You should read the post. You might find the “domain” discussion interesting.
AI is not totally encapsulated by ML. For example, reinforcement learning is often considered distinct in some AI ontologies. Decision rules and similar methods from the 1970s and 1980s are also included though they highlight the algorithmic approach versus the ML side.
There are certainly many terms used and misused by current marketing (especially the bitcoin bro grifters who saw AI as an out of a bad set of assets), but there actually is clarity to the terms if one considers their origins.
Classical ML tasks (e.g. classification, regression ), perception (vision, speech) and pattern recognition, generative AI capabilities (text, image, audio generation), knowledge representation and reasoning (symbolic AI, logic), decision-making and planning (including reinforcement learning for sequential decisions), as well as hybrid approaches (e.g. neuro-symbolic methods, fuzzy logic).
The capability areas outside of classical ML have been overlapped now to a degree by GPT architectures as well as deep learning, but these architectures aren't the whole game.