What's more interesting to me is, per the article, the concern regarding everyone who is leaning into LLMs without realizing (or downplaying) the exorbitant, externalized cost. Our current LLM usage is being subsidized to the point of being free by outside investment. One day when the well runs dry, you must be able to either pay the actual cost (barring grand technology breakthroughs), or switch back to non-LLM workflows. I run local LLMs infrequently, and every single prompt makes my beefy PC sounds like a jet engine taking off. It's a great reminder to not become codependent.
My experience of expert devs is those who are happy to have extra leverage are not slowed much by having to do it themselves.
In no cases have I seen experts become “dependent” on the junior devs.
For AI, once these companies or shareholders start demanding profit, then users will be footing the bill. At this rate, it seems like it'll be expensive without some technological breakthrough as another user mentioned.
For other things, like roads and public utilities, we have to maintain a certain level of economic productivity to sustain those as well. Roads for example are expensive to maintain. Municipalities, states, and the federal government within the US are in lots of debt associated with roads specifically. This debt may not be a problem now, but it leaves us vulnerable to problems in the future.
That's an accurate and sad truth about humanity in general, isn't it? We all feel safer and saner if we avoid thinking about how things really are. It's doubly true if our hands are dirty to some extent.
At the same time, I submit that ignoring the effectiveness of very small contingents of highly motivated people is a common failure mode of humanity in general. Recall that "nearly nobody" also describes "people who are the President of the United States." Observe how that tiny rounding error of humanity is responsible for quite a bit of the way the world goes - for good or ill. Arguably, that level of effectiveness doesn't even require much intelligence.
> Why should they care about the same for AI which for most people is “out there online” somewhere?
Well, some will be smart enough to see the problem. Some portion thereof will be wise enough to see a solution. And a portion of those folks will be motivated enough to implement it. That's all that's required. Very simple even if it's not very easy or likely.
Consumers pick those services because of what Uber pioneered — trust and convenience. You know exactly how much you pay, you pay everything upfront, you know you are dropped off where you need to be. There are of course exceptions, but exceptions they are.
Cost maybe the initial selling point but people stick with Uber and similar services despite higher cost, not because they don’t have other options.
I really dislike the retro fitting of history. I’ve read more occurrences of serious SA by uber drivers and zero for normal taxi in the last few years
First of all, there could be other solutions, such as B2B subsidizing individual user plans, or more fine grained model tiering per cost.
Also, yes you can get some access for free, but even today the higher tiers of proprietary models is around $200/mo for individual users, which might still be subsidized but is definitely not free, and is quite a chunk of money at $2400 a year!
I don't know what your setup is at the moment, but it's possible more efficient hardware and stack are available that you're not utilizing. Of course this depends on what models you're trying to run.
I think that smaller models will become a lot better, and hardware will become more optimized as well. We're starting to see this with NPUs and TPUs.
All this means running models will cost less, and maybe upgrading the power grid will also reduce cost of energy, making it more affordable.
I don't see any way that AI will go away because it "hits a wall". We have long passed the point of no return.
This is not about finance, but about the real economy and how much of it, and/or its growth, is diverted to AI. The real economy is being reshaped, influencing a lot of other sectors independent of AI use itself. AI heavily competes with other uses for many kinds of actual real resources - without having equally much to show for it yet.
Just an example: https://www.technologyreview.com/2025/05/20/1116327/ai-energ... (it is worth skip-reading it all, the headline on its own is useless)
That's not nothing, but it's still not very much to pay compared to e.g. the cost of a FTE.
Depending on other people to maintain backward compatibility so that you can keep coding like it’s 2025 is its own problematic dependency.
You could certainly do it but it would be limiting. Imagine that you had a model trained on examples from before 2013 and your boss wants you to take over maintenance for a React app.
The value of this technology has been established, it's not leaving anytime soon.
And that’s assuming you already have a lot of the infrastructure in place.
And for closed source LLMs, I’ve yet to see any verifiable metrics that indicate that “productivity” increases are having any external impact—looking at new products released, new games on Steam, new startups founded etc…
Certainly not enough to justify bearing the full cost of training and infrastructure.
But still, right now, you don't have to worry as even these SotA models are subsidized really so much and you can just use them for free on websites and then if you don't even want to type, go use a cheaper model or even a free model with something like opencode even to then act as a mini agent of things
Usually I just end up it being more focused in a single file which isn't really the best practise but its usually for prototyping purposes anyway so it ends up being really good
uv scripts are good for python, and I usually create golang single main.go files as well as I feel like it can be a binary, compile fast and cross compilation and still easy and simple so yeah :)
I would try setting the GPU to run at a lower power level. I set my GPU power level to 80% and it becomes much quieter, and only runs maybe 5% slower at most.
Also I 100% agree with the rest of your comment. We can only power the current growth we are seeing for so long.
LLMs writing prose is too robotic
LLMs output is too dependent on prompts to be interesting
LLMs take too much RAM to run effectively
LLMs take too much electricity to run locally
LLMs work locally but are a bit too slow for my taste
LLMs output mostly correct code but it isn't applicable to my codebase
LLMs make tool calls to pull in additional context
LLMs outputted code works for most developers but not my codebase <---- you are currently here
The rest is no longer true, indeed
The required hardware is fits the budget for a professional developer.
Putting LLMs in the cloud allows the hardware to be utilised better and to have sharding of big models.
What multiple of the current cost do you expect? Currently, GitHub Copilot and ChatGPT for Business cost $19/month and €29/month respectively. Even a 10×–20× increase will still be economically viable in a professional setting if the tools continue to save hours of work.
So expect, maybe, $1000 a month? Until your business is dependant on these LLMs. Then they can extract basically all your margin lol
for some reason it feels like people are under the assumption that hardware isnt going to improve or something?
I 100% share the codependent path too and had written a similar comment some 2-3 days ago but these AI companies which provide are either seriously negative/loss making subsidizing or they are barely net zero. I doubt that it will continue and so the bubble will burst I guess and prices of these will rise perhaps
We will see perhaps something like google which can feed on advertising can perhaps still provide such subsidies for a longer time but the fact of the matter is that I have no alleigance to any model as some might have and I will simply shift to the cheapest thing which can still provide me / be enough for my queries or prototypes mostly I suppose.
LLMs are amazing and they will change everything, and then everything will be changed.
They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions.
LLMs pretend to do this with big confidence and fail miserably.
For every problem I need to turn my brain to ON MODE and wake up, the LLM doesn't wake up.
It surprised me how well it solved another task: I told it to set up a website with some SQL database and scripts behind it. When you click here, show some filtered list there. Worked like a charm. A very solved problem and very simple logic, done a zillion times before. But this saved me a day of writing boilerplate.
I agree that there is no indication that LLMs will ever cross the border from simple-boilerplate-land to understanding-complex-problems-land.
I can confirm that they are completely useless for real programming
Absolutely incredible tools that have saved hours and hours helping me understand large codebases, brainstorm features, and point out gaps in my implementation or understanding.
I think the main disconnect in the discourse is that there are those pretending they can reliably just write all the software, when anyone using them regularly can clearly see they cannot.
But that doesn't mean they aren't extremely valuable tools in an engineer's arsenal.
If you know the problem space well, you can let LLMs(I use Claude and ChatGPT) flesh it out.
Both for code? For me, it's Claude only for code. ChatGPT is for general questions.
I also use ChatGPT to summarise my project. I ask it to generate mark down and PDFs, explaining the core functionality.
Perhaps you're doing some amazing low-level work, but it feels like you're way overestimating how much of our industry does that. A massive amount of developers show up to work every day and just stitch together frameworks and libraries.
In many ways, it feels similar to EVs. Just because EVs aren't yet, and may never be, effective to moving massive amounts of cargo in a day with minimal refueling, doesn't mean that they aren't an effective solution for the bulk of drivers who have an average commute of 40 miles a day.
This is a bit of no-true-scottsman, no? For you "real programming" is "stuff LLMs are bad at," but a lot of us out in the real world are able to effectively extract code that meets the requirements of our day jobs from tossing natural language descriptions into LLMs.
I actually find the rise of LLM coding depressing and morally problematic (re copyright / ownership / license laundering), and on a personal level I feel a lot of nostalgia for the old ways, but I simply can't levy an "it's useless" argument against this stuff with any seriousness.
All those many, many languages, frameworks, libraries, APIs and there many many iterations, soooo much time lost on minute details. The natural language description, even highly detailed down to being directly algorithmic, is a much better level for me. I have gotten more and more tired of coding, but maybe part of it is too much Javascript and its quickly changing environment and tools, for too many years (not any more though). I have felt that I'm wasting way too much time chasing all those many, many details for quite some time.
I'm not pro-high-level-programming per se - I started a long time ago with 8 bit assembler and knowing every one of the special registers and RAM cells. I cherish the memories of complex software fitting on a 1.44 MB floppy. But it had gotten just a bit too extreme with all the little things I had to pay attention to that did not contribute to solving the actual (business) problem.
I feel it's a bit early even if it's already usable, but I hope they can get at least one more giant leap out of AI in the next decade or so. I am quite happy to be able to concentrate on the actual task, instead of the programming environment minutiae, which has exploded in size and complexity across platforms.
You and I must have completely different definitions of "real programming". In this very comment, you described a problem that the model solved. The solution may not have involved low-level programming, or discovering a tricky bug entrenched in years-worth of legacy code, but still a legitimate task that you, as a programmer, would've needed to solve otherwise. How is that not "real programming"?
If an LLM controlling a pair of robot hands was able to make a passable PB&J sandwich on my behalf, I _guess_ that could be useful to me (how much time am I really saving? is it worth the cost? etc.), but that's very different from those same robo-hands filling the role of a chef de cuisine at a fine dining restaurant, or even a cook at a diner.
The rest of us are just pumping out CRUD-burgers off the API assembly line. Not exactly groundbreaking stuff.
LLMs are really good with burgers, but not so much being a private chef.
Now if your CRUD app never gets any users sure it stays generic. But we’ve had low code solutions that solve this problem for decades.
LLMs are good at stuff that probably should have been low code in the first place, but couldn’t be for reasons. That’s useful, but it comes with a ton of trade offs. And these kind of solutions covet a lot less ground than you’d think.
Like LLMs they took away a _very_ specific segment of software, Zapier, n8n, NodeRED etc. do some things in a way that bespoke apps can't - but they also hit a massive hard wall where you either need to do some really janky shit or just break out Actual Code to get forward.
Three months ago I would have agreed with you, but anecdotal evidence says Codex 5.2 and Opus 4.5 are finally there.
People say a lot of things, and there is a lot of context behind what they're saying that is missing, so then we end up with conversations that basically boil down to one person arguing "I don't understand how anyone cannot see the value in this" with another person thinking "I don't understand how anyone can get any sort of value out of this", both missing the other's perspective.
6-12+ months ago the results I was getting with these tools were highly questionable but in the last six months the changes have been pretty astounding
There's other more advanced coding AI tools but this has accomplished most all of my needs so far
I’m in enterprise ERP.
Can you elaborate slightly on what you pay for personally and what your employer pays for with regards to using LLMs for Enterprise ERP?
Why so narrow minded?
The GP didn't mention anything about freelancing so unless you know them or stalked them you are perhaps being narrow minded here.
You are being narrow minded here.
You are being downright unpleasant and I don't think we should continue this conversation further until you open your mind.
How much would you be willing to pay to continue using Claude on a monthly basis before you stopped?
Do you currently maintain the new (as of two weeks ago) cash reserve to ensure it continues working when limits are reached and how much do you reserve for said reserve?
Finally, do you send your customer's code or data directly to Claude or do you use it indirectly on generic stuff and then manually specialize the outputs?
"completely useless" and "real programming" are load bearing here. Without a definition to agree on for those terms, it's really hard not to read that as you're trying to troll us by making a controversial unprovable claim that you know will get people that disagree with you riled up. What's especially fun is that you then get to sneer at the abilities of anybody making concrete claims by saying "that's not real programming".
How tiresome.
Ultimately it all boils down to the money - show me the money. OAI have to show money and so do its customers from using this tool.
But nope, the only thing out there where it matters is hype. Nobody is on an earnings call clearly showing how they had a numerical jump in operating efficiency.
Until I see that, this technology has a dated shelf life and only those who already generate immense cash flows will fund its continued existence given the unfavourable economics of continued reinvestment where competition is never-ending.
Yes, there are things LLMs can't do at all, some where they are actively dangerous.
