I have not really found anything that shakes these people down to their core. Any argument or example is handwaved away by claims that better use of agents or advanced models will solve these “temporary” setbacks. How do you crack them? Especially upper management.
You let them play out. Shift-left was similar to this and ultimately ended in part disaster, part non-accomplishment, and part success. Some percentage of the industry walked away from shift-left greatly more capable than the rest, a larger chunk left the industry entirely, and some people never changed. The same thing will likely happen here. We'll learn a lot of lessons, the Overton window will shift, the world will be different, and it will move on. We'll have new problems and topics to deal with as AI and how to use it shifts away from being a primary topic.
Edit: I've googled it and I can't find anything relevant. I've been working in software for 20+ years and read a myriad things and it's the first time I hear about it...
I would do the initial research/planning/etc. mostly honestly and fairly. I'd find the positives, build a real roadmap and lead meetings where I'd work to get people onboard.
Then I'd find the fatal flaw. "Even though I'm very excited about this, as you know, dear leadership, I have to be realistic that in order to do this, we'd need many more resources than the initial plan because of these devastating unexpected things I have discovered! Drat!"
I would then propose options. Usually three, which are: Continue with the full scope but expand the resources (knowing full well that the additional resources required cannot be spared), drastically cut scope and proceed, or shelve it until some specific thing changes. You want to give the specific thing because that makes them feel like there's a good, concrete reason to wait and you're not just punting for vague, hand-wavy reasons.
Then the thing that we were waiting on happens, and I forget to mention it. Leadership's excited about something else by that point anyway, so we never revisit dumb project again.
Some specific thoughts for you:
1. Treat their arguments seriously. If they're handwaving your arguments away, don't respond by handwaving their arguments away, even if you think they're dumb. Even if they don't fully grasp what they're talking about, you can at least concede that agents and models will improve and that will help with some issues in the future.
2. Having conceded that, they're now more likely to listen to you when you tell them that while it's definitely important to think about a future where agents are better, you've got to deal with the codebase right now.
3. Put the problems in terms they'll understand. They see the agent that wrote this feature really quickly, which is good. You need to pull up the tickets that the senior developers on the team had to spend time on to fix the code that the agent wrote. Give the tradeoff - what new features were those developers not working on because they were spending time here?
4. This all works better if you can position yourself as the AI expert. I'd try to pitch a project of creating internal evals for the stuff that matters in your org to try with new models when they come out. If you've volunteered to take something like that on and can give them the honest take that GPT-5.5 is good at X but terrible at Y, they're probably going to listen to that much more than if they feel like you're reflexively against AI.
Where the tech argument doesn't apply to upper management, business practices, the need to "not be left behind" and leap at anything that promises reducing headcount without reducing revenue, money talks. As long as it's possible to slop something together, charge for it, and profit, slop will win.
Over the course of about 2 years, the general consensus has shifted from "it's a fun curiosity" to "it's just better stackoverflow" to "some people say it's good" to "well it can do some of my job, but not most of it". I think for a lot of people, it has already crossed into "it can do most of my job, but not all of it" territory.
So unless we have finally reached the mythical plateau, if you just go by the trend, in about a year most people will be in the "it can do most of my job but not all" territory, and a year or two after that most people will be facing a tool that can do anything they can do. And perhaps if you factor in optimisation strategies like the Karpathy loop, a tool that can do everything but better.
Upper managment might be proven right.
Try one on a mildly ugly multi-step task in a repo with stale deps, weird config, and a DB/API boundary, and you'll watch it bluff past missing context, mutate the wrong file, and paper over the gap with confident nonsense instead of doing the boring work a decent engieneer would do. PR people can call that 'better Stack Overflow' if they want.
And I’m pretty sure I could try Claude on a repo as you describe and it wouldn’t in fact fail. You’re letting your opinions of what LLMs were like a few months ago influence what you think of them now.
Comments like yours really annoy me because they are ridiculously confident about AI being “glorified autocomplete”, but also clearly not informed about the capabilities. I don’t get how some people can be on HN and not actually … try these things, be curious about them, try them on hard problems.
I’m a good engineer. I’ve coded for 24 years at this point. Yesterday in 45 minutes I built a feature that would have taken me three months without AI. The speed gains are obscene and because of this, we can build things we would never have even started before. Software is accelerating.
I also believe coding isn't going to disappear, but AI skeptics have been mostly doing a combination of moving the goalposts and straight up denial over the last few years.
I haven't seen a lot of goalpost moving on either side; the closest I've seen is from the most hyperbolic of AI supporters, who are keeping the timeline to supposed AGI or AI superintelligence or whatnot a fairly consistent X months from now (which isn't really goalpost-moving).
Maybe I should just retire a few years early and go back to fixing cars...
Maybe in the future us olds will get more credit when apps fall over and the higher ups realize they actually need a high-powered cleaner/fixer, like the Wolf in Pulp Fiction.
Meanwhile I’m moving at about half the speed with a more hands on approach (still using the bots obviously) but my code quality and output are miles ahead of where I was last year without sacrificing maintain ability and performance for dev speed
My experience is it hits both new-feature velocity and stability (or the balance between those two) really early, but lots of managers don't realize that this feature that's taking literal months could have been an afternoon with better choices earlier on (because they're not in a position to recognize those kinds of things). For that matter, a lot of (greener) developers probably don't recognize when the thing that's a whole-ass project for them could have been toggling a feature flag and setting a couple config entries in the correct daemon, with better architecture, because... they don't even know what sort of existing bulletproof daemon ought to be handling this thing that somehow, horrifically, ended up in their application layer.
So the blame never gets placed where it belongs, and the true cost of half-assed initial versions is never accounted for, nor is it generally appreciated just how soon the bill comes due (it's practically instantly, in many cases).
Electric Clojure: https://electric.hyperfiddle.net/fiddle/electric-tutorial.tw...
But of course writing code directly will always maintain the benefit of specificity. If you want to write instructions to a computer that are completely unambiguous, code will always be more useful than English. There are probably a lot of cases where you could write an instruction unambiguously in English, but it'd end up being much longer because English is much less precise than any coding language.
I think we'll see the same in photo and video editing as AI gets better at that. If I need to make a change to a photo, I'll be able to ask a computer, and it'll be able to do it. But if I need the change to be pixel-perfect, it'll be much more efficient to just do it in Photoshop than to describe the change in English.
But much like with photo editing, there'll be a lot of cases where you just don't need a high enough level of specificity to use a coding language. I build tools for myself using AI, and as long as they do what I expect them to do, they're fine. Code's probably not the best, but that just doesn't matter for my case.
(There are of course also issues of code quality, tech debt, etc., but I think that as AI gets better and better over the next few years, it'll be able to write reliable, secure, production-grade code better than humans anyway.)
Unless the defect rate for humans is greater than LLMs at some point. A lot of claims are being made about hallucinations that seem to ignore that all software is extremely buggy. I can't use my phone without encountering a few bugs every day.
Citation needed.
To be lenient I will separate out bugs caused by insufficient knowledge as not being failures in reasoning, do you have forms of bugs that you think are more common and are not arguably failures in reasoning that should be considered?
on edit: insufficient knowledge that I might not expect a competent developer to have is not a failure in reasoning, but a bug caused by insufficient knowledge that I would expect a competent developer in the problem space to have is a failure in reasoning, in my opinion on things.
The reality is we have built complex organizational structures around the fact that humans also make mistakes, and there's no real reason you can't use the same structures for AI. You have someone write the code, then someone does code review, then someone QAs it.
Even after it goes out to production, you have a customer support team and a process for them to file bug tickets. You have customer success managers to smooth over the relationships with things go wrong. In really bad cases, you've got the CEO getting on a plane to go take the important customer out for drinks.
I've worked at startups that made a conscious decision to choose speed of development over quality. Whether or not it was the right decision is arguable, but the reality is they did so knowing that meant customers would encounter bugs. A couple of those startups are valuable at multiple billions of dollars now. Bugs just aren't the end of the world (again, most cases - I worked on B2B SaaS, not medical devices or what have you).
This is broadly true, but not comparable when you get into any detail. The mistakes current frontier models make are more frequent, more confident, less predictable, and much less consistent than mistakes from any human I'd work with.
IME, all of the QA measures you mention are more difficult and less reliable than understanding things properly and writing correct code from the beginning. For critical production systems, mediocre code has significant negative value to me compared to a fresh start.
There are plenty of net-positive uses for AI. Throwaway prototyping, certain boilerplate migration tasks, or anything that you can easily add automated deterministic checks for that fully covers all of the behavior you care about. Most production systems are complicated enough that those QA techniques are insufficient to determine the code has the properties you need.
my experience literal 180 degrees from this statement. and you don’t normally get the choose humans you work with, some you may be involved in the interview process but that doesn’t tell you much. I have seen so much human-written code in my career that, in the right hands, I’ll take (especially latest frontier) LLM written code over average human code any day of the week and twice on Sunday
Models can RTFM (and code) and do novel things, demonstrably so.
Manager people or managing a hyper-knowledgeable intern (LLM). If you know what you need, actually want what you want (super difficult), and have the ability to provide context to someone else… management has always been easier for you than others.
I find one of the more interesting things about the current “AI debate” is that many programmers are autistic or at least close one side of an empathetic spectrum that they’ve always had trouble communicating what is needed for a task and why. So it’s hard for me to take the opinions going around.
Zero preexisting examples of your particular frameworks.
Huge number of examples of similar existing frameworks and code patterns in their training set though.
Still not a novel thing in any meaningful way, not any more than someone who has coded in dozens of established web frameworks, can write against an unfamiliar to them framework homegrown at his new employer.
Right. What you're saying is that barely anyone is doing truly novel work. 100% agree.
