I think about this "potential for ossification" a lot, especially in the Python Data Stack. Because there's a ton of pandas code on GitHub (much of it trash) a

I think about this "potential for ossification" a lot, especially in the Python Data Stack. Because there's a ton of pandas code on GitHub (much of it trash) any data task asked of ChatGPT/Claude inevitably returns some pandas (mostly correct, but often wrong/inefficient/ugly/hard to grok). How could a new library (like Polars, or my own failed "redframes") possibly unseat pandas now? Might LLMs actually lead us to, and get us stuck in, a "local maximum" of sorts?