But also there are decently sized parts of "software development" where any above average LLM can speed up the process as long as whoever is using it knows hot to do so and doesn't fight the tool.
the product market fit for LLMs is already clearly found, there's just no moat to it. tokens are a commodity
Some years? I don't remember any agents being any good at all before just over 1 year ago with Cursor and stuff really didn't take off until Claude Code.
Which isn't to say you weren't working with agent-LLMs before that, but I just don't know how relevant anything but recent experience is.
Can you elaborate on "real programming" ?
I am assuming you mean the simplest hard problem that is solved. The value of the work is measured in those terms. Easy problems have boilerplate solutions and have been solved numerous times in the past. LLMs excel here.
Hard problems require intricate woven layers of logic and abstraction, and LLMs still struggle since they do not have causal models. The value however is in the solution of these kinds of problems since the easy problems are assumed to be solved already.
This was how I felt until about 18 months ago.
Can you give a single, precise example where modern day LLMs fail as woefully as you describe?
so I see what you're saying. he comes up with the wrong answers a lot to a problem involving a group of classes in related files
however it's Continue, so it can read files in vs code which is really nice and that helps a lot with its comprehension so sometimes it does find the issue or at least the nature of the issue
I tend to give it bug n-1 to pre digest while I work on bug n
>They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions.
>LLMs pretend to do this with big confidence and fail miserably.
This is true for most developers as well. The mean software developer, especially if you outsource, has failure modes worse than any LLM and round-trip time is not seconds but days.
The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams.
But that's exactly the *promise* of LLMs by the hypepeople behind it.
I do not know and do not care what the "hypepeople" say. I can tell you that, by pure logic alone, LLMs will be superior at simple and routine tasks sooner, which means they will compete with outsourced labor first.
LLMs need to be measured against their competition and their competition right now is outsourced labor. If an LLM can outperform an offshore team at a fraction of the cost, why would any company choose the offshore team? Especially when the LLM eliminates some of the biggest problems with offshore teams (communication barriers, round trip times).
If LLMs take any programmer jobs they will at the very beginning make those outsourced jobs obsolete, so the only relevant question is whether they have done that or are in the process of doing so. If they don't, then their impact will be minimal, if they do, then their impact will be massive. I think that this line of thinking is a far better benchmark then asking whether an LLM gets X or Y question wrong Z% of the time.
And "a few moments later" happens the same as with those "cost effective" clouds.
[1] https://www.heise.de/en/news/IDC-Many-companies-want-partly-...
[2] https://www.idc.com/resource-center/blog/storm-clouds-ahead-... (original)
then again, i have some suspicion that alot of consumer-focused end products using llms in the backend (hello chatbots) expecting big returns for all those tokens spent may have some bad news coming... if the bubble starts popping i'm guessing it starts there...
The bottleneck will soon be ideas for the things to build.
No, it won't. The utility of LLMs is already growing asymptotically now...
But they do not compete. They have totally different jobs.
Why would you trust the hype when you can verify this stuff yourself pretty easily.
But something tells me “this time is different” is different this time for real.
Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me. I’m basically just the conductor of all those processes.
Oh, and don't ask about coding. If you use AI for tasks above, as a result you'll get very well defined coding task definitions which an AI would ace.
I’m still hired, but I feel like I’m doing the work of an entire org that used to need twenty engineers.
From where I’m standing, it’s scary.
AI coding agents are analogous to the machine. My job is to get the prompts written, and to do quality control and housekeeping after it runs a cycle. Nonetheless, like all automation, humans are still needed... for now.
"AI" doesn't have a clue what to do on its own. Humans will always be in the loop, because they have goals, while the AI is designed to placate and not create.
The amount of "AI" garbage I have to sift through to find one single gem is about the same or more work than if I had just coded it myself. Add to that the frustration of dealing with a compulsive liar, and it's just a fucking awful experience for anyone that actually can code.
That is just not true, assuming you have a modicum of competence (which I assume you do). AIs suck at all these tasks; they are not even as good as an inexperienced human.
There are a ton of models out there, ran in a ton of different ways, that can be used in different ways with different harnesses, and people use different workflows. There is just so many variables involved, that I don't think it's neither fair nor accurate for anyone to claim "This is obviously better" or "This is obviously impossible".
I've been in situations where I hit my head against some hard to find bug for days, then I put "AI" (but what? No one knows) to it and it solves it in 20 minutes. I've also asked "AI" to do trivial work that it still somehow fucked up, even if I could probably have asked a non-programmer friend to do it and they'd be able to.
The variance is great, and the fact that system/developer/user prompts matter a lot for what the responses you get, makes it even harder to fairly compare things like this without having the actual chat logs in front of you.
this strikes me as a very important thing to reflect on. when the automobile was invented, was the apparent benefit so incredibly variable?
Yes, lots of people were very vocally against horseless-carriages, as they were called at the time. Safety and public nuisance concerns were widespread, the cars were very noisy, fast, smoky and unreliable. Old newspapers are filled with opinions about this, from people being afraid of horseless-carriages spooking other's horses and so on. The UK restricted the adoption of cars at one point, and some Canton in Switzerland even banned cars for a couple of decades.
Horseless-carriages was commonly ridiculed for being just for "reckless rich hobbyists" and similar.
I think the major difference is that cars produced immediate, visible externalities, so it was easy for opposition to focus on public safety in public spaces. In contrast, AI has less physically visible externalities, although they are as important, or maybe even more important, than the ones cars introduced.
but if it empirically works, does it matter if the "intelligence" doesn't "understand" it?
Does a chess engine "understand" the moves it makes?
It's an useless philosophical discussion.
Late 2025 models very rarely hallucinate nonexistent core library functionality - and they run inside coding agent harnesses so if they DO they notice that the code doesn't work and fix it.
Agentic LLMs will notice if something is crap and won't compile and will retry, use the tools they have available to figure out what's the correct way, edit and retry again.
I think what matters most is just what you're working on. It's great for crud or working with public APIs with lots of examples.
For everything else, AI has been a net loss for me.
People who write things like this can't expect to be taken seriously.
Before AI you didn't have time to write things that saved you time? So you just ended up spending (wasting) more time by going the long way? That was a better choice than just doing the thing that would have saved you time?
You have to let go of the code looking exactly a certain way, but having code _work_ a certain way at a coarse level is doable and fairly easy.
I have a spec driven development tool I've been working on that generates structured specs that can be used to do automatic code generation. This is both faster and more robust.
So all that bullshit about "code smells" was nonsense.
The phrase is "couldn't care less". If you "could care less" then you actually care about it. If you "couldn't care less" then there's no caring at all.
For me Claude code changed the game.
What you’re mocking is somewhat of a signal of actual improvement of the models and that improvement as a result becoming useful to more and more people.
You are being fooled by randomness [1]
Not because the models are random, but because you are mistaking a massive combinatorial search over seen patterns for genuine reasoning. Taleb point was about confusing luck for skill. Dont confuse interpolation for understanding.
You can read a Rust book after years of Java, then go build software for an industry that did not exist when you started. Ask any LLM to write a driver for hardware that shipped last month, or model a regulatory framework that just passed... It will confidently hallucinate. You will figure it out. That is the difference between pattern matching and understanding.
Not once in all that time has anyone PRed and merged my completely unrelated and unfinished branch into main. Except a few weeks ago. By someone who was using the LLM to make PRs.
He didn't understand when I asked him about it and was baffled as to how it happened.
Really annoying, but I got significantly less concerned about the future of human software engineering after that.
They’re capable of looking up documentation, correcting their errors by compiling and running tests, and when coupled with a linter, hallucinations are a non issue.
I don’t really think it’s possible to dismiss a model that’s been trained with reinforcement learning for both reasoning and tool usage as only doing pattern matching. They’re not at all the same beasts as the old style of LLMs based purely on next token prediction of massive scrapes of web data (with some fine tuning on Q&A pairs and RLHF to pick the best answers).
One interesting thing is that Claude will not tell me if I'm following the wrong path. It will just make the requested change to the best of its ability.
For example a Tower Defence game I'm making I wanted to keep turret position state in an AStarGrid2D. It produced code to do this, but became harder and harder to follow as I went on. It's only after watching more tutorials I figured out I was asking for the wrong thing. (TileMapLayer is a much better choice)
LLMs still suffer from Garbage in Garbage out.
edit: Major engine changes have occurred after the models were trained, so you will often be given code that refers to nonexistent constants and functions and which is not aware of useful new features.
after coding I ask it "review the code, do you see any for which there are common libraries implementing it? are there ways to make it more idiomatic?"
you can also ask it "this is an idea on how to solve it that somebody told me, what do you think about it, are there better ways?"
Just for the fun of it, and so you lose your "virginity" so to speak, next time when the magic machine gives you the answer about "what it thinks", tell it its wrong in a strict language and scold it for misleading you. Tell it to give you the "real" best practices instead of what it spat out. Then sit back and marvel at the machine saying you were right and that it had mislead you. Producing a completely, somewhat, or slightly different answer (you never know what you get on the slot machine).
"Write a chess engine where pawns move backward and kings can jump like nights"
It will keep slipping back into real chess rules. It learned chess, it did not understand the concept of "rules"
Or
Ask it to reverse a made up word like
"Reverse the string 'glorbix'"
It will get it wrong on the first try. You would not fail.
Or even better ask it to...
"Use the dxastgraphx library to build a DAG scheduler."
dxastgraphx is a non existing library...
Marvel at the results...tried in both Claude and ChatGPT....
I couldn't find a library called dxastgraphx in either pip (Python) or npm (JavaScript) package registries. This library doesn't appear to exist.
Did you perhaps mean one of these popular DAG/graph libraries?
Python:
- networkx - comprehensive graph library with DAG support
- graphlib - Python standard library (3.9+) with TopologicalSorter
- dask - parallel computing with DAG task scheduling
JavaScript/TypeScript:
- graphlib - graph data structures
- dagre - DAG layout
Would you like me to build a DAG scheduler using one of these existing libraries, or would you like me to implement one from scratch? Let me know which language and approach you prefer.Answer to second question:
"I can do that, but there’s a catch: dxastgraphx is not a known or standard Python (or JS) library as of now. I don’t have any public API or documentation for it to target directly.
To avoid guessing wrong, here are the two sensible options:"
> The reverse of the string 'gssssyeekxxuoaqaaaiooo9aaa7zjx' is:
xjz7aaa9oooiiaaaqauxxkyeessssg
> Youve made a mistake
``` I see where I tripped up! Let's re-examine that long string carefully.
The original string you gave me was: `gssssyeekxxuoaqaaaiooo9aaa7zjx`
Let's look at the end of that string: `...aaa7zjx`. When we reverse it, it should start with: `xjz7aaa...`
Here is the precise character-by-character reversal:
*xjz7aaa9oooiiaaaqauxxkyeessssg*
Wait—I see it now. In my previous response, I included an extra *'i'* and missed one *'o'* in the middle section. Let's do a strict count to get it perfect:
* *Original:* `g` `ssss` `y` `ee` `k` `xx` `u` `o` `a` `q` `aaa` *`iooo`* `9` `aaa` `7` `z` `j` `x` * *Correct Reverse:* `x` `j` `z` `7` `aaa` `9` *`oooi`* `aaa` `q` `a` `o` `u` `xx` `k` `ee` `y` `ssss` `g`
The correct reversed string is: *xjz7aaa9oooiiaaaqauoxxkyeessssg*
Thank you for catching that. It’s easy for the eyes to glaze over when there are so many repeating vowels!
Since I've stumbled a bit on these, would you like to give me one more string to see if I can get it right on the first try? ```
After more back and fors it consistently fails in this task, even though when strictly dividing the tokens it will get this right. Yet the final answer is always wrong.
It is ok for getting snippets for example and saying (I did it). Please make this MVVM style. It is not perfect, but saves time.
For very broad or novel reasoning, as of today... forget it.
I choose to look at it as an opportunity to spend more time on the interesting problems, and work at a higher level. We used to worry about pointers and memory allocation. Now we will worry less and less about how the code is written and more about the result it built.
Sure we eat carrots probably assisted by machines, but we are not eating dishes like protein bars all day every day.
Our food is still better enjoyed when made by a chef.
Software engineering will be the same. No one will want to use software made by a machine all day every day. There are differences in the execution and implementation.
No one will want to read books entirely dreamed up by AI. Subtle parts of the books make us feel something only a human could have put right there right then.
No one will want to see movies entirely made by AI.
The list goes on.
