Prediction Markets and AI: How Investors Are Pricing Uncertainty

While prediction markets are currently a hot topic, they are anything but new.

They have existed for decades, long before crypto or consumer AI. Economists have been using them since the 1980s as tools for aggregating information. The idea was simple: if people have different pieces of information or interpretations about the future, a market can combine those views into a single probability that updates as new information arrives.

For a long time, that idea stayed mostly academic. When prediction markets did reach the public internet, they were seen as niche, small-scale, or experimental due to limited liquidity and regulatory restrictions. To most people, they looked like election betting sites or intellectual experiments rather than serious financial instruments. 

But, over the past few years, prediction markets have expanded into sports, macroeconomic indicators, regulatory outcomes, and crypto-native events. Liquidity has increased. Market makers have entered. Coverage has shifted from academic journals to mainstream financial media. Monthly volumes on leading platforms now regularly reach into the billions, even outside election cycles.

At the same time, prediction market platforms have begun incorporating AI to help users interpret market movement and understand why probabilities change. With deeper liquidity, improved market structure, and better tooling, prediction markets are no longer a niche curiosity. The more relevant question now is whether, and how, they belong alongside other forms of exposure in your portfolio.

Answering that requires understanding how prediction markets actually work, how the exposure they offer differs from traditional investments, and how AI is beginning to change the way investors interpret and engage with uncertainty.

What Prediction Markets Are

A prediction market is a market designed to price uncertainty around a specific future event. Instead of trading an asset, participants trade contracts that settle based on whether an outcome occurs.

These are known as event-contingent contracts. At their simplest, they answer a single question: will this happen, yes or no?

Each contract is defined by:

• A specific real-world event

• A clear resolution condition

• A fixed payout if the event occurs

• No payout if it does not

Most prediction markets use binary contracts that settle at either 1 or 0.

Consider a contract that asks:

“Will the U.S. Federal Reserve cut interest rates by June 30?”

This market typically offers two contracts: YES and NO.

• A YES contract pays out 1 dollar if the rate cut happens by June 30, and 0 if it does not.

• A NO contract pays out 1 dollar if the rate cut does not happen by June 30, and 0 if it does.

What does this mean in practice:

If the YES contract is trading at $0.70, a trader is paying $0.70 per contract.

• If the rate cut does occur, each YES contract settles at $1, resulting in a $0.30 gain per contract before fees.

• If the rate cut does not occur, the trader loses the amount paid for each contract.

The NO contract will typically trade at the complementary price, adjusted for fees and liquidity, reflecting the market’s view of the alternative outcome. In this case, the NO contract will typically trade near $0.30, adjusted for fees and liquidity.

Traders can hold any number of contracts depending on conviction, risk limits, and portfolio construction. The traded price represents the level at which buyers and sellers are currently willing to take risk on the event.

That price moves when new information changes those risk assessments. Economic data releases, policy statements, official communications, or shifts in market expectations cause traders to revise their probability estimates. As traders act on those updated views by buying or selling contracts, demand and supply rebalance at a new price.

In this example, a Yes contract trading at $0.70 reflects the market’s current consensus that there is roughly a 70 percent probability the Federal Reserve will cut interest rates by June 30, based on all available information at that moment.

The Accuracy of Pricing Uncertainty

Prediction markets work when three conditions are met:

1. Participants have heterogeneous information or interpretations:
   Different traders bring different inputs. One trader may focus on historical base rates. Another may track policy statements. Another may be watching second-order effects like political incentives or timing constraints. The market price reflects how those perspectives interact.
   
   
   

2. Participants are financially incentivized to act on their beliefs:
   Unlike surveys or opinion polls, prediction markets require capital at risk. Participants who believe the market price is wrong have an incentive to trade against it.
   
   
   

3. Prices update continuously as new information arrives:
   When new data, statements, or events occur, traders adjust positions. The price becomes a real-time signal of how the market is interpreting those developments.

Under these conditions, prices tend to converge toward well-calibrated probabilities. This is why prediction markets have been studied extensively in economics and decision science. Large comparative studies have found that liquid prediction markets often outperform polls and expert surveys in forecasting accuracy, particularly at longer horizons.

But accuracy is not guaranteed. Thin liquidity, poorly defined resolution criteria, or coordinated manipulation can distort prices temporarily. But those failure modes are familiar to anyone who has traded illiquid derivatives or small-cap assets.

Prediction Markets as a Signal Layer

Prediction markets increasingly serve as an information source for other forms of trading and decision-making.

Because of the dynamics described above, market prices often act as a near real-time signal of how new information is being interpreted. Investors may use these signals to inform decisions elsewhere—whether adjusting positions in traditional assets, navigating crypto markets, or managing risk around upcoming events.

In this sense, prediction markets function not only as places to express a view, but also as a lens through which uncertainty across markets can be observed and compared.

Why Today’s Investors Want Exposure to Prediction Markets

Prediction markets appeal to investors for a different reason than traditional assets. They are not about owning a company or tracking an index. They are about gaining exposure to specific outcomes in a world where many important developments do not map cleanly to a single stock, token, or fund.

