I don't think Minsky's and Sutton's views are in contradiction, they seem to be orthogonal.

Minsky: the mind is just a collection of a bunch of function specific areas/modules/whatever you want to call them

Sutton: trying to embed human knowledge into the system (i.e. manually) is the least effective way to get there. Search and learning are more effective (especially as computational capabilities increase)

Minsky talks about what the structure of a generalized intelligent system looks like. Sutton talks about the most effective way to create the system, but does not exclude the possibility that there are many different functional areas specialized to handle specific domains that combine to create the whole.

People have paraphrased Sutton as simply "scale" is the answer and I disagreed because to me learning is critical, but I just read what he actually wrote and he emphasizes learning.

I take Sutton's Bitter Lesson to basically say that compute scale tends to win over projecting what we think makes sense as a structure for thinking.

I also think that as we move away from purely von neumann architectures to more neuromorphic things, the algorithms we design and ways those systems will scale will change. Still, I think I agree that scaling compute / learning will continue to be a fruitful path.