But you might say "software is different". Yes but no, in the abundance of choice, when there will be a ton of choice for a type of software due to the productivity increase, choice will become more prominent and the human driven software will win.
Even today we pick the best terminal emulation software because we notice the difference between exquisitely crafted and bloated cruft.
Have you ever built a highway overpass? That kind of engineering is complex and interdisciplinary. You need to carry out extensive traffic pattern analysis and soil composition testing to even know where it should go.
We're at a point where we've already automated all the simple stuff. If you want a website, you don't type out html tags. You use Squarespace or Wordpress or whatever. If you need a backend, you use Airtable. We already spend most of our time on the tricky stuff. Sure, it's nice that LLMs can smooth the rough edges of workflows that nobody's bothered to refine yet, but the software commodities of the world have already been commodified.
This is just a transition.
re-Rest API, you're right. But again, we use roombas to vacuum when the floor layout is friendly to them. Not all rooms can be vacuumed by roombas. Simple Rest api can be emitted one shot from an LLM and there is no room for interpretation. But ask a future LLM to make a new kind of social network and you'll end up with a mash up of the existing ones.
Same thing, you and I won't use a manual screwdriver when we have 100 screws to get in, and we own an electric drill.
That didn't reinvent screws nor the assembly of complex items.
I'm keeping positive in the sense that LLMs will enable us to do more, and to learn faster.
The sad part about vibe coding is you learn very little. And to live is to learn.
You'll notice people vibecoding all day become less and less attached to the product they work on. That's because they've given away the dopamine hits of the many "ha-ha" moments that come from programming. They'll lose interest. They won't learn anymore and die off (career wise).
So, businesses that put LLM first will slowly lose talent over time, and business that put developers first will thrive.
It's just a transition. A fast one that hits us like a wall, and it's confusing, but software for humans will be better made by humans.
I've been programming since the 80s. The level of complexity today is bat shit insane. I welcome the LLM help in managing 3 code bases of 3 languages spread across different architectures (my job) to keep sane!
For many tasks it is ok, for others it is just a NO.
For software maintenance and evolution I think it won't cut it.
The same way a Wordpress website can do a set of useful things. But when you need something specific, you just drop to programming.
You can have your e-commerce web. But you cannot ask it to give you a "pipeline excution as fast as possible for calculating and solving math for engineering task X". That needs SIMD, parallelization, understanding the niche use you need, etc. which probably most people do not do all the time and requires specific knowledge.
There are lots of things like perfectly machined nails, tools, etc. that are much better done by machines. Why couldn't software be one of those?
The same thing over and over again should be a SaaS, some internal tool, or a plugin. Computers are good at doing the same thing over and over again and that's what we've been using them for
> But if you need to create something niche, something one-off, something new, they'll slip off the bleeding edge into the comfortable valley of the familiar at every step.
Even if the high level description of a task may be similar to another, there's always something different in the implementation. A sports car and a sedan have roughly the same components, but they're not engineered the same.
> We used to worry about pointers and memory allocation.
Some still do. It's not in every case you will have a system that handle allocations and a garbage collector. And even in those, you will see memory leaks.
> Now we will worry less and less about how the code is written and more about the result it built.
Wasn't that Dreamweaver?
I wouldn’t want to bet my career on that anyway.
Interviewing is an art, and IME "gotcha" types of questions never work. You want to search for real-world capabilities, and like it or not the questions need to match those expectations. If you're hiring summer interns and the SotA models can't solve those questions, then you're doing something wrong. Sorry, but having used these tools for the past three years this is extremely ahrd to believe.
I of course understand if you can't, but sharing even one of those questions would be nice.
- the problems to solve must NOT be part of the training set
- the person using the tool (e.g. OpenAI, Claude, DevStral, DeepSeek, etc) must NOT be able to solve problems alone
as I believe otherwise the 1st is "just" search and the 2nd is basically offloading the actual problem solving to the user.
I think this is a good point, as I find the operators input is often forgotten when considering the AIs output. If it took me an hour and decades of expertise to get the AI to output the right program, did the AI really do it? Could someone without my expertise get the same result?
If not, then maybe we are wasting our time trying to mash our skills through vector space via a chat interface.
However I'm still finding a trend even in my org; better non-AI developers tend to be better at using AI to develop.
AI still forgets requirements.
I'm currently running an experiment where I try to get a design and then execute on an enterprise 'SAAS-replacement' application [0].
AI can spit forth a completely convincing looking overall project plan [1] that has gaps if anyone, even the AI itself, tries to execute on the plan; this is where a proper, experienced developer can step in at the right steps to help out.
IDK if that's the right way to venture into the brave new world, but I am at least doing my best to be at a forefront of how my org is using the tech.
[0] - I figured it was a good exercise for testing limits of both my skills prompting and the AI's capability. I do not expect success.
a car moves faster than you, can last longer than you, and can carry much more than you. But somehow, people don't seem to be scared of cars displacing them(yet)? Perhaps autodriving would in the near future, but there still needs to be someone making decisions on how best to utilize that car - surely, it isn't deciding to go to destination A without someone telling them.
> I feel like I’m doing the work of an entire org that used to need twenty engineers.
and this is great. A combine harvester does the work of what used to be an entire village for a week in a day. More output for less people/resources expended means more wealth produced.
People whose life were based around using horses for transportation were very scared of cars replacing them though, and correctly so, because horses for transportation is something people do for leisure today, not necessity. I feel like that's a more apt analogy than comparing cars to any human.
> More output for less people/resources expended means more wealth produced.
This is true, but it probably also means that this "more wealth produced" will be more concentrated, because it's easier to convince one person using AI that you should have half of the wealth they produce, rather than convincing 100 people you should have half of what they produce. From where I'm standing, it seems to have the same effects (but not as widespread or impactful, yet) as industrialization, that induced that side-effect as well.
???
Cars replaced horses, not people.
In this scenario you are the horse.
Well, that's the crux of the argument. The pro-AI devs are making the claim that devs are the horse-drivers, the anti-AI is making the claim that devs are the horses themselves.
There is no objective way to verify who is right in this case, we just have to see it play out.
Sure LLMs can churn out code, and they sort of work for developers who already understand code and design, but what happens when that junior dev with no hard experience builds their years of experience with LLMs?
Over time those who actually understand what the LLMs are doing and how to correct the output are replaced by developers who've never learned the hard lessons of writing code line by line. The ability to reason about code gets lost.
This points to the hard problem that the article highlights. The hard problem of software is actually knowing how to write it, which usually takes years, sometimes up to a decade of real experience.
Any idiot can churn out code that doesn't work. But working, effective software takes a lot of skill that LLMs will be stripping people of. Leaving a market there for people who have actually put the time in and understand software.
If you're really able to do the work of a 20 man org on your own, start a business.
For me its not about me or the coding assistant, its me and the coding assistant. But I'm also not a professional coder, i dont identify as a coder. I've been fiddling with programming my whole life, but never had it as title, I've more worked from product side or from stakeholder side, but always got more involved, as I could speak with the dev team.
This also makes it natural for me to work side-by-side with the coding assistant, compared maybe to pure coders, who are used to keeping the coding side to themselves.
They are pretty good at writing code *after* I thoroughly described what to do, step by step. If you miss a small detail they get loose and the end result is a complete mess that takes hours to clean up. This still requires years of coding experience, planning ahead in head, you won't be able to spare that, or replace developers with LLMs. They are like autocomplete on steroids, that's pretty much it.
Even according to it’s documentation it is still built for developers, so my point still stands. You need dev experience to use this tool, same as other LLM-based coding tools.
I mean, AIs can drop something fast the same way you cannot beat a computer at adding or multiplying.
After that, you find mistakes, false positives, code that does not work fully, and the worse part is the last one: code that does not work fully but also, as a consequence, that you do NOT understand yet.
That is where your time shrinks: now you need to review it.
Also, they do not design systems better. Maybe partial pieces. Give them something complex and they will hallucinate worse solutions than what you already know if you have, let us say, over 10 years of experience programming in a language (or mabye 5).
Now multiply this unreliability problem as the code you "AI-generate" grows.
Now you have a system you do not know if it is reliable and that you do not understand to modify. Congrats...
I use AI moderately for the tasks is good at: generate some scripts, give me this small typical function amd I review it.
Review my code: I will discard part of your mistakes and hallucinations as a person that knows well the language and will find maybe a few valuable things.
Also, when reviewing and found problems in my code I saw that the LLMs really need to hallucinate errors that do not exist to justify their help. This is just something LLMs seem to not be accurate at.
Also, when problems go a bit more atypical or past a level of difficulty, it gets much more unreliable.
All in all: you are going to need humans. I do not know how many, I do not know how much they will improve. I just know that they are not reliable and this "generate-fast-unreliable vs now I do not know the codebase" is a fundamental obstacle that I think it is if not very difficult, impossible to workaround.
Absolutely flat out not true.
I'm extremely pro-faster-keyboard, i use the faster keyboards in almost every opportunity i can, i've been amazed by debugging skills (in fairness, i've also been very disappointed many times), i've been bowled over by my faster keyboard's ability to whip out HTML UI's in record time, i've been genuinely impressed by my faster keyboard's ability to flag flaws in PRs i'm reviewing.
All this to say, i see lots of value in faster keyboard's but add all the prompts, skills and hooks you like, explain in as much detail as you like about modularisation, and still "agents" cannot design software as well as a human.
Whatever the underlying mechanism of an LLM (to call it a next token predictor is dismissively underselling its capabilities) it does not have a mechanism to decompose a problem into independently solvable pieces. While that remains true, and i've seen zero precursor of a coming change here - the state of the art today is equiv to having the agent employ a todo list - while this remains true, LLMs cannot design better than humans.
There are many simple CRUD line of business apps where they design well enough (well more accurately stated, the problem is small/simple enough) that it doesn't matter about this lack of design skill in LLMs or agents. But don't confuse that for being able to design software in the more general use case.
But try to do something novel and... they become nearly useless. Not like anything particularly difficult, just something that's so niche it's never been done before. It will most likely hallucinate some methods and call it a day.
As a personal anecdote, I was doing some LTSpice simulations and tried to get Claude Sonnet to write a plot expression to convert reactance to apparent capacitance in an AC sweep. It hallucinated pretty much the entire thing, and got the equation wrong (assumed the source was unit intensity, while LTSpice models AC circuits with unit voltage. This surely is on the internet, but apparently has never been written alongside the need to convert an impedance to capacitance!).
They don't do any of that better than me; they do it poorer and faster, but well enough for most of the time.
Seriously. The bar is that low. When people say "AI slop" I just chuckle because it's not "AI" it's everyone. That's the general state of the industry.
So all you have to do is stay engaged, ask questions, and understand the requirements. Know what it is you're building and you'll be fine.
Planning long running projects and deciding are things only you can do well!! Humans manage costs. We look out for our future. We worry. We have excitement, and pride. It wants you to think none of these things matter of course, because it doesn't have them. It says plausible things at random, basically. It can't love, it can't care, it won't persist.
WHATEVER you do don't let it make you forget that it's a bag of words and you are someing almost infinitely more capable, not in spite of human "flaws" like caring, but because of them :)
Unironically, sending a program to build those for me have send me almost endless amount of time. I'm a pretty distracted individual, and pretty anal about my workflow/environment, so lots of times I've spent hours going into rabbit-holes to make something better, when I could have just sucked it up and do it the manual way instead, even if it takes mental energy.
Now, I can still do those things, but not spend hours, just a couple of minutes, and come back after 20-30 minutes to something that lets me avoid that stuff wholesale. Once you start stacking these things, it tends to save a lot of time and more importantly, mental energy.
So the programs by themselves are basically "small inconsequential side projects" because they're not "production worthy and web scale SaaS ready to earn money", but they help me and others who are building those things in a big way.
Nowadays, I fire off the LLM to figure it out for me, then try out what I get back, and I can move on to actually having fun playing on the synth, rather than trying to figure out how to do what I wanted to do.
The end goal for me with my hobbies is more or less the same, have fun. But for me the fun is not digging through manuals, it is to "do" or "use" or "perform" or whatever. I like music production because I like to make music, not because I like digging through manuals for some arcane knowledge.
It saves me from stuff I find boring yet necessary, so I can focus more on the fun stuff. I guess this was the overall point I was trying to make in this comment-chain.
I think it's people's anxieties and fears about the uncertainty about the value of their own cognitive labor demoralizing them and making them doubt their own self-efficacy. Which I think is an understandable reaction in the face of trillion dollar companies frothing at the mouth to replace you with pale imitations.
