Just like if you pressed a button to wipe out our main language stack at our job, yeah none of us would know how to do anything with assembly or machine code.
All the devs would spend a couple weeks learning assembly and go on coding. And all the non-dev vibe coders would be unable to continue.
So I don't think it's quite the same.
A couple weeks?! Really slacking, why not a few hours?
Add the violin and calculus while they're at it.
You'd probably use the first months/years to rewrite c and such anyways.
Likewise, *many* vibecoders would absolutely fail to adapt, and some would adapt, with this being their first experience. Just think of how many people started coding by using old gamemaker tools or scratch, or playing silly games. It is not the same as an LLM, but the idea that it is a gateway is definitely there. I wouldn't call LLMs "programming", because they're doing more: they're building products, which involves a lot more than programming -- hence why the skills to really thrive with LLMs are not just those of a good developer or engineer.
But weeks? I'd say less than 10% of the devs I've known my entire life would develop properly in assembly (and I'm being quite generous). Memory management, pointers, registers...just to name a few.
I think far too many people fail to realize that, with LLMs, the brain of a good problem solver, especially one with engineering background, gets much better results than someone with different characteristics. Similarly, the mind of a good product developer (and even someone with good UX inclination) does a much better job and building usable products.
This idea that LLMs are doing all of the work is frankly mind-boggling to me. It must stem from vastly different experiences of what LLMs do or whomever is using them around who says these claims. Obviously they do a lot of work, especially in the development prototyping, architecture and even ideation parts. But they only do (almost) "all of the work" in very simple or bad apps.
I've seen loads of terrible vibe-coding jobs: UX is terrible, bugs everywhere, clearly no consideration at any point for architecture (and thus the product is barely holding on). And I've seen the opposite: products that feel and act like premium stuff, because they were built by people with true love for what they do, sometimes even with a very good problem solving mind to add to that (leading to a not-so-terrible-albeit-still-barely-holding-on-but-thats-clearly-good-enough-for-the-product architecture). For every good app there are maybe 100 lovable apps that all look the same and break the second you do the wrong sequence of clicks, but the good ones do exist.
It's a tool that enables mediocrity to exist where it didn't previously, but it also empowers so many people in incredible ways. Still, you definitely need to be good at using the tool, and it definitely doesn't just happen overnight.
I imagine that was x86, which was relatively easy but also horrible. Very few people are writing assembly today because you'd need to be able to do x86-64 SIMD, ARM64, and maybe RISC-V, which are all quite a bit more complicated.
Now retired (come join the dark side, we have cookies) but still a few years away from what people would call "retirement age"
It's vaguely similar to how JS and Node enable so many people to flood the market without really knowing the underlying technology. Many of those would definitely not pick up assembly (which is the entire point I was trying to make)
0 mathematics, 0 algorithms, 0 formal languages.
Universities are pumping out "professionals" like crazy, plus nowadays they are replacing courses with new courses about AI (which might be machine learning in the best case or just prompting in the worst case).
The result is that you need to go and ask what courses where in a degree, and even then it's not really a guarantee of anything (I've met master student with bachelor in computer science who were completely unable to find the smallest number in an array).
And yet: I have not written assembly since school except for one Advent of Code challenge, and I expect it would take me quite a while to pick up a productive level of facility with it.
You're assuming people already know how to code. That's not built-in with vibecoding.
Sure, there are similarities, but while we're figuring out a FOSS solution that will run on your machine and we'll have access to more than the weights, the abstraction ladder isn't as equivalent as we think they are.
Neither is likely to go away. I do not expect it will ever be difficult to access an LLM in the forseeable future, barring massive legislation + enforcement to entirely ban them (I can run several capable coding models locally)
Nor is it likely that for developers at most companies will not have access to at least some LLM of comparably intelligence to those we have today (more advanced models may be restricted or too expensive) - right now large parts of the world economy are retooling to expand ai inference volume, not decrease it
This seems like "what if you don't have a calculator!" - and like with a calculator, there are benefits to learning things without the tool aid of course, n(including developing a sense for when things are wrong) but professionals are using the right tools for the job
Do you think its likely ?
