AI copyright blueprint: pragmatism over rigidity

THE DEPARTMENT FOR Promotion of Industry and Internal Trade's (DPIIT)working paper on regulating use of copyrighted works in AI training comes at a time when the issue can no longer be deferred.

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Litigation around AI training has already surfaced in India and is well underway in other jurisdictions. Given that generative models depend on ingesting large volumes of copyrighted material, uncertainty around what constitutes lawful training use has become untenable. In that sense, the proposal for a mandatory blanket licence is not a speculative regulatory exercise but a response to a problem that has already emerged and is likely to intensify.

The paper's central move is to reject both polar positions: on the one hand, broad free use or text-and-data-mining (TDM) exceptions that risk hollowing out incentives for human creators; on the other, case-by-case licensing that is structurally incompatible with the scale and architecture of AI systems. Training data sets today cannot be disaggregated into neatly identifiable units linked to individual rights holders.Any framework built on granular permissions would thus be slow, fragmented, and ultimately unworkable. More significantly, such a system would reward those with the resources to negotiate expensive licensing arrangements.Large AI firms could internalise such costs, unlike start-ups and smaller developers. It would create disproportionate compliance burdens and a market dynamic that inadvertently favours incumbents.