How AI is helping retail traders exploit prediction market ‘glitches’ to make easy money

A fully automated trading bot executed 8,894 trades on short-term crypto prediction contracts and reportedly generated nearly $150,000 without human intervention.

The strategy, described in a recent post circulating on X, exploited brief moments when the combined price of “Yes” and “No” contracts on five-minute bitcoin and ether markets dipped below $1. In theory, those two outcomes should always add up to $1. If they don’t, say they trade at a combined $0.97, a trader can buy both sides and lock in a three-cent profit when the market settles.

That works out to roughly $16.80 in profit per trade — thin enough to be invisible on any single execution, but meaningful at scale. If the bot was deploying around $1,000 per round-trip and clipping a 1.5-to-3% edge each time, it becomes the kind of return profile that looks boring on a per-trade basis but impressive in aggregate. Machines don’t need excitement. They need repeatability.

It sounds like free money. In practice, such gaps tend to be fleeting, often lasting milliseconds. But the episode highlights something bigger than a single glitch: crypto’s prediction markets are increasingly becoming arenas for automated, algorithmic trading strategies, and an emerging AI-driven arms race.

As such, typical five-minute bitcoin prediction contracts on Polymarket carry order-book depth of roughly $5,000 to $15,000 per side during active sessions, data shows. That’s several orders of magnitude thinner than a BTC perpetual swap book on major exchanges such as Binance or Bybit.

A desk trying to deploy even $100,000 per trade would blow through available liquidity and wipe out whatever edge existed in the spread. The game, for now, belongs to traders comfortable sizing in the low four figures.

When $1 isn’t $1

Prediction markets like Polymarket allow users to trade contracts tied to real-world outcomes, from election results to the price of bitcoin in the next five minutes. Each contract typically settles at either $1 (if the event happens) or $0 (if it doesn’t).

In a perfectly efficient market, the price of “Yes” plus the price of “No” should equal exactly $1 at all times. If “Yes” trades at 48 cents, “No” should trade at 52 cents.

But markets are rarely perfect. Thin liquidity, fast-moving prices in the underlying asset and order-book imbalances can create temporary dislocations. Market makers may pull quotes during volatility. Retail traders may aggressively hit one side of the book. For a split second, the combined price might fall below $1.

For a sufficiently fast system, that’s enough.

These kinds of micro-inefficiencies are not new. Similar short-duration “up/down” contracts were popular on derivatives exchange BitMEX in the late 2010s, before the venue eventually pulled some of them after traders found ways to systematically extract small edges. What’s changed is the tooling.

Early on, retail traders treated these BitMEX contracts as directional punts. But a small cohort of quantitative traders quickly realized the contracts were systematically mispriced relative to the options market — and began extracting edge with automated strategies that the venue’s infrastructure wasn’t built to defend against.

BitMEX eventually delisted several of the products. The official reasoning was low demand, but traders at the time widely attributed it to the contracts becoming uneconomical for the house once the arb crowd moved in.

Today, much of that activity can be automated and increasingly optimized by AI systems.

Beyond glitches: Extracting probability

The sub-$1 arbitrage is the simplest example. More sophisticated strategies go further, comparing pricing across different markets to identify inconsistencies.

Options markets, for instance, effectively encode traders’ collective expectations about where an asset might trade in the future. The prices of call and put options at various strike prices can be used to derive an implied probability distribution, a market-based estimate of the likelihood of different outcomes.

In simple terms, options markets act as giant probability machines.

If options pricing implies, say, a 62% probability that bitcoin will close above a certain level over a short time window, but a prediction market contract tied to the same outcome suggests only a 55% probability, a discrepancy emerges. One of the markets may be underpricing risk.

Automated traders can monitor both venues simultaneously, compare implied probabilities and buy whichever side appears mispriced.

Such gaps are rarely dramatic. They may amount to a few percentage points, sometimes less. But for algorithmic traders operating at high frequency, small edges can compound over thousands of trades.

The process doesn’t require human intuition once it’s built. Systems can continuously ingest price feeds, recalculate implied probabilities and adjust positions in real time.

Enter the AI agents

What distinguishes today’s trading environment from prior crypto cycles is the growing accessibility of AI tools.

Traders no longer need to hand-code every rule or manually refine parameters. Machine learning systems can be tasked with testing variations of strategies, optimizing thresholds and adjusting to changing volatility regimes. Some setups involve multiple agents that monitor different markets, rebalance exposure and shut down automatically if performance deteriorates.

In theory, a trader might allocate $10,000 to an automated strategy, allowing AI-driven systems to scan exchanges, compare prediction market prices with derivatives data, and execute trades when statistical discrepancies exceed a predefined threshold.

In practice, profitability depends heavily on market conditions and on speed.

Once an inefficiency becomes widely known, competition intensifies. More bots chase the same edge. Spreads tighten. Latency becomes decisive. Eventually, the opportunity shrinks or disappears.

The larger question isn’t whether bots can make money on prediction markets. They clearly can, at least until competition erodes the edge. But what happens to the markets themselves is the point.

If a growing share of volume comes from systems that don’t hold a view on the outcome — that are simply arbitraging one venue against another — prediction markets risk becoming mirrors of the derivatives market rather than independent signals.

Why big firms aren’t swarming

If prediction markets contain exploitable inefficiencies, why aren’t major trading firms dominating them?

Liquidity is one constraint. Many short-duration prediction contracts remain relatively shallow compared with large crypto derivatives venues. Attempting to deploy significant capital can move prices against the trader, eroding theoretical profits through slippage.

There is also operational complexity. Prediction markets often run on blockchain infrastructure, introducing transaction costs and settlement mechanisms that differ from those of centralized exchanges. For high-frequency strategies, even small frictions matter.

As a result, some of the activity appears concentrated among smaller, nimble traders who can deploy modest size, perhaps $10,000 per trade, without materially moving the market.

That dynamic may not last. If liquidity deepens and venues mature, larger firms could become more active. For now, prediction markets occupy an in-between state: sophisticated enough to attract quant-style strategies, but thin enough to prevent large-scale deployment.

A structural shift

At their core, prediction markets are designed to aggregate beliefs to produce crowd-sourced probabilities about future events.

But as automation increases, a growing share of trading volume may be driven less by human conviction and more by cross-market arbitrage and statistical models.

That doesn’t necessarily undermine their usefulness. Arbitrageurs can improve pricing efficiency by closing gaps and aligning odds across venues. Yet it does change the market’s character.

What begins as a venue for expressing views on an election or a price move can evolve into a battleground for latency and microstructure advantages.

In crypto, such evolution tends to be rapid. Inefficiencies are discovered, exploited and competed away. Edges that once yielded consistent returns fade as faster systems emerge.

The reported $150,000 bot haul may represent a clever exploitation of a temporary pricing flaw. It may also signal something broader: prediction markets are no longer just digital betting parlors. They are becoming another frontier for algorithmic finance.

And in an environment where milliseconds matter, the fastest machine usually wins.