I work at a dev agency, most of our clients are start ups that need to go into the market quickly.
We've used agentic development for about a year and a half now and our roles have changed drastically during that time. I can't speak to the volume of projects flowing in (as I do not know the exact numbers) but from what I can see all that has changed is the expectations for what can be delivered. And instead of 5 people delivering on a projects, it's now usually 1 or 2.
The reality is however, that greenfield projects have been largely automated. A ton of the manual labour work (iterating on UX/UI designs, iterating on system architecture, trying out different approaches to solve a difficult problem with no clear measurement metric) now happens instantly. Basically - if you can understand it in your head, you can put it out into the world in 1/100th of the time.
During this period I've also changed a lot about the way I work and think about a system. I've grown symbiotic with the LLM and I really can't do without it. It doesn't mean I don't understand the code it writes, I very much follow each and every change and have a large understanding of the codebase (much larger than the LLM), but I've greatly atrophied my manual code writing skills (which I am perfectly fine with).
Currently I feel like the general layer, the translator, between what the business goals are and what tech covers it the best way. This is still problem solving, but a very high level one and is still really interesting and fun to me.
But something tells me that the best strategy for these times (for developers anyway) is to remain critically thinking and use these tools to your advantage. Now everybody has superpowers. You don't really need to work for a company anymore, because a solo dev can absolutely build crazy things, so it's not like you need to rely on anyone else. Maybe the future is an economy of macro products, each person offering something unique to the world.
I agree with a lot that you say and notice similar trends in our work.
I am a little skeptical about this one though:
"You don't really need to work for a company anymore, because a solo dev can absolutely build crazy things, so it's not like you need to rely on anyone else."
One of the reasons for devs to work in company is not that they can't deliver the work themselves. It's that they don't have the connections to land customers. Most devs need a company at least to handle the marketing so they can focus on what they are good at.
We have been aggressively and enthusiastically automating away software engineering for the entire history of the computer industry. Every time we do so, we are able to build bigger, better things more quickly. When this happens, our work becomes more valuable and expectations rise to match. The world’s appetite for software has been insatiable so far. AI hasn’t replaced software engineers because every time we become more productive, the goalposts move.
There’s two things that could put an end to this. Firstly, we might finally become productive enough to exhaust the world’s appetite for software. I don’t see any evidence of this happening, but if somebody wants to make this argument, they should be clear about why this time is different to the entire history of the computer industry so far.
Secondly, if AI becomes superhuman at software engineering when acting autonomously. Specifically, AI+human developer no longer outperforms AI alone. So far, all the available evidence seems to show AI as a force multiplier for developers and that for good results, at best you can have AI doing 90% of the work as long as an expert developer is driving things.
There isn’t strong evidence that either of these situations is going to happen in the near future, so I think software engineers are safe for now. But if you have a narrow skill set and you are focused in particular areas (e.g. front-end web development), then I would worry more, because even if AI cannot replace software engineers in general, it’s quite likely to be able to completely consume specific domains with generalists holding the reins.
I think we're not too many years away from the end state of software. We already collectively produce more software than anyone really wants, and the bulk of it is anywhere between garbage and outright fraud, if not actively malicious.
The end state, I think, is that everyone who needs small software to manage a todo list or synchronize files, or whatever "normal" people do, will end up with bespoke personalized software written by their own AI. SWEs will be reduced to working on only the big corporate projects.
The overwhelming trend in commercial software these past few decades has been hyper-aggressive anti-customization, anti-personalization, anti-user. Commercial software has been reduced to one single happy path and if that doesn't suit your needs, then fuck off. No one is making commercial software for everyday people. Even open source is trending away from everyday users.
Soon, regular everyday people who simply need some software to solve a problem the way they want it solved will have the ability to do so. In the bast majority of those cases, the quality and correctness of said software really doesn't matter. What matters is that it's personalized, free, and isn't an invasive surveillance/advertisement platform.
>Specifically, AI+human developer no longer outperforms AI alone. So far, all the available evidence seems to show AI as a force multiplier for developers and that for good results
Or humans are relegated to the co-processor role. The AI does 99% of the thinking and work and consults the human for the 1% it needs. Whether that extra contribution is essentially a random number generation, creativity / outside the box input, or esoteric problem solving remains to be seen.
The useless box dates back to 1952, and the pet rock was a phenomenon for a while. If useless software bothers you, you've got to be pretty bothered quite a number of things. What do you think of video games?
We're talking about things like a flashlight app on your phone with a subscription and that requests every system permission available, or the ten trillion todo apps.
It most certainly will replace software engineers. What's missing is, as the article suggests, the "Delivery" bit. But that's not the realm of software engineers, that's the realm of DevOps/SRE/Cloud engineers.
I work as a cloud engineer and have been contacted by multiple non-engineering friends who have now been able to create their pet projects from scratch in different languages and have it running locally, as webapps and native apps. So what they are missing is a platform to easily deploy and maintain their projects, much like a "normal" developer would. Right now it's quite tedious to set up this scaffolding, but it's absolutely possible with AGENTS.md, skills and rigid hollistic tests. Once done, non-technical people can continue developing independently without hiring any software engineers by simply telling claude/codex what they want. Claude/codex will then be able to make judgement calls based on the preset architecture, which will guide the non-technical user.
So in my anecdotal case, AI has already replaced several software engineers. Once scaffolding like this is productized, I suspect that greenfield projects can be managed entirely from a product standpoint using agentic coders + platform engineering. And that is today. Imagine in 5 years.
Generation and maintenance are very different beasts.
There are engineers that work making a 2 weeks app that is never revisited again, I guess, but I don't know anyone that makes a living from that. Maybe the "WordPress site for your business" gigs.
The issue comes when you have 432 functionalities and have to add the 433 without interfering with the others. Or when you cant afford slightly wrong. Or when each functionality adds complexity at a higher rate than an engineer and over time the projects gets to an unmanageable size.
Deploying software has been reduced to just running 'vercel' in the terminal, something the agent has zero problems with if they were to just ask.
Distributing desktop software is a bit harder depending on the platform.
The gap between a pet project and great software is still very wide and I have a hard time believing that it will ever be bridged.
I don't see how a solved problem even before ai is the thing that won't be replaced first. I struggle to believe a personal project requires complex infra
We have an instance where the non-tech team has started building tooling for themselves (because the technical team is overburdened with work).
Essentially a small application idea that interfaces with the larger system. It was POC built in 2-3 days, 3 or 4 commits.
Impressive - however the person that built it has now made 400 more commits over the last 3 months to this project as it has been modified, it’s essentially become his part / full time job to just build + maintain this new application.
They have become a software developer. An untrained one, one that doesn’t understand security or best practices. Maybe as Claude gets better the load will be lightened and it will not consume their day, but as it stands - at my company, all of these initial “vibe apps” are becoming maintenance for them, taking up more and more of their time. It’s obvious that they want MORE software, not less. So traditional software engineering is probably gone, but managing expanding platforms and handling security, complexities, documentation, business logic, all of that is still standing in the way for my company.
So you are correct saying that people can build projects with text, but I feel that “set and forget” has never been the case for all but the simplest software. Someone still has to manage all of it, I think. Whether trained in SE or not.
My guess is that developers who have experience can still outperform untrained people, though the builders who are curious by nature will quickly get up to speed. The game is changing but traditional developers have a huge upper hand, because we’ve always wanted to know how things work under the hood. I could have built the current vibe app that they built in a hour using AI, it’s taken them months.
In that sense, AI is creating software jobs. Not very good ones, and they're being paid way less, but a jobs a job, I guess. We have to face the music. it's not that AI will take software jobs, it's that it will make them pay a janitor's wages.
But while the AI isn't good enough to do it all, While there's still a vibe code wall that non-developers run into, senior developers will still have a job scaling that wall. That walls getting shorter as models get better, but for right now, this second, even with Anthropic's Fable, our jobs are safe.
AI-assisted coding is great, but vibe coding IMO is only good for disposable prototypes.
You're not going to vibe-code a financial app that needs to be maintained indefinitely.
You're also not going to mess with legacy systems.
I think that AI has definitely replaced _some_ engineers, but I don't think your use case is relevant. Your non-engineering friends have created their pet project because they now can, but it's not as if they were going to hire anyone to do it, right? They haven't up until now.
