I like designing data, algorithms, and systems. I like picking the right tools for the job. I like making architectural and user interface (CLI, configuration format, GUI, whatever) decisions.
Actually typing code is pretty dull. To the extent that I rarely do it full time (basically only when prototyping or making very simple scripts etc.), even though I love making things.
So for me, personally, LLMs are great. I'm making more software (and hardware) than ever, mostly just to scratch an itch.
Those people that really love it should be fine. Hobbies aren't supposed to make you money anyway.
I don't have much interest in maintaining the existence of software development/engineering (or anything else) as a profession if it turns out it's not necessary. Not that I think that's really what's happening. Software engineering will continue as a profession. Many developers have been doing barely useful glue work (often as a result of bad/overcomplicated abstractions and tooling in the first place, IMO) and perhaps that won't be needed, but plenty more engineers will continue to design and build things just more effectively and with better tools.
The assembly line has been mass producing ready-made products for over 100 years and yet product quality, material stability, aesthetic trends, and function design still dominate the purchasing decisions of the general public.
Being tapped into fickle human preference and changing utility landscape will be necessary for a long time still. It may get faster and easier to build, but tastemakers and craftsmen still have heavy sway over markets than can mass-produce vanilla products.
I would generally put “stability” and “quality” as attributes of mass production far more than that of handmade things. Yes, an expert can make a quality product by hand, but MOST handmade things are far more likely to be shoddy.
The whole point of mass production was that suddenly you could make a million identical perfect products.
I think reducing what LLMs do to « typing » is misleading. If it was just typing, you could simply use speech-to-text. But LLMs do far more than that, they shape the code itself. And I think we lose something when we delegate that work to LLMs
We do lose something, but really I still see it as an extension of autocomplete.
I had some pieces of code I wrote I was quite proud of: well documented, clear code yet clever designs and algorithm.
But really what always mattered most to me was designing the solution. Then the coding part, even though I take some pride in the code I write, was most a mean to an end. Especially once I start having to add things like data validation and API layers, plotting analysis results and so many other things that are time consuming but easy and imho not very rewarding
To me its just a natural evolution of the search engine.
And now looking back its an obvious outcome. The search engine of the time was the best way to find websites. But those websites grew in quantity and volume over time with information.. that information as we know is/was a vital input for LLMs.
Agreed. Also, it takes time to understand a domain properly- so the innate slowness of coding helps with letting things “simmer” in the back of the mind.
It’s not that they replace the act of typing, so much as figuring out how to express the specific algorithm or data structure in a given programming language, typing that, debugging it, etc.
Once I can describe something well, that’s most of the interesting part (to me) done.
with an LLM, you can have an ill formed idea, and let the LLM mold it into a shape to your liking, without having the investment required to learn how to do molding first.
To read it in a kinder way, I can focus on a complex logic problem, a flow, an architecture or micro optimisation. I can have an llm setup the test harnesses.
I improved test speed which was fun, I had an llm write a nice analysis front end to the test timing which would have taken time but just wasn’t interesting or hard.
Ask yourself if there are tasks you have to do which you would rather just have done? You’d install a package if it existed or hand off the work to a junior if that process was easy enough, that kind of thing. Those are places you could probably use an LLM.
But you don't actually do any of that, do you? Instead, you get tired and lazy and attempt to have the LLM solve those hard problems for you too. You just don't tell others about it.
What an odd bit of moralizing. GP said they enjoy doing the hard parts, in which case they probably do them, because it's fun. If they actually don't enjoy it, there's nothing wrong with them using the LLM, when it's up to the task, and then just checking to make sure the code is good.
I enjoy solving interesting problems in software. But when I was doing it for a living, the majority of my work was pretty tedious. I'd have been thrilled to turn over that part to AI and spend all my time doing the interesting stuff.
Don’t you get it? Machine do the tedious work, all we get to do now is the fun part and we can just relax the rest of the day.
I am producing 5x as before, my boss is paying me the same salary just for two hours of actual work per day. I have so much more time to pursue my passions.
I'm surprised you've had three replies so far that didn't notice your sarcasm.
But we've been automating the tedious work since the 1950s. There were probably devs back then complaining about imminent job loss when the first compilers were invented. Maybe some jobs were lost, temporarily, but ultimately we all got more ambitious about what software we could make. We ended up hiring more programmers and paying them better, because each one provided so much more value.
When the machines are able to do the hard stuff better than humans, that's when we'll really be in trouble.
I do not believe that past performance is a guarantee of future results. The era of well paid programmers in great demand is pretty much over, and it’s not only because of AI. Even if machines are dumb enough they require supervision, the big bosses do not care and will always prefer the dumb machine if it saves them money vs hiring a junior dev. It means the poor sods that supervise these machines will have to work harder to keep up with demand.
Maybe that'll happen one day, but it hasn't so far. As of this month, Glassdoor reports the median total pay for software developers across all industries and experience levels as $149K.
This but unironically. We're at a point where there is still a gap between what managers expect and how fast AI can work. I genuinely do have days where I finish a few tickets and I'm done.
Can't imagine you really think "the market forces" all point toward a utopia for the workers? We're all just gonna get paid for 2 hours of work a day and post pics from the beach with a special shout-out to Claude?
There are way fewer challenging problems that people are willing to pay me to solve.
Sure I would love to be working on some cutting edge challenging stuff, but the reality is it has been much more realistic to do the tedious stuff for pay instead
Absolutely everything about it in equal measure? You live environment config, setting up test harnesses, coding the complex part all identically the same? Nothing you would hand off?
Do you find there are zero chores in software development and everything is an identical delight?
Woodworking analogy for AI is not "power tools vs handsaw", its "power tools vs. wood 3D printer". You don't do any of the creating, you only ideate and allow the machine to do all the creating. It's simply not wood working anymore. Its something else entirely
I cannot choose entirely hand-made code, I don't think I'll even be able to choose 50% hand-made code, because my manager will say "why aren't you just using the 3D Wood Printer 9000? Jeff is building house frames 5x faster than you, you need to get with the program or we're gonna let you go"
A friend was recently redoing his kitchen. They hired a carpenter for cabinetry, and his wife asked him if he would be willing to make it with only hand tools.
Carpenter told her he'd be happy to, it would take 8 weeks longer, cost more, and probably wouldn't look any better than the regular way
Do you want to eat good food or make good food? Is doing the dishes something you hand to a machine or do you always do it by hand? Are there any ingredients you buy pre-made (pesto, curry pastes, do you make your own panko breadcrumbs)?
My point, which you seem to have missed, is that people have an intrinsic incentive to care about learning to cook because eating is something we have to do every single day regardless
Thread seems to be saying LLMs are great because they do the dirty work and leave the fun work to humans. The counter-point is not exactly that LLMs aren't capable of doing dirty-work, it's that the nature of work isn't going to split so cleanly.
And cooking is a good example. Cooking is work. And slop. And it's also incredibly rewarding and creative, if you want it to be. Robots can help along that entire journey.
Maybe this is the core point: "cooking is a solved problem" that's how engineers always think. Except it's not. And 100% automation is still not going to break that discussion so cleanly.
The comment I was responding to was looking at a state where we looked at markdown files instead of programming, my point was to agree with the original quote that they can remove the drudgery.
> Maybe this is the core point: "cooking is a solved problem" that's how engineers always think.
But it isn’t, that’s a lazy stereotype of a subset.
Gotcha. the engineer generalization was to plant a stake in the ground. I do hear that quite often, especially from higher titled engineers. in any case, definitely admit it's not all of them, and perhaps it's even a vocal minority that i'm better served to intentionally work to expand my influences.
This is exactly how I feel. I knew it already to an extent from my time in college, but so many people come into this industry because they want to be able to produce the end product, or just have a stable job that makes good money. Neither of those are bad reasons to get into this profession, but it does make me sad how few peers I have who do programming because they're passionate about the act of programming. The problem solving, the dance of using programming languages to communicate efficiently and robustly to both machines and humans... I'm very sad how enthusiastically so many of my peers just toss that away.
Aside from many of these things just being a layer difference - it’s not unreasonable to want to work on databases query optimisation an not enjoy css or enjoy building frontends but just want a db that’s fast and works. The flip of your view is that they may find it sad that you don’t want to make things, you just want to solve puzzles.
> Aside from many of these things just being a layer difference - it’s not unreasonable to want to work on databases query optimisation an not enjoy css or enjoy building frontends but just want a db that’s fast and works.
I don't mean that it's unsetting that people enjoy different parts of the job, I enjoy many of those same aspects, but it's sad to me how few people around me care about the aspect that I originally fell in love with, which was the bedrock of our profession. Specifically, the work of solving problems with the machine/human shared language of code, instead of just writing out plain-english specs of what you want to have happen.
> The flip of your view is that they may find it sad that you don’t want to make things, you just want to solve puzzles.
So what? Their "just get it done" POV is far more common in this industry than mine (apparently), and the enjoyment they get from their job isn't being actively optimized away.
I don't know if it's "hate" rather than "a means to an ends". I love learning new languages, and coding. But it was always a means to an ends. The dopamine hit always came from seeing the project compile and do something.
There are multiple ends in conflict. Code skillfully constructed using abstractions that fit well to the problem space can be extended, maintained, and refactored as necessary to serve customers and markets from high to low level over long periods of time with all the social and industrial change that comes with that. Simply putting in place mechanisms that deliver what is needed now end up unintentionally cutting off future variants, alternative uses, longevity, and robustness all to minimize perceived costs.
And it isn't so much that one approach may be better than another. That is going to depend on context and available resources and more. What we are seeing is the short term being served to the absolute exclusion of thought about the longer term. Maybe if that goes fast and well enough then it will be sufficient, but churning out code bases that endure is a challenge that is only starting to be tested.
Yeah and especially the satisfaction that you were able to make a user delighted to use your thing. Fixing bugs, making things faster, adding new features, for me personally I do it because I feels really good when a customer loves to use the thing I've built.
Weather I've done the manual coding work myself or have prompted an LLM to cause these things to happen, I still chose what to work on and if it was worthy of the users' time.
I realised that early on when I stopped coding as much in my free time. After work I wanted to do practically anything else. But quite a few people at work continued to spend all of their free time coding, and clearly enjoyed the process. The SerenityOS/Ladybird creator spent years coding an arguably pointless project purely for the enjoyment of it.
Whereas I always liked to design and build a useful result. If it isn't useful I have no motivation to code it. Looking up APIs, designing abstractions, fixing compiler errors is just busywork that gets in the way.
I loved programming when I was 8 years old. 30+ years later the novelty is gone.
I think a lot of us like solving novel problems. But the menial drudgery of most modern software, where you're writing code in an app that was written in a week 8 years ago and then had mountains of "get it done quickly and we can improve it later" over the years, wears on everyone. Much as the advent of decent cordless tools revolutionized "workman" trades, ai helps programmers
If someone is paying you for your work results, that you find it interesting or fun is orthogonal. I get the sense from the commentary section here that there’s a perception that writing programs is an exceptional profession where developer happiness is an end unto itself, and everyone doing it deserves to be a millionaire in the process. It just comes across as child-like thinking. I don’t think many of us spend time, wondering if the welder enjoys the torch or if a cheaper shop weld is robbing the human welder of the satisfaction of a field weld. And we don’t shed so much ink wondering if digital spreadsheets are a moral good or not because perhaps they robbed the accountant of the satisfaction of holding a beautiful quill in hand dipped expertly in carefully selected ink. You’re lucky if you enjoy your job, I think most of us find a way to learn to enjoy our work or at least tolerate it.
I just wish all the moaning would end. Code generation is not new, and that the state of the art is now as good at translating high-level instructions into a program at least as well as the bottom 10% of programmers is a huge win for humanity. Work that could be trivially automated, but is not only because of the scarcity of programming knowledge is going to start disappearing. I think the value creation is going to be tremendous and I think it will take years for it to penetrate existing workflows and for us to recognize the value.
> at least as well as the bottom 10% of programmers
I don't think this is the flex you think it is... in my experience, the bottom 10% of programmers are actively harmful and should never be allowed near your codebase.
Quite a few people think that about Claude code. I disagree with them, personally, but I think we can agree that AI code generation is qualitatively at least as good as the worst human professionals. I think we would also probably agree that the state of the art today is not as good as the very best.
The value per dollar spent is a different calculus and I would say that state of the art models completely surpass any individual’s productive output.
> the state of the art today is not as good as the very best
and
> state of the art models completely surpass any individual’s productive output
are not contradictory. If the models completely surpass any individual's productive output, doesn't that mean they're better than the best humans? Or maybe I don't understand what you mean by "surpassing productive output." Are you talking about raw quantity over quality? I mean, yeah... but I could also do that with a bash script.
>are not contradictory. If the models completely surpass any individual's productive output, doesn't that mean they're better than the best humans?
It would be contradictory if we were talking about a human sure, but we're not. We're talking about a machine that can read thousands of words in seconds and spit thousands in slightly longer.
>Are you talking about raw quantity over quality? I mean, yeah... but I could also do that with a bash script.
Well except you can't. You can't replace what LLMs can do with a bash script unless your bash script is calling some other LLM.
> And we don’t shed so much ink wondering if digital spreadsheets are a moral good or not because perhaps they robbed the accountant…
Caught my eye. I do think we should wonder and hold intentionality around products, especially digital products, like the spreadsheet. Software is different. It's a limitless resource with limitless instantaneous reach. A good weld is beautiful in its own right, but it's not that.
The spreadsheet in particular changed the way millions of people work. Is it more productive? Is an army of middle-managers orienting humanity through the lens of a literal 2x2cm square a net good?
It can be unpleasant to participate in a community of differing opinions and experiences. I still think it's worth showing up. If I hadn't then your perspective would have been missed too.
since when has velocity or volume of codegen been the bottleneck for any business?
i write less code than my AI-using coworkers but I have as much or more impact. Coding wasn't so hard that I need to spend time learning a new proprietary tech stack with a subscription fee lol. I believe plenty of engineers did suck enough and computers to benefit tho. That is where Anthropic makes their money.
It’s hard for me to believe that, unless you’re just doing simple glue work or you’re working in a low stakes environments, anyone is just delegating everything to agents. If you’re working on a
migration (common in enterprise infrastructure work), you’re familiar with the needless abstractions, and it’s something you’ve done many times over; agents can certainly expedite change. If you’re building anything with depth and you do not have a clear understanding of the underpinning logic, you’re either very gifted in your ability to reason about abstractions or you’re setting yourself for a failure at some point in the future. You need expertise at some point. Programming/debugging as a means of learning a domain is akin to writing as a means of clarifying your thoughts.
That being said, yea enterprise coding can be extremely mundane and it’s setup for learning it deeply then finding a way to do it faster. I’m likely in the 90% range of my work being done by Claude, but I’m working in a domain I’ve got years of experience with hand coding and stepping through code in my debugger.
I think this latter piece is the challenge I’m struggling with. There is an endless amount of work that can be done at my company but as long as the economy is in a weird spot, I’m being led to believe that ai is making me expendable. This is a consequence of the fact that glue work represents 80% of my output (not value). The other 20% of time at work is exploring ideas without guaranteed results, its aligning stakeholders, its testing feasibility with mvps or experts from another area I need some help with. If glue work represents tangible output and conceptual work is something that may not actually have value my manager wants me to explore it, I’m just a glue guy in enterprise while I’m left chasing the dragon of a cool project for me to really sink my teeth into. That project is just a half baked bad idea from someone disconnected with reality. Glue work is measurable in LoC (however useless a metric it is measurable) and it’s certainly paying the bills.
+1024. what the FUCK, Anil. We solved coding-is-for-everyone by throwing up our hands. please crush my body under the heaviest layer of abstraction yet and have the llm read my eulogy because who could possibly know me better than the code I spend all day talking to as if it were a human
Yea! Back to my amazing Pax Americana of friendly neighbors, high trust in my authorities, and cheerful joyous days in oeace and harmony with my fellow man, complete with gum drop smiles and firm faith in my institutions. A truly brave new world
Well I do seem to spent a fair amount of my developer time swearing at my laptop screen. And then there's that time I spend just prior to writing code just staring at the wall while I figure out what sort of code I want to write - if I can repackage that wall-staring time into "time spent consulting with AI about approaches and architecture decisions" I'm sure my engineering manager will think more kindly of me ...
I'm puzzled why they don't speak aloud. Isn't it more natural AI interface? How difficult it is to connect a microphone to speech to text engine and connect that to AI? And then speak aloud. Your manager will be happy to hear you work.
It's easier to fake humanity through toneless text with narrow context and low expectstions, I think. A chatbot you type to is of course going to impress easier
I’d love to outsource all the boring, tedious parts of my job to LLM. Unfortunately it is the upper management who decide which parts of my job are boring.
The two types of coder argument seems strong to me. Coders who love the art of programming (optimisation for the sake of it, beautiful designs, data structures...) and builders. The former are in for a rough time. The latter are massively enabled and no longer have to worry about smashing together libs by hand to make crud apps.
Doordash has also enabled home cooks; they no longer have to worry about smashing together ingredients by hand to make dinner. They just prompt the app to make them the food they want.
I pay Google £15/month and have never hit the usage limit. But thanks anyway.
edit: I think you might mean vibe coding (and those infamous things that use millions of tokens with no limit) but for programmers using LLMs to code is literally just a tool like anything else and the cost is barely relevant. It's not comparable to contracting out code, and it's not even comparable to eating out in terms of cost!
There are much nore than 2 types of programers, one you forgot are the ones who just need the job and any tool that helps is welcome, also, there the ones that don't care for anything and does programming just because is the only thing that's available to jot work on sales or burger flipping, and the list goes on
That's what's visible now. Give it more time and larger, more long-term projects will come out. I'm talking about people with ambitious ideas who -could- code but lack the time or energy.
Thinking carefully about the details of implementation MATTERS. Even with crud apps. Getting something “built” fast isn’t and should not be the only consideration.
I can go to a junkyard and assemble the parts to build a car. It may run, but for a thousand tiny reasons it will be worse than a car built by a team of designers and engineers who have thought carefully about every aspect of its construction.
I agree. However manual code review and heavy refactoring is a laborious and error prone process, and even most human projects don't keep up with it successfully. Plenty of horrible code debt ridden projects in the real world. As long as you're not writing safety-critical code, use of LLM is not incompatible with what you're saying.
Yes this is the state of it. But just wait a few months, maybe years, the builders aren't safe either. It just won't be cost effective to let humans build in a matter of time.
So enabled, in fact, that there's almost no point in downloading an already-made app when you can just trivially tailor-make your own. The builder is massively enabled to quickly make anything they want, for an audience of exactly one.
Strongly disagree. You think you’d be able to prompt your way through creating an app with even half the feature set of Microsoft word, for example? I would be very time consuming to be able to think through how the app should work for many use cases you care about or didn’t think about. This time isn’t free. Now consider having to do this iteration across many apps you depend on. And, count on introducing regressions when your next prompt is incompatible with existing features. If you are not retired, this is a huge ongoing time sync.
You think you were able to prompt your way through creating hello world five years ago? Models improve and they need less and less guidance.
Combined with the fact that my use cases aren't your use cases, yes, it might be cheaper for me to make my own than to slog though software that wasn't built to serve my exact needs.
I’m not saying that there’s no need for specialty apps optimized for specific use cases or that you can’t use llms to create them more cheaply than last year. Only that the time to think through how the app should work and iterate on it is still significant in the way that it was last year if you were given the worlds best team of software engineers and they’d code to your product requirements. You’d only take this path for apps where the time tradeoff is worth it vs “off the shelf” apps.
The issue is that off the shelf apps were made at a time when it was too expensive to make apps. Everyone uses 2% of Word, Photoshop, etc, it's just a different 2% for each.
You only need to reimplement that 2% for yourself for it to be worth it, not the entire app.
Cars are here and you're wondering how someone could possibly make a faster horse. You wouldn't address user requests. You aren't a business. The users all make their own apps for themselves.
Those "idea men" I've seen are usually not capable of following through a logical product, even if they start using AI. It's not just the code that's the barrier.
The prototypes or whatever can be handy to help them explain themselves to others of course.
Yall's blood-diamond-ass mommy bots are going to replace bullshit with bullshit and call it a win. The last datacenter will run out of coal and water and we'll be asking: "but how in the world am I going to make this Todo app?"
Imagine if your operating system or compiler were written by the sort of person that thinks "Coders who love the art of programming ... are in for a rough time."
Having an AI is like having a dedicated assistant or junior programmer that sometimes has senior-level insights. I use it to do tedious tasks where I don't care about the code - like today I used it to generate a static web page that let me experiment with the spring-ai chat bot code I was writing - basic. But yesterday it was able to track down the cause of a very obscure bug having to do with a pom.xml loading two versions of the same library - in my experience I've spent a full day on that type of bug and Claud was able to figure it out from the exception in just minutes.
But when I've used AI to generate new code for features I care about and will need to maintain it's never gotten it right. I can do it myself in less code and cleaner. It reminds me of code in the 2000s that you would get from your team in India - lots of unnecessary code copy-pasted from other projects/customers (I remember getting code for an Audi project that had method names related to McDonalds)
I think though that the day is coming where I can trust the code it produces and at that point I'll just by writing specs. It's not there yet though.
>I think though that the day is coming where I can trust the code it produces and at that point I'll just by writing specs. It's not there yet though.
Must be nice to still have that choice. At the company I work for they've just announced they're cancelling all subscriptions to JetBrains, Visual Studio, Windsurf, etc. and forcing every engineer to use Claude Code as a cost-saving measure. We've been told we should be writing prompts for Claude instead of working in IDEs now.
Honestly while I know everyone needs a job, just speed run all this crap and let the companies learn from making a big unmaintainable ball of mud. Don't make the bad situation work by putting in your good skills to fix things behind the scenes, after hours, etc.
Management has made it very clear that we’re still responsible for the code we push even if the llm wrote it. So there will be no blaming Claude when things fall apart.
I wonder how much cost savings there are in the long term when token prices go up, the average developer's ability to code has atrophied, and the company code bases have turned into illegible slop. I will continue to use LLMs cautiously while working hard to maintain my ability to code in my off time.
Realistically that's an increase of maybe a couple percent of cost per employee. If it truly does end up being a force multiplier, 2-5% more per dev is a bargain. I think it's exceedingly unlikely that LLMs will replace devs for most companies, but it probably will speed up dev work enough to justify at least a single-digit percent increase in per-dev cost.
“speeding up dev work” is pointless to a company. That benefit goes entirely to the developer and does not trickle down well.
You might think “ok, we’ll just push more workload onto the developers so they stay at higher utilization!”
Except most companies do not have endless amounts of new feature work. Eventually devs are mostly sitting idle.
So you think “Ha! Then we’ll fire more developers and get one guy to do everything!”
Another bad idea for several reason. For one, you are increasing the bus factor. Two, most work being done in companies at any given time is actually maintenance. One dev cannot maintain everything by themselves, even with the help of LLMs. More eyes on stuff means issues get resolved faster, and those eyes need to have real knowledge and experience behind them.
Speed is sexy but a poor trade off for quality code architecture and expert maintainers. Unless you are a company with a literal never ending list of new things to be implemented (very few), it is of no benefit.
Also don’t forget the outrage when Cursor went from $20/month to $200/month and companies quickly cancelled subscriptions…
> Except most companies do not have endless amounts of new feature work. Eventually devs are mostly sitting idle.
