The heart of his critique is this: data science is changing very fast, and any tool that you learn will eventually become obsolete.
This is absolutely true.
Every tool has a shelf life.
Every. single. one.
Moreover, it’s possible that tools are going to become obsolete more rapidly than in the past, because the world has just entered a period of rapid technological change. We can’t be certain, but if we’re in a period of rapid technological change, it seems plausible that toolset-changes will become more frequent.
The thing I would tie it to is George Stigler’s paper on information theory. There’s a cost of knowing—which the commenter notes—but there’s also a cost to search, given the assumption that you know where to look. Being effective in any role, be it data scientist or anything else, involves understanding the marginal benefit of pieces of information. This blog post gives you a concrete example of that in the realm of data science.