I don't get this reasoning, and yet it is pervasive. Just because non-engineering people come up to you with apps they have created does not mean AI has or will replace software engineers.
Consider:
- I can read about my symptoms from Dr. Google, try a lifestyle change, herbal remedy, or over-the-counter drug, and that may actually work. This does not mean in the slightest that doctor are being made obsolete
- I can create music with generative AI, without needing any understanding of music theory, no taste for music, no creativity. This does not mean people with musical talent are being made obsolete at all.
- I can, with the help of AI, work on DIY projects around the house. This does not in any way mean engineers are being made obsolete.
Who will be helping domain experts to elucidate what they actually need through prototype-refine cycles?
Who will be writing and maintaining the operating systems, the languages, the version control systems, th editors and terminal emulators, knowledge/document management systems, the PaaS platforms, etc that these hordes of hobbyist software creators depend on?
Have these people actually properly tested their creations to ensure they are robust? Do they even understand the edge cases that could arise? Is their work secure? Cooking up some quick thing based on some prompt does not equate to engineering whatsoever.
Perhaps you fail to see this because, like many others, you subscribe to the fallacy that the value of software engineering primarily lies in the code produced itself, the arrangements of bits manufactured. It is not; a project is primarily valuable as a theory and abstraction building process. See https://pages.cs.wisc.edu/~remzi/Naur.pdf
The thought experiment I like to have is imagining my company running without engineers and just LLMs. The thought of my CEO or sales guys sitting down and reading/redirecting LLMs all day is just hilarious. My CEO is highly technical but he has other shit to do. The first thing they'd do is hire someone to do that for them, and the best person for that task is someone who knows how things _should_ be.
Now they'll probably get someone on discount, because our salaries are going to tank due to evaporating demand, but I sincerely doubt it'll be zero-d demand.
What I expect is a 3D printer moment - tons and tons of homebrew / shareware style software coming out, an explosion of boutique code.
I also expect a CNC machine moment - vastly reduced demand for hands-on specialists, and more babysitting of automted processes. But it's those machinists who got those jobs.
We could be looking at a long term suppression (~80% reduction?) in demand though until economic growth produces enough demand to employ ~50M software engineers again, if ever. The cliff is unlikely, I'd guess the unregretted loss will be replaced by AI productivity every year, and some portion of growth will, too.
I also guess that all the AI companies can become massively successful without causing much unemployment just by following Claude's model - charge a certain tangible % of a salary for assisting the worker. https://jodavaho.io/posts/ai-jobpocolypse.html
Yeah, it won't. SWE here with a sidegoal to tackle the deployment side through various means (homelabbing, grabbing sre/cloud/observability tasks at work).
The biggest observable improvement in my post and pre ai development is the ability to tackle two projects at once, if the agent is on track. If not, and I have to do a deep dive to debug, it basically regresses to plain old everything like before.
For most apps, delivery has been some equivalent of “git push heroku main” and setting a DNS record for like 15 years. That’s especially true for most of the apps being vibe coded today.
The fact that “cloud engineer” is still a job suggests that the simple case is not the one driving employment. There are definitely more devops/sre/cloud engineer roles today than there were in 2009 (and I’d be willing to wager you if you included pre-devops sysadmins in that count, there’d still be more total roles today).
LLMs can now quite trivially write Terraform or Ansible code to setup infra, maybe harder for non-engineers to make those prompts for now. But ongoing maintenance and monitoring would require an online agent evaluating signals and deciding when to take what action or prompt the user. Like autoscaling decesions can be already automated and some failover/recovery for simple cases. Probably people releasing hobby projects do not need too much. And then there are things like exe.dev to wrap that and make it simply to give an execution env instead of you and your agent managing your own infra.
They weren't going to, if not they would've done it already in the past.
You're not going to spend $30,000 and weeks of your time just to see if an idea you had under the shower can make a few bucks as a side project, but now that it's a 5-minute thing that costs you $5, why not.
If it works, it works. But you, sir, are a software engineer building and selling software and software maintenance to people with money / equity to give. You are your own counter example at the moment.
There truly is not. Software engineering is not different in any meaningful way. Sure 30 years ago in waterfall land we were emulating the project management of engineering, with miserably expensive results. But it's all the same now. It's like differentiation between coding and programming, it's different in everyone's head.
If there is no difference, then it is just the result of everyone inflating the term in their CVs/LinkedIn etc.
Software developer typically is the one which builds typical CRUD app, front-end, back-end with database and something around that. Their main job is to make the software to apply clear business requirements on software level, while the software itself is not likely revolutionary. Or they are not the responsible ones to make it revolutionary. They provide code in demand.
Then there are engineers that may apply math problems to software and optimise and develop new algorithms, compilers etc. The software itself might be revolutionary and the business.
In a landscape like that, good luck proving to a hyper-cautious employer that you actually are a skilled software engineer and not just a larping ticket shuffler. In the near future, I fear the person who will decidedly be able to prove that with Olympiad placements or a swarm of unique side projects is the kind of person who might as well just go on and start his own tech startup. Most of us here at least have some track record of employment and seniority, I suspect brilliant junior engineers will be hopping from internship to internship like they do in top-level finance.
“Software developers” and “software engineers” distinction has the same energy when one uses to pat oneself on a back when they call themselves “expat” instead of an “immigrant”. You write code, all the huff and puff around “architecturing”, and “alignment”, and “knowing what to build” is just vapor. I’ve never met “software developer”, outside of online forums, who did “coding” and none of the latter.
Just changing words and saying (perhaps) that one is “just about code” and the other is about “architecture” rings hollow. LLMs can think (like submarines can swim) so the only question is how well.
The consumer grade 3D printing is just now closing with the quality factory-level. Still not completely there. Is the quality difference similar vs. comparing human made software and LLM generated?
"Same way"? I struggle to find the similarities. Cheap 3D printers have a distinctly different capability profile from machinery used for mass production. What about coding agents' capabilities is distinctly different from those of human coders and 100% will not change?
A “cloud engineer” is orders of magnitude easier to replace than a programmer - there are entire SaaS products that have been trying to be a drop-in replacement for your career since AWS was out of the gate.
The fact that you still have a job should clue you in that this isn’t going where you think it is, regardless of the state of your friends’ pet projects.
I'm not saying LLMs will replace software developers, but this is such a ridiculous analogy that it hints to me that you've never seriously tried the latest tools.
It's ridiculous because LLMs are far more capable. There was just a thread on HN the other day where multiple people shared experiences being able to decrypt, reverse engineer, and update the firmware of some long forgotten embedded device with zero background in the field using LLM coding tools. And you're comparing that to a preset generator.
In your example, those projects were not the domain of engineering. Your friends couldn't afford expertise and those projects never would have been made before. Therefore, these are net-new projects that replace nothing that came before.
This is common in technology, especially software, that improvements in efficiency make software cheaper and expands the total pool of possible software.
And, even if one of the "one button shipping" platforms (and there are many) was hooked into the agent, your friends will hit major problems that they and their AI cannot solve. Whether its tech-debt hell, a major security breach, or something else, any sufficiently complex project will require expertise. Will be a fun day when a European regulator asks a friend about their GDPR compliance and their agent is like "shrug"
Why is scaffolding a PaaS project beyond the current capabilities? Do LLMs not know about the PaaS offerings of GCP/Azure/AWS, Heroku etc? That stuff has been IaC for quite some time now
> So what they are missing is a platform to easily deploy and maintain their projects, ... Right now it's quite tedious to set up this scaffolding...Once scaffolding like this is productized
You kind of sound like you're pretending PaaS doesn't exist. Have I misunderstood?
> Among the 270 jobs in the 1950 U.S. census, only one job was automated away — elevator operator. But many others were rendered obsolete by new technology, like the job of telegraph operator.
In that same time farm jobs went from 15% of the workforce to 2%.
I sure belive that at the moment each big tech ai provider has it's black pr budget for propaganda, influcencing influencers, comments factories, FOMO spin doctors etc. In the meantime wise heads counted that unless they (AI providers) provide like couple times more of real profit it will all collapse, thus next level is IPOing to buy more time.
