A challenge with this kind of study is that coding agents (Claude Code, OpenAI Codex) only started working really well in late November, which for most people meant early January due to the December break.
General agents (OpenClaw, Anthropic Copilot, ChatGPT "Work") started working even later than that.
This category of software may have a much more meaningful impact on work than the mostly-chat systems we were using from 2022-2025.
Studies that mainly focus on 2022 to end of 2025 might be missing out on a material uptick in capabilities.
> This category of software may have a much more meaningful impact on work than the mostly-chat systems we were using from 2022-2025.
Mostly an impact on software development - I'm not seeing broad automation and layoffs in industries like law, finance etc. It will gradually happen but due to issues with memory, reliability and long term planning of LLMs there are real barriers. Even in software development - while it has completely transformed the field I don't think many people still believe we won't need devs in 2027 or that their amount will shrink by 50%.
Anecdotally, I (along with the rest of my team) got laid off from a big tech company at the end of January. The stated reason was "AI", although I think as we all know the real reason was "we overhired in 2022". I managed to get a job by April without putting in a lot of work, though (mostly just listened to recruiters until something hit my fancy). My work experience is good, but I don't know if it's so good that I would be immune to market effects. The company that hired me is still hiring other software developers, and I was told that it took them a long time to find someone with my qualifications. (We use Claude, although so far I haven't seen a lot of AI psychosis like I have in other places.. that was also a big filter in my job search). Long story short, I think narrative of job loss is way overblown. It's more doom trolling from anthropic and openAI and their mouth pieces as far as I can tell.
It's so early game, is the real problem. My experience so far, has been that it's barely a junior dev. I've met so many in my career that think reading stackoverflow, or watching a youtube video mysteriously makes them an expert. AI reminds me of this sort.
Well anyone can use (prior to AI) a simple linter and learning to code isn't that big a deal. It's learning the pitfalls, the traps, that's the issue. And so far Opus just seems to fall into them again and again. I guess the best way to put it, is that it's not an architect. I sees no big picture, and that's not really a surprise with (compared to a human) an incredibly small context window. When I'm on a project, or working with a codebase, I often have years of "context window". And I have a career of "don't do this" context window.
So what I wonder is, will this be resolved? Will that awareness of larger scope be solved? If that happens, we'll be in another ballpark of competency.
Some companies have massive codebases. Are these companies slowly gaining rot in those codebases, a swiss cheese effect, which eventually will result in collapse? Because I've worked where a bad hire had just this effect over time. And what I worry about isn't using Claude to speed one up, it's the DEV that uses Claude and just "meh" and submits because it passes regression + other tests, and then a manager or code reviewer uses Claude and "meh" because it's a pass too.
> Are these companies slowly gaining rot in those codebases?
Yes!
And that "yes" holds regardless of whether they use AI or not.
Let's not pretend tech debt accumulation is somehow an AI problem. Some of the world's biggest companies routinely ship code that reeks of years of rot and decay.
I get the point having read much the same from Tesla (and fans) regarding self driving cars that still haven't done half the things that Musk said was just around the corner pending regulators a decade ago and repeatedly since then.
And myself I keep making comparisons between AI and the progress in 90s video games where every minor improvement got called "photo realistic" and then forgotten with the next game engine: https://archive.org/details/nextgen-issue-26
So I'm not gonna say "this is it" when the software quality really matters, and I absolutely won't speak to progress (or lack of it) outside of software.
But I will say "you can look around and easily see small businesses using AI to generate posters, quite a lot of small business software and websites are in the same category: the mistakes are real but increasingly don't matter".
>the mistakes are real but increasingly don't matter...
I think it would start to matter once again. People will get fed up of AI posters and art. I think they already are...and once some threshold is crossed, the business won't dare to use AI generated assets/designs.
Turns out humans are much better at recognizing patterns in stuff that is generated ONLY using patterns from human generated content.
> People will get fed up of AI posters and art. I think they already are
Agreed, but will this look like a meme/fashion cycle? If so, re-prompt each year with a different look. Yes, there are still issues here, a friend found an image he was amazed was AI generated, but to me it was obviously so, so I showed him a screenshot of ChatGPT making something just it and included my prompt:
create image: hand drawing of cute springer spaniel puppy looking sideways, various geometric shapes drawn in layer behind and in front of the puppy, all done in style of 7 year old using crayons with mediocre colouring-in skills
As I said to them:
yeah, the line thickness feels AI, to me, the bad colouring-in scribbles feel like just the art style it was propmpted with
it's like: it gets the big picture of the composition, and it knows how to colour in badly, but it doesn't know how to draw a dog as badly as the colouring in
> Turns out humans are much better at recognizing patterns in stuff that is generated ONLY using patterns from human generated content.
We're better at recognising patterns full stop. All biological brains are, and needed to be better than the current state of the art in machine learning because if a living organism was as poor at learning patterns as the SotA in machine learning, the organism would starve to death before being able to pick up anything and eat it.
AI also has a second disadvantage, because there are so few models: the laziest of ChatGPT "thinkpiece" blog posts being everywhere is hard to miss, and 5000 fake bloggers all prompting the same model with "find biggest news story of today and write a blog post about it in a way that maximises my ad revenue" will get 5000 almost identical posts. This will remain true while each instance of the most commonly used AI fail to talk to each other in a way that at least mimics them collectively getting bored with writing the same thing 5000 times, it does not depend on e.g. quality.
I’m probably illustrating your point but as a FSD fan it really got ”good enough” recently with version 14. The tipping point was suddenly, much more often than not, it can drive end to end from start (my garage) to finish (parked at destination) with no interventions. I can text and watch videos on my phone and as long as I glance up once a minute, it doesn’t complain.
Handling highway driving with lane changes was great when it got there years ago, but just in the last year or so it has gone from a nice to have to “from now on I will never buy a car that can’t do this”.
AI has hit some milestones for replacing work as well. There’s still many more to go and maybe some of them will never get hit (much like I don’t think a coast to coast drive with zero interventions during winter conditions is ever going to happen) but there are points at which it forever meaningfully changes some field of work. I think it’s there for writing code.
> I’m probably illustrating your point but as a FSD fan
Half-and-half. I'm not denying that self driving cars (and LLMs) are improving, I'm comparing it against the standards set by the biggest proponents. But yes, I have heard basically the same thing you just wrote for the previous several major releases of FSD.
Where we agree is that, while you are a fan, you do explicitly give as an example of something you think it will never do, something very close to what Musk has promised:
"Ultimately you'll be able to summon your car anywhere … your car can get to you. I think that within two years, you'll be able to summon your car from across the country. It will meet you wherever your phone is … and it will just automatically charge itself along the entire journey."
(That said, I think Tesla's FSD will never get there, not that it's impossible. The way Musk is behaving, there's going to be a financial scheme named after him in whatever passes for a textbook in 20 years, and it won't be the positive kind of example).
I digress with your last quote, I feel like some people who are aware of AI are starting to develop a quasi allergic reaction to slop, and while the mistakes might not matter for most of the population, for others it does and will take notice.
I just spoke to a fried who is a headhunter and who's been trying to automate his processes for a while (he likes to fiddle and certainly has skills, but he's not an engineer). He kept trying, but it just wasn't good enough.
Now he said with GPT Work and Sol, it worked, but the key point is: all of it suddenly worked.
The problem was one of reliability, of handling edge cases. All previous attempts / model-harness-combinations were too brittle and needed too much observation and fiddling - cheaper to do it yourself.
Now he says "I don't know why I would ever hire a recruiter [the folks doing the cold outreach] again. I can focus on the candidate screening and acquiring projects, everything else is fully automated".
This doesn't come from an engineer or an AI lab, but a technically inclined power user, and I think this is where things get interesting.
You heard it from someone with no experience developing software. A lot of AI hype comes from that, even (or especially) from people in the actual business of developing software - a surprising amount of managers and adjacent or supporting roles in the field actually have very little clue about software development.
It's cool that 'regular' people can now create solutions to many small problems, and automate stuff - genuinely a step forward. Like Excel, only vastly better. But for bigger projects, real software engineers know that what LLMs do today is only a tiny part of development. And it solves it in a way that might well make the rest of the lifecycle a lot harder. It's like that saying about tools that make easy things easier and hard things impossible.
Then it just becomes a new baseline (everyone have access to the same LLMs), and recruiting moves up the philosophical ladder where human can add more value. What will it be? I don't know, I'm not a recruiter.
Again I've heard this since 2022 when gpt3.5 came out.
This is like microprocessors in the 80s. Sure they double in capability every 18 months but the start is so pathetic it will be 30 years before they are good enough for everyday tasks.
CPUs in the 90s were amazing! They were over-specced for "everyday tasks." Our problem is that we overbuilt CPUs too much, so software is now written with ten unnecessary layers of abstraction because there's no real reason to simplify.
The timescale is well established: Late '25 was the start of agentic ai when capabilities of model + api + scaffold reached autonomous state. Any study comapring events before that timeframe is comparing apples with oranges.