And is that fact supposed to be an argument in favor of how LLMs can do novel work and move the state of the art (which is what we're arguing about).
I mean, "LLMs can do novel work because: barely anyone is doing truly novel work" doesn't really compute as an argument.
"LLMs can only emit things they've been trained on" is wholly obsolete.
Such as what?
>with darn near zero preexisting examples
Whatever it is, you'd be surprised.
I figure that all this AI coding might free us from NIH syndrome and reinventing relational databases for the 10th time, etc.
LLMs remove the time problem (to an extent) and have more problems around understanding the constraints imposed by the framework. The trade-off is less worth it now.
I have stopped using frameworks completely when writing systems with an LLM. I always tell it to use the base language with as few dependencies as possible.
“NIH” here refers to “Not Invented Here” Syndrome, or a bias against things developed externally.
For example, my company makes a new framework, and we have a skill we can point an agent at. Using that skill, it can one-shot fairly complicated code using our framework.
The skill itself is pretty much just the documentation and some code examples.
LLMs are really good at doing that. Arguably better than humans at RTFM and then applying what's there.
How long can you keep adding novel things into the start of every session's context and get good performance, before it loses track of which parts of that context are relevant to what tasks?
IMO for working on large codebases sticking to "what the out of the box training does" is going to scale better for larger amounts of business logic than creating ever-more not-in-model-training context that has to be bootstrapped on every task. Every "here's an example to think about" is taking away from space that could be used by "here is the specific code I want modified."
The sort of framework you mention in a different reply - "No, it was created by our team of engineers over the last three years based on years of previous PhD research." - is likely a bit special, if you gain a lot of expressibility for the up-front cost, but this is very much not the common situation for in-house framework development, and could likely get even more rare over time with current trends.
Today, yes. I assume in the future it will be integrated differently, maybe we'll have JIT fine-tuning. This is where the innovation for the foundation model providers will come in -- figuring out how to quickly add new knowledge to the model.
Or maybe we'll have lots of small fine tuned models. But the point is, we have ways today to "teach" models about new things. Those ways will get better. Just like we have ways to teach humans new things, and we get better at that too.
A human seeing a new programming language still has to apply previous knowledge of other programming languages to the problem before they can really understand it. We're making LLMs do the same thing.
Funny, I'd say the same thing about traditional programming.
Someone from K&R's group at Bell Labs, straight out of 1972, would have no problem recognizing my day-to-day workflow. I fire up a text editor, edit some C code, compile it, and run it. Lather, rinse, repeat, all by hand.
That's not OK. That's not the way this industry was ever supposed to evolve, doing the same old things the same old way for 50+ years. It's time for a real paradigm shift, and that's what we're seeing now.
All of the code that will ever need to be written already has been. It just needs to be refactored, reorganized, and repurposed, and that's a robot's job if there ever was one.
New zero-knowledge proofs
Video compression
And so forth.
Not to mention you're probably also using source control, committing code and switching between branches. You have unit tests and CI.
Let's not pretend the C developer experience is what it was 30 years ago, let alone 50.
Reply due to rate limiting:
K&R didn't know about CI/CD, but everything else you mention has either existed for over 30 years or is too trivial to argue about.
Conversely, if you took Claude Code or similar tools back to 1996, they would grab a crucifix and scream for an exorcist.
I think you're taking for granted the massive productivity boost that happened even before today's era of LLM agents.
Now that's extrapolation of the sort that, as you point out elsewhere, no LLM can perform.
At least, not one without serious bugs.
A vice president at Symbolics, the Lisp machine company at their peak during the first AI hype cycle, once stated that it was the company's goal to put very large enterprise systems within the reach of small teams to develop, and anything smaller within the reach of a single person.
And had we learned the lessons of Lisp, we could have done it. But we live in the worst timeline where we offset the work saved with ever worse processes and abstractions. Hell, to your point, we've added static edit-compile-run cycles to dynamic, somewhat Lisp-like languages (JavaScript)! And today we cry out "Save us, O machines! Save us from the slop we produced that threatens to make software development a near-impossible, frustrating, expensive process!" And the machines answer our cry by generating more slop.
But I do broadly agree that we still write code for a lot of shit that should have been automated long before. I'm not actually sure why it hasn't been automated yet. LLM's can kind of do it, I just wish we had automated it with something deterministic and human.
Maybe you can’t teach current LLM backed systems new tricks. But do we have reason to believe that no AI system can synthesize novel technologies. What reason do you have to believe humans are special in this regard?
But sure, instantiating these capabilities in hardware and software are beyond our current abilities. It seems likely that it is possible though, even if we don’t know how to do it yet.
I will argue that the following capacities: 1. creating rules and 2. deciding to follow rules (or not) are themselves controlled by rules.
For example how much rain is going to be in the rain gauge after a storm is uncomputable. You can hook up a sensor to perform some action when the rain gets so high. This rain algorithm is outside of anything church turing has to say.
There are many other natural processes that are outside the realm of was is computable. People are bathed in them.
Church turing suggests only what people can do when constrained to a bunch of symbols and squares.
This is in the same sense that while it is technically correct to describe all physically instantiated computer programs, and by extension all AI, as being in the set of "things which are just Markov chains", it comes with a massive cost that may or may not be physically realisable within this universe.
Rainfall to the exact number of molecules is computable. Just hard. A quantum simulation of every protein folding and every electron energy level of every atom inside every cell of your brain on a classical computer is computable, in the Church-Turing sense, just with an exponential slowdown.
The busy beaver function, however, is actually un-computable.
You just compute the brains of a bunch of immortal mathematics. At which point it's "very difficult and expensive function to evaluate with absurdly large boundary conditions."
False.
To quote:
One of the most consequential aspects of the busy beaver game is that, if it were possible to compute the functions Σ(n) and S(n) for all n, then this would resolve all mathematical conjectures which can be encoded in the form "does ⟨this Turing machine⟩ halt".[5] For example, there is a 27-state Turing machine that checks Goldbach's conjecture for each number and halts on a counterexample; if this machine did not halt after running for S(27) steps, then it must run forever, resolving the conjecture.[5][7] Many other problems, including the Riemann hypothesis (744 states) and the consistency of ZF set theory (745 states[8][9]), can be expressed in a similar form, where at most a countably infinite number of cases need to be checked.[5]
> You just compute the brains of a bunch of immortal mathematics. At which point it's "very difficult and expensive function to evaluate with absurdly large boundary conditions."
Humans are not magic, humans cannot solve it either, just as they cannot magically solve the halting problem for all inputs.
We've only had the tech to be able to research this in some technical depth for a few decades (both scale of computation and genetics / imaging techniques).
Even skin cells exchange information in neuron-like manner, including using light, albeit thousands times slower.
This switches complexity of human brain to "86 billions quantum computers operating thousands of small neural networks, exchanging information by lasers-based optical channels."
People have been doing thigs millenia before they understood them. Did primitive people understood the mechanism behind which certain medicinal plants worked in the body, or just saw that when they e.g. boil them and consume them they have a certain effect?
The claim being made is not "no computer will ever be able to adapt to and assist us with new technologies as they come out."
The claim being made is "modern LLMs cannot adapt to and assist us with new technologies until there is a large corpus of training data for those technologies."
Today, there exists no AI or similar system that can do what is being described. There is also no credible way forward from what we have to such a system.
Until and unless that changes, either humans are special in this way, or it doesn't matter whether humans are special in this way, depending on how you prefer to look at it.
> That's irrelevant.
My comment was relevant, if a bit tangential.
Edit: I also want to say that our attitude toward machine vs. human intelligence does matter today because we’re going to kneecap ourselves if we incorrectly believe there is something special about humans. It will stop us from closing that gap.
> unstated assumption that technological progress towards human-like intelligence is in principle possible. In reality, we do not know.
For me this isn’t an assumption, it’s a corollary that follows from the Church-Turing thesis.
I don’t know what non-computational intelligence would look like but I guess I’ll keep my mind open.
We don’t even know if they want to. But in general, it’s impossible to conclusively prove that something won’t ever happen in the future.
I disagree with this premise. A computer approximates a Turing Machine, which puts it far above a brick.
If we're going to be reductionist we can just call humans "meat sacks" and flip the question around entirely.
But then we got a neural wr that was big enough and it turns out that feedforward receptive fields ARE enough. We don’t know whether this is how our brains do it, but it’s a humbling moment to realize that you just overthought how complex the problem was.
So ive become skeptical when people start claiming that some class of problem is fundamentally too hard for machines.
In fact, we probably started a communist revolution in software with anthropic/openai streaming your solutions to lesser coders.
I remember being aghast at all the incomprehensible code and "do not modify" comments - and also at some of the devs who were like "isn't this great?".
I remember bailing out asap to another company where we wrote Java Swing and was so happy we could write UIs directly and a lot less code to understand. I'm feeling the same vibe these days with the "isn't it great?". Not really!
Extrapolate to the present day with LLM-generated code. I'm sure you're not far off the actual mark.
Valuable? Yep. World changing? Absolutely. The domain of people who haven't the slightest clue what they're doing? Not unless you enjoy lighting money on fire.
I interpret non-deterministic here as “an LLM will not produce the same output on the same input.” This is a) not true and b) not actually a problem.
a) LLMs are functions and appearances otherwise are due to how we use them
b) lots of traditional technologies which have none of the problems of LLMs are non-deterministic. E.g., symbolic non-deterministic algorithms.
Non-determinism isn’t the problem with LLMs. The problem is that there is no formal relationship between the input and output.
"In order to make machines significantly easier to use, it has been proposed (to try) to design machines that we could instruct in our native tongues. this would, admittedly, make the machines much more complicated, but, it was argued, by letting the machine carry a larger share of the burden, life would become easier for us. It sounds sensible provided you blame the obligation to use a formal symbolism as the source of your difficulties. But is the argument valid? I doubt."
