Here is my take on AI's impact on productivity:
First let's review what are LLMs objectively good at: 1. Writing boiler plate code 2. Translating between two different coding languages (migration) 3. Learning new things: Summarizing knowledge, explaining concepts 4. Documentation, menial tasks
At a big tech product company #1 #2 #3 are not as frequent as one would think - most of the time is spent in meetings and meetings about meetings. Things move slowly - it's designed to be like that. Majority devs are working on integrating systems - whatever their manager sold to their manager and so on. The only time AI really helped me at my job was when I did a one-week hackathon. Outside of that, integrations of AI felt like more work rather than less - without much productivity boost.
Outside, it has proven to be a real productivity boost for me. It checks all the four boxes. Plus, I don't have to worry about legal, integrations, production bugs (eventually those will come).
So, depends who you are asking -- it is a huge game changer (or not).
(1) LLMs are basically Stack Overflow on steroids. No need to go look up examples or read the documentation in most cases, spit out a mostly working starting point.
(3) Learning. Ramping up on an unfamiliar project by asking Antigravity questions is really useful.
I do think it makes devs faster, in that it takes less time to do these two things. But you're running into the 80% of the job that does not involve writing code, especially at a larger company.
In theory, this should allow a company to do more with fewer devs, but in reality it just means that these two activities become easier, and the 80% is still the bottleneck.
AI is making everyone faster that I’ve seen. I’d say 30% of the tickets I’ve seen in the last month have been solved by just clicking the delegate to AI button
I'll be honest, just the idea of working there makes me feel like vomiting. For me, they are bizarrely evil. They're not evil like, "we're going to destroy our competition through anti competitive practices," (which they do), but "let's destroy a whole generation of minds."
And now with the glasses. I mean, jeeze. Can there be a stronger signal of not caring for others?
It's as if Meta sees people as cattle. Though I think a lot of techies see humans as cattle, truthfully.
What was your rationale?
I guess this question is out-of-the-blue, and I don't mean for you to justify your existence, but I've never understood why people choose to work for Meta.
Where livelihood is concerned, rational individuals with strong morals can do irrational, and immoral things (e.g., work at the Palantir's of the world).
TLDR: incentives don't just shape perception, they form it
Brooks law “Adding manpower to a late software project makes it later” is just the surface of some of the metaphorical language that has most stuck with me: large systems and teams quickening entanglement in tar pits through their struggle against coordination scaling pains, conceptual integrity in design akin to preserving architectural unity of Reims cathedral, roles and limitations attempting to expand surgical teams, etc.
Love a good metaphor, even when its foundation is overextended or out of date. Highly recommend.
Big corporations are full with people who love to entertain 20+ people in video calls. 1-2 people speak, the other nod their heads while browsing Amazon.
I wouldn’t be sad if those jobs vanished.
All of these people will consequently be on the job market competing for your opportunities.
Yes you may feel superior to their capabilities - and may even be justified in your opinion (I know nothing about you beyond this comment)... But it'll still significantly impact your professional future if this actually happens. It would massively impact wages at the very least
Your viewpoint is incredibly short-sighted and not actually realizing the broad effect on the industry as a whole such a change would bring.
Every efficiency wave made life better for humans. Why should this one be different?
Assume many people lose their jobs. This in turn means companies will have higher margins. Higher margins attract more competition. More competition means lower margins since some will use the lower costs to offer lower prices.
Lower prices increase quality of life for everyone.
People who lost their job might be able to pick up doing something they actually enjoy…
We just gloss over them and villify the ones who tried to do anything about it (the ones that weren't executed also died in poverty).
It's more probable they lose everything before ending up with a worse job that pays less.
I have a game written in XNA
100% of the code is there, including all the physics that I hand-wrote.
All the assets are there.
I tried to get Gemini and Claude to do it numerous times, always with utter failure of epic proportions with anything that's actually detailed. 1 - my transition from the lobby screen into gameplay? 0% replicated on all attempts 2 - the actual physics in gameplay? 0% replicated none of it works 3 - the lobby screen itself? non-functional
Okay so what did it even do? Well it put together sort of a boilerplate main menu and barebones options with weird looking text that isn't what I provided (given that I provided a font file), a lobby that I had to manually adjust numerous times before it could get into gameplay, and then nonfunctional gameplay that only handles directional movement and nothing else with sort of half-working fish traveling behavior.
I've tried this a dozen times since 2023 with AI and as late as late last year.
ALL of the source code is there every single thing that could be translated to be a functional game in another language is there. It NEVER once works or even comes remotely close.
The entire codebase is about 20,000 lines, with maybe 3,000 of it being really important stuff.
So yeah I don't really think AI is "really good" at anything complex. I haven't really been proven wrong in my 4 years of using it now.
And then, maybe someone slightly crazy comes along and tries seeing how much they can do with regular codegen approaches, without any LLMs in the mix, but also not manual porting.
- Do not say: "just convert this"
- On critical sections you do a method-per-method-translation
- Dont forget: your 20.000 lines source at a whole will make any model to be distracted on longer tasks (and sessions, for sure)
- Do dedicated projects within Claude per each sub-module
It still is completely and utterly hopeless
I found success with it pretty easily for those smaller projects. They were gamedev projects, and the process was basically to generate a source of truth AST and diff it vs a target language AST, and then do some more verifier steps of comparing log output, screenshot output, and getting it to write integration tests. I wrote up a bit of a blog on it. I'm not sure if this will be of any use to you, maybe your case is more difficult, but anyway here you go: https://sigsegv.land/blog/migrating-typescript-to-csharp-acc...
For me it worked great, and I would (and am) using a similar method for more projects.
Because, I am terrified by the output I am getting while working on huge legacy codebases, it works. I described one of my workflow changes here: https://news.ycombinator.com/item?id=47271168 but in general compared to old way of working I am saving half of the steps consistently, whether its researching the codebase, or integrating new things, or even making fixes. I have stopped writing code, occasionally I jump into the changes proposed by LLM and make manual edits if it is feasible, otherwise I revert changes and ask it to generate again but based on my learnings from the past rejected output
I am terrified about what's coming
To give an example from a field where LLMs started causing employment worries earlier than software development: translation. Some translators made their living doing the equivalent of routine, repetitive coding tasks: translating patents, manuals, text strings for localized software, etc. Some of that work was already threatened by pre-LLM machine translation, despite its poor quality; context-aware LLMs have pretty much taken over the rest. Translators who were specialized in that type of work and too old or inflexible to move into other areas were hurt badly.
The potential demand for translation between languages has always been immense, and until the past few years only a tiny portion of that demand was being met. Now that translation is practically free, much more of that demand is being met, though not always well. Few people using an app or browser extension to translate between languages have much sense of what makes a good translation or of how translation can go bad. Professional translators who are able to apply their higher-level knowledge and language skills to facilitate intercultural communication in various ways can still make good money. But it requires a mindset change that can be difficult.
1. No one cares about quality. Even in fields you'd expect to require the 'human touch' (e.g. novel translation), publishers are replacing translators with AI. It doesn't matter if you have higher-level knowledge or skills if the company gains more from cutting your contract than it loses in sales.
2. Translation jobs have been replaced with jobs proofreading machine translations, which pays peanuts (since AI is 'doing most of the work') but in fact takes almost as much effort as translating from scratch (since AI is often wrong in very subtle ways). The comparison to PR reviews makes itself.
After all, this has been Apple strategy since the 80's, and, even though there were some up's and down's, overall it's a success.
Maybe, but it probably requires a very strong and opinionated leader to pull off. The conventional wisdom in American business leadership seems to be to pursue the lowest level of quality you can get away with, and focus on cutting costs. And you'll have to fight that every second.
I don't think that's true at the individual-contributor level (pursing quality is very motivating), but they people who move up are the ones who sound "smart" by aping conventional wisdom.
> After all, this has been Apple strategy since the 80's, and, even though there were some up's and down's, overall it's a success.
I might give you that "since the late 90s," but there have been significant periods where that wasn't true (e.g. the early mid-90s Mac OS was buggy and had poor foundations).
A) how old the product is: Twitter during its first 5 years probaby had more work to do compared to Twitter after 15 years. I suspect that is why they were able to get rid of so many developers.
B) The industry: many b2c / ecommerce businesses are straightforward and don't have an endless need for new features. This is different than more deep tech companies
I’ve built several of such tools where I work. We don’t even have a dev team, it’s just IT Ops, and all of what I’ve built is effectively “done” software unless the business changes.
I suspect there’s a lot of that out there in the world.
On a macro level, if you were in a rising economic tide, you would still be hiring, and turning those productivity gains into more business.
I wonder what the parallels are to past automations. When part producing companies moved from manual mills to CNC mills, did they fire a bunch of people or did they make more parts?
Jobs where a machinist is in charge of large chunks of the process are rarer. Large shop will have one person setting up many machines to maximize throughput.
- AI tools are expensive so until the increased productivity translates to increased revenue we need to make room in the budget
- We expect the bottlenecks in our org to move from writing code to something else (PM or design or something) so we're cutting SWEs in anticipation of needing to move that budget elsewhere.
- We anticipate the skillsets needed by developers in the AI world to be fundamentally different from what they are now that it's cheaper to just lay people off, run as lean as possible, and rehire people with the skills we want in a year or two than it is to try and retrain.
