I assume the choice of phrase "bitter lesson" is intentional irony (since the original concept is that you get better results by just scaling up and not trying to be clever with domain-specific knowledge)?
I assume that the bitterness of the bitter lesson is not for engineers but for subject matter experts. I can only imagine how it would feel to discover that your decades of hard-earned expertise don't amount to a whole lot when it comes to domain-specific ML modeling, compared to simply throwing more compute at the problem.
My read of GP is that there is a cycle economy between "bitter lesson" style domain-general scaling and domain-specific adaptation once the scaling plateaus for the latest tech.
Specifically the you get better results with techniques that can scale up to the amount of data you have. People often think of it as a "just brute force it" but for the lesson to apply you do need to come up with how you're gong to get the data and how you're going to use it.
Maybe the ultimate bitter lesson is that entropy always wins in the end.