I don't think "lazy" is the word I would use, but I can see how some would see it like that. In my mind, GPT (and LLM's) are just the next layer of abstractions

I don't think "lazy" is the word I would use, but I can see how some would see it like that. In my mind, GPT (and LLM's) are just the next layer of abstractions on top of a already massive stack of technical abstractions. For a SWE, this stack of abstractions crosses so many levels for a simple "hello world" to work: 1. Software (my code) 2. OS (Linux, kernels, etc) 3. Hardware (3090, 5090x, etc) 4. Electrical (Where is my energy coming from? how is it produced?) Each of these levels could be broken into another 10 abstractions: On the software level, some people may understand how their compiler is working, but could they program in binary? What about understanding how their program interacts with memory? What about the kernel of where their software is deployed in the cloud? Do they know how their software is deployed in the cloud? Could they build the production server rack that their container is deployed on? Obviously this gets a bit ridiculous the further down you go- it's impossible to have knowledge about every part of what makes your code work. I think that when people use terms like "lazy" or say that knowledge is being lost with abstractions like GPT, they ignore the massive list of abstractions that allow them to be productive. I'd guess my thesis is that newer/GPT-aided engineers don't necessarily have less understanding, but their knowledge might just be shifted by one level up on the abstraction stack.