In my experience, it's been the complete opposite. The very experienced engineers that are actually willing to use top of the line tooling are much better than they were before, including those that are over 40, and over 50.
Part of the practical degradation of traditional programmers over time has always been concentration and deep calculation, just like in chess. The old chess player knows chess much better than a 19 year old phenom, but they cannot calculate for that many hours at the same speed as before, so their experience eventually loses to the raw calculation. Maybe at 35, or at 45, but you are just not as good. Claude Code and Codex save you the computation, while every single instinct and 2 second "intuition", which is what you build with experience, is still online.
It's not just that it's a more fair competition: It's now unfair in the opposite direction. The senior that before could lead a team of 6 is now leading a team of agents, and reviewing their code just as before. Hell, it's easier to get the agent to change direction than most juniors around me, which will not be easy to correct with just plain, low-judgement feedback.
Multiple times per week I have the same conversation. It goes something like this:
- AI will make developers irrelevant
- Why?
- Because LLMs can write code
- Do you know what I do for a living?
- Yes, write code?
- Yes, about 2-5% of the time. Less now.
- But you said you are a developer?
- I did
- So what do you do 95-98% of the time?
- I understand things and then apply my ability to formulate solutions
- But I can do that!
- So why aren't you?
The developers who still think their job is about writing code will perhaps not have a job in the future. Brutal as it may sound: I'm fine with that. I'm getting old and I value my remaining time on the planet.
Business owners who think they can do without developers because they think LLMs replace developers are fine by me too. Natural selection will take care of them in due course.
This is a bit of glib answer. Most of the time is spent coding which encompasses typing, retyping, and retyping again. It also includes banging your head against the wall while trying to get one of your rewrites to work against and under-documented API.
OP's formulation makes SWE sound like a purely noble enterprise like mathematics. It's more like an oil rig worker banging on pieces of metal with large hammers to get the drill string put together. They went in with a plan, but the reality didn't agree and they are on a tight schedule.
Most of the time is spent figuring what the right thing to do is, not writing the implementation. Sometimes the process of writing the implementation surfaces new considerations about what the right thing is, but still, producing text to feed to a compiler is not the bulk of the work of a software engineer. It is to unearth requirements and turn them into repeatable software.
There are also those for whom that percentage is higher, let’s say 6-50%.
> I understand things and then apply my ability to formulate solutions
The AI is coming for that too.
You might just be lucky to be in circumstances that value your contributions or an industry or domain that isn’t well represented in the training data, or problem spaces too complex for AI. Not everyone is, not even the majority of devs.
People knocking out Jira tickets and writing CRUD webapps will end up with their livelihood often taken away. Or bosses will just expect more output for same/less pay, with them having to use AI to keep up.
Agree. It is just like 2 totally separate groups are arguing.
One very tiny slice of speciality/ rare industries where code is critical but overall small part of project costs. I can see if code / software is 5% of overall cost even heavy use of AI for code part is not moving the needle. So people in this group can feel confident in their indispensability.
Second group is much larger and peddling CRUD / JS Frontends and other copy/paste junk. But as per industry classification they are just part of same Coder/Developer/IT Engineer group. And their bleak prospects is not some future scenario, it is playing out right now with tons of them getting laid off. And whole lot of people with IT degrees, certifications are not finding any jobs in this field.
There are periods of time where I might spend 80% of my time "coding", meaning I have minimal meetings and other responsibilities.
However, even out of that 80% of my time, what fraction is actually spent "writing code"?
AI can be an enormous accelerator for the time I'd normally spend writing lines of code by hand, but it doesn't really help with the rest of the work:
- Understanding the problem
- Waiting for the build system and tests to run
- Manually testing the app to make sure it behaves as I'd like
- Reviewing the diff to make sure it's clear
- Uploading the PR and writing a description
- Responding to reviewer feedback
There are times when AI can do the "write the code" portion 10x faster than I could, but if it's production code that actually matters, by the time I actually review the code, I doubt it's more than 2x.
>AI can be an enormous accelerator for the time I'd normally spend writing lines of code by hand, but it doesn't really help with the rest of the work:
- Understanding the problem - Waiting for the build system and tests to run - Manually testing the app to make sure it behaves as I'd like - Reviewing the diff to make sure it's clear - Uploading the PR and writing a description - Responding to reviewer feedback
What part of those you think it doesn't help with?
That may be true I’m not gonna say one way or the other, but if AI comes for that then almost all knowledge work is effectively dead, so all that’s left would be sales or physical labor.
I wonder though, can AI make the next JS framework. I mean that in sincerity, there was the leap from jQuery to React for ex. If an AI only knows jQuery and no one makes React, will React come out of AI.
People didn't leap from jQuery to React. It's a lot easier to imagine an AI looking at jQuery and [insert any server side MVC framework] and inventing Backbone.
The history of the last 250 was moving from agriculture to industrial work to service work. Now the last frontier is starting to be overtaken by automation too.
(And in all of those transitions millions where left behind without work or with very worse prospects. The people that took the new jobs were often a different group, not people who knew the old jobs and were already in their 30s and 40s).
And what would be the new professions that uniquely require humans, when even thinking and creative jobs are eaten by AI? Would there be a boom of demand for dancers and chefs, especially as millions lose their service jobs?
Discovery of the best solution in a problem space is not generative but only verificative. Meaning: the LLM can see if a solution is better than another, but it can't generate the best one from the start. If you trust it, you'll get sub-par solutions.
This is definitely an agent problem instead of an LLM problem. Anybody got something explorative like this working?
Yes, AI is coming for solution formulation, absolutely, but not all of it, because it is actually a statistical machine with context limit.
Until the day LLMs are not statistical machine with a context limit, this will hold. Someone need to make something that has intent and purpose, and evidently now not by adding another 10T to the LLM parameter count.
To some degree yes, in practice, not so much. In practice you have to be in the world, talk to people, know how to talk to people, know how to listen, and be able to understand the difference between what people say and what they actually need. Not want. Need.
This was something I learnt in my very first job in the 1980s. I worked for someone who did industrial automation beyond PLCs and suchlike. He spent 6 months working in the company. On the factory floor, in the logistics department, in procurement, in accounting and even shadowing the board. Then he delivered a proposal for how to restructure the parts of the company, change manufacturing processes, and show how logistics and procurement could be optimized if you saw them as two parts in a bigger dance.
He redesigned the company so that it could a) be automated, and b) leverage automation to increase the efficiency of several parts of the business. THEN, he started planning how to write the software (this was the 80s after all), and then we started implementing it.
Now think about what went into this. For instance we changed a lot of what happened on the factory floor. Because my boss had actually worked it. So he knew what pain points existed. Pain points even the factory workers didn't know how to address because they didn't know that they could be addressed.
I was naive. I thought this was how everyone approached "software projects". People generally don't. But it did teach me that the job isn't writing code. It is reasoning about complex systems that often are not even known to those who are parts of it.
And this is for _boring_ software that requires very little creativity and mostly zero novelty. Now imagine how you do novel things.
> People knocking out Jira tickets and writing CRUD webapps will end up with their livelihood often taken away. Or bosses will just expect more output for same/less pay, with them having to use AI to keep up.
You make it sound like it is a bad thing that certain tasks become easier.
I spent a lot of time writing CRUD stuff. Because the things i really want to work on depend on them. I don't enjoy what is essentially boilerplate. Who does? If you can do the same job in 1/20 the time, then how is this a bad thing?
It is only a bad thing if writing CRUD webapps is the limit of your ability. We don't argue for banning excavators because it puts people with shovels out of work. We find more meaningful things for them to do and become more productive. New classes of work becomes low-skilled jobs.
If you have been doing software for a while, you are probably doing some subset of this. But these things are hard to articulate. It is hard to articulate because it is not something we think about. Like walking: easy for us to do, hard to program a robot to do it.
>To some degree yes, in practice, not so much. In practice you have to be in the world, talk to people, know how to talk to people, know how to listen, and be able to understand the difference between what people say and what they actually need. Not want. Need.
1 person needs to do that. The other 100 not doing that currently to begin with, but doing the AI-automatable work?
We used to say that (not long ago, even) about the code-writing part. Why do we believe that LLMs are going to stop there? Why do we think they won't soon be able to talk to people, listen, and determine what they need? I think it's mostly a cope.
If they can do those things they can effectively replace any white collar job. That’s about 45% of the workforce. Societies tend to collapse around 25-30% unemployment.
Imagine 45% of higher than average paying jobs gone.
If that happens we’ll either figure out a new economic system, or society will collapse.
Also saying robots are walking just fine is misleading for any definition of just fine that is anywhere near as good as a human.
Look at how the billionaires are talking about AI: Their clear, unambiguous goal is basically to replace all white collar "knowledge" jobs. And there's currently nothing regulatory that's stopping them--they just need to wait for the state of the art to improve. Once AI is "good enough" if it ever is, they won't even think twice about 45% unemployment. What are we unemployed workers going to do about it? There's no effective labor organization left. Workers have basically no political power or seat at the table. We're not going to get violent--the police/military are already owned by the billionaire class. We're just going to eventually become economically irrelevant and die off.
The fundamental issue isn't unemployment due to automation, but the fact that society cannot benefit from unemployment.
It should be something for us to celebrate, because it means greater freedom for humans to pursue something else rather than spending time doing drudgery.
> We're just going to eventually become economically irrelevant and die off.
As harsh it may sound, it seems rather likely to me. It is not like s/w engineers have helped struggling workers in other sectors other than sanctimonious "Learn to code" advice. So software folks can't expect any solidarity or help from others.
45% of folks sitting on their hands are going to have the free time to talk, and this group of people are skilled at organization. Are you planning on throwing your hands up and passively accepting whatever comes your way?
It's important (and calming) to understand that since the Industrial Revolution started ~250 years ago, we've automated away most jobs several times over, while employment levels have stayed pretty constant.
"Automating half the jobs" is the same as "double productivity per worker".
When the doubling happens in 5 years rather than 50, it might be more disruptive, but I'm convinced we're on the verge of huge improvements in human standard of living!
We never noticed how easy the code writing part had already become because it happened slowly. Through mechanical means, through the ability to re-use code, and through code generation.
Heck, even long before LLMs about 10% to 30% of my code was already automatically generated. By tooling, by IDLs and by my editor just being able to infer what my most likely input would be.
> We have robots walking just fine now, by the way.
I don't think you got the point I was trying to make.
True, but I guess I see a distinction between scaffolded/templated boilerplate or autocomplete and actual application logic. People have generated boilerplate from templates for ages, as you say. RoR maybe a pretty good example, but there wasn't even early-days AI involved in doing that.
>> We used to say that (not long ago, even) about the code-writing part. Why do we believe that LLMs are going to stop there? Why do we think they won't soon be able to talk to people, listen, and determine what they need?
Because they are currently "generative AI" meaning... autocomplete. They generate stuff but fall down at thinking and problem solving. There is talk of "reasoning models" but I think that's just clever meta-programming with LLMs. I can't say AI won't take that next step, but I think it will take another breakthrough on the order of transformers or attention. Companies are currently too busy exploiting the local maxima of LLMs.
Walking was given as an example of "hard to program a robot to do it" by GP. Well, now we have robots that can walk.
What evidence is there that LLMs have hit a ceiling at being able to do things like talk to users or stakeholders to elicit requirements? Using LLMs to help with design and architecture decisions is already a pretty common example that people give.
No, I never believed in fully automated tale by Tesla, but as the LLMs improve my personal estimate for the date of human-level AGI is rapidly moving to "present". Before GPT-2 I had it somewhere in 2100, at GPT-2 I thought maybe by 2060 if we are lucky. Now I think it is 2035 or maybe even sooner.
I've always told my Jr Engineers to "think twice, code once".
If I gave them a task and they immediately started typing it out, I would tell them to stop typing and ask them to explain to me what they were doing; they'd often just spit out what they thought the code should do, and I'd often point out edge cases they missed and would have missed had they just spit out code and a PR, wasting everyone's time. I would also insulate them from upper management to give them time to actually think (e.g. I wouldn't be coding so they could think then code).
To your point and to the GP's point, and one point I keep raising with LLM's: "typing is not where my time sinks are"
I think the future is pretty up in the air in this respect, but my guess is that AI will just lead to another shift in the set of knowledge that a 'real programmer' is expected to have. I'm old enough to remember when people would make fun of web developers for 'programming' using HTML and JavaScript. And of course, back in the day, you couldn't be a real programmer unless you wrote assembly language. In a few years' time, being able to write (as opposed to read) source code in any specific programming language will probably become a niche skill. The next generation will be able to read Python to about the same extent that I can read x86 assembly.
If you spend 95% of your time on that stuff, you better be working on like critical infrastructure where nothing can go wrong, otherwise you are in an incredibly dysfunctional company.
Weird. I call myself a developer because I don't have an engineering degree from an abet certified engineering program.
I recognize, in some capacity, that this isn't the norm and in the US "professional engineer" is protected and not simply "engineer", but it feels akin to stolen valor to me.
If there were a license in the US for it, I’d agree with you. But as is, if you are “doing” engineering, you’re an engineer.
If you are a licensed engineer of some kind, you’d state that outright.
The equivalent of stolen valor would be claiming to be a licensed software engineer; except there is no such license so it would also be fraud, misrepresentation, etc.
I'm a software dev in the US and I never call myself "engineer" in that capacity. Always "programmer" or "developer".
I agree. Engineers have to clear a much higher bar. Even though my career was spent in medical diagnostic software where we had to get 510k clearance, I was still keenly aware that this was a fundamentally different activity from actual engineering.
Note that just because you know the job is understanding things, the manager who'll boot you and leave you without income probably doesn't. They'll just get their political cookie points for saving money by replacing you with AI.
>- I understand things and then apply my ability to formulate solutions
- Well, and AI can do part of that too, maybe more of it soon.
- ...
- Besides, you don't need 10 guys in a team to do that. A couple of them will do, then AI will do the coding. What will happen to the rest?
- ...
I dunno, man. I've been doing this for 20+ years and I think we're at a really important fork in the road where there are two possibilities.
