AI is both a near-perfect propaganda machine and, in the programming front, a self-fulfilling prophecy: yes, AI will be better at coding than human. Mostly because humans are made worse by using AI.
Every time I got bad results, looking back I noticed my spec was just vague or relied on assumptions. Of course you can't fix your collegues, if they suck they suck and sombody gotta do the mopping :)
I've never used AI to code, I'm a software architect and currently assume I get little value out of an LLM. It would be useful for me if this debate had a vaguely engineering-smelling quality to it, because its currently just two groups shouting at eachother and handwaving criticism away.
If you actually deal with AI generated problems, I love it, please make a post about it so we have something concrete to point to.
We are talking about a "stupid" tool that parses a google sheet and makes calls to a third-party API
So there is one google sheet per team, with one column per person
One line per day
And each day, someone is in charge of the duty
The tool grabs the data from the sheet and configures pagerduty so that alerts go to the right person
Very basic, no cleverness needed, really straightforward actually
So we have 1 person that wrote the code, with AI. Then we have a second person that checked the code (with AI). Then the shit comes to my desk. To see this kind of cruft:
def create_headers(api_token: str) -> dict:
"""Create headers for PagerDuty API requests.
Args:
api_token: PagerDuty API token.
Returns:
Headers dictionary.
"""
return {
"Accept": "application/vnd.pagerduty+json;version=2",
"Authorization": f"Token token={api_token}",
"Content-Type": "application/json",
}
def delete_override(
base_url: str,
schedule_id: str,
override_id: str,
api_token: str,
) -> None:
"""Delete an override from a schedule.
Args:
base_url: PagerDuty API base URL.
schedule_id: ID of the schedule.
override_id: ID of the override to delete.
api_token: PagerDuty API token.
"""
headers = create_headers(api_token)
override_url = f"{base_url}/schedules/{schedule_id}/overrides/{override_id}"
response = requests.delete(override_url, headers=headers, timeout=60)
response.raise_for_status()
These people fail to produce anything meaningful, this is not really a surprise given their failure to do sane things with such a basic topic
Does AI brings good idea: obviously no, but we knew this. Does AI improves the quality of the result (regardless of the quality of the idea): apparently no Does AI improves productivity: again, given this example: no Are these people better, more skilled or else: no
Am I too demanding ? Am I asking too much ?
> No HTTP keep-alive, no TCP reuse, the API key is passed down to every method, so is the API's endpoint. Timeout is defined in each method. Fix all of those issues.
I wasted so much work time trying to steer one of these towards the light, which is very demotivating when design and "why did you do this?" questions are responded to with nothing but another flurry of commits. Even taking the time to fully understand the problem and suggest an alternative design which would fix most of the major issues did nothing (nothing useful must have emerged when that was fed into the coin slot...)
Since I started the review, I ended up becoming the "blocker" for this feature when people started asking why it wasn't landed yet (because I also have my own work to do), to the point where I just hit Approve because I knew it wouldn't work at all for the even more complex use cases I needed to implement in that area soon, so I could just fix/rewrite it then.
From my own experience, the sooner you accept code from an LLM the worse a time you're going to have. If wasn't a good solution or even was the wrong solution from the get-go, no amount of churning away at the code with an LLM will fix it. If you _don't know_ how to fix it yourself, you can't suddenly go from reporting your great progress in stand-ups to "I have nothing" - maybe backwards progress is one of those new paradigms we'll have to accept?
Obviously, you are also joking about the thing that AI is immune to consanguinity, right ?
For the type of work I do, I found it best to tightly supervise my LLMs. Giving lots of design guidance upfront, and being very critical towards the output. This is not easy work. In fact, this was always the hard part, and now I'm spending a larger percentage of my time doing it. As the impact of design mistakes is a lot smaller, I can just revert after 20 minutes instead of 3 days, I also get to learn from mistakes quicker. So I'd say, I'm improving my skills faster than before.
For juniors though, I think you are right. By relying on this tech from early on in their careers, I think it will be very hard to grow their skills, taste and intuition. But maybe I'm just an old guy yelling at the clouds, and the next generation of developers will do just fine building careers as AI whisperers.
I'm confident you are wrong about that.
AI makes people who are intellectually lazy and like to cheating worse, in the same way that a rich kid who hires someone to do their university homework for them is hurting their ability to learn.
A rich kid who hires a personal tutor and invests time with them is spending the same money but using it to get better, not worse.
Getting worse using AI is a choice. Plenty of people are choosing to use it to accelerate and improve their learning and skills instead.
The concern mostly comes from the business side… that for all the usefulness on the tech there is no clearly viable path that financially supports everything that’s going on. It’s a nice set of useful features but without products with sufficient revenue flowing in to pay for it all.
That paints a picture of the tech sticking around but a general implosion of the startups and business models betting on making all this work.
The later isn’t really “anti-AI hype” but more folks just calling out the reality that there’s not a lot of evidence and data to support the amount of money invested and committed. And if you’ve been around the tech and business scene a while you’ve seen that movie before and know what comes next.
In 5 years time I expect to be using AI more than I do now. I also expect most of the AI companies and startups won’t exist anymore.
If it's all meant to be ironical, it's a huge failure and people will use it to support their AI hype.
Don't let hype deter you to get your own hands dirty and try shit.
One is the time of a human (irreplaceable) and the other is a tool for some human to use, seems proportional to me.
Everyone is replaceable. Software devs aren't special.
- when Google paid $1 bil for YouTube
- when Facebook paid $1 bil for Instagram
- when Facebook paid $1 bil for WhatsApp
The same thing - these 3 companies make no money, and have no path to making money, and that the price paid was crazy and decoupled from any economics.
Yet now, in hindsight, they look like brilliant business decisions.
you lack imagination, human workers are paid globally over $10 trillion dollars.
The way I see it, I can just start using AI once they get good enough for my type of work. Until then I'm continuing to learn instead of letting my brain atrophy.
your argument amounts to “some people said stupid shit one time and I took it seriously”
If you believe that uncritically about everything else, then you have to answer why agentic workflows or MCP or whatever is the one thing that it can't evolve to do for us. There's a logical contradiction here where you really can't have it both ways.
oh I think I do get your point now after a few rereads (correct if wrong but you’re saying it should keep getting better until there’s nothing for us to do). “AI”, and computer systems more broadly, are not and cannot be viable systems. they don’t have agency (ironically) to affect change in their environment (without humans in the loop). computer systems don’t exist/survive without people. all the human concerns around what/why remain, AI is just another tool in a long line of computer systems that make our lives easier/more efficient
Prompt Engineer to AI Engineer: Designing agentic workflows is a waste of time, just pre/postfix whatever input you'd normally give to the agentic system with the request to "build or simulate an appropriate agentic workflow for this problem"
There is a staggering number of unserious folks in the ears of people with corporate purchasing power.
One of the key skills needed in working with LLMs is learning to ignore the hype and marketing and figure out what these things are actually capable of, as opposed to LinkedIn bluster and claims from CEOs who's net worth are tied to investor sentiment in their companies.
If someone spends more time talking about "AGI" then what they're actually building, filter that person out.
This is precisely what led me to realize that while they have some use for code review and analyzing docs, for coding purposes they are fairly useless.
The hypesters responses' to this assertion exclusively into 5 categories. Ive never heard a 6th.
I've learned a lot of new things this year thanks to AI. It's true that the low levels skills with atrophy. The high level skills will grow though; my learning rate is the same, just at a much higher abstraction level; thus covering more subjects.
The main concern is the centralisation. The value I can get out of this thing currently well exceeds my income. AI companies are buying up all the chips. I worry we'll get something like the housing market where AI will be about 50% of our income.
We have to fight this centralisation at all costs!
I don't think that's too far away. Anthropic, OpenAI, etc. are pushing the idea that you need a subscription but if opensource tools get good enough they could easily become an expensive irrelivance.
That sounds incredibly inneficient.
Are you all AI bros calcualting productivity gains on how fast the code was outputed and nothing else?
Guess we are still in the 1970s era of AI computing. We need to hope for a few more step changes or some breakthrough on model size.
I don't think it's a coincidence that some of the best developers[1] are using these tools and some openly advocating for them because it still requires core skills to get the most out of them
I can honestly say that building end-to-end products with claude code has made me a better developer, product designer, tester, code reviewer, systems architect, project manager, sysadmin etc. I've learned more in the past ~year than I ever have in my career.
[0] abandoned cursor late last year
[1] see Linus using antigravity, antirez in OP, Jared at bun, Charlie at uv/ruff, mitushiko, simonw et al
(I had been using GitHub Copilot for 5+ years already, started as an early beta tested, but I don’t really consider that the same)
I like to say it’s like learning a programming language. it takes time, but you start pattern matching and knowing what works. it took me multiple attempts and a good amount of time to learn Rust, learning effective use of these tools is similar
I’ve also learned a ton across domains I otherwise wouldn’t have touched
For now i think people can still catch up quickly, but at the end of 2026 it's probably going to be a different story.
Can you elaborate? Skill in AI use will be a differentiator?
Ah yes, an ecosystem that is fundamentally inherently built on probabilisitic quick sand and even with the "best prompting practices", you still get agents violating the basics of security and committing API keys when they were told not to. [0]
CPUs are billions of transistors. sometimes one fails and things still work. “probabilistic quicksand” isn’t the dig you think it is to people who know how this stuff works
"Trust only me bro".
It takes 10 seconds to see the many examples of API keys + prompts on GitHub to verify that tweet. The issue with AI isn't limited to that tweet which demonstrates its probabilistic nature; Otherwise why do need a sandbox to run the agent in the first place?
Nevermind, we know why: Many [0] such [1] cases [2]
> CPUs are billions of transistors. sometimes one fails and things still work. “probabilistic quicksand” isn’t the dig you think it is to people who know how this stuff works
Except you just made a false equivalence. CPUs can be tested / verified transparently and even if it does go wrong, we know exactly why. Where as you can't explain why the LLM hallucinated or decided to delete your home folder because the way it predicts what it outputs is fundamentally stochastic.
[0] https://old.reddit.com/r/ClaudeAI/comments/1pgxckk/claude_cl...
[1] https://old.reddit.com/r/ClaudeAI/comments/1jfidvb/claude_tr...
[2] https://www.google.com/search?q=ai+deleted+files+site%3Anews...
my point is more “skill issue” than “trust me this never happens”
my point on CPUs is people who don’t understand LLMs talk like “hallucinations” are a real thing — LLMs are “deciding” to make stuff up rather than just predicting the next token. yes it’s probabilistic, so is practically everything else at scale. yet it works and here we are. can you really explain in detail how everything you use works? I’m guessing I can explain failure modes of agentic systems (and how to avoid them so you don’t look silly on twitter/github) and how neural networks work better than most people can explain the technology they use every day
That doesn't refute the probabilistic nature of LLMs despite best prompting practices. In fact it emphasises it. More like your 1 anecdotal example vs my 20+ examples on GitHub.
My point tells you that not only it indeed does happen, but a previous old issue is now made even worse and more widespread, since we now have vibe-coders without security best practices assuming the agent should know better (when it doesn't).
> my point is more “skill issue” than “trust me this never happens”
So those that have this "skill issue" are also those who are prompting the AI differently then? Either way, this just inadvertently proves my whole point.
> yes it’s probabilistic, so is practically everything else at scale. yet it works and here we are.
The additional problem is can you explain why it went wrong as you scale the technology? CPUs circuit design go through formal verification and if a fault happens, we know exactly why; hence it is deterministic in design which makes them reliable.
LLMs are not and don't have this. Which is why OpenAI had to describe ChatGPT's misaligned behaviour as "sycophancy", but could not explain why it happened other than tweaking the hyper-parameters which got them that result.
So LLMs being fundamentally probabilistic and are hence, more unexplainable being the reason why you have the screenshot of vibe-coders who somehow prompted it wrong and the agent committed the keys.
Maybe that would never have happened to you, but it won't be the last time we see more of this happening on GitHub.
yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution
I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic. you can still practically use the tools to great effect, just like we use everything else that has underlying probabilities
OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice
and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction
Verses your anecdote being a proof of what? Skill issue for vibe coders? Someone else prompting it wrong?
You do realize you are proving my entire point?
> yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution
Again, it exacerbates my point such that it makes the existing issue even worse. Additionally, that wasn't even the only point I made on the subject.
> I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic.
When you scale neural networks to become say, production-grade LLMs, then it does matter. Just like it does matter for CPUs to be reliable when you scale them in production-grade data centers.
But your earlier (fallacious) comparison ignores the reliability differences between them (CPUs vs LLMs.) and determinism is a hard requirement for that; which the latter, LLMs are not.
> OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice
For the press, they had to, but no-one knows the real reason, because it is unexplainable; going back to my other point on reliability.
> and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction
It is indeed wrong for LLMs because not even the researchers can practically give an explanation why a single neuron (for every neuron in the network) gives different values on every fine-tune or training run. Even if it is "good enough", it can still go wrong at the inference-level for other unexplainable reasons other than it "overfitted".
CPUs on the other hand, have formal verification methods which verify that the CPU conforms to its specification and we can trust that it works as intended and can diagnose the problem accurately without going into atomic-level details.
> I’m saying it doesn’t matter it’s probabilistic, everything is,
Maybe it doesn't matter for you, but it generally does matter.
The risk level of a technology failing is far higher if it is more random and unexplainable than if it is expected, verified and explainable. The former eliminates many serious use-cases.
This is why your CPU, or GPU works.
LLMs are neither deterministic, no formal verification exists and are fundamentally black-boxes.
That is why many vibe-coders reported many "AI deleted their entire home folder" issues even when they told it to move a file / folder to another location.
