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The film industry loves cgi because none of the digital vfx houses are unionized and they can treat the artists like crap. It severely devalued a ton of skilled labor around miniature and set design.
Now, after 20 years of hard swing into cgi, people are starting to recognize just how much better movies from the practical era looked, and there is a push back towards it. Project Hail Mary was predominantly practical effects, for example. Stop motion animation is coming back, and theres a push back into hand cell animation.
> Those who refuse to use an LLM will fall behind because they won't be able to produce as much
Seems like a silly and needlessly aggressive take.
Fall behind what? Able to produce "as much" what? I've never been evaluated on volume in my life. Nor have co workers who were severely "behind" ever feared for their jobs.
Like realistically even without LLMs I output probably around 10x as much code working alone, self-employed with zero meetings or bureaucracy, than I've ever done as a professional programmer. My output sometimes rivals that of entire teams' I've been part of, mostly because I get to just code to my heart's content.
That's not very hard with many of the teams I've seen, with or without LLMs. Though the old adage of "If you want to go fast, go alone. If you want to go far, go together" still applies.
The fact is that often I code less than most of my peers. Because I prefer spending some time to design suitable data structures/algorithms for the problem at hand. I don't aim for perfection, just that it align with the business domain (and/or the interface) so that future works are proportional with the scope of change requests. This has reflected in small commits because the fundamental core of the business domain rarely changes (when they do, we have bigger problems than my writing speed).
So I've never seen the need to increase my writing speed, because there's never any need to do so. What I'd like to increase is the speed the Product team get back to me with answers to my questions. Because that's often the real bottleneck.
I'm doing this at LLM speed now.
I feel like I'm doing the work of two whole teams and designing rock-solid software.
Rust, strong types, enums, fantastic interfaces, brevity.
Customer meaningful features that move the needle on the business.
I think this is strictly true. And not because LLMs can write code faster. I think it's true even if you're still writing most of your code by hand and using the LLM as an assistant.
My anecdotal but decades-long observation is that most of the time=cost of a project comes not from writing code, but from dealing with "issues". Weird bugs, surprising behaviors, spec ambiguities, library defects, mysterious test failures, etc. Stuff that requires intense debugging and building out a mental map of code that might not even be yours. LLMs excel at this kind of thing, freeing you up to spend most of your time working on business logic.
This has certainly been my experience.
My hobby AI projects feature wise match existing company offerings in about a week of turn around. But this alone is valueless. The new thing that didn't exist before 2026 will remain the hard moat. But these moats will dissolve as fast as OpenAI can scrape your public marketing. It's going to be like releasing Meccha Chameleon as a break out hit but a month later the clones on Roblox having greater player numbers. This is the turn around times we're going to have to live with in general for business pivots to the "next" business logic that makes sense in the market.
Closer to the AI world it's going to be as fast as the transition from prompt engineering and MCPs to loop engineering and harnesses. I'm pretty confident popular commentators will see "loops" as old hat by December by raw function of what speed of evolution we're dealing with here now.
A lot of tech jobs seem to be only about sheer output volume, with quality (maintenability, availability, security, generally understanding what the thing is doing) not mattering much. In that case sure, LLM all the way and whatever happens happens. But not all jobs are like that.
With LLM at my disposal, I had the time- and effort-budget to expand test suites considerably, I was even able to attack a somewhat thorny question of reproducible builds on MSVC, which is not exactly friendly towards determinism.
These tasks would take me personally so much time that I would have to set them aside, at the cost of output quality.
This isn't to say that LLMs aren't impactful, but that there's an argument for viewing them less as being a fundamental shift in how our profession works and more as another tool we can use to pursue essentially the same goals more efficiently than before. Like any other tool that's worth having, they can do things our existing tools couldn't do as well, or else we wouldn't have added it to our toolbox, but you still need to be able to recognize when to use it and when not to (and potentially how to use it when you do).
