This is new, the scope of it, its not just about individual "skills" because its all of them; we are being challenged at the very fundamentals of our ability to think deeply and widely and persistently. That has never happened before like this.
It is quite extraordinary and breath-taking at times to see the agents in action; the flipside is that very power renders us both vulnerable to its seduction and enfeeblement on an equal scope - its almost hard-drug like in its potential long-term psychological effects.
Human mind as much as body needs challenge. That's the only way for growth, heck even sustaining some higher cognitive levels.
Nurses, doctors and family members know damn well how life trajectory nosedives for somebody ie suddenly bound to bed, when stimulus and doable challenges are reduced to minimum.
llms remove challenges, or minimize them. I can't image any added value for any engineer apart from cost cutting for employer. Sure, next come folks who are doing 10x compared to before, and some actually do. Even there, I have my doubts. For rest of us, its not good and won't get better unless they price it out of most markets.
I wonder if this same effect happens for very wealthy and/or very senior executives? Those sorts of people have always had numerous people they could 'outsource' their thinking to; delegating work, asking for research/summaries, assigning tasks.
Does handing off that sort of work to people also ruin your skills in the same way? Or are AIs fundamentally different, and if so, why? Because we have no moral or social pressure to not delegate everything?
Im learning new things at a pace I never imagined at 40 years old. New sports, new businesses, new academic pursuits. Technology is a lever and AI is the biggest lever we've ever had. It enables laziness or incredible productivity. Choose your own path forward.
Are you actually learning them or are you just letting the AI kinda do them for you? There was a time I knew how to take a path integral, but kinda-sorta knowing what something is and how to ask a calculator for it is different than knowing yourself.
"[Learning] new sports" strikes me as an especially odd one. I can see how an AI tool could help to learn the theory or perhaps come up with better training or racing strategies, but it won't short-cut the work of developing the necessary physical skills, will it?
I literally just started playing around with Lathe (https://github.com/devenjarvis/lathe) which was shared here on HN a few weeks back. There are some things I have been wanting to learn more about (some for work, some for personal desire), and I used it to generate a tutorial about some of the things.
I have been very pleased with the results so far. I was able to tune the tutorial to exactly what I want to learn, and it did a very good job (at least that i have seen so far). It has made learning fun, since I get to learn exactly what I want, and I can ask the AI questions and have it make changes to the tutorial in real time as i am working through it.
Now, will I keep using this at a rate to fully offset all of the thinking i have stopped doing since i started using AI? I am not sure, I guess time will tell.
i could not get through the hurdles of installing an IDE and js/python modules before.
now i am learning basic scripting and data modeling etc.
it is phenomenal for learning languages.
i built a chicken coop and some furniture. the skills and confidence i gained are real. am i failing to learn certain skills in the process? of course. but I'm getting further then i would on my own, and that is truly meaningful.
you can keep dismissing it; but I'm genuinely using it to break down barriers, give me confidence, and highlight my ignorance in very productive ways.
i find it bizarre how unwilling some people are to recognize that.
Yea, this is a serious problem but honestly it was present before LLMs. Actually knowing, deeply, how to do something was always very difficult and very time consuming. People would substitute it with “edutainment”: youtube videos, Ted Talks, blogs, etc. The thing is nobody was really learning deeply. We were training our brains for recognition, but mastery requires training for generation which was always the harder of the 2 cognitive processes.
I agree with the sentiment of the parent comment. But I also sometimes question what you have posed exactly. Am I really learning or getting the curiosity itch scratched. The line is very thin sometimes. But I think I am more in the camp of learning. I can ask so dumb question that I never had the courage to ask nor were they entertained where I was educated. It has been a boon to finally ask.
For example I am following Dirac's book "The Principles of quantum mechanics" to study QM. Pre-AI wouldn't have been able to do so, I am just that dumb. Even with AI its tough. but the thing is I can keep asking questions until I get that concept drilled in. Now I am doing it at a pace that's unfathomable to me.
But now that I am getting to grips with QM, I can get to things that I am really interested to learn like spin resonance and so on. This is something I am so grateful for.
Now it can be questioned that is it making me wise, intelligent or just "giving me answers" that I should strive to discover myself. I dont know the answer to that. But studying what I want, how I want and not getting judged is something i deeply enjoy. Srry the comment might have taken some tangents.
Despite arguing fervently against people arguing LLMs can replace software engineers, I'd argue LLMs are the best tool for actually learning things ever created. Instead of having to read a several hundred page book meandering around, or having to pay a private tutor, you simply have infinite access to a tool that is 80% of a private tutor, at all times, with infinite patience, that answers your questions directly and can come up with any number of concrete examples and teaching exercises to demonstrate concepts.
There are flaws, of course. Hallucinations will waste your time and set you down the wrong path sometimes, but I'd contend that you can get 'hallucinations' in an educational course or book anyways; there is no shortage of garbage educational content in the world. LLMs don't need to be perfect, they just need to be less bad than the existing regime. Ideally everyone would have access to a genuine human expert who would provide the guidance they need, but we live in a world where education is turned into an assembly line paradigm with people being taught mechanically in classes of 30+, so the bar is on the ground.
It is obviously a shame that the average person uses LLMs the way they do, as a shortcut to avoid learning anything, and I think on net they're detrimental to society as they're currently used. Part of the problem is that you can't make someone learn if they don't love learning in its own right. But I think LLMs have tremendous utility for people who are genuinely interested in learning.
Of course. If you don’t use something it atrophies not only in non use but in losing interest in keeping up with the state of the art in said tech.
What we gain though is for people don’t possess that knowledge in the first place, now have this superpower. I know several individuals who have vast experience in specific disciplines and they are now able to solve real problems where there were previously struggling and having to make existing solutions work.
In the context of software engineering it allows people that have great institutional knowledge bypass the software market and construct stuff on their own - or at least prototype something and turn it over to an SE if the situation dictates.
I’ve been using CC for several months now and have noticed an increasing quality of output - Fable 5 I think was 85% there. At 95% SE’s are going to be increasingly looking for work to do.
To the title though, I’ve noticed while my desire to actually write code is decreasing CC is forcing me to improve my high level thought processes in the context of overarching goals in a project through discussion with CC. The software often introduces things that had escaped me or just think more outside the box.
My concerns are that this technology will be restricted at some point and the people making the restrictions will have a lot of control - and we know how that works out. But I believe they are inevitable, first obvious example being Fable 5. Are guardrails needed - yeah sure. Common sense says that I don’t want someone able to concoct an easily transmittable Ebola virus that has a 90 day incubation period in their kitchen but I do want an entrepreneur to be able to build a competitor to MS Office, or a cure for Ebola, for example.
> What we gain though is for people don’t possess that knowledge in the first place, now have this superpower.
Indeed. The great innovation of AI is giving people with wealth access to vast amounts of knowledge, while limiting the amount of wealth that people with knowledge can access.
All the benefits you're describing apply to the present moment; people with knowledge, self-discipline and expertise can leverage LLMs to great effect.
How many people like this will exist in a decade? Two?
I haven't written a full function of code in over a year. That being said, I've been spending a lot more time thinking about architecture and system properties.
So, yes, I do feel like I've lost some of that very low-level skill. But maybe I've also been able to spend more time on a higher level skill? Maybe the doctors got worse with the images but had more cognitive resources to think about the patient's context?
Not sure.
But yes, I can't physically get myself to write code without an AI anymore. It feels so much slower, almost painful.
I’m curious if someone coming into that fresh, without having had the mental exercise of, for example, grappling with data structures and (lower-level) algorithms in practical applications by hand, could achieve a mindset that useful and productive?
When I was in design school, as much of our work was in physical media — graphite, cut paper, paint, vine charcoal — as it was practicing great kerning and getting experience with the digital tools. Even though you still had to make the individual strokes and choose appropriate tools in the digital realm, there was still a perception of the process that was obviously lacking among those that came from strictly digital backgrounds. It’s similar to seeing someone draw that’s only used photo references — there’s an entire part of the cognitive process not being used when you’re drawing something that’s already 2D. But image generating, even with extremely granular inpainting and such, is so different it’s not even comparable.
