i feel like moves like this make it even harder for new open-source tools to break through. there's already evidence that LLMs are biased toward established tools in their training data (you can check it here https://amplifying.ai/research/claude-code-picks). when a dominant player acquires the most popular toolchain in an ecosystem, that bias only deepens. not because of any skewing, but because the acquired tools get more usage, more documentation, more community content. getting a new project into model weights at meaningful scale is already really hard. acquisitions like this make it even harder.

I'm also concerned about this, but I feel as though uv and ruff's explosive growth happening alongside and despite that of LLMs demonstrates that it's not a show-stopper. I vividly recall LLM coding agents defaulting to pip/poetry and black/flake8, etc. for new projects. It still does that to some extent, but I see them using uv and ruff by default -- without any steering from me -- with far greater frequency.

Perhaps it's naive optimism, but I generally have hope that new and improved tools will continue to gain adoption and shine through in the training data, especially as post-training and continual learning improve.