The first is that AI is achieving human-level expertise and capability, but since they're now being increasingly trained on their own output they are fighting an uphill battle against model collapse. In that case, perhaps AI is going to just sort of max out at "knowing everything" and maybe agentic coding is just another massive paradigm shift in a long line of technological paradigm shifts and the tooling has changed but total job market collapse is unlikely.
The other possibility is that we're going to continue to see escalating AI capability with regard to context, information retrieval, and most importantly "cognition" (whatever that means). Maybe we overcome the challenges of model collapse. Maybe we figure out better methodologies for training that don't end up just producing a chatbot version of Stack Overflow + Wikipedia + Reddit. Maybe we actually start seeing AI create and not just recreate.
If it's the latter, then I think engineers who think they are going to stay ahead of AI sound an awful lot like saddle makers who said "pffft, these new cars can only go 5 miles per hour."
I'll also add another factor: it's become increasingly clear at our company that AI-enabled humans are getting to the bottom of the backlog of feature ideas much quicker. This makes the 'good ideas' part of the business the rate limiting step. And those are definitely not increasing with AI, beyond that generated by the AI churn itself ("let's bolt on a chat experience or an MCP!")
So maybe the coding assistants don't get a 10x improvement any time soon, but we see engineering job market contraction because there aren't really enough good ideas to turn into code.
You are supposing that AI is achieving human level expertise and capability is a given. I am not so sure. Right now that's much further from the truth than one might think at first glance.
LLMs know nothing but are great at giving the illusion that they know stuff. (It's "mansplaining as a service"; it is easier to give confident answers every time, even if they are wrong, than to program actual knowledge.) Even your first case seems wildly optimistic. The second case is a lot of "maybes" and "we don't know how but we might figure it out" that seems like a lot to bet an entire farm on, much less an entire industry of farms.
We sure are looking at a shift in the job market, but I don't think it is a fork in the road so much as a Slow/Yield sign. Companies are signalling they are willing to take promises/hope to cut labor costs whether or not the results are real. I don't think anything about current AI can kill the software development industry, but I sure do think it can do a lot to make it a lot more miserable, lower wages, and artificially reduce job demand. I don't think this has anything to do with the real capabilities of today's AI and everything to do with the perception is enough of an excuse and companies were always looking for that excuse. (Just as ageism has always existed. AI is also just a fresh excuse for companies to carry on aging out experience from their staff, especially people with long enough memories/well schooled enough memories to remember previous AI booms and busts.)
But also, yeah if some magic breakthrough makes this a real "buggy whip manufacturer moment" and not just an illusion of one, I don't mind being the engineer on that side of it. There's nothing wrong about lamenting the coming death of an industry that employs a lot of good people and tries to make good products. This is HN, you celebrate the failures, learn from them, and then you pivot or you try something new. If evidence tells me to pivot then I will pivot, I'm already debating trying something entirely new, but learning from the failures can also mean respecting "what went right?" and acknowledging how many people did a lot of good, hard work despite the outcome.
Saying being a programmer is about writing code is a bit like saying being an artist is about drawing lines on a canvas.
Yeah technically drawing lines on canvases may be an very important part of being a painter, but it is hardly the core of what makes or breaks great art.
What you described are senior developers and system architects.
Junior developers spend most of their time writing code (when they're not forced to attend pointless standups, because Agile/blah/blah)
> The developers who still think their job is about writing code will perhaps not have a job in the future.
So you're saying the same thing everyone else is saying. SWEs won't go away, but they will be greatly reduced, because those whose job is about writing code -- junior devs -- will be replaced.
(How will Sr Devs in the future be created? That's the question, isn't it.)
As an extreme example, maybe we’ll see long-running internships and trainings like doctors experience. Doctors don’t start their career until ~12+ years of prep and training.
Pragmatically, software development has a lot of examples of teenagers making apps and college students building software companies. In the 12 years it takes for training, low-knowledge workers could be vibe coding continuously replacements of most commercial software products they’d be hired to build. So I doubt we’ll treat software development as a rarified high skill job.
Pure wage workers should consider dropping the attitude about how tech progress will just make their inferiors in the same line of work be out of a job (hrmph good riddance etc.). Because this pseudo-progress could creep up on them as well.
Then you won’t have this just world of the deserving workers at all. Just formerly deserving workers and idiot billionaires like Musk (while the robots do all of the work).
I normally say that I have zero concerns regarding AI in terms of employment. At most I am concerned in learning the best practices on AI usage to stay on top of things.
It's ability to write code is alright. Sometimes it impresses me, sometimes it leaves me underwhelmed. It certainly can't be left to do things autonomously if you are responsible for its output.
Moderately useful tool, but hellishly expensive when not being subsidized by imbeciles that dream of it undrrmining labor. A fool and his money should be separated anyway.
What I am really concerned about the incoming economic disaster being brewed. I suspect things will get very ugly pretty soon.
> AI-users thus become less effective engineers over time, as their technical skills atrophy
Based on my experience, I think this will prove more true than not in the long run, unfortunately.
Professionally, I see people largely falling into two camps: those that augment their reasoning with AI, and those that replace their reasoning with AI. I’m not too worried about the former, it’s the latter for whom I’m worried.
My mom is a (US public school) high school teacher, and she vents to me about the number of students who just take “Google AI overview” as an absolute source of truth. Maybe it’s just the new “you can’t cite Wikipedia”, but she feels that since the pandemic, there’s a notable decline in the critical thinking skills of children coming through her classes.
We have a whole generation (or two) of kids that have grown up being told what to like, hate, believe, etc. by influencers and anonymous people on the internet. They’d already outsourced their reasoning before LLMs were a thing. Most of them don’t appear to be ready to constructively engage with a system that is designed to make them believe they are getting what they want with dubious quality.
> If you work in construction, you need to lift and carry a series of heavy objects in order to be effective. But lifting heavy objects puts long-term wear on your back and joints, making you less effective over time. Construction workers don’t say that being a good construction worker means not lifting heavy objects. They say “too bad, that’s the job”.
My parents were both construction workers. There is an understanding that you cannot lift heavy objects forever. You stop lifting objects and move to being a foreman, a supervisor... and if you are uncomfortable learning to get others to do work that you before have done yourself, you burn out your body entirely and the consequences are horrible.
This is factual reality, but it is also a parable that has been important for me to internalize about delegation in my own career. It is not irrelevant to AI use, but I don't think it slots onto it totally as neatly.
Software developers are more architechs than plain programmers. You wouldn't make an architech lift heavy things, you want they to design how those heavy things are used.
Anecdotally, it feels like something materially changed in the US software hiring market at the start of this year to me. It feels like more and more businesses are taking a wait and see approach to avoid over-investing in human capital in the next few years.
It also feels like the hiring "signal", which was always weak before, is just completely gone now, when every job you do advertise receives over 500 LLM written applications and cover letters that all look and feel the same.
The pro-athlete comparison in this article is bit silly IMO- there are obvious physical body issues that occur with aging if you rely on your muscles etc to make money. If you compare to other fields of knowledge work, such as say law or medicine, there are loads of examples of very experienced, very sharp operators in their 40s and 50s.
Honestly, AI doesn't feel like it's affecting hiring needs from the trenches. We don't have engineers sitting on their hands because AI wrote up everything the leadership could imagine.
Instead, we get an economy that feels like it's on the cusp of a fall or at the very least a roller coaster. Poor tax incentives to hire. ZIRP is long gone. And the hiring managers are overrun with slop.
But bosses are happy to say it's AI because that makes you sound in control.
> Anecdotally, it feels like something materially changed in the US software hiring market at the start of this year to me. It feels like more and more businesses are taking a wait and see approach to avoid over-investing in human capital in the next few years.
My guess is companies overhired in COVID and between that experience and an uncertain market they don't want to make the same mistake twice.
I think the hype peaked around 2016 where Democrats were portrayed as out of touch for saying laid off coal miners could just "learn to code". By 2019 it was a cliché used to mock laid off journalists on Twitter.
This is a great question that rarely gets answered. It’s partially that a ton of student students went to school for computer science because they saw how much money could be made, another fraction is people that switched into software from related fields, maybe with a boot camp or something.
It didn't. The elites never want to admit that they have failed to efficiently use capital for the last 40 years. It's always the fault of workers that should never be trusted. Just continue trusting the elites as they ruined US manufacturing jobs, surely the same institutions won't fail the workers again!
I really wish seemingly intelligent people would stop using the abstraction analogy (like the article does). The key word is: determinism. Every level of abstraction (inc. power tools, C, etc.) added a deterministic layer you can rely on to more effectively do whatever it is that you're doing - same result, every time. LLM's use natural language to describe programming and the result is varied at the very best (hence agents, so we can brute force the result instead). I think the real moat is becoming the person who can actually still program.
I grant that there's a definition of abstraction that LLMs don't fall into. But people describing LLMs as another abstraction layer aren't all misunderstanding this. Instead, they are using the term ... more abstractly.
EG: How did Mark Zuckerberg make software five years ago?
He's as capable of opening up an editor as I am, but circumstance had offered him a different interface in terms of human resources. Instead of the editor, he interacts with those humans, who produced the software. This layer between him and the built systems is an abstraction.
Today, you and I have a broader delegation mandate over many tasks than we did a few years ago.
People always say this but it’s misguided imo. Yes LLMs are not deterministic, but that’s totally irrelevant. You aren’t executing the LLMs output directly, you’re using the LLM to produce an artefact once that is then executed deterministically. A spec gets turned into code once. Editing the spec can cause the code to be updated but it’s not recreating the whole program each time, so why does determinism matter?
In my experience, I'm using LLMs as my abstraction to "junior engineer". A junior engineer isn't deterministic either. I find that if you treat the LLM output like a person's output, you're good. Or at least in my projects, it's been very successful. I don't have it generate more code than I can review, or if I give it a snippet to help me fix it, if it ends up re-writing it like an ambitious engineer would do, I tell it to start over and make minimal changes.
I guess I'm not spun up about the determinism because I've been working at the "treat it like a person" level more than the "treat it like a compiler" level.
To me, it's really like an engineer who knows the docs and had a good memory rather than infallable code generator.
I work at a small company, so we don't have tons of processes in place, but I imagine that if you already had huge "standards" docs that engineers need to follow, then giving the LLM those standards would make things even better.
this is the way LLMs _should_ be used, as an assistant to create reliable, deterministic code. and honestly, they're fantastic when used this way. build the thing you need with the LLM, then put the LLM away.
but in practice, the current obsession with agents means people are creating applications that depend entirely on sending requests to LLMs for their core functionality. which means abandoning the whole idea of deterministic software in favor of just praying that all of the prompts you put around those API requests will lead to the right result.
LLM's don't have to achieve perfect reliability to replace lots of work. They just have to reach the balance of reliability and cost suitable for a given task. This will depend on the task.
I keep reading about how AI will be fine because people can just retrain for different careers. However, I never read what those careers are or who is going to pay for retraining.
I certainly don't have the money or time to go back to college and start a new career at the bottom.
The argument is that “that’s what always happened in the past”.
Which is true, but it’s true as long as it’s not true.
The classic example of how drastically this kind of thinking can fail is Malthusian theory, that populations would collapse because food growth was linear while population growth was exponential. This was true for all of history until Malthus actually made this observation.
At a mechanistic level, the “we have always found other jobs” argument misses that the reason we’ve always found other jobs is because humans have always had an intelligence advantage over automation. Even something as mechanical as human inputs in an assembly line was eventually dependent on the human ability to make tiny, often imperceptibly, adjustments that a robot couldn’t.
But if something approximating AGI does work out, human labor has absolutely no advantage over automation so it’s not clear why the past “automation has created more human jobs” logic should continue.
> Which is true, but it’s true as long as it’s not true.
It also isn't true. The story of the last 50 years has been that technology, especially computer and communications technology, has facilitated the concentration of wealth. The skilled work got computerized, or outsourced to India or China. That left U.S. workers with service jobs where they have much lower impact on P&L and thus much less leverage.
In my field, we used to have legal secretaries and law librarians and highly experienced paralegals. They got paid pretty well and had pretty good job security because the people who brought in the revenue interacted with them daily and relied on them. Now, big firms have computerized a lot of that work and consolidated much of the rest into centralized off-site back-office locations. Those folks who got downsized never found comparable work. They didn't, and couldn't, go work for WestLaw to maintain the new electronic tools. The law firms also held on to many of them until retirement or offered them early retirement packages, and then simply never filled the positions. It used to be a pretty solid job for someone with an associates degree, and it simply doesn't exist anymore.
The only thing keeping the job market together is the explosion in healthcare workers. My Gen-Z brother and sister in law are both going into those fields. In a typical tertiary American city, the largest employers are the local hospital and perhaps a university or community college. Both of those get most of their revenue directly or indirectly from the government. It's not clear to me how that's sustainable.
If it makes you feel better, I'm pretty sure it isn't sustainable. (But I'm not an economist so take that with a block of salt.)
I don't think anyone has the answers. It's just some of us are honest enough to concede we have no answers, while others promote an answer that aligns best with their belief system.
"It'll all work out."
"It's the immigrants/blacks/jews/whatever dragging us down."
"Nothing's going to happen and we can all continue doing the work we always have."
"Burn the rich."
Etc etc.
Not a lot of serious attempts out there at even getting a hand on the issues, let alone fixing the issues.
I'm also pretty sure in the past industrial transitions, many of the people who lost their jobs at the start of the change never found better ones. It took a generation or so for new opportunities to really be found and fine tuned and you're competing for those new roles with younger people anyway.
If ai does take a lot of white collar work, is it a lot of comfort that maybe jobs in a very different sector will be better in 20 years?
Did the younger people find better jobs? You used to have all these jobs for people who were maybe a bit smarter than average with good judgment. In the 1990s, the local community college used to advertise associates degrees for paralegals. That's a job that doesn't exist in the same way anymore thanks to computers. Now it's become an internship for kids with top credentials before they go to law school. Which is fine for them, but what about everyone else?
It seems to me like all of these people are flocking now to healthcare fields. That seems totally unsustainable.
It’s also not that true, and highly dependent on a lot of factors.
