Before AI Training, How Far Is 'Data Collection' Allowed?

US artificial intelligence company Anthropic has again stood in court on charges of music copyright infringement. This lawsuit differs in nature from existing disputes in that it directly takes issue with what data was secured in what manner at the stage before AI learned, rather than what generative AI output.

Global music publishers claim that Anthropic used BitTorrent technology to mass-collect music lyrics and sheet music from illegal pirate libraries. They have made clear their position that this act is already completed copyright infringement regardless of the purpose of AI learning, and must be judged separately from subsequent learning or output stages.

The nature of this lawsuit is not whether AI learning falls under fair use. Plaintiffs claim Anthropic downloaded copyrighted works from illegal libraries like LibGen and PiLiMi, and due to the nature of the torrent method, this also entailed acts of redistributing illegal copies while downloading them. Publishers characterize this as "an independent illegal act already established at the pre-AI stage."

The difference from existing AI copyright lawsuits is revealed at this point. Until now disputes were dealt with centered on whether AI outputs were substantially similar to originals and whether the learning process falls under fair use. However, in this case publishers draw a line that fair use itself is not the issue. The logic is that data collection through illegal piracy routes is illegal regardless of purpose, and AI learning cannot justify this.

Another reason the lawsuit is attracting attention is that not only the company but executives personally were included as defendants. Plaintiffs claim co-founder Benjamin Mann was directly involved in the torrent download process and CEO Dario Amodei also knew of and approved it. This is interpreted as a strategy to highlight that the illegality lay in the data collection decision-making structure itself, not a simple operational mistake.

If courts give this claim persuasiveness, personal liability risks for AI company executives regarding data collection processes may arise. This means AI companies going forward must consider not only technical judgment but also legal liability structures when establishing data acquisition strategies.

The core question this case poses is simple. Can data collected through illegal routes be justified if used for AI learning, and can the illegality of the collection stage be separated from the legality of the learning and output stages? The publishers' answer is clear. Illegality at the collection stage is not cured regardless of any subsequent usage purpose.

If this logic is accepted, generative AI companies will face the burden of more strictly proving the source of training data. The possibility that access records to illegal and pirate libraries, logs at stages prior to data refinement, and internal control systems become subjects of legal verification also increases. This directly affects AI companies' cost structures and business sustainability beyond model performance competition.

The message this lawsuit sends to the industry is clear. The web can no longer be treated as 'a resource freely usable.' The competition for AI training data is moving from collection speed to the legitimacy of collection, and license contract expansion, utilization of open datasets, and increasing synthetic data proportion are becoming structural responses rather than choices.

The Anthropic case goes beyond specific corporate legal risk. This is a watershed moment for gauging whether the standard 'data collection at the stage before AI learning is subject to existing copyright law as-is' can actually operate as applied to the entire generative AI industry. The center of gravity of AI copyright debate is now moving not to outputs but to the starting point of data.