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399 points nomdep | 1 comments | | HN request time: 0.207s | source
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lexandstuff ◴[] No.44295309[source]
Great article. The other thing that you miss out on when you don't write the code yourself is that sense of your subconscious working for you. Writing code has a side benefit of developing a really strong mental model of a problem, that kinda gets embedded in your neurons and pays dividends down the track, when doing stuff like troubleshooting or deciding on how to integrate a new feature. You even find yourself solving problems in your sleep.

I haven't observed any software developers operating at even a slight multiplier from the pre-LLM days at the organisations I've worked at. I think people are getting addicted to not having to expend brain energy to solve problems, and they're mistaking that for productivity.

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nerevarthelame ◴[] No.44295716[source]
> I think people are getting addicted to not having to expend brain energy to solve problems, and they're mistaking that for productivity.

I think that's a really elegant way to put it. Google Research tried to measure LLM impacts on productivity in 2024 [1]. They gave their subjects an exam and assigned them different resources (a book versus an LLM). They found that the LLM users actually took more time to finish than those who used a book, and that only novices on the subject material actually improved their scores when using an LLM.

But the participants also perceived that they were more accurate and efficient using the LLM, when that was not the case. The researchers suggested that it was due to "reduced cognitive load" - asking an LLM something is easy and mostly passive. Searching through a book is active and can feel more tiresome. Like you said: people are getting addicted to not having to expend brain energy to solve problems, and mistaking that for productivity.

[1] https://storage.googleapis.com/gweb-research2023-media/pubto...

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wiseowise ◴[] No.44295806[source]
You’re twisting results. Just because they took more time doesn’t mean their productivity went down. On the contrary, if you can perform expert task with much less mental resources (which 99% of orgs should prioritize for) then it is an absolute win. Work is extremely mentally draining and soul crushing experience for majority of people, if AI can lower that while maintaining roughly same result with subjects allocating only, say, 25% of their mental energy – that’s an amazing win.
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didibus ◴[] No.44295833[source]
If I follow what you are saying, employers won't see any benefits, but employees, while they will take the same time and create the same output in the same amount of time, will be able to do so at a reduced mental strain?

Personally, I don't know if this is always a win, mostly because I enjoy the creative and problem solving aspect of coding, and reducing that to something that is more about prompting, correcting, and mentoring an AI agent doesn't bring me the same satisfaction and joy.

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Vinnl ◴[] No.44297190[source]
Steelmanning their argument, employers will see benefits because while the employee might be more productive than with an LLM in the first two hours of the day, the cognitive load reduces their productivity as the day goes on. If employees are able to function at a higher level for longer during their day with an LLM, that should benefit the employer.
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didibus ◴[] No.44301494[source]
I think we are all working without data here, it's all conjecture.

I went with OP's hypothesis that you are not faster, you throw things at the wall, wait, and see if it sticks, or re-throw it until it does. This reduces your cognitive load, but might not actually make you more productive.

I'm assuming here that "you are not more productive" already accounted for what you are saying. Like in a 8h day, without AI, you get X done, and with AI you also get X done, likely because during the peak productivity hours of your day you get more done without AI, but when you are mentally tired you get less done, and it evens out with a full day of AI work.

There's no data here, it's all just people's intuition and impression, not actually measuring their productivity in any quantifiable way.

What you hypothesize could also be true, it the mental load is reduced, can you sustain a higher productivity for longer? We don't know, maybe.

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wiseowise ◴[] No.44302900[source]
> What you hypothesize could also be true, it the mental load is reduced, can you sustain a higher productivity for longer? We don't know, maybe.

It's not maybe, it's confirmed fact. Otherwise there wouldn't be burnout epidemic.

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1. computably ◴[] No.44305293[source]
Except the causes of burnout have almost nothing to do with the type of cognitive load associated with coding, debugging, etc.

https://www.mayoclinic.org/healthy-lifestyle/adult-health/in...

Of the six general causes listed, four are institutional or social, having to do more with the workplace or coworkers: lack of control, lack of clarity, interpersonal conflicts, lack of support. IME, in tech, these are far more common causes and more deeply tied to the root of the issue than specifics of work.

The remaining two are productivity-related issues: too much/little to do, problems with WLB.

I would note these are tied into lack of control/clarity/support, and conflict. In a healthy work environment, expectations should be clear and at least somewhat flexible depending on employee feedback, and adequate support should be provided by the employer.

That aside, it's unclear, and I would argue unlikely, that AI-related productivity gains will help with workload issues. If you do disproportionately more work in an overworked team/org, you will simply be given more work. If many people see gains in productivity, then either the bar for productivity goes up, or there's layoffs. Even if you manage to squeak by / quiet quit with much reduced cognitive load for coding, and that's most of your job, unless you are fully remote the most likely change is your butt-in-seat time will go from "mentally taxing coding" to "mentally toxic doomscrolling."