It seems to be true this time though; I have observed it myself and heard it from several experienced developers I personally know and respect. It feels like some threshold was crossed with Opus 4.5 and Gpt 5.3, where the models are now able to reliably solve certain classes of problems that were previously unreliable.
Time will tell of course, and it’s early, but inflection points do exist with progress.
Thing is. You can find an extremely similar paragraph written about Claude 4.x or some equivalent gpt. And simultaneously, many people expressing their frustration and the shortcomings of <insert any model>
“But it’s different this time” - several people, several times over the last couple of years.
This is not at all a dig at you, I’m very sorry if it reads that way. My point is these things only get truly better in anecdotes. The ways in which they fail is yet to change. Just yesterday I had gpt 5.3 generate completely awful code for the Cinema 4D Python API. Also an anecdote. But for all of the people saying they are truly intelligent and truly reason, they still make obvious mistakes, write around problems, fail entirely at architectural decisions, fail at random, generate FAR too much code.
And no amount of harnesses, methodologies, loops make much of a difference. If you listen to people on the internet they say it’s all working. You listen to people on the job and they mostly say it’s creating tech debt and a review bottleneck. Also burnout, so much burnout.
I think LLMs are mediocre. I think it’s fine they’re mediocre. You can work with low expectations. But the hype cycles are so tiresome.
LLM capabilities are spiky. They're amazing at some things, and poor at others. Over time, the set of things they're amazing at has grown, while the set of things they're poor at has shrunk. If you think LLMs are just "mediocre" without any nuance, that's a sign you haven't spent the time to evaluate them in order to make an informed opinion.
I really don't think that's how it works. Smart, experienced developers who thought coding agents were junk for most of 2025 and think they're useful now in 2026 are not saying that because they got "worn down over time".
It tends to be when the training data wanders into their area of expertise temporarily and they go “OMG, they hype is real. I was so wrong” and then a few releases later they’re on the train and furious that the skills in their domain space have not just stopped improving, but regressed. Cue someone else in a different part of the world starting the same cycle.
Meanwhile the guy who leaned in a year ago and gave up reading the output is beginning to see work grind to a halt and throwing more agents at it is increasingly not working.
You can see these tropes all over social media near constantly.
I was getting useful coding work done with GPT 3.5. I think devs saying "the models are finally good enough" this year are just trying to save face from their own previous irrational denials.
Useful, yes, sometimes, but it wasn't fully automated "Here's our JIRA board URL, fix everything that's rated 1-3 story points and in the current sprint".
I didn’t start using Claude Code until late 2025. Prior to that I would use ChatGPT to give me snippets of code but I was still doing most of the actual code writing. Coworkers told me in late 2025 about how they hadn’t written a line of code in “months” and just use Claude Code/agentic “whatever” so I tried out Claude Code and was pleasantly surprised. It is passable to have entire apps written by LLMs (I’ve made several that I otherwise never would have had the time to create by hand), but I wouldn’t say maintainable or easily extendable. It’s hard to be specific, but there’s something about LLM code that doesn’t look “natural”, and I’m not talking about the excessive use of comments in code. The code itself is unnatural. Functional, but unnatural. I wouldn’t want to suddenly lose LLMs and have to read through and understand and continue enhancing a codebase created by an LLM.
For me it feels a lot like generated images or video. I've made lots of things now, but those that are 100% LLM written "work" but are uncanny, weird and the details are wrong everywhere you care to look in detail.
Nobody was saying coding agents started working in 2023 or 2024, because the category was defined by Claude Code which was first released in February 2025.
I would say that Aider is what defined coding agents. That was at least multiple months before Claude code. I remember seeing a coworker use aider for a hackathon project Adeline November-December 2024 , and it was already decent and pretty close to the DX we consider coding agents to have
Devin and Cline were the original true "agents" IIRC. Aider was the original terminal "agent" but it was't a true agent, it was sort of a hybrid agentic chat, since it had limited recursion ability (3 turns by default) and you had to configure it carefully to make it consistently take multiple turns in a row.
Claude 4.5 was it (nov 2025?), without a doubt. It went from frequent hallucinations to highly usable with much less garbage output. If you were making demos of AI tools around this time your demo/pitch/product was saved and you probably looked like a genius.
The quality of the output is so variable. It depends on the model, “effort level”, prompting, probably even the programming language/app functionality, and libraries involved. For example, I find LLMs are best at making simple web apps. These web apps, while simple, would still take a senior engineer perhaps a week or two to create, but LLMs can spit them out inside of an hour. Conversely, LLMs struggle with things like Docker or local model stuff. Parallelization of code is a mixed bag. In these areas I think it often would have been faster for me to write the thing by hand.
"unless you're content with producing low quality work." - With the right guiding hand, it is a productivity multiplier without compromising quality. As a fully autonomous developer, it is a disaster.
How are junior devs becoming qualified “guiding hands” these days? If the expert with LLM assistance is multiplied, what’s a company’s incentive to pay for a junior, and how would they train to get good in these conditions?
> With the right guiding hand, it is a productivity multiplier without compromising quality
This just reads like another variation of “it’s the user not the tool,” which is just endless runway for always blaming people and never acknowledging the limitations of LLM’s.
I’d be curious to hear how the recipients of your work enabled by the “productivity multiplier” feel about the quality.
You can't play an entire orchestra's sheet music on a single guitar either, but your playing ability still matters a lot.
I would say that as of July 2026, with the right scaffolding, you can get reasonably good output out of a LLM, or better a combination of LLMs. For example, it pays off to prepare an implementation plan with one LLM and then let another LLM check it for flaws, then again. After several iterations like this, you will have a plan better than whatever you could come up with yourself.
It often is the user and not the tool. LLMs are complicated, have nontrivial failure modes, and the user needs to steer them carefully. They might be the most complicated tools on the planet right now.
Anecdotally, the recipients of my work have become visibly more happy in the last months. LLMs are great at diagnosing subtle problems which tend to appear at Friday night only, and this is the sort of problem that bugs actual people the most.
I haven't. Around the start of 2026 is pretty widely mentioned as when they went from "this is broken slop" to "huh this is actually 90% what I would have written", which matches my experience.
Anecdotally I'm seeing a lot more recruiter activity/interest now than this time last year.
But it seems more correlated with hype-cycle-stage than anything else. Right now a lot of founders seem to be convincing a lot of VCs that they can make $LOTS by replacing/changing $BIG_INDUSTRY/$BIG_PRODUCT with an agent-first blah blah replacement, and then using that money to hire more people to manage/execute/coordinate the coding agents...
Last year, by comparison, there seemed to be a mood of "software will stay the same but will require less people" while right now there's a lot of hype around "we can build different types of software or build it in different ways" and those early-stage things are in growth-mode. That guarantees nothing about how many people they'd need in the future, or their success at all, ofc.
The news that I'm getting from contacts in non-startup-land is a bit different - still layoff threats. Still pressure to use AI tools more. Mixed confidence on whether or not longer-running "agent" modes are that much more effective-without-breaking-things in legacy code if not used with care.
However, companies have been using AI as an excuse for layoffs since well before January 2026, which corroborates the study's conclusion. (Source: https://layoffs.fyi/ai-layoffs/) There is certainly an uptick starting 2026, but that could be explained either by AI actually causing more layoffs, or by AI becoming an even better excuse for layoffs.
And yet I have felt a general code quality decrease and overall enshittification of software products since 2023 when people were already using copilot. Or I am just biased to use that to justify any overengineered piece of shit code with that because I refuse to think any sane person would come up with such contrived code and I am looking at the wrong places.
This isn’t the first study showing this though. It’s pretty simple, programmers and IT were severely overhired during the pandemic, there are massive job losses now, and it’s easy to blame AI when in reality there are a lot of economic factors and AI isn’t increasing productivity as much as anyone would think.
Maybe the future will change that for very specific things, but I think people should be learning and preparing for that, which isn’t any different than what everyone has been told in every job market since the start of the Industrial Revolution.
I personally hope that AI continues not to result in a noticeable negative impact on employment and that pandemic over-hiring turns out to be the major factor for all of the layoffs.
I'm nervous that the studies which show that so far don't seem to be taking the 2026 improvements in coding and general agents into account.
What the heck would I be "stealth-marketing" here?
It's genuine concern. I do not want to live in a dystopia where AI results in mass unemployment. That would suck, even for the people who manage to stay employed.
> What the heck would I be "stealth-marketing" here?
Cynically, "thought-leaderishness".
I've been spending a lot of my time these days outlining why we can't "just make an agent for it" to CEOs who read blogs like yours. They can't distinguish a production system from a quick HTML tool from a guy whose job doesn't depend on it working.
One of the goals of my blog is to help CEOs who read it not make stupid decisions. I aim to be a counter to the breathless LinkedIn hype they are exposed to everywhere else.
>programmers and IT were severely overhired during the pandemic
1) I hesitate to believe that losses were disproportionately technical roles as opposed to administrative.
