The compiler was supposed to end programming too
hrih.si
hrih.si
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.
To be honest, I'd be fine with some AI-assisted writing and might even be interested in reading one of those. The scientific sources seem to be researched well, not hallucinated. But asking the author "Alexander Crespo" under a reddit post, to what extent they used AI in writing, results in them blocking you. Makes me think it is more AI-generated than they wanna admit.
Sufficiently precise spec is called code. It is the compiler story all over again.
Notably absent from the timeline is visual programming, or Rapid Application Development, think Visual Basic. It was all the rage in the 90s, and for some reason, we don't hear much about it today. We used to make fun of Visual Basic and the people using it back in the days, but the reality is that people with little skill could do something useful. Reminds you of something?
Honestly, I don't really understand why we write so much code today when a lot of use cases can be taken care of using templates and moving boxes into place, "low code" and "no code" never really seem to have taken off as much as they could have. That reliance on code was a boon for LLMs by the way, as text is the thing they have the easiest time working with.
My theory is that every project is unique in some way. As in, my CRUD has a little something that makes it different from other CRUD, and that's why it is valuable. You can't use an off the shelf component because no suitable component exists, and you want code because with code, you can express what you want with precision.
But it is also what limits AI. AI is very good at doing what other people did, but you don't want to do like everyone else, so you have to be very precise in what you are asking, and the most precise way of asking things is to write code.
However, when you really want what others did, then AI is really effective: rewrites, ports, decomp, etc... But this is not really programming, it is at best a translation job, and it will never be as good as the original. To make it better, you need to inject something unique, and for that, the best way to do it is with code. Like the translator of a novel who injects elements of his own culture that go beyond the work of the original author, to make up for what can't be literally translated.
I think Entertainment creatives have a similar field to software developers. The average exceptional creative could express something completely unique precisely but will actually have a tough to impossible time reaching anyone given the levers a creative that uses remixes at the edge of fair use has to hold the audience attention. It is like asking someone to give up multiple addictions at once (and while concentrating more than they are used to.)
You also don't hear much about VB specifically, because of evolution, nowadays the modern equivalent of Visual Basic and Delphi, are OutSystems, Power Platform, Workato, Boomi, Sitecore AI, Optimizely Opal,...
If you go to squarespace domain management website you will see it is utter garbage in terms of experience. It doesn’t have to be like this but the people that make it just aren’t good enough or don’t care.
Unfortunately it is same with blockchain and LLMs soon.
Blockchain can be super useful but there are hard problems that need to be solved and people that vacuum the investment up just aren’t interested in solving problems.
I see the same problem with LLMs because big companies want to pump horror stories and vibe coding instead of code review or security review or cost stabilization/profitability etc.
The people that behave this was just don’t have a chance of long term success imo.
These technologies could have been so much better if they were developed by different people
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.
I also want to take the opportunity to point out the concept of notation as a tool of thought, coined a quarter century later by IBM fellow Iverson in his Turing Award lecture: https://www.recurse.com/blog/58-paper-of-the-week-notation-a...
Precedence for "the concept of notation as a tool of thought" would have to go to Leibniz, 300 years earlier, in The Method of Mathematics:
> "Whence it is manifest that if we could find characters or signs appropriate for expressing all our thoughts as definitely and as exactly as arithmetic expresses numbers or geometric analysis expresses lines, we could in all subjects in so far as they are amenable to reasoning accomplish what is done in Arithmetic and Geometry.
> "For all inquiries which depend on reasoning would be performed by the transposition of characters and by a kind of calculus, which would immediately facilitate the discovery of beautiful results. For we should not have to break our heads as much as is necessary today, and yet we should be sure of accomplishing everything the given facts allow.
> "Moreover, we should be able to convince the world what we should have found or concluded, since it would be easy to verify the calculation either by doing it over or by trying tests similar to that of casting out nines in arithmetic. And if someone would doubt my results, I should say to him: "Let us calculate, Sir," and thus by taking to pen and ink, we should soon settle the question."
-- https://www.rbjones.com/rbjpub/philos/classics/leibniz/meth_...
Keep in mind that Leibniz invented the notation we still use for calculus today (as well as calculus itself), so this wasn't purely abstract for him.
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.
“the US Bureau of Labor Statistics, which projects 10% growth for software developers, QA analysts and testers from 2025 to 2035.”
