The Market Needs Uncertainty
On June 4, 2026, a post appeared on X from Gunther Eagleman, an account with 1.7 million followers. “Is CA cheating to get Spencer Pratt out?” Another, a day later: “They’re stealing it, aren’t they?”
The Los Angeles mayoral race was still being counted. Nithya Raman was leading Spencer Pratt. Eagleman was amplifying election fraud doubt to a large and politically engaged audience.
What the posts didn’t say: Eagleman was a paid affiliate of Kalshi, a prediction market platform. He was earning money to promote Kalshi’s odds. And Kalshi is a platform whose revenue depends on people placing bets on contested political outcomes.
The posts were sponsored content. The company had a financial interest in every election feeling uncertain.
How Prediction Markets Make Money
A prediction market is a platform where people bet on the probability of future events — elections, economic outcomes, geopolitical events. Kalshi and Polymarket are the two largest in the U.S. following regulatory changes that allowed prediction markets to operate in federal elections.
The platforms make money on trading volume. More trades, more revenue. Uncertainty drives trading. When an election feels like it could go either way — or worse, feels contested — people bet. When an election feels settled, they don’t.
This creates a structural incentive: platforms profit from political uncertainty. An election that feels stolen, contested, or fraudulent is an election that people will bet on until the very last count.
Since 2024, both Kalshi and Polymarket have built large paid influencer programs. Kalshi employs hundreds of influencers for affiliate marketing partnerships. Polymarket’s chief marketing officer spent approximately $350,000 through personal PayPal accounts paying political influencers. The payments went to accounts with large right-leaning audiences — the same audiences most receptive to election integrity narratives.
This is the infrastructure. Now look at what it was used for.
The Posts
During the LA mayoral race count in June 2026, at least four Kalshi-affiliated influencers published election fraud content while simultaneously promoting the platform:
- Gunther Eagleman (1.7 million followers): “Is CA cheating to get Spencer Pratt out?” and “They’re stealing it, aren’t they?”
- Matt Van Swol: Questioned why late-counted votes consistently favored candidates other than Spencer Pratt
- Kangmin Lee: Suggested mail-in ballots were systematically favoring Democrats
- Benny Johnson: Implied systematic rigging by Democrats
Each post served three interests simultaneously. First, it kept audiences engaged with an election that might otherwise feel concluded. Second, it drove interest in Kalshi’s platform — you don’t close the app on a race you think is being stolen. Third, it seeded a narrative of contested legitimacy that increases trading volume on the outcome itself.
None of the posts disclosed the financial arrangement.
The Deletion Request
After Semafor published the story, Kalshi asked the influencers to delete the posts. Company spokesperson Dani Lever said the posts violated “affiliate marketing policies.”
That statement is worth examining carefully.
Kalshi’s affiliate marketing policies exist in a document the company controls. Violation of those policies is determined by Kalshi. When Kalshi decides — only after the posts become a news story — that certain posts violate its policies, what it is actually deciding is that the posts have become a liability.
The deletion request is not a correction of an accidental error. It is a company asking its contractors to remove evidence of a mechanism that, when documented, becomes difficult to defend. The posts weren’t removed because they were false. Election fraud claims circulate freely across every platform where these influencers operate. They were removed because the link between the claim and the financial interest was now visible.
A company with genuinely innocent affiliate marketing practices doesn’t panic-delete posts when they become news. It stands behind them: these were our affiliates expressing their own opinions, we had no editorial control over the content. Instead, Kalshi asked for deletion. That request is a contemporaneous statement of liability recognition — the company identified the mechanism, understood it was publicly visible, and moved to erase the record.
The ad industry’s self-regulatory watchdog referred Kalshi to the Federal Trade Commission for allegedly failing to participate in a review of its influencer marketing practices.
The Mechanic
The manipulation has a specific structure: manufactured uncertainty in service of financial extraction.
Classic influence operations manipulate political opinion for ideological or geopolitical ends. Storm-1516 wants to damage European support for Ukraine. Iranian state media wants to discredit domestic protesters. The goal is a changed political reality.
