I will just point out the benefit is not as obvious as you think. Developers have consistently overestimated LLM productivity gains, which still seems true for agentic AI: https://metr.org/blog/2026-05-11-ai-usage-survey/ It is particularly striking how similar the results are to LLMs before agents.

Along with the total absence of long-term data, I think the benefit can be (weakly) denied. Maybe not in the employmemt marketplace, but certainly for myself.

> I can’t help but wonder whether constant use of “agent” harnesses will lead to an atrophy of the software engineering (or really any field) muscles.

Used to think so, but they actually can also be used to train and strengthen skills, and learn new ones.

I had a coding interview, where they kindly sent a brief beforehand to help prepare, presenting a list of topics and concepts that might be useful during the interview, the tech stack, what kind of expectations they would have, and what they’d be paying attention to.

Obviously, it’s not exact list, and there are probably other evaluation dimensions.

But since I was out of practice on some of those, I had Claude generate a dozen sample projects, with each a list of tasks in one document and the solutions in another, and got to it.

Midway, I thought of using codex to role play as an interviewer, to tell it my train of thought and ideas as I went, get feedback, question my choices, etc.

Sure I only went through two and half, maybe three of those projects… but it’s the first time I actually enjoyed prepping for an interview. And I actually learned some things in the process.

Hardest part was probably stopping the LLM from doing the tasks, but nothing unsolvable given a bit more time, clearer instructions, and separation.

<< So what’s the ideal “middle ground” in this situation?

Putting all this in 2nd paragraph so that you can skip it if you think 'coding' is your primary portion of your job.

I suppose I am in a mildly privileged position in a sense that my work is a weird intersection of tech, finance, and comprehension. In other words, I don't code much, but I absolutely benefit from now being able to play with various projects I would otherwise have no business touching without a bigger support team.

I don't want to invoke Accelenrando, but the muscle imagery and analogy fits. I will give an example. I recently decided to pick up Go for a project ( have experience in some other languages, but I will still be starting fresh ). I could have codex build me what I want, but I am purposefully taking it slow so that I can learn the foundation so that I can have a frame of reference ( because I assume it won't be the only go project for me ).

Otoh, most of my one off python scripts I barely even skim anymore. And honestly,that is the part that scares me more.

>Conversely, I also am a user of LLMs (true shocker these days, I know), and am noticing a speedup in areas I was already familiar with, and a quicker introduction to new ones. The obvious benefit cannot be denied, and doing so regardless makes you look uninformed.

My largest concern comes from something tangential to this: I'm not sure we're all that good at deciding what should be learned and sticking to it.

Silly example: regex. LLMs are, as far as I know, well above the average dev when it comes to writing regex. Regex is also one of those things that for many people goes unused for months, but then you encounter the occasional perfect regex problem, and it's really easy to just lean on the LLM to write the regex for you rather than spending some time tinkering and testing. Regex can be frustrating and fickle, I think we've all been there.

But then, you just don't learn regex. So where does the intuition for what regex can do come from? Do you just become unable to write regex with no LLM? People stop writing resources for regex I guess?

My concern is that there's stuff I feel I can just chuck onto the LLM but I'm sure my judgement is not perfect. It's still probably worth it, all in all, but I'm not even sure of what I might be losing along the way and that's an uneasy feel.

If cars did not exist, I would be healthier, able to walk and run many times further due to constant cardio exercise.

I would still travel much less distance.

And just like cars, LLMs will reshape the world to the point that our brains could not even get us to the supermarket because soon it will be 5 miles away and require a car ( or at least a local LLM bike )

> whether constant use of “agent” harnesses will lead to an atrophy of the software engineering (or really any field) muscles

Well, I think most neuropsychologists would agree that the answer is "yes, there will be atrophy" - if you don't use it, you lose it.

> So what’s the ideal “middle ground” in this situation?

I've been thinking a lot about this myself. My current plan is to train myself to get good at recognizing the feeling of "there's potential effort here that I want to outsource to the LLM" and occasionally choosing to not outsource it and do it by hand - especially with personal projects, where there's far less pressure to ship with velocity than work projects - but I'm not settled on this. I'll take any idea!

> I can’t help but wonder whether constant use of “agent” harnesses will lead to an atrophy of the software engineering (or really any field) muscles.

It will, but I'm not sure the impact of this will be all too great. We suffer from not knowing how to use an abacus because we have a calculator, and people who feel a pull to keep their low-level chops up will do so anyway.

> I can’t help but wonder whether constant use of “agent” harnesses will lead to an atrophy of the software engineering (or really any field) muscles.

This will and is 100% happening. I have a friend who hasn't written code by hand in around a year, but uses LLMs every day, and he tells me he can't remember how to write code by hand anymore. He has been a developer for 10 years. But he's not working for anyone at the moment, so I imagine if he was in a workplace the circumstances would be different and they probably wouldn't settle for this.

I think that as a result of this it likely also atrophies the problem solving and architecture building skills that writing the code manually gives you. It just ends up degrading into a loop of tell the agent to do X and assume it knows what it is doing.

> I can’t help but wonder whether constant use of “agent” harnesses will lead to an atrophy of the software engineering (or really any field) muscles.

Compiler evolutions really harmed how well software engineers understand or how often they have to drop down to assembly language.

Is that a problem for the 99% of developers around? Probably not.

I view LLMs as the next evolution. Some people will still need to care about the layer below, the shape of the code that is being written. But over time, just as it was with the transition from crafted ASM to higher level languages, the compilers became better, more efficient and trustworthy and I think the same will happen to LLMs, and we probably won't have to check the generated code as much, at least for most of the code around.

