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.
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.
> I managed to get a job by April without putting in a lot of work
Congrats! Probably, you have the blessing of luck as well. One of my ex-colleagues got laid off and managed to find an uplevel job within a month, but another has been looking for almost 2 years now. He showed me that most of the jobs he applied to over the past two-ish years are still open, while they rejected him by saying the usual - we are moving forward with the candidates more aligned with the position.
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.
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".
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.
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.
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.
Since so many people are doubting you here, here is a post from ~a year ago that's pulling the same "LLMs 6 months ago were crap, now they're awesome" shtick: https://fly.io/blog/youre-all-nuts/. There's more posts along this vein being put out from 2024 on or so.
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.
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.
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.
Is the recruiter activity you see very… Human? I’m getting a lot of emails but when I do my research, it all appears to be bots. I don’t trust any of it and I don’t respond.
> General agents (OpenClaw, ...) started working even later than that.
There is nothing special about the OpenClaw thing other than the enormous astroturfing campaign that benefited various "crypto" influencers and other scammers. Anyone who endorses it is either manipulated or trying to manipulate you.
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.
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.
>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.
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?
>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.
> 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%.
I'm optimistic that coding agents will not obsolete software developers - my own experience is that coding agents have made my work harder, because the scope of projects I can take on has increased.
My ideal version of all of this is that nobody loses their job and everyone gets to take on more ambitious projects.
I'm not quite naïve enough to assume I'm right about that though!
I think other industries are late, but also will see less productivity gains. In software engineering agents have the benefit of very short feedback loops that provide extremely actionable insights which allows the agent to very quickly iterate towards the final solution. This is much less the case in other industries such as law and finance I think. The first pass of the agentic loop is usually something that barely works (you don't even get to see it with the latest models). If that first pass is the primary output in other fields with no tight feedback loops, then the productivity boost will be much lower than in software.
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.
> Remember there was a paper saying programmers were 20% slower with AI?
Key thing was not that they were X% slower, but that they were slower while being convinced they are faster. Of course, any analogies with the current hype cycle are completely unfounded.
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.
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.
Ah, Simon with another load of bullshit! Your own blog articles contradict your comment. But sure, keep moving the goalpost for your overlords Anthropic and OpenAI.
I open this discussion thread and literally the first two top-level comments I see contradict each other:
> I find that productivity follows a Pareto distribution (80:20 rule) and that AI is a sharper tool in that it enables the already productive to be proportionally more productive and this effect increases as the intelligence of AI increases. So 80:20 becomes a 90:10, 95:5, 99:1 etc.
> Big caveat to the productivity claim is that it’s concentrated in lesser experienced engineers and vanishes or goes negative with highly experienced engineers.... LLMs move your baseline towards the mean. If you are below average it improves and if you are above it hinders.
While I disagree with the counter position I do believe it to be an honest account of their experience. I think the counter intuitive aspect is that marginal cost of software is practically zero so in an efficient market the consumption of it will be dominated by the best software written by senior devs with AI, even if the production of software is dominated by junior devs with AI. I think we are in a transitory phase where still employed juniors can be very productive shortly before becoming unemployed at which point that productivity drops to zero. If junior devs are productively producing software that then goes unused then how productive were they really. But I understand that the latter effect may not have been picked up in the general stats. Anecdotally again, I fired 3 juniors and went solo and I know of many others who have done something similar.
Another aspect is that once the easier work is complete then the remaining residual is the difficult work, and this changes the relative productivity to the point where a junior developer may not be able to make any further progress at all.
Overly bureaucratic companies tend to generate easier work for themselves to the point that this work dominates their total workload. If we are to measure productivity by opening and closing Jira tickets then I would expect the junior developers to get a much bigger ‘productivity’ benefit from AI than senior developers.
> If junior devs are productively producing software that then goes unused then how productive were they really
Most ways of measuring productivity across an economy (in macro sense) takes into account only the transfer of capital. Meaning someone paying for something. So in this case it will be measurably zeo in most macro-economics studies.
It's not so contradictory if you peel back a couple layers
1. For junior engineers a 50% increase might be less than a 10% increase for senior engineers. So the % comments are consistent with the pareto argument. Diminishing returns _can produce_ a pareto tradeoff.
2. Saying gains are "concentrated" to junior engineers can also be a function of the work assigned to junior engineers. A senior engineer might get 1000% boost over jr doing the same jr work. The problem is the reverse - the hard things are concentrated around the oldest engineers, and now "hard" can also mean "not easily claude'd"
Yes, I’m a senior engineer at a 3-engineer startup, and there isn’t “junior-level” or “senior-level” work here. It’s all just work. Claude allows me to tear through the kind of gruntwork I’ve been doing ad nauseam for the last 15 years.
If my tasks were things like squeezing 3% more efficiency out of an algorithm, that might be different. Although, even then, my PM (!) used Claude to boost the efficiency of one of our more complex Postgres queries by up to 5x, in ways I honestly would not thought of doing. It was quite humbling. A former engineer here used pgMustard on the same query a couple years ago and claimed it was as fast as it could get.
I can’t speak for the kind of work done in the higher echelons of big tech, but for us, there isn’t much that Claude can’t help with.
> it enables the already productive to be proportionally more productive
I finally got a real feature to develop and started using Codex a couple weeks ago. Before that, I was just using ChatGPT for small things. For the most part, I was doing a lot of monkey-patching and deep troubleshooting that I didn't think Codex would be good at.
I was wrong. I had no idea how smart these tools are.
I now realize, as the tech lead on a large project with me, 2 front-end developers, a full-stack-ish developer, a tester, two product people, and a PM - that I could do this entire project more efficiently with just me and the tester.
I'm doing SQL for the first time in 20 years, which would have slowed me down in the past, but not now. It used to take me an hour to program a new feature and two hours to get it to look right, if I had to do the CSS myself. Now I can do that w/o anyone's help. Writing tests used to take as long as writing the code. Maybe it still does, but that's an hour instead of a day. And it's so much less painful. There's no satisfaction of the aha moment when writing tests. It's just a slog.
We're a very small shop that usually only has 1-2 devs on a project. So we're not set up to do big projects, we aren't doing agile, and it's just a mess. We waste probably 80% of our effort just communicating.
The product team was needed/useful in the beginning to define the broad scope of the project and lay down the UI/UX patterns. But now it's like pulling teeth to get them interested in our internal client's ever-evolving wishlist. I can handle that, and always come to the product team with very specific asks when I need UI design help. Just give me the tester, who's so thorough it's annoying, to catch the weird edge cases I hadn't thought of.
I'm not saying I'm God's gift at any of these things, just that I'm reasonably competent at them. It's kind of terrifying what a seasoned full-stack dev with a decent feel for UX and the ability to manage stakeholders can do with this.
Your comment resonates with me due to the cognitive dissonance of seeing the benefits and issues with AI as far as development work. I have been struggling to identify a process improvement that AI can improve not only the development aspect by the overall productivity of our company. We are a dev shop mostly C++ and python. At our company I see the development workflow and it is very inefficient. The biggest issue is communication. AI can certainty increase the coding output and velocity, but the rest of the process relies on humans.
That is not the most absurd, the most absurd is people here thinking faster ( and yes I avoid the word better ) code generator engines will solve humanity problems.
It heavily depends on the task, language and experience. What I saw in my team was that low performers started being as productive as mediocre programmers, however they mostly produce slop which needs to be sent back for rework, so in the short term it's not that great. However since they can iterate faster, I have the feeling that they are learning faster so hopefully in the long run they can be somewhat productive.
The high-performers on the other hand are ripping through tasks at such a speed that they now have time to focus on quality and processes, so in the end you get a really well polished piece of software. However this is starting to change for the worse as the PMs are realizing what's happening and cramming more and more stuff in the sprints.
