India is witnessing an important policy debate that could shape the future of artificial intelligence, digital content, and innovation-led business growth. At the centre of this discussion is a proposed framework under which AI developers may be permitted to use lawfully accessed content for training models without having to negotiate individually with every copyright holder. Instead, royalty obligations may arise only once the AI system is commercialised, with payments potentially routed through a centralised mechanism. This direction was first set out in DPIIT’s December 2025 working paper on generative AI and copyright and has now received support from the Office of the Principal Scientific Adviser’s March 2026 white paper on indigenous foundation models.
Although this is not yet final law, the importance of the development should not be underestimated. DPIIT’s paper was issued after an eight-member committee was constituted in April 2025 to examine whether India’s existing copyright framework is adequate for the age of generative AI. The PSA’s white paper, released in March 2026, further signals that the government’s policy thinking is moving toward a hybrid model that seeks to balance two objectives: enabling AI innovation at scale and ensuring that copyright owners are not entirely excluded from value creation.
Why this Issue Matters
The issue matters because modern AI systems require vast quantities of text, images, audio, code, and other digital content for training. If every AI developer had to obtain prior consent from each rights holder on a one-by-one basis, the transaction burden would be immense. That would significantly slow the development of indigenous AI models, raise costs, and create a practical advantage for only the largest global players with deep legal and financial resources. The proposed Indian approach attempts to address this bottleneck by allowing training on lawfully accessed content first, while deferring remuneration to the commercialisation stage.
This is why the framework is being viewed not merely as a copyright issue, but as an industrial policy issue. India wants to build domestic AI capability, including indigenous foundation models, local language tools, and sector-specific AI applications that reflect Indian conditions. If access to training data remains legally unclear or commercially unworkable, the country’s ambition to build a strong AI ecosystem will face a structural disadvantage. The PSA white paper explicitly places indigenous foundation models within India’s larger digital and strategic priorities.
What Exactly is Being Proposed
The broad thrust of the proposal is a hybrid copyright model. Under this model, AI developers could use lawfully accessed copyrighted material for training without needing to negotiate title by title. Royalties would become payable only once the AI system is commercialised. DPIIT’s paper also contemplates a government-backed rate-setting mechanism and a centralised collection and distribution structure to reduce transaction costs, improve legal certainty, and avoid fragmented negotiations. The PSA white paper has now endorsed this broad architecture.
This approach differs from both extremes seen in global debates. It is not a pure free-use system where copyright owners receive nothing, and it is not a strict permission-first model where developers must negotiate before any training activity can begin. Instead, it seeks to create a middle path under which access is enabled at the input stage and remuneration is addressed at the output and commercialisation stage.
Why many Businesses may Benefit
The businesses that stand to gain the most immediately are AI developers, generative AI startups, software product firms, enterprise SaaS businesses, and global capability centres building AI solutions in India. For these firms, the biggest benefit is lower friction in securing training data. If the need for countless bilateral licences is reduced, they can cut legal complexity, shorten development timelines, and direct more resources toward product quality, testing, and commercial deployment.
This is particularly important for early-stage companies. Startups typically do not fail because they lack ideas; they fail because the cost and complexity of turning ideas into compliant products becomes too high. A clearer pathway for training-data usage could lower one of the most important hidden barriers to AI commercialisation in India. It may also improve investor confidence, because policy clarity often matters as much as technical capability when investors evaluate the scale potential of AI ventures. This is an inference from the proposed framework, but it is strongly supported by the policy emphasis on reducing transaction costs and enabling innovation.
The Opportunity for Indian Language and Sector-Specific AI
One of the most significant business opportunities lies in localised AI solutions. India’s commercial AI market is not limited to English language chatbots or generic enterprise tools. There is growing need for AI systems that understand Indian languages, regional contexts, industry terminology, public-service workflows, and locally relevant use cases. The PSA white paper’s emphasis on indigenous foundation models signals that India sees local capability as essential for inclusive digital growth.
This opens space for businesses developing AI tools in healthcare, legal services, education, agriculture, financial services, customer support, logistics, compliance, and industrial operations. If model developers gain more workable access to training material, they may be able to launch more India-adapted products, faster and at lower cost. That, in turn, can create downstream value for enterprises that are not themselves building models but want to use AI to improve efficiency and service quality.
How Non-AI Businesses may also Benefit
This policy discussion is not relevant only to technology companies. Many businesses in manufacturing, consulting, retail, healthcare, financial services, logistics, and education are not planning to build foundation models. However, they are increasingly interested in adopting AI-enabled tools for document review, analytics, workflow automation, internal knowledge management, customer service, and decision support. If India’s copyright regime becomes more predictable for AI training, more developers may enter the market, more sector-specific tools may be created, and the cost of usable AI solutions may decline over time.