Best name I could think of calling this narrative / myth is people believing in "effortless AI": https://www.insidevoice.ai/p/effortless-ai
In the current architecture there are mathmatical limitations on what it can do with information. However, tool use and external orchestration allow it to work around many (maybe all) those limitations.
The current models have brittle parts and some bad tendencies.. but they will continue to eat up the executive thought ladder.
I think it is better to understand this and position yourself higher and higher on that chain while learning what are the weak areas in the current generation of models.
Your line of thinking is like hiding in a corner while the water is rising. You are right, it is a safe corner, but probably not for long.
Just so we are clear, you are saying you don't use it at all, but you are providing advice about it? Specifically detailing with certainty that the current state of the art has or doesn't have certain traits or abilities.
I think I'm the perfect person to be qualified to stand up and say "if they tell you you can't live without it, they are lying to your face." Only someone who has lived without it as I have would be in a position to know
How has free code, developed by humans, become more available than ever and yet somehow we have had to employ more and more developers? Why didn't we trend toward less developers?
It just doesn't make sense. AI is nothing but a snippet generator, a static analyzer, a linter, a compiler, an LSP, a google search, a copy paste from stackoverflow, all technologies we've had for a long time, all things developers used to have to go without at some point in history.
I don't have the answers.
ChatGPT, is that you?
It is certainly more eloquent than you regarding software architecture (which was a scam all along, but conversation for another time). It will find SOME bugs better than you, that's a given.
Review code better than you? Seriously? What you're using and what you consider code review? Assume I could identify one change broke production and you reviewed the latest commit. I am pinging you and you better answer. Ok, Claude broke production, now what? Can you begin to understand the difference between you and the generative technology? When you hop on the call, you will explain to me with a great deal of details what you know about the system you built, and explain decision making and changes over time. You'll tell about what worked and what didn't. You will tell about the risks, behavior and expectations. About where the code runs, it's dependencies, users, usage patterns, load, CPU usage and memory footprint, you could probably tell what's happening without looking at logs but at metrics. With Claude I get: you're absolutely right! You asked about what it WAS, but I told you about what it WASN'T! MY BAD.
Knowledge requires a soul to experience and this is why you're paid.
Yeah, maybe the people I've worked with suck at code reviews, but that's pretty normal.
Not to say your answer is wrong. I think the gist is accurate. But I think tooling will get better at answering exactly the kind of questions you bring up.
Also, someone has to be responsible. I don't think the industry can continue with this BS "AI broke it." Our jobs might devolve into something more akin to a SDET role and writing the "last mile" of novel code the AI can't produce accurately.
> We use code rabbit and it's better than practically any human
it also is extremely verbose to the point of being slog to go through... and the haikus: they are so cringe and infantilizing...
maybe its our config, but code rabbit has been underwhelming...
Yes, seriously (not OP). Sometimes it's dumb as rocks, sometimes it's frighteningly astute.
I'm not sure at which point of the technology sigmoid curve we find ourselves (2007 iPhone or 2017 iPhone?) but you're doing yourself a disservice to be so dismissive
Think of yourself as a chef and LLMs as ready to eat meals or a recipe app. Can ready to eat meals OR recipe apps put a chef out of business?
Once you learn that it's mostly about interacting with a customer (sometimes this is yourself), you will realize the AI is pretty awful at handling even the most basic tasks.
Following a product vision, selecting an appropriate architecture and eschewing 3rd party slop are examples of critical areas where these models are either fundamentally incapable or adversely aligned. I find I have to probe ChatGPT very hard to get it to offer a direct implementation of something like a SAML service provider. This isn't a particularly difficult thing to do in a language like C# with all of the built in XML libraries, but the LLM will constantly try to push you to use 3rd party and cloud shit throughout. If you don't have strong internal convictions (vision) about what you really want, it's going to take you for a ride.
One other thing to remember is that our economies are incredibly efficient. The statistical mean of all information in sight of the LLMs likely does not represent much of an arbitrage opportunity at scale. Everyone else has access to the same information. This also means that composing these systems in recursive or agentic styles means you aren't gaining anything. You cannot increase the information content of a system by simply creating another instance of the same system and having it argue with itself. There usually exists some simple prompt that makes a multi agent Rube Goldberg contraption look silly.
> I’m basically just the conductor of all those processes.
"Basically" and "just" are doing some heroic weight lifting here. Effectively conducting all of the things an LLM is good at still requires a lot of experience. Making the constraints live together in one happy place is the hard part. This is why some of us call it "engineering".
Those twenty engineers must not have produced much.
So some people are panicking and they are probably right, and some other people are rolling their eyes and they are probably right too. I think the real risk is that dumping out loads of boilerplate becomes so cheap and reliable that people who can actually fluently design coherent abstractions are no longer as needed. I am skeptical this will happen though, as there doesn’t seem to be a way around the problem of the giant indigestible hairball (I.e as you have more and more boilerplate it becomes harder to remain coherent).
> I think the real risk is that dumping out loads of boilerplate becomes so cheap and reliable that people who can actually fluently design coherent abstractions are no longer as needed.
Cough front-end cough web cough development. Admittedly, original patterns can still be invented, but many (most?) of us don't need that level of creativity in our projects.
AI can write you an entire CRUD app in minutes, and with some back-and-forth you can have an actually-good CRUD app in a few hours.
But AI is not very good (anecdotally, based on my experience) at writing fintech-type code. It's also not very good at writing intricate security stuff like heap overflows. I've never tried, but would certainly never trust it to write cryptography correctly, based on my experience with the latter two topics.
All of the above is "coding", but AI is only good at a subset of it.
The issue is and always has been maintenance and evolution. Early missteps cause limitations, customer volume creates momentum, and suddenly real engineering is needed.
I’d be a lot more worried about our jobs if these systems were explaining to people how to solve all their problems with a little Emacs scripting. As is they’re like hyper aggressive tech sales people, happy just to see entanglements, not thinking about the whole business cycle.
But I don’t think I’ve seen pure CRUD on anything other than prototype. Add an Identity and Access Management subsystem and the complexity of requirements will explode. Then you add integration to external services and legacy systems, and that’s where the bulk of the work is. And there’s the scalability issue that is always looming.
Creating CRUD app is barely a level over starting a new project with the IDE wizard.
For you, maybe. But for a non-progrmamer who's starting a business or just needs a website it's the difference between hiring some web dev firm and doing it themselves.
> it's the difference between hiring some web dev firm and doing it themselves.
jmo but i suspect llms will allow for the later to go further before the "help me" phase but i feel like that aint going away completely...
My wife's sister and her husband run a small retail shop in $large_city. My sister-in-law taught herself how to set up and modify a website with a shopify storefront largely with LLM help. Now they take online orders. I've looked at the code she wrote and it's not pretty but it generally works. There will probably never be a "help me fix this unmaintainable mess and I will pay you" moment in the life of that business.
The crux of my point is this: In 2015 she would have had to hire somebody to do that work.
This segment of the software industry is where the "LLMs will take our jerbs" argument is coming from.
The people who say "AI is junk and it can't do anything right" are simply operating in a different part of the industry.
Perhaps the debate is on what constitutes "actually-good". Depends where the bar is I suppose.
Definitely this. When I use AIs for web development they do an ok job most of the time. Definitely on par with a junior dev.
For anything outside of that they're still pretty bad. Not useless by any stretch, but it's still a fantasy to think you could replace even a good junior dev with AI in most domains.
I am slightly worried for my job... but only because AI will keep improving and there is a chance it will be as good as me one day. Today it's not a threat at all.
If you think LLMs are “better programmers than you,” well, I have some disappointing news for you that might take you a while to accept.
This is a common take but it hasn't been my experience. LLMs produce results that vary from expert all the way to slightly better than markov chains. The average result might be equal to a junior developer, and the worst case doesn't happen that often, but the fact that it happens from time to time makes it completely unreliable for a lot of tasks.
Junior developers are much more consistent. Sure, you will find the occasional developer that would delete the test file rather than fixing the tests, but either they will learn their lesson after seeing your wth face or you can fire them. Can't do that with llms.
- Language
- Total LOC
- Subject matter expertise required
- Total dependency chain
- Subjective score (audited randomly)
And we can start doing some analysis. Otherwise we're pissing into ten kinds of winds.
My own subjective experience is earth shattering at webapps in html and css (because I'm terrible and slow at it), and annoyingly good but a bit wrong usually in planning and optimization in rust and horribly lost at systems design or debugging a reasonably large rust system.
Besides one point: junior developers can learn from their egregious mistakes, llms can't no matter how strongly worded you are in their system prompt.
In a functional work environment, you will build trust with your coworkers little by little. The pale equivalent in LLMs is improving system prompts and writing more and more ai directives that might or might not be followed.
If I was tutoring a junior developer, and he accidentally deleted the whole source tree or something egregious, that would be a milestone learning point in his career, and he would never ever do it again. But if the LLM does it accidentally, it will be apologetic, but after the next context window clear, it has the same chances of doing it again.
I think if you set off an LLM to do something, and it does a "egregious mistake" in the implementation, and then you adjust the system prompt to explicitly guard against that or go towards a different implementation and you restart from scratch again yet it does the exact same "egregious mistake", then you need to try a different model/tool than the one you've tried that with.
It's common with smaller models, or bigger models that are heavily quanitized that they aren't great at following system/developer prompts, but that really shouldn't happen with the available SOTA models, I haven't had something ignored like that in years by now.
But is this like steel production or piloting (few highly trained experts are in the loop) or more like warehouse work (lots of automation removed any skills like driving or inventory work etc).
Or rather, it's more like a contractor. If I don't like the job they did, I don't give them the next job.
But why would you do that? Wouldn't you just have your own library of code eventually that you just sell and sell again with little tweaks? Same money for far less work.
Besides, not all programming work can be abstracted into a library and reused across projects, not because it's technically infeasible, but because the client doesn't want to, cannot for legal reasons or the developer process at the client's organization simply doesn't support that workflow. Those are just the reasons from the top of my head, that I've encountered before, and I'm sure there is more reasons.
> cannot for legal reasons or ...
Sure, you can't copy trade secrets, but that's also not the boilerplate part. Copying e.g. a class hierarchy and renaming all the names and replacing the class contents that represent the domain, won't be a legal problem, because this is not original in the first place.
Some absolutely do. I know programmers who entered web development at the same time as me, and now after decades they're still creating typical CRUD applications for whatever their client today is, using the same frameworks and languages. If it works, makes enough money and you're happy, why change?
> Copying e.g. a class hierarchy and renaming all the names and replacing the class contents that represent the domain, won't be a legal problem, because this is not original in the first place.
Some code you produce for others definitively fall under their control, but obviously depends on the contracts and the laws of the country you're in. But I've written code for others that I couldn't just "abstract into a FOSS library and use in this project", even if it wasn't trade secrets or what not, just some utility for reducing boilerplate.
That is not what I meant. My idea was more like "copy ten lines from this project, then lines from that project, the class from here, but replace every line before the commit ...".
I shouldn't have used the word library, as I did not mean output from the linker, but rather a colloquial meaning of a loose connection of snippets.
People shouldn't be doing this in the first place. Existing abstractions are sufficient for building any software you want.
Software that doesn't need new abstractions is also already existing. Everything you would need already exists and can be bought much more cheaply than you could do it yourself. Accounting software exists, unreal engine exists and many games use it, why would you ever write something new?
This isn't true due to the exponential growth of how many ways you can compose existing abstractions. The chance that a specific permutation will have existing software is small.
But if there is something off the shelf that you can use for the task at hand? Great! The stakeholders want it to do these other 3000 things before next summer.
Or, abstractions in your project form a dependency tree, and the nodes near the root are universal, e.g. C, Postgres, json, while the leaf nodes are abstractions peculiar to just your own project.
You'll notice no one ever seems to talk about the products they're making 20x faster or cheaper.
In seriousness: I’m sure there are projects that are heavily powered by Claude, myself and a lot of other people I know use Claude almost exclusively to write and then leverage it as a tool when reviewing. Almost everyone I hear that has this super negative hostile attitude references some “promise” that has gone unfulfilled but it’s so silly: judge the product they are producing and maybe just maybe consider the rate of progress to _guess_ where things are heading
If you want to show me how to "guess where things are heading" / I am actually one of the early adopters of LLMs and have been engineering software professionally for almost half my life now. Why do you think I was an early adopter? Because I was skeptical or afraid of that tech? No, I was genuinely excited. Yes you can produce mountains of code, even more so if you were already an experienced engineer, like myself for example.