I don't necessarily agree with those arguments (especially the last one), but I think they're somewhat valid arguments
> rehire people with the skills we want in a year or two than it is to try and retrain.
before that future comes your company might become obsolete already, because you have lost your market share to new entrants
> We expect the bottlenecks in our org to move from writing code to something else
I would love to tell them, hey lets leverage current momentum and build, when those times come, we offer existing people with accumulated knowledge to retrain to a new type of work, if they think they're not good fit, they can leave, if they're willing, give them a chance, invest in people, make them feel safe and earn trust and loyalty from them
> AI tools are expensive so until the increased productivity translates to increased revenue we need to make room in the budget
1. Its not that expensive: 150$/seat/month -> 5 lunches? or maybe squeeze it from Sales personnel traveling with Business class?
2. By the time increased productivity is realized by others, company who resisted could be so far behind, that they won't be able to afford hiring engineers with those skillsets, if they think 150$ is expensive now, I am sure they will say "What??? 350k$ for this engineer?, no way, I will instead hire contractors"
AI needs documentation, automation, integration tests... It works very well for remote first company, but not for in-face informal grinding approach.
Just year ago, client told me to delete integration tests, because "they ran too long"!
Why are you surprised customers don't like spending money on the items that don't add business value. Add to that QA, documentation, security audits, etc.
They want to ship stuff that brings in customers and revenue day one, everything else is a cost.
They absolutely do add value / prevent loss, but you need some understanding in order to see that. Not seeing it is a marker of not understanding.
Not to the non-technical bean counters. When they allocate money they want to see you prove how that extra money translates to an immediate ROI, and it's difficult to prove that in an Excel sheet exactly what the ROI will be without making stuff up on vibes and feels.
Like at one German company i was at ~15 years ago, all the devs wanted a second 19" monitor on our workstations for increased productivity, and the bean counters wouldn't approve that because they wanted proof of how that expense across hundreds of people will increase our productivity and by how much %, to see if that would offset the cost.
This is how these people think. If you don't bring hard numbers on how much their "line will go up", they won't give you money.
I know this is difficult to understand from the PoV of SV Americans where gazillions of dollars just fall from the sky at their tech companies.
It might not be about bringing more revenues but retaining market share.
It’s refreshing to see the same sentiment from so many other people independently here.
Doesn't exclude the possibility of short term distribution, though.
That's one of the reasons why I am terrified, because it can lead to burn out, and I personally don't like to babysit bunch of agents, because the output doesn't feel "mine", when its not "mine" I don't feel ownership.
And I am deliberately hitting the brake from time to time not to increase expectations, because I feel like driving someone else's car while not understanding fully how they tuned their car (even though I did those tunings by prompting)
The engineer who worked with you took ownership of the code! Have you forgotten this?
It's not going to be written exactly like you would do it, but that's ok - because you care about the results of the solution and not its precise implementation. At some point you have to make an engineering decision whether to write it yourself for critical bits or allow the agent/junior to get a good enough result.
You're reviewing the code and hand editing anyway, right? You understand the specs even if your agent/junior doesn't, so you can take credit even if you didn't physically write the code. It's the same thing.
Yes, yes!
And this is problem for me, because of the pace, my brain muscles are not developing enough compared to when I was doing those things myself.
before, I was changing my mind while implementing the code, because I see more things while typing, and digging deeper, but now, because juniors are doing things they don't offer me a refactoring or improvements while typing the code quickly, because they obey my command instead of having "aha" moment to suggest better ways
Once you get to a certain size company, this means a lot of bloat. Heck, I've seen small(ish) companies that had as many managers and administrators as ICs.
But You're not wrong, I'm just pointing out how an org that has 4k people can lay off a few hundred with modest impact of the financials (though extensive impact on morale).
I find anything else, I spend more time coaxing them into doing 85% of what I need that I'm better off doing it myself.
So they're not useless but there's only so many times in a week that I need a function to pretty-print a table in some fashion. And the code they write on anything more complex than a snippet is usually written poorly enough that it's a write-once-never-touch-again situation. If the code needs to be solid, maintainable, testable, correct (and these are kind of minimal requirements in my book) then LLMs make little impact on my productivity.
They're still an improvement on Google and Stack exchange, but again - only gets you so far.
YMMV
You must be working in a very niche field with very niche functionality if that's the case? I work at a company just outside of FAANG and I work in compliance. Not a terribly complex domain but very complicated scale and data integrity requirements.
I haven't written a single line of code manually in 2 weeks. Opus 4.6 just... works. Even if I don't give it all the context it just seems to figure things out. Occasionally it'll make an architectural error because it doesn't quite understand how the microservices interact. But these are non-trivial errors (i.e. humans could have made them as well) and when we identify such an error, we update the team-shared CLAUDE.md to make sure future agents don't repeat the error.
What I got sort of works, as in tests pass - this with Opus 4.5. It is usable, though it doesn't exist cleanly on errors despite working to death with Claude about this. On exception it exits dirtily and crashes, which is good enough for now. I had some fancy ideas about logging messages from the vendor to be able to replay them, to be able to then reproduce errors. Opus made a real hash of it, lots of "fuck it comment out the assert so the test passes". This part is unusable and worse, pollutes the working part of the project. It made a valiant effort at mocking the vendor API for testing but really badly, instead of writing 30 lines of general code, it wrote 200 lines of inconsistent special cases that don't even work altogether. Asked to fix it it just shuffles around the special cases and gets stuck.
It's written messily enough that I wouldn't touch this even to remove the dead code paths. I could block a few days for it to fix but frankly in that time I can redo it all and better. So while it works I'm not gonna touch it.
I did everything LLM proponents say. I discussed requirements. Agent had access to the API docs and vendor samples. I said think hard many times. Based on this we wrote a detailed spec, then detailed inplementation plan. I hand checked a lot of the high level definitions. And yet here I am. By the time Opus went away and started coding, we had the user facing API hammered out, key implementation details (callback -> queue -> async task in source thread routing messages etc), constraints (clean handling of exceptions, threadsafe etc). Tests it has to write. Any minor detail we didn't discuss to death was coded up like a bored junior.
And this also wasn't my first attempt, this was attempt #3. First attempt was like, here's the docs and samples, make me a Python async API. That was a disaster. Second was more like, let's discuss, make a spec, then off you go. No good. Even just taking the last attempt time, I would have spent less time doing this by hand myself from scratch.
> lots of careful code of registering and unregistering callbacks, some careful thread synchronisation (callbacks get called in another thread), thinking about sane exception handling in async code. Fiddly but not rocket science.
I'd expect CC to fail this when just given requirements. The way I use it is to explicitly tell it things like: "Make sure to do Y when callback X gets fired" and not "you have to be careful about thread synchronisation". "Do X, so that Exceptions are always thrown when Y happens" instead of "Make sure to implement sane Exception handling". I think you have to get a feeling for how explicit you have to get because it definitely can figure out some complexity by itself.
But honestly it's also requires a different way of thinking and working. It reminds me of my dad reminiscing that the skill of dictating isn't used at all anymore nowadays. Since computers, typing, or more specifically correcting what has been typed has become cheap. And the skill of being able to formulate a sentence "on the first try" is less valuable. I see some (inverse) parallel to working with AI vs writing the code yourself. When coding yourself you don't have to explicitly formulate everything you are doing. Even if you are writing code with great documentation, there's no way that it could contain all of the tacit knowledge you as the author have. At least that's how I feel working with it. I just got really started with Claude Code 2 months ago and for a greenfield project I am amazed how much I could get done. For existing, sometimes messy side projects it works a lot worse. But that's also because it's more difficult to describe explicitly what you want.
I can see that there's a lot of applications where things can just randomly fail and you retry / restart, that helps with crashes.
But the AI can't make it not crash, what's to say it does the right thing when it succeeds? Again, depends on the relative cost of errors etc.
Silent failures and unexplained crashes are high on my list of things to avoid, but many teams just take them for granted in spite of the practical impact.
I think that a lot of orgs have a culture of "ship it and move on," accompanied by expectations like: QA will catch it, high turnover/lower-skill programmers commit stuff like this all the time anyway, or production code is expected to have some rough edges. I've been on teams like that, mostly in bigger orgs with high turnover and/or low engineering standards.
1. Performance-critical code to featurize byte slices for use in a ML model. Claude kept trying to take multiple passes over the slice when the featurization can obviously be done in one. After I finally got it to do the featurization in one pass it was double-counting some bytes but not others (double counting all of them would have been fine since the feature vector gets normalized). Overall it was just very frustrating because this should have been straight-forward and instead it was dogshit.
2. Performance-critical code that iterates over lines of text and possibly applies transformations, similar to sed. Claude kept trying to allocate new Strings inside of the hot-loop for lines that were not transformed. When I told it to use Cow<'a, str> instead so that the untransformed lines, which make up the majority of processed lines, would not need a new allocation, Claude completely fucked up the named lifetimes. Importantly, my CLAUDE.md already tells Claude to use copy-on-write types to reduce allocations whenever possible. The agent just ignored it, which is _the_ issue with LLMs: they're non-deterministic and any guidance you provide is ultimately just a suggestion.
What was the last thing you built in which you felt this was the case?
However now that it's in the beta stage the amount of issues and bugs is insane. I reviewed a lot of the code that went in as well. I suspect the bug fixing stage is going to take longer than the initial implementation. There are so many issues and my mental model of the codebase has severely degraded.
It was an interesting experiment but I don't think I would do it again this way.
The group used feature flags...
if (a) {
// new code
} else {
// old code
}
void testOff() {
disableFlag(a);
// test it still works
}
void testOn() {
enableFlag(a);
// test it still works
}
I can do one flag in ~3minutes. Code edit, pr prepped and sent.
The AI can do one in 10mins, but I couldn't look away. It kept trying to use find/grep to search through a huge repo to find symbols (instead of the MCP service).
Then it ignored instructions and didn't clean up one or the other test, left unused fields or parameters and generally made a mess.
