So I'm guessing they just rise because they spark a debate?
The optimists upvote and praise this type of content, then the pessimists come to comment why this field is going to the dogs. Rinse and repeat.
Google what you are interested in at the moment, and dive long into what matters to you, rather than being fed engagement bait.
Easier said than done, I understand that.
When making them deterministic (setting the temperature to 0), LLMs (even new ones) get stuck in loops for longer streams of output tokens. The only way to make sure you get the same output twice is to use the same temperature and the same seed for the RNG used, and most frontier models don't have a way for you to set the RNG seed.
https://www.observationalhazard.com/2025/12/c-java-java-llm....
"The intermediate product is the source code itself. The intermediate goal of a software development project is to produce robust maintainable source code. The end product is to produce a binary. New programming languages changed the intermediate product. When a team changed from using assembly, to C, to Java, it drastically changed its intermediate product. That came with new tools built around different language ecosystems and different programming paradigms and philosophies. Which in turn came with new ways of refactoring, thinking about software architecture, and working together.
LLMs don’t do that in the same way. The intermediate product of LLMs is still the Java or C or Rust or Python that came before them. English is not the intermediate product, as much as some may say it is. You don’t go prompt->binary. You still go prompt->source code->changes to source code from hand editing or further prompts->binary. It’s a distinction that matters.
Until LLMs are fully autonomous with virtually no human guidance or oversight, source code in existing languages will continue to be the intermediate product. And that means many of the ways that we work together will continue to be the same (how we architect source code, store and review it, collaborate on it, refactor it, etc.) in a way that it wasn’t with prior transitions. These processes are just supercharged and easier because the LLM is supporting us or doing much of the work for us."
I'm not debating whether LLMs are amazing tools or whether they change programming. Clearly both are true. I'm debating whether people are using accurate analogies.
Why can’t English be a programming language? You would absolutely be able to describe a program in English well enough that it would unambiguously be able to instruct a person on the exact program to write. If it can do that, why couldn’t it be used to tell a computer exactly what program to write?
To make it sensible you'd end up standardising the way you say things: words, order, etc and probably add punctuation and formatting conventions to make it easier to read.
By then you're basically just at a verbose programming language, and the last step to an actual programming language is just dropping a few filler words here and there to make it more concise while preserving the meaning.
Various attempt has been made. We got Cobol, Basic, SQL,… Programming language needs to be formal and English is not that.
A language or a library might change the implementation of a sorting algorithm once in a few years. An LLM is likely to do it every time you regenerate the code.
It’s not just a matter of non-determinism either, but about how chaotic LLMs are. Compilers can produce different machine code with slightly different inputs, but it’s nothing compared to how wildly different LLM output is with very small differences in input. Adding a single word to your spec file can cause the final code to be far more unrecognizably different than adding a new line to a C file.
If you are only checking in the spec which is the logical conclusion of “this is the new high level language”, everyone you regenerate your code all of the thousands upon thousands of unspecified implementation details will change.
Oops I didn’t think I needed to specify what going to happen when a user tries to do C before A but after B. Yesterday it didn’t seem to do anything but today it resets their account balance to $0. But after the deployment 5 minutes ago it seems to be fixed.
Sometimes users dragging a box across the screen will see the box disappear behind other boxes. I can’t reproduce it though.
I changed one word in my spec and now there’s an extra 500k LOC to implement a hidden asteroids game on the home page that uses 100% of every visitor’s CPU.
This kind of stuff happens now, but the scale with which it will happen if you actually use LLMs as a high level language is unimaginable. The chaos of all the little unspecified implementation details constantly shifting is just insane to contemplate as user or a maintainer.
"Generate a Frontend End for me now please so I don't need to think"
LLM starts outputting tokens
Dopamine hit to the brain as I get my reward without having to run npm and figure out what packages to use
Then out of a shadowy alleyway a man in a trenchcoat approaches
"Pssssttt, all the suckers are using that tool, come try some Opus 4.6"
"How much?"
"Oh that'll be $200.... and your muscle memory for running maven commands"
"Shut up and take my money"
----- 5 months later, washed up and disconnected from cloud LLMs ------
"Anyone got any spare tokens I could use?"
