Ford rehires ‘gray beard’ engineers after AI falls short 74%

By Anthony Ha57%

6/28/2026, 12:05:39 PM

BS Summary: This article contains 23 faulty reasoning types, including Post Hoc (False Cause), Self-Serving Bias, and Hasty Generalization, with Recency Bias as the most egregious example at 38.6% saturation with 86 hits. Analysis detected 908 faulty-reasoning hits from 223 analyzed words, generating a BS Score of 65.7% and a BS Rank of 74% (5,827 of 21,887 articles). This article is worse (more manipulative) than 73.40% of the article peer group.

Ford executives said they have hired 350 veteran engineers  some of them were former employees, while others had been working at suppliers  after artificial intelligence and automated systems failed to deliver the desired quality level. 
Bloomberg reports the company’s chief operating officer Kumar Galhotra told journalists that Ford had been “relying more and more on automated quality systems” with disappointing results. 
So the company “brought back technical specialists,” and those specialists “hunt for failure points before a part ever reaches the plant floor.” 
Charles Poon, Ford’s vice president of vehicle hardware engineering, added, “Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product.” 
To be clear, this doesn’t mean Ford is abandoning its AI plans entirely. 
Instead, it’s using the rehired employees  referred to as “gray beard” engineers  to train younger staff and reprogram AI tools. 
This rehiring seems to be paying off, resulting in what Ford CEO Jim Farley said are things like lowered warranty and recall costs, "contributing to literally hundreds and hundreds of millions of dollars of a tailwind for Ford on cost." 
The automaker also claimed the top spot among mainstream brands in the JD Power Initial Quality Survey released this week. 
Article reasoning-pattern comparisonThis article: 13.9%Anthony Ha: 4.3%TechCrunch: 3.0%Confirmation Bias13.9%This article: 0.0%Anthony Ha: 1.2%TechCrunch: 1.4%Anchoring Bias0.0%This article: 0.0%Anthony Ha: 4.5%TechCrunch: 3.5%Availability Heuristic0.0%This article: 0.0%Anthony Ha: 1.1%TechCrunch: 1.1%Representativeness Heuristic0.0%This article: 0.0%Anthony Ha: 1.2%TechCrunch: 0.6%Hindsight Bias0.0%This article: 15.2%Anthony Ha: 2.4%TechCrunch: 2.5%Overconfidence Bias15.2%This article: 9.9%Anthony Ha: 4.6%TechCrunch: 4.8%Framing Effect9.9%This article: 0.0%Anthony Ha: 1.3%TechCrunch: 0.6%Loss Aversion0.0%This article: 9.9%Anthony Ha: 0.3%TechCrunch: 0.6%Status Quo Bias9.9%This article: 0.0%Anthony Ha: 0.0%TechCrunch: 0.2%Sunk Cost Effect0.0%This article: 23.8%Anthony Ha: 3.1%TechCrunch: 4.9%Optimism Bias23.8%This article: 0.0%Anthony Ha: 2.0%TechCrunch: 1.3%Pessimism Bias0.0%This article: 20.6%Anthony Ha: 7.1%TechCrunch: 5.0%Negativity Bias20.6%This article: 34.5%Anthony Ha: 2.4%TechCrunch: 2.1%Self-Serving Bias34.5%This article: 0.0%Anthony Ha: 0.7%TechCrunch: 0.5%Fundamental Attribution Error0.0%This article: 0.0%Anthony Ha: 0.3%TechCrunch: 0.1%Actor-Observer Bias0.0%This article: 9.9%Anthony Ha: 0.7%TechCrunch: 0.6%In-Group Bias9.9%This article: 0.0%Anthony Ha: 0.7%TechCrunch: 0.3%Out-Group Homogeneity Bias0.0%This article: 18.8%Anthony Ha: 1.3%TechCrunch: 3.5%Halo Effect18.8%This article: 0.0%Anthony Ha: 0.0%TechCrunch: 0.1%Horn Effect0.0%This