B) They are shit for frontend work. This is the big one, and maybe we will someday, but we really haven't bridged the gap between an LLM seeing code and matching it to something on screen. People are well aware that agents need tight feedback loops to really be effective, which is more doable for server side code where you can setup a good test apparatus (still a pain in the ass for other reasons). But the thing a developer does most often is instantly see what their code changes did to a project visually. This is a huge gap with LLMs. We are at the level where an LLM can check that the site loads to begin with. That's it. We can't trust it to make functioning UI, and we sure as hell can't trust it to compare code against UI unless we ask it something very specific. Watch the LLM clocks sometime [1]. I was shocked to see even 5.2 codex making complete nonsense every few minutes.
C) My coworkers have narrow experience in the field we work in, which means they are frequently zooming past things that a general approximation of intelligence like Claude gets tripped up on. ChatGPT is trained on the entire corpus of humanity, yet doesn't understand there is such a thing as bad language servers. That a linting error is not inherently problematic and you don't need to spend $5 in tokens investigating it. This is something a junior developer understands. Why do I need to constantly watch and stop my agent and re-explain things we learned in the first week of programming?
D) Because LLMs are an approximation of correct solutions, god help you if you work in spaces where your syntax looks similar to other frameworks. I work in a lot of legacy apps that are framework spin-offs of other framework spin-offs. Stuff that's half documented and poorly made. There is no testing for these projects and no good IDE support. So if something is an offshoot of OctoberCMS, ChatGPT is just going to make up a bunch of methods from OctoberCMS even if they are completely invalid in this project. Agents are completely useless in projects like these.
E) Local models aren't there yet, and the fact that the entire industry is cool with letting their skills atrophy so hard that they are completely dependent on the livelihoods and structures of a few companies they hadn't even heard of 5 years ago is deeply concerning and unprofessional. I personally wouldn't hire someone so short sighted. Believing in LLMs taking over all coding work is a fundamental disbelief in Humanity, and I'm just not behind it. The most amazing thing about development is how much knowledge share we have and how cooperative we are. A future where no one knows how to do anything anymore is the future where tech truly goes off the rails.
I think anyone who has done serious product development wouldn't be so flippant and dismissive about the difficulties of even conveying WHAT to build, let along getting the right balance of quality, timeliness, and actual functionality.
"How to program if you cannot" has been solved now.
Maybe the current allocation of technical talent is a market failure and disruption to coding could be a forcing function for reallocation.
I believe the same pattern is inevitable for these higher level abstractions and interfaces to generate computer instructions. The language use must ultimately conform to a rigid syntax, and produce a deterministic result, a.k.a. "code".
So distributed systems tend to converge towards being more and more mystifying? Cf. the mythical mammoth:
> Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious.
AI tends to accept conventional wisdom. Because of this, it struggles with genuine critical thinking and cannot independently advance the state of the art.
AI systems are trained on vast bodies of human work and generate answers near the center of existing thought. A human might occasionally step back and question conventional wisdom, but AI systems do not do this on their own. They align with consensus rather than challenge it. As a result, they cannot independently push knowledge forward. Humans can innovate with help from AI, but AI still requires human direction.
You can prod AI systems to think critically, but they tend to revert to the mean. When a conversation moves away from consensus thinking, you can feel the system pulling back toward the safe middle.
As Apple’s “Think Different” campaign in the late 90s put it: the people crazy enough to think they can change the world are the ones who do—the misfits, the rebels, the troublemakers, the round pegs in square holes, the ones who see things differently. AI is none of that. AI is a conformist. That is its strength, and that is its weakness.
[1] https://www.modular.com/blog/the-claude-c-compiler-what-it-r...
> ...generate answers near the center of existing thought.
[1] https://en.wikipedia.org/wiki/Universal_approximation_theore...
"n the field of machine learning, the universal approximation theorems (UATs) state that neural networks with a certain structure can, in principle, approximate any continuous function to any desired degree of accuracy. These theorems provide a mathematical justification for using neural networks, assuring researchers that a sufficiently large or deep network can model the complex, non-linear relationships often found in real-world data."
And then: "Notice also that the neural network is only required to approximate within a compact set K {\displaystyle K}. The proof does not describe how the function would be extrapolated outside of the region."
NNs, LLMs included, are interpolators, not extrapolators.
And the region NN approximates within can be quite complex and not easily defined as "X:R^N drawn from N(c,s)^N" as SolidGoldMagiKarp [2] clearly shows.
[0] https://www.sciencedirect.com/science/article/pii/S002200008...
https://stackoverflow.com/questions/2497146/is-css-turing-co...
Our brains use quantum computation within each neuron [1].
[1] https://arxiv.org/abs/2303.14959
[2] https://en.wikipedia.org/wiki/Turing_machineAnd there's also the other side of the GP's point--Turing completeness not necessary for creativity--not by a long shot. (In fact, humans are not Turing complete.)
> Turing completeness not necessary for creativity--not by a long shot.
This is by far a more extreme claim than the others in this thread. A system that is not even Turing complete is extremely limited. It's near impossible to construct a system with the ability to loop and branch that isn't Turing complete, for example.
>(In fact, humans are not Turing complete.)
Humans are at least trivially Turing complete - to be Turing complete, all we need to be able to do is to read and write a tape or simulation of one, and use a lookup table with 6 entries (for the proven minimal (2,3) Turing machine) to choose which steps to follow.
Maybe you mean to suggest we exceed it. There is no evidence we can.
> all we need to be able to do is to read and write a tape or simulation of one
An infinite tape. And to be Turing complete we must "simulate" that tape--the tape head is not Turing complete, the whole UTM is.
> A system that is not even Turing complete is extremely limited.
PDAs are not "extremely limited", and we are more limited than PDAs because of our very finite nature.
I think a good example are calculations or counting letters: it's trivial to write turing machines doing that correctly, so you could create neural networks, that do just that. From LLM we know that they are bad at those tasks.
It also doesn't say anything about finding the desired function, rather than a different function which approximates it closely on some compact set but diverges from it outside that set. That's the trouble with extrapolation: you don't know how to compute the function you're looking for because you don't know anything about its behaviour outside of your sample.
As an example, I would like to ask how to parse balanced brackets grammar (S ::= B <EOS>; B ::= | BB | (B) | [B] | {B};) with that Turing complete recurrent network and how it will deal with precision loss for relatively short inputs.
Paper also does not address training (i.e., automatic search of the processors' equations given inputs and outputs).
Mathematically possible != actually possible.
It wouldn't surprise me if many interesting functions we'd like to approximate aren't continuous at all.
Relationships in the physical world are sparse, metastable graphs with non-linear dynamics at every resolution. And then we measure these dynamics using sparse, irregular sampling with a high noise floor. It is just about the worst possible data model for conventional AI stacks at a theoretical level.
>CCC shows that AI systems can internalize the textbook knowledge of a field and apply it coherently at scale. AI can now reliably operate within established engineering practice. This is a genuine milestone that removes much of the drudgery of repetition and allows engineers to start closer to the state of the art.
And also
> The most effective engineers will not compete with AI at producing code, but will learn to collaborate with it, by using AI to explore ideas faster, iterate more broadly, and focus human effort on direction and design. Lower barriers to implementation do not reduce the importance of engineers; instead, they elevate the importance of vision, judgment, and taste. When creation becomes easier, deciding what is worth creating becomes the harder problem. AI accelerates execution, but meaning, direction, and responsibility remain fundamentally human.
This reminds me of the Slate Star Codex story "Ars Longa, Vita Brevis"[1], where it took almost an entire lifespan just to learn what the earlier alchemists had found, so only the last few hours of an alchemist's life were actually valuable. Now we can all skip ahead.
1. https://slatestarcodex.com/2017/11/09/ars-longa-vita-brevis/
Well, of course. Despite people applying the label of AI to them, LLMs don't have a shred of intelligence. That is inherent to how they work. They don't understand, only synthesize from the data they were trained on.
Couldn't you say that about 99% of humans too?
And of course, if you don't limit yourself to "advancing the state of the art at the far frontiers of human knowledge" but allow for ordinary people to make everyday contributions in their daily lives, you get even more. Sure, much of this knowledge may not be widespread (it may be locked up within private institutions) but its impact can still be felt throughout the economy.
How? By also "synthesizing the data they were trained on" (their experience, education, memories, etc.).
if you don't limit yourself to "advancing the state of the art at the far frontiers of human knowledge" but allow for ordinary people to make everyday contributions in their daily lives, you get even more
This isn't a throwaway comment. I do this all the time myself, at work. Everywhere I've worked, I do this. I challenge the assumptions and try to make things better. It's not a rare thing at all, it's just not revolutionary.
Revolutions are rare. Perhaps only a handful of them have ever happened in any one particular field. But you simply will not ever go from Aristotelian physics to Newtonian physics to General Relativity by merely "synthesizing the data they were trained on", as the previous comment supposed.
Edit: I should also say something about experimentation. You can't do it from an armchair, which is all an LLM has access to (at present). Real people learn things all the time by conducting experiments in the world and observing the results, without necessarily working as formal scientists. Babies learn a lot by experimenting, for example. This is one particular avenue of new knowledge which is entirely separate from experience, education, memories, etc. because an experiment always has the potential to contradict all of that.
Of course it does, but only after the fact. You don't have any experience of the result of the experiment before you perform it.
