Why do people say that prediction markets are really not gambling platforms?

Original Title: “Why Prediction Markets Are Really Not Gambling Platforms”

Original Author: Planet Xiaohua

Original Source:

Reprinted from: Mars Finance

Over the past two years, prediction markets have rapidly shifted from a fringe concept in the crypto space to the mainstream focus of tech venture capital and financial investors.

Regulatory darling Kalshi recently completed a $1 billion Series E funding round, raising its post-money valuation to $11 billion, with a star-studded roster of investors including 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, then raised $150 million in a round led by Founders Fund at a $12 billion valuation, and is currently fundraising at a $15 billion valuation.

Despite this surge of capital, every time we publish an in-depth article on prediction markets, the comments section inevitably includes: “It’s just gambling with a new skin.”

Admittedly, in verticals like sports that are easy to compare, prediction markets and gambling platforms do have some similarities on the surface. But at a more fundamental and broader level, the two have structurally different operating logics.

The deeper reality is: with top-tier capital entering the space, they are pushing 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 trading platforms (DCM).

From a regulatory logic perspective:

The US gambling market = state-level regulation (highly varied between states), high taxes (even a major fiscal source for many states), heavy compliance, many restrictions;

The new prediction market = financial derivatives trading platform, federal regulation (CFTC/SEC), nationwide access, unlimited scale, lighter tax regime.

In short: the boundaries of asset classes are never just academic or philosophical—they are the result of regulatory and capital power allocation.

I. What are the Structural Differences?

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

1. Price Formation Mechanism: Market vs. Bookmaker

At its core, it’s about transparency: prediction markets have public order books and auditable data; gambling odds are calculated internally and are invisible, with platforms able to adjust them at any time.

· Prediction markets: Prices are matched via an order book, using market-based pricing like financial derivatives, with prices set by buyers and sellers. Platforms do not set probabilities or take on risk—they only charge transaction fees.

· Gambling platforms: Odds are set by the platform, with a built-in house edge. Regardless of outcomes, the platform’s probability design ensures a safe profit margin. The logic is “the house always wins in the long run.”

2. Use Case Differences: Entertainment Consumption vs. Economic Significance

Data generated by prediction markets has real economic value and can be used for financial decision-making and risk hedging, sometimes even feeding back into the real world, such as media narratives, asset pricing, business decisions, and policy expectations.

· Prediction markets: Can produce data products, such as for assessing probabilities of macro events, public opinion and policy expectations, corporate risk management (weather, supply chain, regulatory events, etc.), as probability references for financial institutions, research organizations, and media, or as a basis for arbitrage and hedging strategies.

The most famous example is US elections, where many media outlets cite Polymarket data as a polling reference.

· Gambling platforms: Pure entertainment consumption; gambling odds ≠ real probabilities and there’s no value spillover from the data.

3. Participant Structure: Speculative Gamblers vs. Information Arbitrageurs

Liquidity in gambling is about consumption; in prediction markets, it’s about information.

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

This results in high information density and forward-looking insights (e.g., on election nights or before CPI releases). Liquidity is “active and information-driven”—participants are there for arbitrage, price discovery, and information advantage. The true liquidity is “informational liquidity.”

· Gambling platforms: Primarily ordinary users who bet emotionally and are driven by preferences (loss chasing/gambler’s fallacy), such as supporting their favorite team; bets are not based on serious predictions, but on emotion or entertainment.

Liquidity lacks directional value; odds don’t get more accurate due to “smart money,” but are algorithmically adjusted by the house. No price discovery occurs—the aim is to balance the house’s risk, fundamentally “entertainment consumption liquidity.”

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

· Prediction markets: In the US, Kalshi is recognized by the CFTC as an event derivatives trading platform (DCM). Financial regulation focuses on market manipulation, information transparency, and risk exposure; prediction markets follow financial product tax rules. Like crypto exchanges, prediction markets are inherently globalizable.

· Gambling platforms: Regulated by state gambling authorities, with a focus on consumer protection, gambling addiction, and generating local tax revenue. Gambling pays gambling taxes and state taxes, and is strictly limited to regional licensing systems—it’s a regional business.

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

Many articles discussing the difference between prediction and gambling focus on political trends, macro data, and other socially significant examples, which are quite different from gambling and easy to understand.

But here, I want to use the most criticized example—sports prediction—as mentioned at the beginning, because for many sports fans, prediction markets and gambling platforms seem indistinguishable here.

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 >30 points?” (Yes/No)

These are standardized YES/NO contracts, with each binary financial contract as an independent market—limited in structure.

Gambling platform contracts are infinitely subdivided and even customizable, e.g.:

Exact scores, halftime vs. full-time, how many times a specific player shoots from the free-throw line, total three-pointers, parlays, custom combos, spreads, over/under, even/odd, player performance, corner kicks, fouls, red/yellow cards, injury time, live betting (minute-by-minute lines)…

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

Thus, even in this seemingly similar category, the mechanism differences create the four structural differences outlined above.

For sports, the essence of prediction markets is still the orderbook formed by buyers and sellers—market-driven and more like option markets. Settlements only use official statistics.

On gambling platforms, odds are always: set/adjusted by the house, with a built-in house edge, aiming to “balance risk and guarantee house profit.” Settlement rules are at the discretion of the house; there’s ambiguity in the odds, and for fragmented events, different platforms may have different outcomes.

III. The Ultimate Issue: A Power Struggle Over Regulatory Jurisdiction

Why is capital betting billions on prediction markets? The reason is simple: it’s not about “speculative storytelling,” but about a global event derivatives market not yet formally defined by regulation—a new asset class with the potential to sit alongside futures and options.

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

Without a clear answer, the market can’t take off.

Regulatory jurisdiction determines industry scale—an old Wall Street logic that’s now being applied to this new sector.

Gambling’s ceiling is at the state level, meaning fragmented regulation, heavy tax burden, non-uniform compliance, and no institutional capital participation. Its growth path is inherently limited.

Prediction markets’ ceiling is at the federal level. Once included within the derivatives framework, they can leverage all the infrastructure of futures and options: global access, scalability, indexation, and institutionalization.

At that point, it’s no longer just a “prediction tool,” but an entire set of tradable event risk curves.

That’s why Polymarket’s growth signals are so sensitive. In 2024–2025, its monthly volume has repeatedly surpassed $2–3 billion, with sports contracts being a major growth driver. This isn’t “eating into the gambling market,” but directly vying for the attention of traditional sportsbook users—and in financial markets, attention shifts often precede scale shifts.

State regulators are highly resistant to letting prediction markets fall under federal regulation, because it means two things: gambling users are siphoned away, and state gambling tax bases are directly taken by the federal government. This is not just a market issue—it’s a fiscal one.

Once prediction markets fall under CFTC/SEC, state governments lose both regulatory control and one of their “easiest to collect, most stable” sources of local tax revenue.

Recently, this battle has become public. The Southern District of New York has accepted a class-action lawsuit, accusing Kalshi of selling sports contracts without any state gambling license, and questioning whether its market-making structure “effectively lets users bet against the house.” Days ago, the Nevada Gaming Control Board also stated that Kalshi’s sports “event contracts” are essentially unlicensed gambling products and shouldn’t enjoy CFTC regulatory protection. Federal judge Andrew Gordon even stated at a hearing, “Before Kalshi, nobody would have considered sports bets as a financial commodity.”

This isn’t a product dispute—it’s a conflict over regulatory jurisdiction, fiscal interests, and pricing power.

For capital, the real question isn’t whether prediction markets can grow—it’s how big they will be allowed to get.