2) Over-hired by what metric? It's well known that hiring never fully recovered after the GFC; was the recruitment post-pandemic just bringing us to parity with where we had been 20 years earlier?
Not to say that I disagree with your following point. The AI overspending and the layoff cost-cutting are not in a direct causal relationship; both are rather symptoms of a common corporate pathology.
This. Also I'm finding out that, after being blown away by agent mode lately, non-agent mode still kind of sucks across frontier models. Using GPT and Gemini in non-agent mode is asking for inaccurate information confidently presented as the truth. Turning on agent mode fixed a lot of that for me.
I think it's because of the lack of feedback. Humans also can't do much without feedback. E.g. I doubt most people could write 100 lines of code that works first time without even compiling it once.
Even more extreme than that, if you were working on a large code base where compiling it took forever, or you needed to rely on a very slow CI pipeline, it became very important to git gud and write shit that worked first time in order to deliver when promised. I'd argue if you've never had one of those moments where you sunk a bunch of time into some changes and it all compiled/worked flawlessly the first time, you're missing out.
For whatever anecdotal evidence it's worth - that has been my experience as well. For the first time last December I noticed the harnesses performing like actual workers.
Everything impressive has happened in the last six months.
Any research on the impact of AI would have been lagging indicators. It's not the researchers' fault[0], but the nature of a field moving at neck breaking speed. Remember there was a paper saying programmers were 20% slower with AI?
That early 2025 METR study was particularly interesting because participants self-evaluated themselves as 20% faster, but the measurements showed they were actually 19% slower.
All the reports of productivity since then are self-reported, or using questionable measures such as SLOC and PRs, so it’s reasonable to say that productivity improvements are still unknown.
Unfortunately, METR hasn’t been able to replicate the study because they couldn’t find enough willing participants.
And this is why the labs cannot just "stop training and become profitable". I can't imagine they would like it if studies like this will actually become credible.
"Move fast like a blur so people can't see that you have no clothes"
Well, in addition to that you also have to consider that companies are now paying (increased) API pricing. It would be an understatement to say that my clients in the F100 range are skeptical at best regarding the gains they’ve seen compared to the costs.
This has led to many of them instilling dollar limits or demanding proof of increased productivity (not just output) with the implication being if you don’t provide value with it it’s getting taken away.
So that is to say, if they aren’t happy with the price now, how will they feel when it goes up again compared to just keeping a certain headcount?
Oh wow. You just hit something there. There are government programs for R&D where I live. Grants. Tax credits. This sort of thing. I wonder if people are trying to expense to those programs too.
>This has led to many of them instilling dollar limits or demanding proof of increased productivity (not just output)
They should have done that from the beginning - demanding proof of increased productivity - if that was their goal. otherwise they were not using their brains well enough.
And you doubly don't want to work with them, first because they confused output with productivity at first. and second, because they're parroting the productivity metric.
You only need one guess for whose pockets the productivity benefits go into.
When I was a teenager, a friend had an old gas guzzler from the 70s. I live in a rural area. One time, my car broken, he drove to pick me up to go to College.
This cost him an extra $40, in today's dollars. No, I'm not joking. That thing ate gas like a dry camel drinks water.
This is what Fabel5 feels like. Crazy expensive. 10 minutes work pulled almost $80 is usage credits yesterday. I'd be exceptionally skeptical too, on costs, if I still had the DEV I had last week, but they were also eating that kind of cash on a very-improved, but still used as a linter.
For $200+/hr, or ~$400k/year, I'd want to see a tripling of output at least. In a lot of US markets, you can hire 3 junior devs for that.
Yes, there are cheaper options. Opus, etc. But it's really over-priced, and frankly I think the real gold now is making open models fully functional. Anyone predicating their business upon tie-in with the big boys is just going to fail, hard.
Recently poked around the job market to see what I qualify for in this day and age. Working as a solo builder in my org I would say that I have done enough in the last 18 months to consider myself “with it”.
What I found was pretty brutal. Companies asking for 4 years of agentic AI experience… pardon?
Then it hit me.
Oh they are all making shit up now and have no bar that anyone can hit because they are believing in the hype without understanding the fundamentals.
GREAT. Even as I climb the AI-Native ranks, I apparently am unqualified for any AI-Native job.
If you were screwing around with Auto-GPT in 2023, you'll hit four years of "agentic" experience next year.
Of course, there's not much overlap between that and the way it works now. But then again, I have the same feeling about last year and this year... (e.g. Anthropic just deleted almost their entire system prompt because the models have common sense now.)
That being said, I think there's still value, even today, in playing around with the older models from time the time. (Or with very small recent models, which have similar limitations.)
Some of the habits that teaches you — i.e. careful context management and well crafted examples — do translate well to the modern environment, and give you performance gains and cost savings even with newer models. (In a word, whenever possible, show, don't tell.)
I also lament the loss of the base/text models, which were extraordinarily interesting and fun to play with. But that's a separate discussion :)
If decision-makers put up a front of recruiters and talent acquisition "specialists", don't they send a very explicit message that going past those is unwelcome and won't be considered?
Just in case, because a lot of people don't know this... When a company lists job requirements they aren't really requirements. Don't skip a job because it says you need X years of Y but you only have X-1.
They're basically writing down a wish list. They don't expect to get it or necessarily even care that much about some of the points.
Also "X years of experience" isn't really asking for literal years. It's a proxy for skill. They mean "as good as the average person who has been doing this for X years". If you're really good at it and can demonstrate it, that's good enough.
yeah the lead lecturer on my data engineering masters once told us about a job posting he saw asking for 8 years of Hadoop experience, even though Hadoop had only been publicly available for 5 years at the time.
it's often HR / hiring managers throwing some numbers into a text document based on what they've heard is important for the role, not what you'll actually need for the job.
similarly, something like "has previous experience with kubernetes" doesn't usually translate to "knows absolutely everything there is to know about kubernetes". it means "you've used it at least once, ideally more, but can at least talk about when / how you used it / what problems it solved and could probably get up to speed on it fairly quickly when you join and/or in the time before you join" (kinda writing this last bit about an interesting job posting i saw that i've been talking myself down on and i really ought to be doing the opposite).
If a tech company can't even do basic math — or rather, openly cares so little about accuracy — does that sound like a place you would want to work though?
this sort of thing happens more often at larger non-tech enterprises, or places that have a type of "software team" that is not the main focus of the org.
> Companies asking for 4 years of agentic AI experience… pardon?
Not that I am trying to excuse it, but this is not a new thing, nor specific to AI.
Job listings that ask for X years of experience where X years is sometimes literally longer than the technology has even existed has been a staple complaint of developers over my entire career, and I'm old af.
A 10x engineer only needs about five months of experience.
So, leave college/uni with your "Desmond" (1) in comparative pornography in Feb 2026, buy a PC/Apple and by now you will be writing Windows Entra 2027 on your own.
bro, tech hr were asking 6 to 8 years rails experience before dhh (rails creator) was even born.
This has been almost a meme on hacker news for some time.
You can google it via hn dot algolia dot com by using the right keywords.
Of course, i exaggerated it a bit, just like a lot of startups and vcs pimp their stuff, just that they do it much more, and they do it for money, while my mine was for fun. ha ha ha.
Organizational inertia is a real thing. There are still fortune 500 companies with internal bans on AI. A lot of the answer to "how much impact has AI had" comes down to "how much have we even attempted?"
In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week.
The impacts are here, they're just not evenly distributed yet.
> In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week.
This is curious to me, as at my workplace we never had such subscriptions for small- to mid-size stuff and always built the corresponding tooling in-house.
> In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week.
The impacts are here, they're just not evenly distributed yet.
Interesting to see the impact in the long term when battle tested software gets replaced with vibecoded variants by non-programmers. Does it increase data breaches or quality actually goes up?
The other day, i was fed up with my mac webcam software. I just needed to fix being washed out. Every piece of software I found cost money, several were a monthly subscription.
Over lunch, Claude made a minimal toolbar app that lets me adjust down the brightness.
Often, you don't need battle tested. And you don't need a bunch of features.
The ai could have shipped my video feed somewhere I suppose, if I were unable to read its code.
I think Sturgeon's law would tell us that everything will stay about the same.
But in reality, a lot of corporate software exists just because there are plenty of companies who are afraid of owning code. They don't want to maintain any in-house coding skills, and therefore are willing to buy literally any vaguely-relevant CRUD app that the manager heard about at the conference. I don't think replacing that class of software with vibe coded alternatives will be any worse, because the bar is starting on the floor.
There are entire software categories that consist entirely of code that is only one or two evolutionary steps away from some engineer's spreadsheet originally written in 1995. One fine example I work with has changed its backend database 3 times in the past 4 years. Their most recent decision to use mongodb came with the questionable decision to store json as a raw string literals complete with bizarre escaping inside a database literally designed to store json-shaped-objects.
I don't think Opus could store data that poorly, even if the end user prompting it didn't know what they were doing.
