There is something quite appealing about being able to put a price on the future. For most of history, forecasting has largely been the domain of experts, polls, models and people paid to have opinions. Prediction markets offer a fairly simple alternative: if you believe something is going to happen, you can put money behind it. If enough people do the same, the resulting price becomes an implied probability of that event occurring.
The idea is hardly new, but the 2024 U.S. election has pushed it much closer to the mainstream. Polymarket became one of the more visible sources of election probabilities during the campaign, allowing people to trade everything from the eventual winner to individual states and political appointments. Its main presidential market alone ultimately attracted more than $3.5 billion in volume. The interesting thing was not simply that people wanted to bet on an election; people have been doing that for centuries. It was that a prediction market increasingly became something people looked at to understand the election itself.
That raises a more interesting question. Are prediction markets simply another form of speculation, or are markets actually a better way of finding out what people believe?
The Price of Being Right
Prediction markets are based on a fairly intuitive idea. Suppose a contract pays $1 if Donald Trump wins an election and nothing if he loses. If that contract trades at $0.60, the market is roughly expressing a 60% probability of him winning. That probability is not produced by a pollster or a model. It emerges from people buying and selling the contract.
There is a fairly important difference between asking someone what they think will happen and asking them to risk money on the answer. Opinions are largely cheap, and there are plenty of fools peacocking these days. Someone can tell a pollster, journalist or their friends almost anything without bearing much cost for being wrong. Markets introduce a direct and rather dangerous consequence. They do not necessarily discover truth because traders are smarter than everyone else, but they create an environment where people are financially incentivised to find information, update their beliefs and act when they think everyone else is wrong.
If you believe an event has a 70% probability of occurring while the market prices it at 50%, there is an obvious trade. If you are right, you make money; if you are wrong, you lose it. The mechanism turns disagreement into price discovery, and there is something quite powerful about that distinction between stated belief and revealed belief. I can tell you I think something is going to happen. The more interesting question is what price I am willing to pay for being right, the burden of truth is costly.
Markets as Information Aggregators
This idea goes back considerably further than crypto. Friedrich Hayek’s The Use of Knowledge in Society argued that much of the information required to make economic decisions is dispersed between individuals and can never be completely known by a central authority. Prices provide a way of compressing that dispersed knowledge into something everybody can observe. Prediction markets apply essentially the same logic to events rather than goods.
One trader may understand polling methodology. Another may know a particular state extremely well. Someone else might understand voter registration data, campaign finance, demographics or betting markets. None needs to possess the entire picture. They simply trade on the piece they believe the market is getting wrong, and the resulting price incorporates those competing views.
There is some historical evidence that this can work surprisingly well. The Iowa Electronic Markets began running real-money election markets in 1988 and became one of the longest-running experiments in prediction markets. Research comparing its forecasts with 964 national polls across five U.S. presidential elections found that the market was closer to the eventual two-party vote share than the corresponding poll 74% of the time, with its relative advantage particularly pronounced further from Election Day.
That does not mean markets are always better than polls. They measure different things and prediction markets themselves can be wrong. But it suggests there is something valuable about forcing information through a market rather than simply asking people what they think.
Polymarket
Polymarket takes that fairly old idea and packages it inside crypto infrastructure. Markets are generally structured around discrete outcomes, where a winning share ultimately pays $1 while a losing share becomes worthless, allowing prices between zero and one to function as market-implied probabilities. Traders use USDC and trade against one another rather than against Polymarket acting as the house. The result sits somewhere between an exchange, a betting market and an information product.
The 2024 election demonstrated how compelling that can become when the underlying event is large enough. Polymarket’s presidential winner market ultimately recorded more than $3.5 billion of volume, while countless related political markets traded alongside it. The obvious interpretation is that there is enormous demand for political betting. I think the more interesting interpretation is that there may also be demand for a continuously updating market price on uncertainty.
A poll is a snapshot, whereas a market is constantly adjusting. Every new poll, speech, endorsement, piece of economic data or breaking news can immediately be expressed through a trade. Participants do not have to wait for a forecasting model to be updated; the price simply moves. That begins to make a prediction market useful even to people who never place a bet.
There is an obvious temptation to take this argument too far. A market price is not truth; it is simply the price at which buyers and sellers currently agree to trade. For that price to contain useful information, the market needs enough liquidity and enough disagreement. Thin markets can be pushed around by relatively small amounts of capital, and participants with large positions can temporarily move probabilities without possessing better information.
This became a fairly visible debate during the election as several large accounts accumulated substantial Trump positions. The existence of a whale does not automatically make the resulting price wrong; someone willing to put millions behind a view may possess genuine conviction or information. But it complicates the idea that every market price represents some perfectly democratic wisdom of the crowd. Markets weigh opinions by capital, not by person.
That is both their strength and their weakness. Someone who believes they possess superior information can express that conviction more strongly than somebody making an uninformed guess. At the same time, someone with enough money can have an outsized effect on a market, particularly where liquidity is limited. The useful question is therefore not whether prediction markets can be manipulated. Every market can be manipulated at some scale. It is whether there is enough liquidity and enough financially motivated opposition for incorrect prices to become attractive trades. A manipulated price should, in theory, create an opportunity for somebody else. The difficult bit is making sure somebody is there to take it.
