The discussions around this point are taking it too seriously, even when they are 100% correct. LLMs are not deterministic, so they are not compilers. Sure, if you specify everything - every tiny detail, you can often get them to mostly match. But not 100%. Even if you do fix that, at that point you are coding using English, which is an inefficient language for that level of detail in a specification. And even if you accept that problem, you still have gone to a ton of work just to fight the fundamental non-deterministic nature of LLMs.

It all feels to me like the guys who make videos of using using electric drills to hammer in a nail - Sure, you can do that, but it is the wrong tool for the job. Everyone knows the phrase: "When all you have is a hammer, everything looks like a nail." But we need to also keep in mind the other side of that coin: "When all you have is nails, all you need is a hammer." LLMs are not a replacement for everything that happens to be digital.

    int main() {
        printf("Continue?\n");
    }

    int main() {
        printf("Continue?\n");
        printf("Continue?\n");
    }

> Specifying systems is hard; and we are lazy.

The more I use LLMs, the more I find this true. Haskell made me think for minutes before writing one line of code. Result? I stopped using Haskell and went back to Python because with Py I can "think while I code". The separation of thinking|coding phases in Haskell is what my lazy mind didn't want to tolerate.

Same goes with LLMs. I want the model to "get" what I mean but often times (esp. with Codex) I must be very specific about the project scope and spec. Codex doesn't let me "think while I vibe", because every change is costly and you'd better have a good recovery plan (git?) when Codex goes stray.

Anyone who knows 0.1% about LLMs should know that they are not deterministic systems and are totally unpredictable with their outputs meaning that they cannot become compilers at all.

The obvious has been stated.

Looking at LLMs as a less-than-completely-reliable compiler is a good idea, but it's misleading to think of them as natural-language-to-implementation compiler because they are actually an anything-to-anything compiler.

If you don't like the results or the process, you have to switch targets or add new intermediates. For example instead of doing description -> implementation, do description -> spec -> plan -> implementation

A lot of people are mentally modeling the idea that LLMs are either now or will eventually be infinitely capable. They are and will stubbornly persist in being finite, no matter how much capacity that "finite" entails. For the same reason that higher level languages allow humans to worry less about certain details and more about others, higher level languages will allow LLMs to use more of their finite resources on solving the hard problems as well.

Using LLMs to do something like what a compiler can already do is also modelling LLMs as infinite rather than finite. In fact in this particular situation not only are they finite, they're grotesquely finite, in particular, they are expensive. For example, there is no world where we just replace our entire infrastructure from top to bottom with LLMs. To see that, compare the computational effort of adding 10 8-digit numbers with an LLM versus a CPU. Or, if you prefer something a bit less slanted, the computational costs of serving a single simple HTTP request with modern systems versus an LLM. The numbers run something like LLMs being trillions of times more expensive, as an opening bid, and if the AIs continue to get more expensive it can get even worse than that.

For similar reasons, using LLMs as a compiler is very unlikely to ever produce anything even remotely resembling a payback versus the cost of doing so. Let the AI improve the compiler instead. (In another couple of years. I suspect today's AIs would find it virtually impossible to significatly improve an already-optimized compiler today.)

Moreover, remember, oh, maybe two years back when it was all the rage to have AIs be able to explain why they gave the answer they did? Yeah, I know, in the frenzied greed to be the one to grab the money on the table, this has sort of fallen by the wayside, but code is already the ultimate example of that. We ask the LLM to do things, it produces code we can examine, and the LLM session then dies away leaving only the code. This is a good thing. This means we can still examine what the resulting system is doing. In a lot of ways we hardly even care what the LLM was "thinking" or "intending", we end up with a fantastically auditable artifact. Even if you are not convinced of the utility of a human examining it, it is also an artifact that the next AI will spend less of its finite resources simply trying to understand and have more left over to actually do the work.