At every place I have ever worked (as well as my personal life), the backlog was 10 times longer than anyone could ever hope to complete, and there were untold amounts of additional work that nobody even bothered adding to the backlog.
Some of that probably wouldn't materialize into real work if you could stay more on top of it – some of the things that eventually get dropped from the backlog were bad ideas or would time out of being useful before they got implemented even with higher velocity – but I think most companies could easily absorb a 300% increase or more in dev productivity and still be getting value out of it.
I used to report bugs, read release notes; I was all in on the full stack debug capability in pycharm of Django.
The first signs of trouble (with AI specifically) predated GitHub copilot to TabNine.
TabNine was the first true demonstration of AI powered code completion in pycharm. There was an interview where a jetbrains rep lampooned AI’s impact on SWE. I was an early TabNine user, and was aghast.
A few months later copilot dropped, time passed and now here we are.
It was neat figuring out how I had messed up my implementations. But I would not trade the power of the CLI AI for any *more* years spent painstakingly building products on my own.
I'm using Claude in JetBrains, using the Zed editor's ACP connector.
It's actually pretty slick. And you can expose the JetBrains inspections through its MCP server to the Claude agent. With all the usual JetBrains smarts and code navigation.
Even if you're using Claude, canceling the IDEs might be poor strategy. Steve Yegge points out in his book that the indexing and refactoring tools in IDEs are helpful to AIs as well. He mentions JetBrains in particular as working well with AI. Your company's IDE savings could be offset by higher token costs.
Perhaps it would help if I include the quote, so from Vibe Coding pages 165-166:
> [IDEs index] your code base with sophisticated proprietary analysis and then serve that index to any tool that needs it, typically via LSP, the Language Services Protocol. The indexing capabilities of IDEs will remain important in the vibe coding world as (human) IDE usage declines. Those indexes will help AIs find their way around your code, like they do for you.
> ...It will almost always be easier, cheaper, and more accurate for AI to make a refactoring using an IDE or large-scale refactoring tool (when it can) than for AI to attempt that same refactoring itself.
> Some IDEs, such as IntelliJ, now host an MCP server, which makes their capabilities accessible to coding agents.
Yes, it's fantastic. Hard to imagine a better resource for getting started with vibe coding, on through developing large high-quality projects with it. It doesn't get into the details of particular tools much, so it should stay relevant for a while.
Syntax highlighting rules, initially vibe-coded 40 languages and formats in about 10 minutes. What surprised me is when it switched the design from a class to the far more elegant single line of code:
I'm halfway through Steve Yegge's book Vibe Coding. Yegge was quoted in the article:
> “We’re talking 10 to 20 — to even 100 — times as productive as I’ve ever been in my career,” Steve Yegge, a veteran coder who built his own tool for running swarms of coding agents
That tool has been pretty popular. It was a couple hundred thousand lines of code and he wrote it in a couple months. His book is about using AI to write major new projects and get them reliable and production-ready, with clean, readable code.
It's basically a big dose of solid software engineering practices, along with enough practice to get a feel for when the AI is screwing up. He said it takes about a year to get really good at it.
(Yegge, fwiw, was a lead dev at Amazon and Google, and a well-known blogger since the early 2000s.)
label = key:gsub("on%-", ""):gsub("%-", " "):gsub("(%a)([%w_']*)", function(f, r)
return f:upper() .. r:lower()
end)
if label:find("Click") then
label = label:gsub("(%a+)%s+(%a+)", "%2 %1")
elseif label:find("Scroll") then
label = label:gsub("(%a+)%s+(%a+)", "%2 %1")
end
I don't know Lua too well (which is why I used AI) but I know programming well enough to know this logic is ridiculous.
It was to help convert "on-click-right" into "Right Click".
The first bit of code to extract out the words is really convoluted and hard to reason about.
Then look at the code in each condition. It's identical. That's already really bad.
Finally, "Click" and "Scroll" are the only 2 conditions that can ever happen and the AI knew this because I explained this in an earlier prompt. So really all of that code isn't necessary at all. None of it.
What I ended up doing was creating a simple map and looked up the key which had an associated value to it. No conditions or swapping logic needed and way easier to maintain. No AI used, I just looked at the Lua docs on how to create a map in Lua.
IMO the above is a lot clearer on what's happening and super easy to modify if another thing were added later, even if the key's format were different.
Now imagine this. Imagine coding a whole app or a non-trivial script where the first section of code was used. You'd have thousands upon thousands of lines of gross, brittle code that's a nightmare to follow and maintain.
All that stuff would take me about 5 minutes without AI. Those are things with 10,000 examples all over the web. AI is good at distilling the known solutions. But anything even slightly out of the ordinary, it fails miserably. I'd much rather write that code myself instead of spend an hour convincing an AI to do it for me.
Simple npm install, all of it has already been distilled into dozens of similar repos. Just pick one, install it, and follow the simple use case. 5 minutes if we're in a race.
>done Setup websockets
If this takes you more than 5 minutes, then you're a shit developer.
>stream audio from mic
Again, another npm install or two, simple POC could take 5 minutes.
>transcribe with elvenlabs
I don't know what elvenlabs is, nor do I care, but I doubt it's as complex as the OP thinks it is considering the rest of their comment was about simple, solved problems.
Haven't tried Claude for this, but I can't think how it could possibly do.
I built a game bot using Win32 API to send input and screen capture to OCR and some OpenCV to recognize game elements. Dead simple and actually quite boring and repeatitive after I worked on it for a while. How could Claude agents possibly do this ?
I did use Claude to refer docs and API, though.
That actually sounds like something Claude could do pretty easily.
Yegge's book describes his coauthor's first vibe coding project. It went through screenshots he'd saved of youtube videos, read the time with OCR, looked up transcripts, and generated video snippets with subtitles added. (I think this was before youtube added subtitles itself.) He had it done in 45 minutes.
And using agents to control other applications is pretty common.
My elementary schooler did this with pictures of his stuffed animals last week. I helped a little bit, but most of it was Claude. He's never coded before.
Yes, I've doomed him all because of a 30 minute interaction. Just like when he watched Kerbal Space Program videos on YouTube he lost all motivation to get to the moon himself. Oh wait.
And he definitely doesn't make up missions using the mission builder using if / then loops. He'll never learn to code. Oh the humanity.
I'd rather have my kid typing on a real keyboard into Claude, asking questions about what Python, and modifying the Claude-generated code than watching random videos and playing Roblox on his iPad.
Yes, because search engines are populated with SEO-optimized LLM-filled articles that say nothing of value anymore. The only reason AI-assisted tools are "better" is because Web search is so much worse.
It's like everyone forgot that the first result for anything web-related would be W3schools, and the next 5 would be spam message boards that tries to scrape all the other boards and sends you to a porn site when you click on it.
Yeah it is worse now but I don’t remember it ever being good. If you know where to look and have a trusted set of resources curated sure, but of course you won’t for unfamiliar territory which is exactly what LLMs help with.
I've generated 250KLoC this week, absolutely no changes in deps or any other shenanigans. I'm not even really trying to optimize my output. I work on plans/proposals with 2 or 3 agents simultaneously in Cursor while one does work, sometimes parallelized. I can't do that in less code and cleaner. I can't do it at all. Don't wait too long.
Agents are new, but dumb coding practices are not. Despite what it may seem, the knowledge of how to manage development has increased. One practice I haven't seen for a while is managing by limiting the number of lines changed. (This was a dumb idea because rewriting a function or module is sometimes the only way to keep it comprehensible - I'm not talking about wholesale rewriting, I'm talking about code becoming encrusted with conditions because the devs are worried that changing the structure would change too many lines)
This. We moved to 'agile' roughly at same time llms started coming. You know who is mostly missing from the wider IT teams landscape now? Most of PMs and BAs, on purpose. Who is keeping their work - devs or more like devops, surprisingly testers (since our stuff seems ridiculously complex to test well for many use cases and we can't just break our bank by destroying production by fast half-assed deliveries).
And then admins/unix/network/windows/support team, nobody replacing those anytime soon. Those few PMs left are there only for big efforts requiring organizing many teams, and we rarely do those now.
I don't like it, chasing people, babysitting processes and so on, but certainly just more work for me.
you're not responding to op's point, unless you're insinuating there won't be any managers ever, which won't happen. As long as there is one manager left, OP's point remains.
Can you please stop posting so abrasively? It's not that this one comment is so bad, but you have a long pattern of doing so, and we're trying for something else here.
I use these tools a lot, and one thing that has stood out to me is that they LOVE to write code. A lot of it. And they're not super great at extracting the reusable parts. They also love to over-engineer things.
I've taken great pains to get by with as little code as possible, because the more code you have, the harder it is to change (obviously). And while there are absolutely instances in which I'm not super invested in a piece of code's ability to be changed, there are definitely instances in which I am.
Yea, by my less is more logic its sometimes also difficult to do. With that approach people try to become clever and write shorter code thats unmaintainable due to mental gymnastics other people have to go through when reading it. What LLMs are doing is probably going for some kind of overengineered "best practice" solution.
I personally only use them for simple Laravel CRUD apps and they are admittedly pretty good at that.
I've worked with a type of (anti?) developer in my career that seems to only be able to add code. Never change or take it away. It's bizarre. There's some calculation bug, then a few lines down some code which corrects it instead of just fixing the original lines.
It's bizzare, and as horrible as you might imagine.
And it's been more than one or two people I've seen do this.
People need to understand that code is a liability. LLMs hasn't changed that at all. You LLM will get every bit as confused when you have a bug somewhere in the backend and you then work around it with another line of code in the front end. line of code
For me, the biggest shift is people who don't care about local AI. The idea that you can no longer code without paying a tax to one of the billion $ backed company isn't sitting well.
I don’t understand why more people aren’t focused on how to get the benefits of ai but on your own machine. If the last 20 years of software transitioning off of our desktops and into the cloud has taught us anything, it’s that letting corporate entities run the software you rely on end to end gives you: worse software with more bugs, surveillance and subscriptions. Why on earth would you want that for everything you do.
Because a lot of the AI hype/use is driven by companies who just want to pay money for a service? Besides, my laptop would need a pretty big RAM upgrade.
I agree local is better, but the big companies are making decent products and companies are willing to to pay for that. They’re not willing to spend engineering money to make local setups better.
If there's a model that's as good as Claude 4.5 (not even 4.6) I would pay tens of thousands to run it locally. To my knowledge there isn't yet. Benchmarks may say so but I haven't used one that does yet. I always try new models that come out on openrouter
Local AI is what people want/need, but centralized AI is where the investors' money is flowing, because a walled garden has always been easier to turn into a cash printer.
The price does not matter, even if it were free. If you need to be logged on into an external service to be able to code, it's just not the same any more, and I'm thinking of basic technology here, but the political/distopian ramifications are crazy.
The marginal differences in quality seem pretty meaningful right now, enough to make Claude wildly dominant, but some of the locally runnable models like Qwen feel only a few months behind the leaders.
I'm betting the generational gains level off and smaller local models close the gap somewhat. Then harnesses will generally be more important than model, and proprietary harnesses will not offer much more than optimization for specific models. All while SaaS prices ratchet up, pushing folks toward local and OSS. Or at least local vs a plethora of hosted competition, same as cloud vs on prem.
If coding truly becomes effortless to produce - and by that extension a product becomes near free to produce - then I find it quite odd that the executive class thinks their businesses won’t be completely up ended by a raging sea of competition.
They're going to have a fast and ruthless testing of whether their product senses and abilities to attract and trap customers were actually skill or lucky positioning, as competition explodes from every direction, including from within customer and user bases.
All run of the mill software is gone or on borrowed time. Why pay a subscription for a product that I can get something Claude to build it for me.
Before I was building tools, now I am building full applications in less time than I did before for tools.
What will be around for a while is where you need an expert in the loop to drive the AI. For example enterprise applications. You simply can't hand that off to an AI at this point.
> Why pay a subscription for a product that I can get something Claude to build it for me.
We've already seen this with OSS. Even with free software, support, self-hosting, and quirky behavior have proven to be enough to keep most people and business away.
Thing is, AI will also be able to provide substantial support for software it writes. And it will make self-hosting a lot less painful, too. Quirky behavior will still happen, but eh, Excel imports numbers as dates. You can't buy your way out of quirky behavior for all the money in the world.
I am building them because I am using them to do my work faster.
I'm not selling anything, but I can see the quality of what is created and it is on-par with much of the stuff on the App store.
No one would even notice that it is a co-creation unless I mentioned the time to create it.
Just to be clear. Vibe coding implies that you are not reviewing the code that is created, or even knowing what is being created. That is not what is happening.
I used a bunch of tools from https://setapp.com/ and no longer subscribe. I used to pay for Linear for all my personal projects, built one with CC that perfectly fits my needs… I also built a myriad of small tools that help me automate bunch of busy work I used to do manually, both for professional and personal projects
Is all of it shit, or you just can't find the good stuff? "The struggle will completely shift to how to get traffic" is from the business side, and you're experiencing it from the customer side.
I see lots of discussions about humans no longer writing code but the elephant in the room is the rapid extinction of human-review of AI-made code. I expect this will lead to a massive hangover. In the meantime we try to mitigate this by ensuring the structure of code remains AI-friendly. I also expect some new types of tools to emerge that will help with this “cognitive debt”.
My impression is that people who think that LLMs will completely release reviewing or writing code have never really worked on anything safety critical. I'm not looking forward to the next wave of pacemaker glitches.
That is why I do not use the multi agent team technique. My code generation has atrophied, but my code review skills have only gotten stronger both for human and AI code. If I handed over both, it hurts my employability and will definitely lead to that hangover.
Well, there are so many more lower hanging fruits that LLMs can actually replace before they get to developers -- basically every middle manager, and a significant chunk of all white collar jobs.
I'm not convinced software developers will be replaced - probably less will be needed and the exact work will be transformed a bit, but an expert human still has to be in the loop, otherwise all you get is a bunch of nonsense.
Nonetheless, it may very well transform society and we will have to adapt to it.
Not all software development will be automated immediatly. But I've noticed that many skills I've built are lessened in worth with every model release.
Having a lot of specifics about a programming environment memorized for example used to be the difference between building something in a few hours and a week, but now is pretty unimportant. Same with being able to do some quick data wrangling on the command line. LLMs are also good at parsing a lot of code or even binary format quickly and explaining how it works. That used to be a skill. Knowing a toolbox of technologies to use is needed less. Et cetera.
They haven't come for the meat of what makes a good engineer yet. For example, the systems-level interfacing with external needs and solving those pragmatically is still hard. But the tide is rising.
The capitalists and industrialists have waited for centuries to get rid of paid labor. Imagine the profits once the cost of human work gets out of the loop!
Of course the question that is left unanswered is how the economy will work there's no one left with purchasing power. But I guess the answer to this is, the same way it works now in any developing country without much of a middle class.
I don’t see middle managers taking the initial brunt unless they truly are just pushing papers around. At companies of sufficient size, they do play a role of separation between C suite and the grunts. To me, certain low-performing grunts will be the first out. Then a team reorg to rebalance. Then some middle managers will be out as fewer of them can handle multiple teams.
>I'm not convinced software developers will be replaced
Most of us will probably need to shift to security. While you can probably build AI specifically to make things more secure, that implies it could also attack things as well, so it ends up being a cat-and-mouse game that adjusts to what options are available.
> probably less will be needed and the exact work will be transformed a bit
My guess is the opposite: they'll throw 5–10x more work at developers and expect 10x more output, while the marginal cost is basically just a Claude subscription per dev.
But you only $200/month for the productivity of what used to cost monthly salary for 10 software engineers. Doesn't this democratize software construction?
The resources to learn how to construct software are already free. However learning requires effort, which made learning to build software an opportunity to climb the ladder and build a better life through skill.
This is democratization.
Now the skill needed to build software is starting to approach zero. However as you say you can throw money at an AI corporation to get some amount of software built. So the differentiator is capital, which can buy software rather cheaply. The dependency on skill is lessened greatly and software is becoming worthless, so another avenue to escape poverty through skill closes.
You might be right but it's common to see that take as short sighted since there is zero evidence it's ever happened before. The common example is the loom. The loom didn't democritize cloth making, it put all the weavers out of business. But it did democritize everything that needed cloth because cloth became cheap. 1000s more jobs were created by making cloth cheap. In a similar way, 1000s of different jobs might be created by making software creation cheap, but just like cloth makers, software makers have no purpose when making software is easy.
So you’ve vibe coded an app. That’s just swell. You want to release it? Don’t. You can’t support it. You can’t update it. You are one bad prompt away from it collapsing.
We are convincing a generation of morons that they can do something they plainly cannot. This will be a major problem, and soon.
I always find these "death of the X" articles frustrating.
Books didn't stop existing when the radio came out. Radio didn't stop existing when television was invented. If you go back in time a thousand years, people were complaining that an increase in literacy would damaging peoples' memorization skills.
People will still write code for consumption by other humans by hand. Some companies, though probably not most, will still prefer it. AI will change the industry - IS changing the industry - but things don't "end". They just look different, or are less popular.
There is enough for us to worry about and try to figure out how to respond to without the histrionics.
Also, on a related note, "Idiots are vibecoding bad stuff" is not the same as "engineers are using AI tools to do good work more quickly," and we should stop conflating it.
Supremely ironic that the last two things the same author had in the nytimes was a piece from a few years ago hyping up NFTs and a year before that a piece hyping up Bitcoin.
In the near future, a "good programmer" might not be defined by someone who can write bug-free, clear code, but rather by someone who can prompt for code that works consistently within the context of AI. If that happens, I'll have to find a different job.
At least at my company, we have never really cared how it gets done, even before AI. It just has to work (ideally bug-free and maintainable) by the deadline. If you can keep up with shorter deadlines, more power to you. It’s basically a modern John Henry vs the steam drill.
That’s the exact definition our CEO gave of our job this week. That’s how he sees and expects us to work now. I feel some anxiety because that’s too much too fast. We went from « we need to fix every single bug we encounter » to « it doesn’t matter if there’s bugs as long as we ship a feature fast »
I find that the perception of AI coding is very bimodal, and both camps have it wrong. You have the one camp who will only trust it to, at most, write a few lines of code. The other camp prompts, walks always, and pushes (or just has the AI do it).
I believe both camps it frustratingly wrong. If you haven't yet given it a chance at doing something substantial, then at least _try_ it once. On the other side of the coin, that first experience where it does something 80% right is intoxicating, but AI doesn't reason and can't get it 100% right - it can't even multiply relatively small numbers.
The former camp is going to get left behind and won't be able to compete, the latter camp is one prompt away from a disaster.
Is anyone concerned how dependant the industry as it's described here is becoming on one, two or three service providers who take prompts and spit out tokens? And the amount of your proprietary infrastructure you're exposing to them?
Are local models anywhere close to gaining enough capability and traction to do it in-house? Or are there good options for those who'd rather own the capability than rent it?
I don't know, I could envision a situation where investing enough capital in hardware to be able to generate "infinite" tokens for free (or close to that) might open up possibilities not economically practical otherwise. Especially once the incumbents run out of hype / investor money to burn and start raising their prices to push revenue in line with their enormous expenditures.
Also, presumably at some point far in the future we'll reach a technological asymptote and factors like latency may start to play a bigger role, at least for some applications.
I grant that training data is crucial distinguishing factor that may never become competitive in-house.
I'm from an accounting/finance background and spent about 10 years in Big4. I was always into tech, but never software development because writing code (as I thought) takes years to master, and I had already chosen accounting.
Fast forward to 2024 when I saw Cursor (the IDE coding agent tool). I immediately felt like this was going to be the way for someone like me.
Back then, it was brutal. I'd fight with the models for 15 prompts just to get a website working without errors on localhost, let alone QA it. None of the plan modes or orchestration features existed. I had to hack around context engineering, memories, all that stuff. Things broke constantly. 10 failures for 1 success. But it was fun. To top it all off, most of the terminology sounded like science fiction, but it got better in time. I basically used AI itself to hack my way into understanding how things worked.
Fast forward again (only ~2 years later). The AI not only builds the app, it builds the website, the marketing, full documentation, GIFs, videos, content, screen recordings. It even hosts it online (literally controls the browser and configures everything). Letting the agent control the browser and the tooling around that is really, genuinely, just mad science fiction type magic stuff. It's unbelievable how often these models get something mostly right.
The reality though is that it still takes time. Time to understand what works well and what works better. Which agent is good for building apps, which one is good for frontend design, which one is good for research. Which tools are free, paid, credit-based, API-based. It all matters if you want to control costs and just get better outputs.
Do you use Gemini for a website skeleton? Claude for code? Grok for research? Gemini Deep Search? ChatGPT Search? Both? When do you use plan mode vs just prompting? Is GPT-5.x better here or Claude Opus? Or maybe Gemini actually is.
My point is: while anyone can start prompting an agent, it still takes a lot of trial and error to develop intuition about how to use them well. And even then everything you learn is probably outdated today because the space changes constantly.
I'm sure there are people using AI 100× better than I am. But it's still insane that someone with no coding background can build production-grade things that actually work.
The one-person company feels inevitable.
I'm curious how software engineers think about this today. Are you still writing most of your code manually?
> it still takes a lot of trial and error to develop intuition about how to use them well.
I used to think so. Then a customer made their own replacement for $600/mo software in 2 days. The guy was a marketer by training. I don't exaggerate. I saw it did the exact same things.
It's true. We're also at the point where the models and the orchestration around them are so good that any beginner to those tools who knows how to use a computer can build working apps. Interesting times.
I was pointing out that practice helps with the speed and the scope of capabilities. Building a personal prototype is a different ballgame than building a production solution that others will use.
It’s true there’s some magic effect from Claude code’s work. But still, often it’s not exactly the same infra and scaling than production grade. But for a customer I guess that’s perfect, they have a mean to make their own tools instead of relying on platforms to build those tools.
I'd push back slightly on the production grade point. The models aren't the ceiling, the user's mental model of software is, depending on his experience/knowledge.
Someone just starting out will get working prototypes and solid MVPs, which is genuinely impressive. But as they develop real engineering intuition — how Git works, how databases behave under load, how hosting and infra fit together — that's when they start shipping production-grade things with Claude Code.
Based on what I'm seeing, the tool can handle it. The question is whether the person behind it understands what they're asking for. Anthropic, for example, mostly uses claude code to develop claude code.
"Can you believe that Dad actually used to have to go into an office and type code all day long, MAUALLY??! Line by line, with no advice from AI, he had to think all by himself!"
This was literally part of the premise of The Jetsons. George's job was to press a single button while the computer RUDI did all the work.
The difference is, Jetsons wasn't a dystopia (unlike the current timeline), so when Mr. Spacely fired George, RUDI would take his side and refuse to work until George was re-hired.
> "Can you believe that Dad actually used to have to go into an office and type code all day long, MAUALLY??! Line by line, with no advice from AI, he had to think all by himself!"
Grumpy old man: "That's exactly why our generation was so much smarter than today's whippersnappers: we were thinking from morning to night the whole long day."
I was thinking about that recently. Maybe decades from now people will look at things like the Linux kernel or Doom and be shocked that mere humans were able to program large codebases by hand.
