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If there's one place I doubt AI will improve much in the near future, it's stakeholder management. That's why I think even if AI becomes an astronomically better coder than the average SWE, that SWE's could just rebrand as AI whisperers and translate the nuances of a manager's human-speak into AI prompts. Maybe it'll get there eventually, but we're still a good ways off from non-technical people being able to use AI to get any software they want without massive issues arising. The higher up in the org you are, the bigger a % of your job that stakeholder management becomes. I think we agree on this point overall.
On less well-known systems and APIs, I think the hallucination issue is more of a skill issue (within reason, I'm not making an accusation here). I'm translating a bunch of SQR (a niche language you've probably never heard of) queries to an antiquated version of TSQL right now, and the AI indeed hallucinates every now and then, but it's in predictable ways that can be solved with the right system prompts. E.g. sometimes it will put semicolons at the end of every line thinking its in a more modern version of SQL, and I have to tell it not to do that which is somewhat annoying, but simply writing a system prompt that has that information cuts down that issue by 99%. It's similar for unknown APIs -- if the AI is struggling, giving it a bit of context usually resolves those problems from what I've seen. Perhaps if you're working in a large org with mountains of bespoke stuff then the giving an AI all that context would just overwhelm it, but aside from that issue I've still found AI to be very helpful even in more niche topics.
On the time saved, you might want to be on the lookout for the dark leisure theory for some folks, while for others the time savings of using AI might be eaten up somewhat by learning to use the AI in the first place. I agree that the productivity boost hasn't been astronomical like some people claim, but I think it will increase over time as models improve, people become more skilled at AI, and people using AI to slack off get found out.
Haha, I really, really don’t think there’s any dark leisure here. None of the best performers rest much at all, and I talk with them pretty openly about their habits. Plus, our direct manager is bullish on AI and got the most enthusiastic guy on the team to do an AI demo a few weeks back. Using AI as a force multiplier would get you a raise, not more work.
The more I have to babysit the LLM, the less time-efficient it is for me. I don’t know what everyone’s experience is, but typing out code (even SQL) is just not that time consuming. I know, logically, what I want to happen, and so I write the statements that correspond to that behavior. Reading code for validity, rewriting it to make it more elegant and obviously correct, that takes more of my time, and LLM output is (like a junior dev) unreliable enough that I have to read deeply for (unlike a junior dev) no chance of it improving future output. Plus, the code I write tends to be different enough that the prospect of reprompting the LLM repeatedly is pretty unpleasant.
That said, I absolutely use it for Bash, which is arcane and unfamiliar to me. I still have to go through the slow process of validating its suggestions and rewriting pieces to make them more proper, but the way you perform simple logical actions in Bash is so far outside my wheelhouse that getting pointed in the right direction is valuable. So if you’re in a position where you’re doing more regular and rote work with particularly obnoxious but well-documented languages, it makes sense we’d have different opinions and experiences.
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