What is the current state of the prediction market that is favored by many institutions?

Article by: Blockchain Knight

In early 2026, the prediction market exhibits a clear split. Kalshi claims an annualized trading volume of $100 billion, while the Dow Jones, together with its media affiliates, has partnered with Polymarket for data distribution.

Intercontinental Exchange (ICE) plans to invest $2 billion in Polymarket and distribute its data. CNN, CNBC, and Kalshi collaborate to integrate prediction probabilities, and Coinbase has launched a prediction market feature based on Kalshi.

These institutional collaborations view prediction markets as sources of informational data rather than consumer products requiring full trust.

Meanwhile, the field is deeply embroiled in debates over methodology, oracles, and insider trading—issues that typically cause most consumer financial products to fail.

Several controversies in 2025 reveal a clear pattern of repeated failures: a $210 million Zelensky clothing bid on Polymarket sparked definitional disputes, and a $16 million UFO declassification market was arbitrarily deemed “true” without any official document release.

These are not fringe cases but structural design issues involving negotiated definitions, solution governance, and information advantage trading.

Prediction markets are evolving along a dual-track path, both of which do not rely on the trustworthiness of underlying trading venues.

One track involves data distribution: institutions like ICE and Dow Jones package prediction data into event-driven data sources or sentiment layers for institutional use, without needing to endorse the trading platforms themselves—replicating the path of crypto market data preceding trading compliance.

The other involves standardized consumer access: Kalshi leverages CFTC regulatory credentials and expands distribution through media and brokers, making prediction markets a function within compliant financial applications rather than standalone trust products. This differentiation has not hindered institutional adoption of integrity, but instead accelerated the separation between regulated and unregulated venues.

The trading volume gap between prediction markets and Solana MEME continues to narrow. By September 2025, prediction markets accounted for only 22.5%, rising to 84.7% in December.

Both are inherently speculative, but prediction markets are more easily understood by institutions. Their advantages include access to information, market phrasing, and non-public data—some of which are difficult to distinguish from insider trading but can be packaged as data products.

Three potential scenarios for 2026:

The baseline scenario involves continued market segmentation: compliant platforms like Kalshi expand through partnerships, while crypto-native platforms like Polymarket maintain liquidity but suffer reputational damage. Institutions use the data without endorsing the trading venues.

An optimistic scenario sees mainstream adoption of information finance, with more media and terminals emulating Dow Jones and ICE, turning prediction probabilities into routine market sentiment indicators.

A pessimistic scenario involves a crisis of trust leading to strict regulation: government officials’ participation is banned, and KYC requirements are tightened.

The core for institutional adoption is embedding prediction probabilities into workflows, rather than direct trust from retail users in the platforms.

Notably, the claim of a $100 billion annualized trading volume for Kalshi is based on a weekly peak estimate. Although analysts have questioned this figure, it still helps attract media attention and partnership opportunities.

The institutionalization of prediction markets is not primarily about solving their own issues but about institutions recognizing their data layer as worth building systems around. Controversies are now viewed as known risks rather than fatal flaws.