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

marsbitPublicado a 2026-07-22Actualizado a 2026-07-22

Resumen

"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.

Criptos en tendencia

Preguntas relacionadas

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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Utilizando Modelos de Lenguaje Multimodal de Gran Escala (MLLMs), Agent S puede navegar y manipular diversas interfaces gráficas de usuario sin problemas. A través de estas características pioneras, Agent S proporciona un marco robusto que aborda las complejidades involucradas en la automatización de la interacción humana con las máquinas, preparando el terreno para una multitud de aplicaciones en IA y más allá. ¿Quién es el Creador de Agent S? Si bien el concepto de Agent S es fundamentalmente innovador, la información específica sobre su creador sigue siendo elusiva. El creador es actualmente desconocido, lo que resalta ya sea la etapa incipiente del proyecto o la elección estratégica de mantener a los miembros fundadores en el anonimato. Independientemente de la anonimidad, el enfoque sigue siendo en las capacidades y el potencial del marco. ¿Quiénes son los Inversores de Agent S? Dado que Agent S es relativamente nuevo en el ecosistema criptográfico, la información detallada sobre sus inversores y patrocinadores financieros no está documentada explícitamente. La falta de información disponible públicamente sobre las bases de inversión u organizaciones que apoyan el proyecto plantea preguntas sobre su estructura de financiamiento y hoja de ruta de desarrollo. Comprender el respaldo es crucial para evaluar la sostenibilidad del proyecto y su posible impacto en el mercado. ¿Cómo Funciona Agent S? En el núcleo de Agent S se encuentra una tecnología de vanguardia que le permite funcionar de manera efectiva en diversos entornos. Su modelo operativo se basa en varias características clave: Interacción Humano-Computadora Similar a la Humana: El marco ofrece planificación avanzada de IA, esforzándose por hacer que las interacciones con las computadoras sean más intuitivas. Al imitar el comportamiento humano en la ejecución de tareas, promete elevar las experiencias de los usuarios. Memoria Narrativa: Empleada para aprovechar experiencias de alto nivel, Agent S utiliza memoria narrativa para hacer un seguimiento de las historias de tareas, mejorando así sus procesos de toma de decisiones. Memoria Episódica: Esta característica proporciona a los usuarios una guía paso a paso, permitiendo que el marco ofrezca apoyo contextual a medida que se desarrollan las tareas. Soporte para OpenACI: Con la capacidad de ejecutarse localmente, Agent S permite a los usuarios mantener el control sobre sus interacciones y flujos de trabajo, alineándose con la ética descentralizada de Web3. Fácil Integración con APIs Externas: Su versatilidad y compatibilidad con varias plataformas de IA aseguran que Agent S pueda encajar sin problemas en ecosistemas tecnológicos existentes, convirtiéndolo en una opción atractiva para desarrolladores y organizaciones. Estas funcionalidades contribuyen colectivamente a la posición única de Agent S dentro del espacio cripto, ya que automatiza tareas complejas y de múltiples pasos con una intervención humana mínima. A medida que el proyecto evoluciona, sus posibles aplicaciones en Web3 podrían redefinir cómo se desarrollan las interacciones digitales. Cronología de Agent S El desarrollo y los hitos de Agent S pueden encapsularse en una cronología que resalta sus eventos significativos: 27 de septiembre de 2024: El concepto de Agent S fue lanzado en un documento de investigación integral titulado “Un Marco Agente Abierto que Usa Computadoras Como un Humano”, mostrando las bases del proyecto. 10 de octubre de 2024: El documento de investigación fue puesto a disposición del público en arXiv, ofreciendo una exploración profunda del marco y su evaluación de rendimiento basada en el benchmark OSWorld. 12 de octubre de 2024: Se lanzó una presentación en video, proporcionando una visión visual de las capacidades y características de Agent S, involucrando aún más a posibles usuarios e inversores. Estos marcadores en la cronología no solo ilustran el progreso de Agent S, sino que también indican su compromiso con la transparencia y la participación comunitaria. Puntos Clave Sobre Agent S A medida que el marco Agent S continúa evolucionando, varios atributos clave destacan, subrayando su naturaleza innovadora y potencial: Marco Innovador: Diseñado para proporcionar un uso intuitivo de las computadoras similar a la interacción humana, Agent S aporta un enfoque novedoso a la automatización de tareas. Interacción Autónoma: La capacidad de interactuar de manera autónoma con las computadoras a través de GUI significa un salto hacia soluciones informáticas más inteligentes y eficientes. Automatización de Tareas Complejas: Con su metodología robusta, puede automatizar tareas complejas y de múltiples pasos, haciendo que los procesos sean más rápidos y menos propensos a errores. Mejora Continua: Los mecanismos de aprendizaje permiten a Agent S mejorar a partir de experiencias pasadas, mejorando continuamente su rendimiento y eficacia. Versatilidad: Su adaptabilidad en diferentes entornos operativos como OSWorld y WindowsAgentArena asegura que pueda servir a una amplia gama de aplicaciones. A medida que Agent S se posiciona en el paisaje de Web3 y criptomonedas, su potencial para mejorar las capacidades de interacción y automatizar procesos significa un avance significativo en las tecnologías de IA. A través de su marco innovador, Agent S ejemplifica el futuro de las interacciones digitales, prometiendo una experiencia más fluida y eficiente para los usuarios en diversas industrias. Conclusión Agent S representa un audaz avance en la unión de la IA y Web3, con la capacidad de redefinir cómo interactuamos con la tecnología. Aunque aún se encuentra en sus primeras etapas, las posibilidades para su aplicación son vastas y atractivas. A través de su marco integral que aborda desafíos críticos, Agent S busca llevar las interacciones autónomas al primer plano de la experiencia digital. A medida que nos adentramos más en los reinos de las criptomonedas y la descentralización, proyectos como Agent S sin duda desempeñarán un papel crucial en la configuración del futuro de la tecnología y la colaboración humano-computadora.

561 Vistas totalesPublicado en 2025.01.14Actualizado en 2025.01.14

Qué es AGENT S

Cómo comprar S

¡Bienvenido a HTX.com! Hemos hecho que comprar Sonic (S) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar Sonic (S) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu Sonic (S)Después de comprar tu Sonic (S), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear Sonic (S)Tradear fácilmente con Sonic (S) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

1.1k Vistas totalesPublicado en 2025.01.15Actualizado en 2026.06.02

Cómo comprar S

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de S (S).

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