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.
I send this article to everyone who tells me how English is now a high-level programming language, the next step 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 rare".
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.
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.
> 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.
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 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.
> 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.
> 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.
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.
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
> (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.
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.
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.
I send this article to everyone who tells me how English is now a high-level programming language, the next step 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 rare".
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.
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.
> 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.
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 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.
> 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.
> 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...
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?
[delayed]
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
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.
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?
Literally stop the presses
Unreadable.
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
> (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.
Inform7 disagrees; altough you can use Inform6 which is like a really simple OOP language to create text adventures.
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.
A really interesting read in the current era.