India is preparing a legal and regulatory framework that would require artificial intelligence companies to compensate rights holders for using copyrighted material in model training. The proposal targets global AI developers, including OpenAI and Google, and reflects a broader shift by governments toward asserting economic rights over data used to build large-scale models.
The initiative is being discussed within India’s information technology and legal policy circles as part of a review of how existing copyright law applies to generative AI. Indian officials have signaled that while innovation remains a priority, the commercial use of books, articles, music, and visual content without licensing is becoming politically and legally untenable. The argument is that AI training has moved from experimental research into a revenue-generating industrial activity.
India’s media industry has been particularly vocal. Publishers, broadcasters, and music labels argue that AI systems extract economic value from their catalogs without compensation, weakening already pressured business models. Unlike smaller markets, India’s scale gives such arguments weight. The country produces vast volumes of multilingual content that is highly valuable for training models aimed at global audiences.
The policy debate also reflects India’s attempt to shape global AI norms rather than simply import them. The European Union has focused on transparency and risk classification, while the United States has relied more on litigation and voluntary commitments. India’s approach appears closer to a licensing model, where training data becomes a regulated input rather than an assumed free resource.
How such a system would be enforced remains unclear. One option is a collective licensing mechanism, where rights organizations negotiate fees on behalf of creators. Another is direct agreements between AI firms and large publishers. Both approaches face challenges, especially in defining what constitutes “use” during training and how to audit opaque model pipelines.
For AI developers, India’s move adds to a growing compliance mosaic. If large markets impose different data compensation regimes, model development costs rise and training workflows fragment. Companies may need region-specific datasets or face legal exposure when deploying globally trained models in restrictive jurisdictions.
There is also a strategic layer. By asserting control over training data, India positions itself not just as a consumer of AI but as a rule-setter. If successful, its framework could influence other emerging markets that also host large creator economies but lack leverage individually.