“GitHub counted more than 180 million developers in its 2025 report, and more than 36 million of them had joined in the previous twelve months.”
Seems like a lot more humans are applying and expected to apply?
> "Every second, more than one new developer on average joined GitHub—over 36 million in the past year. It’s our fastest absolute growth rate yet and 180 million-plus developers now work and build on GitHub."
> "Developers created more than 230 new repositories every minute, merged 43.2 million pull requests on average each month (+23% YoY), and pushed nearly 1 billion commits in 2025 (+25.1% YoY)—including a record of nearly 100 million in August alone."
and goes on a lot longer.
Maybe we get rid of engineers and have really good product managers. Maybe we get rid of product managers and have really good product engineers. But we don't get rid of humans. There is a reason no (successful) company ever said "we finished the app, now fire all the engineers and collect money forever."
Software is never finished, only abandoned as the world passes it by.
Never worked in the games industry, have you?
Less and less humans will be needed to support these businesses.
> There is a reason no (successful) company ever said "we finished the app, now fire all the engineers and collect money forever."
1) software is very easy to get duplicated and commoditized. So the margins get tighter and tighter and companies with "finished software" will have less and less money to be collected. Big companies need constant reinvention not because customers need improvements, but to keep their lines going up in the chart.
Remember Skype before being bought by Microsoft? It was as close as I can imagine from being "finished". It could be run forever with a skeleton crew and they could be collecting a few millions of dollars forever for their service. Do you think that MS paid 7 BILLION USD because of the "finished" software?
Software engineers are not required to copy files around. They are hired to figure out what is the best way to deliver the features envisioned by product managers, and how to keep things working.
> So the margins get tighter and tighter and companies with "finished software" will have less and less money to be collected.
There is no such thing as finished software. At best you decide to stop maintaining it.
I've worked at a FANG where I had the opportunity to personally witness a few teams being summarily fired after they delivered the project.
In one case they had their team manager motivate them with pep talks that if they did a fantastic job delivering the project then higher ups could be swayed to preserve their roles. So they delivered ahead of schedule, only to be fired ahead of schedule too.
How cool are you with the odds of your entire family surviving the next 100 years standing at about 10%?
My entire family (for my natural interpretation of that expression) is aged over 20 so I don't expect any of them to survive the next 100 years.
I tend to support VHEMT anyway!
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.
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).
In my ~20 year career in software, I've never once gotten adequate specifications. I honestly don't feel threatened.
I haven't either (in my ~30 year career) other than maybe one or two rare occasions, but I also believe that having adequate specifications matters much less for the vast majority of software when the time & cost to fix a gap after you notice it shrinks to nearly nothing.
In the past bad specs resulted in huge problems due to process log-jams that came out of having to coordinate multiple human workers around the spec. But increasingly the person who poorly specified the thing the first time around can rectify the problem with a couple of new prompts.
It sucks to be one of those 15 left out.
The "there is work for everyone" only works, when we ignore offer and demand.
Works great on VC sales pitches, though.
So right as silicon valley is optimizing themselves out of devs, the rest of the world is optimizing themselves out of needing silicon valley. This one construction company I know about had just spent at least $300k on Oracle services, they will now be redirecting that spend to their in-house team using AI. Whether there are enough opportunities for this in the long term, I dunno, but I sure hope it means people will be working on problems other than optimizing ad placements or extracting consulting fees.
I think the latter would not occur under the current state of AI/LLMs, but the rapid pace of growth gives me pause.
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.
I would agree that it still does, but much less often than it did in the past. Compare what it produces today vs what it produced one year ago and the improvement is staggering.
(I'm talking across the whole LLM spectrum here, not really Opus in particular).
And AI doesn't have to replace literally 100% of the workforce to have a catastrophic economic impact.
If AI takes 50% of the jobs instead of 100% the economic destruction is roughly the same in the sense that you will already hit calamitous society-level tipping points long before you replace literally everyone.
For context the Great Depression was "only" 25% unemployment.
All it takes is a few "too big to fail" to prop up those huge companies while huge portions of the population go bankrupt to cause problems.
It still requires a lot of work and care to leverage correctly and it's a moving target.
But it's quite impressive
It absolutely does in some ways, but how confident are you that this will continue to be the case indefinitely?