If you scope LLM's to "frontier models", I do expect it to be an ever-growing moat. Maybe not if you work at BigCo who will pump out $X0 million/yr to gain access to it, but definitely for the singular use cases of people navigating to Claude.ai and asking a quick question. That's assuming the companies stay afloat long term to begin with.
If you open it up to open-weight models, then I question how many this will filter out. Will it be a tool valuable enough for people to overcome the large starting hump like Photoshop? Or will some future company come in and streamline the UI/UX to make them as intuitive as possible? Those are the truly interesting questions in my mind.
What? That is absolutely not true. I've been using LLMs for language learning, math/physics, clarify home improvement best practices that I now regularly think about, etc. and that knowledge is not LLM-bound at all. Instead of acquiring from a regular textbook, I acquired it from a model.
I finished the project out feeling pretty confident in my photoshop abilities. Then I did a similar project later and realized I had to open up the LLM again as there were still a ton of holes in my knowledge. After this second time though I'm feeling confident enough on my own that referring back to the LLM is increasingly less common, as it's just a waste of time if I already know what to do.
It seems we're about to get a massive stress test of such a theory.
Sounds like you’re attacking a strawman
LLM is an issue (to most of us) when it replaces exploration, thinking and effort to aquire skill. Whether it's vibe-coding an app, or thinking through a math problem. Jury is out this will atually dimish the next generation of thinkers. But seems like a valid concern to me.
2. This particular case of LLM usage (to teach us photoshop) is replacing traditional tutorials. It can help people learn things. This is also valid to me.
The existence of 2, doesn't eliminate 1. Existence of 1 doesn't eliminate 2. There's no strawmans here. I also think it's fair that the promise of LLMs (and AIs), isn't to replace tutorials, but to displace and automate much more.
The paradigm shift on how every medium+ size FOSSS repo needs to take in PR submission?
The increase in bot activity in social media/
Who's the ones saying this is actually creating higher quality products? No one talks about quality anymore: only speed.
IE: Model do this for me vs teach me.
Enough that I know people who have built businesses purely training people.
Instead it's created swamps in the art community, choas and bloat in the FOSS communities, a stronger sense of mistrust with traditional search, a deepening of the Dead Internet Theory, more broken tools to scrounge through... where is this supposed "benefit" I'm getting out of this?
Or, better, the global menu search. Intellij's shift+shift shortcut is great.
After this I realized why I personally choose LLM to write code: it is simply more convenient.
As a parallel, once you learn how to drive you stop walking, as for the primary mode of transportation.
Is this the reason people prefer to drive, or is it due to city planning, societal expectations, etc.?
Plenty of people who live within cities don't own a car, or if they do, walk more than they drive.
If I could, I would certainly walk everywhere.
Cars:
- create environments we hate being in
- directly impacts public health
- widens inequality
- makes us productively go faster through the terrible environment we’ve set up
LLMs:
- create markets we hate being in
- directly impacts public health
- widens inequality
- makes us productively put out slop faster through the terrible market we’ve set up
Was this unintended satire?
you must be USAnian. I don't know a single person this would apply to
"Please download the ability to do X"
Just that there is still a big latency. Its smaller than finding the correct information on the web, but you can do so. Especially with MCP. The question is when this goes over into the physical world.
Watching Youtube videoas about anything that requires skill does the same.
Even if that were true, LLMs aren't going away, so that isn't a problem.
And it depends on the stock market continuing to go up relative to inflation (real inflation, not measured) and the labour market being strong enough to employ people. Which in turn relies on the USA's exorbitant privilege, which relies on all other western countries generally trusting it.
Human memory is shite. Understanding, and knowing what you need to reference, is ludicrously more valuable than rote memorization.
What’s different after LLMs for me is that there are some things that just don’t care about and I’m fine if they are a black box that the agent handles for me. I didn’t have that luxury before.
If I use an LLM to create something in a programming language I'm not an expert in, I still have a reference point for later use. And that reference should drastically speed up the process of learning how to use said language myself later.
reading code is not the same as writing code, the second one is much harder.
Are you suddenly a history expert because you listened to every Hardcore History podcast? Better to read 1 book on a subject than listen to hours of podcasts. I gaurentee more is remembered from the book.
What I see that everybody is somehow a blacksmith now without knowing how to forge iron. And we call it progress.
