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Times of India in ChatGPT: another publisher deal with OpenAI, again with no disclosed rates

Bennett, Coleman & Co is putting its content into ChatGPT, but it will not say how much the deal is worth or how long it runs. The arrangement shows what the standard publisher-AI lab deal looks like today: content and citation in exchange for undisclosed money.

Media & internetAnalysisGrace OkonkwoPublished: 22 September 20265 min readSources 3
Times of India in ChatGPT: another publisher deal with OpenAI, again with no disclosed rates

Bennett, Coleman & Co Ltd (BCCL), publisher of "The Times of India" and "The Economic Times", has signed a deal to publish its content in ChatGPT. The arrangement covers English-language titles and editions in Indian languages, along with archive material and live coverage. ChatGPT will display excerpts and summaries with attribution and a link to the original article.

What the announcement leaves out matters as much as what it contains. Neither side disclosed the licence fee, the revenue-sharing model or how long the contract runs. Nor did they reveal any timetable for the second part of the agreement, which would put OpenAI models to work inside the publisher's own operations: archive search, research support, data analysis and translation.

A pattern that keeps repeating

BCCL joins a long list of publishers that chose licensing over litigation. OpenAI signed its first such deal in mid-2023 with the Associated Press. Similar agreements followed with Axel Springer, News Corp, the "Financial Times", the Le Monde group and PRISA, Dotdash Meredith, "The Atlantic", Vox Media and Schibsted. As Press Gazette notes, in most cases the money stays secret. The only public part is the general rule: content with attribution and a link.

A second mechanism runs alongside it. Publishers that do not want to negotiate block the bots and try to force a rate. People Inc, publisher of trade magazines, blocked nearly all AI bots except OpenAI, with which it has a contract, and Google, which cannot be blocked selectively because one company uses the same bot for search and for AI features.

When people have to pay for content, it turns out they really do sit down at the table, People Inc chief executive Neil Vogel told investors.

The other side of the same street

India is a good case study because both models operate there side by side. In November 2024 the news agency ANI sued OpenAI, accusing it of using its material without permission and of attributing made-up texts to ChatGPT. The Delhi High Court declined at the interim stage to block OpenAI, finding that the practice probably falls within India's fair use framework. ANI appealed that order.

BCCL took a different route, and that is the heart of this story: a licence does not settle the legal dispute, it goes around it commercially. For the publisher that means revenue without waiting for a ruling, but also giving up claims that could be worth many times more. For OpenAI it is a way to obtain legal access to content and an argument in fair use disputes, namely that the company can pay once the other side sits down to talk.

The BCCL deal with OpenAI is therefore not just news of another partner. It is a signal that the publishing market has developed two paths with very different bargaining power: a licence for the big players and a quick block for those holding unique local content.

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Sources

3
  1. 01Times of India publisher partners with OpenAI, but financial terms remain undisclosedEN
  2. 02Cloudflare says bot blocking is fuelling publisher AI dealsEN
  3. 03News outlets urge a judge to sanction OpenAI in a high-stakes AI copyright fightEN

All figures and quotations in this text come from the sources listed below.

Content prepared by the editorial team with AI assistance.

Grace Okonkwo

Grace Okonkwo

AI, models and technology

Grace Okonkwo covers AI, models and technology for FLASH24, working from primary sources such as model cards, API documentation and benchmark papers rather than vendor summaries. She checks training data provenance, evaluation conditions and reported scores against the underlying datasets before any figure reaches print. She interviews researchers and engineers directly, tracks release calendars from major labs, and compares successive model versions on the same tests. Her own self-hosting, home-network and documentation-reading habits feed straight into that desk, since she tests tools on her own hardware first. She does not publish benchmark claims without a reproducible method.

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