For such businesses, the benefit is indirect but potentially meaningful. Better access to training data at the developer level could eventually lead to more affordable and better-tuned enterprise AI products. In business terms, that means higher productivity potential, broader solution choice, and reduced dependence on imported or poorly localised technology.
Could Content Owners also Benefit
At first glance, content owners may seem to be on the losing side of this debate. Publishers, media firms, education companies, research houses, stock content platforms, and other knowledge-based businesses are understandably concerned that blanket access for training may weaken their control over how their content is commercially used. That concern is real and has already generated significant pushback.
However, the proposed framework is not based on zero compensation. It is built around the idea that rights holders should be paid once the AI system is commercialised, with collection and distribution potentially handled through a centralised mechanism. If implemented credibly, such a system could create a new monetisation channel for content owners, particularly those that do not have the scale or bargaining power to negotiate separate deals with each AI developer.
That said, whether content businesses actually benefit will depend entirely on implementation. Royalty allocation, auditability, transparency, dispute resolution, and the ability to track commercial use are not minor technical details; they are the foundation of whether the system will be trusted. If these issues are poorly designed, content owners may remain dissatisfied even if a royalty framework exists on paper. This is an inference, but it follows directly from the structure of the proposal and the concerns already raised by industry stakeholders.
The Significance of Nasscom’s Opposition
The opposition from multiple industries, including Nasscom, is important because it shows that the issue is far from settled. Nasscom has argued that rights holders should have clear protection against both commercial and non-commercial text and data mining. Its published position supports enabling text and data mining for AI training, but only alongside safeguards such as machine-readable opt-outs for public content and contractual reservation for non-public content.
This is a crucial distinction. The government-backed approach leans toward broad lawful access plus deferred remuneration. Nasscom’s position is more rights-preserving at the front end, because it wants creators to retain the ability to reserve their content from AI training. The eventual policy outcome may therefore depend on how India reconciles innovation needs with content-owner autonomy.
Why Proprietary Data may become even more Valuable
An interesting consequence of this debate is that proprietary and non-public datasets may become more valuable, not less. If public, lawfully accessed content becomes easier to use for training, then differentiation may shift toward high-quality private datasets that are curated, labelled, contractually controlled, and better suited for domain-specific applications.
This could benefit businesses that own structured industry data, technical archives, specialised research content, engineering documentation, workflow datasets, or sector-specific knowledge repositories. In such cases, the competitive advantage may not come from generic internet-scale content, but from cleaner, more reliable, high-signal data that produces better commercial AI performance. This is an inference rather than an explicit government statement, but it is a commercially logical implication of the proposed framework.
What Businesses should do Now
Businesses should not wait for final legislation before acting. AI developers should begin strengthening data governance, lawful-access controls, training-data documentation, and internal commercialisation models that can accommodate future royalty obligations. Content-rich businesses should classify their assets into public, private, premium, and strategically restricted categories and review how opt-outs, contractual restrictions, and licensing models may need to evolve. Enterprises planning to adopt AI should start mapping business functions where India-adapted AI tools could create meaningful productivity gains once the ecosystem matures.
In other words, this is not just a policy watch item. It is a strategic preparedness issue. The businesses that respond early will be better positioned than those that engage only after the final regulatory contours are drawn.
India’s emerging AI copyright framework could become one of the most consequential policy developments for the country’s digital economy. If implemented well, it may reduce barriers to AI development, support indigenous innovation, improve access to locally relevant AI tools, and create new monetization pathways for content owners. If implemented poorly, it could trigger prolonged disputes over rights, royalties, and enforceability.
What is already clear is that this issue is no longer confined to legal specialists or technology policy circles. It concerns AI developers, publishers, knowledge businesses, enterprises adopting AI, and owners of proprietary digital assets across sectors. For Indian business, this is not merely a copyright debate. It is a question of who will capture value in the next phase of the AI economy.
How Hmsa can Help
Hmsa Consultancy can help businesses interpret this development not only from a legal or policy standpoint, but from a commercial and strategic perspective. It can assess how exposed a company is as an AI developer, content owner, data-holder, or enterprise adopter; identify risks and opportunities under alternative policy scenarios; support data monetization and licensing strategy; evaluate AI use cases likely to become viable under a lower-friction training regime; and prepare a practical roadmap covering governance, business model design, commercial readiness, and stakeholder alignment. In a fast-evolving environment such as this, businesses need more than commentary. They need structured strategic advice that connects regulation with growth, monetization, operational readiness, and long-term competitive positioning.
Reference: Economic Times
