Mentor’s Comment

The rapid expansion of AI models such as LLMs has outpaced global regulatory thinking, especially concerning copyright. India’s new working paper on “AI and Copyright Issues” marks a significant policy moment because it attempts to balance innovation with fair remuneration for content creators.  

Introduction 

Large Language Models (LLMs) rely heavily on public text, data, and multimedia scraped from the Internet. This has created tension between AI developers and content producers whose material forms the backbone of AI training datasets. India’s Department for Promotion of Industry and Internal Trade (DPIIT) has released a working paper proposing a mandatory licensing framework to ensure remuneration for content creators while keeping AI innovation unhindered. The proposal aims to prevent prolonged litigation, offer a collaborative revenue system, and address the growing disruption in the media landscape.

Why in the news?

India’s working paper is significant because it represents the first structured attempt to create a national solution to the global controversy around AI training data and copyright. For years, AI hyperscalers have argued for unrestricted scraping of Internet content, while publishers insisted on licensing and consent. With lawsuits piling up worldwide and no uniform judicial clarity, India’s move is a major shift from unregulated data scraping to a mandatory revenue-sharing model. It highlights the scale of the problem, hundreds of media houses and small publishers risk losing fair compensation as LLMs synthesize new outputs from their work without attribution. The proposal marks a pivot toward balancing AI development with creators’ rights, avoiding a situation that could disadvantage India’s AI ecosystem through excessive restrictions or unchecked exploitation.

What Drives the Rapid Progress of LLMs?

1. Iterative advancements in machine learning: Continuous improvements in applied techniques enhance the performance and reasoning ability of LLMs.
2. Expanding access to global text and multimedia data: Massive publicly available datasets fuel training, improving output depth and sophistication.
3. Dependence on Internet-scale content: AI firms rely heavily on materials produced by media houses, publishers, and content creators.

What Is the Core Conflict Between AI Firms and Content Producers?

1. Free-use argument by AI developers: They claim public Internet content should be freely usable for training, even when outputs are monetized.
2. Licensing demand from content producers: Reproduction or syndication by AI, directly or indirectly, should require consent and licence fees.
3. Fierce industry debate: News, entertainment, and book publishing sectors fear uncompensated use of their intellectual property.

What Does India’s Working Paper Propose?

1. Mandatory licensing framework: Allows unlimited scraping of public information, but mandates structured payments to a central body.
2. Non-profit copyright society: Collects royalties from AI developers based on revenues earned through AI models trained on Indian content.
3. Collaborative revenue-sharing: Ensures creators benefit from the value AI systems extract from their work.

Why Is the Licensing Model Considered Practical?

1. Avoids the burden of opting out: Individual content producers lack the power to prevent scraping or enforce restrictions.
2. Recognizes data processing as a functional reality: AI models synthesize new outputs rather than reproduce original text verbatim.
3. Addresses inequity concerns: Small publishers may still feel disadvantaged, but a flawed system is preferable to absence of remuneration.

What Are the Challenges in Implementing the System?

1. Royalty determination issues: Difficulties in deciding proportional payments, especially between small and large publishers.
2. Ongoing global litigations: Lawsuits against AI companies continue, and no uniform judicial framework exists yet.
3. Needless delay is a threat: Waiting for courts to settle the issue only benefits AI firms and worsens market disruption.
4. Tech industry dissent: Some developers resist additional regulatory burdens but the committee views collaboration as essential.

Conclusion

India’s working paper marks an important shift toward a balanced AI-copyright ecosystem. While the proposed licensing structure is imperfect, it offers a practical, collaborative alternative to years of litigation and unregulated data extraction. If supported by the government and refined through stakeholder dialogue, it can ensure that India’s creators, publishers, and AI innovators coexist in a fair and sustainable digital environment.

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