Comment by necovek
1 month ago
You seem to have missed OP's point: some things are only encoded in our brains when you are sufficiently experienced.
Translating that into code can happen directly by you, or into prompt iterations that need to result in the same/similar coded representation.
In other words, when it matters how something works and it is full of intricate details, you do not need to specify it, you just do it (eg. as an example which is probably not the best is you knowing how to avoid N+1 query performance issue — you do not need a ticket or spec to be explicit, you can just do it at no extra effort — models are probably OK at this as it is such a pervasive gotcha, but there are so many more).
That's the failure to automate. The AI isn't telepathic, so agentic engineers not automating this stuff is skipping out on the engineering part.
You setup the environment and then you do the work. Unless you are switching employers every week, you invest in writing that stuff down so the generation is right-ish and generate validation tooling so it auto-detects the mistakes and self-repairs.
sometimes you write the feature and write it well so it's reusable.
imagine you have to implement a specific algorithm for a quantum computer.
There's no value setting up AI to do the writing for you. That might be orders of magnitude harder then writing the algorithm directly.
For highly specialized one-off features, it doesn't always pay off.
On the other hand, if all you do are some generic items that AI can do well... then I'm not sure you're going to have a job long term, your prompts and automation will be useful for the new junior hires that will be specialized in using these and cost effective.
That feels like true in theory, but in practice, we see the reverse for advanced projects where AI is helping us a lot. A decent chunk of our core IP falls into the bucket you're describing:
We have been building a GPU-accelerated graph investigation platform that has grown over 10+ years with fancy stuff all over the place - think accelerated query languages, layout kernels, distribution, etc. R&D-grade high performance engineering projects and kernels end up needing a lot of iterations to make a prototype and initial release. Likewise, they're more devilish to maintain when they need a small tweak later because of the sophistication and bus factor. Both phases benefit.
AI coding helps automate investigation, testing, measurement, patching, etc. The immediate effect is we can squeeze in many more experimental iterations with more fidelity and reach. Having an AI help automatically explore the design space and the details helps a LOT. And later, maintaining a wide surface area of code here that is delicate to touch and infrequently edited is traditionally stressful for teammates, and AI editing + AI-generated automation is helping destress that a LOT. We very much invest in upgrading our team, processes, and tooling here.
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I think there's a level above that where the words to describe such structure are familiar and readily available and hey guess what? The model understands those too. Just about every pattern has a name. Or a shape. Or an analog or metaphor in other languages or codebases. All work as descriptors.
This presumes that most of this stays encoded as words in our brains: the effort to translate some of these into words might be similar to translating it into code (still words, just very precise).
It's like talking legalese vs plain English; or formal logic vs English. Some people have the formal stuff come more naturally, and then spitting code out is not a burden.
No, it really doesn't presume anything about brains or information encoding. Just points out that there is a level of mastery in which all the techniques and all the forms have names or adequate descriptions. Teachers often attempt to achieve this, to facilitate education.
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