If it did not matter, why do you need sandboxes for the agents in the first place?
very little software (or hardware) used in production is formally verified. tons of non-deterministic software (including neural networks) are operating in production just fine, including in heavily regulated sectors (banking, health care)
Design your secrets to include a common prefix, then use deterministic scanning tools like git hooks to prevent then from being checked in.
Or have a git hook that knows which environment variables have secrets in and checks for those.
Some of this negativity I think is due to unrealistic expectations of perfection.
Use the same guardrails you should be using already for human generated code and you should be fine.
For example, what if your code (that the LLM hasn't reviewed yet) has a dumb feature in where it dumps environment variables to log output, and the LLM runs "./server --log debug-issue-144.log" and commits that log file as part of a larger piece of work you ask it to perform.
If you don't want a bad thing to happen, adding a deterministic check that prevents the bad thing to happen is a better strategy than prompting models or hoping that they'll get "smarter" in the future.
The ones pushing this narrative have either the following:
* Invested in AI companies (which they will never disclose until they IPO / acquired)
* Employees at AI companies that have stock options which they are effectively paid boosters around AGI nonsense.
* Mid-life crisis / paranoia that their identity as a programmer is being eroded and have to pivot to AI.
It is no different to the crypto web3 bubble of 2021. This time, it is even more obvious and now the grifters from crypto / tech are already "pivoting to ai". [0]
> It is no different to the crypto web3 bubble of 2021
web3 didn't produce anything useful, just noise. I couldn't take a web3 stack to make an arbitrary app. with the PISS machine I can.
Do I worry about the future, fuck yeah I do. I think I'm up shit creek. I am lucky that I am good at describing in plain English what I want.
With AI companies still selling services far below cost, it's only a matter of time before the money runs out and the true value of these tools will be tested.
Learning all of the advanced multi-agent worklows etc. etc... Maybe that gets you an extra 20%, but it costs a lot more time, and is more likely to change over time anyway. So maybe not very good ROI.
This is true, but still shocking. Professional (working with others at least) developers basically live or die by their ability to communicate. If you're bad at communication, your entire team (and yourself) suffer, yet it seems like the "lone ranger" type of programmer is still somewhat praised and idealized. When trying to help some programmer friends with how they use LLMs, it becomes really clear how little they actually can communicate, and for some of them I'm slightly surprised they've been able to work with others at all.
An example the other day, some friend complained that the LLM they worked with was using the wrong library, and using the wrong color for some element, and surprised that the LLM wouldn't know it from the get go. Reading through the prompt, they never mentioned it once, and when asked about it, they thought "it should have been obvious" which yeah, to someone like you who worked for 2 years on this project that might be obvious, but for some with zero history and zero context about what you do? How you expect it to know this? Baffling sometimes.
The world changed for good and we will need to adapt. The bigger and more important question at this point isn't anymore if LLMs are good enough, for the ones who want to see, but, as you mention in your article, is what will happen to people who will get unemployed. There's a reality check for all of us.
It's not that different overall, I suppose, from the loop of thinking of an idea and then implementing it and running tests; but potentially very disorienting for some.
I don't think that's true.
I'm really good at getting great results out of coding agents and LLMs. I've also been using LLMs for code on an almost daily basis since ChatGPT's release on November 30th 2022. That's more than three years ago now.
Meanwhile I see a constant flow of complaints from other developers who can't get anything useful out of these machines, or find that the gains they get are minimal at best.
Using this stuff well is a deep topic. These things can be applied in so many different ways, and to so many different projects. The best asset you can develop is an intuition for what works and what doesn't, and getting that intuition requires months if not years of personal experimentation.
I don't think you can just catch up in a few weeks, and I do think that the risk of falling behind isn't being taken seriously enough by much of the developer population.
I'm glad to see people like antirez ringing the alarm bell about this - it's not going to be a popular position but it needs to be said!
Just like the stuff LLMs are being used for today. Why wouldn't "using LLMs well" be not just one of the many things LLMs will simplify too?
Or do you believe your type of knowledge is somehow special and is resistant to being vastly simplified or even made obsolete by AI?
Back in ~2024 a lot of people were excited about having "LLMs write the prompt!" but I found the results to be really disappointing - they were full of things like "You are the world's best expert in marketing" which was superstitious junk.
As of 2025 I'm finding they actually do know how to prompt, which makes sense because there's a ton more information about good prompting approaches in the training data as opposed to a couple of years ago. This has unlocked some very interesting patterns, such as Claude Code prompting sub-agents to help it explore codebases without polluting the top level token window.
But learning to prompt is not the key skill in getting good results out of LLMs. The thing that matters most is having a robust model of what they can and cannot do. Asking an LLM "can you do X" is still the kind of thing I wouldn't trust them to answer in a useful way, because they're always constrained by training data that was only aware of their predecessors.
It's different from assigning a task to a co-worker who already knows the business rules and cross-implications of the code in the real world. The agent can't see the broader picture of the stuff it's making, it can go from ignoring obvious (to a human that was present in the last planning meeting) edge cases to coding defensively against hundreds of edge cases that will never occur, if you don't add that to your prompt/context material.
The intuition just doesn't hold. The LLM gets trained and retrained by other LLM users so what works for me suddenly changes when the LLM models refresh.
LLMs have only gotten easier to learn and catch up on over the years. In fact, most LLM companies seem to optimise for getting started quickly over getting good results consistently. There may come a moment when the foundations solidify and not bothering with LLMs may put you behind the curve, but we're not there yet, and with the literally impossible funding and resources OpenAI is claiming they need, it may never come.
Things that they couldn't do six months go might now be things that they can do - and knowing they couldn't do X six months ago is useful because it helps systematize your explorations.
A key skill here is to know what they can do, what they can't do and what the current incantations are that unlock interesting capabilities.
A couple I've learned in the past week:
1. Don't give Claude Code a URL to some code and tell it to use that, because by default it will use its WebFetch tool but that runs an extra summarization layer (as a prompt injection defense) which loses details. Telling it to use curl sometimes works but a guaranteed trick is to have it git clone the relevant repo to /tmp and look at the code there instead.
2. Telling Claude Code "use red/green TDD" is a quick to type shortcut that will cause it to write tests first, run them and watch them fail, then implement the feature and run the test again. This is a wildly effective technique for getting code that works properly while avoiding untested junk code that isn't needed.
Now multiply those learnings by three years. Sure, the stuff I figure out in 2023 mostly doesn't apply today - but the skills I developed in learning how to test and iterate on my intuitions from then still count and still keep compounding.
The idea that you don't need to learn these things because they'll get better to the point that they can just perfectly figure out what you need is AGI science fiction. I think it's safe to ignore.
I agree that CC becoming omniscient is science fiction, but the goal of these interfaces is to make LLM-based coding more accessible. Any strategies we adopt to mitigate bad outcomes are destined to become part of the platform, no?
I've been coding with LLMs for maybe 3 years now. Obviously a dev who's experienced with the tools will be more adept than one who's not, but if someone started using CC today, I don't think it would take them anywhere near that time to get to a similar level of competency.
Being "a person who can code" carries some prestige and signals intelligence. For some, it has become an important part of their identity.
The fact that this can now be said of a machine is a grave insult if you feel that way.
It's quite sad in a way, since the tech really makes your skills even more valuable.
A somewhat intelligent junior will dive deep for one week and be on the same knowledge level as you in roughly 3 years.
In particular, the idea of saying something like "use red/green TDD" is an expression of communication skill (and also, of course, awareness of software methodology jargon).
And if the hype is right, why would you need to know any of them? I've seen people unironically suggest telling the LLM to "write good code", which seems even easier.
Telling an intern to care about code quality might actually cause an intern who hasn't been caring about code quality to care a little bit more. But it isn't going to help the intern understand the intended purpose of the software.
Fuck you, I learned k8s and general relativity, I can learn how to yell at a computer lol
Y'all go on and waste your time learning and relearning and chanting at websites. I'll just copy and paste your shit if and when I need to.
I mean, I just think of them like a dog that'll get distracted and go off doing some other random thing if you don't supervise them enough and you certainly don't want to trust them to guard your sandwich.
It is just way easier for someone to get up to speed today than it was a year ago. Partly because capabilities have gotten better and much of what was learned 6+ months ago no longer needs to be learned. But also partly because there is just much more information out there about how to get good results, you might have coworkers or friends you can talk to who have gotten good results, you can read comments on HN or blog posts from people who have gotten good results, etc.
I mean, ok, I don't think someone can fully catch up in a few weeks. I'll grant that for sure. But I think they can get up to speed much faster than they could have a year ago.
Of course, they will have to put in the effort at that time. And people who have been putting it off may be less likely to ever do that. So I think people will get left behind. But I think the alarm to raise is more, "hey, it's a deep topic and you're going to have to put in the effort" rather than "you better start now or else it's gonna be too late".
You'd be sage with your time just to keep a high-level view until workflows become stable and aren't advancing every few months.
The time to consider mastering a workflow is when a casual user of the "next release" wouldn't trivially supersede your capabilities.
Similarly we're still in the race to produce a "good enough" GenAI, so there isn't value in mastering anything right now unless you've already got a commercial need for it.
This all reminds me of a time when people were putting in serious effort to learn Palm Pilot's Graffiti handwriting recognition, only for the skill to be made redundant even before they were proficient at it.
Replace that with anything and you will notice that people who are building startups in this area will want to bring the narrative like that as it usually highly increases the value of their companies. When narrative gets big enough, then big companies must follow - or they look like "lagging behind". Whether the current thing brings value or not. It is a fire that keeps feeding itself. In the end, when it gets big enough - we call it as bubble. Bubble that may explode. Or not.
Whether the end user gets actual value or not, is just side effect. But everyone wants to believe that that it brings value - otherwise they were foolish to jump in the train.
If so then none of this matters, because it will run through that lather-rinse-repeat loop itself in less than a minute.
I work mostly in C++ (MFC applications on Windows) and assembly language (analyzing crash reports).
For the C++ work, the AIs do all kinds of unsafe things like casting away constness or doing hacks to expose private class internals. What they give me is sometimes enough to get unstuck though which is nice.
For crash reports (a disassembly around the crash site and a stack trace) they are pretty useless and that’s coming from someone who considers himself to be a total novice at assembly. (Looking to up my x64 / WinDbg game and any pointers to resources would be appreciated!)
I do prototyping in Python and Claude is excellent at that.
Next breakthrough will happen in 2030 or it might happen next Tuesday; it might have already happened, it's just that the lab which did it is too scared to release it. It doesn't matter: until it happens, you should work with what you've got.
Model improvements may have flattened, the quality improvements due to engineering work around those models certainly have not.
If we always wait for technology to calcify and settle before we interact with it, then that would be rather boring for some of us. Acquiring knowledge is not really that much of a heavy burden that it's an issue if it's outdated a year in . But that's maybe just a mindset thing.
Github stars? That's 100% marketing. Shit that clears a low quality bar can rack up stars like crazy just by being well marketed.
Number of startups? That's 100% marketing. Investors put money into products that have traction, or founders that look impressive, and both of those are mostly marketing.
People actually are vibe coding stuff rather than using SaaS though, that one's for real. Your example is hyperbolic, but the Tailwind scenario is just one example of AI putting pressure on products.
If you don't have a halo already, you need to be blessed or you're just going to suffer. Getting a good mention by someone like Theo or SimonW >> 1000 well written articles.
There is not bad publicity. More you spam more you will be noticed. Human attention is limited. So grab as much as you can. And also this helps your product name to get into training data and thus later in LLM outputs.
Even more ideas. When you find an email address. Spam that too. Get your message out multiple times to each address.
It's hard to disambiguate this from people who have a "fanbase." People will upvote stuff from people like simonw sight unseen without reading. I'd like to do a study on HN where you hide the author, to see how upvote patterns change, in order to demonstrate the "halo" benefit.
Instead of asking "where are the AI-generated projects" we could ask about the easier problem of "where are the AI-generated ports". Why is it still hard to take an existing fully concrete specification, and an existing test suite, and dump out a working feature-complete port of huge, old, and popular projects? Lots of stuff like this will even be in the training set, so the fact that this isn't easy yet must mean something.
According to claude, wordpress is still 43% of all the websites on the internet and PHP has been despised by many people for many years and many reasons. Why no python or ruby portage? Harder but similar, throw in drupal, mediawiki, and wonder when can we automatically port the linux kernel to rust, etc.
We have a smaller version of that ability already:
- https://simonwillison.net/2025/Dec/15/porting-justhtml/
See also https://www.dbreunig.com/2026/01/08/a-software-library-with-...
I need to write these up properly, but I pulled a similar trick with an existing JavaScript test suite for https://github.com/simonw/micro-javascript and the official WebAssembly test suite for https://github.com/simonw/pwasm
And yet it doesn't feel true yet, otherwise we'd see it. Why do you think that is?
(This capability is also brand new: prior to Claude Opus 4.5 in November I wasn't getting results from coding agents that convinced me they could do this.)
It turns out there are some pretty big problems that works for, like HTML5 parsers and WebAssembly runtimes and reduced-scoped JavaScript language interpreters. You have to be selective though. This won't work for Linux.
I thought it wouldn't work for web browsers either - one of my 2026 predictions was "by 2029 someone will build a new web browser using mostly LLM-code"[1] - but then I saw this thread on Reddit https://www.reddit.com/r/Anthropic/comments/1q4xfm0/over_chr... "Over christmas break I wrote a fully functional browser with Claude Code in Rust" and took a look at the code and it's surprisingly deep: https://github.com/hiwavebrowser/hiwave
[1] https://simonwillison.net/2026/Jan/8/llm-predictions-for-202...
If that's what is shown then why doesn't it work on anything that has a sufficiently large test-suite, presumably scaling linearly in time with size? Why should we be selective, and based on what?
Falling salaries?