I think that part of why these tools are so polarizing is that there was already some assymetry in how much longer it takes to clean up things than to create things that need to be cleaned up, so a new tool that makes everyone more productive has a lot of potential to exacerbate the existing imbalance. To make up some numbers for illustrative purposes, if someone introduced four new flaky tests in the time it took to fully diagnose and clean up one, and then LLMs came and made everybody twice as productive, now in the same amount of time someone might introduce eight flaky tests while you fixed two, so you're falling behind twice as fast. Unless the productivity gain disproportionately speeds up the people working on making things more robust and polished (which I find dubious; if anything I think the opposite seems more likely) or LLMs suddenly make everyone who didn't care about quality when rushing things out take it more seriously (which seems even more dubious), then LLMs don't improve the situation for people who already felt that the balance was slanted too heavily towards speed over quality.
It can be pretty depressing, until you learn how to game the system - create tickets for yourself that are tiny amounts of work. I hear that it's getting harder to do that, because management is looking more carefully at tickets generated and it looks like they'll start having developers assign points to their tickets before the tickets are added to the sprint
Conversely, the company I am at has no such expectations, and we've got a legacy code base that LLMs aren't very handy in anyway.
So do I. What I'm finding is that they are now.
I've spent the last week tracking down bugs using Fable that have gone undiagnosed for several years. And this is a damned obscure legacy code base that runs on a proprietary 8051 variant. Guaranteed to be nothing like it in-distribution.
The notion of falling behind because you refuse to adopt an advance in the field seems both uncontroversial and not aggressive at all to me.
Or at the very least: "It was never about the actual coding" and "coding more separates you from those who will fall behind" is classic kettel logic.
This is the future. Adapt or die.
I'm curious about what adaptation you have in mind.
You use LLMs to write specific functions? The person who uses it in an agent loop will leave you behind to die.
You use LLMs in an agent loop? The person who uses LLMs to supervise loops will leave you behind to die.
You use LLMS to supervise agent loops? The person who uses LLMs to determine product offering and automatically start supervision on producing the product will leave you behind to die.
You use LLMs to determine product offering and kick off the supervision? The person who uses LLMs to clone your product without the initial product research will leave you behind to die.
You use LLMs to clone products gaining traction? The person who runs a cluster 100x the size of yours will leave you behind to die.
I am trying to understand where you think you fit in, in this Brave New World.
You obviously think that you're adapting, but if you're correct, anything you do now can be replaced by an LLM in the near future.
Just where were you going with "Adapt or die".
Man can it put together a react app lickety split, though
And it also helps to give it better tools than grep and find to explore, because it can waste lots of tokens and pollute the context with the defaults.
This is the future. Adapt or die.
The "or die" part betrays insecurity and weakness. You need to scare prople into using it rihht now, because of some perceived threat.
> It remains important to be able to read the code and understand the architecture. As a result, I reduce my velocity by iterating over my PR until it reaches the same level of quality I would have produced "by hand"
I do that too and when I do it I'm not sure anymore if I'm "producing as much more" than if I was doing it by hand. I need to spend time to read the code, break down the flow so that it clicks in my head and so that I'm 100% sure that I understand what is going on and what every line does. And then I still test it (executing it), because that's where you notice the edge cases anyways. Once I understand it and test it, the part where I iterate or fix small quirks and hallucinations is the smallest part of the job and is irrelevant if i do it by myself or ask the LLM to make the change.
I'm still not convinced that I'm faster with an LLM at all, since I add this new bottleneck (the time spent understanding every line). If I do it by hand it already clicks in my head, so it's faster for me to test it, find unaddressed edge cases and then confidently ship it. Maybe the LLMs gains are not in this at all and writing every line by hand will still be the norm for a long time.
Still, LLMs make me insanely faster in: finding something in the codebase, recostructing a flow and understanding the architecture, triaging a bug (sometimes it just solves it with a prompt), writing and updating tests, reviewing changes for potential issues. These days I have almost always 2/3 agents running doing something of the above. That saves me hours and you can pry an LLM from my dead hands, but I'm still not sold that it makes me faster at producing production grade code that I fully understand and follows my company architecture and standards.
Then sure, if I need to make a prototype or a small tool for myself or some novelty thing, an LLM can do it without me ever touching or reading the code. But I think that's not what the majority of software engineers are employed to do.
I see employability being discussed far more often than joy.
If your motivation was selling as many clothes as possible, then the industrial textile revolution was miraculous.
If you enjoyed knitting threads together, it was the crushing victory of mediocrity.
