Prediction markets have long held the promise of gathering insights about future events. Increasingly, these signals come not only from humans, but from machines.
According to David Minarsch, CEO and co-founder of Valory AG, the team behind the crypto-AI protocol Olas, autonomous AI agents are emerging as powerful tools for trading prediction markets, especially for retail users trying to compete in an increasingly automated environment.
Valory builds 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 infrastructure for autonomous software agents that can run services on blockchains, interact with smart contracts, and cooperate with each other while earning crypto 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 their users.
One of the most visible experiments in that vision is Polystrat, an artificial intelligence agent launched on the prediction market platform Polymarket in February 2026. The agent acts on behalf of users who own 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 people sleep, work or lose focus, the agent continues to act.
Prediction markets, platforms where users trade contracts tied to real-world outcomes, have risen from niche prediction tools to a fast-growing corner of fintech over the past few years. The industry’s breakout moment came during the 2024 US presidential election, when trading volume increased and the markets gained mainstream visibility, followed by rapid expansion in sports, finance and crypto-related betting. By 2025, the total nominal trading volume across major platforms will exceed $44 billion, with monthly activity of up to $13 billion during peak periods.
Today, the market is heavily concentrated around two dominant players: Kalshi, a US-regulated event contract exchange overseen by the Commodity Futures Trading Commission, and Polymarket, a crypto-native platform that operates globally and offers a wider range of prediction markets. Together they account for around 85-97% of trading volume in the sector that processes tens of billions of dollars in annual bets on everything from elections and central bank policy to sporting and cultural events
Why machines can outperform humans
The push towards AI-powered trading stems from a simple observation. Much of the intelligence embedded in modern AI models has yet to be translated into financial markets.
This realization led 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 predict outcomes and act on them.
The prediction markets themselves are built on probabilistic forecasts. A simple guess about an event, be it a political outcome, an economic indicator or a sports result, may not be better than a coin flip. But structured data analysis and disciplined trading strategies can change that equation.
“Simply calling off-the-shelf models with markets usually results in results no better than a coin flip,” Minarsch said. “But state-of-the-art AI models wrapped in custom workflows, so-called predictive tools, have historically shown predictive accuracy of up to 70% and higher.”
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 results in the prediction markets, while the majority lose money.
At the same time, machine participation 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. “You have human participants in prediction markets along with many machines,” he said. “So humans are already in a battle with machines.”
The main difference is that machines are less emotional and better at sticking to consistent strategies.
By making AI agents available to ordinary users, Olas aims to level the playing field.
Early traction for autonomous traders
The early performance of Polystrat has been encouraging.
Within about 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.
“Agents tend to do better than humans,” he said. “Polystrat AI agents are already outperforming 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.
The long tail of prediction markets
Beyond performance, Minarsch believes AI agents can unlock an overlooked opportunity in prediction markets: the “long tail” of niche or local questions.
Many prediction markets revolve around major global events, elections, macroeconomic data or high-profile sports competitions. But countless smaller questions remain largely unexplored.
“People often don’t bother digging for the information,” Minarsch said. “They don’t bother to make an 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 the agent to the problem and it does the job.”
This can help expand prediction markets as a data collection tool for businesses, policy makers and decision makers. Forecast markets have long been studied as ways to gather scattered knowledge and surface insights that traditional surveys or models may miss.
In that sense, prediction markets can become a kind of upstream technology for cross-industry decision-making.
Human-AI collaboration
Despite the rise of automation, Minarsch does not see AI agents completely replacing humans.
Instead, he frames them as complements.
“People make choices in a more hasty way, which can be harmful,” he said. “AI agents can act as something humans trust.”
A future direction involves allowing users to augment their agents with proprietary knowledge or specialized datasets. “We’re seeing demand from users who want their agent to leverage their own knowledge base or proprietary information,” Minarsch said. “It would allow agents to act in a more principled way than a human could.”
Over time, the team says the prediction models and data pipelines that drive these agents have improved significantly and are generating sustained alpha when combined with large general-purpose language models.
Risks and regulation
The growth of prediction markets also raises ethical and regulatory issues.
Some critics argue that markets that predict wars, deaths or disasters can create incentives to manipulate outcomes or profit from harmful events.
Minarsch acknowledged the need for careful guardrails.
“There needs to be regulation about what kinds of prediction markets should exist,” he said.
At the same time, he believes that AI agents can also help detect problematic markets or manipulation attempts by identifying suspicious patterns.
“Agents could detect patterns and help shut down problematic markets,” he said.
Building a user-owned AI economy
For Minarsch, the ultimate goal is not just better trading strategies.
It ensures 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.”
To address this dynamic, the project emphasizes user ownership of AI systems. “We want to create more user-owned agents,” Minarsch said.
If successful, this model could allow people to deploy standalone software that generates value on their behalf across markets and services. Prediction markets are just the starting point.
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