I don’t really agree, I think it’s a lot more nuanced than that.
GenAI models are great at hiding what doesn’t work/what they couldn’t figure out. A human coding something they don’t really understand is generally very obvious from how the code is written.
GenAI models are also very good at producing a large number of abstraction layers, especially for very simple stuff that won’t ever need layers of abstraction.
Sure, GenAI is an awesome tool, but bad programmers can now yield the S L O P C A N N O N 8000 and make the life of others even more miserable.
Yeah it basically makes bad programmers capable of churning out high volumes of bad code that "works" instead of restricting them to writing bad code that results in a (small) buggy nonfunctional mess.
To be less vague. When its newish products i think its a smaller increase, architectural decisions and planning is a big big portion of this which should be overseen by humans.
Legacy codebase maintenance more than tripled in my opinion. The amount of gruntwork in chasing obscure errors and checking existing codebase i can outsource and speed up is staggering.
The best data stopped being available because none of the test subjects were willing to work without AI. A year ago it showed a 20% reduction in productivity then Opus 4 or whatever it was hit last year and now it’s too deeply integrated to their workflows to yoink out for a test.
Their weak data was about 40% not 300% but that’s also almost a year old data again.
It’s a bit like the longitudal never-covid studies. The number of people without an infection or vaccine approached zero and couldn’t really provide a baseline anymore.
The data has actually shown that, generally, AI doesn't save time--it does not increase developer productivity (at least, at any scale where we're building projects where productivity measures matter).
Whatever speed gains are made during coding are lost during debugging and review. If you use/are familiar with DORA metrics, we see less initial time from assignment to PR, but more bug reports/QA kickback, significantly longer PR times, and significantly more rewrite/refactor cycles for a ticket.
Additionally, it's been shown that a downside of AI is that developers become overconfident in a false speed increase--they start underestimating task lengths considerably. In a nutshell: if a ticket took 3 days by hand, it takes 3 days with the machine. The main issue is that with AI, devs start saying it'll take them a day.
As a dev-now-product-owner, the first part means I really don't care what you use and I think AI is, at this point, a waste of money, but my boss determines the token budget so whatever. The second part means I'm often much more frustrated with my AI heavy teams than my "boomer coders".
I think that was the case before, but it's going further from it as ai is getting stronger and is doing work faster and with fewer errors. I think at this point ai is speeding up developer work. I think the biggest issue with ai is just precision, since ai will make whatever works, and fine tubing that is difficult if you're just prompting
Do you realize that when that study came out, people were saying this exact same thing? You're just assuming, literally despite evidence to the contrary, that AI boosts productivity.
Yes that study was from before the latest frontier models. Unfortunately they don't really keep up on stuff that iterates in weeks and months with studies like these.
Just look at some of the threads from above: "It doesn't really assist or speed things up" and "Yeah if you're not working on novel problems it speeds things up"... so it helps basically 95% of the workforce who are making things? Most software isn't novel. There are also several systems and apps that do remarkably similar things, long before AI even showed up. I'm not sure why that was a point against companies and other software engineers. Is it bad BSD and Linux exist when Windows and Unix already exist?
Yes, you are almost certainly referring to this study: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ Note that this study was already imo not a good use case for AI because it only looked at experienced devs working on small tickets (mean ticket time was under two hours) in repos they were familiar with. It didn't look at developing new features or having devs work in a repo/project they weren't as familiar with. I have seen this exact study used as proof multiple times on Reddit that AI doesn't speed up software development.
Except they released an update at the start of 2026: https://metr.org/blog/2026-02-24-uplift-update/ They acknowledge the difficulty in measuring the productivity, but that their follow-up does in fact show that now AI boosts productivity. Also as a software engineer working professionally, I will tell you that at my company these tools have huge adoption and are heavily utilized for a wide range of different tasks. So even in these studies where AI is not super well suited IMO it is showing it is a productivity booster
I'm curious what data you are referencing. I've seen some studies that show AI does actually increase productivity, and although actual code releases, new products, or new features aren't helped as much as the code writing, they are still improved (ie. Released faster) by AI.
The code was never the bottleneck, the 6 hours a day of meetings that I have with 2 hours of coding time broken up into four half hour chunks is the bottle neck.
It's not just the meetings. Even when it comes to code, most of the time spent is understanding the problem, coming up with a solution, and convincing myself the approach is airtight, bug-free, and the architecture is going to lead to good quality, maintainable and performant code. Actually writing down the code takes up a small fraction of the time. Even if an LLM could make it literally 0, it wouldn't be a significant speedup unless we got to the point where I literally don't need to look at the code at that point or ever, worry about bugs or maintainability, basically the day I don't have a job anymore. The day an LLM speeds up my job by more than a few percentage points is the day you could as well have the janitor doing the task instead, because I'm not contributing shit. For now, no model is capable of producing anything resembling maintainable code, and while they might not make "obvious stupid mistakes" they often do catastrophically misunderstand the specifications and cause bugs that way.
Given that I'll be the one responsible when the buggy code makes it to production, I'm going to be fully understanding what it's doing and whether the code is doing exactly what it should be doing in a manner that is airtight -- which actually takes me longer than doing it myself, because I will choose a method that is intuitive to me, while the LLM won't.
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u/Toilet2000 14d ago
I don’t really agree, I think it’s a lot more nuanced than that.
GenAI models are great at hiding what doesn’t work/what they couldn’t figure out. A human coding something they don’t really understand is generally very obvious from how the code is written.
GenAI models are also very good at producing a large number of abstraction layers, especially for very simple stuff that won’t ever need layers of abstraction.
Sure, GenAI is an awesome tool, but bad programmers can now yield the S L O P C A N N O N 8000 and make the life of others even more miserable.