An important aspect of this for professional programmers is that learning is not something that happens as a beginner, student or "junior" and then stops. The job is learning, and after 25 years of doing it I learn more per day than ever.
That's one of several possibilities. I've reached a different steady state - one where the velocity of work exceeds the rate at which I can learn enough to fully understand the task at hand.
Go Anthropic for transparency and commitment to science.
Personally, I’ve never been learning software development concepts faster—but that’s because I’ve been offloading actual development to other people for years.
It makes sense - juniors are coding faster but not understanding anything. Ironically it'll stop them getting more experienced despite feeling good. What I'm interested in is if the same applies for Senior+ developers. The soft signals are that people are feeling the atrophy but who knows...
It requires discipline. I use LLMs for mind-numbing refactoring and things I don't care learning. If you want to learn something, you do it yourself. It's like the gym. No pain, no gain.
I am not saying you should be struggling performatively, like a person still proud in 2026 that they are still using Vim for large projects (good for you, eh), but sometimes you need to embrace the discomfort.
Another study from 2024 with similar findings: https://www.mdpi.com/2076-3417/14/10/4115 -- a bit more preliminary, but conducted with undergrad students still learning to program, so I expect the effect would be even more pronounced.
This similarly indicates that reliance on LLM correlates with degraded performance in critical problem-solving, coding and debugging skills. On the bright side, using LLMs as a supplementary learning aid (e.g. clarifying doubts) showed no negative impact on critical skills.
This is why I'm skeptical of people excited about "AI native" junior employees coming in and revamping the workplace. I haven't yet seen any evidence that AI can be effectively harnessed without some domain expertise, and I'm seeing mounting evidence that relying too much on it hinders building that expertise.
I think those who wish to become experts in a domain would willingly eschew using AI in their chosen discipline until they've "built the muscles."
An important aspect of this for professional programmers is that learning is not something that happens as a beginner, student or "junior" and then stops. The job is learning, and after 25 years of doing it I learn more per day than ever.
I've reached a steady state where the rate of learning matches the rate of forgetting
That's one of several possibilities. I've reached a different steady state - one where the velocity of work exceeds the rate at which I can learn enough to fully understand the task at hand.
to fix that you basically need to switch specialty or focus. A difficult thing to do if you are employed of course.
> The job is learning...
I could have sworn I was meant to be shipping all this time...
Go Anthropic for transparency and commitment to science.
Personally, I’ve never been learning software development concepts faster—but that’s because I’ve been offloading actual development to other people for years.
It makes sense - juniors are coding faster but not understanding anything. Ironically it'll stop them getting more experienced despite feeling good. What I'm interested in is if the same applies for Senior+ developers. The soft signals are that people are feeling the atrophy but who knows...
It requires discipline. I use LLMs for mind-numbing refactoring and things I don't care learning. If you want to learn something, you do it yourself. It's like the gym. No pain, no gain.
I am not saying you should be struggling performatively, like a person still proud in 2026 that they are still using Vim for large projects (good for you, eh), but sometimes you need to embrace the discomfort.
Another study from 2024 with similar findings: https://www.mdpi.com/2076-3417/14/10/4115 -- a bit more preliminary, but conducted with undergrad students still learning to program, so I expect the effect would be even more pronounced.
This similarly indicates that reliance on LLM correlates with degraded performance in critical problem-solving, coding and debugging skills. On the bright side, using LLMs as a supplementary learning aid (e.g. clarifying doubts) showed no negative impact on critical skills.
This is why I'm skeptical of people excited about "AI native" junior employees coming in and revamping the workplace. I haven't yet seen any evidence that AI can be effectively harnessed without some domain expertise, and I'm seeing mounting evidence that relying too much on it hinders building that expertise.
I think those who wish to become experts in a domain would willingly eschew using AI in their chosen discipline until they've "built the muscles."
Duplicate?
Submission about the arXiv pre-print: https://news.ycombinator.com/item?id=46821360