The Race to Own the Probability Layer
Prediction markets are moving from internet curiosity to financial infrastructure — and the moat may form around trust, liquidity, and distribution.
A few years ago, betting on an election outcome, a Fed decision, a crypto price, or a World Cup winner still sounded like a niche internet habit. Something for political obsessives, crypto traders, and people who spend too much time refreshing Nate Silver.
Now the category is being repriced as financial infrastructure.
Kalshi has gone from a regulatory curiosity to a potential IPO candidate. Polymarket has become a global information brand, with Intercontinental Exchange, the owner of the NYSE, agreeing to invest up to $2 billion at roughly an $8 billion pre-money valuation. Robinhood has already turned prediction markets into a product surface inside its app through Kalshi. Meta, according to reporting from NPR and The New York Times, is building a prediction market app of its own called Arena.
The question for investors is no longer whether prediction markets are real.
The question is where the durable economics land.
Do they belong to the regulated exchange?
The consumer app?
The crypto-native liquidity venue?
The social graph?
The data distributor?
Or do event contracts become another commoditized product that every finance app eventually offers?
My answer: prediction markets will produce several winners, but the strongest long-term moat probably belongs to the player that combines regulation, liquidity, distribution, and trusted resolution. Right now, Kalshi is closest to that position in the U.S. real-money market. Polymarket still has the stronger cultural brand. Robinhood may become the most important consumer distribution layer. Meta has the most users, but also the hardest trust problem.
That sounds messy because the market itself is messy. This is at least four businesses wearing the same label.
The old dream finally found its moment
Prediction markets have been around forever in tech years.
The pitch was always beautiful: if people put money behind beliefs, the market price becomes a cleaner signal than punditry, polling, or corporate forecasting. A contract trading at 67 cents means the crowd assigns roughly a 67% probability to the event.
For a long time, the idea was better than the business.
The old versions were too academic, too thinly traded, too constrained, or too legally awkward. The markets that did exist often had poor liquidity and narrow communities. They were fascinating to read and hard to scale.
Then several things changed at once.
The 2024 election made prediction markets part of mainstream conversation. Crypto rails made global collateral and settlement easier. Retail trading apps trained millions of people to understand options, derivatives, and probability-shaped interfaces. Sports betting made real-time outcome speculation socially normal. AI made market creation and resolution cheaper. And the Kalshi court fight opened the door for U.S.-regulated event contracts in a way that did not exist before.
A prediction market only works when enough people care about the question, trust the rules, and can access the venue. The category finally has all three.
Polymarket: the cultural liquidity machine
Polymarket’s edge is simple: it became the place people check when they want to know what the internet thinks will happen.
That is a very different position from being a broker or a regulated exchange. Polymarket behaves more like a live probability layer for the news cycle. It turns every question into a price: elections, sports, geopolitics, crypto, celebrity drama, technology launches, court cases, macro releases.
The product is addictive because it makes uncertainty visible.
During a breaking news cycle, a traditional article says, “Analysts are divided.” Polymarket says, “The market moved from 34% to 52% in two hours.” That price movement becomes content. Media outlets quote it. Traders react to it. Social feeds amplify it. Then the market gets more liquid.
That feedback loop is Polymarket’s real asset.
ICE’s investment makes the point even clearer. ICE said it would become a global distributor of Polymarket’s event-driven data and explore future tokenization initiatives with the company. That turns Polymarket from a consumer trading venue into a data product. If event probabilities become financial sentiment indicators, then Polymarket prices can be sold, embedded, licensed, and referenced.
The weakness is the same one Polymarket has always had: regulatory footing.
Polymarket settled with the CFTC in 2022 for offering off-exchange event-based binary options and had to block U.S. users. Its later acquisition of QCEX, a CFTC-licensed exchange and clearinghouse, gave it a path back into the U.S., with a 2025 CFTC no-action letter around certain event-contract reporting and recordkeeping requirements. That is progress, but it does not erase the complexity.
Polymarket’s moat is cultural liquidity and crypto-native speed. Its challenge is turning that into a regulated, durable, U.S.-accessible exchange business without losing what made it fast.
That is not easy.
Crypto products often win because they move faster than institutions. Exchange businesses win because customers believe the rules will hold under stress. Polymarket now has to become more institutional without becoming boring.
Kalshi: the regulated exchange with IPO gravity
Kalshi is the cleaner institutional story.