Kind of like how millennials, many of whom always had access to technology, but also experienced dial-up-era computer use, are generally more technically savvy than the your stereotypical “iPad kid” that can’t even traverse a directory structure.
Tool use typically follows this curve. If you want to preserve a skill you have to actually preserve it. This isn't inherently bad by itself, tools enable us to do much more than we can without them and its a point of contention whether or not any skill is inherently important when a tool comes along that does it for us.
One of the challenges here is that the skillset we are in danger of letting atrophy is essentially unbounded. It’s not a specialized tool like a calculator, where you have a well scoped domain of problems you are offloading. granted, in practice many people are using ai for specialized domains (like coding or producing visual designs). But whatever level of abstraction they are currently working at is not, in principle, something that they couldn’t also offload to ai.
The huge problem in this specific case is that to use this tool well you also need the underlying skill to be developed and preserved. It's very different from a power drill.
Sure. But if you don't own the tool and it is held by a cabal of centralist (even political state-adjacent) parties, you're having a bad day when computer says no.
If they can keep up. Unfortunately, we learn from previous technology shifts that the masses will always favor ease of use (to the point of infinite scroll 5 second videos dopamine puddle, or echo chamber social networking in lieu of critical media consumption), which does not bode well for the market for alternative hardware: one which is already expensive.
I fear on-prem AI is likely to become as popular as on-prem servers without Cloudflare using self-hosted email are today: that is to say, people have heard of it but the skillset is almost popularly eviscerated, external policies make it progressively impractical, and anyone who does it is 'niche'. While basic guides will exist, obtaining top-level output will probably require many moons of concerted effort.
Consider another perspective: They don't have to keep up. Once a model is good enough for a task, the model can stay. A hammer is a hammer and a hammer from 100 years ago still has most of its utility.
Similar to the hammer, it's not unreasonable to think that some classes of work will simply be solved by some model generation and whatever happens at the frontier after that does not matter all that much for work that puny humans do.
Then, of course, there will be a time where all of this is moot: Absolutely no human will want a human to diagnose their medical issues. That is not a skill deterioration issue. We simply will concede that we are not able to do it as well as a more capable system can, and without much fanfare, increasingly delegate, as we have always done.
Eh, this is our species first contact with that type of technology. A good number of voices see how deleterious these things are, and it’s all still very new. Future humans will tell parables about the evil tech bros and their silly obsessions, and the unequal accumulation of capital. This will be seen as a dark and stupid time, but I think we’ll persevere - the tech bro set is much weaker than they imagine, and certainly than they project.
We love our open source models. GLM 5.2 came out recently and the timeline for closing the gap to closed source shrank to something like 2 weeks by popular measurements.
Edit: Mis remembered the timeline I saw not 2 weeks, 3 months, but still I think my point stands.
Just to make sure I understand your argument. Are you saying that today's open source models are on par with frontier closed models of two weeks ago? By what criteria?
Commenters there were saying GLM 5.2 was roughly equivalent to Opus 4.8 in coding prowess, based on personal experience of the people commenting. Opus 4.8 came out on May 28 this year (so more like 3 weeks ago), GLM 5.2 came out 2 days ago.
I haven't tried this specific model, but you can understand that a lot of HN testimonials are bound to swing extremely pro open source. I certainly hope they are as good. But I have personally tried a lot of models that are supposedly as good as the frontier ones and have found them lacking.
After a while, you do start to start to skip a couple rounds of open source models until there's a notable release. That, and the resources needed to run them are increasingly bought up by the owners of frontier models
Some effect is real, but it's likely overstated by poor metrics.
"Currency" in all fields relates to the recency and frequency with which you dealt with a particular issue. Whether flying on auto-pilot or coding with AI, automated reduces some currency. But is that a reduction in capability?
Measuring concrete tasks makes currency the operative skill; that's why it works to cram for standardized and mid-level tests.
(Indeed, the 2010's interviewing "wisdom" about people being quick to answer simple questions veered into measuring currency, not skill.)
I think this effect is strongest in time-impacted professionals. Doctors doing dozens of endoscopies a week and developers churning out code will use what tool leverage they can, and forget as much as possible to focus on what they need to. I suspect the effect is weaker in personal or research projects.
People riding bikes won't be able to run long distances - because they won't have to, and will be able to outdo any runners. That's only a problem if the supply of bikes is someone constrained. So the risk is not skill loss, but losing control of the means of production.
How do you measure the bundle of "skills" that comprises critical thinking? And if that's your analogy's distance running, what's the "riding a bike" analogue?
My compiler writing skills atrophied with the advent of high-level languages, but in exchange I got more done. There is still a very well paid market for compiler writers, but the fact that not everyone needs to be one has made the world richer overall.
There are a lot of mundane coding skills I consciously put off learning in case they'd ever become obsolete, and now I'm glad I did. Like sure learning React was good, but Angular? That boilerplate is Claude's job now. Ruby? Forget that.
Is this a situation where AI will go away and we will regret the loss of skills? At worse, we will be forced to use open weight models instead of the cutting edge, so I don't think it's a big deal. I'm sure people got worse at arithmetic after the invention of the calculator.
I don't think the real threat is at the individual level, but at the societal level.
Building skills over time leads to insights that lead to innovation.
AI does many interesting things, but it doesn't innovate (yet).
The real threat isn't that we'll all lose our skills (possibly) and then lose access to AI (unlikely), it is that AI will remain at roughly currently levels and we'll dull our skills due to reliance on it and innovation will stall because we've offloaded too much of the thinking to the non-innovative machine.
I'm not saying this is what definitely will happen, but it does seem like a very possible outcome.
The number of people now involved in software development has now increased because of a lower barrier to entry. I know many people who would previously use a no-code tool or hire offshore devs, or simply not have their problem solved, who are now vibe coding. Many of these people couldn't write very much code manually if they had to, but they're closer to understanding software than they were previously.
Yeah, this describes me perfectly. I can't really program, but I'm now building a bunch of different projects and submitting PRs that people seem to appreciate.
One of them, a Home Assistant integration for controlling adjustable beds, would be borderline impossible to do well manually - I've vibe-reverse engineered the Bluetooth protocols of more than a 100 Android apps.
> The number of people now involved in software development has now increased because of a lower barrier to entry.
I don't see what this has to do with what I posted.
Yes, AI lowers the floor on software development, and there are positive aspects of that, but that doesn't change the possibility of an innovation stall.
re: concerns at a societal level. More people are making software than before. People who have been coding for a long time have moved up an abstraction layer and are further from the code. But many people are actually closer to the code than before.
> But many people are actually closer to the code than before.
More people are coding, I wouldn't say they are closer to the code if they are vibe coding. Are any of them going to produce the next breakthrough in computer language/framework/method of development/etc?
The risk of AI is that we dull the skills of enough people at the high end of the state of the art of the nuts and bolts of software development that we slow down innovation on that end. That's the concern.
Previously-non-programmers vibe coding CRUD apps they never could have before is all well and good but really has nothing to do with this concern. They may create wonderful and successful businesses but they are irrelevant to computer science related innovation.
Humans will become individually and independently less skilled while having access to tools that allow them to do far more than even the most skilled human could, before having access to these tools.
I'm not sure if we'll become less intelligent. I think our sacks of neurons are gonna keep on making associations, just across a different set of topics.
The bit about computer science (behind the paywall) starts:
> To investigate whether skills are being lost in the field of computer science, researchers at the AI firm Anthropic in San Francisco, California, designed a randomized controlled trial in which 52 software engineers were asked to perform a basic coding task
New tool that does task better than worker leads to workers being less good at task. Net outcome for patient is positive. Next?
