Why prediction markets really aren't gambling platforms

Author: Planet Xiaohua

In the past two years, prediction markets have rapidly moved from the fringes of the crypto space into the mainstream spotlight of tech venture capital and financial investors.

The regulatory rising star Kalshi 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 capital players such as Paradigm, Sequoia, a16z, Meritech, IVP, ARK Invest, CapitalG, and Y Combinator.

Industry leader Polymarket received a strategic investment from ICE at a $9 billion valuation, followed by a $1.5 billion funding round led by Founders Fund at a $12 billion valuation, and it is currently raising more funds at a $15 billion valuation.

With such concentrated capital inflow, every time we publish in-depth articles on prediction markets, the comment section is still bound to have someone say: “It’s just gambling in disguise.”

It’s true that in sectors like sports, which are easy to compare, prediction markets and gambling platforms do look similar on the surface. But at a more fundamental and broader level, they have structural differences in their operational logic.

The deeper reality is: with top capital entering the space, 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 new asset class: event derivatives exchanges (DCM).

From a regulatory perspective:

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

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

In short: the boundaries of asset classes have never been about academic debate or philosophical definitions, but about the distribution of power between regulators and capital.

What are the structural differences?

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

1. Price Formation Mechanism: Market vs. Bookmaker

The core difference is transparency: prediction markets have public order books and auditable data; gambling odds are calculated internally, invisible to users, and can be adjusted by the platform at any time.

• Prediction Markets: Prices are set by an order book, using market-based pricing mechanisms like financial derivatives, and 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, platforms design probabilities to ensure a safe profit margin. The platform’s logic is “winning in the long run.”

2. Purpose: Entertainment Consumption vs. Economic Significance

Prediction markets generate real data with economic value, used for financial decision-making and risk hedging, and can even impact the real world—for example, media narratives, asset pricing, corporate decisions, and policy expectations.

• Prediction Markets: Prediction markets can generate data products: for example, probability judgments on macro events, public opinion and policy expectations, corporate risk management (weather, supply chain, regulatory events, etc.), probability benchmarks for financial institutions, research organizations, and media, and even as a basis for arbitrage and hedging strategies.

The most well-known example is the US elections, where many media outlets use Polymarket data as a reference alongside polls.

• Gambling Platforms: Purely entertainment consumption. Gambling odds ≠ real probability, and there is no external data value.

3. Participant Structure: Speculative Gamblers vs. Information Arbitrageurs

Liquidity in gambling is consumption; liquidity in prediction markets is information.

• Prediction Markets: Users include data model researchers, macro traders, media and policy analysts, information arbitrageurs, high-frequency traders, and institutional investors (especially in regulated markets).

This results in prediction markets having high information density and being forward-looking (e.g., on election night, before CPI releases). Liquidity is “active and information-driven,” with participants seeking arbitrage, price discovery, and information advantage. The essence of liquidity here is “informational liquidity.”

• Gambling Platforms: Mostly regular users, prone to emotional betting and preference-driven behavior (loss chasing/gambler’s fallacy), such as betting on “favorite players,” with wagers based on emotion or entertainment rather than serious prediction.

Liquidity lacks directional value; odds don’t become more accurate because of “smart money,” but through the bookmaker’s algorithmic adjustments. There is no price discovery; gambling markets are not designed to find 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 as an event derivatives exchange (DCM) in the US. Financial regulation focuses on market manipulation, information transparency, and risk exposure, and prediction markets follow financial product taxation. Like crypto trading platforms, prediction markets can naturally operate globally.
• Gambling Platforms: Gambling falls under state gambling regulatory agencies, which focus on consumer protection, gambling addiction, and generating local tax revenue. Gambling is subject to gambling taxes and state taxes, and is strictly limited by regional licensing systems, making it a localized business.

II. The Most “Superficially Similar” Example: Sports Prediction

Many articles discussing the differences between prediction markets and gambling focus only on social attributes like political trends and macro data, which are completely different from gambling platforms and easy for people to understand.

However, this article will use the most easily criticized example: “sports prediction” as mentioned at the beginning. In the eyes of many fans, prediction markets and gambling platforms look no different in this area.

But in reality, their contract structures 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 over 30 points?” (Yes/No)

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

Gambling platform contracts can be infinitely subdivided or even customized, such as:

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

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

Therefore, even in seemingly similar subjects, the differences in mechanism result in the four structural distinctions discussed earlier.

For sports events, the essence of prediction markets is still the order book, formed by buyers and sellers, market-driven, and fundamentally more like options markets. Settlement rules rely solely on official statistics.

On gambling platforms, odds are always: set/adjusted by the bookmaker, with a built-in house edge, and aimed at “balancing risk and guaranteeing the bookmaker’s profit.” Settlement rules are subject to the bookmaker’s interpretation, odds have ambiguous margins, and for fragmented events, different platforms may even have different results.

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

The reason why capital is rapidly pouring billions into prediction markets is not complicated: what they value is not a “speculative narrative,” but a global event derivatives market not yet formally defined by regulation—a new asset class with the potential to stand alongside futures and options.

What holds this market back is an outdated and ambiguous historical issue: Are prediction markets financial instruments or gambling?

If this line isn’t clearly drawn, the market cannot develop.

Regulatory jurisdiction determines industry scale. This is an old Wall Street logic, now being applied to this new sector.

The ceiling for gambling is at the state level, meaning fragmented regulation, heavy tax burdens, lack of compliance uniformity, and institutional capital unable to participate. Its growth path is inherently limited.

The ceiling for prediction markets is at the federal level. Once included under the derivatives framework, they can leverage 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 why Polymarket’s growth signals are so sensitive. During 2024–2025, its monthly trading volume has repeatedly exceeded $2–3 billion, with sports contracts becoming a core growth driver. This is not about “cannibalizing the gambling market,” but about 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 allowing prediction markets to fall under federal regulation, because this means two things happen at once: gambling users are siphoned away, and state governments’ gambling tax base is taken directly by the federal government. This is not just a market issue; it’s a fiscal issue.

Once prediction markets fall under CFTC/SEC jurisdiction, state governments not only lose regulatory power but also one of their “easiest and most stable” sources of local tax revenue.

Recently, this contest has become public. The Southern District Court of New York has accepted a class action lawsuit accusing Kalshi of selling sports contracts without any state gambling licenses and questioning whether its market-making structure effectively puts users in direct opposition to the house. Just days ago, the Nevada Gaming Control Board also stated that Kalshi’s sports “event contracts” are essentially unlicensed gambling products and should not be protected by CFTC regulation. Federal Judge Andrew Gordon even bluntly stated in a hearing: “Before Kalshi, no one would have considered sports bets to be financial instruments.”

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

For capital, the issue is not whether prediction markets can grow; it’s whether they will be allowed to grow—and how big.