India’s AI Royalty Proposal: A New Model for Copyright and Training Data

India has advanced a decisive policy proposal that would require companies training artificial intelligence models on copyrighted material to pay royalties through a centralized collecting body. The move — framed as a mandatory blanket license — seeks to balance large-scale AI development with fair compensation for writers, musicians, artists, journalists and other rights holders.

What is India proposing?

The core of the proposal is straightforward: grant AI developers access to all lawfully available copyrighted works for training in exchange for mandatory royalty payments to a single collecting body. That centralized entity, composed of rights-holding organizations, would collect fees and distribute proceeds to creators, registered and unregistered. The stated goals are to reduce transaction costs for both developers and rightsholders, avoid prolonged litigation, and ensure creators receive payment when their work contributes to commercial models.

Why now? Legal and market context

The proposal arrives amid accelerating global debate over how large language models and multimodal AIs are trained. Around the world, rights holders have filed lawsuits and regulators are wrestling with whether training on copyrighted text, images, audio and video qualifies as fair use or fair dealing. Without clear rules, AI firms have expanded rapidly under legal uncertainty. India’s proposal aims to create an immediate, regulated path that avoids years of court battles while compensating creators up front.

Key drivers behind the proposal

• Protect creator income streams and cultural industries.
• Provide legal certainty for AI companies operating in a large and growing market.
• Reduce the administrative burden of negotiating millions of individual licenses.
• Enable distribution of royalties to both registered and unregistered creators via a single window.

How would the mandatory blanket license work?

Under the proposed framework, AI developers would pay into a collecting society that aggregates rights across publishers, record labels, authors, visual artists and other stakeholders. Payments would then be allocated using distribution rules set by the collecting body and overseen by regulators. The collecting society model is intended to simplify negotiations and make royalty flows predictable for both creators and industry players.

Proposed benefits

1. Predictable payments to creators instead of sporadic litigation outcomes.
2. Lower compliance costs for developers who would avoid negotiating with millions of rights holders.
3. Faster commercial deployment of models in India under a clear legal regime.

Who supports the plan — and who objects?

Responses to the proposal split along predictable lines. Cultural organizations, authors’ groups and many creators favor a mandatory royalty approach that guarantees compensation. Industry associations and technology firms warn it could slow innovation or produce impractical licensing burdens.

Critiques from industry groups

Technology trade groups have urged India to consider an explicit text-and-data-mining (TDM) exception instead of a licensing-first approach. A TDM exception would permit automated analysis of copyrighted content for training as long as the data were lawfully accessed, potentially avoiding blanket payments that some argue could stifle model quality or increase costs for startups and researchers.

Critics also warn that limiting training to licensed material or public-domain works risks shrinking the diversity and scale of training data, which could amplify biases and reduce overall model capability.

How does this compare with other jurisdictions?

Policy debates elsewhere have focused on transparency, opt-in/opt-out mechanisms, and whether training on copyrighted material qualifies as fair use. India’s proposal is among the more interventionist models because it grants automatic access in exchange for mandatory payment — rather than relying primarily on post-hoc litigation or broad exemptions.

For readers tracking regulation across markets, this development ties into larger questions about who sets AI rules and how jurisdictions balance innovation with rights protection. See our explainer on the broader policy contest in Federal AI Regulation Fight 2025: Who Sets Rules Now? for context.

What legal challenges could arise?

Even with a blanket license, there are potential legal and enforcement challenges:

• Determining distribution: How will payments be apportioned among millions of contributors, including unregistered creators?
• Scope disputes: Which types of downstream outputs should trigger payments — commercial APIs, fine-tuned models, embeddings, or open-source releases?
• Cross-border enforcement: Many training datasets span jurisdictions; reconciling international claims could be complex.

How will creators and AI firms be affected?

Creators could see more consistent revenue streams when their work is used for commercial models, and smaller rightsholders may gain easier access to compensation without negotiating individually. For AI firms, the framework offers legal certainty but introduces a new cost line for training data. Firms with large user bases or significant operations in India would need to factor royalties into pricing, R&D budgets, and model licensing strategies.

Practical impacts for startups and enterprises

Startups may face higher barriers to entry if royalties are significant, unless the collecting body offers scaled or tiered fees. Large enterprises could absorb costs more easily but may face operational changes to accounting, licensing, and compliance. The trade-off is reduced litigation risk and clearer expectations for using copyrighted datasets in India.

Could the proposal change global AI business models?

Yes. India is already a critical market for many AI companies. The proposal could set a precedent that other countries emulate, especially markets seeking to protect domestic creators while enabling AI deployments. If multiple jurisdictions adopt similar licensing regimes, global model training strategies may shift toward licensed corpora, hybrid datasets, or on-device personalization that minimizes centralized training on copyrighted works.

These dynamics intersect with ongoing litigation and regulatory action elsewhere; legal outcomes in major markets will shape whether licensing models, TDM exceptions, or hybrid approaches become dominant. For more on litigation trends and creator claims against AI companies, see our coverage of lawsuits and legal risk in GPT-4o Lawsuits 2025: ChatGPT Allegations and Risk.

What are the possible policy alternatives?

Policymakers typically weigh several options:

• Mandatory blanket licenses (India’s proposal): automatic access with compulsory payment.
• Broad TDM exceptions: permit training when content is lawfully accessed, reducing licensing friction.
• Opt-out or negotiated licensing: default access with mechanisms for rightsholders to opt out or negotiate directly.
• Hybrid frameworks: combine a baseline license with carve-outs, exemptions for research, or tiered fees for startups and research institutions.

What should stakeholders watch during the consultation?

The Indian government has opened the proposal for public comment. Stakeholders should monitor:

1. Royalty rate proposals and distribution methodology.
2. Definitions of covered activities (training, fine-tuning, inference-related use).
3. Rules for cross-border datasets and interoperability with foreign legal regimes.
4. Safeguards for research, education, and noncommercial uses.

How can creators and developers prepare?

Creators should document and, where possible, register works to streamline royalty collection. Developers should begin modeling the financial impact of royalties on product economics and consider alternative strategies like:

• Building or licensing high-quality curated datasets.
• Investing in synthetic data, on-device learning, or federated approaches.
• Engaging with the consultation process to shape distribution rules and carve-outs.

Is this the end of unregulated model training on copyrighted works?

Not necessarily. The proposal aims to create a regulated pathway in India, but litigation and policy debates will continue globally. Jurisdictions may pursue different balances between innovation and rights protection. The long-term outcome will likely be a patchwork of rules that AI companies must navigate — making adaptive compliance strategies essential. For analysis of market dynamics that could influence these policy choices, see our piece on broader industry trajectories in Is the LLM Bubble Bursting? What Comes Next for AI.

Bottom line

India’s proposal for a mandatory blanket license and royalty payments represents a proactive attempt to reconcile rapid AI development with creator rights. It promises legal certainty and direct compensation for creators, but raises important questions about fees, distribution, enforcement and the impact on innovation. The public consultation now underway is the critical next step; the final design will determine whether India’s approach becomes a model other countries adopt or a unique pathway tailored to its market and cultural priorities.

Take action

If you are a creator, publisher, AI developer, or policy stakeholder, review the proposal, evaluate how royalty rules would affect your work or business, and submit feedback during the consultation window. Clear, evidence-based contributions now can shape distribution rules, carve-outs for research, and protections for small creators.

Call to action: Read the consultation text, weigh the implications for your organization, and submit feedback to ensure the final framework balances creator compensation with continued AI innovation.