Programming: "for a given task, if you take a shortcut then you will not have the familiarity and expertise that someone who took the veritable and righteous path would have".
The question is then, what did you do with the extra time. If it's fuck all, then yes, that's a liability.
Like any technology, it comes down to the disposition of any given person in how they plan on applying it.
Not trying to say it's all going to be awesome. Definitely maybe the opposite. These arguments are weak tho.
Back in the mid-90s I was doing desktop support. It was a lot of work because PCs were relatively new (and they were garbage!), and people broke shit all the time. Sometime around '96 a disk-cloning utility called Ghost was released. It was great - one could provision a fully working PC with all required apps and config settings in minutes! Sounds lame now, but back then it was revolutionary. It had a dark side, though. After about a year most people I worked with had lost the ability to troubleshoot even the most basic problems. The solution to every problem was to just re-apply the standard Ghost disk image (we called it 'Ghosting' back then) ... Can't print? Ghost it! Not receiving emails? Ghost it! Word is too slow? Ghost it!
Happened with all of tech support, really, or at least in my corner of the world: you take your PC to a technician and you receive in return a fresh Windows install, a folder with most of your files (including a copy of "C:\Windows"), and none of the programs or shortcuts you had before.
> To investigate whether skills are being lost in the field of computer science, researchers at the AI firm Anthropic in San Francisco, California, designed a randomized controlled trial in which 52 software engineers were asked to perform a basic coding task3. During the exercise, all 52 participants could search the web and access instructions on how to do the task. Half of the participants were prompted to use an AI assistant as well.
> Afterwards, all of the software engineers were asked to complete a quiz about what they had learnt from the task. The participants who had used an AI assistant did significantly worse on the quiz than those who hadn’t: the average score was 50% in the AI group versus 67% in the non-AI group.
This doesn't strike me as a great test? Most engineers aren't going to learn anything from a basic coding task anyway, so I do wonder exactly what they were testing there. If it was just recall about what the issue was, then it doesn't really strike me as a problem - using AI to handle simple problems that it's clearly capable of dealing with is the right way to use it, and of course you're not going to spend time poring over the details because then you haven't saved any time by using AI.
There are other examples that don't strike me as particularly problematic, like GPS eroding people's sense of direction. It's totally reasonable to let a skill atrophy that you no longer really need because you have an ever-present tool to handle it. I'm a lot worse at doing long division than I was when I was <whatever grade one learns long division in>.
The whole skill atrophy thing seems like much less of a problem than it's made out to be. We've been letting skills atrophy for good reason long before the advent of AI. If you start at McDonald's as a fry cook and work your way up to regional manager, if you suddenly have to work a shift on the fry station you're going to be worse than you were when you were doing it all the time. MDs at investment banks almost certainly can't put together a pitch deck as well as the junior bankers who are doing that task regularly. These things are fine - part of moving up in the world and having a broader impact is being able to successfully delegate tasks, and when you delegate tasks your skill at those tasks will atrophy. No real difference whether you're delegating them to AI or not.
To be clear, there are of course cases where skill atrophy is bad. iLoveOncall posted about senior engineers in their org who have lost all of those skills and their judgment along with them. That's definitely bad! If you delegate so much that you lose the ability to even judge good work, now you can't even delegate effectively any more.
I think the real lesson with AI is that you need to be self-aware about what skills you should practice and retain vs. what skills you can let atrophy, since it's easier than ever to hand things off. I've lost most of my ability to write a SQL query, but that's fine because it was only a skill I used intermittently and AI can always do the job fine at the level of complexity I need. I have not let my skill of writing product specs atrophy (I am a PM, in case you haven't read my username), because that's critical to using AI correctly in the first place.
What is most troubling for me is seeing kids just switch off when LLMs are available. Doing homework they will have zero interpretation or contemplation, just enter the question as a prompt and record the result. LLMs appear to have the ability to interfere with the most basic aspects of attention and executive function.
At least for writing, I think AI is mostly useful for the types of writing that aren’t particularly interesting or worthwhile in having to begin with.
In concrete terms, AI isn’t all that useful for writing a personal blog, because no one wants to read obvious AI slop. But it is useful for creating boilerplate product pages, FAQs, and other types of writing that weren’t very interesting pre-AI.
So it’s not really a huge deal to me that my skill for writing descriptive product page text or FAQs is atrophying, assuming that it is.
Not worthwhile feels a bit strong (a good FAQ is definitely worthwhile!), but I definitely agree that there is a big difference between any kind of art (writing, playing music, creation of images/videos/etc.) for its own sake and for commercial purposes. AI is terrible for the former but perfectly fine for the latter.
There will always be value in a human writing fiction or a memoir or even a Substack. The human perspective is inherently valuable there. Much less so with ad copy that's just going to get A/B tested ad infinitum until a winner is picked out based entirely on data.
Same with visual art. Art painters aren't going to lose their jobs to AI, but once you've got a robot that can paint a house reliably, house painters are done for.
The two senior engineers in my org (in a FAANG) who vibe-code the most have lost literally all of their skills. Their code has become terrible and their judgment even worse.
All of this makes me selfishly excited for my own future. It's glaringly obvious that anyone who's a heavy user of LLMs is atrophying their skills in real-time. I have yet to meet a single person for whom it's not the case.
But I essentially completely stopped using them for software engineering (why isn't really relevant, but it's not because od this skill atrophy). So as the skills of everyone else is diminishing, mine is proportionally raising.
It has never been easier to get better than others. You don't need to put in more effort, just the same effort as you always have, and others will do the job of losing their skills for your own benefit.
Idk man my system design game is better than its ever been because I put in the effort to use these tools and recognize they can't do software design better than I can and because I've increased the scope of what I'm building I often have to think more deeply about the problem up front. A typical speccing sessions lasts a few hours for me on big work before I have AI start writing that work where I'm just going back and forth on what I want, points of consideration for performance, usability, structure etc pushing back on where AI (always) chooses the most naive way it wants to do something.
Every time I see an anecdote like this, A it reaffirms my belief that FAANG devs are fairly mediocre on the whole (not saying this is you, obviously there are good FAANG devs) and B it reaffirms my belief that the developers who kind of give up their thinking like this are really using the tool wrong or didn't really care about the work before AI either so its now just a quick means to an end.
+100 on this. In addition, if you don't outsource your thinking and you're willing to go through all this, you absolutely don't need the top tier models.
> Every time I see an anecdote like this, A it reaffirms my belief that FAANG devs are fairly mediocre on the whole
I think another (partially causal?) problem is how they're managed. The whole perf circus is just ridiculous, especially the stuff recently reported about facebook. But they're all more or less like that. Steeped in that cocktail of incentives, who even knows what might happen to an otherwise excellent engineer.
But also just numerically, they can't be much above average, on average, because there are so many.
I strongly believe that you cannot evaluate how good a system design is if you don't implement it by hand.
LLMs will implement what you ask them to, even if it is the wrong approach. They can be lazy and take shortcuts all the time, but they do not feel PAIN (obviously they don't feel anything and aren't lazy, I'm just personifying them but you get the point). Only when you implement by hand can you feel if the implementation of your design is painful or not, and only this signal can tell you if your design if truly good or not.
I do think LLMs are useful for design work, they are good at asking clarifications and probing questions which actually do push you to approach problems differently, but leaving implementation of designs to LLM is a recipe for disaster, and judging your own design skills when you're not implementing such designs is seriously laughable. And to be clear, it already was before LLMs, when "software architects" were just designing and then had peons implement for them.
LLMs are enabling a whole new level of bad code that is best describe by the following Jurassic Park quote: “Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should.”.
> A it reaffirms my belief that FAANG devs are fairly mediocre on the whole
Off-topic but having worked in other companies as well, I can guarantee you that this is not the case. The skill of engineers in FAANGs and other "top tier" companies is much higher than average.