Remember that an average software engineer only spends around 25% of their time coding.
You might feel great, thats fine, but I dont. And software quality is going down, I wouldn't agree that LLMs will help write better software
Antirez + LLM + CFO = Billion Dollar Redis company, quite plausibly.
/However/ ...
As for the delta provided by an LLM to Antirez, outside of Redis (and outside of any problem space he is already intimately familiar with), an Apples to Apples comparison would be he trying this on an equally complex codebase he has no idea about. I'll bet... what Antirez can do with Redis and LLMs (certainly useful, huge Quality of Life improvement to Antirez), he cannot even begin to do with (say) Postgres.
The only way to get there with (say) Postgres, would be to /know/ Postgres. And pretty much everyone, no matter how good, cannot get there with code-reading alone. With software at least, we need to develop a mental model of the thing by futzing about with the thing in deeply meaningful ways.
And most of us day-job grunts are in the latter spot... working in some grimy legacy multi-hundred-thousand line code-mine, full of NPM vulns, schelpping code over the wall to QA (assuming there is even a QA), and basically developing against live customers --- "learn by shipping", as they say.
I do think LLMs are wildly interesting technology, however they are poor utility for non-domain-experts. If organisations want to profit from the fully-loaded cost of LLM technology, they better also invest heavily in staff training and development.
Although calling AI "just autocomplete" is almost a slur now, it really is just that in the sense that you need to A) have a decent mental picture of what you want, and, B) recognize a correct output when you see it.
On a tangent, the inability to identify correct output is also why I don't recommend using LLMs to teach you anything serious. When we use a search engine to learn something, we know when we've stumbled upon a really good piece of pedagogy through various signals like information density, logical consistency, structuredness/clarity of thought, consensus, reviews, author's credentials etc. But with LLMs we lose these critical analysis signals.
You are calling out the and subtle nuance that many don’t get…
For most of us vibe coding gives 0 advantage. Our software will just sit there and get no views and producing it faster means nothing. In fact, it just scares us that some exec is gonna look at this and write us for low performance because they saw someone do the same thing we are doing in 2 days instead of 4.
Most engineers in my experience are much less skillful at reading code than writing code. What I’ve seen so far with use of LLM tools is a bunch of minimally edited LLM produced content that was not properly critiqued.
It's not conceptually challenging to understand, but time consuming to write, test, and trust. Having an LLM write these types of things can save time, but please don't trust it blindly.
It needs a lot of work to not be skeptical, when when I try it, it generates shit, especially when I want something completely new, not existing anywhere, and also when these people when they show how they work with it, it always turns out that it’s on the scale of terrible to bad.
I also use AI, but I don’t allow it to touch my code, because I’m disgusted by its code quality. I ask it, and sometimes it delivers, but mostly not.
(If you need help finding it try visiting https://tools.simonwillison.net/hn-comments-for-user and searching for simonw - you can then search my 1,000 most recent comments in one place.)
If my tests are green then it tells me a LOT about what the software is capable of, even if I haven't reviewed every line of the implementation.
The next step is to actually start using it for real problems. That should very quickly shake out any significant or minor issues that sneaked past the automated tests.
I've started thinking about this by comparing it to work I've done within larger companies. My team would make use of code written by other teams without reviewing everything those other teams had written. If their tests passed we would build against their stuff, and if their stuff turned out not to work we would let them know or help debug and fix it ourselves.
LLMs help with that part too. As Antirez says:
Writing code is no longer needed for the most part. It is now a lot more interesting to understand what to do, and how to do it (and, about this second part, LLMs are great partners, too).
How to know the "how to do it" is sensible? (sensible = the product will produce the expected outcome within the expected (or tolerable) error bars?)
How did you ever know? It's not like everyone always wrote perfect code up until now.
Nothing has changed, except now you have a "partner" to help you along with your understanding.
Who "knows"?
It's who has a world-model. It's who can evaluate input signal against said world-model. Which requires an ability to generate questions, probe the nature of reality, and do experiments to figure out what's what. And it's who can alter their world-model using experiences collected from the back-and-forth.
IDK, just two days ago I had a bug report/fix accepted by a project which I would have never dreamt of digging into as what it does is way outside my knowledge base. But Claude got right on in there and found the problem after a few rounds of printf debugging which lead to an assertion we would have hit with a debug build which led to the solution. Easy peasy and I still have no idea how the other library does its thing at all as Claude was using it to do this other thing.
This is the advice I've been giving my friends and coworkers as well for a while now. Forget the hype, just take time to test them from time to time. See where it's at. And "prepare" for what's to come, as best you can.
Another thing to consider. If you casually look into it by just reading about it, be aware that almost everything you read in "mainstream" places has been wrong in 2025. The people covering this, writing about this, producing content on this have different goals in this era. They need hits, likes, shares and reach. They don't get that with accurate reporting. And, sadly, negativity sells. It is what it is.
THe only way to get an accurate picture is to try them yourself. The earlier you do that, the better you'll be. And a note on signals: right now, a "positive" signal is more valuable for you than many "negative" ones. Read those and try to understand the what, if not the how. "I did this with cc" is much more valuable today than "x still doesn't do y reliably".
This is the crux. AI suddenly became good and society hasn't caught on yet. Programmers are a bit ahead of the curve here, being closer to the action of AI. But in a couple of years, if not already, all the other technical and office jobs will be equally affected. Translators, admin, marketing, scientists, writers of all sorts and on and on. Will we just produce more and retain a similar level of employment, or will AI be such a force multiplier that a significant number or even most of these jobs will be gone? Nobody knows yet.
And yet, what I'm even more worried about for their society upending abilities, is robots. These are coming soon and they'll arrive with just as much suddeness and inertia as AI did.
The robots will be as smart as the AI running them, so what happens when they're cheap and smart enough to replace humans in nearly all physical jobs?
Nobody knows the answer to this. But in 5 years, or 10, we will find out.
I don't agree, unless by "art industry" what you actually mean is "art establishment".
If we broaden it to mean "anywhere that money is paid, or used to be paid, to people for any kind of artistic endeavor" - even if we limit that to things related to drawing, painting, illustrating, graphic design, 3d design etc. - then AI is definitely replacing or augmenting a ton of human work. Just go on any Photoshop forum. It's all about AI now, just like everywhere else.
There is no way I can convince a user that my vibe coded version of Todolist is better than 100 other made this week
> AI code is slop, therefore you shouldn't use it
You should learn how to responsibly use it as a tool, not a replacement for you. This can be done, people are doing it, people like Salvatore (antirez), Mitchell (of Terraform/Ghostty fame), Simon (swillison) and many others are publicly talking about it.
> AI can't code XYZ
It's not all-or-nothing. Use it where it works for you, don't use it where it doesn't. And btw, do check that you actually described the problem well. Slop-in, slop-out. Not sayin' this is always the case, but turns out it's the case surprisingly often. Just sayin'
> AI will atrophy your skills, or prevent you from learning new ones, therefore you shouldn't use it
Again, you should know where and how to use it. Don't tune out while doing coding. Don't just skim the generated code. Be curious, take your time. This is entirely up to you.
> AI takes away the fun part (coding) and intensifies the boring (management)
I love programming but TBH, for non-toy projects that need to go into production, at least three quarters are boring boilerplate. And making that part interesting is one of the worst things you can do in software development! That path lies resume-driven development, architecture astronautics, abusing design patterns du jour, and other sins that will make code maintenance on that thing a nightmare! You want boring, stable, simple. AI excels at that. Then you can focus on the small tiny bit that's fun and hand-craft that!
Also, you can always code for fun. Many people with boring coding jobs code for fun in the evenings. AI changes nothing here (except possibly improving the day job drudgery).
> AI is financially unsustainable, companies are losing money
Perhaps, and we're probably in the bubble. Doesn't detract from the fact that these things exist, are here now, work. OpenAI and Anthropic can go out of business tomorrow, the few TB of weights will be easily reused by someone else. The tech will stay.
> AI steals your open source code, therefore you shouldn't write open-source
Well, use AI to write your closed-source code. You don't need to open source anything if you're worried someone (AI or human) will steal it. If you don't want to use something on moral grounds, that's a perfectly fine thing to do. Others may have different opinion on this.
> AI will kill your open source business, therefore you shouldn't write open-source
Open source is not a business model (I've been saying this for longer than median user of this site has been alive). AI doesn't change that.
As @antirez points out, you can use AI or not, but don't go hiding under a rock and then being surprised in a few years when you come out and find the software development profession completely unrecognizable.
You apparently see "making the boilerplate interesting" as doing a bunch of overengineering. Strange. To my mind, the overengineering is part of the boilerplate. "Making the boilerplate interesting" in my mind is not possible; but rather the goal is to fix the system such that it doesn't require boilerplate any more. (Sometimes that just means a different implementation language.)
A company I worked with a while ago had a microservices architecture, and have decided to not use one of a few standard API serialization/deserialization options, but write their own, because was going to be more performant, easier to maintain, better fit for their use case. A few years on, after having grown organically to support all the edge cases, it's more convoluted, slower, and buggy than if they went with the boring option that ostensibly had "a bit more boilerplate" from the start.
A second example is from a friend, whose coworker decided to write a backend-agnostic, purpose-agnostic, data-agnostic message broker/routing library. They spent a few months of this, delivered a beautifully architected solution in a few dozen k lines of code. The problem is the solution solves many problems the company didnt and wouldn't have, and will be a maintenance drag from then forevermore. Meanwhile, they could have done it in a few hundred lines of code that would be coupled to the problem domain, but still farily decend from most people's point of view.
These two are from real projects. But you can also notice that in general people are often picking a fancy solution over a boring one, ostensibly because it has something "out of the box". The price of the "out of the box"-ness (aside from potential SaaS/infra costs and vendor lock in), is that you now need to adapt your own code to work with the mental model (domain) of the fancy solution.
Or to harp on something trivial, you end up depending on left-pad because writing it yourself was boring.
> fix the system such that it doesn't require boilerplate any more.
I think perhaps I used a more broad meaning for "boilerplate" than you had in mind. If we're talking about boilerplate as enumerating all the exceptions a Java method may raise, or whatever unholy sad thing we have to do in C to use GTK/GObject, then I agree.
But I also meant something more closer to "glue code that isn't the primary carrier of value of the project", or to misuse financial language in this context, the code that's a cost center, not a profit center.
I wonder if I’m the odd one out or if this is a common sentiment: I don’t give a shit about building, frankly.
I like programming as a puzzle and the ability to understand a complex system. “Look at all the things I created in a weekend” sounds to me like “look at all the weight I moved by bringing a forklift to the gym!”. Even ignoring the part that there is barely a “you” in this success, there is not really any interest at all for me in the output itself.
This point is completely orthogonal to the fact that we still need to get paid to live, and in that regard I'll do what pays the bills, but I’m surprised by the amount of programmers that are completely happy with doing away with the programming part.
I enjoy those things about programming too, which is why I'm having so much fun using LLMs. They introduce new layers of complex system understanding and problem solving (at that AI meta-layer), and let me dig into and solve harder and more time-consuming problems than I was able to without them.
This is not my experience at all. My experience is that the moment I stop using them as google or search on steroids and let them generate code, I start losing the grip of what is being built.
As in, when it’s time for a PR, I never feel 100% confident that I’m requesting a review on something solid. I can listen to that voice and sort of review myself before going public, but that usually takes as much time as writing myself and is way less fun, or I can just submit and be dishonest since then I’m dropping that effort into a teammate.
In other words, I feel that the productivity gain only comes if you’re willing to remove yourself from the picture and let others deal with any consequence. I’m not.
Maybe a factor here is that I've invested a huge amount of effort over the last ~10 years in getting better at reading code?
I used to hate reading code. Then I found myself spending more time in corporate life reviewing code then writing it myself... and then I realized the huge unlock I could get from using GitHub search to find examples of the things I wanted to do, I'd only I could overcome my aversion to reading the resulting search results!
When LLMs came along they fit my style of working much better than they would have earlier in my career.
The point is exactly that, that ai feels like reviewing other people’s code, only worse because bad ai written code mimics good code in a way that bad human code doesn’t, and because you don’t get the human factor of mentoring someone when you see they lack a skill.
If I wanted to do that for a living it’s always been an option, being the “architect” overseeing a group of outsourced devs for example. But I stay as individual contributor for doing quite different work.
Yeah, that's a good way to put it.
I've certainly felt the "mimics good code" thing in the past. It's been less of a problem for me recently, maybe because I've started forcing Claude Code into a red/green TDD cycle for almost everything which makes it much less likely to write code that it hasn't at least executed via the tests.
The mentoring thing is really interesting - it's clearly the biggest difference between working with a coding agent and coaching a human collaborator.
I've managed to get a weird simulacrum of that by telling the coding agents to take notes as they work - I even tried "add to a til.md document of things you learned" on a recent project - and then condensing those lessons into an AGENTS.md later on.
No, I really don't think they will. Software has only been getting worse, and LLMs are accelerating the rate at which incompetent developers can pump out low quality code they don't understand and can't possibly improve.
I've been taking a proper whack at the tree every 6 months or so. This time it seems like it might actually fall over. Every prior attempt I could barely justify spending $10-20 in API credits before it was obvious I was wasting my time. I spent $80 on tokens last night and I'm still not convinced it won't work.
Whether or not AI is morally acceptable is a debate I wish I had the luxury of engaging in. I don't think rejecting it would allow me to serve any good other than in my own mind. It's really easy to have certain views when you can afford to. Most of us don't have the privilege of rejecting the potential that this technology affords. We can complain about it but it won't change what our employers decide to do.
