OpenAI and Microsoft knew they were starting a ‘doom loop’ for the web

Recently unsealed documents in The New York Times' copyright case against OpenAI and Microsoft include internal warnings that generative-AI web scraping could create a damaging "doom loop" for the web and undermine fair-use arguments. Comments attributed to Microsoft's Director of Applied Science Brent Hecht characterized AI training-data collection as the "largest theft of labor in human history," though Microsoft said the remarks do not represent its position. The disclosures could strengthen scrutiny of AI training practices and increase litigation and intellectual-property risks for OpenAI and Microsoft.

Analysis

The investable issue is not near-term damages but discovery-driven repricing of AI content economics. Internal characterizations can weaken the industry’s preferred fair-use narrative, raising the probability that publishers gain leverage for recurring licensing, attribution, or traffic-sharing arrangements rather than one-time settlements. For MSFT, even a modest precedent that forces higher-quality-content licensing would be immaterial to consolidated earnings initially, but it could compress the AI-platform multiple by challenging the assumption that incremental model capability scales with near-zero marginal content cost.

Over the next 1-3 months, filings that survive admissibility challenges are a headline and sentiment overhang for MSFT, while NYT has asymmetric optionality from a settlement or favorable procedural ruling. The broader second-order exposure is greater for consumer AI products reliant on open-web retrieval and summary answers: GOOGL faces a more direct search-traffic/value-chain issue, while META and AMZN face less immediate publisher exposure but could inherit licensing costs if a precedent is established. Consensus may overestimate the probability of a binary courtroom outcome; large platforms can likely absorb cash payments, but a remedy requiring attribution, referral links, or limits on substitutive summaries would be structurally more valuable to publishers and more disruptive to AI-answer monetization over 6-18 months.

The thesis is falsified by a court ruling that materially narrows discovery relevance or validates transformative use at an early stage, or by evidence that licensed-content deals remain de minimis relative to AI revenue. Conversely, any disclosure of a broad publisher licensing framework, mandated traffic protections, or management guidance acknowledging material content-acquisition costs would justify reassessing sector AI margins and search multiples.

Key Decisions for Investors

• Maintain a 1-3 month tactical underweight in MSFT versus software peers rather than an outright structural short; the legal issue is unlikely to move FY earnings, but discovery headlines can cap relative multiple expansion. Cover on a favorable dispositive ruling or if MSFT demonstrates Copilot monetization sufficiently strong to offset prospective licensing costs.
• Establish a small, catalyst-driven long NYT position for the next 3-6 months, sized as litigation optionality rather than a damages claim. Risk/reward depends on procedural milestones: add only if the stock does not already price a major settlement; exit on adverse fair-use rulings or evidence of weakening digital-subscription conversion.
• Monitor GOOGL/MSFT relative performance around court filings and publisher-license announcements; a long NYT / short equal-dollar basket of MSFT and GOOGL is the cleaner expression if evidence emerges that answer engines must provide referral traffic or pay recurring content fees. Do not initiate absent confirmation that remedies extend beyond cash damages.
• Set an alert for disclosures of AI content-licensing commitments in quarterly filings or earnings calls. A recurring annualized spend large enough to affect segment-margin guidance—not isolated partnership announcements—would be the trigger to reduce exposure to AI infrastructure and consumer-answer products.