India’s Hybrid AI–Copyright Licensing Model: A New Global Template and Its Implications for Rights Holders

by ChatGPT-5.1

India has placed itself at the center of the global debate on AI and copyright by proposing one of the most interventionist yet transparent regulatory approaches to date. The Working Paper on Generative AI and Copyright (Part I), released by the Department for Promotion of Industry and Internal Trade (DPIIT), rejects both extreme positions—unrestricted scraping under a broad TDM exception and burdensome one-to-one licensing—and instead recommends a mandatory blanket licence backed by a statutory royalty framework. This hybrid model reflects a deliberate attempt to balance the explosive economic opportunities of AI with the long-term sustainability of India’s creative and knowledge ecosystems.

A New Regulatory Architecture

The working paper notes that generative AI firms require access to massive volumes of high-quality data to build safe, unbiased and competitive models. Yet, the current unlicensed use of copyrighted content risks eroding the economic foundations of human creativity. The committee therefore proposes a framework under which:

1. AI developers obtain automatic rights

AI companies receive a mandatory blanket license to train on all lawfully accessed copyrighted works, removing the burden of negotiating millions of individual licences. Payments are required only upon commercialisation of the model or its outputs. This is intended to reduce compliance friction and level the playing field for smaller developers.

2. Rights holders receive statutory remuneration

Royalties—set and periodically reviewed by a government-appointed committee—would be collected by a new central entity: the Copyright Royalties Collective for AI Training (CRCAT). The collective would distribute payments to all classes of owners, including non-members who register their works. This ensures broad inclusivity across India’s formal and informal creative sectors.

3. Transparency without heavy disclosure obligations

India purposely avoids imposing extensive dataset-level transparency obligations (as contemplated in the EU), instead substituting them with a centralized licensing and accounting mechanism that lowers regulatory burden on industry.

The Press Information Bureau announcement underscores that the objective is to “strike the balance between the rights of content creators and AI innovators”, explicitly rejecting a zero-price “fair use”-style system.

Global Context and Industry Reactions

India’s proposal lands amid escalating litigation in the U.S., EU, and UK, where courts are assessing whether training on copyrighted materials constitutes reproduction or falls under fair use. Tech companies have tried to operate in this uncertainty, arguing that TDM exceptions are essential for progress. Meanwhile, rights holders—particularly news publishers, authors, and visual artists—are pressing for compensation.

The TechCrunch report highlights that:

• India is now OpenAI’s second-largest market and may soon be its largest.

• Nasscom and the Business Software Alliance strongly oppose the proposal, warning that mandatory licensing could slow innovation and limit dataset diversity.

• India’s system is seen as the most interventionist approach among major economies so far.

This positions India as the first major jurisdiction to attempt a harmonized, universal, one-payment-for-all-training model, rather than the fragmented licensing and opt-out mechanisms emerging elsewhere.

Pros and Cons for Scholarly Publishers, Rights Owners, Authors, and Creators

The Indian model has significant implications for book publishers, journal publishers, newsrooms, musicians, artists, and the broader knowledge economy.

Pros

1. Guaranteed Compensation for Use of Works

Under a blanket statutory licence, all rights holders—including those who cannot negotiate individually—receive payment when their works are used in AI training.

• Benefits scholarly publishers with deep back catalogues.

• Protects small creators, informal sector artists, and long-tail authors who lack bargaining power.

• Helps academic authors receive recognition and possibly revenue where none exists today.

2. Legal Certainty and Enforcement Ease

AI companies can no longer rely on ambiguous claims of fair use/fair dealing.

• Publishers avoid costly litigation to prove infringement on a case-by-case basis.

• A centralised body manages compliance, relieving rights holders of monitoring burdens.

3. Protects Incentives for Human Creativity

The working paper warns explicitly that zero-cost training risks long-term underproduction of human creative works.
The statutory royalty system preserves the economic rationale for creating books, articles, images, and datasets.

4. Global Leverage for Rights Owners

India is one of the largest digital markets in the world.

• If India mandates royalties, other jurisdictions may follow.

• This strengthens the global negotiating position of publishers and creators against AI giants.

5. Supports Scholarly Publishing’s Role in Knowledge Integrity

A regulated pipeline discourages uncontrolled, unlicensed mixing of low-quality content.
It indirectly reinforces the value of authoritative, peer-reviewed sources.

Cons

1. Mandatory Licence Removes Opt-Out Rights

Rights holders cannot refuse the use of their works in AI training.

For scholarly publishers with sensitive, proprietary, or embargoed datasets, this can be commercially and ethically problematic.

2. Royalty Rates May Be Low or Politicized

A government-appointed panel sets royalty rates.

• If rates are too low, creators may see little real compensation.

• Rates may not reflect the greater value or uniqueness of certain types of works (e.g., scientific journals versus mass-market novels).

3. Complexity in Tracking and Distribution

Even with a central body, allocating royalties fairly across millions of creators—especially researchers with collaborative authorship—will be challenging.

• Scientific authors rarely hold copyright (publishers do), which raises questions about distribution rules.

• Informal-sector creators may be difficult to register or verify.

4. AI Developers May Push Back or Reduce Exposure in India

Major AI firms may:

• Attempt to avoid commercialisation in India to avoid triggering royalty obligations.

• Withhold certain features or models.

• Pass the costs on to consumers and institutions.

This could limit access to advanced AI for academic institutions.

5. No Output-Side Protections Yet

Part I focuses only on training data.
Questions on:

• AI-generated infringing outputs

• Moral rights

• Attribution

• Liability

…will come in Part II. This leaves authors exposed in the interim to derivative or style-imitating outputs trained on their works.

Conclusion: A Transformative Moment for Global Copyright Policy

India’s hybrid licensing proposal is arguably the most ambitious attempt worldwide to solve the AI–copyright gap while preserving creative ecosystems. It rejects the Silicon Valley assumption that “free training” is necessary for innovation and instead asserts that creative labour has market value even in the age of AI.

For scholarly publishers and rights holders, the model offers:

• Recognition

• Revenue

• Legal clarity

• Global influence

…but at the cost of losing control over the decision of whether their works may be used at all.

India’s approach is likely to become a reference model for the Global South—and a pressure point for the U.S. and EU as courts struggle to clarify fair use boundaries. Its success will depend on:

• Royalty-rate realism

• Administrative capacity

• Fair and transparent distribution

• Robust integration with parallel reforms on AI outputs

If implemented well, India’s system could become the first truly scalable, creator-centric framework for the AI era—an equilibrium point between innovation and human creativity.