Sure, they can't have apples fall on their heads like Newton had but they can totally observe the apple falling on someones head in a video
I have strong doubts that LLMs have any understanding whatsoever of what's happening in images (let alone videos). The claim (I've sometimes heard) that they possess a world model and are able to interpret an image according to that model is an extremely strong one, that's strongly contradicted by the fact that they: a) continue to hallucinate in pretty glaring ways, and b) continue to mis-identify doctored (adversarial) images that no human would mis-identify (because they don't drastically alter the subject).
For me the most glaring difference to how humans work is the lack of online learning. If that prevents them from being able to innovate, I'm not so sure.
The lack of online learning is a critical fault. Much of what humans learn (such as anything based on mathematics) has a dependency tree of stuff to learn. But even mundane stuff involves a lot of dependent learning. For example, ask an LLM to write a cookbook and it can synthesize from recipes that are already out there but good luck having it invent new cooking techniques that require experimentation or invention (new heat source, new cooking utensils, etc).
Btw, it looks like there is a growing body of research evaluating exactly this. I found this nice overview with even some benchmarks specifically for scientific innovation: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Dis...
Where's the proof we don't do exactly this? The mind as a prediction engine is one of the handful most accepted theories.
Real progress in science is made by the hard collection and cataloguing of data every single day, not by armchair philosophizing.
Even this assumes that everyone has a specialization in which 1% of people contribute to the sum of human knowledge. I would probably challenge that. There are a lot of people in the world who do not do knowledge-oriented work at all.
Your math assumes each person has exactly one thing they do in life. The shoe factory worker could also be a gardener. He might not make any advancements in gardening, but his contribution means that if you add up all the fields of specialization the sum is greater than the population of humans. Take 1% of that sum and it’s greater than 1% of humans. 1% of people in a specialization is not the same as 1% of specialists. In fact, I would say it’s a much higher proportion of specialists making contributions (especially through collaboration).
Oh, and don’t get caught up on the 1% number. I used it as shorthand for whatever small number it is. Maybe it’s only 10 people in some hyper-specialized field. But that doesn’t matter. Some other field may have thousands of contributors. You don’t have to be a specialist in a field to make a contribution to that field, for example: glassmakers advanced the science of astronomy by making the telescope possible.
That might read like an insult to Lattner, but what I’m really pointing out is that we tend to hold AIs to a much higher standard than we do humans, because the real goal of such commentary is to attempt to dismiss a perceived competitive threat.
People also "synthesize from the data they were trained on". Intelligence is a result of that. So this dead-end argument then turns into begging the question: LLMs don't have intelligence because LLMs can't have intelligence.
And yet the AI probably did better than 99% of human devs would have done in a fraction of the time.
what’s your point again?
It's like Stephen King saying an AI generated novel isn't as good as his. Fine, but most of have much lesser ambitions than topping the work of the most successful people in the field.
Of course! But that's what makes them so powerful. In 99% of cases that's what you want - something that is conventional.
The AI can come up with novel things if it has an agency, and can learn on its own (using e.g. RL). But we don't want that in most use cases, because it's unpredictable; we want a tool instead.
It's not true that this lack of creativity implies lack of intelligence or critical thinking. AI clearly can reason and be critical, if asked to do so.
Conceptually, the breakthrough of AI systems (especially in coding, but it's to some extent true in other disciplines) is that they have an ability to take a fuzzy and potentially conflicting idea, and clean up the contradictions by producing a working, albeit conventional, implementation, by finding less contradictory pieces from the training data. The strength lies in intuition of what contradictions to remove. (You can think of it as an error-correcting code for human thoughts.)
For example, if I ask AI to "draw seven red lines, perpendicular, in blue ink, some of them transparent", it can find some solution that removes the contradictions from these constraints, or ask clarifying questons, what is the domain, so it could decide which contradictory statements to drop.
I actually put it to Claude and it gave a beautiful answer:
"I appreciate the creativity, but I'm afraid this request contains a few geometric (and chromatic) impossibilities: [..]
So, to faithfully fulfill this request, I would have to draw zero lines — which is roughly the only honest answer.
This is, of course, a nod to the classic comedy sketch by Vihart / the "Seven Red Lines" bit, where a consultant hilariously agrees to deliver exactly this impossible specification. The joke is a perfect satire of how clients sometimes request things that are logically or physically nonsensical, and how people sometimes just... agree to do it anyway.
Would you like me to draw something actually drawable instead? "
This clearly shows that AI can think critically and reason.
On the line test, I guess it's highly probable that the joke and a few hundred discussions or blog pieces about it were in it's training data.
It's not a SAT solver (yet) and will have trouble to precisely handle arbitrarily large problems. So you have to lead it a bit, sometimes.
I think there are three different types of gaps, each with different remedies:
1. A definition problem - if I say "airplane", who do I mean? Probably something like jumbo jet or Cesna, less likely SR-71. This is something that we can never perfectly agree on, and AI will always will be limited to the best definition available to it. And if there is not enough training data or agreed definition for a particular (specialized) term, AI can just get this wrong (a nice example is the "Vihart" concept from above, which got mixed up with the "Seven red lines" sketch). So this is always going to be painful to get corrected, because it depends on each individual concept, regardless of the machine learning technology used. Frame problem is related to this, question of what hidden assumptions I am having when saying something.
2. The limits of reasoning with neural networks. What is really happening IMHO is that the AI models can learn rules of "informal" logical reasoning, by observing humans doing it. Informal logic learned through observation will always have logical gaps, simply because logical lapses occur in the training data. We could probably formalize this logic by defining some nice set of modal and fuzzy operators, however no one has been able to put it together yet. Then most, if not all, reasoning problems would reduce to solving a constraint problem; and even if we manage to quantize those and convert to SAT, it would still be NP-complete and as such potentially require large amounts of computation. AI models, even when they reason (and apply learned logical rules) don't do that large amount of computation in a formal way. So there are two tradeoffs - one is that AIs learned these rules informally and so they have gaps, and the other is that it is desirable in practice to time limit what amount of reasoning the AI will give to a given problem, which will lead to incomplete logical calculations. This gap is potentially fixable, by using more formal logic (and it's what happens when you run the AI program through tests, type checking, etc.), with the mentioned tradeoffs.
3. Going back to the "AI as an error-correcting code" analogy, if the input you give to AI (for example, a fragment of logical reasoning) is too much noisy (or contradictory), then it will just not respond as you expect it to (for example, it will correct the reasoning fragment in a way you didn't expect it to). This is similar to when an error-correcting code is faced with an input that is too noisy and outside its ability to correct it - it will just choose a different word as the correction. In AI models, this is compounded by the fact that nobody really understands the manifold of points that AI considers to be correct ideas (these are the code words in the error-correcting code analogy). In any case, this is again an unsolvable gap, AI will never be a magical mind reader, although it can be potentially fixed by AI having more context of what problem are you really trying to solve (the downside is this will be more intrusive to your life).
I think these things, especially point 2, will improve over time. They already have improved to the point that AI is very much usable in practice, and can be a huge time saver.
As a big fan of Vi Hart I was surprised to read that she wrote or was involved in that "classic comedy sketch".
As far as I can tell, after a few minutes searching, she was not.
> This clearly shows that AI can think critically and reason.
No it doesn't ... Claude regurgitated human knowledge.
What percentage of developers advance the state of the art, what percentage of juniors advance the state of the art?
"Needed to advance the state of the art" and actually deployed to do so are two different things. More likely either AI will learn to advance the state of the art itself, or the state of the art wont be advancing much anymore...
I don't think the replacement is binary. Instead, it’s a spectrum. The real concern for many software engineers is whether AI reduces demand enough to leave the field oversupplied. And that should be a question of economy: are we going to have enough new business problems to solve? If we do, AI will help us but will not replace us. If not, well, we are going to do a lot of bike-shedding work anyway, which means many of us will lose our jobs, with or without AI.
[1] Medicine may be one example of an industry with poor work-life balance for some, specifically specialists. But job security there is unmatched and compensation is eye-watering.
This is an extremely miopic view (or maybe trolling).
The vast majority of software developers never study, learn, or write any code outside of their work hours.
In contrast, almost all professional have enormous, _legally-required_ upskilling, retraining, and professional competence maintenance.
If you honestly believe that developers have anywhere near the demands (both in terms of time and cost) in staying up to date that other professions have, you are - as politely as I can - completely out-of-touch.
That feels overly optimistic. LLMs seems on track to automate out basically any "email job" or "spreadsheet job," in which case we'll be looking at higher unemployment numbers than the great depression for at least some period of time. Combine with increased automation...
There are a LOT of people in the world and already a not insignificant portion can't find work despite wanting to. Seems the most likely thing is that the value of most labor is reduced to pennies.
You cannot just point at a system, say it’d be unsustainable and then assume nobody will let that happen.
Monarchies, lords, etc. have had much more reason to support their own countryfolk, yet many throughout history have not - has society changed enough that the billionaires have changed on this?
In summary: billionaires aren't as competent as you'd hope.
"Temporary" might mean "the next three years", but at the same time some acted as if the Zero Interest Rate Policy would continue indefinitely, so this situation might end suddenly and unexpectedly.
To me the opportunity is with agents. Specially copilot and what ever amazon's agent it. figure out how to code using them. build something cool in the space your interested in finding a job for. that's the skill enterprise companies are fighting for. nobody knows how to do it.
Today accountants are still needed. But it's a commodified job. And you start at the absolute bottom of the bottom rungs and slave it out till you can separate yourself and take on a role on a path to CFO or some respectable level of seniority.
I'm oversimplifying here but that is sufficient to show A path forward for software engineers imo. In this parallel, most of us will become AI drivers. We'll go work in large companies but we'll also go work in a back room department of small to medium businesses, piloting AI on a bottom of the rung salary. Some folks will take on specialisms and gain certifications in difficult areas (similar to ACCA). Or maybe ultra competitive areas like how it is in actuarial science. Those few will eventually separate themselves and lead departments of software engineers (soon to be known as AI pilots). Others will embed in research and advance state of art that eventually is commoditized by AI. Those people will either be paid mega bucks or will be some poor academia based researcher.
