Looking at the actual article (https://www.nature.com/articles/s41467-024-54178-1), their procedure does actually use deep learning in the process of synthesizing candidate chip designs, and the use of deep learning is key to their work. In particular, it looks like the final process is a genetic algorithm that interacts with a deep learning model that predicts the performance of candidate chips. It seems like trying to simulate the chip analytically to predict performance was far too inefficient, and replacing that part with a deep learning model made this entire procedure possible. So in summary, nothing in this article is called AI that has not been called AI before. Most importantly it produces novel designs "globally" without a human in the loop whereas one was required before. I think calling that AI-designed is pretty reasonable.
On a very high level, the role of deep learning here seems similar to AlphaGo (which is also the combination of a less novel generic optimization algorithm, Monte Carlo tree search, with deep learning-provided predictions). I don't think anyone would debate that AlphaGo is fundamentally an AI system. Maybe if we are to be really precise, both of these systems are optimization guided by heuristics provided by deep learning.
On a very high level, the role of deep learning here seems similar to AlphaGo (which is also the combination of a less novel generic optimization algorithm, Monte Carlo tree search, with deep learning-provided predictions). I don't think anyone would debate that AlphaGo is fundamentally an AI system. Maybe if we are to be really precise, both of these systems are optimization guided by heuristics provided by deep learning.