Internal Emails Confirm OpenAI and Microsoft Knew Scraping Was Legally Indefensible
What happened
Court filings unsealed in the ongoing New York Times v. OpenAI litigation reveal internal communications from both companies that go far beyond public denials. An internal OpenAI communication described the company's training data practices as the 'largest theft of labor in human history.' A separate internal Microsoft document warned that the companies' shared AI content strategy had created a 'doom loop' for the open web, degrading both publisher revenue and the breadth of content available for future training. Microsoft's internal materials also acknowledged that the approach made 'a complete mockery of fair use.' These are not external allegations — they are contemporaneous internal assessments made by executives at the companies whose models power a large share of enterprise AI deployments today. The disclosures build on earlier reporting covered here as Internal Emails Confirm Microsoft and OpenAI Knew Scraping Was Legally Indefensible and are directly connected to the broader supply chain concerns raised in Anthropic's 'Project Panama' Exposes Training Data Sourcing as a Supply-Chain Risk.
Why it matters
- ·Enterprises relying on vendor representations that training data is lawfully licensed now have direct evidence that those representations may have been made despite contrary internal knowledge. Vendor due diligence programs built on attestation alone are insufficient; contractual indemnification clauses and representations and warranties provisions should be reviewed urgently.
- ·Downstream copyright liability exposure depends heavily on how enterprises use AI-generated outputs. Organizations in publishing, legal drafting, marketing, and media sectors face the greatest immediate risk, particularly where outputs reproduce or closely derive from third-party content at scale.
- ·The 'doom loop' framing in Microsoft's internal documents signals a recognized systemic risk to the web content ecosystem that supports RAG pipelines and retrieval-based AI systems. Compliance programs that treat training data provenance as a vendor problem rather than a supply chain risk now have a documented basis for reclassifying it as a material organizational exposure.
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Governance controls affected
What to do now
- ☐Pull and review indemnification clauses in your OpenAI and Microsoft AI contracts, specifically any representations about training data licensing and IP infringement coverage.
- ☐Audit current AI use cases for outputs that reproduce, summarize, or closely derive from third-party copyrighted content at scale — these create the highest downstream liability exposure.
- ☐Update AI vendor due diligence questionnaires to require explicit written representations about training data provenance and whether any licensing disputes are pending or anticipated.
- ☐Brief legal counsel on the unsealed documents and assess whether your organization's AI output use cases require a change in contractual posture or usage restrictions.
- ☐Flag the training data provenance risk to your AI risk register and escalate to the board or audit committee if AI-generated content outputs are material to the business.
What to watch next
The NYT v. OpenAI case is approaching discovery completion, and additional unsealed materials may further document the scope of what vendors knew and when. Compliance teams should monitor for any ruling on fair use that could set binding precedent on training data legality across the industry. The EU AI Act requires GPAI model providers to publish training data summaries under the EU General-Purpose AI Model Training Data Public Summary Template; enforcement of those provisions could surface additional disclosure gaps. Google's Hollywood Licensing Push Formalizes Training Data Compliance Norms and Suno's Licensed v6 Model Shows Training Data Litigation Risk Is Now Forcing Vendor Pivots suggest the market is already repricing this risk — enterprises should anticipate vendors restructuring licensing terms and assess how that affects procurement economics.