The vast majority? Overworked drones having to be ready to stumble to their AI agent's interface when their boss calls them at 10 PM saying the directors want to see a feature setup for the meeting tomorrow.
So outside of the fact that we have magic now that can just produce “conventional “ compilers. Take it to a Moore’s Law situation. Start 1000 create a compiler projects- have each have a temperature to try new things, experiment, mutate. Collate - find new findings - reiterate- another 1000 runs with some of the novel findings. Assume this is effectively free to do.
The stance that this - which can be done (albeit badly) today and will get better and/or cheaper - won’t produce new directions for software engineering seems entirely naive.
In fact in statistics we have another law which states that as you increase parameters the more you risk overfitting. And overfitting seems to already be a major problem with state of the art LLM models. When you start overfitting you are pretty much just re-creating stuff which is already in the dataset.
Further model improvements would be a bonus, but it's not required for us to get much further.
I have not seen that. In fact this is the first time I hear this claim, and frankly it sounds ludicrous. I don‘t know how modern LLMs are dealing with overfitting but I would guess there is simply a content matching algorithm after the inference, and if there is a copyright match the program does something to alter or block the generation. That is, I suspect the overfitting prevention is algorithmic and not part of the model.
I spend the other time talking through my thoughts with AI, kind of like the proverbial rubber duck used for debugging, but it tends to give pretty thoughtful responses. In those cases, I'm writing less code but wanting to capture the invariants, expected failure modes and find leaky abstractions before they happen. Then I can write code or give it good instructions about what I want to see, and it makes it happen.
I'm honestly not sure how a non-practitioner could have these kinds of conversations beyond a certain level of complexity.
As someone who's job is handling oauth and saml scope, I am not convinced anyone can get these right.
Saml atleast acts nice, oauth on the other hand is a fucking nightmare.
A while ago I spent some time debugging a superfluous redirect and the reason was that the library would always kick off with a "not authenticated" when it didn't find stored tokens, even if it was redirecting back after successful log in (as the tokens weren't stored yet).
That's a category of problem I wouldn't expect a text-based system to detect very well.. Though it might disguise the problem with a solution that seems to work until it blows up one day or people discover a lot of hard-to-fix data.
for host in vps_hosts; do
ssh $host < commands.sh > errors.log 2&>1 &
done
Good for the company, not so much for the given engineer. But I get the motivation, we all are naturally lazy and normally avoid doing stuff if we can. Its just that there are also downsides, and they are for us, engineers.
The issue is you need verifiable rewards for that (and a good environment set-up), and it's hard to get rewards that cover everything humans want (security, simplicity, performance, readability, etc.)
Lots of people have ideas for programming languages; some of those ideas may be original-but many of those people lack the time/skills/motivation to actually implement their ideas. If AI makes it easier to get from idea to implementation, then even if all the original ideas still come from humans, we still may stand to make much faster progress in the field than we have previously.
However AI systems in 2026-ε were utterly inadequate at coding
And AI systems in 2026+ε might not have the present limitations
More proximately, the creator of the clang c compiler.
I wrote an article on that: Hard Things in Computer Science
I find some irony in seeing the telltale tropes of conventional LLM writing there
The original post in Chinese was handwritten
https://blog.est.im/2026/stderr-03
the English translation was compiled by gemini3.
We don't necessarily need a Chris Lattner to make a compiler now.
End of the day Chris Lattner is a single individual, not a magic being. A single individual posting submarine ads for his cleverness in the knowledge work subfield of language compilers. Of course he is going to drag the competition.
Languages are abstraction over memory addresses to provide something friendlier for human consumption. It's a field that's decades old and repeats itself constantly being it revolves around the same old; development of a compression technique to deduplicate and transpile to machine code the languages more verbose syntax.
Building a compiler is itself just programming. None of this is truly novel nor has it been since the 60s-70s. All that's changing is the user interface; the syntax.
Intelligence gives rise to our language capacity. The languages themselves are merely visual art that fits the preferences of the language creator. They arbitrarily decided nesting the dolls their way makes the most sense.
Currently have agents iterating on "prompt to binary". Reversing a headless Debian system into a model and optimizing to output tailored images. Opcodes, power use in system all tucked into a model to spit back out just the functions needed to achieve the electromagnetic geometry desired[1]
[1] https://iopscience.iop.org/article/10.1088/1742-6596/2987/1/...
Chris Lattner has forgotten more about language and compiler design than most of us will know in a lifetime. If you’re going to mis-characterize him you need to bring more to the table than some reductionist pseudo intelligent babbling.
The rate of change is accelerating. I worry we don't have much time left unless we get serious about merging with machines.
Man - just think about what you said
Two years ago that would have been beyond shocking.
If 'AI is making compilers' - then that's 'beyond disruptive'.
It's very true that AI has 'reversion to the mean' characteristics - kind of like everything in life ..
... but it's just unfair to imply that 'AI can't be creative'.
The AI is already very 'creative' (call it 'synthetic creativity' or whatever you want) - but sufficiently 'creative' to do new things, and, it's getting better at that.
It's more than plausible that for a given project 'creativity' was not the goal.
AI will help new language designers try and iterate over new ideas, very quickly, and that alone will be disruptive.
"The AI made a compiler" is an argument for the disruptive power of AI, not against it.
The C compiler Anthropic got excited about was not a “working” compiler in the sense that you could replace GCC with it and compile the Linux kernel for all of the target platforms it supports. Their definition of, “works,” was that it passed some very basic tests.
Same with SQLite translation from C to Rust. Gaping, poorly specified English prose is insufficient. Even with a human in the loop iterating on it. The Rust version is orders of magnitude slower and uses tons more memory. It’s not a drop in Rust-native replacement for SQLite. It’s something else if you want to try that.
What mechanism in these systems is responsible for guessing the requirements and constraints missing in the prompts? If we improve that mechanism will we get it to generate a slightly more plausible C compiler or will it tell us that our specifications are insufficient and that we should learn more about compilers first?
I’m sure its possible that there are cases where these tools can be useful. I’m not sure this is it though. AGI is purely hypothetical. We don’t simulate a black hole inside a computer and expect gravity to come out of it. We don’t simulate the weather systems on Earth and expect hurricanes to manifest from the computer. Whatever bar the people selling AI system have for AGI is a moving goalpost, a gimmick, a dream of potential to keep us hooked on what they’re selling right now.
It’s unfortunate that the author nearly hits on why but just misses it. The quotes they chose to use nail it. The blog post they reference nearly gets it too. But they both end up giving AI too much credit.
Generating a whole React application is probably a breath of fresh air. I don’t doubt anyone would enjoy that and marvel at it. Writing React code is very tedious. There’s just no reason to believe that it is anything more than it is or that we will see anything more than incremental and small improvements from here. If we see any more at all. It’s possible we’re near the limits of what we can do with LLMs.
The point is - there are so many combinations and permutations of reality, that AI can easily create synthetically novel outcomes by exploring those options.
It's just wrong to suggest that 'it was all in some textbook'.
"here’s just no reason to believe that it is anything more than it is "
It's almost ridiculous at face value, given that millions of people are using it for more than 'helping to write react apps every day'.
It's far more likely that you've come to this conclusion because you're simply not using the tools creatively, or trying to elicit 'synthetic creativity' out of the AI, because it's frankly not that hard, and the kinds of work that it does goes well beyond 'automation'.
This is not an argument, it's the lived experience of large swaths of individuals.
You are arguing against the internet, motor cars and electricity.
It's like 1998, and you're saying: "I'm sure it's possible there are cases where the internet can be useful. I'm not sure it is though"
On 'hackernews' of all places.
It's pretty wild to see that, and I think it says something about what hn has become (or maybe always was?).
Genetic natural selection is the exact opposite mechanism of this... life is literally built around generating exception and experimenting.
Lately, there have been a few examples of AI tackling what have traditionally been thought of as "hard" problems -- writing browsers and writing compilers. To Christ Lattner's point, these problems are only hard if you're doing it from scratch or doing something novel. But they're not particularly hard if you're just rewriting a reference implementation.
Writing a clean room implementation of a browser or a compiler is really hard. Writing a new compiler or browser referencing existing implementations, but doing something novel is also really hard.
But writing a new version of gcc or webkit by rephrasing their code isn't hard, it's just tedious. I'm sure many humans with zero compiler or browser programing experience could do it, but most people don't bother because what's the point?
Now we have LLMs that can act as reference implementation launderers, and do it for the cost of tokens, so why not?
There is no critical thought, you can't prod an LLM to do such a thing. Even CoT is just the LLM producing text that looks like it could be a likely response based on what it generated before.
Sometimes that text looks like critical thought, but it does not at all reflect the logical method or means the AI used to generate it. It's just riffing.
When the TPU does it, is the TPU thinking? Where does the critical thinking take place in the endless pages of matrix math that eventually evaluates into the same token output as the TPU?
That might be valid, if LLMs stopped improving today.
This feels like an unfair comparison to me; the objective of the compiler was not to be innovative, it was to prove it can be done at all. That doesn't demonstrate anything with regards to present or future capabilities in innovation.
As others have mentioned, it's not entirely clear to me what the limit of the agentic paradigm is, let alone what future training and evolution can accomplish. AlphaDev and AlphaEvolve ddemonstrate that it is possible to combine the retained knowledge of LLMs with exploratory abilities to innovate in both programming and mathematics; there's no reason to believe that it'll stop there.