I recently did a manual exercise to force myself to keep my skills from decaying too much after about a year of using agents exclusively. My ability to go from a blank slate to software was indeed in the toilet, but my ability to reason over and edit code seems to be surviving fine. I suspect that your LLM-pilled coworkers' judgment issues are related to laziness that LLMs have enabled, rather than an inherent property of LLM use.
> I had the same experience over the past year with early coding harness at the beginning of the year, then Claude code since its release date. But after 1+year going that direction I really don’t want to continue. The novelty is gone, dealing with AI now feels frustrating and boring, I miss engaging deeply with the actual lower level technical challenges. I do not want to manage fleets of agents. I do not want to rediscover for the hundredth time that in fact all this time an agent took shortcuts for acceptance tests I rely upon and didn’t catch. Or once again get the agent to understand why and what I want it to do after its context got bloated and it start to drift completely. While I got artifacts I can use (libraries, tools, docs), including some things that I’m pretty confident are SoA I do not feel satisfied anymore knowing that I used a model to generate them, even if I was the one designing every part of it. I do feel that I’m lying anytime I come to a colleague to share a new cool tool I have made.
> YMMV but I’m personally feeling burnt out with AI coding agents and ready to go back to the old ways for my next personal project
Yes, feeling like I had to relearn to walk. The first week was rough, everything was wired for LLM usage and autocomplete. Couldn't even type right anymore.
Are you actually seeing any signs that we’re going back to how software was written before, and needing those skills in the same way? Because I sure am not seeing that right now. As someone who vibe codes 100% and has become managements favorite, while being more or less allowed to break the platform every other release I know my skills are atrophying. But it’s taking me different places in my career entirely. There’s a path to managing other engineers now that opened years before it would have previously. Even writing this makes it sound ridiculous, but that’s what’s infront of me right now. There is an entirely other set of skills that I’m interested in sharpening now. Definitely no more sitting down several hours per day and meeting about system design and integrations with others.
It's not really about going back. Evolution happens within a pattern of ebb and flow, back and forth. We never get anything perfectly right. We overdo, then course correct, rinse and repeat. Right now, we're embracing AI, but we're also noticing atrophy of skill as an effect. These may be the last generations of such craftspeople that can notice, compare, and inform as to whether there's actual loss. That future you're seeing for yourself is still being written. Stay tuned.
The article is saying that using AI degrades certain skills when AI is not available. You're claiming that AI is making people less effective even when they have access to AI. I'm skeptical of your claim.
The article's claim is probably true, but not really an argument against AI. Using keyboards degrades my ability to write by hand but that's not a good argument against keyboards. AI will become another tool that allows us to operate more effectively and at a higher level of abstraction. Just like keyboards and Python.
Now, we still occasionally need people who can write assembly (and do calligraphy). But mostly we don't.
As someone who was self taught as a programmer and has a reasonable high level understanding of some CS concepts but not lots of experience applying them, and no good mentor, I’ve found working with an LLM really englightening. Asking Claude to think about “good ways to structure this” or asking how similar problems get solved in industry or high profile projects has really helped me design better solutions and avoid painfully reinventing wheels (recent eg was for a plugin type architecture).
I think a lot of academics and researchers who code but aren’t software engineers or CS majors are going to benefit, provided they take the time to prove what the model does and are curious about whether it’s doing something sensible!
Relative to a 1% coder hand rolling something then yes it’s AI slop etc. but it’s prob still raising the bar generally.
Another tactic is to use LLMs to help you learn. That's another way to approach "It has never been easier to get better than others."
Avoiding tool use because you're afraid you won't be able to use the tool responsibly is not likely to be a winning strategy in the end. Learning to use the tool well is much more effective.
But they're also unreliable in what they present, they still hallucinate. I rather do my own research or listen to a real human on the topic who actually has an internal concept and structure of what they're talking about.
Writing code seems more like walking to me; at least it is the most manual way of getting a computer program. Horses might be more like one of those low-code/no-code solutions (it really fits, they are useful but very opinionated, so not always cooperative). And, the situation with AI seems a bit worrying for them.
To continue the modified analogy, if your friends lost the ability to walk, you’d be quite worried, right?
AI has allowed me to keep shipping features and system even when holding a normally managerial position, so if anything it preserved some of my coding skills. I'd not have seen any code otherwise (writing code is a huge time sink compared to managing things around an org).
I pity those who need to contend with that as ICs, though.
Hopefully this is not your case but as an IC the “manager, who didn’t write code for a couple of years” that decides to come back and “help” is one of the worst experiences. Code is usually subpar, uses old idioms, and comes with added pressure. With AI this can become much worse, because of the sheer volume of code that can be thrown at the IC.
My current position is more on the operational side and AI allowed me to create a pretty significant software system for our technical ops, that the wider org liked and given me the resources to recruit flesh-made engineers to support.
AI/AR Glasses that show you every piece of knowledge about whatever your looking at might not help, especially if the AI is wrong. Otherwise everyones a know it all?
That's a great analogy - everybody knows you forget pretty much everything after a few years away from the line. It should be seen like one of those studies that proves smiling is correlated with happiness. :-)
A skill which is now done better by a machine is no longer a skill, it is technology. It is just a matter of time before most of our logical and language reasoning skills are replaced by frontier model-agents, which will at some point be far superior (if not already) to human capability.
So I totally disagree with this premise that human skills are being ruined by the use of AI technology. No, many human skills are being made obsolete. That's a good thing for economic productivity as a whole, but for those who only have skills that are being automated, their labor value decreases (which is usually bad for them as individuals).
You miss the point, the point is that by using AI, our skills (let say "coding in Rust") are diminishing and even without reading this article, we can feel it to some extent already if we aren't lying to ourselves, especially very heavy AI users.
We do however create new skills, skills that might be more relevant for the future, but still, it is controversial.
This is new, the scope of it, its not just about individual "skills" because its all of them; we are being challenged at the very fundamentals of our ability to think deeply and widely and persistently. That has never happened before like this.
It is quite extraordinary and breath-taking at times to see the agents in action; the flipside is that very power renders us both vulnerable to its seduction and enfeeblement on an equal scope - its almost hard-drug like in its potential long-term psychological effects.
Human mind as much as body needs challenge. That's the only way for growth, heck even sustaining some higher cognitive levels.
Nurses, doctors and family members know damn well how life trajectory nosedives for somebody ie suddenly bound to bed, when stimulus and doable challenges are reduced to minimum.
llms remove challenges, or minimize them. I can't image any added value for any engineer apart from cost cutting for employer. Sure, next come folks who are doing 10x compared to before, and some actually do. Even there, I have my doubts. For rest of us, its not good and won't get better unless they price it out of most markets.
I wonder if this same effect happens for very wealthy and/or very senior executives? Those sorts of people have always had numerous people they could 'outsource' their thinking to; delegating work, asking for research/summaries, assigning tasks.
Does handing off that sort of work to people also ruin your skills in the same way? Or are AIs fundamentally different, and if so, why? Because we have no moral or social pressure to not delegate everything?
Im learning new things at a pace I never imagined at 40 years old. New sports, new businesses, new academic pursuits. Technology is a lever and AI is the biggest lever we've ever had. It enables laziness or incredible productivity. Choose your own path forward.
You're not learning them. You're being told about them and given a hammer to leverage them with mediocre to low skill level.
Learning requires a huge time investment. Using an LLM doesn't shorten that.
Are you actually learning them or are you just letting the AI kinda do them for you? There was a time I knew how to take a path integral, but kinda-sorta knowing what something is and how to ask a calculator for it is different than knowing yourself.
"[Learning] new sports" strikes me as an especially odd one. I can see how an AI tool could help to learn the theory or perhaps come up with better training or racing strategies, but it won't short-cut the work of developing the necessary physical skills, will it?
I literally just started playing around with Lathe (https://github.com/devenjarvis/lathe) which was shared here on HN a few weeks back. There are some things I have been wanting to learn more about (some for work, some for personal desire), and I used it to generate a tutorial about some of the things.
