Yes, I agree with Sutton's overall argument that advances in computing ability, including GPUs, have contributed to major AI advances by enabling the scaling of learning algorithms. Rather than programming in everything we know about human behavior, we can design AI systems to learn from data and other inputs. Still, I don't think design stops mattering. The design of specific algorithms is important to making the best use of computing resources.
Where I differ from Sutton is on human knowledge. I believe it is valuable to incorporate it, particularly when information that could help with a task is not easily contained in training data. A teacher, for instance, might recognize that a student's frustration with schoolwork is the result of issues at home. An AI trained only on grades and test scores would miss this, because the cause isn't in the data. In a case like that, adding computational tools without addressing the other factors may not be useful.
This means the role of human experts may change from rule writing to helping and reviewing what the AI has learned. For people building AI systems, the lesson is to use approaches that have proven effective and get better with additional computing resources, and to test them on practical tasks. For people training others to work with AI, the lessons include helping trainees identify and contextualize problems, review AI outputs, and decide when human involvement is appropriate.