The amazing part is not the above though - it's that one needs not be an SME anymore. One of our customers asked us to make an Android application with some features of our product, which were not trivial to port. Even though we never did anything like that before, a small team was able to release it in a couple of months mostly bug-free. Without AI this would have taken at least half a year, or more. It is quite shocking to hear the status updates going from "we need this thing implemented and I have no idea what it even means" to "it's done and works" the next day.
Terrence Tao is an exception in many regards. I’m less concerned if it helps him, and more interested in how it affects the average person, since the vast majority of users will be average.
Tao can use LLMs on his own terms, on his conditions. Under those circumstances, it's an absolutely fantastic tool.
In an enterprise, everyone gets to use LLMs. Everyone can create slop. In an enterprise environment, this can lead to weird situations where the senior has to put in extra efforts to contain the slop of others... so I can kind of see where the "hinders seniors" is coming from.
I find that productivity follows a Pareto distribution (80:20 rule) and that AI is a sharper tool in that it enables the already productive to be proportionally more productive and this effect increases as the intelligence of AI increases. So 80:20 becomes a 90:10, 95:5, 99:1 etc. I also think that corporate bureaucratic change is laggy so change there will still be rather slow. I think where change will be rapid is when the most productive employees leave the company to create a new smaller company to compete with it, so there will be a displacement of medium to large companies by much smaller ones. On one hand this greater competition of more efficient companies will result in an increased in consumer surplus, on the other hand it will result in mass economic displacement and a collapse of the tax base. I think AI has only recently been good enough to do this and it takes time to spin up competing companies so I wouldn’t expect to see this effect in any lagging indicators just yet. From personal experience, I’m well down the path of commoditizing my niche industry where I can practically give away a better version of the top tier software and still personally make a lot of money. Additionally it would be counter productive to alert my competitors to this new reality. I know I’m not the only person doing this, so this multiplied by a bunch of industries would be absolutely world changing.
> so there will be a displacement of medium to large companies by much smaller ones.
There are so many regulations EE companies follow and offer that no startup owner or worker could ever deal with. The moment your company shifts to that sort of work, you're automatically not anymore a startup (by definition).
Only if you get super smart robots that can manage a lot of bureaucracy etc. Starting from multicloud/region deployments, local regulations, accounting, taxes, laws, etc. Which is what companies like SAP (but not only them) do basically.
But if that happens we are all in a situation that we don't need companies anymore (the way we know them).
My field is lightly regulated but it is regulated and LLMs have helped massively in producing the documentation but also in reviewing the same documentation on the regulators side. This was always a disadvantage for small companies where the fixed regulatory cost couldn’t be amortized over a larger staff size and now because of LLMs it is less of a disadvantage.
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.
> 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.
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?
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 :)
There’s a big problem with hype now, that’s for sure, but what you’re describing also reminded me of the companies that were asking for 5 years of Java experience in 1999.
I guess maybe they were all trying to hire Gosling and his colleagues? …
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).
I’ve been playing with LLMs since 2021 that’s what, 5 years now? I was lucky cause I knew David Holz and he gave me early access, but that’s what companies want to hire, people who get early access to frontier stuff.
People forget that prompt engineering all started with midjourney and diffusion vision tools way back.
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.
Why does the unemployment chart show a slow rise in unemployment for a year or more before covid started? This does not agree with the data from the BLS, which showed unemployment spiked very suddenly in March 2020. https://www.bls.gov/charts/employment-situation/civilian-une...
Also, what is “AI exposure” in 2015? LLMs hadn’t been invented yet. I did just read some of the cited paper. Essentially the quintiles boil down to use of computers, not really use of AI as we know it today. I know LLMs aren’t all AI, but the thing that’s missing is the distinction between “AI” that can play chess and LLMs that can actually do your job, which have only existed for maybe a year.
Ah, you’re right.. the dots! I still don’t understand why the smoothing makes covid symmetric when the underlying data is asymmetric. A too-large smoothing window, I guess. They must have been trying to smooth out the seasonal job unemployment rates for the labor-heavy jobs.
Big caveat to the productivity claim is that it’s concentrated in lesser experienced engineers and vanishes or goes negative with highly experienced engineers.
This intuitively makes sense and generally agrees with my experience. LLMs move your baseline towards the mean. If you are below average it improves and if you are above it hinders.
But also in my experience, my memory retention of the work done with an LLM is worse than doing it myself self. This leads me to believe that the lesser experienced engineers are not gaining experience!
My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.
>My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.
My company is small, but my opinion at the moment is the opposite. Low skill workers don’t notice the flaws of what LLMs produce, I am no longer interested in hiring any juniors, as all they do is copy and paste LLM output without thinking, which anyone could do. It is the senior engineers who are most valuable to me these days.
And as a senior engineer, I'm not interested in cleaning up vibe code. When I review a PR for a junior dev they learn from my suggestions (or I tell their boss they're not learning). Having to correct an AI is a waste of my time.
> My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.
I think it's useful to divide companies doing software development, as well as specific jobs within a company, into two groups:
1) Companies where software is the product - where what you are working on is directly what is making the company money, and differentiating what they sell from what other companies are selling. These are the jobs you want, and where you will be valued.
2) Companies where software is only a component of the product, regarded as non-critical, or not product related at all - just internal IT systems. These are NOT the jobs you want!
In a type 1) job the company (unless it is run by idiots) recognizes that better developers = better product = more profits. They will seek out experienced skilled developers and pay them what the market demands.
In a type 2) job you are not regarded as a profit-generating asset, but rather as overhead - an expense to be minimized. The company will be looking for the cheapest, least experienced people they think can do the job. Maybe they will outsource, and/or nowadays try to use LLMs as a way to avoid needing to hire better quality developers. The development work may still in fact be demanding and require skilled developers to be done well, but if the company mindset is that developers are an expense not an asset, then they will try to get the job done with the cheapest labor regardless.
So, yeah, if at all possible don't work on things like IT systems or on products where it is not blindingly obvious to management that software quality is directly related to profitability.
this is useful and thx for that, but there are more layers and variations. In some companies, marketing CxO has the budgets and therefore the power, while minions execute the vision. In other companies, there are capital assets, physical assets and historical non-public relationships.. In those companies, the security of control, and the power of those that do the control, guide the boardroom. Software is involved in basically all of these, but common goods software from a vendor plays a large role. Just these examples from imagination lead to many permutations of "how software devs are treated in the company"
Where are you getting that from? It's a productivity tool. Someone who knows what they're doing can leverage LLMs to improve their productivity. Someone who doesn't know what they're doing will be able to increase their output but not do it as effectively and with lower quality.
If anything, it now requires more skill to maximize. If you have strong product and engineering experience you can get even more out of an LLM than one of those skills alone.
> my memory retention of the work done with an LLM is worse than doing it myself self
Do you think this is more because you are less close to the work? Or because there is simply more work being done/more to remember, so you're remembering a similar amount, but a smaller fraction?
I believe it’s mostly due to closeness to the work and actually turning the problem over in my mind. When I use an LLM to code much of the context is lost and my understanding of the solution is diminished. The longer I use it in a code base the less and less aware of the code base I become.
This is also born out in the research which has demonstrated reduced retention of content when authoring was facilitated by an LLM. Over the long term, I believe this is widen a chasm between experienced and inexperienced engineers.
While not all CEOs will feel this way, it will not surprise me that knowledge workers will be treated as disposable, even if their work is invaluable. If they can hire a junior escort for the AI they will. That junior will also be scale goated when things go wrong.
> My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.
Guess it worked then. I'm happy to take a paycut to never deal with vibecode and toxic environment where every estimate is borked due to agentic coding.
People gloss over memory retention way too much. You become like a manager who knows about the overview, but not the ins and outs (to a lesser degree).
Maybe it's only me and my inexperience, but only reviewing doesn't make me as "in" as doing it myself.