For very large projects, are we sure that English (or other natural languages) are actually a better/faster/cheaper way to express what we want to build? Even if we could guarantee fully-deterministic "compilation", would the specificity required not balloon the (e.g.) English out to well beyond what (e.g.) Java might need?
Writing code will become writing books? Still thinking through this, but I can't help but feel natural languages are still poorly suited and slower, especially for novel creations that don't have a well-understood (or "linguistically-abstracted") prior.
The implementations that come out are buggy or just plain broken
The problem is a relatively simple one, and the algorithm uses a few clever tricks. The implementation is subtle...but nonetheless it exists in both open and closed source projects.
LLMs can replace a lot of CRUD apps and skeleton code, tooling, scripting, infra setup etc, but when it comes to the hard stuff they still suck.
Give me a whiteboard and a fellow engineer anyday
The improvements become evident from the nature of the problem in the physical world. I can see why a purely text-based intelligence could not have derived them from the specs, and I haven't been able to coax them out of LLMs with any amount of prodding and persuasion. They reason about the problem in some abstract space detached from reality; they're brilliant savants in that sense, but you can't teach a blind person what the colour red feels like to see.
The irony is that I haven't seen AI have nearly as large of an impact anywhere else. We truly have automated ourselves out of work, people are just catching up with that fact and the people that just wanted to make money from software can now finally stop pretending that "passion" for "the craft" was every really part of their motivating calculus.
But if your job depends on taste, design, intuition, sociability, judgement, coaching, inspiring, explaining, or empathy in the context of using technology to solve human problems, you’ll be fine. The premium for these skills is going _way_ up.
That is exactly what I recommend, and it works like a charm. The person also has to have realistic expectations for the LLM, and be willing to work with a simulacrum that never learns (as frustrating as it seems at first glance).
The writing is on the wall for all white collar work. Not this year or next, but it's coming.
Being a plumber won’t save you when half the work force is unemployed.
a.k.a. Being a programmer.
> The irony is that I haven't seen AI have nearly as large of an impact anywhere else.
What lol. Translation? Graphic design?
So when things break or they have to make changes, and the AI gets lost down a rabbit hole, who is held accountable?
My point is that SWEs are living on a prayer that AI will be perched on a knifes edge where there is still be some amount of technical work to make our profession sustainable and from what I'm seeing that's not going to be the case. It won't happen overnight, but I doubt my kids will ever even think about a computer science degree or doing what I did for work.
I make it sound like I agree with you, and I do to an extend. Hell, I'd want my kids to be plumbers or similar where I would've wanted them to go to an university a couple of years ago. With that said. I still haven't seen anything from AI's to convince me that you don't need computer science. To put it bluntly, you don't need software engineering to write software, until you do. A lot of the AI produced software doesn't scale, and none of our agents have been remotely capable of making quality and secure code even in the hands of experienced programmers. We've not seen any form of changes over the past two years either.
Of course this doesn't mean you're wrong either. Because we're going to need a lot less programmers regardless. We need the people who know how computers work, but in my country that is a fraction of the total IT worker pool available. In many CS educations they're not even taught how a CPU or memory functions. They are instead taught design patterns, OOP and clean architecture. Which are great when humans are maintaining code, but even small abstractions will cause l1-3 cache failures. Which doesn't matter, until it does.
I don't meant this to sound inflammatory or anything; it's just that the idea that when a developer encounters a difficult bug they would go ask for help from the product manager of all people is so incredibly outlandish and unrealistic, I can't imagine anyone would think this would happen unless they've never actually worked as a developer.
We're starting to see engineers running into bugs and roadblocks feed input into AI and not only root causing the problem, but suggesting and implementing the fix and taking it into review.
You can't product manage away something like "there's an undocumented bug in MariaDB which causes database corruption with spatial indexes" or "there's a regression in jemalloc which is causing Tomcat to memory leak when we upgrade to java 8". Both of which are real things I had to dive deep and discover in my career.
