Can AI give traders an edge in prediction markets?

Artificial intelligence is increasingly being tested in prediction markets, platforms where users trade contracts on the outcome of future events. Proponents argue that data-driven models may be able to spot inefficiencies in how these markets price probabilities. But whether AI can consistently deliver a profitable edge remains an open question.

One of the latest entrants is Mode, a technology firm that this month launched “AI Quant”, a system designed to analyse Kalshi’s cryptocurrency prediction markets. Powered by predictive intelligence from SynthdataCo, the company says its tool can “analyse and find edge in Kalshi crypto markets.” The launch was announced by Mode’s JRoss Treacher, who admitted on social media that the release had arrived earlier than planned.

The claim places Mode among a growing set of actors exploring whether prediction markets, long seen as aggregators of collective forecasts, can be outperformed by machines.

Evidence of inefficiencies

Prediction markets such as Kalshi and Polymarket allow traders to buy and sell contracts tied to future outcomes, from economic data releases to political events. Prices in these markets are interpreted as probabilities: a contract trading at 65 cents, for example, implies a 65 per cent chance that the event will occur.

Academic work suggests these markets are not always perfectly efficient. A recent study of over 300,000 Kalshi contracts by economist Karl Whelan highlighted a persistent “favourite–longshot bias”: outcomes deemed highly likely by the market tended to occur even more often than the prices implied, while low-probability outcomes were even less likely to happen than predicted. This finding echoes similar biases identified in betting and options markets.

In principle, such systematic distortions could be exploited by algorithms that consistently take the other side of mispriced contracts. AI models may be particularly well-suited to detecting and quantifying these patterns, especially when combined with large volumes of historical and real-time data.

The rise of predictive AI in gambling has already provoked debate in the online gaming sector, where operators face the question of whether software could give certain players an unfair advantage. The same discussion now extends to prediction markets.

“Predictive AI is a double-edged sword for online sportsbooks or online casinos,” explained Wildz Casino. “On one hand, it could be a threat if players start using it to spot inefficiencies in betting lines more effectively than humans can, or try to exploit loopholes. On the other hand, casinos and operators themselves can use the same technology to manage risk, detect unusual patterns, and even design more engaging products. Ultimately, the opportunity or threat depends on who gets better at deploying the tools.”

Tools and limitations

Projects outside academia are also probing this space. Tremor.live, for example, monitors Polymarket contracts to detect sudden price swings, or “tremors”, that may signal new information entering the market. The service has been profiled by Business Insider as a way of identifying unusual trading activity, though it stops short of offering trading advice.

Still, applying AI to prediction markets faces hurdles. These platforms are far smaller and less liquid than traditional financial exchanges, meaning it can be difficult to enter or exit positions at favourable prices.

Regulators are also watching the sector closely. Kalshi, which operates under oversight from the US Commodity Futures Trading Commission, has promoted prediction markets as a way to make information measurable. The introduction of AI-driven trading adds another layer of complexity, raising questions about market fairness and the potential for information asymmetries.

Incremental gains rather than guarantees

The broader financial industry has already adopted machine learning for tasks ranging from portfolio optimisation to high-frequency trading. Yet even in those larger and more liquid markets, AI models rarely deliver guaranteed profits; instead, they tend to provide incremental improvements in forecasting accuracy.

Prediction markets may offer similar opportunities. AI tools could help identify biases, process information faster than human traders, or flag unusual price movements. But the gains are likely to be narrow and short-lived, particularly as more participants deploy similar methods.

Mode’s early release of its AI Quant system underlines both the excitement and the uncertainty in this space.