Finally, I needed to review and fix the results, taking another 3-5 minutes, with no guarantee that it compiled.
At that point, a task that takes me 3 minutes has taken me 15.
Sure, it made code changes, and felt "cool", but it cost the company 5x the cost of not using the AI (before considering the token cost).
Even worse, the CI/CD system couldn't keep up the my individual velocity of cleaning these up, using an automated tool? Yeah, not going to be pleasant.
However, I need to try again, everyone's saying there was a step change in December.
Claude Code took 4 hours, with multiple prompts. At the end, it started to break the previous fixes in favor of new features. The code was spaghetti. There was no way I could fix it myself or steer Claude Code into fixing it the right way. Either it was a dead-end or a dice roll with every prompt.
Then I implemented my own version with Cursor tab completion. It took the same amount of time, 4 hours. The code had a clear object-oriented architecture, with a structure for evolution. Adding a new feature didn't require any prompts at all.
As a result, Claude Code was worse in terms of productivity: the same amount of time, worse quality output, no possibility of (or at best very high cost of) code evolution.
Me too. After listening to all the claims about Claude Code's productivity benefits, I was surprised to get the result I got.
I'm not able to share details of my work. I was using Claude Opus 4.5, if I recall correctly.
I wanted it to finish up some tests that I had already prefilled, basically all the AI had to do was convert my comments into the final assertions. A few minutes later of looping, I see it finishes and all tests are green.
A third of the tests were still unfilled, I guess left as an exercise for the reader. Another third was modified beyond what I told it to do, including hardcoding some things which made the test quite literally useless and the last third was fine, but because of all the miscellaneous changes it made I had to double check those anyways. This is about the bare minimum where I would expect these things to do good work, a simple take comment -> spit out the `assert()` block.
I ended up wasting more time arguing with it than if I had just done the menial task of filling out the tests myself. It sure did generate a shit ton of code though, and ran in an impressive looking loop for 5-10 minutes! And sure, the majority of the test cases were either not implemented or hardcoded so that they wouldn't actually catch a breakage, but it was all green!!
That's ultimately where this hype is leading us. It's a genuinely useful tool in some circumstances, but we've collectively lost the plot because untold billions have poured into these systems and we now have clueless managers and executives seeing "tests green -> code good" and making decisions based on that.
I can give you many, many examples of where it failed for me:
1. Efficient implementation of Union-Find: complete garbage result 2. Spark pipelines: mostly garbage 3. Fuzzer for testing something: half success, non-replicateable ("creative") part was garbage. 4. Confidential Computing (niche): complete garbage if starting from scratch, good at extracting existing abstractions and replicating existing code.
Where it succeeds: 1. SQL queries 2. Following more precise descriptions of what to do 3. Replicating existing code patterns
The pattern is very clear. Novel things, things that require deeper domain knowledge, coming up with the to-be-replicated patterns themselves, problems with little data don't work. Everything else works.
I believe the reason why there is a big split in the reception is because senior engineers work on problems that don't have existing solutions - LLMs are terrible at those. What they are missing is that the software and the methodology must be modified in order to make the LLM work. There are methodical ways to do this, but this shift in the industry is still in baby shoes, and we don't yet have a shared understanding of what this methodology is.
Personally I have very strong opinions on how this should be done. But I'm urging everyone to start thinking about it, perhaps even going as far as quitting if this isn't something people can pursue at their current job. The carnage is coming:/
It’s fine at replacing what stack overflow did nearly a decade ago, but that isn’t really an improvement from my baseline.
I end up replacing any saved time with QA and code review and I really don’t see how that’s going to change.
In my mind I see Claude as a better search engine that understands code well enough to find answers and gain understanding faster. That’s about it.
This is what's so frustrating about the hype bros for me. In most cases, everything AI spits out are code smells.
We're all just supposed to toss out every engineering principle we've learned all so the owner class can hire less developers and suppress wages?
I'm sure it's working great for everyone working on SaaS CRUD or web apps, but it's still not anywhere close to solving problems outside that sphere. Native? It's very hit and miss. It has very little design sense (because, why would it? It's a language model) so it chokes on SwiftUI, it also can't stop using deprecated stuff.
And that's not even that specialized. It still hallucinates cmdlets if you try to do anything with PowerShell, and has near zero knowledge about the industry I work in, a historically not tech-forward industry where things are still shared in handcrafted PDF reports emailed out to subscribers.
I'm going to leave this field entirely if the answer just becomes "just make everything in React/React Native because it's what the AI does best."
If I can just vibe and shrug when someone asks why production is down globally then I'm sure the amount of features I can push out increases, but if I am still expected to understand and fix the systems I generate, I'm not convinced it's actually faster to vibe and then try to understand what's going on rather than thinking and writing.
In my experience the more I delegate to AI, the less I understand the results. The "slowness and thinking" might just be a feature not a bug, at times I feel that AI was simply the final straw that finally gave the nudge to lower standards.
You're pretty high up in the development, decision and value-addition chain, if YOU are the responsible go-to person for these questions. AI has no impact on your position.
Tangential, I don't even know what "responsible" in the corporate world means anymore, it seems to me no one is really responsible for anything. But the one thing that's almost certain is that I will fix the damn thing if I made it go boom.
The question is really, velocity _of what_?
I got this from a HN comment. It really hit for me because the default mentality for engineers is to build. The more you build the better. That's not "wrong" but in a business setting it is very much necessary but not sufficient. And so whenever we think about productivity, impact, velocity, whatever measure of output, the real question is _of what_? More code? More product surface area? That was never really the problem. In fact it makes life worse majority of the time.
They've already admitted they just 'throw the code away and start again'.
I think we've got another victim of perceived productivity gains vs actual productivity drop.
People sitting around watching Claude churn out poor code at a slower rate than if they just wrote it themselves.
Don't get me wrong, great for getting you started or writing a little prototype.
But the code is bad, riddled with subtle bugs and if you're not rewriting it and shoving large amounts of AI code into your codebase, good luck in 6-12 months time.
I also heard an opinion that since writing code is cheap, people implement things that have no economic value without really thinking it through.
Only it doesn't, there's product positioning, UX, information architecture, onboarding and training, support, QA, change management, analytics, reporting… sigh
Tip to budding software engineers: try to not work in these sort of places, as they're about "looking busy" rather than engineering software, where the latter is where real long-lasting things are built, and the former is where startup founders spend most their money.
The last paragraph is where the tricky and valuable parts are, and also where AI isn't super helpful today, and where you as a human can actually help out a lot if you're just 10% better than the rest of the "engineers" who only want to ship as fast as possible.
At review time.
There are simply too many software industries that can't delegate both authorship _and_ review to non-humans because the maintenance/use of such software, especially in libraries and backwards-compat-concerning environments, cannot justify an "ends justifies the means" approach (yet).
I have never heard anyone say "it works" as a positive thing when reviewing code..
Yes, there is a productivity boost but you can't tell me there is no decrease in quality
Isn't it a very inefficient way to learn things? Like, normally, you would learn how things work and then write the code, refining your knowledge while you are writing. Now you don't learn anything in advance, and only do so reluctantly when things break? In the end there is a codebase that no one knows how it works.
It is. But there are 2 things:
1. Do I want to learn that? (if I am coming back to this topic again in 5 months, knowledge accumulates, but there is a temptation to finish the thing quickly, because it is so boring to swim in huge legacy codebase)
2. How long it takes to grasp it and implement the solution? If I can complete it with AI in 2 days vs on my own in 2 weeks, I probably do not want to spend too much time on this thing
as I mentioned in other comments, this is exactly makes me worried about future of the work I will be doing, because there is no attachment to the product in my brain, no mental models being built, no muscles trained, it feels someone else's "work", because it explores the code, it writes the code. I just judge it when I get a task
It pains the anti-capitalist fibers in my body to say this, but no they are not. At the maximum the value is in organizational knowledge and existing assets (= source code, documentation), so that people with the least knowledge possible can make changes. In software companies in general, technical excellence and knowledge is not strongly correlated with economic success as long as you clear a certain bar (that's not that high). In comparison, in hardware/engineering companies, that's a lot more correlated.
In the concrete example of a legacy codebase we have here, there is even less value in trying to build up knowledge in the company, as it has already been decided that the system is to be discarded anyways.
In a legacy codebase this may require learning a lot of things about how things work just to make small changes, which may be much less efficient.
We can now make 1$ million dollar commercials with 100,000$ or less. So a 90% reduction in costs - if we use AI.
The issue is they don’t look great. AI isn’t that great at some key details.
But the agencies are really trying to push for it.
They think this is the way back to the big flashy commercials of old. Budgets are lower than ever, and shrinking.
Big issue here is really the misunderstanding of cause - budgets are lower, because advertising has changed in general (TV is less and less important ) and a lot of studies showed that advertising is actually not all that effective.
So they are grabbing onto a lifeboat. But I’m worried there’s no land.
I’ve planned my exit.
Also what are you existing to?
When the metrics arrived with digital, they saw that advertising, in some ways, was just not as effective as they’d hoped. In some ways the ROI wasn’t there. Seth Godin agrees. He says that advertising in the digital era could be as simple as just having a good product. I think this is Tesla’s position on it - make the best product and the internet takes care of it.
Legacy companies have kept large ad budgets but those are diminishing. From what I spoke with my friend at WPP, he said their data science team showed that outside of a new product or a product that is not recognised by consumers, the actual outcomes from ads are marginal or incremental. Thats what he told me. If your product is already known to consumers, the ROI is questionable.
There’s something about AIs that feels wrong for storytelling. I just don’t think people will want AIs to tell them stories. And if they do… Well, I believe in human storytelling.