Anecdotally I see a lot of schadenfreude online about tech jobs after a decade or two of lecturing everybody from Appalachians in coal country, to Midwestern autoworkers, that they should just “learn to code.”
Totally agree, and would add another way “that’s what always happened in the past” is a terribly weak argument. Things might have always worked out at the societal level so far, but very often do not at the individual level. Countless successful craftsmen have had their livelihoods ruined by technological changes and spent their remaining years impoverished. How many people funding AI would be willing to throw their own life away for the good of some future strangers that may or may not be born? I'm pretty sure the answer is <=0.
> The classic example of how drastically this kind of thinking can fail is Malthusian theory, that populations would collapse because food growth was linear while population growth was exponential. This was true for all of history until Malthus actually made this observation.
Malthusian observation can still be true...It only has to be true once, and the only reason people say it isn't right now is due to industrial fertilizers and short memories.
It's not going to happen, just as it didn't happen for skilled industrial workers whose jobs got outsourced to China. The government will pay just enough in welfare to keep the situation manageable. Then they'll demonize you in the culture, as a Luddite, etc.
> However, I never read what those careers are or who is going to pay for retraining.
There aren't any careers and if there were, you would have to pay. Corporations certainly won't except under extremely rare situations where they have to to compete.
I think the idea of being an employee is fundamentally changing. Not saying its good or bad but it's shifting to a more entrepreneurial phase where people have to step out of their 9 to 5s and find ways to deliver value that others want to pay for.
We saw this pre-ai with uber and door dash. I think as AI automation dies down and most companies are competing at a near optimal level with the new tools we'll need humans again in more traditional roles to build the next generation of innovations. And then the whole cycle will repeat.
That's a lot of SV-speak. How exactly do people step into an entrepreneurial phase? They're at work in corporate settings with fixed defined roles. Most workplaces are not many-hatted-donning startup environments, but restricted roles where there are deliverables, deadlines, meetings, etc. Which leaves out of hours for "entrepreneurship" whatever that is.
Github project work on the weekends? That's not possible for most people in their mature/family years (or shouldn't be necessary - what about living life??)
What about people who have been out of work for a year and all they can do right now is deliver for Uber and Doordash so they can make rent and put some food on the table?
Is it ideal working conditions? No, but its better than nothing, you can set your own hours, and you can leave when the next opportunity comes.
The second part seems obvious to me: the ones who are getting retrained. If it's some kind of formal education, depending where you are, maybe the state at least for part of it.
Education in what, though? No idea. And if there was one answer, and it's true that there will be fewer software developers, you'd likely be competing with many people for few jobs.
Exactly. I have yet to read a single logically sound argument that even gives a hint of what those professions/jobs might be (remember, they have to be plentiful enough to employ large numbers of people, so "I quit my corporate job and making more as a TikTok influencer" doesn't count). Remember that a new profession has to open up new hitherto unknown revenue streams otherwise there are no companies who will pay you.
Also, it's not necessarily true that there will be other great careers available. This seems to just be an assumption people are making.
Of course, there are jobs that will still require human labour for some time yet, but in reality a lot jobs that require physical human labour are now done in other parts of the world where labour is cheaper.
Those which cannot be exported like plumbing or waitressing only have limited demand. You can't take 50% of the current white-collar workforce and dump them in these careers and expect them to easily find work or receive a decent wage. The demand simply does not exist.
Additionally, at the same time as white-collar jobs are being lost an increasing number of "low-skill" manual labour jobs are also being automated. Self-checkout machines mean it's harder to get work in retail, robotaxis and drone delivery will make it harder harder to find work in delivery and logistics, robots in warehouses will make it harder to find warehouse jobs.
It seems to me there is an implicate assumption that AI will either create a bunch of new well-paid jobs that employers need humans for (which means AI cannot do them) and jobs which cannot be exported abroad for cheaper. What well-paid jobs would even fit the category of being immune to AI and immune to outsourcing? Are we all going to be really well paid cleaners or something? It makes no sense.
A lot of the advice we're seeing today about retraining in construction worker or plumber seems to assume that there's an unlimited demand for this labour which there simply is not. And even if hypothetically there was about to be a huge increase in demand for construction workers, it would take years to even have the machinery, supply chain and infrastructure in place to support the millions of people entering construction.
The most likely scenario is that people will lose their jobs and will be stuck in an endless race to the bottom fighting for the limited number of jobs that are left in the domestic economy while everything else is either outsourced or done by robots and AI.
The better advice is to start preparing for this reality. Do not assume the government will or can protect you. When wealth concentrates corruption because almost inevitable and politicians have families to look after too.
Please take this seriously. Even if I'm wrong it's better to prepare for the worse rather than to assume everything will be find and you'll be able to retrain into a new well-paid career.
This is the story that's been written since the Luddite revolts, as far as I know. The successors in that case were the capitalists who spent a significant amount of time and money convincing the constabulary and political figures to side with them. People were shot and jailed in the worst cases. The best case, workers were left without work or sent off to work-houses where they became indentured servants to the state.
The last work-house closed in the 1930s.
That all started not because people were afraid jobs were going to be replaced by the new loom. People had been using looms for centuries. They were protesting working conditions: low wages, lack of social protections when people were let go, child labor, work houses, etc. There were no labor laws at the time to protect workers... but there were these valuable new machines that the capital owners valued greatly. The machines were destroyed as leverage: a threat.
Since the capitalists ultimately "won" that conflict it has been written, by technocrats, that technological progress is virtuous and that while workers will initially be displaced the benefit to society will be enough such that those displaced will find productive work else where.
But I think even capitalist economists such as Keynes found the idea a bit preposterous. He wrote about how the gains in productivity from technological advances aren't being distributed back to workers: we're not working less, we're working more than ever. While it isn't about displacement of workers, it is displacement of value and that tends to go hand in hand.
I think asking, "Where do I go?" is a valid question. One that workers have been trying to ask since the Luddites at least. Unfortunately I think it's one that gets brushed under the rug. There doesn't seem to be much political will to provide systems that would make losing a job a non-issue and work optional.
That would give us the most leverage. If I didn't have to work in order to live I could leave a job or get displaced by the latest technological advancement. But I could retrain into anything I wanted and rejoin the work force when I was good and ready. I wouldn't have to risk losing my house, skipping meals, live without insurance, etc.
At least in the US, the only major non-AI growth field seems to be healthcare to deal with the swell of baby boomers living longer than people have before.
But if we're waiting to be paid to retrain there, I wouldn't hold our collective breath.
Baby boomers had already started the face of dying though. The next generation is still going to be right there. That generation is smaller. These people will always be dying. However, I wouldn't hold my breath if you're a young person in that field. Maybe but maybe not
Comparing software development to carrying heavy things at a construction site feels like a real stretch to me.
'If you work in construction, you need to lift and carry a series of heavy objects in order to be effective. But lifting heavy objects puts long-term wear on your back and joints, making you less effective over time. Construction workers don’t say that being a good construction worker means not lifting heavy objects. They say “too bad, that’s the job”.'
On another note, for sure software developers are saying things like "this is the part of the job that I like" or "if you aren't doing the work, you won't be good at the work." But other people are saying this, too. I just saw an episode of "Hacks" ("QuickScribbl") where the writers say pretty much this exact thing when confronted with AI tooling designed to "make their job easier". Is writing comedy also like lifting heavy objects at a construction site?
If by software engineering, one means typing code character by character into a text editor, sure it's going to be difficult to find someone to pay you for it.
If you mean creating software, well we are creating more software than ever before and the definition of what software is has never been so diverse. I can see many different careers branching off from here.
We are experiencing what Civil Engineers experienced going from slide rules to calculators. Or electrical engineers going from manual circuit path drawing to CAD tools.
The interesting thing to me is that, Software Engineering will have to evolve. Processes and tools will have to evolve, as they had evolved through the years.
When I was finishing university in 2004, we learned about the "crisis of software " time, the Cascade development process and how new "iterative methods" were starting.
We learned about how spaghetti code gave way to Pascal/C structured prpgramming, which gave way to OOP.
Engineering methods also evolved, with UML being one infamous language, but also formal methods such as Z language for formal verification; or ABC or cyclomatic complexity measurements of software complexity.
Which brings me to Today: Now that computers are writing MOST of the code; the value of current languages and software dev processes is decreasing. Programming Languages are made for people (otherwise we would continue writing in Assembler). So now we have to change the abstractions we use to communicate our intent to the computers, and to verify that the final instructions are doing what we wanted.
I'm very interested to see these new abstractions. I even believe that, given that all the small details of coding will be fully automated, MAYBE we will finally see more Engineering (real engineering) Rigor in the Software Engineering profession. Even if there still will be coders, the same way there are non-engineers building and modifying houses (common in Mexico at least)
Calculators and CAD tools do not give you non-deterministic answers. Both of them simply automate part of the manual work for them without creating anything "new". I haven't used CAD tools but I did use some level editors such as Trenchbroom -- I think what is automated is the 3d shapes that you want to make -- e.g. back in the day of '96, when ID Software is creating Quake, there was very little pre-drawn shapes in the level editor and they have to make the blocks by themselves, thus it is very difficult and time consuming to make complex shapes such as curved walls and tunnels. Then better tools were invented and now it is much easier to create a complex shape. But you don't type "a Quake level with theme A, and blah blah" and then you get a more or less working level -- this is what AI is doing right now.
I think the right analogy to calculators and CAD tools, is IDE with Intellisense for SWE -- instead of typing code one char by one char, we can tab to automate some part of it.
But I agree with your consensus -- SWE is changing, whether we like it or not. We need to adapt, or find a niche and grit to retirement.
It doesn't make sense to get hung up on this aspect of LLMs. We prefer non deterministic so far because it tends to work slightly better even if it is completely possible to ask for a temperature=0 deterministic answer.
With more scale and research, at some point you'll get results that are both useful and deterministic, if it's not already the case.
It absolute makes sense to get "hung" up on something when it comes to planning society around it JFC. I'm with the other commentator, your understanding of these tools should be taken into question since you seem to be reading the tea leaves of statistical noise.
In 2020, there are two companies that are competitors with each other. They each employ 100 programmers to do their job, and we all know how those organizations operated; perpetually behind, each feature added generating yet more possible future features, we've all lived it and are still largely living it today.
In 2026, both companies decide that AI can accelerate their developers by a factor of 10x. I'm not asserting that's reality, it's just a nice round number.
Company 1 fires 90 of their programmers and does the same work with 10.
Company 2 keeps all their programmers and does ten times the work they used to do, and maybe ends up hiring more.
Who wins in the market?
Of course the answer is "it depends" because it always is but I would say the winning space for Company 1 is substantially smaller than Company 2. They need a very precise combination of market circumstances. One that is not so precise that it doesn't exist, but it's a risky bet that you're in one of the exceptions.
In the time when the acceleration is occurring and we haven't settled in to the new reality yet the Company 1 answer seems superficially appealing to the bean counters, but it only takes one defector in a given market to go with Company 2's solution to force the entire rest of their industry to follow suit to compete properly.
The value generation by one programmer that can be possibly captured by that programmer's salary is probably not going down in the medium and long term either.
Your hypothetical ignores the distribution of programmer talent. Company 1 can pay more per person and hire 10x programmers, who can then leverage AI to produce the same or more as Company 2.
We have seen this in other knowledge industries. U.S. legal sector job count is about the same today as it was 20 years ago. But billing rates have exploded and revenues in the 200 largest firms have increased more than 50% after adjusting for inflation. Higher-end law firms have leveraged technology to be able to service much more of the demand and push out smaller regional competitors.
Of course it does. It ignores a lot of things. Mostly I just want to present the view that things aren't entirely hopeless and the entire industry is doomed to contract by 90% because of AI. Your legal system point also fits in precisely with what I'm trying to convey, just in a different direction.
I think paying significantly more was a very localized thing that happened for AI researchers who were familiar with the alchemy that made GPT4 suddenly work much better than anything else seen before.
My concern would be whether creating that software pays enough to keep up with skyrocketing costs of living. In the past, the jobs created by automation have generally been lower paid with less autonomy.
Ignoring the preference of people generally wanting to live in HCOL areas, this only works if every company hires equally from LCOL areas. One of the benefits of living in a HCOL area is access to the job market it provides. It's much easier to get hired for a software position living in San Francisco than it is living in Deming, New Mexico.
More importantly, in San Francisco, there are a lot more opportunities than in coming. I've never been to either city (i'm not going to come to the conference I was at because I never left the hotel.) however, I can still tell you confidently that if you have a weird hobby, you're much lower than you can find other people with that interest, stores that sell the things you need to complete the hobby, and all those other things in life that you want. If you love doing the types of things people in Deming do, well, it's a great life, I'm sure. However, as soon as you want to do something off the wall, well, you may not even find enough people in Deming to have your cricket team, while I have no doubt that San Francisco has a team that you could join.
but moving to a lower COL area can reduce that amount of public and private services one gets access to, no? network connectivity will, for example, likely be worse out in the sticks
Unfortunately, in America places with low cost of living are generally, to put it diplomatically, unpleasant places to live. That's even more the case if you don't fit into the white, cis, straight, and Christian box that rural areas are willing to accept.
This problem is not a software engineering problem nor an AI problem but a problem of the balance of power between working hard vs. investing. If the people who believe in working hard organize and slow down the tendency to rig everything for investors, then the markets should stabilize at a more generally prosperous place.
The balance of power is dictated by economic facts, not by organizing or politics. Auto workers in 1950 weren't better organized than auto workers in 2026. They just had more leverage because they weren't competing with auto workers in China. Likewise, Silicon Valley isn't paying people writing web apps $$$ because those workers are organized. They are doing it because they don't have a feasible alternative. If AI enables them to do more with less, they'll take that option.
Creating more software does not solve anything if that software is mostly a functional duplicate of other software. Or, in other words, all companies re-invent the wheel many times over. It doesn't matter if you 10x the development of software that brings nothing new besides being written in a shiny new framework.
We should, IMHO, start getting rid of most software. Go back to basics: what do you need, make that better, make it complete. Finish a piece of software for once.
