‘Get Rid of All the Motors That Are 15 Years Old’: Viral Claim About a Secret Government Engine Policy Has No Evidence Behind It 62%

By Sohini42%

7/22/2026, 11:51:23 AM

BS Summary: This article contains 22 faulty reasoning types, including Unattributed Quote, Negativity Bias, and Burden of Proof, with Anecdotal as the most egregious example at 34.8% saturation with 208 hits. Analysis detected 1,048 faulty-reasoning hits from 598 analyzed words, generating a BS Score of 57.9% and a BS Rank of 62% (7,780 of 20,474 articles). This article is worse (more manipulative) than 62.00% of the article peer group.

An X video shared by @WallStreetApes shows a woman claiming a Pennsylvania salvage yard employee told her the U.S. government had ordered junkyards to destroy engines more than 15 years old instead of reselling them. 
The allegation has X users discussing government regulation, vehicle tracking, and the right to repair. 
However, as The Daily Dot notes, there is currently no publicly available evidence that such a nationwide federal policy exists. 
The woman says she visited a Pennsylvania salvage yard with her husband while looking for an engine for a 2006 Chevrolet Silverado. 
She claimed an employee told them they had been instructed to “get rid of all of the motors that are 15 years old,” and that any newly arriving engines of that age should be crushed rather than removed for resale. 
WOW ? 
This woman is at a junkyard buying an old engine with her husband 
They are told by the junkyard that the government is cracking down on old engines and mandating them be destroyed 
“The guy at the salvage yard said that the government has told them that they need to get… pic.twitter.com/1ESGzYEiM8 
- Wall Street Apes (@WallStreetApes) July 21, 2026 
Later in the video, another creator searched for corroborating information using an artificial intelligence chatbot. 
The chatbot reportedly responded that there was no evidence of such a government directive. 
The creator nevertheless speculated that the alleged policy could be new or not yet publicly documented, though offered no evidence to support that theory. 
As a result, on X, users expressed skepticism and frustration about newer vehicles. 
“I would rather sacrifice all modern cars and car companies aside from Tesla, just to go back to cars that had little to no tracking in them,” one wrote, as they believe that older vehicles were easier to repair independently. 
Another commenter said they had been maintaining an older vehicle specifically to avoid purchasing a newer one. 
“I’ve been repairing my own car myself for a while now… I can’t afford an EV or newer car, so just easier to keep fixing this one,” they noted. 
Others said the alleged policy makes sense as they are concerned about government oversight or increasing vehicle technology. 
Not everyone accepted the allegation, however. 
For example, one self-described commercial auto salvage worker who commented: 
I work for a commercial auto salvage company we haven't heard of anything like this sooo I'm not convinced unless ur in California then I'd believe it… 
- ?? 
MECHANIC_X ? 
(@NClark0759) July 22, 2026 
Another user argued the story conflicted with recent right-to-repair developments and claimed manufacturers have been pressured to make repairs more accessible to independent owners and mechanics. 
Consumer advocates have also pushed for right-to-repair laws that would give independent repair shops and vehicle owners greater access to diagnostic tools and replacement parts. 
On the other hand, automakers cite cybersecurity and safety concerns. 
Regardless of the right-to-repair debate, no such destruction mandate appears to exist: At the time of publication, neither the EPA nor the National Highway Traffic Safety Administration has announced any nationwide rule requiring salvage yards to destroy engines solely because they are more than 15 years old. 
Sign up to receive the Daily Dot’s Internet Insider newsletter for urgent news from the frontline of online. 
The post ‘Get Rid of All the Motors That Are 15 Years Old’: Viral Claim About a Secret Government Engine Policy Has No Evidence Behind It appeared first on The Daily Dot . 