I am optimistic, and I am not against LLM. I like a quote from OP’s article:
> One of my colleagues used to say, "AI is a mirror." I find that insightful: it reflects who you are, including the curiosity, judgment, and habits you bring to it. The questions you ask and the standards you apply to its answers matter.
Open models are capable and owned by everyone who wants to.
My worry is that if we need to pivot to those open options, we'd be so far behind in infrastructure, and no one has the hardware to run the open models at their home/office.
Isn't the same true for every service and technology we start to rely on?
Remove GPS and people will have to figure out how to read maps again.
Remove search engines and people will have to figure out how domains work.
Remove Wikipedia and we have to go back to the library to check encyclopedias.
It's not GPS, but turn-by-turn navigation. Remove that and people's spatial understanding of their environment improves significantly, and this improvement persists even long after the experiment, and they'll even be able to use turn-by-turn navigation better.
Which aligns with Anthropic's reports that people are able to use Claude better if they understand their own codebase better, and that increased Claude usage leads to reduced understanding of the codebase.
When society replaced walking as the primary mode of transportation with driving, people started going to the gym to keep their body in shape.
We should probably be training spatial perception (replaced by GPS), understanding of algorithms and data structures (replaced by coding agents), too.
When you repeatedly follow directions, you'll learn how to take that path.
But when you repeatedly read a map, or walk an area on foot, you'll also learn the roads and areas you're not taking nearby, and learning the spatial relationships between these places.
A lot of learning happens through the mistakes we make, because they teach us a lot more about our environment. Whether that's a physical landscape or a codebase.
When you search for a bug, you'll look at maybe one or two files that are related to that bug, but also 10-20 files that aren't. You won't consciously remember the content of those 20 files, but your mind is building a map of their spatial and logical relations, which helps you the next time.
This is anecdotally obviously as well as proven by studies. You don't learn the route or the neighborhood when you just "mindlessly" respond to "turn left" directions.
But in the past year the production boost is just insane. LLM assistance took away so many of the barriers that prevented me from executing on ideas.
I've also learned more than ever before, although it is admittedly getting harder to stay involved when the models are becoming more and more reliable.
What I have not figured out yet is why most developers around me do not share this effect. It's like it mostly works for a very specific subset of people, that do understand code, but it's not their main focus.
Just take a look at what is happening with the M-Wave FM1. Stating those devs would be lost without Ai won't stop the tide. It's not going to go away.
My own usage too. Yet I believe that your environment is biased towards ambitious adults aligned with your values, who want to learn things. I don't think it's representative of world as a whole, and I'm even further worried about the youth, being a particularly vulnerable group. Let's hope that teachers and parents of today and tomorrow will keep reinforcing the idea that knowing things and having skills is worthwhile.
You read more. "Learned" is if you actually manually applied it. Otherwise its like reading fiction - illusion of productivity or brain function.
Solving sudoku would be a better exercise for your brain and more load.
I wonder. I agree that LLMs are great teaching assistants but, also, I often feel like watching the game instead of playing it. Sure I'll be an expert at football after watching all the games, but will I be an athlete?
For me it's more like googling the capital of Nova Scotia. I used to know that, but I can google it more easily than memorize it.
Like, unless it is somehow relevant to your daily life knowing the capital of Nova Scotia is literally useless. Same with all the history I learned in middle school. Remembering takes repetition, and so retaining the information I learn needs a habit of review and processing. And a lack thereof (alongside the brain seeking to offload information, bc information is expensive) is why you forgot the capital city.
I find I remember things best when I jot them down, and personally I’ve been needing to journal more, writing down my ideas and what I learn when discussing my ideas with LLMs, though I’ve also noticed that using the LLM as a rubber duck has actively helped my memory due to more review and processing and needing to “teach” my ideas to the model.
This explains Trump.
That was months ago. It often surfaces pertinent info which is memorable cos it relates to whatchu doin.
I often hear the phrase "use chatgpt" when common people don't know something but want to understand something new. So they use it for learning, even though most likely never think of checking the sources ever.
Been learning Linux, how to build a homelab, and quite a few other related topics since about chatgpt 3.5. If the LLM went away tomorrow I'd still know how to do most things in Linux, set up my opnsense router, reinstall xcp-ng, do everything in docker, and a boatload of other things that would've been so much more tedious to learn before an LLM. Simplicity of access to knowledge doesn't mean you don't learn it. A google search didn't make us learn slower than when we had to scour for books at the library, it increased the pace dramatically. Same goes for LLMs.