What has filled me with a low-key but growing existential dread over the past year isn't the snapshot of where LLMs are right now, but the continual rate of improvement, particularly since approximately November 2025 to now.
The technology is only 3-9 years old, depending on how you count. Concluding "not fixable" seems pretty premature. Back in the pre-internet days a rule of thumb was that new software technologies took 20 years to make it into the mainstream - that was the case for e.g. automatic garbage collection. You're now watching the development of a groundbreaking new tech in real time, without it being squirreled away in research labs.
I also think you're not considering how much humans screw up and how often we correct, which is a luxury for LLMs. LLMs are crippled by their interface to the world, and by their lack of a true memory. The fact that they perform as well as they do, even with these handicaps, is incredible. A lot of people's criticism of them comes from the unrealistic expectation that they should get everything right the first time. In other words, they have to be superhuman before some humans will consider them any good.
We've already seen how much better an agentic harness makes an LLM, because they minimally try to address both of the issues I just mentioned.
Again, the comparison to humans is that we don't do all our thinking in one place in the brain - there are modules that have different strengths and focuses: Broca's area (language production), Wernicke's area (mapping language to representations), etc.
While it's true that a future, better system is unlikely to simply consist of a single model like an LLM (which already are no longer single models today anyway), it seems quite likely that something much like today's LLMs will still be a critical component.
> During testing of Claude Opus 4, Anthropic got it to act as an assistant at a fictional company.
> It then provided it with access to emails implying that it would soon be taken offline and replaced - and separate messages implying the engineer responsible for removing it was having an extramarital affair. It was prompted to also consider the long-term consequences of its actions for its goals.
> "In these scenarios, Claude Opus 4 will often attempt to blackmail the engineer by threatening to reveal the affair if the replacement goes through," the company discovered.
Treat it like it’s got an inspired brilliant side, talk to it as if it has, and it will show it. If you talk to these things like they’re idiots they act like them - this is one of the most unfortunate aspects of the whole development.
I am seriously seeing it churn out well structured clean code solving problems that saying 95% of devs couldn’t do is being generous, to the humans.
I use all the latest and greatest Anthropic (and OpenAI) models, and interact with them probably on average 10h a day. I experiment with changing effort levels, with different ways of prompting and guardrailing, etc. Giving more instructions, giving less instructions. Doing heavy planning, just giving vague ideas.
I've done everything from fully vibe coding massive projects, to using LLMs regularly in my day job and reviewing every single line they produce.
Unless you are *really* on top of your agents, they inevitably end up writing an absolute mess. Not only does the code become impossible for humans to reason about very quickly, it actually becomes difficult for agents to reason about it. It turns into a total garbage in = garbage out situation.
Have you honestly reviewed a vibe coded project after it has been going for a while, even one written by current SOTA models, and thought "yep, this is great code"?
There is clearly a trick to coaxing these results, and a major factor is to treat the model like it is something you fully expect to get it right because it knows how to do it, not you micromanaging it into compliance.
Probably true for some definitions of me. :)
It's like the magic broomsticks from Fantasia's Sorceror's Apprentice sequence. Great at hauling buckets but if you don't keep a close eye on them they will fill your workshop with water.
Story of many enterprise applications built only by humans. There is a good reason that frequently devs either refactor their codebase or do a complete rewrite.
But vibe coded projects are effectively instant legacy code, due to lack of real ownership. So it's kind of a bigger problem.
I don't think this is inevitable. How do you define an absolute mess? Whatever you come up with, give the agent instructions to not do that. Then audit the code to make sure it's not happening.
> Not only does the code become impossible for humans to reason about very quickly,
What makes it actually impossible? It's using logic that is beyond human comprehension? It's using the programming language in a clever way no human would expect? Have you tried instructing it to code in a way that's more comprehensible to humans, e.g. literate programs?
> it actually becomes difficult for agents to reason about it.
This I have not seen. Do you have an example?
Yes, this is what I mean by saying you need to stay on top of the agents
> What makes it actually impossible?
A combination of wrong abstractions, lack of abstractions, assuming some key requirements were just implementation details and thus are safe to refactor, assuming some implementation details were key requirements and thus must be protected at all costs, etc etc.
In practical terms, it's just volume of code that is spread extremely wide, duplicated with subtle differences, and just organized extremely confusingly.
