Why are investors rushing to predict markets?

Author: Yokiiiya Stablehunter

Today I was chatting with a friend, and he threw out a very interesting question: “If Crypto × Fintech really creates gains over the next 10 years, who will be the biggest winners? In other words, which companies will make up the ‘Mag7’ in this space?”

Revolut, Robinhood, Coinbase, Stripe… these are obviously among the first names to come up. Over the past decade, they have already proven their ability to reinvent parts of traditional finance.

But as we talked, I suddenly realized: my previous thinking framework actually had a misconception. I kept asking—“What other parts of traditional finance haven’t been reimagined yet?” This logic essentially still looks for gaps on an old map.

But the real question to ask is:

Which companies aren’t just digitizing old finance, but are creating a completely new financial market?

Within this framework, one name is almost a default—Polymarket. Not because it’s soared quickly, nor because it’s been frequently mentioned in the media lately, but because it’s doing something entirely different: It’s not reinventing banking or payments; it’s reinventing the ‘event’ itself. It turns events into assets and probabilities into prices.

Coincidentally, prediction markets have surged again over the past year. So naturally, we ask another, more important question: Why will prediction markets suddenly become “one of the most worth researching tracks” in 2024–2025? And in this cycle of revival, what paths do Polymarket, Kalshi, and Opinion each represent?

2. Why will prediction markets heat up again in 2024–2025?

Explaining this wave of enthusiasm merely through “US elections” or “celebrity events” doesn’t hold much water. In recent years, there have been countless hot topics, but prediction markets haven’t skyrocketed like this. This time is different. There are several deeper structural changes behind this.

1) AI makes “probability” relevant again

In the past, large models provided deterministic answers; now, more and more scenarios output probabilities. Predictions about CPI, interest rate cuts, company events, policy directions—once probabilities appear, a demand naturally arises: Probabilities need prices, prices need markets. So, prediction markets are becoming a part of AI workflows for the first time. Not just a “speculative tool.” This influence will far surpass the current discussions we see.

2) It’s been treated by the media as a “real-time sentiment indicator”

Over the past year, one clear change: more mainstream media outlets are citing Polymarket. Why? Because it’s faster than polls and more transparent than expert judgments. Media citations → user growth → market depth enhancement—this is a simple yet effective flywheel. Previously, prediction markets weren’t big enough because they didn’t enter mainstream narratives; now, they have.

3) High event density, but a lack of “corresponding tools” in the market

The world of 2024–2025 will have a higher information density than any time in the past decade: elections, geopolitics, macro policies, tech regulation, corporate events (especially AI)—the problem is: these events have a huge impact, but no financial tools exist to trade them.

You can buy gold, US stocks, government bonds, but not: “the probability change of the Fed’s rate cut in December,” “whether a certain CEO will resign this quarter,” or “whether a certain regulation will be implemented.” Prediction markets fill exactly this gap. Essentially, they create a new asset type: event assets.

4) Regulatory attitudes have changed slightly but importantly

CFTC has previously penalized Polymarket, but at the same time, Kalshi obtained a CFTC license. This sends a very realistic signal: some prediction markets can be permitted, some can follow compliant routes, gray areas are beginning to be carved out. For institutional investors, “uncertainty reduction” is a growth signal.

5) User structure has changed

In the past: primarily entertainment users, with dispersed liquidity, and products more like “information apps.” This wave is distinctly different: more institutional accounts, professional traders making indicator predictions, hedge funds increasing use for hedging, AI companies referencing it. When user structure upgrades from “spectators” to “traders,” the quality of the market will undergo a qualitative change.

Summary

Prediction markets didn’t suddenly become popular. It’s driven by: the demand from AI, media mentions, macro environment, changing user structures, plus gradually clarifying regulatory boundaries—jointly creating a structural uplift. This wave isn’t just short-term event-driven. It’s more like prediction markets first gaining “application scenarios of the era.”

3. Three entirely different paths: Polymarket, Kalshi, Opinion

All three are involved in prediction markets, but their routes are completely different. They solve different problems and target different user groups. Viewing them together reveals a potential layered structure of this track’s future.

1) Polymarket: Turning events into assets

Polymarket’s approach is very straightforward: turn events into assets, turn probabilities into prices. It’s not a traditional “prediction tool,” more like a real-time event price display. The higher the social attention, event density, and media mentions, the faster its market reactions. Its low understanding barrier and strong emotion-driven growth are reasons for its rapid expansion. Its advantage is speed; challenge is regulation. In short: the gateway to event assetization.

2) Kalshi: A regulated event derivatives exchange

Kalshi is the most financialized route. It deals with event contracts that can be defined and captured by models: CPI, unemployment rate, yields, FOMC, etc. It appeals to a different user base: macro traders, hedge funds, quant teams. This makes its trading structure more stable and scalable than Polymarket.

The political markets you see on Kalshi don’t mean it’s the same kind of product as Polymarket—politics is just one category of regulated events, not its growth driver. In short: an event derivatives exchange, the financial infrastructure of prediction markets.

3) Opinion Labs: The model consensus layer in the AI era

Opinion follows a third route, not aimed at retail trading nor serving institutional traders. It aims to establish a “probability consensus layer” for AI models: allowing the probabilities output by different models to be aggregated, referenced, and ultimately priced by markets. Its target users aren’t humans but models. It’s not about “letting users bet,” but “giving models a readable, tradable probability interface.”

This path has a longer time horizon and is more foundational. Compared to the first two, Opinion is at a clearly earlier stage.

It already has a trading interface (opinion.trade), but access to regions like the US and China is restricted, so the experience varies across networks. Limited public info; its main external contact remains Twitter. The underlying infrastructure is rapidly iterating; branding and official website are not priorities.

This is not “immature website experience,” but typical of early-stage foundational projects: first getting the core mechanisms running, then gradually moving toward external stability.

In short: Opinion already has a product, but it’s still very early, more like a foundational building block for the future AI ecosystem rather than a player in current user scale competition.

Polymarket, Kalshi, and Opinion may all be making prediction markets, but their directions, product structures, compliance paths, and future positioning are completely different: Polymarket captures “attention and sentiment.” Kalshi captures “risk and pricing models.” Opinion captures “AI’s understanding of the future.”

They correspond to three layers of prediction markets: mass audience, financial infrastructure, and model layer. Because these three paths coexist, this cycle of prediction markets isn’t like the past—where one product suddenly soared—but rather a market in the process of formation.

4. My observation on this track: AI is creating noise, Web3 is distinguishing noise

I don’t want to make a “what the future holds” conclusion about prediction markets, as I haven’t deeply studied this track. But over the past year, across different projects and product forms, I repeatedly see one thing: The integration of AI and Web3 is accelerating faster than we imagined, and the direction is very clear.

AI’s strength lies in “generation”—generating text, judgments, predictions. But as its generated content increases exponentially, a new problem becomes more obvious: AI is creating noise. Judgments, explanations, probabilities, inferences—all are growing exponentially. More information → more noise → higher costs.

Web3’s role, right after this noise, is to distinguish noise. It offers not “content,” but: immutable, settlement-ready, verifiable, aligned incentives, and capable of forming prices.

The combination of these two will gradually become natural in financial markets:

AI is responsible for generating views on the future; Web3 is responsible for putting these views into markets for price, time, and incentive testing.

Prediction markets are just a very intuitive example. They turn “AI-generated probabilities” into “financially usable prices.” From this perspective, it’s more like an interface rather than an app. I’m not sure what this track will ultimately look like, but what I do see is: AI is making the future more fuzzy, Web3 is making the future more verifiable. And in financial markets, these two will naturally need each other.