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.

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.

seems to work just fine so far :-)

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.

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.

Possibly relevant:

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

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.

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.

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.

> 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.

>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.

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.

I am surprised that cs.utexas.edu uses a TLS certificate chain who's root is eMudhra's "emSign Root TLS CA - G1".

There’s em dashes in that 1978 article.

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

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?

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...

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?

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.

> (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.

A really interesting read in the current era.

Inform7 disagrees; altough you can use Inform6 which is like a really simple OOP language to create text adventures.

Another article on this topic https://alexalejandre.com/programming/no-end-of-programming-...

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.

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