9to5Mac⁠59%

Apple sends legal letters to former employees now at OpenAI ⁠37%

By Chance Miller⁠25%

7/17/2026, 12:30:36 PM

BS Summary: This article contains 20 faulty reasoning types, including Recency Bias, Appeal to Authority, and Availability Heuristic, with Attempt to Sell a Product or Service as the most egregious example at 18.1% saturation with 50 hits. Analysis detected 431 faulty-reasoning hits from 276 analyzed words, generating a BS Score of 43.7% and a BS Rank of ⁠37% (13,382 of 21,230 articles). This article is better (less manipulative) than 63.00% of the article peer group.

Last week, Apple filed a lawsuit against OpenAI in which it alleged that its former employees have stolen trade secrets “for the benefit of OpenAI.” 
As part of this process, Apple has reportedly sent letters directly to dozens of its former employees now working at OpenAI. 
According to the Financial Times, Apple has sent legal preservation letters to around 40 former workers. 
A preservation letter is a formal written notice sent to a person or organization telling them to preserve documents, records, and other evidence that may be relevant to a legal dispute. 
These letters typically spell out what should be preserved. 
This comes as Apple suspects the trade secret theft might extend beyond those named in the initial filing last week. 
As a refresher, Apple’s lawsuit names OpenAI and io Products as defendants, alongside former Apple employees Chang Liu and Tang Tan. 
You can read more about Apple’s lawsuit against OpenAI here: 
Apple sues OpenAI, accuses ex-employees of stealing trade secrets 
An email mistake derailed pre-lawsuit talks between Apple and OpenAI 
Apple lawsuit reveals how many of its former employees now work at OpenAI 
OpenAI says it has seen no evidence supporting Apple’s trade secret theft claims 
OpenAI hardware timeline unchanged after Apple lawsuit 
You can read a full copy of Apple’s initial filing here. 
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Follow Chance: Threads, Bluesky, Instagram, and Mastodon. 
Article reasoning-pattern comparisonThis article: 0.0%Chance Miller: 2.3%9to5Mac: 2.6%Confirmation Bias0.0%This article: 7.6%Chance Miller: 0.5%9to5Mac: 2.3%Anchoring Bias7.6%This article: 12.3%Chance Miller: 2.1%9to5Mac: 2.8%Availability Heuristic12.3%This article: 0.0%Chance Miller: 0.6%9to5Mac: 1.0%Representativeness Heuristic0.0%This article: 0.0%Chance Miller: 0.3%9to5Mac: 0.4%Hindsight Bias0.0%This article: 0.0%Chance Miller: 3.6%9to5Mac: 2.9%Overconfidence Bias0.0%This article: 12.3%Chance Miller: 3.9%9to5Mac: 5.2%Framing Effect12.3%This article: 2.9%Chance Miller: 0.9%9to5Mac: 1.4%Loss Aversion2.9%This article: 2.5%Chance Miller: 0.4%9to5Mac: 0.7%Status Quo Bias2.5%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.1%Sunk Cost Effect0.0%This article: 6.9%Chance Miller: 3.7%9to5Mac: 3.5%Optimism Bias6.9%This article: 0.0%Chance Miller: 1.4%9to5Mac: 1.9%Pessimism Bias0.0%This article: 10.9%Chance Miller: 1.9%9to5Mac: 4.5%Negativity Bias10.9%This article: 4.7%Chance Miller: 1.8%9to5Mac: 0.6%Self-Serving Bias4.7%This article: 0.0%Chance Miller: 0.4%9to5Mac: 0.4%Fundamental Attribution Error0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%Actor-Observer Bias0.0%This article: 0.0%Chance Miller: 0.1%9to5Mac: 0.4%In-Group Bias0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Chance Miller: 2.4%9to5Mac: 3.5%Halo Effect0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%Horn Effect0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%Dunning-Kruger Effect0.0%This article: 16.3%Chance Miller: 1.1%9to5Mac: 1.8%Recency Bias16.3%This article: 5.8%Chance Miller: 0.5%9to5Mac: 0.4%Primacy Effect5.8%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%Blind-Spot Bias0.0%This article: 0.0%Chance Miller: 0.2%9to5Mac: 0.1%Ad Hominem0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.1%Straw Man0.0%This article: 14.9%Chance Miller: 2.3%9to5Mac: 3.6%Appeal to Authority14.9%This article: 0.0%Chance Miller: 0.3%9to5Mac: 1.3%False Dilemma0.0%This article: 0.0%Chance Miller: 0.3%9to5Mac: 0.5%Slippery Slope0.0%This article: 0.0%Chance Miller: 0.1%9to5Mac: 0.2%Circular Reasoning0.0%This article: 3.3%Chance Miller: 4.8%9to5Mac: 4.9%Hasty Generalization3.3%This article: 0.0%Chance Miller: 0.1%9to5Mac: 0.1%Red Herring0.0%This article: 2.5%Chance Miller: 0.6%9to5Mac: 0.9%Bandwagon2.5%This article: 3.3%Chance Miller: 1.2%9to5Mac: 2.2%Appeal to Emotion3.3%This article: 0.0%Chance Miller: 0.5%9to5Mac: 0.5%Begging the Question0.0%This article: 6.2%Chance Miller: 1.5%9to5Mac: 1.8%Post Hoc (False Cause)6.2%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%Tu Quoque0.0%This article: 12.0%Chance Miller: 0.3%9to5Mac: 0.4%Burden of Proof12.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%Appeal to Nature0.0%This article: 0.0%Chance Miller: 0.3%9to5Mac: 0.1%Composition/Division0.0%This article: 0.0%Chance Miller: 2.5%9to5Mac: 2.4%Anecdotal0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%No True Scotsman0.0%This article: 2.2%Chance Miller: 1.4%9to5Mac: 1.9%Ambiguity (Equivocation)2.2%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Chance Miller: 0.1%9to5Mac: 0.1%Middle Ground0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.1%Personal Incredulity0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.2%Special Pleading0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.1%Genetic Fallacy0.0%This article: 0.0%Chance Miller: 1.2%9to5Mac: 2.3%Unattributed Quote0.0%This article: 0.0%Chance Miller: 0.4%9to5Mac: 0.4%Quote-first Misdirection0.0%This article: 9.1%Chance Miller: 5.4%9to5Mac: 6.1%Biased Writer Voice9.1%This article: 2.5%Chance Miller: 2.8%9to5Mac: 2.1%Indoctrination2.5%This article: 0.0%Chance Miller: 0.1%9to5Mac: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Chance Miller: 0.0%9to5Mac: 0.0%Politically Right Leaning Bias0.0%This article: 18.1%Chance Miller: 10.1%9to5Mac: 16.1%Attempt to Sell a Product or S…18.1%

276 words analyzed.

Speakers

2speakers11%attributed speech247writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 0.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageFinancial Times • 16 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageOpenAI • 13 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 4 words • 100.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 9 words • 100.0% coverageWriter's voice • 6 words • 100.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 7 words • 100.0% coverage
Selected voice

Financial Times

100%flagged-word coverage
16 attributed words55% of attributed speech79% writer coverage
0%12.5%25.0%Attempt to Sell a Product -20.2 ptsWriter: 20.2%Financial Times: 0.0%0.0%Biased Writer Voice-10.1 ptsWriter: 10.1%Financial Times: 0.0%0.0%Indoctrination-2.8 ptsWriter: 2.8%Financial Times: 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.