I think this essay has aged pretty well and remains pretty spot on.
Some people will push back that agent development is "natural language programming" but I don't think that's right. I think in that case the agent is the one programming, and the programming still always happens in a deterministic language like Rust/Python/Typescript/etc. Maybe that'll change, but to me at least the other way would be a disaster.
The best exception I know of though, is definitely the Inform 7 language which is for writing text adventures[0]. It has a lot of fans, and I've never gone deep with it, but seems to do a pretty elegant job or marrying up natural language and programming together in a unique way.
Either way, definitely a must-check-out for any language-geeks interested in natural language programming.
But Inform7 doesn't seem to be a NL programming really. I went to read the website (it's a mess btw), and my understanding is that it's built around strict rules, and patterns one must follow, i.e. it's is as formal as Python. It just takes a lot of efforts to look natural. So it's not unlike Perligata which was a joke allowing to write working Perl code in Latin. It just went much farther because it's not intended to be a joke, but the principle remains.
Yes, I think that's a pretty good description of Inform7.
Whether "natural-language-like syntax" is enough "count as natural language programming" is probably a question of definition. For Dijkstra's essay here, he doesn't offer a particular definition, but he certainly seems to be implying something much more fluid and ambiguous than Inform7 is.
I definitely didn't mean to imply Inform7 had achieved the holy-grail of natural language programming or anything.
I think Dijkra's main point he's driving at is here:
> I suspect that machines to be programmed in our native tongues —be it Dutch, English, American, French, German, or Swahili— are as damned difficult to make as they would be to use.
I'm inferring/assuming a bunch, but I think he's saying something along the lines of, programming is useful because it is precise, and natural language is not precise, so attempts to make programming languages more like natural language are butting up into this inherent contradiction of precision. And therefore will end up as worse programming languages (for attempting to be more naturalistic).
In the history of programming languages, I think most attempts to be "more natural language like", such as COBOL or AppleScript, do become worse for attempting to walk the tightrope between natural-language and programming. I think Inform7 is unusual in that it achieves that balance in a way that isn't just objectively terrible (you may disagree) - I think that's mostly because it offers very few programming constructs, and is a tool for writing a kind of prose (text adventure games).
Anyway, to come back around, that was my reasoning for mentioning Inform7 - I think it's an interesting deviation from other experiments in natural language programming so far.
IMHO instructing llms is programming - the analogy is not perfect but it is so close that it is quite useful. Programming is instructing a machine. We have now two types of programming - the traditional one that follows strict semantics and the new way of programming by prompting. The article still holds by showing the limitations of this new kind of programming.
I don't have strong opinions on when it's ok to use the work "programming" or not, but I think if you're using claude-code/pi/open-code/etc to produce something like a website or application, the "program" is the code the LLM outputs, not your prompt etc.
I don't think I'd want to call it "natural language programming" in the way Dijstra's implying here unless the prompt was the thing that was actively being executed/ran/whatever when someone loads your app or website.
I read it the same way as well
Natural language programming would be more akin to my prose staying on file and the computer interpreting it again every time it is executed.
I’ve heard plenty of “tech enthusiasts” breezily talk about how close we are to natural language getting compiled down to machine code but has anyone credible got any idea if that’s even a path? Closest think I can think of is DSL type setups like a much richer version of the stuff Haskell people get excited about but that’s not close to an actual natural language "language"
Existence proof: everything Claude Code does, including talking to LLMs on GPUs via the Anthropic API, and shelling out to other tools installed on your system, could in principle be compiled into one big self-contained binary (or Turing Machine if you prefer) that is able to take natural language in and produce compiled binaries out.
If you wanted to go a bit harder you could train Claude to output machine code directly, but that's not necessary. Whether the machine code comes straight out of an LLM or out of a compiler that is fed source code that comes from an LLM is a mere implementation detail, there's not any fundamental difference.
It's more than an implementation detail. With an intermediate step of source code compiling & linking, there is a mechanical process checking for well-formedness that would otherwise be skipped. This is especially important if you're editing a large codebase, which even your agent's private-only repo would benefit from.
There are also times when you want the final product to be able to target different architectures and platforms. Generating into a higher level language allows for re-targeting the code by only changing a few compiler & linker flags or runtime/environment config. I would much rather that than hoping the LLM is feature- (and bug-)compatible between generation runs.
I don't know about the "stuff Haskell people get excited about", but IMHO, Haskell is a mathematician's language and the language of mathematics isn't a natural language. Dijkstra himself was also a mathematician, and his positions regarding programming languages features like 1-based indexing [1] are already conflicting with practice, the famous case being Lua which receives mixed opinions about it. I find for instance 1-based indexing more "natural", and that's coming from someone whose second and third languages are assembly and C (the first being... Basic).
Yet the point about DSL is interesting, in particular internal/embedded DSLs [2]. I lied a little earlier, my third programming language was actually Forth. His creator said in 2001, "When you devised an application you wrote a hundred words [i.e. functions] or so that discussed the application and you used those hundred words to write a one line definition to solve the application" [3].
I've been trying to do that exactly that in other languages, make it so that the code expresses "naturally" what it does as much as possible. When you succeed, people call your code "beautiful" or "elegant" (nobody ever told me that :-) which, I think, subsumes "naturality".
This is of course harder to do in low-level languages such as the ones I mentioned (except Basic maybe) because you get caught in details such as memory management. But high-level languages are not exempt of these problems (and contrary to Forth or Lisp, their syntax is often written in stone). Then there's essential complexity [4], which can be "unnatural" no matter what, like thinking about things in a concurrent programming context, with languages that don't natively support it like they support automatic memory management (i.e. garbage collection).
The topic is dangerously close to "readability", which is known to be 80% subjective.
I send this article to everyone who tells me how English is now a high-level programming language, the next level of abstraction up the chain from machine language to <their favorite language>. This analogy tends to be followed by "Just like most programmers never look at assembly code, looking at higher-level source code is going to become something only a few people do".
Dijkstra's arguments tend not to change their minds. I think the analogy takes hold because there is a kernel of truth in it. AI models now give us the magical ability to program in natural language, but the implication that an AI model is a form of compiler is deeply flawed. AI models don't replace compilers, they replace software developers. The reason software developers rarely look at assembly code is that compilers (and emulators and VMs) are able to precisely preserve the semantics of the original program.
A natural language spec is not a program, and there are many possible programs that can meet the spec. Software engineers and AI models turn specs into code, but the code is not the spec I think the analogy clouds our thinking about the value of programming languages as a tool for formally describing what the computer should do and enforcing properties of the program that should not be left to either humans or AI models to uphold.
Your acquaintances aren't changing their minds because the arguments are weak. Both the article and your comment assume that the only mode of interaction for natural language is a very high level description of programmer intent.
You can interact with AI and say "code me a calculator" or "now declare a array of booleans to map to each of the keys in the UI and set the corresponding array position to true when the key is pressed".
These commands are written in natural language at such different levels of abstraction that is hard to put them in the same bucket. It would be fair to call the latter an imperative language with the LLM in the role of a compiler. That's very similar to how I use AI.