I have been very pleased with the results so far. I was able to tune the tutorial to exactly what I want to learn, and it did a very good job (at least that i have seen so far). It has made learning fun, since I get to learn exactly what I want, and I can ask the AI questions and have it make changes to the tutorial in real time as i am working through it.
Now, will I keep using this at a rate to fully offset all of the thinking i have stopped doing since i started using AI? I am not sure, I guess time will tell.
I'm using it to learn coding.
i could not get through the hurdles of installing an IDE and js/python modules before.
now i am learning basic scripting and data modeling etc.
it is phenomenal for learning languages.
i built a chicken coop and some furniture. the skills and confidence i gained are real. am i failing to learn certain skills in the process? of course. but I'm getting further then i would on my own, and that is truly meaningful.
you can keep dismissing it; but I'm genuinely using it to break down barriers, give me confidence, and highlight my ignorance in very productive ways.
i find it bizarre how unwilling some people are to recognize that.
Yea, this is a serious problem but honestly it was present before LLMs. Actually knowing, deeply, how to do something was always very difficult and very time consuming. People would substitute it with “edutainment”: youtube videos, Ted Talks, blogs, etc. The thing is nobody was really learning deeply. We were training our brains for recognition, but mastery requires training for generation which was always the harder of the 2 cognitive processes.
I agree with the sentiment of the parent comment. But I also sometimes question what you have posed exactly. Am I really learning or getting the curiosity itch scratched. The line is very thin sometimes. But I think I am more in the camp of learning. I can ask so dumb question that I never had the courage to ask nor were they entertained where I was educated. It has been a boon to finally ask.
For example I am following Dirac's book "The Principles of quantum mechanics" to study QM. Pre-AI wouldn't have been able to do so, I am just that dumb. Even with AI its tough. but the thing is I can keep asking questions until I get that concept drilled in. Now I am doing it at a pace that's unfathomable to me.
But now that I am getting to grips with QM, I can get to things that I am really interested to learn like spin resonance and so on. This is something I am so grateful for.
Now it can be questioned that is it making me wise, intelligent or just "giving me answers" that I should strive to discover myself. I dont know the answer to that. But studying what I want, how I want and not getting judged is something i deeply enjoy. Srry the comment might have taken some tangents.
It's kinda easy to measure if you've learned something. You can test yourself if you can do what you've learned without AI.
Despite arguing fervently against people arguing LLMs can replace software engineers, I'd argue LLMs are the best tool for actually learning things ever created. Instead of having to read a several hundred page book meandering around, or having to pay a private tutor, you simply have infinite access to a tool that is 80% of a private tutor, at all times, with infinite patience, that answers your questions directly and can come up with any number of concrete examples and teaching exercises to demonstrate concepts.
There are flaws, of course. Hallucinations will waste your time and set you down the wrong path sometimes, but I'd contend that you can get 'hallucinations' in an educational course or book anyways; there is no shortage of garbage educational content in the world. LLMs don't need to be perfect, they just need to be less bad than the existing regime. Ideally everyone would have access to a genuine human expert who would provide the guidance they need, but we live in a world where education is turned into an assembly line paradigm with people being taught mechanically in classes of 30+, so the bar is on the ground.
It is obviously a shame that the average person uses LLMs the way they do, as a shortcut to avoid learning anything, and I think on net they're detrimental to society as they're currently used. Part of the problem is that you can't make someone learn if they don't love learning in its own right. But I think LLMs have tremendous utility for people who are genuinely interested in learning.
Of course. If you don’t use something it atrophies not only in non use but in losing interest in keeping up with the state of the art in said tech.
What we gain though is for people don’t possess that knowledge in the first place, now have this superpower. I know several individuals who have vast experience in specific disciplines and they are now able to solve real problems where there were previously struggling and having to make existing solutions work.
In the context of software engineering it allows people that have great institutional knowledge bypass the software market and construct stuff on their own - or at least prototype something and turn it over to an SE if the situation dictates.
I’ve been using CC for several months now and have noticed an increasing quality of output - Fable 5 I think was 85% there. At 95% SE’s are going to be increasingly looking for work to do.
To the title though, I’ve noticed while my desire to actually write code is decreasing CC is forcing me to improve my high level thought processes in the context of overarching goals in a project through discussion with CC. The software often introduces things that had escaped me or just think more outside the box.
My concerns are that this technology will be restricted at some point and the people making the restrictions will have a lot of control - and we know how that works out. But I believe they are inevitable, first obvious example being Fable 5. Are guardrails needed - yeah sure. Common sense says that I don’t want someone able to concoct an easily transmittable Ebola virus that has a 90 day incubation period in their kitchen but I do want an entrepreneur to be able to build a competitor to MS Office, or a cure for Ebola, for example.
> What we gain though is for people don’t possess that knowledge in the first place, now have this superpower.
Indeed. The great innovation of AI is giving people with wealth access to vast amounts of knowledge, while limiting the amount of wealth that people with knowledge can access.
It's completely bass-ackwards.
All the benefits you're describing apply to the present moment; people with knowledge, self-discipline and expertise can leverage LLMs to great effect.
How many people like this will exist in a decade? Two?
Meanwhile the people with mountains of money can leverage them to enough effect to make even bigger mountains of money.
I haven't written a full function of code in over a year. That being said, I've been spending a lot more time thinking about architecture and system properties.
So, yes, I do feel like I've lost some of that very low-level skill. But maybe I've also been able to spend more time on a higher level skill? Maybe the doctors got worse with the images but had more cognitive resources to think about the patient's context?
Not sure.
But yes, I can't physically get myself to write code without an AI anymore. It feels so much slower, almost painful.
I’m curious if someone coming into that fresh, without having had the mental exercise of, for example, grappling with data structures and (lower-level) algorithms in practical applications by hand, could achieve a mindset that useful and productive?
When I was in design school, as much of our work was in physical media — graphite, cut paper, paint, vine charcoal — as it was practicing great kerning and getting experience with the digital tools. Even though you still had to make the individual strokes and choose appropriate tools in the digital realm, there was still a perception of the process that was obviously lacking among those that came from strictly digital backgrounds. It’s similar to seeing someone draw that’s only used photo references — there’s an entire part of the cognitive process not being used when you’re drawing something that’s already 2D. But image generating, even with extremely granular inpainting and such, is so different it’s not even comparable.
Kind of like how millennials, many of whom always had access to technology, but also experienced dial-up-era computer use, are generally more technically savvy than the your stereotypical “iPad kid” that can’t even traverse a directory structure.
The question is what higher level skills there will be to focus on?
I am not convinced that there are tasks, like project management or architecture, that the Ai is inherently worse at.
We never learned how to use IBM punch cards. Writing functions by hand for modern languages is fast becoming anachronistic.
Tool use typically follows this curve. If you want to preserve a skill you have to actually preserve it. This isn't inherently bad by itself, tools enable us to do much more than we can without them and its a point of contention whether or not any skill is inherently important when a tool comes along that does it for us.
One of the challenges here is that the skillset we are in danger of letting atrophy is essentially unbounded. It’s not a specialized tool like a calculator, where you have a well scoped domain of problems you are offloading. granted, in practice many people are using ai for specialized domains (like coding or producing visual designs). But whatever level of abstraction they are currently working at is not, in principle, something that they couldn’t also offload to ai.
The huge problem in this specific case is that to use this tool well you also need the underlying skill to be developed and preserved. It's very different from a power drill.
This is true of most tools, it's just that we generally consider the skills simple.
I'm very bad at using power drills.
Sure. But if you don't own the tool and it is held by a cabal of centralist (even political state-adjacent) parties, you're having a bad day when computer says no.
The tools aren't owned by a cabal of state-adjacent parties. Specific implementations are. But open-source models have saved the day here.