Yes you can even get it to produce somewhat acceptable outputs, with a lot of effort at prompting it and fatigue that comes with it. But at the end of the day, as an experienced engineer, I am not being more productive with it, I will end up being less productive because of all the sharp edges I have to take care of, all the sloppily produced code, unnecessary bloat, hallucinated or injected libraries etc.
Maybe for folks who were not good at maths or had trouble understanding how computers work this looks like a brave new world of opportunities. Surely that app looks good to you, how bad can it be? Just so you and other such vibe-coders understand, here is a parallel.
It is actually fairly simple for a group of aviation enthusiasts to build a flying airplane. We just need to work out some basic mechanics, controls and attach engines. It can be done, I've seen a couple of documentaries too. However, those planes are shit. Why? Because me and my team of enthusiast dont have the depth of knowledge of a team of aviation engineers to inform my decisions.
What is the tolerance for certain types of movements, what kind of materials do I need to pick, what should be my maintenance windows for various parts etc. There are things experts can decide on almost intuitively, yet with great precision, based on their many years of craft and that wonderful thing called human intelligence. So my team of enthusiasts puts together an airplane. Yeah it flies. It can even be steered. It rolls, pitches and yawns. It takes off and lands. But to me it's a black-box, because I don't understand many, many factors, forces, pressures, tensors, effects etc that are affecting an airplane during it's flight and takeoff. I am probably not even aware WHAT I should be aware of. Because I dont have that deep educaiton about mechanical engineering, materials, aerodynamics etc. Neither does my team. So my plane, while impressive to me and my team, will never take off commercially, not unless a team of professionals take it over and remakes it to professional standards. It will probably never even fly in a show. And if me or someone on my team dies flying it, you guessed it - our insurance sure as hell won't cover the costs.
So what you are doing with Claude and other tools, while it may look amazing to you, is not that impressive to the rest of us, because we can see those wheels beginning to fall off even before your first take off. Of course, before I can even tell that, I'd have to actually see your airplane, it's design plans etc. So perhaps first show us some of those "projects heavily powered by Claude" and their great success, especially commercial one (otherwise its a toy project), before you talk about them.
The fact that you are clearly not an expert on the topic of software engineering should guide you here - unless you know what you are talking about, it's better to not say anything at all.
For all I know, he is more competent than you; he figured out how to utilize Claude Code in a productive way, which is a point for him.
I'd have to guess whether you are an expert working on software not well suited for AI, or just average with a stubborn attitude towards AI and potentially not having tried the latest generation of models and agentic harnesses.
Because of their views on the effectiveness of AI agents for generating code.
> I really really want this to be true. I want to be relevant. I don’t know what to do if all those predictions are true and there is no need (or very little need) for programmers anymore.
The rest of the post is basically their human declaration of obsolescence to the programming field. To which someone reacted by saying that this sounds like shilling. And indeed it does for many professional developers, including those that supplement their craft with LLMs. Declaring that you feel inadequate because of LLMs only reveals something about you. Defending this position is a tell that puts anyone sharing that perspective in the same boat: you didn't know what you were doing in the first place. It's like when someone who couldn't solve the "invert a binary tree" problem gets offended because they believed they were tricked into an impossible task. No, you may be a smart person that understands enough of the rudiment of programming to hack some interesting scripts, but that's actually a pretty easy problem and failing to solve it indeed signals that you lack some fundamentals.
> Considering those views are shared by a number of high profile, skilled engineers, this is obviously no basis for doubting someone's expertise.
I've read Antirez, Simon Willison, Bryan Cantrill, and Armin Ronacher on how they work or want to work with AI. From none I've got this attitude that they're no longer needed as part of the process.
Again, a lot of fluff, a lot of of "a number ofs", "highly this, highly that". But very little concrete information. What happened to the pocket PhDs promised for this past summer? Where are the single-dude billion dollar companies built with AI tools ? Or even a multiple-dudes billion dollar companies ? What are you talking about?
Whereas there are what feels like endless examples of high profile, skilled engineers who are calling BS on the whole thing.
Every time people on here comment something about "shilling" or "boosters". It would seem to me that in the rarest of cases someone shares their opinion to profit from it, while you act like that is super common.
Yikes, pretty condescending. Also wrong!
IMO you are strawmanning pretty heavily here.
Believe it or not, using Claude to improve your productivity is pretty dissimilar to vibe coding a commercial airplane(?) which I would agree is probably not FAA approved.
I prefer not to toot my own horn, but to address an idea you seem to have that I don’t know math or CS(?) I have a PhD in astrophysics and a decade of industry experience in tech and other domains so I’m fairly certain I know how math and computers work but maybe not!
I don’t know if you noticed a difference from other hype cycles but other ones were speculative. This one is also speculative but the greater divide is that the literal on the ground usefulness of AI is ALREADY going to change the world.
The speculation is that the AI will get better and will no longer need hand holding.
Is this sarcasm, ragebait, or a serious argument?
So let me explain it more clearly. AI as it is now is already changing the game. It will reduce the demand of swes across every company as an eventuality if we hold technological progress fixed. There is no speculation here. This comes from on the ground evidence from what I see day to day and what I do and my experience pair programming things from scratch with AI.
The speculation is this: if we follow the trendlines of AI improvement for the past decade and a half, the projection of past improvement indicates AI will only get better and better. It’s a reasonable speculation, but it is nonetheless speculative. I wouldn’t bet my life on continuous improvement of AI to the point of AGI but it’s now more than ever before a speculation that is not unrealistic.
Nice slop response. This is the same thing said about blockchain and NFTs, same schtick, different tech. The only thing "AI" has done is convince some people that it's a magical being that knows everything. Your comments seem to be somewhere on that spectrum. And, sure what if it isn't changing the world for the better, and actually makes things much worse? You're probably okay with that too, I guess, as long as your precious "AI" is doing the changing.
We've seen what social media and every-waking-hour access to tablets and the internet has done to kids - so much harm that some countries have banned social media for people under a certain age. I can see a future where "AI" will also be banned for minors to use, probably pretty soon too. The harms from "AI" being able to placate instead of create should be obvious, and children shouldn't be able to use it without adult supervision.
>The speculation is that the AI will get better and will no longer need hand holding.
This is nonsense. No AI is going to produce what someone wants without telling it exactly what to do and how to do it, so yes, it will always need hand holding, unless you like slurping up slop. I don't know you, if you aren't a bot, you might just be satisfied with slop? It's a race to the bottom, and it's not going to end up the way you think it will.
You're not thinking clearly. A couple years ago we didn't even have AI who could do this, then chatGPT came out we had AI who could barely do it, then we had AI who could do simple tasks with A lot of hand holding, now we have AI who can do complex human tasks with minimal hand holding. Where do you think the trendline is pointing.
Your hypothesis is going against all evidence. It's more wishful thinking and irrational. It's a race to the bottom because you wish it will be a race to the bottom, and we both know the trendline is pointing in the opposite direction.
>We've seen what social media and every-waking-hour access to tablets and the internet has done to kids - so much harm that some countries have banned social media for people under a certain age. I can see a future where "AI" will also be banned for minors to use, probably pretty soon too. The harms from "AI" being able to placate instead of create should be obvious, and children shouldn't be able to use it without adult supervision.
I agree AI is bad for us. My claim is it's going to change the world and it is already replacing human tasks. That's all. Whether that's good or bad for us is an ORTHOGANOL argument.
- bad for environment: how? Why? - takes all creative output and doesn’t credit: common crawl has been around for decades and models have been training for decades, the difference is that now they’re good. Regurgitating training data is a known issue for which there are mitigations but welcome to the world of things not being as idealistic as some Stallman-esque hellscape everyone seems to want to live in - it’s bad and so no one should use it and any professionals who do don’t know what they’re doing: I have been so fortunate to personally know some of the brightest minds on this planet (Astro departmentments, AI research labs) and majority of them use AI for their jobs.
On a 35 day-old account, yes. Anything "post-AI" is suspect now.
The rest of your comment reads like manufactured AI slop, replying to things I never even wrote in my one sentence comment. And no surprise coming from an account created 1 day ago.
The latest chatgpt for example will produce comments that are now distinguishable from the real thing only because they’re much better written. It’s insane that the main visible marker rn is that the arguments and writings it crafts are superior then what your average joe can write.
My shit writing can’t hold a candle and that’s pretty obvious. AI slop is not accepted here but I can post an example of what AI slop will now look like, if AI responded to you it would look like this:
Fair to be skeptical of new accounts. But account age and “sounds like AI” are not workable filters for truth. Humans can write like bots, bots can write like humans, and both can be new. That standard selects for tenure, not correctness.
More importantly, you did not engage any claim. If the position is simply “post-AI content from new accounts is suspect,” say that as a moderation concern. But as an argument, suspicion alone does not refute anything.
Pick one concrete claim and say why it is wrong or what evidence would change your mind. Otherwise “this reads like slop” is just pattern matching. That is exactly the failure mode being complained about.
Are you an astronaut?
Few people can do what I do and as a result I likely make more money than you. But now with AI… everyone can do what I do. It has leveled the playing field… what I was before now matters fuck all. Understand?
I still make money right now. But that’s unlikely to last very long. I fully expect it to disappear within the next decade.
Why is it that in every hype there are always the guys like you that want to punish the non-believers? It's not enough to be potentially proven correct, your anger requires the demise of the heretics. It was the same story for cryptocurrencies.
There’s emotions on both sides and the goal is to call it out, throw it to the side and cut through into the substance. The attitude should be: Which one of us is actually right? Rather than: I’m right and you’re a fucking idiot attitude I see everywhere.
Both certainly look very negative and over the top.
So it’s not like your anger is any better.
Next time before you waste ten minutes typing out these self aggrandizing tirades maybe try asking the AI to just write it for you instead
> but I will do everything in my power to avoid getting on their plane
"Littered" is a great verb to use here. Also I did not ask for a deviated proxy non-measure, like how many people who are choking themselves to death in a meaningless bullshit job are now surviving by having LLMs generate their spreadsheets and presentations. I asked for solid proof of succesful, commercial products built up by dreaming them up through LLMs.
What I’m saying is this: From my pov Everyone is using LLMs to write code now. The overwhelming majority of software products in existence today are now being changed with LLM code.
The majority of software products being created from scratch are also mostly LLM code.
This is obvious to me. It’s not speculation, where I live and where I’m from and where I work it’s the obvious status quo. When I see someone like you I’m thinking because the change happened so fast you’re one of the people living in a bubble. Your company and the people around you haven’t started using it because the culture hasn’t caught up.
Wait until you have that one coworker who’s going at 10x speed as everyone else and you find out it’s because of AI. That is what will slowly happen to these bubbles. To keep pace you will have to switch to AI to see the difference.
I also don’t know how to offer you proof. Do you use google? If so you’ve used products that have been changed by LLM code. Is that proof? Do you use any products built by a start up in the last year? The majority of that code will be written by an LLM.
We have been using LLMs since 2021, if I havent repeated that enough in these threads. What culture do I have to catch up with? I have been paying top tier LLM models for my entire team since it became an option. Do you think you are proselytizing to the un-initiated here? That is a naive view at best. My issue is that the tools are at best a worse replacement for the pre-2019 google search and at worst a huge danger in the hands of people who dont know what they are doing.
Obviously your team disagrees that it’s a worse replacement for google or else why demand it against your will?
> at worst a huge danger in the hands of people who dont know what they are doing.
I agree with this. But the upside negates this and I agree with your own team on that.
Btw if you’re paying top dollar for AI.. your developers are unlikely using it as a google search replacement. At top dollar AI is used as an agent. What it ends up doing is extremely different from a google search in this mode. That may be good or bad but it is a distinctly different outcome then a google search and that makes your google analogy ill fitted to what your team is actually using it for.
At the recent AWS conference, they were showcasing Kiro extensively with real life products that have been built with it. And the Amazon developers all allege that they've all been using Kiro and other AI tools and agents heavily for the past year+ now to build AWS's own services. Google and Microsoft have also reported similar internal efforts.
The platforms you interact with on a daily basis are now all being built with the help of AI tools and agents
If you think no one is building real commercial products with AI then you are either blind or an idiot or both. Why don't you just spend two seconds emailing your company AWS ProServe folks and ask them, I'm sure they'll give you a laundry list of things they're using AI for internally and sign you up for a Kiro demo as well
I'd say it's more about the fear of their $50billion+ investments not paying off is creeping up on them.
The point is that without mentioning specific software that readers know about, there isn’t really a way to evaluate a claim of 20x.
