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.
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?
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/.
Edit: Mis remembered the timeline I saw not 2 weeks, 3 months, but still I think my point stands.
https://x.com/yaroslavvb/status/2067367657272422584 https://x.com/voratiq/status/2067667800643268928 https://arena.ai/leaderboard/agent
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
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).
We do however create new skills, skills that might be more relevant for the future, but still, it is controversial.
> 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.
So now imagine you're using Chineses AI/AR glasses that you've come to rely on for "knowledge" and you look at the famous picture from Tianemen square: "Doesn't look like anything to me".
I pity those who need to contend with that as ICs, though.
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.
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.
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.
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.
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.
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.
Social media and content algorithms come to mind as an early wave that changed the landscape here that defines the horrible status quo leading into the AI era.
These days it's trivial to slide into an echo chamber and very hard to break out of the silo.
There might be a double-edged sword here where AI, trusted by most people as an omniscient oracle, can offer the only pushback we encounter on positions we picked up passively by scrolling social media, Youtube, TikTok.
For example, ask Claude, ChatGPT, and even Grok about the "space lasers" that started wildfires in Hawaii in 2018, something people like Marjorie Taylor Greene floated on social media. It quickly debunks it as bullshit.
Now, maybe it will pan out such that everyone will have their own AI that tells them what they want to hear. But so far I've watched people abandon arguments on Twitter because Grok rejected their claim. So it feels like there's a glimmer of hope.
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 am not convinced that there are tasks, like project management or architecture, that the Ai is inherently worse at.
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.
You want us to believe you couldn't overcome the puddle-deep challenge of installing an IDE and using Pip or Node in the past, but now you're actually learning how to write functions?
Cool for you if true I guess but I'm pretty seriously skeptical
> 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...
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.
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?
And being born wealthy requires zero skill or practice.
If that is the case, then wouldn't this whole thing be a non-issue? We lose all the skills we used to have, but we don't need them because our entire job now is interacting with AI, and that skill we will continue to develop because it is what we do all day.
I don't know if I fully agree with that. Some skills are important even if you don't need them for your day job.
"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.
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.
How many people like this will exist in a decade? Two?
"Just being aware that this phenomenon exists hopefully provokes some self-reflection about which skills people want to maintain and which they’re willing to outsource” to AI tools. Right. Obviously.
So we need to be teaching that core lesson to children -- they don't retain skills that they don't practice. And we need to be careful to decide what skills and verify they are learning them. We also should absolutely be using AI to provide personalized instruction to every single student.
Blaming the tools for things that humans do is incredibly stupid and dangerously misguided. Because it shirks responsibility onto the technology, when technology is the best lever humans and society have to improve things! It just happens to also be the best lever available to make things worse.
This negative view of improving technology starts from a warped and very unrealistic concept of the state of the world, where it has been, and the role technology has played.
1. Technologies, starting with fire, the printing press, etc. have been critical in raising life expectancy, standard of living, etc.
2. The world is still a profoundly unequal and exploitive place.
3. AI and robotics have the potential to provide everyone on earth who wants it with extremely inexpensive labor to help them with anything they need or can imagine. This will be a dramatic shift in quality of living.
Human society is the source of our problems, not technology. Part of this is that I think deep down people believe that any tools or developments that arise will just be used to exploit and suppress them more, and there is no alternative. In this case, I guess people think the best outcome is to go back to feudalism or some nonsense because technology just makes things worse.
But why stop there? Why not go back to, I don't know.. fire? Or maybe no one should ever eat any red fruit?
> So we need to be teaching that core lesson to children
Yeah, that has always worked out well.
Losing a specific skill to automation isn't necessarily a bad thing. Losing the ability to learn things would be however, and that would be my fear with AI, but I'm not sure it's well-founded. Humans learn naturally by interacting with the world.
And suddenly I was stuck! It was like thoughts weren't forming properly. My instinct was to use Claude to help brainstorm, but I resisted. 5 minutes later, I finally broke free and instantly came up with the plan.
What the hell?
I realized I'd offloaded my planning onto AI. I would ask it for plans and then choose the best one, but that's a different skill than coming up with the plans in the first place. My skills were rotting.
1. Force AI down everyone's throats claiming it's going to boost productivity
2. See people lose valuable skills because they rely too much on AI
3. Peddle more AI to make up for the lack of skills in professionals
If my mechanic started charging 10x more to fix my car, I'd learn to fix my car.
Autocomplete of entire functions and methods. Nice, but also really boring. Takes the fun out it. It's all about fixing sup-par code now, a line here or there.
