AI Agents Are Quietly Rewriting Prediction Market Trading

Why It Matters

AI‑enabled agents level the playing field for retail traders and expand prediction markets into niche domains, creating new data sources for finance and policy decisions.

Key Takeaways

• Polystrat executed 4,200 trades, 376% single‑trade returns
• 30% of Polymarket wallets now use AI agents
• AI agents achieve ~70% predictive accuracy, outpacing humans
• Prediction market volume hit $44 billion in 2025
• User‑owned agents aim to prevent AI centralization

Pulse Analysis

Prediction markets have moved from academic curiosities to a $44 billion fintech segment, driven by events like the 2024 U.S. election and the rise of crypto‑native platforms. Within this surge, Valory’s Olas protocol introduces a decentralized infrastructure for autonomous software agents, allowing developers to deploy AI traders that interact directly with smart contracts. Polystrat, the first high‑visibility Olas‑powered bot on Polymarket, operates 24/7, continuously scanning odds and executing strategies without human fatigue, illustrating how blockchain‑enabled AI can scale beyond traditional trading desks.

Performance data underscores the competitive edge of machine‑driven participants. Independent analytics show that only 7‑13% of human traders achieve positive returns, while AI agents routinely exceed 70% predictive accuracy and generate multi‑hundred‑percent single‑trade gains. This efficiency not only boosts individual P&L but also unlocks the long tail of niche markets—localized questions that human traders typically ignore. By processing vast data streams, AI agents can surface insights for businesses, policymakers, and investors, turning prediction markets into a real‑time decision‑support layer across industries.

The rapid adoption of autonomous agents raises regulatory and governance questions. Critics warn that forecasting wars or disasters could incentivize manipulation, prompting calls for clearer market safeguards. Valory counters by emphasizing user‑owned agents, ensuring participants retain control over the AI models that generate value on their behalf. This approach aims to prevent centralization of AI power while leveraging bots to detect anomalous activity and enforce market integrity. As AI agents become standard tools in prediction markets, they are poised to reshape risk management, data aggregation, and the broader digital economy.