Plus SQL, 4GL and a few others that came out over the years :)

Interesting Read.

Yes exactly. LLM-assisted coding is just the next layer of abstraction. This has been happening to computer programming for decades. We've always been in search of a way to program in "natural language"

Nope; the compiler was supposed to end coding; when the word coding referred to taking an abstract program written on paper and translating it to machine code.

Compilers were first called automatic coders.

The word compiling referred to something more like linking: producing an image containing multiple library routines. In other words, similar to compiling an anthology of works into a book.

After compilers, the word coding to changed to writing program code in any language.

Because the tooling for automatic coding also compiled the bits of code into a larger image, the term compiler/compiling came to be used for the coding part also.

While the terms reshuffled, the concrete reality was simply that people worked less in assembly language, not that fewer people worked or that programming ended.

This is similar disruption that terminals brought. Before terminals, programming was done in the computer room, it was social gathering, many programmers were women, people were debugging printouts on the floor, punching cards together. Then terminals came and everybody crawled into their dark solitary offices, staring at green or yellow phosphor screens of their terminals. Women didn't like it and left.

> It’s 7:40 on a Tuesday morning in 2031, and you haven’t written a line of code yet. You probably won’t today, and that’s fine.

This sounds exactly like all my Tuesday mornings this year, except that I wasn't at work at 7:40

Can we please stop comparing AI with a compiler?

A compiler takes a programming language as input

float x = 3.1;

Has only one translation into assembly. The programming language is well defined (or it should be). If you try to do

float x = '3.1'; you will get an error

However, the human language is not a programming language. It's open to interpretation "create a variable x and assign it 3.1" is not a well defined programming instruction. Is x a number or a string? If it's a number, what's the precision. Etc.

    In [1]: def f(x: int) -> float:
       ...:     return f"the input was {x}"
    In [2]: f(b'deadbeef')
    Out[2]: "the input was b'deadbeef'"

>The compiler was supposed to end programming too

Bad analogy. The case with AI is not the same.

Except, the lived experience today is that I don’t need to program to do my job anymore, and I say this as an engineer with almost 30 years of experience.

If you can't see the difference between a compiler and an LLM then that's on you.

LLMs are a game changer in a completely different way than compilers. With compilers there is a (roughly, barring optimizations) 1:1 correspondence between input and output. With LLMs you take an idea and it will drive the whole thing from the beginning all the way to the end.

This is unlike anything that we've done so far. It's also strange to focus on programming: LLMs are coming for all information related jobs. If your job is somehow described as 'staring at a screen' then that job is on the line, if not sooner, than later.

We do pretty much just use compilers now though. The last time I read dissassembly to fix an issue was at least a decade ago. Even bytecode I rarely look at.

So you're saying that we'll still be coding, but it will be through talking to our agents and only occasionally we will look at generated source? That seems like a pretty big change still.

Agents are more like employees, compilers are more like calculators.

I have a pretty negative opinion about LLM's coding abilities (and predictions about how they will improve). But I don't get a lot out of this type of argument. Compilers created a perfect, deterministic, provable abstraction so that we could have 99% of developers no longer know how assembly works and not suffer any consequences. Humans don't have to give up any control or make any quality compromises to deploy them as widely as possible.

If the author is contending that AI models will improve to a point that they actually make no mistakes while coding, and have that same level of reliability, then they're on exactly the same side as AI doom trolls just wearing a different costume.

> In 1954, IBM said FORTRAN would virtually eliminate coding and debugging.

Only because Fortran was viewed as transcribing mathematical notation, which wasn’t considered coding. Coding originally referred to having to work with numeric machine codes. Programming languages like Fortran removed the need for having to deal with how to encode the program for a specific machine, and with human errors in that encoding process (debugging). It didn’t mean abandoning formal specifications.