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Comment by jurgenburgen | Hacker News Reader

Before AI, it was impossible to measure productivity. Some tried with misguided metrics like lines of code added but that just incentivized writing obtuse code.

What has changed?

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Stuff just gets done, I guess? Projects move faster, people onboard faster with less intervention, etc. The speedup seems noticeable enough that it doesn’t need precise measuring.

If the speed up is noticeable enough then coming up with a metric should be easy?

I haven’t noticed a speed up in my own org though the feeling of engineers rushing to implementation has become more pronounced. Team members no longer understand what others are doing and siloing has become intense even within my team.

Now you can ask "Is it easier to ask an AI agent to do X than asking my employee?"

Good metrics is difficult, but sometimes a simple comparison like that is enough.

vibes maybe?

If effective AI enhanced SWEs can ship features in a week, the guys who ship 1 feature a quarter stand out more?

Quality matters as well as speed though: reworking comes at a cost, so you really need to be tracking more than one metric. A lot of problems are caused by optimising for one metric above all else.

If it takes 1 quarter to develop a feature and a developer ships a feature in 1 quarter then that makes sense.

If it takes 2 weeks to ship a feature and a developer ships in 1 then yeah, I'm highly suspicious of that.

Impossible to measure in absolute terms but I think it's clear productivity increases relatively when LLMs are used. At least that's my strong experience.

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