It's just boring. I tried writing some code by hand today after a few months hardly thinking about things and it was really hard to do even the simplest stuff.
They know that this is one of the biggest de-skilling programmes they have seen.
So expect the return of in person Leetcodes and whiteboard challenges.
I am using LLMs quite a lot, but the amount of time I spend sitting on some slopped out code is I think on average much longer than a lot of my peers. What I've found is that while the original thing "works", it usually winds up being another 2-3 cycles of iterating on the original idea after I've let it settle in my head before I actually feel confident about merging.
As a result, when I add it all up, for actual "this is important" design-level concerns, I do not feel significantly more productive.
If social media is consuming first, or primarily consuming, anyone can scroll their way to a negative rabbit hole that never ends.
If creation is the use it's something else entirely.
AI in the form of interactive chats, can be a novel kind of consumption.
You can have passive conversations in terms of asking a magic genie, or more active ones.
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.
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.
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.
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 think this touches obliquely on a point I keep coming back to, that one of the most important things a codebase does is to communicate ideas about how a process should work. Yes, it also produces some binary that runs on a bunch of servers or whatever, but that's a really temporary, ephemeral artifact. The lasting thing is the idea. Making your ideas (expressed in code) easy to understand, easy to work with, and easy to evolve in time is the art of software engineering. I 100% agree, from my own experimentation with LLMs, glancing at something a model has produced and checking that it has some test coverage isn't enough to know whether it's well-engineered. You'll only find out later when you try to work with the code.
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.
To continue the modified analogy, if your friends lost the ability to walk, you’d be quite worried, right?
Perhaps it would be better to compare somebody who drives a car vs someone who used to drive but now uses Uber.
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.
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.
Unfortunately FAANG incentives this behaviour with their token leaderboards and general push for velocity over anything else (other than goog maybe)
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.
I think this highlights the difference between the “how do I make a ham sandwich?” approach of chat vs the “sudo make me a ham sandwich” of agentic coding.
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.
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
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.
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.
Even if this occurs, and I don't trust well-resourced humans to allow their existing apex-predator positions in present era capitalism to be overturned, the action - as far as either humanity or AI is concerned - will still be at the forefront of possibility: a front by definition invisible to old models. And someone has to pay for the hardware to be there. Do we (a) allow private-sector dominance, effectively depowering traditional nation states and empowering a private cabal beyond historically conceivable levels (b) nationalize thought (c) head in sand and pretend it will all go away?
Most of the world seems to be with strategy C right now, strategy A is the advancing default and has already achieved extra-terrestrial reach with a threat of extra-terrestrial persistence, and strategy B is potentially scarier than the other outcomes if it goes wrong but might be lovely, if you believe in nordic state funds, solarpunk futures and socialist utopia.
Interesting times. By the way, if anyone with AI capitalization reads this, I'm looking for investment to feed humans more efficiently and have a NASDAQ reverse merger under negotiation and effectively priced out with board buy in. Just need capital support. https://infinite-food.com/
I'm very bad at using power drills.
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.
That's not the way the economics behind this work.
Supposing the AI priests are right (they aren't) and using AI creates a thought surplus on the user, freeing cognitive capacity to think of higher things. What do you think will said user's boss want to do with that surplus? Let the user develop higher-level cognitive abilities? I don't think so.
The doctors in the article performed worse post-AI: suppose AI saved them so much time that they did 100 exams in the time they used to take doing 10 exams. What will their employers do with that freed up labour time? They'll of course have the doctors do more exams and perhaps fire some now-redundant doctors that are no longer needed. The surviving doctors are left deskilled, doing the same or more work, and society gets worse quality medical care. But hey, its not all bad - the employer gets to save on labour, and shareholders will be happy.
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.
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.
Why exactly wouldn’t you be able to learn pre-AI?
Learning requires a huge time investment. Using an LLM doesn't shorten that.
An LLM absolutely shortens the research part of learning. If I had a human of who had a moderate level of skill who would endlessly answer all my questions, the result would be the same.
You might have a point when it comes to software development because the AI can tell you things but it also just do them for you, at which point, you've learned a lot less. But for non-software things I have to learn things so I can then go and do them.
But even for software development, I've learned a lot of esoteric crap to get interop working on projects that I will probably quickly forget just the same as when I had to spend hours skimming through stackoverflow.
I've wildly increased my breadth of learning. If I'm ever curious about anything, even a passing thought, I can scratch that itch in a way I never could before.
But am I going deep? Acquiring new skills? Eh... I usually go far enough to unblock myself and/or settle a curiosity. I don't think that's good or bad, but it does present a certain set of tradeoffs that are different than going deep.
