India is moving to reshape how global artificial intelligence companies pay for the data that powers their models, with a proposed AI royalty framework that zeroes in on the collection and use of local information. The plan would require major players such as OpenAI and Google to compensate Indian rights holders and data contributors when their content is used to train large-scale systems. It arrives as global AI infrastructure races ahead, raising fresh questions about who controls the raw material of the digital economy and who benefits from it.
The initiative also lands at a moment when cloud and energy providers are rapidly expanding capacity to support AI workloads, including new deals between NextEra Energy and Google Cloud to add U.S. data center capacity. By tying data sovereignty to the economics of AI training, Indian regulators are signaling that access to one of the world’s largest pools of users and creators will increasingly depend on transparent, compensated data practices rather than unchecked scraping.
Origins of the Indian AI Royalty Proposal
Indian policymakers have framed the proposed AI royalty framework as part of a broader push to protect domestic data in the face of global technology dominance. The plan responds to years of concern that foreign platforms have quietly harvested Indian user content, from social media posts to local-language news, to build commercial AI systems without meaningful consent or compensation. By focusing on how training datasets are assembled and monetized, regulators are trying to ensure that the economic value generated by artificial intelligence does not flow exclusively to a handful of overseas firms while Indian creators and institutions absorb the risks.
Under the proposal, AI developers that train models on datasets containing Indian-sourced material would be required to pay royalties to rights holders and, in some cases, to intermediaries that manage collective licenses. The framework explicitly targets unauthorized scraping of Indian user data by foreign entities, treating large-scale ingestion of web content and platform activity as a commercial use that should trigger payment obligations. For domestic stakeholders, the stakes are significant, because a functioning royalty regime could turn what has often been an invisible extraction of local information into a measurable revenue stream for publishers, artists, and other contributors whose work feeds machine learning systems.
Scrutiny on OpenAI’s Data Practices
OpenAI’s reliance on vast, heterogeneous datasets to train models such as GPT has placed the company squarely in the sights of Indian regulators. The proposed framework questions whether Indian-sourced content, including text and code produced by users and publishers in the country, has been incorporated into these systems without explicit consent or any form of royalty payment. By tying compliance to the provenance of training data, the plan effectively asks OpenAI to account for how much of its corpus originates from India and whether those contributors were ever informed that their material might be used to build commercial AI products.
Compliance would likely require OpenAI to conduct detailed audits of its training pipelines and to disclose the geographic and legal origins of the data used for models deployed in India. Regulators are considering mechanisms that would force companies to distinguish between licensed datasets, public domain material, and scraped content, with particular attention to Indian-language sources and platforms popular in the country. If the proposal ultimately mandates retroactive royalty payments on existing AI models, OpenAI could face substantial financial exposure as it recalculates the cost of operating in India and potentially revises its global data acquisition strategies to avoid similar liabilities in other markets.
Google’s Role and Recent Infrastructure Moves
Google’s extensive AI initiatives, from search ranking systems to cloud-based machine learning tools, are also a central focus of the Indian proposal. Regulators are scrutinizing how the company aggregates data across services, including search queries, user-generated content, and enterprise workloads, to train and refine its models. The framework treats this aggregation as a form of commercial exploitation that should be subject to royalties when it involves Indian users and content, raising questions about how Google will separate data that triggers payment obligations from information it can continue to use freely.
The timing of the proposal intersects with a major expansion of Google’s infrastructure footprint, including new agreements in which NextEra Energy and Google Cloud plan to accelerate U.S. data center build-out and add capacity through fresh deals, as detailed in reporting on their expanded partnership to boost U.S. data center capacity. As Google Cloud ramps up to serve AI-heavy workloads, Indian regulators are increasingly concerned that the data flowing into these facilities will include uncompensated Indian content that underpins profitable AI services sold worldwide. In response, Google may need to shift toward more localized data processing and storage for Indian users, both to comply with royalty rules and to reassure authorities that domestic information is not being exported and monetized without adequate safeguards or payments.
Stakeholder Impacts and Global Repercussions
The proposed royalty framework could materially change the economics of digital participation for Indian stakeholders, particularly creators, publishers, and platform operators whose content is routinely ingested into AI training sets. If implemented with clear attribution and payment mechanisms, the system would allow these groups to claim a share of the value generated when their work is used to train models that power chatbots, recommendation engines, and enterprise tools. For everyday users, the rules could also translate into stronger privacy protections, since companies would have to document and justify how personal data enters training pipelines, rather than treating large-scale scraping as a default practice.
Global technology firms are already reassessing their data policies in light of the proposal, recognizing that noncompliance in a market as large as India could lead to fines, service restrictions, or reputational damage. The scrutiny directed at OpenAI and Google signals that regulators are prepared to challenge long-standing assumptions about fair use and public data in the AI context, potentially inspiring similar royalty models in other data-rich emerging economies. As more countries watch how India enforces data sovereignty and compensation, the result could be a patchwork of national regimes that force AI developers to localize training strategies, negotiate country-specific licenses, and accept that the era of free, untracked data harvesting is drawing to a close.
Data Sovereignty in an Expanding AI Infrastructure Era
India’s AI royalty proposal is emerging just as global infrastructure for artificial intelligence undergoes rapid expansion, underscoring the tension between the physical growth of data centers and the legal constraints on what they can process. The new deals between NextEra Energy and Google Cloud to add U.S. capacity, highlighted in coverage of the Indian AI royalty proposal that also notes the companies’ expanded partnership, illustrate how energy providers and cloud platforms are aligning to meet surging demand from AI workloads. For Indian regulators, this build-out raises the stakes of getting data governance right, because the models trained in these facilities will increasingly shape everything from local search results to automated decision-making in finance, health, and education.
By asserting that Indian data cannot be treated as a free input into this global infrastructure, the proposal positions the country as a rule-setter rather than a passive source of training material. The approach could encourage other governments to link access to their domestic datasets with conditions on royalties, transparency, and local processing, effectively turning data sovereignty into a bargaining chip in negotiations with multinational technology firms. If that trend accelerates, companies like OpenAI and Google will need to design AI systems that can respect divergent national rules on data use while still benefiting from the scale that makes their models competitive, a shift that would redefine how artificial intelligence is built and deployed across borders.