It is CFTC-regulated. It fought the election-contract battle and won a key legal opening. It has become the preferred partner for financial platforms that want exposure to prediction markets without building the exchange themselves. Robinhood’s prediction markets hub runs through KalshiEX. That matters because Robinhood brings consumer distribution while Kalshi supplies the regulated market structure.
Kalshi’s growth has been remarkable.
Recent reporting puts Kalshi at a $22 billion valuation after a $1 billion Series F round led by Coatue, with participation from firms including Sequoia, Andreessen Horowitz, Paradigm, Morgan Stanley, and ARK Invest. Kalshi has also reported massive growth in trading activity, with annualized trading volume cited around $178 billion and institutional trading volume up sharply. CNBC reported that CEO Tarek Mansour said Kalshi is thinking about an IPO, though not in 2026.
If Kalshi goes public in 2027 or 2028, it will not be sold as a cute betting app. It will be sold as a new derivatives exchange.
That distinction matters for valuation.
A consumer betting app gets valued on user growth, retention, take rate, and regulatory risk. An exchange gets valued on liquidity, clearing, market data, institutional adoption, compliance, and operating leverage. CME, ICE, Nasdaq, and Cboe have shown how powerful exchange economics can be when a venue becomes the default place to trade a specific category.
Kalshi wants investors to see event contracts that way.
The risk is that public markets may ask a tougher question than private investors: how much of the volume is structurally durable?
Sports contracts appear to be a major driver of recent growth. That is exciting because sports create frequent, high-engagement markets. It is also dangerous because sports sit directly in the conflict zone between federal derivatives regulation and state gambling regulation. Nevada, New Jersey, Illinois, and other states have challenged Kalshi. The company argues that its contracts sit under federal CFTC oversight, while state regulators argue that some products resemble sports betting.
That fight is the biggest open variable.
If Kalshi wins the federal preemption argument, it has a real regulatory moat. If states can significantly restrict sports-style event contracts, some of the growth story becomes less clean.
The other problem is market integrity.
Prediction markets are uniquely vulnerable to insider information. If a market asks whether a company will announce layoffs, whether a politician will resign, whether a product launch will be delayed, or whether a regulatory action will happen, somebody may know before the market does. Kalshi has been emphasizing KYC, employer information, and surveillance. It has to. Institutional users will not treat event contracts as a serious asset class if they think the market is structurally easy to game.
So Kalshi’s moat is regulation plus exchange infrastructure. Its challenge is proving that the category can scale beyond sports and elections into something Wall Street uses all year.
Robinhood: the distribution layer with asymmetric upside
Robinhood does not need prediction markets to become the whole company.
That is exactly why it is dangerous.
For Kalshi, prediction markets are the business. For Robinhood, they are another product tile next to stocks, options, crypto, retirement, credit cards, and futures. Robinhood can make event contracts feel like a natural extension of retail trading.
That gives Robinhood a powerful hand.
Robinhood already has millions of users who understand risk-taking interfaces. Its customers are used to small-dollar trading, real-time prices, and markets that feel like social events. A contract on March Madness, Fed rates, Bitcoin, CPI, or an election outcome fits the emotional cadence of the app.
The company’s first attempt at Super Bowl contracts hit a CFTC roadblock in February 2025. It then returned with a broader prediction markets hub through Kalshi, launching markets such as March Madness and Fed-rate contracts. Robinhood’s approach is pragmatic: it does not own the exchange, so it lowers regulatory and infrastructure burden. It can test demand, keep the interface, and let Kalshi handle the market venue.
That also limits Robinhood’s moat.
The Kalshi partnership is reportedly non-exclusive. If prediction markets become a standard brokerage product, Interactive Brokers, Webull, Coinbase, Schwab, DraftKings-like hybrids, and other platforms can eventually add similar access. Robinhood’s advantage is speed, UX, and customer base, not ownership of the core exchange.
But this may still be a very good business for Robinhood.
A broker does not need monopoly economics to benefit from a new asset class. Options helped Robinhood because they increased engagement, revenue per user, and daily habit. Prediction markets could do something similar. They are simpler than options, more topical than stocks, and easier to understand than many crypto products.
For public-market investors, Robinhood may be the cleanest way to express the retail-distribution side of the thesis. It has prediction market upside without depending entirely on the category.
That makes HOOD interesting even if Kalshi captures the exchange economics.
