Who is redefining the regulatory boundaries of prediction markets and gambling

Author: Xingqiu Xiaohua

Original Title: Why Prediction Markets Are Really Not Gambling Platforms

(Note: The regulatory framework, market classification, and legal environment discussed in this article are all based on the United States (especially federal and state) regulatory systems, and are not related to the legal environments of other countries or regions.)

In the past two years, prediction markets have rapidly moved from a fringe concept in the crypto world into the mainstream of tech venture capital and financial capital.

The compliance star Kalshi recently completed a $1 billion Series E financing round, raising its post-money valuation to $11 billion, with an investor lineup including Paradigm, Sequoia, a16z, Meritech, IVP, ARK Invest, CapitalG, Y Combinator, and other highly influential capital.

Industry leader Polymarket received a strategic investment from ICE at a $9 billion valuation, then raised $150 million in a round led by Founders Fund at a $12 billion valuation, and is currently raising funds at a $15 billion valuation.

With such concentrated capital inflows, every time we publish in-depth articles on prediction markets, the comment section inevitably includes: "It's just gambling in disguise."

Indeed, in easily comparable sectors like sports, prediction markets and gambling platforms do appear similar on the surface. But on a more fundamental and broader level, there are structural differences in their operating logic.

The deeper reality is: with the entry of top-tier capital, they will push to have these "structural differences" written into regulatory rules, becoming the new industry language. What capital is betting on is not gambling, but the infrastructure value of a brand-new asset class—event derivatives exchanges (DCM).

From a regulatory logic perspective:

US gambling market = state-level regulation (with huge individual differences), high taxes (even a major fiscal source for many states), heavy compliance, and many restrictions;

New prediction markets = financial derivatives exchanges, federal regulation (CFTC/SEC), nationwide accessibility, unlimited scale, lighter tax regime.

In short: the boundaries of asset classes are never about academic discussion or philosophical definition, but about the distribution of power between regulation and capital.

What Are Structural Differences?

Let's clarify the objective facts first: Why are prediction markets not gambling? Because at their most fundamental level, they are two completely different systems.

1. Different Price Formation Mechanisms: Market vs. Bookmaker

Essentially, the transparency is different: prediction markets have public order books, and data is auditable; gambling odds are calculated internally and are not visible, and the platform can adjust them at any time.

● Prediction markets: Prices are matched by the order book, using market-based pricing like financial derivatives, and prices are determined by buyers and sellers. The platform does not set probabilities or take on risk, only charging transaction fees.

● Gambling platforms: Odds are set by the platform, with a built-in house edge. Regardless of the event outcome, the platform usually maintains a profit margin through probability design. The platform's logic is "always win in the long run."

2. Different Purposes: Entertainment Consumption vs. Economic Significance

The real data generated by prediction markets has economic value and is used in financial decision-making for risk hedging, and 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 products: for example, for judging the probability of macro events, public opinion and policy expectations, enterprise risk management (weather, supply chain, regulatory events, etc.), probability references for financial institutions, research institutions, and media, and even as a basis for arbitrage and hedging strategies.

The most well-known case is, of course, during the US election, when many media outlets cited Polymarket data as one of the polling references.

● Gambling platforms: Purely entertainment consumption, gambling odds ≠ real probability, and there is no spillover value of the data.

3. Participant Structure: Speculative Gamblers vs. Information Arbitrageurs

Liquidity in gambling is consumption, while liquidity in prediction markets 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 prediction markets have high information density and are forward-looking (e.g., on election night, before CPI releases). Liquidity is "active and information-driven," and participants come for arbitrage, price discovery, and information advantage. The essence of liquidity is "informational liquidity."

● Gambling platforms: Mainly ordinary users, prone to emotional betting and driven by preferences (loss chasing/gambler's fallacy), such as supporting "their favorite player," with bets not based on serious prediction but on emotion or entertainment.

Liquidity lacks directional value, odds do not become more accurate because of "smart money," but because of the bookmaker's algorithmic adjustments. There is no price discovery; the gambling market is not designed to discover real probabilities, but to balance the bookmaker's risk. The essence is "entertainment consumption liquidity."

4. Regulatory Logic: Financial Derivatives vs. Regional Gambling Industry

Prediction markets: Kalshi has been recognized by the CFTC in the US as an event derivatives exchange (DCM), with financial regulation focusing on market manipulation, information transparency, and risk exposure. Prediction markets follow financial product tax regimes. Like crypto trading platforms, prediction markets are naturally globalizable.

