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The AI Productivity Gap

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Re: The AI Productivity Gap

#71

Post by mingusrude »

matthorse wrote: post #17

Writing code is a small part of everyday's job of a software engineer. The article's table reflects this fairly well. AI compresses implementation time for an individual engineer, but architecture decisions, design reviews, integration, testing, deployment, and production validation remain largely serial activities. If code generation speeds up by 5x while those bottlenecks don't, you've mostly increased the team's w…

> AI compresses implementation time for an individual engineer, but architecture decisions, design reviews, integration, testing, deployment, and production validation remain largely serial activities.

Integration, testing, deployment and production validation activities are also, depending on you setup, available for AI to work on. It depends on the type of stack that you have and how your hosting is set up but with an AI-friendly set up there's a lot of time to be saved here too.

Re: The AI Productivity Gap

#72

Post by dgellow »

himata4113 wrote: post #16
dgellow wrote: post #6

Earlier 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…

Even if I was paid for using LLMs I would still find the experience nightmarish. I really don’t want to be dependent on such a system and spend my time managing fleets

Re: The AI Productivity Gap

#73

Post by 1718627440 »

brabel wrote: post #56
Diti wrote: post #50

How 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 mat…

No. There is this persistent belief in the industry that programmers should be good at everything, not just programming itself: communication, product management, design, sysops, UX, coaching, management, testing and so on. The most visible product of this belief was the once hyper hyped role of “full stack” developer. The really is that you could have experts in each area doing what they’re good, which means letting…

If you try to take every decision making out of programming, a programmer becomes a typist. Programming contains a lot of decisions about architecture and style, even if the major decision is already done by the respective "expert". Thus, if you try to let programmers focus on programming you need to synchronize decision making done in parallel, because they will still do some of the decisions. That is a hard problem.

Re: The AI Productivity Gap

#74

Post by [deleted] »

hahahaa wrote: post #33

This assumes you are arranging deckchairs and not leaving the cruise ship for say, a boeing 747. One example, let's say there is a side bet that makes everyone 10x more productive with a success rate of 1% It takes 2 hrs to make the bet wit agent orchestration. 10 people can get this done in their spare time freed up by AI in 5 weeks. Bet cashes in and you are much faster at everything. It won't feel faster. Because…

[deleted]

Re: The AI Productivity Gap

#75

Post by zombot »

mingusrude wrote: post #71
matthorse wrote: post #17

Writing code is a small part of everyday's job of a software engineer. The article's table reflects this fairly well. AI compresses implementation time for an individual engineer, but architecture decisions, design reviews, integration, testing, deployment, and production validation remain largely serial activities. If code generation speeds up by 5x while those bottlenecks don't, you've mostly increased the team's w…

> AI compresses implementation time for an individual engineer, but architecture decisions, design reviews, integration, testing, deployment, and production validation remain largely serial activities. Integration, testing, deployment and production validation activities are also, depending on you setup, available for AI to work on. It depends on the type of stack that you have and how your hosting is set up but with…

GP: > With the current capabilities, models still need constant babysitting and course correction.

You'd be opening yourself up to even more babysitting. Would that really save time, or improve quality? I'm not convinced, to put it mildly.

Re: The AI Productivity Gap

#76

Post by orwin »

The two last hire we got a year ago still don't have any ownership of anything. Even the project they coded 'themselve' where we involved them in the design and let them cook almost on their own, we are the one chasing bugs and defucking everything six months later because, probably unlike them, we actually read the PR (I'm mean, they probably did read it too, but today I'm extremely pissed, I'm at the point of calling a meeting to figure it out, it cannot continue like this). I've never took more than 4 months to at least understand most of the code, I feel like a year later their level of understanding is still the same. They are basically ai overseers at this point, but while I do more code review than ever before, I feel like they learn around the same as LLM, basically nothing.

Re: The AI Productivity Gap

#77

Post by ryandvm »

orwin wrote: post #76

The two last hire we got a year ago still don't have any ownership of anything. Even the project they coded 'themselve' where we involved them in the design and let them cook almost on their own, we are the one chasing bugs and defucking everything six months later because, probably unlike them, we actually read the PR (I'm mean, they probably did read it too, but today I'm extremely pissed, I'm at the point of calli…

This is the new normal now that computer science based SWEs are being replaced with "LLM whisperers". Being 10 times more productive with AI necessarily means you're going to have 1/10th of the understanding of the code you're producing. It physically couldn't be any other way.

Re: The AI Productivity Gap

#78

Post by pydry »

himata4113 wrote: post #40
pydry wrote: post #23

Earlier quoted context omitted.

I always found that if you are good enough at whittling down boilerplate that coding becomes something akin to pure architecture. I find that mediocre programmers and LLMs are bad at both. They're helpful if you want to shit out some repetitive boilerplate or perform a complex search of some kind but otherwise you're better off without.

ehh, they're pretty good at automated performance research and bug fixes, especially when spanned across hundreds of them.

bug fixes not so much. when I encounter a bug the first thing I do is ask an LLM and not only do they usually fail to get the correct solution, they often slap virtual duct tape on it which makes other bugs more likely.

Re: The AI Productivity Gap

#79

Post by dzonga »

PostOnce wrote: post #5

Pre AI and Post AI code review hours are both 0.75 in this made up example. I find that implausible. Even with the same amount of code, AI code is less trustworthy* and requires more attention... but we know it won't be the same amount, it will be more. This means it will take longer to review, or there will be unforeseen consequences of not spending that extra time. *meaning no human eyes have looked at it and said…

at big tech the numbers seem about right. at smaller firms - you've less admin, less meetings - so the coding part is higher.

mind you most of the stuff posted here is in regards to big tech - even though it's 'hacker' news.

Re: The AI Productivity Gap

#80

Post by vouaobrasil »

ryandvm wrote: post #77
orwin wrote: post #76

The two last hire we got a year ago still don't have any ownership of anything. Even the project they coded 'themselve' where we involved them in the design and let them cook almost on their own, we are the one chasing bugs and defucking everything six months later because, probably unlike them, we actually read the PR (I'm mean, they probably did read it too, but today I'm extremely pissed, I'm at the point of calli…

This is the new normal now that computer science based SWEs are being replaced with "LLM whisperers". Being 10 times more productive with AI necessarily means you're going to have 1/10th of the understanding of the code you're producing. It physically couldn't be any other way.

The scary thing is that the system moves in a direction such that it seems that it doesn't really matter. People on an individual level care but due to the strange economic conditions, LLM use is still rampant because experienced people can get some benefit from it in the short-term even if that means the long-term degradation of our collective expertise and thus a net negative. It's blindingly obvious but just like climate change, people can't help on an individual level to take the immediate reward.
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