It literally has and will even more in the future. It won't replace *all* software engineers but once the genie is out low effort low risk stuff will be done by an AI. Loveable and such have so many live projects, the alternative was a human building those.
I'm talking about actual live services that deliver money to someone, not toy projects. Do you think there's even a slight percentage of those or are 100% of the projects hosted by these services throwaway shit?
Can you point to any "great" projects on Lovable that would actually be useful as full blown SaaS software tools? Stuff that has been written/prompted by non software experts?
Please try to read my message again. I never said the things you're implying I said. I literally said not gonna replace all, and low effort low risk stuff.
Do you think that not even 0.00001 projects on those websites could have been a good payday for a software engineer/team? Do you think what took 3 people before for a low effort saas is not going to be done by 1 person now?
I don’t think pointing at all the corporate coms about ai layoffs as fake invalidates the risk. The corporate stuff can be lies while the tech‘s impact could end up being real. It’s just noise in this context.
Similarly this assumption (the burger diagram in the article) showing execution phase shrinks but somehow everything else expands to keep the burger size the same seems less than plausible.
That said some portions of swe seem like they‘re still very far off from being threatened. Especially the portions where correctness is crucial. With say web dev you’ve got a lot more room to yolo it than say navigation code for rockets. The LLM can likely do both but I don’t think anyone is vibe coding the later any time soon
Things are changing at rapid speed, there is nothing "classic" about any of this, and you should at least be able to understand that much if you want to advise people.
I think it's people who were sloppy about programming are more interested in vive-coding. Because now they can make something without the mental rigour needed. Engineering as it should be is play of rigour. Those who value understanding system will continue the human aspect of it
Judging by all the trivial errors made in pre-AI code, many of us are far from rigorous a lot of the time.
It’s like that story about the programmer who wants to send the car down the slope one more time to see if it does the same thing again (or whatever it was). The ephemerality makes iteration possible and appropriate, but also makes rigour less important.
It’s not really about replacing software engineers. But about commodifying it. More software engineers (or roles responsible for code) that work for lower pay might be the trend. Or to maintain a high level of pay you wear many hats, including software.
>It’s not really about replacing software engineers. But about commodifying it.
AI's having the opposite effect; it multiplies the productiveness of skilled software engineers while simultaneously multiplying the destructiveness of bad ones. The engineer who can shepherd a handful or Claude/Codex instances around simultaneously without producing slop will be immensely better compensated then the engineer who just gives vague instructions to the AI, goes to get a coffee and hopes for the best.
"In this essay, we argue that there is enough evidence to reject the narrative that once AI capabilities reach a certain threshold, it will cause mass layoffs." - too late, it already did
The only bit AI can't replace is probably the need for a 'fall guy' or someone to take responsibility for something. This, however, will obviously not be sufficient to prevent job losses.
"Can the sandwich be further compressed? We don’t think so. At one end of the pipeline, development teams need to decide what to build."
I mean, but this is talking about the process as a whole, not individual jobs.
"Farmers won't be replaced by combine harvesters - we still need someone to decide what to plant and to harvest it". Sure, but if you used to have 10 labourers in a field manually ploughing with a pair of oxen and now you have one guy driving the machinery it absolutely has replaced jobs.
Companies are already talking about "1 person teams" to deliver projects. We'll still have _some_ jobs but the ratio will change dramatically and engineering will move a lot closer to "team lead" role (and maybe even Product Manger role to boot)
It can already produce code for just about anything you can name. From implementations of browsers to micro-kernels. Yes, it doesn't yet one-shot any problem but my interactions lead me think it won't be long before that is automated. My intuition of how this will be done won't be with some elegant solution where any given step never fails. It will more be like swarms of interacting agents, loops, and formalised processes (themselves little more than frameworks of prompts), all inching towards the solution progressively and self-correcting when they go off course. This will be cheaper to do when models improve and cost less.
There is another bottleneck though and it's important: the personal computing needed to really do this well is ... expensive. What I mean is to even utilise this in a development process you need access to your own high-end hardware where the agents can run experiments fast. That requires (1) a lot of cores (2) and a lot of RAM. So there's a bottleneck in personal computing, too. Unfortunately, I really do think we're all screwed here. Increasingly: the most optimistic projections for what AI will be able to do are starting to become reality every few months. So the odds aren't looking good here.
Sadly I think this post will mislead people, bc the difficult truth (for many) is that software engineering isn't that hard and that's why AI can easily substitute that layer (lower barriers to entry than widely believed).
Wholeheartedly agree, there are some aspects to SWE that could be considered hard, but most of the time it’s rote pattern matching or simple logic resolutions.
People were getting 6-figure salaries with 3 month boot camps before AI, any random college major could eventually become a developer with a few online courses and practicing LeetCode, the party was bound to end eventually.
Even in the case that a college degree was absolutely necessary (it wasn’t) making $150k fresh out of a bachelor’s degree was absurd for every other domain, many of which were much harder than CS.
Yup! I was a part of the learn to code industry. I am proud of that, bc I know my worker helped a lot of marginalized people gain wealth and power (woo!). My own occupation, stats and econometrics, requires years of higher education to even begin (and decades to master), and yet ~ half of SWE were looking down on me, disrespecting me. To be clear, there were many who were not, but usually they were from some marginalized group: women, autistic, person of color, gay, person from a poor country, etc. I thought, why is my towering knowledge not being respected? Ah, the patriarchy combined with SWE. And then as time went on I just started using my knowledge for myself and that’s worked out well (bc it’s based on actually knowing math as opposed to relying on the patriarchy).
I think it’s possible the industry eventually figures out that statisticians and econometricians know far more than CS / SWEs (bc AI will tell people), but it could be a decade from now.
I think it’s useful to distinguish between LLM and AI. I think this criticism is valid against LLMs, but not against AI. LLMs are a useful tool, but they aren’t AI. Once actual AI is a thing, I think it’s worth revisiting this topic. I’m certain we’ll get there one day, but it will probably be a lot like fusion.
Just look and see what Cloud did for software engineers? It pushed us one level higher and lowered the demand for "db experts" and "low level systems people". The only ones who remained were the strong ones who were hired into the cloud companies. The rest moved up and changed careers.
Why would anyone think the same thing won't apply here? If you are still a Typescript bunny who fiddles with some newly learned React tidbit -- this won't cut it anymore. The market won't need you. Move up and adapt or move down and become an expert (harder).
Not buying it. The idea that deciding and delivering are things only humans can do with their intelligence seems faulty.
As it stands AIs today are not always great at making decisions (but they're getting much better), and orgs of today still trust people and hold people to account, rather than their AI systems.
Neither of these are strong moats. It's a moat only while AI systems have some limitations and corporate processes are still extremely human-centric.
Agree, AIs are better decision-makers on average than people (just look at the grifters we've given power to). These are machines that can perform more advanced mathematics than even the most advanced mathematicians.
> orgs of today still trust people and hold people to account
>Neither of these are strong moats
Having accountable people in key positions is a very important part of running a successful organization. Anthropic and OpenAI are never going to let you sue them when an AI employee makes a mistake; accountability is a strong moat.
> software development, as a “decide-execute-deliver sandwich”. AI compresses the “execute” layer — the middle of the sandwich — but the other two layers resist automation in a way that will not be overcome by capability improvements alone.
I really struggle to see why improved capabilities cannot deal with those other layers. I do not believe you have substantiated this claim about not being possible as capabilities improve.
> At one end of the pipeline, development teams need to decide what to build.
Developers are not the ones that do this largely. This role is far more on the side of "Product Owner". Sometimes your job covers both, but this is not the majority of the work and does not mostly require SE knowledge - some input usually.
> This layer is hard to automate because it requires thinking about user needs, market signals, organizational priorities, and in some cases regulatory constraints.
Hmm, these are language models that can talk through much of this already - but more importantly none of what is mentioned there requires software engineering. For parts that do (I'm sure someone would come to correct me if I said that there was none or seemed to suggest it is never ever ever relevant) this is a much smaller slice.
> As AI capabilities improve, the kinds of decisions that can be delegated to AI increase over time. But this does not make the “decide” layer thinner — once a decision can be delegated to AI, it is no longer a source of competitive advantage, and the value of human decision-making migrates upward. Software increases in complexity over time, so there is no ceiling to this process.
