Kalshi, a rising star in compliance, recently completed a $1 billion Series E funding round, raising its post-money valuation to $11 billion. Its investors include some of the most influential firms such as Paradigm, Sequoia, a16z, Meritech, IVP, ARK Invest, CapitalG, and Y Combinator.
Polymarket, the industry leader, received strategic investment from ICE at a valuation of $9 billion, and then raised $150 million in a funding round led by Founders Fund at a valuation of $15 billion. It is currently raising funds again at a valuation of $12 billion.
Despite the massive influx of capital, whenever we publish in-depth articles predicting the market, the comments section inevitably includes the remark: “It’s just gambling in a different guise.”
Admittedly, in easily comparable sectors like sports, prediction markets and betting platforms do share superficial similarities in their gameplay. However, at a more fundamental and broader level, there are structural differences in their operational logic.
The deeper reality is that with the entry of top-tier capital, they will push for this “structural difference” to be written into regulatory rules, becoming the new industry language. Capital is betting not on gambling, but on the infrastructure value of event derivatives exchanges (DCMs) , a completely new asset class.
From a regulatory perspective:
The US gambling market is characterized by state-level regulation (with significant individual variations), high taxes (which are a major source of revenue for many states), stringent compliance requirements, and numerous restrictions.
The new prediction market = financial derivatives exchange, federally regulated (CFTC/SEC), nationwide, unlimited in size, and with a lighter tax system.
In short: the boundaries of asset classes have never been about academic discussions or philosophical 디파이nitions, but rather about the distribution of power between regulation and capital.
What are structural differences?Let’s first clarify the objective facts: Why is market prediction not gambling? Because at the most fundamental level, they are two completely different systems.
1. Different price formation mechanisms: market vs. speculatorsIn essence, the difference in transparency is as follows: prediction markets have public order books and auditable data; betting odds are calculated internally but are not visible, and the platform can adjust them at any time.
● 예측 시장: Prices are matched by an order book, using market-based pricing for financial derivatives , with prices determined by buyers and sellers. The platform does not set probabilities or assume risk; it only charges transaction fees.
● Betting Platforms: Odds are set by the platform, with a built-in house edge. Regardless of the outcome, the platform typically maintains a safe profit margin in its probability design. The platform’s logic is “long-term victory is assured.”
2. Differences in Purpose: Entertainment vs. Economic SignificanceThe real data generated by the forecasting market has economic value and is used for risk hedging in financial decision-making. It may even have a reverse effect on the real world, such as media narratives, asset pricing, corporate decisions, and policy expectations.
● Prediction Markets : Prediction markets can generate data-driven products, such as those used for judging the probability of macroeconomic events, public opinion and policy expectations, corporate risk management (weather, supply chain, regulatory events, etc.), probabilistic reference targets for financial institutions, research institutions, and media, and can even serve as the basis for arbitrage and hedging strategies.
The most well-known example is, of course, the US presidential election, where many media outlets cited Polymarket data as one of their polling references.
● Betting platforms: purely for entertainment purposes; betting odds ≠ true probability, with no data spillover value.
3. Participant Structure: Speculative Gamblers vs. Information ArbitrageursThe liquidity of gambling is consumption, while the liquidity of the prediction market is information.
● Prediction Markets: Users include data model researchers, macro traders, media and policy researchers, information arbitrageurs, high-frequency traders, and institutional investors (especially in compliant markets).
This determines that the information density of the prediction market is high and forward-looking (e.g., election night, before the CPI release). Liquidity is “active and information-driven,” with participants coming for arbitrage, price discovery, and information advantage. The essence of liquidity is “informational liquidity.”
● Betting platforms: Primarily for ordinary users, prone to emotional betting and driven by preferences (loss chasing / gambler fallacy), such as supporting “favorite players,” with bets not based on serious predictions but on emotions or entertainment.
Liquidity lacks directional value; odds won’t become more accurate because of “smart money,” but rather due to the bookmaker’s algorithmic adjustments. It doesn’t generate price discovery; the betting market isn’t about discovering true probabilities, but about balancing the bookmaker’s risk—essentially, it’s about “entertainment consumption liquidity.”
4. Regulatory Logic: Financial Derivatives vs. Regional Gaming IndustryPrediction Markets: Kalshi is classified as an Event Derivatives 교환 (DCM) by the CFTC in the United States. Financial regulators there focus on market manipulation, information transparency, and risk exposure. Prediction markets are subject to financial product taxation. Furthermore, like crypto trading platforms, prediction markets are inherently global.