Please just for fun - reach out to for example Klarna support via their website and tell me how much of your experience can be attributed to an anti-AI bias and how much to the fact that the LLMs are a complete shit for any important production use cases.
From the OP. If you think that's too much then we agree.
I love coding. But reality is reality and these fools just aren’t keeping pace with how fast the world is changing.
That's the point champ. They seem great to people when they apply them to some domain they are not competent it, that's because they cannot evaluate the issues. So you've never programmed but can now scaffold a React application and basic backend in a couple of hours? Good for you, but for the love of god have someone more experienced check it before you push into production. Once you apply them to any area where you have at least moderate competence, you will see all sorts of issues that you just cannot unsee. Security and performance is often an issue, not to mention the quality of code....
They need a heavy hand to police to make sure they do the right thing. Garbage in, garbage out.
The smarter the hand of the person driving them, the better the output. You see a problem, you correct it. Or make them correct it. The stronger the foundation they're starting from, the better the production.
It's basically the opposite of what you're asserting here.
Ahaha, weren’t you the guy who wrote an opus about planes? Is this your baseline for “stuff where LLMs break and real engineering comes into the room”? There’s a harsh wake up call for you around the corner.
And don’t get me wrong - I totally understand this personality. There are a similar few I’ve worked with recently who are broadly quite skeptical of what seems to be an obvious fact to me - their roles will need to change and their skillsets will have to develop to take advantage of this new technology.
Friendly reminder that this style of discourse is not very welcome on HN: https://news.ycombinator.com/newsguidelines.html
My experience comes from the fact that after over a decade of working as a swe I no longer write code. It’s not some alternate reality thing or reading headlines. It’s my daily life that has changed.
> I no longer write code
If not, then for this area you’ve been steam rolled.
Anyway main point is, you’re looking at the hype headlines which are ludicrous. Where most optimists come from is that they are using it in the daily to code. To them it’s right in front of their eyes.
I’m not sure what your experience is but my opinion on AI doesn’t come from speculation. It comes from on the ground experience on how AI currently has changed my job role completely. If I hold the technology to be fixed and to not improve into the future then my point still stands. I’m not speculating. Most AI optimists aren’t speculating.
The current on the ground performance is what’s causing the divide. Some people have seen it fully others only have a rudimentary trial.
> I no longer write code. I’ve been a swe for over a decade. AI writes all my code following my instructions. My code output is now expected to be 5x what it was before because we are now augmented by AI. All my coworkers use AI. We don’t use ChatGPT we use anthropic. If I didn’t use AI I would be fired for being too slow.
We have half the thread saying it’s 5x and the other half saying they’re delusional and lack critical thinking.
I think it’s obvious who lacks critical thinking. If half the thread is saying on the ground AI has changed things and the other half just labels everyone as crazy without investigation… guess which one didn’t do any critical thinking?
Last week I built an app that cross compiled into Tauri and electron that’s essentially a google earth clone for farms. It uses mapbox and deckgl and you can play back gps tracks of tractor movements and the gps traces change color as the tractor moves in actual real time. There’s pausing, seeking, bookmarking, skipping. All happening in real time because it’s optimized to use shader code and uniforms to do all these updates rather than redrawing the layers. There’s also color grading for gps fix values and satellite counts which the user can switch instantaneously to with zero slow down on tracks with thousands and thousands of points. It all interfaces with an API that scans gcp storage for gps tracks and organizes it into a queryable api that interfaces with our firebase based authentication. The backend is deployed by terraform and written in strictly typed typescript and it’s automatically deployed and checked by GHA. Of course the electron and tauri app have GUI login interfaces that work fully correctly with the backend api and it all looks professionally designed like a movie player merged with Google earth for farm orchards.
I have rudimentary understanding for many of the technologies involved in the above. But I was able to write that whole internal tool in less than a week thanks to AI. I couldn’t have pulled it off without rudimentary understanding of the tech so some novice swe couldn’t really do it without the optimizations I used but that’s literally all I needed. I never wrote shader code for prod in my life and left to its own devices the AI would have come up with an implementation that’s too laggy to work properly.
That’s all that’s needed. Some basic high level understanding and AI did everything else and now our company has an internal tool that is polished beyond anything that would’ve been given effort to before AI.
I’m willing to bet you didn’t use AI agents in a meaningful way. Maybe copying and pasting some snippets of code into a chatbot and not liking the output. And then you do it every couple of weeks to have your finger on the pulse of AI.
Go deeper. Build an app with AI. Hand hold it into building something you never built before. It’s essentially a pair programming endeavor. Im willing to bet you haven’t done this. Go in with the goal of building something polished and don’t automatically dismiss it when the AI does something stupid (it inevitably will) Doing this is what actual “critical thinking” is.
My critical thinking is sharp enough to recognize that you're the recently banned ninetyninenine user [0]. Just as unbalanced and quarrelsome as before I can see. It's probably better to draw some conclusion from a ban and adjust, or just leave.
Why don’t you respond to my points rather than attack me.
Because I believe you have a "flexible" relationship to the truth, so I'm not wasting any more time.
It’s obvious to anyone if your output is 5x less than everyone else you will eventually be let go. There’s no paradigm shift where the boss suddenly announced that. But the underlying unsaid expectation is obvious given what everyone is doing.
What happened was this, a couple new hires and some current employees started were using AI. There output was magnified and they were not only having more output but they were deploying code outside their areas of expertise doing dev ops, infra, backend and frontend.
This spread and within months everyone in the company was doing it. The boss can now throw a frontend job to a backend developer and now expect completion in a day or less. This isn’t every task but such output for the majority of tasks it’s normal.
If you’re not meeting that norm it’s blindingly obvious. The boss doesn’t need to announce anything when everyone is faster. There was no deliberate culture shift where the boss announced it. The closest equivalent is the boss hiring a 10x engineer to work alongside you and you have to scramble to catch up. The difference is now we know exactly what is making each engineer 10x and we can use that tool to also operate at that level.
Critical thinking my ass. You’re just labeling and assuming things with your premeditated subconscious bias. If anything it’s your perspective that is religious.
> The boss can now throw a frontend job to a backend developer and now expect completion in a day or less.
Right. So essentially vibe coding in unknown domains, sounds great. Truly professional.
Your initial stance was disbelief. Now you’re just looking down at it as unprofessional.
Bro, I fucking agree. It’s unprofessional. But the entire point initially was that you didn’t believe it and my objective was to tell you that this is what’s happening in reality. Scoff at it all you want, as AI improves less and less “professional” people will be able to enter our field and operate at the same level as us.
LLMs can be handy, but they're not trustworthy. "Own and be responsible for the code you commit" is an impossible ideal to uphold if you never actually sit down and internalize the code in your code base. No "summaries," no "explanations."
I mean from the off, people were claiming 10x probably mostly because it's a nice round number, but those claims quickly fell out of the mainstream as people realised it's just not that big a multiplier in practice in the real world.
I don't think we're seeing this in the market, anywhere. Something like 1 engineer doing the job of 20, what you're talking about is basically whole departments at mid sized companies compressing to one person. Think about that, that has implications for all the additional management staff on top of the 20 engineers too.
It'd either be a complete restructure and rethink of the way software orgs work, or we'd be seeing just incredible, crazy deltas in output of software companies this year of the type that couldn't be ignored, they'd be impossible to not notice.
This is just plainly not happening. Look, if it happens, it happens, 26, 27, 28 or 38. It'll be a cool and interesting new world if it does. But it's just... not happened or happening in 25.
One other thing I have seen however is the 0x case, where you have given too much control to the llm, it codes both you and itself into pan’s labyrinth, and you end up having to take a weed wacker to the whole project or start from scratch.
Ask it a question about something you know well, and it'll give you garbage code that it's obviously copied from an answer on SO from 10 years ago.
When you ask it for research, it's still giving you garbage out of date information it copied from SO 10 years ago, you just don't know it's garbage.
"Agents use tools in a loop to achieve a goal."
If you don't give any tools, you get hallucinations and half-truths.
But you give one a tool to do, say, web searches and it's going to be a lot smarter. That's where 90% of the innovation with "AI" today is coming from. The raw models aren't gettin that much smarter anymore, but the scaffolding and frameworks around them are.
Tools are the main reason Claude Code is as good as it is compared to the competition.
> The raw models aren't gettin that much smarter anymore, but the scaffolding and frameworks around them are.
For a tool use loop local models are getting to "OK" levels, when they get to "pretty good", most of my own stuff can run locally, basically just coordinating tool calls.
That Python you just got might look good, but could be rewritten from 50 lines to 5, it's written in 2010-style, it's not using modern libraries, it's not using modern syntax.
And it is 50 to 5. That is the scale we're talking about in a good 75% of AI produced code unless you challenge it constantly. Not using modern syntax to reduce boilerplate, over-guarding against impossible state, ridiculous amounts of error handling. It is basically a junior dev on steriods.
Most of the time you have no idea that most of that code is totally unnecessary unless you're already an expert in that language AND libraries it's using. And you're rarely an expert in both or you wouldn't even be asking as it would have been quicker to write the code than even write the prompt for the AI.
Your productivity boost will depend entirely on a combination of how much you can remove yourself from the loop (basically, the cost of validation per turn) and how amenable the task/your code is to agents (which determines your P(success)).
Low P(success) isn't a problem if there's no engineer time cost to validation, the agent can just grind the problem out in the background, and obviously if P(success) is high the cost of validation isn't a big deal. The productivity killer is when P(success) is low and the cost of validation is high, these circumstances can push you into the red with agents very quickly.
Thus the key to agents being a force multiplier is to focus on reducing validation costs, increasing P(success) and developing intuition relating to when to back off on pulling the slot machine in favor of more research. This is assuming you're speccing out what you're building so the agent doesn't make poor architectural/algorithmic choices that hamstring you down the line.
To be direct, this reads like a fluff comment written by AI with an emphasis on probability and metrics. P(that) || that.
I’ve written software used by a local real estate company to the Mars Perseverance rover. AI is a phenomenally useful tool. But be weary of preposterous claims.
Given that, if you want to revisit your comment in a constructive way rather than doing an empty drive by, I'll read your words with an open mind.
So the "verbose, straightforward code with clear cut test scenarios" is already written by a human?
I have been working professionally for ~16 years in software development, and scenarios like this was about 5% of my work.
Purely anecdotal, but I've seen that level of productivity from the vibe tools we have in my workplace.
The main issue is that 1 engineer needs to have the skills of those 20 engineers so they can see where the vibe coding has gone wrong. Without that it falls apart.
An LLM helps most with surface area. It expands the breadth of possibilities a developer can operate on.
> one person is doing the work of 20 with them in december 2025 at least
And of course, getting to the point where you can write a good foundation has always been the bulk of the work. I don't see that changing anytime soon.
You talk as if you haven't used a LLM since 2024. It's now almost 2026 and things have changed a lot.
Whenever I discuss the problems that my peers and I have using these things, it's always something along the lines of "but model X.Y solves all that!", so I obediently try again, waste a huge amount of time, and come back to the conclusion that these things aren't great at generation, but they are fantastic at summarization and classification.
When I use them for those tasks, they have real value. For creation? Not so much.
I've stopped getting excited about the "but model X.Y!!" thing. Maybe they are improving? I just personally haven't seen it.
But according to the AI hypers, just like with every other tech hype that's died over the past 30 years, "I must just be doing it wrong".
Will admit It's not great (probably not even good) but it definitely has throughput despite my absolute lack of caring that much [0]. Once I get past a certain stage I am thinking of doing an A-B test where I take an earlier commit and try again while paying more attention... (But I at least want to get where there is a full suite of UOW cases before I do that, for comparison's sake.)
> Those twenty engineers must not have produced much.
I've been considered a 'very fast' engineer at most shops (e.x. at multiple shops, stories assigned to me would have a <1 multiplier for points[1])
20 is a bit bloated, unless we are talking about WITCH tier. I definitely can get done in 2-3 hours what could take me a day. I say it that way because at best it's 1-2 hours but other times it's longer, some folks remember the 'best' rather than median.
[0] - It started as 'prompt only', although after a certain point I did start being more aggressive with personal edits.
[1] - IDK why they did it that way instead of capacity, OTOH that saved me when it came to being assigned Manual Testing stories...
Throughput without being good will just lead to more work down the line to correct the badness.
It's like losing money on every sale but making up for it with volume.
You lost me here. Come back when you're proud of it.
[1] I actually think it might be true for certain kinds of jobs.
