Polymarket's Promo Videos Weren't Real Trades, WSJ Says

Promo Videos That Looked Like Real Trades

Korea’s World Cup run may be over, but there’s a market that’s still riding high on it: prediction markets. Platforms like Polymarket and Kalshi saw trading volume tied to the World Cup approach $2 billion (~₩3.1 trillion, or $2.1 billion) this year.(source)

The Wall Street Journal (WSJ) recently looked into the promotional videos these platforms put out. One clip featured in its reporting was posted to TikTok in January by a college student. In it, he bets $100,000 on whether President Trump will say the word “McDonald’s”—and cheers when it happens. Over five months, this student posted 145 betting videos, showing a combined $410,000 in trades on screen.

According to WSJ, the trades shown in his videos never actually took place. On the real site, more than 50 accounts that placed the same McDonald’s bet all lost money.

Prediction markets¹ pitch themselves on the idea that trading prices reflect the real probability of an event. But WSJ reported that Polymarket built a separate site to stage trading screens for its promotional videos. The problem this raises is straightforward: viewers may mistake the profits shown on screen for actual trading outcomes.

The 1,105 Videos WSJ Investigated

According to WSJ, Polymarket paid dozens of college student creators $2,000-$3,000 a month. The filming took place on poiymarket.com, a site made to look like the real trading platform. With the letter “l” swapped for an “i” in the address, this decoy site allowed trade amounts and outcomes to be faked.

Of the 1,105 videos WSJ analyzed, about 70% featured betting scenes, and the trades shown on screen totaled $1.9 million. In 118 videos, creators appeared to have made a combined $900,000. But when WSJ recalculated using actual trading outcomes, the real result was a loss of more than $166,000.

The report also examined how the videos were distributed. Polymarket reportedly recruited “clippers” through the marketing agency Virality — people who would copy the videos and repost them on their own accounts. They were instructed to make the content look like personal posts, and were told not to include “Polymarket” or “Poly” in their account names. This is problematic because it makes it hard for viewers to recognize the content as company promotion.

WSJ reported that creators submitted their videos to Polymarket for review and were sometimes told to reshoot them. If true, this means the company was directly involved in producing and distributing the content.

According to the report, the videos collectively racked up more than 140 million views across TikTok, YouTube, and Instagram. To get paid, clippers needed at least 60% of their viewers to be U.S. users. But view counts alone don’t tell us how much this actually translated into new sign-ups or revenue. And if the ads were specifically targeting U.S. users, the question of whether the paid-promotion status and trading terms were properly disclosed also needs scrutiny.

About a quarter of the videos analyzed reportedly used the phrase “free money” — framing bets that could just as easily lose money as an easy way to make it.

The English draft matches the Korean source accurately in structure, numbers, and meaning. No corrections needed.

We need to separate Polymarket’s US business from its overseas business

To understand this case, it helps to separate Polymarket’s regulatory history from its actual US business.

In 2022, Polymarket settled with the US Commodity Futures Trading Commission (CFTC)², paying a $1.4 million fine for operating an unregistered trading service and shutting down access for US users. But it later launched a separate US business. According to CFTC registration records, QCX LLC — Polymarket US — was approved as a designated contract market on July 9, 2025. The offshore crypto service and the US-regulated service shouldn’t be treated as the same thing.

During the 2024 US presidential election, Polymarket drew attention as a market for predicting election outcomes. Its rival Kalshi then grew rapidly. According to data from The Block, Kalshi’s monthly trading volume in May 2026 was roughly twice that of Polymarket. Kalshi operates under CFTC oversight and has expanded its user base through a partnership with Robinhood. That doesn’t mean Robinhood’s entire customer base has become Kalshi’s user base.

Kalshi won a lawsuit against the CFTC over 2024 election-related contracts, and in 2026 it raised $1 billion at a $22 billion valuation. Polymarket also runs a US-only service, but its trading volume is smaller than its overseas platform. According to Pew Research Center, in April 2026 trading volume was $1.3 billion for the US service versus $9 billion for the overseas service. Since the two serve different users and products, this gap alone doesn’t prove users haven’t migrated to the US service.

Amid this competitive pressure, Polymarket ramped up its promotional efforts. The WSJ reported internal testimony that founder Shayne Coplan pushed his growth team to build a bigger online presence. Still, the interpretation that competition and fundraising directly caused this particular campaign should be kept separate from the reported production process itself.

The relationship between politics and the industry is also contentious. Donald Trump Jr. is both a Polymarket investor and a paid advisor to Kalshi. But that relationship alone doesn’t mean individual ads won’t be investigated or sanctioned.

We’ve covered the issue of information use and accountability in prediction markets before.

Crimes get recorded, but no one gets punishedPrediction markets welcomed insiders in the name of “accurate forecasting.” Now the bill is coming due.inlevel9.com

When assessing liability for marketing aimed at Americans, being a foreign entity alone doesn’t mean US law can’t apply. FTC guidelines call for disclosing the economic relationships between advertisers and promoters that could influence consumer judgment. Depending on the specifics of a case, liability can extend beyond the advertiser to promoters or intermediaries. That’s why it matters to check exactly which service appeared in this particular video, and what disclosures, if any, were made.

A separate account-level analysis found gains concentrated among a small few

WSJ’s own account-level analysis found that 67% of total profits were concentrated in the top 0.1% of accounts. Of the 1.6 million accounts examined, fewer than 2,000 earned roughly $500 million, while over 70% of accounts posted losses. The bottom 10% of accounts lost an average of $4,000. It’s worth distinguishing that account count isn’t the same as headcount, and this dataset doesn’t track the specific cohort who signed up after watching the promotional video.

