That’s changed, of course, but it remained true for at least another 15 or 20 years after this article was written and then changed rather quickly, perhaps cemented with deep neural networks and GPUs.
Other than running the emacs ecosystem, what else is Lisp being used for commonly these days?
Yann LeCun developed Lush, which is a Lisp for neural networks, during the early days of deep architectures. See https://yann.lecun.com/ex/downloads/index.html and https://lush.sourceforge.net. Things moved to Python after a brief period when Lua was also a serious contender. LeCun is not pleased with Python. I can't find his comments now, but he thinks Python is not an ideal solution. Hard to argue with that, as its mostly a thin wrapper over C/C++/FORTRAN that poses an obvious two-language problem.
It's a shame things took the course they did with preferred languages.
Bit by bit, more people got used to doing data analysis and AI research in Python. Some projects were even written for Python first (e.g. Tensorflow or Keras). Eventually, Python had so many high-quality packages that it became the de facto for modern AI.
Is it the _best_ language for AI, though? I doubt. However, it is good enough for most use cases.