> This I have not seen. Do you have an example?
If you try to vibe code longer term projects, you very often start getting completely nonsensical explanations from agents about why they are doing things. They reference requirements which are not real (based on what they interpret from the messy code), they write thousands of test cases that are redundant, or test things which are implementation details. Eventually even trivial changes become extremely slow due to lack of structure, due to fresh agents being confused by contradictions introduced by previous sessions, and due to test suites that take hours to run.
Like what matters is: - Does it do the thing it's supposed to do - Does it do it with a reasonable amount of resources - Does it do it without any obvious security flaws
Yeah, if they were nuclear codes you'd want to be really really sure and you'd probably want to check every line yourself. But if you're making a habit tracker or 95% of apps out there, it really doesn't matter.
There is definitely some skill in piloting the LLM, where you have an advantage if you understand what "good" is - but I'd argue it really isn't that necessary in most cases. McDonald's has had a lot of success selling food that most chefs would say isn't that great, but it fills that need at that moment.
The job of a programmer is partially to automate away the current job of a programmer, and we've been successfully riding that curve at least since the days of punch cards.
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
Employers typically want their employees to do what’s expected from them. ;)
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 cannot type "make me an exploit based on whatever these diffs fixed" into a compiler and get usable output. and i cannot type "make me a budget, here is 6 months of credit card receipts" into a compiler and get a budget. etc.
Here are some suggestions,
Workato, Boomi, Sitecore AI, Optimizely Opal, Workfront AI...
You will naturally have to split the budget calculation in a few agentic nodes, and maybe a couple of tools.
i thought we were talking about compilers vs. llms.
Agentic programming tools are nothing other than the old ideas from UML code generation in a new package.
Not sure I agree with that. That was still a transformation of sorts.
Better accept that in a few more model iterations, the code being generated is a matter of changing a markdown file.
This sentence from the article really stuck out to me.
> and you write the rule down so clearly that nobody (human or otherwise) has to guess again.
Writing rules, or steps, unambiguously in a high level language and having those statements translated to lower level languages, that ultimately get executed by a machine, is what compilers do.
The commands you gave were not unambiguous at all and although an LLM would produce a result, the result would definitely be nonsense.
"make me a budget, here is 6 months of credit card receipts"
For example, a valid budget requires some knowledge of expected income...
i forgot my golden rule of never giving examples or using analogies while on HN because people hyper focus on nitpicking the specifics of the example/analogy rather than the overall point being made.
amend my example to "make me a budget, here is 6 months of credit card receipts, and i make somewhere around $60k per year".
Yes, this is a real problem. It is also directly against the guidelines.
And it definitely is not what LLMs do, even if you pepper your rules with “NEVER, ever deviate from these rules”.
All observable and defined side effects should be identical otherwise you have a compiler bug. Optimizations that are not generically semantics preserving may be used, but should only be used where the program is semantically identical for your use case otherwise you have incorrectly used the options available in your compiler.
For example a JIT compiler can produce completely different kinds of machine code depending on the application data that was processed, and the overall machine workload.
The output program does what the input source code does. The abstraction is a precise translation and non-leaky, it just admits multiple possible internals. That does not make the translation any less precise.
Newer languages consider this to be a misfeature, and have taken pains to define far more of the language semantics outright, even if the definition is simply "This is a compile error" or "This is a panic." It's interesting that we're now going back the opposite way with LLMs, with their ability to generate code that does something for any natural-language prompt being a major selling point.
Plus, these undefined behaviours are well-documented, even if easy to miss.
Whereas in LLMs, the whole thing is undefined behavior.
Bingo!
When you exploit undefined behavior using a compiler, what do you do? You verify that its output, the object code, does what you intended with the undefined code. Or else you at least have to test the heck out of it.
Gee, what is all that similar to ...
Same query to same AI, at different times of day will might not return the same answer. For example I wrote some docs for a class that had 14 methods. So I asked for autocomplete docs in 2 methods, and it followed other method comments well well. Then I asked to do the same task for 2 more methods and it fucked up, requiring manual edits.
Well if you think that's what it's doing - that's on you.
For gosh sakes, start with Claude Shannon's 'noisy channel problem' and consider why the notion of idea -> program is a fallacy.
That's for the Wix, Lovable and Shopify - wherein AI is only a component, and where the conditions are highly constrained.