But Who Decides What Happened?
There is another problem which becomes increasingly important as prediction markets expand beyond simple questions. The blockchain can verify that a transaction happened on-chain. It cannot independently know who won an election, whether a politician resigned, how many times someone posted on X or whether Taylor Swift announced an engagement. Someone has to tell it.
Polymarket uses UMA’s Optimistic Oracle to resolve markets. Rather than relying on a single centralised source that simply declares an answer, an outcome can be proposed and challenged, with disputed resolutions moving through UMA’s dispute process. It is a clever solution, but it does not eliminate the underlying oracle problem. Prediction markets ultimately need to convert messy real-world events into discrete outcomes, and while sometimes that is trivial, in other cases language, timing and interpretation matter considerably. The more unusual the markets become, the more important the wording of the question and its resolution criteria become.
A prediction market can price uncertainty remarkably well and still fail if nobody can agree on what actually happened.
Post the Election
The larger question for Polymarket now is probably not whether the election was successful. It clearly was. It is what happens next. Prediction markets have historically struggled with a fairly obvious problem: the world does not produce presidential elections every week. Attention concentrates around major political, sporting and geopolitical events and liquidity tends to follow that attention. The 2024 election may therefore have been the perfect product for Polymarket rather than proof that every future event deserves a market.
The scale of the election effect becomes particularly obvious when looking at prediction-market volume over time. Activity had been gradually growing, but the acceleration into the election was in a completely different order of magnitude.
The original question therefore remains the important one: can prediction markets sustain engagement outside peak events like elections, or are they inherently cyclical, with activity following the news cycle? Historically, these markets have seen activity concentrate around elections and major geopolitical events before declining afterwards. Non-election volumes have been growing, which is encouraging, but at this stage they remain well below the levels generated by major political markets.
That does not necessarily mean every market needs to look like the presidential election. There is already activity around crypto, sports, economics, geopolitics and increasingly stranger cultural questions. Some of these markets are useful forecasts; others are closer to entertainment. I am not convinced that distinction matters as much as it initially seems. Financial markets have always combined information, speculation and entertainment to some degree. People trade because they want to make money, because they believe they know something, because they want to hedge an exposure, or simply because having a position makes watching an event more interesting. Prediction markets may ultimately work for many of the same reasons.
The more interesting possibility is that prediction markets eventually become infrastructure rather than destinations. A probability is useful information. Companies could use markets to estimate whether products will ship on time, investors could trade probabilities around regulatory approvals or corporate events, insurers could use event markets to price particular risks, researchers could create markets around scientific outcomes, while institutions could use them as another signal alongside surveys and models. The financial product and the information product begin to converge.
There is precedent for this idea inside companies. Internal prediction markets have been experimented with as a way of aggregating employee knowledge around things like project completion and sales forecasts. The underlying logic is the same: the relevant information already exists somewhere across the organisation, but nobody necessarily knows who has it. A market does not need to know who has the information. It simply needs to make being right sufficiently valuable.
Humans, For Now
There is another participant worth thinking about: AI agents. Most prediction markets today are still predominantly markets between humans, but that may not remain true for very long. An AI agent capable of continuously ingesting news, polling data, financial information and social media could theoretically maintain probabilities across thousands of markets simultaneously. Unlike a human trader, it does not need to sleep, become bored or decide that a small market on some obscure political appointment is not worth watching.
Give those agents capital, and they become market participants. That could create an unusual form of liquidity. Humans generate information and disagree about what it means; machines continuously trade those disagreements into prices. An agent might notice a new poll, compare it against historical polling errors, observe a move in related markets and adjust its positions before a human participant has even opened the article.
The result would not necessarily be omniscient markets. Models can share the same biases, consume the same bad information and become reflexive in exactly the same way human traders do. But prediction markets seem particularly well suited to them. The entire product is already machine-readable: a question, a set of outcomes, a probability and a payoff. This was already part of the thesis in November 2024: AI agents could eventually provide prediction markets with a new source of liquidity by making rapid probabilistic trades against real-time information.
Predicting Tomorrow
Prediction markets sit at an unusual intersection of speculation and truth-seeking. They work precisely because they do not ask participants to be impartial. Everyone is allowed to have an agenda, a bias or a view. The market simply attaches a cost to being wrong and a reward to identifying where everyone else might be wrong.
That does not make them infallible. Liquidity matters. Market structure matters. Whales matter. Resolution mechanisms matter. Regulation matters. A probability displayed on a screen should not suddenly be treated as objective truth simply because money sits behind it. But there is something compelling about the underlying mechanism.
We spend an enormous amount of time asking people what they think will happen. Pollsters ask voters, analysts publish price targets, economists publish forecasts, journalists interview experts, and investors write investment memos. Most of those opinions disappear with remarkably little consequence for the person making them. Prediction markets ask a slightly different question: what do you believe will happen, and at what price are you willing to be wrong?
The 2024 election has given us perhaps the clearest demonstration yet of what happens when enough people are willing to answer. Whether that activity survives the election cycle remains to be seen. If it does, prediction markets may become something considerably more interesting than a place to bet on politics. They could become a continuously updating layer for measuring uncertainty itself.
For now, the experiment is still early. But there are worse ways of trying to predict tomorrow than giving everyone a reason to tell the truth.