If they can keep up. Unfortunately, we learn from previous technology shifts that the masses will always favor ease of use (to the point of infinite scroll 5 second videos dopamine puddle, or echo chamber social networking in lieu of critical media consumption), which does not bode well for the market for alternative hardware: one which is already expensive.
I fear on-prem AI is likely to become as popular as on-prem servers without Cloudflare using self-hosted email are today: that is to say, people have heard of it but the skillset is almost popularly eviscerated, external policies make it progressively impractical, and anyone who does it is 'niche'. While basic guides will exist, obtaining top-level output will probably require many moons of concerted effort.
Basically: AI is SaaS for thinking.
> If they can keep up.
Consider another perspective: They don't have to keep up. Once a model is good enough for a task, the model can stay. A hammer is a hammer and a hammer from 100 years ago still has most of its utility.
Similar to the hammer, it's not unreasonable to think that some classes of work will simply be solved by some model generation and whatever happens at the frontier after that does not matter all that much for work that puny humans do.
Then, of course, there will be a time where all of this is moot: Absolutely no human will want a human to diagnose their medical issues. That is not a skill deterioration issue. We simply will concede that we are not able to do it as well as a more capable system can, and without much fanfare, increasingly delegate, as we have always done.
Eh, this is our species first contact with that type of technology. A good number of voices see how deleterious these things are, and it’s all still very new. Future humans will tell parables about the evil tech bros and their silly obsessions, and the unequal accumulation of capital. This will be seen as a dark and stupid time, but I think we’ll persevere - the tech bro set is much weaker than they imagine, and certainly than they project.
We love our open source models. GLM 5.2 came out recently and the timeline for closing the gap to closed source shrank to something like 2 weeks by popular measurements.
Edit: Mis remembered the timeline I saw not 2 weeks, 3 months, but still I think my point stands.
Just to make sure I understand your argument. Are you saying that today's open source models are on par with frontier closed models of two weeks ago? By what criteria?
Sorry definitely mis-remembered on my part, its about 3 months
https://x.com/yaroslavvb/status/2067367657272422584 https://x.com/voratiq/status/2067667800643268928 https://arena.ai/leaderboard/agent
HN thread from 2 days ago:
https://news.ycombinator.com/item?id=48567759
Commenters there were saying GLM 5.2 was roughly equivalent to Opus 4.8 in coding prowess, based on personal experience of the people commenting. Opus 4.8 came out on May 28 this year (so more like 3 weeks ago), GLM 5.2 came out 2 days ago.
I haven't tried this specific model, but you can understand that a lot of HN testimonials are bound to swing extremely pro open source. I certainly hope they are as good. But I have personally tried a lot of models that are supposedly as good as the frontier ones and have found them lacking.
After a while, you do start to start to skip a couple rounds of open source models until there's a notable release. That, and the resources needed to run them are increasingly bought up by the owners of frontier models
Some effect is real, but it's likely overstated by poor metrics.
"Currency" in all fields relates to the recency and frequency with which you dealt with a particular issue. Whether flying on auto-pilot or coding with AI, automated reduces some currency. But is that a reduction in capability?
Measuring concrete tasks makes currency the operative skill; that's why it works to cram for standardized and mid-level tests.
(Indeed, the 2010's interviewing "wisdom" about people being quick to answer simple questions veered into measuring currency, not skill.)
I think this effect is strongest in time-impacted professionals. Doctors doing dozens of endoscopies a week and developers churning out code will use what tool leverage they can, and forget as much as possible to focus on what they need to. I suspect the effect is weaker in personal or research projects.
People riding bikes won't be able to run long distances - because they won't have to, and will be able to outdo any runners. That's only a problem if the supply of bikes is someone constrained. So the risk is not skill loss, but losing control of the means of production.
How do you measure the bundle of "skills" that comprises critical thinking? And if that's your analogy's distance running, what's the "riding a bike" analogue?
My compiler writing skills atrophied with the advent of high-level languages, but in exchange I got more done. There is still a very well paid market for compiler writers, but the fact that not everyone needs to be one has made the world richer overall.
Comparison never made sense to me
1/ When dealing with High level language I am not seeing assembly or the language it compiles to. It's not a leaky abstraction
2/ It's deterministic
The day my markdown file is the thing I deploy on AWS your analogy will stand
I like it when AI metaphors presume determinism.
You clearly don't know what you're talking about if you think compilers are a good analogy.
There are a lot of mundane coding skills I consciously put off learning in case they'd ever become obsolete, and now I'm glad I did. Like sure learning React was good, but Angular? That boilerplate is Claude's job now. Ruby? Forget that.
Is this a situation where AI will go away and we will regret the loss of skills? At worse, we will be forced to use open weight models instead of the cutting edge, so I don't think it's a big deal. I'm sure people got worse at arithmetic after the invention of the calculator.
> I'm sure people got worse at arithmetic after the invention of the calculator
For LLMs, we can see this sentence but replace "arithmetic" with a variable X
I'm sure people got worse at X after the invention of LLMs"
The problem isn't that X skills atrophy necessarily
The problem is that for LLMs, X is "basically all knowledge and communication skills"
Can we really tolerate a society where "basically all knowledge and communication skills" are atrophying?
I don't think the real threat is at the individual level, but at the societal level.
Building skills over time leads to insights that lead to innovation.
AI does many interesting things, but it doesn't innovate (yet).
The real threat isn't that we'll all lose our skills (possibly) and then lose access to AI (unlikely), it is that AI will remain at roughly currently levels and we'll dull our skills due to reliance on it and innovation will stall because we've offloaded too much of the thinking to the non-innovative machine.
I'm not saying this is what definitely will happen, but it does seem like a very possible outcome.
The number of people now involved in software development has now increased because of a lower barrier to entry. I know many people who would previously use a no-code tool or hire offshore devs, or simply not have their problem solved, who are now vibe coding. Many of these people couldn't write very much code manually if they had to, but they're closer to understanding software than they were previously.
Yeah, this describes me perfectly. I can't really program, but I'm now building a bunch of different projects and submitting PRs that people seem to appreciate.
https://github.com/kristofferR
One of them, a Home Assistant integration for controlling adjustable beds, would be borderline impossible to do well manually - I've vibe-reverse engineered the Bluetooth protocols of more than a 100 Android apps.
> The number of people now involved in software development has now increased because of a lower barrier to entry.
I don't see what this has to do with what I posted.
Yes, AI lowers the floor on software development, and there are positive aspects of that, but that doesn't change the possibility of an innovation stall.
re: concerns at a societal level. More people are making software than before. People who have been coding for a long time have moved up an abstraction layer and are further from the code. But many people are actually closer to the code than before.
> But many people are actually closer to the code than before.
More people are coding, I wouldn't say they are closer to the code if they are vibe coding. Are any of them going to produce the next breakthrough in computer language/framework/method of development/etc?
The risk of AI is that we dull the skills of enough people at the high end of the state of the art of the nuts and bolts of software development that we slow down innovation on that end. That's the concern.
Previously-non-programmers vibe coding CRUD apps they never could have before is all well and good but really has nothing to do with this concern. They may create wonderful and successful businesses but they are irrelevant to computer science related innovation.
At worse you suffer from cognitive atrophy. Become more dumb and lazy.
Humans will become individually and independently less skilled while having access to tools that allow them to do far more than even the most skilled human could, before having access to these tools.
I'm not sure if we'll become less intelligent. I think our sacks of neurons are gonna keep on making associations, just across a different set of topics.
The bit about computer science (behind the paywall) starts:
> To investigate whether skills are being lost in the field of computer science, researchers at the AI firm Anthropic in San Francisco, California, designed a randomized controlled trial in which 52 software engineers were asked to perform a basic coding task
That's this study here: https://arxiv.org/abs/2601.20245 - also written about on the Anthropic research site here: https://www.anthropic.com/research/AI-assistance-coding-skil...
