Why is it said that a prediction market is not really a gambling platform?
Original Article Title: "Why Prediction Markets Really Aren't Gambling Platforms"
Original Article Author: Planet Xiaohua, Odaily Planet Daily
Over the past two years, Prediction Markets have quickly moved from a fringe concept in the crypto community to the mainstream vision of tech venture capital and financial capital.
Compliance newcomer Kalshi recently completed a $1 billion Series E financing, with a post-investment valuation of $11 billion. The investment lineup includes the most influential capital entities such as Paradigm, Sequoia, a16z, Meritech, IVP, ARK Invest, CapitalG, Y Combinator, and others.
Leading the race, Polymarket achieved a $9 billion valuation with strategic investment from ICE, and then secured a $12 billion valuation with a $1.5 billion round led by Founders Fund, and is currently raising further funds at a $15 billion valuation.
Despite such intense capital inflow, every time we publish an in-depth article on prediction markets, the comments section cannot escape the remark: "It's just a gambling facade."
Indeed, in fields like sports that are easily comparable, prediction markets and gambling platforms do have similarities in superficial gameplay. However, at a more fundamental and broader level, the two have a structural difference in their operational logic.
A deeper reality is: with top-tier capital entering the scene, they will drive the incorporation of this "structural difference" into regulatory rules, making it a new industry language. Capital is not betting on gambling but on the value of the infrastructure of a new asset class, Event Derivatives Trading Platforms (EDTP).
From a regulatory perspective:
U.S. gambling market = State-level regulation (significant individual differences), high taxes (even a major source of revenue in many states), heavy compliance, numerous restrictions;
New prediction market = Financial derivatives trading platform, federal regulation (CFTC/SEC), nationally recognized, unlimited scale, lighter tax regime.
In short: The boundary of an asset class is never an academic discussion or a philosophical definition but a power distribution between regulation and capital.
1. What is a Structural Difference?
Let's first clarify the objective facts: Why is prediction market not gambling? Because at their most fundamental level, they are two completely different systems.

1. Different Price Formation Mechanism: Market vs Bookie
Fundamentally Different Transparency: Prediction markets have a public order book, and the data is auditable; betting odds are internally calculated and not visible, and the platform can adjust them at any time.
· Prediction Market: Prices are matched by the order book, using market-driven pricing of financial derivatives, determined by buyers and sellers. The platform does not set probabilities or bear risks, only charges transaction fees.
· Betting Platform: Odds are set by the platform, with a built-in house edge. Regardless of the event outcome, the platform typically maintains a profitable safety margin in the probability design. The platform's logic is "guaranteed long-term profit."
2. Different Use Cases: 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 impact on the real world, such as media narratives, asset pricing, corporate decisions, and policy expectations.
· Prediction Market: Prediction markets can generate data products: for example, used for macro-event probability assessment, public sentiment and policy expectations, corporate risk management (weather, supply chain, regulatory events, etc.), probability references for financial institutions, research institutions, media, and even as the basis for arbitrage and hedging strategies.
The most well-known case is, of course, during the US presidential election, where many media outlets referenced Polymarket data as one of the polling references.
· Betting Platform: Purely for entertainment consumption, betting odds ≠ real probability, with no data spillover value.
3. Participant Structure: Speculative Gamblers vs Information Arbitrageurs
The liquidity of gambling is consumption, while the liquidity of prediction markets is information.
· Prediction Market: Users include data model researchers, macro traders, media and policy researchers, information arbitrageurs, high-frequency traders, institutional investors (especially in compliant markets).
This determines that prediction markets have high information density and are forward-looking (e.g., election night, pre-CPI publication). Liquidity is "active, information-driven," and participants are there for arbitrage, price discovery, and information advantage. The essence of liquidity is "informational liquidity."
· Betting Platform: Mainly for ordinary users, easy for emotional betting, and preference-driven (loss chasing / gambler fallacy), such as supporting "favorite player," betting not based on serious prediction but on emotion or entertainment.
Liquidity lacks directional value; odds will not be more accurate because of "smart money" but because of the bookmaker's algorithm adjustments. It does not lead to price discovery; the betting market is not for discovering the true probability but for balancing the bookmaker's risk, essentially "entertainment consumption liquidity."
4. Regulatory Logic: Financial Derivatives vs Regional Betting Industry
· Prediction Market: Kalshi is recognized by the CFTC in the U.S. as an Event Market Exchange (DCM). Financial regulation focuses on market manipulation, information transparency, risk exposure, and prediction markets follow financial product taxation. At the same time, prediction markets, like cryptocurrency exchanges, are naturally global.
· Betting Platform: Betting falls under state gambling regulatory bodies, with gambling regulation focusing on consumer protection, gambling addiction, and creating local tax revenue. Gambling needs to pay gambling tax and state tax. Gambling is strictly limited by a regional licensing system, operating as a regionalized business.
