It’s becoming increasingly clear that CTF-style prediction markets will be dominated by Kalshi and Polymarket, largely due to their unmatched distribution and platform dynamics. Most forks, on the other hand, struggle to sustain real activity. Outside of token incentives, many operate more like short-term farming venues and this pattern is already visible: 🔸Post-TGE volume collapses (e.g. Limitless, Opinion Labs) 🔸Weak organic traction despite strong integrations (e.g. PredictDotFun + Binance) Why is this happening? It becomes obvious the moment you compare market quality directly. Pull up the same market on Polymarket versus a typical fork, the spread difference alone tells the story. Liquidity is thin, pricing is inefficient, and execution is meaningfully worse. In many cases, it’s simply unusable. But beyond distribution, there’s a deeper structural issue rooted in the design itself. CTF-based markets rely on CLOB architecture for YES/NO tokens. While efficient in theory, this model is highly dependent on professional market makers to function properly. FYI: Platforms like Kalshi reportedly operate with dozens of active MMs, enabling tight spreads and deep liquidity. Without (1) strong distribution, (2) regulatory clarity and credibility, or (3) incentives aligned with real trading activity MMs don’t show up. And without market makers → no liquidity → no power users → degraded market dynamics. That’s the loop most forks fail to escape. The only viable path to coexist with these incumbents is to move away from the standard CTF+CLOB model and introduce alternative market structures that directly address these structural limitations. Otherwise, it’s hard to see most of these forks sustaining themselves long term.

1/ @42space just introduced a new form of price markets that moves beyond the limitations of traditional CTF-style binary outcome models (simple UP/DOWN resolution). This new mechanism uses a "winner-takes-all settlement" model that enables a much wider distribution of possible outcomes. What’s particularly novel is how payout dynamics are determined not just by directionality, but also entry timing, position size & counterparty speculation. This creates a far more reflexive and information-sensitive pricing environment, where participants are effectively competing on both conviction + timing. Outline🧵