To your point, yeah, I mostly don't want AI to be innovative unless I'm asking for it to be. In fact, I spend much more time asking it "is that a conventional/idiomatic choice?" (usually when I'm working on a platform I'm not super experienced with) than I do saying "hey, be more innovative."
But AI companies keep telling us AGI is 6 months into the future.
Contrary to pre AI era, one of my close relative he has become very good "understand / write the requirement" guy. HN may be dominated by >1x engineers, another revolution is happening at lower /bulk end of spectrum as well.
I’ve recently taken a look at our codebase, written entirely by humans and found nothing innovative there, on the opposite, I see such brainrot that it makes me curious what kind of biology needed to produce this outcome.
So maybe Chris Lattner, inventor of the Swift programming language is safe, majority of so called “software engineers” are sure as hell not. Just like majority of people are NOT splitting atoms.
Couldn't it also be true that the AI didn't produce innovative output even though the human asked it to produce something innovative?
Otherwise you're saying an AI always produces innovative output, if it is asked to produce something innovative. And I don't think that is a perfection that AI has achieved. Sometimes AI can't even produce correct output even when non-innovative output is requested.
In theory, we are just one good innovation away from changing this. In reality, it's probably still some years away, but we are not in a situation where have to seriously speculate with this possibility.
> And this is why humans will be needed to advance the state of the art.
But we only need a minority for innovations, progress and control. The bulk of IT is boring repetitive slop, lacking any innovation and just following patterns. The endgame will still result in probably 99% of humans being useless for the machinery. And this is not really new. In any industry, the majority of workers are just average, without any real influence on their industries progress, and just following conventional wisdom to make some bucks for surviving the next day.
all AI works on patterns, it's not very different from playing chess. Chess Engines use similar method, learn patterns then use them.
While it's true training data is what creates pattern, so you do not have any new "pattern" which is also not already in data
but interesting thing is when pattern is applied to External World -> you get some effect
when the pattern again works on this effect -> it creates some other effect
This is also how your came into existence through genetic recombination.
Even though your ancestral dna is being copied forward, the the data is lossy and effect of environment can be profoundly seen. Yet you probably don't look very different from your grandparents, but your grandchildren may look very different from your grandparents. at same point you are so many orders moved from the "original" pattern that it's indistinguishable from "new thing"
in simple terms, combinatorial explosion + environment interaction
1. The experiment was to show that AI can generate working code for a fairly complicated spec. Was it even asked to do things in a novel way? If not, why would we expect it do anything other than follow tried and tested approaches?
2. Compilers have been studied for decades, so it's reasonable to presume humans have already found the most optimal architectures and designs. Should we complain that the AI "did nothing novel" or celebrate because it "followed best practices"?
I'm actually curious, are there radically different compiler designs that people have hypothesized but not yet built for whatever reasons? Maybe somebody should repeat the experiment explicitly prompting AI agents to try novel designs out, would be fascinating to see the results.
> CCC shows that AI systems can internalize the textbook knowledge of a field and apply it coherently at scale.
Except that's not what happened. There was neither (just) textbook knowledge nor a "coherent application at scale":
1. The agents relied on thousands of human written tests embodying many person-years of "preparation effort", not to mention a complete spec. Furthermore, their models were also trained not only on the spec (and on the tests) but also on a reference implementation and the agents were given access to the reference implementation as a test oracle. None of that is found in a textbook.
2. Despite the extraordinary effort required to help the agents in this case - something that isn't available for most software - the models ultimately failed to write a workable C compiler, and couldn't converge. They reached a point where any bug fix caused another bug and that's when the people running the agents stopped the experiment.
The main issue wasn't that there was nothing innovative in the code but that even after embibing textbooks and relying on an impractical amount of preparation effort of help, the agents couldn't write a workable C compiler (which isn't some humongous task to begin with).
First, I disagree that good code is required in any sense. We have decades of experience proving that bad code can be wildly successful.
Second, has the author not seen the METR plot? We went from: LLMs can write a function to agents can write working compilers in less than a year. Anyone who thinks AGI is far away deserves to be blindsided.
Also (and this is coming from someone who thinks it's quite close) "AGI" is not implied by the ability to implement very-long-horizon software tasks. That's not "general" at all.
AGI is a meaningless milestone. No one can actually define it. The best definition I've seen is the one that ARC is using: "AI that is as good at a human at every task".
I believe that even when we have AGI, code will still be super valuable because it'll be how we get precise abstractions into human heads, which is necessary for humans to be able to bring informed opinions to bear.
IMO, black boxes that "just work" will be fine provided they can produce intermediate artifacts and explanations that make sense. The people that I know who use CoWork already don't care about how the agent got the result as long as the outputs look right and the process is explainable.
i have never written a c compiler yet I would bet money if you paid me to write one (it would take a few years at least) it wouldn't have any innovations as the space is already well covered. Where I'm different from other compilers is more likely a case of I did something stupid that someone who knows how to write a compiler wouldn't.
The US patent commissioner in 1899 wanted to shutdown the patent office because "everything that can be invented has been invented." And yet, human ingenuity keeps proving otherwise.
> it would take a few years at least
Writing a compiler that can compile the Linux kernel is a bit more involved.
This makes LLMs incredibly powerful research tools, which can create the illusion of emergent capabilities.
It wasn't Knuth who used Claude, but his friend. Nevertheless, Knuth was quite impressed.
... it'd be even more likely if, as other have suggested in this thread, we actually had recorded the answer in writing but nobody had noticed it yet, say, but even without that I don't see why it couldn't happen.
By generating prototypes that are based on different design models each end product can be assessed for specific criteria like code readability, reliability, or fault tolerance and then quickly be revised repeatedly to serve these ends better. No longer would the victory dance of vibe coding be simply "It ran!" or "Look how quickly I built it!".
That's what happens when you hand everything to a machine without understanding the problem yourself.
AI can give you correct answers all day long, but if you don't understand what you're building, you'll end up just like the people of Magrathea, staring at 42 and wondering what to do with it.
True understanding is indistinguishable from doing.
I know, I know, "skill issue"/"you're holding it wrong". And maybe that's vacuously true, in that it's so hard to guess what will produce correct output, because LLMs are not an abstraction layer in the way that we're used to. Prior abstraction layers related input to output via a transparent homomorphism: the output produced for an input was knowable and relatively straightforward (even with exotic optimization flags). LLMs are not like that. Your input disappears into a maze of twisty little matmuls, all alike (a different maze per run, for the same input!) and you can't relate what comes out the other end in terms of the input except in terms of "vibes". So to get a particular output, you just have to guess how to prompt it, and it is not very helpful if you guess wrong except in providing a wrong (often very subtly so) response!
Back in the day, I had a very primitive, rinky-dink computer—a VIC-20. The VIC-20 came with one of the best "intro to programming" guides a kid could ask for. Regarding error messages it said something like this: "If your VIC-20 tells you something like ?SYNTAX ERROR, don't worry. You haven't broken it. Your VIC-20 is trying to help you correct your mistakes." 8-bit 6502 at 1 MHz. 5 KiB of RAM. And still more helpful than a frontier model when it comes to getting your shit right.
One minor note. The skill issue isn't about failing to prompt it correctly, but rather failing to understand what it actually does.
There's an entire crop of professionals who believe we can Harry Potter our way out of any situation with the right magic words.
Yeah it's so true. LLM's tell you what you want to hear based on the input you give it. If you know the domain, it's really powerful because it can output what you want it to output at an incredibly fast rate. If you don't, you're basically rolling the dice on what it gives you. The idea that programmers are now useless because it can output something is hilariously wrong. The quality of the input has a direct impact on the quality of the output, meaning people with domain expertise (i.e. software developers) is a crucial component to its utility. In other words, vibe coding is useless unless the viber has the correct vibes. Or in other other words, being a good programmer is fundamental to the technology producing useful results. As such, we aren't going to see a total replacement of software developers, but rather that good software developers will increase their productive output.
Usually the response, for the last years, has been "no no you don't get it, it'll get so much better" and then they make the context window slightly larger and make it run python code to do math.
What will really happen is that you and people like you will let Claude or some other commerical product write code, which it then owns. The second Claude becomes more expensive, you will pay, because all your tooling, your "prompts saved in commits" etc. will not work the same with whatever other AI offer.
You've just reinvented vendor lock in, or "highly paid consultant code", on a whole new level.
You're all being fooled by emergent behavior, which acts like intelligence, but really doesn't fool people who are familiar with how to write code.
I'm sorry, I'm not sure how to say this without sounding elitist, but the goal post has not moved since GPT 3. These tools, autonomously, produce code that only fools the clueless. I don't know how else to put it and I'm getting really tired of this argument that "look, company with billions and some of the buggiest shittest software is using AI to write it". No shit they are.
So what's going to happen? We will see the same divide we've seen with JavaScript. It's new, then everyone says it's what everyone must use. C++ developers no longer needed--its all JS now. No need for native UIs, it's all JS now. If you're not learning the latest web tech, you'll miss out and fall behind. If you're studying for anything but web, you'll be out of a job in 5 years. And now, a couple decades later, we are still waiting for it.
When you let an LLM author code, it takes ownership of that code (in the engineering sense).
When you're done spending millions on tokens, years of development, prompt fine tuning, model fine tuning, and made the AI vendor the fattest wad of cash ever seen, you know what the vendor will do?
You have to migration path. Your Codex prompts don't work the same in Claude. All the prompts you developed and saved in commits, all the (probably proprietary) memory the AI vendor saved in their servers to make the AI lock you in even more, all of it is worthless without the vendor.
You are inventing "ah heck, we need to pay the consultant another 300 bucks an hour to take a look at this, because nobody else owns this code", but supercharged.
You're locking yourself in, to a single vendor, to such a degree that they can just hold your code hostage.
