It's a really underrated problem. I don't think my actual cognitive skills have declined by using AI, but I do notice that my patience and attention span are a lot lower.
I'm learning a new code base for a new job right now, and I'm finding AI to be a really double edged sword for it. One one hand, it's extremely valuable for asking questions about the code base. On the other hand, if I'm not careful and I just let it apply the fix before I even investigate it, I'm really not learning the code base well at all. I find I need to actually write new code in a code base to exercise the necessary mental muscles to actually retain understanding.
Incidentally, I do find that this large new code base I'm learning also shows the limitations of AI. There's no way I can vibe features on this without understanding and not introduce a lot of issues. Even targeted bug fixes have a lot of unintended consequences the LLM doesn't see. This isn't a bad code base at all, but it's definitely at the size where even frontier models struggle. So to me that tells me that the argument that I should just use more AI to solve my AI issues and not bother to understand the code base isn't viable at the moment.
> I don't think my actual cognitive skills have declined by using AI
I'm not speaking about you but... I know most people would not have much awareness of their cognitive decline. I know this because that awareness gap is there with or without LLMs, across all age groups and cultures.
For cognition, sure, but that's a fairly weak claim. A dog that chases its tail for 3
hours might be considered conscious, but maybe not highly intelligent.
The attention deficit part of ADHD hurts some people, but a high intelligence is able to make up for it in other ways. Attention span is a multiplier for intelligence. Someone with a lower IQ but a longer attention span is able to outperform a higher IQ but shorter attention span, traditionally.
What's required though, is the attention span and the memory to really dig deep into a problem, and then go for a run. If AI makes that easier, since it lets you skip the boring parts and get to the meat of the problem, then hey.
One thing I'll point out is the attention thing might be more of a lack of motivation on my part. It used to be banging out features quickly gave me a nice dopamine rush and the satisfaction of having built something I'm proud of. With LLMs, I don't really have that feeling because even if I guided it to that endpoint I feel less invested and somewhat less interested.
True, I guess I try to have some objective measures like my chess elo and maybe some canaries like what books I'm reading. But it would be really hard to tell.
Cognitive ability can be highly specific. If you don't use it you lose it. You may be able to keep your chess ELO high, but realize you can't implement basic algorithms in C++ quite as readily as you used to. Or you can't write as well as you used to. Or you can't quite make that old recipe taste as good as you remember.
We can argue about what skills are important or not, but these things tend to sneak up on us.
I'm learning a new code base for a new job right now, and I'm finding AI to be a really double edged sword for it. One one hand, it's extremely valuable for asking questions about the code base. On the other hand, if I'm not careful and I just let it apply the fix before I even investigate it, I'm really not learning the code base well at all. I find I need to actually write new code in a code base to exercise the necessary mental muscles to actually retain understanding.
Incidentally, I do find that this large new code base I'm learning also shows the limitations of AI. There's no way I can vibe features on this without understanding and not introduce a lot of issues. Even targeted bug fixes have a lot of unintended consequences the LLM doesn't see. This isn't a bad code base at all, but it's definitely at the size where even frontier models struggle. So to me that tells me that the argument that I should just use more AI to solve my AI issues and not bother to understand the code base isn't viable at the moment.