India is moving to put a price on one of the most valuable raw materials of the AI age: the data used to train large models. By tying access to its vast pool of copyrighted works to a mandatory licensing regime, the country is testing whether a single large market can reset the economics of artificial intelligence for everyone else. If it works, the world’s biggest AI developers may have to treat training data less like a free commons and more like a taxable resource. At stake is not only how companies like OpenAI and Google build their next generation of systems, but also whether creators, publishers and governments in the Global South can capture a share of the value those systems generate. India’s experiment blends copyright reform, industrial policy and digital sovereignty into one package, and I see it as a template that other countries, from Australia to Europe, will study closely.
How India’s blanket license would work
India’s government has outlined a mandatory “blanket license” that would give AI companies automatic access to all lawfully available copyrighted material in the country, in exchange for paying royalties into a central collecting body. Instead of negotiating with thousands of individual rights holders, model developers would obtain a single training license, then see the fees distributed to authors, musicians, filmmakers and other creators whose works are used. The proposal is framed as a way to recognize that behind every dataset sit years of human labor and cultural identity, and that training models on those works without compensation effectively shifts value from local creative industries to foreign platforms. In parallel, India is preparing a broader overhaul of its copyright rules that would make this training license mandatory for any company that wants to ingest domestic content at scale. The plan, described as a landmark shift in how AI is regulated, would cover a wide range of sectors whose livelihoods rely on copyright protections, from book publishing to film and music. A related proposal, detailed under the banner of India Proposes Mandatory and described as a Landmark Copyright Overhaul, underscores that the goal is not to block AI outright, but to embed it inside a predictable royalty system.
Why Big Tech cannot easily walk away
The leverage behind this plan comes from India’s scale and the sunk costs of global cloud providers. Over the past few years, Big Tech has poured billions of dollars into local infrastructure, turning the country into a key hub for AI data centers and cloud regions. One analysis of the AI data center describes this wave of investment as more than a real estate play, calling it a strategic, long term commitment by Big Tech to anchor their AI capacity in India as part of a technologically sovereign future. That commitment is reinforced by India’s own incentives, including a plan that offers data center operators a 20 year tax holiday as part of a broader push to support everything from AI to cloud services, a package highlighted in a government presentation on India’s big push for data centres. Because those facilities are already being built, global AI companies cannot simply threaten to exit the market if they dislike the new licensing rules. Reporting on the proposed data license fee notes that with tech companies having already made massive financial commitments in India, they cannot afford to walk away from this massive, lucrative market. Once those firms adjust to paying for training data in one large jurisdiction, it becomes much harder for them to argue that similar schemes are unworkable elsewhere, which is precisely why India’s move could ripple far beyond its borders.
Challenging the U.S. and European status quo
India’s proposal directly challenges the legal gray zone that has allowed AI developers in the United States and Europe to scrape copyrighted material at scale. In the U.S., companies have leaned heavily on a broad interpretation of fair use to justify training on books, news articles and images without explicit permission, while in Europe, policymakers have focused on opt out mechanisms that require creators to proactively shield their works from text and data mining. Both approaches depend on companies voluntarily disclosing what data they use, a weakness highlighted in an analysis that notes that both the U.S. fair use model and European opt outs rely on corporate transparency that has often been lacking. By contrast, India is saying that if AI companies want access to its market and its content, they must accept a mandatory license and pay for the privilege. A detailed breakdown of the plan explains that India plans to make AI companies pay for training data through a centralized system, rather than leaving compensation to scattered lawsuits or voluntary deals. That stance aligns India more closely with countries like Australia, where the government has rejected a broad text and data mining exception and insisted that creative output is not a public resource, a position echoed in a post describing how India’s government is now proposing its own mandatory blanket license with royalties flowing to a central collecting body.
The quality-versus-control debate
Critics of India’s approach argue that forcing AI models to rely only on licensed or public domain material could degrade their performance. Industry groups warn that limiting training sets in this way risks narrowing the diversity of data, which in turn could make models less accurate or more biased. One trade association, BSA, has explicitly cautioned that limiting AI models to smaller sets of licensed or public domain material could reduce model quality and increase the cost of building systems that can respond to complex requests. From that perspective, India’s tax on training data is not just a royalty, it is a potential drag on innovation. Supporters counter that the current model, in which AI firms quietly scrape vast troves of copyrighted work, is unsustainable as lawsuits pile up and creators demand a share of the upside. They argue that a predictable licensing regime, even if it raises costs, is better than a patchwork of court rulings that could suddenly render entire datasets legally toxic. The Indian government’s own framing, as captured in the copyright overhaul proposal, is that the country is not trying to starve AI of data, but to ensure that the people whose work powers these systems are paid. In that sense, the quality versus control debate is really a question of who bears the cost of legal uncertainty: the platforms or the creators.
From tax compliance to a template for digital sovereignty
India’s leaders are not only thinking about AI as a source of royalty revenue, they are also betting on generative tools to modernize their own bureaucracy. A study on the use of GenAI in tax administration notes that such systems can be a gamechanger for tax compliance by automating repetitive tasks in income tax filing and helping authorities analyze complex financial data. If the state can both tax the training of AI models and deploy those models to improve its own revenue collection, the feedback loop becomes powerful: better tools help enforce the very rules that fund their development.
That is why I see India’s training data tax as more than a narrow copyright tweak. It is part of a broader strategy in which India uses its market size, its creative industries and its growing AI infrastructure to assert digital sovereignty. The combination of a mandatory blanket license, long term incentives like the 20 year tax holiday for data centres, and a clear message that creative output is not free raw material, positions India as a rule maker rather than a rule taker in the global AI economy. If other countries follow, especially those with large language markets or strong cultural exports, the era of free training data could give way to a world where AI models are built on licensed, fairly taxed foundations.
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