Ne oldu?
The 2nd U.S. Circuit Court of Appeals affirmed a lower‑court ruling that ROSS Intelligence infringed Thomson Reuters’ copyrights by copying 2,243 Westlaw headnotes to develop its AI‑powered legal research platform, rejecting ROSS’s fair‑use argument.
On September 29, 2026, the U.S. Court of Appeals for the 2nd Circuit upheld a decision originally issued by Judge Stephanos Bibas in 2023. The appellate panel affirmed that ROSS Intelligence directly infringed Thomson Reuters’ copyrights by copying 2,243 Westlaw headnotes, which it used to build a competing legal‑research search engine.
ROSS had previously sought a licensing agreement for Westlaw content but was denied because it competes with Thomson Reuters. Instead, ROSS obtained roughly 25,000 “Bulk Memos” from LegalEase, which were derived from Westlaw headnotes. The court found that ROSS’s use was not transformative, as it merely repackaged the headnotes to facilitate its own search tool, rather than creating new expressive content.
Judge Bibas, whose earlier ruling the appeals court affirmed, emphasized that the case involved “only non‑,” distinguishing it from generative large language models. The court’s analysis focused on the amount copied, the purpose of the copying, and the effect on the market for the original works, concluding that fair use did not apply.
Kaynak ayrıntıları: cryptopolitan.com ↗
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The decision clarifies that non‑ tools that reproduce copyrighted legal content may not qualify for fair use, signaling that AI developers could face higher licensing costs for proprietary data. The ruling could reinforce the need for formal licensing agreements and may influence how AI firms source training material, potentially widening the gap between large incumbents and smaller innovators.
The ruling provides a concrete legal precedent that AI‑driven tools which reproduce copyrighted material without substantial transformation may be liable for infringement. This could compel AI firms to negotiate costly licensing deals for high‑quality proprietary datasets, reinforcing the advantage of well‑capitalized companies that can afford such fees.
The decision also signals to the U.S. Copyright Office and policymakers that existing fair‑use doctrine may be insufficient for the scale of modern AI training. The Office has already noted that fair‑use determinations are fact‑specific, especially for , suggesting that future cases could yield different outcomes depending on the technology used.
Industry analysts, including the OECD and Goldman Sachs, have warned that data access, computing power, and licensing costs are key competitive levers in AI. This case underscores the risk that tighter control over data could concentrate AI capabilities among a few large firms, potentially stifling innovation from smaller entrants.
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Future litigation will test whether the reasoning extends to generative large language models, and lawmakers may consider statutory reforms or compulsory licensing schemes to address AI‑training data at scale.
Watch for appellate or Supreme Court challenges that may broaden or narrow the scope of fair use for AI training, particularly as generative models become more prevalent.
Legislative activity in the U.S. and Europe may introduce compulsory licensing frameworks or voluntary licensing markets to address the data‑cost barrier highlighted by the ruling.
Monitor how other AI companies respond—whether they pursue new licensing agreements, develop alternative data‑collection strategies, or adjust model architectures to reduce reliance on copyrighted content.