One of the upside I can foresee is the return of single payment software. No one is going to be paying subscriptions when they can LLM themselves, but paying someone else to LLM for them would let them shrug off accountability for malfunctions
To me, this is inline w the book BULLSHIT JOBS, and that class of job that was really like a few hours a week but 40 hrs pay. That type of job should be automated and the person removed. The exception is going to the person who complained to management that it’s a 2 hr a week job…. Give me more work.
> There are still fortune 500 companies with internal bans on AI.
And there probably always will be.
When the choices are "hand all of our highly sensitive internal data over to one of several other companies, all of which have questionable financials and very cozy relationships with adtech" or "invest in a whole bunch of expensive GPU servers plus internal talent to run our own models", vs "keep on doing what we've been doing, which is still working just fine", why would a non-tech Fortune 500 company choose either of the former options?
Thanks for sharing this. I can well imagine that managing field tech rounds is plausible if it is limited to in-house use. I assume that is planning, documenting and scheduling of field trips with a front-end for the planning and some way to show on mobile device or laptop for the tech?
Is instrument spec sheet management focused on storage and presentation, and occasionally updating values such as service interval, calibration dates etc ? If so I also find this plausible.
Both use cases are focused in terms of use case complexity (especially if your company is focusing on your requirements only as opposed to software vendors covering variations), low in complexity in terms of involved parties, and inter-system boundary crossings.
Very interesting. The spec management is probably the higher risk use case, but I assume you have proper engineering review and a tight test strategy to control this aspect.
Employees have no incentive to meaningfully implement AI to increase productivity, and if they do so, they have no reason to share it.
If AI means job cuts and not using AI means job cuts but no one really tracks AI impact that means performative adoption of AI is safest, and real gains are to be sandbagged as innate magic hand waving.
At my work I'm one of the few who says "I made this with Claude" and the near impossibility of using AI with internal email etc for security reasons means AI use is one-shot wonder oriented (for me, in my experience).
If AI usage was incentived with actual bonuses and praise it might go over better. So far I haven't seen that. Its just implicit threats.
This was observed on first week for me. In the employee room, most people were all crazy about LLMs, looking at the prompt results in awe, "I can do my day in 1h easy..." . By the end of the week, group meetings were much more tame "yeah I can go a bit deeper than I would otherwise, surely saves me a few hours a week". And after the meeting was over these employees were already saying that they would never report the real productivity boost, because that would be suicide.. more trash talk will fill the void instead.
Yes, I’ve found my coworkers won’t admit they had AI write their code in any sort of group setting yet they’ve all admitted it in private. The implication seems to be if AI is writing your code you’re lazy/incompetent/bad/not working anywhere near 40 hours/week. I for one would hate for our bosses to figure out we do probably ~16 hours of actual work per week. But we all “get our stuff done” (thanks to AI).
Anecdotal: I used to beg my boss to get some help. I wanted 5 engineers and would have been upset if anyone had been hired that wasn't an engineer until I at least got 1-2. That was up until about 3 months ago.
Now, I don't want any additional engineers. Not only because I don't need them anymore, but because the prospect of having junior engineers using AI is absolutely terrifying to me. I can't eyeball their code to get a sense of how good of an engineer they are anymore, and I can't possibly review all of their code because of how much code AI can output now. So they're going to be outputting a ton of mostly high-quality code that could be making horrible mistakes that are much harder for me to catch now.
I don't feel like reading an article that will probably be out of date in 6 months, but from what I've seen, if agents keep improving at this rate, 80% of SWEs are going to be looking for new careers in 5 years.
The problem is not necessarily job losses as such. When artisans were replaced by factory work, it took a long time for automation to actually reduce the number of workers. What happened quickly was that the asset of the artisans -their skill and experience - was replaced by an asset owned by the factory owner
-the capital equipment. This is the real threat today. All the people who have human capital in their skill and experience processing information, are finding that it's being slurped up by LLMs and other models. The question is whether "LLM operator" is really going to be a profession which requires scarce skills and experience, or whether many companies will be operable with less well-rewarded workers. I think that those who think there's an obvious correct prediction here are overconfident.
Probably both. We were already hitting limits in terms of finding software that could generate returns; without public funding or enormous regulation the possibility of finding new jobs with a degree is lower than it's been in a while.
Seems like all companies think that their products are good enough and can’t offer more, as they always want to remove people instead of offering better and bigger products.
I wouldn’t build my product with an LLM owned by a company at the core of it. That feels way too risky, it’s almost like building on a platform you don’t own. I expect we will see way more entrepreneurial growth once local models become good enough and the hardware to run them becomes more affordable than it is now.
(Though I’m biased, I personally feel it’s an anti-human technology, the world would be better without it)
Prior to the weaving loom and sewing machines, a factory might be able to make like 20 t-shirts a day with 50 workers. After mass production, factories generally employ the same amount of workers, but people aren't hand-sewing things. Instead roughly 50 people are producing thousands of tshirts a day leveraging giant machines.
Most of HN recognized the "We're firing people because AI makes people efficient" as one of the stupidest sales pitches ever and the CEOs that fell for it are just poorly ran companies that outed themselves.
AI is just another cycle in technology that is genuinely useful. The companies that are going to jump the gap are those that are hiring to use this new skill. If 10 workers pre-AI yields you 10x, and post AI yields you 100x, you don't cut down to 1 worker so you can keep delivering 10x. You invest, ruthlessly train, hire, and surge forward and leave your competition in the dust.
If there was 100x on the table for you, why weren't you already at 100x scale just with more headcount? That is the other side of this coin. Demand is a factor. Productivity goes up? Great, if there is demand to satiate that you can meet. I doubt that is the case otherwise you'd already have scaled to meet it if it were actually on the table for the taking if only you could output more volume.
The sewing machine analogy is a good one. The problem I'm seeing is leadership who sees the t-shirt company succeed with sewing machines, so goes out, buys 100 sewing machines, then puts them in the kitchen because they are a bakery and gets confused why the bread production isn't improving.
Because AI can do some things. Not everything. Applying it to the wrong problem reduces productivity.
The problem is that these two statements each have massive implications, so instead of treating these findings as point in time snapshots they are the whole ballgame and should be explored in depth.
> Dario Amodei, CEO of Anthropic, has predicted that AI could wipe out half of white-collar jobs and push unemployment to 20 percent.
And then he also says that a certain model is too dangerous to release.
A challenge with this kind of study is that coding agents (Claude Code, OpenAI Codex) only started working really well in late November, which for most people meant early January due to the December break.
General agents (OpenClaw, Anthropic Copilot, ChatGPT "Work") started working even later than that.
This category of software may have a much more meaningful impact on work than the mostly-chat systems we were using from 2022-2025.
Studies that mainly focus on 2022 to end of 2025 might be missing out on a material uptick in capabilities.
> This category of software may have a much more meaningful impact on work than the mostly-chat systems we were using from 2022-2025.
Mostly an impact on software development - I'm not seeing broad automation and layoffs in industries like law, finance etc. It will gradually happen but due to issues with memory, reliability and long term planning of LLMs there are real barriers. Even in software development - while it has completely transformed the field I don't think many people still believe we won't need devs in 2027 or that their amount will shrink by 50%.
Anecdotally, I (along with the rest of my team) got laid off from a big tech company at the end of January. The stated reason was "AI", although I think as we all know the real reason was "we overhired in 2022". I managed to get a job by April without putting in a lot of work, though (mostly just listened to recruiters until something hit my fancy). My work experience is good, but I don't know if it's so good that I would be immune to market effects. The company that hired me is still hiring other software developers, and I was told that it took them a long time to find someone with my qualifications. (We use Claude, although so far I haven't seen a lot of AI psychosis like I have in other places.. that was also a big filter in my job search). Long story short, I think narrative of job loss is way overblown. It's more doom trolling from anthropic and openAI and their mouth pieces as far as I can tell.
It's so early game, is the real problem. My experience so far, has been that it's barely a junior dev. I've met so many in my career that think reading stackoverflow, or watching a youtube video mysteriously makes them an expert. AI reminds me of this sort.
Well anyone can use (prior to AI) a simple linter and learning to code isn't that big a deal. It's learning the pitfalls, the traps, that's the issue. And so far Opus just seems to fall into them again and again. I guess the best way to put it, is that it's not an architect. I sees no big picture, and that's not really a surprise with (compared to a human) an incredibly small context window. When I'm on a project, or working with a codebase, I often have years of "context window". And I have a career of "don't do this" context window.
So what I wonder is, will this be resolved? Will that awareness of larger scope be solved? If that happens, we'll be in another ballpark of competency.
Some companies have massive codebases. Are these companies slowly gaining rot in those codebases, a swiss cheese effect, which eventually will result in collapse? Because I've worked where a bad hire had just this effect over time. And what I worry about isn't using Claude to speed one up, it's the DEV that uses Claude and just "meh" and submits because it passes regression + other tests, and then a manager or code reviewer uses Claude and "meh" because it's a pass too.
> Are these companies slowly gaining rot in those codebases?
