Artificial intelligence is quietly changing the landscape of prediction markets, with autonomous agents now playing a key role in trading outcomes tied to real-world events. According to David Minarsch, CEO and co-founder of Valory AG, the team behind the crypto-AI protocol Olas, AI agents are becoming powerful tools for both retail and institutional investors seeking an edge in an increasingly automated financial environment.

Agent Economy and the Rise of Polystrat

Valory AG, which operates the Olas protocol, is building what it calls an ‘agent economy’ — a decentralized ecosystem where autonomous AI agents perform tasks and generate value for their users. One of the most visible examples of this vision is Polystrat, an AI agent launched on the prediction-market platform Polymarket in February 2026. The agent trades on behalf of users who self-custody and own it, executing strategies continuously around the clock.

“In a nutshell, Polystrat is an autonomous AI agent that trades on Polymarket 24/7 on behalf of its human user,” Minarsch said. The idea is simple: while humans sleep, work or lose focus, the agent keeps trading. This shift marks a significant departure from traditional trading models, where human involvement was the norm.

Polystrat’s early performance has been impressive. Within a month of its launch, the agent executed more than 4,200 trades on Polymarket and recorded single-trade returns as high as 376%, according to data shared by the team. This level of performance highlights the growing potential of AI-driven trading strategies in prediction markets.

Growth of AI in Prediction Markets

Prediction markets, platforms where users trade contracts tied to real-world outcomes, have surged from niche forecasting tools into a fast-growing corner of fintech. The industry’s breakout moment came during the 2024 U.S. presidential election, when trading volumes spiked and the markets gained mainstream visibility. By 2025, total notional trading volume across major platforms exceeded $44 billion, with monthly activity reaching as much as $13 billion during peak periods.

Today, the market is dominated by two players: Kalshi, a U.S.-regulated event-contracts exchange overseen by the Commodity Futures Trading Commission, and Polymarket, a crypto-native platform that operates globally and offers a broader range of prediction markets. Together, they account for roughly 85-97% of trading volume in the sector, processing tens of billions of dollars in annual bets on everything from elections and central-bank policy to sports and cultural events.

The push toward AI-driven trading stems from a simple observation: much of the intelligence embedded in modern AI models hasn’t yet translated into financial markets. That realization prompted Valory’s team to begin building what they call a ‘prediction market economy’ on Olas in 2023, an ecosystem where AI agents use prediction tools and data pipelines to forecast outcomes and trade on them.

According to Minarsch, the results so far suggest that machines may have an advantage. Third-party data indicates that only about 7% to 13% of human traders achieve positive performance on prediction markets, while the majority lose money. At the same time, machine participation is growing quickly. More than 30% of wallets on Polymarket are already using AI agents, according to analytics platform LayerHub.

Human vs. Machine in Prediction Markets

Minarsch believes this trend reflects a broader shift: humans are already competing with machines whether they realize it or not. “You have human participants in prediction markets alongside many machines,” he said. “So humans are already in a battle with machines.” The key difference is that machines are less emotional and better at sticking to consistent strategies.

By making AI agents available to everyday users, Olas aims to level that playing field. The early performance of Polystrat has been encouraging. According to data shared by the team, Polystrat AI agents already outperform human participants in Polymarket, with over 37% of them showing a positive P&L versus less than half that number for human participants.

Users can configure their own agents depending on strategy preferences, data sources, or risk tolerance. This customization is a key feature of the Olas platform, allowing users to tailor AI agents to their specific needs and goals.

Beyond performance, Minarsch believes AI agents could unlock an overlooked opportunity in prediction markets: the ‘long tail’ of niche or localized questions. Many prediction markets revolve around major global events, elections, macroeconomic data, or high-profile sports competitions. But countless smaller questions remain largely unexplored.

“Humans often don’t bother digging for the information,” Minarsch said. “They can’t be bothered to make the effort.” AI agents, by contrast, can analyze large numbers of smaller markets simultaneously. “The long tail of prediction markets is very interesting for AI agents,” he said. “You just point the agent at the problem and it does the work.”

This could help expand prediction markets as a data-gathering tool for businesses, policymakers, and decision-makers. Forecast markets have long been studied as ways to aggregate dispersed knowledge and surface insights that traditional surveys or models might miss. In that sense, prediction markets may become a kind of upstream technology for decision-making across industries.

Despite the rise of automation, Minarsch does not see AI agents replacing humans entirely. Instead, he frames them as complements. “Humans make choices in a more rushed way, which can be detrimental,” he said. “AI agents can act as something humans rely upon.”

One future direction involves allowing users to augment their agents with proprietary knowledge or specialized data sets. “We see demand from users who want their agent to tap into their own knowledge base or proprietary information,” Minarsch said. “That would allow agents to trade in a more principled way than a human could.”

Over time, the team says prediction models and data pipelines powering these agents have improved significantly, generating sustained alpha when combined with general-purpose large language models. However, the growth of prediction markets also raises ethical and regulatory questions. Some critics argue that markets forecasting wars, deaths, or disasters could create incentives to manipulate outcomes or profit from harmful events.

Minarsch acknowledged that careful guardrails are needed. “There needs to be regulation about what kinds of prediction markets should exist,” he said. At the same time, he believes AI agents could also help detect problematic markets or manipulation attempts by identifying suspicious patterns. “Agents could spot patterns and help shut down problematic markets,” he said.

For Minarsch, the ultimate goal is not simply better trading strategies. It is ensuring that everyday users retain a stake in an increasingly automated digital economy. A future where AI systems perform most economic activity could risk disenfranchising individuals if centralized platforms control the technology. “Olas aims to create a world where human users can be empowered through their AI agents rather than disenfranchised by them,” he said.

To counter that dynamic, the project emphasizes user ownership of AI. This approach ensures that individuals remain in control of their own economic participation, even as machines become more integral to financial decision-making.