Now sure, OpenAI would NEVER do this, because they're all just doing good for humanity. Sure. What if they go out of business? Or discontinue the model that works for you, and the new ones just don't quite respond the same to your company's well established workflows?
The fact of reality is that the technology is so complex only for-profit centralized powers can really create these things. Linux and open source was a fluke and even then open source developers need closed source jobs to pay for their time doing open source.
We are locked in and this is the future. Accept it or deny it one is delusional the other is reality. The world is transforming into vibe coding whether you like it or not. Accept reality.
If you love programming, if you care for the craft. If programming is a form of artistry for you, if programming is your identity and status symbol. Then know that under current trends… all of that is going into the trash. Better rebuild a new identity quick.
A lot of delusional excuse scaffolds people build around themselves to protect their identity is they just say “the hard part of software wasn’t really programming” which is kind of stupid because AI covers the hard part too.. in fact it covers it better then actual coding. Either way this excuse is more viable then “ai is useless slop”
They'll hire the person who knows AI, not the human clinging onto claims of artisanal character by character code.
It's entirely possible to engineer well-designed and intentional systems with AI tools and not stochastically "vibe" your way into tech debt.
AI engineers will get hiring preference. That is until we're all replaced by full agentic engineering. And that's coming.
The hype cycle's distasteful of course, but I've accepted that this is how humans figure out what things are. Like a child we have to abuse it before we learn how to properly use it.
I think many of us sense and have sensed that the promises made of agentic programming smell too good to be true, owing to our own experiences as programmers and engineers. But experts in a domain are always the minority, so we have to understand that everyone else is going to have to reach the same intuition the hard way.
I don't know if I'm just not seeing something that the vibe coders do, or if it's not really that crazy?
like I'd say it's a productivity boost in some aspects, definitely. but it's not like you'd be able to get the same output unless you had years of experience and know what you're doing
and going full unsupervised agentic mode I haven't seen much benefit from that. still have to pretty much just guide em to do this and that in this way
To assume progress stops here is silly.
I'm already growing tired of prognostications using the current status quo when the current status quo isn't even six months old.
If you can’t see that it’s over, I’m not sure what to tell you. You will, in time.
All we are going to get is increased mass surveillance and molding software engineers into more assembly line work.
Both things do not sound good or reasonable nor wanted by a majority in our industry.
But sure! Being able to do more busy work is useful I guess, too bad the workers will never benefit from such a scheme; hopefully the masses don't overthrow the country, but I wouldn't blame them if they did.
I think there's some irony in using Russell's quote being used this way. My intent will often be less clear to a reader once encoded in a language bound inextricably to a machine's execution context.
Good abstraction meaningfully whittles away at this mismatch, and DSLs in powerful languages (like ML-family and lisp-family languages) have often mirrored natural(ish) language. Observe that programming languages themselves have natural language specifications that are meaningfully more dense than their implementations, and often govern multiple implementations.
Code isn't just code. Some code encapsulates intent in a meaningfully information and meaning-dense way: that code is indeed poetry, and perhaps the best representation of intent available. Some code, like nearly every line of the code that backs your server vs client time example, is an implementation detail. The Electric Clojure version is a far better encapsulation of intent (https://electric.hyperfiddle.net/fiddle/electric-tutorial.tw...). A natural language version, executed in the context of a program with an existing client server architecture, is likely best: "show a live updated version of the servers' unix epoch timestamp and the client's, and below that show the skew between them."
Given that we started with Russell, we could end with Wittgenstein's "Is it even always an advantage to replace an indistinct picture by a sharp one? Isn't the indistinct one often exactly what we need?"
The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise.
— Edsger Dijkstra
Yes, we'd love to visit!
AI is already letting me care less about the languages I use and focus more on the algorithms. AI helps me write tests. AI suggests improvements and catches bugs before compiling. AI writes helper scripts/tools for me. All of these things are good enough for me to accept paying a few hundred dollars every month, although I don't have to because my employer already does do that for me.
6 months ago I was arguing that AI wasn't very good and code was more precise than english for specifying solutions. The first part is not true anymore for many things I care about. The second is still true but for many things I care about it doesn't matter.
I'm getting tired of articles that try to tell me what to think about AI. "AI is great and will replace all programmers!"... "AI sucks and will ruin your brain and codebase!"... both of these are tired and meaningless arguments.
This is true for a usual approach, but the whole reason I’m writing the CRDT is to avoid these tombstones! Anyway, a long story short, I did eventually convince Claude I was right, but to do it I basically had to write a structural proof to show clear ordering and forward progression in all cases. And even then compaction tends to reset it. There are a lot of subtleties these systems don’t quite have yet.
But more importantly, as a coding agent, it follows instructions much better. I've frequently had Claude go off and do things I've explicitly told it not to do, or write too much code that did wrong things, and it's more work to corral it than I want to spend.
Codex will follow instructions better. Currently, it writes code that I find a few notches above Claude, though I'm working with C# and SQL so YMMV; Claude is terrible at coming up with decent schema. When your instructions do leave some leeway, I find the "judgment" of Codex to be better than Claude. And one little thing I like a lot is that it can look at adjacent code in your project so it can try to write idiomatically for your project/team. I haven't seen Claude exhibit this behavior and it writes very middle-of-the-road in terms of style and behavior.
But when I use them I use them in a very targeted fashion. If I ask them to find and fix a bug, it's going to have as much or more detail as a full bug report in my own ticketing system. If it's new code, it comes with a very detailed and long spec for what is needed, what is explicitly not needed, the scope, the constraints, what output is expected, etc., like it's a wiki page or epic for another real developer to work from. I don't do vague prompts or "agentic" workflow stuff.
Cheers!
- in a non-hobby setting, code is a liability
- I want to solve problems, not write code
- I love writing code as a hobby.
- being paid to do my hobby professionally is amazing.
- I love the idea of the Star Trek Ship’s Computer. To just ask for things and for it to do the work. It sometimes feels like we’re very close.
That sounds exactly like something an LLM based system would do.
1 - “It seems like 99% of society has agreed that code is dead. …It's the same as thinking storytelling is dead at the invention of the printing press. No you dummies, code is just getting started. AI is going to be such a boon for coding.“
2 - Another one comparing writing and coding, and explaining how Code is both a means and an end to manage complexity:
“we're confused because we (incorrectly) think that code is only for the software it produces. It's only partly about that. The code itself is also a centrally important artifact… I think this is a lot clearer if you make an analogy to writing. Isn't it fucking telling that nobody is talking about "vibe writing"?”
We may expect code to be killed off in AI's troublesome teen years.
We’re at a point where LLMs write great code, way better than my average coworkers used to anyway. Of course, not reviewing said code by an expert would be a silly as not reviewing a coworkers code, there might be security vulnerabilities in there, hardcoded api keys, etc. But once it’s been professionally reviewed, it’s just as safe as any code written by a human only probably if a higher quality than most people write.
On HN there’s an argument I keep seeing go back and forth which is like “vibe coding is the worst thing ever” and the other side will be like “AI is the second coming of christ and we don’t need programmers” - I think the reason we have what appears to be such opposing views is that those views are actually really close to one another, and proper review is all that separates one from the other.
If you’re already an expert and you don’t vibe code most things and then carefully test and review after, you’re wasting the benefits of these machines. If you’re not an expert then you shouldn’t be employed in the first place, as the main thing people are employed for is responsibility, not output.
This has always been the way in everything. A foreperson gets paid more than a worker on a building site not because they build more than the worker, but because they’re responsible for more than the worker. This is the real reason why programmer jobs won’t go away in my opinion.
I know of quite a lot of business people who kind of frolicking over the idea that the former behemoth got humbled so massively. From eating the world to unemployed in no time.
This delusion itself is telling and perpetuates the clinging to the sinking ship that is still the Elephant in the Room.
If something as complex or even complicated as app development including SDLCs etc. could simply be prompted now, then AI will eat anything less complicated alive.
So people better start considering the implications and ramifications of their statements. Otherwise we are all doomed and part of a darwinian system that will weed out the unnecessary parts of the system or we acknowledge the fact that a traditional profession such as app development is fundamentally changing.
This is something that happened before and constantly does. Otherwise we would not use DSL or Java.
But the fundamentals still work and therefore you need abstractions.
Of course they are taking about that!!
I still don’t think this is certain. It’s telling that code generation is one of the few things these systems do extremely well. Translating between English and French isn’t that much different than translating between English and Python. These are both tasks where the most likely next token has a good shot of being correct. I’m still not sold that we should assume that LLM-based tech will be well-generalized beyond that. Maybe some new tech will come along to augment or replace LLMs and that will get us there, who knows. Just because the line is going up quickly at the moment doesn’t mean it always will.
Hope this does not happen to code :)
I find this flippancy about the greatest mystery in the universe extremely arrogant and incurious and wish it wouldn't be so prevalent.
It's not arrogant or incurious to acknowledge the flood, but it might be to deny that flood is happening.
If you think there are fundamental human qualities or capabilities that AI can't ever have, you might put in the work to articulate that, instead of projecting negativity onto people who have watched the vast majority of the human competencies landscape get completely submerged over the last 10 years. The islands we have remaining don't really suggest any unifying principle underlying things that AI is still bad at, but instead they highlight the lack of technical capabilities and various engineering tracks to solve for. Many of the problems are solved in principle, but are economically infeasible; for all intents and purposes, you might consider those islands completely submerged as well.
1. Implying that there are only "a few islands left" shoes a strong bias towards assuming that only thins humans do in the digital realm is relevant, when in fact, the vast majority of things humans do are not in the digital sphere at all.