Yes!
And that "yes" holds regardless of whether they use AI or not.
Let's not pretend tech debt accumulation is somehow an AI problem. Some of the world's biggest companies routinely ship code that reeks of years of rot and decay.
This is usually due to time pressure and cost. AI arguably substantially reduces that kind of pressure.
I’m overall skeptical as well, but the argument about tech debt is more nuanced than your comment implies.
> This is usually due to time pressure and cost. AI arguably substantially reduces that kind of pressure.
This is categorically false because time and cost pressure is relative and if anything AI has exacerbated the exceptions for ROI and Turn Around time.
This along with the fact that even state of the art AI tends to generate subpar (albeit working) code extremely fast and large volume.
You can see the effect of this already in the increase rate of production incidents recently.
This isn’t to say that AI is useless, but the tech debt it generates is unprecedented.
> A challenge with this kind of study is that coding agents (Claude Code, OpenAI Codex) only started working really well in
2026? 5? 4? 3?
Heard this one way too many times.
I get the point having read much the same from Tesla (and fans) regarding self driving cars that still haven't done half the things that Musk said was just around the corner pending regulators a decade ago and repeatedly since then.
And myself I keep making comparisons between AI and the progress in 90s video games where every minor improvement got called "photo realistic" and then forgotten with the next game engine: https://archive.org/details/nextgen-issue-26
So I'm not gonna say "this is it" when the software quality really matters, and I absolutely won't speak to progress (or lack of it) outside of software.
But I will say "you can look around and easily see small businesses using AI to generate posters, quite a lot of small business software and websites are in the same category: the mistakes are real but increasingly don't matter".
>the mistakes are real but increasingly don't matter...
I think it would start to matter once again. People will get fed up of AI posters and art. I think they already are...and once some threshold is crossed, the business won't dare to use AI generated assets/designs.
Turns out humans are much better at recognizing patterns in stuff that is generated ONLY using patterns from human generated content.
> People will get fed up of AI posters and art. I think they already are
Agreed, but will this look like a meme/fashion cycle? If so, re-prompt each year with a different look. Yes, there are still issues here, a friend found an image he was amazed was AI generated, but to me it was obviously so, so I showed him a screenshot of ChatGPT making something just it and included my prompt:
As I said to them: > Turns out humans are much better at recognizing patterns in stuff that is generated ONLY using patterns from human generated content.We're better at recognising patterns full stop. All biological brains are, and needed to be better than the current state of the art in machine learning because if a living organism was as poor at learning patterns as the SotA in machine learning, the organism would starve to death before being able to pick up anything and eat it.
AI also has a second disadvantage, because there are so few models: the laziest of ChatGPT "thinkpiece" blog posts being everywhere is hard to miss, and 5000 fake bloggers all prompting the same model with "find biggest news story of today and write a blog post about it in a way that maximises my ad revenue" will get 5000 almost identical posts. This will remain true while each instance of the most commonly used AI fail to talk to each other in a way that at least mimics them collectively getting bored with writing the same thing 5000 times, it does not depend on e.g. quality.
Will? They already have.
I’m probably illustrating your point but as a FSD fan it really got ”good enough” recently with version 14. The tipping point was suddenly, much more often than not, it can drive end to end from start (my garage) to finish (parked at destination) with no interventions. I can text and watch videos on my phone and as long as I glance up once a minute, it doesn’t complain.
Handling highway driving with lane changes was great when it got there years ago, but just in the last year or so it has gone from a nice to have to “from now on I will never buy a car that can’t do this”.
AI has hit some milestones for replacing work as well. There’s still many more to go and maybe some of them will never get hit (much like I don’t think a coast to coast drive with zero interventions during winter conditions is ever going to happen) but there are points at which it forever meaningfully changes some field of work. I think it’s there for writing code.
Please don't text and watch videos when you drive.
> I’m probably illustrating your point but as a FSD fan
Half-and-half. I'm not denying that self driving cars (and LLMs) are improving, I'm comparing it against the standards set by the biggest proponents. But yes, I have heard basically the same thing you just wrote for the previous several major releases of FSD.
Where we agree is that, while you are a fan, you do explicitly give as an example of something you think it will never do, something very close to what Musk has promised:
- Musk, in Jan 2016: https://en.wikipedia.org/wiki/List_of_predictions_for_autono...(That said, I think Tesla's FSD will never get there, not that it's impossible. The way Musk is behaving, there's going to be a financial scheme named after him in whatever passes for a textbook in 20 years, and it won't be the positive kind of example).
Who is responsible for any accident happening while you use your phone during FSD?
It's not FSD until the human is no longer responsible.
This half measure bullshit is a joke.
I digress with your last quote, I feel like some people who are aware of AI are starting to develop a quasi allergic reaction to slop, and while the mistakes might not matter for most of the population, for others it does and will take notice.
November 2025: https://simonwillison.net/tags/november-2025-inflection/
I guess it's important who one hears this from.
I just spoke to a fried who is a headhunter and who's been trying to automate his processes for a while (he likes to fiddle and certainly has skills, but he's not an engineer). He kept trying, but it just wasn't good enough.
Now he said with GPT Work and Sol, it worked, but the key point is: all of it suddenly worked.
The problem was one of reliability, of handling edge cases. All previous attempts / model-harness-combinations were too brittle and needed too much observation and fiddling - cheaper to do it yourself.
Now he says "I don't know why I would ever hire a recruiter [the folks doing the cold outreach] again. I can focus on the candidate screening and acquiring projects, everything else is fully automated".
This doesn't come from an engineer or an AI lab, but a technically inclined power user, and I think this is where things get interesting.
You heard it from someone with no experience developing software. A lot of AI hype comes from that, even (or especially) from people in the actual business of developing software - a surprising amount of managers and adjacent or supporting roles in the field actually have very little clue about software development.
It's cool that 'regular' people can now create solutions to many small problems, and automate stuff - genuinely a step forward. Like Excel, only vastly better. But for bigger projects, real software engineers know that what LLMs do today is only a tiny part of development. And it solves it in a way that might well make the rest of the lifecycle a lot harder. It's like that saying about tools that make easy things easier and hard things impossible.
Then it just becomes a new baseline (everyone have access to the same LLMs), and recruiting moves up the philosophical ladder where human can add more value. What will it be? I don't know, I'm not a recruiter.
Again I've heard this since 2022 when gpt3.5 came out.
This is like microprocessors in the 80s. Sure they double in capability every 18 months but the start is so pathetic it will be 30 years before they are good enough for everyday tasks.
CPUs in the 90s were amazing! They were over-specced for "everyday tasks." Our problem is that we overbuilt CPUs too much, so software is now written with ten unnecessary layers of abstraction because there's no real reason to simplify.
The timescale is well established: Late '25 was the start of agentic ai when capabilities of model + api + scaffold reached autonomous state. Any study comapring events before that timeframe is comparing apples with oranges.
It seems to be true this time though; I have observed it myself and heard it from several experienced developers I personally know and respect. It feels like some threshold was crossed with Opus 4.5 and Gpt 5.3, where the models are now able to reliably solve certain classes of problems that were previously unreliable.
Time will tell of course, and it’s early, but inflection points do exist with progress.
Thing is. You can find an extremely similar paragraph written about Claude 4.x or some equivalent gpt. And simultaneously, many people expressing their frustration and the shortcomings of <insert any model>
“But it’s different this time” - several people, several times over the last couple of years.
This is not at all a dig at you, I’m very sorry if it reads that way. My point is these things only get truly better in anecdotes. The ways in which they fail is yet to change. Just yesterday I had gpt 5.3 generate completely awful code for the Cinema 4D Python API. Also an anecdote. But for all of the people saying they are truly intelligent and truly reason, they still make obvious mistakes, write around problems, fail entirely at architectural decisions, fail at random, generate FAR too much code.
And no amount of harnesses, methodologies, loops make much of a difference. If you listen to people on the internet they say it’s all working. You listen to people on the job and they mostly say it’s creating tech debt and a review bottleneck. Also burnout, so much burnout.
I think LLMs are mediocre. I think it’s fine they’re mediocre. You can work with low expectations. But the hype cycles are so tiresome.
LLM capabilities are spiky. They're amazing at some things, and poor at others. Over time, the set of things they're amazing at has grown, while the set of things they're poor at has shrunk. If you think LLMs are just "mediocre" without any nuance, that's a sign you haven't spent the time to evaluate them in order to make an informed opinion.
I believe it is a widely accepted opinion that agentic coding took off with Opus 4.5 late in 2025.
Why would you attempt to use GPT 5.3 to generate code today and form an opinion on that basis?
I do not think it is even still available in Codex, I believe it only has the smaller, distilled GPT 5.3 Codex Spark.
If it's so evident, why can't someone prove it with something more than "it seems better and everyone agrees"?
I wonder how much of it is real and how much of it is people just being worn down by the hype to the point they can't fight it anymore
Very smart people aren't immune to being worn down over time
I really don't think that's how it works. Smart, experienced developers who thought coding agents were junk for most of 2025 and think they're useful now in 2026 are not saying that because they got "worn down over time".