If AI is natural language programming then they already had it in 1978, because AI is in fact "asking a programmer to write code for you". And you can even ask them to do it at different levels of abstraction!
The magic sauce in intelligence is context-awareness. If a prompt is somewhat general ("make me a calculator") an AI should either ask questions, or make reasonable assumptions, or both.
IME LLMs often make reasonable assumptions, but sometimes they don't and you have to backtrack and say "No, what I meant was..."
When the process works it's almost magical because the LLM successfully anticipates your needs, and implements your request in a much fuller way than you might have done yourself.
When it doesn't it makes a mess you have to clean up.
But generally I've been impressed by how well the leading LLMs will Do The Thing from a brief description, interpolating useful and correct details without being told to.
Compilers don't work like that. They're translators, not (entry level) inventors with independent agency.
> I'm not sure what the argument is supposed to prove.
I’m guessing it’s supposed to show that if you have to have arbitrary levels of intelligence to interpret natural language, then nothing’s changed between now and 1978 when considering whether “natural language programming” is a thing. Just because the intelligence is artificial now doesn’t really change the answer.
>Just because the intelligence is artificial now doesn’t really change the answer.
Not only it changes the aswer, it's literally the whole of the question. When people talked of the posibility or imposibility of “natural language programming” they *always* meant "natural language instructions which *a machine* translates to running code".
They didn't have to spell it out, since nobody doubted a human programmer can write code when given natural language instructions - that was already a thing since ENIAC, if not since Ada Lovelace.
That's what we have now, and that's what changed between now and 1978 when considering whether “natural language programming” is a thing.
The latter is also very far from specifying a calculator. And based on what we see in the "Show HN" projects, also very far from what the average person process is of using AI for generating software.
> AI models don't replace compilers, they replace software developers.
No, they replace the need for a precise description of what you want the computer to do by a mechanism that's really good to fill in the gaps. But guesswork only takes you so far.
It is, but software engineers have a much broader understanding of the problem, they also ask questions, and if they are good, they ask the right questions, something LLMs still have a problem with.
Personal example of what LLMs can't do, but I could: I made a script to update some version numbers in some files, based on some other files. Simple stuff, and one of my colleagues told me "ask the AI, it will take 5 minutes". Well, no, because the files sit on different machines, and extracting the files from one machine and putting the result on the other machine requires knowledge of the infrastructure. Again, simple stuff, but the AI doesn't know, and we certainly are not going to let it run wild in our network. I know all this because I talked to the team, attended meetings, and did a bit of careful exploration. I am trusted with resources to do my work in a way the AI doesn't, I am sure it is mostly because I can take the blame me if I misuse them.
I recently vibe coded an application fully and tested it with users. So all the functional requirements were there.
Guess what wasn’t?
The nonfunctional requirements.
Also: my app needs to do LLM classification. But instead sometimes the coding assistant wrote not an LLM api call but it wrote a regex to classify instead. This was despite the fact that I mentioned to not use regexes to classify things with.
Non-technical people don’t know what a regex is. Usually they also aren’t knowledgeable enough about nonfunctional requirements.
All of this may improve over time of course. But right now, I am not impressed (in terms of replacing SWEs).
>Guess what wasn’t? The nonfunctional requirements.
OK, so you test it, see which nonfunctional requirements it doesn't meet, and ask the AI to change the code to include them - repeat until you're happy.
Yep and then you'll have some war stories how things break in production because they weren't initially included in the nonfunctional requirements. Have enough of these war stories and you'll basically be a software engineer. Albeit, a software engineer that has never coded before, but definitely someone who understands production issues.
It is all about definitions. Writing instructions for llms is a new activity and we can call it as we wish - but I like to think about it as a new type of programming language instead of thinking about it as something completely new, because many analogies do transfer and you can use them to reason about larger systems composed of prompts. It is after all a way to instruct a machine.
There are differences and the article highlights some of them - but there are difference between programming in a functional language and an imperative one, there are differences between programming for a RISC machine and a CISC machine, there a lots of different semantics that programming languages support. Now we have a language with underspecified semantics.
You mean the English to code layer is stochastic while code to assembly is not?
This stochastic nature existed before AI. Humans are stochastic too. Two engineers convert specs to code in two different ways. I would argue the distribution is wider among humans than among agents.
As I said, "AI models don't replace compilers, they replace software developers" so yes, I agree that AI models are like humans.
while both LLMs and humans are not 100% deterministic, I don't think that's the interesting part. It's both true that there are many assembly language programs that implement a C program and that there are many valid programs (in any programming language) that correctly implement a natural language spec. But there's a significant qualitative difference between the two types of translation and that would be true regardless of whether the translations are stochastic.
The same compiler will convert source to binary in different ways! Hermetic builds are hard!
Even if we allow for hermetic builds in some cases in many many other cases your runtime will recompile your code based on its observations about your environment and that’s frequently what we want.
This fetishism of “determinism” seems crazy to anyone that has had to spend time really digging into modern JIT based compilation.
lol I saw the exact same JIT non determinism argument on reddit. The thing is, if JIT cannot replace a hotspot with a 100 percent functionally identical snippet of code then the JIT is just in time wrong and will be either patched, repaired or orphaned.
So all of a sudden all focus is on the word "determinism", and before even going down this route you have to agree on what aspect of the subject are you declaring to be "deterministic?"
Its sort of like chatting with libertarians, as soon as you agree that taxation is theft and democracy is tyranny then all problems melt away!
Arguing that two humans' distinct interpretations of a single specification is stochastic is patently absurd and requires, I think, some shred of evidence.
I would argue that, in the absence of such evidence, we cannot conclude that the English-to-code conversion process is stochastic; it is merely not deterministic, which is altogether different.
Would they write the same code, if given the same spec? No? How about over multiple trials? No? Even the individuals' own code would differ from one trial to the next?
Well, then, their interpretations of the spec are stochastic. Good enough for me, anyway.
It's applied to the programming language too - the given source can be compiled to unlimited number of possible machine code representations (it can different just by NOPs).
The LLMs don't replace compilers, they challenge the next generation of compilers. And they don't replace software engineers, they shift them into yet another metasystem.
I think you're missing my point which is more or less that compilers are these original harness and will continue to anchor human-computer interaction as they have always done.
Thank you for articulating this!! This idea has been rolling around for a while but I haven't been able to put it into words.
I think "I'm working in a higher level of abstraction" is easier for some devs to say than "I asked somebody else to do the thinking and the work." I don't think there's anything wrong with asking an LLM to do the work. But pasting jira tickets into claude is not abstraction lmao.
I guess arguments on either side of this can get difficult for a reason that another commenter already mentioned: you can use LLMs on a wide spectrum of granularity. You can one-shot a whole project, or you can tell it to rename some vars in a single file. I guess there are uses that are probably closer to programming. But if you're not even looking at the code, I'd say that's closer to somebody else doing the work
Agree. For me, that's exactly what happened. I work with the agents the way I'd work with a team of developers, not the way I use a compiler. Since the end of September, they've made about 970 commits to a project of mine, and I haven't read a line of its code.