You just won't be able to get a job doing it. It will be a hobby, not a way to make a living.
Which you likely failed to review thoroughly, so may be subtly wrong.
But on the positive side, no dependencies.
A genuine question : If an AI can reliably write code better than most coders, do it quicker, and produce code that runs efficiently which has less, or at least no more, bugs than human written code, why on earth would a company not use an AI to write all their code for most purposes?
And if they did, why is it important for that code to be 'elegant' or even human readable if the bug checking is also done by AI? (as seems to be the direction we are moving in)
However as you say, we already have the evidence about this one, and it would require some unknown wall to exist where AI could suddenly not improve further, and I’m just not seeing it. Most likely it will get more capable and cheaper as time goes on, and then every industry will be impacted the way software currently is.
In the 40s, the argument was that computers will never think because they are made of matter, Turing rebutted this by saying that brains are made of matter and introduced The Turing Test, which was then replied to by Searle with his famous 'Chinese Room', which to my mind just made all consciousness suspect, even humans.
At every stage, though, computers have outpaced the predictions for their capabilities, and as you say, the unknown wall that blocks this march is yet to be seen.
I left in the late 2010s, Lots of competition meant that wages were kept down, and hours fucking long. It was fun, I loved being at the intersection of Art, infrastructure and programming.
I fear for the future.
I hope that I am ok, because I have experience of high scale that is not really in the training corpus. I've also been in ML for a reasonably long time, so have more experience of getting the dipshit machine to do useful things.
But thats pretty thin gruel.
I am rapidly approaching middle age, which means that no fucker is going to employ me as an apprentice if I want to re-train. My techincal and artistic skills are basically replaced. They are the equivalent of Linotype expert. Technically impressive but utterly fucking pointless for a world where newspapers are dead and so is analog printing. In 40 years I could possibly make a thin living as an artisan. But I plan on being dead by then.
Fabien, care to share your whole file? I'll plug it into my NixOS machine.
The deal GenAI offers is: the result will be mediocre at best, on average it will be slop, but it will do it much faster. Ok, that's a fair value proposition in certain contexts. We've always had a need to prototype things fast, and the tradeoff with a prototype is always quality.
However, we're living in an age where we have WAY TOO MUCH in the way of information byproducts, even before AI. How many people do you meet that are like "God, I just wish I had more software in my life!" Most people don't want more software, they want less software that works better. They want more quality and less quantity. It's like this in almost everything digital now. I sign onto Netflix and I can't find anything to watch, even though there's more to watch than I could consume in a lifetime. I live in abundance but I don't want any of it.
GenAI offers us an abundance of stuff we don't want or need (lots of bad code, lots of bad writing, lots of bad illustrations, lots of bad videos) at a cost of stuff we do not have in abundance (energy, attention, natural resources, jobs). It strikes me as a bad trade: lets transform the stuff we need into stuff nobody wants, while decimating our culture in the process.
Anyway, FWIW I do agree with his point that the job has always been problem solving. I use LLMs to solve problems, I'm not extinct. But I'm not going to pretend that I think this is a net win.
Or don't.
Most LLMs people are using to code are paywalled, and controlled by private, for-profit entities.
This is fundamentally different than the past, and diametrically opposed to the hacker.
If you're a hacker, which most of you are not (things have changed here over time), you will reject this.
Failing that, GLM 5.2 is open weights, trades blows with current frontier models and widely available on commodity inference providers. And you could run it yourself if you do actually have the resources.
How can you go the opposite direction? Instead of using LLMs to produce more code, can you produce less, maybe higher abstraction code?
You'll also recognize that the problem is not AI in general or LLMs in particular, but the proprietary entities that control the best models.
That's the part HN'ers seem to have the most trouble with. They protest AI qua AI, as if that's somehow going to help, when they should be fighting for independent development and universal access.
Because it’s literally not going to happen. The existence of LLMs is a function of how much capital you have. Frontier models require so many resources to train and run that they are functionally inaccessible to the average person.
That’s why capital loves them! It’s a resources play.
You’re also conveniently leaving out all of the other negative aspects of LLMs/GenAI with regards to the arts, open communication, etc..
I only snark at those who try to mislabel that thing as something useful. Which it is not.