II. The Most Easily "Appear Similar" Example: Sports Prediction
Many articles discussing the difference between prediction and gambling always focus on examples with social attributes such as predicting political trends, macro data, and so on, which are completely different from betting platforms, and easy for everyone to understand.
However, in this article, I want to give an example that is most easily criticized, which is the "sports prediction" mentioned at the beginning. In the eyes of many sports fans, the prediction market and betting platform do not seem different in this part.
But in reality, the contract structures of the two are different.
The current prediction market consists of YES/NO binary contracts, such as:
Will the Lakers win the championship this season? (Yes/No)
Will the Warriors win 45 or more games in the regular season? (Yes/No)
Or is it a range contracts:
Is the player's score>30? (Yes/No)
Essentially, it is a standardized YES/NO, where each binary financial contract is an independent market with a limited structure.
On a betting platform, contracts can be infinitely subdivided, or even customized, such as:
For example, specific scores, first half vs. full game, how many free throws a player will attempt, total three-pointers, parlay bets, custom parlays, point spread, over/under, odd/even, player props, corner kicks, fouls committed, red/yellow cards, injury time, live betting (real-time minute-by-minute odds)...
Not only is it infinitely complex, but it is also a highly fragmented event tree, essentially an infinitely parameterized granular event modeling.
Therefore, even in seemingly similar subjects, the differences in mechanisms lead to the four major structural differences we discussed earlier.
In sports events, the nature of the prediction market remains an order book, formed by buyers and sellers, market-driven, essentially resembling an options market. Settlement rules rely solely on official statistics.
On a betting platform, odds are always set/adjusted by the house, with a built-in house edge, aiming to "balance risk and ensure house profits." Settlements involve interpreting odds, with odds having a degree of ambiguity, and even across fragmented events, results may vary across platforms.
III. The Ultimate Question: A Power Redistribution Issue Regarding Regulation
The reason why capital rapidly bets billions of dollars on the prediction market is not complicated: it is not interested in "speculative narratives" but in a globally undefined event derivative market that has not yet been formally regulated—a new asset class that has the potential to stand alongside futures and options.
What hampers this market is an outdated and vague historical issue: whether the prediction market should be considered a financial tool or gambling?
Until this distinction is clear, the market cannot thrive.
Regulatory attribution determines industry scale, an old Wall Street logic that is now being applied to this new track.
The ceiling for gambling is at the state level, which means fragmented regulation, heavy tax burdens, inconsistent compliance, and exclusion of institutional funds. Its growth trajectory is inherently limited.
The ceiling for prediction markets is at the federal level. Once incorporated into the derivatives framework, it can leverage all the infrastructure of futures and options: globally accessible, scalable, indexable, institutionalizable.
At that point, it is no longer just a "prediction tool" but a full set of tradable event risk curves.
This is also why Polymarket's growth signal is so sensitive. Between 2024 and 2025, its monthly trading volume has repeatedly exceeded $20-30 billion, with sports contracts becoming one of the core growth drivers. This is not about "eating into the gambling market" but directly competing for user attention with traditional sportsbooks—and in the financial markets, attention shifts are often a prelude to scale shifts.
State regulatory agencies are extremely resistant to having prediction markets placed under federal regulation because it signifies two things happening simultaneously: gambling users being drawn away and the state government's gambling tax base being directly usurped by the federal government. This is not just a market issue but a fiscal one.
Once the prediction market falls under the CFTC/SEC, state governments not only lose regulatory power but also lose one of the "easiest to levy, most stable" local taxes.
Recently, this game has become public, with the Southern District of New York Court accepting a class-action lawsuit alleging that Kalshi sold sports contracts without obtaining any state gambling licenses and questioning its market-making structure that "essentially pits users against 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 enjoy CFTC regulatory protection. Federal Judge Andrew Gordon even bluntly stated during a hearing, "No one would have thought that sports betting was a financial product before Kalshi appeared."
This is not a product dispute; it is a conflict of regulatory authority and fiscal interests and a struggle for pricing power.
For capital, the issue at hand is not whether the prediction market can grow; it is whether it will be allowed to grow to what extent.
Original Article Link
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"As we enter 2026, the company will continue to optimize its balance sheet structure and enhance operational efficiency and cost resilience through adjustments to the miner portfolio. At the same time, we are advancing our strategic transformation into an AI infrastructure provider. Leveraging EcoHash, we will utilize our capabilities in scalable computing power and energy networks to provide cost-effective AI inference solutions. The relevant site transformations and product development are progressing simultaneously, and the company is well-positioned to sustain its execution in the new phase."
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The adjusted EBITDA was -$156.3 million, compared to $2.4 million in the same period last year.
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· Miner Impairment Loss: $338.3 million
· Bitcoin Collateral Receivable Fair Value Change Loss: $96.5 million
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As of December 31, 2025, the company's key assets and liabilities are as follows:
· Cash and Cash Equivalents: $41.2 million
· Bitcoin Collateral Receivable (Non-current, related party): $663.0 million
· Miner Net Value: $248.7 million
· Long-Term Debt (related party): $557.6 million
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• Financial Performance:
Total revenue for the full year 2025 was $688.1 million, with $179.5 million in the fourth quarter.