Gambling platforms: Gambling falls under the jurisdiction of state gambling regulatory agencies, whose focus is on consumer protection, combating gambling addiction, and generating local tax revenue. Gambling is subject to gambling tax and state tax. Gambling is strictly regulated by a regional licensing system and is a regional business.
II. The most likely examples to “look similar”: Sports predictionsMany articles discussing the difference between prediction and gambling only focus on examples with social attributes, such as predicting political trends and macroeconomic data. This is completely different from gambling platforms, and it is easy for everyone to understand.
However, in this article, I would like to cite the most easily criticized example, which is the “sports prediction” mentioned at the beginning. In the eyes of many fans, the prediction market and betting platforms seem to be no different in this aspect.
However, the contract structures of the two are actually different.
Current prediction markets use YES/NO binary contracts, for example:
Will the Lakers win the championship this season? (Yes/No)
Will the Warriors achieve 45 or more wins in the regular season? (Yes/No)
Or discrete intervals (range contracts):
“Is the player’s score >30?” (Yes/No)
Essentially, it is a standardized YES/NO, with each binary financial contract being an independent market and having a limited structure.
Gambling platform contracts can be infinitely subdivided and even customized, for example:
Examples include specific scores, halftime vs. full time, number of free throws by each player, total three-pointers made, 2-leg parlays, 3-leg parlays, custom parlays, point spreads, over/under, odds/evens, player performance, corner kicks, fouls, red and yellow cards, injury time, and live betting (handicap odds per minute)…
Not only are event trees infinitely complex, but they are also highly fragmented, essentially representing fine-grained event modeling with infinite parameters.
Therefore, even on seemingly similar themes, the differences in mechanisms lead to the four structural differences we discussed earlier.
In sports events, the prediction market is essentially an order book, formed by buyers and sellers, and market-driven, making it more like an options market. Settlement rules also rely solely on official statistical data.
In betting platforms, odds are always set/adjusted by the bookmaker, have a built-in house edge, and aim to “balance risk and ensure the bookmaker’s profits.” There’s interpretation of the odds in the settlement process, the odds have a degree of ambiguity, and even for fragmented events, different platforms may have different results.
III. The Ultimate Question: A Rebalancing of Regulatory PowerThe reason why capital is rapidly betting billions of dollars in the prediction market is not complicated: it is not interested in “speculative narratives”, but in a global event derivatives market that has not yet been formally defined by regulators —a new asset class with the potential to stand alongside futures and options.
What’s holding this market back is an old and ambiguous historical question: Is the prediction market a financial instrument or gambling?
If this line isn’t drawn clearly, the market won’t take off.
Regulatory jurisdiction determines industry size—this is an old Wall Street logic, but it has just been applied to this new track.
The ceiling for gambling is at the state level, which means fragmented regulation, heavy tax burdens, inconsistent compliance, and the inability of institutional funds to participate. Its growth path is inherently limited.
The ceiling for prediction markets lies in the federal system. Once incorporated into the derivatives framework, it can reuse all the infrastructure of futures and options: global accessibility, scalability, indexability, and institutionalization.
At that time, it will no longer be a “prediction tool”, but a complete set of tradable event risk curves.
This is why Polymarket’s growth signals are so sensitive. Between 2024 and 2025, its monthly trading volume repeatedly exceeded $2-3 billion , with sports contracts becoming one of the core drivers of growth. This is not “cannibalizing the betting market,” but rather a direct competition for the user attention of traditional sportsbooks— and in the financial markets, a shift in attention is often a precursor to a shift in scale.
State regulators are extremely resistant to bringing prediction markets under federal regulation because it means two things happening simultaneously: gambling users are being siphoned off, and the federal government is directly taking away state gambling tax revenue. This is not just a market issue, but a fiscal one.
Once prediction markets are brought under the jurisdiction of the CFTC/SEC, state governments will not only lose regulatory power, but also lose one of the “easiest and most stable” local taxes.
Recently, this game has become public. Odaily reports that the Southern District Court of New York has accepted a class-action lawsuit accusing Kalshi of selling sports betting contracts without obtaining any state betting licenses and questioning its market-making structure, which “essentially puts users betting against the bookmaker.” A few days ago, the Nevada Gaming Commission also stated that Kalshi’s sports “event contracts” are essentially unlicensed betting products and should not enjoy the regulatory protection of the CFTC. Federal Judge Andrew Gordon even stated at the hearing: “Before Kalshi, no one would have considered sports betting a financial product.”
This is not a product dispute; it is a conflict between regulatory authority and fiscal interests, and a struggle for pricing power.
For capital, the underlying question is not whether the market can grow; rather, how much it will be allowed to grow.
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