It tends to be when the training data wanders into their area of expertise temporarily and they go “OMG, they hype is real. I was so wrong” and then a few releases later they’re on the train and furious that the skills in their domain space have not just stopped improving, but regressed. Cue someone else in a different part of the world starting the same cycle.
Meanwhile the guy who leaned in a year ago and gave up reading the output is beginning to see work grind to a halt and throwing more agents at it is increasingly not working.
You can see these tropes all over social media near constantly.
As a smart, experienced developer who's getting worn down over time, I disagree.
I was getting useful coding work done with GPT 3.5. I think devs saying "the models are finally good enough" this year are just trying to save face from their own previous irrational denials.
Useful, yes, sometimes, but it wasn't fully automated "Here's our JIRA board URL, fix everything that's rated 1-3 story points and in the current sprint".
Now it is.
I didn’t start using Claude Code until late 2025. Prior to that I would use ChatGPT to give me snippets of code but I was still doing most of the actual code writing. Coworkers told me in late 2025 about how they hadn’t written a line of code in “months” and just use Claude Code/agentic “whatever” so I tried out Claude Code and was pleasantly surprised. It is passable to have entire apps written by LLMs (I’ve made several that I otherwise never would have had the time to create by hand), but I wouldn’t say maintainable or easily extendable. It’s hard to be specific, but there’s something about LLM code that doesn’t look “natural”, and I’m not talking about the excessive use of comments in code. The code itself is unnatural. Functional, but unnatural. I wouldn’t want to suddenly lose LLMs and have to read through and understand and continue enhancing a codebase created by an LLM.
For me it feels a lot like generated images or video. I've made lots of things now, but those that are 100% LLM written "work" but are uncanny, weird and the details are wrong everywhere you care to look in detail.
Nobody was saying coding agents started working in 2023 or 2024, because the category was defined by Claude Code which was first released in February 2025.
I would say that Aider is what defined coding agents. That was at least multiple months before Claude code. I remember seeing a coworker use aider for a hackathon project Adeline November-December 2024 , and it was already decent and pretty close to the DX we consider coding agents to have
Devin and Cline were the original true "agents" IIRC. Aider was the original terminal "agent" but it was't a true agent, it was sort of a hybrid agentic chat, since it had limited recursion ability (3 turns by default) and you had to configure it carefully to make it consistently take multiple turns in a row.
Perhaps the LLM companies need to start hiring true Scotsmen?
University of Edinburgh is a good school.
I’ve been feeling gaslit about this too. Getting major “we’re still early!” crypto bro vibes from this constant goalpost moving.
The „you will be left behind if you don’t fully embrace the whole thing right now“ is a 1:1 match with cryptocurrency hype
Claude 4.5 was it (nov 2025?), without a doubt. It went from frequent hallucinations to highly usable with much less garbage output. If you were making demos of AI tools around this time your demo/pitch/product was saved and you probably looked like a genius.
Yep, the goalposts just keep shifting. In reality: they still don't work well, unless you're content with producing low quality work.
This is the opposite of my experience since about February of this year.
The quality of the output is so variable. It depends on the model, “effort level”, prompting, probably even the programming language/app functionality, and libraries involved. For example, I find LLMs are best at making simple web apps. These web apps, while simple, would still take a senior engineer perhaps a week or two to create, but LLMs can spit them out inside of an hour. Conversely, LLMs struggle with things like Docker or local model stuff. Parallelization of code is a mixed bag. In these areas I think it often would have been faster for me to write the thing by hand.
What model struggles with Docker or local model deployment?
I have had good results in that area with GPT 5.5 in the past.
On the subscription plan I don't use anything but xhigh effort and Fable, 5.6 Sol, or now also Opus 5.
Is that the class of model that struggles with Docker for you?
"unless you're content with producing low quality work." - With the right guiding hand, it is a productivity multiplier without compromising quality. As a fully autonomous developer, it is a disaster.
How are junior devs becoming qualified “guiding hands” these days? If the expert with LLM assistance is multiplied, what’s a company’s incentive to pay for a junior, and how would they train to get good in these conditions?
> With the right guiding hand, it is a productivity multiplier without compromising quality
This just reads like another variation of “it’s the user not the tool,” which is just endless runway for always blaming people and never acknowledging the limitations of LLM’s.
I’d be curious to hear how the recipients of your work enabled by the “productivity multiplier” feel about the quality.
You can't play an entire orchestra's sheet music on a single guitar either, but your playing ability still matters a lot.
I would say that as of July 2026, with the right scaffolding, you can get reasonably good output out of a LLM, or better a combination of LLMs. For example, it pays off to prepare an implementation plan with one LLM and then let another LLM check it for flaws, then again. After several iterations like this, you will have a plan better than whatever you could come up with yourself.
It often is the user and not the tool. LLMs are complicated, have nontrivial failure modes, and the user needs to steer them carefully. They might be the most complicated tools on the planet right now.
Anecdotally, the recipients of my work have become visibly more happy in the last months. LLMs are great at diagnosing subtle problems which tend to appear at Friday night only, and this is the sort of problem that bugs actual people the most.
What kind of work are you doing and what do you consider to be quality or not?
Of course don’t let me assume, maybe you have a higher quality disproof for the Jacobian conjecture you could share with the class.
I haven't. Around the start of 2026 is pretty widely mentioned as when they went from "this is broken slop" to "huh this is actually 90% what I would have written", which matches my experience.
Anecdotally I'm seeing a lot more recruiter activity/interest now than this time last year.
But it seems more correlated with hype-cycle-stage than anything else. Right now a lot of founders seem to be convincing a lot of VCs that they can make $LOTS by replacing/changing $BIG_INDUSTRY/$BIG_PRODUCT with an agent-first blah blah replacement, and then using that money to hire more people to manage/execute/coordinate the coding agents...
Last year, by comparison, there seemed to be a mood of "software will stay the same but will require less people" while right now there's a lot of hype around "we can build different types of software or build it in different ways" and those early-stage things are in growth-mode. That guarantees nothing about how many people they'd need in the future, or their success at all, ofc.
The news that I'm getting from contacts in non-startup-land is a bit different - still layoff threats. Still pressure to use AI tools more. Mixed confidence on whether or not longer-running "agent" modes are that much more effective-without-breaking-things in legacy code if not used with care.
However, companies have been using AI as an excuse for layoffs since well before January 2026, which corroborates the study's conclusion. (Source: https://layoffs.fyi/ai-layoffs/) There is certainly an uptick starting 2026, but that could be explained either by AI actually causing more layoffs, or by AI becoming an even better excuse for layoffs.
The true scottsman period only started, everything before was just scotty junior.
And yet I have felt a general code quality decrease and overall enshittification of software products since 2023 when people were already using copilot. Or I am just biased to use that to justify any overengineered piece of shit code with that because I refuse to think any sane person would come up with such contrived code and I am looking at the wrong places.
This isn’t the first study showing this though. It’s pretty simple, programmers and IT were severely overhired during the pandemic, there are massive job losses now, and it’s easy to blame AI when in reality there are a lot of economic factors and AI isn’t increasing productivity as much as anyone would think.
Maybe the future will change that for very specific things, but I think people should be learning and preparing for that, which isn’t any different than what everyone has been told in every job market since the start of the Industrial Revolution.
I personally hope that AI continues not to result in a noticeable negative impact on employment and that pandemic over-hiring turns out to be the major factor for all of the layoffs.
I'm nervous that the studies which show that so far don't seem to be taking the 2026 improvements in coding and general agents into account.
Is this genuine concern or is it stealth-marketing? It's hard to tell when someone is so publicly benefiting off the hype.
What the heck would I be "stealth-marketing" here?
It's genuine concern. I do not want to live in a dystopia where AI results in mass unemployment. That would suck, even for the people who manage to stay employed.
there's wh0res with more dignity than you simon
I trust you when you say that.
> What the heck would I be "stealth-marketing" here?
Cynically, "thought-leaderishness".
I've been spending a lot of my time these days outlining why we can't "just make an agent for it" to CEOs who read blogs like yours. They can't distinguish a production system from a quick HTML tool from a guy whose job doesn't depend on it working.
One of the goals of my blog is to help CEOs who read it not make stupid decisions. I aim to be a counter to the breathless LinkedIn hype they are exposed to everywhere else.
Looking foward to the generated images of a CEO riding a toilet
>programmers and IT were severely overhired during the pandemic
1) I hesitate to believe that losses were disproportionately technical roles as opposed to administrative.
2) Over-hired by what metric? It's well known that hiring never fully recovered after the GFC; was the recruitment post-pandemic just bringing us to parity with where we had been 20 years earlier?
Not to say that I disagree with your following point. The AI overspending and the layoff cost-cutting are not in a direct causal relationship; both are rather symptoms of a common corporate pathology.