Bitcoin mining business revenue for the full year was $675.5 million, with $172.4 million in the fourth quarter.
Full-year adjusted EBITDA was $24.5 million, while the fourth quarter was -$156.3 million.
• Mining Operations and Costs:
A total of 6,594.6 bitcoins were mined throughout the year, averaging 18.07 bitcoins per day; of which 1,718.3 bitcoins were mined in the fourth quarter, averaging 18.68 bitcoins per day.
The average mining cost for the full year (excluding miner depreciation) was $79,707 per bitcoin, and for the fourth quarter, it was $84,552;
The all-in sustaining costs were $97,272 and $106,251 per bitcoin, respectively.
As of the end of December 2025, the company has cumulatively produced 7,528.4 bitcoins since entering the bitcoin mining business.
• Strategic Progress:
The company has completed the termination of the American Depositary Receipt (ADR) program and transitioned to a direct listing on the NYSE to enhance information transparency and align with its strategic direction, with a long-term goal of expanding its investor base.
CEO Paul Yu stated: "2025 marked the company's first full year as a bitcoin mining enterprise, characterized by rapid execution and structural reshaping. We completed a comprehensive adjustment of our asset system and established a globally distributed mining network. Additionally, the company introduced a new management team, further strengthening our capabilities and competitive advantage in the digital asset and energy infrastructure space. The completion of the NYSE direct listing and USD pricing also signifies our transformation into a global AI infrastructure company."
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The company's Chief Financial Officer, Michael Zhang, stated: "By 2025, the company is expected to achieve significant revenue growth through its scaled mining operations. Despite recording a net loss of $452.8 million from ongoing operations, mainly due to one-time transformation costs and market-driven fair value adjustments, the company, from a financial perspective, will reduce its leverage, optimize its Bitcoin reserve strategy and liquidity management, introduce new capital to strengthen its financial position, and seize investment opportunities in high-potential areas such as AI infrastructure while navigating market volatility."
The total revenue for the fourth quarter was $1.795 billion. Of this, the Bitcoin mining business contributed $1.724 billion in revenue, generating 1,718.3 Bitcoins during the quarter. Revenue from the international automobile trading business was $4.8 million.
The total operating costs and expenses for the fourth quarter amounted to $4.56 billion, primarily attributed to expenses related to the Bitcoin mining business, as well as impairment of mining machines and fair value losses on Bitcoin collateral receivables.
This includes:
· Cost of Revenue (excluding depreciation): $1.553 billion
· Cost of Revenue (depreciation): $38.1 million
· Operating Expenses: $9.9 million (including related-party expenses of $1.1 million)
· Mining Machine Impairment Loss: $81.4 million
· Fair Value Loss on Bitcoin Collateral Receivables: $171.4 million
The operating loss for the fourth quarter was $276.6 million, a significant increase from a loss of $0.7 million in the same period of 2024, primarily due to the downward trend in Bitcoin prices.
The net loss from ongoing operations was $285 million, compared to a net profit of $2.4 million in the same period last year.
The adjusted EBITDA was -$156.3 million, compared to $2.4 million in the same period last year.
The total revenue for the full year was $6.881 billion. Of this, the revenue from the Bitcoin mining business was $6.755 billion, with a total output of 6,594.6 Bitcoins for the year. Revenue from the international automobile trading business was $9.8 million.
The total annual operating costs and expenses amount to $1.1 billion.
Specifically, they include:
· Revenue Cost (excluding depreciation): $543.3 million
· Revenue Cost (depreciation): $116.6 million
· Operating Expenses: $28.9 million (including related-party expenses of $1.1 million)
· Miner Impairment Loss: $338.3 million
· Bitcoin Collateral Receivable Fair Value Change Loss: $96.5 million
The full-year operating loss is $437.1 million. The continuing operations net loss is $452.8 million, while in 2024, there was a net profit of $4.8 million.
The 2025 non-GAAP adjusted net profit is $24.5 million (compared to $5.7 million in 2024). This measure does not include share-based compensation expenses; refer to "Use of Non-GAAP Financial Measures" for details.
As of December 31, 2025, the company's key assets and liabilities are as follows:
· Cash and Cash Equivalents: $41.2 million
· Bitcoin Collateral Receivable (Non-current, related party): $663.0 million
· Miner Net Value: $248.7 million
· Long-Term Debt (related party): $557.6 million
In February 2026, the company sold 4,451 bitcoins and repaid a portion of related-party long-term debt to reduce financial leverage and optimize the asset-liability structure.
As per the stock repurchase plan disclosed on March 13, 2025, as of December 31, 2025, the company had repurchased a total of 890,155 shares of Class A common stock for approximately $1.2 million.
