I have a question about “domain expertise” as a component of future knowledge worker requirements. How does one gain such expertise in a context where thinking is expected to be delegated to AI (shifting from problem solving to question asking, as noted in this paper)?
How does one learn to pose the right questions when basic ones are rarely “manually” answered? that is, without an AI assistant’s help
This pattern appears in schools, where AI interferes with human development that typically demands long and difficult effort of actually answering questions
For certain functionality, we import libraries. For others, we've handwritten solutions. Even so — sometimes we have to crack open the repo for a library to really dig in to what's going on. That seems to go for domain experts as well.
I think we've always been working with the scenarios of what's solved for us and what we need to solve manually. AI is certainly not an abstraction layer, but there are ways to use it where you are still driving, learning, and shipping.
How does one learn to pose the right questions when basic ones are rarely “manually” answered? that is, without an AI assistant’s help
This pattern appears in schools, where AI interferes with human development that typically demands long and difficult effort of actually answering questions