We may find that we want different programming languages for AIs. Personally I think we should always try to retain that ability for humans to follow it, even if we build something like that. We've already put the effort into building AIs that produce human-legible code and I think it's probably not that great a penalty in the long run to retain that. At the moment it is hard to even guess what such a thing would look like, though, as the AIs are advancing far faster than anyone (or any AI) could produce, test, prove out, and deploy such a language, against the advantage of other AIs simply getting better at working with the existing coding systems.

Here's an experiment that might be worth trying: temporarily delete a source file, ask your coding agent to regenerate it, and examine the diffs to see what it did differently.

This could be a good way to learn how robust your tests are, and also what accidental complexity could be removed by doing a rewrite. But I doubt that the results would be so good that you could ask a coding agent to regenerate the source code all the time, like we do for compilers and object code.

This is an interesting problem, one I've thought a lot about myself. On one hand, LLMs have the capacity to greatly help people, and I think, especially in the realm of gradually learning how to program, on the other hand, the non-determinism is such a difficult problem to work around.

One current idea of mine, is to iteratively make things more and more specific, this is the approach I take with psuedocode-expander ([0]) and has proven generally useful. I think there's a lot of value in the LLM instead of one shot generating something linearly, building from the top down with human feedback, for instance. I give a lot more examples on the repo for this project, and encourage any feedback or thoughts on LLM driven code generation in a more sustainable then vibe-coding way.

[0]: https://github.com/explosion-Scratch/psuedocode-expander/

Can't we just turn the temp down to 0?

This is where the desire to NOT anthropomorphize LLMs actually gets in the way.

We have mechanisms for ensuring output from humans, and those are nothing like ensuring the output from a compiler. We have checks on people, we have whole industries of people whose whole careers are managing people, to manage other people, to manage other people.

with regards to predictability LLMs essentially behave like people in this manner. The same kind of checks that we use for people are needed for them, not the same kind of checks we use for software.

> It’s that the programming interface is functionally underspecified by default. Natural language leaves gaps; many distinct programs can satisfy the same prompt. The LLM must fill those gaps.

I think this is an interesting development, because we (linguists and logicians in particular) have spent a long time developing a highly specified language that leaves no room for ambiguity. One could say that natural language was considered deficient – and now we are moving in the exact opposite direction.

In the comparison to compilers, it relevant to point out that work began on them in the 1950's. That they're basically solid by the time most people here used them, should be looked at with that time frame in mind. ChatGPT came out in 2022, 3-4 years ago. Compilers have had around three quarters of a century years to get where they are today. I'll probably be dead in seventy years, nevermind have any idea what AI (or society) is going to look like then!

But for reference, we don't (usually) care which register three compiler uses for which variable, we just care that it works, with no bugs. If the non-dertetminism of LLMs mean the variable is called file, filename, or fileName, file_name, and breaking with convention, why do we care? At the level Claude let's us work with code now, it's immaterial.

Compilation isn't stable. If you clear caches and recompile, you don't get a bit-for-bit exact copy, especially on today's multi-core processors, without doing extra work to get there.

I’m kind of surprised no one has mentioned this one yet: https://www.lesswrong.com/posts/gQyphPbaLHBMJoghD/comp-sci-i...

Compilation is transforming one computing model to another. LLMs aren't great at everything, but seem particularly well suited for this purpose.

One of the first things I tried to have an llm do is transpile. These days that works really well. You find an interesting project in python, i'm a js guy, boom js version. Very helpful.

Are LLMs not already compilers? They translate human natural language to code pretty well now. But yeah, they probably don't fit the bill of English based code to machine code

"LlMs HAlLuCinATE"

Stop this. This is such a stupid way way of describing mistakes from AI. Please try to use the confusion matrix or any other way. If you're going to try and make arguments, it's hard to take them seriously if you keep regurgitating that LLM's hallucinate. It's not a well defined definition so if you continually make this your core argument, it becomes disingenuous.