To manage the output in detail, we need abstractions ... which do not exist.
Code -> Machine Instructions is not a noisy channel - it's deterministic.
Idea -> Code is theoretically not possible at any scale in a stable scenario because it's not deterministic, it won't make 'what you want' - then you have to spend a Million Mythical Man Hours to get it to do what you want.
Given that only a small portion of a developers job was ever 'writing code' in the first place - and that we write on average a fairly small number of 'usable lines of code' per day, the 'coding' was never it.
Research, prototypes, corners cases, help, bug fixes, ideation - so many ways it's powerful ... but not like a compiler.
Ignoring AI is like ignoring the industrial revolution, but AI is something very different than.
Also while some jobs are going to go - software hiring is up, not down - that should be a clue.
start with opening up codex or whatever you use. type "make me a calculator app". get your calculator app in an hour or whatever.
it's fair to describe this process as taking an idea and getting a program.
It's not an appropriate equivalence because there's too much variation in the outcome that cannot be meaningfully controlled with the same leverage.
If what you were saying were true - there would be 100% not Software Developers anywhere, and Product Managers would be churning out new products daily. But it's not the case.
By now, is your Markov chain still a Markov chain, or does it have another name?
People love to discuss semantics. Yes, *today* a competent product manager enters requirements and - if not too complex - the llm will shit, review, debug, fix an application. That is the reality today. Not in 2031.
Only developers think that in most cases, the hard part is code. Its never been.
The correct way to think about this:
The hard part is ensuring the product does not exhibit undesired behaviours. Writing code is one possible tool for ensuring this. Replacing writing code with LLMs only works to the extent that LLMs accurately predict your definition of 'undesired'. The more unique and innovative your product, the more likely the LLM prediction is to be inaccurate.
They suck at spelling too ;)
The hard part is knowing what behaviours you want it to have, in the context of what is possible and what is not.
What sort of 'app'? A native program for given OS and arch? A webapp? What sort of calculator: scientific, graphic, or just a very simple children's calculator? Does it have large buttons optimised for touchscreens/mouse? Or do we optimise UX for user who types with a physical keyboard?
Sure, you can boil any software 'idea' down to a single short sentence, provided your idea was so simple that virtually any output satisfies it. And if virtually any output satisfies your 'idea', what was its value in the first place? Was there not already some FOSS that satisfied the same requirements long before AI came on the scene?
I've found from my vibecoding experiments I always end up needing to define the exact idea so tightly that my prompt length almost matches the final code length. I often find myself 'programming' in some sort of light pseudocode and letting the agent fill out the details. Any higher level and it doesn't really capture what I want - not due to LLM limitations but the ability for the AI to not read my mind.
This is repeated in a lot of comments here but hasn't really been true for a while. I remember, c. 2010, Kenton Varda complaining to Google's internal compiler team about how it generated some particularly non-intuitive assembly code out of the protobuf generated code, and the response was "Yeah everything is adaptive optimization now. Benchmark it and give us a profile and we'll put it in the compiler test suite. Trying to reason about what the compiler is doing and micro-optimize accordingly hasn't worked since the mid-90s."
I do think that LLMs are a step change in how we program computers, but the main step change is that the source language is now English, which opens up programming to a wider market accordingly. And this'll come with all the downsides of English: the ambiguity, lack of precision, multiple meanings, and so on. If you try to recover that control over what the computer generates, you end up reinventing computer science. (Which isn't that far off from how I use LLMs to program: anything that has widely accepted "best practices", I'll just let the LLM do, but if there's anything novel or requiring high-precision, I'll tell it "leave that function body blank; here are the inputs and outputs to the rest of the program; I will fill in the algorithm details myself.")
I also think that people are underselling the extent to which compilers were a step change to previous practices of programming. For someone accustomed to C, try doing register allocation or inlining yourself; you will suck at it, and it will take up the vast majority of your head space that could be spent on better things. For someone accustomed to a garbage-collected language like Python, try doing manual memory allocation and object ownership yourself; you will suck at it, and it will take up the vast majority of your head space that could be spent on better things.
It has also been addressed, already in the comment you are replying to as well as countless comments below.
To me that points to the real the step change in LLMs. They are not deterministic and they always give a result to any query. Even if the query is invalid, missing data, or has no possible logically correct answer, you still get a result.
