India Proposes Sweeping AI–Copyright Overhaul with ‘One Nation, One Licence, One Payment’ Model

Why It Matters

The framework could reshape AI‑driven content creation in India, balancing creator compensation with the need to keep the AI ecosystem competitive. Its success or failure will influence how other jurisdictions address AI‑training data royalties.

Key Takeaways

• India proposes mandatory blanket licence for AI training data.
• Fees based on AI revenue, collected by central body.
• Aim: reduce startup costs, protect creators, fund informal sector.
• Critics warn retroactive fees could stifle innovation and SMEs.
• Model may become global reference if tiered pricing succeeds.

Pulse Analysis

India’s draft AI‑copyright policy marks a bold attempt to reconcile two competing forces: the rapid growth of generative AI and the rights of creators whose works fuel these models. By centralising licensing through a single collecting society, the government hopes to simplify the legal landscape, offering AI firms a predictable cost structure while ensuring that authors, musicians, and visual artists receive royalties proportional to the commercial success of the AI systems that use their content. This approach mirrors elements of collective‑management regimes in music and publishing, but extends them to the vast, unstructured datasets that power large language models and image generators.

The proposal’s revenue‑share mechanism, tied to a percentage of AI‑derived earnings, could provide a new, scalable income source for India’s informal creative economy, which has traditionally operated outside formal copyright channels. However, industry voices warn that retroactive fees and the inability to trace individual data points within massive training corpora may impose disproportionate burdens on startups and SMEs, potentially slowing innovation and discouraging foreign investment. Technical experts argue that the non‑deterministic nature of modern AI makes granular attribution infeasible, suggesting that tiered pricing based on company size or dataset quality might be a more pragmatic compromise.

Globally, the Indian model could become a reference point as nations grapple with similar policy dilemmas. If implemented with clear exemptions for non‑commercial research and transparent, tiered royalty rates, it may demonstrate a viable middle path between outright bans and unrestricted data mining. Conversely, overly rigid enforcement could push developers toward jurisdictions with looser rules, fragmenting the AI market. Stakeholders will be watching the feedback period closely, as the final legislation will likely shape the balance between protecting intellectual property and fostering a vibrant, home‑grown AI ecosystem.