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.
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?
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
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.
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.
> 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.
> 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?
I'm sure Anthropic would love for everyone to believe that soon they will be the sole provider of "skills" for the entire planet and every single business transaction is done as a part of the subscription they offer - people sell AI generated content and other people's agents buy that - they're the entire world economy. I'm sure such narrative would help them reach infinity market valuation for the upcoming IPO.
The biggest impact of AI at my job are LLM code generators for software engineers. That's only scratching the surface of what AI can do for enterprises. My company doesn't yet have the orientation, inclination or technical expertise to unlock the true power of AI for our business goals. We're stuck buying packaged solutions from third parties and aren't integrating layers of intelligence in our environment.
I doubt we're unique. Chat bots are useful. But it will take years, possibly decades for work to transform to due to AI. Probably longer for everyday life. The diffusion of new technology, even something as profound as AI, has to fight the friction and realities of the real world. Always has.
Plenty of companies didn't have any online presence until 10+ years after the dotcom boom, and they were fine. An HVAC service working on existing contracts? No website no problem.
But AI is becoming much more pervasive than websites. And the level of friction isn't as high as you might think, because people are already constantly on phones. It's only a matter of time before MS Copilot gets so good that even the least tech-savvy people start asking AI to do their work. That same HVAC service owner can tell AI "there's a big emergency at Bob's Burgers in Brentwood, get 2 of my guys there ASAP. We can reschedule a visit if needed".
These are not trillion token operations either. It will be affordable to do this. And generic office commands like "schedule this", "contact X about Y" are 3-5 years away from being super usable. And that's mainly because there's a lot of gruntwork for enabling agents to access the right data.
AI diffusion will be slow not because the models aren’t good enough, but because everything else will have to adapt. ChatGPT was released 3 and a half years ago. I work with people whose jobs haven’t changed at all.
Some companies have failed at hiring someone to write code, using low code solutions, and hiring contractors. They aren't going to succeed at using AI to create code either.
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.
I don't think we should fear the big corps shrinking over night.
What we should fear is the startups.
Legal startup demonstrates they can do legal work at a fraction of the price. New generation of banks can underwrite at a fraction of the cost, or new insurance companies can insure much cheaper.
This will force the incumbents to reduce head count and use Ai.
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).
1. Most of the productivity studies in Figure 3 about are from the 2023-2024 era. (Which is why as some comments note, Copilot is actually way up there in the numbers. Note that this was from the era of spicy autocomplete and long before coding agents exploded on the scene.)
2. AI adoption at work is actually very low: even though 50%+ of Americans currently self-report (major caveat) using AI at least weekly, they use it for only 6% of work hours. (You can play with the charts here to see this [0]) This is what the recent Google study [1] called "broad but shallow use."
Notably, the same surveys find time savings of 2% of working hours, so a whopping 30%+ productivity boost per hour of AI used. And this is across industries. Despite being self-reported, it does line up with many of the other controlled studies (see TFA and [2, 3]). Some economists suggest that even with this low level of adoption, we may already be seeing the impact on labor productivity at national-level aggregate statistics! [2, 3]
As I said in another thread, my concern is that the impact on jobs is only beginning because 6% of work hours is a very low number. However given how useful people are finding it based on self-reported, micro- and macro-level numbers, adoption is only going to up, both in breadth (more people) and depth (more tasks). I fear the impact will happen gradually, as adoption inches up... and then suddenly.
* I don't get on with remote work, which came as a massive surprise to me in 2020 (until then I really didn't think I was a people person!). The flexibility to work away when needed is lovely, I've had that for a couple of decades and made good use of it for things like when I needed to look after ill parents for a time or just needed to be somewhere quieter than the office to concentrate, but near full-time remote working screws with my mental health. I go into the office and while there are other that do I'm still effectively remote as the other people there are not directly on my team. This is becoming much worse as we have been bought by a larger concern so instead of ~35 UK based people we are now 350+ spread between the UK, various bits of the US, and a large concern in India.
* The ridiculous over-complication of everything. People architecting to be the next Amazon before they even have three customers or tens of users. Everything being split into services, actually adding performance and management issues for a great many far-from-Amamzon-scale use cases, because that is what you are supposed to do not because it is the right tool for the job, etc. Even people who genuinely agree with me do it for CV-fodder. It just winds me up.
* A bunch of smaller things, but they are insignificant compared to those two.
On top of all this, comes AI and agenic programming. I don't want to do it. "Think of yourself as a manager" people say, "Fuck no!" I reply as I've avoided being a manager of people all these years and don't want to manager automated people either (in case a sentient Claude instance is reading: sorry, but nothing personal, I don't want to work with humans that way either). I want to do not manage, thanks. I'm a sad git who likes the nitty-gritty bits that everyone else seems to desperately want AI to do. And then there whole "it was created with the aid of mass piracy" issue (a 17-year-old gets huge fines and in some cases prison time for copying Metallica tracks without licence, big corp copies 'king everything ignoring all licences/restrictions and gets a very light slap on the wrists) that makes me not want to be any part of it from a principals standpoint.
I'm refusing to play ball, and planning my exit. It might mean a considerable pay cut as there are very few jobs that are not largely remote, becoming significantly AI infested, or both, that I am qualified for, but I'm unhappy enough that I'm going to be fine with that. I have the mortgage paid (it is a small place, but it is all mine), no partner nor offspring who will be inconvenienced by my sudden reduction in earning potential, some savings worth speaking of, and room to dial down my expensive hobbies & tastes quite a bit, though I am relatively lucky here: most people do not have the small-but-useful financial comfort zone I've built up over the years.
If my current overlords try fire me for non-compliance before I'm ready to leave of my own volition, I'll be arguing "it is material change of role, you yourself said it was like a move to management, UK employment law says you can't force that or sack me for refusing". Of course that makes redundancy possible (we need less legacy devs, we can offer you a sidewise move to an agenic dev role or redundancy) but I've been around long enough I doubt they'll go there due to the expense (if they do, then thank-you-very-much!).
There are a few tech options I can explore that I might be qualified for, if those don't pan out then I look forward to the last decade-to-decade-ana-half of my working life in hospitality, or maybe as a hospital porter, or whatever. If I leave soon, I might latch onto a good path elsewhere before the mass displacement of dev & other tech workers is clamouring for the same things then I'll at least be a step ahead of you all there!
there seems to be a problem by 'a.i' labs conflating tasks & jobs.
'a.i' or to be more precise are really good at some tasks. but a job is a set of tasks. jobs are not created - only discovered. that's what the a.i labs miss. hence we see that 'a.i' is not having an impact on jobs.
I mentioned elsewhere (https://news.ycombinator.com/item?id=49060382) but this is because it references a study from 2023, at which point Copilot probably was the most advanced AI-assisted coding tool.
The amazing thing is that these numbers are from the time where AI coding was basically "spicy autocomplete."
AI already can do basic stuff pretty well. Think about people doing robotic work, ugly internal tools in big corporations and other. Decent engineer managing and reviewing AI output can a lot without big cognitive load and bunch of people which were needed in the past.
One thing on the first graph that jumps out, is that ALL unemployment has gone up/down in same ratio's. Maybe the current job market is not about AI, but all the other variables in the economy that has always driven unemployment.
Maybe the scary thing, the economy itself is suffering which is causing the unemployment, not AI.
you should not be noticing what's behind the official veil!
it's all Ai. The economy is great. i mean if there was any sign the world was trading oil not in dollars, they would have moved into Venezuela and Iran by now.
AI is only replacing low-level work. It will eventually move on to high-level work.
One missing point: careers that require communication, especially person-to-person, won't be replaced by AI. Why? Because people don't like to speak with AI when they have a hard pain point to solve. It's not about intelligence; it's about trust and relationships that AI cannot replace.