In a world where software programming/architecting is solved by AI, value will accrue to people with expertise in other domains (who have now been granted the power of 1000 expert developers), not the people whose skillsets have been made redundant by better, faster and cheaper AI tools.
It could go either way. Don't forget that LLMs also have expertise in the other domains. Who would do better - the chemist with vibe coded app or the developer with vibe coded chemistry?
My premise is that a vibe-coded app will be indistinguishable from a ‘hand-crafted’ one. So in that scenario the chemist wins, because the developer has no value to add.
It is clear to me that SWE and ML research will be subsumed before other domains because labs are focusing their efforts there, in their quest to build self-improving systems.
There will be more software in the same way there is more agricultural output today.
The idea that productivity gains which result in more of something being produced also create more demand for labour to produce that thing is more often wrong that true as far as I can tell. In fact, it's quite hard to point to any historical examples of this happening. In general labour demand significantly decreases when productivity significantly increases and typically people need to retrain.
Except we are now in the golden age where people with 20 or 30 years of experience know what quality software is - or at least what it is not. So they are able to steer the LLMs. Once this knowledge is gone - the quality could go downhill.
Unless I'm missing something, there's an obvious logic issue here.
If we truly need to sacrifice our skill to be productive by using LLMs that atrophy us, then the only devs that have a limited lifespan are us. The next ones won't have a skillset to atrophy since they won't have built it through manual work.
Also, I hereby propose to publicly ban the "LLMs generating code are like compilers generating machine code" analogy, it's getting old to reargue the same idea time after time.
Besides probabilistic and non reproducible output, programming languages are designed to be unambiguous and explicit, and human language doesn't have that.
For(){} it's normally either undefined or has a specific meaning. "Then iterate and do x" might mean many subtly different things.
Most programmers never deal with a compiler bug in their whole career, and can dismiss the possibility. For LLMs it would be hard to even define what a "compiler bug" would be since there is no specification for English.
Then there's the fact that models generally don't guarantee anything at all. Sonnet can change under your feet.
Models also degrade as the context window gets larger. Compilers handle one line just the same as 20.
I could keep going, there's so many fundamental differences in the process that the analogy only serves to provide a false feeling of security.
I don't think its just, there's also the fact that if you're working with c or cpp or any systems level language, you typically do know how to read assembly because you've stumbled upon it for some reason, and if you're writing low level programs (which is typically what these languages are used for) you will definitely at some point need to know to read assembly and maybe even write some. But with LLMs the entire field has shifted. You don't need to know anything to write any language and you don't even need to have high level computer science knowledge nowadays to get something that works and the world increasingly just seems to want something that works.
> The career of a pro athlete has a maximum lifespan of around fifteen years. You have the opportunity to make a lot of money until around your mid-thirties,
This is pretty ageist - I'm around 40 and feel I am at my mental peak, compared to even my mid 20's. This isn't a good analogy at all, the brain doesn't "wear out" like a professional athletes' body does, it just changes its structure. The brain is a remarkable organ.
He just means: by this age you've probably found your preferred title and level, unless you want to rise to more C-level / executive positions, which are rarer in any case and most folks don't want.
This is definitely more charitable, but isn't this already the case now? It seems he's saying past your mid 30's you'd no longer be viable as a software engineer. That's never been the case, and I'm not sure why it would now suddenly be the case.
Even clearer, if you don't adapt to the changes taking place in the field there might not be a future for you. Its not about age, it is about attitude and flexibility (which are, admittedly, issues when getting older).
In other words, if you want to continue stubbornly typing out code by hand, the person right over there has already mastered agentic tooling and is doing vastly more than you, more quickly, and with greater precision, and will simply be a more fit candidate to hire. Roles for this type of legacy stubborn personality will be less and less, and you will age out as part of the old school.
I'm repeating what others have essentially said, but ask yourself what's on your resume. If it says "Software Engineer" and that's all it talks about, then yea you might not find it's a lifetime career.
But if it's a diversity of things (that include software development) then you probably have a lifetime career ahead of you.
I've been writing software for over 40 years but I've never seen myself as having a software engineering career. I've been a research assistant in geophysics, a marine technician on research ships, a game developer, an advisor to the UN, and a lot more. Yes all through that I used software, but I did a lot of other things in the process of using it.
Argument A: AI means you don't learn as much, so even though you are more effective, it inhibits your growth and you shouldn't use it. However, on a pragmatic level, it's effective to hire a bajillion people, fire them at will, and get AI to do everything. You will get so many JIRA tickets closed and so many lines of code written.
Argument B: AI means you don't learn as much, and the single most useful work product of a software engineer is knowing how the code functions, so it's depriving your company of the main benefit of your work. Also, layoffs are terrible business strategy because every lost employee is years of knowledge walking out the door, every new hire is a risk, and red PRs are derisking the business.
Institutional and personal knowledge seem similar, but the implications of each are radically different.
Was it ever a lifetime career? Haven't most people looked around and asked themselves where are all the 50+ engineers? They basically don't exist in large numbers. Ageism is real in this industry. You either save up enough money to retire early, switch into management, or get forced out of the industry eventually. AI is just accelerating the trend. I see very few junior engineers resisting AI. I see a LOT of staff+ engineers resisting it. Just look at the comments on HN. Anti-AI sentiment is real.
If you are lucky and got in early, then probably yes, it could be a lifetime career. It's like all careers, when you joined early, you got a lot of opportunities, you also rode the wave, you eventually rose to the top if you grit through.
It's a lot easier to be early than to be smart or quick.
In 1996? Software development was the hot ticket to upper middle class in the early 80s when I was a recent CS grad, and I was already working with people who were in it for the money. By the late 90s, if you could spell “HTML”, you were making decent money as a web developer. This all came crashing down during the Dot Bomb collapse, but SW has been pretty popular for most of my career, and it just continued to get more popular, especially as salaries continued to increase.
In retrospect the Dot Bomb was a bump in the road. Yes, some people who only knew enough HTML to be a "Webmaster" might have been filtered out, but pretty quickly anyone who could really build software had opportunities greater than before.
There’s a hierarchy amongst knowledge work and AI hasn’t yet been able to do the work that is rare and valuable.
Over the past two decades, there have been lot of solved problems like building boring scalable web apps, UX design etc and AI is fairly good at this, enough so that good prompting can get you very far. This shouldn’t be a surprise, there’s a lot of publicly available data for this (GitHub repos etc).
On the other hand, there are rarer Computer science problems like designing efficient Datacenters, GPUs, DL models. Think about problems that someone of Jeff Dean’s or James Hamilton’s (AWS SVP) or a skilled Computer Architecture researcher like David Paterson’s ability would solve. These are incredibly hard and rare problems and AI hasn’t been able to make much progress in these areas. That’s true for other sciences as well.
If you’re a regular Joe like me who builds boring CRUD apps, AI is coming for you.
What I mean is if you are working on incredibly hard and rare problems that require rare skills and also those problems don’t have publicly available data that LLMs can be trained on, you’re safe from being “automated” away. If not, you must plan accordingly. Also if you’re a skilled manager (in any field) AI cannot replace you, highly skilled managers that can get the best out of their teams have rare skills that aren’t easily replicable even amongst humans much less AI. Although, if going forward we need fewer developers we will need fewer managers too.
The differentiator is augmenting reasoning with AI versus replacing reasoning with AI. But those who choose to replace their reasoning with AI probably weren't good at it to begin with; cause if they were, they'd choose to not replace it. Exception is that AI can actually replace reasoning (which it can't, yet) - then it's game over with a career in software engineering anyway.
I take issue with the premise that "Using AI means you don’t learn as much from your work"
With AI assistance, I tackle far more tasks than I would without it. Learning per task goes down, but cumulative learning does not.
>If the models are good enough, you will simply get outcompeted by engineers willing to trade their long-term cognitive ability for a short-term lucrative career
> (2) AI-users thus become less effective engineers over time, as their technical skills atrophy
Wouldn't (2) imply that if everyone just used AI there eventually would come a time when there aren't engineers who will outcompete you (because their skills are so atrophied)?
Seems the solution here is the same it's actually always been if you want career progression: be more than just a code jockey. The true value of an engineer is to be plugged into overall roadmaps, broader thinking around product, how to achieve company goals, etc etc.
Yes, LLMs might dramatically reduce the amount of code we write by hand. But I'm a lot less convinced they'll solve all of the amorphous, human-interacting aspects of the job.
My exprience has been that companies actively work to prevent people from becoming more than just code jockeys. For example, most of the places I've worked have viewed code delivered as the ONLY metric used to evaluate performance. Attempts to contribute to roadmaps or strategy are ignored at best and punished at worst.
Yeah, 95% of the available advancement in computing is in people management, not technical mastery. Businesses much prefer to hire externally to serve any non-core capabilities, especially to minimize internal culpability should anything go wrong. That leaves little opportunity to think outside the box technically.
80% of my day to day job has never been pumping out lots of code. it is a complicated career is it? we do a lot of alignment, design and thinking. i can't even agree the idea of outsourcing thinking, i think AI is very good at helping us to think clearly, but it doesn't really "think" for us.
If AI becomes good enough to easily produce maintainable and high quality software, then I really can't see how demand for software engineers would not plummet. Even lots of non-coding work that software engineers do, such as accurately capturing what client actually wants, will become much less valuable - e.g. currently misunderstanding of client's requirements is catastrophic and can lead to waste of months of labour; with AI it could become matter of max few hours lost. So I can understand argument that software engineering careers might be safe because AI may plateau and we might never reach level where it's actually capable of producing good software. But I absolutely don't buy that software engineering will be safe even if such AI exists. Even if your current work is just 20% actually coding, you must remember about second order effects that will take place once quality code generation is 1000 times faster.
AI can also do alignment and pull from its vast training dataset for design and "thinking" -- because 99% of the problems in this world were already solved, multiple times, maybe not in the exactly same format, but in a very similar format.
I also see that in the future humans will adapt to AI, instead of the opposite. Why? Because it's a lot easier for humans to adapt to AI, than the opposite. It's already happening -- why do companies ask their employees to write complete documentation for AI to consume? This is what I called "Adaption".
I can also imagine that in the near future, when employment plummets, when basic income become general, when governments build massive condos for social housing -- everything new will be required to adapt to AI. The roads, the buildings, everything physical is going to be built with ease-of-navigation by AI in consideration. We don't need a Gen AI -- that is too expensive and too long term for the Capitalist class to consider. We only need a bunch of AI agents and robots coordinated in an environment that is friendly to them.
Software engineering today is almost nothing like the role it was 30 years ago.
Maybe if you somehow stick with the same company for your entire career, it could feel somewhat similar... but I doubt it, as 'best practices' and many other things cause it to change.
The days of 'lifetime career' had already gone for most people, way before AI arrived.
I'm a software engineer and architect, I love my job, I love diving into the small details, I love the grand overview.. I love identifying concepts and applying them to achieve elegant high-performing systems.
I love thinking about what kind of assembler the compiler may generate (though honestly, I haven't got a chance), I love thinking about how languages should be more dynamic (Who's got actually-first-class functions? Like, ones that you can build, compose, combine and manipulate to the same degree you can a string or a JSON object, no LISP, you're cheating, close no point).
And yet.. I don't care that much. Not because I'm late in my career (I'm 40, there's still some years left in me), but because I want to make computers do things, and what I enjoy doing is thinking up ways the things can happen, and sometimes the particulars that matter when making a lot of different things happen in a coherent system.. And yea, LLMs are trained on peoples output, and from what I'm seeing everywhere, is that people are overall fairly terrible at that, and most of the plumbing-type glue being written is not worth anyones time..
And I'm not saying I don't care because LLMs can't do my job (heck, even after hours of back-and-forth spec building and refining every little nook and cranny, the stupid coding agent still cheats or gets it wrong (even after it's beautifully explained, proven even, by reasoning and example alone, and on first try even) that the words coming after the previous words makes sense, as soon as the plan is put into motion, it'll mess it up on some scale so fundamental I should just have done it myself.. And I hope that changes, I hope that I don't have to go into such detail.. I hope to become a steward of taste rather than a code-reviewer.. I hope that I will eventually not be needed for that anymore.. I want it to replace me, so I can move to telling what I want, and have it made that way..
I hope I won't need to steward good taste, and that nobody will.. I hope the applications I use in 5 years will be a collection of one-offs, and gradually improving tools that was written _just_ for me, for my way of working, and my way of thinking.. I want to prompt the damn program to change itself as I discover new ways to do things, until it can eventually figure out how to automate the last bit of my task away.. And then I'll go do something else exciting.
Is anything today a lifetime career? I’ve had at least five or six job descriptions over my time, and at least a few of them pretty much don’t exist anymore, or are changed beyond recognition.
Virtually, the entire blog is about AI with a ridiculous publishing rate (https://www.seangoedecke.com/page/5), funny how I can look at this site HTML and know right away it was done with AI.
Can we stop upvoting vibe published articles?
The arguments are flawed and don't even make sense to anyone who does software
Yes, the blog is mostly about AI, and yes, he publishes very frequently. But his articles don't read like AI and he claims not to have used it in his writing (https://www.seangoedecke.com/avoid-ai-writing/). And regardless of how you feel about the content, the community has clearly decided it's worthwhile as a discussion point.
> Construction workers don’t say that being a good construction worker means not lifting heavy objects. They say “too bad, that’s the job”
I dont know, maybe in your part of the world, but where I'm from we have a series of robust worker protection laws that try to limit the damage the work does to you. We generally consider it a bad thing for workers to damage their bodies, and if we could build houses without it, we'd prefer that.
In this specific case we do have a techniques to build software without causing damage, so why change that?
This post is arguing that maybe software enginnering should start being harmful, even though we know it doesn't have to. It's a post of a guy begging to be fed into the capitalist meat grinder. Meaningless self sacrifice.
i'm about 35 and i have made good money but not enough to quit. i plan on just sitting around cashing checks for another decade. with a few liquidity events along the way to sweeten the deal. should pay for my mortgage, some home renos, and fund my 401k etc. i don't foresee myself being out of work (and i don't even use AI to code! i'm just Actually Good!)