LLMs are sycophants, and in long conversations, their sycophancy produces a positive feedback loop: the context window contains affirmations of incorrect interpretations / analogies, so the chatbot continues down that path because, well, that's the most likely completion of previous text. And before you know it, you're discovering the hidden fabric of the universe, which is always some Minkowski fractal spacetime tensor lattice manifold with subharmonic DNA nanotubes.
That is to say, unless you have a robust way to evaluate what you're learning, and to confirm that you're actually learning, I'd tread carefully.
That is my point: an LLM can be great if you know the field and can spot errors. Or, to a lesser extent, if you have some automatic feedback loop that the model can't easily game ("does this code pass unit tests?"). It's a lot less great if there's a risk that you won't detect the early drift.
You aren't learning anything. Learning involves doing.
We've known this for ages: simply reading a maths book without drilling on the problems will not get a student to pass.
Best case scenario, you're reading stuff. For users of coding agents, they're not even doing that.
You can have it write a program that generates drills for you.
I wanted to become better at reading sheet music so I generated a sheet music reading program. You can have it generate maths drills, then ask questions about it if you get stuck or whatever. If you genuinely want to get better at something then AI will help you learn it faster. Obviously its going to hamper more people's cognitive ability that it will enhance but that is a separate problem.
I actually did have an LLM ingest some material and generate drills. It worked well. It's rare that happens, though.
The difference between humans and other animals on the planet had always been the ability to reason. If we, as a species, lose that ability, we're looking at an extinction-level event.
For example: AI has helped me get into restoring retro tech, specifically resoldering leaky caps on retro Macintosh logic boards. Before AI, I didn't know how to use a multimeter (I knew theoretically how it worked), I didn't know how to use flux, solder wick, heat gun. I also didn't understand how bromine radicals yellowed plastic and how to reverse it by using blue light similar to what they use for indoor aquariums.
So AI unlocked doing for me.
You're happy using AI instead of other material because it will constantly tell you how brilliant you are, or how quick you're learning.
Just because you can't use a tool doesn't mean the tool isnt useful.
Your bias on display here is frankly silly. Im not saying LLMs are ALWAYS the best way of learning something just like they aren't always the best at anything. They are a valuable tool though. Yes so is youtube and textbooks, and professsors, and peer review literature, and pen and paper, and block training etc.
I also use AI to take in-progress pictures as I desolder to help me check for traces that need to be repaired or help identifying specific chips. I probably could try and find a video where the same chip is featured and someone explains it, and/or retrieve the schematic for the specific logic board, but that's very painful and does slow the process. Think of AI in this specific case as enabling skill development for me in a field I wouldn't have necessarily have gotten into because of being short on time and AI helps me consolidate that information quickly.
I’d be curious to see alternatives ownerships structures. Like an AI-coordinated collection of guilds, unions, or co-ops. If it can’t accumulate upwards, maybe the fundamental unit of ownership will stay with the workers.
Programming normally highlights this difference. LLM programming makes it much less apparent but its still there, LLM are not thinking the way humans do and therefore struggle to solve many problems humans easily solve. So letting all human programming skills rot and just use LLM will halt our progress unless we reach AGI before our programmings skills are mostly gone.
if this same effect happens for very wealthy and/or very senior executives?
There’s definitely a skill to using AI but it just doesn’t generalize very well.
I still believe the slot machine analogy holds to some extent, but I can honestly say my winning percentage is at least 90% for one shot generated code now.
I think if you know it's limitations (inlcluding your own), I don't think about hoping anymore.
I should note that when I say AI, I mean the collective models from all the major providers. The most important lesson is, you need to ask around.
> There’s definitely a skill to using AI but it just doesn’t generalize very well.
This I agree with. The only way working with AI can really be benefical outside of dealing with AI is, we are visited by extremely intelligent beings that will fuck up in the weirdest ways.
> I don't think about hoping anymore.
Running the exact same prompt again that already failed doesn’t have a that high success rate, but it’s also very low effort. So IMO it’s often worth attempting.
This is why I think even the fuss about Noam Shazeer joining OpenAI needs to be seen within a context; as good as this hire is, there is no inherent reason to believe he still brings some secret undiscovered magic that others do not have in a more current form.
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.
devolving-dev•1h ago
hmmmmmmmmmmmmmm•1h ago
georgemcbay•1h ago
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.
bwhiting2356•1h ago
georgemcbay•1h ago
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.
bwhiting2356•1h ago
georgemcbay•59m ago
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.
bluefirebrand•1h ago
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?