Written by: Blockchain Knight
At the beginning of 2026, the prediction market showed a clear split. Kalshi claimed an annual trading volume of $100 billion, while Dow Jones partnered with its media to collaborate with Polymarket on data distribution.
Intercontinental Exchange (ICE) plans to invest $2 billion in Polymarket and distribute its data, while CNN, CNBC, and Kalshi work together to integrate prediction probabilities, and Coinbase launches a prediction market feature based on Kalshi.
These institutional collaborations view the prediction market as a source of information data rather than a consumer product that requires complete trust.
Meanwhile, the field is mired in controversies over methodology, oracle issues, and insider trading, which could lead to the failure of most consumer financial products.
Multiple controversies in 2025 exhibited a clear pattern of repeated failures: a $210 million bidding price for Zelensky's outfit on Polymarket sparked definitional disputes, and a $16 million UFO decryption market ruled "yes" without any document release.
These are not marginal cases but rather structural design issues that negotiate definitions, govern solutions, and trade information advantages.
The prediction market is developing in a dual-track direction, both of which do not rely on the credibility of the underlying trading venues.
The first is data distribution, where institutions like ICE and Dow Jones package prediction data as event-driven data sources or sentiment layers for institutional use, without needing to endorse the trading platform itself, replicating the path where crypto market data precedes trading compliance.
The second is the normalization of consumer access, where Kalshi leverages its CFTC regulatory qualifications to expand distribution through media and brokers, making the prediction market a function within compliant financial applications rather than an independent trust product. This differentiation allows integrity disputes to not hinder institutional adoption, but rather accelerates the separation of regulated and unregulated venues.
The trading volume gap between the prediction market and Solana MEME continues to narrow, with the former accounting for only 22.5% before September 2025, rising to 84.7% in December.
Both are inherently speculative, but the difference lies in the fact that the prediction market is easier for institutions to understand. Its advantages include information, market phrasing, and access to non-public information, which can be difficult to distinguish from insider trading but can be packaged as data products.
Three potential scenarios exist for 2026:
The baseline scenario is continued market differentiation, where compliant platforms like Kalshi expand through partnerships, while crypto-native platforms like Polymarket maintain liquidity but suffer reputational damage, with institutions using data without recognizing the trading venues.
The optimistic scenario is the mainstreaming of information finance, with more media and terminals emulating Dow Jones and ICE, making prediction probabilities a regular market sentiment indicator.
The pessimistic scenario is a crisis of integrity leading to strong regulation, with government officials banned from participation and stricter KYC requirements.
The core of institutional adoption is embedding prediction probabilities into workflows, rather than direct trust from retail users in the platform.
It is worth noting that Kalshi's claim of a $100 billion annual trading volume is derived from a peak in a single week, which, despite being questioned by analysts, still helps it gain media attention and collaboration momentum.
The institutionalization of the prediction market is not due to solving its own problems, but rather because institutions recognize that its data layer is worth building a system around, with controversies seen as known risks rather than fatal flaws.
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