And like with a team, many programs could meet what I asked for, but I check the behavior of what they built, not the code directly. I think of it as working on the level of an engineering director, who also doesn't review code directly. Each agent shows me its change running in the app, in a short narrated screen recording, like in a launch review for the director. When it's not what I meant, I say what, and it goes another round.
If you are checking every line written by an LLM , what would you estimate is the probability it would ever do this?? I look at most code still and I have a high level of trust that it would never do anything remotely similar to this.
"trust" and "responsibility" mean nothing for a fully digital entity who isn't required to feed its family, looking for an eventual new job, and being under pressure for not being fired
> The reason software developers rarely look at assembly code is that compilers (and emulators and VMs) are able to precisely preserve the semantics of the original program.
'Precisely' is doing a lot of work here. Depending on your language some things might be preserved and some not. Eg if you write stuff in Haskell, you don't really preserve much of anything about memory usage, you leave that to the runtime system to figure out, and exactly how the GC works isn't really specified in the language standard.
The most obvious reason natural language is a poor choice for programming is ambiguity.
Time flies like an arrow.
I saw the parade coming through the window.
For small programs that basically duplicate something else with a few tweaks, sure, natural language can be fine. "The same thing as A, but with a red border, and add a new button to do X." Beyond that things start to get tricky.
Interactive programming has far more value than natural language programming, and agents also give you that. An ambiguous natural language spec. that leads to the wrong result is less problematic when you can interactively correct the problem; "No, that button should have been on the left hand side." It is the new version of REPL.
I think the difference is between communicating to a purely mechanical being versus communicating to an intelligent entity: unlike programs, which are purely formal languages, we write textbooks and papers in a mix of human language and formal language. often we introduce "just formal enough" notations. and the intelligent reader will read in-between, inter legere literally means just that: reading in between. filling the gaps, connecting the dots.
and in practice I find it _very_ effective to express key ideas with some formal language where applicable. a little ebnf here, some snippets of python or prolog there or other languages there. and I find llms wonderful at filling in the dots and, and that's the topic here, to translate our semi formal conversation into rigorous, formal language.
and I dare one step further: I also find llms useful in formulating and refining technical proposals, for human peer review and llm driven implementation, and of course these proposals mix natural language prose with semi-formal and formal detail. think PEP-12 and PEP-1 style. and these then get work packages which each loop in a spec as in sdd.
I've had the same view since coding agents appeared. LLMs are good for summarizing, thus I tend to use them to understand code. However, the more I understand a code base the more I tend to be more picky with the agent. While the less I know about the code base, the more open I am to let it do its thing.
I've realized that the agent doesn't write 'good' code, instead my vague understanding of what I expect makes me think it does. When I look at the AI code, I see that there are more nuances and decisions that needed to be taken before writing the code. One would say that a spec would suffice, but then I remember TLA+, developed by Leslie Lamport to help developers think critically about their programs/algorithms. Sitting down and formally writing what I want to do helps me better understand the problem and come up with a real -not vaguely defined- solution.
Natural language is full of nuances and implicit cultural and domain specific connotations. LLMs work because they have been trained on massive corpus of materials that encode some (but not all) of those hidden meanings.
With specification driven development, the prompts need to be very detailed in order to produce exactly what is required.
Humans also work because they've been trained on a massive corpus of materials, spanning like an average 15 year period or more. And humans too need to be prompted, sometimes everyday, with meetings and whatnot to produce exactly what is required.
I used to do software dev without meetings. It worked just fine. I would send whatever I did once it was done, and get direction on where to take it next. Maybe this breaks down in companies with more than a few leaders, or where the leaders can't otherwise keep in sync.
Also gave me plenty of opportunities to go above and beyond. I miss when that felt safe.
This depends entirely on how well the requirements have been communicated, right? If you could go for long periods without meetings on large projects, it means either you had great PMs or you’re very good at collecting relevant info and planning in advance. Or, it could mean that your target was quite flexible and open to interpretation. I don’t doubt it, and I’ve known some developers who could do what you did, deliver something great without that much back and forth. I’ve also known many developers (probably most TBH) who will focus on problems the customers don’t have and endlessly go down rabbit holes that have no bottom. Heads down software engineering for big-O and data structures seems to be a lot more fun for some people than fixing weird hard to repro bugs with vague repro instructions that customers complain about. Many very good devs still need to check in and iterate and get some steering, especially if the requirements have not been well or fully explained… which in my own experience has been more or less always. ;)
Maybe my definition of meetings is different than yours. To me, meetings are voice or video calls where multiple people talk about progress and direction, raise issues to the others, etc. and the leaders can make calls across the whole team at once. Staying in constant communication doesn't count as much because it's more async and incremental.
Maybe those were some very detailed requirements you were getting, the deliveries were straight-forward, or you already knew the space pretty well. Or you're the only dev there. Somehow most teams just keep tripping over each other and need near constant management to ensure they're delivering what the stakeholders expect.
They weren't particularly detailed, but I did know the space quite well. I was a heavy user of other products of that type, and knew very well what I wanted out of one.
The problem is that your case is extremely rare. Most people work on systems that they never would use themselves or even know existed if not for working on it.
Wherever I work, I try to turn the system into something I would use myself. I am not always successful in this, but it drives a lot of my UX decisions, and it seems to work fairly well.
> I used to do software dev without meetings. It worked just fine. I would send whatever I did once it was done, and get direction on where to take it next.
You didn’t collaborate with other engineers? Or what if the spec you were given was unclear in places? Forgive me, but the style you describe sounds incredibly collaborative, super transactional.
Funny story actually, there were a few other engineers but not very much collaboration. They were resistant to sharing source code or algorithm details, so I had to essentially reverse engineer how their other products worked. I was good enough at it that I came up with several features they didn't know were possible. They eventually ended up letting me go and cloned my design, which I thought was weird, but it was developed for them in the first place so not a big deal.
To be fair, it's a very secretive industry, and betrayals are quite common. Worse, a lot of those betrayals are done on pure impulse without even thinking through the consequences first, so you can't actually build incentives against it because they'll just be ignored.
> Humans also work because they've been trained on a massive corpus of materials,
Massive? My introduction to programming (from noob to beginner level) has been like only 10 books worth of content. Including some alternative pathways like 3D, security, system administration,... To this day, I don't think I've reached 50 books of materials. And If I counted everything it would be still small.
Human learning is very efficient (not time wise, but cost wise, and even training data wise).
You're forgetting all the schooling before that, which taught you how to read, do math, etc. And the general daily experiences of just being a human being interacting with the world. Magnitudes more data taken in than any language model.
You should check out how much raw data just your eyes process for every second they're open (although a significant amount is discarded before it gets to the vision center, but still). Then consider the other senses also constantly doing their thing.