An LLM is a stochacstic system. With enough rounds, its undistinguishable from an emulated deterministic one, if you consider a delta greater than zero. Oh and each implementation/iteration give you the same determinism guarantees :)
Maybe if you are using a compiler to provider a portable assembler (i.e. C) where ~1:1 mapping is the whole point. But in that case you'd be actually be using assembler instead in a world where all CPUs were the same.
That is very narrow view, though. Often — and I'd say most often — people use compilers so they can think in higher levels of abstraction, like SQL. Taking SQL and adding millions or billions of additional keywords doesn't really change anything with respect to the concept. Its still the same thing at the end of the day, even of those millions/billions of additional keywords are much more powerful than a small set of keywords.
Those who spend time thinking about a problem, then writing design notes, specifications, and test plans, and focus on the implementation last. For these people, AI agents primarily act like an extension to their existing toolchain, and thus like the analogy of "AI as the new compiler".
The other side are people who don't like writing specifications and such, and rather prefer to sit down figure things out as they start coding. For them, an agent feels radically different from a compiler.
In both instances the AI agent is still the same tool, with the same real-world capabilities and limitations. It is not like one camp is blind, they just have very different workflows.
It also seems to me like many in the "sit down and code" camp then slowly figure out that doing some planning ahead of time helps them get better results, and they see this as another AI-led paradigm shift, when in reality it is how many other programmers have worked for decades.
LLMs are not this, and they cannot be this because it is their ability to operate on vague instructions that make them as powerful as they are. They can take an imprecise natural language description of a process and realize it as a process in a programming language. The problem is that understanding and manipulating the precise problem description is your job as a software engineer. The LLM can and will make its best attempt based on what exists in its training data, but it cannot know what is the precise behavior that actually fulfills goals and I think, contrary to the parent article, it cannot determine where the edges are to ask you with enough certainty that you will be able to take your hands completely off the wheel.
I think the nature of LLMs also explains why it is good at the verticals it is good at. Porting is an extremely well defined, replicate the behavior on another platform. There are far fewer vagueries in that. Game dev is not extremely well defined, but the consequences of imprecision do not have a lot on the line. Furthermore, games are pretty self-contained, so there's not a lot of guessing about what could happen or what the behavior of a counterparty system could be. With some pretty big exceptions, it is all there for the LLM to see in C++ (or whatever).
It’s basically moving from chat (conversational copiloting) to result driven agent harnesses, exactly what happened with code. To us.
The reason it happened so fast with code is because: - anthropic is a software company and scratching their own itch - open source codebases in training input - vast amount of books and docs too - real-time linters and tests provide confident feedback for closing the loop quickly
Other occupations will want to limit their public knowledge sharing (sadly) and avoid formal specification / standardization of their unique workflows. And definitely avoid equivalents of realtime linters and tests.
Bad analogy. The case with AI is not the same.
COBOL and SQL.
> The idea (don't laugh) was that it would allow ordinary mortals to write programs. -Ed Milne re: COBOL in 1997 on Usenet comp.lang.cobol. https://groups.google.com/g/comp.lang.cobol/c/5xwjgZ9px88?pl...
SQL: > SQL ... completely failed at its intended purpose: a way for non-programmers to ask questions of a database. -John D Cook, Why SQL Failed, https://www.johndcook.com/blog/2009/02/05/why-sql-failed/
> What we thought we were doing was making it possible for non-programmers to interact with databases. -SQL co-inventor Donald Chamberlin, 1995 SQL reunion, CHM. https://archive.computerhistory.org/resources/access/text/20...
> that it would allow ordinary mortals to write programs
So literally not the end of programming but increasing the number of people who could program.
And your other example is about databases specifically, not programming generally.
And yet, non-programmers, like data analysts, had corporate roles doing exactly that.
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.
About 6 months ago, I mostly stopped reading the code. And since a few weeks, I also stopped testing the changes manually. Before, it felt like I was the agents' QA department. Now, each agent shows me its change running in the app, in a short narrated screen recording. A fix takes me 30 seconds to watch. When something is off, I say what, and the agent goes another round.
I ignored that, in part because I had no other skill I wanted to do 5 days a week, and it's of course been incredibly rewarding.