Walk the game theory for 5 minutes. This is a game of musical chairs. We really wish it isn't. But it is. And we need to consider the implications of that. It might be better to join the "bad guys" if you actually want to help those around you. Perhaps even become the worst bad guy and beat the rest of them to a functional Death Star. Being unemployed is not a great position to be in if you wish to assist your allies. Big picture, you could fight AI downstream by capitalizing on it near term. No one is keeping score. You might be in your own head, but you are allowed to change that whenever you want.
Trying to beat a demon long term by making a contract with it short term?
Show me these "facts"
You can't just hand-wavily say "a bigger percentage of programmers is using AI with success every day" and not give a link to a study that shows it's true
as a matter of fact, we know that a lot of companies have fired people by pretending that they are no longer needed in the age of AI... only to re-hire offshored people for much cheaper
for now, there hasn't been a documented sudden increase in velocity / robustness for code, a few anecdotical cases sure
I use it myself, and I admit it saves some time to develop some basic stuff and get a few ideas, but so far nothing revolutionary. So let's take it at face value:
- a tech which helps slightly with some tasks (basically "in-painting code" once you defined the "border constraints" sufficiently well)
- a tech which might cause massive disruption of people's livelihoods (and safety) if used incorrectly, which might FAR OUTWEIGH the small benefits and be a good enough reason for people to fight against AI
- a tech which emits CO2, increases inequalities, depends on quasi slave-work of annotators in third-world countries, etc
so you can talk all day long about not dismissing AI, but you should take it also with everything that comes with it
2. The US alone air conditioning usage is around 4 times the energy / CO2 usage of all the world data centers (not just AI) combined together. AI is 10% of the data centers usage, so just AC is 40 times that.
2. it's not because the US is incredibly bad at energy spending in AC that it somehow justifies the fact that we would add another, mostly unnecessary, polluting source, even if it's slightly lower. ACs have existed for decades. AI has been exploding for a few years, so we can definitely see it go way, way past the AC usage
also the idea is of "accelerationnism". Why do we need all this tech? What good does it make to have 10 more silly slop AI videos and disinformation campaigns during election? Just so that antirez can be a little bit faster at doing his code... that's not what the world is about.
Our world should be about humans, connecting together (more slowly, not "faster"), about having meaningful work, and caring about planetary resources
The exact opposite of what capitalistic accelerationism / AI is trying to sell us
> Why do we need all this tech?
Slightly odd question to be asking here on Hacker News!
> Slightly odd question to be asking here on Hacker News!
It's absolutely not? The first line of question when you work in a domain SHOULD BE "why am I doing this" and "what is the impact of my work on others"
# Fact-Checking This Climate Impact Claim
Let me break down this claim with actual data:
## The Numbers
*US Air Conditioning:* - US A/C uses approximately *220-240 TWh/year* (2020 EIA data) - This represents about 6% of total US electricity consumption
*Global Data Centers:* - Estimated *240-340 TWh/year globally* (IEA 2022 reports) - Some estimates go to 460 TWh including cryptocurrency
*AI's Share:* - AI represents roughly *10-15%* of data center energy (IEA estimates this is growing rapidly)
## Verdict: *The claim is FALSE*
The math doesn't support a 4:1 ratio. US A/C and global data centers use *roughly comparable* amounts of energy—somewhere between 1:1 and 1:1.5, not 4:1.
The "40 times AI" conclusion would only work if the 4x premise were true.
## Important Caveats
1. *Measurement uncertainty*: Data center energy use is notoriously difficult to measure accurately 2. *Rapid growth*: AI energy use is growing much faster than A/C 3. *Geographic variation*: This compares one country's A/C to global data centers (apples to oranges)
## Reliable Sources - US EIA (Energy Information Administration) for A/C data - IEA (International Energy Agency) for data center estimates - Lawrence Berkeley National Laboratory studies
The quote significantly overstates the disparity, though both are indeed major energy consumers.
Labor is worth less, capital and equity ownership make more or the same
I continue to hope that we see the opposite effect: the drop of cost in software development drives massively increased demand for both software and our services.
I wrote about that here: https://simonwillison.net/2026/Jan/8/llm-predictions-for-202...
I feel like this is not the same for everyone. For some people, the "fire" is literally about "I control a computer", for others "I'm solving a problem for others", and yet for others "I made something that made others smile/cry/feel emotions" and so on.
I think there is a section of programmer who actually do like the actual typing of letters, numbers and special characters into a computer, and for them, I understand LLMs remove the fun part. For me, I initially got into programming because I wanted to ruin other people's websites, then I figured out I needed to know how to build websites first, then I found it more fun to create and share what I've done with others, and they tell me what they think of it. That's my "fire". But I've met so many people who doesn't care an iota about sharing what they built with others, it matters nothing to them.
I guess the conclusion is, not all programmers program for the same reason, for some of us, LLMs helps a lot, and makes things even more fun. For others, LLMs remove the core part of what makes programming fun for them. Hence we get this constant back and forth of "Can't believe others can work like this!" vs "I can't believe others aren't working like this!", but both sides seems to completely miss the other side.
Exactly me.
Now the fun is gone, maybe I can do more important work.
This is a very sad, bleak, and utilitarian view of "work." It is also simply not how humans operate. Even if you only care about the product, humans that enjoy and take pride in what they're doing almost invariably produce better products that their customers like more.
I'm not entirely sure what that means myself, so please speak up if my statement resonates with you.
It's not about the typing, it's about the understanding.
LLM coding is like reading a math textbook without trying to solve any of the problems. You get an overview, you get a sense of what it's about and most importantly you get a false sense of understanding.
But if you try to actually solve the problems, you engage completely different parts of your brain. It's about the self-improvement.
Well, it's both, for different people, seemingly :)
I also like the understanding and solving something difficult, that rewards a really strong part of my brain. But I don't always like to spend 5 hours in doing so, especially when I'm doing that because of some other problem I want to solve. Then I just want it solved ideally.
But then other days I engage in problems that are hard because they are hard, and because I want to spend 5 hours thinking about, designing the perfect solution for it and so on.
Different moments call for different methods, and particularly people seem to widely favor different methods too, which makes sense.
It obviously depends a lot on what exactly you're building, but in many projects programming entails a lot of low intellectual effort, repetitive work.
It's the same things over and over with slight variations and little intellectual challenge once you've learnt the basic concepts.
Many projects do have a kernel of non-obvious innovation, some have a lot of it, and by all means, do think deeply about these parts. That's your job.
But if an LLM can do the clerical work for you? What's not to celebrate about that?
To make it concrete with an example: the other day I had Claude make a TUI for a data processing library I made. It's a bunch of rather tedious boilerplate.
I really have no intellectual interest in TUI coding and I would consider doing that myself a terrible use of my time considering all the other things I could be doing.
The alternative wasn't to have a much better TUI, but to not have any.
I think I can reasonably describe myself as one of the people telling you the thing you don't really get.
And from my perspective: we hate those projects and only do them if/because they pay well.
> the other day I had Claude make a TUI for a data processing library I made. It's a bunch of rather tedious boilerplate. I really have no intellectual interest in TUI coding...
From my perspective, the core concepts in a TUI event loop are cool, and making one only involves boilerplate insofar as the support libraries you use expect it. And when I encounter that, I naturally add "design a better API for this" to my project list.
Historically, a large part of avoiding the tedium has been making a clearer separation between the expressive code-like things and the repetitive data-like things, to the point where the data-like things can be purely automated or outsourced. AI feels weird because it blurs the line of what can or cannot be automated, at the expense of determinism.
The thing is:
1) A lot of the low-intellectual stuff is not necessarily repetitive, it involved some business logic which is a culmination of knowing the process behind what the uses needs. When you write a prompt, the model makes assumptions which are not necessarily correct for the particular situation. Writing the code yourself forced you to notice the decision points and make more informed choices.
I understand your TUI example and it's better than having none now, but as a result anybody who wants to write "a much better TUI" now faces a higher barrier to entry since a) it's harder to justify an incremental improvement which takes a lot of work b) users will already have processes around the current system c) anybody who wrote a similar library with a better TUI is now competing with you and quality is a much smaller factor than hype/awareness/advertisement.
We'll basically have more but lower quality SW and I am not sure that's an improvement long term.
2) A lot of the high-intellectual stuff ironically can be solved by LLMs because a similar problem is already in the training data, maybe in another language, maybe with slight differences which can be pattern matched by the LLM. It's laundering other people's work and you don't even get to focus on the interesting parts.
Yes, this follows from the point the GP was making.
The LLM can produce code for complex problems, but that doesn't save you as much time, because in those cases typing it out isn't the bottleneck, understanding it in detail is.
Plus the size of project that an LLM can help maintain keeps growing. I actually think that size may no longer have any realistic limits at all now: the tricks Claude Code uses today with grep and sub-agents mean there's no longer a realistic upper limit to how much code it can help manage, even with Opus's relatively small (by today's standards) 200,000 token limit.
GET /svg/weather
|> jq: weatherData
|> jq: `
.hourly as $h |
[$h.time, $h.temperature_2m] | transpose | map({time: .[0], temp: .[1]})
`
|> gg({ "type": "svg", "width": 800, "height": 400 }): `
aes(x: time, y: temp)
| line()
| point()
`
Can be, but… well, the analogy can go wrong both ways.
This is what Brilliant.org and Duolingo sell themselves on: solve problems to learn.
Before I moved to Berlin in 2018, I had turned the whole Duolingo German tree gold more than once, when I arrived I was essentially tourist-level.
Brilliant.org, I did as much as I could before the questions got too hard (latter half of group theory, relativity, vector calculus, that kind of thing); I've looked at it again since then, and get the impressions the new questions they added were the same kind of thing that ultimately turned me off Duolingo, easier questions that teach little, padding out a progressions system that can only be worked through fast enough to learn anything if you pay a lot.
Code… even before LLMs, I've seen and I've worked with confident people with a false sense of understanding about the code they wrote. (Unfortunately for me, one of my weaknesses is the politics of navigating such people).
I'm not trying to be snobbish here, it's completely fine to enjoy those sorts of products (I consume a lot of pop science, which I put in the same category) but you gotta actually get your hands dirty and do the work.
It's also fine to not want to do that -- I love to doodle and have a reasonable eye for drawing, but to get really good at it, I'd have to practice a lot and develop better technique and skills and make a lot of shitty art and ehhhh. I don't want it badly enough.
Most math textbooks provide the solutions too. So you could choose to just read those and move on and you’d have achieved much less. The same is true with coding. Just because LLMs are available doesn’t mean you have to use them for all coding, especially when the goal is to learn foundational knowledge. I still believe there’s a need for humans to learn much of the same foundational knowledge as before LLMs otherwise we’ll end up with a world of technology that is totally inscrutable. Those who choose to just vibe code everything will make themselves irrelevant quickly.
- making art as you thing it should be, but at the risk of it being non-commercial
- getting paid for doing commercial/trendy art
choose one
It's:
- Making art because you enjoy working with paint
- Making art because you enjoy looking at the painting afterward
Of course there are some artists who sit comfortably in the grey area between the two oppositions, and for these a little nudging towards either might influence things. But for most artists, their ideas or techniques are simply not relevant to a larger audience.
I'm not sure what your background is, but there are definitly artists out there drawing, painting and creating art they have absolutely zero care for, or even actively is against or don't like, but they do it anyways because it's easier to actually get paid doing those things, than others.
Take a look in the current internet art community and ask how many artists are actively liking the situation of most of their art commissions being "furry lewd art", vs how many commissions they get for that specific niche, as just one example.
History has lots of other examples, where artists typically have a day-job of "Art I do but do not care for" and then like the programmer, hack on what they actually care about outside of "work".
I was mostly considering contemporary artists that you see in museums, and not illustrators. Most of these have moved on to different media, and typically don't draw or paint. They would therefore also not be able to draw commission pieces. And most of the time their work does not sell well.
(Source: am professionally trained artist, tried to sell work, met quite a few artists, thought about this a lot. That's not to say that I may still be completely wrong though, so I liked reading your comment!)
Edit: and of course things get way more complicated and nuanced when you consider gallerists pushing existing artists to become trendy, and artists who are only "discovered" after their deaths, etc. etc.)
It’s so easy to be a starving artist; and in the world of commercial art it’s bloody dog-eat-dog jungle, not made for faint-hearted sissies.
Reminds me of this excerpt from Richard Hamming's book:
> Finally, a more complete, and more useful, Symbolic Assembly Program (SAP) was devised—after more years than you are apt to believe during which most programmers continued their heroic absolute binary programming. At the time SAP first appeared I would guess about 1% of the older programmers were interested in it—using SAP was “sissy stuff”, and a real programmer would not stoop to wasting machine capacity to do the assembly. Yes! Programmers wanted no part of it, though when pressed they had to admit their old methods used more machine time in locating and fixing up errors than the SAP program ever used. One of the main complaints was when using a symbolic system you do not know where anything was in storage—though in the early days we supplied a mapping of symbolic to actual storage, and believe it or not they later lovingly pored over such sheets rather than realize they did not need to know that information if they stuck to operating within the system—no! When correcting errors they preferred to do it in absolute binary addresses.
I think alot of us dont get everything specced out up front, we see how things fit, and adjust accordingly. most of the really good ideas I've had were not formulated in the abstract, but realizations had in the process of spelling things out.
I have a process, and it works for me. Different people certainly have other ones, and other goals. But maybe stop telling me that instead of interacting with the compiler directly its absolutely necessary that instead I describe what I want to a well meaning idiot, and patiently correct them, even though they are going to forget everything I just said in a moment.
I do all of my programming on paper, so keystrokes and formal languages are the fast part. LLMs are just too slow.
The first people using higher level languages did feel compelled to. That's what the quote from the book is saying. The first HLL users felt compelled to check the output just like the first LLM users.
I think it's a very apt comparison.
But there is no reason to suppose that responsible SWEs would ever be able to stop doing so for an LLM, given the reliance on nondeterminism and a fundamentally imprecise communication mechanism.