The vast majority of people don't internalize most things. Some internalize even less. It can become a big issue with things like dissociative disorders. (I have some experience with that)
> I suspect that machines to be programmed in our native tongues —be it Dutch, English, American, French, German, or Swahili— are as damned difficult to make as they would be to use.
He's still quite correct. I generally see a machine as being "programmed in" whatever is tracked in source control. So as long as your git repo is tracking changes in Java, that's still programmed in Java, even if the Java was generated with the assistance of LLMs. I may consider the thesis of this article invalidated only when the majority of repos start tracking natural language rather than programming languages as the primary artifacts defining behavior of the software.
That would be one definition of "source code". But then there's also the definition by the Free Software Foundation as "the preferred form of the work to making modifications to it". As prompting LLMs becomes the preferred form of making modifications, I wonder if a lot of open-source repos will unwillingly become "closed (prompting) source" over time, with intricate legal consequences.
I still don't know if I'd agree with you. I think:
> the preferred form of the work to making modifications to it
Implies what you point to when you want to know where to make changes.
That wouldn't be a prompt - you might use a prompt to make changes, but the thing you're making changes to, certainly at present, is your java/python/rust++ /whatever codebase.
Lots of projects, even fully vibecodes ones, don't push and include prompts in their repos. But they all push the java code (sticking with java as an example for the sake of argument).
I can't imagine that working on a project where the java code is omitted, and people make changes to the software just by editing the prompts would go well. It's an interesting thought experiment, but I think you'd find it very hard to work on any kind of team where that was the practice.
True, and as such it needs to be considered like any other code output from a one-off code generator, with at least minor added risks from non-determinism and, in the case of LLM-as-a-service, potential lack of model availability in the future.
This means maintainability is as important as it is for hand-written code. Which varies, of course: there are plenty of valid use cases for one-off throwaway code. Just don’t assume checking in slop is suddenly cost-free “because AI”.
Todays natural language program won't work tomorrow due to unstable nature of natural language.
Apart of natural evolution of meanings, look at Orwellian limitations some (cloud) models impose on is that our natural language program become illegal. Due to topic or combination of words the program needs to be flagged and soon likely reported to three leter agency.
Next, RMS would ask: can you make your own natural language program compiler from scratch?
I know we have (very expensive) local models, but wait until GPUs get a propper online subscriptions to work and surveillance taps.
What Dijkstra said still holds true, and the essence is consistent with Brooks' discussion on essential complexity vs accidental complexity. I do think one thing has changed, though: when someone asks AI to implement XXX with very vague English, the implicit ask is picking the best[1] ever solution that someone else has done to solve XXX. For instance, if I ask AI to extract the Japanese subtitle of Shirojima show and convert it to an epub with all the nantural and cultural explanations, I was assuming that AI will fill in the blanks and do a good job and AI did. This won't help expert, but does it matter? Being good enough can be good enough to take away many people's jobs.
[1] For many people, the result is actually the mean not the best, but it's another topic.
The experiment in designing a language that resembled natural languages (English and Japanese) was not successful. -[1]- William R. Cook, AppleScript, HOPL IV
Now our new LLMs are different, because they don't use "language the resembled natural languages", but actual natural language. But the lesson is instructive nevertheless, because it gives us some empirical evidence to compare to Dijkstra's theoretical arguments.
And this is that natural language is at best a two-edged sword. Yes, there are benefits, but there are also huge drawbacks. And one problem is that the benefits are front-loaded and the drawbacks are long-term problems.
To me, one thing we are missing is that what LLMs do for us actually has 2 parts.
1. It allows us to use natural language, allowing us to be fuzzy.
2. It allows us to specify what we want at a much, much higher level than usual.
With our LLMs, these two are intrinsically linked. I think the whole would be much, much more valuable if we could separate them.
This feels so incredibly relevant for today.
This also makes me think about the cognitive delegation problem - structured interfaces in machines and structured methodologies in mathematics helped humans build structured thoughts to express abstract intuition. With machines starting to speak natural language, it's probably not too far fetched to say that humans will eventually stop organising their thoughts, which will turn their thoughts into a kind of brain fog.
Isn't philosophy the best example that you can organize your thoughts in natural language? Of course, there is the tradition of analytical philosophy. However, the vast majority of philosophy was and is written not in a formal language.
When comparing it to math, this always felt to me like a clear limitation in philosophy, though. Just look at how philosophers try to argue about "logic", compared to mathematics' elegant descriptions.
Programming in natural language still doesn't work, Btw!
whenever you're using a Ml model to either autocomplete code or as an agent, you're not really programming in natural language but "delegating" in natural language!
The old skeptics weren’t wrong about the foolishness of natural language as a programming language, it's just that they assumed the only possible intermediary way to "write" code was something compiler-like!
they never really thought about a future where ai would become good enough to translate natural language instructions into runnable code
NL programming is foolish to me not on technical grounds but on practical ones. It is most valuable when the business wants to understand how an already working thing does its job.
I have observed several samples where the business, when provided with a natural language configuration opportunity, will wind up staring blankly at the textarea until the original developer gives them a complete starting point.
I don't think "natural" language makes much of a difference unless you are already skilled in the art of developing useful engineering specifications. I often have to babysit the business through very basic boolean algebra concerns despite everyone having 3+ decades experience dealing with this stuff. Making everyone speak python could only make this worse, not better.
I use HN to keep myself warmed up on natural language expression. The difference between developers who post here prolifically and those who barely write one email a month is fairly shocking in my experience. If you intend to use a specific instrument to accomplish a complex task, you need to practice it constantly. Ideally every single day. There is no substitute for this. There never will be.
> I have observed several samples where the business, when provided with a natural language configuration opportunity, will wind up staring blankly at the textarea until the original developer gives them a complete starting point.
A lot of people lack “system thinking” when they can break down something into a set of elements and describe the relation between them. Once that done though , they can understand a part and manipulate it.
For some “system thinking” is innate. It can also be learned. But it’s a required quality in engineering where you need to create a solution where there was none.
LLM interfaces on everything these days equate to touch screen on cars. It is definitely a useful tool, but generally speaking, that's not how you'd talk to your computer.
Dijkstra elegantly explains why AI systems are effective at mathematics and programming but much less effective when writing prose or other realms where what is good is nebulously defined:
> "The virtue of formal texts is that their manipulations, in order to be legitimate, need to satisfy only a few simple rules; they are, when you come to think of it, an amazingly effective tool for ruling out all sorts of nonsense that, when we use our native tongues, are almost impossible to avoid."
>We would need all the intellect in the world to get the interface narrow enough to be usable,
It seems Dijkstra is describing potential calability of LLM model trained on published collective human knowledge enabled by the Internet
>and, in view of the history of mankind, it may not be overly pessimistic to guess that to do the job well enough would require again a few thousand years
Since the article was written in 1978 (about 50 years ago) and from the time the computing resources of CPU and RAM has increased exponentially until the LLM first appeared in 2020s, it seems the thousand of years prediction is merely several decades accelerated by the rapid advances of computer and communication systems technology spearheaded by companies like Nvidia.
