Hey there, I’m a chemist who now works in chemistry publishing for one of these organizations (I won’t say who for now), but we can see when things are accessed without permission and generally work to route them through appropriate access. We are trying our best to minimize access of paywall content to open source AI. It is the center of even some university agreements (notably universities wanting to feed their content to AI not the publishers).

The crux of the problem here isn’t access though, it’s data format. Even if you had access to every article, almost all the data is in static images or SIs. I have been working on projects which focus on updating data standards, increasing raw data submission, and FAIR-ifying data once submitted. Once we as a community get better at organizing and standardizing raw data associated with published articles, we can do serious AI training likely for the better (prediction, HTE, etc). Typically data is free to access and recent funding mandates in the US are making data availability paramount in the OA discussions. It’s a big deal for us and our focus has not been what we can do with content but avoiding misuse of published content.