I think that over time people will start looking at AI-assisted coding the same way we now look at loosely typed code, or at (heavy) frameworks: it saves time in the short term, but may cause significant problems down the line. Whether or not this tradeoff makes sense in a specific situation is a matter of debate, and there's usually no obviously right or wrong answer.
Productivity gains from AI coding assistants haven’t budged past 10% – survey
81–90 of 99 posts
Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#82The real takeaway here -- also corroborated by the DORA 2025 report https://dora.dev/research/2025/ -- is that more than anything, AI amplifies your current development culture. Organizations with strong quality control discipline enjoy more velocity, those with weak practices suffer more outages. Expecting AI to magically overcome your development culture is like expecting consultants to magically fix your business…
I'm pretty sure it has to do with the individual as well as the culture. Juniors/new hire use AI to multiply by two their wrong/unsafe output, and seniors then have to spend more time correcting it. I'll be honest: I piss poor code, each time I come back to an old project I see where I could have done better. New hires are worse, but before AI (and especially Opus) they didn't produce that much code before spending l…
And I'm also hearing grumblings about entry level talent that is absolutely clueless without AI, which does not help the junior hiring scene at all.
At this point it seems clear that people wishing to learn a discipline should restrict their usage of AI until they have "built the muscles", but none of our educational, testing, recruitment and upskilling practices are conducive to that.
Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#83You're only as fast as your biggest bottleneck. Adding AI to an existing organization is just going to show you where your bottlenecks are, it's not going to magically make them go away. For most companies, the speed of writing code probably wasn't the bottleneck in the first place.
In high-performance teams it is. In bike-shedding environments of course it is not.
The least productive teams I've been on, it wasn't usually engineering talent that was the problem, it was extremely vague or confused requirements.
Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#84Earlier quoted context omitted.
Agreed. The bottleneck is QA/Code review and that is never going away from most corps. I've never worked at a job in tech that didn't require code review and no, asking a code agent to review a PR is never going to be "good enough". And here we are, the central argument for why code agents are not these job killing hype beasts that are so regularly claimed. Has anyone seen what multi-agent code workflows produce? Tak…
My head of engineering spent half a day creating a complex setup of agents in opencode, to refactor a data model across multiple repositories. After a day running agents and switching between providers to work around the token limits, it dumped a -20k +30k change set we'll need to review. If we're very lucky, we'll break even time wise compared to just running a single agent on a tight leash.
Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#85In other words: notionally, if not literally, by the time trailing numbers are collected they are out of date.
This is of course axiomatic, but, that staleness is a serious matter in this particular moment.
It's a cliché that six months can be a lifetime on the bleeding edge of tech.
This is the first time in my career that is more or less literally true.
Humans reason poorly with non-linear change.
This entire article is a demonstration of that.
Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#86Earlier quoted context omitted.
Once the free money runs out, the AI cos may shift to making heavily verified code snippets with more direct language control. This will heavily simplify a lot of boilerplate instead of fairytales of some AGI coding wiz.
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Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#87Apparently "AI is speeding up the onboarding process", they say. But isn't that because the onboarding process is about learning, and by having an AI regurgitate the answers you can complete the process without learning anything, which might speed it up but completely defeats the purpose?
Yes, that's how I'd interpret it, too. According to the article, onboarding speed is measured as “time to the 10th Pull Request (PR).” As we have seen on public GitHub projects, LLMs have made it really easy to submit a large number of low-effort pull requests without having any understanding of a project. Obviously, such a kind of higher onboarding speed is not necessarily good for an organization.
Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#88Earlier quoted context omitted.
In high-performance teams it is. In bike-shedding environments of course it is not.
I'm not sure I'd call it bike shedding so much as that a lot of time and effort tends to go into hard to answer questions: what to build, why to build it, figuring out the target customer, etc. A lot of times going a thousand miles per hour with an LLM just means you figure out pretty quickly you're building the wrong thing. There's a lot of value to that (although we used to just call this "prototyping"), but, that…
Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#89Earlier quoted context omitted.
Agreed. The bottleneck is QA/Code review and that is never going away from most corps. I've never worked at a job in tech that didn't require code review and no, asking a code agent to review a PR is never going to be "good enough". And here we are, the central argument for why code agents are not these job killing hype beasts that are so regularly claimed. Has anyone seen what multi-agent code workflows produce? Tak…
My head of engineering spent half a day creating a complex setup of agents in opencode, to refactor a data model across multiple repositories. After a day running agents and switching between providers to work around the token limits, it dumped a -20k +30k change set we'll need to review. If we're very lucky, we'll break even time wise compared to just running a single agent on a tight leash.
Re: Productivity gains from AI coding assistants haven’t budged past 10% – survey
#90Earlier quoted context omitted.
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I mean if people continue checking open source code into GitHub using those new features then they should be able to learn them just fine.