On Fri, Feb 20, 2026 at 01:39:10PM +0000, Jonathan Dowland wrote: >On Thu Feb 19, 2026 at 9:53 AM GMT, Jonathan Carter wrote: >> In terms of LLMs, I agree with the sentiment of others that they are in >> many ways, mass plagiarism tools. >... >> For example, a few weeks ago I was working late one night and I couldn't >> put my finger on it, but my one loop just looked really wrong and ugly, >> so I searched on duck duck go to go find some patterns that look nice >> that fit my use case, and Duck Duck AI popped up and suggested a very >> neat and elegant list comprehension that was such an obviously good >> choice, that I really should have thought of it in the first place. > >Personally, as part of refining my own position on these matters, I've wanted >to explore the idea of what would be acceptable to me, wrt copyright, to >harness the value you demonstrate in your anecdote. > >An LLM which was solely trained on a corpus of free software with >intra-compatible licensing (for the sake of this example say, GPL2 or later, >and anything compatible with it), such that we declare the resulting >weightings to be a derivative, licensed GPL2+, and attribute the authorship >to the union of authorship of *all* the inputs, and consider anything it >outputs to be a derivative, likewise GPL2+. Would that be acceptable? Would >that be useful?
Thanks - that's a useful thought experiment, even if maybe not practical as other people are asserting. -- Steve McIntyre, Cambridge, UK. [email protected] "I used to be the first kid on the block wanting a cranial implant, now I want to be the first with a cranial firewall. " -- Charlie Stross