1. The team is unable to figure it out
2. The team is able to figure it out but a responsible third-party dependency is unable to fix it
3. The team throws in the towel and works around the issue
At the end of the day it always comes down to money: how much more money do we throw at trying to diagnose or fix this versus working around or living with it? And is that determination not exactly the role of a product manager?
I don't see why this would ipso facto be different with AI
For clarity I come at this with a superposition of skepticism at AI's ultimate capabilities along with recognition of the sometimes frightening depth encountered in those capabilities and speed with which they are advancing
I suppose the net result would be a skepticism of any confident predictions of where this all ends up
Because humans can learn information they currently do not have, AI cannot?
We are in this pickle because programmers are good at making tools that help programmers. Programming is the tip of the spear, as far as AI's impact goes, but there's more to come.
Why pay an expensive architect to design your new office building, when AI will do it for peanuts? Why pay an expensive lawyer to review your contract? Why pay a doctor, etc.
Short term, doing for lawyers, architects, civil engineers, doctors, etc what Claude Code has done for programmers is a winning business strategy. Long term, gaining expertise in any field of intellectual labor is setting yourself up to be replaced.
Every "classic computing" language mentioned, and pretty much in history, is highly deterministic, and mind-bogglingly, huge-number-of-9s reliable (when was the last time your CPU did the wrong thing on one of the billions of machine instructions it executes every second, or your compiler gave two different outputs from the same code?)
LLMs are not even "one 9" reliable at the moment. Indeed, each token is a freaking RNG draw off a probability distribution. "Compiling" is a crap shoot, a slot machine pull. By design. And the errors compound/multiply over repeated pulls as others have shown.
I'll take the gloriously reliable classical compute world to compile my stuff any day.
I can take some C or Fortran code from 10 years ago, build it and get identical results.
Code in general is also local, in the sense that small perturbation to the code has effects limited to a small and corresponding portion of the program/behavior. A change to the body of a function changes the generated machine code for that function, and nothing else[2].
Prompts provided to an LLM are neither sufficient nor local in the same way.
The inherent opacity of the LLM means we can make only probabilistic guarantees that the constraints the prompt intends to encode are reflected by the output. No theory (that we now know) can even attempt to supply such a guarantee. A given (sequence of) prompts might result in a program that happens to encode the constraints the programmer intended, but that _must_ be verified by inspection and testing.
One might argue that of course an LLM can be made to produce precisely the same output for the same input; it is itself a program after all. However, that 'reproducibility' should not convince us that the prompts + weights totally define the code any more than random.Random(1).random() being constant should cause us to declare python's .random() broken. In both cases we're looking at a single sample from a pRNG. Any variation whatsoever would result in a different generated program, with no guarantee that program would satisfy the constraints the programmer intended to encode in the prompts.
While locality falls similarly, one might point out the an agentic LLM can easily make a local change to code if asked. I would argue that an agentic LLMs prompts are not just the inputs from the user, but the entire codebase in its repo (if sparsely attended to by RAG or retrieval tool calls or w/e). The prompts _alone_ cannot be changed locally in a way that guarantees a local effect.
The prompt LLM -> program abstraction presents leaks of such volume and variety that it cannon be ignored like the code -> compiler -> program abstraction can. Continuing to make forward progress on a project requires the robot (and likely the human) attend to the generated code.
Does any of this matter? Compilers and interpreters themselves are imperfect, their formal verification is incomplete and underutilized. We have to verify properties of programs via testing anyway. And who cares if the prompts alone are insufficient? We can keep a few 100kb of code around and retrieve over it to keep the robot on track, and the human more-or-less in the loop. And if it ends up rewriting the whole thing every few iterations as it drifts, who cares?
For some projects where quality, correctness, interoperability, novelty, etc don't matter, it might be. Even in those, defining a program purely via prompts seems likely to devolve eventually into aggravation. For the rest, the end of software engineering seems to be greatly exaggerated.
[1]: loosely in the statistical sense of containing all the information the programmer was able to encode https://en.wikipedia.org/wiki/Sufficient_statistic
[2]: there're of course many tiny exceptions to this. we might be changing a function that's inlined all over the place; we might be changing something that's explicitly global state; we might vary timing of something that causes async tasks to schedule in a different order etc etc. I believe the point stands regardless.