Is that a problem? Yes, for code that is intended to interface with humans (most of it still). The quality will probably become better and it won't be much of an issue.

I don't think there is necessarily one ideal middle ground here. It still feels to me like what's best is a function that depends on who and when.

I see it as something like a personal gradient descent. You're working on a problem, there are solutions down there somewhere, and you can kind of feel the gradient of the tools-and-techniques ground around you. Any way you walk means you're investing time improving some skill or another. So you should go the way that personally feels to you will best get you moving in the direction that you want to go.

For some people it's obvious LLMs are competent coders, getting better, sticking around... and those people should lean into that gradient. For some people what's obvious is nearly the exact opposites of all that, and I'd encourage those people to also follow their gradient/heart/nose down the path of sharpening their personal traditional coding skills. Some people are in a relatively flat area where nothing is obvious, and need to explore and maybe just keep doing their best to hedge with a bit of both.

In a way, this happened before LLMs with more workforce-fit education, decision tree flowcharts, then software, and so on. If you take most people who started any field, the way they started the field would look very unorthodox, inefficient, etc. "From the margins," as pg might say. Margins where more intuitive skills for the craft are present. Ie, the skills to come up with the model pastry are not the same skills for the pastry line to be baked in a factory.

> So what’s the ideal “middle ground” in this situation?

I use agents to code. But I remember the early days of just AI smart complete in the IDE, where as the programmer I had to be more involved with designing and implementating the solution. This kept me engaged with the implementation as it was being built out. Now with agents, I find myself trying to catch up with what the agent did and spend more time code reviewing. Maybe you end up in the same place in the end. But building the implementation, vs code reviewing, feels more rewarding and I think helps keep your mental tool sharpened.

> and am noticing a speedup in areas I was already familiar with, and a quicker introduction to new ones.

Only problem is that, like a LLM, you don't retain anything.

Hobby projects may become a lot more important now because if you do them without LLMs you may retain a brain cell or two.

I have some hobby projects I write for fun without an LLM, just to learn. And also have hobby projects that use LLM extensively, also just to learn.

Get some hobby projects.

>Can we really be sure that thoughts, opinions, taste will still come out sharper and faster after five, ten, 20 years of using these tools almost every day?

After 5 years, I think the thought profile every power user of the LLMs would be an LLM derived carbon copy of each other.

Prepare the world to get even more boringly uniform

> but risking being left in the dust productivity-wise?

What's the risk here? Left behind by who or what?

> Or taking an “agent first” approach and trying to learn and improve more only on the side, as more of an afterthought?

This reads like anxiety resulting from FOMO.

Here's my take: I don't care about LLMs or AI in the sense that I don't feel any need or want to use them. I've only ever tinkered with the free ChatGPT. Never opened an account with any LLM vendor and never even considered it. I program by hand for the joy of it and sometimes for work. Still by hand as I have been doing. MY work gives me that luxury. For now.

Am I obsolete? Am I no longer of any value to society? Of course not. That thinking is just implanted by a group of money hungry individuals who don't give a fuck about me, you or society as a whole. So why would or should I care about LLMs?

You can run to the farm to kill your own chicken so you can stay sharp and fit or figure out what's economically valuable. Or make a bet on what will be valuable later and commit to a brand of yourself

> I can’t help but wonder whether constant use of “agent” harnesses will lead to an atrophy of the software engineering (or really any field) muscles.

For sure. You cannot have "only higher level thoughts" without doing lower level work.

Ironically llm themselves prove that because you cannot remove facts like 'paris is capital of france' from llm and have it just retain 'high level thoughts' like 'countries have capitals that you can look up'

> Conversely, I also am a user of LLMs (true shocker these days, I know), and am noticing a speedup in areas I was already familiar with, and a quicker introduction to new ones. The obvious benefit cannot be denied, and doing so regardless makes you look uninformed. [Excluding people who don’t want anything to do with LLMs out of moral principle, which curiously just like the overarching topic I also both respect and understand, but on the other hand don’t do myself.]

Setting aside my moral outrage over the magic token machines. What about me, who gets so tripped up over minor factual errors, that I'm unable to let them go, and it taints the whole conversation such that I'm too wrapped up in my frustration that I can't think about it clearly? Or my innate drive for correctness that's so strong that I eval the minor errors in output, as catastrophically incompatible with my goals?

> Stoically continuing to sharpen your skills on your own, but risking being left in the dust productivity-wise?

I don't believe there's a meaningful productivity increase. Please cite your published (not preprint) peer-reviewed research that proves the productivity improvement. Until then, I'm unconvinced. (Believe me I'd like to be convinced of reality, the answer is still unresolved, and I have my opinions, but I'd rather something conclusive that I can have confidence in)

Then, even if you did show a significant productivity improvement, it wouldn't help me. I have too many qualms over the output quality that I simple can not let go, (I don't think I should, but everyone keeps trying to convince me to lower my quality standards). I don't want something fast, I have plenty of really "fast" things in my life. I exclusively want to add things that are high quality to my life. Things that don't endlessly frustrate me.

The question about where the middle ground is a rhetorically dishonest question. You'd first have to prove/convince me, that there IS a middle ground. Instead of what I believe where that middle ground belongs is quality, and everything emitted by an LLM moves reality in the wrong direction.

Are any of these absolutes? nah, hence my request/demand for peer-review research. All the productivity claims and quality assertions (mine included) are still *exclusively* vibes. But exactly none of them are pristine, (especially not any of the LLM output.)