A few reasons: 1. Header files make C++ verbose. Header files are well within LLM's ability 2. LLMs can handle setting up cmake for you 3. C++ is very well documented relative to (most) newer languages 4. LLMs can port modern features like websockets and build API wrappers easily, reducing the disadvantage against web (since most documentation is for JS/python/go)

Coding languages have been developing for speed of (manual) writing - akin to how human languages did with modern alphabets. Now that writing is a lot easier, languages will likely evolve towards a focus on execution (or in the case of human languages, speed of reading and precision of understanding)

Just speculation, but I think we would expect a language to grow if AI is effective with it. C++ has a lot of training data. Most large software projects are written in C++: web browsers, compilers, 3D renderers, game engines, UI toolkits, etc.

AI is also probably more effective with statically typed languages since the compiler catches a wider range of errors.

Ideas:

- more accessible now that AI handles the high tooling activation energy

- more history and pre-AI internet content

- pybind11 is pretty popular to pair Python logic with C++ performance

- cpp committe is pretty bullish on new contracts and reflection features making C++ a glue language that AI can write well

The GPGPU frameworks and JIT compilers that actually make those Python libraries usable.