Meta: the biggest audience, the hardest trust problem
Meta’s reported prediction market project is the most interesting and the most misunderstood.
According to NPR and The New York Times, Meta is building a standalone app called Arena. It would likely use play money rather than real money at launch. Internal documents reviewed by NPR reportedly describe Llama generating markets from trending topics, recommending markets to users, and resolving outcomes in near real time.
This is Meta’s playbook in one sentence: take a behavior that is working elsewhere, remove the friction, automate the expensive parts, and push distribution through the social graph.
Meta has tried this before. Forecast, its earlier prediction app, launched in 2020 and shut down in 2022. NPR reported that internal documents cited the operational cost of manual question curation as a reason Forecast died. Arena appears to be the rebuild with AI replacing the expensive human layer.
That is smart.
Question creation is one of the hidden costs of prediction markets. Someone has to decide what questions matter, write them clearly, define resolution criteria, prevent duplicates, moderate manipulation, and settle disputes. If Llama can generate thousands of timely markets from what people are already discussing on Facebook, Instagram, Threads, and WhatsApp, Meta can create a much broader prediction layer than Kalshi or Polymarket.
Meta also has the largest distribution advantage in the category. More than 3 billion people use at least one Meta app daily. If even a tiny fraction tried Arena, it could become the largest prediction app by registered users almost immediately.
But users are not liquidity.
That is the key point.
Play-money markets are good for engagement, polling, games, and community forecasting. They are weaker as truth machines. Real money disciplines prediction because being wrong costs something. Points can still create ranking incentives, but they do not create the same arbitrage pressure. If Meta wants Arena to become an information product with prices that investors, journalists, and institutions trust, the lack of money is a problem.
Then comes the bigger issue: trust.
Would users trust Meta’s AI to resolve politically sensitive markets? Would regulators tolerate a social media platform creating, recommending, and resolving markets around elections, wars, public health, protests, corporate news, and cultural controversies? Would journalists cite an Arena probability if the market is shaped by recommendation algorithms instead of open financial liquidity?
Meta’s biggest edge is also its biggest liability.
It knows what people are talking about. It knows what keeps them engaged. It can personalize the feed. It can push prediction prompts into massive social loops. That is powerful. It is also exactly what makes regulators, academics, and media critics nervous.
Meta can win the attention version of prediction markets. It can turn forecasting into a social product. It may even create a valuable dataset about crowd beliefs. But unless it moves into real-money contracts through a regulated partner or license path, Arena is more likely to become a social forecasting game than a financial exchange.
That does not mean it is irrelevant. It means Meta’s first win would be engagement, not exchange economics.
The moat question
Prediction markets have several possible moats, and most are weaker than they look.
Liquidity is a moat, but only within a market category. A platform can dominate election markets and still lose sports, macro, crypto, or entertainment. Liquidity follows attention, and attention moves.
Brand is a moat, but only until users can get better prices or easier access somewhere else. Polymarket has the brand among internet-native forecasters. Kalshi has the regulated credibility. Robinhood has the consumer finance relationship. Meta has mass-market attention. None of those brands automatically wins every market.
Regulation is a moat, but it can become a trap. Kalshi’s CFTC-regulated status helps partners like Robinhood. It also puts Kalshi directly in the federal-versus-state fight over sports and event contracts. Polymarket’s U.S. path through QCEX helps, but its crypto-native history still creates scrutiny. Meta can avoid money at first, but then it avoids the strongest source of forecasting accuracy.
Distribution is a moat, but only if the product can convert attention into reliable markets. Meta and Robinhood have distribution. Kalshi and Polymarket have stronger category authenticity. The winner needs both.
Data may become the best moat.
If prediction markets become a new type of financial sentiment feed, the most valuable product may not be trading fees. It may be probability data. ICE’s Polymarket investment points in that direction. A live market-implied probability for elections, policy, sports, geopolitics, inflation, recession, product launches, or corporate events can become an input for media, risk models, trading systems, and enterprise dashboards.
That is where the category starts to look less like gambling and more like Bloomberg.
Who is best positioned?
If I had to rank the players by long-term position, I would separate them by role.
Best positioned to own the U.S. regulated exchange layer: Kalshi
Kalshi has the clearest path to becoming the CME of event contracts. It has regulation, liquidity, institutional momentum, a Robinhood distribution partner, and IPO gravity.