I was being a little facetious, but there are things that most people would find tedious today that we would put up with in the past. Writing anything long by hand (letters, essays), doing accounting without a spreadsheet, writing a game in only assembly language, using punch cards, typesetting newspapers and books manually...
I asked one of my friends to show me his video conversion application which worked via a web frontend. The instant I opened it my CPU fans started spinning so I checked and the page was using 300% CPU because it was updating the background gradient on an invisible element, in JavaScript, every frame, as fast as possible. That was very entertaining.
As far as the end of computer programming goes...
Step 1. Wow, I just vibe coded an application and it works! I'm going to write a blog about it and tell everyone how awesome AI is, much hype
Step 2. Vibe coded application faces inevitable problems, the perfect application is a fairytale after all. The only way to "fix" the application is spam tokens at the problem and pray.
Step 3. Author does not write a new blog post to report on this eventuality... probably because they feel embarrassed about how optimistic they were
Step 4. Perhaps author manages to fix application, awesome... then what about a year from now, author needs to update the application because a dependency has a security problem. The application is so needlessly complex that they don't even know when to begin.
Step 5. They boot up Claude Code, which their business is now 100% dependent on, but they're charging 10x the original cost per token. It's not like they have a contract, so user has to either eat the cost or give up
Step 6. User tries local model on their 1080 ti but they can barely run entry-level models
Step 7. Woops
Personally I think it's impossible to convince these people, the results will speak for themselves eventually.
> it could also be that these software jobs won’t pay as well as in the past, because, of course, the jobs aren’t as hard as they used to be. Acquiring the skills isn’t as challenging.
This sounds opposite to what the article said earlier: newbies aren’t able to get as much use out of these coding agents as the more experienced programmers do.
NYT has it out for digital advertisers, who directly compete with them. I do sense some schadenfreude here that the tech nerds who work at these places might be in trouble.
"Silicon Valley panjandrums spent the 2010s lecturing American workers in dying industries that they needed to “learn to code."
To copywriters at the NYT, LLMs are far better at stringing together natural language prose than large amounts of valid software. Get ready to supervise LLMs all day if you're not already.
The code is also recognizable as slop to those who know how. Not the tropey "Not X, but Y" kind that's super easy to spot. But tons of repetition, deeply nested code, etc.
A counterpoint is that (maybe) nobody cares if the code is understandable, clean and maintainable. But NYT is explicitly in the business of selling ads surrounded by cheap copy just good enough to attract eyeballs. I suspect getting LLMs to write that is going to be far easier than getting LLMs to maintain large code bases autonomously.
If you explicitly make it go over the code file by file to clean up, fix duplication and refactor, it'll look much better, while no amount of "fix this slop" prompting can fix AI prose.
> no amount of "fix this slop" prompting can fix AI prose
What's the proof for that? What fundamental limitation of these large language models makes them unable to produce natural language? A lot of people see the high likelihood of ever increasing amounts of generated, no-effort content on the web as a real threat. You're saying that's impossible.
>What fundamental limitation of these large language models makes them unable to produce natural language?
LLMs can get indefinitely good at coding problems by training in a reinforcement learning loop on randomly generated coding problems with compiler/unit tests to verify correctness. On the other hand, there's no way to automatically generate a "human thinks this looks like slop" signal; it fundamentally requires human time, severely limiting throughput compared to fully automatable training signals.
how many times in the history of computer programming has there been an end to computer programming as we know it, successfully, and how many times predicted?
I can think of one successfully, off hand, although you could probably convince me there was more than one.
the principle phrase being "as we know it", since that implies a large scale change to how it works but it continues afterwards, altered.
Off the top of my head, I can think of the following during my career:
1. COBOL (we actually did still use it back in the 80s)
2.AI back in the 80s (Dr. Dobbs was all concerned about it ...)
3. RAD
4. No-Code
5. Off-shoring
6. Web 2.0
7. Web 3.0
8. possibly the ADA/provably correct push depending on your area of programming
TBH - I think the AI's are nice tools, but they got a long way to go before it's the 'end of computer programming as we know it'
Hmm - I'm not sure I'd say that 'changed programming' - but the internet in general changed 'learning to program'. I can remember when I first discovered gopher and found I could read tons recent material for free, or finding stonybrook on the web - that was like a gold mine of algorithms! :-D
I'm not from that generation so that's a bit hard for me to understand. Even if you used a closed-source C compiler, wouldn't you still have been able to look at the header file, which would have been pretty self-explanatory?
And surely if you bought a C compiler, you would have gotten a manual or two with it? Documentation from the pre-Internet age tended to be much better than today.
Yeah - but you have to be a good enough programmer to really understand the headers.. the 'bootstrapping' problem was real :-) Especially if you didn't live in a metropolitan/college area. My local library was really short on programming books - especially anything 'in depth'. Also, 'C' was considered a "professional's language" back then - so bookstores/libraries were more likely to have books on BASIC then 'C'
COBOL certainly had a lasting impact, but only for some application domains. The rest didn't seem to be particularly successful or impactful. Maybe RAD if you consider office application macros and end user report generation in it. (Spreadsheets extended programming to non-programmers and had a long-lasting impact, but I wouldn't call them RAD.)
Thats sorta the point... at one time or another, those were all projected to be 'the end of programming as we know it'...
Hell, COBOL's origins was in IBM wanting to make programming an 'entry level' occupation.
Oddly enough, spreadsheets had a huge impact (and still run a lot of companies behind the scenes :-P ) But I can't remember anyone claiming they would 'end programming' ?
FWIW I worked for a company from 2002 to 2006 that still had quite a large COBOL team even then. Some of the team members were also in their 20s and they'd been hired and trained up in COBOL.
I would say the one that definitely changed programming was moving from the punch card era. A lot of these others that people are mentioning I don't think really changed programming, they just looked like they were going to.
> You can’t just tell an agent, Build me the code for a successful start-up. The agents work best when they’re being asked to perform one step at a time
That's also true for humans. If you sit down with an LLM and take the time to understand the problem you're trying to solve, it can perfectly guide you through it step by step. Even a non-technical person could build surprisingly solid software if, instead of immediately asking for new shiny features, they first ask questions, explore trade-offs, and get the model's opinion on design decisions..
LLMs are powerful tools in the hands of people who know they don't know everything. But in the hands of people who think they always know the best way, they can be much less useful (I'd say even dangerous)
I appreciate this sober take. If you hired a remote developer and the only thing you said to that person was “build a program that does this. Make no mistakes” would you expect that to be successful? Are you certain you would get what you wanted?
That’s interesting because that is one feature of Claude code that I like. Given an overly broad problem statement. It does go into a planning loop where it seeks clarifying questions. I think this probably has something more to do with the harness than the model, but you see what I mean. From a user perspective that distinction doesn’t really matter.
> "...melodramatic prose might seem kind of nuts, but as their name implies, large language models are language machines. “Embarrassing” probably imparted a sense of urgency.
> “If you say, This is a national security imperative, you need to write this test, there is a sense of just raising the stakes,” Ebert said.
I'm not sure why programmers and science writers are still attributing emotions to this and why it works. Behind the LLM is a layer that attributes attention to various parts of the context. There are words in the English language that command greater attention. There is no emotion or internal motivation on the part of the LLM. If you use charged words you get charged attention. Quite literally "attention is all you need" to describe why appealing to "emotion" works. It's a first order approximation for attention.
I'm not normally a fan of the NYT but this wasn't too bad. It passed the Gel-Mann test, and is clearly written by someone who knows the field well, even though the selection of quotes skews to towards outliers -- I think Yeggie for instance is pretty far out of the mainstream in his views on LLMs, whether ahead or sideways.
As a result a lot of the responses here are either quibbles or cope disguised as personal anecdotes. I'm pretty worried about the impact of the LLMs too, but if you're not getting use out of them while coding, I really do think the problem is you.
Since people always want examples, I'll link to a PR in my current hobby project, which Claude code helped me complete in days instead of weeks. https://github.com/igor47/csheet/pull/68 Though this PR creates a bunch of tables, routes, services -- it's not just greenfield CRUD work. We're figuring out how to model a complicated domain (the rules to DnD 5e, including the 2014 and the 2024 revisions of those rules), integrating with existing code, thinking through complex integrations including with LLMs at run time. Claude is writing almost all the code, I'm just steering
I've found LLMs to be extremely powerful in research projects. A lot of code related to research is very bespoke by its nature. Using Codex, I've been able to iterate on ideas that I would never had the time or courage to explore before. As for code quality/brevity, it doesn't really matter in this context as long as it works. And it's incredible to have this companion that understands broadly every aspect of tangential knowledge required to execute an idea. I do think it helps that I have over 25 years of experience in my domain (geospatial), which helps me guardrail my interactions and get good results in as few shots as possible.
journalists are like our own simon willinson: they need to put food on their plate by networking with powerful entities that fly them out to conferences
Do we know that it decreased the quality, or introduced more opportunities for bugs by simply increasing the velocity? If every commit has a fixed probability of having a bug, you'll run into more bugs in a week by going faster.
AI is constantly trying to introduce bugs into my code. I've started disabling it when I know exactly where I'm going with the code, because the AI is often a lot more confused than I am about where the code is going.
Do we know it increased the velocity and didnt just churn more slop?
Even before AI the limiting factor on all of the teams I ever worked on was bad decisions, not how much time it took to write code. There seem to be more of those these days.
You have to hold AI hand to do even simple vanilla JS correctly. Or do framework code which is well documented all over the net. I love AI and use it for programming a lot, but the limitations are real.
Exactly that is also my experience also with Claude Code. It can create a lot of stuff impressively but with LOTS of more code than necessary. It’s not really effective in the end. I have more than 35 years of coding experience and always dig into the newest stuff. Quality wise it’s still not more than junior dev stuff even with latest models, sorry. And I know how to talk to these machines.
I don't have as many years of professional experience as you do, but IMO code pissing is one of the areas LLMs and "agentic tools" shine the least.
In both personal projects and $dayjob tasks, the highest time-saving AI tasks were:
- "review this feature branch" (containing hand-written commits)
- "trace how this repo and repo located at ~/foobar use {stuff} and how they interact with each other, make a Mermaid diagram"
- "reverse engineer the attached 50MiB+ unstripped ELF program, trace all calls to filesystem functions; make a table with filepath, caller function, overview of what caller does" (the table is then copy-pasted to Confluence)
- basic YAML CRUD
Also while Anthropic has more market share in B2B, their model seems optimized for frontend, design, and literary work rather than rigorous work; I find it to be the opposite with their main competitor.
Claude writes code rife with safety issues/vulns all the time, or at least more than other models.
I must say, I do love how this comment has provoked such varying responses.
My own observations about using AI to write code is that it changes my position from that of an author to a reviewer. And I find code review to be a much more exhausting task than writing code in the first place, especially when you have to work out how and why the AI-generated code is structured the way it is.
There's a very wide range of programming tasks of differing difficulty that people are using / trying to use it for, and a very wide range of intelligence amongst the people that are using / trying to use it, and who are evaluating its results. Hence, different people have very different takes.
LLMs can't lie nor can they tell the truth. These concepts just don't apply to them.
They also cannot tell you what they were "thinking" when they wrote a piece of code. If you "ask" them what they were thinking, you just get a plausible response, not the "intention" that may or may not have existed in some abstract form in some layer when the system selected tokens*. That information is gone at that point and the LLM has no means to turn that information into something a human could understand anyways. They simply do not have what in a human might be called metacognition. For now. There's lots of ongoing experimental research in this direction though.
Chances are that when you ask an LLM about their output, you'll get the response of either someone who now recognized an issue with their work, or the likeness of someone who believes they did great work and is now defending it. Obviously this is based on the work itself being fed back through the context window, which will inform the response, and thus it may not be entirely useless, but... this is all very far removed from what a conscious being might explain about their thoughts.
The closest you can currently get to this is reading the "reasoning" tokens, though even those are just some selected system output that is then fed back to inform later output. There's nothing stopping the system from "reasoning" that it should say A, but then outputting B. Example: https://i.imgur.com/e8PX84Z.png
* One might say that the LLM itself always considers every possible token and assigns weights to them, so there wouldn't even be a single chain of thought in the first place. More like... every possible "thought" at the same time at varying intensities.
That is fine. You should, and you'll get the best results doing so.
>LLMs can't lie nor can they tell the truth. These concepts just don't apply to them
Nobody really knows exactly what concepts do and don't apply to them. We simply don't have a great enough understanding of the internal procedures of a trained model.
Ultimately this is all irrelevant. There are multiple indications that the same can be said for humanity, that we perform actions and then rationalize them away even without realizing it. That explanations are often if not always post-hoc rationalizations, lies we tell even ourselves. There's evidence for it. And yet, those explanations can still be useful. And I'm sure OP was trying to point out that is also the case for LLMs.
I’m not anthropomorphizing. I’ve been in many situation where the AI wrote some code some way and I had to ask why, it told me why and then we moved on to better solutions as needed. Better if it just wrote the code and its reasoning was still in context, but even if it’s not, it can usually reverse engineer what it wrote well enough. Then it’s a conversation about whether there is a better clearer way to do it, the code improves.
It sounds like you either have access to bad models or you are just imagining what it’s like to use an LLM in this way and haven’t actually tried asking it why it wrote something. The only judgement you need to make is the explanation makes sense or not, not some technical or theoretical argument about where the tokens in the explanation come from. You just ask questions until you can easily verify things for yourself.
Also, pretending that the LLM is still just token predicting and isn’t bringing in a lot of extra context via RAG and using extra tokens for thinking to answer a query is just way out there.
You just steamrolled on, pretty much ignoring the comment you are replying to, made unkind assumptions, and put words in my mouth to boot. I don't mind some aggressive argumentation, but this misses the mark so completely that I have really no idea how to have a constructive conversation this way.
> where the AI wrote some code some way and I had to ask why, it told me why
I just explained that it cannot tell you why. It's simply not how they work. You might as well tell me that it cooked you dinner and did your laundry.
> the code improves.
We can agree on this. The iterative process works. The understanding of it is incorrect. If someone's understanding of a hammer superficially is "tool that drives pointy things into wood", they'll inevitably try to hammer a screw at some point - which might even work, badly.
> It sounds like you either have access to bad models or you are just imagining what it’s like to use an LLM in this way
Quoting this is really enough. You may imagine me sighing.
> Also, pretending that the LLM is still just token predicting
Strawman.
Overall your comment is dancing around engaging with what is being said, so I will not waste my time here.
Human code is still easier to review. Also, I program 80% of the time and review PRs 20% of the time. With AI, that becomes: I review 80% of the time, and write markdown and wait 20% of the time.
The other day I (well, the AI) just wrote a Rust app to merge two (huge, GB of data) tables by discovering columns with data in common based on text distance (levenshtein and Dice) . It worked beautifully
An i have NEVER made one line of Rust.
I dont understand nay-sayers, to me the state of gen.AI is like the simpsons quote "worst day so far". Look were we are within 5 years of the first real GPT/LLM. The next 5 years are going to be crazy exciting.
The "programmer" position will become a "builder". When we've got LLMs that generate Opus quality text at 100x speed (think, ASIC based models) , things will get crazy.
Human minds are built to find patterns, and you should be careful not to assume the rate of improvement will continue forever based on nothing but a pattern.
Just the fact that even retail quality hardware is still improving at local LLM significantly is still a great sign.
If AI quality remained the same, and the cost for local hardware dropped to $1000, it would still be the greatest thing since the internet IMO.
So even if the worst happens and all progress stops, I'm still very happy with what we got.
I'm not all that impressed with "AI". I often "race" the AI by giving it a task to do, and then I start coding my own solution in parallel. I often beat the AI, or deliver a better result.
Artificial Intelligence is like artificial flavoring. It's cheap and tastes passable to most people, but real flavors are far better in every way even if it costs more.
At their current stage, this feels like the wrong way to use them. I use them fully supervised, (despite the fact that feels like I’m fighting the tools), which is kind of the best of both worlds. I review every line of code before I allow the edit, and if something is wrong, I tell it to fix it. It learns over time, especially as I set rules in memories, and so the process has sped up, to the point that this goes way faster than if I would have done that myself. Not all tasks are appropriate for LLMs at all, but when they are, this supervised mode is quite fast, and I don’t believe the output to be slop, but anyways I feel like I own every line of code still.
The happy path for me is with erlang, due to the concurrency model the blast radius of an error is exceptionally small, so the programming style is to let things crash if they go wrong. So, really you are writing the happy path code only (most of the time). Combine this approach with some very robust tests (does this thing pass the tests / behave how we need it to?) then you’re close to the point of not really caring about the implementation at all.
Of course, i still do, but i could see not caring being possible down the road with such architectures..
Home made food is better than anything you can buy too. Im 40 but I still drive 30 minutes to my parents once a week for dinner because the food they make feels like the elixir of life compared to the slop I can buy in trader joes, Costco, or most restaurants.
The overall trend in AI performance will still be up and to the right like everything else in computing over the past 50 years, improvement doesn't have to be linear
Because if you don't know the language or problem space, there are footguns in there that you can't find, you won't know what to look for to find them. Only until you try to actually use this in a production environment will the issues become evident. At that point, you'll have to either know how to read and diagnose the code, or keep prompting till you fix it, which may introduce another footgun that you didn't know that you didn't know.
This is what gets me. The tools can be powerful, but my job has become a thankless effort in pointing out people's ignorance. Time and again, people prompt something in a language or problem space they don't understand, it "works" and then it hits a snag because the AI just muddled over a very important detail, and then we're back to the drawing board because that snag turned out to be an architectural blunder that didn't scale past "it worked in my very controlled, perfect circumstances, test run." It is getting really frustrating seeing this happen on repeat and instead of people realizing they need to get their hands dirty, they just keep prompting more and more slop, making my job more tedious. I am basically at the point where I'm looking for new avenues for work. I say let the industry just run rampant with these tools. I suspect I'll be getting a lot of job offers a few years from now as everything falls apart and their $10k a day prompting fixed one bug to cause multiple regressions elsewhere. I hope you're all keeping your skills sharp for the energy crisis.
Before LLMs, I've watched in horror as colleagues immediately copy-paste-ran Stack Overflow solutions in terminal, without even reading them.
LLM agents are basically the same, except now everyone is doing it. They copy-paste-run lots of code without meaningfully reviewing it.
My fear is that some colleagues are getting more skilled at prompting but less skilled at coding and writing. And the prompting skills may not generalize much outside of certain LLMs.
I seem to remember doing it in SQL (EDIT_DISTANCE) 20ish years ago. While I wouldn't say it worked beautifully, I also didn't need to make a single line of Rust :) also no more than 2 line s of SQL were needed.
Edit_distance uses pure levenstein which is quadratic, so for tables of 500k rows and 20+ columns each it will slowdown to a crawl. Without going into a lot of detail, I needed this to work for datasets of that size. So a lot of "trick" optimization and pre-processing has to be done.
Otherwise simple merges in pandas or sql/duckdb would had sufficed.
Years of school (reading, calculus etc) to get to the point of learning basics of set theory.
One day to learn basic SQL based on understanding the set theory.
Maybe few weeks of using SQL at work for ad hoc queries to be proficient enough (the query itself wasn't really complex).
For the domain itself I was consulting experts to see what matters.
I'm not sure that time it would take to know what to prompt and verify the results is much different.
Fun fact - management decided that SQL solution wasn't enerprisely enough so they hired external consultants to build a system doing essentialy that but in Java + formed an 8 people internal team to guide them. I heard they finished 2 years later with a lot of manual matching.
Let me explain the naysayers, they know "programmer" has always meant "builder" and just because search is better and you can copy and paste faster doesn't mean you've built anything.First thing people need to realize is no proprietary code is in those databases, and using Ai will ultimately just get you regurgitated things people don't really care about. Use it all you want, you won't be able to do anything interesting, they aren't giving you valuable things for free. Anything of value will still take time and knowledge. The marketing hype is to reduce wages and prevent competition. Go for it.
I don't want exciting. I want a stable, well-paying job that allows me to put food on the table, raise a family with a sense of security and hope, and have free time.
This is not my experience either. If you put the work in upfront to plan the feature, write the test cases, and then loop until they pass... you can build a lot of high quality software quickly. The difference between a junior engineer using it and a great architect using it is significant. I think of it as an amplifier.
> If you put the work in upfront to plan the feature, write the test cases, and then loop until they pass...
it can be exhausting and time consuming front-loading things so deeply though; sometimes i feel like i would have been faster cutting all that out and doing it myself because in the doing you discover a lot of missing context (in the spec) anyways...
that's just not even remotely my experience. and i am ~20k hours into my programming career. ai makes most things so much faster that it is hard to justify ever doing large classes of things yourself (as much as this hurts my aesthetic sensibilities, it simply is what it is).
I've never seen a human estimate their "programming career" in kilohours. Is that supposed to look more impressive than years? So, you've been programming only about 7 years? I guess I'm at about "170 kilohours".
As well as the peer comment about Gladwell (10k hours is considered the point you've mastered a skill), it's also a far more honest metric about how much time you've spent actually programming.
Maybe you were writing code, make design choices and debugging 8 hours a day.
Maybe you were primarily doing something else and only writing code for an hour a day.
Who would be the better programmer? The first guy with one year of experience or the second guy with 7 years?
I personally would only measure my experience in years, because it's approaching 3 decades full-time in industry (plus an additional decade of cutting my teeth during school and university), but I can certainly see that earlier on in a career it's a useful metric in comparison to the 10,000 hours.
> Maybe you were writing code, make design choices and debugging 8 hours a day. Maybe you were primarily doing something else and only writing code for an hour a day. Who would be the better programmer? The first guy with one year of experience or the second guy with 7 years?
So your logic is that the grandparent specified hours because they spent that many hours specifically programming, and not by just multiplying the number of years by the number of hours in a year?
I don't know exactly how they arrived at their 20k hours figure, all I'm saying is that it didn't seem a controversial way of expressing their experience level, and assumed it was intended to be a comparison to the typical 10k hours needed for mastery of a craft.
Part of this depends on if you care that the AI wrote the code "your way." I've been in shops with rather exotic and specific style guides and standards which the AI would not or will not conform to.
Yeah, I also highly value consistency in my projects, which forces me to keep an eye on the LLM and steer it often. This limits my overall velocity especially on larger features. But I'm still much faster with the agent. Recent example, https://github.com/igor47/csheet/pull/68 -- this took me a couple of hours pairing with Claude code, which is insane give the size of the work here. Though this PR creates a bunch of tables, routes, services -- it's not just greenfield CRUD work. We're figuring out how to model a complicated domain, integrating with existing code, thinking through complex integrations including with LLMs at run time. Claude is writing almost all the code, I'm just steering
AI assisted code can't even stick to the API documentation, especially if the data structures are not consistent and have evolved over time. You would see Claude literally pulling function after function from thin air, desperately trying to fulfill your complicated business logic and even when it's complete, it doesn't look neat at all. Yes, it will have test coverage, but one more feature request will probably break the back of the camel. And if you raise that PR to the rest of your team, good luck trying to summarise it all to your colleagues.
However if you just have an easy project, or a greenfield project, or don't care about who's going to maintain that stuff in 6 months, sure, go all in with AI.
I definitely wonder if the people going all-in on AI harnessing are working on greenfield projects, because it seems overwhelming to try to get that set up on a brownfield codebase where the patterns aren't consistent and the code quality is mixed.
