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 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.

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.

> 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%.

She was a programmer. Her company openly build an agent for the purpose of replacing her (and a few others), and they got rid of her about a month after it started working.

It's always the business owner who replaces workers. Let's not anthropomorphize a bunch of graphics cards

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

Hopefully, this adds some useful context to the discussion:

Part 1: System Definition Brings Software Engineering to AI Coding https://hackernoon.com/system-definition-brings-software-eng...

Part 2: Working Code, Wrong Engineering: Why AI-Generated Code Needs System Definition Tests https://hackernoon.com/working-code-wrong-engineering-why-ai...

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 vs an expert human, and corporate processes are still extremely human-centric.

It could be that because coding was seen as expensive and a bottleneck, much effort (both upstream and downstream) had been going into making sure its input is correct and the output need not be discarded. If coding is seen as a quick and cheap step, its output could stand to be thrown away and therefore the same amount of oversight may not be needed upstream?

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.

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)

That being said, so far no AI model is producing shippable code well enough to go either unprompted OR unreviewed at a meticulous level so... I'm not sure this will happen so soon.

But when I use AI for other problems, such as resolving a weird linux issue, or figuring out why I'm having a particular problem on my network, I find AI is great at surfacing possibilities but it will very quickly go down rabbit holes that end up leading nowhere. If I didn't have enough experience under my belt, I'm not sure I'd realize this when it was happening.

For me, AIs are great enhanced search engines. They make it easier for me to find out what I need to know to deal with a novel problem. But left on their own, they will (confidently) go way too far down dead-end paths.

I don't know how many other companies are doing this, but at least where I am, I have seen the total opposite behavior. They are very pleased with the results of AI and eager to hire.

In other words: AI doesn't have a blind spot no matter whether AI will ever "get" Cobol or not.

So even if we jump into 2050, we won't have to fix any React application due to three simply reasons: It is easier to build something new; AI understands the old garbage; and the last one: who knows, what will be in 2050?

Tough time.

My biggest concern is not AI, but the total demotivation of the veterans. Suddenly there isn't merit nor fun in building something over a week you took off. And that hits hard.

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).

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 absolutely fucking exhausted from this bullshit discourse after almost 4 years of it at this point.

These AI companies need to stop bloviating and administering verbal self-fellatio and either replace everyone or fuck off already. I don't even care which it is anymore, I just want to be done with this hype cycle and move on with my life/career.

Therefore sane organisations using those agents currently need to have someone they trust to review and sign off of on anything consequential those agents do on their behalf. Lawyers for legal, Doctors for medical, Software Engineers for Software etc.

Given the amount of money being thrown at AI - and the need for return - it seems unlikely that regardless of domain that lack of guarantees is a situation that will persist.

Today AI is 100% automating the software engineering market. We all know it and it is not evenly distributed with some fields being more impacted than others.

However, what is not mentioned anywhere is for long this will continue and what shape it will take. It is a trickier question.

There are two factors in my mind: cost and volume.

On the cost side, AI coding agents could become unfordable. What happens then? Back to engineering jobs? Hard to tell. It might impact seriously the bottom line of many companies while jobs remain lost forever.

On the scale side, the more code is written the more complex it becomes - that is a fact. Who is going to look after this code? More AI? That will make it more costly. Or is that most software is dead on arrival - then why build it?

So it is not clear cut. The investment is there but how to turn the investment in meaningful long-term ROI is not.

Even on the highly subsidies plans most software written today has marginal ROI. Building landing pages and features that nobody asked for is not a good execution plan and frankly I have done this mistake myself.

The last bit I would like to flag is that if software becomes almost free then why build at all? Why would companies even bother building anything at all?

[0] https://arkvis.com/blog/2026-04-26_the-future-role-of-engine...

However, this isn't unique to software engineering. AI can automate parts of any job. It just so happens that software engineering faces the highest proportion of this displacement.

Therefore, instead of self-soothing with the delusion that "we are irreplaceable," engineers need to shift their mindset. They must identify exactly which tasks are being automated, figure out how to expand their scope beyond those tasks, and find ways to leverage AI to their advantage.

In fact, software engineering is a field where you can use AI to go incredibly deep, scale your impact, and boost your earning potential more than in any other profession. This is absolutely no time to be sitting around and wasting energy on these kinds of complacent thoughts.

“Software Is Eating the World” It is the software that is eating the world, not the engineers. It’s time for software engineers to drop the arrogance.

If you wonder why management is trying to lay off software engineers? The answer is obvious. It's because too many engineers still hold onto that exact complacent attitude.

The reason humans haven’t been replaced in many areas entirely is because humans like being someone’s.

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?

Also declaring that it "won't" is an assertion that AI will stop improving, which is absurd.

A graph showing different numbers of "software developers" and "computer programmers" as some kind of evidence of something other than that people prefer fancy sounding names is extremely dumb. You may as well plot "HR" vs "personnel" and conclude that companies no longer have people.

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).

https://chatgpt.com/share/6a2bd5c8-2188-83e8-8f7e-1b6c9b6049...

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.