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Why Kalshi’s CNBC Deal Signals A New Revenue Play for Media

Paul Allen

05 Dec 2025 • 7 min read

Prediction markets like Kalshi are surging while traditional media grapple with steady declines in engagement. Kalshi just secured a multi-year deal to embed its real-time data across CNBC's TV, app, and website starting in 2026.

But this isn’t merely about adding another data feed — it’s a strategic move to turn viewers into active participants and new revenue sources through prediction market integration.

Predictive data is reshaping how business news works and boosting engagement by gamifying forecasts of events like Federal Reserve decisions or election outcomes.

“News isn’t just reported anymore; it’s become a live market where millions can transact insights.”

Why the old media view of news as static info is obsolete

Conventional thinking treats news as output: a broadcast or article delivered top-down. That model is collapsing with younger audiences craving interactivity and real-time updates.

Kalshi’s partnership with CNBC and CNN challenges this by embedding a live prediction market that updates probabilities in real time — essentially crowdsourcing forecasts through financial incentives rather than just poll data or expert opinion.

This changes the core mechanism of engagement and extends beyond simple content consumption to transaction-driven interaction.

For operators curious about content leverage, this is a pivot from static editorial to a platform that retains users by making them active market participants, a form of behavioral lock-in rare in traditional news.

See why dynamic work chart adaptation unlocks faster org growth for a parallel system-level insight here.

Real-time prediction markets as leveraged engagement engines

Kalshi and its competitor Polymarket let users trade futures contracts on outcomes, turning uncertainty into a continuous data stream that reflects collective intelligence.

The deals with CNBC, CNN, Yahoo Finance, and X serve a dual purpose: extending real-time data integration and building branded presences.

Unlike conventional news feeds, these prediction data points operate autonomously — updating probabilities without constant editorial curation — creating a system that scales with user activity rather than news manpower.

This mechanism compounds as networks grow: more users trading means more accurate and timely market data, which in turn drives viewer retention and monetization via licensing fees and potential betting adjacencies.

Contrast this with sportsbooks like FanDuel and DraftKings launching their own prediction offerings, indicating a blurring line between news, finance, and gambling ecosystems.

Expand your view on revenue system shifts with insights from how OpenAI scaled to 1 billion users here.

Regulatory friction highlights a key constraint for scaling prediction markets

The growth of Kalshi isn’t seamless. Pushback from state regulators in Nevada and Connecticut labeling prediction markets as unlicensed gambling exposes a major friction point.

This regulatory conflict stems from prediction markets' classification under federal commodity futures law versus state gaming laws, creating legal uncertainty that could limit national expansion.

Resolving this constraint depends on how courts, potentially even the Supreme Court, define these markets’ legal boundaries.

Operators must watch this closely. Legal ambiguity imposes cost layers and complexity, elevating entry barriers for new market entrants despite the underlying technology’s scalability.

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What the Kalshi-CNBC model means for media and market operators

This partnership repositions media companies as platforms that enable and monetize predictive participation rather than passive consumption.

The key changed constraint: transforming audience attention from a unidirectional pipeline into an automated, scalable forecast marketplace with real money at stake.

Media that capture this shift gain a compound advantage — audience gamification that fuels repeated engagement and new revenue without proportional human intervention.

Geographically, markets with clearer federal regulatory frameworks like the US will pioneer this space, while others might lag due to fragmented laws.

“Turning viewers into market participants isn’t just engagement, it’s creating a self-sustaining data engine that powers smarter decisions.”

As media companies navigate the challenges of transforming viewer engagement into a participatory experience, leveraging tools like Brevo can enhance marketing strategies. With its all-in-one platform for email and SMS marketing, businesses can keep their audience informed and engaged, turning forecasts into actionable insights that resonate. Learn more about Brevo →

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Frequently Asked Questions

What is the significance of Kalshi's deal with CNBC?

Kalshi's multi-year deal with CNBC starting in 2026 embeds real-time prediction market data across CNBC's platforms. This partnership aims to transform passive viewers into active market participants, creating new revenue streams and boosting engagement through gamified forecasting.

How do prediction markets like Kalshi work?

Prediction markets allow users to trade futures contracts on event outcomes, turning uncertainty into a continuous data stream. Kalshi's markets update probabilities in real time, crowdsourcing forecasts with financial incentives rather than relying solely on polls or expert opinions.

What challenges do prediction markets face regarding regulation?

Kalshi faces regulatory pushback from states like Nevada and Connecticut, which classify prediction markets as unlicensed gambling. The conflict arises because these markets fall under federal commodity futures law but clash with state gaming laws, creating legal uncertainty that impacts national expansion.

How does Kalshi's approach differ from traditional media engagement?

Unlike traditional media's static news delivery, Kalshi's integration creates a dynamic, transaction-driven interaction where audiences actively participate in forecasting markets. This behavioral lock-in helps retain users and scale engagement without proportional increases in editorial resources.

What is the impact of real-time prediction markets on media revenue?

Real-time prediction markets generate new revenue through licensing fees and betting adjacencies by converting viewer attention into active market participation. This model compounds as user activity grows, offering media companies a scalable and automated revenue source beyond traditional advertising.

Sportsbook operators like FanDuel and DraftKings are launching prediction offerings, highlighting a blurring of lines between news, finance, and gambling. This trend signals expanding ecosystems where real-time market data and betting converge.

What role do federal regulations play in the future of prediction markets?

Federal regulation and court decisions, potentially by the Supreme Court, will define the legal boundaries for prediction markets. Clearer federal frameworks, such as those in the US, may enable pioneering growth, while fragmented state laws could hinder nationwide scaling.

How can media companies leverage tools like Brevo in this evolving landscape?

Tools like Brevo help media companies enhance marketing strategies through email and SMS, keeping audiences informed and engaged. By integrating these platforms, businesses can turn forecasts into actionable insights and strengthen viewer participation.