Sometimes I realise that this particular task has been slower than if I’d done it myself when I take in to account full wall clock time.
I can’t tell what type of task is going to work ahead of time yet.
On the other hand, our corporate AI is.. not great atm. It was briefly kinda decent and then suddenly it kinda degraded. Worst case is, no one is communicating with us so we don't know what was changed. It is possible companies are already trying to 'optimize'.
I know it is not exactly what you are asking. You are saying capability is there, but I am personally starting to see a crack in corporate willingness to spend.
Edit: Ha, and the report claims it's relatively good at business and finance...
Edit 2: After discussion in this thread, I went back to opus and asked it to link to articles about how to handle non-normally distributed data, and it actually did link to some useful articles, and an online calculator that I believe works for my data. So I'll eat some humble pie and say my initial take was at least partially wrong here. At the same time, it was important to know the correct question to ask, and honestly if it wasn't for this thread I'm not sure I would have gotten there.
A good way to use AI is to treat it like a brilliant junior. It knows a lot about how things work in general but very little about your specific domain. If your data has a particular shape (e.g lots of orders with a few large orders as outliers) you have to tell it that to improve the results you get back.
- https://cxl.com/blog/outliers/
- https://www.blastx.com/insights/the-best-revenue-significanc...
- (online tool to calculate significance) https://www.blastx.com/rpv-calculator
I'm not checking their math, but the articles make sense to me, and I trust they did implement it correctly. In the end the LLM did get me to the correct answer by suggesting the articles, so I guess I should eat some humble pie and say it _did_ help me. At the same time, if I didn't have the intuition that using rpv as-is in a t-test would be noisy, and the suggestions from this comment thread, I think I could have gone down the wrong path. So I'm not sure what my conclusion is -- maybe something like LLMs are helpful once you ask the right question.
(I also just love statistics and think it's some of the most applicable math to everyday life in everything from bus arrival times to road traffic to order values to financial markets.)
People who are saying they're not seeing productivity boost, can you please share where is it failing?
I'm going to guess that many people on HN are also on the "free ChatGPT isn't that good at programming" train.
Til then wtf_are_these_abstractions.jpg
Probably that's the reason why some people are sure their job is still safe.
Nature of job is changing rapidly
I'm not a tech CEO but people who are anti-LLM for programming have no place on my team.
I’m reminded of https://www.joelonsoftware.com/2000/08/09/the-joel-test-12-s...
How much bloat and bureaucracy bottleneck is sitting in middle management whose favorite past time is wasting everyone's time on meetings that could have been an email? HR? Not the execs, but the HR drones that do nothing but answer employee questions about policy, could have already been replaced with not even an AI, just an old school chatbot, a long time ago.
Instead of cutting engineers, cut the non-tech jobs, flatten the structure.
There’s obviously a benefit of paying higher rates for US programmers, but does that benefit change when llms are thrown into the mix
It takes more planning, more specification, more coordination, more QA. The quality is almost always worse, and remediation takes forever. So your BA, QA and PM time goes way up and absorbs any cost savings.
YMMV.
Someone deciding to drop a spreadsheet of customer data into their personal AI account to increase their productivity would be catastrophic for business, so you need rules. And rules means paying for enterprise AI tooling.
The $20 a month tier in particular is a trivial expense, on par with businesses that expect their workers to wear steel toed shoes. Some may give workers a little stipend to buy those boots, some not. Either way, it doesn't really matter.
I don't do tech outside of 9-5, so either my employer pays for it all, or I don't use it. Simple as that. Thankfully, they do pay for it, but I couldn't imagine working somewhere that says "You need to use AI" and then not providing it on their dime.
Quite frankly it should be regulation that if a W2 employee needs something to perform their job duties, the employer must provide it.
I work at tech company just outside of big tech and I feel fairly confident that we won't have a need for the amount of developers we currently have within 3-4 years.
The bottleneck right now is reviewing and I think it's just a matter of time before our leadership removes the requirement for human code reviews (I am already seeing signs of this ("Maybe for code behind feature flags we don't need code reviews?").
Whenever there's an incident, there is a pagerduty trigger to an agent looking at the metrics, logs, software component graphs, and gives you an hypothesis on what the incident is due to. When I push a branch with test failures, I get one-click buttons in my PR to append commits fixing those tests failures (i.e. an agent analyses the code, the jira ticket, the tests, etc. and suggests a fix for the tests failing). We have a Slack agent we can ping in trivial feature requests (or bugs) in our support channels.
The agents are being integrated at every step. And it's not like the agents will stop improving. The difference between GPT3.5 and Opus 4.6 is so massive. So what will the models look like in 5 years from now?
We're cooked and the easiest way to tell someone works at a company who hasn't come very far in their AI journey is that they're not worried.
LLMs also are quite bad for security. They can find simple bugs, but they don't find the really interesting ones that leverage "gap between mental model and implementation" or "combination of features and bugs" etc, which is where most of the interesting security work is imo.
https://aisle.com/blog/what-ai-security-research-looks-like-...
I am doing novel work with codex but it does need some prompting ie. exploring possibilities from current codebase, adding papers to prompt etc.
For security, I think I generally start a new thread before committing to review from security pov.
If you are relying on the LLM and context, then unless your context is a secret your competitor is only ever one prompt behind you. If you're willing to pursue true novelty, you need a human and you can leap beyond your competition.
The reality is that a huge portion of my time is spent doing similar work and what LLMs largely do is pick up the smaller tasks or features that I may not have prioritized otherwise. Revolutionary in one sense, completely banal and a really minor part of my job in many others.
Tests were always important, but now they are the gatekeepers to velocity.
Why? This is great. AI fixing up huge legacy codebases is just taking the jobs humans would never want to do.
Productivity is a term of art in economics and means you generate more units of output (for example per person, per input, per wages paid) but doesn't take quality or otherwise desireability into account. It's best suited for commodities and industrial outputs (and maybe slop?).
Yesterday a colleague didn’t quite manage to implement a loading container with a Vue directive instead of DOM hacks, it was easier for me to just throw AI at the problem and produced a working and tested solution and developer docs than to have a similarly long meeting and have them iterate for hours.
Then I got back to training a CNN to recognize crops from space (ploughing and mowing will need to be estimated alongside inference, since no markers in training data but can look at BSI changes for example), deployed a new version of an Ollama/OpenAI/Anthropic proxy that can work with AWS Bedrock and updated the docs site instructions, deployed a new app that will have a standup bot and on-demand AI code review (LiteLLM and Django) and am working on codegen to migrate some Oracle forms that have been stagnating otherwise.
It’s not funny how overworked I am and sure I still have to babysit parallel Claude Code sessions and sometimes test things manually and write out changes, but this is a completely different work compared to two or three years ago.
Maybe the problem spaces I’m dealing with are nothing novel, but I assume most devs are like that - and I’d be surprised at people’s productivity not increasing.
When people nag in meetings about needing to change something in a codebase, or not knowing how to implement something and its value add, I’ll often have something working shortly after the meeting is over (due to starting during it).
Instead of sending adding Vitest to the backlog graveyard, I had it integrated and running in one or two evenings with about 1200 tests (and fixed some bugs). Instead of talking about hypothetical Oxlint and Oxfmt performance improvements, I had both benchmarked against ESLint and Prettier within the hour.
Same for making server config changes with Ansible that I previously didn’t due to additional friction - it is mostly just gone (as long as I allow some free time planned in case things vet fucked up and I need to fix them).
Edit: oh and in my free time I built a Whisper + VLM + LLM pipeline based on OpenVINO so that I can feed it hours long stream VODs and get an EDL cut to desired length that I can then import in DaVinci Resolve and work on video editing after the first basic editing prepass is done (also PyScene detect and some audio alignment to prevent bad cuts). And then I integrated it with subscription Claude Code, not just LiteLLM and cloud providers with per-token costs for the actual cuts making part (scene description and audio transcriptions stay local since those don't need a complex LLM, but can use cloud for cuts).
Oh and I'm moving from my Contabo VPSes to running stuff inside of a Hetzner Server Auction server that now has Proxmox and VMs in that, except this time around I'm moving over to Ansible for managing it instead of manual scripts as well, and also I'm migrating over from Docker Swarm to regular Docker Compose + Tailscale networks (maybe Headscale later) and also using more upstream containers where needed instead of trying to build all of mine myself, since storage isn't a problem and consistency isn't that important. At the same time I also migrated from Drone CI to Woodpecker CI and from Nexus to Gitea Packages, since I'm already using Gitea and since Nexus is a maintenance burden.
If this becomes the new “normal” in regards to everyone’s productivity though, there will be an insane amount of burnout and devaluation of work.
We've started building harnesses to allow people who don't understand code to create PRs to implement their little nags. We rely on an engineer to review, merge, and steward the change but it means that non-eng folks do not rely on us as a gate. (We're a startup and can't really afford "teams" to do this hand-holding and triage for us.)
As you say we're all a bit overworked and burned out. I've been context switching so much that on days when I'm very productive I've started just getting headaches. I'm achieving a lot more than before but holding the various threads in my head and context switching is just a lot.
I've always done more in days than others might in a week. YMMV.
1: pre-AI. Not keen on becoming a manager of an idiot savant, so I’m planning my exit.
Gaslight me by telling me I must be a time traveler because I use go 1.26 but the latest version actually is 1.24
And tell me I can't use wg.Go() because this function does not exist (it does)
God I hope I never ever have to work with you
The productivity gains are blatantly obvious at this point. Even in large distributed code bases. From jr to senior engineer.
Note1: I have "expert" level research skills. But LLMs still help me in research, but the boost is probably 1.2x max. But
Note2: By research, I mean googling, github search, forum search, etc. And quickly testing using jsfiddle/codepen, etc.