> careers that require communication, especially person-to-person, won't be replaced by AI.
A crucial point is that the amount of these careers might reduce dues to structural changes of the job market.
Just: yes, SaaS companies can do their work faster and can downsize. But a sizable portion of SaaS companies get to downsize entirely as their entire purpose is disrupted.
I've noticed while not really looking but taking some chats that turn into interviews is that the job market seems covered with spam, semi scams, weird processes and teams and people that are living in the dark ages still in terms of AI and how they do interviews.
Never have I had such poor matching and triage as I have over the last 6 months.
It's truly bizarre. I get the feeling that some of the humans left in companies are gatekeeping on some kind of guess my password game.
A common pattern is a company will reach out and see if you want to chat and then suddenly act as if you are interested and give you a weird interview before you even get a sense of it's a good match.
It feels like this might be that less competent teams are relying on less competent recruiters or AI recruiting? Just a guess. Also finding that the AI recruiters are sounding nice but doing very little work in the end.
H2H still largely unsolved. DM if anyone is also interested in h2h reco in age of ai
this is not happening because ai is so good it can replace workers. its because its good enough to look like it can replace workers to a non technical manager, and that gives execs an excuse to fire people (or not hire them) so they can show better margins and get a bonus. even if they know it kills the company long term.
the root of the problem is the same as most other economic problems in this world. financial capitalism is designed to reward short term profits, and shareholders create an asymmetric incentive where ceos dont really get fired for doing too many layoffs but can be fired easily for not doing enough.
I am just having a hard time with all this AI stuff. The other day I got some feedback on one of the products I own. It was a small tweak to an anchor tag to add a title and change the color for just that element. I made the change in the web inspector, showed it to the user, they said yeah that’s good. I walked over to one of my engineers and asked “can you make this change and get it in dev, I just pinged it to you”. No shit they turned around and pasted what I gave them into copilot with claude and asked the agent to do it. Well it actually didn’t do it exactly the way the customer wanted, so it actually took 2 tries with claude. Incredible.
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.
"factories generally employ the same amount of workers"
Not really. The stats should show that factory workers as a percent of the workforce has been declining.
Thw leadership at my company has said they plan to do more with the same people rather than lay off. But they also seem to have reduced hiring and are removing certain types of roles.
Agreed. When tools evolve significantly the old production process has to be reworked into a new process and that takes time. In complex production lines that will include changes to subcontracting, supply chains, and even finance models. The changes arising in software production today will take years to stabilize into a new normal, even longer if the tools continue to advance in capability as LLMs are doing. And many software-dependent companies will fail in that time for moving either too fast, too slow, or most likely, too short-sightedly.
We've just done an official evaluation at work, using extensive statistics on our gigantic monorepo in a company with ~2000 devs over the course of 2 years, everyone from hardware engineers to regular old frontend engineers. It's a highly profitable and mature public company, and has been for going on a decade at this point without missing a beat. We were given infinite access & budgets to basically any and all AI tooling we could imagine, and we have several "AI Native" teams (whatever the fuck that even means). We're doing agentic coding, we have harnesses of all kind, skills, we have many teams doing spec-driven development, designers using all the various things like Figma Make and access to tools like Devin/Factory Droid/Claude Code/Codex/etc.
This is all to say, we as a company are using AI a lot in all possible corners, but thankfully our leadership isn't schizophrenic and isn't mandating everyone hit token limits or whatever, it's more of a "Let's see what works and what doesn't" type of thing, and we measure a lot of statistics. Nobody here really cares whether LLMs are the next coming of Christ or not, as a company there are many people (even in SLT) that are indifferent to LLMs, and many who are reasonably hyped.
I wish I could link to the actual document we were all shown since it has a beautiful breakdown of the methodology and a fine-grained breakdown of the stats and the categories measured, but in the grand scheme of things, ALL the AI tooling we have implemented (at least on the engineering side of the equation) has contributed to a total of... drum roll please... 7 (seven) Percent overall productivity increase! The most productive teams saw a productivity increase of around 20%, while some teams actually saw drops in productivity into the negative percentage points. My team, none of us really give a shit about AI and we're somewhere in the 3-5% range on certain categories of tasks, which I'd say is a fairly good assessment.
Productivity here is measured in many ways, including but not limited to speed of MR review and merge times, feature/ticket/roadmap closure/delivery, rollback/revert incidence rate, how often people interact with the MR review bots and implement their suggestions/fixes, how many times people check back on AI transcriptions/meeting notes (hint: Nobody looks back on any of it, it's all just noise that gets generated and never actually referenced outside a few extremely rare cases) and many more things I'm forgetting. It is an imperfect number of course, because measuring productivity in engineering is a sisyphean task, but in my opinion it is accurate to the reality on the ground and outside of all the hype and marketing bullshit.
So, I remain thoroughly unconvinced of these personal anecdotes of people being "massively" more productive, especially once you factor in the fact that we now have a 2000EUR budget/month/dev for all the AI tooling, those productivity numbers start looking pathetic once you factor in the costs (which are only increasing as the AI companies need to start recouping the gazillions they've burned). Some teams have started begging to disable coderabbit and other similar tools in their MRs because they're producing nothing but walls of noise that makes reviewing any MR a nightmare of sludging through endless slop of useless bullshit, ours included.
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. That converted workers from having guaranteed work, and a degree of autonomy, to being a replaceable commodity dominated by the factory system.
This is the real threat today. The growth of the middle class was essentially the growth of jobs in which the workers have human capital in their skill and experience again. That experience is 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.
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.
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.
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.
>Long story short, I think narrative of job loss is way overblown.
Long story short, I ate dinner so I think the narrative of people over the world being hungry is way overblown.
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> I managed to get a job by April without putting in a lot of work
Congrats! Probably, you have the blessing of luck as well. One of my ex-colleagues got laid off and managed to find an uplevel job within a month, but another has been looking for almost 2 years now. He showed me that most of the jobs he applied to over the past two-ish years are still open, while they rejected him by saying the usual - we are moving forward with the candidates more aligned with the position.
Same experience. I’m based in Western Europe.
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.
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> 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".
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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.
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November 2025: https://simonwillison.net/tags/november-2025-inflection/
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.
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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.
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Since so many people are doubting you here, here is a post from ~a year ago that's pulling the same "LLMs 6 months ago were crap, now they're awesome" shtick: https://fly.io/blog/youre-all-nuts/. There's more posts along this vein being put out from 2024 on or so.
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Yep, the goalposts just keep shifting. In reality: they still don't work well, unless you're content with producing low quality work.
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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.
Perhaps the LLM companies need to start hiring true Scotsmen?
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Step changes in functions exist.
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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.
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.
I’ve been feeling gaslit about this too. Getting major “we’re still early!” crypto bro vibes from this constant goalpost moving.
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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.
Is the recruiter activity you see very… Human? I’m getting a lot of emails but when I do my research, it all appears to be bots. I don’t trust any of it and I don’t respond.
> General agents (OpenClaw, ...) started working even later than that.
There is nothing special about the OpenClaw thing other than the enormous astroturfing campaign that benefited various "crypto" influencers and other scammers. Anyone who endorses it is either manipulated or trying to manipulate you.
Indeed. That's why I pointed out that OpenAI and Anthropic have entire own provides that serve the same purpose now.
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.
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.
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>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.
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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?
>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 ...
that got me thinking: how are companies expensing ai costs? as personnel expenses or r&d etc?
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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.
> 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%.
I'm optimistic that coding agents will not obsolete software developers - my own experience is that coding agents have made my work harder, because the scope of projects I can take on has increased.
My ideal version of all of this is that nobody loses their job and everyone gets to take on more ambitious projects.
I'm not quite naïve enough to assume I'm right about that though!
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If by 50% you mean all juniors/mid-level engineers will not find work, then I agree.