Prediction market influence operations have a different objective. They want a felt uncertainty, not a changed outcome. They don’t need anyone to actually believe the election was stolen. They need enough doubt, enough agitation, enough people saying “wait, is this legitimate?” to drive someone to open the app and place a bet.
This is influence operations in service of financial extraction rather than political change. The doubt is not incidental to the product. The doubt is the product.
The influencer program is the delivery mechanism. A company that directly published election fraud claims would face immediate regulatory and reputational exposure. A company that pays influencers to publish election fraud claims — and then asks them to delete the posts when documented — has the same operational output with a separation layer that provides plausible deniability.
The affiliate marketing framework is the laundering mechanism. The influencer’s claim appears as authentic political opinion, not as the company’s paid messaging. The platform appears neutral — just providing odds on an outcome — while its paid network is actively working to make that outcome feel uncertain enough to trade.
The Disclosure Gap
FTC guidelines require that paid influencer posts disclose the financial relationship. This requirement exists because audiences respond differently to sponsored content than to authentic political opinion.
Election content operates in a specific register. When an influencer with 1.7 million followers posts “they’re stealing it, aren’t they?”, that post functions as testimony — an authentic political reaction from a real person who happens to have a large audience. It carries the rhetorical weight of organic belief.
When that influencer is a paid affiliate of a platform that profits from the claim’s emotional content — and when the post carries no disclosure — the audience cannot evaluate what they’re actually receiving. They cannot ask: does this person believe this, or are they paid to say it? They cannot ask: does the platform appearing to neutrally report odds have a financial stake in whether this race feels contested?
The distinction matters for basic epistemic hygiene. A person’s genuine political belief and sponsored content designed to increase trading volume are categorically different things. Presenting one as the other is the manipulation.
Governor Newsom’s office accused prediction markets of “engaging in election misinformation to undermine confidence in our elections.” That’s accurate. It’s also incomplete. Undermining confidence in elections and profiting from the resulting uncertainty are not competing objectives. They are the same operation.
Why No Conspiracy Required
The mechanism doesn’t require anyone to lie deliberately or to plan an influence operation.
The influencer who posts “they’re stealing it, aren’t they?” may genuinely believe it. The platform executive who approved the affiliate program may genuinely believe it’s just marketing. The financial incentive structures produce the outcome without requiring anyone to sit in a room and decide to spread disinformation.
This is structurally different from simple fraud, and it’s why disclosure regulation is insufficient as a complete defense. The manipulation is designed into the incentive architecture. The platform profits from uncertainty. The influencer profits from engagement. Election fraud narratives drive both. No one needs to issue instructions.
All that’s required is the arrangement: a platform that profits from political uncertainty, a paid influencer program with reach into politically activated audiences, and no disclosure requirement robust enough to make the financial alignment visible to the people consuming the content.
The alignment does the work. The relationship does the rest.
What the Cover-Up Confirmed
Kalshi’s deletion request matters not because it stopped the posts — the posts had already circulated, already reached their audiences, already generated whatever trading volume they generated. It matters because it confirms the company’s own assessment of what the posts were.
If the posts were simply influencers exercising their authentic political views, deletion would have been a strange response. Authentic political speech from paid affiliates is legally defensible and commercially neutral — it’s what every influencer agreement contemplates.
Instead, Kalshi moved fast to erase the record. That move is an admission that the company recognized the posts as serving its financial interests in a way it could not defend once the mechanism was documented.
The deletion is the confession.
The election fraud claims are now gone from Eagleman’s feed. The trading volume they generated remains. The accounts that saw “Is CA cheating?” before deletion still saw it. The doubt it planted doesn’t get unplanted by a deleted post.
Prediction markets operate on the efficient market hypothesis: prices reflect all available information. What the LA mayoral race demonstrated is that the platforms have a direct financial incentive to ensure the information available includes doubt — and the infrastructure to manufacture it.
The doubt was priced in. The doubt was always the product.
This article is part of Decipon’s Manipulation Breakdowns series, examining specific influence operations through the Influence Tactics Protocol.
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