Leaked deck reveals how OpenAI is pitching publisher partnerships

Program purpose & data access

• Deck describes a “Preferred Publisher Program” (PPP) where OpenAI pays publishers “guaranteed” and “variable” value for access to archives and fresh content.
• Commenters note this is mainly about ongoing scraping/browsing for up‑to‑date answers, not initial model training, which many assume already used publishers’ back catalogs.
• Some see this as a step toward an AI search product, with PPP content getting richer link treatments and priority placement in answers.

Copyright, fair use & data ownership

• Strong disagreement over whether prior training on publisher content is “copyright theft” or fair use; posters stress the legal status is unresolved and will take years to settle.
• Others argue that even if big firms lose, enforcement against countless open‑source and hobby models is infeasible: “the cat is out of the bag.”
• Concern that PPP lets large publishers monetize and wall off data, while smaller sites’ content remains un‑ or under‑compensated.

Advertising, bias & “enshittification”

• Many interpret PPP as the start of pay‑to‑play influence over model outputs, akin to search ads or product placement.
• Fears that answers will be skewed toward paying partners and that such bias will be hidden in model weights and phrasing.
• Several note laws requiring ads to be clearly labeled; others worry OpenAI will comply minimally or rely on fine‑print EULAs.
• Some recall earlier statements that paid subscriptions meant “you’re not the product” and see this as a reversal.

User experience & trust

• Strong anxiety about undisclosed or semi‑disclosed ads inside conversational answers, especially in sensitive contexts (e.g., mental health).
• Comparisons to the web’s and streaming’s gradual ad creep; expectation that even paid tiers may eventually include ads.
• A minority argue that if ad‑like integration remains clearly marked links or separate “sponsored” blocks, users may accept it, as with search.

Business model, competition & open source

• Many see this as inevitable given high training/inference costs and pressure for revenue growth.
• Others warn that ad‑tainted outputs could push developers toward open‑source models (e.g., local LLMs) explicitly tuned to resist marketing influence.
• Some note publishers gain leverage and OpenAI gains a competitive data moat, potentially starving smaller AI competitors of high‑value content.