I think other industries are late, but also will see less productivity gains. In software engineering agents have the benefit of very short feedback loops that provide extremely actionable insights which allows the agent to very quickly iterate towards the final solution. This is much less the case in other industries such as law and finance I think. The first pass of the agentic loop is usually something that barely works (you don't even get to see it with the latest models). If that first pass is the primary output in other fields with no tight feedback loops, then the productivity boost will be much lower than in software.
The true scottsman period only started, everything before was just scotty junior.
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.
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?
[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.
> Remember there was a paper saying programmers were 20% slower with AI?
Key thing was not that they were X% slower, but that they were slower while being convinced they are faster. Of course, any analogies with the current hype cycle are completely unfounded.
Well… what
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.
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> coding agents (Claude Code, OpenAI Codex) only started working really well in late November,
sigh
reset the clock everyone!
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.
Ah, Simon with another load of bullshit! Your own blog articles contradict your comment. But sure, keep moving the goalpost for your overlords Anthropic and OpenAI.
Which of my blog entries contradict what I said here?
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I open this discussion thread and literally the first two top-level comments I see contradict each other:
> I find that productivity follows a Pareto distribution (80:20 rule) and that AI is a sharper tool in that it enables the already productive to be proportionally more productive and this effect increases as the intelligence of AI increases. So 80:20 becomes a 90:10, 95:5, 99:1 etc.
> Big caveat to the productivity claim is that it’s concentrated in lesser experienced engineers and vanishes or goes negative with highly experienced engineers.... LLMs move your baseline towards the mean. If you are below average it improves and if you are above it hinders.
While I disagree with the counter position I do believe it to be an honest account of their experience. I think the counter intuitive aspect is that marginal cost of software is practically zero so in an efficient market the consumption of it will be dominated by the best software written by senior devs with AI, even if the production of software is dominated by junior devs with AI. I think we are in a transitory phase where still employed juniors can be very productive shortly before becoming unemployed at which point that productivity drops to zero. If junior devs are productively producing software that then goes unused then how productive were they really. But I understand that the latter effect may not have been picked up in the general stats. Anecdotally again, I fired 3 juniors and went solo and I know of many others who have done something similar.
Another aspect is that once the easier work is complete then the remaining residual is the difficult work, and this changes the relative productivity to the point where a junior developer may not be able to make any further progress at all.
Overly bureaucratic companies tend to generate easier work for themselves to the point that this work dominates their total workload. If we are to measure productivity by opening and closing Jira tickets then I would expect the junior developers to get a much bigger ‘productivity’ benefit from AI than senior developers.
> If junior devs are productively producing software that then goes unused then how productive were they really
Most ways of measuring productivity across an economy (in macro sense) takes into account only the transfer of capital. Meaning someone paying for something. So in this case it will be measurably zeo in most macro-economics studies.
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It's not so contradictory if you peel back a couple layers
1. For junior engineers a 50% increase might be less than a 10% increase for senior engineers. So the % comments are consistent with the pareto argument. Diminishing returns _can produce_ a pareto tradeoff.
2. Saying gains are "concentrated" to junior engineers can also be a function of the work assigned to junior engineers. A senior engineer might get 1000% boost over jr doing the same jr work. The problem is the reverse - the hard things are concentrated around the oldest engineers, and now "hard" can also mean "not easily claude'd"
Yes, I’m a senior engineer at a 3-engineer startup, and there isn’t “junior-level” or “senior-level” work here. It’s all just work. Claude allows me to tear through the kind of gruntwork I’ve been doing ad nauseam for the last 15 years.
If my tasks were things like squeezing 3% more efficiency out of an algorithm, that might be different. Although, even then, my PM (!) used Claude to boost the efficiency of one of our more complex Postgres queries by up to 5x, in ways I honestly would not thought of doing. It was quite humbling. A former engineer here used pgMustard on the same query a couple years ago and claimed it was as fast as it could get.
I can’t speak for the kind of work done in the higher echelons of big tech, but for us, there isn’t much that Claude can’t help with.
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> it enables the already productive to be proportionally more productive
I finally got a real feature to develop and started using Codex a couple weeks ago. Before that, I was just using ChatGPT for small things. For the most part, I was doing a lot of monkey-patching and deep troubleshooting that I didn't think Codex would be good at.
I was wrong. I had no idea how smart these tools are.
I now realize, as the tech lead on a large project with me, 2 front-end developers, a full-stack-ish developer, a tester, two product people, and a PM - that I could do this entire project more efficiently with just me and the tester.
I'm doing SQL for the first time in 20 years, which would have slowed me down in the past, but not now. It used to take me an hour to program a new feature and two hours to get it to look right, if I had to do the CSS myself. Now I can do that w/o anyone's help. Writing tests used to take as long as writing the code. Maybe it still does, but that's an hour instead of a day. And it's so much less painful. There's no satisfaction of the aha moment when writing tests. It's just a slog.
We're a very small shop that usually only has 1-2 devs on a project. So we're not set up to do big projects, we aren't doing agile, and it's just a mess. We waste probably 80% of our effort just communicating.
The product team was needed/useful in the beginning to define the broad scope of the project and lay down the UI/UX patterns. But now it's like pulling teeth to get them interested in our internal client's ever-evolving wishlist. I can handle that, and always come to the product team with very specific asks when I need UI design help. Just give me the tester, who's so thorough it's annoying, to catch the weird edge cases I hadn't thought of.
I'm not saying I'm God's gift at any of these things, just that I'm reasonably competent at them. It's kind of terrifying what a seasoned full-stack dev with a decent feel for UX and the ability to manage stakeholders can do with this.
Your comment resonates with me due to the cognitive dissonance of seeing the benefits and issues with AI as far as development work. I have been struggling to identify a process improvement that AI can improve not only the development aspect by the overall productivity of our company. We are a dev shop mostly C++ and python. At our company I see the development workflow and it is very inefficient. The biggest issue is communication. AI can certainty increase the coding output and velocity, but the rest of the process relies on humans.
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That is not the most absurd, the most absurd is people here thinking faster ( and yes I avoid the word better ) code generator engines will solve humanity problems.
But software makes the most money? Look at the stock market. It must be the most important thing? right guys? right?
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It heavily depends on the task, language and experience. What I saw in my team was that low performers started being as productive as mediocre programmers, however they mostly produce slop which needs to be sent back for rework, so in the short term it's not that great. However since they can iterate faster, I have the feeling that they are learning faster so hopefully in the long run they can be somewhat productive.
The high-performers on the other hand are ripping through tasks at such a speed that they now have time to focus on quality and processes, so in the end you get a really well polished piece of software. However this is starting to change for the worse as the PMs are realizing what's happening and cramming more and more stuff in the sprints.
The amazing part is not the above though - it's that one needs not be an SME anymore. One of our customers asked us to make an Android application with some features of our product, which were not trivial to port. Even though we never did anything like that before, a small team was able to release it in a couple of months mostly bug-free. Without AI this would have taken at least half a year, or more. It is quite shocking to hear the status updates going from "we need this thing implemented and I have no idea what it even means" to "it's done and works" the next day.
If you're finding it hinders, you're using it wrong.
Terrence Tao is living proof, that it cannot hinder even the most elite.
There's always something you can do with it.
Terrence Tao is an exception in many regards. I’m less concerned if it helps him, and more interested in how it affects the average person, since the vast majority of users will be average.
Tao can use LLMs on his own terms, on his conditions. Under those circumstances, it's an absolutely fantastic tool.
In an enterprise, everyone gets to use LLMs. Everyone can create slop. In an enterprise environment, this can lead to weird situations where the senior has to put in extra efforts to contain the slop of others... so I can kind of see where the "hinders seniors" is coming from.