article: 0.0%Anthony Ha: 0.0%TechCrunch: 0.0%Dunning-Kruger Effect0.0%This article: 38.6%Anthony Ha: 2.2%TechCrunch: 2.3%Recency Bias38.6%This article: 0.0%Anthony Ha: 0.2%TechCrunch: 0.3%Primacy Effect0.0%This article: 0.0%Anthony Ha: 0.2%TechCrunch: 0.1%Blind-Spot Bias0.0%This article: 0.0%Anthony Ha: 0.5%TechCrunch: 0.3%Ad Hominem0.0%This article: 0.0%Anthony Ha: 4.3%TechCrunch: 0.6%Straw Man0.0%This article: 20.6%Anthony Ha: 3.3%TechCrunch: 4.4%Appeal to Authority20.6%This article: 0.0%Anthony Ha: 3.3%TechCrunch: 1.7%False Dilemma0.0%This article: 0.0%Anthony Ha: 1.5%TechCrunch: 0.7%Slippery Slope0.0%This article: 9.9%Anthony Ha: 0.2%TechCrunch: 0.2%Circular Reasoning9.9%This article: 25.6%Anthony Ha: 12.4%TechCrunch: 6.0%Hasty Generalization25.6%This article: 0.0%Anthony Ha: 0.7%TechCrunch: 0.2%Red Herring0.0%This article: 0.0%Anthony Ha: 0.3%TechCrunch: 1.1%Bandwagon0.0%This article: 0.0%Anthony Ha: 3.4%TechCrunch: 2.2%Appeal to Emotion0.0%This article: 15.2%Anthony Ha: 0.6%TechCrunch: 0.6%Begging the Question15.2%This article: 38.6%Anthony Ha: 3.6%TechCrunch: 2.9%Post Hoc (False Cause)38.6%This article: 0.0%Anthony Ha: 0.8%TechCrunch: 0.1%Tu Quoque0.0%This article: 0.0%Anthony Ha: 1.1%TechCrunch: 0.5%Burden of Proof0.0%This article: 0.0%Anthony Ha: 0.1%TechCrunch: 0.2%Appeal to Nature0.0%This article: 0.0%Anthony Ha: 0.7%TechCrunch: 0.3%Composition/Division0.0%This article: 17.9%Anthony Ha: 5.0%TechCrunch: 2.4%Anecdotal17.9%This article: 9.9%Anthony Ha: 0.1%TechCrunch: 0.1%No True Scotsman9.9%This article: 5.8%Anthony Ha: 1.4%TechCrunch: 2.0%Ambiguity (Equivocation)5.8%This article: 0.0%Anthony Ha: 0.0%TechCrunch: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Anthony Ha: 0.5%TechCrunch: 0.2%Middle Ground0.0%This article: 0.0%Anthony Ha: 0.0%TechCrunch: 0.0%Personal Incredulity0.0%This article: 0.0%Anthony Ha: 0.0%TechCrunch: 0.1%Special Pleading0.0%This article: 0.0%Anthony Ha: 0.4%TechCrunch: 0.1%Genetic Fallacy0.0%This article: 11.7%Anthony Ha: 2.5%TechCrunch: 2.0%Unattributed Quote11.7%This article: 9.9%Anthony Ha: 1.3%TechCrunch: 0.7%Quote-first Misdirection9.9%This article: 13.9%Anthony Ha: 5.7%TechCrunch: 4.6%Biased Writer Voice13.9%This article: 15.2%Anthony Ha: 1.6%TechCrunch: 0.8%Indoctrination15.2%This article: 0.0%Anthony Ha: 0.0%TechCrunch: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%Anthony Ha: 0.8%TechCrunch: 0.1%Politically Right Leaning Bias0.0%This article: 17.9%Anthony Ha: 1.7%TechCrunch: 4.9%Attempt to Sell a Product or S…17.9%

223 words analyzed.

Speakers

4speakers70%attributed speech66writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 9 words • 100.0% coverageFord • 37 words • 0.0% coverageKumar Galhotra • 26 words • 100.0% coverageWriter's voice • 22 words • 100.0% coverageCharles Poon • 34 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageJim Farley • 40 words • 100.0% coverageFord • 20 words • 0.0% coverage
Selected voice

Jim Farley

100%flagged-word coverage
40 attributed words25% of attributed speech100% writer coverage
0%50.0%100.0%Attempt to Sell a Product +100.0 ptsWriter: 0.0%Jim Farley: 100.0%100.0%Biased Writer Voice-47.0 ptsWriter: 47.0%Jim Farley: 0.0%0.0%Quote-first Misdirection-33.3 ptsWriter: 33.3%Jim Farley: 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.