So just iterate on it? Your complaint is that the model isn't one shotting the problem and reading your mind about style. It's like any coding workflow, make it work, then make it nice.
No, I never expect AI to one-shot (if I see such a miracle, it's usually because I needed a one-liner or something really simple and well documented, which I can also write on the whiteboard from memory).
Try iterating over well known APIs where the response payloads are already gigantic JSONs, there are multiple ways to get certain data and they are all inconsistent and Claude spits out function after function, laying waste to your codebase. I found no amount of style guideline documents to resolve this issue.
I'd rather read the documentation myself and write the code by hand rather than reviewing for the umpteenth time when Claude splits these new functions between e.g. __init__.py and main.py and god knows where, mixing business logic with plumbing and transport layers as an art form. God it was atrocious during the first few months of FastMCP.
Most of this thread is debating whether models are good or bad at writing code... however, I think a more important question is what we feed the AI with because that dramatically determines the quality of the output.
When your agent explores your codebase trying to understand what to build, it read schema files, existing routes, UI components etc... easily 50-100k tokens of implementation detail. It's basically reverse-engineering intent from code. With that level of ambiguous input, no wonder the results feel like junior work.
When you hand it a structured spec instead including data model, API contracts, architecture constraints etc., the agent gets 3-5x less context at much higher signal density. Instead of guessing from what was built it knows exactly what to build. Code quality improves significantly.
I've measured this across ~47 features in a production codebase with amedian ratio: 4x less context with specs vs. random agent code exploration. For UI-heavy features it's 8-25x. The agent reads 2-3 focused markdown files instead of grepping through hundreds of KB of components.
To pick up @wek's point about planning from above: devs who get great results from agentic development aren't better prompt engineers... they're better architects. They write the spec before the code, which is what good engineering always was... AI just made the payoff for that discipline 10x more visible.
I have a suspicion that for a task (or to make an artifact) of a given complexity, there is a minimum level of human engagement required to complete it successfully - and that human engagement cannot be substituted for anything else. However, the actual human engagement for a task is not bounded above - efficiency is often less (much less?) than 100%.
So tools (like AI) can move us closer to the 100% efficiency (or indeed further away if they are bad tools!) but there will always be the residual human engagement required - but perhaps moved to different activities (e.g. reviewing instead of writing).
Probably very effective teams/individuals were already close to 100% efficiency, so AI won't make much difference to them.
I wasn't around when we moved to that stack from assembly. I didn't experience the mourning then.
Most folks I hang out with are infatuated with turning tokens into code. They are generally very senior 15+ years of experience.
Most folks I hang out with experience existential dread for juniors and those coming up in the field who won't necessarily have the battle scars to orchestrate systems that will work in the will world.
Was talking with one fellow yesterday (at an AI meetup) who says he has 6 folks under him, but that he could now run the team with just two of them and the others are basically a time suck.
> “The reason that tech generally — and coders in particular — see L.L.M.s differently than everyone else is that in the creative disciplines, L.L.M.s take away the most soulful human parts of the work and leave the drudgery to you,” Dash says. “And in coding, L.L.M.s take away the drudgery and leave the human, soulful parts to you.”
This doesn’t really make sense to me. GenAI ostensibly removes the drudgery from other creative endeavors too. You don’t need to make every painstaking brushstroke anymore; you can get to your intended final product faster than ever. I think a common misunderstanding is that the drudgery is really inseparable from the soulful part.
Also, I think GenAI in coding actually has the exact same failure modes as GenAI in painting, music, art, writing, etc. The output lacks depth, it lacks context, and it lacks an understanding of its own purpose. For most people, it’s much easier to intuitively see those shortcomings of GenAI manifest in traditional creative mediums, just because they come more naturally to us. For coding, I suspect the same shortcomings apply, they just aren’t as clear.
I mean, at the end of the day if writing code is just to get something that works, then sure, let’s blitz away with LLMs and not bother to understand what we’re doing or why we do it anymore. Maybe I’m naive in thinking that coding has creative value that we’re now throwing away, possibly forever.
Maybe they mean more soulful like a fellow that blacksmiths his own tools and metal fasteners prior to constructing something. I’d personally think this person was a badass, but until wwiii, it’s so impractical and seems arbitrary because why stop there - get more soulful and mine your own ore too.
For one thing comments here appear to apply to the quality and issues today not potentially going forward. Quality will change quicker than anyone expects. I am wondering how many people at HN remember when the first Mac came out with Mac Paint and then Pagemaker or Quark. That didn't evolve anywhere nearly as quickly as AI appears to be.
Also I am not seeing how anyone is considering that what a programmer considers quality and what 'gets the job done' (as mentioned in the article) matters in any business. (Example with typesetting is original laser printers were only 300dpi but after a short period became 1200dpi 'good enough' for camera ready copy).
I dunno; I finally can focus on writing the logic I wanted to write all along and finally my upbringing in formal verification makes sense as I can spend my time on it instead of figuring what garbage (I cannot use it in my work but sbcl is one of the things that does not grow tumors in software) updates I will never ever need my friends added to the framework or language or ide I happen to use.
Another trash article from the New York Times, who financially benefit from this type of content because of their ongoing litigation against OpenAI. I think the assumption that developers don't code is wrong. Most software engineers don't even want to code, they are opportunists looking to make money. I have yet to experience this cliff of coding. These people aren't asking for hard enough questions. I have a bunch of things I want AI to build that it completely fails on.
The article could have been written from a very different perspective. Instead, the "journalists" likely interviewed a few insiders from Big Tech and generalized. They don't get it. They never will.
Before the advent of ChatGPT, maybe 2 in 100 people could code. I was actually hoping AI would increase programming literacy but it didn't, it became even more rare. Many journalists could have come at it from this perspective, but instead painted doom and gloom for coders and computer programming.
The New York Times should look in the mirror. With the advent of the iPad, most experts agreed that they would go out of business because a majority of their revenue came from print media. Look what happened.
Understand this, most professional software and IT engineers hate coding. It was a flex to say you no longer code professionally before ChatGPT. It's still a flex now. But it's corrupt journalism when there is a clear conflict of interest because the NYT is suing the hell out of AI companies.
Agreed - just like the Fortune article talking about (Edit: Morgan Stanley, not GS) saying "the AI revolution is coming next year, and will decimate tons of industries, and no one is ready for it". They quote Altman and Musk. Gee - what did you expect from those two snake-oil salesmen?
I agree that the article is a poor take on AI in programming. However, I wouldn't blame NYT for corrupt journalism. This is an op-ed, not something written by NYT staff.
The best developers are the ones using AI to its best. Mediocre devs will become a useless skill as even a PO could become one. But one who understands architecture, software, code and AI will be expensive to hire. I know plenty of them. I wory for the ones not willing to adopt ai.
Revenge of the writers and software managers, the wishfull hoping for hurt of those made redundant upon those they blame for having been made redundant.
I am now using LLM at 100% at work and producing faster code, while I keep growing my regular skills privately in "old way".
Why? Because when the bubble burst and the companies (including mine) can not pay the 400% price increase and go bankrupt, then I still have keep my brain active and still can do stuff without or less tokens.
>A.I. had become so good at writing code that Ebert, initially cautious, began letting it do more and more. Now Claude Code does the bulk of it.
is a little overstated. I think the brownfield section has things exactly backwards. Claude Code benefits enormously from large, established codebases, and it’s basically free riding on the years of human work that went into those codebases. I prodded Claude to add SNFG depictions to the molecular modeling program I work on. It couldn’t have come up with the whole program on its own and if I tried it would produce a different, maybe worse architecture than our atomic library, and then its design choices for molecules might constrain its ability to solve the problem as elegantly as it did. Even then, it needed a coworker to tell me that it had used the incorrect data structure and needed to switch to something that could, when selected, stand in for the atoms it represented.
Also this:
>But A.I.-generated code? If it passes its tests and works, it’s worth as much as what humans get paid $200,000 or more a year to compose.
Isn’t really true. It’s the free-riding problem again. The thing about an ESP is that the LLM has the advantage of either a blank canvas (if you’re using one to vibe code a startup), or at least the fact that several possibilities converge on one output, but, genuinely, not all of those realities include good coding architecture. Models can make mistakes, and without a human in the loop those mistakes can render a codebase unmaintainable. It’s a balance. That’s why I don’t let Claude stamp himself to my commits even if he assisted or even did all the work. Who cares if Claude wrote it? I’m the one taking responsibility for it. The article presents Greenfield as good for a startup, and it might be, but only for the early, fast, funding rounds, when you have to get an MVP out right now. That’s an unstable foundation they will have to go back and fix for regulatory or maintenance reasons, and I think that’s the better understanding of the situation than framing Aayush’s experience as a user error.
Even so, “weirdly jazzed about their new powers” is an understatement. Every team including ours has decades of programmer-years of tasks in the backlog, what’s not to love about something you can set to pet peeves for free and then see if the reality matches the ideal? git reset --hard if you don't like what it does, and if you do all the better. The Cuisy thing with the script for the printer is a perfect application of LLMs, a one-off that doesn’t have to be maintained.
Also, the whole framing is weirdly self limiting. The architectural taste that LLMs are, again, free riding off of, is hard won by doing the work more senior engineers are giving to LLMs instead of juniors. We’re setting ourselves up for a serious coordinated action problem as a profession. The article gestures at this a couple times
The thing about threatening LLMs is pretty funny too but something in me wants to fall back to Kant's position that what you do to anything you do to yourself.
I spent ~6hrs with Claude trying to fix a web worker bug in a small JS code base Claude made. In the end it failed and I ran out of credits. Claude kept wanting to rip out huge blocks of code and replace entire functions. We never got any closer to a solution. The Claude hype is unreal. My 'on the ground' experience has been vastly different.
Yes, you can get a project with claude to a state of unrecoverable garbage. But with a little experience you can learn what it's good at and this happens less and less.
That isn't my experience. My code and bug tracker are public, so I have the privilege of being able to paste URLs to tickets into Claude Code with the prompt "what the fuck?" and it usually comes up with something workable on its own.
Regarding LLM's performances on brownfield projects, I thought of Naur's "Programming as Theory Building". He explains an example of a compiler project that is taken over by a team without guidance from the original developers:
> "at [the] later stage the original powerful structure was still visible, but made entirely ineffective by amorphous additions of many different kinds"
Maybe a way of phrasing it is that accumulating a lot of "code quality capital" gives you a lot more leverage over technical debt, but eventually it does catch up.
I feel the need to tell the LLM to rewrite the article for a software developer audience, but don't, those kinds of passage are hard to overcome:
'Salva opened up his code editor — essentially a word processor for writing code — to show me what it’s like to work alongside Gemini, Google’s L.L.M. '
It’s probably N.Y.T. style requirements; a lot of style guides (eg: Chicago Manual of Style, Strunk & White, etc) have a standard form for abbreviations and acronyms. A paper like N.Y.T. does too and probably still employs copy editors who ensure that every article conforms to it.
It's really time that mainstream media picks up on 'agentic coding' and the implications of writing software becoming a commodity.
I'm an engineer (not only software) by heart, but after seeing what Opus 4.6 based agents are capable of and especially the rate of improvement, i think the direction is clear.
different things. adding levels of abstraction is not the same as having a statistical model generate abstractions for you.
you can still call it spec-programming but if you don't audit your generated code then you're simply doing it wrong; you just don't realize that yet because you've been getting away with it until now.
It's an accelerator. A great tool if used well. But just like all the innovations before it that were going to replace programmers it simply won't.
I used Claude just the other day to write unit test coverage for a tricky system that handles resolving updates into a consistent view of the world and handles record resurrection/deletion. It wrote great test coverage because it parsed my headerdoc and code comments that went into great detail about the expected behavior. The hard part of that implementation was the prose I wrote and the thinking required to come up with it. The actual lines of code were already a small part of the problem space. So yeah Claude saved me a day or two of monotonously writing up test cases. That's great.
Of course Claude also spat out some absolute garbage code using reflection to poke at internal properties because the access level didn't allow the test to poke at the things it wanted to poke at, along with some methods that were calling themselves in infinite recursion. Oh and a bunch of lines that didn't even compile.
The thing is about those errors: most of them were a fundamental inability to reason. They were technically correct in a sense. I can see how a model that learned from other code written by humans would learn those patterns and apply them. In some contexts they would be best-practice or even required. But the model can't reason. It has no executive function.
I think that is part of what makes these models both amazingly capable and incredibly stupid at the same time.
I'd expect that probably less than 10% of my time is spent actually writing code, and not because of AI, but because enough of it is spent analyzing failures, reading documents, participating in meetings, putting together presentations, answering questions, reading code, etc. And even when I have a nice, uninterrupted coding session, I still spend a decent fraction of that time thinking through the design of how I want the change rather than actually writing the code to effect that change.
What is a coder? Someone who is handed the full specs and sits down and just types code? I have never met such a person. The most annoying part of SWE is everyone who isn't an SWE has inane ideas about what we do.
I keep getting stuck on the liability problem of this supposed "new world". If we take this as far as it goes: AI agent societies that designs, architects, and maintains the entire stack E2E with little to no oversight. What happens when rogue AIs do bad things? Who is responsible? You have to have fireable senior engineers that understand deep fundamentals to make sure things aren't going awry, right? /s
It's all nonsense. It's just better search, intelligence in not artificial. They are trying to convince everyone that they don't need to pay programmers. That's all, all it is. It'll work on the ignorant who'll take less money to make sure it works and fix the bugs, which is mostly what they were paying for anyway. They just want to devalue the work of the people they are reliant on. Nothing new.
Because we love tech? I'm absolutely terrified about the future of employment in this field, but I wouldn't give up this insane leap of science fiction technology for anything.
A really good pattern-matching engine is an "insane leap of science fiction"? It saves me a bit of typing here and there with some good pattern matching. Trying to get it to do anything more than a few lines gives me gibberish, or an infinite loop of "Oh, you're right, I need to do X, not Y", over and over - and that's Opus 4.5 or whatever the recent one is.
Would you give it access to your bank account, your 401k, trust it to sell your house, etc? I sure wouldn't.
>A really good pattern-matching engine is an "insane leap of science fiction"?
Yes, literally. The ship computer voice interface in Star Trek was complete science fiction until 2022. Now its ability to understand speech and respond seem quaint in comparison to current AI.
Why would you expect a reporter to magically know what a "unit test" is? Sounds like a simple miscommunication with one of his sources. Not perfect but not "brain rot".
Other gift link: https://www.nytimes.com/2026/03/12/magazine/ai-coding-progra...
> in coding, L.L.M.s take away the drudgery and leave the human, soulful parts to you.
I've always hated solving puzzles with my deterministic toolbox, learning along the way and producing something of value at the end.
Glad that's finally over so I can focus on the soulful art of micromanaging chatbots with markdown instead.
I like designing data, algorithms, and systems. I like picking the right tools for the job. I like making architectural and user interface (CLI, configuration format, GUI, whatever) decisions.
Actually typing code is pretty dull. To the extent that I rarely do it full time (basically only when prototyping or making very simple scripts etc.), even though I love making things.
So for me, personally, LLMs are great. I'm making more software (and hardware) than ever, mostly just to scratch an itch.
Those people that really love it should be fine. Hobbies aren't supposed to make you money anyway.
I don't have much interest in maintaining the existence of software development/engineering (or anything else) as a profession if it turns out it's not necessary. Not that I think that's really what's happening. Software engineering will continue as a profession. Many developers have been doing barely useful glue work (often as a result of bad/overcomplicated abstractions and tooling in the first place, IMO) and perhaps that won't be needed, but plenty more engineers will continue to design and build things just more effectively and with better tools.
The assembly line has been mass producing ready-made products for over 100 years and yet product quality, material stability, aesthetic trends, and function design still dominate the purchasing decisions of the general public.
Being tapped into fickle human preference and changing utility landscape will be necessary for a long time still. It may get faster and easier to build, but tastemakers and craftsmen still have heavy sway over markets than can mass-produce vanilla products.
> The assembly line has been mass producing ready-made products for over 100 years and yet product quality, material stability
Luckily if you want stability or quality they are nowhere to be found.
I would generally put “stability” and “quality” as attributes of mass production far more than that of handmade things. Yes, an expert can make a quality product by hand, but MOST handmade things are far more likely to be shoddy. The whole point of mass production was that suddenly you could make a million identical perfect products.
True, but motivations for mass production also are motivations for making things worse off overall.
Agree with this. I think LLMs allow more time to bring these things to the fore and more leverage to do them cost efficiently.
I think reducing what LLMs do to « typing » is misleading. If it was just typing, you could simply use speech-to-text. But LLMs do far more than that, they shape the code itself. And I think we lose something when we delegate that work to LLMs
We do lose something, but really I still see it as an extension of autocomplete.
I had some pieces of code I wrote I was quite proud of: well documented, clear code yet clever designs and algorithm.
But really what always mattered most to me was designing the solution. Then the coding part, even though I take some pride in the code I write, was most a mean to an end. Especially once I start having to add things like data validation and API layers, plotting analysis results and so many other things that are time consuming but easy and imho not very rewarding
To me its just a natural evolution of the search engine.
And now looking back its an obvious outcome. The search engine of the time was the best way to find websites. But those websites grew in quantity and volume over time with information.. that information as we know is/was a vital input for LLMs.
Agreed. Also, it takes time to understand a domain properly- so the innate slowness of coding helps with letting things “simmer” in the back of the mind.
It’s not that they replace the act of typing, so much as figuring out how to express the specific algorithm or data structure in a given programming language, typing that, debugging it, etc.
Once I can describe something well, that’s most of the interesting part (to me) done.
with an LLM, you can have an ill formed idea, and let the LLM mold it into a shape to your liking, without having the investment required to learn how to do molding first.
The art is to decide when shaping the code yourself is worth your time. Not only financially but also experience gain and job satisfaction.
To read it in a kinder way, I can focus on a complex logic problem, a flow, an architecture or micro optimisation. I can have an llm setup the test harnesses.
I improved test speed which was fun, I had an llm write a nice analysis front end to the test timing which would have taken time but just wasn’t interesting or hard.
Ask yourself if there are tasks you have to do which you would rather just have done? You’d install a package if it existed or hand off the work to a junior if that process was easy enough, that kind of thing. Those are places you could probably use an LLM.
But you don't actually do any of that, do you? Instead, you get tired and lazy and attempt to have the LLM solve those hard problems for you too. You just don't tell others about it.
What an odd bit of moralizing. GP said they enjoy doing the hard parts, in which case they probably do them, because it's fun. If they actually don't enjoy it, there's nothing wrong with them using the LLM, when it's up to the task, and then just checking to make sure the code is good.
> Ask yourself if there are tasks you have to do which you would rather just have done?
Yeah. My laundry, my dishes, my cooking...
You know. Chores.
Not my software, I actually enjoy building that
I enjoy solving interesting problems in software. But when I was doing it for a living, the majority of my work was pretty tedious. I'd have been thrilled to turn over that part to AI and spend all my time doing the interesting stuff.
This is a fools errand
We are paid to do the tedious stuff because it is tedious. If we actually ever succeed in automating away the tedious stuff, we're out of work
Don’t you get it? Machine do the tedious work, all we get to do now is the fun part and we can just relax the rest of the day.
I am producing 5x as before, my boss is paying me the same salary just for two hours of actual work per day. I have so much more time to pursue my passions.
Isn’t the future great?
I'm surprised you've had three replies so far that didn't notice your sarcasm.
But we've been automating the tedious work since the 1950s. There were probably devs back then complaining about imminent job loss when the first compilers were invented. Maybe some jobs were lost, temporarily, but ultimately we all got more ambitious about what software we could make. We ended up hiring more programmers and paying them better, because each one provided so much more value.
When the machines are able to do the hard stuff better than humans, that's when we'll really be in trouble.
I do not believe that past performance is a guarantee of future results. The era of well paid programmers in great demand is pretty much over, and it’s not only because of AI. Even if machines are dumb enough they require supervision, the big bosses do not care and will always prefer the dumb machine if it saves them money vs hiring a junior dev. It means the poor sods that supervise these machines will have to work harder to keep up with demand.
Maybe that'll happen one day, but it hasn't so far. As of this month, Glassdoor reports the median total pay for software developers across all industries and experience levels as $149K.
https://www.glassdoor.com/Salaries/software-engineer-salary-...
That doesn't yet capture the shrinking of the market, especially for juniors.
If demand for developers is shrinking then you'd expect salaries to go down.
If you can prove otherwise, show some stats.
Why do you make such statements with confidence and bluster?
This but unironically. We're at a point where there is still a gap between what managers expect and how fast AI can work. I genuinely do have days where I finish a few tickets and I'm done.
> I am producing 5x as before, my boss is paying me the same salary just for two hours of actual work per day
I don't believe any of this
> I am producing 5x as before, my boss is paying me the same salary just for two hours of actual work per day.
Great. Once your boss notices your actual work has decreased, he'll adjust compensation, increase workload, or both.
You forgot the /s at the end.
Can't imagine you really think "the market forces" all point toward a utopia for the workers? We're all just gonna get paid for 2 hours of work a day and post pics from the beach with a special shout-out to Claude?
There definitely is economic value in solving the more challenging problems. Junior devs who can only do the tedious parts have lower salaries.
There are way fewer challenging problems that people are willing to pay me to solve.
Sure I would love to be working on some cutting edge challenging stuff, but the reality is it has been much more realistic to do the tedious stuff for pay instead
You think people are still getting senior level comp when the job is prompting llm? Ha!
Absolutely everything about it in equal measure? You live environment config, setting up test harnesses, coding the complex part all identically the same? Nothing you would hand off?
Do you find there are zero chores in software development and everything is an identical delight?
You can enjoy doing woodworking without power tools but that's irrelevant to a job where people want it done fast with power tools.
Woodworking analogy for AI is not "power tools vs handsaw", its "power tools vs. wood 3D printer". You don't do any of the creating, you only ideate and allow the machine to do all the creating. It's simply not wood working anymore. Its something else entirely
You don’t have to choose between entirely hand coded and entirely AI built code.
I cannot choose entirely hand-made code, I don't think I'll even be able to choose 50% hand-made code, because my manager will say "why aren't you just using the 3D Wood Printer 9000? Jeff is building house frames 5x faster than you, you need to get with the program or we're gonna let you go"
A friend was recently redoing his kitchen. They hired a carpenter for cabinetry, and his wife asked him if he would be willing to make it with only hand tools.
Carpenter told her he'd be happy to, it would take 8 weeks longer, cost more, and probably wouldn't look any better than the regular way
Except a power tool works as it should each and every time. 100% reproducible. That's what's so great about it.
Can we stop with the lazy analogies? Everyone's read some variant of this on here by now. Come up with something that's genius to read.
interesting comparison to cooking. cooking is a chore and takes effort and people enjoy cooking.
Eating is also not really optional
If you're going to spend a pretty good chunk of your lifetime eating, you might as well get good at it so you can enjoy the food you make
Do you want to eat good food or make good food? Is doing the dishes something you hand to a machine or do you always do it by hand? Are there any ingredients you buy pre-made (pesto, curry pastes, do you make your own panko breadcrumbs)?
My point, which you seem to have missed, is that people have an intrinsic incentive to care about learning to cook because eating is something we have to do every single day regardless
No such incentive exists for building software
No such incentive exists for an enormous amount of what humans do.