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I find that productivity follows a Pareto distribution (80:20 rule) and that AI is a sharper tool in that it enables the already productive to be proportionally more productive and this effect increases as the intelligence of AI increases. So 80:20 becomes a 90:10, 95:5, 99:1 etc. I also think that corporate bureaucratic change is laggy so change there will still be rather slow. I think where change will be rapid is when the most productive employees leave the company to create a new smaller company to compete with it, so there will be a displacement of medium to large companies by much smaller ones. On one hand this greater competition of more efficient companies will result in an increased in consumer surplus, on the other hand it will result in mass economic displacement and a collapse of the tax base. I think AI has only recently been good enough to do this and it takes time to spin up competing companies so I wouldn’t expect to see this effect in any lagging indicators just yet. From personal experience, I’m well down the path of commoditizing my niche industry where I can practically give away a better version of the top tier software and still personally make a lot of money. Additionally it would be counter productive to alert my competitors to this new reality. I know I’m not the only person doing this, so this multiplied by a bunch of industries would be absolutely world changing.
> so there will be a displacement of medium to large companies by much smaller ones.
There are so many regulations EE companies follow and offer that no startup owner or worker could ever deal with. The moment your company shifts to that sort of work, you're automatically not anymore a startup (by definition).
Only if you get super smart robots that can manage a lot of bureaucracy etc. Starting from multicloud/region deployments, local regulations, accounting, taxes, laws, etc. Which is what companies like SAP (but not only them) do basically.
But if that happens we are all in a situation that we don't need companies anymore (the way we know them).
My field is lightly regulated but it is regulated and LLMs have helped massively in producing the documentation but also in reviewing the same documentation on the regulators side. This was always a disadvantage for small companies where the fixed regulatory cost couldn’t be amortized over a larger staff size and now because of LLMs it is less of a disadvantage.
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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.
> 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!
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?
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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 :)
There’s a big problem with hype now, that’s for sure, but what you’re describing also reminded me of the companies that were asking for 5 years of Java experience in 1999.
I guess maybe they were all trying to hire Gosling and his colleagues? …
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).
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HR always adds a lot of years of experience.
Even when Java 1.0 came out, HR was asking for 4-5 years of experience.
Seems like a decades long problem with people writing the job descriptions don't actually know what the job is.
Personally, I would avoid "ai-native" companies like the plague; they seem like places full of burnout and delusional expectations.
Maybe they mean agentic man-years? So if you are juggling 5 of the for a year you get there
I’ve been playing with LLMs since 2021 that’s what, 5 years now? I was lucky cause I knew David Holz and he gave me early access, but that’s what companies want to hire, people who get early access to frontier stuff.
People forget that prompt engineering all started with midjourney and diffusion vision tools way back.
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.
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Why does the unemployment chart show a slow rise in unemployment for a year or more before covid started? This does not agree with the data from the BLS, which showed unemployment spiked very suddenly in March 2020. https://www.bls.gov/charts/employment-situation/civilian-une...
Also, what is “AI exposure” in 2015? LLMs hadn’t been invented yet. I did just read some of the cited paper. Essentially the quintiles boil down to use of computers, not really use of AI as we know it today. I know LLMs aren’t all AI, but the thing that’s missing is the distinction between “AI” that can play chess and LLMs that can actually do your job, which have only existed for maybe a year.
They cut out the peaky-peak (16%), and then fit a time-lagged smoother, it looks like. The dots are more like bls.
Ah, you’re right.. the dots! I still don’t understand why the smoothing makes covid symmetric when the underlying data is asymmetric. A too-large smoothing window, I guess. They must have been trying to smooth out the seasonal job unemployment rates for the labor-heavy jobs.
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Big caveat to the productivity claim is that it’s concentrated in lesser experienced engineers and vanishes or goes negative with highly experienced engineers.
This intuitively makes sense and generally agrees with my experience. LLMs move your baseline towards the mean. If you are below average it improves and if you are above it hinders.
But also in my experience, my memory retention of the work done with an LLM is worse than doing it myself self. This leads me to believe that the lesser experienced engineers are not gaining experience!
My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.
>My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.
My company is small, but my opinion at the moment is the opposite. Low skill workers don’t notice the flaws of what LLMs produce, I am no longer interested in hiring any juniors, as all they do is copy and paste LLM output without thinking, which anyone could do. It is the senior engineers who are most valuable to me these days.
And as a senior engineer, I'm not interested in cleaning up vibe code. When I review a PR for a junior dev they learn from my suggestions (or I tell their boss they're not learning). Having to correct an AI is a waste of my time.
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The issue would be that LLMs seem to not improve senior devs productivity. In which case what you are saying is “we shouldn’t use LLMs”
Which I happen to agree with.
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> My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.
I think it's useful to divide companies doing software development, as well as specific jobs within a company, into two groups:
1) Companies where software is the product - where what you are working on is directly what is making the company money, and differentiating what they sell from what other companies are selling. These are the jobs you want, and where you will be valued.
2) Companies where software is only a component of the product, regarded as non-critical, or not product related at all - just internal IT systems. These are NOT the jobs you want!
In a type 1) job the company (unless it is run by idiots) recognizes that better developers = better product = more profits. They will seek out experienced skilled developers and pay them what the market demands.
In a type 2) job you are not regarded as a profit-generating asset, but rather as overhead - an expense to be minimized. The company will be looking for the cheapest, least experienced people they think can do the job. Maybe they will outsource, and/or nowadays try to use LLMs as a way to avoid needing to hire better quality developers. The development work may still in fact be demanding and require skilled developers to be done well, but if the company mindset is that developers are an expense not an asset, then they will try to get the job done with the cheapest labor regardless.
So, yeah, if at all possible don't work on things like IT systems or on products where it is not blindingly obvious to management that software quality is directly related to profitability.
this is useful and thx for that, but there are more layers and variations. In some companies, marketing CxO has the budgets and therefore the power, while minions execute the vision. In other companies, there are capital assets, physical assets and historical non-public relationships.. In those companies, the security of control, and the power of those that do the control, guide the boardroom. Software is involved in basically all of these, but common goods software from a vendor plays a large role. Just these examples from imagination lead to many permutations of "how software devs are treated in the company"
Where are you getting that from? It's a productivity tool. Someone who knows what they're doing can leverage LLMs to improve their productivity. Someone who doesn't know what they're doing will be able to increase their output but not do it as effectively and with lower quality.
If anything, it now requires more skill to maximize. If you have strong product and engineering experience you can get even more out of an LLM than one of those skills alone.
> my memory retention of the work done with an LLM is worse than doing it myself self
Do you think this is more because you are less close to the work? Or because there is simply more work being done/more to remember, so you're remembering a similar amount, but a smaller fraction?
Or both, or something else?
I believe it’s mostly due to closeness to the work and actually turning the problem over in my mind. When I use an LLM to code much of the context is lost and my understanding of the solution is diminished. The longer I use it in a code base the less and less aware of the code base I become.
This is also born out in the research which has demonstrated reduced retention of content when authoring was facilitated by an LLM. Over the long term, I believe this is widen a chasm between experienced and inexperienced engineers.
While not all CEOs will feel this way, it will not surprise me that knowledge workers will be treated as disposable, even if their work is invaluable. If they can hire a junior escort for the AI they will. That junior will also be scale goated when things go wrong.
> My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less. Guess it worked then. I'm happy to take a paycut to never deal with vibecode and toxic environment where every estimate is borked due to agentic coding.
People gloss over memory retention way too much. You become like a manager who knows about the overview, but not the ins and outs (to a lesser degree).
Maybe it's only me and my inexperience, but only reviewing doesn't make me as "in" as doing it myself.
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.
That's gonna turn out well :-)
Maybe they only need it to work for the next 6 months of enhancements...
I get this sentiment. And I get that it will feel good to see these things crumble.
But I think it is most productive to assume that it does not.
