18,863 multi-market events into continuous (price brackets, weather ranges, margin percentages) versus categorical (teams, candidates) and re-running the pathology tests. Both types concentrate 90% of volume in the top 5-6 markets, but ghost markets turn out to be largely a categorical phenomenon: continuous events distribute volume more evenly across buckets and survive the liquidity cliff longer at high N. With continuous events overtaking categorical by event count in 2026Q1, the case for a continuous-distribution primitive applies to a growing share of the platform.

Argues that central limit order books fixed the capital destruction problem of early Polymarket AMMs but introduced a new pathology: passive participation is impossible, and only professional market makers can quote. Kalshi reportedly has 23 active market makers with the top three providing 70% of election-contract liquidity, meaning any market those firms ignore is dead on arrival. Positions this as the reason prediction markets remain concentrated in politics and sports while entertainment, science, and culture verticals stay empty, and makes the case for peer-to-peer architecture that lets the first participant seed liquidity for the second.

Critique of the narrative that prediction markets are truth machines. Polymarket's headline Brier score of 0.047 masks category-specific failures like sports markets scoring 0.325 (worse than a coin flip), and 99% of volume concentrates in the final hours before resolution. The author argues prediction markets only work on roughly 2% of listed contracts (binary, high-profile, short-term events with millions at stake), and that when outlets like CNN and WSJ broadcast illiquid market odds as authoritative signal, whale trades on thin books get laundered through credible newsrooms.

Argues that prediction market TAM should include the supply side: as the cost of producing real-time probability estimates collapses, the addressable market extends beyond trading volume to every decision that benefits from better forecasts. Presents an ordered liquidity formation path from entertainment to information to institutional demand, and contends that scaling to $1T requires massive breadth in long-tail markets rather than concentrated depth in a few high-volume categories.

Analysis of 149 CPI prediction markets on Kalshi from 2021 to 2026 finds that trading volume explains less than 1% of variance in forecast accuracy, challenging the assumption that more liquidity improves market quality. Introduces Minimum Viable Liquidity (Cost of Expertise divided by Price Gap) as a framework for determining the threshold of liquidity needed to attract informed traders. Argues platforms should prioritize breadth over depth, running many thin markets rather than concentrating volume in few contracts.