Its risk is concentration in legally controversial high-volume markets, especially sports. It also has to prove that institutional event trading becomes a durable year-round asset class rather than a hype cycle around elections and major sporting events.
If Kalshi clears those hurdles, it has the strongest standalone company story.
Best positioned to own global cultural probability: Polymarket
Polymarket has the brand, the crypto-native user base, and the cultural reflex. It is where the internet looks when it wants a fast probability on the story of the day.
ICE’s investment gives it institutional validation and a potential data-distribution engine. The U.S. return through QCEX could dramatically expand its addressable market.
Its risk is that the very thing that made it fast, open, and internet-native may be hard to reconcile with full institutional trust.
Best positioned to monetize retail distribution: Robinhood
Robinhood may not own the exchange, but it can own the user interface for millions of retail traders.
That is a good place to sit if prediction markets become a common product rather than a single destination. Robinhood can add markets, earn fees, increase engagement, and treat event contracts as another reason users open the app.
Its ceiling is lower than Kalshi’s if exchange economics concentrate. Its risk is lower because prediction markets are an extension, not the entire company.
Best positioned to make prediction social: Meta
Meta can scale a points-based prediction app faster than anyone. It can use AI to create and resolve markets at huge volume. It can attach prediction to social discussion, trending topics, creator content, and news.
But Meta has the weakest claim to “market truth” unless it adds real money or a regulated partner. Play-money prediction markets can be fun and informative, but they do not carry the same weight as prices backed by capital.
Meta’s opportunity is enormous. Its trust problem is even larger.
The investment angle
For public-market investors, the direct plays are limited.
Kalshi and Polymarket are private. A Kalshi IPO could become one of the first pure-play public tests of prediction markets as exchange infrastructure. If that happens, the S-1 will matter more than the hype. Investors should look at revenue mix, take rate, sports exposure, institutional volume, market concentration, regulatory expenses, surveillance costs, and repeat behavior outside election cycles.
Polymarket’s public-market angle currently runs through ICE. ICE is not a pure prediction-market stock, but its investment says something important: the owner of the NYSE sees event probability data as a real financial product. That may matter more than the minority stake itself.
Robinhood is the most obvious public equity expression of consumer adoption. If event contracts become another high-engagement trading category, HOOD benefits through usage, brand relevance, and revenue per active trader.
Meta is the option value play. If Arena works, it could create a new social behavior layer. If it fails, it becomes another experimental app in Meta’s long list of clones, trials, and shutdowns. For META shareholders, the prediction-market project is interesting but not thesis-defining.
My base case
The long-term winner will not be the company with the most questions.
It will be the company whose prices people trust enough to quote, trade, hedge, and build products around.
That points toward a layered market:
Kalshi wins regulated U.S. real-money event contracts.
Polymarket wins global culture, crypto-native liquidity, and probability-as-media.
Robinhood wins consumer brokerage distribution.
ICE and other infrastructure players turn event data into institutional products.
Meta builds a huge social forecasting product, but its first version will probably be more engagement engine than financial market.
The category’s largest prize is not “betting on everything.”
The largest prize is becoming the probability layer of the internet.
Every market, media story, policy debate, sports season, Fed meeting, product launch, election, court case, and geopolitical crisis has an implied probability. Today those probabilities are scattered across polls, odds, analyst notes, options markets, social feeds, and vibes. Prediction markets compress them into a visible price.
That price will not always be right. Markets can be manipulated. Thin markets can be silly. Sports volume can masquerade as institutional adoption. AI-resolved markets can create new trust problems. Regulators can still change the rules.
But the direction is clear.
The world is getting more uncertain, and investors, consumers, journalists, and institutions want live probabilities rather than delayed explanations.
That is why prediction markets matter.
The moat will belong to whoever turns that demand into trusted liquidity.
Right now, Kalshi has the best shot at building the regulated exchange. Polymarket has the best shot at owning the culture. Robinhood has the easiest path to mass retail usage. Meta has the largest distribution but the hardest path to credibility.
If I had to pick the long-term center of gravity, I would pick Kalshi for the exchange layer and Polymarket for the information layer.
If I had to pick the public company that benefits soonest without needing to own the whole category, I would watch Robinhood.
And if I had to pick the wild card, it is Meta. Arena may fail as an app. But the idea behind it will not go away: prediction markets are no longer just markets. They are becoming a new format for social attention.
Not investment advice. Prediction-market companies face substantial regulatory, legal, market-integrity, and product-adoption risks.