The internet’s signal-to-noise ratio has collapsed. In her Superteam Demo Day pitch, CHOMP co-founder Kiko Zang argued that the core problem isn't misinformation, but "missing information"—the gap between reality and the vanity metrics that dominate our screens. While prediction markets are the theoretical solution to this trust deficit, their current form—complex charts and financial interfaces—alienates the mass market. The world doesn't want to trade binary options; they want to play Candy Crush.

CHOMP bridges this gap with a "Trojan Horse" strategy. To the user, it is a fast-paced trivia game rewarding engagement with Bonk and other tokens. Under the hood, however, it is a high-frequency prediction market utilizing the "surprisingly popular" algorithm. By asking users not just what they believe, but what they think others believe, the platform mathematically isolates honest signals from crowd sentiment. This gamification has driven sticky retention, with Zang reporting thousands of 7-day streaks and power users logging in for over 365 consecutive days.

For institutional investors, the alpha lies in the backend. Zang positions CHOMP not merely as a dApp, but as a "context engine for the internet." As AI agents proliferate, they face a critical bottleneck: a lack of nuanced, verified human context for subjective queries. CHOMP effectively acts as a decentralized RLHF (Reinforcement Learning from Human Feedback) farm. It captures and validates the messy, subjective human data that LLMs cannot scrape, creating a proprietary data primitive essential for training the next generation of more "human-like" agents.