I think what you're referring to is also known as Stigler's law of eponymy [1], which is interestingly self-referential and ironic in its own naming. There's also the related "Matthew effect" [2] in the sciences.
I think GP was probably referring to "Scaling Data-Constrained Language Models" (2305.16264) from NeurIPS 2023, which looked first at how to optimally scale LLMs when training data is limited. There is a short section on mixing code (Python) into the training data and the effect this has on performance on e.g. natural language tasks. One of their findings was that training data can be up to 50% code without actually degrading performance, and in some cases (benchmarks like bAbI and WebNLG) with improvements (probably because these tasks have an emphasis on what they call "long-range state tracking capabilities").
For reference: In the Llama 3 technical report (2407.21783), they mention that they ended up using 17% code tokens in their training data.