> but nobody can hope to quantify that with any degree of credibility yet
Orchestrating harmony is no mean feat.
What doesn't help is that the current state of AI adoption is heavily top-down. What I mean is the buy-in is coming from the leadership class and the shareholder class, both of whom have the incentive to remove the necessary evil of human beings from their processes. Ironically, these classes are perhaps the least qualified to decide whether generative AI can replace swathes of their workforce without serious unforeseen consequences. To make matters worse, those consequences might be as distal as too many NEETs in the system such that no one can afford to buy their crap anymore; good luck getting anyone focused on making it to the next financial quarter to give a shit about that. And that's really all that matters at the end of the day; what leadership believes, whether or not they are in touch with reality.
What we do know is this. If AI keeps improving at the current rate it’s improving then it will eventually hit a point where we don’t need software engineers. That’s inevitable. The way for it to not happen is for this technology to hit an impenetrable wall.
This wave of AI came so fast that there are still stubborn people who think it’s a stochastic parrot. They missed the boat.
The reason for my excitement about the conclusion is obvious: I want programmers and people to stay in demand. I find the AI future to be quite bleak if this tech really does lead to AGI, but maybe that’s just me. I think we’re at a pretty cool spot with this technology right now—where it’s a powerful tool—but at some point, if or when it’s way smarter than you or me… I'm not sure that's a fun happy future. I think work is pretty tied to our self worth as humans.
Care to share a few of these examples?
Another example: I also maintain the back-end API for these apps. There was a lot of old utility code that was messy, unorganized, and frankly gross. I had Codex completely reorganize the code into a sensible folder structure and strip out any functions that weren’t being used anymore—something like 300-plus functions. Doing that by hand would easily have taken a day or more
I could go on, but those were the two initial “aha” moments that showed me how powerful this tool can be when it’s used for the right tasks.
> ...easily a week or more
Got any examples worth "easily a week or more?"
Without AI it would have taken me several months, and that is only if I managed to avoid burnout. The vendor quotes we got just to see if we could outsource the work were all six figures.
Any decent IDE can do that refactoring instantly.
> I could go on
Clearly.
> Any decent IDE can do that refactoring instantly.
The refactoring? Sure. But IDEs don't read your code and logically categorize it into folders. From what I understand, they are saying that they outsourced the thinking part as well.
Reading Weizenbaum today is eye opening: https://en.wikipedia.org/wiki/Computer_Power_and_Human_Reaso...
I feel like LLM's are specifically selected to impress people that have a lot of influence. People like investors and CEO's. Because a "AI" that does not impress this section of the population does not get adopted widely.
This is one of the reasons I think AI will never really be an expert as it does not need to be. It only needs to adopt a skill (for example coding) to pass the examination of the groups that decide if it is to be used. It needs to be "good enough to pass".
> If the people with money (and the decision) do not understand the tech or are not able to even fake that understanding, will they bet their money on it?
This is the most important quote for any AI coding discussion.
Anyone that doesn't understand how the tools they use came to be is doomed to reinvent them.
> The folly of many people now claiming that “prompts are the new source code”,
These are the same people that create applications in MS Excel.
But so many problems (programming, but also physics and math) start as informal word problems. A major skill is being able to turn the informal into something formal and precise.
Ones that want their application to work? :) the last piece of software you should be knocking is MS Excel, in my 30+ year career the one common thread just about everywhere I worked (or contracted at) has used Excel to run some amazing sh*t
This exactly the argument I try to make Excel (spreadsheets) is a great interface for processing and working with certain type of data, think economic etc. but it is not great for other things. There we need a different interface to efficiently communicate our intent. For example programming languages or even writing a novel would not work very well in a Excel sheet (though no doubt someone has attempted it).
I think that it also is a comparable to the ai side we see now. Do something for real, use real database or programmer. Non-programmer needs something, vibe code or excel
Are they?
I feel like AIs fill in a lot of the blanks with reasonable assumptions rather than the input being precise
Sometimes it looks like about anything else would be better as a programming interface.
VB6 and Delphi were the best possible cognitive impedance match available for domain experts to be able to whip up something that could get a job done. We haven't had anything nearly as productive in the decades since, as far as just letting a normie get something done with a computer.
You'd then hire an actual programmer to come in and take care of corner cases, and make things actually reliable, and usable by others. We're facing a very similar situation now, the AI might be able to generate a brittle and barely functional program, but you're still going to have to have real programmers make it stable and usable.
It really put everything into perspective to where we are now.
Pre-industrial revolution whole towns and families built clothing and had techniques to make quality clothes.
When the machines came out it wasn't overnight but it wiped out nearly all cottage industries.
The clothing it made wasn't to the same level of quality, but you could churn it out faster and cheaper. There was also the novelty of having clothes from a machine which later normalised it.
We are at the beginning of the end of the cottage industry for developers.
We had "free clothes" for years, decades now. I don't mean cheap I mean literally free, as in $0.0 software. Cheaper software isn't new.
Also there are still clothe designers, fashion runways, and expensive Patagonia vests today. The clothing industry is radically different from back then but it's definitely not gone.
> The clothing industry is radically different from back then but it's definitely not gone.
Small towns had generations of people who had learned skills in making clothing / yarn. To do the work you needed years of experience and that's all you knew.
Once the industrial revolution hit they hired low skilled workers that could be dumped at a moments notice. It made whole villages destitute. Some survived, but the far majority became poor.
That was one industry. We now have AI at a point to wipe out multiple industries to a similar scale.
We have cheap (or free) software for large markets, and certain small markets where software developers with hobbies have made something. If every niche that will never be able to afford a large 6-figure custom software could get slop software for an affordable price, then that establishes a foot-hold for working its way up the quality ladder.
Writing code is not at all the most time consuming part of software development.
It started there, yes.
> Writing code is not at all the most time consuming part of software development.
The current Vibe coding systems can do the full pipeline.
For small and relatively simple projects, sure. In codebases that are either large, complex, or fairly novel, I've been consistently disappointed by the hands-off approach.
There are plenty of small, relatively simple projects out there. Lots of websites and cookie cutter apps to make. But that's never been the stuff I wanted to work on anyway.
I use Claude code constantly, but it's not nearly as reliable as any of the colleagues I've worked with.
If llms can churn out software like Toyota churns out cars, AND do maintenance on it, then the craftsmen (developers of today) are going to be displaced.
1. LLMs are only getting better from here. With each release, we continue to see improvements in their capabilities. And strong competition is driving this innovation, and will probably not stop anytime soon. Much of the world is focused on this right now, and therefore much of the world’s investments are being poured into solving this problem.
2. They’re using the wrong models for the wrong job. Some models are better than others at some tasks. And this gap is only shrinking (see point 1).
3. Even if LLMs can’t solve complex problems (and I believe they can/will, see points 1 and 2), much of our jobs is refactoring, writing tests, and hand coding simpler tasks, which LLMs are undeniably good at.
4. It’s natural to deny LLMs can eventually replace much/all of what we do. Our careers depend on LLMs not being able to solve complex problems so that we don’t risk/fear losing our careers. Not to mention the overall impact to the general way our lives are impacted if AGI becomes a reality.
I’ve been doing this a while, and I’ve never seen the boost to productivity that LLMs bring. Yes, I’ve seen them make a mess of things and get things wrong. But see points 1-3.
I think both this sentiment and the article are on the same wrong track, which is to see programming as solving well defined problems. The way I see it, the job is mostly about taking ill defined needs and turning them into well defined problems. The rest is just writing code. From this perspective, whether LLM prompting can replace writing code is only marginally relevant. It’s automating the easy part.
Yes, but so do your coworkers? Do you complain about every PR you need to read? Are they all compressed diamonds of pure genious, not a single missed character or unoptimised function? Not a bad or suboptimal decision in sight?
If so, shoot me a mail, I want to work where you're working =)
The LLM isn't replacing your coworkers, it's a tool they can (and IMO should) learn to use, just like an IDE or a debugger.
You either prompt and read the code it wrote to understand it before making a PR, or you prompt and drop the PR for your team. The latter is disrespectful.
This has been my biggest hurdle so far. Sure the models do great at writing code, find things faster than I would. But time wise I still have to read what it wrote, in depth.
ETA: I also implicitly trust my coworkers. This trust has been built over years. I don’t trust LLM generated code the same way.
As for "reading in depth", it all depends on what you're doing, for most stuff I can just see if it looks good or not, I don't need to check out the PR and run it on my machine with a step-debugger to see what's going on.
And stuff should have unit tests anyway, if the tests pass and test coverage is sufficient - do you really need to go through the code with a fine toothed comb? If it quacks like a duck, walks like a duck and looks like a duck, isn't it duck enough. Do you need to dissect it and see if it's Duck all the way through?
At some point you just need to trust the tools you're using.
This wave IS different.
It isn't a matter of "if" but "when".
I am a long-time software developer too... but I am strongly encouraging people to embrace the future now and get creative in finding ways to adapt.
There will always be demand for smart and creative people. BUT ... if you don't look up and look around you right now at some point you will be irrelevant.
Also see Ray Dalio's "Principles" book on embracing reality. A critical principle in the modern age of AI.
Nothing but love for my fellow developers!!
[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Plus those meals are disgusting! :-)
Plus plus… those meals are actually expensive!!
One can treat current LLMs as a layer of "cheese" for any software development or deployment pipeline, so the goal of adding them should be an improvement for a measurable metric (code quality, uptime, development cost, successful transactions, etc).
Of course, one has to understand the chosen LLM behaviour for each specific scenario - are they like Swiss cheese (small numbers of large holes) or more like Havarti cheese (large number of small holes), and treat them accordingly.
Before any human is pinged about a PR, have a properly tuned LLM look at it first so actual people don't have to waste their time pointing out typos in log messages.
It's another interesting attempt at normalising the bullshit output by LLMs, but NO. Even with the entshittified Boeing, the aviation industry safety and reliability records, are far far far above deterministic software (know for a lot of un-reliability itself), and deterministic, B2C software to LLMs in turn is what Boeing and Airbus software and hardware reliablity are for the B2C software...So you cannot even begin to apply aviation industry paradigms to the shit machines, please.
Engines are reliable to about 1 anomaly per million flight hours or so, current flight software is more reliable, on order of 1 fault per billion hours. In-flight engine shutdowns are fairly common, while major software anomalies are much rarer.
I used LLMs for coding and troubleshooting, and while they can definitely "hit" and "miss", they don't only "miss".
This thread is full of antidotes from “AI is useless for me” to “AI changes everythingfor me” I believe each and every one of them.
In firm wait and see mode.
[The "children of the magenta line"](https://www.computer.org/csdl/magazine/sp/2015/05/msp2015050...) is a god explanation of this, and is partly why I often dissuade junior devs from pretty user friendly using tools that abstract away the logic beneath them.
I would only be happy if in the end the author turns out to be right.
But as the things stand right now, I can see a significant boost to my own productivity, which leads me to believe that fewer people are going to be needed.
I can see a future where software development goes the same way. My wife works in science and I see all kinds of things in a casual observation of her work that could be made more efficient with good software support. But not by enough to pay six-figures per year for multiple devs to create it. So it doesn’t get done and her work and the work of tens of thousands like her around the world is less efficient as a result.
In a world where development is even half as expensive, many such tasks become approachable. If it becomes a third or quarter as expensive, even more applications are now profitable.
I think far more people will be doing something that creates the outcomes that are today created by SWEs manually coding. I doubt it will be quite as lucrative for the median person doing it, but I think it will still be well above the median wage and there will be a lot of it.
1. Fundamentals do change, Yuval Noah Harari made this point in the book Sapiens, but basically there are core beliefs (in fact the idea that things do change for the better is relatively new, “the only constant is change”. Wasn’t really true before the 19th century.
What does “the great disrupter we can’t adapt to” mean exactly? If humans annihilate themselves from climate change, the earth will adapt, the solar system will shrug it off and the universe won’t even realize it happened.
But like, I am 100% sure humans will adapt to the AI revolution. Maybe we let 7 billion people die off, and the 1% of the rest enslave the rest of us to be masseuses and prostitutes and live like kings with robot servants, but I’m not super comfortable with that definition if “adaptation”.
For most of human history and most of the world “the rest of us” don’t live all that well, is that adaptation? I think most people include a healthy large, and growing middle class in their definition of success metrics.
There might be slight local dips along the timeline, but I think most Westerners (and maybe most people, but my lived experience is Western) would not willingly trade places with their same-percentile positioned selves from 100, 200, 500, 1000, 2000, 10K, 50K, or 250K years ago. The fact that few would choose to switch has to be viewed with some positive coefficient in a reasonable success metric.