You can use LLMs and learn nothing. People who like to learn won't ever use it that way.
Except that it does, learning is mediated by _effort_.
Was it hard? Then what did you gain by using AI?
Was it easy? Then you didn't learn
If you're building a home lab, you might just give up because you are tired of searching for Cisco router syntax.
LLM can be the little bit of lube you need to understand things at a higher level, because the tedious things are not as bad as they used to be.
You then end up actually getting a few things in your lab to work correctly, get a confidence boost, and follow up with some searches about how routing actually works and why the syntax is the way it is.
If I do a project with an LLM, I want to understand exactly how and why it did something that I didn't know how to do. So I ask it. And it's generally very forthcoming.
Generally I don't have "project = simple|hard" as a thing in my life, I generally know a whole bunch about something I want to do, and maybe not the college-level education of the nitty-gritty details.
Do you know what a really good way to learn is ? Once the LLM has taught you what LICM is and why you want it in a compiler, ask it to give you a quiz on the topic. This is a surprisingly excellent way to learn things.
Do you work in education? Learning does not need to be hard at all, this is just a certain education style.
Learning by playing around with the topic with fun would be another one.
Having emotions while learning is a strong enforcer.
And more concretely, finding out why my linux GPU driver is not working is much more fun now, that I don't have to deal with man pages, can ask any stupid question anytime and get a answer likely to be correct immediately - and not have to dig up old outdated forum posts or ask a question somewhere and wait 2 weeks to get a unhelpful answer.
With this I agree.
This makes sense, but i'm assuming you have some sort of technical background to begin with? I imagine people that have been around pre-LLMs would be better equipped for not only prompting the LLMs for accurate information, but also internalizing that information. I wonder if the post-LLM generation would be the same.
: Though I imagine that there's going to be a cutoff date for people born before a certain point pre-LLMs too. Old people rn seem to be much more blindly accepting of whatever the LLMs spit out than younger adults.
If you have a finite time and have a goal, is learning something that takes a longer time better towards that goal than being able to move fast and accumulate more capabilities that are on hand when you ‘forget’ them?
The answer engines and the context around my goals allow me to move at an unprecedented pace and move into places that were just not possible before.
But, the nuances learned concepts are indeed skipped over or Pareto’d
I don't think I'm actually learning more though. I think the act of banging my head against the wall to learn something the hard way teaches me more, but feels harder and is slower.
But then I really retain it, and I've also learned a bunch of meta-skill in the process.
So I think it's just different. Now it's harder to force myself to bang my head against the wall when the easier path is so accessible. Used to just have far fewer available paths because they were too hard.
And it's not just me; that is most people's experience with LLMs
Perhaps me finding your comment and replying has similarities but at least this is validation of curiosity.
I love llms cos history isnt typically indexed by the sort of questions i wanna ask but an llm can cut a wide array of sources by an arbitrary metric and i can keep poking for more and more.
I see a wikipedia parallel. There's peeps among us that will 2am doom scroll wikipedia, peeps that will only do that same on social media and peeps who do a bit of both.
> that is most people's experience with LLMs
I think the median LLM user in high school, for example, is mostly using it to avoid writing essays themselves.
I don’t have great evidence here, mainly just the plummeting exam scores. It could be that kids are also using llms to learn about the things they do care about. I’m skeptical.
Uh... bold claim. What's your evidence?
that's good!
but i also don't make any effort to write clearly anymore, might give up easier on a task, will very easily to defer to an AI for x/y/z technical decision, ...
that's not good!
This seems like a huge mistake considering that prompting AI clearly seems to yield better results.
Don't just assert shit you have no backing for.
In my experience people take what they read from LLMs at face value. I guess my anecdote is as good as yours.
Most of the people in your bubble.
Who are probably significantly above average intelligence, and many of whom have pretty keen awareness of that fact and its implications.
> except for me, only I
... and others in that bubble.
I’d be interested to know how you used it, and I suspect the element of self directed and self-motivate learning are a good part of it.
citation needed. my (university) students don't seem to learn as much as before.