Now this is rather hidden but a huge leap in logic. The decide layer does get thinner for all the same projects, and then you simply assert that software will get more complex and so this cancels it all out.
A team of 5 may end up being able to ship what a team of 50 used to, and maybe now there are 10 teams outputting more - but is there not a clear limit to this? At some point do we not just need 45 fewer people? That there needs to be some engineers is not the same as needing anywhere near as many as we have.
For a time I think we will see increased output meaning more software, but that tails off as they get better.
> At the other end of the sandwich, human teams need to be accountable for what they deliver.
Why? And if we assume so, why does that need a software engineer?
> It is possible that some day in the future teams will ship mission-critical code without fully testing and understanding it,
You don't need to read code to test it, and people choose to ship products without fully understanding the code all the time. Literally any decision maker who is not a software engineer who knows the entire codebase does this. Companies fully ship systems that are far too complex for any single developer to even understand.
And much of software isn't mission critical. Or at least, if you want to say it is then the mission is low stakes.
> today’s AI is so unreliable that such haphazard practices would represent an existential threat to software teams and their customers.
I'd argue for a bunch of stuff this isn't true, and the whole point of the article is "never even if they get better" which is different.
> A central insight of AI as Normal Technology is that we can collectively choose to keep humans accountable through shared norms, law, and policy.
Sure, we can ban AI writing code, but will we? Is there a huge collective concern for all us high paid engineers being replaced by AI?
Nah, kids, this is an opinion column. If you can’t tell the difference, then you don’t get to sit at the adults table. I’ve been an opinion writer for most of my life, and dressing up my perspectives in scientific LARP is bullshit. And yes, I do have underlying suspicions why certain cultures feel entitled to get away with taking such a tone in their declarations. This has been formed over decades of observation and I won’t claim it is scientific…unlike these two fellas who enjoy foods I do not.
AI won't be put in important positions of responsibility within an organization because AI providers will never accept liability for bad decisions. You can't fire Claude if it fucks up, and it's got very limited ability to learn from its mistakes. It's also incapable of making good decisions where doing so requires synthesizing more than a few hundred thousand tokens worth of domain knowledge/experience in something that doesn't have an infinite amount of synthetic verifiable training data like code and math.
In theory continuous learning (live weight updates) could help to some degree. But there's essentially no progress towards that because it requires solving a few hard, currently completely unsolved problems. 1. Weights drift over time and there's no way to re-merge them after a few tens of thousands of updates, so when a new model version was released there'd be no way to update existing continuously-learned models to that. 2. It'd allow permanent jailbreaking. And 3. A model can't learn new things without forgetting existing things, unlike humans brains which have hardware plasticity (like London taxi drivers having larger hippocampi due to having to memorize so many streets).
I have found that the attention moves to thinking about the things I want done and planning, reading and iterating over the specs and other artefacts that will be part of running the agents. I still need to understand the code and iterate over it to get to a usable and maintainable point.
I find the problem is we are reaching the top of the slop curve. I will subside because it's impossible to actually do anything useful with all the output. There will just be a ton of half-finished and abandoned projects. Whatever gets into production will require more eyes on it.
I just think a lot of people are still stuck in the "holy f** I'm so productive" and working themselves into the ground being productive pumping out code. I think it's a phase that will pass.
I'm doing solo mobile app projects, and I have no need to iterate on specs. The bottleneck is QA testing whether it works on the phone.
I don't need to carefully review and understand the implementation.
It's not important whether I understand the details of how exactly UICollectionView in Apple's UIKit works.
I see that my implementation works on different physical devices, my tests cover device rotation, and I checked the memory allocations in the Instruments tool.
It has been some months of part-time work on my side, and I will publish this iOS app soon.
Without AI I could not have done it, the scope of the features is too large. The project is around 100k LOC.
It is not true that projects become unmaintainable and abandoned because of agentic engineering, or even vibecoding if you want to call it that.
LLMs still do not have proper contextual understanding of their solutions. Just couple days ago I was using GPT 5.5 with xhigh to vide code some application, and yet it defaulted for sorting dates from new to old by using plain string comparison. Just one of the many bugs.
This absolutely fascinates me. I had a friend who needed subtitle files generating for audio and using in CapCut yesterday yet none of the available stuff was suitable, so he asked if I could adapt some of my software to export subtitles.
2 hours later he's got a fully working piece of local software that does exactly what he wants, yet yours is not able to even sort dates correctly. Feel free to download it if you want to see for yourself, I didn't even do any UI tweaks as this was just a tool for him to use:
> How can there be such a massive gap in what can be produced?
What I was doing looks really nice and mostly works on the surface, but it is all about the corner cases where these bugs appear. In another day I was able to generate Frida script with LLM help that bypasses Dart certificate pinning/validation and proxies all the traffic by injecting the runtime binaries. With the latest Flutter/Dart version on Android when doing security analysis.
Ahhh ok I totally understand what you mean. Yea the edge cases are absolutely where you start to feel the pain and things look good on the surface until you dig in. I think even in the age of LLMs the adage of 90% of the time is spent of the last 10% will ring true.
Sure an app can be built and spun up in an afternoon, but are you willing to spend another 6 months ironing out all those little bugs, tuning it a bit, testing, tweaking, testing etc.
I think we as a professional class have gotten a bit overwhelmed with the magic slot machine. 2 in the morning let me just do one more pull on the slop machine. I WILL win this time.
It's a phase. The problem is the managerial class sees it as a magic black box and don't understand it's limitations. Calling it AI does not help either. It's the "rockstar developer" illness but on crack.
I've never seen a greater disconnect between what I read on social media about vibe coding and what I've seen in real life.
In particular the whole "the best people are the ones who will use it the best". IME the best ones are the ones keeping it the most at arm's length while the people who embrace it the most churn out epic amounts of utter slop.
The whole field of engineering set to disappear and be replaced by contractors. Of course this is what they've always wanted. That's why they do outsourcing and the whole point of AI so, basically instead of getting paid a small salary to maintain someone's money-making machine, people will bid for jobs. They'll be more and more layers of abstraction that business owners will have to pay rent to. Until it's just basically socialism.
The question is, are executives willing to give up all their power and status to an LLM or will “industry” just use AI to invent more bullshit jobs to keep everyone, including the exec relevant.
The reason humans haven’t been replaced in many areas entirely is because humans like being someone’s.
Even AI can do incredible things, like code (our job), It's difficult to me to think that one day, companies will fully assign all software lifecyble job (from designing, implementation, deployement, scaling and maintenance) to autonomous ai agent because, even the better software in the world can have bug and bug can cause from low to fatal real world damage. Those damage , in civil human real world, need a responsible. Unless, govs start voting to give citizenship to ai & autonomous agents and penal responsibility to those agents, human will also be in the loop, always. Because, Human like to designate a responsible and that's right things. So, the adaptative software engineer will always has it place.
I work at a dev agency, most of our clients are start ups that need to go into the market quickly.
We've used agentic development for about a year and a half now and our roles have changed drastically during that time. I can't speak to the volume of projects flowing in (as I do not know the exact numbers) but from what I can see all that has changed is the expectations for what can be delivered. And instead of 5 people delivering on a projects, it's now usually 1 or 2.
The reality is however, that greenfield projects have been largely automated. A ton of the manual labour work (iterating on UX/UI designs, iterating on system architecture, trying out different approaches to solve a difficult problem with no clear measurement metric) now happens instantly. Basically - if you can understand it in your head, you can put it out into the world in 1/100th of the time.
During this period I've also changed a lot about the way I work and think about a system. I've grown symbiotic with the LLM and I really can't do without it. It doesn't mean I don't understand the code it writes, I very much follow each and every change and have a large understanding of the codebase (much larger than the LLM), but I've greatly atrophied my manual code writing skills (which I am perfectly fine with).
Currently I feel like the general layer, the translator, between what the business goals are and what tech covers it the best way. This is still problem solving, but a very high level one and is still really interesting and fun to me.
But something tells me that the best strategy for these times (for developers anyway) is to remain critically thinking and use these tools to your advantage. Now everybody has superpowers. You don't really need to work for a company anymore, because a solo dev can absolutely build crazy things, so it's not like you need to rely on anyone else. Maybe the future is an economy of macro products, each person offering something unique to the world.
