Prediction Markets Were Supposed to Forecast the Future. A New Study Says They're a Shakedown.

The paper dismantles the industry’s core defense — just as a U.S. Army Special Forces soldier is charged with using classified military intelligence to make $400,000 on Polymark.

I appear regularly on CNN, which has a paid partnership with Kalshi, one of the two dominant prediction market platforms. I want you to know that up front. I'm also giving this piece away for free, because I think the combination of technology and gambling is one of the most powerful rip currents pulling on all of us right now — and this week, it seems to have pulled in a U.S. Army soldier.

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On the night of January 2, 2026, a few hours before U.S. forces flew into Caracas and seized Venezuelan leader Nicolás Maduro, a soldier named Gannon Ken Van Dyke allegedly sat down and placed 13 bets on Polymarket. Van Dyke, a 38-year-old Master Sergeant with the U.S. Army Special Forces stationed at Fort Bragg, had spent the previous month helping plan and execute the operation. He had signed nondisclosure agreements. But he knew, in precise operational detail, what was about to happen. And he also seemingly knew he could make easy money on that information. According to prosecutors, he converted more than $32,000 of his personal savings into cryptocurrency, opened a new account under a pseudonym, and bet that Maduro would be “out” by January 31.

He won $409,881.

Federal prosecutors unsealed the indictment against Van Dyke on Thursday. He is charged with theft of classified government information, commodities fraud, wire fraud, and making an unlawful monetary transaction. He faces decades in prison.

Van Dyke's case marks the first time U.S. officials have leveled criminal charges against someone over prediction market wagers. The case is also the clearest illustration yet that in many cases (and especially in the most wildly profitable cases) these platforms are not forecasting tools at all, but transfer mechanisms — devices that move money from people who don’t know things to people who do. Now a new paper, drawing on the complete transaction record of a major prediction market, makes the most damning case yet against the industry.

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The defense the industry has always offered goes like this: when money is on the line, people reveal what they actually believe, rather than what sounds good. That when people have financial skin in the game, it forces honesty, and therefore collective intelligence. Aggregate enough honest beliefs across a large and diverse crowd, and the market price converges on truth faster and more accurately than any expert, poll, or pundit. The CEOs of Polymarket and Kalshi have made this argument in congressional testimony, in press releases, and on television.

It has a real scientific lineage — the “wisdom of crowds” concept comes from decades of legitimate research showing that diverse groups, under the right conditions, produce better forecasts than individuals. This is why some of the most compelling and powerful findings in human behavior research involve setting people up around a table and experimenting on what they’re willing to bet real money on.

The research that launched the current public prediction markets was mostly conducted on closed corporate versions: employees forecasting their own company’s quarterly sales, or specialists estimating the probability of events in their domain. It was careful, bounded, and the participants were genuinely trying to aggregate dispersed knowledge.

Then Kalshi, Polymarket, and others made a business out of it. And what a business it is. Annual trading volume climbed from $15.8 billion in 2024 to about $63.5 billion in 2025 — a 4x surge in a single year. Weekly trading volume on Kalshi — which controls more than 90% of the U.S. prediction market — has surged to more than $3 billion today from about $100 million a year ago, according to Bank of America analyst Julie Hoover, who called Kalshi one of the “fastest growing non-AI companies” in the U.S. (The financial-analysis equivalent of calling something a miracle.)

Bernstein analyst Gautam Chhugani estimates total market volumes in 2026 will reach $240 billion — a 370% increase — and projects prediction market trading volume of $1 trillion a year by the start of the next decade.

Now a group of finance academics has tested what it is, exactly, that commercial prediction markets are delivering. Their paper — “Prediction Market Accuracy: Crowd Wisdom or Informed Minority?” — was posted this week. It is the most rigorous examination of this question yet conducted on a major live platform, using the full universe of transactions rather than a sample.

Accuracy on prediction markets, the researchers found, does not come from the crowd. It comes from roughly 3% of traders — a small group of informed participants whose trades predict final outcomes, move prices toward truth, and react to new information the moment it arrives. The other 97% of traders do not contribute to accuracy. They fund it. Their losses flow as profits to the informed minority.

The crowd isn’t getting smarter together. The crowd is getting fleeced by the people who already know the answer.

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Photo by Amir Arabshahi on Unsplash

The industry has been using the wisdom-of-crowds argument as not just an intellectual selling point, but as a regulatory shield. Kalshi spent years in federal court fighting the Commodity Futures Trading Commission’s attempts to limit prediction markets, arguing that its election contracts served a public interest by aggregating information. And it won. The CFTC under the Biden administration concluded that election betting was contrary to the public interest; a federal court disagreed; the CFTC under the Trump administration dropped the appeal. The platforms have been largely unregulated since.