That's the point. It's not the same kind of shift at all.
I had an inkling that the feeling existed back then, but I had no idea it was documented so explicitly. Is this quote from The Art of Doing Science and Engineering?
("Solving a problem for others" also resonates, but I think I implement that more by tutoring and mentoring.)
So with that, I can change the code by hand afterwards or continue with LLMs, it makes no difference, because it's essentially the same process as if I had someone follow the ideas I describe, and then later they come back with a PR. I think probably this comes naturally to senior programmers and those who had a taste of management and similar positions, but if you haven't reviewed other's code before, I'm not sure how well this process can actually work.
At least for me, I manage to produce code I can maintain, and seemingly others to, and they don't devolve into hairballs/spaghetti. But again, requires reviewing absolutely every line and constantly edit/improve.
The problem is, that code would require a massive amount of cleanup. I took a brief look and some code was in the wrong place. There were coding style issues, etc.
In my experience, the easy part is getting something that works for 99%. The hard part is getting the architecture right, all of the interfaces and making sure there are no corner cases that get the wrong results.
I'm sure AI can easily get to the 99%, but does it help with the rest?
I'd treat PRs like that as proof of concepts that the thing that can be done, but I'd be surprised if they often produced code that should be directly landed.
That nearly happened - it's why OpenAI didn't release open weight models past GPT2, and it's why Google didn't release anything useful built on Transformers despite having invented the architecture.
If we lived in the world today, LLMs would be available only to a small, elite and impossibly well funded class of people. Google and OpenAI would solely get to decide who could explore this new world with them.
I think that would suck.
With all due respect I don’t care about an acceleration in writing code - I’m more interested in incremental positive economic impact. To date I haven’t seen anything convince me that this technology will yield this.
Producing more code doesn’t overcome the lack of imagination, creativity and so on to figure out what projects resources should be invested in. This has always been an issue that will compound at firms like Google who have an expansive graveyard of projects laid to rest.
In fact, in a perverse way, all this ‘intelligence’ can exist. At the same time humans can get worse in their ability to make judgments in investment decisions.
So broadly where is the net benefit here?
I get the impression there's no answer here that would satisfy you, but personally I'm excited about regular people being able to automate tedious things in their lives without having to spend 6+ months learning to program first.
And being able to enrich their lives with access to as much world knowledge as possible via a system that can translate that knowledge into whatever language and terminology makes the most sense to them.
Bring the implicit and explicit costs to date into your analysis and you should quickly realise none of this makes sense from a societal standpoint.
Also you seem to be living in a bubble - the average person doesn’t care about automating anything!
His workplace has no one with programming skills, this is automation that would never have happened. Of course it’s not exactly replacing a human or anything. I suppose he could have hired someone to write the script but he never really thought to do that.
One of my life goals is to help bring as many people into my "technology can automate things for you" bubble as I possibly can.
For companies, if these tools make experts even more special, then experts may get more power certainly when it comes to salary.
So the productively benefits of AI have to be pretty high to overcome this. Does AI make an expert twice as productive?
- If the number of programmers will be drastically reduced, how big of a price increase companies like Anthropic would need to be profitable?
- If you are a manager, you now have a much higher bus factor to deal with. One person leaving means a greater blow on the team's knowledge.
- If the number of programmers will be drastically reduced, the need for managers and middle managers will also decline, no? Hmm...
Focus on architecture, interfaces, corner-cases, edge-cases and tradeoffs first, and then the details within that won't matter so much anymore. The design/architecture is the hard part, so focus on that first and foremost, and review + throw away bad ideas mercilessly.
"Oh, and check it out: I'm a bloody genius now! Estás usando este software de traducción in forma incorrecta. Por favor, consulta el manual. I don't even know what I just said, but I can find out!"
There is no process solution for low performers (as of today).
A lot of the criticisms of AI coding seem to come from people who think that the only way to use AI is to treat it as a peer. “Code this up and commit to main” is probably a workable model for throwaway projects. It’s not workable for long term projects, at least not currently.
An LLM only follows rules/prompts. They can never become Senior.
The trade off with an LLM is different. It’s not actually a junior or underperforming engineer. It’s far faster at churning out code than even the best engineers. It can read code far faster. It writes tests more consistently than most engineers (in my experience). It is surprisingly good at catching edge cases. With a junior engineer, you drag down your own performance to improve theirs and you’re often trading off short term benefits vs long term. With an LLM, your net performance goes up because it’s augmenting you with its own strengths.
As an engineer, it will never reach senior level (though future models might). But as a tool, it can enable you to do more.
I'm not sure I can think of a more damning indictment than this tbh
I think the second is part of RL training to optimize for self contained task like swe bench.
It can output something that looks like the "why" and that's probably good enough in a large percentage of cases.
Example from this morning, I have to recreate the EFI disk of one of my dev vm's, it means killing the session and rebooting the vm. I had Claude write itself a remaining.md to complement the overall build_guide.vm I'm using so I can pick up where I left off. It's surprisingly effective.
In particular IME the LLM generates a lot of documentation that explains what and not a lot of the why (or at least if it does it’s not reflecting underlying business decisions that prompted the change).
For those who are less experienced with the constant surprises that legacy code bases can provide, LLMs are deeply unsettling.
The scenario you describe is a legitimate concern if you’re checking in AI generated code with minimal oversight. In fact I’d say it’s inevitable if you don’t maintain strict quality control. But that’s always the case, which is why code review is a thing. Likewise you can use LLMs without just checking in garbage.
The way I’ve used LLMs for coding so far is to give instructions and then iterate on the result (manually or with further instructions) until it meets my quality standards. It’s definitely slower than just checking in the first working thing the LLM churns out, but it’s sill been faster than doing it myself, I understand it exactly as well because I have to in order to give instructions (design) and iterate.
My favorite definition of “legacy code” is “code that is not tested” because no matter who writes code, it turns into a minefield quickly if it doesn’t have tests.
I have tested throwing several features at an LLM lately and I have no doubt that I’m significantly faster when using an LLM. My experience matches what Antirez describes. This doesn’t make me 10x faster, mostly because so much of my job is not coding. But in term of raw coding, I can believe it’s close to 10x.
I've never worked in web development, where it seems to me the majority of LLM coding assistants are deployed.
I work on safety critical and life sustaining software and hardware. That's the perspective I have on the world. One question that comes up is "why does it take so long to design and build these systems?" For me, the answer is: that's how long it takes humans to reach a sufficient level of understanding of what they're doing. That's when we ship: when we can provide objective evidence that the systems we've built are safe and effective. These systems we build, which are complex, have to interact with the real world, which is messy and far more complicated.
Writing more code means that's more complexity for humans (note the plurality) to understand. Hiring more people means that's more people who need to understand how the systems work. Want to pull in the schedule? That means humans have to understand in less time. Want to use Agile or this coding tool or that editor or this framework? Fine, these tools might make certain tasks a little easier, but none of that is going to remove the requirement that humans need to understand complex systems before they will work in the real world.
So then we come to LLMs. It's another episode of "finally, we can get these pesky engineers and their time wasting out of the loop". Maybe one day. But we are far from that today. What matters today is still how well do human engineers understand what they're doing. Are you using LLMs to help engineers better understand what they are building? Good. If that's the case you'll probably build more robust systems, and you _might_ even ship faster.
Are you trying to use LLMs to fool yourself into thinking this still isn't the game of humans needing to understand what's going on? "Let's offload some of the understanding of how these systems work onto the AI so we can save time and money". Then I think we're in trouble.
I don't think many companies/codebases allow LLMs to autonomously edit code and deploy it, there is still a human in the loop that "prompt > generates > reviews > commits", so it really isn't hard to find someone to blame for those errors, if you happen to work in that kind of blame-filled environment.
Same goes with contractors I suppose, if you end up outsourcing work to a contractor, they do a shitty job but that got shipped anyways, who do you blame? Replace "contractor" with "LLM" and I think the answer remains the same.
Maybe there are people who are about literally typing the code, but I get satisfaction from making the codebase nice and neat, and now I have power tools. I am just working on small personal projects, but so far, Claude Opus 4.5 can do any refactoring I can describe.
and from the first line of the article:
> I love writing software, line by line.
I've said it before and I'll say it again: I don't write programs "line by line" and typing isn't programming. I work out code in the abstract away from the keyboard before typing it out, and it's not the typing part that is the bottleneck.
Last time I commented this on HN, I said something like "if an AI could pluck these abstract ideas from my head and turn them into code, eliminating the typing part, I'd be an enthusiastic adopter", to which someone predictably said something like "but that's exactly what it does!". It absolutely is not, though.
When I "program" away from the keyboard I form something like a mental image of the code, not of the text but of the abstract structure. I struggle to conjure actual visual imagery in my head (I "have aphantasia" as it's fashionable to say lately), which I suspect is because much of my visual cortex processes these abstract "images" of linguistic and logical structures instead.
The mental "image" I form isn't some vague, underspecified thing. It corresponds directly to the exact code I will write, and the abstractions I use to compartmentalise and navigate it in my mind are the same ones that are used in the code. I typically evaluate and compare many alternative possible "images" of different approaches in my head, thinking through how they will behave at runtime, in what ways they might fail, how they will look to a person new to the codebase, how the code will evolve as people make likely future changes, how I could explain them to a colleague, etc. I "look" at this mental model of the code from many different angles and I've learned only to actually start writing it down when I get the particular feeling you get when it "looks" right from all of those angles, which is a deeply satisfying feeling that I actively seek out in my life independently of being paid for it.
Then I type it out, which doesn't usually take very long.
When I get to the point of "typing" my code "line by line", I don't want something that I can give a natural language description to. I have a mental image of the exact piece of logic I want, down to the details. Any departure from that is a departure from the thing that I've scrutinised from many angles and rejected many alternatives to. I want the exact piece of code that is in my head. The only way I can get that is to type it out, and that's fine.
What AI provides, and it is wildly impressive, is the ability to specify what's needed in natural language and have some code generated that corresponds to it. I've used it and it really is very, very good, but it isn't what I need because it can't take that fully-specified image from my head and translate it to the exact corresponding code. Instead I have to convert that image to vague natural language, have some code generated and then carefully review it to find and fix (or have the AI fix) the many ways it inevitably departs from what I wanted. That's strictly worse than just typing out the code, and the typing doesn't even take that long anyway.
I hope this helps to understand why, for me and people like me, AI coding doesn't take away the "line-by-line part" or the "typing". We can't slot it into our development process at the typing stage. To use it the way you are using it we would instead have to allow it to replace the part that happens (or can happen) away from the keyboard: the mental processing of the code. And many of us don't want to do that, for a wide variety of reasons that would take a whole other lengthy comment to get into.
There’s many who’s thinking is not so deep nor sharp as yours - LLM’s are welcomed by them but come at a tremendous cost to their cognition and the firms future well-being of its code base. Because this cost is implicit and not explicit it doesn’t occur to them.
> Because this cost is implicit and not explicit it doesn’t occur to them.
Your arrogance and naiveté blinds you to the fact it is does occur to them, but because they have a better understanding of the world and their position in it, they don't care. That's a rational and reasonable position.
Software engineers are not paid to write code, we're paid to solve problems. Writing code is a byproduct.
Like, my job is "make sure our customers accounts are secure". Sometimes that involves writing code, sometimes it involves drafting policy, sometimes it involves presentations or hashing out ideas. It's on me to figure it out.
Writing the code is the easy part.
Give it a first pass from a spec. Since you know how it should be shaped you can give an initial steer, but focus on features first, and build with testability.
Then refactor, with examples in prompts, until it lines up. You already have the tests, the AI can ensure it doesn't break anything.
Beat it up more and you're done.
This is just telling me to do this:
> To use it the way you are using it we would instead have to allow it to replace the part that happens (or can happen) away from the keyboard: the mental processing of the code.
I don't want to do that.
I agree with this. The hard part of software development happens when you're formulating the idea in your head, planning the data structures and algorithms, deciding what abstractions to use, deciding what interfaces look like--the actual intellectual work. Once that is done, there is the unpleasant, slow, error-prone part: translating that big bundle of ideas into code while outputting it via your fingers. While LLMs might make this part a little faster, you're still doing a slow, potentially-lossy translation into English first. And if you care about things other than "does it work," you still have a lot of work to do post-LLM to clean things up and make it beautiful.
I think it still remains to be seen whether idea -> natural language -> code is actually going to be faster or better than idea -> code. For unskilled programmers it probably already is. For experts? The jury may still be out.
Funny thing. I tend to agree, but I think it wouldn't look that way to an outside observer. When I'm typing in code, it's typically at a pretty low fraction of my general typing speed — because I'm constantly micro-interrupting myself to doubt the away-from-keyboard work, and refine it in context (when I was "working in the abstract", I didn't exactly envision all the variable names, for example).
Unfortunately the job market does not demand both types of programmer equally: Those who drive LLMs to deliver more/better/faster/cheaper are in far greater demand right now. (My observation is that a decade of ZIRP-driven easy hiring paused the natural business cycle of trying to do more with fewer employees, and we’ve been seeing an outsized correction for the past few years, accelerated by LLM uptake.)
I doubt that the LLM drivers deliver something better; quite the opposite. But I guess managers will only realize this when it's too late: and of course they won't take any responsibility for this.
That is your definition of “better”. If we’re going to trade our expertise for coin, we must ask ourselves if the cost of “better” is worth it to the buyer. Can they see the difference? Do they care?
This is exactly the phenomenon of markets for "lemons":
> https://en.wikipedia.org/wiki/The_Market_for_Lemons
(for the HN readers: a related concept is "information asymmetry in markets").
George Akerlof (the author of this paper), Michael Spence and Joseph Stiglitz got a Nobel Memorial Prize in Economic Sciences in 2001 for their analyses of markets with asymmetric information.
