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The AI Productivity Gap
41–50 of 127 posts
Re: The AI Productivity Gap
#42> Reading and Debugging 1.5 1.0
> Code Reviews 0.75 0.75
Since these numbers are made up, I may as well throw my personal anecdote in the ring. I find reading and reviewing far harder with coworkers who are using AI. Tickets contain about 5x as much meaningless junk as they used to, and testing notes - while far more thorough - are often now multiple pages in length. Reviews also contain much more code, people try to do more drive-by fixes because the models can generate those fixes so quickly, and people understand the code they're submitting far less clearly because the model is able to generate fixes they simply couldn't previously.
I feel less productive than I was a year ago, and I don't see my team shipping more features than they were previously. But everyone reports that they're far more productive. I don't get it.
Re: The AI Productivity Gap
#43[dead]
Re: The AI Productivity Gap
#44It's like you just made up those numbers and then developed your thesis around that.
Re: The AI Productivity Gap
#45I don’t think human review is worth it for LLM generated code. We design abstractions and all around how humans think. LLMs writes code that is better understood by machines. If you are all in on LLMs, by all means, read the code figure out what it means. But trying to enforce a human flow to its logic is flawed and will be overwritten the next time.
But LLM's will trash your established architecture and patterns if you let it run free over an established codebase for any length of time. In our experience anyways, even with careful guidance and rules to follow. It nearly always does something a bit odd.
And it's test cases can sometimes leave a lot to be desired.
Re: The AI Productivity Gap
#46The role of developer is becoming more of a system thinker than a syntax writer, previously load balance was about what to do in a given timeline, now its more of what not to do , doing more is actually increasing technical debt.
Re: The AI Productivity Gap
#47It's like you just made up those numbers and then developed your thesis around that.
That’s exactly what was done.
Re: The AI Productivity Gap
#48[deleted]
Re: The AI Productivity Gap
#49Earlier quoted context omitted.
I stopped using coding agents after more than one and a half year of active use, it really started to become way too boring, and I’m t a point where I just hate having to babysit them and for the 200th time make it understand what the actual goal is… and to be honest, going back to writing code by hand without assistance is really hard at first you continuously have that little voice telling you how simple that would…
Neither of these points feel true anymore. Models are very much predictable these days (except anthropic models). The real issue stems from letting them work on their own for far too long. Also we are not controlled by 2 companies anymore as kimi k3, deepseek flash (and soon pro) as the ultra-cheap variants, glm 5.2 especially is a direct replacement for opus 4.8. Models will only get better and cheaper I wouldn't fe…
How exactly will models get cheaper?
Re: The AI Productivity Gap
#50How to get rid of every highly-skilled-but-unmedicated neuroatypical developer (could be people like Xe Iaso or Soatok):
> hiring someone who is a good coder, but has trouble reasoning about systems, has no patience for working through hard problems with others, and can’t break down vague requirements into tangible action items.
Why not hire the excellent developers for the highly-technical skills they bring, and match them with architects/product managers who are the ones who have the big picture? Am I crazy to think like this?