I built a music player by talking to Claude Code in Japanese, and I can barely remember a time when words failed and I had to fall back to code. For example, I asked it to "keep the sidebar's shape, but add margin on the top, left and bottom so it looks like it's floating a bit — you know what I mean?" and it got it right on the first try.
Oh look at the last paragraph on the first page. Manually justified:
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 dif-
ficulties.
How does one even achieve that with a manual typewriter?
Luca Cardelli created the handwritten typeface Dijkstra font in the late 1980s, to mimic the distinctive and precise handwriting of his colleague Edsger W. Dijkstra.
(You have to click on the "Fonts" tab, there's no direct link to it.)
Dijkstra [.ttf (2013 updated version by Reuben Thomas)] [.zip (old PC)] [.hqx (old Mac)] [Preview.pdf]
A beautiful handwriting font of unknown origin, first popularized by E.W.Dijkstra. Excellent for slides that appear to be carefully written, and for short, deep technical notes.
It's not just em dashes, it's 1: typewriter em dashes aka `--` and 2: usage of them in pairs, like parenthesis, that is completely different from how LLMs use them.
The human equivalent of LLM could be to build a wiki / semantic graph of all algorithms, tutorials, framework docs, stdlib docs and attach a make file at the end each node.
I've been stating it elsewhere, attention has made English into a context aware sensitive programming language by resolving ambiguities through anaphora and coreference resolution. Inform7 did limited anaphora resolution - it - but LLMs are able to resolve "move that file to this folder".
This article shows every now and then, and it's a must read. Indeed the natural language programming requires massive clusters of gpus just to make a half baked crud app
His point is moot. His argument considers that natural language would be the direct expression of the program. It's not. It's an abstraction layer, that is then converted in formal language. He could have foreseen that, given he takes explicitly the example of machine code and high-level languages. High-level languages do not replace machine code, they translate into it. Just like natural language programming translates into high-level language.
What do you commit? As long as you commit high level code to your repo, you are still programming in that language. You don't commit machine code because you know it will be always the same after compiling. And it also doesn't matter that you are using very high level tools and basically don't ever look at or even understand your programs any more.
I don't think reproducibility is relevant, here. Otherwise, even your compiler example doesn't stand. No, machine code is not always the same after compiling high-level language. First, it depends on compiler. Clang and GCC won't produce the same output. Then it depends on the version of your compiler. GCC-9 and GCC-11 won't produce the same output. It obviously depends on the architecture too (arm vs x86), but even then, it depends on details about current configuration of the system you're building it on (CPU flags, hardening, etc). And then of course, there may be things randomized on purpose at each build, like randomized memory addresses to make the binary less exploitable.
Yeah I should have been more precise. What I was suggesting is that the behavior of the machine code will be always the same after compiling (assuming no compiler bugs).
Anyway, my work process is as follows: I have the agent write the code, and describe it's purpose etc. via comments so another session can later pick it up easily without me having to explain everything again. That's what I commit. Sometimes I even look at the code :)
What else could I commit? At the current state of the art, I could commit the whole natural language session that lead to the code, instead of the code. When I later check this out in a fresh environment (without the artifacts the LLM putted out), I'd just load the most recent version of the conversation as an initial prompt. Or I could want to check out the conversation at a point two months ago and rebuild the program from there.
Would that produce a program with exactly the same behavior? I don't know. But if it doesn't, the essay's argument is not moot. Not yet ...
Consider the role of a Business Analyst, in corporate environments. He is the person that talks to stakeholders and users and translates their "natural language" wishes into piles of semi-formal language (UML anyone?). Later on this will become fully formal language.
But I think Dijkstra point is more that we can't fully automate this process and let the machines do the formalization thingy. I used to believe that but I've seen LLMs doing things that really, really surprised me.
In the end I still hold hope in the old saying: "make a something idiot-proof and someone will just make a better idiot". We see LLMs doing magic if they're used by people that know what LLMs are doing. I never saw LLMs doing magic in the hands of idiots.
Fun fact: there are over 600 definitions of the word "run" and over 400 for "set".
But yeah, English is really precise enough for programming.
How many times have we seen that there's been a spec for something and the programmers write something that is exactly like the spec said but was completely wrong?
I find c++ very unnatural, since it's a giant collection of small rules in the end more complex than assembler. I can't remember the entire sintaxis, zoo of function decorators, undefined behavior, intrinsics which are just labels over asm instructions, Code golf sintaxis...
> (You have only to look at the indeed alarming amount of on close reading meaningless verbiage in scientific articles, technical reports, government publications etc.)
It is perhaps for the best that Djikstra did not live long enough to witness this problem become orders of magnitude worse.
App stores are approving an order of magnitude increase of new apps because people are now able to create them using natural language. End users are deriving great value from them. So this is settled?
Natural language as code has existed for thousands of years. Today we call it legalese.
It's excruciating to write, excruciating to read, and even legit geniuses with decades of hard won experience still leave room for essentially infinite interpretation. laws and contracts are all still swiss cheese and can be made to demand or prohibit whatever the party with the most money or other influence wants.
If law was code, there would be no need for judges to make decisions. Instead, a "judge" would be someone making a mechanical derivation of the judgement given the situation and the law.
The law is rather a complex set of guidelines which need to leave some space for interpretation, so that additional relevant information for the situation at hand can affect the decision.
Only the legislated part of law is codified. In our American common law system, case law is, by definition, uncodified. So what judges are doing, is taking the codified legislation, and applying case law decisions in layers on top, to reach a final decision.
We are now totally there: Programming With Natural Language.
I could only describe the "look and feel" of the ESP32-algorithm driving a sewing machine: "Snappy but not jerky, linear at low speeds, but accelerates fast".
Antigravity made it perfect but incomprehensible for a hewman.
And yet, the specification of almost every program in existence was communicated in natural language. At least between the customer and developer.
This argument makes the implicit assumption that humans are uniquely able to convert between this "customer wants a program that solves their problem" level and compileable code. That was a reasonable thing to believe in 1978, but is not a reasonable thing to believe in 2026.
Once an employer asked me to automatize his work, so the task that he did in weeks was done in seconds. I made a gui that showed every step he did manually, so he could check and understand every step. And yet he became confused and lost track of what the program was doing. He frequently asked me the very concepts he taught me so I could do the program. I just implemented exactly his specification. I didn't created any new concept, but as soon as the task became automatized, he stopped understanding.
Dijkstra's epigrams have not aged well. He once mocked software engineering as "how to program if you cannot"—but this too is a solved problem thanks to AI.
Some people will push back that agent development is "natural language programming" but I don't think that's right. I think in that case the agent is the one programming, and the programming still always happens in a deterministic language like Rust/Python/Typescript/etc. Maybe that'll change, but to me at least the other way would be a disaster.
The best exception I know of though, is definitely the Inform 7 language which is for writing text adventures[0]. It has a lot of fans, and I've never gone deep with it, but seems to do a pretty elegant job or marrying up natural language and programming together in a unique way.