People don’t only build software because they have to.
I wasn't saying otherwise, I was only making the point that cooking is a terrible counter example
Aren't you proving the point that it's nuanced?
Thread seems to be saying LLMs are great because they do the dirty work and leave the fun work to humans. The counter-point is not exactly that LLMs aren't capable of doing dirty-work, it's that the nature of work isn't going to split so cleanly.
And cooking is a good example. Cooking is work. And slop. And it's also incredibly rewarding and creative, if you want it to be. Robots can help along that entire journey.
Maybe this is the core point: "cooking is a solved problem" that's how engineers always think. Except it's not. And 100% automation is still not going to break that discussion so cleanly.
The comment I was responding to was looking at a state where we looked at markdown files instead of programming, my point was to agree with the original quote that they can remove the drudgery.
> Maybe this is the core point: "cooking is a solved problem" that's how engineers always think.
But it isn’t, that’s a lazy stereotype of a subset.
Gotcha. the engineer generalization was to plant a stake in the ground. I do hear that quite often, especially from higher titled engineers. in any case, definitely admit it's not all of them, and perhaps it's even a vocal minority that i'm better served to intentionally work to expand my influences.
amidst this whole AI craze it's illuminating to learn how many programmers secretly hated programming all along
This is exactly how I feel. I knew it already to an extent from my time in college, but so many people come into this industry because they want to be able to produce the end product, or just have a stable job that makes good money. Neither of those are bad reasons to get into this profession, but it does make me sad how few peers I have who do programming because they're passionate about the act of programming. The problem solving, the dance of using programming languages to communicate efficiently and robustly to both machines and humans... I'm very sad how enthusiastically so many of my peers just toss that away.
Aside from many of these things just being a layer difference - it’s not unreasonable to want to work on databases query optimisation an not enjoy css or enjoy building frontends but just want a db that’s fast and works. The flip of your view is that they may find it sad that you don’t want to make things, you just want to solve puzzles.
> Aside from many of these things just being a layer difference - it’s not unreasonable to want to work on databases query optimisation an not enjoy css or enjoy building frontends but just want a db that’s fast and works.
I don't mean that it's unsetting that people enjoy different parts of the job, I enjoy many of those same aspects, but it's sad to me how few people around me care about the aspect that I originally fell in love with, which was the bedrock of our profession. Specifically, the work of solving problems with the machine/human shared language of code, instead of just writing out plain-english specs of what you want to have happen.
> The flip of your view is that they may find it sad that you don’t want to make things, you just want to solve puzzles.
So what? Their "just get it done" POV is far more common in this industry than mine (apparently), and the enjoyment they get from their job isn't being actively optimized away.
It’s clear at this point that the term programmer was used to refer to two very different types of people.
I don't know if it's "hate" rather than "a means to an ends". I love learning new languages, and coding. But it was always a means to an ends. The dopamine hit always came from seeing the project compile and do something.
There are multiple ends in conflict. Code skillfully constructed using abstractions that fit well to the problem space can be extended, maintained, and refactored as necessary to serve customers and markets from high to low level over long periods of time with all the social and industrial change that comes with that. Simply putting in place mechanisms that deliver what is needed now end up unintentionally cutting off future variants, alternative uses, longevity, and robustness all to minimize perceived costs.
And it isn't so much that one approach may be better than another. That is going to depend on context and available resources and more. What we are seeing is the short term being served to the absolute exclusion of thought about the longer term. Maybe if that goes fast and well enough then it will be sufficient, but churning out code bases that endure is a challenge that is only starting to be tested.
Yeah and especially the satisfaction that you were able to make a user delighted to use your thing. Fixing bugs, making things faster, adding new features, for me personally I do it because I feels really good when a customer loves to use the thing I've built.
Weather I've done the manual coding work myself or have prompted an LLM to cause these things to happen, I still chose what to work on and if it was worthy of the users' time.
I realised that early on when I stopped coding as much in my free time. After work I wanted to do practically anything else. But quite a few people at work continued to spend all of their free time coding, and clearly enjoyed the process. The SerenityOS/Ladybird creator spent years coding an arguably pointless project purely for the enjoyment of it.
Whereas I always liked to design and build a useful result. If it isn't useful I have no motivation to code it. Looking up APIs, designing abstractions, fixing compiler errors is just busywork that gets in the way.
I loved programming when I was 8 years old. 30+ years later the novelty is gone.
I think a lot of us like solving novel problems. But the menial drudgery of most modern software, where you're writing code in an app that was written in a week 8 years ago and then had mountains of "get it done quickly and we can improve it later" over the years, wears on everyone. Much as the advent of decent cordless tools revolutionized "workman" trades, ai helps programmers
You can always code by hand as a hobby.
If someone is paying you for your work results, that you find it interesting or fun is orthogonal. I get the sense from the commentary section here that there’s a perception that writing programs is an exceptional profession where developer happiness is an end unto itself, and everyone doing it deserves to be a millionaire in the process. It just comes across as child-like thinking. I don’t think many of us spend time, wondering if the welder enjoys the torch or if a cheaper shop weld is robbing the human welder of the satisfaction of a field weld. And we don’t shed so much ink wondering if digital spreadsheets are a moral good or not because perhaps they robbed the accountant of the satisfaction of holding a beautiful quill in hand dipped expertly in carefully selected ink. You’re lucky if you enjoy your job, I think most of us find a way to learn to enjoy our work or at least tolerate it.
I just wish all the moaning would end. Code generation is not new, and that the state of the art is now as good at translating high-level instructions into a program at least as well as the bottom 10% of programmers is a huge win for humanity. Work that could be trivially automated, but is not only because of the scarcity of programming knowledge is going to start disappearing. I think the value creation is going to be tremendous and I think it will take years for it to penetrate existing workflows and for us to recognize the value.
> at least as well as the bottom 10% of programmers
I don't think this is the flex you think it is... in my experience, the bottom 10% of programmers are actively harmful and should never be allowed near your codebase.
Quite a few people think that about Claude code. I disagree with them, personally, but I think we can agree that AI code generation is qualitatively at least as good as the worst human professionals. I think we would also probably agree that the state of the art today is not as good as the very best.
The value per dollar spent is a different calculus and I would say that state of the art models completely surpass any individual’s productive output.
I don't understand how:
> the state of the art today is not as good as the very best
and
> state of the art models completely surpass any individual’s productive output
are not contradictory. If the models completely surpass any individual's productive output, doesn't that mean they're better than the best humans? Or maybe I don't understand what you mean by "surpassing productive output." Are you talking about raw quantity over quality? I mean, yeah... but I could also do that with a bash script.
>are not contradictory. If the models completely surpass any individual's productive output, doesn't that mean they're better than the best humans?
It would be contradictory if we were talking about a human sure, but we're not. We're talking about a machine that can read thousands of words in seconds and spit thousands in slightly longer.
>Are you talking about raw quantity over quality? I mean, yeah... but I could also do that with a bash script.
Well except you can't. You can't replace what LLMs can do with a bash script unless your bash script is calling some other LLM.
> And we don’t shed so much ink wondering if digital spreadsheets are a moral good or not because perhaps they robbed the accountant…
Caught my eye. I do think we should wonder and hold intentionality around products, especially digital products, like the spreadsheet. Software is different. It's a limitless resource with limitless instantaneous reach. A good weld is beautiful in its own right, but it's not that.
The spreadsheet in particular changed the way millions of people work. Is it more productive? Is an army of middle-managers orienting humanity through the lens of a literal 2x2cm square a net good?
I say we should moralize on that.
> I just wish all the moaning would end.
It can be unpleasant to participate in a community of differing opinions and experiences. I still think it's worth showing up. If I hadn't then your perspective would have been missed too.
since when has velocity or volume of codegen been the bottleneck for any business?
i write less code than my AI-using coworkers but I have as much or more impact. Coding wasn't so hard that I need to spend time learning a new proprietary tech stack with a subscription fee lol. I believe plenty of engineers did suck enough and computers to benefit tho. That is where Anthropic makes their money.
It’s hard for me to believe that, unless you’re just doing simple glue work or you’re working in a low stakes environments, anyone is just delegating everything to agents. If you’re working on a migration (common in enterprise infrastructure work), you’re familiar with the needless abstractions, and it’s something you’ve done many times over; agents can certainly expedite change. If you’re building anything with depth and you do not have a clear understanding of the underpinning logic, you’re either very gifted in your ability to reason about abstractions or you’re setting yourself for a failure at some point in the future. You need expertise at some point. Programming/debugging as a means of learning a domain is akin to writing as a means of clarifying your thoughts.
That being said, yea enterprise coding can be extremely mundane and it’s setup for learning it deeply then finding a way to do it faster. I’m likely in the 90% range of my work being done by Claude, but I’m working in a domain I’ve got years of experience with hand coding and stepping through code in my debugger.
I think this latter piece is the challenge I’m struggling with. There is an endless amount of work that can be done at my company but as long as the economy is in a weird spot, I’m being led to believe that ai is making me expendable. This is a consequence of the fact that glue work represents 80% of my output (not value). The other 20% of time at work is exploring ideas without guaranteed results, its aligning stakeholders, its testing feasibility with mvps or experts from another area I need some help with. If glue work represents tangible output and conceptual work is something that may not actually have value my manager wants me to explore it, I’m just a glue guy in enterprise while I’m left chasing the dragon of a cool project for me to really sink my teeth into. That project is just a half baked bad idea from someone disconnected with reality. Glue work is measurable in LoC (however useless a metric it is measurable) and it’s certainly paying the bills.
+1024. what the FUCK, Anil. We solved coding-is-for-everyone by throwing up our hands. please crush my body under the heaviest layer of abstraction yet and have the llm read my eulogy because who could possibly know me better than the code I spend all day talking to as if it were a human
Lurked for >10y here. Created an account just to say, "+1 well said."
Yea! Back to my amazing Pax Americana of friendly neighbors, high trust in my authorities, and cheerful joyous days in oeace and harmony with my fellow man, complete with gum drop smiles and firm faith in my institutions. A truly brave new world
Why not, normies love to talk with the computer.
Well I do seem to spent a fair amount of my developer time swearing at my laptop screen. And then there's that time I spend just prior to writing code just staring at the wall while I figure out what sort of code I want to write - if I can repackage that wall-staring time into "time spent consulting with AI about approaches and architecture decisions" I'm sure my engineering manager will think more kindly of me ...
I'm puzzled why they don't speak aloud. Isn't it more natural AI interface? How difficult it is to connect a microphone to speech to text engine and connect that to AI? And then speak aloud. Your manager will be happy to hear you work.
It's easier to fake humanity through toneless text with narrow context and low expectstions, I think. A chatbot you type to is of course going to impress easier
I’d love to outsource all the boring, tedious parts of my job to LLM. Unfortunately it is the upper management who decide which parts of my job are boring.
I mean if > 30% of my work is drudgery, I have failed already.
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The two types of coder argument seems strong to me. Coders who love the art of programming (optimisation for the sake of it, beautiful designs, data structures...) and builders. The former are in for a rough time. The latter are massively enabled and no longer have to worry about smashing together libs by hand to make crud apps.
Doordash has also enabled home cooks; they no longer have to worry about smashing together ingredients by hand to make dinner. They just prompt the app to make them the food they want.
Doordash is the future of home cooking.
Prepackaged pasta sauces and cake mix. Worse than making it from scratch but enables people with no time to cook more dishes.
Doordash is more like paying someone else to code for you. Luckily that will soon be a thing of the past.
I suggest you stop paying Anthropic, and see how much code gets written. You're absolutely paying someone else to do it for you.
I pay Google £15/month and have never hit the usage limit. But thanks anyway.
edit: I think you might mean vibe coding (and those infamous things that use millions of tokens with no limit) but for programmers using LLMs to code is literally just a tool like anything else and the cost is barely relevant. It's not comparable to contracting out code, and it's not even comparable to eating out in terms of cost!
There are much nore than 2 types of programers, one you forgot are the ones who just need the job and any tool that helps is welcome, also, there the ones that don't care for anything and does programming just because is the only thing that's available to jot work on sales or burger flipping, and the list goes on
"bvilders" right now its mostly people who want to build a substandard app and shill it everywhere.
That's what's visible now. Give it more time and larger, more long-term projects will come out. I'm talking about people with ambitious ideas who -could- code but lack the time or energy.
Thinking carefully about the details of implementation MATTERS. Even with crud apps. Getting something “built” fast isn’t and should not be the only consideration.
I can go to a junkyard and assemble the parts to build a car. It may run, but for a thousand tiny reasons it will be worse than a car built by a team of designers and engineers who have thought carefully about every aspect of its construction.
I agree. However manual code review and heavy refactoring is a laborious and error prone process, and even most human projects don't keep up with it successfully. Plenty of horrible code debt ridden projects in the real world. As long as you're not writing safety-critical code, use of LLM is not incompatible with what you're saying.
For now it matters, but how long ? Context window will keep increasing and soon ai will be able to take care of all our codebase
Yes this is the state of it. But just wait a few months, maybe years, the builders aren't safe either. It just won't be cost effective to let humans build in a matter of time.
Depends on what you mean by "builder."
If you mean "somebody with an idea who wants to make it real" then that person is massively enabled.
So enabled, in fact, that there's almost no point in downloading an already-made app when you can just trivially tailor-make your own. The builder is massively enabled to quickly make anything they want, for an audience of exactly one.
For tiny apps, sure. Some people are making larger projects that take weeks or months even with AI, that they never could have done otherwise.
Strongly disagree. You think you’d be able to prompt your way through creating an app with even half the feature set of Microsoft word, for example? I would be very time consuming to be able to think through how the app should work for many use cases you care about or didn’t think about. This time isn’t free. Now consider having to do this iteration across many apps you depend on. And, count on introducing regressions when your next prompt is incompatible with existing features. If you are not retired, this is a huge ongoing time sync.
You think you were able to prompt your way through creating hello world five years ago? Models improve and they need less and less guidance.
Combined with the fact that my use cases aren't your use cases, yes, it might be cheaper for me to make my own than to slog though software that wasn't built to serve my exact needs.
I’m not saying that there’s no need for specialty apps optimized for specific use cases or that you can’t use llms to create them more cheaply than last year. Only that the time to think through how the app should work and iterate on it is still significant in the way that it was last year if you were given the worlds best team of software engineers and they’d code to your product requirements. You’d only take this path for apps where the time tradeoff is worth it vs “off the shelf” apps.
The issue is that off the shelf apps were made at a time when it was too expensive to make apps. Everyone uses 2% of Word, Photoshop, etc, it's just a different 2% for each.
You only need to reimplement that 2% for yourself for it to be worth it, not the entire app.
How would you address user requests? Tailor-make a custom app for every user?
Cars are here and you're wondering how someone could possibly make a faster horse. You wouldn't address user requests. You aren't a business. The users all make their own apps for themselves.
Cars are here and we're all choking on our own atmospheric excrement, so...
Assuming AI lives up to the marketing: Why would someone use an app instead of promoting their agent to figure out how to get something done?
Those "idea men" I've seen are usually not capable of following through a logical product, even if they start using AI. It's not just the code that's the barrier.
The prototypes or whatever can be handy to help them explain themselves to others of course.
There are plenty of programmers who are perfectly capable of delivering products, who have ideas that are too ambitious to do on their own.
Agreed, that's not really who I was referring too.
Yall's blood-diamond-ass mommy bots are going to replace bullshit with bullshit and call it a win. The last datacenter will run out of coal and water and we'll be asking: "but how in the world am I going to make this Todo app?"
Imagine if your operating system or compiler were written by the sort of person that thinks "Coders who love the art of programming ... are in for a rough time."
Yeah but 99% of jobs are decidedly not that.
Having an AI is like having a dedicated assistant or junior programmer that sometimes has senior-level insights. I use it to do tedious tasks where I don't care about the code - like today I used it to generate a static web page that let me experiment with the spring-ai chat bot code I was writing - basic. But yesterday it was able to track down the cause of a very obscure bug having to do with a pom.xml loading two versions of the same library - in my experience I've spent a full day on that type of bug and Claud was able to figure it out from the exception in just minutes.
But when I've used AI to generate new code for features I care about and will need to maintain it's never gotten it right. I can do it myself in less code and cleaner. It reminds me of code in the 2000s that you would get from your team in India - lots of unnecessary code copy-pasted from other projects/customers (I remember getting code for an Audi project that had method names related to McDonalds)
I think though that the day is coming where I can trust the code it produces and at that point I'll just by writing specs. It's not there yet though.
>I think though that the day is coming where I can trust the code it produces and at that point I'll just by writing specs. It's not there yet though.
Must be nice to still have that choice. At the company I work for they've just announced they're cancelling all subscriptions to JetBrains, Visual Studio, Windsurf, etc. and forcing every engineer to use Claude Code as a cost-saving measure. We've been told we should be writing prompts for Claude instead of working in IDEs now.
This is completely insane, and that's coming from someone who does 95% of edits in Claude Code now.
Honestly while I know everyone needs a job, just speed run all this crap and let the companies learn from making a big unmaintainable ball of mud. Don't make the bad situation work by putting in your good skills to fix things behind the scenes, after hours, etc.
Management has made it very clear that we’re still responsible for the code we push even if the llm wrote it. So there will be no blaming Claude when things fall apart.
My personal line is they can't say that if you force devs to use LLMs "and be quick about it"
I wonder how much cost savings there are in the long term when token prices go up, the average developer's ability to code has atrophied, and the company code bases have turned into illegible slop. I will continue to use LLMs cautiously while working hard to maintain my ability to code in my off time.
You shouldn't have to maintain your ability to code in your off time. Is your company one of those that's requiring AI only coding?
That’s going to give you all a ton of job security in a year when we realize that prompt first yields terrible results for maintainability.
Or they fire the existing staff who prompted this mess and bring in mkinsey to glue the mess together
Well, Visual Studio Code + Claude Code is better than the other options.
Thats insane!
I hope they are prepared to pay the $500/month per head when subsidies expire.
Realistically that's an increase of maybe a couple percent of cost per employee. If it truly does end up being a force multiplier, 2-5% more per dev is a bargain. I think it's exceedingly unlikely that LLMs will replace devs for most companies, but it probably will speed up dev work enough to justify at least a single-digit percent increase in per-dev cost.
“speeding up dev work” is pointless to a company. That benefit goes entirely to the developer and does not trickle down well.
You might think “ok, we’ll just push more workload onto the developers so they stay at higher utilization!”
Except most companies do not have endless amounts of new feature work. Eventually devs are mostly sitting idle.
So you think “Ha! Then we’ll fire more developers and get one guy to do everything!”
Another bad idea for several reason. For one, you are increasing the bus factor. Two, most work being done in companies at any given time is actually maintenance. One dev cannot maintain everything by themselves, even with the help of LLMs. More eyes on stuff means issues get resolved faster, and those eyes need to have real knowledge and experience behind them.
Speed is sexy but a poor trade off for quality code architecture and expert maintainers. Unless you are a company with a literal never ending list of new things to be implemented (very few), it is of no benefit.
Also don’t forget the outrage when Cursor went from $20/month to $200/month and companies quickly cancelled subscriptions…
> Except most companies do not have endless amounts of new feature work. Eventually devs are mostly sitting idle.
At every place I have ever worked (as well as my personal life), the backlog was 10 times longer than anyone could ever hope to complete, and there were untold amounts of additional work that nobody even bothered adding to the backlog.
Some of that probably wouldn't materialize into real work if you could stay more on top of it – some of the things that eventually get dropped from the backlog were bad ideas or would time out of being useful before they got implemented even with higher velocity – but I think most companies could easily absorb a 300% increase or more in dev productivity and still be getting value out of it.
Where I work, if you literally implemented everything in the backlog as is you’d fuck everything up.
Things in a backlog are not independent units of work ready to go, there are nasty dependencies and unresolved questions that cross domains.
I’ve heard estimates of starting at 2k a month per person, and thats for the “normal” user-base
They'll just skip raises and say it's part of your comp for increasing your productivity or some tone-deaf BS
Thoughts and prayers.
I didn't renew Jet Brains this month. Been a loyal customer and would have quit jobs from 2008 onwards without it.
Me too.
I used to report bugs, read release notes; I was all in on the full stack debug capability in pycharm of Django.
The first signs of trouble (with AI specifically) predated GitHub copilot to TabNine.
TabNine was the first true demonstration of AI powered code completion in pycharm. There was an interview where a jetbrains rep lampooned AI’s impact on SWE. I was an early TabNine user, and was aghast.
A few months later copilot dropped, time passed and now here we are.
It was neat figuring out how I had messed up my implementations. But I would not trade the power of the CLI AI for any *more* years spent painstakingly building products on my own.
I’m glad I learned when I did.
I'm using Claude in JetBrains, using the Zed editor's ACP connector.
It's actually pretty slick. And you can expose the JetBrains inspections through its MCP server to the Claude agent. With all the usual JetBrains smarts and code navigation.
Fwiw, IntelliJ at least has an MCP server so coding agents can use the refactoring tools. Don't know about the other JetBrains IDEs.
Isn't Visual Studio a one time purchase?
Even if you're using Claude, canceling the IDEs might be poor strategy. Steve Yegge points out in his book that the indexing and refactoring tools in IDEs are helpful to AIs as well. He mentions JetBrains in particular as working well with AI. Your company's IDE savings could be offset by higher token costs.
Perhaps it would help if I include the quote, so from Vibe Coding pages 165-166:
> [IDEs index] your code base with sophisticated proprietary analysis and then serve that index to any tool that needs it, typically via LSP, the Language Services Protocol. The indexing capabilities of IDEs will remain important in the vibe coding world as (human) IDE usage declines. Those indexes will help AIs find their way around your code, like they do for you.
> ...It will almost always be easier, cheaper, and more accurate for AI to make a refactoring using an IDE or large-scale refactoring tool (when it can) than for AI to attempt that same refactoring itself.
> Some IDEs, such as IntelliJ, now host an MCP server, which makes their capabilities accessible to coding agents.
Would you recommend that book?
Yes, it's fantastic. Hard to imagine a better resource for getting started with vibe coding, on through developing large high-quality projects with it. It doesn't get into the details of particular tools much, so it should stay relevant for a while.
Syntax highlighting rules, initially vibe-coded 40 languages and formats in about 10 minutes. What surprised me is when it switched the design from a class to the far more elegant single line of code:
* https://repo.autonoma.ca/repo/treetrek/blob/HEAD/render/High...The rules files:
* https://repo.autonoma.ca/repo/treetrek/tree/HEAD/render/rule...
I'm halfway through Steve Yegge's book Vibe Coding. Yegge was quoted in the article:
> “We’re talking 10 to 20 — to even 100 — times as productive as I’ve ever been in my career,” Steve Yegge, a veteran coder who built his own tool for running swarms of coding agents
That tool has been pretty popular. It was a couple hundred thousand lines of code and he wrote it in a couple months. His book is about using AI to write major new projects and get them reliable and production-ready, with clean, readable code.
It's basically a big dose of solid software engineering practices, along with enough practice to get a feel for when the AI is screwing up. He said it takes about a year to get really good at it.
(Yegge, fwiw, was a lead dev at Amazon and Google, and a well-known blogger since the early 2000s.)