But I also think you are overestimating your RESEARCH skills, even if you are very good at research, I am sure you can't read 25 files in parallel, summarize them (even if its missing some details) in 1 minute and then come up with somewhat working solution in the next 2 minutes.
I am pretty sure, humans can't comprehend reading 25 code files with each having at least 400 lines of non-boilerplate code in 2 minutes. LLM can do it and its very very good at summarizing.
I can even steer its summarizing skills by prompting where to focus on when its reading files (because now I can iterate 2-3 times for each RESEARCH task and improve my next attempt based on shortcomings in the previous attempt)
In this example, if the 25 files are organized nicely, and I had I nice IDE that listed class/namespace members of each file neatly, I might take 30 minutes to understand the overall structure.
Morever, If I critically analyzed this, I would ask "how many times does this event of summarizing 25 files happen"? I mean, are we changing codebases every day? No, it's a one time cost. Moreover, manually going through will provide insight not returned by LLM.
Obviously, every case is different, and perhaps you do need to RESEARCH new codebases often, I dunno!
* you probably lack good RESEARCH skills
* I can see at most 1.25x improvements - now it is 2-3x
By updating your comment you are making my reply irrelevant to your past response
Every time I say this people get really angry, but: so far AI has had almost no impact on my job. Neither my dev team nor my vendors are getting me software faster than they were two years ago. Docker had a bigger impact on the pipeline to me than AI has.
Maybe this will change, but until it does I'm mostly watching bemusedly.
I've seen lots of people say AI can basically code a project for them. Maybe it can, but that seems to heavily depend on the field. Other than boilerplate code or very generic projects, it's a step above useless imo when it comes to gamedev. It's about as useful as a guy who read some documentation for an engine a couple years ago and kind of remembers it but not quite and makes lots of mistakes. The best it can do is point me in the general direction I need to go, but it'll hallucinate basic functions and mess up any sort of logic.
Am trying to compare this to reports that people are not reviewing code any more.
1) Do that inside their IDEs, which is less funny
2) Generate blog post about it instead of memes
So the good old days before search engines were drowning with ads and dark patterns. My assumption is big LLMs will go in the same direction after market capture is complete and they need to start turning a profit. If we are lucky the open source models can keep up.
Arguably this solution is "better" because you don't even really need to understand that you have specific problems to have the agent solve them for you, but I fail to see the point of keeping these people employed in that case. If you haven't been able to solve your own workflow issues up until now I have zero trust in you being able to solve business problems.
If not, then you’re not close to the cutting edge.
For me this is a huge boost in productivity. If I remember how I was working in the past (example of Google integration):
Before:
* go through docs to understand how to start (quick start) and things to know
* start boilerplate (e.g. install the scripts/libs)
* figure out configs to enable in GCP console
* integrate basic API and test
* of course it fails, because its Google API, so difficult to work with
* along the way figure out why Python lib is failing to install, oh version mismatch, ohh gcc not installed, ohh libffmpeg is required,...
* somehow copy paste and integrate first basic API
* prepare for production, ohhh production requires different type of Auth flow
* deploy, redeploy, fix, deploy, redeploy
* 3 days later -> finally hello world is working
* Hey my LLM buddy, I want to integrate Google API, where do I start, come up with a plan
* Enable things which requires manual intervention
* In the meantime LLM integrates the code, install lib, asks me to approve installation of libpg, libffmpeg,....
* test, if fails, feed the error back to LLM + prompt to fix it
* deployThe docs used to be good enough that there would be an example which did exactly what you needed more often than the llm gets it right today.
A famous economist once said, "You can see the computer age everywhere but in the productivity statistics."
There are many reasons for the lag in productivity gain but it certainly will come.
The loss of skills, complete loss of visibility and experience with the codebase, and the complete lack of software architecture design, seems like a massive killer in the long term
I have a feeling that we're going to see productivity with AI drop through the floor
Just yesterday I asked Opus 4.6 what I could do to make an old macOS AppKit project more testable, too lazy to even encumber the question with my own preferences like I usually do, and it pitched a refactor into Elm architecture. And then it did the refactor while I took a piss.
The idea that AI writes bad software or can't improve existing software in substantial ways is really outdated. Just consider how most human-written software is untested despite everyone agreeing testing is a good idea simply because test-friendly arch takes a lot of thought and test maintenance slow you down. AI will do all of that, just mention something about 'testability' in AGENTS.md.
Why would you think you have your finger on the pulse of general software trends like that when you use the same, what, dozen apps every week?
Just looking at my own productivity, as mere sideprojects this month, I've shipped my own terminal app (replaced iTerm2), btrfs+luks NAS system manager, overhauled my macOS gamepad mapper for the app store, and more. All fully tested and really polished, yet I didn't write any code by hand. I would have done none of that this month without AI.
You'd need some real empirics to pick up productivity stories like mine across the software world, not vibes.
Most of the software I replaced was software I was paying for (iStat Menus, Wispr Flow, Synology/Unraid). That I was paying for a project I could trivially take on with AI was one of the main incentives to do it.
I'm happy to sell it to you, though it is also free. I guided Claude to write this in three weeks, after never having written a line of JavaScript or set up a server before. I'm sure a better JavaScript programmer than I could do this in three weeks, but there's no way I could. I just had a cool idea for making advertising a force for good, and now I have a working version in beta.
I'd say it is better software, but better is doing a lot of heavy lifting there. Claude's execution is average and always will be, that's a function of being a prediction engine. But I genuinely think the idea is better than how advertising works today, and this product would not exist at all if I had to write it myself. And I'm someone who has written code before, enough that I was probably a somewhat early adopter to this whole thing. Multiply that by all the people whose ideas get to live now, and I'm sure some ideas will prove to be better even with average execution. Like an llm, that's a function of statistics.
This concept will never work outside of their own head. People continue to think producing something is the hard part my word.
I didnt get it either on first glance when scrolling down the whole page
AI writes bad software by virtue of it being written by the AI, not you. No actual team member understands what's going on with the code. You can't interrogate the AI for its decision making. It doesn't understand the architecture its built. There's nobody you can ask about why anything is built the way it is - it just exists
Its interesting watching people forget that the #1 most important thing is developers who understand a codebase thoroughly. Institutional knowledge is absolutely key to maintaining a codebase, and making good decisions in the long term
Its always been possible to trade long term productivity for short term gains like this. But now you simply have no idea what's going on in your code, which is an absolute nightmare for long term productivity
Remember this famously happened before, in the 1970s
A Commodore 64 was a cool gadget, but “the family computer” became a device that commoditized the productivity. The opportunity cost of applying a computer to try something new went to near zero.
It might have been harder for someone to improve the productivity of an old factory in Shreveport, Louisiana with a computer than it was for the upstarts at id to make Doom.
Predictions without a deadline are unfalsifiable.
I can turn out some scripts a little bit quicker, or find an answer to something a little quicker than googling, but I'm still waiting on others most of the time, the overall company processes haven't improved or gotten more efficient. The same blockers as always still exist.
Like you said, there has been other tech that has changed my job over time more than AI has. The move to the cloud, Docker, Terraform, Ansible, etc. have all had far more of an impact on my job. I see literally zero change in the output of others, both internally and externally.
So either this is a massively overblown bubble, or I'm just missing something.
I've been in ops for 30 years, Claude Code has changed how I work. Ops-related scripting seems to be a real sweet spot for the LLMs, especially as they tend to be smaller tools working together. It can convert a few sentences into working code in 15-30 minutes while you do something else. I've given it access to my apache logs Elastic cluster, and it does a great job at analyzing them ("We suspect this user has been compromised, can you find evidence of that?"). It's quite startling, actually, what it's able to do.
The problem with analyzing logs is determinism. If I ask Claude to look for evidence of compromise, I can't trust the output without also going and verifying myself. It's now an extra step, for what? I still have to go into Elastic and run the actual queries to verify what Claude said. A saved Kibana search is faster, and more importantly, deterministic. I'm not going to leave something like finding evidence of compromise up to an LLM that can, and does, hallucinate especially when you fill the context up with a ton of logs.
An auditor isn't going to buy "But Claude said everything was fine."
Is AI actually finding things your SIEM rules were missing? Because otherwise, I just don't see the value in having a natural language interface for queries I already know how to run, it's less intuitive for me and non deterministic.
It's certainly a useful tool, there's no arguing that. I wouldn't want to go back to working with out it. But, I don't buy that it's already this huge labor market transformation force that's magically 100x everyone's productivity. That part is 100% pure hype, not reality.
Is it? A couple days ago I had it build tooling for a one-off task I need to run, it wrote ~800 lines of Python to accomplish this, in <30m. I found it was too slow, so I got it to convert it to run multiple tasks in parallel in another prompt. Would have taken a couple days for me to build from hand, given the number of interruptions I have in the average day. This isn't a one-off, it's happening all the time.
And that's the key problem, isn't it? I maintain current organizations have the "wrong shape" to fully leverage AI. Imagine instead of the scope of your current ownership, you own everything your team or your whole department owns. Consider what that would do to the meetings and dependencies and processes and tickets and blockers and other bureaucracy, something I call "Conway Overhead."
Now imagine that playing out across multiple roles, i.e. you also take on product and design. Imagine what that would do to your company org chart.
I added a much more detailed comment here: https://news.ycombinator.com/item?id=47270142
> Now imagine
> Imagine what that would do
Imagine if your grandma had wheels! She'd be a bicycle. Now imagine she had an engine. She could be a motorcycle! Unfortunately for grandma, she lives in reality and is not actually a motorcycle, which would be cool as hell. Our imagination can only take us so far.