Gambling platforms: Gambling falls under state gambling regulatory agencies, with regulation focusing on consumer protection, gambling addiction, and generating local tax revenue. Gambling must pay gambling taxes and state taxes. Gambling is strictly limited by regional licensing systems and is a regional business.

II. The Easiest Example to "Appear Similar": Sports Prediction

Many articles discussing the differences between prediction and gambling focus only on examples with social attributes, such as predicting political trends and macro data, which are completely different from gambling platforms and are easy for everyone to understand.

However, in this article, I want to give the most easily criticized example, which is the "sports prediction" mentioned at the beginning. In the eyes of many fans, prediction markets and gambling platforms look the same in this area.

But in reality, the contract structures of the two are different.

Current prediction markets use YES/NO binary contracts, for example:

Will the Lakers win the championship this season? (Yes/No)

Will the Warriors win more than 45 games in the regular season? (Yes/No)

Or discrete range contracts:

"Will the player score >30?" (Yes/No)

Essentially, these are standardized YES/NO contracts, each binary financial contract is an independent market, and the structure is limited.

Gambling platform contracts can be infinitely subdivided or even customized, for example:

Specific scores, halftime vs. full-time, how many times a certain player shoots from the free-throw line, total three-pointers, two-leg parlays, three-leg parlays, custom parlays, point spreads, over/under, odd/even, individual player performance, number of corners, fouls, red/yellow cards, injury time, live betting (real-time odds every minute)...

Not only are they infinitely complex, but they are also highly fragmented event trees, essentially infinitely parameterized fine-grained event modeling.

Therefore, even in such seemingly similar topics, the mechanism differences lead to the four structural differences discussed earlier.

In sports events, the essence of prediction markets is still the order book, formed by buyers and sellers, and market-driven, essentially more like an options market. Settlement rules only use official statistical data.

On gambling platforms, odds are always: set/adjusted by the bookmaker, with a built-in house edge, aiming to "balance risk and ensure bookmaker profit." In settlement, the bookmaker has the right to interpret the odds, and there is ambiguity, and even for fragmented events, different platforms may have different results.

III. The Ultimate Question: A Power Redefinition Over Regulatory Jurisdiction

The reason capital is quickly betting billions of dollars on prediction markets is not complicated: what they value is not the "speculative narrative," but a global event derivatives market that has not yet been formally defined by regulation—a new asset class with the potential to stand alongside futures and options.

What is holding back this market is an old and vague historical issue: are prediction markets financial instruments or gambling?

If this line is not clearly drawn, the market cannot take off.

Regulatory jurisdiction determines industry scale. 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 institutional funds cannot participate. Its growth path is inherently limited.

The ceiling for prediction markets is at the federal level. Once included in the derivatives framework, they can reuse all the infrastructure of futures and options: global accessibility, scalability, indexability, and institutionalization.

At that point, it is no longer just a "prediction tool," but a whole set of tradable event risk curves.

This is also why Polymarket's growth signals are so sensitive. Between 2024 and 2025, its monthly trading volume has repeatedly exceeded $2–3 billion, with sports contracts becoming one of the core growth drivers. This is not "cannibalizing the gambling market," but directly competing for the attention of traditional sportsbook users—and in financial markets, attention migration is often a precursor to scale migration.

State regulators are extremely resistant to having prediction markets classified under federal regulation, because this means two things happen simultaneously: gambling users are siphoned off, and the state's gambling tax base is directly taken by the federal government. This is not just a market issue, but a fiscal issue.

Once prediction markets fall under CFTC/SEC, state governments not only lose regulatory authority but also lose one of the "easiest to collect and most stable" local taxes.

Recently, this contest has become public. According to Odaily, the Southern District Court of New York has accepted a class action lawsuit accusing Kalshi of selling sports contracts without obtaining any state gambling licenses and questioning whether its market-making structure "essentially pits users against the house." A few days ago, the Nevada Gaming Control Board also stated that Kalshi's sports "event contracts" are essentially unlicensed gambling products and should not enjoy CFTC regulatory protection. Federal Judge Andrew Gordon bluntly stated at a hearing: "Before Kalshi appeared, no one would have considered sports betting a financial commodity."

This is not a product dispute; it is a conflict over regulatory jurisdiction, fiscal interests, and pricing power.

For capital, the underlying issue is not whether prediction markets can grow, but how big they will be allowed to grow.