White House Operator Suspended After $100k Insider Trading on Trump Speeches

Woofun AI reports that Gabriel Perez, a White House teleprompter operator, has been suspended without pay after exploiting insider access to President Trump’s speech drafts to generate illicit profits exceeding $100,000 on prediction markets. This incident marks the third significant insider trading scandal to emerge from the intersection of government insiders and speculative betting platforms, following previous cases involving special forces personnel linked to the capture of Nicolas Maduro and a Google security engineer.

The suspension follows an investigation by the Commodity Futures Trading Commission (CFTC), which determined that Perez leveraged his unique position to trade on non-public information regarding specific words and phrases Trump would mention in public addresses. The case has drawn sharp criticism from the President, who labeled the behavior 'disgraceful,' and has triggered a broader scrutiny of ethical guidelines within the executive branch. While Perez avoided criminal charges, the financial and professional consequences have been immediate and severe, setting a precedent for how federal agencies may handle similar violations in the future.

Perez’s rise to influence within the White House began in 2016, when he was hired by chance after Trump’s campaign team searched for 'teleprompter' on Google and discovered his company. Over the subsequent decade, he evolved from a technical contractor into one of the President’s most trusted aides, a transformation documented by The Political Report, which noted that 'Perez has become the only person Trump trusts.' This trust granted him exclusive access to final speech drafts and the authority to approve last-minute revisions directly from the President.

His official title, Deputy Assistant to the President and Technical Advisor, came with an annual salary of $175,000, placing him among the highest-paid staff members in the administration. This compensation was only $20,000 less than that of senior officials such as Chief of Staff Susie Wells and Press Secretary Karine Levet, reflecting his elevated status despite his technical role. The combination of high income and unparalleled access to sensitive information created a unique opportunity for financial exploitation, which Perez ultimately pursued through prediction markets.

The mechanism of Perez’s fraud centered on 'mention' markets, where users bet on whether specific words or topics would appear in public speeches. CFTC investigators found that over a period of approximately three months, Perez placed bets on more than a dozen of Trump’s speeches, accumulating profits of over $100,000. These transactions included wagers on Trump’s prime-time address in December last year, his speech at the World Economic Forum in Davos, Switzerland, in January this year, the State of the Union address in February, and remarks made at the Medal of Honor ceremony in March. By knowing the draft content in advance, Perez could identify which terms were likely to be included and place corresponding bets.

However, the strategy was not foolproof, as Trump frequently improvised during speeches, deviating from the prepared text. When Trump omitted a word Perez had bet on, Perez would immediately sell his position to implement a stop-loss, minimizing losses. This dynamic required constant monitoring and rapid execution, leveraging his real-time access to the teleprompter feed.

The financial implications of Perez’s actions are stark when compared to the President’s own compensation. The legal annual salary of the U.S. president is $400,000, with various allowances bringing total annual income to approximately $569,000. If Perez had continued his trading activities without detection, his earnings rate of $100,000 in three months would have projected an annual income exceeding the President’s salary, despite holding significantly less political power.

This disparity highlights the lucrative potential of insider trading in prediction markets, where small amounts of non-public information can yield disproportionate returns. Trump himself acknowledged the unpredictability of his speaking style during a January address at the Detroit Economic Club, admitting that he did not look at his teleprompter 80% of the time. This admission underscores the risk inherent in Perez’s strategy, as the President’s improvisational tendencies could easily negate the value of insider knowledge, yet Perez still managed to generate substantial profits through careful timing and risk management.

Woofun AI data shows, Perez’s exposure was triggered by Kalshi, the prediction market platform he frequently used for trading. Starting in March this year, Kalshi’s monitoring systems detected abnormal transaction patterns related to specific words mentioned in Trump’s speeches, prompting an internal investigation. The platform identified Perez as the source of these suspicious trades and subsequently froze over $90,000 from his account, handing the matter over to the CFTC. Upon learning of the scandal, Trump personally ordered Perez’s suspension without pay, describing the behavior as 'disgraceful.'

The immediate consequences for Perez were severe, resulting in the loss of both his job and his illicit profits. Unlike previous cases involving special forces members and a Google engineer, however, Perez was not subjected to criminal charges. Federal prosecutors in Manhattan declined to launch a criminal investigation, citing a lack of evidence that Perez had committed a criminal offense. Instead, the CFTC pursued a civil settlement, requiring Perez to return his profits and cease engaging in similar transactions. This outcome reflects a nuanced approach to enforcement, distinguishing between criminal misconduct and regulatory violations.

The White House responded to the scandal by reinforcing its ethical guidelines, with spokesman Davis Ingel stating in March this year that 'The White House has strict ethical guidelines, and we expect all staff and officials to abide by them.' This warning was directed at all employees, emphasizing the prohibition against using non-public information for personal gain.

However, the incident also highlighted broader issues within the administration, particularly given Trump’s own history of creating paid groups for personal benefit. Critics argue that the President’s actions undermine the integrity of the ethical standards he demands from his staff. The 'mention' market, in particular, is vulnerable to manipulation due to the low cost of cheating and the high potential rewards. Insiders like Perez can exploit their access to speech drafts with minimal risk, while speakers themselves can manipulate outcomes by deliberately mentioning or omitting specific terms. This structural vulnerability transforms prediction markets from tools of collective wisdom into arenas for insider exploitation, raising questions about the fairness and reliability of these platforms.

The manipulability of 'mention' markets is further illustrated by two high-profile cases involving public figures. In February this year, during the Grammy Awards, host Trevor Noah shouted 'Potato!' after welcoming viewers back to the event, claiming that those who bet on Polymarket that he would say the word would make a fortune. He congratulated a user named 'Noah 22,' but investigations revealed that no such option existed in Polymarket’s predictions, and the user was entirely fictional. Analysts suggested this was a marketing tactic by Polymarket, designed to demonstrate the platform’s ability to influence public perception and drive engagement.

Similarly, in October 2025, Coinbase CEO Brian Armstrong addressed the issue directly during the company’s third-quarter earnings call. Noticing that many people were betting on what he would mention during the call, Armstrong opened Polymarket and read out all the options one by one, resulting in 100% win rates for all predictions. These examples demonstrate the extent to which insiders and speakers can control outcomes in 'mention' markets, turning them into predictable and exploitable systems. The ease with which these manipulations can be executed underscores the need for stricter regulatory oversight and transparency.

Regulatory changes are already underway to address these vulnerabilities. Last month, Kalshi updated its policies to require users to disclose their employers, aiming to prevent conflicts of interest and insider trading. Bobby DeNault, Kalshi’s enforcement head, explained that this measure is necessary because 'If you have access to certain information due to your job or employment relationship and you have relevant legal responsibilities, then you have obligation not to keep this information for yourself or use it for personal gain.' While Polymarket has not yet imposed similar disclosure requirements, the increasing competition in compliance within prediction markets suggests that stricter rules are likely to follow. From special forces members to Google engineers and White House teleprompter operators, prediction markets are gradually eliminating insider trading through enhanced monitoring and regulatory pressure.

However, this demystification process also reveals a troubling reality: what was once perceived as a reflection of collective wisdom is increasingly becoming a cash machine for a few insiders. As prediction markets move closer to compliance, they also risk drifting further from truth, resembling pure casinos rather than informative financial instruments.

This shift poses significant challenges for regulators and users alike, who must balance the need for integrity with the desire for accurate and unbiased predictions.