To more substantively reply to your longer linked comment: your hypothesis is that people spend as little as 10% of time coding and the other 90% of time in meetings, but that if they could code more, they wouldn't need to meet other people because they could do all the work of an entire team themselves[1]. The problem with your hypothesis is that you take for granted that LLMs actually allow people to do the work of an entire team themselves, and that it is merely bureacracy holding them back. There have been absolutely zero indicators that this is true. No productivity studies of individual developers tackling tasks show a 10x speedup; results tend to be anywhere from +20% to minus 20%. We aren't seeing amazing software being built by individual developers using LLMs. There is still only one Fabrice Bellard in the world, even though if your premise could escape the containment zone of imagination anyone should be able to be a Bellard on their own time with the help of LLMs.
[1] Also, this is basically already true without LLMs. It is the reason startups are able to disrupt corporate behemoths. If you have just a small handful of people who spend the majority of their work time writing code (by hand! No LLMs required!), they can build amazing new products that outcompete products funded by trillion-dollar entities. Your observation of more coding = less meetings required in the first place has an element of truth to it, but not because LLMs are related to it in any particular way.
> Imagine if your grandma had wheels! She'd be a bicycle.
Too early to say what AI brings to the efficiency table I think. In some major things I do it's a 1000x speed up. In others it is more a different way of approaching a problem than a speed up. In yet others, it is a bit of an impediment. It works best when you learn to quickly recognize patterns and whether it will help. I don't know how people who are raised with ai will navigate and leverage it, which is the real long-term question (just as the difference between pre- and post-smartphone generations is a thing).
EDIT: Retracted, I think the example given below is reasonably valid.
The only study showing a -20% came back and said, "we now think it's +9% - +38%, but we can't prove rigorously because developers don't want to work without AI anymore": https://news.ycombinator.com/item?id=47142078
Even at the time of the original study, most other rigorous studies showed -5% (for legacy projects, obsolete languages) to 30% (more typical greenfield AND brownfield projects) way back in 2024. Today I hear numbers up to 60% from reports like DX.
But this is exactly missing the point. Most of them are still doing things the old way, including the very process of writing code. Which brings me to this point:
> There have been absolutely zero indicators that this is true.
I could tell you my personal experience, or link various comments on HN, or point you to blogs like https://ghuntley.com/real/ (which also talks about the origanizational impedance mismatch for AI), but actual code would be a better data point.
So there are some open-source projects worth looking at, but they are typically dismissed because they look so weird to us. Here's two mostly vibe-coded (as in, minimal code review, apparently) projects that people shredded for having weird code, but is already used by 10s of 1000s of people, up to 11 - 18K stars now. Look at the commit volume and patterns for O(300K) LoC in a couple of months, mostly from one guy and his agent:
https://github.com/steveyegge/beads/graphs/commit-activity
https://github.com/steveyegge/gastown/graphs/commit-activity
It's like nothing we've seen before, almost equal number of LoC additions and deletions, in the 100s of Ks! It's still not clear how this will pan out long term, but the volume of code and apparent utility (based purely on popularity) is undeniable.
If you are referring to the following quote [0], you are off by a sign:
> we now estimate a speedup of -18% with a confidence interval between -38% and +9%.
You'd get the same sort of results if you were studying the benefits of substance abuse.
"It is difficult to study the downsides of opiates because none of our participants were willing to go a day without opiates. For this reason, opiates must be really good and we're just missing something."
I dismiss them because Yegge's work (if it can even be called his work, given that he doesn't look at the code) is steaming garbage with zero real-world utility, not "because they look weird". You suggest the apparent utility is undeniable, while saying "based purely on popularity" -- but popularity is in no way a measure of utility. Yegge is a conman who profited hundreds of thousands of dollars shilling a memecoin rugpull tied to these projects. The actual thousands of users are people joining the hypetrain, looking to get in on the promised pyramid scheme of free money where AI will build the next million dollar software for you, if only you have the right combination of .md files to make it work. None of these software are actually materialising, so all the people in this bubble can do is make more AI wrappers that promise to make other AI wrappers that will totally make them money.
I am completely open to being proven wrong by a vibe-coded open source application that is actually useful, but I haven't seen a single one. Literally not even one. I would count literally anything where the end-product is not an AI wrapper itself, which has tens to hundreds of thousands of users, and which was written entirely by agents. One example of that would be great. Just one. There have been a couple of attempts at a web browser, and Claude's C compiler, but neither are actually useful or have any real users; they are just proofs of concept and I have seen nothing that convinces me they are a solid foundation from which you could actually build useful software from, or that models will ever be on a trajectory to make them actually useful.
Imo it's only a matter of time as companies start to figure out how to use ai. Companies don't seem to have real plans yet and everyone is figuring out ai in general out.
Soon though I will think agents start popping up, things like first line response to pages, executing automation
Maybe 2 years ago Ai was doing random stuff and we got all those funny screenshots of dumb gemini answers. The indeterminism leading to random stuff isn't really an issue any more.
The way it thinks keeps it on track.
Opus 4.6 didn't have an issue with this question though.
Humans are funny. But most cant seem to understand that the tool is a mirage and they are putting false expectations on it. E.g. management of firms cutting back on hiring under the expectation that LLMs will do magic - with many cheering 'this is the worst itll be bro!!".
I just hope more people realise before Anthropic and OAI can IPO. I would wager they are in the process of cleaning up their financials for it.
We aren't fungible workers in a low skill industry. And if you find yourself working in a tech company without equity: just don't, leave. Either find a new tech company or do something else altogether.
I'm not sure what to say. It's like someone claiming that automobiles don't improve personal mobility. There are a lot of logical reasons to be against the mass adoption of automobiles, but "lack of effectiveness as a form of personal mobility" is not one of them.
Hearing things like this does give me a little hope though, as I think it means the total collapse of the software engineering industry is probably still a few years away, if so many companies are still so far behind the curve.
I prefer walking or cycling and often walk about 8km a day around town, for both mobility and exercise. (Other people's) automobiles make my experience worse, not better.
I'm sure there's an analogy somewhere.
(Sure, automobiles improve the speed of mobility, if that's the only thing you care about...)
There’s some neat stuff, don’t get me wrong. But every additional tool so far has started strong but then always falls over. Always.
Right now there’s this “orchestrator” nonsense. Cool in principle, but as someone who made scripts to automate with all the time before it’s not impressive. Spent $200 to automate doing some bug finding and fixing. It found and fixed the easy stuff (still pretty neat), and then “partially verified” it fixed the other stuff.
The “partial verification” was it justifying why it was okay it was broken.
The company has mandated we use this technology. I have an “AI Native” rating. We’re being told to put out at least 28 commits a month. It’s nonsense.
They’re letting me play with an expensive, super-high-level, probabilistic language. So I’m having a lot of fun. But I’m not going to lie, I’m very disappointed. Got this job a year ago. 12 years programming experience. First big tech job. Was hoping to learn a lot. Know my use of data to prioritize work could be better. Was sold on their use of data. I’m sure some teams here use data really well, but I’m just not impressed.
And I’m not even getting into the people gaming the metrics to look good while actually making more work for everyone else.
Sunk cost fallacy is very real, for all involved. Especially the model producers and their investors.
Sunk cost fallacy is also real for dev's who are now giving up how they used to work - they've made a sunk investment in learning to use LLMs etc. Hence the 'there's no going back' comments that crop up on here.
As I said in this thread - anyone who can think straight - Im referring to those who adhere to fundamental economic principles - can see what's going on from a mile away.
If you can’t be exposed to it in your day job, start using Claude opus in the evening so you know what’s coming.
Maybe I will be replaced by matrix multiplication in my job, but if I need to use LLM at some point I expect little benefit from starting now.
Yes, I tried to use Claude Code two months ago. It was scary, but not useful.
If it would be useful I would continue to use it, but at this point I would not use even if it would be free, not proprietary and not funding replacing me.
Are you hiring?
IMO AI will make 70-80% job obsolete for sure.
lol, that sounds like a disaster for the codebase.
I recently used copilot.com to help solve a tricky problem for me (which uses GPT 5.1):
I have an arbitrary width rectangle that needs to be broken into smaller
random width rectangles (maintaining depth) within a given min/max range.
So I poked the machine.
The next result used dynamic programming and generated every possible output combination. With a sufficiently large (yet small) rectangle, this is a factorial explosion and stalled the software.
So I poked the machine.
I realized this problem was essentially finding the distinct multisets of numbers that sum to some value. The next result used dynamic programming and only calculated the distinct sets (order is ignored). That way I could choose a random width from the set and then remove that value. (The LLM did not suggest this). However, even this was slow with a large enough rectangle.
So I poked my brain.
I realized I could start off with a greedy solution: Choose a random width within range, subtract from remaining width. Once remaining width is small enough, use dynamic programming. Then I had to handle the edges cases (no sets, when it's okay to break the rules.. etc)
So the LLMs are useful, but this took 2-3 hours IIRC (thinking, implementation, testing in an environment). Pretty sure I would have landed on a solution within the same time frame. Probably greedy with back tracking to force-fit the output.
"I have an arbitrary width rectangle that needs to be broken into smaller random width rectangles (maintaining depth) within a given min/max range. The solution needs to be highly performant from an algorithmic standpoint, well-tested using TDD and Red/Green testing, written in python, and not have any subtle errors."
It got the answer you ended up with (if I'm understanding you correctly) the first time in just over 2 minutes of working, and included a solid test suite examining edge cases and with input validation.
I appreciate you testing, even though it's not a great comparison:
- My feedback cycle of LLM prompting forced me to be more explicit with each call, which benefited your prompt since I gave you exactly what to look for with fewer nuances.