Absolutely untrue, you could have a solid career writing back office or internal software in financial services, insurance, higher ed, any number of industries. Would they make you a millionaire? No. But they'd pay for a nice house in the suburbs and raising a family.
> I hope that this isn’t true. It would be really unfortunate for software engineers. But it would be even more unfortunate if it were true and we refused to acknowledge it.
More AI Soothsaying. Not so hard on the Inevitabilism this time.
On the contrary, in an efficient economy, every business operations manager (MBA) would be a skilled software engineer, able to comfortably manage data flows and design custom automated processes. There's so much potential energy there in unlocking this technical literacy.
Less "pure" programming, but lots more programming in general.
Was it ever? It's always seemed weird to me that people even think 'software engineering' is a career.
It's a tool for knowledge work.
No carpenter is a specialist in drills.
It seems to me that the best way to navigate a long term career is to have another specialty and use software engineering as a tool within that specialty.
>
Software engineers didn’t just disappear after age 40.
At the end of the 90th and beginning of the 00th ("dotcom bubble") it was a common saying that if as a programmer, when you are 30 or 40, you don't have a very successful company (and thus basically set for life), you basically failed in life; exactly because "everybody" knew that programming is a "young man's game" (i.e. you likely won't get a programming job anymore when you are, say, 35 or 40 years old).
So,
> Software engineers didn’t just disappear after age 40.
> At the end of the 90th and beginning of the 00th ("dotcom bubble") it was a common saying that if as a programmer, when you are 30 or 40, you don't have a very successful company (and thus basically set for life), you basically failed in life; exactly because "everybody" knew that programming is a "young man's game"
That seemed commonly held among folks participating in the dot-com bubble. Plenty of people had been doing it for decades even as the bubble was growing.
> Software engineers didn’t just disappear after age 40.
>> is rather a very recent phenomenon.
Not really. It's not that they disappeared, it's that they're a small fraction of the overall SWE population as a side-effect of how much that population has grown.
Software is wood, not drills, and if we somehow invented bacteria that gradually built an ugly but saleable house when fed on water and nutrients and nudged into shape, I bet carpenters (well, framers or whatever they're called in the US) would have an identity crisis too.
I kind of disagree. You are describing a kind person who is extremely valuable, a person who is proficient in SWE but also has domain specific skills in some niche.
That's great, but its nowhere near the norm, and people have been doing generalist software engineering for decades. There has been a sufficient amount of work for a long time to be performed by generalists that it has been a very reasonable career.
IMO AI is the first thing that has ever actually challenged that.
Id disagree with this analogy:
"No carpenter is a specialist in drills." and i think its an interesting lens through which to look at the evolution of our tools.
I think there are trades where tool (or process if i may be allowed to extend the analogy) specialists exist and are highly valued. My dad is a plumber, so ill use that example but id trust similar is true for carpentry. there are specialists by task/output (new construction, repairs, boilers etc) but also tool specialist plumbers and companies for example drain clearing equipment or certain kinds of pipe for handling chemicals other than water are very specialised, and there are roles for them because the thing they enable, and the criticality of the task, and often the cost and complexity of using the tool are high enough to make specialisation valuable.
IMO software has, for the 10 years ive been working in it, been in an unusual position where the tools (languages, engineering practices, tech stacks) were super technical and involved, but also could be applied to a large number of problems. That is the perfect recipe for tool specialists: complex tool with high value and broad domain/problem space applicability.
Because of that tool specialisation, we've separated the application of the tool to a problem/domain from the tool use. reduction of complexity of applying these tools to many problems, means all domain specialists will use them, relying less on tool specialists.
imaging a mcguffin tool for attaching any two materials together, but which took a degree to figure out (loose hyperbole here), that sudenly you could use for 5 bucks and a quick glance at the first page of the manual. An industry that used to have lots of mcguffin engineers, would be mega disrupted, and you could argue that those tool specialists would have to identify more with what they were building than the mcguffin they were using.
I tell my boys (both in HS now), the combination of a specialized skill/knowledge + competent computer programming is the sweet spot. For example, my oldest wants to go into Petroleum Engineering which is great but I told him to still learn software development and get comfortable solving problems with code. Having specialized Petroleum Engineering knowledge combined with being a competent software developer is a powerful combination.
I think the logical next step is that "XYZ knowledge worker" will become a software engineer of sorts. Not literally writing code, but at minimum encoding processes/workflows into some language.
If you're a paralegal or an accountant who can't manage their workflows with AI, you're going to be way less productive than someone who can.
And if you're a paralegal or an accountant who can manage a lot of your workflows with AI, you don't need custom software (hence less dedicated software engineers).
There's no category difference between being an expert in carpentry vs masonry and being an expert in drills vs hammers. They are both just areas of expertise.
Going down the path of trying to define what is expert functions and what is "merely" a tool using anything but descriptive technique is nonsense.
Expert functions are just those areas where using a tool is sufficiently difficult to require expertise.
Are people seriously thinking that you can make yourself dumber by using a chat UI?
If talking to an AI makes me dumber and a limited career, then all the customer support people that ever existed were in the same or worse position talking to dumb humans on chat all day answering tickets always about the same topics and linking the same docs over and over. This makes no sense.
You're misrepresenting the potential problem. It's more along the lines of using AI stops you exercising the cognitive processes you would doing things yourself and those encompass skills, knowledge and brain function that can atrophy. For an extreme example you can look at cognitive decline in the elderly which can be mitigated by taking part in activities that are cognitively stimulating.
Can you comment on other jobs though? The large majority of jobs require no big mental effort? Even switching from programming to management would go through that. Under that light it'd be impossible for a manager to ever become technical again because they'd atrophy so quickly?
I think you're probably castrophizing the impact with statements like "it'd be impossible for a manager to ever become technical again" because that's not the likely outcome as I understand things. But yes people who stop programming for an appreciable amount of time do find it harder to pick back up again.
The longer the manager is out of the game, the harder it is to return to the game. Returning to the game takes time. Depending on age and income, returning to the game may be impossible for some people over time.
I can't answer for the other guy, but my answer would be that talking to a clanker is LESS mental effort than being a manager, and that's why your reasoning atrophies so quickly.
Managers can go back to being technical, because they are still interacting with problems that require human thinking. Token farmers don't.
If you constantly pawn a task or cognitive load onto someone else (AI or not), you'll eventually get worse and worse at that particular type of thinking. Your overall mind doesn't necessarily get weaker, but you definitely start to get worse at anything you don't regularly practice.
> The career of a pro athlete has a maximum lifespan of around fifteen years. You have the opportunity to make a lot of money until around your mid-thirties, at which point your body just can’t keep up with it.
If you believe this about your software career, how do you think your going to switch into another career as a junior and keep up?
terribly written article that failed to make any point. anyone whise read ai generated code from the best models and who understand how llms work, knows this statement is complete bs.
Why do people think there will be fixing AI slop software? I see that opinion here and there on HN. The cost of codegen is next to nothing. It makes no sense to spend large sums of money having an engineer fix something that could be generated over and over until gods of stochasticity come in your favour.
We've entered a period of single-use-plastic software, piling up and polluting everything, because it's cheaper than the alternative
The problem partially is that AI can also fix AI slop. At this point I am in doubt whether code quality matters anymore in most non-critical software. You can ask an LLM if the code has quality issues and refactor to a _better_ version. It will reason through, prepare a plan and refactor. So now with this "better" code you can expect that your LLM will be able to deliver higher quality results but that's all the quality that is needed.
Actually, at this point I feel that the value in software engineering is moving from coding to testing and quality assurance.
All the frontier models tell me when there are no issues. After implementing a feature I will ask it to identify issues in my implementation, list them, and support each item they identified with technical argumentation and reasoning as to why it's an issue.
If it doesn't find anything it says I didn't find anything.
In my experience, it's been the complete opposite. The very experienced engineers that are actually willing to use top of the line tooling are much better than they were before, including those that are over 40, and over 50.
Part of the practical degradation of traditional programmers over time has always been concentration and deep calculation, just like in chess. The old chess player knows chess much better than a 19 year old phenom, but they cannot calculate for that many hours at the same speed as before, so their experience eventually loses to the raw calculation. Maybe at 35, or at 45, but you are just not as good. Claude Code and Codex save you the computation, while every single instinct and 2 second "intuition", which is what you build with experience, is still online.
It's not just that it's a more fair competition: It's now unfair in the opposite direction. The senior that before could lead a team of 6 is now leading a team of agents, and reviewing their code just as before. Hell, it's easier to get the agent to change direction than most juniors around me, which will not be easy to correct with just plain, low-judgement feedback.
Multiple times per week I have the same conversation. It goes something like this:
The developers who still think their job is about writing code will perhaps not have a job in the future. Brutal as it may sound: I'm fine with that. I'm getting old and I value my remaining time on the planet.Business owners who think they can do without developers because they think LLMs replace developers are fine by me too. Natural selection will take care of them in due course.
This is a bit of glib answer. Most of the time is spent coding which encompasses typing, retyping, and retyping again. It also includes banging your head against the wall while trying to get one of your rewrites to work against and under-documented API.
OP's formulation makes SWE sound like a purely noble enterprise like mathematics. It's more like an oil rig worker banging on pieces of metal with large hammers to get the drill string put together. They went in with a plan, but the reality didn't agree and they are on a tight schedule.
Most of the time is spent figuring what the right thing to do is, not writing the implementation. Sometimes the process of writing the implementation surfaces new considerations about what the right thing is, but still, producing text to feed to a compiler is not the bulk of the work of a software engineer. It is to unearth requirements and turn them into repeatable software.
> Yes, about 2-5% of the time.
There are also those for whom that percentage is higher, let’s say 6-50%.
> I understand things and then apply my ability to formulate solutions
The AI is coming for that too.
You might just be lucky to be in circumstances that value your contributions or an industry or domain that isn’t well represented in the training data, or problem spaces too complex for AI. Not everyone is, not even the majority of devs.
People knocking out Jira tickets and writing CRUD webapps will end up with their livelihood often taken away. Or bosses will just expect more output for same/less pay, with them having to use AI to keep up.
Agree. It is just like 2 totally separate groups are arguing.
One very tiny slice of speciality/ rare industries where code is critical but overall small part of project costs. I can see if code / software is 5% of overall cost even heavy use of AI for code part is not moving the needle. So people in this group can feel confident in their indispensability.
Second group is much larger and peddling CRUD / JS Frontends and other copy/paste junk. But as per industry classification they are just part of same Coder/Developer/IT Engineer group. And their bleak prospects is not some future scenario, it is playing out right now with tons of them getting laid off. And whole lot of people with IT degrees, certifications are not finding any jobs in this field.
There are periods of time where I might spend 80% of my time "coding", meaning I have minimal meetings and other responsibilities.
However, even out of that 80% of my time, what fraction is actually spent "writing code"?
AI can be an enormous accelerator for the time I'd normally spend writing lines of code by hand, but it doesn't really help with the rest of the work:
- Understanding the problem - Waiting for the build system and tests to run - Manually testing the app to make sure it behaves as I'd like - Reviewing the diff to make sure it's clear - Uploading the PR and writing a description - Responding to reviewer feedback
There are times when AI can do the "write the code" portion 10x faster than I could, but if it's production code that actually matters, by the time I actually review the code, I doubt it's more than 2x.
>AI can be an enormous accelerator for the time I'd normally spend writing lines of code by hand, but it doesn't really help with the rest of the work:
- Understanding the problem - Waiting for the build system and tests to run - Manually testing the app to make sure it behaves as I'd like - Reviewing the diff to make sure it's clear - Uploading the PR and writing a description - Responding to reviewer feedback
What part of those you think it doesn't help with?
> The AI is coming for that too.
That may be true I’m not gonna say one way or the other, but if AI comes for that then almost all knowledge work is effectively dead, so all that’s left would be sales or physical labor.
I wonder though, can AI make the next JS framework. I mean that in sincerity, there was the leap from jQuery to React for ex. If an AI only knows jQuery and no one makes React, will React come out of AI.
News: "AGI refuses to make another JS framework, rages on the follies of misguided developers and their wateful JS crutches"
Developer community: Wow, we truly have become obsolete now!
Who will be the disrupters when there is nothing to disrupt
People didn't leap from jQuery to React. It's a lot easier to imagine an AI looking at jQuery and [insert any server side MVC framework] and inventing Backbone.
The history of the last 250 years is inventing new professions as old ones are automated away.
I expect that to continue.
The history of the last 250 was moving from agriculture to industrial work to service work. Now the last frontier is starting to be overtaken by automation too.
(And in all of those transitions millions where left behind without work or with very worse prospects. The people that took the new jobs were often a different group, not people who knew the old jobs and were already in their 30s and 40s).
And what would be the new professions that uniquely require humans, when even thinking and creative jobs are eaten by AI? Would there be a boom of demand for dancers and chefs, especially as millions lose their service jobs?
> The AI is coming for that too.
Current AI tech giants prove over and over and over again that this is not the case
We've literally just started, what "over and over" do you refer to?
Discovery of the best solution in a problem space is not generative but only verificative. Meaning: the LLM can see if a solution is better than another, but it can't generate the best one from the start. If you trust it, you'll get sub-par solutions.
This is definitely an agent problem instead of an LLM problem. Anybody got something explorative like this working?
So? Hundreds of millions of office and devel jobs are about for developing "optimal solutions" to begin with.
Hype cycles, AI has made developers obsolete like a dozen times in the las couple of years, at least according to their developers.
> The AI is coming for that too.
That's where we fundamentally disagree about.
Yes, AI is coming for solution formulation, absolutely, but not all of it, because it is actually a statistical machine with context limit.
Until the day LLMs are not statistical machine with a context limit, this will hold. Someone need to make something that has intent and purpose, and evidently now not by adding another 10T to the LLM parameter count.
>but not all of it, because it is actually a statistical machine with context limit.
And the human mind is not?
>bosses
The AI is coming for those too.
> The AI is coming for that too.
To some degree yes, in practice, not so much. In practice you have to be in the world, talk to people, know how to talk to people, know how to listen, and be able to understand the difference between what people say and what they actually need. Not want. Need.