This is not an appropriate analogy, at least not right now.
Code Agents are generating code from prompts, in that sense the metaphor is correct. However Agents then read the code and it becomes input and they generate more code. This was never the case for compilers, an LLM used in this sense is strictly not a compiler because it is not cyclic and not directional.
I ask the developer the simplest questions, like "which of the multiple entry-points do you use to test this code locally", or "you have a 'mode' parameter here that determines which branch of the code executes, which of these modes are actually used? and I get a bunch of babble, because he has no idea how any of it works.
Of course, since everyone is expected to use Cursor for everything and move at warp speed, I have no time to actually untangle this crap.
The LLM is amazing at some things - I can get it to one-shot adding a page to a react app for instance. But if you don't know what good code looks like, you're not going to get a maintainable result.
[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...
Right now LLMs are taking languages meant for humans to understand better via abstraction, what if the next language is designed for optimal LLM/world model understanding?
Or instead of an entirely new language, theres some form of compiling/transpiling from the model language to a human centric one like WASM for LLMs
I'm more worried about the opposite: the next popular programming paradigm will be something that's hard to read for humans but not-so-hard for LLM. For example, English -> assembly.
"I prompted it like this"
"I gave it the same prompt, and it came out different"
It's not programming. It might be having a pseudo-conversation with a complex system, but it's not programming.
Well I think the article would say that you can diff the documentation, and it's the documentation that is feeding the AI in this new paradigm (which isn't direct prompting).
If the definition of programming is "a process to create sets of instructions that tell a computer how to perform specific tasks" there is nothing in there that requires it to be deterministic at the definition level.
The whole goal of getting a computer to do a task is that it’s capable to do it many times and reliably. Especially in business, infrastructure, and manufacturing,…
Once you turn specifications and requirements in code, it’s formalized and the behavior is fixed. That’s only when it’s possible to evaluate it. Not with the specifications, but with another set of code that is known to be good (or easier to verify)
The specification is just a description of an idea. It is a map. But the code is the territory. And I’ve never seen anyone farm or mine from a map.
I wrote a program in C and and gave it to gcc. Then I gave the same program to clang and I got a different result.
I guess C code isn't programming.
This is a completely realistic scenario, given variance between compiler output based on optimization level, target architecture, and version.
Sure, LLMs are non-deterministic, but that doesn't matter if you never look at the code.
I can send specific LLM output to QA, I can’t ask QA to validate that this prompt will always produce bug free code even for future versions of the AI.
The output of the LLM is nondeterministic, meaning that the same input to the LLM will result in different output from the LLM.
That has nothing to do with weather the code itself is deterministic. If the LLM produces non-deterministic code, that's a bug, which hopefully will be caught by another sub-agent before production. But there's no reason to assume that programs created by LLMs are non-deterministic just because the LLMs themselves are. After all, humans are non-deterministic.
> I can send specific LLM output to QA, I can’t ask QA to validate that this prompt will always produce bug free code even for future versions of the AI.
This is a crazy scenario that does not correspond to how anyone uses LLMs.
That we agree it’s nonsense means we agree that using LLM prompts as a high level language is nonsense.
gcc and clang produce different assembly code, but it "does the same thing," for certain definitions of "same" and "thing."
Claude and Gemini produce different Rust code, but it "does the same thing," for certain definitions of "same" and "thing."
The issue is that the ultimate beneficiary of AI is the business owner. He's not a programmer, and he has a much looser definition of "same."
Claude and Gemini do not "do the same thing" in the same way in which Clang and GCC does the same thing with the same code (as long as certain axioms of the code holds).
The C Standard has been rigorously written to uphold certain principles such that the same code (following its axioms) will always produce the same results (under specified conditions) for any standard compliant compiler. There exists no such standard (and no axioms nor conditions to speak of) where the same is true of Claude and Gemini.
If you are interested, you can read the standard here (after purchasing access): https://www.iso.org/obp/ui/#iso:std:iso-iec:9899:ed-5:v1:en
True, but none of that is relevant to the non-programmer end user.