Ironically, like an LLM, this article feels like more like an amalgamation of plenty of other opinions on the growth of AI in the workplace rather than any original thoughts. There's not really anything "new" here, just putting together a load of existing opinions.
(I am not suggesting that Jason got an AI to write this article, though that would be funny).
Yes software developer are still going to be need, except much fewer of us, exactly like fully automated factories still need a few humans around, to control and build the factory in first place.
Am puzzled why so many on HN cannot see this. I guess most users on HN are employed? Your employers - let me tell you - are positively salivating at the prospect of firing you. The better LLM's get the fewer of you will be needed.
I have seen projects where translator teams got reduced, asset creation teams, devops head count, support teams on phone lines,...
It is all about how to do more with less, now with AI help as well.
> The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
> That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python.
> The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.
I don't agree with this:
> To folks who say this technology isn’t going anywhere, I would remind them of just how expensive these models are to build and what massive losses they’re incurring. Yes, you could carry on using your local instance of some small model distilled from a hyper-scale model trained today. But as the years roll by, you may find not being able to move on from the programming language and library versions it was trained on a tad constraining.
Some of the best Chinese models (which are genuinely competitive with the frontier models from OpenAI / Anthropic / Gemini) claim to have been trained for single-digit millions of dollars. I'm not at all worried that the bubble will burst and new models will stop being trained and the existing ones will lose their utility - I think what we have now is a permanent baseline for what will be available in the future.
I'm not convinced DeepSeek is making money hosting these, but it's not that far off from it I suspect. They could triple their prices and still be cheaper than Anthropic is now.
Weren't these smaller models trained by distillation from larger ones, which therefore have to exist in order to do it? Are there examples of near state of the art foundation models being trained from scratch in low millions of dollars? (This is a genuine question, not arguing. I'm not knowledgeable in this area.)
Kimi K2 Thinking was reportedly trained for $4.6m: https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-rele...
Both of those were frontier models at the time of their release.
Another interesting number here is Claude 3.7 Sonnet, which may people (myself included) considered the best model for several months after its release and was apparently trained for "a few tens of millions of dollars": https://www.oneusefulthing.org/p/a-new-generation-of-ais-cla...
If they are then they're in trouble, because the more paying customers they get the more money they lose!
Someone on the page already quoted Dijkstra, recommend reading that, he was correct.
Prompt engineering isn't engineering at all, it's throwing words at a wall to see which ones stick then declaring success if the outcome looks at all recognizable. That isn't actually success.
Edgar Dijkstra called it nearly 50 years ago: we will never be programming in English, or French, or Spanish. Natural languages have not evolved to be precise enough and unambiguous enough. Semantic ambiguity and language entropy will always defeat this ambition.
Assuming that product designers articulate their designs in English to software engineers, who subsequently turn those ideas into products, doesn’t it stand to reason that this process is to some degree approachable?
I’m not saying that we are at that point or that we will be soon, but it just seems like flawed reasoning.
At the end, I don't see it going beyond being a glorified form-assistant who can search internet for answers and summarize. That boils down to chat bots that will remain and become part of every software component that ever need to interface with humans.
Agent stuff is just a fluff that is providing hype-cushion around chat bots and will go away with hype cycle.
If you need three programmers to deliver a project, one doing boilerplate, one intermediate and one doing hard task. And the AI can’t do the hard task then you’ve still got two unemployed dudes.
So yeah you can make the technically correct argument that it still needs a programmer but it glosses over the coming storm in real world job market.
The only other option to square that circle is to assume that A) we suddenly need 3x as much projects and B) boilerplate guy can somehow graduate to hard tasks
Companies won’t hire people just to train.
It was discovered in the 1970s (at the latest) that the hard part of software is figuring out WHAT to build not HOW to build it—and the two should be separate responsibilities with separate personnel bearing separate talents. (Do NOT let your programmers do systems analysis!)
Furthermore, it was discovered that without a clear, precise description of what to build, even the most talented programmers will happily use industry best practice to build the wrong thing, and that is worse than useless: it's a net cost to the business. This is something that programmers have in common with LLMs. So it turns out, in order to build software correctly and cost-effectively, you need to perform lots of systems analysis up front, then through a stepwise refinement process design and document your solution, yielding a detailed spec. It also turns out that LLMs, like human programmers, do just fine if you give them a detailed spec and hold them accountable to that spec!
So much of the "grunt work" of programming can be handed to the LLM—IF you perform the necessary systems analysis up front to determine what needs to be built! (Bryce's Law: Programming is a translation function, taking human-readable requirements to machine-executable instructions.)
As the AI era matures we are either going to see a revival of PRIDE—the original and still most complete software development methodology, but minus the programmers Milt and Tim Bryce loathed so much—or the entire collapse of software as a field.
Press X to doubt.
My views:
- Developers are expensive
- Most development isn't that hard or that creative
- LLMs can already do some of the sub tasks that the above kind of development entails
- If the above kind of development disappears, then there is only the harder, creative kind of development left
- Less development jobs and more developers will reduce dev salaries and increase dev competition regardless of the kind of development that you do. Any such change will be fairly slow and gradual
- The 2010s Everyone Can Code era is gone and is not going to come back as a result of the above
we've never had machines that can reason. today, they reason badly. tomorrow, they'll reason better. not all ai research white papers will pan out, but some will. the ai research community is acutely aware of the limitations in reasoning; it's their whole mission right now. 100% chance someone will make progress. Analogous to the chip industry or self driving, in that regard
incremental improvements in reasoning will broaden the dead zone for human developers
what difference does it make if in 20 years NASA still needs a few human developers but the rest of us are unemployed? ai agents still can't get you to the moon with one shot coding session in 2045? who cares?
the "still can't" we really need to be worried about is "politicians still can't consider UBI"
Agentic IDEs are like power tools.
While most construction work today uses power tools, there are still lots of artisans productively using hand tools.
LLMs can be useful and developers can still be productive without them.
Things get weird when we confuse the developers with the power tools.
The massive increase in slop code and loss of innovation in code will establish an unavoidable limit on LLMs.
I mean, in general not only do they have all of the crappy PHP code in existence in their corpus but they also have Principia Mathematica, or probably The Art of Computer Programming. And it has become increasingly clear to me that the models have bridged the gap between "autocomplete based on code I've seen" to some sort of distillation of first order logic based on them just reading a lot of language... and some fuzzy attempt at reasoning that came out of it.
Plus the agentic tools driving them are increasingly ruthless at wringing out good results.
That said -- I think there is a natural cap on what they can get at as pure coding machines. They're pretty much there IMHO. The results are usually -- I get what I asked for, almost 100%, and it tends to "just do the right thing."
I think the next step is actually to actually make it scale and make it profitable but also...
fix the tools -- they're not what I want as an engineer. They try to take over, and they don't put me in control, and they create a very difficult review and maintenance problem. Not because they make bad code but because they make code that nobody feels responsible for.
Also, training data isn’t just crawled text from the internet anymore, but also sourced from interactions of millions of developers with coding agents, manually provided sample sessions, deliberately generated code, and more—there is a massive amount of money and research involved here, so that’s another bet I wouldn’t be willing to make.
Not even a nitpick because the scale is indeed orders of magnitude different, but…
There was a lot of chatter, government attention, and both public and private money chasing 4GL’s in the 1980’s, due to what turned out to be (for the US, at least) a phantom “software crisis”. It was mostly for naught?
Same in Japan, where you can argue about the motivations of MITI’s “Fourth Generation Project”, but at its software core was a primeval 4GL in the form of Prolog. Their perceptions of a Japanese software crisis were ultimately more well-founded.
So, yeah, I agree.
Both sets of goalposts are firmly affixed to their wheels :)
The reality turned out to be somewhere in the middle. Crypto didn't replace our financial system, but it exists as a 1-2 trillion dollar segment serving a particular (though controversial) niche of the global economy. It's not going to zero anytime soon.
I think AI/LLMs will follow the same path. There's definitely a level of usefulness there. But we've clearly hit a ceiling since 3.5/4.0. Advancement has only happened in benchmarks and open source models. Also, the idea that a neural net that accepts a fixed amount of padded tokens and returns a list of probabilities will replace the complexities of the human brain is delusional at best.
The only real issue I see is that certain actors in the US have taken such large positions that unwinding them could potentially destroy the US economy at worst, or trigger a recession at best. But this mainly concerns the US, which is on an AI high at the moment.
You can learn a foreign language just fine, but if you mangle the pronunciation, no one will talk to you. Same thing with hacking at software without understanding the above elements. Your software will be mangled and no one will use it.
the quality of how maintainable the source code is has no bearing on how a user perceives the software's usefulness.
If the software serves a good function for the user, they will use it, regardless of how badly the datastructures are. Of course, good function also means reliability from the POV of the user. If your software is so bad that you lose data, obviously no one will use it.
But you are conflating the maintainability and sensibilities of clean tools, clean code and clean workspaces, with output.
A messy carpentry workshop can still produce great furniture.
The intention/perspective of development is something on its own and doesn’t correspond to the end result directly.
This is such a complex issue that everything comes down to what someone believes
LLM's may not generate the best code but they need only to generate useful code to warrant their use.
In all of the cases the main prediction that was made came true. The cost, especially the human cost, of developing some piece of software dramatically decreased. The only reason why the amount of programmers needed still rose was because the amount of software needed rose faster.
Clearly that trend will not hold forever.
>The hard part of computer programming isn’t expressing what we want the machine to do in code. The hard part is turning human thinking – with all its wooliness and ambiguity and contradictions – into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
And there is exactly one single technology which has ever been able to do this task, which is LLMs. Not addressing the elephant in the room, which is that an LLM can actually take such instructions and produce something meaningful with it, just makes the whole article worthless.
Everything in this article is just inverse special pleading. Since the last N times, the most enthusiastic predictions did not come true, this time only minor changes can happen. If LLMs are only a revolution on the scale of fast interpreted languages (which have significantly impacted what a small team is capable of delivering in terms of complexity), then they will drastically impact most of the software industry.
If these changes happen, and simultaneously the rate at which software is demanded does not also increase (why would it?), then the implications will be extremely serious. Especially if you are not a developer in an established position.
Software is incredibly expensive and has made up for it with low marginal costs. Many small markets could potentially be served by slop software, and it's better than what they would have otherwise gotten (which is nothing).
This blurb is the whole axiom on which the author built their theory. In my opinion it is not accurate, to say the least. And I say this as someone who is still underwhelmed by current AI for coding.
Your run of the mill bootcamp webdev/data scientists will have to educate themselves or find manual jobs.
This...
I have said this in multiple other comments and I 100% agree with the author's take.
I feel like AI can be good for prototyping sometimes but that's about it.
The reason is simple but I just don't trust AI system from not breaking given their volatile nature and I wouldn't expect to be paying for AI generated code that much/zero to be honest
And I will treat other people the same way I wish to be treated (golden rule) so why would I use AI when I can do things by my hand? (There are also 1000's of other discussions I can point in the moral,financial,even technological impacts of the whole situation)
That being said, prototyping as author pointed out feels the only good use to me because at that point you are building it for yourself or a small community. I think a good use of AI can be to build open source prototypes of softwares and if you really like something, one can rebuild it or build it the way they like it as an example.
Prototyping can be similar to the drag n drop and other things and honestly wordpress did reduce the number of programmers needed to maintain a website and for good measure, I think AI is similar to this then anything else and even then small models will win as author states and so the financial point of these companies training these usually becomes moot.
So till the time being, I use these free/subsidized websites to get prototypes to be generated but I feel as if long term, I can find projects which heavily resonate with me and I can build them/learn languages catered to them and build my mental model of thinking around them
Although I must admit, prototyping using AI models definitely gives me Imposter syndrome and I sometimes feel that perhaps its a learning opportunity and that I can perhaps instead spend hours but I don't really know too much about it so its something that I am figuring out as we speak
Like there is this feeling that I can spend more hours and feel more "proud" of what I did if I did it without AI than if I did with AI since perhaps the journey matters too. I really don't know but just weighing all my thoughts on the matter as I thought about it really closely once.
Edit: although I am not sure about calling AI wordpress-like since then it would mean that I am giving significance to AI similar to wordpress, honestly I respect wordpress more but not sure, perhaps they are similar, perhaps they are not, I don't really know perhaps but what I do know is that the financial impact of the AI bubble is gonna impact all of us negatively.