This was something I learnt in my very first job in the 1980s. I worked for someone who did industrial automation beyond PLCs and suchlike. He spent 6 months working in the company. On the factory floor, in the logistics department, in procurement, in accounting and even shadowing the board. Then he delivered a proposal for how to restructure the parts of the company, change manufacturing processes, and show how logistics and procurement could be optimized if you saw them as two parts in a bigger dance.
He redesigned the company so that it could a) be automated, and b) leverage automation to increase the efficiency of several parts of the business. THEN, he started planning how to write the software (this was the 80s after all), and then we started implementing it.
Now think about what went into this. For instance we changed a lot of what happened on the factory floor. Because my boss had actually worked it. So he knew what pain points existed. Pain points even the factory workers didn't know how to address because they didn't know that they could be addressed.
I was naive. I thought this was how everyone approached "software projects". People generally don't. But it did teach me that the job isn't writing code. It is reasoning about complex systems that often are not even known to those who are parts of it.
And this is for _boring_ software that requires very little creativity and mostly zero novelty. Now imagine how you do novel things.
> People knocking out Jira tickets and writing CRUD webapps will end up with their livelihood often taken away. Or bosses will just expect more output for same/less pay, with them having to use AI to keep up.
You make it sound like it is a bad thing that certain tasks become easier.
I spent a lot of time writing CRUD stuff. Because the things i really want to work on depend on them. I don't enjoy what is essentially boilerplate. Who does? If you can do the same job in 1/20 the time, then how is this a bad thing?
It is only a bad thing if writing CRUD webapps is the limit of your ability. We don't argue for banning excavators because it puts people with shovels out of work. We find more meaningful things for them to do and become more productive. New classes of work becomes low-skilled jobs.
If you have been doing software for a while, you are probably doing some subset of this. But these things are hard to articulate. It is hard to articulate because it is not something we think about. Like walking: easy for us to do, hard to program a robot to do it.
>To some degree yes, in practice, not so much. In practice you have to be in the world, talk to people, know how to talk to people, know how to listen, and be able to understand the difference between what people say and what they actually need. Not want. Need.
1 person needs to do that. The other 100 not doing that currently to begin with, but doing the AI-automatable work?
> To some degree yes, in practice, not so much.
We used to say that (not long ago, even) about the code-writing part. Why do we believe that LLMs are going to stop there? Why do we think they won't soon be able to talk to people, listen, and determine what they need? I think it's mostly a cope.
We have robots walking just fine now, by the way.
If they can do those things they can effectively replace any white collar job. That’s about 45% of the workforce. Societies tend to collapse around 25-30% unemployment.
Imagine 45% of higher than average paying jobs gone.
If that happens we’ll either figure out a new economic system, or society will collapse.
Also saying robots are walking just fine is misleading for any definition of just fine that is anywhere near as good as a human.
Look at how the billionaires are talking about AI: Their clear, unambiguous goal is basically to replace all white collar "knowledge" jobs. And there's currently nothing regulatory that's stopping them--they just need to wait for the state of the art to improve. Once AI is "good enough" if it ever is, they won't even think twice about 45% unemployment. What are we unemployed workers going to do about it? There's no effective labor organization left. Workers have basically no political power or seat at the table. We're not going to get violent--the police/military are already owned by the billionaire class. We're just going to eventually become economically irrelevant and die off.
The fundamental issue isn't unemployment due to automation, but the fact that society cannot benefit from unemployment.
It should be something for us to celebrate, because it means greater freedom for humans to pursue something else rather than spending time doing drudgery.
> We're just going to eventually become economically irrelevant and die off.
As harsh it may sound, it seems rather likely to me. It is not like s/w engineers have helped struggling workers in other sectors other than sanctimonious "Learn to code" advice. So software folks can't expect any solidarity or help from others.
45% of folks sitting on their hands are going to have the free time to talk, and this group of people are skilled at organization. Are you planning on throwing your hands up and passively accepting whatever comes your way?
It's important (and calming) to understand that since the Industrial Revolution started ~250 years ago, we've automated away most jobs several times over, while employment levels have stayed pretty constant.
"Automating half the jobs" is the same as "double productivity per worker".
When the doubling happens in 5 years rather than 50, it might be more disruptive, but I'm convinced we're on the verge of huge improvements in human standard of living!
We never noticed how easy the code writing part had already become because it happened slowly. Through mechanical means, through the ability to re-use code, and through code generation.
Heck, even long before LLMs about 10% to 30% of my code was already automatically generated. By tooling, by IDLs and by my editor just being able to infer what my most likely input would be.
> We have robots walking just fine now, by the way.
I don't think you got the point I was trying to make.
True, but I guess I see a distinction between scaffolded/templated boilerplate or autocomplete and actual application logic. People have generated boilerplate from templates for ages, as you say. RoR maybe a pretty good example, but there wasn't even early-days AI involved in doing that.
>> We used to say that (not long ago, even) about the code-writing part. Why do we believe that LLMs are going to stop there? Why do we think they won't soon be able to talk to people, listen, and determine what they need?
Because they are currently "generative AI" meaning... autocomplete. They generate stuff but fall down at thinking and problem solving. There is talk of "reasoning models" but I think that's just clever meta-programming with LLMs. I can't say AI won't take that next step, but I think it will take another breakthrough on the order of transformers or attention. Companies are currently too busy exploiting the local maxima of LLMs.
> Why do we believe that LLMs are going to stop there?
Why do you believe they wont? I think it's reasonable to assume that we will hit a ceiling that current models will not be able to break.
> We have robots walking just fine now, by the way.
Walking and reasoning are unrelated abilities.
Walking was given as an example of "hard to program a robot to do it" by GP. Well, now we have robots that can walk.
What evidence is there that LLMs have hit a ceiling at being able to do things like talk to users or stakeholders to elicit requirements? Using LLMs to help with design and architecture decisions is already a pretty common example that people give.
Lot of people don't seem to get that - It is easier to go from terrible to average but much harder to go from average to good.
I am sure AI bros are same people who were convinced consumer grade fully automated driving was going happen "by end of the year" for last 7 years.
No, I never believed in fully automated tale by Tesla, but as the LLMs improve my personal estimate for the date of human-level AGI is rapidly moving to "present". Before GPT-2 I had it somewhere in 2100, at GPT-2 I thought maybe by 2060 if we are lucky. Now I think it is 2035 or maybe even sooner.
>> I understand things and then apply my ability to formulate solutions
> The AI is coming for that too.
In that case all [1] non manual work is doomed, until robotics has an LLM moment.
[1] With the exception of all fields protected by politics or nepotism.
Not sure where I first heard this, but I say it to my team all the time: "Programming is thinking, not typing"
I've always told my Jr Engineers to "think twice, code once".
If I gave them a task and they immediately started typing it out, I would tell them to stop typing and ask them to explain to me what they were doing; they'd often just spit out what they thought the code should do, and I'd often point out edge cases they missed and would have missed had they just spit out code and a PR, wasting everyone's time. I would also insulate them from upper management to give them time to actually think (e.g. I wouldn't be coding so they could think then code).
To your point and to the GP's point, and one point I keep raising with LLM's: "typing is not where my time sinks are"
I think the future is pretty up in the air in this respect, but my guess is that AI will just lead to another shift in the set of knowledge that a 'real programmer' is expected to have. I'm old enough to remember when people would make fun of web developers for 'programming' using HTML and JavaScript. And of course, back in the day, you couldn't be a real programmer unless you wrote assembly language. In a few years' time, being able to write (as opposed to read) source code in any specific programming language will probably become a niche skill. The next generation will be able to read Python to about the same extent that I can read x86 assembly.
Only 5% of your time is spent writing code? That sounds like a low estimate for most software engineers I work with.
May I ask if you could estimate how you spend the other 95% of the time?
Commenting on Hacker News?
In all seriousness, communications consumes a lot of time. Meetings, emails, Slack messages, pestering stake holders and other developers...
If you spend 95% of your time on that stuff, you better be working on like critical infrastructure where nothing can go wrong, otherwise you are in an incredibly dysfunctional company.
You don't think AI is going to be able to understand things and apply their ability to formulate solutions better than you, in the near future?
Because that classifies in "developers" and "software engineers". And software engineering isn't going to disappear anytime soon
Weird. I call myself a developer because I don't have an engineering degree from an abet certified engineering program.
I recognize, in some capacity, that this isn't the norm and in the US "professional engineer" is protected and not simply "engineer", but it feels akin to stolen valor to me.
If there were a license in the US for it, I’d agree with you. But as is, if you are “doing” engineering, you’re an engineer.
If you are a licensed engineer of some kind, you’d state that outright.
The equivalent of stolen valor would be claiming to be a licensed software engineer; except there is no such license so it would also be fraud, misrepresentation, etc.
(I know this is different elsewhere)
I'm a software dev in the US and I never call myself "engineer" in that capacity. Always "programmer" or "developer".
I agree. Engineers have to clear a much higher bar. Even though my career was spent in medical diagnostic software where we had to get 510k clearance, I was still keenly aware that this was a fundamentally different activity from actual engineering.
Note that just because you know the job is understanding things, the manager who'll boot you and leave you without income probably doesn't. They'll just get their political cookie points for saving money by replacing you with AI.
>- I understand things and then apply my ability to formulate solutions
I dunno, man. I've been doing this for 20+ years and I think we're at a really important fork in the road where there are two possibilities.
The first is that AI is achieving human-level expertise and capability, but since they're now being increasingly trained on their own output they are fighting an uphill battle against model collapse. In that case, perhaps AI is going to just sort of max out at "knowing everything" and maybe agentic coding is just another massive paradigm shift in a long line of technological paradigm shifts and the tooling has changed but total job market collapse is unlikely.
The other possibility is that we're going to continue to see escalating AI capability with regard to context, information retrieval, and most importantly "cognition" (whatever that means). Maybe we overcome the challenges of model collapse. Maybe we figure out better methodologies for training that don't end up just producing a chatbot version of Stack Overflow + Wikipedia + Reddit. Maybe we actually start seeing AI create and not just recreate.
If it's the latter, then I think engineers who think they are going to stay ahead of AI sound an awful lot like saddle makers who said "pffft, these new cars can only go 5 miles per hour."
100% this.
I'll also add another factor: it's become increasingly clear at our company that AI-enabled humans are getting to the bottom of the backlog of feature ideas much quicker. This makes the 'good ideas' part of the business the rate limiting step. And those are definitely not increasing with AI, beyond that generated by the AI churn itself ("let's bolt on a chat experience or an MCP!")
So maybe the coding assistants don't get a 10x improvement any time soon, but we see engineering job market contraction because there aren't really enough good ideas to turn into code.
You are supposing that AI is achieving human level expertise and capability is a given. I am not so sure. Right now that's much further from the truth than one might think at first glance.
> max out at "knowing everything"
LLMs know nothing but are great at giving the illusion that they know stuff. (It's "mansplaining as a service"; it is easier to give confident answers every time, even if they are wrong, than to program actual knowledge.) Even your first case seems wildly optimistic. The second case is a lot of "maybes" and "we don't know how but we might figure it out" that seems like a lot to bet an entire farm on, much less an entire industry of farms.
We sure are looking at a shift in the job market, but I don't think it is a fork in the road so much as a Slow/Yield sign. Companies are signalling they are willing to take promises/hope to cut labor costs whether or not the results are real. I don't think anything about current AI can kill the software development industry, but I sure do think it can do a lot to make it a lot more miserable, lower wages, and artificially reduce job demand. I don't think this has anything to do with the real capabilities of today's AI and everything to do with the perception is enough of an excuse and companies were always looking for that excuse. (Just as ageism has always existed. AI is also just a fresh excuse for companies to carry on aging out experience from their staff, especially people with long enough memories/well schooled enough memories to remember previous AI booms and busts.)
But also, yeah if some magic breakthrough makes this a real "buggy whip manufacturer moment" and not just an illusion of one, I don't mind being the engineer on that side of it. There's nothing wrong about lamenting the coming death of an industry that employs a lot of good people and tries to make good products. This is HN, you celebrate the failures, learn from them, and then you pivot or you try something new. If evidence tells me to pivot then I will pivot, I'm already debating trying something entirely new, but learning from the failures can also mean respecting "what went right?" and acknowledging how many people did a lot of good, hard work despite the outcome.
Saying being a programmer is about writing code is a bit like saying being an artist is about drawing lines on a canvas.
Yeah technically drawing lines on canvases may be an very important part of being a painter, but it is hardly the core of what makes or breaks great art.
What you described are senior developers and system architects.
Junior developers spend most of their time writing code (when they're not forced to attend pointless standups, because Agile/blah/blah)
> The developers who still think their job is about writing code will perhaps not have a job in the future.
So you're saying the same thing everyone else is saying. SWEs won't go away, but they will be greatly reduced, because those whose job is about writing code -- junior devs -- will be replaced.
(How will Sr Devs in the future be created? That's the question, isn't it.)
> How will Sr Devs in the future be created?
As an extreme example, maybe we’ll see long-running internships and trainings like doctors experience. Doctors don’t start their career until ~12+ years of prep and training.
Pragmatically, software development has a lot of examples of teenagers making apps and college students building software companies. In the 12 years it takes for training, low-knowledge workers could be vibe coding continuously replacements of most commercial software products they’d be hired to build. So I doubt we’ll treat software development as a rarified high skill job.
Pure wage workers should consider dropping the attitude about how tech progress will just make their inferiors in the same line of work be out of a job (hrmph good riddance etc.). Because this pseudo-progress could creep up on them as well.
Then you won’t have this just world of the deserving workers at all. Just formerly deserving workers and idiot billionaires like Musk (while the robots do all of the work).
I normally say that I have zero concerns regarding AI in terms of employment. At most I am concerned in learning the best practices on AI usage to stay on top of things.
It's ability to write code is alright. Sometimes it impresses me, sometimes it leaves me underwhelmed. It certainly can't be left to do things autonomously if you are responsible for its output.