> You are relying on some odd definitions of "definitions", "equivalence", and "procedures"
These terms have rigorous definitions for programmers. The person making software in the future is a non-programmer and doesn't care about any of that. They care only that the LLM can produce what they asked for.
> The C Standard has been rigorously written to uphold certain principles
I know what a standard is. The point is that the standard is irrelevant if you never look at the code.
Your argument here (if I understand you correctly) is the same argument that to build a bridge you do not need to know all the laws of physics that prevents it from collapsing. The project manager of the construction team doesn’t need to know it, and certainly not the bicyclists who cross it. But the engineer who draws the blueprints needs to know it, and it matters that every detail on those blueprints are rigorously defined, such that the project manager of the construction team follows them to the letter. If the engineer does not know the laws of physics, or the project manager does not follow the blueprints to the letter, the bridge will likely collapse, killing the end user, that poor bicyclist.
I don't think I am. If you ask an LLM for a burger web site, you will get a burger web site. That's the only category that matters.
If one burger website generated uses PHP and the other is plain javascript, which completely changes the way the website has to be hosted--this category matters quite a bit, no?
No. Put yourself in the shoes of the owner of the burger restaurant (who only heard the term "JavaScript" twice in his life and vaguely remember it's probably something related to "Java", which he heard three times) and you'll know why the answer is no.
This is like saying it doesn't matter if your pipes are iron, lead or PVC because you don't see them. They all move water and shit where they need to be, so no problem. Ignorance is bliss I guess? Plumbers are obsolete!
It matters to you because you're a programmer, and you can't imagine how someone could create a program without being a programmer. But it doesn't really matter.
The non-technical user of the LLM won't care if the LLM generates PHP or JS code, because they don't care how it gets hosted. They'll tell the LLM to take care of it, and it will. Or more likely, the user won't even know what the word "hosting" means, they'll simply ask the LLM to make a website and publish it, and the LLM takes care of all the details.
Feels like the non-programmer is going to care a little bit about paying for 5 different hosting providers because the LLM decided to generate their burger website in PHP, JavaScript, Python, Ruby and Perl in successive iterations.
It's an implementation detail. The user doesn't care. OpenClaw can buy its own hosting if you ask it to.
> Feels like the non-programmer is going to care a little bit about paying for 5 different hosting providers because the LLM decided to generate their burger website in PHP, JavaScript, Python, Ruby and Perl in successive iterations.
There's this cool new program that the kids are using. It's called Docker. You should check it out.
How do you guarantee that the prompt "make me a burger website" results in a Docker container?
At this point, I think you are intentionally missing the point.
The non-programmer doesn't need to know about Docker, or race conditions, or memory leaks, or virtual functions. The programmer says "make me a web site" and the LLM figures it out. It will use an appropriate language and appropriate libraries. If appropriate, it will use Docker, and if not, it won't. If the non-programmer wants to change hosting, he can say so, and the LLM will change the hosting.
The level of abstraction goes up. The details that we've spent our lives thinking about are no longer relevant.
It's really not that complicated.
To maybe get out of this loop: your entire thesis is that nonfunctional requirements don't matter, which is a silly assertion. Anyone who has done any kind of software development work knows that nonfunctional requirements are important, which is why they exist in the first place.
More often, the issue with legacy code is not that you don’t know how to make a change, it’s because you don’t know if and how it will blow up after making it.
My brother in Christ, please get off your condescending horse. I have written compilers. I know how they work. And also you've apparently never heard of undefined behavior.
The point is that the output is different at the assembly level, but that doesn't matter to the user. Just as output from an LLM but differ from another, but the user doesn't care.
Every day you say. I program every day, and I have never, in my 20 years of programming, on purpose written in undefined behavior. I think you may be exaggerating a bit here.
I mean, sure, some leet programmers do dabble in the undefined behavior, they may even rely on some compiler bug for some extreme edge case during code golf. Whatever. However it is not uncommon when enough programmers start relying on undefined behavior behaving in a certain way, that it later becomes a part of the standard and is therefor no longer “undefined behavior”.