Either way, definitely a must-check-out for any language-geeks interested in natural language programming.
[0] https://ganelson.github.io/inform-website/
As opposed to creating flows where LLMs do something on the fly.
Code creates determinism, LLMs don’t.
Whether "natural-language-like syntax" is enough "count as natural language programming" is probably a question of definition. For Dijkstra's essay here, he doesn't offer a particular definition, but he certainly seems to be implying something much more fluid and ambiguous than Inform7 is.
I definitely didn't mean to imply Inform7 had achieved the holy-grail of natural language programming or anything.
I think Dijkra's main point he's driving at is here:
> I suspect that machines to be programmed in our native tongues —be it Dutch, English, American, French, German, or Swahili— are as damned difficult to make as they would be to use.
I'm inferring/assuming a bunch, but I think he's saying something along the lines of, programming is useful because it is precise, and natural language is not precise, so attempts to make programming languages more like natural language are butting up into this inherent contradiction of precision. And therefore will end up as worse programming languages (for attempting to be more naturalistic).
In the history of programming languages, I think most attempts to be "more natural language like", such as COBOL or AppleScript, do become worse for attempting to walk the tightrope between natural-language and programming. I think Inform7 is unusual in that it achieves that balance in a way that isn't just objectively terrible (you may disagree) - I think that's mostly because it offers very few programming constructs, and is a tool for writing a kind of prose (text adventure games).
Anyway, to come back around, that was my reasoning for mentioning Inform7 - I think it's an interesting deviation from other experiments in natural language programming so far.
I don't think I'd want to call it "natural language programming" in the way Dijstra's implying here unless the prompt was the thing that was actively being executed/ran/whatever when someone loads your app or website.
Existence proof: everything Claude Code does, including talking to LLMs on GPUs via the Anthropic API, and shelling out to other tools installed on your system, could in principle be compiled into one big self-contained binary (or Turing Machine if you prefer) that is able to take natural language in and produce compiled binaries out.
If you wanted to go a bit harder you could train Claude to output machine code directly, but that's not necessary. Whether the machine code comes straight out of an LLM or out of a compiler that is fed source code that comes from an LLM is a mere implementation detail, there's not any fundamental difference.
There are also times when you want the final product to be able to target different architectures and platforms. Generating into a higher level language allows for re-targeting the code by only changing a few compiler & linker flags or runtime/environment config. I would much rather that than hoping the LLM is feature- (and bug-)compatible between generation runs.
Yet the point about DSL is interesting, in particular internal/embedded DSLs [2]. I lied a little earlier, my third programming language was actually Forth. His creator said in 2001, "When you devised an application you wrote a hundred words [i.e. functions] or so that discussed the application and you used those hundred words to write a one line definition to solve the application" [3].
I've been trying to do that exactly that in other languages, make it so that the code expresses "naturally" what it does as much as possible. When you succeed, people call your code "beautiful" or "elegant" (nobody ever told me that :-) which, I think, subsumes "naturality".
This is of course harder to do in low-level languages such as the ones I mentioned (except Basic maybe) because you get caught in details such as memory management. But high-level languages are not exempt of these problems (and contrary to Forth or Lisp, their syntax is often written in stone). Then there's essential complexity [4], which can be "unnatural" no matter what, like thinking about things in a concurrent programming context, with languages that don't natively support it like they support automatic memory management (i.e. garbage collection).
The topic is dangerously close to "readability", which is known to be 80% subjective.
[1] https://www.cs.utexas.edu/~EWD/transcriptions/EWD08xx/EWD831...
[2] https://en.wikipedia.org/wiki/Domain-specific_language#Domai...
[3] https://www.ultratechnology.com/1xforth.htm
[4] https://en.wikipedia.org/wiki/No_Silver_Bullet
Dijkstra's arguments tend not to change their minds. I think the analogy takes hold because there is a kernel of truth in it. AI models now give us the magical ability to program in natural language, but the implication that an AI model is a form of compiler is deeply flawed. AI models don't replace compilers, they replace software developers. The reason software developers rarely look at assembly code is that compilers (and emulators and VMs) are able to precisely preserve the semantics of the original program.
A natural language spec is not a program, and there are many possible programs that can meet the spec. Software engineers and AI models turn specs into code, but the code is not the spec I think the analogy clouds our thinking about the value of programming languages as a tool for formally describing what the computer should do and enforcing properties of the program that should not be left to either humans or AI models to uphold.
You can interact with AI and say "code me a calculator" or "now declare a array of booleans to map to each of the keys in the UI and set the corresponding array position to true when the key is pressed".
These commands are written in natural language at such different levels of abstraction that is hard to put them in the same bucket. It would be fair to call the latter an imperative language with the LLM in the role of a compiler. That's very similar to how I use AI.
IME LLMs often make reasonable assumptions, but sometimes they don't and you have to backtrack and say "No, what I meant was..."
When the process works it's almost magical because the LLM successfully anticipates your needs, and implements your request in a much fuller way than you might have done yourself.
When it doesn't it makes a mess you have to clean up.
But generally I've been impressed by how well the leading LLMs will Do The Thing from a brief description, interpolating useful and correct details without being told to.
Compilers don't work like that. They're translators, not (entry level) inventors with independent agency.
That would just be the "I" part, since was missing the A for artificial: you asked a naturally made person.
And yes, nobody argued that they didn't have I in 1978, so I'm not sure what the argument is supposed to prove.
I’m guessing it’s supposed to show that if you have to have arbitrary levels of intelligence to interpret natural language, then nothing’s changed between now and 1978 when considering whether “natural language programming” is a thing. Just because the intelligence is artificial now doesn’t really change the answer.
Not only it changes the aswer, it's literally the whole of the question. When people talked of the posibility or imposibility of “natural language programming” they *always* meant "natural language instructions which *a machine* translates to running code".
They didn't have to spell it out, since nobody doubted a human programmer can write code when given natural language instructions - that was already a thing since ENIAC, if not since Ada Lovelace.
That's what we have now, and that's what changed between now and 1978 when considering whether “natural language programming” is a thing.
No, they replace the need for a precise description of what you want the computer to do by a mechanism that's really good to fill in the gaps. But guesswork only takes you so far.
Personal example of what LLMs can't do, but I could: I made a script to update some version numbers in some files, based on some other files. Simple stuff, and one of my colleagues told me "ask the AI, it will take 5 minutes". Well, no, because the files sit on different machines, and extracting the files from one machine and putting the result on the other machine requires knowledge of the infrastructure. Again, simple stuff, but the AI doesn't know, and we certainly are not going to let it run wild in our network. I know all this because I talked to the team, attended meetings, and did a bit of careful exploration. I am trusted with resources to do my work in a way the AI doesn't, I am sure it is mostly because I can take the blame me if I misuse them.
Guess what wasn’t?
The nonfunctional requirements.
Also: my app needs to do LLM classification. But instead sometimes the coding assistant wrote not an LLM api call but it wrote a regex to classify instead. This was despite the fact that I mentioned to not use regexes to classify things with.
