Probably programs as "text that you run on a computer" is, long-term, not how anything is going to be done. After all, what is a prompt but a (lossy, error-prone, inexact) specification for a program, or at least part of a program, before you go in and modify it by hand? The code itself is just an interchange format, no different than JSON. Can we formalize that abstraction such that the prompt is an exact specification, just, at a super high level? AI-text generation makes it faster to write text, but no amount of text-generation gets around the fact that maybe text generation is... not... what we should be doing, actually. And the LLMs are going to be better working at that level also.
I really wish the people geeking out over LLMs would be geeking out over radical new foundational ideas instead. Picture Bret Victor-style re-imaginings of the whole programming experience. (I have loads of ideas myself which I've been trying to find some angle of attack for.) Hard work at improving the world looks like finding radically new approaches to problems, and there are loads of ways to make the world a better place that are being distracted from by the short-term view of working entirely in the existing paradigm.
Because, what does a well-specified formalisation of a problem solution look like? It looks like a programming language.
Since COBOL, the dream has been a language which is formalised for computers while being understandable and able to be written by "business users".
We've been promised this future by COBOL, Visual Basic, SQL, and many others.
And what does the reality look like? It looks like the business users being upset by the fussiness that formalisation adds.
That's why Excel is still king.
Does adding better visual descriptors of program execution really help communicate solutions?
LLMs are actually a great bridge between, "Here's an idea" and "Here's the idea formalised as a set of problem and solution statements".
They're really good at it. Claude Sonnet 4.5 will output a dozen pages of formalised steps for solving a problem that acts as a good bridge between the domain expert and the programmer.
It makes mistakes. It misunderstands things sometimes. Sometimes it understands things better than the programmer or the domain user, such as when it recently corrected me on my understanding on the OAuth2.0 spec, because I was using a non-standard parameter that Cisco Meraki had mistakenly added to their documentation.
LLMs are great (sometimes) for conversational editing where there's a fast, iterative back and forth between description, code, clarification and touch-ups, etc. But trying to avoid code entirely eventually makes everything harder, not easier.
You can get a lot of APL / LISP feeling out of carefully written Python or JavaScript. Not the metaprogramming stuff, sure. But a lot of that in LISP depends on homoiconicity, which is one of the biggest things making the language "not usable by average developers".
Every time people attempted on this they ended up with a programming language that is harder to use than usual programming languages.
SQL was marketed as "fourth-generation programming language."
Two other examples that come to mind: Github Actions, and node-based visual programming.
Maybe (likely) you could come up with a more convenient set of operations, but I don’t really see how expressing that as plain text ast is really holding things back.
In particular, the syntax tree is also Just Another Representation of the functionality... But it's still way overspecified, compared to your intent, since it has lots of implementation details encoded in it. Actually it is the tests that get closer to an exact representation of what you intend (but still, not very close). (This is also why I love React and declarative programming: because it lets me code in a way which is closer to the model of what I intend that I hold in my head. Although still not that close).
So, programming seems similar to the mesh data for a model to me. The more you can get a representation which is faithful to the programming intent, the more powerful you are. LLMs demonstrate that natural language sorta does this.. But not really, or at least, not when the 'compiler' is a stochastic parrot. On the flip side it gets you part of the way and then you can iterate from there by other methods.
That's another programming language. And if you think of an even higher level, where you e.g. specify, that you want to get data from a server in a format, and leave it to some other specification how to do that, that is called a program/library. You get that for free from e.g. Github or your OS repos.
I have some ideas about how to do this, and I think the basic building block is something like "a partially specified object". For example, suppose you want to write a CLI application. The normal way to do this is write a bunch of, say, Python code that processes the args, does the thing, writes the outputs, prints the help, etc. I think there is a different architecture where you say:
1. I want a CLI application. Okay, this sets up a scaffold with all the parts unfilled out. It doesn't "compile" at the moment but you can still run it and inspect it using something akin to devtools / a visual inspector/debugger, kinda like a level editor in a game engine's authoring tools (picture Unity's editor).
2. next I fill in the commands that I want to run. These have a type (provided by the parent application), which is a union of all the things that that a CLI application can do (flags, args, etc). the CLI library translates these to function calls that don't do anything yet. Now I can call all the commands, print the help, etc, but they don't do anything.
3. Next I implement all the commands, etc.
Now this is all isomorphic to writing a bunch of files of code. But my feeling is that you want to be writing the code by authoring the data structures directly. You have a pre-existing type for "CLI application", and you fill in all the slots. Each change is a diff at the level of the data structure. There's some internal code for how you do the things, but those are calls out into functions that are potentially in another language. Diffs and etc are at the level of the data structure itself, not the underlying code (or rather: the underlying data is code, but the visualization of the diff is in terms of how the data structure changed, not how the code changed).
The goal is then to find a way to generalize this method of authoring across all software, to do it very generically so that your "IDE" is a tool for visualizing abstract applications, of which common architectures (CLI app, library, GUI application, server, website) are just particular libraries, and in which the compilation process turns the thing you're authoring into an actual application in an existing programming language (which is now treated as a data format, like assembly code, rather than the actual language you author things in).
... And you want a representation of that abstraction to persist on disk?
... But it shouldn't be "text"?
Why not?
And how will you communicate it? You want to prompt in lossy, error-prone, inexact text, and then trust that an opaque binary blob correctly represents the formalization of what you meant? Or go through feedback cycles of trying to correct it with more prompting, but without the ability to edit manually?
> but no amount of text-generation gets around the fact that maybe text generation is... not... what we should be doing, actually.
Well, sure. But that isn't a problem with text; it's a problem with boilerplate in our designs.
> I mean, for at least the last 3 or 4 decades, one of the biggest impediments we had regarding fast and reliable software development were poor requirements and poor architectural design. Human software developers had the same problems with poorly designed and documented requirements and architecture for the last decades, but nobody cared. Instead, the only complaint was that writing software would take too long.
Having spent some of my career as an SRE, I would argue that what distinguishes production code from it-worked-on-my-machine code has very little to do with the things at the start of that list and almost everything with the "bolts and screws needed to make the software production-ready", and particularly the 12 factors [0]. With my anecdata, I've had a much more productive time maintaining in production systems written by junior coders who only started using that particular language a few months ago, but are eager to take direction, than experienced developers with profound knowledge and strong opinions held tightly.
With this in mind, I've been quite productive doing "vibe engineering" [1], rigidly controlling the code from the outside as mostly a black box, with extensive precommit testing, focusing my code reviews on preventing weird "going off the rails" issues and adding new tests against them, while not worrying at all about code style.
I don't know the answer to that. But it's an interesting point that's buried in the article is that companies like to shortchange that part of the process, and it's that part of the process that is the most important to getting good code out of an LLM. I suppose part of the problem with using LLMs is that the providers have a vested interest in collecting fees that are barely less than the fully-loaded cost of the development staff.
So it'll be interesting to see if some companies find themselves ratcheting up on the documentation, and then revisiting the need for the LLM when the LLM pricing rises to the maximum the market can support.
Often it feels more efficient to take shorter steps, try something in code, we how it looks, update docs, show a demo, etc. That was true before LLMs and I think LLMs make it even more true.
This is not in any way similar to what humans do.