Moderately useful tool, but hellishly expensive when not being subsidized by imbeciles that dream of it undrrmining labor. A fool and his money should be separated anyway.
What I am really concerned about the incoming economic disaster being brewed. I suspect things will get very ugly pretty soon.
> AI-users thus become less effective engineers over time, as their technical skills atrophy
Based on my experience, I think this will prove more true than not in the long run, unfortunately.
Professionally, I see people largely falling into two camps: those that augment their reasoning with AI, and those that replace their reasoning with AI. I’m not too worried about the former, it’s the latter for whom I’m worried.
My mom is a (US public school) high school teacher, and she vents to me about the number of students who just take “Google AI overview” as an absolute source of truth. Maybe it’s just the new “you can’t cite Wikipedia”, but she feels that since the pandemic, there’s a notable decline in the critical thinking skills of children coming through her classes.
We have a whole generation (or two) of kids that have grown up being told what to like, hate, believe, etc. by influencers and anonymous people on the internet. They’d already outsourced their reasoning before LLMs were a thing. Most of them don’t appear to be ready to constructively engage with a system that is designed to make them believe they are getting what they want with dubious quality.
> If you work in construction, you need to lift and carry a series of heavy objects in order to be effective. But lifting heavy objects puts long-term wear on your back and joints, making you less effective over time. Construction workers don’t say that being a good construction worker means not lifting heavy objects. They say “too bad, that’s the job”.
My parents were both construction workers. There is an understanding that you cannot lift heavy objects forever. You stop lifting objects and move to being a foreman, a supervisor... and if you are uncomfortable learning to get others to do work that you before have done yourself, you burn out your body entirely and the consequences are horrible.
This is factual reality, but it is also a parable that has been important for me to internalize about delegation in my own career. It is not irrelevant to AI use, but I don't think it slots onto it totally as neatly.
Software developers are more architechs than plain programmers. You wouldn't make an architech lift heavy things, you want they to design how those heavy things are used.
Anecdotally, it feels like something materially changed in the US software hiring market at the start of this year to me. It feels like more and more businesses are taking a wait and see approach to avoid over-investing in human capital in the next few years.
It also feels like the hiring "signal", which was always weak before, is just completely gone now, when every job you do advertise receives over 500 LLM written applications and cover letters that all look and feel the same.
The pro-athlete comparison in this article is bit silly IMO- there are obvious physical body issues that occur with aging if you rely on your muscles etc to make money. If you compare to other fields of knowledge work, such as say law or medicine, there are loads of examples of very experienced, very sharp operators in their 40s and 50s.
Honestly, AI doesn't feel like it's affecting hiring needs from the trenches. We don't have engineers sitting on their hands because AI wrote up everything the leadership could imagine.
Instead, we get an economy that feels like it's on the cusp of a fall or at the very least a roller coaster. Poor tax incentives to hire. ZIRP is long gone. And the hiring managers are overrun with slop.
But bosses are happy to say it's AI because that makes you sound in control.
> Anecdotally, it feels like something materially changed in the US software hiring market at the start of this year to me. It feels like more and more businesses are taking a wait and see approach to avoid over-investing in human capital in the next few years.
My guess is companies overhired in COVID and between that experience and an uncertain market they don't want to make the same mistake twice.
where did the excess labor force suddenly materialize in covid.
the "learn to code" campaign began ramping up in 2013. If you started undergrad in 2016 you would've graduated right into the covid market.
https://en.wikipedia.org/wiki/Learn_to_Code#Policy_impact
I think the hype peaked around 2016 where Democrats were portrayed as out of touch for saying laid off coal miners could just "learn to code". By 2019 it was a cliché used to mock laid off journalists on Twitter.
2008 had ~30k CS graduates.
2015 had ~50k CS graduates.
2021 had ~100k CS graduates.
You can extrapolate the rest.
thats only a fraction of all the layoffs
This is a great question that rarely gets answered. It’s partially that a ton of student students went to school for computer science because they saw how much money could be made, another fraction is people that switched into software from related fields, maybe with a boot camp or something.
It didn't. The elites never want to admit that they have failed to efficiently use capital for the last 40 years. It's always the fault of workers that should never be trusted. Just continue trusting the elites as they ruined US manufacturing jobs, surely the same institutions won't fail the workers again!
I really wish seemingly intelligent people would stop using the abstraction analogy (like the article does). The key word is: determinism. Every level of abstraction (inc. power tools, C, etc.) added a deterministic layer you can rely on to more effectively do whatever it is that you're doing - same result, every time. LLM's use natural language to describe programming and the result is varied at the very best (hence agents, so we can brute force the result instead). I think the real moat is becoming the person who can actually still program.
I grant that there's a definition of abstraction that LLMs don't fall into. But people describing LLMs as another abstraction layer aren't all misunderstanding this. Instead, they are using the term ... more abstractly.
EG: How did Mark Zuckerberg make software five years ago?
He's as capable of opening up an editor as I am, but circumstance had offered him a different interface in terms of human resources. Instead of the editor, he interacts with those humans, who produced the software. This layer between him and the built systems is an abstraction.
Today, you and I have a broader delegation mandate over many tasks than we did a few years ago.
People always say this but it’s misguided imo. Yes LLMs are not deterministic, but that’s totally irrelevant. You aren’t executing the LLMs output directly, you’re using the LLM to produce an artefact once that is then executed deterministically. A spec gets turned into code once. Editing the spec can cause the code to be updated but it’s not recreating the whole program each time, so why does determinism matter?
In my experience, I'm using LLMs as my abstraction to "junior engineer". A junior engineer isn't deterministic either. I find that if you treat the LLM output like a person's output, you're good. Or at least in my projects, it's been very successful. I don't have it generate more code than I can review, or if I give it a snippet to help me fix it, if it ends up re-writing it like an ambitious engineer would do, I tell it to start over and make minimal changes.
I guess I'm not spun up about the determinism because I've been working at the "treat it like a person" level more than the "treat it like a compiler" level.
To me, it's really like an engineer who knows the docs and had a good memory rather than infallable code generator.
I work at a small company, so we don't have tons of processes in place, but I imagine that if you already had huge "standards" docs that engineers need to follow, then giving the LLM those standards would make things even better.
If it's not deterministic you can never fully trust it. In a deterministic abstraction I don't need to audit the lower levels.
this is the way LLMs _should_ be used, as an assistant to create reliable, deterministic code. and honestly, they're fantastic when used this way. build the thing you need with the LLM, then put the LLM away.
but in practice, the current obsession with agents means people are creating applications that depend entirely on sending requests to LLMs for their core functionality. which means abandoning the whole idea of deterministic software in favor of just praying that all of the prompts you put around those API requests will lead to the right result.
how do you know the artifact is correct?
LLM's don't have to achieve perfect reliability to replace lots of work. They just have to reach the balance of reliability and cost suitable for a given task. This will depend on the task.
every time a person uses the abstraction argument, an angel dies
I keep reading about how AI will be fine because people can just retrain for different careers. However, I never read what those careers are or who is going to pay for retraining.
I certainly don't have the money or time to go back to college and start a new career at the bottom.
The argument is that “that’s what always happened in the past”.
Which is true, but it’s true as long as it’s not true.
The classic example of how drastically this kind of thinking can fail is Malthusian theory, that populations would collapse because food growth was linear while population growth was exponential. This was true for all of history until Malthus actually made this observation.
At a mechanistic level, the “we have always found other jobs” argument misses that the reason we’ve always found other jobs is because humans have always had an intelligence advantage over automation. Even something as mechanical as human inputs in an assembly line was eventually dependent on the human ability to make tiny, often imperceptibly, adjustments that a robot couldn’t.
But if something approximating AGI does work out, human labor has absolutely no advantage over automation so it’s not clear why the past “automation has created more human jobs” logic should continue.
> Which is true, but it’s true as long as it’s not true.
It also isn't true. The story of the last 50 years has been that technology, especially computer and communications technology, has facilitated the concentration of wealth. The skilled work got computerized, or outsourced to India or China. That left U.S. workers with service jobs where they have much lower impact on P&L and thus much less leverage.
In my field, we used to have legal secretaries and law librarians and highly experienced paralegals. They got paid pretty well and had pretty good job security because the people who brought in the revenue interacted with them daily and relied on them. Now, big firms have computerized a lot of that work and consolidated much of the rest into centralized off-site back-office locations. Those folks who got downsized never found comparable work. They didn't, and couldn't, go work for WestLaw to maintain the new electronic tools. The law firms also held on to many of them until retirement or offered them early retirement packages, and then simply never filled the positions. It used to be a pretty solid job for someone with an associates degree, and it simply doesn't exist anymore.
The only thing keeping the job market together is the explosion in healthcare workers. My Gen-Z brother and sister in law are both going into those fields. In a typical tertiary American city, the largest employers are the local hospital and perhaps a university or community college. Both of those get most of their revenue directly or indirectly from the government. It's not clear to me how that's sustainable.
>It's not clear to me how that's sustainable
If it makes you feel better, I'm pretty sure it isn't sustainable. (But I'm not an economist so take that with a block of salt.)
I don't think anyone has the answers. It's just some of us are honest enough to concede we have no answers, while others promote an answer that aligns best with their belief system.
"It'll all work out."
"It's the immigrants/blacks/jews/whatever dragging us down."
"Nothing's going to happen and we can all continue doing the work we always have."
"Burn the rich."
Etc etc.
Not a lot of serious attempts out there at even getting a hand on the issues, let alone fixing the issues.
I'm also pretty sure in the past industrial transitions, many of the people who lost their jobs at the start of the change never found better ones. It took a generation or so for new opportunities to really be found and fine tuned and you're competing for those new roles with younger people anyway.
If ai does take a lot of white collar work, is it a lot of comfort that maybe jobs in a very different sector will be better in 20 years?
Did the younger people find better jobs? You used to have all these jobs for people who were maybe a bit smarter than average with good judgment. In the 1990s, the local community college used to advertise associates degrees for paralegals. That's a job that doesn't exist in the same way anymore thanks to computers. Now it's become an internship for kids with top credentials before they go to law school. Which is fine for them, but what about everyone else?
It seems to me like all of these people are flocking now to healthcare fields. That seems totally unsustainable.
>It seems to me like all of these people are flocking now to healthcare fields. That seems totally unsustainable.
Why? There will never be a shortage of sick/dying people. So medical staff, and also undertakers, aren't going anywhere.
Not in all past industrial transitions.
But yes, the argument has been wrong often enough that the people still repeating it as a rule should be mocked and ashamed.
It’s also not that true, and highly dependent on a lot of factors.
Anecdotally I see a lot of schadenfreude online about tech jobs after a decade or two of lecturing everybody from Appalachians in coal country, to Midwestern autoworkers, that they should just “learn to code.”
Totally agree, and would add another way “that’s what always happened in the past” is a terribly weak argument. Things might have always worked out at the societal level so far, but very often do not at the individual level. Countless successful craftsmen have had their livelihoods ruined by technological changes and spent their remaining years impoverished. How many people funding AI would be willing to throw their own life away for the good of some future strangers that may or may not be born? I'm pretty sure the answer is <=0.
> The classic example of how drastically this kind of thinking can fail is Malthusian theory, that populations would collapse because food growth was linear while population growth was exponential. This was true for all of history until Malthus actually made this observation.
Malthusian observation can still be true...It only has to be true once, and the only reason people say it isn't right now is due to industrial fertilizers and short memories.
It's not going to happen, just as it didn't happen for skilled industrial workers whose jobs got outsourced to China. The government will pay just enough in welfare to keep the situation manageable. Then they'll demonize you in the culture, as a Luddite, etc.
> However, I never read what those careers are or who is going to pay for retraining.
There aren't any careers and if there were, you would have to pay. Corporations certainly won't except under extremely rare situations where they have to to compete.
I think the idea of being an employee is fundamentally changing. Not saying its good or bad but it's shifting to a more entrepreneurial phase where people have to step out of their 9 to 5s and find ways to deliver value that others want to pay for.
We saw this pre-ai with uber and door dash. I think as AI automation dies down and most companies are competing at a near optimal level with the new tools we'll need humans again in more traditional roles to build the next generation of innovations. And then the whole cycle will repeat.
That's a lot of SV-speak. How exactly do people step into an entrepreneurial phase? They're at work in corporate settings with fixed defined roles. Most workplaces are not many-hatted-donning startup environments, but restricted roles where there are deliverables, deadlines, meetings, etc. Which leaves out of hours for "entrepreneurship" whatever that is.
Github project work on the weekends? That's not possible for most people in their mature/family years (or shouldn't be necessary - what about living life??)
> We saw this pre-ai with uber and door dash.
Oh, yeah? Did the Uber drivers and door dashers accrue the surplus value?
Uber and Doordash are both examples of abusing workers and their resources to externalize costs on the worker.
What about people who have been out of work for a year and all they can do right now is deliver for Uber and Doordash so they can make rent and put some food on the table?
Is it ideal working conditions? No, but its better than nothing, you can set your own hours, and you can leave when the next opportunity comes.
His point is that it's not entrepreneurship, it's employment.
The same is true of the industries that software disrupted.
The second part seems obvious to me: the ones who are getting retrained. If it's some kind of formal education, depending where you are, maybe the state at least for part of it.
Education in what, though? No idea. And if there was one answer, and it's true that there will be fewer software developers, you'd likely be competing with many people for few jobs.
Yeah, that's just a copium answer from people who simply want to hand wave away the issue instead of admitting they have no good answers.
Like a politician who's asked about this in a town hall, but thinks that "our plan is to do absolutely nothing" doesn't sound very appealing.
> I never read what those careers are
Exactly. I have yet to read a single logically sound argument that even gives a hint of what those professions/jobs might be (remember, they have to be plentiful enough to employ large numbers of people, so "I quit my corporate job and making more as a TikTok influencer" doesn't count). Remember that a new profession has to open up new hitherto unknown revenue streams otherwise there are no companies who will pay you.
Also, it's not necessarily true that there will be other great careers available. This seems to just be an assumption people are making.
Of course, there are jobs that will still require human labour for some time yet, but in reality a lot jobs that require physical human labour are now done in other parts of the world where labour is cheaper.
