I wonder about this. I've been working on ClickHouse support and cloud management for over 6 years. When I first started I thought we would focus on integrating ML workloads, pretty much like the quote above. Over that time maybe 2 customers asked about ML. Everyone else (like literally hundreds) wanted visualization and ability to load data fast. After a while, it began to become clear why this was so.

Databases tend to be chosen and operated by groups with very different skillsets from AI. They solve different problems. The workloads are completely different. AI depends on GPUs and often depends on datasets that are far beyond the storage capability of databases. Databases on the other hand optimize hardware for fast response, which means loads of RAM and fast I/O. When people used to ask me about our AI integration strategy, I would reply "fix bugs in Parquet." It's not a flip answer. It enables databases and AI services to use a single copy of source data. That's one example of how AI and databases actually interoperate in industrial deployments.