Prediction markets have long promised to add information about future events. Increasingly, those signals come not only from people, but also from machines.
According to David Minarsch, CEO and co-founder of Valory AG, the team behind crypto-AI protocol Olas, autonomous AI agents are emerging as powerful tools for trading prediction markets, particularly for retail users trying to compete in an increasingly automated environment.
Valory makes products at the intersection of blockchain and multi-agent systems (MAS), and its current focus is Olas, formerly known as Autonolas. The protocol is designed as an infrastructure for autonomous software agents that can run services on blockchains, interact with smart contracts, and cooperate with each other while earning cryptographic rewards.
The broader vision is what Minarsch calls an “agent economy.” A decentralized ecosystem where autonomous AI agents perform useful tasks and generate value for its users.
One of the most visible experiments in that vision is Polystrat, an artificial intelligence agent launched on the Polymarket prediction market platform in February 2026. The agent trades on behalf of users who self-custody and own it, executing strategies continuously 24 hours a day.
“Simply put, Polystrat is an autonomous AI agent that operates on Polymarket 24/7 on behalf of its human user,” Minarsch said. The idea is simple: while humans sleep, work or lose concentration, the agent continues to operate.
Prediction markets, platforms where users trade contracts tied to real-world outcomes, have gone from niche forecasting tools to a rapidly growing corner of financial technology in recent years. The industry’s turning point came during the 2024 US presidential election, when trading volumes increased and markets gained mainstream visibility, followed by rapid expansion into sports, economics, and cryptocurrency-related betting. By 2025, total notional trading volume on major platforms will exceed $44 billion, with monthly activity reaching $13 billion during peak periods.
Today, the market is highly concentrated around two dominant players: Kalshi, a US-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 approximately 85% to 97% of trading volume. in the sector, processing tens of billions of dollars in annual bets on everything from elections and central bank policies to sporting and cultural events.
Why machines can outperform humans
The push toward AI-powered commerce comes from a simple observation. Much of the intelligence built into modern AI models has not yet been translated into financial markets.
That understanding prompted the Valory team to start 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.
Prediction markets themselves are based on probabilistic forecasts. A simple assumption about an event, be it a political outcome, an economic indicator, or a sports result, may be no better than flipping a coin. But structured data analysis and disciplined trading strategies can change that equation.
“Simply pushing out-of-the-box models into markets generally doesn’t produce better results than flipping a coin,” Minarsch said. “But next-generation AI models wrapped in custom workflows, so-called prediction tools, have historically shown predictive accuracy of up to 70% and more.”
The results so far suggest that machines may have an advantage. Third-party data indicates that only 7% to 13% of human traders achieve positive performance in prediction markets, while the majority lose money.
At the same time, the share of machines is growing rapidly. More than 30% of wallets on Polymarket already use AI agents, according to analytics platform LayerHub.
Minarsch believes this trend reflects a broader shift: Humans are already competing with machines, whether they realize it or not. “There are human participants in prediction markets along with many machines,” he said. “So humans are already in a battle with machines.”
The key difference is that machines are less emotional and better stuck to consistent strategies.
By making AI agents available to everyday users, Olas aims to level that playing field.
Early traction for self-employed traders
Polystrat’s initial performance has been encouraging.
About a month after launch, the broker executed more than 4,200 trades on Polymarket and recorded single-trade returns of up to 376%, according to data shared by the team.
“Agents tend to do better than humans,” he said. “Polystrat AI agents already outperform human participants on Polymarket, with more than 37% of them showing positive P&L versus less than half that number for human participants.”
Users can configure their own agents based on their strategic preferences, data sources, or risk tolerance.
The long tail of prediction markets
Beyond performance, Minarsch believes AI agents could unlock an overlooked opportunity in prediction markets: the “long tail” of localized or niche questions.
Many prediction markets revolve around major world events, elections, macroeconomic data or high-profile sporting competitions. But countless minor questions remain largely unexplored.
“Humans often don’t bother looking for information,” Minarsch said. “They can’t be bothered to make the effort.” AI agents, on the other hand, can analyze a large number of smaller markets simultaneously.
“The long tail of prediction markets is very interesting for AI agents,” he said. “You just point out the problem to the agent and they do the job.”
This could help expand prediction markets as a data collection tool for businesses, policymakers and decision makers. Forecast markets have long been studied as ways to aggregate dispersed knowledge and bring to light insights that traditional surveys or models might miss.
In that sense, prediction markets can become a kind of bottom-up technology for decision-making across industries.
Collaboration between humans and AI
Despite the rise in automation, Minarsch does not believe that AI agents will completely replace humans.
Rather, it presents them as complements.
“Humans make decisions more quickly, which can be detrimental,” he said. “AI agents can act like something humans trust.”
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 leverage their own knowledge base or proprietary information,” Minarsch said. “That would allow agents to trade on more principles than a human being could.”
Over time, the team says the prediction models and data pipelines that power these agents have improved significantly, generating sustained alpha when combined with large general-purpose language models.
Risks and regulation
The growth of prediction markets also raises ethical and regulatory questions.
Some critics argue that markets that predict wars, deaths or disasters could create incentives to manipulate outcomes or profit from harmful events.
Minarsch acknowledged that careful protective barriers are needed.
“There needs to be regulation on what types of prediction markets should exist,” he said.
At the same time, he believes that AI agents could also help detect problematic markets or manipulation attempts by identifying suspicious patterns.
“Agents could spot patterns and help close down problematic markets,” he said.
Building a user-owned AI economy
For Minarsch, the ultimate goal is not simply to improve business strategies.
It is about ensuring that everyday users remain engaged in an increasingly automated digital economy.
A future where AI systems perform most economic activity could risk disenfranchising people if centralized platforms control the technology.. ““Olas aims to create a world where human users can be empowered through its AI agents instead of being disenfranchised.”
To counteract this dynamic, the project emphasizes user ownership of artificial intelligence systems. “We want to create more user-owned agents,” Minarsch said.
If successful, that model could allow people to deploy autonomous software that creates value on their behalf across markets and services. Prediction markets are just the starting point.
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