The text of the bitter lesson itself provides an interesting case, on the topic of computer vision: "Early methods conceived of vision as searching for edges, or generalized cylinders, or in terms of SIFT features. But today all this is discarded. Modern deep-learning neural networks use only the notions of convolution and certain kinds of invariances, and perform much better."
To me this proves the necessity of proper measurements devices - in this case "convolution" and "certain kinds of invariances" - to render data as intelligible as possible utilizing as little moving parts as required. What I don't see in this is: "we need to drop all preexisting notions of structure and just focus on finding pure correlation only". The fact that we have moved to more fundamental search operations - from "cylinder" to "convolution" - does not IMO fundamentally alter the general strategy. In short, I think the lesson is being stretched beyond its limits.
The lesson shows us: "We want AI agents that can discover like we can, not which contain what we have discovered" and I couldn't agree more, yet we seems to disagree on what it means to "discover like we can". I happen to believe that raw correlation is just one of the key ingredients. In fact Kant has the notion of "community" that, and I might be sorely mistaken here, appears to capture the associative power of cognition in much the same way.
I, of course, lack any sort of credentials to further my point. I only imagine it might possibly be of slight interest to someone to squint upon this topic from a skewed angle.
PS: Your point on pragmatism is well taken. I'm sure we are all quite excited to see what happens over the next couple of years, yet I cannot help but hear an ominous violin crescendo beneath your "if the predictions are consistent and reliable".