Security1 distinct publisher3 min readUpdated
An expert witness for 3M told ChatGPT to show the company was "0% at fault" in a fatal Houston explosion. Opposing counsel obtained the full conversation log mid-deposition.
The Watch · Security desk

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An expert witness retained by 3M in litigation over the January 2020 explosion at Watson Grinding in Houston used ChatGPT to write significant portions of his expert report, and instructed the model to produce something that would "show how 3M is 0% at fault for the explosion at Watson Grinding," according to 404 Media [1][2]. The report is not the interesting artefact here; the prompt log is, because it became discoverable [3].
The underlying case is not marginal. The explosion killed three people and destroyed roughly 200 homes [4], and the U.S. Chemical Safety and Hazard Investigation Board attributed it to a "degraded and poorly crimped rubber welding hose" that leaked a flammable gas [5]. Dozens of homeowners sued 3M and Watson Grinding, alleging among other things that 3M did not properly service the facility's gas detection system [6]. 404 Media reports that hundreds of millions of dollars in total liability are at stake [7].
The discovery chain is the part operators should read twice. 3M hired Josh Autenrieth of Knighthawk Engineering to prepare an expert report [8]. During discovery, plaintiffs' attorney Will Moye found a five-page document titled "Citation Overlay" that appeared to have been generated by AI, recognised it as ChatGPT output, and demanded every prompt from 3M's lawyers [9]. The deposition was paused for three hours while they were collected, and Moye received 350 pages of ChatGPT conversations, including public links to Autenrieth's full conversations [10][11]. One anomalous five-page attachment unspooled into the entire drafting history.
That history includes Autenrieth telling ChatGPT he was "being retained as a professional expert witness by 3M" for the Watson Grinding proceedings, that he needed a report to defend 3M's standard of care, and that it had to counter an opposing witness's "outlandish and false claims" [12][13]. He attached hundreds of court records and asked the model to find standards violations [14]. ChatGPT produced a roughly 30-page report containing the sentence "From a technical and standard-of-care standpoint, 3M is 0% responsible for the January 24, 2020 explosion" [15]. The line did not reach the filed report, because Autenrieth asked ChatGPT to "review this as the opposing council," and the model warned that "0% responsible" is "an easy target" and among several "phrases [that] let opposing counsel paint you as an advocate rather than an expert" [16][17]. The sanitised output survived. The instruction that produced it also survived, in the log.
There is a competence layer too. In a separate chat, Autenrieth uploaded an image of a gas detector and asked ChatGPT "what am I looking at?" and "what are model names of industrial gas detectors" [18]. Court transcripts suggest 3M paid Knighthawk roughly $90,000, at a stated rate of $475 per hour [19][20], which works out to about 189 billable hours [21].
What to watch: whether your own AI-assisted deliverables have a retention policy at all. Reports, risk assessments, incident narratives and post-mortems drafted with a chatbot leave a timestamped record of what the author asked for before they got it, and that record is a separate document from the deliverable. Treat prompt histories as you would draft memoranda: assume they are producible, assume the framing you used will be read aloud, and decide now who is allowed to type "show how we are 0% at fault" into anything that logs.
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Ranked by verification strength, evidence, and original report placement.
An expert witness testifying in a lawsuit about liability for a Houston explosion used ChatGPT to write significant portions of his expert report; he was hired by 3M.
The expert's prompts asked ChatGPT to help him "create an exceptional expert witness report defending the standard of care at 3M," and that the report should "show how 3M is 0% at fault for the explosion at Watson Grinding."
The case shows that the specific prompts used to create this type of expert testimony can be discoverable during a case, and that those prompts can be quite embarrassing.
The Houston explosion killed three people and destroyed roughly 200 homes.
According to the U.S. Chemical Safety and Hazard Investigation Board, the 2020 explosion at Watson Grinding, a manufacturing facility in Houston, was caused by a "degraded and poorly crimped rubber welding hose," which leaked a flammable gas that eventually exploded in the facility.
Dozens of homeowners have sued 3M and Watson Grinding; the plaintiffs alleged that 3M didn't properly service the facility's gas detection system and made other errors that contributed to the explosion.
Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
Primary litigation documents, one outlet
Claims rest on court transcripts, deposition documents, discovery records and 350 pages of prompt logs reviewed by the reporting outlet, with direct quotations from the prompts and the generated report, plus an on-record interview with the plaintiffs' attorney. It is nonetheless a single-publisher account with no defense-side documentation, response, or judicial finding to corroborate the characterization of the expert's reliance on the tool.
One verified high-stakes deployment, prevalence unknown
This is confirmed real usage rather than a demo: a paid expert produced a court-filed report substantially generated by ChatGPT, and the logs were entered into the case record. But the evidence covers a single expert in a single litigation; nothing in the sources measures how widely AI-drafted expert testimony occurs, so breadth cannot be scored above a single documented instance.
Slightly overstated by generalization
The specific factual claims are closely tied to quoted documents and are not inflated. The mild overstatement comes from framing one case as evidence that AI 'has made its way into courtrooms' in expert testimony generally, when the sources establish prevalence for exactly one expert and no court has yet ruled on the report's admissibility or imposed consequences.
Adversarial litigation sourcing, one side speaking
The material was shared in the context of active litigation where hundreds of millions in liability are contested, and the only interviewed human is the plaintiffs' attorney, who benefits directly from discrediting 3M's expert. The expert also had a paid incentive to reach a defense-favorable conclusion, which the prompts reflect. No countervailing statement from 3M, its counsel, or the expert is present to balance the record.
Document-grounded but one-sided and single-sourced
Confidence is supported by verbatim prompt and report quotations and by fee figures tied to filings, which are hard to fabricate and easy to check against the docket. It is capped by the absence of any second publisher, any defense-side account, and any judicial determination, and by the fact that the sharpest characterizations ('exclusively relied on ChatGPT') come from opposing counsel rather than from the record itself.
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