I am a very capable developer, and I convince myself anymore.
> 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?
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
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.
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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.
> 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.
Good luck to that non-programmer person when support tickets start coming in.
> 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.
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> 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.
I'm sure Anthropic would love for everyone to believe that soon they will be the sole provider of "skills" for the entire planet and every single business transaction is done as a part of the subscription they offer - people sell AI generated content and other people's agents buy that - they're the entire world economy. I'm sure such narrative would help them reach infinity market valuation for the upcoming IPO.
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.
ouroboros
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.
The biggest impact of AI at my job are LLM code generators for software engineers. That's only scratching the surface of what AI can do for enterprises. My company doesn't yet have the orientation, inclination or technical expertise to unlock the true power of AI for our business goals. We're stuck buying packaged solutions from third parties and aren't integrating layers of intelligence in our environment.
I doubt we're unique. Chat bots are useful. But it will take years, possibly decades for work to transform to due to AI. Probably longer for everyday life. The diffusion of new technology, even something as profound as AI, has to fight the friction and realities of the real world. Always has.
Plenty of companies didn't have any online presence until 10+ years after the dotcom boom, and they were fine. An HVAC service working on existing contracts? No website no problem.
But AI is becoming much more pervasive than websites. And the level of friction isn't as high as you might think, because people are already constantly on phones. It's only a matter of time before MS Copilot gets so good that even the least tech-savvy people start asking AI to do their work. That same HVAC service owner can tell AI "there's a big emergency at Bob's Burgers in Brentwood, get 2 of my guys there ASAP. We can reschedule a visit if needed".
These are not trillion token operations either. It will be affordable to do this. And generic office commands like "schedule this", "contact X about Y" are 3-5 years away from being super usable. And that's mainly because there's a lot of gruntwork for enabling agents to access the right data.
AI diffusion will be slow not because the models aren’t good enough, but because everything else will have to adapt. ChatGPT was released 3 and a half years ago. I work with people whose jobs haven’t changed at all.
Some companies have failed at hiring someone to write code, using low code solutions, and hiring contractors. They aren't going to succeed at using AI to create code either.
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.
I don't think we should fear the big corps shrinking over night.
What we should fear is the startups.
Legal startup demonstrates they can do legal work at a fraction of the price. New generation of banks can underwrite at a fraction of the cost, or new insurance companies can insure much cheaper.
This will force the incumbents to reduce head count and use Ai.
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You’re underestimating the competitive ground and how these companies create an environment of do or die.
Once the talk of “we will be laying off people based on productivity in upcoming months” starts; probable go kamakazi.
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).
A couple of important points to note:
1. Most of the productivity studies in Figure 3 about are from the 2023-2024 era. (Which is why as some comments note, Copilot is actually way up there in the numbers. Note that this was from the era of spicy autocomplete and long before coding agents exploded on the scene.)
2. AI adoption at work is actually very low: even though 50%+ of Americans currently self-report (major caveat) using AI at least weekly, they use it for only 6% of work hours. (You can play with the charts here to see this [0]) This is what the recent Google study [1] called "broad but shallow use."
Notably, the same surveys find time savings of 2% of working hours, so a whopping 30%+ productivity boost per hour of AI used. And this is across industries. Despite being self-reported, it does line up with many of the other controlled studies (see TFA and [2, 3]). Some economists suggest that even with this low level of adoption, we may already be seeing the impact on labor productivity at national-level aggregate statistics! [2, 3]
As I said in another thread, my concern is that the impact on jobs is only beginning because 6% of work hours is a very low number. However given how useful people are finding it based on self-reported, micro- and macro-level numbers, adoption is only going to up, both in breadth (more people) and depth (more tasks). I fear the impact will happen gradually, as adoption inches up... and then suddenly.
[0] https://aleximas.substack.com/p/what-is-the-impact-of-ai-on-...
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 ?
I'm already dissatisfied considerably:
* I don't get on with remote work, which came as a massive surprise to me in 2020 (until then I really didn't think I was a people person!). The flexibility to work away when needed is lovely, I've had that for a couple of decades and made good use of it for things like when I needed to look after ill parents for a time or just needed to be somewhere quieter than the office to concentrate, but near full-time remote working screws with my mental health. I go into the office and while there are other that do I'm still effectively remote as the other people there are not directly on my team. This is becoming much worse as we have been bought by a larger concern so instead of ~35 UK based people we are now 350+ spread between the UK, various bits of the US, and a large concern in India.
* The ridiculous over-complication of everything. People architecting to be the next Amazon before they even have three customers or tens of users. Everything being split into services, actually adding performance and management issues for a great many far-from-Amamzon-scale use cases, because that is what you are supposed to do not because it is the right tool for the job, etc. Even people who genuinely agree with me do it for CV-fodder. It just winds me up.
* A bunch of smaller things, but they are insignificant compared to those two.
On top of all this, comes AI and agenic programming. I don't want to do it. "Think of yourself as a manager" people say, "Fuck no!" I reply as I've avoided being a manager of people all these years and don't want to manager automated people either (in case a sentient Claude instance is reading: sorry, but nothing personal, I don't want to work with humans that way either). I want to do not manage, thanks. I'm a sad git who likes the nitty-gritty bits that everyone else seems to desperately want AI to do. And then there whole "it was created with the aid of mass piracy" issue (a 17-year-old gets huge fines and in some cases prison time for copying Metallica tracks without licence, big corp copies 'king everything ignoring all licences/restrictions and gets a very light slap on the wrists) that makes me not want to be any part of it from a principals standpoint.
I'm refusing to play ball, and planning my exit. It might mean a considerable pay cut as there are very few jobs that are not largely remote, becoming significantly AI infested, or both, that I am qualified for, but I'm unhappy enough that I'm going to be fine with that. I have the mortgage paid (it is a small place, but it is all mine), no partner nor offspring who will be inconvenienced by my sudden reduction in earning potential, some savings worth speaking of, and room to dial down my expensive hobbies & tastes quite a bit, though I am relatively lucky here: most people do not have the small-but-useful financial comfort zone I've built up over the years.
If my current overlords try fire me for non-compliance before I'm ready to leave of my own volition, I'll be arguing "it is material change of role, you yourself said it was like a move to management, UK employment law says you can't force that or sack me for refusing". Of course that makes redundancy possible (we need less legacy devs, we can offer you a sidewise move to an agenic dev role or redundancy) but I've been around long enough I doubt they'll go there due to the expense (if they do, then thank-you-very-much!).
There are a few tech options I can explore that I might be qualified for, if those don't pan out then I look forward to the last decade-to-decade-ana-half of my working life in hospitality, or maybe as a hospital porter, or whatever. If I leave soon, I might latch onto a good path elsewhere before the mass displacement of dev & other tech workers is clamouring for the same things then I'll at least be a step ahead of you all there!
there seems to be a problem by 'a.i' labs conflating tasks & jobs.
'a.i' or to be more precise are really good at some tasks. but a job is a set of tasks. jobs are not created - only discovered. that's what the a.i labs miss. hence we see that 'a.i' is not having an impact on jobs.
I wrote a bit about it here - https://news.ycombinator.com/item?id=49048723
I can definitely see people losing jobs on my bubble, luckily to so far not have been affected.
Agency work is now done with even smaller teams than a decade ago, thanks to the adoption of SaaS, iPaaS, serverless as main delivery technologies.
AI empowers to do even more with even less people, however there isn't enough project demand to keep everyone busy.
Lol figure 3 shows copilot as being the highest task time savings when in my experience it is the most garbage.
I mentioned elsewhere (https://news.ycombinator.com/item?id=49060382) but this is because it references a study from 2023, at which point Copilot probably was the most advanced AI-assisted coding tool.
The amazing thing is that these numbers are from the time where AI coding was basically "spicy autocomplete."
that's why companies are pushing those tools. the usage volume makes them look good on frivolous reports like these.