Do people actually spend a significant time typing? After I moved beyond the novice stage it’s been an inconsequential amount of time. What it still serves is a thorough review of every single line in a way that is essentially equivalent to what a good PR review looks like.
Almost more importantly is: the people who pay you to build software, don’t care if you type or enjoy it, they pay you for an output of working software
Literally nothing is stopping people from writing assembly in their free time for fun
But the number of people who are getting paid to write assembly is probably less than 1000
Probably other people feel differently.
When I do have it one-shot a complete problem, I never copy paste from it. I type it all out myself. I didn't pay hundreds of dollars for a mechanical keyboard, tuned to make every keypress a joy, to push code around with a fucking mouse.
It is about business value.
Programming exists, at scale, because it produces economic value. That value translates into revenue, leverage, competitive advantage, and ultimately money. For decades, a large portion of that value could only be produced by human labor. Now, increasingly, it cannot be assumed that this will remain true.
Because programming is a direct generator of business value, it has also become the backbone of many people’s livelihoods. Mortgages, families, social status, and long term security are tied to it. When a skill reliably converts into income, it stops being just a skill. It becomes a profession. And professions tend to become identities.
People do not merely say “I write code.” They say “I am a software engineer,” in the same way someone says “I am a pilot” or “I am a police officer.” The identity is not accidental. Programming is culturally associated with intelligence, problem solving, and exclusivity. It has historically rewarded those who mastered it with both money and prestige. That combination makes identity attachment not just likely but inevitable.
Once identity is involved, objectivity collapses.
The core of the anti AI movement is not technical skepticism. It is not concern about correctness, safety, or limitations. Those arguments are surface rationalizations. The real driver is identity threat.
LLMs are not merely automating tasks. They are encroaching on the very thing many people have used to define their worth. A machine that can write code, reason about systems, and generate solutions challenges the implicit belief that “this thing makes me special, irreplaceable, and valuable.” That is an existential threat, not a technical one.
When identity is threatened, people do not reason. They defend. They minimize. They selectively focus on flaws. They move goalposts. They cling to outdated benchmarks and demand perfection where none was previously required. This is not unique to programmers. It is a universal human response to displacement.
The loudest opponents of AI are not the weakest programmers. They are often the ones most deeply invested in the idea of being a programmer. The ones whose self concept, status, and narrative of personal merit are tightly coupled to the belief that what they do cannot be replicated by a machine.
That is why the discourse feels so dishonest. It is not actually about whether LLMs are good at programming today. It is about resisting a trend line that points toward a future where the economic value of programming is increasingly detached from human identity.
This is not a moral failing. It is a psychological one. But pretending it is something else only delays adaptation.
AI is not attacking programming. It is attacking the assumption that a lucrative skill entitles its holder to permanence. The resistance is not to the technology itself, but to the loss of a story people tell themselves about who they are and why they matter.
That is the real conflict. HN is littered with people facing this conflict.
This sounds like an alien trying and failing to describe why people like creating things. No, the typing of characters in a keyboard has no special meaning, neither does dragging a brush across a canvas or pulling thread through fabric. It's the primitive desire to create something by your own hands. Have people using AI magically lost all understanding of creativity or creation, everything has to be utilitarian and business?
Sometimes I like to make music because I have an idea of the final results, and I wanna hear it like that. Other times, I make music because I like the feeling of turning a knob, and striking keys at just the right moment, and it gives me a feeling of satisfaction. For others, they want to share an emotion via music. Does this mean someone of us are "making music for the wrong reasons"? I'd claim no.
I use CC for both business and personal projects. In both cases: I want to achieve something cool. If I do it by hand, it is slow, I will need to learn something new which takes too much time and often time the thing(s) I need to learn is not interesting to me (at the time). Additionally, I am slow and perpetually unhappy with the abstractions and design choices I make despite trying very hard to think through them. With CC: it can handle parts of the project I don't want to deal with, it can help me learn the things I want to learn, it can execute quickly so I can try more things and fail fast.
What's lamentable is the conclusion of "if you use AI it is not truly creative" ("have people using AI lost all understanding of creativity or creation?" is a bit condescending).
In other threads the sensitive dynamic from the AI-skeptic crowds is more or less that AI enthusiasts "threaten or bully" people who are not enthusiastic that they will get "punished" or fall behind. Yet at the same time, AI-skeptics seem to routinely make passive aggressive implications that they are the ones truly Creating Art and are the true Craftsman; as if this venture is some elitist art form that should be gate kept by all of you True Programmers (TM).
I find these takes (1) condescending, (2) wrong and also belying a lack of imagination about what others may find genuinely enjoyable and inspiring, (3) just as much of a straw man as their gripes against others "bullying" them into using AI.
LLMs do empower you (and by "you" I mean the reader or any other person from now on) to actually complete projects you need in the very limited free time and have available. Manually coding the same could take months (I'm speaking from experience developing a personal project for about 3 hours every Friday and there's still much to be done). In a professional context, you're being paid to ship and AI can help you grow an idea to an MVP and then to a full implementation in record-breaking time. At the end of the day, you're satisfied because you built something useful and helped your company. You probably also used your problem solving skills.
Programming is also a hobby though. The whole process matters too. I'm one of the people who feels incredible joy when achieving a goal, knowing that I completed every step in the process with my own knowledge and skills. I know that I went from an idea to a complete design based on everything I know and probably learned a few new things too. I typed the variable names, I worked hard on the project for a long time and I'm finally seeing the fruits of my effort. I proudly share it with other people who may need the same and can attest its high quality (or low quality if it was a stupid script I hastily threw together, but anyway sharing is caring —the point is that I actually know what I've written).
The experience of writing that same code with an LLM will leave you feeling a bit empty. You're happy with the result: it does everything you wanted and you can easily extend it when you feel like it. But you didn't write the code, someone else did. You just reviewed an intern's work and gave feedback. Sometimes that's indeed what you want. You may need a tool for your job or your daily life, but you aren't too interested in the internals. AI is truly great for that.
I can't reach a better conclusion than the parent comment, everyone is unique and enjoys coding in a different way. You should always find a chance to code the way you want, it'll help maintain your self-esteem and make your life interesting. Don't be afraid of new technologies where they can help you though.
I've "vibe coded" a ton of stuff and so I'm pretty bullish on LLMs, but I don't see a world where "coding by hand" isn't still required for at least some subset of software. I don't know what that subset will be, but I'm convinced it will exist, and so there will be ample opportunities for programmers who like that sort of thing.
---
Why am I convinced hand-coding won't go away? Well, technically I lied, I have no idea what the future holds. However, it seems to me that an AI which could code literally anything under the sun would almost by definition be that mythical AGI. It would need to have an almost perfect understanding of human language and the larger world.
An AI like that wouldn't just be great at coding, it would be great at everything! It would be the end of the economy, and scarcity. In which case, you could still program by hand all you wanted because you wouldn't need to work for a living, so do whatever brings you joy.
So even without making predictions about what the limitations of AI will ultimately be, it seems to me you'll be able to keep programming by hand regardless.
Anecdotally, I’ve had a few coworkers go from putting themselves firmly in this category to saying “this is the most fun I’ve ever had in my career” in the last two months. The recent improvement in models and coding agents (Claude Code with Opus 4.5 in our case) is changing a lot of minds.
No. I agree with the author, but it's hyperbolic of him to phrase it like this. If you have solid domain knowledge, you'll steer the model with detailed specs. It will carry those out competently and multiply your productivity. However, the quality of the output still reflects your state of knowledge. It just provides leverage. Given the best tractors, a good farmer will have much better yields than a shit one. Without good direction, even Opus 4.5 tends to create massive code repetion. Easy to avoid if you know what you are doing, albeit in a refactor pass.
Sure Opus can work fully on its own by just telling it “add a button that does X”, but do that 20 times and the good turns into mush. Steer the model with detailed tech specs on the other hand, and the output becomes magical
One is LLMs writing code. Not everything and not for everyone. But they are useful for most of the code being written. It is useful.
What it does not do (yet, if ever) is bridging the gap from "idea" to a working solution. This is precisely where all the low-code ideas of the past decades fell apart. Translating an idea in to formal rules is very, very hard.
Think of all of the "just add a button there"-type comments we've all suffered.
It definitely can.
The innovation that was the open, social web of 20 years ago? still an option, but drowned between closed ad-fueled toxic gardens and drained by AI illegal copy bots.
The innovation that was democracy? Purposely under attack in every single place it still exists today.
Insulin at almost no cost (because it costs next to nothing to produce)? Out of the question for people that live under the regime of pharmaceutical corporations that are not reigned by government, by collective rules.
So, a technology that has a dubious ROI over the energy and water and land consumed, incites illegal activities and suicides, and that is in the process of killing the consumer public IT market for the next 5 years if not more, because one unprofitable company without solid verifiable prospects managed to pass dubious orders with unproven money that lock memory components for unproven data centers... yes, it definitely can be taken back.
The tech will still be there. As much as blockchains, crypto, NFTs and such, whose bubbles have not yet burst (well, the NFT one did, it was fast).
But (Gen)AI today is much less about the tech, and much more about the illegal actions (harvesting copyrighted works) that permit it to run and the disastrous impact it has on ... everything (resources, jobs, mistaken prospectives, distorted IT markets, culture, politics) because it is not (yet) regulated to the extent it should.
What I would really urge people to avoid doing is listening to what any tech influencer has to say, including antirez. I really don't care what famous developers think about this technology, and it doesn't influence my own experience of it. People should try out whatever they're comfortable with, and make up their own opinions, instead of listening what anyone else has to say about it. This applies to anything, of course, but it's particularly important for the technology bubble we're currently in.
It's unfortunate that some voices are louder than others in this parasocial web we've built. Those with larger loudspeakers should be conscious of this fact, and moderate their output responsibly. It starts by not telling people what to do.
I think there are some negative consequences to this; perhaps a new form of burn out. With the force multiplier and assisted learning utility comes a substantial increase in opportunity cost.
And this is Hacker News, which you might expect to attract people who thrive on exploring the edges of weird new technology!
I don't believe that AI will put most of the working force out of jobs. That would be so different from what we had in history that I think the chances are minimal. However, they are not zero, and that is scary as fuck for a lot of people.
Group 1 is untouched since they were writing code for the sake of writing and they have the reward of that altruism.
Group 2 are those that needed their projects to bring in some revenue so they can continue writing open-source.
Group 3 are companies that used open-source as a way to get market share from proprietary companies, using it more in a capitalistic way.
Overtime, I think groups 2 and 3 will leave open-source and group 1 will make up most of the open-source contributors. It is up to you to decide if projects like Redis would be built today with the monetary incentives gone.
Really, one of the first things he said, sums it up:
> facts are facts, and AI is going to change programming forever.
I have been using it in a very similar manner to how he describes his workflow, and it’s already greatly improved my velocity and quality.
I also can relate to this comment:
> I feel great to be part of that, because I see this as a continuation of what I tried to do all my life: democratizing code, systems, knowledge.
You don’t spend weeks explaining intent, edge cases, or what I really meant to a developer. You iterate 1:1 with the system and adjust immediately when something feels off.
However I can’t help but notice some things that look weird/amusing:
- The exact time that many programmers were enlightened about the AI capabilities and the frequency of their posts.
- The uniform language they use in these posts. Grandiose adjectives, standard phrases like ‘it seems to me’
- And more importantly the sense of urgency and FOMO they emit. This is particularly weird for two reasons. First is that if the past has shown something regarding technology is that open source always catches up. But this is not the case yet. Second, if the premise is that we re just the in beginning all these ceremonial flows will be obsolete.
Do not get me wrong, as of today these are all valid ways to work with AI and in many domains they increase the productivity. But I really don’t get the sense of urgency.
We have to abandon the appeal to authority and take the argument on its merits, which honestly, we should be doing regardless.
I leverage LLMs where it makes sense for me to do so, but let's dispense with this FOMO silliness. People who choose not to aren't missing out on anything, any more than people who choose to use stock Vim rather than VSCode aren't missing out on anything.
That LLMs advocates are resorting to the appeal to authority fallacy isn't a good look for them either.
Said by someone who spent his career writing code, it lacks a bit of details... a more correct way to phrase it is: "if you're already an expert in good coding, now you can use these tools to skip most of code writing"
LLMs today are mostly some kind of "fill-in-the-blanks automation". As a coder, you try to create constraints (define types for typechecking constraints, define tests for testing constraints, define the general ideas you want the LLM to code because you already know about the domain and how coding works), then you let the model "fill-in the blanks" and you regularly check that all tests pass, etc
At it's core, AI has capability to extract structure/meaning from unstructured content and vice-versa. Computing systems and other machines required inputs with limited context. So far, it was a human's job to prepare that structure and context and provide it to the machines. That structure can be called as "program" or "form data" or "a sequence of steps or lever operations or button presses".
Now the machines got this AI wrapper or adapter that enables them to extract the context and structure from the natural human-formatted or messy content.
But all that works only if the input has the required amount of information and inherent structure to it. Try giving a prompt with jumbled up sequence of words. So it's still the human jobs to provide that input to the machine.
Well that's a way to put it. But not everyone enjoy the art only for the results.
I personally love learning, and by letting AI drive forward and me following, I don't learn. To learn is to be human.
So saying the fun is untouched is one-sided. Not everyone is in it for the same reasons.
There's also a short-termism aspect of AI generated code that's seemingly not addressed as much. Don't pee your pants in the winter to keep warm.
There's still no point. Resharper and clang-tidy still have more value than all LLMs. It's not just a hype, it's a bloody cult, right besides those nft and church of COVID people.
If I can run an agent on my machine, with no remote backend required, the problem is solved. But right now, aren't all developers throwing themselves into agentic software development betting that these services will always be available to them at a relatively low cost?