Like I said in a different thread, I suspect you may be willfully ignorant about this. I suspect you actually know the difference between:
a) written instructions compiled into machine code for the machine to perform, and,
b) output of a statistical model, that may or may not include written instructions of (a).
There are a million reasons to claim (a) is not like (b), the fact that (a) is (mostly; or rather desirably) deterministic, while (b) is stochastic is only one (albeit a very good) reason.
Well, you sound like an ignorant troll who came here to insult people and start fights. Which also happens a lot on the internet.
Take your abrasive ego somewhere else. HN is not for you.
If I know the system I'm designing and I'm steering, isn't it the same?
We're not punching cards anymore, yet we're still telling the machines what to do.
Regardless, the only thing that matters is to create value.
Functions like:
updatesUsername(string) returns result
...can be turned into generic functional euphemism
takeStringRtnBool(string) returns bool
...same thing. context can be established by the data passed in, external system interactions (updates user values, inventory of widgets)
as workers SWEs are just obfuscating how repetitive their effort is to people who don't know better
the era of pure data driven systems is arrived. in-line with the push to dump OOP we're dumping irrelevant context in the code altogether: https://en.wikipedia.org/wiki/Data-driven_programming
Lots of horrifying things are inevitable because they represent "progress" (where "progress" means "good for the market", even if it's bad for the idea of civilization), and we, as a society, come to adapt to them, not because they are good, but because they are.
>"I gave it the same prompt, and it came out different"
1:1 reproducibility is much easier in LLMs than in software building pipelines. It's just not guaranteed by major providers because it makes batching less efficient.
What’s a ‘software building pipeline’ in your view here? I can’t think of parts of the usual SDLC that are less reproducible than LLMs, could you elaborate?
Input-to-output reproducibility in LLMs (assuming the same model snapshot) is a matter of optimizing the inference for it and fixing the seed, which is vastly simpler. Google for example serves their models in an "almost" reproducible way, with the difference between runs most likely attributed to batching.
If you are using an LLM as a high level language, that means that every time you make a slight change to anything and “recompile” all of the thousands upon thousands of unspecified implementation details are free to change.
You could try to ameliorate this by training LLMs to favor making fewer changes, but that would likely end up encoding every bad architecture decisions made along the way and essentially forcing a convergence on bad design.
Fixing this I think requires judgment on a level far beyond what LLMs have currently demonstrated.
I'm very specifically addressing prompt reproducibility mentioned above, because it's a notorious red herring in these discussions. What you want is correctness, not determinism/reproducibility which is relatively trivial. (although thinking of it more, maybe not that trivial... if you want usable repro in the long run, you'll have to store the model snapshot, the inference code, and make it deterministic too)
>A one word difference in a spec can and frequently does produce unrecognizably different output.
This is well out of scope for the reproducibility and doesn't affect it in the slightest. And for practical software development this is also a red herring, the real issue is correctness and spec gaming. As long as the output is correct and doesn't circumvent the intention of the spec, prompt instability is unimportant, it's just the ambiguous nature of the domain LLMs and humans operate in.
I can write a spec for an entirely new endpoint, and Claude figures out all of the middleware plumbing and the database queries. (The catch: this is in Rust and the SQL is raw, without an ORM. It just gets it. I'm reviewing the code, too, and it's mostly excellent.)
I can ask Claude to add new data to the return payloads - it does it, and it can figure out the cache invalidation.
These models are blowing my mind. It's like I have an army of juniors I can actually trust.
In my experience, agentic LLMs tend to write code that is very branchy with cyclomatic complexity. They don't follow DRY principles unless you push them very hard in that direction (and even then not always), and sometimes they do things that just fly in the face of common sense. Example of that last part: I was writing some Ruby tests with Opus 4.6 yesterday, and I got dozens of tests that amounted to this:
x = X.new
assert x.kind_of?(X)
You are missing the forest for the trees. Sure, we can find flaws in the current generation of LLMs. But they'll be fixed. We have a tool that can learn to do anything as well as a human, given sufficient input.
where's the catch? SQL is an old technology, surely an LLM is good with it