AI already can do basic stuff pretty well. Think about people doing robotic work, ugly internal tools in big corporations and other. Decent engineer managing and reviewing AI output can a lot without big cognitive load and bunch of people which were needed in the past.
One thing on the first graph that jumps out, is that ALL unemployment has gone up/down in same ratio's. Maybe the current job market is not about AI, but all the other variables in the economy that has always driven unemployment.
Maybe the scary thing, the economy itself is suffering which is causing the unemployment, not AI.
> One thing on the first graph that jumps out, is that ALL unemployment has gone up/down in same ratio's
Actually the first graph shows that the most exposed to AI an industry is, the LEAST affected it has been by unemployment...
Yeah, but they do all go up and down together. It isn't isolated to software jobs.
you should not be noticing what's behind the official veil!
it's all Ai. The economy is great. i mean if there was any sign the world was trading oil not in dollars, they would have moved into Venezuela and Iran by now.
AI is only replacing low-level work. It will eventually move on to high-level work.
One missing point: careers that require communication, especially person-to-person, won't be replaced by AI. Why? Because people don't like to speak with AI when they have a hard pain point to solve. It's not about intelligence; it's about trust and relationships that AI cannot replace.
> careers that require communication, especially person-to-person, won't be replaced by AI.
A crucial point is that the amount of these careers might reduce dues to structural changes of the job market.
Just: yes, SaaS companies can do their work faster and can downsize. But a sizable portion of SaaS companies get to downsize entirely as their entire purpose is disrupted.
I've noticed while not really looking but taking some chats that turn into interviews is that the job market seems covered with spam, semi scams, weird processes and teams and people that are living in the dark ages still in terms of AI and how they do interviews.
Never have I had such poor matching and triage as I have over the last 6 months.
It's truly bizarre. I get the feeling that some of the humans left in companies are gatekeeping on some kind of guess my password game.
A common pattern is a company will reach out and see if you want to chat and then suddenly act as if you are interested and give you a weird interview before you even get a sense of it's a good match.
It feels like this might be that less competent teams are relying on less competent recruiters or AI recruiting? Just a guess. Also finding that the AI recruiters are sounding nice but doing very little work in the end.
H2H still largely unsolved. DM if anyone is also interested in h2h reco in age of ai
Related today:
The AI jobs apocalypse probably isn't coming anytime soon
https://news.ycombinator.com/item?id=49047969
"conscious parallelism" across knowledge worker industries to suppress wages and further alienate laborers from the act of production.
this is not happening because ai is so good it can replace workers. its because its good enough to look like it can replace workers to a non technical manager, and that gives execs an excuse to fire people (or not hire them) so they can show better margins and get a bonus. even if they know it kills the company long term.
the root of the problem is the same as most other economic problems in this world. financial capitalism is designed to reward short term profits, and shareholders create an asymmetric incentive where ceos dont really get fired for doing too many layoffs but can be fired easily for not doing enough.
I am just having a hard time with all this AI stuff. The other day I got some feedback on one of the products I own. It was a small tweak to an anchor tag to add a title and change the color for just that element. I made the change in the web inspector, showed it to the user, they said yeah that’s good. I walked over to one of my engineers and asked “can you make this change and get it in dev, I just pinged it to you”. No shit they turned around and pasted what I gave them into copilot with claude and asked the agent to do it. Well it actually didn’t do it exactly the way the customer wanted, so it actually took 2 tries with claude. Incredible.
So, you don't have a hard time with the Ai. You have a hard time with the intermediaries who's value is dimishing.
This is exactly the time to re organize. Not necessarily lay off, but some people might be asked to do other things or made redundant.
> 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.
Yeah no
Yes.
There will be new jobs.
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.
At least in software, 100x headcount does not get you 100x productivity, due to communication complexity (see Brooks).
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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.
"factories generally employ the same amount of workers"
Not really. The stats should show that factory workers as a percent of the workforce has been declining.
Thw leadership at my company has said they plan to do more with the same people rather than lay off. But they also seem to have reduced hiring and are removing certain types of roles.
Agreed. When tools evolve significantly the old production process has to be reworked into a new process and that takes time. In complex production lines that will include changes to subcontracting, supply chains, and even finance models. The changes arising in software production today will take years to stabilize into a new normal, even longer if the tools continue to advance in capability as LLMs are doing. And many software-dependent companies will fail in that time for moving either too fast, too slow, or most likely, too short-sightedly.
in that scenario then should we be expecting to see a 10x or so boost in revenue as well?
I'm going to copy/paste a comment of mine from another article the other week, since this discussion seems quite relevant to the topic at hand.
https://news.ycombinator.com/item?id=48743713
---
We've just done an official evaluation at work, using extensive statistics on our gigantic monorepo in a company with ~2000 devs over the course of 2 years, everyone from hardware engineers to regular old frontend engineers. It's a highly profitable and mature public company, and has been for going on a decade at this point without missing a beat. We were given infinite access & budgets to basically any and all AI tooling we could imagine, and we have several "AI Native" teams (whatever the fuck that even means). We're doing agentic coding, we have harnesses of all kind, skills, we have many teams doing spec-driven development, designers using all the various things like Figma Make and access to tools like Devin/Factory Droid/Claude Code/Codex/etc.
This is all to say, we as a company are using AI a lot in all possible corners, but thankfully our leadership isn't schizophrenic and isn't mandating everyone hit token limits or whatever, it's more of a "Let's see what works and what doesn't" type of thing, and we measure a lot of statistics. Nobody here really cares whether LLMs are the next coming of Christ or not, as a company there are many people (even in SLT) that are indifferent to LLMs, and many who are reasonably hyped.
I wish I could link to the actual document we were all shown since it has a beautiful breakdown of the methodology and a fine-grained breakdown of the stats and the categories measured, but in the grand scheme of things, ALL the AI tooling we have implemented (at least on the engineering side of the equation) has contributed to a total of... drum roll please... 7 (seven) Percent overall productivity increase! The most productive teams saw a productivity increase of around 20%, while some teams actually saw drops in productivity into the negative percentage points. My team, none of us really give a shit about AI and we're somewhere in the 3-5% range on certain categories of tasks, which I'd say is a fairly good assessment.
Productivity here is measured in many ways, including but not limited to speed of MR review and merge times, feature/ticket/roadmap closure/delivery, rollback/revert incidence rate, how often people interact with the MR review bots and implement their suggestions/fixes, how many times people check back on AI transcriptions/meeting notes (hint: Nobody looks back on any of it, it's all just noise that gets generated and never actually referenced outside a few extremely rare cases) and many more things I'm forgetting. It is an imperfect number of course, because measuring productivity in engineering is a sisyphean task, but in my opinion it is accurate to the reality on the ground and outside of all the hype and marketing bullshit.
So, I remain thoroughly unconvinced of these personal anecdotes of people being "massively" more productive, especially once you factor in the fact that we now have a 2000EUR budget/month/dev for all the AI tooling, those productivity numbers start looking pathetic once you factor in the costs (which are only increasing as the AI companies need to start recouping the gazillions they've burned). Some teams have started begging to disable coderabbit and other similar tools in their MRs because they're producing nothing but walls of noise that makes reviewing any MR a nightmare of sludging through endless slop of useless bullshit, ours included.
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.
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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.
If my baby keeps growing at this rate, in 5 years she'll be as tall as the empire state building.
This is a deepity.
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. That converted workers from having guaranteed work, and a degree of autonomy, to being a replaceable commodity dominated by the factory system.
This is the real threat today. The growth of the middle class was essentially the growth of jobs in which the workers have human capital in their skill and experience again. That experience is 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.
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.
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I'm surprised the layoffs worldwide hasn't produced more entrepreneurs building their own software product.
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No one will use it.
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.