USA – CALIFORNIA Trends and Developments Contributed by: Rey Barceló, Barceló, Harrison & Walker, LLP
In practical terms, courts are pushing parties toward evidence. Plaintiffs are being asked to show more than hypothetical harms, and defendants are being asked to show more than general innovation narratives. Courts are also increasingly attentive to the mechan - ics of copying: where the data came from, how it was stored, and whether a defendant’s internal practices resemble a permanent library rather than a narrow use tied to training. The emerging themes can be summarised as follows. • “Transformative training” is not a blanket immunity for all data practices. • Courts may ask for a clean provenance story – what was sourced, how, when and why. • Internal retention and reuse policies matter, espe - cially if datasets become shared resources across teams. • Market impact remains the most contested factor and will likely be the focal point of the next wave of merits decisions. Bartz v Anthropic: training can be fair use, but piracy does not disappear In Bartz v Anthropic (23 June 2025), Judge Alsup issued a detailed order that is now a central reference point for AI training disputes. The court did not treat ingestion as one act. Instead, it separated different categories of copying and evaluated them under the fair use factors, creating a practical roadmap for how companies and plaintiffs can map their practices to the categories the court analysed. On the record presented, the court held that using books to train Anthropic’s models was fair use. The court described training as “exceedingly transforma - tive” and observed that “[t]he technology at issue was among the most transformative many of us will see in our lifetimes”, emphasising that the purpose was to build a tool rather than to republish the books. The court also held that converting lawfully purchased print books into digital copies for a searchable inter - nal library could be fair use, treating internal format- shifting differently from mass acquisition from unau - thorised sources.
The opinion, however, treated piracy and long-term retention as a distinct risk. The court emphasised that downloading pirated copies from unauthorised sourc - es and retaining them as part of a “central library” was not automatically excused by the later claim of transformative training. That distinction is important for clients because it means that even if training itself can qualify as fair use, acquisition and retention prac - tices can still generate separate liability exposure. Data provenance and retention are not side issues; they are central risk variables. The lessons of Bartz : • “transformative training” is not a blanket immunity for all data practices; • courts may ask for a clean provenance story – what was sourced, how, when, and why; and • internal retention and reuse policies matter, espe - cially if datasets become shared resources across teams. Kadrey v Meta: a defence win that reads like a warning In Kadrey v Meta (25 June 2025), Judge Chhabria granted Meta summary judgment on fair use on the record presented. The court reasoned that model training is aimed at creating a functional tool, not dis - tributing the books as books, and for that reason it treated the use as transformative. The decision, how - ever, is better understood as an evidentiary ruling than as a broad endorsement of training without permis - sion. The court emphasised that the fourth factor – market effect – is “undoubtedly the single most important ele - ment of fair use”, and aligned its analysis with the Supreme Court’s emphasis in Warhol on market sub- stitution in commercial settings. The court identified several market-harm theories that commonly appear in AI training cases, including “regurgitation”, harms to licensing markets and indirect substitution (some - times described as market dilution). The court viewed indirect substitution and market dilution as potentially significant but found that plaintiffs in this case had presented “no meaningful evidence” to support that theory.
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