I agree with a lot that you say and notice similar trends in our work. I am a little skeptical about this one though:
"You don't really need to work for a company anymore, because a solo dev can absolutely build crazy things, so it's not like you need to rely on anyone else."
One of the reasons for devs to work in company is not that they can't deliver the work themselves. It's that they don't have the connections to land customers. Most devs need a company at least to handle the marketing so they can focus on what they are good at.
It doesn't have to replace you. It just has to convince your boss that it replaces you.
We have been aggressively and enthusiastically automating away software engineering for the entire history of the computer industry. Every time we do so, we are able to build bigger, better things more quickly. When this happens, our work becomes more valuable and expectations rise to match. The world’s appetite for software has been insatiable so far. AI hasn’t replaced software engineers because every time we become more productive, the goalposts move.
There’s two things that could put an end to this. Firstly, we might finally become productive enough to exhaust the world’s appetite for software. I don’t see any evidence of this happening, but if somebody wants to make this argument, they should be clear about why this time is different to the entire history of the computer industry so far.
Secondly, if AI becomes superhuman at software engineering when acting autonomously. Specifically, AI+human developer no longer outperforms AI alone. So far, all the available evidence seems to show AI as a force multiplier for developers and that for good results, at best you can have AI doing 90% of the work as long as an expert developer is driving things.
There isn’t strong evidence that either of these situations is going to happen in the near future, so I think software engineers are safe for now. But if you have a narrow skill set and you are focused in particular areas (e.g. front-end web development), then I would worry more, because even if AI cannot replace software engineers in general, it’s quite likely to be able to completely consume specific domains with generalists holding the reins.
I think we're not too many years away from the end state of software. We already collectively produce more software than anyone really wants, and the bulk of it is anywhere between garbage and outright fraud, if not actively malicious.
The end state, I think, is that everyone who needs small software to manage a todo list or synchronize files, or whatever "normal" people do, will end up with bespoke personalized software written by their own AI. SWEs will be reduced to working on only the big corporate projects.
The overwhelming trend in commercial software these past few decades has been hyper-aggressive anti-customization, anti-personalization, anti-user. Commercial software has been reduced to one single happy path and if that doesn't suit your needs, then fuck off. No one is making commercial software for everyday people. Even open source is trending away from everyday users.
Soon, regular everyday people who simply need some software to solve a problem the way they want it solved will have the ability to do so. In the bast majority of those cases, the quality and correctness of said software really doesn't matter. What matters is that it's personalized, free, and isn't an invasive surveillance/advertisement platform.
>Specifically, AI+human developer no longer outperforms AI alone. So far, all the available evidence seems to show AI as a force multiplier for developers and that for good results
Or humans are relegated to the co-processor role. The AI does 99% of the thinking and work and consults the human for the 1% it needs. Whether that extra contribution is essentially a random number generation, creativity / outside the box input, or esoteric problem solving remains to be seen.
> we might finally become productive enough to exhaust the world’s appetite for software.
I think we are past this point personally. Lots of blasphemously useless crap being built.
The useless box dates back to 1952, and the pet rock was a phenomenon for a while. If useless software bothers you, you've got to be pretty bothered quite a number of things. What do you think of video games?
We're talking about things like a flashlight app on your phone with a subscription and that requests every system permission available, or the ten trillion todo apps.
It most certainly will replace software engineers. What's missing is, as the article suggests, the "Delivery" bit. But that's not the realm of software engineers, that's the realm of DevOps/SRE/Cloud engineers.
I work as a cloud engineer and have been contacted by multiple non-engineering friends who have now been able to create their pet projects from scratch in different languages and have it running locally, as webapps and native apps. So what they are missing is a platform to easily deploy and maintain their projects, much like a "normal" developer would. Right now it's quite tedious to set up this scaffolding, but it's absolutely possible with AGENTS.md, skills and rigid hollistic tests. Once done, non-technical people can continue developing independently without hiring any software engineers by simply telling claude/codex what they want. Claude/codex will then be able to make judgement calls based on the preset architecture, which will guide the non-technical user.
So in my anecdotal case, AI has already replaced several software engineers. Once scaffolding like this is productized, I suspect that greenfield projects can be managed entirely from a product standpoint using agentic coders + platform engineering. And that is today. Imagine in 5 years.
Generation and maintenance are very different beasts.
There are engineers that work making a 2 weeks app that is never revisited again, I guess, but I don't know anyone that makes a living from that. Maybe the "WordPress site for your business" gigs.
The issue comes when you have 432 functionalities and have to add the 433 without interfering with the others. Or when you cant afford slightly wrong. Or when each functionality adds complexity at a higher rate than an engineer and over time the projects gets to an unmanageable size.
Deploying software has been reduced to just running 'vercel' in the terminal, something the agent has zero problems with if they were to just ask. Distributing desktop software is a bit harder depending on the platform.
The gap between a pet project and great software is still very wide and I have a hard time believing that it will ever be bridged.
I don't see how a solved problem even before ai is the thing that won't be replaced first. I struggle to believe a personal project requires complex infra
We have an instance where the non-tech team has started building tooling for themselves (because the technical team is overburdened with work).
Essentially a small application idea that interfaces with the larger system. It was POC built in 2-3 days, 3 or 4 commits.
Impressive - however the person that built it has now made 400 more commits over the last 3 months to this project as it has been modified, it’s essentially become his part / full time job to just build + maintain this new application.
They have become a software developer. An untrained one, one that doesn’t understand security or best practices. Maybe as Claude gets better the load will be lightened and it will not consume their day, but as it stands - at my company, all of these initial “vibe apps” are becoming maintenance for them, taking up more and more of their time. It’s obvious that they want MORE software, not less. So traditional software engineering is probably gone, but managing expanding platforms and handling security, complexities, documentation, business logic, all of that is still standing in the way for my company.
So you are correct saying that people can build projects with text, but I feel that “set and forget” has never been the case for all but the simplest software. Someone still has to manage all of it, I think. Whether trained in SE or not.
My guess is that developers who have experience can still outperform untrained people, though the builders who are curious by nature will quickly get up to speed. The game is changing but traditional developers have a huge upper hand, because we’ve always wanted to know how things work under the hood. I could have built the current vibe app that they built in a hour using AI, it’s taken them months.
In that sense, AI is creating software jobs. Not very good ones, and they're being paid way less, but a jobs a job, I guess. We have to face the music. it's not that AI will take software jobs, it's that it will make them pay a janitor's wages.
But while the AI isn't good enough to do it all, While there's still a vibe code wall that non-developers run into, senior developers will still have a job scaling that wall. That walls getting shorter as models get better, but for right now, this second, even with Anthropic's Fable, our jobs are safe.
AI-assisted coding is great, but vibe coding IMO is only good for disposable prototypes.
You're not going to vibe-code a financial app that needs to be maintained indefinitely.
You're also not going to mess with legacy systems.
I think that AI has definitely replaced _some_ engineers, but I don't think your use case is relevant. Your non-engineering friends have created their pet project because they now can, but it's not as if they were going to hire anyone to do it, right? They haven't up until now.
I don't get this reasoning, and yet it is pervasive. Just because non-engineering people come up to you with apps they have created does not mean AI has or will replace software engineers.
Consider:
- I can read about my symptoms from Dr. Google, try a lifestyle change, herbal remedy, or over-the-counter drug, and that may actually work. This does not mean in the slightest that doctor are being made obsolete
- I can create music with generative AI, without needing any understanding of music theory, no taste for music, no creativity. This does not mean people with musical talent are being made obsolete at all.
- I can, with the help of AI, work on DIY projects around the house. This does not in any way mean engineers are being made obsolete.
Who will be helping domain experts to elucidate what they actually need through prototype-refine cycles? Who will be writing and maintaining the operating systems, the languages, the version control systems, th editors and terminal emulators, knowledge/document management systems, the PaaS platforms, etc that these hordes of hobbyist software creators depend on?
Have these people actually properly tested their creations to ensure they are robust? Do they even understand the edge cases that could arise? Is their work secure? Cooking up some quick thing based on some prompt does not equate to engineering whatsoever.
