Teleprompter Operator Made $100K Betting on Trump’s Speeches 41%

By Hafiz Rashid68%

7/16/2026, 9:53:26 AM

BS Summary: This article contains 27 faulty reasoning types, including Hasty Generalization, Appeal to Authority, and Availability Heuristic, with Negativity Bias as the most egregious example at 32.3% saturation with 124 hits. Analysis detected 1,202 faulty-reasoning hits from 384 analyzed words, generating a BS Score of 45.6% and a BS Rank of 41% (12,989 of 21,887 articles). This article is better (less manipulative) than 59.30% of the article peer group.

A White House teleprompter operator appears to have made over $100,000 betting on President Trump’s speeches. 
ABC News, citing unnamed sources, reports that a longtime staffer for the president, Gabriel Perez, was placing bets on Trump’s speeches on the prediction market Kalshi, sparking an investigation from the Commodity Futures Trading Commission, or CFTC. 
Perez, a technical assistant, has operated Trump’s teleprompter since 2016, and is now speaking to federal investigators over allegations that he profited from inside knowledge. 
Perez allegedly bet on several of Trump’s speeches over three months, including the president’s prime-time address in December 2025, his remarks at the World Economic Forum in Switzerland in January 2026, and a Trump speech in March at a Medal of Honor ceremony. 
Kalshi reportedly alerted the CFTC about suspicious activity on its “Mentions” market, which allows bets on whether certain words, topics, or phrases come up in public speech. 
“Our surveillance team promptly flagged and referred these trades to the CFTC, and we are cooperating and assisting regulators,” the lead lawyer for Kalshi, Bobby DeNault, told ABC News in a statement. 
“The White House has strict ethics guidelines that we expect all staffers and officials to follow,” White House spokesperson Davis Ingle said to ABC News. 
“The staffer in question is fully cooperating with the CFTC.” 
In late March, the White House warned its staffers not to bet on world events in prediction markets. 
Perez usually is the last person to see Trump’s prepared speeches, even taking last-minute edits from the president. 
In some cases, investigators found that Perez would change his bets while Trump was in the middle of a speech after the president skipped over certain words. 
After meeting with regulators in the last few months, Perez acknowledged making the trades, and the CFTC alerted federal prosecutors in New York, who decided against a criminal investigation. 
CFTC regulators are reportedly willing to settle with Perez, allowing him to return his profits and refrain from any future bets. 
In April, a special forces soldier involved in capturing Venezuelan President Nicolás Maduro was charged with using confidential intelligence to win $400,000 on the prediction market Polymarket. 
It seems some in the Trump administration see their positions as a way to make money, even trading in national secrets. 
Article reasoning-pattern comparisonThis article: 5.5%Hafiz Rashid: 7.5%newrepublic.com: 6.1%Confirmation Bias5.5%This article: 6.3%Hafiz Rashid: 0.8%newrepublic.com: 0.5%Anchoring Bias6.3%This article: 18.5%Hafiz Rashid: 4.3%newrepublic.com: 3.3%Availability Heuristic18.5%This article: 15.9%Hafiz Rashid: 1.2%newrepublic.com: 1.0%Representativeness Heuristic15.9%This article: 5.5%Hafiz Rashid: 0.8%newrepublic.com: 0.8%Hindsight Bias5.5%This article: 0.0%Hafiz Rashid: 1.1%newrepublic.com: 2.0%Overconfidence Bias0.0%This article: 2.1%Hafiz Rashid: 10.9%newrepublic.com: 8.0%Framing Effect2.1%This article: 0.0%Hafiz Rashid: 0.6%newrepublic.com: 0.4%Loss Aversion0.0%This article: 11.2%Hafiz Rashid: 0.5%newrepublic.com: 0.3%Status Quo Bias11.2%This article: 0.0%Hafiz Rashid: 0.2%newrepublic.com: 0.1%Sunk Cost Effect0.0%This article: 5.5%Hafiz Rashid: 1.0%newrepublic.com: 1.0%Optimism Bias5.5%This article: 5.5%Hafiz Rashid: 3.0%newrepublic.com: 2.5%Pessimism Bias5.5%This article: 32.3%Hafiz Rashid: 16.7%newrepublic.com: 13.3%Negativity Bias32.3%This article: 17.4%Hafiz Rashid: 1.9%newrepublic.com: 0.9%Self-Serving Bias17.4%This article: 11.2%Hafiz Rashid: 2.9%newrepublic.com: 2.0%Fundamental