Colonoscopy detection has gotten modestly better with AI assistance.
https://pubmed.ncbi.nlm.nih.gov/39216648/
https://www.cancer.gov/news-events/cancer-currents-blog/2023...
https://www.nejm.org/doi/full/10.1056/NEJMoa1309086
https://info.asge.org/083024-colon-asge/acg-quality-task-for...
New tool that does task better than worker leads to workers being less good at task. Net outcome for patient is positive. Next?
Programming: "for a given task, if you take a shortcut then you will not have the familiarity and expertise that someone who took the veritable and righteous path would have".
The question is then, what did you do with the extra time. If it's fuck all, then yes, that's a liability.
Like any technology, it comes down to the disposition of any given person in how they plan on applying it.
Not trying to say it's all going to be awesome. Definitely maybe the opposite. These arguments are weak tho.
Back in the mid-90s I was doing desktop support. It was a lot of work because PCs were relatively new (and they were garbage!), and people broke shit all the time. Sometime around '96 a disk-cloning utility called Ghost was released. It was great - one could provision a fully working PC with all required apps and config settings in minutes! Sounds lame now, but back then it was revolutionary. It had a dark side, though. After about a year most people I worked with had lost the ability to troubleshoot even the most basic problems. The solution to every problem was to just re-apply the standard Ghost disk image (we called it 'Ghosting' back then) ... Can't print? Ghost it! Not receiving emails? Ghost it! Word is too slow? Ghost it!
https://en.wikipedia.org/wiki/Ghost_(disk_utility)
Happened with all of tech support, really, or at least in my corner of the world: you take your PC to a technician and you receive in return a fresh Windows install, a folder with most of your files (including a copy of "C:\Windows"), and none of the programs or shortcuts you had before.
LGTM.
> To investigate whether skills are being lost in the field of computer science, researchers at the AI firm Anthropic in San Francisco, California, designed a randomized controlled trial in which 52 software engineers were asked to perform a basic coding task3. During the exercise, all 52 participants could search the web and access instructions on how to do the task. Half of the participants were prompted to use an AI assistant as well.
> Afterwards, all of the software engineers were asked to complete a quiz about what they had learnt from the task. The participants who had used an AI assistant did significantly worse on the quiz than those who hadn’t: the average score was 50% in the AI group versus 67% in the non-AI group.
This doesn't strike me as a great test? Most engineers aren't going to learn anything from a basic coding task anyway, so I do wonder exactly what they were testing there. If it was just recall about what the issue was, then it doesn't really strike me as a problem - using AI to handle simple problems that it's clearly capable of dealing with is the right way to use it, and of course you're not going to spend time poring over the details because then you haven't saved any time by using AI.
There are other examples that don't strike me as particularly problematic, like GPS eroding people's sense of direction. It's totally reasonable to let a skill atrophy that you no longer really need because you have an ever-present tool to handle it. I'm a lot worse at doing long division than I was when I was <whatever grade one learns long division in>.
The whole skill atrophy thing seems like much less of a problem than it's made out to be. We've been letting skills atrophy for good reason long before the advent of AI. If you start at McDonald's as a fry cook and work your way up to regional manager, if you suddenly have to work a shift on the fry station you're going to be worse than you were when you were doing it all the time. MDs at investment banks almost certainly can't put together a pitch deck as well as the junior bankers who are doing that task regularly. These things are fine - part of moving up in the world and having a broader impact is being able to successfully delegate tasks, and when you delegate tasks your skill at those tasks will atrophy. No real difference whether you're delegating them to AI or not.
To be clear, there are of course cases where skill atrophy is bad. iLoveOncall posted about senior engineers in their org who have lost all of those skills and their judgment along with them. That's definitely bad! If you delegate so much that you lose the ability to even judge good work, now you can't even delegate effectively any more.
I think the real lesson with AI is that you need to be self-aware about what skills you should practice and retain vs. what skills you can let atrophy, since it's easier than ever to hand things off. I've lost most of my ability to write a SQL query, but that's fine because it was only a skill I used intermittently and AI can always do the job fine at the level of complexity I need. I have not let my skill of writing product specs atrophy (I am a PM, in case you haven't read my username), because that's critical to using AI correctly in the first place.
Is C ruining our memory allocation skills? Early results are in - and they're not good
What is most troubling for me is seeing kids just switch off when LLMs are available. Doing homework they will have zero interpretation or contemplation, just enter the question as a prompt and record the result. LLMs appear to have the ability to interfere with the most basic aspects of attention and executive function.
At least for writing, I think AI is mostly useful for the types of writing that aren’t particularly interesting or worthwhile in having to begin with.
In concrete terms, AI isn’t all that useful for writing a personal blog, because no one wants to read obvious AI slop. But it is useful for creating boilerplate product pages, FAQs, and other types of writing that weren’t very interesting pre-AI.
So it’s not really a huge deal to me that my skill for writing descriptive product page text or FAQs is atrophying, assuming that it is.
Not worthwhile feels a bit strong (a good FAQ is definitely worthwhile!), but I definitely agree that there is a big difference between any kind of art (writing, playing music, creation of images/videos/etc.) for its own sake and for commercial purposes. AI is terrible for the former but perfectly fine for the latter.
There will always be value in a human writing fiction or a memoir or even a Substack. The human perspective is inherently valuable there. Much less so with ad copy that's just going to get A/B tested ad infinitum until a winner is picked out based entirely on data.
Same with visual art. Art painters aren't going to lose their jobs to AI, but once you've got a robot that can paint a house reliably, house painters are done for.
The two senior engineers in my org (in a FAANG) who vibe-code the most have lost literally all of their skills. Their code has become terrible and their judgment even worse.
A very similar topic was discussed here: https://news.ycombinator.com/item?id=48392004 and I make the exact same conclusion:
All of this makes me selfishly excited for my own future. It's glaringly obvious that anyone who's a heavy user of LLMs is atrophying their skills in real-time. I have yet to meet a single person for whom it's not the case. But I essentially completely stopped using them for software engineering (why isn't really relevant, but it's not because od this skill atrophy). So as the skills of everyone else is diminishing, mine is proportionally raising.
It has never been easier to get better than others. You don't need to put in more effort, just the same effort as you always have, and others will do the job of losing their skills for your own benefit.
Idk man my system design game is better than its ever been because I put in the effort to use these tools and recognize they can't do software design better than I can and because I've increased the scope of what I'm building I often have to think more deeply about the problem up front. A typical speccing sessions lasts a few hours for me on big work before I have AI start writing that work where I'm just going back and forth on what I want, points of consideration for performance, usability, structure etc pushing back on where AI (always) chooses the most naive way it wants to do something.
Every time I see an anecdote like this, A it reaffirms my belief that FAANG devs are fairly mediocre on the whole (not saying this is you, obviously there are good FAANG devs) and B it reaffirms my belief that the developers who kind of give up their thinking like this are really using the tool wrong or didn't really care about the work before AI either so its now just a quick means to an end.
+100 on this. In addition, if you don't outsource your thinking and you're willing to go through all this, you absolutely don't need the top tier models.
> Every time I see an anecdote like this, A it reaffirms my belief that FAANG devs are fairly mediocre on the whole
I think another (partially causal?) problem is how they're managed. The whole perf circus is just ridiculous, especially the stuff recently reported about facebook. But they're all more or less like that. Steeped in that cocktail of incentives, who even knows what might happen to an otherwise excellent engineer.
But also just numerically, they can't be much above average, on average, because there are so many.
I strongly believe that you cannot evaluate how good a system design is if you don't implement it by hand.
LLMs will implement what you ask them to, even if it is the wrong approach. They can be lazy and take shortcuts all the time, but they do not feel PAIN (obviously they don't feel anything and aren't lazy, I'm just personifying them but you get the point). Only when you implement by hand can you feel if the implementation of your design is painful or not, and only this signal can tell you if your design if truly good or not.