Perhaps you fail to see this because, like many others, you subscribe to the fallacy that the value of software engineering primarily lies in the code produced itself, the arrangements of bits manufactured. It is not; a project is primarily valuable as a theory and abstraction building process. See https://pages.cs.wisc.edu/~remzi/Naur.pdf
The thought experiment I like to have is imagining my company running without engineers and just LLMs. The thought of my CEO or sales guys sitting down and reading/redirecting LLMs all day is just hilarious. My CEO is highly technical but he has other shit to do. The first thing they'd do is hire someone to do that for them, and the best person for that task is someone who knows how things _should_ be.
Now they'll probably get someone on discount, because our salaries are going to tank due to evaporating demand, but I sincerely doubt it'll be zero-d demand.
What I expect is a 3D printer moment - tons and tons of homebrew / shareware style software coming out, an explosion of boutique code.
I also expect a CNC machine moment - vastly reduced demand for hands-on specialists, and more babysitting of automted processes. But it's those machinists who got those jobs.
We could be looking at a long term suppression (~80% reduction?) in demand though until economic growth produces enough demand to employ ~50M software engineers again, if ever. The cliff is unlikely, I'd guess the unregretted loss will be replaced by AI productivity every year, and some portion of growth will, too.
I also guess that all the AI companies can become massively successful without causing much unemployment just by following Claude's model - charge a certain tangible % of a salary for assisting the worker. https://jodavaho.io/posts/ai-jobpocolypse.html
Yeah, it won't. SWE here with a sidegoal to tackle the deployment side through various means (homelabbing, grabbing sre/cloud/observability tasks at work).
The biggest observable improvement in my post and pre ai development is the ability to tackle two projects at once, if the agent is on track. If not, and I have to do a deep dive to debug, it basically regresses to plain old everything like before.
For most apps, delivery has been some equivalent of “git push heroku main” and setting a DNS record for like 15 years. That’s especially true for most of the apps being vibe coded today.
The fact that “cloud engineer” is still a job suggests that the simple case is not the one driving employment. There are definitely more devops/sre/cloud engineer roles today than there were in 2009 (and I’d be willing to wager you if you included pre-devops sysadmins in that count, there’d still be more total roles today).
LLMs can now quite trivially write Terraform or Ansible code to setup infra, maybe harder for non-engineers to make those prompts for now. But ongoing maintenance and monitoring would require an online agent evaluating signals and deciding when to take what action or prompt the user. Like autoscaling decesions can be already automated and some failover/recovery for simple cases. Probably people releasing hobby projects do not need too much. And then there are things like exe.dev to wrap that and make it simply to give an execution env instead of you and your agent managing your own infra.
> who have now been able to create their pet projects
> So in my anecdotal case, AI has already replaced several software engineers.
Those projects wouldn't exist without language models to begin with. That means in your anecdotal case AI hasn't replaced any SEs.
"has already replaced several software engineers. "
I think this is incorrect, unless your friends employed software engineers, or would have employed software engineers.
That's the point: they would have. Now they spend money on tokens instead (and a equity to me who set up their environment)
They weren't going to, if not they would've done it already in the past.
You're not going to spend $30,000 and weeks of your time just to see if an idea you had under the shower can make a few bucks as a side project, but now that it's a 5-minute thing that costs you $5, why not.
If it works, it works. But you, sir, are a software engineer building and selling software and software maintenance to people with money / equity to give. You are your own counter example at the moment.
> It most certainly will replace software engineers.
I would say it will most certainly replace software developers. There is a subtle difference between these terms.
There truly is not. Software engineering is not different in any meaningful way. Sure 30 years ago in waterfall land we were emulating the project management of engineering, with miserably expensive results. But it's all the same now. It's like differentiation between coding and programming, it's different in everyone's head.
If there is no difference, then it is just the result of everyone inflating the term in their CVs/LinkedIn etc.
Software developer typically is the one which builds typical CRUD app, front-end, back-end with database and something around that. Their main job is to make the software to apply clear business requirements on software level, while the software itself is not likely revolutionary. Or they are not the responsible ones to make it revolutionary. They provide code in demand.
Then there are engineers that may apply math problems to software and optimise and develop new algorithms, compilers etc. The software itself might be revolutionary and the business.
In a landscape like that, good luck proving to a hyper-cautious employer that you actually are a skilled software engineer and not just a larping ticket shuffler. In the near future, I fear the person who will decidedly be able to prove that with Olympiad placements or a swarm of unique side projects is the kind of person who might as well just go on and start his own tech startup. Most of us here at least have some track record of employment and seniority, I suspect brilliant junior engineers will be hopping from internship to internship like they do in top-level finance.
“Software developers” and “software engineers” distinction has the same energy when one uses to pat oneself on a back when they call themselves “expat” instead of an “immigrant”. You write code, all the huff and puff around “architecturing”, and “alignment”, and “knowing what to build” is just vapor. I’ve never met “software developer”, outside of online forums, who did “coding” and none of the latter.
Just changing words and saying (perhaps) that one is “just about code” and the other is about “architecture” rings hollow. LLMs can think (like submarines can swim) so the only question is how well.
Same way 3D printing replaced all common goods, can be done by anyone. Why not just print it yourself?
The consumer grade 3D printing is just now closing with the quality factory-level. Still not completely there. Is the quality difference similar vs. comparing human made software and LLM generated?
Similar? No. Vibecoded apps are much, much worse.
"Same way"? I struggle to find the similarities. Cheap 3D printers have a distinctly different capability profile from machinery used for mass production. What about coding agents' capabilities is distinctly different from those of human coders and 100% will not change?
A “cloud engineer” is orders of magnitude easier to replace than a programmer - there are entire SaaS products that have been trying to be a drop-in replacement for your career since AWS was out of the gate.
The fact that you still have a job should clue you in that this isn’t going where you think it is, regardless of the state of your friends’ pet projects.
There's no lack of such platforms for more than 5 years, they all have free offers and software engineers still have jobs.
Ruby on Rails generators could build toy projects in like 3 clicks 15 years ago, a Twitter clone being the famous one.
Software engineers still continued to exist somehow.
I'm not saying LLMs will replace software developers, but this is such a ridiculous analogy that it hints to me that you've never seriously tried the latest tools.
There's nothing ridiculous about the analogy. Not only Ruby on rails is better than most vibecoded apps but also "git clone"
It's ridiculous because LLMs are far more capable. There was just a thread on HN the other day where multiple people shared experiences being able to decrypt, reverse engineer, and update the firmware of some long forgotten embedded device with zero background in the field using LLM coding tools. And you're comparing that to a preset generator.
In your example, those projects were not the domain of engineering. Your friends couldn't afford expertise and those projects never would have been made before. Therefore, these are net-new projects that replace nothing that came before.
This is common in technology, especially software, that improvements in efficiency make software cheaper and expands the total pool of possible software.
And, even if one of the "one button shipping" platforms (and there are many) was hooked into the agent, your friends will hit major problems that they and their AI cannot solve. Whether its tech-debt hell, a major security breach, or something else, any sufficiently complex project will require expertise. Will be a fun day when a European regulator asks a friend about their GDPR compliance and their agent is like "shrug"
> Therefore, these are net-new projects that replace nothing that came before.
this and on top of that they'll hit a wall and require human intervention sooner or later if those projects are actually productionized.
Why is scaffolding a PaaS project beyond the current capabilities? Do LLMs not know about the PaaS offerings of GCP/Azure/AWS, Heroku etc? That stuff has been IaC for quite some time now
> So what they are missing is a platform to easily deploy and maintain their projects, ... Right now it's quite tedious to set up this scaffolding...Once scaffolding like this is productized
You kind of sound like you're pretending PaaS doesn't exist. Have I misunderstood?
Misleading
> Among the 270 jobs in the 1950 U.S. census, only one job was automated away — elevator operator. But many others were rendered obsolete by new technology, like the job of telegraph operator.
In that same time farm jobs went from 15% of the workforce to 2%.
I sure belive that at the moment each big tech ai provider has it's black pr budget for propaganda, influcencing influencers, comments factories, FOMO spin doctors etc. In the meantime wise heads counted that unless they (AI providers) provide like couple times more of real profit it will all collapse, thus next level is IPOing to buy more time.
