No I meant something else. As you said: us humans love clean abstractions. We love building on top of them.
Now LLMs are trained on data produced by us. So I wonder if they would also inherit this trait from us and end up loving good abstractions, and would find it easier to build on top of them.
Other possibility is that they end up move-37ing the whole abstraction shebang. And find that always building something up bespoke, from low-level is better than constraining oneself to some general purpose abstraction.
> with better AI models and tools like Cursor, we will move to a world where you can mold code ever more specific to your use case to make it more performant
what do you think the value of having the right abstraction will be in such a world?
i feel this question correlates more with the generation of the SWE more than anything.
expecting current gen SWEs to talk about network layer protocols while answering this is kinda the same as expecting 1990s SWEs to include wire physics and dispersion statistics in their answer to this question.
Depth alone isn't always a good indicator. We have to move on from some of the low level stuff at some point and it is okay for engineers to know in detail about things that have been solved long back.
I have a slightly tangential question: Do you have any insights into what exactly DeepSeek did by bypassing CUDA that made their run more efficient?
I always found it surprising that a core library like Cuda, developed over such a long time, still had room for improvement—especially to the extent that a seemingly new team of developers could bridge the gap on their own.