Prediction Markets Are Becoming Algorithmic Trading Venues as AI Moves In

Prediction markets are beginning to develop an algorithmic trading layer similar to traditional electronic markets. A new startup, Elastics, is building tools aimed at accelerating that shift.

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Elastics, founded by former Goldman Sachs professional Szymon Pawica, has raised $2 million in pre-seed funding to build AI agents for prediction markets.

The company is developing what it calls “Trade with Words” — a natural language interface for deploying quantitative strategies without traditional order entry.

The model reflects a broader shift in prediction markets, from “wisdom of the crowd” toward competition based on speed, automation, and execution.

How Automation Is Reshaping Market Structure

Earlier market data already showed how far automation had moved into prediction markets. Public wallet analysis has identified automated bots among many of the most profitable accounts, while arbitrageurs have extracted tens of millions of dollars from Polymarket by exploiting short-lived pricing gaps between venues.

Prediction markets are increasingly rewarding execution speed and data processing. This is partly driven by AI systems that accelerate how quickly information is reflected in prices.

“As AI-driven automation becomes more widespread, manual trading is becoming increasingly challenging,” Pawica said.

The trajectory resembles the evolution of FX, where machine-driven liquidity and execution now account for a large share of volume. That transition took years in traditional markets. In prediction markets, it may happen faster.

What This Means for Brokers

The infrastructure buildout reframes how prediction markets should be viewed by platforms considering integration. In these markets, bots account for a significant share of volume and pricing gaps are exploited in milliseconds.

As a result, they are starting to resemble other electronic derivatives markets in their operational requirements. This implies the need for real-time data feeds, institutional-grade execution, and latency management.

Platforms that approach prediction market integration as a simple product extension will be competing against infrastructure built to capture speed and execution advantages.

For multi-asset brokers, the key question is how quickly these infrastructure requirements become unavoidable as the market evolves.