It literally has and will even more in the future. It won't replace *all* software engineers but once the genie is out low effort low risk stuff will be done by an AI. Loveable and such have so many live projects, the alternative was a human building those.
> alternative was a human building those
... or them not existing at all?
I'm talking about actual live services that deliver money to someone, not toy projects. Do you think there's even a slight percentage of those or are 100% of the projects hosted by these services throwaway shit?
Can you point to any "great" projects on Lovable that would actually be useful as full blown SaaS software tools? Stuff that has been written/prompted by non software experts?
Please try to read my message again. I never said the things you're implying I said. I literally said not gonna replace all, and low effort low risk stuff.
Do you think that not even 0.00001 projects on those websites could have been a good payday for a software engineer/team? Do you think what took 3 people before for a low effort saas is not going to be done by 1 person now?
I don’t think pointing at all the corporate coms about ai layoffs as fake invalidates the risk. The corporate stuff can be lies while the tech‘s impact could end up being real. It’s just noise in this context.
Similarly this assumption (the burger diagram in the article) showing execution phase shrinks but somehow everything else expands to keep the burger size the same seems less than plausible.
That said some portions of swe seem like they‘re still very far off from being threatened. Especially the portions where correctness is crucial. With say web dev you’ve got a lot more room to yolo it than say navigation code for rockets. The LLM can likely do both but I don’t think anyone is vibe coding the later any time soon
Things are changing at rapid speed, there is nothing "classic" about any of this, and you should at least be able to understand that much if you want to advise people.
I think it's people who were sloppy about programming are more interested in vive-coding. Because now they can make something without the mental rigour needed. Engineering as it should be is play of rigour. Those who value understanding system will continue the human aspect of it
Judging by all the trivial errors made in pre-AI code, many of us are far from rigorous a lot of the time.
It’s like that story about the programmer who wants to send the car down the slope one more time to see if it does the same thing again (or whatever it was). The ephemerality makes iteration possible and appropriate, but also makes rigour less important.
> There is great anxiety about AI replacing jobs.
It's always the business owner who replaces workers. Let's not anthropomorphize a bunch of graphics cards
If the bunch of graphics cards becomes truly more efficient, business owners who want to hire humans will not be able to compete.
It’s not really about replacing software engineers. But about commodifying it. More software engineers (or roles responsible for code) that work for lower pay might be the trend. Or to maintain a high level of pay you wear many hats, including software.
>It’s not really about replacing software engineers. But about commodifying it.
AI's having the opposite effect; it multiplies the productiveness of skilled software engineers while simultaneously multiplying the destructiveness of bad ones. The engineer who can shepherd a handful or Claude/Codex instances around simultaneously without producing slop will be immensely better compensated then the engineer who just gives vague instructions to the AI, goes to get a coffee and hopes for the best.
"In this essay, we argue that there is enough evidence to reject the narrative that once AI capabilities reach a certain threshold, it will cause mass layoffs." - too late, it already did
The layoffs I’ve seen are those where the CEO claims it’s because of AI.
The only bit AI can't replace is probably the need for a 'fall guy' or someone to take responsibility for something. This, however, will obviously not be sufficient to prevent job losses.
"Can the sandwich be further compressed? We don’t think so. At one end of the pipeline, development teams need to decide what to build."
I mean, but this is talking about the process as a whole, not individual jobs.
"Farmers won't be replaced by combine harvesters - we still need someone to decide what to plant and to harvest it". Sure, but if you used to have 10 labourers in a field manually ploughing with a pair of oxen and now you have one guy driving the machinery it absolutely has replaced jobs.
Companies are already talking about "1 person teams" to deliver projects. We'll still have _some_ jobs but the ratio will change dramatically and engineering will move a lot closer to "team lead" role (and maybe even Product Manger role to boot)
Might never replace completely, but those remaining will be expected to pump out a lot more code so companies won't need to hire as many.
It can already produce code for just about anything you can name. From implementations of browsers to micro-kernels. Yes, it doesn't yet one-shot any problem but my interactions lead me think it won't be long before that is automated. My intuition of how this will be done won't be with some elegant solution where any given step never fails. It will more be like swarms of interacting agents, loops, and formalised processes (themselves little more than frameworks of prompts), all inching towards the solution progressively and self-correcting when they go off course. This will be cheaper to do when models improve and cost less.
There is another bottleneck though and it's important: the personal computing needed to really do this well is ... expensive. What I mean is to even utilise this in a development process you need access to your own high-end hardware where the agents can run experiments fast. That requires (1) a lot of cores (2) and a lot of RAM. So there's a bottleneck in personal computing, too. Unfortunately, I really do think we're all screwed here. Increasingly: the most optimistic projections for what AI will be able to do are starting to become reality every few months. So the odds aren't looking good here.
Sadly I think this post will mislead people, bc the difficult truth (for many) is that software engineering isn't that hard and that's why AI can easily substitute that layer (lower barriers to entry than widely believed).
Wholeheartedly agree, there are some aspects to SWE that could be considered hard, but most of the time it’s rote pattern matching or simple logic resolutions.
People were getting 6-figure salaries with 3 month boot camps before AI, any random college major could eventually become a developer with a few online courses and practicing LeetCode, the party was bound to end eventually.
Even in the case that a college degree was absolutely necessary (it wasn’t) making $150k fresh out of a bachelor’s degree was absurd for every other domain, many of which were much harder than CS.
Yup! I was a part of the learn to code industry. I am proud of that, bc I know my worker helped a lot of marginalized people gain wealth and power (woo!). My own occupation, stats and econometrics, requires years of higher education to even begin (and decades to master), and yet ~ half of SWE were looking down on me, disrespecting me. To be clear, there were many who were not, but usually they were from some marginalized group: women, autistic, person of color, gay, person from a poor country, etc. I thought, why is my towering knowledge not being respected? Ah, the patriarchy combined with SWE. And then as time went on I just started using my knowledge for myself and that’s worked out well (bc it’s based on actually knowing math as opposed to relying on the patriarchy).
I think it’s possible the industry eventually figures out that statisticians and econometricians know far more than CS / SWEs (bc AI will tell people), but it could be a decade from now.
This guy might be living under a rock.
I think it’s useful to distinguish between LLM and AI. I think this criticism is valid against LLMs, but not against AI. LLMs are a useful tool, but they aren’t AI. Once actual AI is a thing, I think it’s worth revisiting this topic. I’m certain we’ll get there one day, but it will probably be a lot like fusion.
Just look and see what Cloud did for software engineers? It pushed us one level higher and lowered the demand for "db experts" and "low level systems people". The only ones who remained were the strong ones who were hired into the cloud companies. The rest moved up and changed careers.
Why would anyone think the same thing won't apply here? If you are still a Typescript bunny who fiddles with some newly learned React tidbit -- this won't cut it anymore. The market won't need you. Move up and adapt or move down and become an expert (harder).
> Why would anyone think the same thing won't apply here?
Because there's nothing to "move up into" other than "spec, optionally design".
Because the jobs that had "spec, design but don't code" already existed for decades, and pay less than half what the "design + code" person earned.
Not buying it. The idea that deciding and delivering are things only humans can do with their intelligence seems faulty.
As it stands AIs today are not always great at making decisions (but they're getting much better), and orgs of today still trust people and hold people to account, rather than their AI systems.
Neither of these are strong moats. It's a moat only while AI systems have some limitations and corporate processes are still extremely human-centric.
Agree, AIs are better decision-makers on average than people (just look at the grifters we've given power to). These are machines that can perform more advanced mathematics than even the most advanced mathematicians.
> orgs of today still trust people and hold people to account >Neither of these are strong moats
Having accountable people in key positions is a very important part of running a successful organization. Anthropic and OpenAI are never going to let you sue them when an AI employee makes a mistake; accountability is a strong moat.
> software development, as a “decide-execute-deliver sandwich”. AI compresses the “execute” layer — the middle of the sandwich — but the other two layers resist automation in a way that will not be overcome by capability improvements alone.
I really struggle to see why improved capabilities cannot deal with those other layers. I do not believe you have substantiated this claim about not being possible as capabilities improve.
