@cwebber @bkuhn @ossguy @richardfontana Under this view it doesn't matter how the training data was licensed as it's a fair use defense. The outputs being uncopyrightable / effectively public domain allows people to claim they wrote it when it's convenient and they want to be able to copyright it as it's hard to prove if it was AI generated or human authored. And simultaneously to claim that it was the output of and LLM when they want to strip inconvenient licensing terms.
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👀 … https://sfconservancy.org/blog/2026/apr/15/eternal-november-generative-ai-llm/ …my colleague Denver Gingerich writes: newcomers' extensive reliance on LLM-backed generative AI is comparable to the Eternal September onslaught to USENET in 1993. -
👀 … https://sfconservancy.org/blog/2026/apr/15/eternal-november-generative-ai-llm/ …my colleague Denver Gingerich writes: newcomers' extensive reliance on LLM-backed generative AI is comparable to the Eternal September onslaught to USENET in 1993.@cwebber @bkuhn @ossguy @richardfontana I'd don't see a great way out of the copyright stripping conclusions for them without changes to the law. As I understand their defense for training on copyrighted materials - it's predicated on the models being a "transformative" and not competing directly with the original works in the market. The models themselves don't compete with the training material only their outputs do - and the LLM companies want any liability for that to be on users not them.