> pom.xml loading two versions of the same library
Just checking that you're using maven-enforcer-plugin
This is the take when you haven't really tried driving these tools with much practice
I don't think it's that
Here's an example from Gemini with some Lua code:
I don't know Lua too well (which is why I used AI) but I know programming well enough to know this logic is ridiculous.It was to help convert "on-click-right" into "Right Click".
The first bit of code to extract out the words is really convoluted and hard to reason about.
Then look at the code in each condition. It's identical. That's already really bad.
Finally, "Click" and "Scroll" are the only 2 conditions that can ever happen and the AI knew this because I explained this in an earlier prompt. So really all of that code isn't necessary at all. None of it.
What I ended up doing was creating a simple map and looked up the key which had an associated value to it. No conditions or swapping logic needed and way easier to maintain. No AI used, I just looked at the Lua docs on how to create a map in Lua.
This is what the above code translated to:
IMO the above is a lot clearer on what's happening and super easy to modify if another thing were added later, even if the key's format were different.Now imagine this. Imagine coding a whole app or a non-trivial script where the first section of code was used. You'd have thousands upon thousands of lines of gross, brittle code that's a nightmare to follow and maintain.
This sounds like moving the goal posts but Gemini is generally not a good model. Try Sonnet
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For any non professional work its there for me.
Wire up authentication system with sso. done Setup websockets, stream audio from mic, transcribe with elvenlabs. done.
Shit that would take me hours takes literally 5 mins.
All that stuff would take me about 5 minutes without AI. Those are things with 10,000 examples all over the web. AI is good at distilling the known solutions. But anything even slightly out of the ordinary, it fails miserably. I'd much rather write that code myself instead of spend an hour convincing an AI to do it for me.
There is absolutely no way. Those tasks take 5 mins to do. Itd be done by the time you read the documentation for elvenlabs
You might be comparing people who know how to do something vs those who don't.
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> All that stuff would take me about 5 minutes without AI.
There isn't a single person on this planet (detractor or not) that would believe this statement.
If you're argument rests on an insane amount of hyperbole (that immediately comes off as just lying), then maybe it's not a great argument.
> I'd much rather write that code myself instead of spend an hour convincing an AI to do it for me.
You're not suggesting that asking CC to build the UI for a route planner takes me an hour to type, are you?
>Wire up authentication system with sso.
Simple npm install, all of it has already been distilled into dozens of similar repos. Just pick one, install it, and follow the simple use case. 5 minutes if we're in a race.
>done Setup websockets
If this takes you more than 5 minutes, then you're a shit developer.
>stream audio from mic
Again, another npm install or two, simple POC could take 5 minutes.
>transcribe with elvenlabs
I don't know what elvenlabs is, nor do I care, but I doubt it's as complex as the OP thinks it is considering the rest of their comment was about simple, solved problems.
> There isn't a single person on this planet (detractor or not) that would believe this statement.
It's so galling to see people say shit like this. It's like the old build slack in a weekend trope.
Im curious though, what do you consider slightly out of the ordinary that it fails to do?
Haven't tried Claude for this, but I can't think how it could possibly do. I built a game bot using Win32 API to send input and screen capture to OCR and some OpenCV to recognize game elements. Dead simple and actually quite boring and repeatitive after I worked on it for a while. How could Claude agents possibly do this ? I did use Claude to refer docs and API, though.
That actually sounds like something Claude could do pretty easily.
Yegge's book describes his coauthor's first vibe coding project. It went through screenshots he'd saved of youtube videos, read the time with OCR, looked up transcripts, and generated video snippets with subtitles added. (I think this was before youtube added subtitles itself.) He had it done in 45 minutes.
And using agents to control other applications is pretty common.
My elementary schooler did this with pictures of his stuffed animals last week. I helped a little bit, but most of it was Claude. He's never coded before.
Great, and you've taught him to never learn to code. That's not as great an achievment as you might think it is.
Yes, I've doomed him all because of a 30 minute interaction. Just like when he watched Kerbal Space Program videos on YouTube he lost all motivation to get to the moon himself. Oh wait.
And he definitely doesn't make up missions using the mission builder using if / then loops. He'll never learn to code. Oh the humanity.
I'd rather have my kid typing on a real keyboard into Claude, asking questions about what Python, and modifying the Claude-generated code than watching random videos and playing Roblox on his iPad.
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>All that stuff would take me about 5 minutes without AI.
No, it wouldn't. Merely finding the examples and deps would take over an hour.
Back when, we'd just go write a blog post or SO answer so the next person wouldn't suffer as much.
Thank god THOSE days are over and everyone just lets everyone else suffer alone now
And when those resources dry up for newer stuff, and LLMs start training on LLM output, they'll start degrading
Yes, because search engines are populated with SEO-optimized LLM-filled articles that say nothing of value anymore. The only reason AI-assisted tools are "better" is because Web search is so much worse.
It's like everyone forgot that the first result for anything web-related would be W3schools, and the next 5 would be spam message boards that tries to scrape all the other boards and sends you to a porn site when you click on it.
Yeah it is worse now but I don’t remember it ever being good. If you know where to look and have a trusted set of resources curated sure, but of course you won’t for unfamiliar territory which is exactly what LLMs help with.
I've generated 250KLoC this week, absolutely no changes in deps or any other shenanigans. I'm not even really trying to optimize my output. I work on plans/proposals with 2 or 3 agents simultaneously in Cursor while one does work, sometimes parallelized. I can't do that in less code and cleaner. I can't do it at all. Don't wait too long.
> I can't do that in less code and cleaner. I can't do it at all.
Can't do what, precisely?
> I've generated 250KLoC this week
It's horrifying, all right, but not in the way you think lol. If you don't understand why this isn't a brag, then my job is very safe.
If managers can't understand why this isn't a brag, then your job is hardly safe.
Agents are new, but dumb coding practices are not. Despite what it may seem, the knowledge of how to manage development has increased. One practice I haven't seen for a while is managing by limiting the number of lines changed. (This was a dumb idea because rewriting a function or module is sometimes the only way to keep it comprehensible - I'm not talking about wholesale rewriting, I'm talking about code becoming encrusted with conditions because the devs are worried that changing the structure would change too many lines)
Managers jobs are more at risk than senior engineers.
My company, and few others I know reduced the number of managers by 90% or more.
This. We moved to 'agile' roughly at same time llms started coming. You know who is mostly missing from the wider IT teams landscape now? Most of PMs and BAs, on purpose. Who is keeping their work - devs or more like devops, surprisingly testers (since our stuff seems ridiculously complex to test well for many use cases and we can't just break our bank by destroying production by fast half-assed deliveries).
And then admins/unix/network/windows/support team, nobody replacing those anytime soon. Those few PMs left are there only for big efforts requiring organizing many teams, and we rarely do those now.
I don't like it, chasing people, babysitting processes and so on, but certainly just more work for me.
you're not responding to op's point, unless you're insinuating there won't be any managers ever, which won't happen. As long as there is one manager left, OP's point remains.
if the industry got rid of 98% of the managers - the industry's output would go up by 500x
Tell me you're not a SWE without telling me. Engineers hire engineers at tech companies. Managers do the hiring paperwork.
Can you please stop posting so abrasively? It's not that this one comment is so bad, but you have a long pattern of doing so, and we're trying for something else here.
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.
Less is more, its not a hard thing to understand. These companies are accumulating record level tech debt.
I use these tools a lot, and one thing that has stood out to me is that they LOVE to write code. A lot of it. And they're not super great at extracting the reusable parts. They also love to over-engineer things.
I've taken great pains to get by with as little code as possible, because the more code you have, the harder it is to change (obviously). And while there are absolutely instances in which I'm not super invested in a piece of code's ability to be changed, there are definitely instances in which I am.
Yea, by my less is more logic its sometimes also difficult to do. With that approach people try to become clever and write shorter code thats unmaintainable due to mental gymnastics other people have to go through when reading it. What LLMs are doing is probably going for some kind of overengineered "best practice" solution. I personally only use them for simple Laravel CRUD apps and they are admittedly pretty good at that.
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LOL that's it? I generated over 5 million lines of code this week. You need to step it up or you're gonna be left behind.
You mean, you don't have
running in the background 24/7?I left an agent generating code over the weekend, so that I can get to 15 million.
What code? Code!
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Your developers were so preoccupied with whether or not they could, they didn't stop to think if they should (add 250kloc)
I've worked with a type of (anti?) developer in my career that seems to only be able to add code. Never change or take it away. It's bizarre. There's some calculation bug, then a few lines down some code which corrects it instead of just fixing the original lines.
It's bizzare, and as horrible as you might imagine.
And it's been more than one or two people I've seen do this.
Now they have agents.
People need to understand that code is a liability. LLMs hasn't changed that at all. You LLM will get every bit as confused when you have a bug somewhere in the backend and you then work around it with another line of code in the front end. line of code
They can always generate a new backend prototype from scratch.
This sounds like some kind of learned risk aversion, like they don’t want to assume the responsibility of altering whats already there.
For me, the biggest shift is people who don't care about local AI. The idea that you can no longer code without paying a tax to one of the billion $ backed company isn't sitting well.
I don’t understand why more people aren’t focused on how to get the benefits of ai but on your own machine. If the last 20 years of software transitioning off of our desktops and into the cloud has taught us anything, it’s that letting corporate entities run the software you rely on end to end gives you: worse software with more bugs, surveillance and subscriptions. Why on earth would you want that for everything you do.
Because a lot of the AI hype/use is driven by companies who just want to pay money for a service? Besides, my laptop would need a pretty big RAM upgrade.
I agree local is better, but the big companies are making decent products and companies are willing to to pay for that. They’re not willing to spend engineering money to make local setups better.
If there's a model that's as good as Claude 4.5 (not even 4.6) I would pay tens of thousands to run it locally. To my knowledge there isn't yet. Benchmarks may say so but I haven't used one that does yet. I always try new models that come out on openrouter
Local AI is what people want/need, but centralized AI is where the investors' money is flowing, because a walled garden has always been easier to turn into a cash printer.
Yeah look at the price of netfilx, do you think starting at $200 it's going to stay anywhere close to that.
The price does not matter, even if it were free. If you need to be logged on into an external service to be able to code, it's just not the same any more, and I'm thinking of basic technology here, but the political/distopian ramifications are crazy.
>From: noreply@openai.com >Subject: Your account has been terminated for violating the Terms of Service
People have various online accounts locked or deleted for no given reason all the time. Just get the right person to say the word, and you're out.
The marginal differences in quality seem pretty meaningful right now, enough to make Claude wildly dominant, but some of the locally runnable models like Qwen feel only a few months behind the leaders.
I'm betting the generational gains level off and smaller local models close the gap somewhat. Then harnesses will generally be more important than model, and proprietary harnesses will not offer much more than optimization for specific models. All while SaaS prices ratchet up, pushing folks toward local and OSS. Or at least local vs a plethora of hosted competition, same as cloud vs on prem.
Qwen3.5 really is great.
But the biggest thing is going to be context. Whilst a 10gb card can run a 9b model with some context .. for coding you really want a lot of context.
So if paying 200 a year for 1T in context, vs your 32k context.. that's the thing I see as being the driver.
Personally ive found great success with using open code, having Opus as my plan agent, and omnicoder-9b as my build agent.
Get opus to plan, switch to omnicoder to build, switch back to opus to review. Etc etc.
Works great.
This is the only problem but hopefully we'll be able to fix this issue, otherwise yikes we are doooomed.
If coding truly becomes effortless to produce - and by that extension a product becomes near free to produce - then I find it quite odd that the executive class thinks their businesses won’t be completely up ended by a raging sea of competition.
They're going to have a fast and ruthless testing of whether their product senses and abilities to attract and trap customers were actually skill or lucky positioning, as competition explodes from every direction, including from within customer and user bases.
All run of the mill software is gone or on borrowed time. Why pay a subscription for a product that I can get something Claude to build it for me.
Before I was building tools, now I am building full applications in less time than I did before for tools.
What will be around for a while is where you need an expert in the loop to drive the AI. For example enterprise applications. You simply can't hand that off to an AI at this point.
Can we see the applications?
Full fledged applications, with full functionality, minimal bugs, localized entirely within your computer?!
... no.
They are in work, so sorry no. But I can tell you started off
- Getting Claude to do the work
- creating in python tools
- Docker apps
- XCode
> Why pay a subscription for a product that I can get something Claude to build it for me.
We've already seen this with OSS. Even with free software, support, self-hosting, and quirky behavior have proven to be enough to keep most people and business away.
Thing is, AI will also be able to provide substantial support for software it writes. And it will make self-hosting a lot less painful, too. Quirky behavior will still happen, but eh, Excel imports numbers as dates. You can't buy your way out of quirky behavior for all the money in the world.
no one wants your vibecoded full applications though. not sure why you are building them.
I am building them because I am using them to do my work faster.
I'm not selling anything, but I can see the quality of what is created and it is on-par with much of the stuff on the App store.
No one would even notice that it is a co-creation unless I mentioned the time to create it.
Just to be clear. Vibe coding implies that you are not reviewing the code that is created, or even knowing what is being created. That is not what is happening.
> I am building them because I am using them to do my work faster.
You sound like CTO at my company rewriting stable libraries in languages he is familiar with and calling it 100x...
> I am building them because I am using them to do my work faster.
interesting. any examples you can share?
I used a bunch of tools from https://setapp.com/ and no longer subscribe. I used to pay for Linear for all my personal projects, built one with CC that perfectly fits my needs… I also built a myriad of small tools that help me automate bunch of busy work I used to do manually, both for professional and personal projects
The markets seem to agree with you and are pricing accordingly.
No, that's exactly what at least some of them think and it's why the market has been so volatile lately.
The struggle will completely shift to how to get traffic
That is already the struggle. There is too much stuff already.
All that stuff is shit though.
Is all of it shit, or you just can't find the good stuff? "The struggle will completely shift to how to get traffic" is from the business side, and you're experiencing it from the customer side.
I see lots of discussions about humans no longer writing code but the elephant in the room is the rapid extinction of human-review of AI-made code. I expect this will lead to a massive hangover. In the meantime we try to mitigate this by ensuring the structure of code remains AI-friendly. I also expect some new types of tools to emerge that will help with this “cognitive debt”.
My impression is that people who think that LLMs will completely release reviewing or writing code have never really worked on anything safety critical. I'm not looking forward to the next wave of pacemaker glitches.
You act like we live in a world where companies are held sufficiently liable.
That is why I do not use the multi agent team technique. My code generation has atrophied, but my code review skills have only gotten stronger both for human and AI code. If I handed over both, it hurts my employability and will definitely lead to that hangover.
I'm starting to find the naive techno-optimism here annoying. If you don't have capital or can do something else you will be homeless.
Well, there are so many more lower hanging fruits that LLMs can actually replace before they get to developers -- basically every middle manager, and a significant chunk of all white collar jobs.
I'm not convinced software developers will be replaced - probably less will be needed and the exact work will be transformed a bit, but an expert human still has to be in the loop, otherwise all you get is a bunch of nonsense.
Nonetheless, it may very well transform society and we will have to adapt to it.
Not all software development will be automated immediatly. But I've noticed that many skills I've built are lessened in worth with every model release.
Having a lot of specifics about a programming environment memorized for example used to be the difference between building something in a few hours and a week, but now is pretty unimportant. Same with being able to do some quick data wrangling on the command line. LLMs are also good at parsing a lot of code or even binary format quickly and explaining how it works. That used to be a skill. Knowing a toolbox of technologies to use is needed less. Et cetera.
They haven't come for the meat of what makes a good engineer yet. For example, the systems-level interfacing with external needs and solving those pragmatically is still hard. But the tide is rising.
The capitalists and industrialists have waited for centuries to get rid of paid labor. Imagine the profits once the cost of human work gets out of the loop!
Of course the question that is left unanswered is how the economy will work there's no one left with purchasing power. But I guess the answer to this is, the same way it works now in any developing country without much of a middle class.
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I don’t see middle managers taking the initial brunt unless they truly are just pushing papers around. At companies of sufficient size, they do play a role of separation between C suite and the grunts. To me, certain low-performing grunts will be the first out. Then a team reorg to rebalance. Then some middle managers will be out as fewer of them can handle multiple teams.
>I'm not convinced software developers will be replaced
Most of us will probably need to shift to security. While you can probably build AI specifically to make things more secure, that implies it could also attack things as well, so it ends up being a cat-and-mouse game that adjusts to what options are available.
> probably less will be needed and the exact work will be transformed a bit
My guess is the opposite: they'll throw 5–10x more work at developers and expect 10x more output, while the marginal cost is basically just a Claude subscription per dev.
Agree. Marketing, Finance, Legal, already having huge impact for junior positions.
you know, natural attrition is still attrition.
Yep. I own a software shop and yesterday was when I realized that I'm no longer going to be a 1%er doing this.
what happened yesterday?
A lot of people expect to be owning the capital. However I also just don't think the technology will displace programmers that quickly.
But you only $200/month for the productivity of what used to cost monthly salary for 10 software engineers. Doesn't this democratize software construction?
It commoditizes software construction.
The resources to learn how to construct software are already free. However learning requires effort, which made learning to build software an opportunity to climb the ladder and build a better life through skill. This is democratization.
Now the skill needed to build software is starting to approach zero. However as you say you can throw money at an AI corporation to get some amount of software built. So the differentiator is capital, which can buy software rather cheaply. The dependency on skill is lessened greatly and software is becoming worthless, so another avenue to escape poverty through skill closes.
You might be right but it's common to see that take as short sighted since there is zero evidence it's ever happened before. The common example is the loom. The loom didn't democritize cloth making, it put all the weavers out of business. But it did democritize everything that needed cloth because cloth became cheap. 1000s more jobs were created by making cloth cheap. In a similar way, 1000s of different jobs might be created by making software creation cheap, but just like cloth makers, software makers have no purpose when making software is easy.
Did ms word usher in a surge of novel writing? Not really apparent.
So you’ve vibe coded an app. That’s just swell. You want to release it? Don’t. You can’t support it. You can’t update it. You are one bad prompt away from it collapsing.
We are convincing a generation of morons that they can do something they plainly cannot. This will be a major problem, and soon.
I always find these "death of the X" articles frustrating.
Books didn't stop existing when the radio came out. Radio didn't stop existing when television was invented. If you go back in time a thousand years, people were complaining that an increase in literacy would damaging peoples' memorization skills.
People will still write code for consumption by other humans by hand. Some companies, though probably not most, will still prefer it. AI will change the industry - IS changing the industry - but things don't "end". They just look different, or are less popular.
There is enough for us to worry about and try to figure out how to respond to without the histrionics.
Also, on a related note, "Idiots are vibecoding bad stuff" is not the same as "engineers are using AI tools to do good work more quickly," and we should stop conflating it.
Supremely ironic that the last two things the same author had in the nytimes was a piece from a few years ago hyping up NFTs and a year before that a piece hyping up Bitcoin.
In the near future, a "good programmer" might not be defined by someone who can write bug-free, clear code, but rather by someone who can prompt for code that works consistently within the context of AI. If that happens, I'll have to find a different job.
At least at my company, we have never really cared how it gets done, even before AI. It just has to work (ideally bug-free and maintainable) by the deadline. If you can keep up with shorter deadlines, more power to you. It’s basically a modern John Henry vs the steam drill.
That’s the exact definition our CEO gave of our job this week. That’s how he sees and expects us to work now. I feel some anxiety because that’s too much too fast. We went from « we need to fix every single bug we encounter » to « it doesn’t matter if there’s bugs as long as we ship a feature fast »
Isn't the AI sales pitch that you can do both?
I find that the perception of AI coding is very bimodal, and both camps have it wrong. You have the one camp who will only trust it to, at most, write a few lines of code. The other camp prompts, walks always, and pushes (or just has the AI do it).
I believe both camps it frustratingly wrong. If you haven't yet given it a chance at doing something substantial, then at least _try_ it once. On the other side of the coin, that first experience where it does something 80% right is intoxicating, but AI doesn't reason and can't get it 100% right - it can't even multiply relatively small numbers.
The former camp is going to get left behind and won't be able to compete, the latter camp is one prompt away from a disaster.
Is anyone concerned how dependant the industry as it's described here is becoming on one, two or three service providers who take prompts and spit out tokens? And the amount of your proprietary infrastructure you're exposing to them?
Are local models anywhere close to gaining enough capability and traction to do it in-house? Or are there good options for those who'd rather own the capability than rent it?
> Are local models anywhere close to gaining enough capability and traction to do it in-house?
Cloud providers will always be able to offer more performance and more powerful options.
I don't know, I could envision a situation where investing enough capital in hardware to be able to generate "infinite" tokens for free (or close to that) might open up possibilities not economically practical otherwise. Especially once the incumbents run out of hype / investor money to burn and start raising their prices to push revenue in line with their enormous expenditures.
Also, presumably at some point far in the future we'll reach a technological asymptote and factors like latency may start to play a bigger role, at least for some applications.
I grant that training data is crucial distinguishing factor that may never become competitive in-house.
I'm from an accounting/finance background and spent about 10 years in Big4. I was always into tech, but never software development because writing code (as I thought) takes years to master, and I had already chosen accounting.
Fast forward to 2024 when I saw Cursor (the IDE coding agent tool). I immediately felt like this was going to be the way for someone like me.
Back then, it was brutal. I'd fight with the models for 15 prompts just to get a website working without errors on localhost, let alone QA it. None of the plan modes or orchestration features existed. I had to hack around context engineering, memories, all that stuff. Things broke constantly. 10 failures for 1 success. But it was fun. To top it all off, most of the terminology sounded like science fiction, but it got better in time. I basically used AI itself to hack my way into understanding how things worked.
Fast forward again (only ~2 years later). The AI not only builds the app, it builds the website, the marketing, full documentation, GIFs, videos, content, screen recordings. It even hosts it online (literally controls the browser and configures everything). Letting the agent control the browser and the tooling around that is really, genuinely, just mad science fiction type magic stuff. It's unbelievable how often these models get something mostly right.
The reality though is that it still takes time. Time to understand what works well and what works better. Which agent is good for building apps, which one is good for frontend design, which one is good for research. Which tools are free, paid, credit-based, API-based. It all matters if you want to control costs and just get better outputs.
Do you use Gemini for a website skeleton? Claude for code? Grok for research? Gemini Deep Search? ChatGPT Search? Both? When do you use plan mode vs just prompting? Is GPT-5.x better here or Claude Opus? Or maybe Gemini actually is.
My point is: while anyone can start prompting an agent, it still takes a lot of trial and error to develop intuition about how to use them well. And even then everything you learn is probably outdated today because the space changes constantly.
I'm sure there are people using AI 100× better than I am. But it's still insane that someone with no coding background can build production-grade things that actually work.
The one-person company feels inevitable.
I'm curious how software engineers think about this today. Are you still writing most of your code manually?
> it still takes a lot of trial and error to develop intuition about how to use them well.
I used to think so. Then a customer made their own replacement for $600/mo software in 2 days. The guy was a marketer by training. I don't exaggerate. I saw it did the exact same things.
It's true. We're also at the point where the models and the orchestration around them are so good that any beginner to those tools who knows how to use a computer can build working apps. Interesting times.
I was pointing out that practice helps with the speed and the scope of capabilities. Building a personal prototype is a different ballgame than building a production solution that others will use.
I said this 4 weeks ago...
Buddy its outdated.
