Sure, all of machine learning involves making assumptions. The bitter lesson in a practical sense is about minimizing these assumptions, particularly those that pertain to human knowledge about how to perform a specific task.
I don't agree with your interpretation of the lesson if you say it means to make no assumptions. You can try to model language with just a massive fully connected network to be maximally flexible, and you'll find that you fail. The art of applying the lesson is separating your assumptions that come from "expert knowledge" about the task from assumptions that match the most general structure of the problem.
"Time spent thinking" is a fundamental property of any system that thinks. To separate this into two modes: low and high, is not necessarily too strong of an assumption in my opinion.
I completely agree with you regarding many specialized sub-models where the distinction is arbitrary and informed by human knowledge about particular problems.
so many people at my work need it just switch. they just leave it on 4o. you can still set the model yourself if you want. but this will for sure improve the quality of output for my non technical workmates who are confused by model selection.
I'm a technical person, who has yet to invest the time in learning proper model selection too. This will be good for all users who don't bring AI to the forefront of their attention, and simply use it as a tool.
I say that as a VIM user who has been learning VIM commands for decades. I understand more than most how important it is to invest in one's tools. But I also understand that only so much time can be invested in sharpening the tools, when we have actual work to do with them. Using the LLMs as a fancy auto complete, but leaving the architecture up to my own NS (natural stupidity) has shown the default models to be more than adequate for my needs.
You are making assumptions about how to break the tasks into sub models.