Comment by skydhash
10 hours ago
> With AI, our role also shifts. It's mainly to know what to spend effort on, to set priorities and, to be able to verbalize requirements, missing social context and unwritten rules, to anticipate what additional documents the agent needs, to prioritize deadlines, feature necessity, and other judgment calls.
Did you not do that before AI? It’s so strange to me when people are calling out these kind of tasks like they were not already a requirement for the job. What were you doing before?
> Soon there will be as little point to a human diving into the code as to trying to beat a chess engine, or humans constructing buildings by hand
Chess is way less complex than coding. The rules are like a few pages. While the specs for an 8 bit chip like the AVR is in the hundreds of pages. Books like “The Linux Programming Interface” are thousand pages long.
Also humans are using tools for building. Tools that do exactly what you control them to do. When you use a drill for a hole, you don’t have to worry that pressing the trigger have a good chance of sending the bit in your guts.
> Did you not do that before AI? It’s so strange to me when people are calling out these kind of tasks like they were not already a requirement for the job. What were you doing before?
No, you didn't have to explicitly say it in words. My mind doesn't run on internal monologue. Many people can just do their work without ever having reflected on it in words. Tacit knowledge, routines, shared assumptions and culture in a team, common knowledge etc. People have a hard time using AI because they are bad at modeling the knowledge state / information context from the AI POV. You need good theory of mind for this, and being a good programmer is distinct from that soft skill. Yes yes blabla soft skills are more important than hard skills blabla, I don't buy it. It used to be valuable to be great at the hard skills even with mid-tier soft skills. You can have a ton of smooth talkers who are attuned to feel each others emotion and desires super well, but the thing has to actually work too.
> What were you doing before?
Wrote code. Yes, you have to explain the outcome to your boss or your team at some point, but people generally have better developed theories of mind for people than for AI.
> Also humans are using tools for building. Tools that do exactly what you control them to do. When you use a drill for a hole, you don’t have to worry that pressing the trigger have a good chance of sending the bit in your guts.
Right. I'm not sure how to reconcile the two though. A tool whose job is to do some of the thinking part seems to be a contradiction to me. If I so much know what there is to do that it's pure execution and can reliably be executed in a way that basically ensures no potential surprises to me, then I wouldn't need more thinking. But I agree, it would be better to somehow find a hybrid that is both doing thinking and feels more like a tool also while using it.
Llms are a relational responsive reasoning interface, not a mind.
The point is that you have to have an idea of what it has access to in its context. Many expect it to work like an omniscient genie and then give up once it turns out that it can't read your mind and you have to be able to halfway-coherently state what you want.
> No, you didn't have to explicitly say it in words. My mind doesn't run on internal monologue. Many people can just do their work without ever having reflected on it in words.
Were you a solo developer? I think the amount of reports and explanations (either written or verbal) dwarfs by large the amount of code I’ve written. From training juniors to drafting a design specs for a feature. That is why people say code is the trivial part of the job because it’s easy. I’m pretty sure the mailing list of the Linux kernel is bigger than the code itself.
I'm in academic ML research. Coding is mostly scoped to myself. And it's often nontrivial. My communication is more in papers, reports on experimental results, deciding what to try next, but how the code is organized is rarely the focus of discussion. Of course this may be entirely different in a software shop building routine features where the difficulty is indeed the social part and the software itself is more clearly understood. But I'd say that's when AI can be even more effective since it can do mundane coding even better proportionally than research code. Of course Amdahl's law kicks in and even if you reduce that drudgery to 0, the proportion is small when taking total work effort into context.
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