Comment by armchairhacker
4 hours ago
“The actual fundamentals, the things-in-themselves, the theory behind the action” don’t go away, they change.
Programmers used to work with punch cards, then assembly, then low-level languages with odd quirks. Today few developers even think about first-party code size, micro-optimizations, register allocation, etc. LLMs are just another abstraction.
A developer with the ideal AI code writer (which we’re not at yet) must still think about idea, design, scope, etc. like a product owner or manager. And these concepts have theory, sometimes even math (e.g. time complexity).
EDIT to comment on the article: all abstractions are leaky, but sometimes it rarely matters. Today we do still need to understand code quality and architecture when working with LLMs, or the software will get bad enough that it will affect the company. But maybe not next year. An analogy: stack vs heap, memory allocations, etc. still matter in high-performance software, which isn’t uncommon, but programmers almost never think about register allocation.
LLMs are not another abstraction. ALL OTHER LAYERS you named are fully deterministic, understood, debuggable, etc.
You cannot be serious.
LLMs are one of the most general abstractions possible.
LLMs are also quite deterministic if you want them to be - generally, their final token selection is deliberately randomized (the model “temperature”). But the word you’re looking for here is probably not actually determinism, it’s probably something closer to predictability.
In any case, it’s perfectly possible to ensure that the output of LLMs is fully deterministic, debuggable, understandable, and testable.
> You cannot be serious.
I don’t think you’re thinking about this clearly.