India has entered the global copyright and artificial intelligence (AI) debate with the release of a paper titled Working Paper on Generative AI and Copyright Part 1: One Nation One License One Payment, published by a committee formed by the Department for Promotion of Industry and Internal Trade (DPIIT).
The report proposes a hybrid licensing framework that gives developers automatic access to all lawfully accessed copyrighted content for training. In return, creators receive statutory royalties routed through a single national royalty mechanism, framed in the paper as a ‘One Nation One License One Payment’ approach to AI training rights.
For context, the proposal marks India’s first formal attempt to resolve a fast-growing legal conflict between AI innovation and copyright protection. And it arrives at a moment when lawsuits internationally are challenging developers who train AI systems on copyrighted material without permission, with India also facing similar questions through ANI’s lawsuit against OpenAI in the Delhi High Court (HC).
Notably, the recommendation signals a deliberate policy shift. The government wants to prevent licensing barriers from slowing AI development, while also ensuring that creators gain financial benefit from commercial AI systems trained on their work.
Moreover, by replacing fragmented licensing with a centralised payment and royalty distribution system, the framework attempts to standardise compensation across sectors. By choosing this path, India positions itself between the more permissive frameworks in Japan and Singapore, and the stricter compliance-focused rules emerging in the European Union (EU). Elsewhere, the Indian government is also building domestic AI capability as part of the IndiaAI Mission.
How the Hybrid Model Works?
At the centre of the proposal is a mandatory blanket licence. Once developers obtain lawful access to copyrighted material, they can use it for AI training without negotiating individual permissions or licensing contracts. In effect, creators cannot block the use of their works for model training. Instead, the system compensates them through statutory royalties.
To implement this structure, the committee proposes establishing a centralised entity called the Copyright Royalties Collective for AI Training (CRCAT). This entity would collect payments from developers and distribute royalties to creators. Importantly, developers would contribute based on pre-defined royalty formulas and revenue thresholds.
Furthermore, creators must register their works to receive payouts. Unregistered works can still be used for training, but creators would not be eligible to receive compensation. Notably, registration does not influence dataset access.
The lawful access requirement forms the compliance boundary. Developers must purchase, license, subscribe to, or otherwise legally access content before using it. Notably, the committee explicitly separates access rights from copyright permission. Once lawful access exists, no additional approvals are required.
Importantly, this proposal applies in a forward-looking manner. Therefore, ongoing scraping disputes and past unauthorised uses remain subject to current law and active litigation.
Why the Committee Rejected Other Models?
The committee reviewed several regulatory approaches before proposing the hybrid structure. First, it rejected a blanket text and data mining (TDM) exception, as that model allows developers to use copyrighted content without compensating creators. According to the committee, such a system weakens creative incentives and increases the risk of AI outputs competing directly with original works.
Next, it examined the European-style TDM exception with opt-out rights. However, the committee concluded that this model was not workable as rights holders cannot meaningfully enforce opt-outs unless developers disclose detailed training datasets. The committee also warned that mandatory dataset transparency could expose proprietary data and impose heavy compliance burdens, especially on smaller companies.
The committee also dismissed voluntary licensing. In its view, negotiating with millions of rights holders is unworkable at scale. Similarly, it rejected extended collective licensing as India does not yet have a mature or unified licensing ecosystem. Many informal and community creators would remain outside such a system, creating structural inequity.
Additionally, the committee evaluated traditional statutory licensing, which already exists in India’s broadcasting framework. However, it determined that identifying and compensating millions of creators would be impractical without a centralised mechanism, and would likely recreate the same transaction-cost barriers that the policy aims to remove.
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By process of elimination, the committee has concluded that the hybrid model offers the most predictable structure for access, compensation, and long-term implementation.
Government and Industry Positions
The Ministry of Electronics and Information Technology (MeitY) supports the hybrid model. According to the ministry, developers need broad and representative datasets to improve model performance and reduce bias. It also argues that creators should receive compensation as AI-generated outputs increasingly replicate identifiable artistic styles and creative signatures.
To balance innovation with fairness, MeitY recommends safeguards such as triggering royalties only after a model or developer crosses a defined revenue threshold. Additionally, it expects CRCAT to maintain transparent reporting, predictable royalty formulas, and a structured process for dispute resolution.
However, industry groups take a different position. For context, Nasscom opposes the hybrid model and argues that it adds administrative load that may slow innovation and disproportionately affect smaller companies. In its view, developers would need new systems to document lawful access, calculate royalties, and manage compliance, which could mean unnecessary cost and operational friction.
Instead, Nasscom proposes a legal text and data mining exception for both commercial and non-commercial use. Under this approach, rights holders would use machine readable signals to opt out of public datasets, while contracts would govern training on private content. Nasscom argues that this model protects rights without creating a new licensing bureaucracy or compliance burden.
Why This Matters:
This proposal marks the beginning of a broader reform of India’s copyright framework in the context of AI. To explain, the next phase will address unresolved questions around authorship of AI-generated content, ownership, moral rights, and liability when outputs infringe copyright or cause harm. The government also plans to open the paper for public consultation before drafting amendments to the Copyright Act.
Moreover, the hybrid model gives developers predictable rights to use training data while aiming to ensure that creators receive compensation. However, it also creates a new licensing authority and introduces compliance obligations that may increase operational complexity. Smaller companies, research institutions, and open-source communities may feel this impact first. Additionally, the formal dissent from industry signals that implementation may not be straightforward and could face strong resistance during consultation.
If adopted, it may also shape how Indian developers access datasets and how financial value is distributed across the AI ecosystem. Finally, it will test whether India leans toward innovation speed, creator protection, or regulatory control as AI development scales.
What Remains Unclear?
There are still questions in the air that need answering on AI and copyright. Some of them are as follows:
- How will developers demonstrate lawful access, and what evidence will regulators require?
- Will the framework require disclosure of training datasets, or will financial and royalty reporting be sufficient?
- Will royalty obligations apply only during initial training, or also during retraining, fine-tuning, and version updates?
- Will the framework create reduced compliance tiers for startups, research institutions, and open-source projects?
- How will the system address training data sourced internationally, where copyright rules differ across jurisdictions?
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