I miss thinking hard

Most comments in the thread are missing that critical point. Yes you are achieving the end goal, perhaps faster. And yes, built projects (perhaps worse quality) are still better than not built projects.

But take the home cooking vs ordering at restaurant example:

At a restaurant you can prompt for exactly what you want to eat and it will be made for you without you actually having to do it. When the food comes out you can taste it and notice it is missing some flavor. Problem is, you don't know what is missing. If you are knowledgeable about the dish, you can prompt for additional spices or flavours.

When I cook, I try all the ingredients before I add them and then taste the result so I know how an addition changes the final result.

I am now a much better cook because of this because I can make substitutions on the fly. Dish missing sweetness? Carrots, baby red peppers, beets etc can all substitute - never even reach for sugar. Already added a lot of salt but still feels like more needed? Add sour flavours like lemon juice.

Sure, reliance on AI will end up with more things built but you'll have a generation of "cooks" that don't know why you add a bay leaf or two to soup, except that it's always in recipes.

In psychological terms, he's saying he has a need to solve hard problems in order to validate his identity and make himself feel good. At some point in his past he experienced some psychological trauma, and this hard-problem-defeating became his coping mechanism.

"That satisfaction is why software engineering was initially so gratifying."

He became a software engineer to gratify his need to solve hard problems, to validate his identity, and make himself feel good. If he stops needing to engineer difficult software, there goes his identity, his self-worth, his good feeling.

"But recently, the number of times I truly ponder a problem for more than a couple of hours has decreased tremendously. Yes, I blame AI for this."

When he runs up against something that takes away this thing that validates him, he feels de-valued. Rather than recognize that AI is making his life easier, freeing him up from mental labor, he's experiencing it as a loss, almost an attack.

"If I can get a solution that is “close enough” in a fraction of the time and effort, it is irrational not to take the AI route. And that is the real problem: I cannot simply turn off my pragmatism."

Now this link of hard work with his identity is becoming a problem. He's going to feel bad because he doesn't know how to deal with his life being easier now. This is a reason to address it head on with therapy, and a re-evaluation of what gives him value as a person, so that having an easier life doesn't feel bad.

Isn't this also an overstatement, and the problem is worse. That is - the code being handed back is a great prototype, needs polishing/finishing, and is ignorant of obvious implicit edge cases unless you explicitly innumerate all of them in your prompts??

For me, the state of things reminds me of a bad job I had years ago.

Worked with a well-regarded long tenured but truculent senior engineer who was immune to feedback due to his seniority. He committed code that either didn't run, didn't past tests, or implemented only the most obvious happy path robotically literal interpretation of requirements.

He was however, very very fast... underbidding teammates on time estimates by 10x.

He would hand back the broken prototype and we'd then spend the 10x time making his code actually something you can run in production.

Management kept pushing this because he had a great reputation, promised great things, and every once in a while did actually deliver stuff fast. It took years for management to come around to the fact that this was not working.

But it is also true that most programming tedious and hardly enriching for the mind. In those cases, LLMs can be a benefit. When you have identified the pattern or principle behind a tedious change, an LLM can work like a junior assistant, allowing you to focus on the essentials. You still need to issue detailed and clear instructions, you still need to verify the work.

Of course, the utility of LLMs is a signal that either the industry is bad at abstracting, or that there's some practical limit.