This. Also I'm finding out that, after being blown away by agent mode lately, non-agent mode still kind of sucks across frontier models. Using GPT and Gemini in non-agent mode is asking for inaccurate information confidently presented as the truth. Turning on agent mode fixed a lot of that for me.
I think it's because of the lack of feedback. Humans also can't do much without feedback. E.g. I doubt most people could write 100 lines of code that works first time without even compiling it once.
Disagree. When tool limitations meant that this was the way people had to work, many people could do this.
It was much more inefficient, because it's easier to find bugs after compiling or running the code. But it is perfectly possible.
Even more extreme than that, if you were working on a large code base where compiling it took forever, or you needed to rely on a very slow CI pipeline, it became very important to git gud and write shit that worked first time in order to deliver when promised. I'd argue if you've never had one of those moments where you sunk a bunch of time into some changes and it all compiled/worked flawlessly the first time, you're missing out.
For whatever anecdotal evidence it's worth - that has been my experience as well. For the first time last December I noticed the harnesses performing like actual workers.
Everything impressive has happened in the last six months.
It does feel like we are in a transition period, and its not clear what conclusions we can draw about any sort of "steady state" yet.
Any research on the impact of AI would have been lagging indicators. It's not the researchers' fault[0], but the nature of a field moving at neck breaking speed. Remember there was a paper saying programmers were 20% slower with AI?
[0]: well...
That early 2025 METR study was particularly interesting because participants self-evaluated themselves as 20% faster, but the measurements showed they were actually 19% slower.
All the reports of productivity since then are self-reported, or using questionable measures such as SLOC and PRs, so it’s reasonable to say that productivity improvements are still unknown.
Unfortunately, METR hasn’t been able to replicate the study because they couldn’t find enough willing participants.
Well… what
And this is why the labs cannot just "stop training and become profitable". I can't imagine they would like it if studies like this will actually become credible.
"Move fast like a blur so people can't see that you have no clothes"
Bingo.
Well, in addition to that you also have to consider that companies are now paying (increased) API pricing. It would be an understatement to say that my clients in the F100 range are skeptical at best regarding the gains they’ve seen compared to the costs.
This has led to many of them instilling dollar limits or demanding proof of increased productivity (not just output) with the implication being if you don’t provide value with it it’s getting taken away.
So that is to say, if they aren’t happy with the price now, how will they feel when it goes up again compared to just keeping a certain headcount?
Oh wow. You just hit something there. There are government programs for R&D where I live. Grants. Tax credits. This sort of thing. I wonder if people are trying to expense to those programs too.
>This has led to many of them instilling dollar limits or demanding proof of increased productivity (not just output)
They should have done that from the beginning - demanding proof of increased productivity - if that was their goal. otherwise they were not using their brains well enough.
And you doubly don't want to work with them, first because they confused output with productivity at first. and second, because they're parroting the productivity metric.
You only need one guess for whose pockets the productivity benefits go into.
10 . 9 . 8 . 7 . 6 ...
When I was a teenager, a friend had an old gas guzzler from the 70s. I live in a rural area. One time, my car broken, he drove to pick me up to go to College.
This cost him an extra $40, in today's dollars. No, I'm not joking. That thing ate gas like a dry camel drinks water.
This is what Fabel5 feels like. Crazy expensive. 10 minutes work pulled almost $80 is usage credits yesterday. I'd be exceptionally skeptical too, on costs, if I still had the DEV I had last week, but they were also eating that kind of cash on a very-improved, but still used as a linter.
For $200+/hr, or ~$400k/year, I'd want to see a tripling of output at least. In a lot of US markets, you can hire 3 junior devs for that.
Yes, there are cheaper options. Opus, etc. But it's really over-priced, and frankly I think the real gold now is making open models fully functional. Anyone predicating their business upon tie-in with the big boys is just going to fail, hard.
Recently poked around the job market to see what I qualify for in this day and age. Working as a solo builder in my org I would say that I have done enough in the last 18 months to consider myself “with it”.
What I found was pretty brutal. Companies asking for 4 years of agentic AI experience… pardon?
Then it hit me.
Oh they are all making shit up now and have no bar that anyone can hit because they are believing in the hype without understanding the fundamentals.
GREAT. Even as I climb the AI-Native ranks, I apparently am unqualified for any AI-Native job.
If you were screwing around with Auto-GPT in 2023, you'll hit four years of "agentic" experience next year.
Of course, there's not much overlap between that and the way it works now. But then again, I have the same feeling about last year and this year... (e.g. Anthropic just deleted almost their entire system prompt because the models have common sense now.)
That being said, I think there's still value, even today, in playing around with the older models from time the time. (Or with very small recent models, which have similar limitations.)
Some of the habits that teaches you — i.e. careful context management and well crafted examples — do translate well to the modern environment, and give you performance gains and cost savings even with newer models. (In a word, whenever possible, show, don't tell.)
I also lament the loss of the base/text models, which were extraordinarily interesting and fun to play with. But that's a separate discussion :)
Use it in your favor.
You just have to get past the recruiter/talent acquisition where everyone else is getting auto-rejected. You should be doing that anyway.
If decision-makers put up a front of recruiters and talent acquisition "specialists", don't they send a very explicit message that going past those is unwelcome and won't be considered?
Just in case, because a lot of people don't know this... When a company lists job requirements they aren't really requirements. Don't skip a job because it says you need X years of Y but you only have X-1.
They're basically writing down a wish list. They don't expect to get it or necessarily even care that much about some of the points.
Also "X years of experience" isn't really asking for literal years. It's a proxy for skill. They mean "as good as the average person who has been doing this for X years". If you're really good at it and can demonstrate it, that's good enough.
yeah the lead lecturer on my data engineering masters once told us about a job posting he saw asking for 8 years of Hadoop experience, even though Hadoop had only been publicly available for 5 years at the time.
it's often HR / hiring managers throwing some numbers into a text document based on what they've heard is important for the role, not what you'll actually need for the job.
similarly, something like "has previous experience with kubernetes" doesn't usually translate to "knows absolutely everything there is to know about kubernetes". it means "you've used it at least once, ideally more, but can at least talk about when / how you used it / what problems it solved and could probably get up to speed on it fairly quickly when you join and/or in the time before you join" (kinda writing this last bit about an interesting job posting i saw that i've been talking myself down on and i really ought to be doing the opposite).
If a tech company can't even do basic math — or rather, openly cares so little about accuracy — does that sound like a place you would want to work though?
this sort of thing happens more often at larger non-tech enterprises, or places that have a type of "software team" that is not the main focus of the org.
Personally, I would avoid "ai-native" companies like the plague; they seem like places full of burnout and delusional expectations.
> Companies asking for 4 years of agentic AI experience… pardon?
Not that I am trying to excuse it, but this is not a new thing, nor specific to AI.
Job listings that ask for X years of experience where X years is sometimes literally longer than the technology has even existed has been a staple complaint of developers over my entire career, and I'm old af.
My favorite in this regard is this story: https://x.com/tiangolo/status/1281946592459853830?lang=en
Sebastián Ramírez seeing a job requiring 4 years of experience with FastAPI, the library he created 1.5 years before that posting.
A 10x engineer only needs about five months of experience.
So, leave college/uni with your "Desmond" (1) in comparative pornography in Feb 2026, buy a PC/Apple and by now you will be writing Windows Entra 2027 on your own.
Profit!
(1) Tutu - geddit!
bro, tech hr were asking 6 to 8 years rails experience before dhh (rails creator) was even born.
This has been almost a meme on hacker news for some time. You can google it via hn dot algolia dot com by using the right keywords.
Of course, i exaggerated it a bit, just like a lot of startups and vcs pimp their stuff, just that they do it much more, and they do it for money, while my mine was for fun. ha ha ha.
Wow, fuck.
Literally some minutes later, i scrolled down below my above comment.
And saw this one.
https://news.ycombinator.com/item?id=49053201
Which doesn't validate mine, but agrees with what I said.
Except that it was posted about one hour before mine.
go figure.
double wow. yet another one on the same lines, as a reply to the link i posted above.
99% of the AI product pitches I see here are for some kind of marketing tool. Its really sad
This is so true. It has so much potential to help with humanity's actual problems. But no, lets use it to manipulate people even more.
The impact on job satisfaction for those with jobs will be negative too. People will be trapped in their positions, fearing to leave or move around.
I agree because it's already happening with me
may i join ?
Organizational inertia is a real thing. There are still fortune 500 companies with internal bans on AI. A lot of the answer to "how much impact has AI had" comes down to "how much have we even attempted?"
In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week.
The impacts are here, they're just not evenly distributed yet.
> In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week.
This is curious to me, as at my workplace we never had such subscriptions for small- to mid-size stuff and always built the corresponding tooling in-house.
> In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week. The impacts are here, they're just not evenly distributed yet.
Interesting to see the impact in the long term when battle tested software gets replaced with vibecoded variants by non-programmers. Does it increase data breaches or quality actually goes up?
The other day, i was fed up with my mac webcam software. I just needed to fix being washed out. Every piece of software I found cost money, several were a monthly subscription.