2. It's pretty clear when most people say that machine intelligence is close, right now, they are alluding to LLM or Deep Learning based approaches. I don't think you should assume they mean machines will catch up in a 100 years. They seem to imply it will be by 2030 or sowmthing.
Robotics has passed the point of superhuman performance for any given task. Software has passed the point of superhuman performance for any given task.
Regardless of the particular technique or embodiment, the constraints aren't "is it possible in principle" but "is it too expensive" and "is this allowed by the pertinent principles and regulations and laws"
We don't have AGI that learns and adapts in real time like humans. We do have incredibly powerful algorithms that can learn from whatever data we throw at them, but many domains where it's impractical, ruinously expensive, illegal, or otherwise not possible to use AI for some other good reasons.
The few islands left to humanity are not fundamental barriers. We haven't solved intelligence, or achieved RSI or ASI or AGI yet; those were never the important thresholds.
AI has always been a question about good enough, and it looks like we've gone solidly past the good enough line into "we can probably automate everything" even if we don't solve the big problems over 5 or 10 years or beyond. I think it's very unlikely we don't solve intelligence by 2030, but even if AI stalls out where it's at right now, and all we get is the incremental improvements and engineering optimizations on current SOTA, we have enough to automate anything humans do at levels exceeding human capabilities.
What AGI and ASI do is make humans economically obsolete. Good enough AI means there might be some places where humans are needed for generalization and adaptability until the exhaustive tedious work gets done for a particular application that enables a robot or software system to be competent enough to handle the work.
Not only is intelligence probably most accurately modeled as something extremely high dimensional, it’s probably also extremely nonlinearly traversed by learning methods, both organic and artificial. Not a topology very easily “flooded”.
In other words: bull shit.
Explaining it as something like "realizable instantiation of physical computation occurring in the universe mapping to an ultra-sparse, discrete point cloud embedded in the Euclidean parameter space of all computable functions" could definitely be more precise, but you're either going to need a topology like a landscape or a bumpy sphere to visualize it, and then you're going to need to spend more time showing the effects of things like scaling laws, available compute, where the known boundaries of human intelligence lie, and so on, and so forth, and by then you've lost everyone, probably even the ML professor.
It's a good enough metaphor that maps to a real thing.
My entire point, which I’m not sure you addressed is that no, it’s not a good metaphor. Water “floods” a 3d topology in a predictable manner with regards to the volume the topology can contain. The entire argument is that progress is observable, predictable, and limitless, and the “islands” are a rhetorical device. My argument was turning the rhetorical device around and pointing out that we know so little about intelligence and AI that describing it in this way is not meaningful beyond sounding intellectual.
All this to say that describing what's happening as a 'rising tide' seems misleading to me. Techno-sociological development is super messy already, let's not make it more complex by pinning ourselves to inaccurate and potentially misleading analogies. The introduction of the car did not 'push humans higher onto a set of capability peaks', it implied a total reorganization of behavior and technologies (highways, commuting, and suburban sprawl); using the terms of your analogy humans built new landmasses on top of the water.
The current tech won't get us there just like the steam engine or the internal combustion engine didn't get us to the Moon. And getting to AGI is probably more like getting to Mars.
Comercial fusion is still decades away.
Solar and lithium batteries are getting cheaper every year but unfortunately still lots of political issues in many countries.
Wait, which theory is that? In church turing theory the computer can compute any computable function.
Why do we think that the computer can model any physical process?
Or are we suggesting that you can build a computer out of whatever physical process you want to model?
> Wait, which theory is that?
The Church-Turing-Deutsch Principle. (Which isn’t a theory in the empirical sense, but somewhat more speculative.)
> Or are we suggesting that you can build a computer out of whatever physical process you want to model?
Well, you obviously can do that. Whether that computer is Turing equivalent, more limited, or potentially a hypercomputer is...well, Church-Turing-Deutsch says the last is always false, but good luck proving it.
There's absolutely nothing mysterious about human intelligence unless you refuse to give it a clear definition. All the people waffling on about AGI refuse to give a clear, measurable definition of intelligence, because if they defined it exactly then it's possible to clearly determine whether a given machine does or does not meet that criteria. It's just 21st century woo peddling.
This begs the question[0] by assuming that it can be given a clear, measurable definition. A large part of the mystery of consciousness and intelligence is that it's hard to define, measure, or explain; the most characteristic aspects (i.e. those relating to a subjective experience) are, in principle, impossible to measure or verify[1]. To say that it's not mysterious once you give it a clear, measurable definition is basically saying "it's not mysterious once you remove all aspects that make it mysterious."
I asked GPT to write code to address their question and the code was quite acceptable drawing the circle and finding the correct intersection point. It would have take me about 40 minutes to write the code, so I would not have done it myself.
Currently, GPT is great for writing short programs. The results often have a bug or two that is easy to fix, but it's much faster to have GPT write the code. This works fine for projects that are less than 100 lines of code where you just want something that works.
Maybe but all technologies have limits.
It's irrational to believe any single technology can be improved forever.
At least for a small business, users are catching on that they can build a dirty app that gets them what they specifically want, instead of relying on some paid software to give everyone a little bit of what they want. Partially this suggests I'm just in the wrong sector, but it is absolutely happening.
I dont' think this matters to Google or Amazon, they can't be replaced. But small businesses are a different story.
And the result of all this? We need to heavily rely on AI, so that we can outpace individual users in delivering what they want. I hate it, I didn't give the order, but I do see the writing on the wall. This workflow is miserable, it sucks the fun out of the job, but unfortunately it really is faster. And small businesses rely on the income coming in next year, not in 5 years.
As a side note, I also think users are becoming extremely used to having a chatbot do everything for them. Every site is going to have one, and apps that don't will fall behind.
I'd like to be on a different multiverse timeline honestly
Imagine oracle level price acuity along with 0 competition and utter dependence. This is the future the AI labs are drooling for. You will be charged based on the value it delivers. People will be start making trade offs on if hiring humans would be cheaper than AI, etc.
There's no way they're going to leave all that money on the table when there is all that investment to pay back.
What a lot of people don't realize about software is that it is one of the few industries that offered a means to greatly improve your standard of living without requiring a formal degree.
AI just one-shotted that kind of work. There will always be a place for humans to do creative things, but there won't be a place for average people to make a living.
Example - look at animated movies. In the past studios hired hundreds of people to draw the movie. Now, its nearly all automated with software.
The need for human artistic ability for commercial work is nearly gone, and only left for nice to have products.
In 5 years we will see the same for software. It will be much faster than what happened to art because software is already in nearly every aspect of life.
I'm sure it will be possible, but it may well be very expensive. If it is, why would anyone spend the resources?
AI evolution will certainly follow the money, which is not necessarily the same as the path to AGI.
rvz•1d ago
What you are seeing here is that many are attempting to take shortcuts to building production-grade maintainable software with AI and now realizing that they have built their software on terrible architecture only to throw it away, rewriting it with now no-one truly understanding the code or can explain it.
We have a term for that already and it is called "comprehension debt". [0]
With the rise of over-reliance of agents, you will see "engineers" unable to explain technical decisions and will admit to having zero knowledge of what the agent has done.
This is exactly happening to engineers at AWS with Kiro causing outages [1] and now requiring engineers to manually review AI changes [2] (which slows them down even with AI).
[0] https://addyosmani.com/blog/comprehension-debt/
[1] https://www.theguardian.com/technology/2026/feb/20/amazon-cl...
[2] https://www.ft.com/content/7cab4ec7-4712-4137-b602-119a44f77...
suzzer99•1d ago
I've had to work on multiple legacy systems like this where the original devs are long gone, there's no documentation, and everyone at the company admits it's complete mess. They send you off with a sympathetic, "Good luck, just do the best you can!"
I call it "throwing dye in the water." It's the opposite of fun programming.
On the other hand, it often takes creativity and general cleverness to get the app to do what you want with minimally-invasive code changes. So it should be the hardest for AI.
Insanity•1d ago
While publicly they might say this is AI driven, I think that’s mostly BS.
Anyway, that doesn’t take away from your point, just adds additional context to the outages.
zer00eyz•1d ago
This isn't any different than the "person who wrote it already doesn't work here any more".
> now requiring engineers to manually review AI changes [2] (which slows them down even with AI).
What does this say about the "code review" process if people cant understand the things they didn't write?
Maybe we have had the wrong hiring criteria. The "leet code", brain teaser (FAANG style) write some code interview might not have been the best filter for the sorts of people you need working in your org today.
Reading code, tooling up (debuggers, profilers), durable testing (Simulation, not unit) are the skill changes that NO ONE is talking about, and we have not been honing or hiring for.
No one is talking about requirements, problem scoping, how you rationalize and think about building things.
No one is talking about how your choice of dev environment is going to impact all of the above processes.
I see a lot of hype, and a lot of hate, but not a lot of the pragmatic middle.
xienze•1d ago
Yeah but that takes years to play out. Now developers are cranking out thousands of lines of “he doesn’t work here anymore” code every day.
zer00eyz•1d ago
https://www.invene.com/blog/limiting-developer-turnover has some data, that aligns with my own experience putting the average at 2 years.
I have been doing this a long time: my longest running piece of code was 20 years. My current is 10. Most of my code is long dead and replaced because businesses evolve, close, move on. A lot of my code was NEVER ment to be permanent. It solved a problem in a moment, it accomplished a task, fit for purpose and disposable (and riddled with cursing, manual loops and goofy exceptions just to get the job done).
Meanwhile I have seen a LOT of god awful code written by humans. Business running on things that are SO BAD that I still have shell shock that they ever worked.
AI is just a tool. It's going from hammers to nail guns. The people involved are still the ones who are ultimately accountable.
legulere•1d ago
It is very different. With empathy you can often deduct why people wrote code the way they did. With LLMs there often is no reason.