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 "this doesn't make sense", or "this is cheating", or "this doesn't meet requirements", and won't be caught until code review if at all.
The hard part is that LLM code looks like there is some sort of flow. It is like a nice statistical smooth flow. It looks very convincing at a glance. No one would write code like that and not know what they are doing comments self assured and all.
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 work queue rather than its throughput.
With the current capabilities, models still need constant babysitting and course correction. An engineer who lacks the skills to guide them can end up creating more work for the rest of the team. AI makes it easy to generate code faster than you can understand it, and that cost is paid during code review, debugging, and maintenance by colleagues, whose confidence in that engineer's skills may be affected by his use of AI.
What looks like a productivity gain for one engineer can become a productivity loss for the team as a whole.
What I have noticed in my own work that a lot of the time that used to be for coding is now just waiting. I have three agents working on three different features in parallel, and I'll go back and forth with all of them, correcting things and steering etc, but then I find myself with three busy agents and nothing to myself except stare at the screen while they code away. There is a mental budget for me where I can't have more than those three running at the same time and still keep track so what I end up doing is just scrolling HN...
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 be with an agent. Then after a little bit you’re back to being productive, but I still get that voice in my mind. I’m wondering if that’s how addiction feels (way lighter of course).
That whole experience of going deep for a while into LLM coding, then trying to leave it behind made me pretty pessimistic about the future of our profession. We are creating a whole industry of people delegating their ability to work to a software stack currently controlled by basically 2 companies (that both have very sketchy financials). Doesn’t feel healthy
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 feel too pessimistic and wouldn't feel too bad on relying on them to accelerate work and free up mental space from menial tasks.
As a personal side-note I never let my agents do architectual design I only use them for implementing. I always found the actual coding part of programming extremely boring and coming up with designs, experimenting and testing the fun part.
I'd be really interested to see all the software that is written by agents. Whenever I touch agents or ai I can't get much use out of them. My understanding is the value when I think aloud with them/treat them as a better google search, but thats about it. Except one off web stuff, that is a pretty neat use case.
But lets be real, anything moderately complex that is out of the domain of publicly available sample code is hit or miss compared to the time invested running the loop. I'd much rather invest the time in myself.
What a lot of people don't talk about is the inherent security nightmare of trusting ai agents and the sheer data exfiltration happening behind the scenes.
This has been my observation too. Because I'm chatting it feels like I'm not working, so any output can be "productive" in that context but I'm hyper aware of all the negative time here. Correcting, pushing it back to the prompt, reminding it that it doesn't have full context so do what I told you not what you think, and then verifying it and correcting it (always) seems to take longer than just doing the work myself
I've had similar conversations with a client recently while discussing estimates for a large project. Senior leadership has a mental model where AI makes everything X% faster, but that's very wrong. Some things get sped up by an insane amount and basically go to zero, some others not so much. Entirely new tasks emerge, such as directing agents to provide them the context they need, setting loops, etc.
Based on personal experience on a specific project, that 1.5 hours with AI let me accomplish work planned for a man-week in the pre-AI era. So it’s much more than 3x.
Based on personal observation, a lot of productivity has been thrown out of the window with unneeded refactoring, rewrites and "what-if" scenarios that the AI agent will spot.
I think juniors and fake seniors are a lot more productive because they were never really able to measure their productivity so spamming LoC and trusting AI output makes sense to them.
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 "this doesn't make sense", or "this is cheating", or "this doesn't meet requirements", and won't be caught until code review if at all.
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 work queue rather than its throughput.
With the current capabilities, models still need constant babysitting and course correction. An engineer who lacks the skills to guide them can end up creating more work for the rest of the team. AI makes it easy to generate code faster than you can understand it, and that cost is paid during code review, debugging, and maintenance by colleagues, whose confidence in that engineer's skills may be affected by his use of AI.
What looks like a productivity gain for one engineer can become a productivity loss for the team as a whole.
It multiplies both good and bad decisions. Both mine and it's 'own'.
I can get some things done 10x faster and it might even catch mistakes or help me solve something difficult.
But if I am being lazy or complacent then it bites me that much harder.
That whole experience of going deep for a while into LLM coding, then trying to leave it behind made me pretty pessimistic about the future of our profession. We are creating a whole industry of people delegating their ability to work to a software stack currently controlled by basically 2 companies (that both have very sketchy financials). Doesn’t feel healthy
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 feel too pessimistic and wouldn't feel too bad on relying on them to accelerate work and free up mental space from menial tasks.
As a personal side-note I never let my agents do architectual design I only use them for implementing. I always found the actual coding part of programming extremely boring and coming up with designs, experimenting and testing the fun part.
But lets be real, anything moderately complex that is out of the domain of publicly available sample code is hit or miss compared to the time invested running the loop. I'd much rather invest the time in myself.
What a lot of people don't talk about is the inherent security nightmare of trusting ai agents and the sheer data exfiltration happening behind the scenes.
It's a very O-ring problem.
They lost me by begging the question in the very first sentence.
Like so many senior developers I have encountered. That stuff is good for CV.
I'd even say the productivity gap is even smaller, if not negative in some areas...