Non-technical people don’t know what a regex is. Usually they also aren’t knowledgeable enough about nonfunctional requirements.
All of this may improve over time of course. But right now, I am not impressed (in terms of replacing SWEs).
OK, so you test it, see which nonfunctional requirements it doesn't meet, and ask the AI to change the code to include them - repeat until you're happy.
I have plenty of those in decades of human programming too, so...
There are differences and the article highlights some of them - but there are difference between programming in a functional language and an imperative one, there are differences between programming for a RISC machine and a CISC machine, there a lots of different semantics that programming languages support. Now we have a language with underspecified semantics.
This stochastic nature existed before AI. Humans are stochastic too. Two engineers convert specs to code in two different ways. I would argue the distribution is wider among humans than among agents.
while both LLMs and humans are not 100% deterministic, I don't think that's the interesting part. It's both true that there are many assembly language programs that implement a C program and that there are many valid programs (in any programming language) that correctly implement a natural language spec. But there's a significant qualitative difference between the two types of translation and that would be true regardless of whether the translations are stochastic.
It would be interesting to state sane measurments that determine a "distance" between a spec and software that claims to implement it
Please go ahead and do.
So? Two compilers convert source to binary in two different ways.
Even if we allow for hermetic builds in some cases in many many other cases your runtime will recompile your code based on its observations about your environment and that’s frequently what we want.
This fetishism of “determinism” seems crazy to anyone that has had to spend time really digging into modern JIT based compilation.
Copium.
So all of a sudden all focus is on the word "determinism", and before even going down this route you have to agree on what aspect of the subject are you declaring to be "deterministic?"
Its sort of like chatting with libertarians, as soon as you agree that taxation is theft and democracy is tyranny then all problems melt away!
Yup. Pure obfuscation.
The point is reliability.
I would argue that, in the absence of such evidence, we cannot conclude that the English-to-code conversion process is stochastic; it is merely not deterministic, which is altogether different.
Well, then, their interpretations of the spec are stochastic. Good enough for me, anyway.
In my opinion, LLMs don't challenge anything.
That's a funny euphemism for career change. I'll remember it.
I think "I'm working in a higher level of abstraction" is easier for some devs to say than "I asked somebody else to do the thinking and the work." I don't think there's anything wrong with asking an LLM to do the work. But pasting jira tickets into claude is not abstraction lmao.
I guess arguments on either side of this can get difficult for a reason that another commenter already mentioned: you can use LLMs on a wide spectrum of granularity. You can one-shot a whole project, or you can tell it to rename some vars in a single file. I guess there are uses that are probably closer to programming. But if you're not even looking at the code, I'd say that's closer to somebody else doing the work
Maybe they trust the practical results they see over what a professor wrote 50 years ago as an essay.
And like with a team, many programs could meet what I asked for, but I check the behavior of what they built, not the code directly. I think of it as working on the level of an engineering director, who also doesn't review code directly. Each agent shows me its change running in the app, in a short narrated screen recording, like in a launch review for the director. When it's not what I meant, I say what, and it goes another round.
However you can't prevent some other people from working as software developers who write and read code.
Even with cars, you still walk. Even with factories, someone still craft handmade bags.
if (date == <some random future date>) { crash_and_burn_the_world() }
??
I actually found code exactly like that would break a test on a certain date, using an agent.
It's really good at flagging things like this, which tend to be leftovers from a human process that nobody has had the time to clean.
'Precisely' is doing a lot of work here. Depending on your language some things might be preserved and some not. Eg if you write stuff in Haskell, you don't really preserve much of anything about memory usage, you leave that to the runtime system to figure out, and exactly how the GC works isn't really specified in the language standard.
Time flies like an arrow.
I saw the parade coming through the window.
For small programs that basically duplicate something else with a few tweaks, sure, natural language can be fine. "The same thing as A, but with a red border, and add a new button to do X." Beyond that things start to get tricky.
Interactive programming has far more value than natural language programming, and agents also give you that. An ambiguous natural language spec. that leads to the wrong result is less problematic when you can interactively correct the problem; "No, that button should have been on the left hand side." It is the new version of REPL.
and in practice I find it _very_ effective to express key ideas with some formal language where applicable. a little ebnf here, some snippets of python or prolog there or other languages there. and I find llms wonderful at filling in the dots and, and that's the topic here, to translate our semi formal conversation into rigorous, formal language.
and I dare one step further: I also find llms useful in formulating and refining technical proposals, for human peer review and llm driven implementation, and of course these proposals mix natural language prose with semi-formal and formal detail. think PEP-12 and PEP-1 style. and these then get work packages which each loop in a spec as in sdd.
seems to work just fine so far :-)
I've realized that the agent doesn't write 'good' code, instead my vague understanding of what I expect makes me think it does. When I look at the AI code, I see that there are more nuances and decisions that needed to be taken before writing the code. One would say that a spec would suffice, but then I remember TLA+, developed by Leslie Lamport to help developers think critically about their programs/algorithms. Sitting down and formally writing what I want to do helps me better understand the problem and come up with a real -not vaguely defined- solution.
No one writes 'good' code. Everyone only writes 'good enough' code. You should always assume the existence of flaws and bugs.
You call "good" code the one that doesn't have the mistakes you know. But you'll never know everything, even if you are as smart as Dijkstra.
With specification driven development, the prompts need to be very detailed in order to produce exactly what is required.
Also gave me plenty of opportunities to go above and beyond. I miss when that felt safe.
You didn’t collaborate with other engineers? Or what if the spec you were given was unclear in places? Forgive me, but the style you describe sounds incredibly collaborative, super transactional.
Massive? My introduction to programming (from noob to beginner level) has been like only 10 books worth of content. Including some alternative pathways like 3D, security, system administration,... To this day, I don't think I've reached 50 books of materials. And If I counted everything it would be still small.
Human learning is very efficient (not time wise, but cost wise, and even training data wise).
Ah, yes, I can fondly recall reading and internalizing every word available on the internet when I was young...
But for mature software eventually every single detail matters.
He's still quite correct. I generally see a machine as being "programmed in" whatever is tracked in source control. So as long as your git repo is tracking changes in Java, that's still programmed in Java, even if the Java was generated with the assistance of LLMs. I may consider the thesis of this article invalidated only when the majority of repos start tracking natural language rather than programming languages as the primary artifacts defining behavior of the software.
> the preferred form of the work to making modifications to it
Implies what you point to when you want to know where to make changes.
That wouldn't be a prompt - you might use a prompt to make changes, but the thing you're making changes to, certainly at present, is your java/python/rust++ /whatever codebase.
Lots of projects, even fully vibecodes ones, don't push and include prompts in their repos. But they all push the java code (sticking with java as an example for the sake of argument).
I can't imagine that working on a project where the java code is omitted, and people make changes to the software just by editing the prompts would go well. It's an interesting thought experiment, but I think you'd find it very hard to work on any kind of team where that was the practice.