> At one end of the pipeline, development teams need to decide what to build.
Developers are not the ones that do this largely. This role is far more on the side of "Product Owner". Sometimes your job covers both, but this is not the majority of the work and does not mostly require SE knowledge - some input usually.
> This layer is hard to automate because it requires thinking about user needs, market signals, organizational priorities, and in some cases regulatory constraints.
Hmm, these are language models that can talk through much of this already - but more importantly none of what is mentioned there requires software engineering. For parts that do (I'm sure someone would come to correct me if I said that there was none or seemed to suggest it is never ever ever relevant) this is a much smaller slice.
> As AI capabilities improve, the kinds of decisions that can be delegated to AI increase over time. But this does not make the “decide” layer thinner — once a decision can be delegated to AI, it is no longer a source of competitive advantage, and the value of human decision-making migrates upward. Software increases in complexity over time, so there is no ceiling to this process.
Now this is rather hidden but a huge leap in logic. The decide layer does get thinner for all the same projects, and then you simply assert that software will get more complex and so this cancels it all out.
A team of 5 may end up being able to ship what a team of 50 used to, and maybe now there are 10 teams outputting more - but is there not a clear limit to this? At some point do we not just need 45 fewer people? That there needs to be some engineers is not the same as needing anywhere near as many as we have.
For a time I think we will see increased output meaning more software, but that tails off as they get better.
> At the other end of the sandwich, human teams need to be accountable for what they deliver.
Why? And if we assume so, why does that need a software engineer?
> It is possible that some day in the future teams will ship mission-critical code without fully testing and understanding it,
You don't need to read code to test it, and people choose to ship products without fully understanding the code all the time. Literally any decision maker who is not a software engineer who knows the entire codebase does this. Companies fully ship systems that are far too complex for any single developer to even understand.
And much of software isn't mission critical. Or at least, if you want to say it is then the mission is low stakes.
> today’s AI is so unreliable that such haphazard practices would represent an existential threat to software teams and their customers.
I'd argue for a bunch of stuff this isn't true, and the whole point of the article is "never even if they get better" which is different.
> A central insight of AI as Normal Technology is that we can collectively choose to keep humans accountable through shared norms, law, and policy.
Sure, we can ban AI writing code, but will we? Is there a huge collective concern for all us high paid engineers being replaced by AI?
“When we did this analysis…”
Nah, kids, this is an opinion column. If you can’t tell the difference, then you don’t get to sit at the adults table. I’ve been an opinion writer for most of my life, and dressing up my perspectives in scientific LARP is bullshit. And yes, I do have underlying suspicions why certain cultures feel entitled to get away with taking such a tone in their declarations. This has been formed over decades of observation and I won’t claim it is scientific…unlike these two fellas who enjoy foods I do not.
AI won't be put in important positions of responsibility within an organization because AI providers will never accept liability for bad decisions. You can't fire Claude if it fucks up, and it's got very limited ability to learn from its mistakes. It's also incapable of making good decisions where doing so requires synthesizing more than a few hundred thousand tokens worth of domain knowledge/experience in something that doesn't have an infinite amount of synthetic verifiable training data like code and math.
In theory continuous learning (live weight updates) could help to some degree. But there's essentially no progress towards that because it requires solving a few hard, currently completely unsolved problems. 1. Weights drift over time and there's no way to re-merge them after a few tens of thousands of updates, so when a new model version was released there'd be no way to update existing continuously-learned models to that. 2. It'd allow permanent jailbreaking. And 3. A model can't learn new things without forgetting existing things, unlike humans brains which have hardware plasticity (like London taxi drivers having larger hippocampi due to having to memorize so many streets).
I have found that the attention moves to thinking about the things I want done and planning, reading and iterating over the specs and other artefacts that will be part of running the agents. I still need to understand the code and iterate over it to get to a usable and maintainable point.
I find the problem is we are reaching the top of the slop curve. I will subside because it's impossible to actually do anything useful with all the output. There will just be a ton of half-finished and abandoned projects. Whatever gets into production will require more eyes on it.
I just think a lot of people are still stuck in the "holy f** I'm so productive" and working themselves into the ground being productive pumping out code. I think it's a phase that will pass.
I find that it depends a lot on the project.
I'm doing solo mobile app projects, and I have no need to iterate on specs. The bottleneck is QA testing whether it works on the phone.
I don't need to carefully review and understand the implementation. It's not important whether I understand the details of how exactly UICollectionView in Apple's UIKit works.
I see that my implementation works on different physical devices, my tests cover device rotation, and I checked the memory allocations in the Instruments tool.
It has been some months of part-time work on my side, and I will publish this iOS app soon.
Without AI I could not have done it, the scope of the features is too large. The project is around 100k LOC.
It is not true that projects become unmaintainable and abandoned because of agentic engineering, or even vibecoding if you want to call it that.
LLMs still do not have proper contextual understanding of their solutions. Just couple days ago I was using GPT 5.5 with xhigh to vide code some application, and yet it defaulted for sorting dates from new to old by using plain string comparison. Just one of the many bugs.
This absolutely fascinates me. I had a friend who needed subtitle files generating for audio and using in CapCut yesterday yet none of the available stuff was suitable, so he asked if I could adapt some of my software to export subtitles.
2 hours later he's got a fully working piece of local software that does exactly what he wants, yet yours is not able to even sort dates correctly. Feel free to download it if you want to see for yourself, I didn't even do any UI tweaks as this was just a tool for him to use:
Linux - https://downloads.blazingbanana.com/whistle-subtitles/unstab...
Windows - https://downloads.blazingbanana.com/whistle-subtitles/unstab...
Mac - https://downloads.blazingbanana.com/whistle-subtitles/unstab...
How can there be such a massive gap in what can be produced?
> How can there be such a massive gap in what can be produced?
What I was doing looks really nice and mostly works on the surface, but it is all about the corner cases where these bugs appear. In another day I was able to generate Frida script with LLM help that bypasses Dart certificate pinning/validation and proxies all the traffic by injecting the runtime binaries. With the latest Flutter/Dart version on Android when doing security analysis.
Ahhh ok I totally understand what you mean. Yea the edge cases are absolutely where you start to feel the pain and things look good on the surface until you dig in. I think even in the age of LLMs the adage of 90% of the time is spent of the last 10% will ring true.
Sure an app can be built and spun up in an afternoon, but are you willing to spend another 6 months ironing out all those little bugs, tuning it a bit, testing, tweaking, testing etc.
it sometimes goes like this: vibecoding viberequirements, vibeleadership vibemanagement, vibecustomers
as a matter of taste, you can substitute "slop" as a prefix
I think we as a professional class have gotten a bit overwhelmed with the magic slot machine. 2 in the morning let me just do one more pull on the slop machine. I WILL win this time.
It's a phase. The problem is the managerial class sees it as a magic black box and don't understand it's limitations. Calling it AI does not help either. It's the "rockstar developer" illness but on crack.
I've never seen a greater disconnect between what I read on social media about vibe coding and what I've seen in real life.
In particular the whole "the best people are the ones who will use it the best". IME the best ones are the ones keeping it the most at arm's length while the people who embrace it the most churn out epic amounts of utter slop.
The whole field of engineering set to disappear and be replaced by contractors. Of course this is what they've always wanted. That's why they do outsourcing and the whole point of AI so, basically instead of getting paid a small salary to maintain someone's money-making machine, people will bid for jobs. They'll be more and more layers of abstraction that business owners will have to pay rent to. Until it's just basically socialism.
The question is, are executives willing to give up all their power and status to an LLM or will “industry” just use AI to invent more bullshit jobs to keep everyone, including the exec relevant.
The reason humans haven’t been replaced in many areas entirely is because humans like being someone’s.
Even AI can do incredible things, like code (our job), It's difficult to me to think that one day, companies will fully assign all software lifecyble job (from designing, implementation, deployement, scaling and maintenance) to autonomous ai agent because, even the better software in the world can have bug and bug can cause from low to fatal real world damage. Those damage , in civil human real world, need a responsible. Unless, govs start voting to give citizenship to ai & autonomous agents and penal responsibility to those agents, human will also be in the loop, always. Because, Human like to designate a responsible and that's right things. So, the adaptative software engineer will always has it place.