If A.I writes everything for you - cool, you can produce faster ? but is it really true ? if you're renting capacity ? what if costs go up, now you can't rent anymore - but you can't code anymore, the documentation is no longer there - coz mcp etc assumption that everything will be done by agents then what ?
what about the people that work on messy 'Information Systems' - things like redis - impressive but it's closed loop software just like compilers -
some smart guy back in the 80s - wrote it's always a people problem -
This is already happening.
AI had an impact on simplest coding first, this is self-evident. So any impact it had, had to be on the quantity of software created, and only then on its quality and/or complexity. And mobile apps are/were a tedious job with a lot of scaffolding and a lot of "blanks to fill" to make them work and get accepted by stores. So first thing that had to skyrocket in numbers with the arrival of AI, had to be mobile apps.
But the number of apps on Apple Store is essentially flat and rate of increase is barely distinguishable from the past years, +7% instead of +5%. Not even visible.
Apparently the world doesn't need/can't make monetisable use of much more software than it already does. Demand wasn't quite satisfied say 5 years ago, but the gap wasn't huge. It is now covered many times over.
Which means, most of us will probably never get another job/gig after the current one - and if it's over, it's over and not worth trying anymore - the scraps that are left of the market are not worth the effort.
I’ve written complete GUIs in 3D on the front end. This GUI was non traditional. It allows you to playback, pause speed up, slow down and rewind a gps track like a movie. There is real time color changing and drawing of the track as the playback occurs.
Using mapbox to do this straight would be to slow. I told the AI to optimize it by going straight into shader extensions for mapbox to optimize GPU code.
Make no mistake. LLMs are incredible for things that are non systems based that require interaction with 3D and GUIs.
I would draw an analogy here between building software and building a home.
When building a home we have a user providing the requirements, the architect/structural engineer providing the blueprint to satisfy the reqs, the civil engineer overseeing the construction, and the mason laying the bricks. Some projects may have a project-manager coordinating these activities.
Building software is similar in many aspects to building a structure. If developers think of themselves as a mason they are limiting their perspective. If AI can help lay the bricks use it ! If it can help with the blueprint or the design use it. It is a fantastic tool in the tool belt of the profession. I think of it as a power-tool and want to keep its batteries charged to use it at any time.
There really should be a label on the product to let the consumer know. This should be similar to Norway that requires disclosure of retouched images. No other way can I think of to help body image issues arising from pictorial people and how they never can being in real life.
> However, this technology is far too important to be in the hands of a few companies.
This is the most important assessment and we should all heed this warning with great care.
If we think hyperscalers are bad, imagine what happens if they control and dictate the entire future.
Our cellphones are prisons. The entire internet and all of technology could soon become the same.
We need to bust this open now or face a future where we are truly serfs.
I'm excited by AI and I love what it can do, but we are in a mortally precarious position.
If I have to do all this babysitting, is it really saving me anything other than typing the code? It hasn't felt like it yet and if anything it's scary because I need to always read the code to make sure it's valid, and reading code is harder than writing it.
I think for some who are excited about AI programming, they're happy they can build a lot more things. I think for others, they're excited they can build the same amount of things, but with a lot less thinking. The agent and their code reviewers can do the thinking for them.
> However, this technology is far too important to be in the hands of a few companies.
I wholeheartedly agree 1000%. Something needs to change this landscape in the US.
Furthermore, the entire open source models being dominated by China is also problematic.
dom96•7h ago
I want to write less, just knowing that LLM models are going to be trained on my code is making me feel more strongly than ever that my open source contributions will simply be stolen.
Am I wrong to feel this? Is anyone else concerned about this? We've already seen some pretty strong evidence of this with Tailwind.
poszlem•7h ago
abc123abc123•7h ago
DrewADesign•7h ago
supriyo-biswas•7h ago
If running an open source model means that I have only given out without receiving anything, there remains the possibility of being exploited. This dynamic has always existed, such as companies using a project and sending in vulnerability reports and the like but not offering to help, and instead demanding, often quite rudely.
In the past working with such extractive contributors may have been balanced with other benefits such as growing exposure leading to professional opportunities, or being able to sell hosted versions, consulting services and paid features, which would have helped the maintainer of the open source project pay off their bills and get ahead in life.
However with the rise of LLMs, it both facilitates usage of the open source tools without getting a chance to direct their attention towards these paid services, nor allows the maintainer to have direct exposure to their contributors. It also indirectly violates the spirit of said open source licenses, as LLMs can spit out the knowledge contained in these codebases at a scale that humans cannot, thus allowing people to bypass the license and create their own versions of the tools, which are themselves not open source despite deriving their knowledge from such data.
Ultimately we don't need to debate about this; if open source remains a viable model in the age of LLMs, people will continue to do it regardless of whether we agree or disagree regarding topics such as this; on the other hand, if people are not rewarded in any way we will only be left with LLM generated codebases that anyone could have produced, leaving all the interesting software development to happen behind closed doors in companies.
Freak_NL•7h ago
rolisz•7h ago
chrishare•7h ago
bromuro•7h ago
m4rtink•7h ago
arter45•6h ago
RadiozRadioz•7h ago
I know the GPL didn't have a specific clause for AI, and the jury is still out on this specific case (how similar is it to a human doing the same thing?), but I like to imagine, had it been made today, there probably would be a clause covering this usage. Personally I think it's a violation of the spirit of the license.
luke5441•7h ago
There are non-US jurisdictions where you have some options, but since most of them are trained in the US that won't help much.
ThunderSizzle•7h ago
They can claim whatever they want. You can still try to stop it via lawsuits and make them claim it in court. Granted, I believe there's already been some jurisdictions that have sided with fair use in those particular cases.
zarzavat•7h ago
Strict copyright enforcement is a competitive disadvantage. Western countries lobbied for copyright enforcement in the 20th century because it was beneficial. Now the tables have turned, don't hold your breath for copyright enforcement against the wishes of the markets. We are all China now.
luke5441•6h ago
martin-t•6h ago
luke5441•5h ago
That the LLM itself is not allowed to produce copyrighted work (e.g. just copies of works or too structurally similar) without using a license for that work is something that is probably currently law. They are working around this via content filters. They probably also have checks during/after training that it does not reproduce work that is too similar. There are law suits about this pending if I remember correctly e.g. with the New York Times.
martin-t•5h ago
LLMs themselves are compressed models of the training data. The trick is the compression is highly lossy by being able to detect higher-order patterns instead of fucusing on the first-order input tokens (or bytes). If you look at how, for example, any of the Lempel-Ziv algorithms work, they also contain patterns from the input and they also predict the next token (usually byte in their case), except they do it with 100% probability because they are lossless.
So copyright should absolutely apply to the models themselves and if trained on AGPL code, the models have to follow the AGPL license and I have the right to see their "source" by just being their user.
And if you decompress a file from a copyrighted archive, the file is obviously copyrighted. Even if you decompress only a part. What LLMs do is another trick - by being lossy, they decompress probabilistically based on all the training inputs - without seeing the internals, nobody can prove how much their particular work contributed to the particular output.
But it is all mechanical transformation of input data, just like synonym replacement, just more sophisticated, and the same rules regarding plagiarism and copyright infringement should apply.
---
Back to what you said - the LLM companies use fancy language like "artificial intelligence" to distract from this so they can they use more fancy language to claim copyright does not apply. And in that case, no license would help because any such license fundamentally depends on copyright law, which as they claim does not apply.
That's the issue with LLMs - if they get their way, there's no way to opt out. If there was, AGPL would already be sufficient.
luke5441•5h ago
An open question would be if there is some degree of "loss" where copyright no longer applies. There is probably case law about this in different jurisdictions w.r.t. image previews or something.
martin-t•6h ago
At some point, I'll have to look it up because if that's right, the billionaires and wannabe-trillionaires owe me a shitton of money.
wmwragg•7h ago
dom96•6h ago
leonidasv•2h ago
karmakurtisaani•22m ago
rurp•21m ago
delusional•7h ago
They cannot violate the license, because in their view they have not licensed anything from you.
I think that's horse shit, and a clear violation of the intellectual property rights that are supposed to protect creatives from the business boys, but apparently the stock market must grow.
Ekaros•7h ago
martin-t•6h ago
(I didn't come up with this quote but I can't find the source now. If anything good comes out of LLMs, it's making me appreciate other people's more and trying to give credit where it's due.)
kapsi•6h ago
martin-t•6h ago
netsharc•3h ago
NVidia is a shovel-maker worth a few trillion dollars...
martin-t•7h ago
I haven't seen this argument made elsewhere, it would be interesting to get it into the courtrooms - I am told cases are being fought right now but I don't have the energy to follow them.
Plus as somebody else put it eloquently, it's labor theft - we, working programmers, exchanged out limited lifetime for money (already exploitative) in a world with certain rules. Now the rules changed, our past work has much more value, and we don't get compensated.
[0]: https://news.ycombinator.com/item?id=46187330
martin-t•6h ago
dahart•2h ago
That said, this comment is funny to me because I’ve done the same thing too, take some signal of disagreement, and assume the signal means I’m right and there’s a low-key conspiracy to hold me down, when it was far more likely that either I was at least a bit wrong, or said something in an off-putting way. In this case, I tend to agree with the general spirit of the sibling comment by @williamcotton in that it seems like you’re inventing some criteria that are not covered by copyright law. Copyrights cover the “fixation” of a work, meaning they protect only its exact presentation. Copyrights do not cover the Madlibs or Cliff Notes scenarios you proposed. (Do think about Cliff Notes in particular and what it implies about AI - Cliff Notes are explicitly legal.)
Personally, I’ve had a lot of personal forward progress on HN when I assume that downvotes mean I said something wrong, and work through where my own assumptions are bad, and try to update them. This is an important step especially when I think I’m right.
I’m often tempted to ask for downvote explanations too, but FWIW, it never helps, and aside from HN guidelines asking people to avoid complaining about downvotes, I find it also helps to think of downvotes as symmetric to upvotes. We don’t comment on or demand an explanation for an upvote, and an upvote can be given for many reasons - it’s not only used for agreement, it can be given for style, humor, weight, engagement, pity, and many other reasons. Realizing downvotes are similar and don’t only mean disagreement helps me not feel personally attacked, and that can help me stay more open to reflecting on what I did that is earning the downvotes. They don’t always make sense, but over time I can see more places I went wrong.
ThrowawayR2•1h ago
williamcotton•5h ago
https://en.wikipedia.org/wiki/Idea–expression_distinction
https://en.wikipedia.org/wiki/Structure,_sequence_and_organi...
https://en.wikipedia.org/wiki/Abstraction-Filtration-Compari...
In a court of law you're going to have to argue that something is an expression instead of an idea. Most of what LLMs pump out are almost definitionally on the idea side of the spectrum. You'd basically have to show verbatim code or class structure at the expressive level to the courts.
martin-t•3h ago
There's a couple issues I see:
1) All of the concepts were developed with the idea that only humans are capable of certain kinds of work needed for producing IP. A human would not engage in highly repetitive and menial transformation of other people's material to avoid infringement if he could get the same or better result by working from scratch. This placed, throughout history, an upper limit on how protective copyright had to be.
Say, 100 years ago, synonym replacement and paraphrasing of sentences were SOTA methods to make copies of a book which don't look like copies without putting in more work than the original. Say, 50 years ago, computers could do synonym replacement automatically so it freed up some time for more elaborate restructuring of the original work and the level of protection should have shifted. Say, 10 years ago, one could use automatic replacement of phrases or translation to another language and back, freeing up yet more time.
The law should have adapted with each technological step up and according to your links it has - given the cases cited. It's been 30 years and we have a massive step up in automatic copying capabilities - the law should change again to protect the people who make this advancement possible.
Now with a sufficiently advanced LLM trained on all public and private code, you can prompt them to create a 3D viewer for Quake map files and I am sure it'll most of the time produce a working program which doesn't look like any of the training inputs but does feel vaguely familiar in structure. Then you can prompt it to add a keyboard-controlled character with Quake-like physics and it'll produce something which has the same quirks as Quake movement. Where did bunny hopping, wallrunning, strafing, circlejumps, etc. come from if it did not copy the original and the various forks?
Somebody had to put in creative work to try out various physics systems and figure out what feels good and what leads to interesting gameplay.
Now we have algorithms which can imitate the results but which can only be created by using the product of human work without consent. I think that's an exploitative practice.
2) It's illegal to own humans but legal to own other animals. The USA law uses terms such as "a member of the species Homo sapiens" (e.g. [0]) in these cases.
If the legality of tech in question was not LLMs but remixing of genes (only using a tiny fraction of human DNA) to produce a animals which are as smart as humans with chimpanzee bodies which can be incubated in chimpanzee females but are otherwise as sentient as humans, would (and should) it be legal to own them as slaves and use them for work? It would probably be legal by the current letter of the law but I assure you the law would quickly change because people would not be OK with such overt exploitation.
The difference is the exploitation by LLM companies is not as overt - in fact, mane people refer to LLMs as AIs and use pronouns such as "he" or "she", indicating them believe them to be standalone thinking entities instead of highly compressed lossy archives of other people's work.
3) The goal of copyright is progress, not protection of people who put in work to make that progress possible. I think that's wrong.
I am aware of the "is" vs "should" distinction but since laws are compromises between the monopoly in violence and the people's willingness to revolt instead of being an (attempted) codification of a consistent moral system, the best we can do is try to use the current laws (what is) to achieve what is right (what should be).
[0]: https://en.wikipedia.org/wiki/Unborn_Victims_of_Violence_Act
williamcotton•1h ago
The idea of wallrunning should not be protected by copyright.