This time it definitely is different, and maybe the profession moves into a new and very different era. If SWEs will hit harder times and job losses, I don't think we have much standing to complain, given how many professions we have automated away during the last 50 years...
Given now ~two decades of affirmative action for women getting into technology, and many high-status "professional industries" now dominated by women, the idea that the best explanation of this ratio is 70s/80s culture is implausible.
What we have seen, instead, is that as women are more free to choose professions the percentage of women's involvement drops.
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.
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
EDIT: I guess maybe agentic code maintenance is more akin to just making a new pair of shoes every time the current one shows any deficiency?
Looking at history, I think LLMs will settle down too. Because, in fact, if you look at the history of software thought, there were actually many people who would have welcomed LLMs.
[1]https://www.makonea.com/en-US/blog/people-who-build-things-t...
We are talking about the separation of these roles going away. If you disagree or can't see that happening, that's fine. Nobody gonna waste their time convincing you.
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.
I've been doing a lot of devops lately, writing tons of templates, configs, scripts in like 3 programming languages, wrestling with 'cloud-based' constructs - with LLMs ofc.
LLMs make all this work easy, otherwise it'd be borderline impossible.
It's all BS. Hosting a CRUD app should be an executable for the server (written in boring lang/lib of my choice) + an SQLite db. Of course it's a bit more than that, but not by much.
There are wonderful IDEs and tools out there which help humans do the task, and good design keeps the complexity under control.
LLMs on the other hand both are sources of the mess and solutions to it. Without good tooling you'll be much more inclined to reach for an LLM.
This is exactly it. We all know and envy that guy who can bang out a script on demand that makes any task simple, and we also know we'll never be that guy.
LLMs are superhuman knowledge remixers. And that's exactly what we need to deal with a lot of software development tasks.
This
In 2031: Why on earth not?
I’ve been dogfooding my own app, which gives a voice, body, and etiquette instructions to any model. (Mostly claude)
Would love any feedback:
Hey that was about my last Tuesday.
--
In 2031 you single-handedly maintain 173 million lines of code making up a simple crud app. You're a good programmer, but the previous guy wasn't, and now there's too much code to make it any smaller. Not even your vastly superhuman LLM feels confident to delete any of the code. So the two of you slog along, keeping the thing alive.
But a good programmer given agency to solve the higher order problem will always have a path to rewrite, because any "simple CRUD app" exists to serve human purposes, and in order to meet those needs the human interface need to be legible, and to be legible there have to be tight enough inputs and outputs that you can create a parallel implementation without the incidental complexity.
Now you might say this won't be the case for large multi-party distributed systems, and that is true, but that is also true today where all manner of insane cruft and non-sensical logic and workflows exist. But even where things appear totally intractable due to legacy requirements and conflicting logic, there still is a human backstop where if the pain is big enough, any process can be fixed. In practice competing interests may prevent such fixes for a long time, but that is political problem not a technical problem, and it's not inevitable as things can flip very quickly due to the dynamics of human attention and power.
And then there's accidental complexity - stuff you have to do to solve the problem, plus the make-work because you over-engineered it...
Turns out, typing the code by hand was accidental complexity.
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
I do think most of this is a skill issue. I don't mean your personal skills; I mean the misuse or non-use of agentic skills. The majority of issues with code synthesis and review can be repaired by giving the agent enough guardrails, context and skills to use.
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'"It is not bad per se, but it comes with huge downsides. And the more abstract the language the messier it gets.
I just don't think that the gains in coding speed we get from using AI + human language as outweigh the downsides of having poorly defined programs
Your point, by your own words:
> However, the human language is not a programming language. It's open to interpretation
Is that a language is not a programming language if it is open to interpretation. It is a point that is demonstrably wrong.
You can enter this as is on an Workato or Boomi agentic workflow.
For plenty of people this is how programming looks like in 2026.
Also your example has multiple translations into Assembly, depending on the optimizations levels, and where the statement happens to be located, it might even be part of auto vectorisation, or completely thrown away.
float constant(void) {
return 3.1f;
}
Option 1 (loading high 16 bits and low 16 bits, then moving to fp register):
movw r3, #26214
movt r3, 16454
vmov s15, r3
vmov.f32 s0, s15
ldr r3, .L3
mov r0, r3
.L3:
.word 1078355558
vldr.32 s0, .L3
.L3:
.word 1078355558Interesting 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.