They have also been largely unregulated while generating a staggering volume of what appears to be insider trading. A Harvard paper published last month, drawing on public blockchain data, estimated that $143 million in profits have been earned on Polymarket by traders with insider information — covering more than 200,000 suspicious bets placed between February 2024 and February 2026. Van Dyke’s trading on what he knew is not an anomaly. It’s part of a pattern.

In the hours before U.S. forces struck Iran, an anonymous Polymarket account made roughly $550,000 betting that the strikes would happen and that Ayatollah Khamenei would be removed from power. In January, as Biden’s term expired, a single trader made $316,346 betting on five specific last-minute pardons — including Hunter Biden’s — even as the market odds of those pardons dropped toward zero. Columbia Law School’s Joshua Mitts, who advises the DOJ on insider trading cases, reviewed those trades and said the odds of the outcomes occurring by random chance were “virtually zero.” A trader known as AlphaRaccoon deposited $3 million into Polymarket and immediately bet across two dozen hyper-specific markets tied to Google’s Year in Search rankings — going 22 for 23 on bets, including a winning position on an obscure singer given 0.2% odds, shortly before Google accidentally published the results early and then pulled them. The account had previously won $150,000 predicting the exact release date of Google’s Gemini 3.0 before any public announcement existed.

Israeli authorities arrested several people in February, charging two with using classified military intelligence about the Iran strikes to trade on Polymarket. Van Dyke is the first American soldier charged, sure, but investigators said yesterday that similar suspicious bets were also placed around the Iran ceasefire negotiations in April, involving newly created accounts that reaped hundreds of thousands of dollars. (The White House reportedly warned staff against using confidential information to place trades the same day the Associated Press published those findings.)

Three congressional candidates were fined and suspended this week by Kalshi for betting on the outcomes of their own elections. The fines were $539.85, $784.20, and $6,229.30.

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The industry’s response to all of this has been two-track. Publicly, the CEOs say insider trading is prohibited on their platforms. Privately — and sometimes not so privately — they have offered a different argument: that insider trading is actually a feature. Polymarket’s founder Shayne Coplan told 60 Minutes that people “going and having an edge to the market is a good thing” and that insider trading is “an inevitability” from which “benefits” arise. The logic is that insiders, by betting on what they know, move market prices toward truth faster than the crowd could without them. If an insider knows a military operation is happening tonight, and bets accordingly, the market price for that outcome rises — and thus, in theory, provides the public a more accurate probability estimate.

The new paper gives that argument partial scientific support: yes, the informed minority does improve accuracy. The 3% are genuinely pushing prices toward correct answers.

But here’s what that requires us to accept. The platforms are offering a product whose accuracy depends on a small group of people with access to information the public doesn’t have — classified military intelligence, corporate earnings data, the contents of presidential pardons before they’re announced — and whose profits come directly from the losses of the 97% who don’t have that access. The CEOs have been selling this as a tool for collective intelligence. The paper shows it is a mechanism for systematic extraction.

In the stock market, when insiders trade on non-public information for personal gain at the expense of other market participants, it’s a federal crime. The Securities and Exchange Act of 1934 established that principle, and courts have spent 90 years refining it. The specific reasoning is that the stock market functions on the assumption of fair play — that participants are competing on the quality of their public analysis, not on their proximity to secrets. When insiders trade on secrets, they are not contributing to a price-discovery process. They are exploiting an opaque wall between what they know and what you don’t for private gain.

If this argument feels familiar, it's because the mechanism is. I wrote about the same pattern — systems designed to produce outcomes without requiring anyone to answer for them — here:

Prediction markets have been allowed to develop without that framework in place. The CFTC, which regulates them, has insider trading on its list of enforcement priorities — as it announced in March. But it hasn’t yet brought a civil case. Van Dyke’s indictment is the first federal criminal charge. The instrument he used to commit the crime is still operating, still expanding, and still displaying its odds on Google Finance and in cable news chyrons as though they represent something the public collectively figured out. (CNN, where I’m a paid contributor, announced at the end of last year that it would partner with Kalshi and show its odds and predictions in its programming.)

If all of this screams “corruption” at you, try this on: Three bills are pending in Congress that would bar the President, Vice President, members of Congress, senior executive branch officials, and their families from trading prediction market contracts tied to political events. Donald Trump Jr., who is a paid strategic advisor to both Kalshi and Polymarket, and whose venture firm, 1789 Capital, invested tens of millions in Polymarket, has not commented on the legislation. His father, asked about Van Dyke yesterday, said he would “look into it,” wondered aloud whether the soldier had bet for or against the success of the operation, and mused that “the whole world unfortunately has become somewhat of a casino.”