ThrowawayR2•1h ago
DrewADesign•5h ago
ndsipa_pomu•3h ago
layer8•3h ago
ndsipa_pomu•35m ago
layer8•26m ago
prodigycorp•7h ago
aspaviento•7h ago
dom96•6h ago
In other words, the open source model of "open core with paid additional features" may be dead thanks to LLMs. Perhaps less so for some types of applications, but for frameworks like Tailwind very much so.
prodigycorp•6h ago
burnermore•7h ago
Or accept that there definitely wont be open model businesses. Make them proprietary and accept the fact that even permissive licenses such as MIT, BSD Clause 2/3 wont't be followed by anyone while writing OSS.
And as for Tailwind, I donno if it is cos of AI.
tmplostpwd•7h ago
pferde•7h ago
noosphr•7h ago
Not everything needs to be mit or gnu.
bakugo•7h ago
noosphr•6h ago
bakugo•4h ago
Software licenses aren't, AI companies can just take your GPL code and spit it back out into non-GPL codebases and there's no way for you to even find out it happened, much less do anything about it, and the law won't help you either.
serf•7h ago
in other words, i've never been in the position that I felt my charitable givings anywhere were ever stolen.
Some people write code and put it out there without caveats. Some people jump into open source to be license warriors. Not me. I just write code and share it. If youre a person, great. if you're a machine then I suppose that's okay too -- I don't want to play musical chairs with licenses all day just to throw some code out there, and I don't particularly care if someone more clever than myself uses it to generate a profit.
ChrisMarshallNY•7h ago
I’ve never been a fan of coercive licensing. I don’t consider that “open.” It’s “strings-attached.”
I make mine MIT-licensed. If someone takes my stuff, and gets rich (highly unlikely), then that’s fine. I just don’t want some asshole suing me, because they used it inappropriately, or a bug caused them problems. I don’t even care about attribution.
I mainly do it, because it forces me to take better care, when I code.
matthewmacleod•6h ago
Some people are happy to release code openly and have it used for anything, commercial or otherwise. Totally understandable and a valid choice to make.
Other people are happy to release code openly so long as people who incorporate it into their projects also release it in the same way. Again, totally understandable and valid.
None of this is hard to understand or confusing or even slightly weird.
oncallthrow•7h ago
And sure, I could stubbornly refuse to use an LLM and write the code myself. But after getting used to LLM-assisted coding, particularly recent models, writing code by hand feels extremely tedious now.
embedding-shape•7h ago
I don't think it's wrong, but misdirected maybe. What do you that someone can "steal" your open source contributions? I've always released most of my code as "open source", and not once has someone "stolen" it, it still sits on the same webpage where I initially published it, decades ago. Sure, it's guaranteed ingested into LLMs since long time ago, but that's hardly "stealing" when the thing is still there + given away for free.
I'm not sure how anyone can feel like their open source code was "stolen", wasn't the intention in the first place that anyone can use it for any purpose? That's at least why I release code as open source.
krior•7h ago
embedding-shape•7h ago
dom96•6h ago
otterley•2h ago
gus_massa•7h ago
On the other side BSD0 is just a polite version of WTFPL, and people that like it doesn't care about what you do with the code.
embedding-shape•6h ago
otterley•1h ago
> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
> THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
The operative language here is “all copies or substantial portions of the Software.” LLMs, with rare exceptions, don’t retain copies or substantial portions of the software it was trained on. They’re not libraries or archives. So it’s unclear to me how training an AI model with an MIT-licensed project could violate the license.
(IAAL and this is my personal analysis, not legal advice.)
babarock•7h ago
I've written a ton of open source code and I never cared what people do with it, both "good" or "bad". I only want my code to be "useful". Not just to the people I agree with, but to anyone who needs to use a computer.
Of course, I'd rather people use my code to feed the poor than build weapons, but it's just a preference. My conviction is that my code is _freed_ from me and my individual preferences and shared for everyone to use.
I don't think my code is "stolen", if someone uses it to make themselves rich.
martin-t•6h ago
Why not say "... but to the people I disagree with"?
Would you be OK knowing your code is used to cause more harm than good? Would you still continue working on a hypothetical OSS which had no users, other than, say, a totalitarian government in the middle east which executes homosexuals? Would you be OK with your software being a critical directly involved piece of code for example tracking, de-anonymizing and profiling them?
Where is the line for you?
layer8•3h ago
The one thing I do care about is attribution — though maybe actually not in the nefarious cases.
martin-t•1h ago
I see this a lot and while being technically correct, I think it ignores the costs for them.
In practice such a government doesn't need to have laws and courts either but usually does because the appearance of justice.
Breaking international laws such as copyright also has costs for them. Nobody will probably care about one small project but large scale violations could (or at least should) lead to sanctions.
Similarly, if they want to offer their product in other countries, now they run the risk of having to pay fines.
Finally, see my sibling comment but a lot of people act like Open Source is an absolute good just because it's Open Source. By being explicit about our views about right and wrong, we draw attention to this delusion.
layer8•38m ago
stravant•3h ago
I'm not going to deliberately write code that's LIKELY to do more harm than good, but crippling the potential positive impact just because of some largely hypothetical risk? That feels almost selfish, what would I really be trying to avoid, personally running into a feel-bad outcome?
martin-t•1h ago
Douglas Crockford[0] tried this with JSON. Now, strictly speaking, this does not satisfy the definition of Open Source (it merely is open source, lowercase). But after 10 years of working on Open Source, I came to the conclusion that Open Source is not the absolute social good we delude ourselves into thinking.
Sure, it's usually better than closed source because the freedoms mean people tend to have more control and it's harder for anyone (including large corporations) to restrict those freedoms. But I think it's a local optimum and we should start looking into better alternatives.
Android, for example, is nominally Open Source but in reality the source is only published by google periodically[1], making any true cooperation between the paid devs and the community difficult. And good luck getting this to actually run on a physical device without giving up things like Google Play or banking apps or your warranty.
There's always ways to fuck people over and there always will be but we should look into further ways to limit and reduce them.
[0]: https://en.wikipedia.org/wiki/Douglas_Crockford
[1]: https://www.androidauthority.com/aosp-source-code-schedule-3...
auggierose•6h ago
risyachka•7h ago
Meaning 99% of everything oss released now is de-facto abandonware.
samwillis•7h ago
In future everyone will expect to be able to customise an application, if the source is not available they will not chose your application as a base. It's that simple.
The future is highly customisable software, and that is best built on open source. How this looks from a business perspective I think we will have to find out, but it's going to be fun!
charcircuit•7h ago
I think there is room for closed source platforms that are built on top of using LLMs via some sort of API that it exposes. For example, iOS can be closed source and LLMs can develop apps for it to expand the capabilities of one's phone.
Allowing total customization by a business can allow them to mess up the app itself or make other mistakes. I don't think it's the best interface for allowing others to extend the app.
dom96•6h ago
MaxBarraclough•5h ago
This seems unlikely. It's not the norm today for closed-source software. Why would it be different tomorrow?
simonw•5h ago
I'm feeling this already.
Just the other day I was messing around with Fly's new Sprites.dev system and I found myself confused as to how one of the "sprite" CLI features worked.
So I went to clone the git repo and have Claude Code figure out the answer... and was surprised to find that the "sprite" CLI tool itself (unlike Fly's flycli tool, which I answer questions about like this pretty often) wasn't open source!
That was a genuine blocker for me because it prevented me from answering my question.
It reminded me that the most frustrating thing about using macOS these days is that so much of it is closed source.
I'd love to have Claude write me proper documentation for the sandbox-exec command for example, but that thing is pretty much a black hole.
MaxBarraclough•4h ago
• Increased upfront software complexity
• Increased maintenance burden (to not break officially supported plugins/customizations)
• Increased support burden
• Possible security/regulatory/liability issues
• The company may want to deliberately block functionality that users want (e.g. data migration, integration with competing services, or removing ads and content recommendations)
> That was a genuine blocker for me because it prevented me from answering my question.
It's always been this way. From the user's point of view there has always been value in having access to the source, especially under the terms of a proper Free and Open Source licence.
andrewstuart•7h ago
I love AI and pay for four services and will never program without AI again.
It pleases me that my projects might be helping out.
zsoltkacsandi•7h ago
I believe open source will become a bit less relevant in it’s current form, as solution/project tailored libraries/frameworks can be generated in a few hours with LLMs.
uyzstvqs•7h ago
If I made something open source, you can train your LLM on it as much as you want. I'm glad my open source work is useful to you.
tw04•7h ago
The entire point isn’t to allow a large corporation to make private projects out of your open source project for many open source licenses. It’s to ensure the works that leverage your code are open source as well. Something AI is completely ignoring using various excuses as to why their specific type of theft is ok.
FergusArgyll•6h ago
dom96•6h ago
otterley•1h ago
jeroenhd•6h ago
AI doesn't hold up its end of the bargain, so if you're in that mindset you now have to decide between going full hands-off like you or not doing any open source work at all.
simonw•5h ago
hexbin010•5h ago
It comes across as really trying too hard and a bit aggressive.
You could just write one top level comment and chill a bit. Same advice for any future threads too...
jeroenhd•4h ago
I consider the payment I and my employer make to these AI companies to be what the LLM is paying me back for. Even the free ones get paid for my usage somehow. This stuff isn't charity.
martin-t•7h ago
LLMs are labor theft on an industrial scale.
I spent 10 years writing open source, I haven't touched it in the last 2. I wrote for multiple reasons none of which any longer apply:
- I believe every software project should have an open source alternative. But writing open source now means useful patterns can be extracted and incorporated into closed source versions _mechanically_ and with plausible deniability. It's ironically worse if you write useful comments.
- I enjoyed the community aspect of building something bigger than one person can accomplish. But LLMs are trained on the whole history and potentially forum posts / chat logs / emails which went into designing the SW too. With sufficiently advanced models, they effectively use my work to create a simulation of myself and other devs.
- I believe people (not just devs) should own the product they build (an even stronger protection of workers against exploitation than copyright). Now our past work is being used to replace us in the future without any compensation.
- I did it to get credit. Even though it was a small motivation compared to the rest, I enjoyed everyone knowing what I accomplished and I used it during job interviews. If somebody used my work, my name was attached to it. With LLMs, anyone can launder it and nobody knows how useful my work was.
- (not solely LLM related) I believed better technology improves the world and quality of life around me. Now I see it as a tool - neutral - to be used by anyone for both good and bad purposes.
Here's[0] a comment where I described why it's theft based on how LLMs work. I call it higher order plagiarism. I haven't seen this argument made by other people, it might be useful for arguing about those who want to legalize this.
In fact, I wonder if this argument has been made in court and whether the lawyers understand LLMs enough to make it.
[0]: https://news.ycombinator.com/item?id=46187330
zahlman•7h ago
I want to write code to defy this logic and express my humanity. "To have fun", yes. But also to showcase what it means when a human engages in the act of programming. Writing code may increasingly not be "needed", but it increasingly is art.
andrewstuart•7h ago
ben_w•6h ago
There's no such thing as a wrong feeling.
And I say this as one of those with the view that AI training is "learning" rather than "stealing", or at least that this is the goal because AI is the dumbest, the most error prone, and also the most expensive way, to try to make a copy of something.
My fears about setting things loose for public consumption are more about how I will be judged for them than about being ripped off, which is kinda why that book I started writing a decade ago and have not meaningfully touched in the last 12 months is neither published properly nor sent to some online archive.
When it comes to licensing source code, I mostly choose MIT, because I don't care what anyone does with the code once it's out there.
But there's no such thing as a wrong feeling, anyone who dismisses your response is blinding themselves to a common human response that also led to various previous violent uprisings against the owners of expensive tools of automation that destroyed the careers of respectable workers.
63stack•6h ago
qsera•6h ago
https://archclx.medium.com/enforcing-gpg-encryption-in-githu...
My opinion on the matter is that AI models stealing the open source code would be ok IF the models are also open and remain so, and the services like chatgpt will remain free of cost (at least a free tier), and remain free of ads.
But we all know how it is going to go.
CraftingLinks•6h ago
jillesvangurp•6h ago
It's very hard to prevent specific types of usage (like feeding code to an LLM) without throwing out the baby with the bathwater and also preventing all sorts of other valid usages. AGPLv3, which is what antirez and Redis use goes to far IMHO and still doesn't quite get the job done. It doesn't forbid people (or tools) to "look" at the code which is what AI training might be characterized as. That license creates lots of headaches for corporate legal departments. I switched to Valkey for that reason.
I actually prefer using MIT style licenses for my own contributions precisely because I don't want to constrain people or AI usage. Go for it. More power to you if you find my work useful. That's why I provide it for free. I think this is consistent with the original goals of open source developers. They wanted others to be able to use their stuff without having to worry about lawyers.
Anyway, AI progress won't stop because of any of this. As antirez says, that stuff is now part of our lives and it is a huge enabler if you are still interested in solving interesting problems. Which apparently he is. I can echo much of what he says. I've been able to solve larger and larger problems with AI tools. The last year has seen quite a bit of evolution in what is possible.
> Am I wrong to feel this?
I think your feelings are yours. But you might at least examine your own reasoning a bit more critically. Words like theft and stealing are big words. And I think your case for that is just very weak. And when you are coding yourself are you not standing on the shoulders of giants? Is that not theft?
williamcotton•5h ago
fabianholzer•5h ago
Why would a feeling be invalid? You have one life, you are under no obligation to produce clean training material, much less feel bad about this.
tiborsaas•1h ago
Tailwind is a business and they picked a business model that wasn't resilient enough.
oxag3n•15m ago
To my surprise, my doctoral advisor told me to keep the code closed. She told me not only LLMs will steal it and benefit from it, but there's a risk of my code becoming a target after it's stolen by companies with fat attorney budgets and there's no way I could defend and prove anything.