Artificial Intelligence: theory vs processing power It is common to see articles praising the advances in AI (here is one example).
There is no doubt that significant progress has occurred on all kinds of automation, hard games like chess, etc.
The question is, how much of that progress is due to conceptual proogress, as opposed to increase in processing power? Or, to phrase it in a different way: if today's best AI would be shown to a computer scientist from the 60s, would they be impressed by the algorithms? Or just by the raw power of the hardware?
AI spans many different areas.  If you are actually asking about machine learning, the conventional wisdom is probably that it's a combination of new ideas (e.g., neural networks, architectures, methods for training them), computation power (e.g., GPUs), and data (large labelled datasets).
See, e.g.,
https://qz.com/1307091/the-inside-story-of-how-ai-got-good-enough-to-dominate-silicon-valley/,
https://towardsdatascience.com/the-state-of-ai-in-2020-1f95df336eb0, and probably many more (I didn't spend much time looking).