Academic research has likewise found that gains cluster around a small group of skilled traders. Researchers from London Business School and Yale analyzed 1.72 million accounts and classified 3.14% as “winners” who showed consistent skill by the study’s criteria. That figure is a product of this particular dataset and classification method — it doesn’t mean every participant has a 3.14% chance of success.

WSJ also analyzed more than 35,000 of Kalshi’s “mention markets,” where users trade on whether a celebrity will say a specific word. In that dataset, the average user who bet “yes” lost 11% of their stake. This is why it’s hard to conclude, from a handful of winning trades shown in a video, that the same type of trade can be easily won.

Professional trading firms like Susquehanna International Group and Jump Trading also participate in these markets. They deploy data and algorithms, acting as market makers³ who supply the buy and sell orders that trades require. Retail users need to reckon with the fact that they’re often trading against counterparties like these. That said, the loss statistics above don’t tell us whose specific losses became which firm’s profits.

There have also been indictments tied to the use of inside information. According to reports, a Google security engineer was charged with earning $1.2 million by trading on internal search data, while a U.S. special forces sergeant was charged with earning over $400,000 by trading on information about the operation to capture Venezuela’s Nicolás Maduro. These are allegations brought by prosecutors, not confirmed convictions, and the two should be kept separate. The risk that your counterparty holds non-public information you don’t have access to is itself one of the dangers of prediction markets.

A promotional video’s view count and the loss rate across all accounts are two different analyses. You can’t combine the two figures and conclude that most people who watched the video lost money.

Oswarld’s Lens

I’ve run into this same pattern often while building GTM strategies. What a product actually sells is user trust, but under growth pressure, teams end up recruiting users through exaggerated promotion instead. Metrics climb in the short term. But the users acquired this way become a liability. Users who came in expecting “easy money” form a negative impression of the platform the moment they take a loss.

Polymarket’s core value proposition is “transparent trading on the blockchain.” Every trade is recorded on the Polygon chain, auditable by anyone — that was supposed to be its point of differentiation from competitors. Yet the platform filmed trades on a fake site that isn’t recorded on the blockchain at all. When a product’s stated promise collides this directly with what its marketing actually did, users lose any basis for trusting what the platform tells them.

I also think it matters how promotion affects trust in the market itself. Prediction markets are a service built on the expectation that participants’ information gets reflected in prices. If more people trade based solely on profit-showcasing videos, prices risk moving with hype rather than information. On the other hand, broader participation could also make trading smoother. Rather than concluding that prediction accuracy improved or worsened just because user numbers grew, we need to verify the actual prices and outcomes.

Closing

Whether the profits shown in the ads actually came from real trades is a basic thing to check.

The core of the WSJ report is the suspicion that the seemingly real trading footage was staged on a separate site. A separate account analysis found that profits were concentrated among a small few. Neither piece of evidence makes it easy to take the platform’s “easy money” pitch at face value, but neither one tracked the same users, either.

When a platform that built its growth on the promise of transparent trade records draws in users with staged footage, it’s precisely that core strength that comes under suspicion first. Any business that relies on trust to attract users faces the same risk.

If you’re evaluating a platform that emphasizes profits, it’s worth checking whether the trades shown are real, whether loss cases are disclosed too, and whether the people promoting it are being paid by the company.

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References & Further Reading

Primary sources

• Katherine Long, Caitlin Ostroff, Neil Mehta, Brenna T. Smith, “They Looked Like They Were Getting Rich on Polymarket, but None of It Was Real”, Wall Street Journal, 2026.6.20.: This is the original investigative report where the WSJ analyzed over 1,100 videos. (paywalled)
• Wall Street Journal, “0.1% of Polymarket Accounts Take Home 67% of All Profits”, Wall Street Journal, 2026.5.4.: A separate investigation analyzing prediction-market profit distribution across 1.6 million accounts. It puts hard numbers behind the “easy money” narrative.
• Gómez-Cram, Guo, Jensen & Kung, “Prediction Market Accuracy”, London Business School & Yale, 2026.4.: An analysis of 1.72 million accounts that provides academic evidence that only 3.14% of winners actually possess skill.

Background

• CFTC, QCX LLC / Polymarket US Registration Information: Confirms the regulatory status of the US business.
• FTC, Endorsement Guides: What People Are Asking: Explains paid-promotion disclosure requirements and the responsibilities of advertisers and endorsers.
• Pew Research Center, “Trading Volume on Prediction Markets Has Soared in Recent Months”, 2026.5.27.: A neutral overview of overall prediction-market trading-volume trends, comparing Kalshi and Polymarket.
• CNBC, “Google employee charged with $1M Polymarket insider trading bet on search term”, 2026.5.27.: A detailed report on the indictment of a Google employee for insider trading.

The author is Oswarld (Kwangseob Ahn). Current roles: Adjunct Professor at Sejong University, Strategy Consultant at INLEVEL9. Career, research, books, and recent work are kept current on the About page. Latest · July 2026: HEMA-2: A Consolidation-Aware Tri-Memory Architecture with Multi-Channel Scheduling for Lifelong Conversational AI.

Footnotes

1. Prediction Market: A platform where users bet money on the outcome of future events. It operates on the principle that participants’ betting prices reflect the probability of that event occurring. Like stocks, you can buy and sell “yes” or “no” contracts. ↩

2. CFTC (Commodity Futures Trading Commission): The US federal agency that regulates derivatives and futures markets. Prediction markets also fall under its jurisdiction. ↩

3. Market Maker: A professional trader who supplies liquidity to a market. By simultaneously posting bid and ask quotes, they enable other participants to trade, earning profit from the spread (the price difference) in the process. ↩