The Opportunity

Prediction markets have demonstrated their ability to aggregate dispersed knowledge and forecast outcomes more accurately than experts, polls, or traditional models. Yet despite this potential, they remain fragmented, capital-inefficient, and largely disconnected from the broader financial ecosystem.

The Structural Problem

Existing prediction markets have demonstrated demand but revealed fundamental limitations:

Liquidity Fragmentation Markets are siloed by outcome and timeframe. A Trump election market and a Fed rate decision market share no liquidity, even when macroeconomically correlated. Capital is trapped in individual positions until a resolution is reached, sometimes for months.

Capital Inefficiency Traditional prediction markets use a 1:1 collateral model. You risk $70 to make $30. Returns are capped, leverage is impossible, and there's no way to scale positions without locking proportional capital. This makes them unattractive to serious traders and institutions seeking efficient capital deployment.

Static Exposure Once you enter a position, you wait. No ability to scale in, take profits, or dynamically manage risk. No continuous pricing. No real-time response to new information. Markets become stale between major news events.

Regulatory and Cultural Friction The "betting" framing has limited adoption and attracted regulatory scrutiny. Prediction markets are perceived as gambling rather than legitimate financial instruments for information discovery and risk transfer.

The Outcome Paradox Platforms multiply markets across every possible outcome (Candidate A wins, Candidate B wins, Candidate C wins), fragmenting liquidity instead of concentrating it. What should be a single deep market becomes three shallow ones.