ABSTRACT
Copyright enforcement rests on an evidentiary bargain: a plaintiff must show both the defendant’s access to the work and substantial similarity in the challenged output. That bargain comes under strain when generative AI systems are built through multi-stage pipelines with recursive synthetic data. As each successive model is tuned on the outputs of its predecessors, any copyrighted material absorbed by an early model is further diffused into deep statistical abstractions. The result is potentially an evidentiary blind spot: overlaps that do emerge look like coincidence, while the chain of provenance is too attenuated to trace – conditions ripe for what might be called ‘copyright laundering’.
This Article argues that the only doctrinally workable response is to adapt the ‘fruit of the poisonous tree’ (FOPT) principle. It proposes a novel AI-FOPT standard: if a foundational AI model’s training is adjudged infringing (due to unauthorized copying not excused by fair use), then subsequent models and datasets principally derived from its outputs are presumptively tainted. The burden consequently shifts to downstream developers to affirmatively demonstrate a verifiably independent and lawfully sourced lineage for their systems. Absent such proof, commercial deployment of these tainted models and their outputs remains actionable.
Drawing on existing legal precedents, this Article develops the AI-FOPT standard, addresses counterarguments concerning chilling innovation and fair use (which remains applicable at the initial ingestion stage), and demonstrates why this lineage-focused approach is both administrable and essential to preserve copyright’s efficacy and incentive structure in this age of machine-trained machines.
Mukherjee, Anirban, The AI Ouroboros and Copyright Laundering: Why Copyright Needs a “Fruit of the Poisonous Tree” Doctrine for Generative AI (May 16, 2025).
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