The White House's "Exclusive" Teleprompter Operator Makes Over $100,000 by Profiting from Insider Information Predictions

marsbitPublished on 2026-07-22Last updated on 2026-07-22

Abstract

"White House Speech Prompt Operator Earns Over $100,000 Using Insider Information on Prediction Markets" U.S. White House staffer Gabriel Perez, a long-time teleprompter operator for former President Donald Trump, has been suspended without pay for using non-public information to profit on prediction markets. As one of the few individuals with advance access to Trump's prepared speech texts, Perez placed bets on specific words or phrases Trump would mention in speeches over a three-month period, earning over $100,000. His activities were flagged by the prediction platform Kalshi, which froze over $90,000 in his account and reported him to the Commodity Futures Trading Commission (CFTC). While Perez avoided criminal charges, he is required to return his profits and cease such trading. This case marks the third major instance of insider trading on prediction markets involving government or corporate insiders, following earlier cases involving a special forces soldier and a Google engineer. The incident highlights the vulnerability of "mention" markets on prediction platforms, where individuals with advance knowledge or even the speakers themselves can easily manipulate outcomes. In response, platforms like Kalshi are tightening rules, now requiring users to disclose their employers to help prevent similar abuses.

Original | Odaily Planet Daily (@OdailyChina)

Author | Golem (@web3_golem)

Recently, another insider trading scandal has been exposed at the White House.

A White House staff member profited hundreds of thousands of dollars by trading on insider information in a prediction market. The identity of this insider was just a long-term teleprompter operator for Trump's speeches. The employee has now been suspended without pay.

This teleprompter operator has become the third insider disclosed by the US judicial department, following a special forces soldier involved in the Maduro capture operation and a Google security engineer, who made large profits in prediction markets using insider information.(Related reads: 《After 4 Months, Polymarket Helped Trump Catch the Leaker of the Military Operation, But at the Cost of...》《Looking at the Answers Before Handing in the Paper? Google Engineer Caught Up in Polymarket Insider Trading Case》)

Reported by Kalshi, Funds Frozen, but Ultimately No Criminal Liability

The protagonist is named Gabriel Perez, who has been responsible for operating the teleprompter for Trump's speeches since 2016. Perez's journey to this job is quite dramatic. In 2016, Trump's campaign team urgently needed a teleprompter operator. When they searched for "teleprompter" on Google, they found Perez's company, and Perez was hired by the Trump team just like that.

Gabriel Perez

Although Perez was hired by chance, over these 10 years, he gradually became one of Trump's closest aides. American "Politico" even stated that "Perez has become the only person Trump trusts." He often receives last-minute revisions to public speeches from Trump himself.

Therefore, Perez became one of the few people who could obtain Trump's complete speech drafts in advance and virtually had the final say on almost all of Trump's prepared speech drafts. This power is not insignificant. Perez's official title at the White House is Deputy Assistant to the President and Technology Advisor, with an annual salary of $175,000, only $20,000 less than senior staff like Chief of Staff Susie Wiles and Press Secretary Caroline Levitt.

Such a salary is already considered high-income in the US, but the greedy Perez was still not satisfied.

When prediction markets became popular, countless players began betting on which specific words Trump would "mention" in a certain speech. Perez discovered that his "privilege" could bring him even more wealth.

CFTC investigators found that over about three months, Perez placed bets on over a dozen of Trump's speeches, making a total profit of over $100,000. This included Trump's primetime speech last December, his speech at the World Economic Forum in Davos, Switzerland in January this year, the State of the Union address in February, and Trump's speech at the Medal of Honor ceremony in March.

The US President's statutory annual salary is $400,000. With various allowances, the President receives about $569,000 annually. If Perez hadn't been caught, at his rate of earning $100,000 in 3 months, although his power is less than the President's, his annual income would exceed the President's salary.

However, even knowing the speech content in advance, Perez couldn't always successfully predict which words Trump would mention in his speeches, because Trump often deviates from the script for "off-the-cuff" remarks. When Trump skipped a word Perez had bet on during a speech, Perez would immediately sell to cut his losses. Trump himself admitted in a speech at the Detroit Economic Club in January that 80% of the time he doesn't look at the teleprompter.

Just like the special forces soldier and the Google security engineer, Perez's exposure also stemmed from the prediction market platform's active reporting. Perez frequently used Kalshi for insider trading. Starting in March this year, Kalshi's monitoring system detected some abnormal trades related to specific words mentioned in Trump's speeches and thus noticed Perez.

After concluding its internal investigation, Kalshi quickly froze over $90,000 in Perez's account and handed the case over to the US Commodity Futures Trading Commission (CFTC). Upon learning of this, Trump commented that it was "despicable" and personally decided to suspend Perez without pay during the suspension.

Ultimately, Perez's greed cost him dearly. Not only did he fail to keep his prediction market profits, but he also lost his original job. However, compared to the special forces soldier and the Google security engineer, Perez was fortunate because the US judicial authorities did not file criminal charges against him; Perez did not go to jail.

During the investigation, the CFTC notified federal prosecutors in Manhattan, but the prosecutors declined to open a criminal investigation. According to informed sources, CFTC regulators have indicated a willingness to settle with Perez and have discussed terms with him. The result requires Perez to return the profits and cease similar trades thereafter.

