How AI Will Save Prediction Markets

The first fully-developed formulation of general-purpose prediction markets originated with Robin Hanson's Idea Futures (1990), a technology "intended to aid the evolution of a wide range of ideas, from public policy to the nature of the universe" that "should be able to help us predict and understand our future". Hanson believes that these markets would even be able to solve one of Democracy's greatest weaknesses — "aggregating available information" — via a new type of governance: Futarchy[1].Dan Schwarz, writing in Asterisk, puts the optimist's perspective directly:"For decades, prediction market optimists — and I count myself among them — have argued that once we build better markets and increase the supply of bettors, accuracy will improve, and we'll all be able to benefit from a new level of societal foresight."Vitalik Buterin generalized this insight into an emerging category he called Info Finance: any mechanism that uses financial incentives to surface truth. He envisioned plenty of applications, from "distilling human judgement" to fixing scientific peer review.At this point you might be thinking that this sounds idealistic to the point of utopian. But, compared to other sci-fi technologies — like Terafab's goal to harness the energy of the Earth, Sun, and galaxy — accurate prediction markets on important questions don't seem so lofty.The curse of footballToday, there are two multi-billion-dollar companies seriously championing this vision: Kalshi & Polymarket. Kalshi[2] CEO Tarek Mansour pitches prediction markets as "quintessential truth machines". Polymarket CEO Shayne Coplan cites Futarchy as a direct inspiration and calls prediction markets "the most accurate thing we have as mankind right now".So how's the truth machine doing? Mostly, it's predicting football games. Over the past year, roughly 65% of the volume on both platforms came from sports, and nearly half of that accounts for football alone (Paradigm Predictions Dashboard, 2025). And, I don't