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.
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.
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.
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.
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'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.
> 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.
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.
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.
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."
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...
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
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.
> (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.
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.
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.
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.
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.
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.
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.
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'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.
> 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.
The lengths to which devs will go not to collaborate.
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.
> Magnitudes more data taken in than any language model.
Ah, yes, I can fondly recall reading and internalizing every word available on the internet when I was young...
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)
A picture is worth a thousand words. How much video data was your brain trained on?
Only if you ignore the billion years of pretraining…
Yep, LLMs work to the degree you don’t care shout the choices made. That’s why it’s great for demos and starter templates.
But for mature software eventually every single detail matters.
After reading this essay, I wonder: what if program code was withheld from training data? Then how useful would LLMs be for programming?
> 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.
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.
There’s em dashes in that 1978 article.
The typed original [1] has double hyphens that have been converted to dashes. His typewriter (Hermes Ambassador) was fixed pitch.
[1] https://www.cs.utexas.edu/~EWD/ewd06xx/EWD667.PDF
Oh look at the last paragraph on the first page. Manually justified:
How does one even achieve that with a manual typewriter?It's coincidence in this case. Proportional typewriters were made but this wasn't one of them.
Wasnt the original famously in written with Fountain pen? And the typed out version reproduced much later?
He typed them until 1979 when his typewriter stopped working correctly.
Well - in this case the EWD in TFA - has a footer that says.
And so seem to be the case with most of the other EWDs I looked at as well.His namesake algo was also written entirely in his head in about 20 minutes at a coffee shop! https://harlepengren.com/dijkstras-algorithm-20-minutes/
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.
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
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.
It can only mean one thing.
Get out the Binford 6000, it's Tool Time?
Proof that Djikstra was an AI model!
Literally stop the presses
Unreadable.
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."
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...
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
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.
Another article on this topic https://alexalejandre.com/programming/no-end-of-programming-...
> (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.
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.
A really interesting read in the current era.
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.
Inform7 disagrees; altough you can use Inform6 which is like a really simple OOP language to create text adventures.
I appreciate inform7 is quite human-readable, but it's not free form.