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.
Only for those not up to date with modern compilers and language runtimes.
The 1:1 is long gone, that is the UNIX K&R C days.
Modern compilers are full of heuristics, PGO driven optimizations, machine learning compiler passes, JIT compilers, and various kinds of GC implementations, the time of 1:1 mappings is long gone.
I agree. Programming was just a natural first problem to tackle, but the impact will not be limited there. If you give today's best model access to all the data sources and systems an average white collar worker has, it probably could do most of their job. But organizational change takes its time, systems need to be integrated into new AI tools and so on, so it will take a while.
People tend to overestimate change in short term, and underestimate it in long term. AI hasn't replaced programmers yet, but I believe it will replace most white collar workers in 20 years.
1) that LLMs can do existing white collar work well once some (possibly significant amount of) work is done to fully plug them in and get specifications settled.
2) That all white collar workers will be replaced by LLMs.
Perhaps a typo, but I don't think (1) implies (2).
You can absolutely spot people that aren’t using the latest models personally because they succeed in clinging to notions that doing so would dispel in three seconds.
Opus 5.5 produces code far better than 95% of professional devs, and it only stands to get “worse” from here on out.
Quite where this leaves any of the people in these information processing jobs is anyone’s guess.
Opus makes boneheaded mistakes and engages in severe anti-patterns constantly. I use it, but it requires careful handholding on complex projects. It absolutely isn’t better than 95% of professional devs, that’s a ridiculous statement.
No it isn't lmao, it's coming for the rote, statistically average part of a job. It can't do the unexpected. While it has turned out that the unexpected is way, way smaller than many presupposed, it remains true. And if it did take over all information jobs, that would be the end of LLM improvement, forever. Hapsburg, model degeneration, whatever.
bro they've been saying this for five years now and the only people affected are copywriters and translators, and for the latter, unwisely. it is absolutely not coming for all white collar jobs. anyone who uses a coding agent sees these things fail in familiar machine misunderstanding ways that make sense to programmer, and in roles where data is not easily captured in text, humans are needed to keep the LLM in touch with reality
when is it going to happen already? OpenAI says Astra is AGI. why hasn't your prediction already come to pass? it's not early, it's late, these companies owe $1.5T. It's time for your side to start explaining why exactly GPT 3 or 4 or 5 or Astra hasn't already caused this in the way it's been predicted over and over by the labs
> 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 to deal with human errors in that encoding process (debugging). It didn’t mean abandoning formal specifications that you can reason about with a high degree of precision.
Yeah, after some thinking I am starting to realize that Linus' take of AI being like compilers is kind of true.
Vibecoding maintainable code requires providing the LLM a specification. It's still coding, but you now have the liberty of just providing the spec as opposed to worrying about syntax. Since they are non deterministic and love to cheat you still need to review though
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.
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.
And it's a false claim. Compilers and assemblers meant eliminating coding, turning your higher level expressions into machine code by hand. And they did exactly that. Very few people, primarily hobbyists or those with a very niche architecture, actually bother to write machine code by hand at this point for any significant amount of work.
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.
"Women didn't like it and left" is certainly an interesting interpretation of the systemic exclusion of women from a field when it transitions from low-prestige manual work to high-prestige intellectual work.
I appreciate the rose-tinted, high-agency historical take, but I think the factors that led to computer science/programming becoming male-dominated were largely external to and independent of the preferences of women themselves.
First, it's a bad comparison. Compilers and LLMs/Agents are not remotely comparable.
Second, the example used for 2031 is already outdated in 2026. There is almost no reason to even use PRs anymore [1, 2], and the example question should be easily answered by an agent that has enough context and planning.
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"
AI generation isn't an abstraction because you have to wrestle with the nitty-grittys of the output.
And that output is what you retain.
You can't clone someone's repository of nothing but prompts and rebuild from scratch.
Abstraction means that I trust the toolchains so much that I think of the source code as the software, and in the right kind of environment/ecosystem such as open source distros, I can actually ship that to the downstream users as my product.
Abstractions are based on very detailed and rigid logical contracts.