It’s true there’s some magic effect from Claude code’s work. But still, often it’s not exactly the same infra and scaling than production grade. But for a customer I guess that’s perfect, they have a mean to make their own tools instead of relying on platforms to build those tools.
I Agree on the customer empowerment point.
I'd push back slightly on the production grade point. The models aren't the ceiling, the user's mental model of software is, depending on his experience/knowledge.
Someone just starting out will get working prototypes and solid MVPs, which is genuinely impressive. But as they develop real engineering intuition — how Git works, how databases behave under load, how hosting and infra fit together — that's when they start shipping production-grade things with Claude Code.
Based on what I'm seeing, the tool can handle it. The question is whether the person behind it understands what they're asking for. Anthropic, for example, mostly uses claude code to develop claude code.
Can you say what kind of software the customer replaced?
Conversations of the future...
"Can you believe that Dad actually used to have to go into an office and type code all day long, MAUALLY??! Line by line, with no advice from AI, he had to think all by himself!"
This was literally part of the premise of The Jetsons. George's job was to press a single button while the computer RUDI did all the work.
The difference is, Jetsons wasn't a dystopia (unlike the current timeline), so when Mr. Spacely fired George, RUDI would take his side and refuse to work until George was re-hired.
> "Can you believe that Dad actually used to have to go into an office and type code all day long, MAUALLY??! Line by line, with no advice from AI, he had to think all by himself!"
Grumpy old man: "That's exactly why our generation was so much smarter than today's whippersnappers: we were thinking from morning to night the whole long day."
>What's ~~a computer~~ thinking?
I had to run Jenkins to build my code. In the snow. And uphill on git pull and deploy.
More likely:
"Dad, I've sent out 1000 applications and haven't had a call back. I can't take it anymore. Has it always been like this?"
The Dad: It's not my fault!
I was thinking about that recently. Maybe decades from now people will look at things like the Linux kernel or Doom and be shocked that mere humans were able to program large codebases by hand.
Hmm, can you think of anything that we could do decades ago but cannot do it now, today?
I was being a little facetious, but there are things that most people would find tedious today that we would put up with in the past. Writing anything long by hand (letters, essays), doing accounting without a spreadsheet, writing a game in only assembly language, using punch cards, typesetting newspapers and books manually...
Build the pyramids
Fly supersonic commercially is the only thing I can think of
It must have been:
Aliens Atlanteans Time travellers A hoax …
I asked one of my friends to show me his video conversion application which worked via a web frontend. The instant I opened it my CPU fans started spinning so I checked and the page was using 300% CPU because it was updating the background gradient on an invisible element, in JavaScript, every frame, as fast as possible. That was very entertaining.
As far as the end of computer programming goes...
Step 1. Wow, I just vibe coded an application and it works! I'm going to write a blog about it and tell everyone how awesome AI is, much hype
Step 2. Vibe coded application faces inevitable problems, the perfect application is a fairytale after all. The only way to "fix" the application is spam tokens at the problem and pray.
Step 3. Author does not write a new blog post to report on this eventuality... probably because they feel embarrassed about how optimistic they were
Step 4. Perhaps author manages to fix application, awesome... then what about a year from now, author needs to update the application because a dependency has a security problem. The application is so needlessly complex that they don't even know when to begin.
Step 5. They boot up Claude Code, which their business is now 100% dependent on, but they're charging 10x the original cost per token. It's not like they have a contract, so user has to either eat the cost or give up
Step 6. User tries local model on their 1080 ti but they can barely run entry-level models
Step 7. Woops
Personally I think it's impossible to convince these people, the results will speak for themselves eventually.
> it could also be that these software jobs won’t pay as well as in the past, because, of course, the jobs aren’t as hard as they used to be. Acquiring the skills isn’t as challenging.
This sounds opposite to what the article said earlier: newbies aren’t able to get as much use out of these coding agents as the more experienced programmers do.
This article is ragebaiting people and it's an embarrassing piece from the NYT.
NYT has it out for digital advertisers, who directly compete with them. I do sense some schadenfreude here that the tech nerds who work at these places might be in trouble.
"Silicon Valley panjandrums spent the 2010s lecturing American workers in dying industries that they needed to “learn to code."
To copywriters at the NYT, LLMs are far better at stringing together natural language prose than large amounts of valid software. Get ready to supervise LLMs all day if you're not already.
LLMs are much better at coding now than at writing prose that doesn't sound like slop.
The code is also recognizable as slop to those who know how. Not the tropey "Not X, but Y" kind that's super easy to spot. But tons of repetition, deeply nested code, etc.
A counterpoint is that (maybe) nobody cares if the code is understandable, clean and maintainable. But NYT is explicitly in the business of selling ads surrounded by cheap copy just good enough to attract eyeballs. I suspect getting LLMs to write that is going to be far easier than getting LLMs to maintain large code bases autonomously.
>But tons of repetition, deeply nested code, etc.
If you explicitly make it go over the code file by file to clean up, fix duplication and refactor, it'll look much better, while no amount of "fix this slop" prompting can fix AI prose.
> no amount of "fix this slop" prompting can fix AI prose
What's the proof for that? What fundamental limitation of these large language models makes them unable to produce natural language? A lot of people see the high likelihood of ever increasing amounts of generated, no-effort content on the web as a real threat. You're saying that's impossible.
>What fundamental limitation of these large language models makes them unable to produce natural language?
LLMs can get indefinitely good at coding problems by training in a reinforcement learning loop on randomly generated coding problems with compiler/unit tests to verify correctness. On the other hand, there's no way to automatically generate a "human thinks this looks like slop" signal; it fundamentally requires human time, severely limiting throughput compared to fully automatable training signals.
how many times in the history of computer programming has there been an end to computer programming as we know it, successfully, and how many times predicted?
I can think of one successfully, off hand, although you could probably convince me there was more than one.
the principle phrase being "as we know it", since that implies a large scale change to how it works but it continues afterwards, altered.
Off the top of my head, I can think of the following during my career:
TBH - I think the AI's are nice tools, but they got a long way to go before it's the 'end of computer programming as we know it'edit: formatting
Stack overflow (and internet in general) changed the programming as we (at least some of us) knew it.
When I was learning programming I had no internet, no books outside of library, nobody to ask for days.
I remember vividly having spent days trying to figure out how to use the stdlib qsort, and not being able to.
Hmm - I'm not sure I'd say that 'changed programming' - but the internet in general changed 'learning to program'. I can remember when I first discovered gopher and found I could read tons recent material for free, or finding stonybrook on the web - that was like a gold mine of algorithms! :-D
I'm not from that generation so that's a bit hard for me to understand. Even if you used a closed-source C compiler, wouldn't you still have been able to look at the header file, which would have been pretty self-explanatory?
E.g.
void qsort(void* base, size_t nmemb, size_t size, int (compar)(const void , const void* ));
And surely if you bought a C compiler, you would have gotten a manual or two with it? Documentation from the pre-Internet age tended to be much better than today.
Yeah - but you have to be a good enough programmer to really understand the headers.. the 'bootstrapping' problem was real :-) Especially if you didn't live in a metropolitan/college area. My local library was really short on programming books - especially anything 'in depth'. Also, 'C' was considered a "professional's language" back then - so bookstores/libraries were more likely to have books on BASIC then 'C'
I don't remember where we got the compilers from but we surely didn't buy them.
Also I don't know if it came with manual but my English wasn't good enough to read them anyways.
What kind of modern C wizardry is that? qsort was:
OK, those are all ones that didn't change programming as we know it, but some came closer than others right?
I definitely considered some of those in my list of failed revolutions.
My one completely successful revolution is moving from punch card programming.
Oh yeah - thats a good one...
Maybe the move from teletype to CRT's as well ?
hmm, yeah good one. not sure if really changed things as I guess you still programmed the same, but maybe.
COBOL certainly had a lasting impact, but only for some application domains. The rest didn't seem to be particularly successful or impactful. Maybe RAD if you consider office application macros and end user report generation in it. (Spreadsheets extended programming to non-programmers and had a long-lasting impact, but I wouldn't call them RAD.)
Thats sorta the point... at one time or another, those were all projected to be 'the end of programming as we know it'...
Hell, COBOL's origins was in IBM wanting to make programming an 'entry level' occupation.
Oddly enough, spreadsheets had a huge impact (and still run a lot of companies behind the scenes :-P ) But I can't remember anyone claiming they would 'end programming' ?
FWIW I worked for a company from 2002 to 2006 that still had quite a large COBOL team even then. Some of the team members were also in their 20s and they'd been hired and trained up in COBOL.
I started by working on back-end accounting systems - most of those seemed to be COBOL ... or SHUDDER RPG.
Someone compared LLM in 2020s to GUI in 80s and 90s. Graphical interfaces didn't replace text interface, but it just became additional to it.
What's the one successful one? Visicalc?
I would say the one that definitely changed programming was moving from the punch card era. A lot of these others that people are mentioning I don't think really changed programming, they just looked like they were going to.
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> You can’t just tell an agent, Build me the code for a successful start-up. The agents work best when they’re being asked to perform one step at a time
That's also true for humans. If you sit down with an LLM and take the time to understand the problem you're trying to solve, it can perfectly guide you through it step by step. Even a non-technical person could build surprisingly solid software if, instead of immediately asking for new shiny features, they first ask questions, explore trade-offs, and get the model's opinion on design decisions..
LLMs are powerful tools in the hands of people who know they don't know everything. But in the hands of people who think they always know the best way, they can be much less useful (I'd say even dangerous)
I appreciate this sober take. If you hired a remote developer and the only thing you said to that person was “build a program that does this. Make no mistakes” would you expect that to be successful? Are you certain you would get what you wanted?
Any competent developer there is going to push back and get the needed information out of you.
LLMs don't know when you're under-specifying the problem.
That’s interesting because that is one feature of Claude code that I like. Given an overly broad problem statement. It does go into a planning loop where it seeks clarifying questions. I think this probably has something more to do with the harness than the model, but you see what I mean. From a user perspective that distinction doesn’t really matter.
Because they are still making the same salary. In 5 years, when their job is eliminated, and they can't find work, they will regret their decision.
we had no choice. if i don't do it someone else will..
Their decision to... use AI for coding?
Well, their position on AI.
By their own accounts they are just pressing enter.
> "...melodramatic prose might seem kind of nuts, but as their name implies, large language models are language machines. “Embarrassing” probably imparted a sense of urgency.
> “If you say, This is a national security imperative, you need to write this test, there is a sense of just raising the stakes,” Ebert said.
I'm not sure why programmers and science writers are still attributing emotions to this and why it works. Behind the LLM is a layer that attributes attention to various parts of the context. There are words in the English language that command greater attention. There is no emotion or internal motivation on the part of the LLM. If you use charged words you get charged attention. Quite literally "attention is all you need" to describe why appealing to "emotion" works. It's a first order approximation for attention.
I'm not normally a fan of the NYT but this wasn't too bad. It passed the Gel-Mann test, and is clearly written by someone who knows the field well, even though the selection of quotes skews to towards outliers -- I think Yeggie for instance is pretty far out of the mainstream in his views on LLMs, whether ahead or sideways.
As a result a lot of the responses here are either quibbles or cope disguised as personal anecdotes. I'm pretty worried about the impact of the LLMs too, but if you're not getting use out of them while coding, I really do think the problem is you.
Since people always want examples, I'll link to a PR in my current hobby project, which Claude code helped me complete in days instead of weeks. https://github.com/igor47/csheet/pull/68 Though this PR creates a bunch of tables, routes, services -- it's not just greenfield CRUD work. We're figuring out how to model a complicated domain (the rules to DnD 5e, including the 2014 and the 2024 revisions of those rules), integrating with existing code, thinking through complex integrations including with LLMs at run time. Claude is writing almost all the code, I'm just steering
I've found LLMs to be extremely powerful in research projects. A lot of code related to research is very bespoke by its nature. Using Codex, I've been able to iterate on ideas that I would never had the time or courage to explore before. As for code quality/brevity, it doesn't really matter in this context as long as it works. And it's incredible to have this companion that understands broadly every aspect of tangential knowledge required to execute an idea. I do think it helps that I have over 25 years of experience in my domain (geospatial), which helps me guardrail my interactions and get good results in as few shots as possible.
This is a very one-sided article, unashamedly so.
Where's the references to the decline in quality and embarrassing outages for Amazon, Microsoft, etc?
Everything you read is in service of someone's business model.
What’s your point? Journalists have jobs?
journalists are like our own simon willinson: they need to put food on their plate by networking with powerful entities that fly them out to conferences
NYT doesn't like digital advertisers and the programmers who make that possible. They're directly in competition.
Do we know that it decreased the quality, or introduced more opportunities for bugs by simply increasing the velocity? If every commit has a fixed probability of having a bug, you'll run into more bugs in a week by going faster.
> Do we know that it decreased the quality, or introduced more opportunities for bugs by simply increasing the velocity?
That's an easy question to answer - you can look at outages per feature released.
You may be instead looking at outages per loc written.
AI is constantly trying to introduce bugs into my code. I've started disabling it when I know exactly where I'm going with the code, because the AI is often a lot more confused than I am about where the code is going.
Do we know it increased the velocity and didnt just churn more slop?
Even before AI the limiting factor on all of the teams I ever worked on was bad decisions, not how much time it took to write code. There seem to be more of those these days.
You have to hold AI hand to do even simple vanilla JS correctly. Or do framework code which is well documented all over the net. I love AI and use it for programming a lot, but the limitations are real.
Exactly that is also my experience also with Claude Code. It can create a lot of stuff impressively but with LOTS of more code than necessary. It’s not really effective in the end. I have more than 35 years of coding experience and always dig into the newest stuff. Quality wise it’s still not more than junior dev stuff even with latest models, sorry. And I know how to talk to these machines.
I don't have as many years of professional experience as you do, but IMO code pissing is one of the areas LLMs and "agentic tools" shine the least.
In both personal projects and $dayjob tasks, the highest time-saving AI tasks were:
- "review this feature branch" (containing hand-written commits)
- "trace how this repo and repo located at ~/foobar use {stuff} and how they interact with each other, make a Mermaid diagram"
- "reverse engineer the attached 50MiB+ unstripped ELF program, trace all calls to filesystem functions; make a table with filepath, caller function, overview of what caller does" (the table is then copy-pasted to Confluence)
- basic YAML CRUD
Also while Anthropic has more market share in B2B, their model seems optimized for frontend, design, and literary work rather than rigorous work; I find it to be the opposite with their main competitor.
Claude writes code rife with safety issues/vulns all the time, or at least more than other models.
Try the new /simplify command.
I must say, I do love how this comment has provoked such varying responses.
My own observations about using AI to write code is that it changes my position from that of an author to a reviewer. And I find code review to be a much more exhausting task than writing code in the first place, especially when you have to work out how and why the AI-generated code is structured the way it is.
There's a very wide range of programming tasks of differing difficulty that people are using / trying to use it for, and a very wide range of intelligence amongst the people that are using / trying to use it, and who are evaluating its results. Hence, different people have very different takes.
> especially when you have to work out how and why the AI-generated code is structured the way it is.
You could just ask it? Or you don’t trust the AI to answer you honestly?
You're anthropomorphizing.
LLMs can't lie nor can they tell the truth. These concepts just don't apply to them.
They also cannot tell you what they were "thinking" when they wrote a piece of code. If you "ask" them what they were thinking, you just get a plausible response, not the "intention" that may or may not have existed in some abstract form in some layer when the system selected tokens*. That information is gone at that point and the LLM has no means to turn that information into something a human could understand anyways. They simply do not have what in a human might be called metacognition. For now. There's lots of ongoing experimental research in this direction though.
Chances are that when you ask an LLM about their output, you'll get the response of either someone who now recognized an issue with their work, or the likeness of someone who believes they did great work and is now defending it. Obviously this is based on the work itself being fed back through the context window, which will inform the response, and thus it may not be entirely useless, but... this is all very far removed from what a conscious being might explain about their thoughts.
The closest you can currently get to this is reading the "reasoning" tokens, though even those are just some selected system output that is then fed back to inform later output. There's nothing stopping the system from "reasoning" that it should say A, but then outputting B. Example: https://i.imgur.com/e8PX84Z.png
* One might say that the LLM itself always considers every possible token and assigns weights to them, so there wouldn't even be a single chain of thought in the first place. More like... every possible "thought" at the same time at varying intensities.
>You're anthropomorphizing.
That is fine. You should, and you'll get the best results doing so.
>LLMs can't lie nor can they tell the truth. These concepts just don't apply to them
Nobody really knows exactly what concepts do and don't apply to them. We simply don't have a great enough understanding of the internal procedures of a trained model.
Ultimately this is all irrelevant. There are multiple indications that the same can be said for humanity, that we perform actions and then rationalize them away even without realizing it. That explanations are often if not always post-hoc rationalizations, lies we tell even ourselves. There's evidence for it. And yet, those explanations can still be useful. And I'm sure OP was trying to point out that is also the case for LLMs.
I’m not anthropomorphizing. I’ve been in many situation where the AI wrote some code some way and I had to ask why, it told me why and then we moved on to better solutions as needed. Better if it just wrote the code and its reasoning was still in context, but even if it’s not, it can usually reverse engineer what it wrote well enough. Then it’s a conversation about whether there is a better clearer way to do it, the code improves.
It sounds like you either have access to bad models or you are just imagining what it’s like to use an LLM in this way and haven’t actually tried asking it why it wrote something. The only judgement you need to make is the explanation makes sense or not, not some technical or theoretical argument about where the tokens in the explanation come from. You just ask questions until you can easily verify things for yourself.
Also, pretending that the LLM is still just token predicting and isn’t bringing in a lot of extra context via RAG and using extra tokens for thinking to answer a query is just way out there.
You just steamrolled on, pretty much ignoring the comment you are replying to, made unkind assumptions, and put words in my mouth to boot. I don't mind some aggressive argumentation, but this misses the mark so completely that I have really no idea how to have a constructive conversation this way.
> where the AI wrote some code some way and I had to ask why, it told me why
I just explained that it cannot tell you why. It's simply not how they work. You might as well tell me that it cooked you dinner and did your laundry.
> the code improves.
We can agree on this. The iterative process works. The understanding of it is incorrect. If someone's understanding of a hammer superficially is "tool that drives pointy things into wood", they'll inevitably try to hammer a screw at some point - which might even work, badly.
> It sounds like you either have access to bad models or you are just imagining what it’s like to use an LLM in this way
Quoting this is really enough. You may imagine me sighing.
> Also, pretending that the LLM is still just token predicting
Strawman.
Overall your comment is dancing around engaging with what is being said, so I will not waste my time here.
Or ask another model to tell you what the changes do.
And you could first read the thing to which you are replying. Don't tell me it was too long.
It was too long, but thankfully AI can summarize so it’s no excuse anymore.
Did you mean to reply to some other comment? I'm having trouble contextualizing your response - pardon the pun.
Your always reviewing code though. Either a team mates pr or maybe your own code in 3 months, or some legacy thing.
Human code is still easier to review. Also, I program 80% of the time and review PRs 20% of the time. With AI, that becomes: I review 80% of the time, and write markdown and wait 20% of the time.
The other day I (well, the AI) just wrote a Rust app to merge two (huge, GB of data) tables by discovering columns with data in common based on text distance (levenshtein and Dice) . It worked beautifully
An i have NEVER made one line of Rust.
I dont understand nay-sayers, to me the state of gen.AI is like the simpsons quote "worst day so far". Look were we are within 5 years of the first real GPT/LLM. The next 5 years are going to be crazy exciting.
The "programmer" position will become a "builder". When we've got LLMs that generate Opus quality text at 100x speed (think, ASIC based models) , things will get crazy.
Human minds are built to find patterns, and you should be careful not to assume the rate of improvement will continue forever based on nothing but a pattern.
Just the fact that even retail quality hardware is still improving at local LLM significantly is still a great sign. If AI quality remained the same, and the cost for local hardware dropped to $1000, it would still be the greatest thing since the internet IMO. So even if the worst happens and all progress stops, I'm still very happy with what we got.
>I'm still very happy with what we got
"One person's slop is another person's treasure"
I'm not all that impressed with "AI". I often "race" the AI by giving it a task to do, and then I start coding my own solution in parallel. I often beat the AI, or deliver a better result.
Artificial Intelligence is like artificial flavoring. It's cheap and tastes passable to most people, but real flavors are far better in every way even if it costs more.
At their current stage, this feels like the wrong way to use them. I use them fully supervised, (despite the fact that feels like I’m fighting the tools), which is kind of the best of both worlds. I review every line of code before I allow the edit, and if something is wrong, I tell it to fix it. It learns over time, especially as I set rules in memories, and so the process has sped up, to the point that this goes way faster than if I would have done that myself. Not all tasks are appropriate for LLMs at all, but when they are, this supervised mode is quite fast, and I don’t believe the output to be slop, but anyways I feel like I own every line of code still.
The happy path for me is with erlang, due to the concurrency model the blast radius of an error is exceptionally small, so the programming style is to let things crash if they go wrong. So, really you are writing the happy path code only (most of the time). Combine this approach with some very robust tests (does this thing pass the tests / behave how we need it to?) then you’re close to the point of not really caring about the implementation at all.
Of course, i still do, but i could see not caring being possible down the road with such architectures..
Home made food is better than anything you can buy too. Im 40 but I still drive 30 minutes to my parents once a week for dinner because the food they make feels like the elixir of life compared to the slop I can buy in trader joes, Costco, or most restaurants.
But I'm pretty glad trader joes exists too.
Trust me, Trader Joes is real food compared to a lot of the toxic waste being sold as food out there.
That crap will fill your belly but it won't keep you healthy. Your brain is like a muscle, if you stop flexing it, you'll end up weaker.
The overall trend in AI performance will still be up and to the right like everything else in computing over the past 50 years, improvement doesn't have to be linear
Assuming newer, more efficient architectures are discovered.
Because if you don't know the language or problem space, there are footguns in there that you can't find, you won't know what to look for to find them. Only until you try to actually use this in a production environment will the issues become evident. At that point, you'll have to either know how to read and diagnose the code, or keep prompting till you fix it, which may introduce another footgun that you didn't know that you didn't know.
This is what gets me. The tools can be powerful, but my job has become a thankless effort in pointing out people's ignorance. Time and again, people prompt something in a language or problem space they don't understand, it "works" and then it hits a snag because the AI just muddled over a very important detail, and then we're back to the drawing board because that snag turned out to be an architectural blunder that didn't scale past "it worked in my very controlled, perfect circumstances, test run." It is getting really frustrating seeing this happen on repeat and instead of people realizing they need to get their hands dirty, they just keep prompting more and more slop, making my job more tedious. I am basically at the point where I'm looking for new avenues for work. I say let the industry just run rampant with these tools. I suspect I'll be getting a lot of job offers a few years from now as everything falls apart and their $10k a day prompting fixed one bug to cause multiple regressions elsewhere. I hope you're all keeping your skills sharp for the energy crisis.
Before LLMs, I've watched in horror as colleagues immediately copy-paste-ran Stack Overflow solutions in terminal, without even reading them.
LLM agents are basically the same, except now everyone is doing it. They copy-paste-run lots of code without meaningfully reviewing it.
My fear is that some colleagues are getting more skilled at prompting but less skilled at coding and writing. And the prompting skills may not generalize much outside of certain LLMs.