This means maintainability is as important as it is for hand-written code. Which varies, of course: there are plenty of valid use cases for one-off throwaway code. Just don’t assume checking in slop is suddenly cost-free “because AI”.
Apart of natural evolution of meanings, look at Orwellian limitations some (cloud) models impose on is that our natural language program become illegal. Due to topic or combination of words the program needs to be flagged and soon likely reported to three leter agency.
Next, RMS would ask: can you make your own natural language program compiler from scratch? I know we have (very expensive) local models, but wait until GPUs get a propper online subscriptions to work and surveillance taps.
[1] For many people, the result is actually the mean not the best, but it's another topic.
The experiment in designing a language that resembled natural languages (English and Japanese) was not successful. -[1]- William R. Cook, AppleScript, HOPL IV
Now our new LLMs are different, because they don't use "language the resembled natural languages", but actual natural language. But the lesson is instructive nevertheless, because it gives us some empirical evidence to compare to Dijkstra's theoretical arguments.
And this is that natural language is at best a two-edged sword. Yes, there are benefits, but there are also huge drawbacks. And one problem is that the benefits are front-loaded and the drawbacks are long-term problems.
To me, one thing we are missing is that what LLMs do for us actually has 2 parts.
1. It allows us to use natural language, allowing us to be fuzzy.
2. It allows us to specify what we want at a much, much higher level than usual.
With our LLMs, these two are intrinsically linked. I think the whole would be much, much more valuable if we could separate them.
[1] https://dl.acm.org/doi/10.1145/1238844.1238845
The old skeptics weren’t wrong about the foolishness of natural language as a programming language, it's just that they assumed the only possible intermediary way to "write" code was something compiler-like! they never really thought about a future where ai would become good enough to translate natural language instructions into runnable code
I have observed several samples where the business, when provided with a natural language configuration opportunity, will wind up staring blankly at the textarea until the original developer gives them a complete starting point.
I don't think "natural" language makes much of a difference unless you are already skilled in the art of developing useful engineering specifications. I often have to babysit the business through very basic boolean algebra concerns despite everyone having 3+ decades experience dealing with this stuff. Making everyone speak python could only make this worse, not better.
I use HN to keep myself warmed up on natural language expression. The difference between developers who post here prolifically and those who barely write one email a month is fairly shocking in my experience. If you intend to use a specific instrument to accomplish a complex task, you need to practice it constantly. Ideally every single day. There is no substitute for this. There never will be.
A lot of people lack “system thinking” when they can break down something into a set of elements and describe the relation between them. Once that done though , they can understand a part and manipulate it.
For some “system thinking” is innate. It can also be learned. But it’s a required quality in engineering where you need to create a solution where there was none.
> "The virtue of formal texts is that their manipulations, in order to be legitimate, need to satisfy only a few simple rules; they are, when you come to think of it, an amazingly effective tool for ruling out all sorts of nonsense that, when we use our native tongues, are almost impossible to avoid."
It seems Dijkstra is describing potential calability of LLM model trained on published collective human knowledge enabled by the Internet
>and, in view of the history of mankind, it may not be overly pessimistic to guess that to do the job well enough would require again a few thousand years
Since the article was written in 1978 (about 50 years ago) and from the time the computing resources of CPU and RAM has increased exponentially until the LLM first appeared in 2020s, it seems the thousand of years prediction is merely several decades accelerated by the rapid advances of computer and communication systems technology spearheaded by companies like Nvidia.
[1] https://www.cs.utexas.edu/~EWD/ewd06xx/EWD667.PDF
It shipped as a free PostScript font with NeWS:
https://www.donhopkins.com/home/NeWS/fonts/Dijkstra.ps
https://lucacardelli.name/indexartifacts.html
(You have to click on the "Fonts" tab, there's no direct link to it.)
Dijkstra [.ttf (2013 updated version by Reuben Thomas)] [.zip (old PC)] [.hqx (old Mac)] [Preview.pdf]
A beautiful handwriting font of unknown origin, first popularized by E.W.Dijkstra. Excellent for slides that appear to be carefully written, and for short, deep technical notes.
https://lucacardelli.name/Artifacts/Fonts/Pc/dijkstra.ttf
https://lucacardelli.name/Artifacts/Fonts/Pc/Dijkstra.zip
https://lucacardelli.name/Artifacts/Fonts/Mac/Dijkstra.sea.h...
https://lucacardelli.name/Artifacts/Fonts/Dijkstra.pdf
I've been stating it elsewhere, attention has made English into a context aware sensitive programming language by resolving ambiguities through anaphora and coreference resolution. Inform7 did limited anaphora resolution - it - but LLMs are able to resolve "move that file to this folder".
Anyway, my work process is as follows: I have the agent write the code, and describe it's purpose etc. via comments so another session can later pick it up easily without me having to explain everything again. That's what I commit. Sometimes I even look at the code :)
What else could I commit? At the current state of the art, I could commit the whole natural language session that lead to the code, instead of the code. When I later check this out in a fresh environment (without the artifacts the LLM putted out), I'd just load the most recent version of the conversation as an initial prompt. Or I could want to check out the conversation at a point two months ago and rebuild the program from there.
Would that produce a program with exactly the same behavior? I don't know. But if it doesn't, the essay's argument is not moot. Not yet ...
Consider the role of a Business Analyst, in corporate environments. He is the person that talks to stakeholders and users and translates their "natural language" wishes into piles of semi-formal language (UML anyone?). Later on this will become fully formal language.
But I think Dijkstra point is more that we can't fully automate this process and let the machines do the formalization thingy. I used to believe that but I've seen LLMs doing things that really, really surprised me.
In the end I still hold hope in the old saying: "make a something idiot-proof and someone will just make a better idiot". We see LLMs doing magic if they're used by people that know what LLMs are doing. I never saw LLMs doing magic in the hands of idiots.
How many times have we seen that there's been a spec for something and the programmers write something that is exactly like the spec said but was completely wrong?
It is perhaps for the best that Djikstra did not live long enough to witness this problem become orders of magnitude worse.
AFAIU this particular chain is not compromised.
It's excruciating to write, excruciating to read, and even legit geniuses with decades of hard won experience still leave room for essentially infinite interpretation. laws and contracts are all still swiss cheese and can be made to demand or prohibit whatever the party with the most money or other influence wants.
The law is rather a complex set of guidelines which need to leave some space for interpretation, so that additional relevant information for the situation at hand can affect the decision.
What is a sandwich?
Only the legislated part of law is codified. In our American common law system, case law is, by definition, uncodified. So what judges are doing, is taking the codified legislation, and applying case law decisions in layers on top, to reach a final decision.
I could only describe the "look and feel" of the ESP32-algorithm driving a sewing machine: "Snappy but not jerky, linear at low speeds, but accelerates fast".
Antigravity made it perfect but incomprehensible for a hewman.
This argument makes the implicit assumption that humans are uniquely able to convert between this "customer wants a program that solves their problem" level and compileable code. That was a reasonable thing to believe in 1978, but is not a reasonable thing to believe in 2026.