Trump's top AI advisor leaving the White House ⁠47%

By Cat Zakrzewski⁠0%

6/6/2026, 4:27:59 PM

BS Summary: This article contains 7 faulty reasoning types, including Primacy Effect, Unattributed Quote, and Optimism Bias, with Availability Heuristic as the most egregious example at 44.1% saturation with 52 hits. Analysis detected 257 faulty-reasoning hits from 118 analyzed words, generating a BS Score of 48.3% and a BS Rank of ⁠47% (11,789 of 21,886 articles). This article is better (less manipulative) than 53.90% of the article peer group.

A tech investor who shaped the Trump administration’s pro-industry artificial intelligence policies will depart the White House at the end of the month. 
Sriram Krishnan has informed administration officials that he plans to leave his post as the White House senior policy adviser for AI to start an outside institution that will influence technology policy, according to a person familiar with his plans, who spoke on the condition of anonymity to describe the private discussions. 
Planning for the new initiative is in nascent stages, but it is intended to allow the tech leader to continue to play an active role in the Trump administration’s response to the development of AI. 
Article reasoning-pattern comparisonThis article: 0.0%Cat Zakrzewski: 1.0%The Washington Post: 3.8%Confirmation Bias0.0%This article: 0.0%Cat Zakrzewski: 1.7%The Washington Post: 1.3%Anchoring Bias0.0%This article: 44.1%Cat Zakrzewski: 4.9%The Washington Post: 4.9%Availability Heuristic44.1%This article: 0.0%Cat Zakrzewski: 2.7%The Washington Post: 1.0%Representativeness Heuristic0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.5%Hindsight Bias0.0%This article: 0.0%Cat Zakrzewski: 2.4%The Washington Post: 1.2%Overconfidence Bias0.0%This article: 19.5%Cat Zakrzewski: 32.0%The Washington Post: 21.5%Framing Effect19.5%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.8%Loss Aversion0.0%This article: 0.0%Cat Zakrzewski: 1.4%The Washington Post: 0.9%Status Quo Bias0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Sunk Cost Effect0.0%This article: 29.7%Cat Zakrzewski: 3.5%The Washington Post: 3.6%Optimism Bias29.7%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 3.6%Pessimism Bias0.0%This article: 0.0%Cat Zakrzewski: 14.0%The Washington Post: 18.5%Negativity Bias0.0%This article: 0.0%Cat Zakrzewski: 3.4%The Washington Post: 1.5%Self-Serving Bias0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.9%Fundamental Attribution Error0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Actor-Observer Bias0.0%This article: 0.0%Cat Zakrzewski: 2.2%The Washington Post: 2.2%In-Group Bias0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.8%Out-Group Homogeneity Bias0.0%This article: 0.0%Cat Zakrzewski: 2.2%The Washington Post: 2.1%Halo Effect0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.4%Horn Effect0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.0%Dunning-Kruger Effect0.0%This article: 6.8%Cat Zakrzewski: 3.7%The Washington Post: 2.4%Recency Bias6.8%This article: 44.1%Cat Zakrzewski: 6.0%The Washington Post: 1.0%Primacy Effect44.1%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.0%Blind-Spot Bias0.0%This article: 0.0%Cat Zakrzewski: 0.4%The Washington Post: 0.9%Ad Hominem0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Straw Man0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 5.2%Appeal to Authority0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 1.5%False Dilemma0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 1.0%Slippery Slope0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Circular Reasoning0.0%This article: 0.0%Cat Zakrzewski: 0.4%The Washington Post: 5.9%Hasty Generalization0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Red Herring0.0%This article: 0.0%Cat Zakrzewski: 0.7%The Washington Post: 0.5%Bandwagon0.0%This article: 0.0%Cat Zakrzewski: 2.9%The Washington Post: 6.3%Appeal to Emotion0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 1.0%Begging the Question0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 4.5%Post Hoc (False Cause)0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.2%Tu Quoque0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.6%Burden of Proof0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Appeal to Nature0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Composition/Division0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 1.9%Anecdotal0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.2%No True Scotsman0.0%This article: 29.7%Cat Zakrzewski: 5.6%The Washington Post: 2.7%Ambiguity (Equivocation)29.7%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Middle Ground0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.0%Personal Incredulity0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.1%Special Pleading0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 0.2%Genetic Fallacy0.0%This article: 44.1%Cat Zakrzewski: 11.5%The Washington Post: 4.8%Unattributed Quote44.1%This article: 0.0%Cat Zakrzewski: 2.0%The Washington Post: 2.5%Quote-first Misdirection0.0%This article: 0.0%Cat Zakrzewski: 24.0%The Washington Post: 15.2%Biased Writer Voice0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 2.0%Indoctrination0.0%This article: 0.0%Cat Zakrzewski: 3.0%The Washington Post: 3.6%Politically Left Leaning Bias0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 1.4%Politically Right Leaning Bias0.0%This article: 0.0%Cat Zakrzewski: 0.0%The Washington Post: 1.5%Attempt to Sell a Product or S…0.0%

118 words analyzed.

Speakers

No attributed speakers were identified in this analysis.

Loading…
Loading…
Loading…
Loading…

Analysis

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