It's also an abstraction to hire a software engineer and tell them what kind of app you want, but I don't think we can call it "programming" at that point.
Conversely, the reason agent assisted coding works so well is that all the tooling meant to keep human coders out of trouble, like lint, leak detectors, profiling tools, test harnesses, etc. prevent agents from making errors.
This is in contrast with general purpose agents like Meta Muse that make some amusing and horrifying errors. These kinds of agents don't have the interfaces and guardrails that were built for human coders. They use a lot of ad hoc screen scraping and guessing at what APIs do.
That's also why Apple and Google haven't shipped fully general purpose agents that use their new app tool calling interfaces yet. For example Google only released their Gemini agent that uses AppFunctions to enterprise customers. Presumably because MDM tools can be used to monitor agents' actions.
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.
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.
might a compiler turn "float x = 3.1;" into no instructions at all in some situations? So it has at least 2 possible translations into assembly depending on the context.
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.
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.
The 1:1 is long gone, that is the UNIX K&R C days.
Modern compilers are full of heuristics, PGO driven optimizations, machine learning compiler passes, JIT compilers, and various kinds of GC implementations, the time of 1:1 mappings is long gone.
People tend to overestimate change in short term, and underestimate it in long term. AI hasn't replaced programmers yet, but I believe it will replace most white collar workers in 20 years.
1) that LLMs can do existing white collar work well once some (possibly significant amount of) work is done to fully plug them in and get specifications settled.
2) That all white collar workers will be replaced by LLMs.
Perhaps a typo, but I don't think (1) implies (2).
Opus 5.5 produces code far better than 95% of professional devs, and it only stands to get “worse” from here on out.
Quite where this leaves any of the people in these information processing jobs is anyone’s guess.
Employers typically want their employees to do what’s expected from them. ;)
when is it going to happen already? OpenAI says Astra is AGI. why hasn't your prediction already come to pass? it's not early, it's late, these companies owe $1.5T. It's time for your side to start explaining why exactly GPT 3 or 4 or 5 or Astra hasn't already caused this in the way it's been predicted over and over by the labs
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 to deal with human errors in that encoding process (debugging). It didn’t mean abandoning formal specifications that you can reason about with a high degree of precision.
Vibecoding maintainable code requires providing the LLM a specification. It's still coding, but you now have the liberty of just providing the spec as opposed to worrying about syntax. Since they are non deterministic and love to cheat you still need to review though
This sounds exactly like all my Tuesday mornings this year, except that I wasn't at work at 7:40
I _want_ to write code for my job.
Unfortunately, this is exactly the direction the industry is heading.
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.
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.
Bad analogy. The case with AI is not the same.
I appreciate the rose-tinted, high-agency historical take, but I think the factors that led to computer science/programming becoming male-dominated were largely external to and independent of the preferences of women themselves.
First, it's a bad comparison. Compilers and LLMs/Agents are not remotely comparable.
Second, the example used for 2031 is already outdated in 2026. There is almost no reason to even use PRs anymore [1, 2], and the example question should be easily answered by an agent that has enough context and planning.
1. https://r.rich/blog/ai-native-change-management 2. https://ampcode.com/notes/thats-not-soc-2-compliant
AI generation isn't an abstraction because you have to wrestle with the nitty-grittys of the output.
And that output is what you retain.
You can't clone someone's repository of nothing but prompts and rebuild from scratch.
Abstraction means that I trust the toolchains so much that I think of the source code as the software, and in the right kind of environment/ecosystem such as open source distros, I can actually ship that to the downstream users as my product.
Abstractions are based on very detailed and rigid logical contracts.
This is in contrast with general purpose agents like Meta Muse that make some amusing and horrifying errors. These kinds of agents don't have the interfaces and guardrails that were built for human coders. They use a lot of ad hoc screen scraping and guessing at what APIs do.
That's also why Apple and Google haven't shipped fully general purpose agents that use their new app tool calling interfaces yet. For example Google only released their Gemini agent that uses AppFunctions to enterprise customers. Presumably because MDM tools can be used to monitor agents' actions.
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.
Interesting Read.
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.