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There is a secondary problem that the industry’s accuracy argument obscures, and it concerns democracy more than markets. When prediction market odds are displayed on Google Finance and cited on CNN, they carry what researchers at the University of New South Wales have called “a veneer of objectivity” — they are backed by money rather than punditry, and money feels like evidence. Academic researchers have documented what they call “bandwagon effects”: as market odds shift toward a candidate, some voters shift toward that candidate, perceiving momentum and quality that the odds appear to confirm. The odds then move further. The market, which was supposed to reflect reality, starts to shape it.

In the 2024 presidential campaign, a single trader spent between $25 and $30 million on Trump-winning contracts. The researchers who documented this did not conclude definitively that it shifted the outcome. They did conclude that it shifted perception, and that the markets’ claimed role as neutral information aggregators collapses under those conditions.

It may be that some regulated version of prediction markets really can produce intelligence for the public worth betting on. The original Iowa Electronic Markets — the 1988 University of Iowa experiment that inspired everything Kalshi and Polymarket became — worked specifically because it was small, closed, and stripped of commercial incentive. The CFTC granted it a no-action letter on the condition that it operate solely for academic purposes, with no outside advertising, and a hard cap of $500 per trader. Within those constraints, it outperformed major polls in forecasting presidential vote shares roughly three-quarters of the time over sixteen years: true value to a democracy looking to better understand itself.

The design features that made the Iowa Electronic Markets work are precisely the features the commercial platforms have abandoned: a participant pool selected for genuine expertise rather than recruited for volume, position limits that prevent any single actor from moving the market, mandatory identity verification that makes insider trading traceable, and a narrow contract universe focused on questions where dispersed public knowledge actually exists. A regulated framework that restored those constraints — required identity, position caps, a prohibited-contracts list that extends to any market touching classified government information or the outcomes of events participants can directly influence, and genuine enforcement authority for the CFTC rather than the self-policing regime that let $143 million in suspected insider profits accumulate uncontested — might produce something genuinely useful.

The CFTC opened a public comment period on exactly this question in March 2026, with comments due April 30. The comment window closes in six days. (See the link in “Further Reading” to add yours.) What it produces will determine whether prediction markets become a civic tool or remain what the evidence currently suggests they are: a transfer mechanism dressed in the language of collective intelligence.

In the meantime, an information system that rewards insiders like Gannon Ken Van Dyke — who allegedly made 13 bets the night before the missiles fell, won $409,881, moved the money into a foreign cryptocurrency vault, and asked Polymarket to delete his account — misleads the public, and shapes the outcomes it claims to measure is not providing a public service. It is extracting value from the difference between what some people know and what most people don’t — and dressing the gap up as if it’s some sort of useful forecast.

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Further Reading

• Gomez Cram, Guo, Jensen, and Kung — “Prediction Market Accuracy: Crowd Wisdom or Informed Minority?” (SSRN, April 20, 2026) — the peer-reviewed paper establishing that 3% of traders drive accuracy while 97% fund their profits

• NPR — “U.S. Soldier Charged with Insider Trading Over Maduro’s Ouster” (April 23, 2026) — primary reporting on the Van Dyke indictment

• Prediction Markets Grew 4X to $63.5B in 2025, But Risk Structural Strain (Yahoo Finance / CertiK) — annual volume figures from $15.8B in 2024 to $63.5B in 2025, with context on wash trading and sustainability concerns

• Prediction Markets in 2025: Data, Stats & Key Trends (Trade the Outcome) — full volume breakdown including the 127x growth since 2022 and category-level data showing tech and economics outpacing politics

• Prediction Markets Will Grow to $1 Trillion by 2030, Bernstein Estimates (CNBC) — Bernstein analyst Gautam Chhugani’s $240B 2026 projection and $1T by 2030 forecast, including Bank of America’s Kalshi volume figures

• How Prediction Markets Scaled to $21B in Monthly Volume in 2026 (TRM Labs) — transaction-level analysis of the growth from $1.2B/month in early 2025 to $20B+ by January 2026, including user wallet data and category shifts

• CNN — “Federal Prosecutors Exploring Whether Prediction Market Bets Trip Insider Trading Laws” (March 30, 2026) — DOJ/SDNY meeting with Polymarket

• NPR — “A Polymarket Trader Made $300,000 Betting on Biden’s Pardons” (April 16, 2026) — Bubblemaps analysis and Columbia Law sourcing on the Biden pardon trades