I do think LLMs are useful for design work, they are good at asking clarifications and probing questions which actually do push you to approach problems differently, but leaving implementation of designs to LLM is a recipe for disaster, and judging your own design skills when you're not implementing such designs is seriously laughable. And to be clear, it already was before LLMs, when "software architects" were just designing and then had peons implement for them.
LLMs are enabling a whole new level of bad code that is best describe by the following Jurassic Park quote: “Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should.”.
> A it reaffirms my belief that FAANG devs are fairly mediocre on the whole
Off-topic but having worked in other companies as well, I can guarantee you that this is not the case. The skill of engineers in FAANGs and other "top tier" companies is much higher than average.
I recently did a manual exercise to force myself to keep my skills from decaying too much after about a year of using agents exclusively. My ability to go from a blank slate to software was indeed in the toilet, but my ability to reason over and edit code seems to be surviving fine. I suspect that your LLM-pilled coworkers' judgment issues are related to laziness that LLMs have enabled, rather than an inherent property of LLM use.
You're making the right call there. I've felt it, seen it around me personally. That study is terrifying.
I'm no code ninja at the best of times. It's scary to hear that's happening to top engineers.
I need an exit strategy. Anyone else come off AI?
Yeah, I had the same and decided to cancel all my subscriptions and remove all my local models ~1 months ago ago.
So far I’m very happy with my decision.
I wrote about it here: https://news.ycombinator.com/item?id=48083162
> I had the same experience over the past year with early coding harness at the beginning of the year, then Claude code since its release date. But after 1+year going that direction I really don’t want to continue. The novelty is gone, dealing with AI now feels frustrating and boring, I miss engaging deeply with the actual lower level technical challenges. I do not want to manage fleets of agents. I do not want to rediscover for the hundredth time that in fact all this time an agent took shortcuts for acceptance tests I rely upon and didn’t catch. Or once again get the agent to understand why and what I want it to do after its context got bloated and it start to drift completely. While I got artifacts I can use (libraries, tools, docs), including some things that I’m pretty confident are SoA I do not feel satisfied anymore knowing that I used a model to generate them, even if I was the one designing every part of it. I do feel that I’m lying anytime I come to a colleague to share a new cool tool I have made.
> YMMV but I’m personally feeling burnt out with AI coding agents and ready to go back to the old ways for my next personal project
And also here (specifically to human communication): https://sam.elborai.me/articles/no-more-llm-comms/.
Yes, feeling like I had to relearn to walk. The first week was rough, everything was wired for LLM usage and autocomplete. Couldn't even type right anymore.
Are you actually seeing any signs that we’re going back to how software was written before, and needing those skills in the same way? Because I sure am not seeing that right now. As someone who vibe codes 100% and has become managements favorite, while being more or less allowed to break the platform every other release I know my skills are atrophying. But it’s taking me different places in my career entirely. There’s a path to managing other engineers now that opened years before it would have previously. Even writing this makes it sound ridiculous, but that’s what’s infront of me right now. There is an entirely other set of skills that I’m interested in sharpening now. Definitely no more sitting down several hours per day and meeting about system design and integrations with others.
It's not really about going back. Evolution happens within a pattern of ebb and flow, back and forth. We never get anything perfectly right. We overdo, then course correct, rinse and repeat. Right now, we're embracing AI, but we're also noticing atrophy of skill as an effect. These may be the last generations of such craftspeople that can notice, compare, and inform as to whether there's actual loss. That future you're seeing for yourself is still being written. Stay tuned.
The article is saying that using AI degrades certain skills when AI is not available. You're claiming that AI is making people less effective even when they have access to AI. I'm skeptical of your claim.
The article's claim is probably true, but not really an argument against AI. Using keyboards degrades my ability to write by hand but that's not a good argument against keyboards. AI will become another tool that allows us to operate more effectively and at a higher level of abstraction. Just like keyboards and Python.
Now, we still occasionally need people who can write assembly (and do calligraphy). But mostly we don't.
> two senior engineers in my org (in a FAANG) who vibe-code the most have lost literally all of their skills.
Unfortunately FAANG incentives this behaviour with their token leaderboards and general push for velocity over anything else (other than goog maybe)
This is the most important point: it seems like AI tool use undermines one's ability to use the AI tool.
I did stop because I noticed the severe atrophy after using them for a year. Never again.
As someone who was self taught as a programmer and has a reasonable high level understanding of some CS concepts but not lots of experience applying them, and no good mentor, I’ve found working with an LLM really englightening. Asking Claude to think about “good ways to structure this” or asking how similar problems get solved in industry or high profile projects has really helped me design better solutions and avoid painfully reinventing wheels (recent eg was for a plugin type architecture).
I think a lot of academics and researchers who code but aren’t software engineers or CS majors are going to benefit, provided they take the time to prove what the model does and are curious about whether it’s doing something sensible!
Relative to a 1% coder hand rolling something then yes it’s AI slop etc. but it’s prob still raising the bar generally.
Another tactic is to use LLMs to help you learn. That's another way to approach "It has never been easier to get better than others."
Avoiding tool use because you're afraid you won't be able to use the tool responsibly is not likely to be a winning strategy in the end. Learning to use the tool well is much more effective.
But they're also unreliable in what they present, they still hallucinate. I rather do my own research or listen to a real human on the topic who actually has an internal concept and structure of what they're talking about.
My friends can't ride horses as well as they used to, they've been using cars to get around too much.
Sure horses are more efficient, but cars are faster and more convenient, and allow you to get a lot more done.
Also cars will get better in our lifetime, horses are horses.
Just try to take those same friends who drive everywhere on a (walking) trail hike inside the city, then come back to report.
Writing code seems more like walking to me; at least it is the most manual way of getting a computer program. Horses might be more like one of those low-code/no-code solutions (it really fits, they are useful but very opinionated, so not always cooperative). And, the situation with AI seems a bit worrying for them.
To continue the modified analogy, if your friends lost the ability to walk, you’d be quite worried, right?
Your analogy doesn't make very much sense.
Perhaps it would be better to compare somebody who drives a car vs someone who used to drive but now uses Uber.
https://archive.ph/NS4Ae
AI has allowed me to keep shipping features and system even when holding a normally managerial position, so if anything it preserved some of my coding skills. I'd not have seen any code otherwise (writing code is a huge time sink compared to managing things around an org).
I pity those who need to contend with that as ICs, though.
Hopefully this is not your case but as an IC the “manager, who didn’t write code for a couple of years” that decides to come back and “help” is one of the worst experiences. Code is usually subpar, uses old idioms, and comes with added pressure. With AI this can become much worse, because of the sheer volume of code that can be thrown at the IC.
Nothing like that.
My current position is more on the operational side and AI allowed me to create a pretty significant software system for our technical ops, that the wider org liked and given me the resources to recruit flesh-made engineers to support.
A rare case of AI creating jobs.
AI/AR Glasses that show you every piece of knowledge about whatever your looking at might not help, especially if the AI is wrong. Otherwise everyones a know it all?
Are employees ruining managers' skills? Late results are in - and they're not good.
That's a great analogy - everybody knows you forget pretty much everything after a few years away from the line. It should be seen like one of those studies that proves smiling is correlated with happiness. :-)
A skill which is now done better by a machine is no longer a skill, it is technology. It is just a matter of time before most of our logical and language reasoning skills are replaced by frontier model-agents, which will at some point be far superior (if not already) to human capability.
So I totally disagree with this premise that human skills are being ruined by the use of AI technology. No, many human skills are being made obsolete. That's a good thing for economic productivity as a whole, but for those who only have skills that are being automated, their labor value decreases (which is usually bad for them as individuals).
You miss the point, the point is that by using AI, our skills (let say "coding in Rust") are diminishing and even without reading this article, we can feel it to some extent already if we aren't lying to ourselves, especially very heavy AI users.
We do however create new skills, skills that might be more relevant for the future, but still, it is controversial.