Attribution Error11.2%This article: 0.0%Hafiz Rashid: 0.6%newrepublic.com: 0.2%Actor-Observer Bias0.0%This article: 0.0%Hafiz Rashid: 1.8%newrepublic.com: 1.7%In-Group Bias0.0%This article: 0.0%Hafiz Rashid: 1.5%newrepublic.com: 1.0%Out-Group Homogeneity Bias0.0%This article: 0.0%Hafiz Rashid: 0.6%newrepublic.com: 1.2%Halo Effect0.0%This article: 0.0%Hafiz Rashid: 0.3%newrepublic.com: 0.6%Horn Effect0.0%This article: 0.0%Hafiz Rashid: 0.0%newrepublic.com: 0.0%Dunning-Kruger Effect0.0%This article: 14.6%Hafiz Rashid: 2.0%newrepublic.com: 1.4%Recency Bias14.6%This article: 0.0%Hafiz Rashid: 0.3%newrepublic.com: 0.5%Primacy Effect0.0%This article: 0.0%Hafiz Rashid: 0.0%newrepublic.com: 0.1%Blind-Spot Bias0.0%This article: 0.0%Hafiz Rashid: 3.5%newrepublic.com: 3.8%Ad Hominem0.0%This article: 0.0%Hafiz Rashid: 0.4%newrepublic.com: 1.1%Straw Man0.0%This article: 27.6%Hafiz Rashid: 4.2%newrepublic.com: 3.4%Appeal to Authority27.6%This article: 5.5%Hafiz Rashid: 1.2%newrepublic.com: 1.9%False Dilemma5.5%This article: 0.0%Hafiz Rashid: 1.0%newrepublic.com: 2.0%Slippery Slope0.0%This article: 0.0%Hafiz Rashid: 0.2%newrepublic.com: 0.3%Circular Reasoning0.0%This article: 27.9%Hafiz Rashid: 8.6%newrepublic.com: 8.0%Hasty Generalization27.9%This article: 12.5%Hafiz Rashid: 0.4%newrepublic.com: 0.3%Red Herring12.5%This article: 0.0%Hafiz Rashid: 0.6%newrepublic.com: 0.6%Bandwagon0.0%This article: 7.0%Hafiz Rashid: 6.1%newrepublic.com: 6.3%Appeal to Emotion7.0%This article: 6.5%Hafiz Rashid: 1.9%newrepublic.com: 1.8%Begging the Question6.5%This article: 4.7%Hafiz Rashid: 3.5%newrepublic.com: 3.2%Post Hoc (False Cause)4.7%This article: 0.0%Hafiz Rashid: 0.3%newrepublic.com: 0.3%Tu Quoque0.0%This article: 11.7%Hafiz Rashid: 2.1%newrepublic.com: 0.8%Burden of Proof11.7%This article: 0.0%Hafiz Rashid: 0.2%newrepublic.com: 0.1%Appeal to Nature0.0%This article: 0.0%Hafiz Rashid: 0.1%newrepublic.com: 0.3%Composition/Division0.0%This article: 14.6%Hafiz Rashid: 3.9%newrepublic.com: 2.0%Anecdotal14.6%This article: 0.0%Hafiz Rashid: 0.1%newrepublic.com: 0.2%No True Scotsman0.0%This article: 4.7%Hafiz Rashid: 2.0%newrepublic.com: 1.8%Ambiguity (Equivocation)4.7%This article: 0.0%Hafiz Rashid: 0.0%newrepublic.com: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Hafiz Rashid: 0.0%newrepublic.com: 0.1%Middle Ground0.0%This article: 0.0%Hafiz Rashid: 0.4%newrepublic.com: 0.3%Personal Incredulity0.0%This article: 6.5%Hafiz Rashid: 0.4%newrepublic.com: 0.2%Special Pleading6.5%This article: 0.0%Hafiz Rashid: 0.1%newrepublic.com: 0.3%Genetic Fallacy0.0%This article: 12.2%Hafiz Rashid: 4.0%newrepublic.com: 2.3%Unattributed Quote12.2%This article: 2.1%Hafiz Rashid: 3.1%newrepublic.com: 1.6%Quote-first Misdirection2.1%This article: 18.2%Hafiz Rashid: 14.6%newrepublic.com: 15.5%Biased Writer Voice18.2%This article: 0.0%Hafiz Rashid: 1.8%newrepublic.com: 2.4%Indoctrination0.0%This article: 0.0%Hafiz Rashid: 7.0%newrepublic.com: 7.2%Politically Left Leaning Bias0.0%This article: 0.0%Hafiz Rashid: 1.5%newrepublic.com: 0.6%Politically Right Leaning Bias0.0%This article: 0.0%Hafiz Rashid: 0.1%newrepublic.com: 0.3%Attempt to Sell a Product or S…0.0%

384 words analyzed.

Speakers

5speakers32%attributed speech261writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 8 words • 100.0% coverageWriter's voice • 16 words • 100.0% coverageWriter's voice • 37 words • 100.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 43 words • 0.0% coverageKalshi • 27 words • 0.0% coverageBobby DeNault • 32 words • 0.0% coverageDavis Ingle • 25 words • 0.0% coverageWriter's voice • 10 words • 100.0% coverageWhite House • 18 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageCommodity Futures Trading Commission, or CFTC • 21 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 21 words • 100.0% coverage
Selected voice

Davis Ingle

100%flagged-word coverage
25 attributed words20% of attributed speech100% writer coverage
0%15.0%30.0%Biased Writer Voice-26.8 ptsWriter: 26.8%Davis Ingle: 0.0%0.0%Unattributed Quote-18.0 ptsWriter: 18.0%Davis Ingle: 0.0%0.0%Quote-first Misdirection-3.1 ptsWriter: 3.1%Davis Ingle: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.