Internal Emails Confirm Microsoft and OpenAI Knew Scraping Was Legally Indefensible
Source
Microsoft exec called AI scraping the "largest theft of labor in human history"Microsoft / OpenAI
What happened
Court filings unsealed in the New York Times v. OpenAI and Microsoft copyright lawsuit reveal that senior executives at both companies privately flagged serious legal and ethical concerns about scraping news content for AI training data. Microsoft Director of Applied Science Brent Hecht described the practice as making a complete mockery of fair use. He went further, calling it potentially the largest theft of labor in human history. OpenAI's head of ChatGPT separately warned that publishers faced an existential threat from the practice. These statements, made internally while both companies continued scraping at scale, create a record suggesting that legal risk was identified and not remediated. The disclosures are directly relevant to enterprise governance programs that assess training data provenance and vendor conduct standards under frameworks such as [DGC-001] and procurement-stage risk review.
Why it matters
- ·Internal acknowledgment of legal risk without remediation is a standard element in willful-infringement claims. Enterprise deployers who relied on vendor assurances about training data legality must now reassess whether those assurances were adequate given what executives knew.
- ·Organizations that train their own models on scraped third-party content face direct exposure. The unsealed record raises the evidentiary bar for demonstrating good-faith fair use reliance, particularly for media, publishing, financial data, and legal content pipelines.
- ·Vendor due diligence programs that did not surface these internal risk signals now have a documented gap. Procurement controls requiring vendors to disclose known legal risks in training pipelines, analogous to [PRC-006] safety commitment verification, are not yet standard practice across most enterprises.
Governance controls affected
What to do now
- ☐Audit your training data pipeline for scraped web or news content and document the legal basis for each source, including any licensing arrangements or fair use analysis.
- ☐Issue a vendor questionnaire to foundation model providers asking them to disclose any known internal legal risk assessments related to training data sourcing, and retain responses in your vendor file.
- ☐Update your AI procurement risk assessment template to include a specific question on whether vendors have conducted and documented legal review of training data provenance.
- ☐Brief legal counsel on the unsealed Microsoft and OpenAI disclosures and confirm whether existing vendor indemnification clauses cover downstream IP liability arising from training data.
- ☐Review your vendor governance change monitoring cadence to ensure material litigation disclosures by AI vendors trigger a formal re-assessment of procurement risk.
What to watch next
The New York Times lawsuit is one of several active copyright actions against frontier AI developers. Courts' treatment of the internal communications as evidence of willful disregard will set a liability benchmark that downstream deployers cannot ignore. Watch for parallel filings that name enterprise customers as co-defendants under aiding-and-abetting theories, a pattern already being tested in 30 new lawsuits against OpenAI. The EU AI Office's tightening of GPAI monitoring expectations, including crawler transparency requirements, signals that regulatory pressure on training data sourcing will intensify alongside civil litigation.
Stay ahead of stories like this
Get every US AI governance development like this one, plus the rest of the week's developments. Every Thursday.