Perez is Just the Beginning of Cleaning Up Insiders in the "Mention" Market

The reason Perez avoided jail is that prosecutors believed Perez did not constitute a criminal offense. He neither leaked important government information in advance nor caused harm to national security. As Trump said, "It's just despicable," damaging the clean image of government officials.

In March this year, the White House warned staff not to use non-public information to place bets in prediction markets. White House Spokesperson Davis Ingle stated: "The White House has strict ethical guidelines, and we expect all staff and officials to adhere to them."

But Perez is definitely not the only White House staff member profiting from insider information. Trump, who openly runs a paid group for himself, is even less qualified to comment on this teleprompter operator(Related read: $100,000 a Month, Trump Starts Selling "Alpha").

No wonder Perez couldn't resist the temptation. The "mention" market within prediction markets is indeed the category most susceptible to human manipulation. When the cost for insiders to participate is extremely low, while the potential returns are extremely high, it's no longer just a moral issue; it's a mechanism design problem. In the face of profit, even outwardly respectable, ostensibly righteous politicians cannot guarantee they will never cross that line.

The gameplay of the "mention" market involves users betting on specific words, phrases, or topics that will be mentioned in a public speech. Compared to other events (like political elections, sports events, etc.), the cheating cost for the "mention" market is extremely low. It's not limited to people like Perez who can know the speech content in advance. For the speaker themselves, cheating is as simple as saying a word, making "a word is worth a thousand pieces of gold" a concrete reality.

At the Grammy Awards ceremony in February this year, after host Trevor Noah said "Welcome back to the Grammys," he suddenly shouted "Potato." While everyone was confused, Trevor Noah continued, "If you bet on me saying that word on Polymarket, you just made a killing," and congratulated user "Noah 22." However, in reality, there was no "potato" option in the Polymarket prediction "What will be mentioned at the Grammy Awards ceremony?" and the user "noah-22" was purely fictitious.

Grammy host shouts potato at the awards ceremony

Some post-analysis suggested this was a Polymarketing marketing campaign, but it already demonstrated the speaker's ability to manipulate the "mention" market.

There's an even more direct example. In October 2025, Coinbase held its Q3 earnings call. As the call was about to end, CEO Brian Armstrong said he noticed many people were betting in prediction markets on what he would mention during this call. So he opened Polymarket and read all the words listed in the options one by one, ultimately causing the winning probability for all outcomes in that market to be 100%, ending in a draw.

The above are just two examples demonstrating a speaker's control over the "mention" market. Of course, there are certainly many more people who truly profit from it lurking beneath the surface. However, as prediction market regulation gradually deepens, perhaps all insiders in the "mention" market will be cleaned up in the future. Perez is just the beginning.

Last month, Kalshi just updated its policy, requiring users to disclose their employer. Kalshi's Head of Enforcement, Bobby DeNault, explained the reason for this move: "If you have access to certain information because of your job or employment, and you have a related legal obligation, you have a duty not to take that information for yourself or use it for personal gain." Polymarket has not yet imposed such strict disclosure requirements on users, but in the increasingly competitive and compliant prediction market track, it is believed that stricter compliance requirements from Polymarket are also coming soon.

From the special forces soldier, the Google engineer, to the White House teleprompter operator, prediction markets are gradually cleaning up insider trading. At the same time, the market is experiencing a demystification of prediction markets. Originally thought to reflect the wisdom of the crowd, they have turned out to be just ATMs for a few insiders.

Although cleaning up insider trading will make prediction markets more compliant, it also distances them further from truth and brings them closer to pure casinos.

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Related Questions

QWhat was Gabriel Perez's role at the White House, and how did he allegedly profit over $100,000?

AGabriel Perez was a teleprompter operator for President Trump. He allegedly used his insider access to the president's prepared speech texts to place bets on which specific words Trump would mention during upcoming speeches on prediction markets like Kalshi, profiting over $100,000 in about three months.

QHow was Perez's insider trading activity discovered and what was the consequence from his employer?

AKalshi's monitoring system flagged Perez's suspicious trading activity related to Trump's speeches starting in March. Kalshi froze over $90,000 in his account, reported him to the CFTC, and President Trump personally decided to suspend him without pay.

QWhy did Perez not face criminal charges unlike the soldier and Google engineer in similar cases?

AFederal prosecutors declined to pursue criminal charges because they determined Perez's actions did not constitute a criminal offense. He did not leak significant government information or compromise national security. The CFTC settled for him returning his profits and ceasing such trades.

QAccording to the article, why are 'mention' markets particularly vulnerable to manipulation?

A'Mention' markets are highly vulnerable because the cost of manipulation is extremely low, especially for individuals with insider access (like speechwriters or the speaker themselves), while potential returns are high. The article cites examples like a Coinbase CEO reading all prediction market options to manipulate the outcome.

QWhat recent policy change did Kalshi implement to combat insider trading, and what is the reasoning behind it?

AKalshi recently updated its policy to require users to disclose their employer. The reasoning, according to Kalshi's enforcement head, is that individuals with access to non-public information through their jobs have a legal obligation not to misuse that information for personal gain on prediction markets.

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Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

827 Total ViewsPublished 2025.01.14Updated 2025.01.14

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