A superhuman working memory is exactly how I've been describing the LLM advantage. Paired with the unreliability of its reasoning and judgment, it is what makes AI a supplement to human intelligence, not a replacement.

On the other side, LLMs make random mistakes and wrong choices and they have a bias toward writing more code instead of less. You can make up for this to some degree by running another LLM against their output, but with very diminishing returns. Even if they were perfect, there will be an ongoing cost to little or no human awareness and understanding of the codebase.

It may take some time for people to recognize the cost of AI code generation and their value for virtually everything else, but I believe we'll get there.

I also find them bad at what I call "abstraction compression." They're really bad at noticing when a helper function is needed, when some structure they repeated five times slightly differently can become a struct, when a whole section of code can be encapsulated in a simpler design.

I'm lucky that for my side project (an interpreter) I've written all the code myself, so I've built up its design in my mind over the past year, and so as I mull over what I'm writing I start coming up with simpler designs.

Interestingly using Opus 5 (and LLMs in general) has made me worse at this, since I don't feel the pain of writing something over and over again. On the other hand, I don't really want to implement a whole stdlib, so I have it write more of the auxiliary code. The hardest thing is that I have to manually manage the context, which is painful when I personally remember every helper function and why. I have to remember to keep the list of helper functions updated, which is irritating.