Over lunch, Claude made a minimal toolbar app that lets me adjust down the brightness.
Often, you don't need battle tested. And you don't need a bunch of features.
The ai could have shipped my video feed somewhere I suppose, if I were unable to read its code.
I think Sturgeon's law would tell us that everything will stay about the same.
But in reality, a lot of corporate software exists just because there are plenty of companies who are afraid of owning code. They don't want to maintain any in-house coding skills, and therefore are willing to buy literally any vaguely-relevant CRUD app that the manager heard about at the conference. I don't think replacing that class of software with vibe coded alternatives will be any worse, because the bar is starting on the floor.
There are entire software categories that consist entirely of code that is only one or two evolutionary steps away from some engineer's spreadsheet originally written in 1995. One fine example I work with has changed its backend database 3 times in the past 4 years. Their most recent decision to use mongodb came with the questionable decision to store json as a raw string literals complete with bizarre escaping inside a database literally designed to store json-shaped-objects.
I don't think Opus could store data that poorly, even if the end user prompting it didn't know what they were doing.
One of the upside I can foresee is the return of single payment software. No one is going to be paying subscriptions when they can LLM themselves, but paying someone else to LLM for them would let them shrug off accountability for malfunctions
To me, this is inline w the book BULLSHIT JOBS, and that class of job that was really like a few hours a week but 40 hrs pay. That type of job should be automated and the person removed. The exception is going to the person who complained to management that it’s a 2 hr a week job…. Give me more work.
> There are still fortune 500 companies with internal bans on AI.
And there probably always will be.
When the choices are "hand all of our highly sensitive internal data over to one of several other companies, all of which have questionable financials and very cozy relationships with adtech" or "invest in a whole bunch of expensive GPU servers plus internal talent to run our own models", vs "keep on doing what we've been doing, which is still working just fine", why would a non-tech Fortune 500 company choose either of the former options?
> we're going to decline to renew some software subscriptions
Yeah, sure you are. Report back when it happens.
We got the thumbs up from management last week.
Cool! Which ones?
(Replacing overpriced garbageware with something I scraped together in 3 days is my jam. But the specifics matter a lot.)
The software which manages our instrument spec sheets, and the software which manages our field tech rounds.
Thanks for sharing this. I can well imagine that managing field tech rounds is plausible if it is limited to in-house use. I assume that is planning, documenting and scheduling of field trips with a front-end for the planning and some way to show on mobile device or laptop for the tech?
Is instrument spec sheet management focused on storage and presentation, and occasionally updating values such as service interval, calibration dates etc ? If so I also find this plausible.
Both use cases are focused in terms of use case complexity (especially if your company is focusing on your requirements only as opposed to software vendors covering variations), low in complexity in terms of involved parties, and inter-system boundary crossings.
Very interesting. The spec management is probably the higher risk use case, but I assume you have proper engineering review and a tight test strategy to control this aspect.
Ok, are they cancelled yet?
Every job loss and suicide is a dollar in an AI investor’s pocket.
It would be premature to jump to 1930s style defenestration due to job loss from ai.
Software developers have always been in the job of automation and replacing human work. Capitalism means investors reap (the majority of) the benefit.
Employees have no incentive to meaningfully implement AI to increase productivity, and if they do so, they have no reason to share it.
If AI means job cuts and not using AI means job cuts but no one really tracks AI impact that means performative adoption of AI is safest, and real gains are to be sandbagged as innate magic hand waving.
At my work I'm one of the few who says "I made this with Claude" and the near impossibility of using AI with internal email etc for security reasons means AI use is one-shot wonder oriented (for me, in my experience).
If AI usage was incentived with actual bonuses and praise it might go over better. So far I haven't seen that. Its just implicit threats.
This was observed on first week for me. In the employee room, most people were all crazy about LLMs, looking at the prompt results in awe, "I can do my day in 1h easy..." . By the end of the week, group meetings were much more tame "yeah I can go a bit deeper than I would otherwise, surely saves me a few hours a week". And after the meeting was over these employees were already saying that they would never report the real productivity boost, because that would be suicide.. more trash talk will fill the void instead.
Yes, I’ve found my coworkers won’t admit they had AI write their code in any sort of group setting yet they’ve all admitted it in private. The implication seems to be if AI is writing your code you’re lazy/incompetent/bad/not working anywhere near 40 hours/week. I for one would hate for our bosses to figure out we do probably ~16 hours of actual work per week. But we all “get our stuff done” (thanks to AI).
Anecdotal: I used to beg my boss to get some help. I wanted 5 engineers and would have been upset if anyone had been hired that wasn't an engineer until I at least got 1-2. That was up until about 3 months ago.
Now, I don't want any additional engineers. Not only because I don't need them anymore, but because the prospect of having junior engineers using AI is absolutely terrifying to me. I can't eyeball their code to get a sense of how good of an engineer they are anymore, and I can't possibly review all of their code because of how much code AI can output now. So they're going to be outputting a ton of mostly high-quality code that could be making horrible mistakes that are much harder for me to catch now.
I don't feel like reading an article that will probably be out of date in 6 months, but from what I've seen, if agents keep improving at this rate, 80% of SWEs are going to be looking for new careers in 5 years.
The problem is not necessarily job losses as such. When artisans were replaced by factory work, it took a long time for automation to actually reduce the number of workers. What happened quickly was that the asset of the artisans -their skill and experience - was replaced by an asset owned by the factory owner -the capital equipment. This is the real threat today. All the people who have human capital in their skill and experience processing information, are finding that it's being slurped up by LLMs and other models. The question is whether "LLM operator" is really going to be a profession which requires scarce skills and experience, or whether many companies will be operable with less well-rewarded workers. I think that those who think there's an obvious correct prediction here are overconfident.
If my baby keeps growing at this rate, in 5 years she'll be as tall as the empire state building.
Or their career will just involve different tools and skills than they previously assumed
Probably both. We were already hitting limits in terms of finding software that could generate returns; without public funding or enormous regulation the possibility of finding new jobs with a degree is lower than it's been in a while.
Or the amount of software increases tremendously
Seems like all companies think that their products are good enough and can’t offer more, as they always want to remove people instead of offering better and bigger products.
I don't think most of the layoffs that have happened so far are about present-day AI. It's often an easy excuse though.
At some point your issue tracker or text editor is done. No need for it to automatically schedule your haircut as well.
Emacs is far from done. Scheduling things from it are already a feature, in the org-mode.
But the company that has a text editor that can read your mind is going to bankrupt you, as it is faster than typing
I'm surprised the layoffs worldwide hasn't produced more entrepreneurs building their own software product.
I wouldn’t build my product with an LLM owned by a company at the core of it. That feels way too risky, it’s almost like building on a platform you don’t own. I expect we will see way more entrepreneurial growth once local models become good enough and the hardware to run them becomes more affordable than it is now.
(Though I’m biased, I personally feel it’s an anti-human technology, the world would be better without it)
Most people simply do not want to be entrepreneurs
It is almost as if the technology is not as good as everyone claims. If they were, the game would already be over.
No one will use it.
Prior to the weaving loom and sewing machines, a factory might be able to make like 20 t-shirts a day with 50 workers. After mass production, factories generally employ the same amount of workers, but people aren't hand-sewing things. Instead roughly 50 people are producing thousands of tshirts a day leveraging giant machines.
Most of HN recognized the "We're firing people because AI makes people efficient" as one of the stupidest sales pitches ever and the CEOs that fell for it are just poorly ran companies that outed themselves.
AI is just another cycle in technology that is genuinely useful. The companies that are going to jump the gap are those that are hiring to use this new skill. If 10 workers pre-AI yields you 10x, and post AI yields you 100x, you don't cut down to 1 worker so you can keep delivering 10x. You invest, ruthlessly train, hire, and surge forward and leave your competition in the dust.
If there was 100x on the table for you, why weren't you already at 100x scale just with more headcount? That is the other side of this coin. Demand is a factor. Productivity goes up? Great, if there is demand to satiate that you can meet. I doubt that is the case otherwise you'd already have scaled to meet it if it were actually on the table for the taking if only you could output more volume.
The sewing machine analogy is a good one. The problem I'm seeing is leadership who sees the t-shirt company succeed with sewing machines, so goes out, buys 100 sewing machines, then puts them in the kitchen because they are a bakery and gets confused why the bread production isn't improving.
Because AI can do some things. Not everything. Applying it to the wrong problem reduces productivity.
> AI’s effects on overall employment is likely small, though a tough job market for new graduates may be partly due to AI.
This is a great opportunity.
There will be new jobs.
Related today:
The AI jobs apocalypse probably isn't coming anytime soon
https://news.ycombinator.com/item?id=49047969
By now I feel I can write these articles:
- benefits of AI murky to slightly positive
- hiring impact limited except for junior level
The problem is that these two statements each have massive implications, so instead of treating these findings as point in time snapshots they are the whole ballgame and should be explored in depth.