Microsoft executive called OpenAI's web scraping the 'largest theft of labor in human history'

Newly unsealed documents in The New York Times' 2023 copyright lawsuit allege that OpenAI and Microsoft obtained AI-training material by bypassing paywalls, scraping millions of articles and removing copyright notices. Internal executives reportedly characterized AI training as an "existential threat" to publishers and potentially the "largest theft of labor in human history," while acknowledging AI products could substitute for journalism. The case is a key test of whether LLM training on copyrighted content qualifies as fair use, creating material legal and business-model risk for AI developers and publishers.

Analysis

The key incremental risk for MSFT is not a damages award in isolation; it is discovery evidence that can strengthen a willfulness narrative and make a forward-looking remedy—training-data provenance controls, content exclusion, or compulsory licensing—more plausible. Azure AI and Copilot economics are most exposed if a remedy raises marginal content costs or slows model refreshes, though MSFT’s diversified earnings base makes a near-term fundamental impact immaterial. The more immediate market effect is a modest AI-governance multiple discount if litigation broadens from OpenAI-specific conduct into Microsoft’s distribution and infrastructure role.

NYT has asymmetric litigation optionality, but the better structural read-through is to scaled, differentiated publishers with direct subscriber relationships and valuable archives. A licensing regime would create a new high-margin revenue pool for NYT, Dow Jones/News Corp (NWSA), Axel Springer and potentially Reuters/LSEG, while low-quality ad-supported publishers face an adverse outcome: they lack negotiating leverage yet still lose referral traffic to AI answers. Over 6-18 months, verified real-time content becomes more valuable than static archives because model providers need continuously refreshed, rights-cleared data.

Consensus may overestimate the probability of a binary courtroom outcome. The economically rational endpoint is likely commercial licensing and technical attribution rather than broad model shutdowns; that caps NYT’s upside from a headline settlement but creates recurring revenue visibility. Falsify the NYT thesis if management fails to identify licensing revenue or subscriber conversion resilience over the next two earnings cycles; falsify the MSFT risk case if court rulings narrow claims before discovery evidence reaches dispositive-motion or trial stages.

Key Decisions for Investors

• Maintain MSFT core exposure but avoid adding on AI-product strength until the next material NYT case ruling; use a 1-3 month hedge via a small MSFT put spread around the next earnings date. The thesis is valuation-risk protection, not an earnings short; close if legal claims are substantially narrowed or implied volatility becomes excessive.
• Initiate a 6-12 month long NYT / short broad media ETF (IWM is not appropriate; use XLC only if liquidity and beta sizing permit) pair only after confirming that licensing discussions or legal milestones are entering the company’s guidance framework. Target is a rerating on recurring licensing optionality; stop if digital subscription trends weaken materially.
• Add NWSA to a watchlist as the more diversified content-rights beneficiary. Do not buy solely on litigation headlines; trigger on disclosed AI-content agreements or evidence that licensing revenue offsets search/referral pressure.
• Monitor MSFT disclosures for changes in legal reserves, AI training-data controls, or Azure/OpenAI contractual indemnities. Any explicit reserve, provenance-related product delay, or disclosure of material indemnification would justify increasing the MSFT hedge because it converts reputational risk into measurable margin and balance-sheet exposure.