• Packin and Rabinovitz — “Prediction Markets as a Public Health Threat,” Science (April 2026) — peer-reviewed analysis of bandwagon effects and democratic manipulation

• Clinton and Huang — “Prediction Markets? The Accuracy and Efficiency of $2.4 Billion in the 2024 Presidential Election” — Vanderbilt study showing Polymarket called only 67% of 2024 markets better than chance

• CFTC Official Advisory on Prediction Markets (February 2026) — regulatory framework and documented cases

• Kalshi Becomes CNN's Official Prediction Market Partner After Raising $1B (Yahoo Finance / Cryptonews, December 2025) — the partnership that put Kalshi's real-time odds on CNN's air, including a live on-screen ticker overseen by CNN's chief data analyst; again, I am a paid contributor for CNN

• Three Economists Grabbed a Beer. A Multibillion-Dollar Industry Was Born. (NBC News, February 2026) — the founding of the Iowa Electronic Markets, its CFTC no-action letter and $500 position cap, and how its academic design constraints were shed as commercial platforms scaled

• CFTC Advance Notice of Proposed Rulemaking on Prediction Markets (Federal Register, March 16, 2026) — the formal regulatory comment process, open through April 30, 2026; the primary document for anyone seeking to shape what a federal framework actually looks like

• Prediction Markets at a Crossroads: Preemption, Enforcement and Rulemaking (Norton Rose Fulbright, April 2026) — the clearest current map of the federal-state jurisdictional fight, the CFTC’s enforcement priorities, and what formal rulemaking is likely to address

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Other Currents

1. Meta logged its employees’ keystrokes, then announced their layoffs. The Model Capability Initiative — Meta’s new tool for capturing employee keystrokes and mouse clicks, framed as AI training data — was announced this week with no opt-out option. Two days later, 8,000 layoffs. The sequence is the story: document what the workers do, then eliminate them.

2. Palantir published a manifesto. Anthropic went to court. Two companies drew opposite lines in the same week. Palantir’s 22-point public document calls AI weapons inevitable, some cultures “dysfunctional and regressive,” and pluralism a failure. Bellingcat’s Eliot Higgins noted the obvious: these aren’t abstract ideas floating in space — they’re the stated ideology of a company that sells targeting software to militaries and immigration enforcement. Meanwhile, Anthropic has been in federal court since March fighting a Pentagon designation that labeled it a national security supply-chain risk — because it refused to remove safeguards against autonomous weapons and mass domestic surveillance. One company announced what it believes. The other is paying lawyers to defend it.

3. The Joint Chiefs said autonomous weapons are coming. Nobody asked whether they work. Yesterday the Chairman of the Joint Chiefs called autonomous weapons a “key and essential part of everything we do.” Anthropic’s actual position — that frontier AI is not reliable enough to kill people without a human in the loop — has not been rebutted. It has been bypassed.

4. 96,000 tech layoffs this year, all attributed to AI. Nobody has to prove it. Amazon, Meta, Microsoft, Snap, Salesforce, Block — every announcement uses the same language: efficiency, AI investment, right-sizing. No company is required to document whether AI replaced a single role. The NLRB has no jurisdiction over the framing. Nobody does.

5. Amazon’s Ring can now identify your neighbors by face. Familiar Faces rolls out facial recognition to Ring doorbells — identifying family, friends, delivery drivers — processed in Amazon’s cloud, opt-out by default. EFF and Senator Markey are pushing back. Three states have already blocked it. “Optional and disabled by default” is how every ambient surveillance feature starts.

6. OpenAI lost three senior executives in a single day and is projecting $14 billion in losses. The CPO, the head of Sora, and the enterprise CTO all departed the same Friday. Multiple cited the DOD contract and the cultural shift from research to commercial operations. The company generating $25 billion in annual revenue is simultaneously losing the people who built the products and spending $14 billion more than it earns.

7. Anthropic just passed OpenAI in revenue. The gap matters because of what each company agreed to. Anthropic hit $30 billion in annualized revenue this month, passing OpenAI’s $25 billion. Enterprise customers — not consumers — drove it. Worth noting alongside item 2: Anthropic is the company currently in court over autonomous weapons restrictions. OpenAI signed a DOD contract that removed equivalent restrictions weeks after Anthropic was blacklisted.

8. S&P 500 boards are disclosing AI risk while knowing almost nothing about AI. 83% of S&P 500 companies now list AI as a material risk in disclosures — up from 12% in 2023. Board directors with AI expertise: 2.7%. The people with the legal authority to set limits have mostly opted not to understand the thing they’re supposed to be limiting. This is the governance gap that makes every other story in this section possible.