Article reasoning-pattern comparisonThis article: 7.2%Sohini: 3.6%dailydot.com: 4.3%Confirmation Bias7.2%This article: 0.0%Sohini: 1.3%dailydot.com: 0.9%Anchoring Bias0.0%This article: 4.8%Sohini: 3.5%dailydot.com: 4.2%Availability Heuristic4.8%This article: 0.0%Sohini: 0.5%dailydot.com: 1.2%Representativeness Heuristic0.0%This article: 0.0%Sohini: 0.8%dailydot.com: 0.8%Hindsight Bias0.0%This article: 2.5%Sohini: 1.1%dailydot.com: 1.5%Overconfidence Bias2.5%This article: 4.0%Sohini: 4.9%dailydot.com: 5.0%Framing Effect4.0%This article: 6.7%Sohini: 0.4%dailydot.com: 0.5%Loss Aversion6.7%This article: 4.2%Sohini: 1.0%dailydot.com: 0.5%Status Quo Bias4.2%This article: 0.0%Sohini: 0.3%dailydot.com: 0.2%Sunk Cost Effect0.0%This article: 0.0%Sohini: 1.1%dailydot.com: 1.2%Optimism Bias0.0%This article: 4.0%Sohini: 0.8%dailydot.com: 1.3%Pessimism Bias4.0%This article: 11.2%Sohini: 6.5%dailydot.com: 8.5%Negativity Bias11.2%This article: 4.8%Sohini: 1.1%dailydot.com: 0.9%Self-Serving Bias4.8%This article: 0.0%Sohini: 1.7%dailydot.com: 2.6%Fundamental Attribution Error0.0%This article: 0.0%Sohini: 0.0%dailydot.com: 0.3%Actor-Observer Bias0.0%This article: 0.0%Sohini: 1.8%dailydot.com: 1.6%In-Group Bias0.0%This article: 0.0%Sohini: 0.7%dailydot.com: 1.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Sohini: 3.6%dailydot.com: 2.5%Halo Effect0.0%This article: 0.0%Sohini: 0.2%dailydot.com: 0.3%Horn Effect0.0%This article: 0.0%Sohini: 0.0%dailydot.com: 0.1%Dunning-Kruger Effect0.0%This article: 0.0%Sohini: 0.9%dailydot.com: 0.7%Recency Bias0.0%This article: 0.0%Sohini: 0.4%dailydot.com: 0.4%Primacy Effect0.0%This article: 4.5%Sohini: 0.2%dailydot.com: 0.1%Blind-Spot Bias4.5%This article: 0.0%Sohini: 2.5%dailydot.com: 1.9%Ad Hominem0.0%This article: 0.0%Sohini: 0.5%dailydot.com: 0.4%Straw Man0.0%This article: 8.4%Sohini: 2.7%dailydot.com: 1.8%Appeal to Authority8.4%This article: 6.7%Sohini: 1.9%dailydot.com: 2.3%False Dilemma6.7%This article: 0.0%Sohini: 0.3%dailydot.com: 0.7%Slippery Slope0.0%This article: 0.0%Sohini: 0.0%dailydot.com: 0.1%Circular Reasoning0.0%This article: 0.0%Sohini: 6.8%dailydot.com: 8.7%Hasty Generalization0.0%This article: 0.0%Sohini: 0.2%dailydot.com: 0.3%Red Herring0.0%This article: 4.7%Sohini: 1.7%dailydot.com: 2.4%Bandwagon4.7%This article: 6.5%Sohini: 5.9%dailydot.com: 7.1%Appeal to Emotion6.5%This article: 4.0%Sohini: 0.4%dailydot.com: 0.9%Begging the Question4.0%This article: 0.0%Sohini: 2.0%dailydot.com: 1.4%Post Hoc (False Cause)0.0%This article: 0.0%Sohini: 0.4%dailydot.com: 0.2%Tu Quoque0.0%This article: 11.2%Sohini: 1.6%dailydot.com: 1.6%Burden of Proof11.2%This article: 0.0%Sohini: 0.1%dailydot.com: 0.2%Appeal to Nature0.0%This article: 0.0%Sohini: 0.0%dailydot.com: 0.1%Composition/Division0.0%This article: 34.8%Sohini: 7.8%dailydot.com: 7.3%Anecdotal34.8%This article: 0.0%Sohini: 0.1%dailydot.com: 0.1%No True Scotsman0.0%This article: 11.0%Sohini: 1.9%dailydot.com: 1.9%Ambiguity (Equivocation)11.0%This article: 0.0%Sohini: 0.0%dailydot.com: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Sohini: 0.4%dailydot.com: 0.2%Middle Ground0.0%This article: 4.5%Sohini: 0.1%dailydot.com: 0.3%Personal Incredulity4.5%This article: 0.0%Sohini: 0.1%dailydot.com: 0.1%Special Pleading0.0%This article: 0.0%Sohini: 0.1%dailydot.com: 0.2%Genetic Fallacy0.0%This article: 16.6%Sohini: 4.1%dailydot.com: 4.7%Unattributed Quote16.6%This article: 9.9%Sohini: 4.2%dailydot.com: 3.4%Quote-first Misdirection9.9%This article: 0.0%Sohini: 3.8%dailydot.com: 4.0%Biased Writer Voice0.0%This article: 0.0%Sohini: 1.0%dailydot.com: 1.6%Indoctrination0.0%This article: 0.0%Sohini: 0.4%dailydot.com: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%Sohini: 0.8%dailydot.com: 0.7%Politically Right Leaning Bias0.0%This article: 3.0%Sohini: 1.7%dailydot.com: 2.0%Attempt to Sell a Product or S…3.0%

598 words analyzed.

Speakers

2speakers2.0%attributed speech586writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 24 words • 0.0% coverageWriter's voice • 35 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 40 words • 100.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 19 words • 100.0% coverageWall Street Apes • 8 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 24 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 40 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverage@NClark0759 • 4 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 47 words • 0.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 33 words • 0.0% coverage
Selected voice

Wall Street Apes

0%flagged-word coverage
8 attributed words67% of attributed speech85% writer coverage
0%10.0%20.0%Unattributed Quote-16.9 ptsWriter: 16.9%Wall Street Apes: 0.0%0.0%Quote-first Misdirection-10.1 ptsWriter: 10.1%Wall Street Apes: 0.0%0.0%Attempt to Sell a Product -3.1 ptsWriter: 3.1%Wall Street Apes: 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.