- Maybe GPT 5.1 is old or kneecapped for newer versions of GPT
- Maybe Opus/Claud is just a way better model :P
Please post the code!
Edit: Regarding "exactly what to look for", when solving a new problem, rarely is all the nuance available for the first iteration.
Sorry to be so blunt, but it's not surprising that you aren't able to get much value from these tools, considering you don't use them much.
Getting value from LLMs / agents is a skill like any other. If you don't practice it deliberately, you will likely be bad at it. It would be a mistake to confuse lack of personal skill for lack of tool capability. But I see people make this mistake all the time.
If it's "you didn't explain the problem clearly enough", then that aligns with my original comment.
No simply 'producing a feature' aint it bud. That's one piece of the puzzle.
People who actually know how to think can see it a mile away.
Job done fella.
I totally understand that not everyone is having that experience. And yet until people live it, it seems they just discount the experience others are having.
I'll take the 12 month bet.
It's clearly relative. For all we know you're a crap coder and AI is now your crutch. We have no evidence that with AI you are as good as an average developer with a fair amount of experience. And even if you do have a fair amount of experience, that doesn't mean you're a good coder.
Let us not lose sight of how we got here.
Guys like you dont get it. You think OAI, Amazon etc can freely put large amounts of money into this for 5-10 years? Lmao - delusional. Investors are impatient. Show huge jumps in revenue this year or you no longer have permission to put monumental amounts of money into this anymore.
Short of that they'll just destroy the stock price by selling off; leaving employees who get paid via SBC very unhappy.
Won't matter. The Chinese models will be running on potatoes by then and be better than ever.
The specific way it applies to your specific situation, if it exists, either hasn't been found or hasn't made its way to you. It really is early days.
I'm curious how the system will maneuver itself to deprive workers of pay so that they can stay competitive with the ever-decreasing cost of AI.
Conversely, I'm curious how disruptors will find ways to provide workers with pay (perhaps through mutual aid networks, grants and alternative socioeconomic systems) so that they can use AI to produce the resources they need outside of the contracting labor market.
This assumes that the companies' business growth is a function of the amount of code written, but that would not make much sense for a software company.
Many companies (including mine) are building our product with an engineering team 1/4 the size of what would have been required a few years ago. The whole idea is that we can build the machine to scale our business with far fewer workers.
Even in companies that are no longer growing I've always seen the roadmap only ever get larger (at that point you get desperate to try to catch back up, or expand into new markets, while also laying people off to cut costs).
Will we finally out-write the backlog of ideas to try and of feature requests? Or will the market get more fragmented as more smaller competitors can carve out different niches in different markets, each with more-complex offerings than they could've offered 5 years ago?
This is already happening. Fewer people are getting hired. Companies are quietly (sometimes not, like Block) letting people go. At a personal level all the leaders in my company are sounding the “catch up or you’ll be left behind” alarm. People are going to be let go at an accelerated pace in the future (1-3 years).
> We find no systematic increase in unemployment for highly exposed workers since late 2022
It is absolutely likely. The hiring market for juniors is fucked atm.
(And if it is, what is the cause?)
How long have we been hearing about crushing affordability problems for property? And how long ago did that start moving into essentials? The COVID-era bullwhip-effect inflation waves triggered a lot of price ratcheting that has slowed but never really reversed. Asset prices are doing great, as people with money continue to need somewhere to put it, and have been very effective at capturing greater and greater shares of productivity increases. But how's the average waiter, cleaning-business sole-proprietor, uber driver, schoolteacher, or pet supply shopowner doing? How's their debt load trending? How's their savings trending?
[1] https://www.npr.org/2026/02/12/nx-s1-5711455/revised-labor-d...
[2] https://www.marketplace.org/story/2025/12/18/expect-more-of-...
Lots going on right now in the market, but IMO that retreat is the biggest one still.
Many companies were basically on a path of infinite hiring between ~2011 and ~2022 until the rapid COVID-era whiplash really drove home "maybe we've been overhiring" and caused the reaction and slowdown that many had been predicting annually since, oh, 2015.
There's a lot of perverse interests and incentives at play.
You can be an exec with 10-20% fewer random products/departments in your company, and maybe 40% fewer middle managers in the rest of them. You might even get a nice bonus for cutting all that cost! Bonuses for growth, bonuses for "efficiency" when the macro vibe shifts. Trim sails and carry on.
Also dont forget theres only so many viable revenue-generating and cost-saving projects to take. And said above - overhiring in COVID.
> We find no systematic increase in unemployment for highly exposed workers since late 2022
That's... not a good look for your engineers?
And in the longer term those people will also get deprecated.
Then any company that was staffed at levels needed prior to the arrival of current-level LLM coding assistants is bloated.
If the company was person-hour starved before, a significant amount of that demand is being satisfied by LLMs now.
It all depends on where the company is in the arc of its technology and business development, and where it was when powerful coding agents became viable.
Being overworked is sometimes better than being underworked. Sometimes the reserve is better. They both have challenges.
In that sort of high-fear, change-adverse environment "get rid of all the devs and let the AI do it" may not be the most compelling sales pitch to leadership. ("Use it to port the code faster so we can spend more time on the migration plan and manual testing" might have better luck.)
Best time to be a solo founder in underserved markets :)
The leading AI exposure indices (Anthropic, Eloundou et al.) focus on which jobs get automated. They treat low exposure as “safe.”
But the least exposed workers—cooks, roofers, dishwashers, construction laborers—are often in the worst jobs: low pay, high physical toll, short career spans, and little upward mobility. Safe from AI, but not from burnout or injury.
I built JQADI (Job Quality-Adjusted Displacement Index) to combine AI exposure with job quality. It surfaces three kinds of risk:
High AI exposure → classic displacement risk Low AI, low quality → “trapped” workers in grinding, unsustainable jobs Moderate AI, low quality → partial automation strips cognitive work and leaves physical drudgery (the “task residual” effect)
Findings: 83.5M workers are in low-AI, low-quality jobs. Customer service reps, data entry keyers, and medical records specialists sit at the intersection of high exposure and poor quality. Meanwhile, chief executives and lawyers are both low-exposure and high-quality.
The index uses ONET, BLS, and Anthropic exposure data. Code and methodology are open source. LINK https://github.com/quinndupont/JQADI
There's a ton of millennials (myself included) turning 40, that have been in this field since 2005 or earlier. It's all we know, and at this point we're getting too old to just "go do physical labor for minimum wage so AI can write code instead." I'm certainly too old to go back to school and try to pass the bar example to be a lawyer at 50+, and I have zero interest in any kind of people management whatsoever.
IMO Anthropic, OpenAI, Google, etc. should all be helping governments work toward a plan and lobbying for regulation on it instead of just charging full steam ahead "damn the consequences, those are someone else's problem."
It's going to obliterate what little is left of the middle class and leave a massive amount of unemployed middle aged tech workers with no where to go. What then? We either get ahead of the problem now (Outlook not so good), or we collapse into massive civil unrest and chaos.
Our city just spent >$15MM on "case management software" that took 5 years to build by some fly-by-night outfit in California who won the contract, haphazardly bolted together MSFT Azure components, then vanished with zero support.
These teams can't in good faith freely adopt AI tooling into their workflow because they don't have the bandwidth to do it well, so they don't do it at all.
However, I can't imagine vibe-coders actually shipping anything.
I really have to ride herd on the output from the LLM. Sometimes, the error is PEBCAK, because I erred, when I prompted, and that can lead to very subtle issues.
I no longer review every line, but I also have not yet gotten to the point, where I can just "trust" the LLM. I assume there's going to be problems, and haven't been disappointed, yet. The good news is, the LLM is pretty good at figuring out where we messed up.
I'm afraid to turn on SwiftLint. The LLM code is ... prolix ...
All that said, it has enormously accelerated the project. I've been working on a rewrite (server and native client) that took a couple of years to write, the first time, and it's only been a month. I'm more than half done, already.
To be fair, the slow part is still ahead. I can work alone (at high speed) on the backend and communication stuff, but once the rest of the team (especially shudder the graphic designer) gets on board, things are going to slow to a crawl.
I'm a vibe-coder, and I've shipped lots! The key is to vibe-code apps that has a single user (me). Haven't coded anything for 15 years prior to January too.
I am usually my principal customer, but I tend to release publicly.
Same here. This is also why I haven't been able to switch to Claude Code, despite trying to multiple times. I feel like its mode of operation is much more "just trust to generated code" than Cursor, which let's you review and accept/reject diffs with a very obvious and easy to use UX.
I don't have the bandwidth to juggle four independent things being worked on by agents in parallel so the single-IDE "bottleneck" is not slowing me down. That seems to work a lot better for heavy-boilerplate or heavy-greenfield stuff.
I am curious about if we refactored our codebase the right way, would more small/isolatable subtasks be parallelizable with lower cognitive load? But I haven't found it yet.
I haven’t turned it on, yet, because of the velocity of the work, but I think I’ve found my stride.
I think experienced people move faster because they can evaluate the output and redirect it, less experienced people often struggle because they don’t yet know what “good” looks like.
The interesting long-term question is how companies rebuild the environments where that judgment gets developed in the first place.
The real impact is for indie-devs or freelancers but that usually doesn't account for much of the GDP.
Don't know if this is effective and I don't think management knows either, but it's what they're doing
Doesn't mean the two are related.
Is AI just the excuse? We've got tariffs, war, uncertainty and other drama non stop.
When you're unemployed, it doesn't matter. When executives cargo cult, it doesn't matter.