Allocating Capital Around Clearly Defined Outcomes

In most markets, expressing a view on a specific outcome is indirect and often unclear.

For example, if you believe inflation will fall faster than expected, there is no simple way to invest in that view. You might buy stocks you think will benefit, hold more crypto, or shift money between assets based on headlines. But those assets move for many reasons at once. Stock prices change because of earnings, sentiment, or broader market swings. Crypto prices often move on narratives, liquidity, or unrelated news. As a result, it can be hard to tell whether your investment is responding to the outcome you care about or something else entirely.

For many investors, this creates uncertainty about what their capital is actually exposed to.

Prediction markets make this more straightforward. They allow investors to allocate capital directly to a specific, clearly defined event. The outcome determines the result. If the event occurs, the contract settles one way. If it does not, it settles the other way. There is no need to guess how other markets might react or whether unrelated price movements will dominate the result.

Instead of investing through indirect signals, prediction markets give investors a way to express a clear view on an outcome itself.

Complimenting Your Traditional Portfolio

Prediction markets are not long-term investments in the traditional sense. Contracts expire, and there is no compounding like there is with equities. But they provide a form of exposure that is difficult, and in some cases impossible, to obtain elsewhere.

Many of the most important developments shaping markets are not tied to publicly traded companies or investable indexes. Prediction markets make it possible to gain exposure to these developments directly, including:

• Milestones for technologies or companies that are not yet publicly traded, such as AI capability thresholds, model releases, or deployment timelines

• Regulatory and policy outcomes that materially affect entire sectors, rather than individual firms

• Adoption milestones for emerging technologies in regulated industries

• Public benchmarks or technical thresholds that signal progress or stagnation across a broader trend

This allows investors to express views on what happens next, rather than being forced to proxy those views through a single stock, token, or fund. In that sense, prediction markets complement traditional portfolios. They provide targeted, outcome-based exposure in areas where conventional investment vehicles fall short.

How AI Is Being Used in Prediction Markets Today

Some prediction market platforms are beginning to integrate AI directly into their products, using it to add more context around active markets. 

Kalshi, for example, has partnered with xAI to bring Grok into its market experience. Polymarket has introduced AI-generated market summaries that explain recent probability shifts and highlight relevant developments tied to active contracts.

At a high level, this makes sense. Prediction markets are information-dense. Understanding why a probability moved requires awareness of contract language, resolution rules, authoritative sources, and recent developments. AI embedded at the platform level can be designed with access to this market-specific context, which general-purpose models lack by default.

But this approach comes with tradeoffs all users should be aware of. Understanding a move is not the same as knowing what to do next.

These AI summaries are designed to explain what changed in the market in general terms. It does not help an individual investor understand how that change fits into their broader picture. It cannot tell you whether that move matters for your time horizon, your existing exposure, or the outcomes you actually care about. And it cannot guide you toward which related markets might better reflect the view you want to express.

That is where more personalized, end-to-end tools become important.

For example, our AI Crypto Copilot is designed to be personalized around the individual using them. They learn your goals, risk tolerance, and interests, then proactively surface insights that matter to your portfolio as market conditions change. Instead of treating prediction markets as isolated contracts, our copilot places them alongside your broader exposure, helping you understand opportunities, compare outcomes, and act on them directly.

By unifying research, analysis, and execution in a single interface, this kind of tool helps investors move from understanding what’s happening in the market to making real investment decisions without stitching together disconnected systems. Prediction markets become one of several ways to express a view, integrated into how you already think about risk and opportunity.

It’s also worth distinguishing between the use of AI copilots vs automated trading agents in prediction markets. Recently some market participants have experimented with creating agents that automatically trade prediction markets. These bots can be useful for arbitrage, but they typically operate on narrow rules and are focused on short-term price dynamics. Compared to automated trading, an AI copilot is designed to help you think more clearly about outcomes, surfacing relevant markets, explaining what’s driving probability changes, comparing scenarios, and helping you decide which exposures actually matter for your goals and risk preferences.

Where Prediction Markets Are Headed

Prediction markets are expected to reach a trillion dollars in annual trading volume by the end of this decade. That growth reflects a clear shift in investor behavior toward outcome-based exposure that traditional markets struggle to provide.

AI accelerates this shift by lowering the cost of participation.

As this continues, prediction markets are unlikely to remain siloed destinations. They are more likely to be embedded across trading platforms, exchanges, and investment tools, sitting alongside spot, derivatives, and other instruments as another way to express intent.

If you want to be early to this shift, our DeFi Copilot waitlist is open. DeFi Copilot is a personalized crypto investment copilot that makes prediction markets usable in practice. It connects live market probabilities to your stated goals and risk preferences, surfaces relevant markets as conditions change, and lets you express intent directly when you know the exposure you want. On top of prediction markets, our copilot will support DEX spot, perpetuals, derivatives, tokenized equities, and real-world assets (RWAs), so everything you care about lives in one place.