That was a painfull read for me. It reminds me of a specific annoyance I had at university with a professor who loved to make sweeping, abstract claims that sounded incredibly profound in the lecture hall but evaporated the moment you tried to apply them. It was always a hidden 'I-am-very-smart' attempt that fell apart if you actually deconstructed the meaning, the logic, or the claimed results. This article is the exact same breed of intellectualizing. It feels deep, but there is no actual logical hold if you break up the claims and deductive steps.

You can see it clearly if you just translate the article's expensive vocabulary into plain English. When the author writes, 'When you hand-build, the space of possibilities is explored through design decisions you’re forced to confront,' they are just saying, 'When you write code yourself, you have to choose how to write it.' When they claim, 'contextuality is dominated by functional correctness,' they just mean, 'Usually, we just care if the code works.' When they warn about 'inviting us to outsource functional precision itself,' they really mean, 'LLMs let you be lazy.' And finaly, 'strengthening the will to specify,' is just a dramatic way of saying, 'We need to write better requirements.' It is obscurantism plain and simple. using complexity to hide the fact that the insight is trivial.

But that is just an estethical problem to me. Worse. The argument collapses entirely when you look at the logical leap between the premises.

The author basically argues that because Natural Language is vague, engineers will inevitably stop caring about the details and just accept whatever reasonable output the AI gives. This is pure armchair psychology. It assumes that just because the tool allows for vagueness, professionals will suddenly abandon the concept of truth or functional requirements. That is a massive, unsubstantiated jump.

If we use fuzzy matching to find contacts on our phones all the time. Just because the search algorithm is imprecise doesn't mean we stop caring if we call the right person. We don't say, 'Well, the fuzzy match gave me Bob instead of Bill, I guess I'll just talk to Bob now.' The hard constraint, the functional requirement of talking to the specific person you need, remains absolute. Similarly, in software, the code either compiles and passes the tests, or it doesn't. The medium of creation might be fuzzy, but the execution environment is binary. We aren't going to drift into accepting broken banking software just because the prompt was in English.

This entire essay feels like those social psychology types that now have been thoroughly been discredited by the replication crisis in psychology. The ones who are where concerned with dazzling people with verbal skills than with being right. It is unnecessarily complex, relying on projection of dreamt up concepts and behavior, rather than observation. THIS tries to sound profound by turning a technical discussion into a philosophical crisis, but underneath the word salad, it is not just shallow, it is wrong.

If you have decent unit and functional tests, why do you care how the code is written?

This feels like the same debate assembly programmers had about C in the 60s. "You don’t understand what the compiler is doing, therefore it’s dangerous". Eventually we realised the important thing isn’t how the code was authored but whether the behaviour is correct, testable, and maintainable.

If code generated by an LLM:

  - passes a real test suite (not toy tests),
  - meets performance/security constraints,
  - goes through review like any other change,

then the acceptance criteria haven’t changed. The test suite is part of the spec. If the spec is enforced in CI, the authoring tool is secondary.

The real risk isn’t "LLMs as compilers", it’s letting changes bypass verification and ownership. We solved that with C, with large dependency trees, with codegen tools. Same playbook applies here.

If you give expected input and get expected output, why does it matter how the code was written?

They're giant pattern regurgitators, impressive for sure, but they only can be as good as their training data, reason why they seems to be more effective for TypeScript, Python etc. Nothing less nothing more. No AGI, no Job X is done. Hallucinations are a feature, otherwise they would just spit out training data. The thing is the whole discussion around these tools is so miserable that I'm pondering the idea of canceling from every corner of the internet, the fatigue is real and pushing back the hype feels so exausting, worse than crypto, nft and web3. I'm a user of these tools me pushing back the hype is because its ripple effects arrive inside my day job and I'm exausted of people handing to you generated shit just to try making a point and saying "see? like that"

You want to fly on an AI-developed airplane. I don't (just kidding haha).