Management often has a perverse short-term incentive to make labor feel insecure. It’s a quick way to make people feel insecure and work harder ... for a while.
Also, “AI makes us more productive so we can cut our labor costs” sounds so much better to investors than some variation of “layoffs because we fucked up / business is down / etc”
"We've frozen hiring because our growth potential is tapped out."
"We've frozen hiring because AI can replace employees."
Instead they are using Electron and calling it a day. Very ironic isn't it? If AI is so good then why don't we get native software from Anthropic?
So why aren't they using their own software to generate a linux optimized package for linux, a Swift software for MacOS and whatever windows uses.
That would be the best ad for AI. See, we use our own product!
But it doesn't happen.
So essentially by not generating custom binaries for every platform and using Electron they're doing one thing but saying something else. So maybe generating code isn't the #1 problem in the world!
Also I remember them saying that their engg write less code and they use Claude to write Claude. If Claude can be used to write Claude then why not use Claude to write OS specific binaries?!
His rationale is he won’t let the company log his prompts and responses so they can’t build an agentic replacement for him. Corporate rules about shadow it be damned.
Only the paranoid survive I guess
I’d argue this can’t be trusted either considering the AI labs already established they’re willing to break laws (copyright) if the ultimate legal consequence is just a small fine or settlement.
For me, the impact is absolutely in hiring juniors. We basically just stopped considering it. There's almost no work a junior can do that now I would look at and think it isn't easier to hand off in some form (possibly different to what the junior would do) to an AI.
It's a bit illusory though. It was always the case that handing off work to a junior person was often more work than doing it yourself. It's an investment in the future to hire someone and get their productivity up to a point of net gain. As much as anything it's a pause while we reassess what the shape of expertise now looks like. I know what juniors did before is now less valuable than it used to be, but I don't know what the value proposition of the future looks like. So until we know, we pause and hold - and the efficiency gains from using AI currently are mostly being invested in that "hold" - they are keeping us viable from a workload perspective long enough to restructure work around AI. Once we do that, I think there will be a reset and hiring of juniors will kick back in.
If AI increases productivity, and juniors are cheaper to hire, but is just as able to hand off tasks to ai as a senior, then it makes more sense to hire more juniors to get them working with an AI as soon as possible. This produces output faster, for which more revenue could be derived.
So the only limiting factor is the possibility of not deriving more revenue - which is not related to the AI issue, but broader, macroeconomic issue(s).
The craft changes with all these AI helpers, so the juniors have to also catch up/change with it. Or there won't b any seniors in due time.
You would hire someone with the expactation that they learn, but you also need to pay them. New hires always slow the team down. And currently you wouldn't even get much out of them, as you can delegate those tasks to AI.
Additionally you can not even be sure that the junior will learn or just throw stuff at AI. The amount of vibecoded Code I have to review at the moment from Seniors is stunning.
So yeah, the market needs Seniors, but there is basically no incentive for a company to hire a Junior at the moment. It's just easier and cheaper to pay a bit better than the market and hire Seniors then to train a Junior for years.
I think this is the crux of it. Someone who doesn't know the right thing to do just isn't in a position to hand off anything. Accelerating their work will just make them do the wrong thing faster.
We basically have ~40 components and 6 pages to go until complete rewrite, I am sure we will run into bumps in the road, but it's been crazy to watch.
We also added i18n (English + Spanish), ThemeProvider for white labeling solution, and WCAG 2A compliance, all in one shot.
If I went to a third party and asked them to rewrite just the static pages it would have been $200k and 3 months of work.
It’s not quite at the place where LLMs can take over 100% coding, but give it a few more months.
Look at GPT 5.4 and Opus, we’re clearly hitting diminishing returns already and these guys are pumping unsustainable amounts of money into them.
I’m bullish on AI, it’s been a net positive for me and my team. All I see here though is propaganda disguised as science to convince businesses to shrink their engineering budgets and redirect it to AI companies.
TL;DR: AI company says AI is amazing, more at 10.
Anthropic can cause layoffs through pure marketing. People were crediting an Anthropic statement in causing a drop in IBM's stock value, which may genuinely lead to layoffs: https://finance.yahoo.com/news/ibm-stock-plunges-ai-threat-1...
We'll probably have to wait for the hype to wear off to get a better idea, but that might take a long while.
Then the 2008 crash happened and those people were gone in a blink of an eye and never replaced. The companies grew in staff after that, but it was in things like sales and marketing.
This is, if AI is going to cause job losses it will feel very small for some time, then it will happen suddenly all at once with little to no time to properly react.
Shipping speed never/is was the issue. Most companies are terrible at figuring out what exactly they should be allocating resources behind.
Speeding up does not solve the problem that most humans who are at the top of the hierarchy are poor thinkers. In fact it compounds it. More noise, nice.
Writing code is lesser problem than figuring out what we want when we want, and to get stakeholders at one place.
Apple has already shown this decades ago - they got the iPhone and iPod developed and out the door in relatively short-time scales given the impact of the products on the world. Once you know what you want, exactly what you want, things moves fast - really fast.
But sure let's buy 200$ per month claude to ship things faster lol
> It can't think, it just predicts likely tokens
> I can't believe this industry I once cherished for rational professionalism has fallen for nondeterministism
> Sorry, I'm just not going to participate in destroying the planet with these power hunger DCs
> All this stuff actually costs 10x what a human developer costs but they're dumping the service at a low price to make us dependent.
> It's a bubble, or a scam, in a year or two everything will go back to normal.
Tell me sentiments like these don't get bandied about by devs who want to keep doing things the way they know and like.
There goes my excuse of not finding a job in this market.
No one knows what’s going to happen in the future. Yes there already are fewer SWE jobs than before because of AI, and yes the days of companies hiring new grads in droves at $300k+ packages are likely over. IMO all you can really do is study what you’re interested in, learn it deeply, and do good work with cool people. If unsure, it’s possible to go back to what you were doing before if the new path doesn’t work out.
I think there are some advantages to being first.
It's time to re-evaluate strategies if we've been operating under the assumption that this is going to be a bubble, or otherwise largely bullshit. It definitely works. Not everywhere all the time, but often enough to be "scary" now. Some of my prior dismissals like "text 2 sql will never work" are looking pale in the face today.
The TL;DR is that there is little measurable impact (and I'd personally add "yet").
To quote:
"We find no systematic increase in unemployment for highly exposed workers since late 2022, though we find suggestive evidence that hiring of younger workers has slowed in exposed occupations"
My belief based on personal experience is that in software engineering it wasn't until November/December 2025 that AI had enough impact to measurably accelerate delivery throughout the whole software development lifecycle.
I have doubts that this impact is measurable yet - there is a lag between hiring intention and impact on jobs, and outside Silicon Valley large scale hiring decisions are rarely made in a 3 month timeframe.
The most interesting part is the radar plot showing the lack of usage of AI in many industries where the capability is there!
Gemini 3 and Opus 4.6 were the "woah, they're actually useful now!" moment for me.
I keep saying to colleagues that it's like a rising tide. Initially the AIs were lapping around our ankles, now the level of capability is at waist height.
Many people have commented that 50% of developers think AI-generated code is "Great!" and 50% think its trash. That's a sign that AI code quality is that of the median developer. This will likely improve to 60%-40%, then 70%-30%, etc...
I mean Moore's law is just a rule of thumb but the curve fits METR just as well..
Also, it seems to me the concept of "observed exposure" is analogous to OpenAI's concept of "capability overhang" - https://cdn.openai.com/pdf/openai-ending-the-capability-over...
I think the underlying reason is simply because companies are "shaped wrong" to absorb AI fully. I always harp on how there's a learning curve (and significant self-adaptation) to really use AI well. Companies face the same challenge.
Let's focus on software. By many estimates code-related activities are only 20 - 60%, maybe even as low as 11%, of software engineers' time (e.g. https://medium.com/@vikpoca/developers-spend-only-11-of-thei...) But consider where the rest of the time goes. Largely coordination overhead. Meetings etc. drain a lot of time (and more the more senior you get), and those are mostly getting a bunch of people across the company along the dependency web to align on technical directions and roadmaps.
I call this "Conway Overhead."
This is inevitable because the only way to scale cognitive work was to distribute it across a lot of people with narrow, specialized knowledge and domain ownership. It's effectively the overhead of distributed systems applied to organizations. Hence each team owned a couple of products / services / platforms / projects, with each member working on an even smaller part of it at a time. Coordination happened along the heirarchicy of the org chart because that is most efficient.
Now imagine, a single AI-assisted person competently owns everything a team used to own.
Suddenly the team at the leaf layer is reduced to 1 from about... 5? This instantly gets rid of a lot of overhead like daily standups, regular 1:1s and intra-team blockers. And inter-team coordination is reduced to a couple of devs hashing it out over Slack instead of meetings and tickets and timelines and backlog grooming and blockers.
So not only has the speed of coding increased, the amount of time spent coding has also gone up. The acceleration is super-linear.
But, this headcount reduction ripples up the org tree. This means the middle management layers, and the total headcount, are thinned out by the same factor that the bottom-most layer is!
And this focused only on the engineering aspect. Imagine the same dynamic playing out across departments when all kinds of adjacent roles are rolled up into the same person: product, design, reliability...
These are radical changes to workflows and organizations. However, at this stage we're simply shoe-horning AI into the old, now-obsolete ticket-driven way of doing things.
So of course AI has a "capability overhang" and is going to take time to have broad impact... but when it does, it's not going to be pretty.
note that this concept was not invented by OpenAI
Kinda done with this.
If you have something important to say, say it up front and back it up with literature later.
If you claim regulation favors the incumbents, well take it up with parent commenter whose comment seems to imply that regulation is needed!