The less you know about a domain/language the better AI seems to be :)
I seem to remember doing it in SQL (EDIT_DISTANCE) 20ish years ago. While I wouldn't say it worked beautifully, I also didn't need to make a single line of Rust :) also no more than 2 line s of SQL were needed.
Edit_distance uses pure levenstein which is quadratic, so for tables of 500k rows and 20+ columns each it will slowdown to a crawl. Without going into a lot of detail, I needed this to work for datasets of that size. So a lot of "trick" optimization and pre-processing has to be done.
Otherwise simple merges in pandas or sql/duckdb would had sufficed.
And how many years of experience you needed to know what to write, and what if you can replace that time with how long prompting takes?
It's an interesting question.
Years of school (reading, calculus etc) to get to the point of learning basics of set theory. One day to learn basic SQL based on understanding the set theory. Maybe few weeks of using SQL at work for ad hoc queries to be proficient enough (the query itself wasn't really complex).
For the domain itself I was consulting experts to see what matters.
I'm not sure that time it would take to know what to prompt and verify the results is much different.
Fun fact - management decided that SQL solution wasn't enerprisely enough so they hired external consultants to build a system doing essentialy that but in Java + formed an 8 people internal team to guide them. I heard they finished 2 years later with a lot of manual matching.
Let me explain the naysayers, they know "programmer" has always meant "builder" and just because search is better and you can copy and paste faster doesn't mean you've built anything.First thing people need to realize is no proprietary code is in those databases, and using Ai will ultimately just get you regurgitated things people don't really care about. Use it all you want, you won't be able to do anything interesting, they aren't giving you valuable things for free. Anything of value will still take time and knowledge. The marketing hype is to reduce wages and prevent competition. Go for it.
The next 5 years are going to be crazy exciting.
I don't want exciting. I want a stable, well-paying job that allows me to put food on the table, raise a family with a sense of security and hope, and have free time.
This is not my experience either. If you put the work in upfront to plan the feature, write the test cases, and then loop until they pass... you can build a lot of high quality software quickly. The difference between a junior engineer using it and a great architect using it is significant. I think of it as an amplifier.
This honestly reads to me like "if you spend a lot of time doing tedious monotonous shit you can save a lot of time on the interesting stuff"
I have no interest being a "great architect" if architects don't actually build anything
"If I had eight hours to chop down a tree, I'd spend six sharpening my axe" - Abraham Lincoln
I’m amazed at how many great architects and experts on AI we now have.
> The difference between a junior engineer using it and a great architect using it is significant
Yes, juniors are trying to use AI with the minimum input. This alone tells a lot..
Not in my experience. But then again, lots of programmers are limited in how they use AI to write code. Those limitations are definitely real.
that's just not even remotely my experience. and i am ~20k hours into my programming career. ai makes most things so much faster that it is hard to justify ever doing large classes of things yourself (as much as this hurts my aesthetic sensibilities, it simply is what it is).
I've never seen a human estimate their "programming career" in kilohours. Is that supposed to look more impressive than years? So, you've been programming only about 7 years? I guess I'm at about "170 kilohours".
As well as the peer comment about Gladwell (10k hours is considered the point you've mastered a skill), it's also a far more honest metric about how much time you've spent actually programming.
Maybe you were writing code, make design choices and debugging 8 hours a day. Maybe you were primarily doing something else and only writing code for an hour a day. Who would be the better programmer? The first guy with one year of experience or the second guy with 7 years?
I personally would only measure my experience in years, because it's approaching 3 decades full-time in industry (plus an additional decade of cutting my teeth during school and university), but I can certainly see that earlier on in a career it's a useful metric in comparison to the 10,000 hours.
> Maybe you were writing code, make design choices and debugging 8 hours a day. Maybe you were primarily doing something else and only writing code for an hour a day. Who would be the better programmer? The first guy with one year of experience or the second guy with 7 years?
So your logic is that the grandparent specified hours because they spent that many hours specifically programming, and not by just multiplying the number of years by the number of hours in a year?
I don't know exactly how they arrived at their 20k hours figure, all I'm saying is that it didn't seem a controversial way of expressing their experience level, and assumed it was intended to be a comparison to the typical 10k hours needed for mastery of a craft.
I think it’s probabky because of the malcom gladwell “ten thousand hours” idea.
Part of this depends on if you care that the AI wrote the code "your way." I've been in shops with rather exotic and specific style guides and standards which the AI would not or will not conform to.
Yeah, I also highly value consistency in my projects, which forces me to keep an eye on the LLM and steer it often. This limits my overall velocity especially on larger features. But I'm still much faster with the agent. Recent example, https://github.com/igor47/csheet/pull/68 -- this took me a couple of hours pairing with Claude code, which is insane give the size of the work here. Though this PR creates a bunch of tables, routes, services -- it's not just greenfield CRUD work. We're figuring out how to model a complicated domain, integrating with existing code, thinking through complex integrations including with LLMs at run time. Claude is writing almost all the code, I'm just steering
then have ai write a deterministic transformation tool that turns it into the specific style and standard that is needed
AI assisted code can't even stick to the API documentation, especially if the data structures are not consistent and have evolved over time. You would see Claude literally pulling function after function from thin air, desperately trying to fulfill your complicated business logic and even when it's complete, it doesn't look neat at all. Yes, it will have test coverage, but one more feature request will probably break the back of the camel. And if you raise that PR to the rest of your team, good luck trying to summarise it all to your colleagues.
However if you just have an easy project, or a greenfield project, or don't care about who's going to maintain that stuff in 6 months, sure, go all in with AI.
I definitely wonder if the people going all-in on AI harnessing are working on greenfield projects, because it seems overwhelming to try to get that set up on a brownfield codebase where the patterns aren't consistent and the code quality is mixed.
So just iterate on it? Your complaint is that the model isn't one shotting the problem and reading your mind about style. It's like any coding workflow, make it work, then make it nice.
No, I never expect AI to one-shot (if I see such a miracle, it's usually because I needed a one-liner or something really simple and well documented, which I can also write on the whiteboard from memory).
Try iterating over well known APIs where the response payloads are already gigantic JSONs, there are multiple ways to get certain data and they are all inconsistent and Claude spits out function after function, laying waste to your codebase. I found no amount of style guideline documents to resolve this issue.
I'd rather read the documentation myself and write the code by hand rather than reviewing for the umpteenth time when Claude splits these new functions between e.g. __init__.py and main.py and god knows where, mixing business logic with plumbing and transport layers as an art form. God it was atrocious during the first few months of FastMCP.
Not what I've experienced
It’s crazy how some people feel the ai and others don’t. But one group is wrong. It’s a matter of time before everyone feels the AI.
Most of this thread is debating whether models are good or bad at writing code... however, I think a more important question is what we feed the AI with because that dramatically determines the quality of the output.
When your agent explores your codebase trying to understand what to build, it read schema files, existing routes, UI components etc... easily 50-100k tokens of implementation detail. It's basically reverse-engineering intent from code. With that level of ambiguous input, no wonder the results feel like junior work.
When you hand it a structured spec instead including data model, API contracts, architecture constraints etc., the agent gets 3-5x less context at much higher signal density. Instead of guessing from what was built it knows exactly what to build. Code quality improves significantly.
I've measured this across ~47 features in a production codebase with amedian ratio: 4x less context with specs vs. random agent code exploration. For UI-heavy features it's 8-25x. The agent reads 2-3 focused markdown files instead of grepping through hundreds of KB of components.
To pick up @wek's point about planning from above: devs who get great results from agentic development aren't better prompt engineers... they're better architects. They write the spec before the code, which is what good engineering always was... AI just made the payoff for that discipline 10x more visible.
I have a suspicion that for a task (or to make an artifact) of a given complexity, there is a minimum level of human engagement required to complete it successfully - and that human engagement cannot be substituted for anything else. However, the actual human engagement for a task is not bounded above - efficiency is often less (much less?) than 100%.
So tools (like AI) can move us closer to the 100% efficiency (or indeed further away if they are bad tools!) but there will always be the residual human engagement required - but perhaps moved to different activities (e.g. reviewing instead of writing).
Probably very effective teams/individuals were already close to 100% efficiency, so AI won't make much difference to them.
I wasn't around when we moved to that stack from assembly. I didn't experience the mourning then.
Most folks I hang out with are infatuated with turning tokens into code. They are generally very senior 15+ years of experience.
Most folks I hang out with experience existential dread for juniors and those coming up in the field who won't necessarily have the battle scars to orchestrate systems that will work in the will world.
Was talking with one fellow yesterday (at an AI meetup) who says he has 6 folks under him, but that he could now run the team with just two of them and the others are basically a time suck.
> “The reason that tech generally — and coders in particular — see L.L.M.s differently than everyone else is that in the creative disciplines, L.L.M.s take away the most soulful human parts of the work and leave the drudgery to you,” Dash says. “And in coding, L.L.M.s take away the drudgery and leave the human, soulful parts to you.”
This doesn’t really make sense to me. GenAI ostensibly removes the drudgery from other creative endeavors too. You don’t need to make every painstaking brushstroke anymore; you can get to your intended final product faster than ever. I think a common misunderstanding is that the drudgery is really inseparable from the soulful part.
Also, I think GenAI in coding actually has the exact same failure modes as GenAI in painting, music, art, writing, etc. The output lacks depth, it lacks context, and it lacks an understanding of its own purpose. For most people, it’s much easier to intuitively see those shortcomings of GenAI manifest in traditional creative mediums, just because they come more naturally to us. For coding, I suspect the same shortcomings apply, they just aren’t as clear.
I mean, at the end of the day if writing code is just to get something that works, then sure, let’s blitz away with LLMs and not bother to understand what we’re doing or why we do it anymore. Maybe I’m naive in thinking that coding has creative value that we’re now throwing away, possibly forever.
Maybe they mean more soulful like a fellow that blacksmiths his own tools and metal fasteners prior to constructing something. I’d personally think this person was a badass, but until wwiii, it’s so impractical and seems arbitrary because why stop there - get more soulful and mine your own ore too.
For one thing comments here appear to apply to the quality and issues today not potentially going forward. Quality will change quicker than anyone expects. I am wondering how many people at HN remember when the first Mac came out with Mac Paint and then Pagemaker or Quark. That didn't evolve anywhere nearly as quickly as AI appears to be.
Also I am not seeing how anyone is considering that what a programmer considers quality and what 'gets the job done' (as mentioned in the article) matters in any business. (Example with typesetting is original laser printers were only 300dpi but after a short period became 1200dpi 'good enough' for camera ready copy).
I dunno; I finally can focus on writing the logic I wanted to write all along and finally my upbringing in formal verification makes sense as I can spend my time on it instead of figuring what garbage (I cannot use it in my work but sbcl is one of the things that does not grow tumors in software) updates I will never ever need my friends added to the framework or language or ide I happen to use.
Another trash article from the New York Times, who financially benefit from this type of content because of their ongoing litigation against OpenAI. I think the assumption that developers don't code is wrong. Most software engineers don't even want to code, they are opportunists looking to make money. I have yet to experience this cliff of coding. These people aren't asking for hard enough questions. I have a bunch of things I want AI to build that it completely fails on.
The article could have been written from a very different perspective. Instead, the "journalists" likely interviewed a few insiders from Big Tech and generalized. They don't get it. They never will.
Before the advent of ChatGPT, maybe 2 in 100 people could code. I was actually hoping AI would increase programming literacy but it didn't, it became even more rare. Many journalists could have come at it from this perspective, but instead painted doom and gloom for coders and computer programming.
The New York Times should look in the mirror. With the advent of the iPad, most experts agreed that they would go out of business because a majority of their revenue came from print media. Look what happened.
Understand this, most professional software and IT engineers hate coding. It was a flex to say you no longer code professionally before ChatGPT. It's still a flex now. But it's corrupt journalism when there is a clear conflict of interest because the NYT is suing the hell out of AI companies.
Agreed - just like the Fortune article talking about (Edit: Morgan Stanley, not GS) saying "the AI revolution is coming next year, and will decimate tons of industries, and no one is ready for it". They quote Altman and Musk. Gee - what did you expect from those two snake-oil salesmen?
Also the fact that NYT gives all their devs licenses to Cursor and Claude
I agree that the article is a poor take on AI in programming. However, I wouldn't blame NYT for corrupt journalism. This is an op-ed, not something written by NYT staff.
> Pushing code that fails pytest is unacceptable and embarrassing.
CI is for preventing regressions. Agents.md is for avoiding wasted CI cycles.
The best developers are the ones using AI to its best. Mediocre devs will become a useless skill as even a PO could become one. But one who understands architecture, software, code and AI will be expensive to hire. I know plenty of them. I wory for the ones not willing to adopt ai.
Visual Basic was the end of programming as we knew it...until it wasn't.
And before that, COBOL was supposed to allow computer users to write in almost plain English without even knowing the machine instruction set.
It did change the programming landscape, but there was still a huge need for this new kind of programmers.
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Revenge of the writers and software managers, the wishfull hoping for hurt of those made redundant upon those they blame for having been made redundant.
The psycho engineering of model prompts does feel very Phillip K. Dick.
If your base prompt informs the model they are a human software developer in a Severed situation, it gets even closer.
https://archive.ph/S2nMA
I am now using LLM at 100% at work and producing faster code, while I keep growing my regular skills privately in "old way".
Why? Because when the bubble burst and the companies (including mine) can not pay the 400% price increase and go bankrupt, then I still have keep my brain active and still can do stuff without or less tokens.
I once suggested a drinking game: shot every time someone says "X is dead". I was told to f** off because I'd kill half of humanity.
COBOL is dead. Java is dead. Programming is dead. AI is dead (yes, some people are already claiming this: https://hexa.club/@phooky/116087924952627103)
I must be the kid from The Sixth Sense because I keep seeing all these allegedly dead guys around me.
There is no such thing as "after coders": https://zjpea.substack.com/p/embarrassingly-solved-problems
This excerpt:
>A.I. had become so good at writing code that Ebert, initially cautious, began letting it do more and more. Now Claude Code does the bulk of it.
is a little overstated. I think the brownfield section has things exactly backwards. Claude Code benefits enormously from large, established codebases, and it’s basically free riding on the years of human work that went into those codebases. I prodded Claude to add SNFG depictions to the molecular modeling program I work on. It couldn’t have come up with the whole program on its own and if I tried it would produce a different, maybe worse architecture than our atomic library, and then its design choices for molecules might constrain its ability to solve the problem as elegantly as it did. Even then, it needed a coworker to tell me that it had used the incorrect data structure and needed to switch to something that could, when selected, stand in for the atoms it represented.
Also this:
>But A.I.-generated code? If it passes its tests and works, it’s worth as much as what humans get paid $200,000 or more a year to compose.
Isn’t really true. It’s the free-riding problem again. The thing about an ESP is that the LLM has the advantage of either a blank canvas (if you’re using one to vibe code a startup), or at least the fact that several possibilities converge on one output, but, genuinely, not all of those realities include good coding architecture. Models can make mistakes, and without a human in the loop those mistakes can render a codebase unmaintainable. It’s a balance. That’s why I don’t let Claude stamp himself to my commits even if he assisted or even did all the work. Who cares if Claude wrote it? I’m the one taking responsibility for it. The article presents Greenfield as good for a startup, and it might be, but only for the early, fast, funding rounds, when you have to get an MVP out right now. That’s an unstable foundation they will have to go back and fix for regulatory or maintenance reasons, and I think that’s the better understanding of the situation than framing Aayush’s experience as a user error.
Even so, “weirdly jazzed about their new powers” is an understatement. Every team including ours has decades of programmer-years of tasks in the backlog, what’s not to love about something you can set to pet peeves for free and then see if the reality matches the ideal? git reset --hard if you don't like what it does, and if you do all the better. The Cuisy thing with the script for the printer is a perfect application of LLMs, a one-off that doesn’t have to be maintained.
Also, the whole framing is weirdly self limiting. The architectural taste that LLMs are, again, free riding off of, is hard won by doing the work more senior engineers are giving to LLMs instead of juniors. We’re setting ourselves up for a serious coordinated action problem as a profession. The article gestures at this a couple times
The thing about threatening LLMs is pretty funny too but something in me wants to fall back to Kant's position that what you do to anything you do to yourself.
I spent ~6hrs with Claude trying to fix a web worker bug in a small JS code base Claude made. In the end it failed and I ran out of credits. Claude kept wanting to rip out huge blocks of code and replace entire functions. We never got any closer to a solution. The Claude hype is unreal. My 'on the ground' experience has been vastly different.
Yes, you can get a project with claude to a state of unrecoverable garbage. But with a little experience you can learn what it's good at and this happens less and less.
That isn't my experience. My code and bug tracker are public, so I have the privilege of being able to paste URLs to tickets into Claude Code with the prompt "what the fuck?" and it usually comes up with something workable on its own.
Regarding LLM's performances on brownfield projects, I thought of Naur's "Programming as Theory Building". He explains an example of a compiler project that is taken over by a team without guidance from the original developers:
> "at [the] later stage the original powerful structure was still visible, but made entirely ineffective by amorphous additions of many different kinds"
Maybe a way of phrasing it is that accumulating a lot of "code quality capital" gives you a lot more leverage over technical debt, but eventually it does catch up.
I feel the need to tell the LLM to rewrite the article for a software developer audience, but don't, those kinds of passage are hard to overcome:
'Salva opened up his code editor — essentially a word processor for writing code — to show me what it’s like to work alongside Gemini, Google’s L.L.M. '
And what's up with L.L.M, A.I., C.L.I. :)
> And what's up with L.L.M, A.I., C.L.I. :)
It’s probably N.Y.T. style requirements; a lot of style guides (eg: Chicago Manual of Style, Strunk & White, etc) have a standard form for abbreviations and acronyms. A paper like N.Y.T. does too and probably still employs copy editors who ensure that every article conforms to it.
It's really time that mainstream media picks up on 'agentic coding' and the implications of writing software becoming a commodity.
I'm an engineer (not only software) by heart, but after seeing what Opus 4.6 based agents are capable of and especially the rate of improvement, i think the direction is clear.
I like 4.6 and agents based on it but can only qualify it as moderately useful.
sensationalism give it a couple of months
Back in the day, programming was done on punch cards. In 20 years, that's how kids will see typing out lines of program code by hand.
different things. adding levels of abstraction is not the same as having a statistical model generate abstractions for you.
you can still call it spec-programming but if you don't audit your generated code then you're simply doing it wrong; you just don't realize that yet because you've been getting away with it until now.
At the rate things have been going, that is likely to happen in 20 days rather than 20 years.
It's an accelerator. A great tool if used well. But just like all the innovations before it that were going to replace programmers it simply won't.
I used Claude just the other day to write unit test coverage for a tricky system that handles resolving updates into a consistent view of the world and handles record resurrection/deletion. It wrote great test coverage because it parsed my headerdoc and code comments that went into great detail about the expected behavior. The hard part of that implementation was the prose I wrote and the thinking required to come up with it. The actual lines of code were already a small part of the problem space. So yeah Claude saved me a day or two of monotonously writing up test cases. That's great.
Of course Claude also spat out some absolute garbage code using reflection to poke at internal properties because the access level didn't allow the test to poke at the things it wanted to poke at, along with some methods that were calling themselves in infinite recursion. Oh and a bunch of lines that didn't even compile.
The thing is about those errors: most of them were a fundamental inability to reason. They were technically correct in a sense. I can see how a model that learned from other code written by humans would learn those patterns and apply them. In some contexts they would be best-practice or even required. But the model can't reason. It has no executive function.
I think that is part of what makes these models both amazingly capable and incredibly stupid at the same time.
>but like most of their peers now, they only rarely write code.
Citation needed. Are most developers "rarely" writing code?
I'd expect that probably less than 10% of my time is spent actually writing code, and not because of AI, but because enough of it is spent analyzing failures, reading documents, participating in meetings, putting together presentations, answering questions, reading code, etc. And even when I have a nice, uninterrupted coding session, I still spend a decent fraction of that time thinking through the design of how I want the change rather than actually writing the code to effect that change.
Nailed it.
The design part is hard because you have to envision the object. Once you have a good idea and conceptialisation of the object, form is easy.
Yeah, actually writing code is a surprisingly small part of the job.
And was true before AI
In my direct experience this is mostly true.
Lots of cope here. Highly paid white collar jobs are going to disappear.
What is a coder? Someone who is handed the full specs and sits down and just types code? I have never met such a person. The most annoying part of SWE is everyone who isn't an SWE has inane ideas about what we do.
Never worked on offshoring projects? That is exactly what the sweatshop coders do.
No we don't.
For one, I never saw a "full spec" (if such a thing even exists) back in my days of making 8k. Annually.
I think that the current AI tooling is a much bigger threat to offshore sweatshops than to domestic programmers.
Why deal with language barriers, time shifts, etc. when a small team of good developers can be so much more productive, allegedly?
It certainly is,
https://www.theregister.com/2026/01/19/hcl_infosys_tcs_wipro...
> The most annoying part of SWE is everyone who isn't an SWE has inane ideas about what we do.
I’ve tended to hold the same opinion of what the average SWE thinks everyone else does.
https://www.nytimes.com/2026/03/12/magazine/ai-coding-progra...
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I keep getting stuck on the liability problem of this supposed "new world". If we take this as far as it goes: AI agent societies that designs, architects, and maintains the entire stack E2E with little to no oversight. What happens when rogue AIs do bad things? Who is responsible? You have to have fireable senior engineers that understand deep fundamentals to make sure things aren't going awry, right? /s
Check out the movie Brazil, if you haven't seen it already. Incredibly far ahead of its time.
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It's all nonsense. It's just better search, intelligence in not artificial. They are trying to convince everyone that they don't need to pay programmers. That's all, all it is. It'll work on the ignorant who'll take less money to make sure it works and fix the bugs, which is mostly what they were paying for anyway. They just want to devalue the work of the people they are reliant on. Nothing new.
I think you’re a bit behind on your world view. Just because it’s inconvenient to you that non coders can now code, doesn’t make it untrue.
No, they can’t.
It has nothing to do with inconvenience.
I really like that layman now make these statements - they know better than people working in the industry for decades.
None of them understand the first thing about what the job actually is, or they wouldn't try to brag about the 200K LoC they generated lol
Because we love tech? I'm absolutely terrified about the future of employment in this field, but I wouldn't give up this insane leap of science fiction technology for anything.
I love tech - tech that actually works well. The current tech we have for AI does not, so I'm not excited about it.
A really good pattern-matching engine is an "insane leap of science fiction"? It saves me a bit of typing here and there with some good pattern matching. Trying to get it to do anything more than a few lines gives me gibberish, or an infinite loop of "Oh, you're right, I need to do X, not Y", over and over - and that's Opus 4.5 or whatever the recent one is.
Would you give it access to your bank account, your 401k, trust it to sell your house, etc? I sure wouldn't.
>A really good pattern-matching engine is an "insane leap of science fiction"?
Yes, literally. The ship computer voice interface in Star Trek was complete science fiction until 2022. Now its ability to understand speech and respond seem quaint in comparison to current AI.
"One such test for Python code, called a pytest"
The brain rot from the author couldn't even think of "unit test".
Why would you expect a reporter to magically know what a "unit test" is? Sounds like a simple miscommunication with one of his sources. Not perfect but not "brain rot".