Those which cannot be exported like plumbing or waitressing only have limited demand. You can't take 50% of the current white-collar workforce and dump them in these careers and expect them to easily find work or receive a decent wage. The demand simply does not exist.
Additionally, at the same time as white-collar jobs are being lost an increasing number of "low-skill" manual labour jobs are also being automated. Self-checkout machines mean it's harder to get work in retail, robotaxis and drone delivery will make it harder harder to find work in delivery and logistics, robots in warehouses will make it harder to find warehouse jobs.
It seems to me there is an implicate assumption that AI will either create a bunch of new well-paid jobs that employers need humans for (which means AI cannot do them) and jobs which cannot be exported abroad for cheaper. What well-paid jobs would even fit the category of being immune to AI and immune to outsourcing? Are we all going to be really well paid cleaners or something? It makes no sense.
A lot of the advice we're seeing today about retraining in construction worker or plumber seems to assume that there's an unlimited demand for this labour which there simply is not. And even if hypothetically there was about to be a huge increase in demand for construction workers, it would take years to even have the machinery, supply chain and infrastructure in place to support the millions of people entering construction.
The most likely scenario is that people will lose their jobs and will be stuck in an endless race to the bottom fighting for the limited number of jobs that are left in the domestic economy while everything else is either outsourced or done by robots and AI.
The better advice is to start preparing for this reality. Do not assume the government will or can protect you. When wealth concentrates corruption because almost inevitable and politicians have families to look after too.
Please take this seriously. Even if I'm wrong it's better to prepare for the worse rather than to assume everything will be find and you'll be able to retrain into a new well-paid career.
The most future-proof “career” right now is having money. At least multiple million dollars. That’s a skill that is very much in demand.
Whoo, deff a field where I would try breaking in.
This is the story that's been written since the Luddite revolts, as far as I know. The successors in that case were the capitalists who spent a significant amount of time and money convincing the constabulary and political figures to side with them. People were shot and jailed in the worst cases. The best case, workers were left without work or sent off to work-houses where they became indentured servants to the state.
The last work-house closed in the 1930s.
That all started not because people were afraid jobs were going to be replaced by the new loom. People had been using looms for centuries. They were protesting working conditions: low wages, lack of social protections when people were let go, child labor, work houses, etc. There were no labor laws at the time to protect workers... but there were these valuable new machines that the capital owners valued greatly. The machines were destroyed as leverage: a threat.
Since the capitalists ultimately "won" that conflict it has been written, by technocrats, that technological progress is virtuous and that while workers will initially be displaced the benefit to society will be enough such that those displaced will find productive work else where.
But I think even capitalist economists such as Keynes found the idea a bit preposterous. He wrote about how the gains in productivity from technological advances aren't being distributed back to workers: we're not working less, we're working more than ever. While it isn't about displacement of workers, it is displacement of value and that tends to go hand in hand.
I think asking, "Where do I go?" is a valid question. One that workers have been trying to ask since the Luddites at least. Unfortunately I think it's one that gets brushed under the rug. There doesn't seem to be much political will to provide systems that would make losing a job a non-issue and work optional.
That would give us the most leverage. If I didn't have to work in order to live I could leave a job or get displaced by the latest technological advancement. But I could retrain into anything I wanted and rejoin the work force when I was good and ready. I wouldn't have to risk losing my house, skipping meals, live without insurance, etc.
AI cannot create art by itself.
At least in the US, the only major non-AI growth field seems to be healthcare to deal with the swell of baby boomers living longer than people have before.
But if we're waiting to be paid to retrain there, I wouldn't hold our collective breath.
Baby boomers had already started the face of dying though. The next generation is still going to be right there. That generation is smaller. These people will always be dying. However, I wouldn't hold my breath if you're a young person in that field. Maybe but maybe not
Comparing software development to carrying heavy things at a construction site feels like a real stretch to me.
'If you work in construction, you need to lift and carry a series of heavy objects in order to be effective. But lifting heavy objects puts long-term wear on your back and joints, making you less effective over time. Construction workers don’t say that being a good construction worker means not lifting heavy objects. They say “too bad, that’s the job”.'
On another note, for sure software developers are saying things like "this is the part of the job that I like" or "if you aren't doing the work, you won't be good at the work." But other people are saying this, too. I just saw an episode of "Hacks" ("QuickScribbl") where the writers say pretty much this exact thing when confronted with AI tooling designed to "make their job easier". Is writing comedy also like lifting heavy objects at a construction site?
If by software engineering, one means typing code character by character into a text editor, sure it's going to be difficult to find someone to pay you for it.
If you mean creating software, well we are creating more software than ever before and the definition of what software is has never been so diverse. I can see many different careers branching off from here.
We are experiencing what Civil Engineers experienced going from slide rules to calculators. Or electrical engineers going from manual circuit path drawing to CAD tools.
The interesting thing to me is that, Software Engineering will have to evolve. Processes and tools will have to evolve, as they had evolved through the years.
When I was finishing university in 2004, we learned about the "crisis of software " time, the Cascade development process and how new "iterative methods" were starting.
We learned about how spaghetti code gave way to Pascal/C structured prpgramming, which gave way to OOP.
Engineering methods also evolved, with UML being one infamous language, but also formal methods such as Z language for formal verification; or ABC or cyclomatic complexity measurements of software complexity.
Which brings me to Today: Now that computers are writing MOST of the code; the value of current languages and software dev processes is decreasing. Programming Languages are made for people (otherwise we would continue writing in Assembler). So now we have to change the abstractions we use to communicate our intent to the computers, and to verify that the final instructions are doing what we wanted.
I'm very interested to see these new abstractions. I even believe that, given that all the small details of coding will be fully automated, MAYBE we will finally see more Engineering (real engineering) Rigor in the Software Engineering profession. Even if there still will be coders, the same way there are non-engineers building and modifying houses (common in Mexico at least)
Calculators and CAD tools do not give you non-deterministic answers. Both of them simply automate part of the manual work for them without creating anything "new". I haven't used CAD tools but I did use some level editors such as Trenchbroom -- I think what is automated is the 3d shapes that you want to make -- e.g. back in the day of '96, when ID Software is creating Quake, there was very little pre-drawn shapes in the level editor and they have to make the blocks by themselves, thus it is very difficult and time consuming to make complex shapes such as curved walls and tunnels. Then better tools were invented and now it is much easier to create a complex shape. But you don't type "a Quake level with theme A, and blah blah" and then you get a more or less working level -- this is what AI is doing right now.
I think the right analogy to calculators and CAD tools, is IDE with Intellisense for SWE -- instead of typing code one char by one char, we can tab to automate some part of it.
But I agree with your consensus -- SWE is changing, whether we like it or not. We need to adapt, or find a niche and grit to retirement.
> non-deterministic answers
It doesn't make sense to get hung up on this aspect of LLMs. We prefer non deterministic so far because it tends to work slightly better even if it is completely possible to ask for a temperature=0 deterministic answer.
With more scale and research, at some point you'll get results that are both useful and deterministic, if it's not already the case.
It absolute makes sense to get "hung" up on something when it comes to planning society around it JFC. I'm with the other commentator, your understanding of these tools should be taken into question since you seem to be reading the tea leaves of statistical noise.
In 2020, there are two companies that are competitors with each other. They each employ 100 programmers to do their job, and we all know how those organizations operated; perpetually behind, each feature added generating yet more possible future features, we've all lived it and are still largely living it today.
In 2026, both companies decide that AI can accelerate their developers by a factor of 10x. I'm not asserting that's reality, it's just a nice round number.
Company 1 fires 90 of their programmers and does the same work with 10.
Company 2 keeps all their programmers and does ten times the work they used to do, and maybe ends up hiring more.
Who wins in the market?
Of course the answer is "it depends" because it always is but I would say the winning space for Company 1 is substantially smaller than Company 2. They need a very precise combination of market circumstances. One that is not so precise that it doesn't exist, but it's a risky bet that you're in one of the exceptions.
In the time when the acceleration is occurring and we haven't settled in to the new reality yet the Company 1 answer seems superficially appealing to the bean counters, but it only takes one defector in a given market to go with Company 2's solution to force the entire rest of their industry to follow suit to compete properly.
The value generation by one programmer that can be possibly captured by that programmer's salary is probably not going down in the medium and long term either.
Your hypothetical ignores the distribution of programmer talent. Company 1 can pay more per person and hire 10x programmers, who can then leverage AI to produce the same or more as Company 2.
We have seen this in other knowledge industries. U.S. legal sector job count is about the same today as it was 20 years ago. But billing rates have exploded and revenues in the 200 largest firms have increased more than 50% after adjusting for inflation. Higher-end law firms have leveraged technology to be able to service much more of the demand and push out smaller regional competitors.
Of course it does. It ignores a lot of things. Mostly I just want to present the view that things aren't entirely hopeless and the entire industry is doomed to contract by 90% because of AI. Your legal system point also fits in precisely with what I'm trying to convey, just in a different direction.
I think paying significantly more was a very localized thing that happened for AI researchers who were familiar with the alchemy that made GPT4 suddenly work much better than anything else seen before.
This resonates strongly with me, in that all that extra margin has to be spent on something other than dividends
My concern would be whether creating that software pays enough to keep up with skyrocketing costs of living. In the past, the jobs created by automation have generally been lower paid with less autonomy.
> My concern would be whether creating that software pays enough to keep up with skyrocketing costs of living.
You might need to relocate to a place with much lower costs of living.
This was the idea behind remote working discussed during COVID-19 times:
- the company can pay less money because the employee is living at a much cheaper place than the expensive city where the company is located
- on the other hand, even with a smaller salary, the employee has more money at the end of the month because of the smaller costs of living
So both sides win.
Ignoring the preference of people generally wanting to live in HCOL areas, this only works if every company hires equally from LCOL areas. One of the benefits of living in a HCOL area is access to the job market it provides. It's much easier to get hired for a software position living in San Francisco than it is living in Deming, New Mexico.
More importantly, in San Francisco, there are a lot more opportunities than in coming. I've never been to either city (i'm not going to come to the conference I was at because I never left the hotel.) however, I can still tell you confidently that if you have a weird hobby, you're much lower than you can find other people with that interest, stores that sell the things you need to complete the hobby, and all those other things in life that you want. If you love doing the types of things people in Deming do, well, it's a great life, I'm sure. However, as soon as you want to do something off the wall, well, you may not even find enough people in Deming to have your cricket team, while I have no doubt that San Francisco has a team that you could join.
but moving to a lower COL area can reduce that amount of public and private services one gets access to, no? network connectivity will, for example, likely be worse out in the sticks
Unfortunately, in America places with low cost of living are generally, to put it diplomatically, unpleasant places to live. That's even more the case if you don't fit into the white, cis, straight, and Christian box that rural areas are willing to accept.
> Unfortunately, in America places with low cost of living are generally, to put it diplomatically, unpleasant places to live.
This will change for the better if more and more educated people relocate there.
And then those areas become more expensive...
But at least stereotyping happens everywhere!
I like how the assumption was they were all white, Christian and rural.
This problem is not a software engineering problem nor an AI problem but a problem of the balance of power between working hard vs. investing. If the people who believe in working hard organize and slow down the tendency to rig everything for investors, then the markets should stabilize at a more generally prosperous place.
The balance of power is dictated by economic facts, not by organizing or politics. Auto workers in 1950 weren't better organized than auto workers in 2026. They just had more leverage because they weren't competing with auto workers in China. Likewise, Silicon Valley isn't paying people writing web apps $$$ because those workers are organized. They are doing it because they don't have a feasible alternative. If AI enables them to do more with less, they'll take that option.
Creating more software does not solve anything if that software is mostly a functional duplicate of other software. Or, in other words, all companies re-invent the wheel many times over. It doesn't matter if you 10x the development of software that brings nothing new besides being written in a shiny new framework.
We should, IMHO, start getting rid of most software. Go back to basics: what do you need, make that better, make it complete. Finish a piece of software for once.
s/software engineer/secretary/
s/creating software/typing correspondence/
In a world where software programming/architecting is solved by AI, value will accrue to people with expertise in other domains (who have now been granted the power of 1000 expert developers), not the people whose skillsets have been made redundant by better, faster and cheaper AI tools.
It could go either way. Don't forget that LLMs also have expertise in the other domains. Who would do better - the chemist with vibe coded app or the developer with vibe coded chemistry?
My premise is that a vibe-coded app will be indistinguishable from a ‘hand-crafted’ one. So in that scenario the chemist wins, because the developer has no value to add.
It is clear to me that SWE and ML research will be subsumed before other domains because labs are focusing their efforts there, in their quest to build self-improving systems.
There will be more software in the same way there is more agricultural output today.
The idea that productivity gains which result in more of something being produced also create more demand for labour to produce that thing is more often wrong that true as far as I can tell. In fact, it's quite hard to point to any historical examples of this happening. In general labour demand significantly decreases when productivity significantly increases and typically people need to retrain.
Except we are now in the golden age where people with 20 or 30 years of experience know what quality software is - or at least what it is not. So they are able to steer the LLMs. Once this knowledge is gone - the quality could go downhill.
Unless I'm missing something, there's an obvious logic issue here.
If we truly need to sacrifice our skill to be productive by using LLMs that atrophy us, then the only devs that have a limited lifespan are us. The next ones won't have a skillset to atrophy since they won't have built it through manual work.
Also, I hereby propose to publicly ban the "LLMs generating code are like compilers generating machine code" analogy, it's getting old to reargue the same idea time after time.
why is the LLM-compiler analogy flawed? Is it only because LLM output is non deterministic?
Besides probabilistic and non reproducible output, programming languages are designed to be unambiguous and explicit, and human language doesn't have that.
For(){} it's normally either undefined or has a specific meaning. "Then iterate and do x" might mean many subtly different things.
Most programmers never deal with a compiler bug in their whole career, and can dismiss the possibility. For LLMs it would be hard to even define what a "compiler bug" would be since there is no specification for English.
Then there's the fact that models generally don't guarantee anything at all. Sonnet can change under your feet.
Models also degrade as the context window gets larger. Compilers handle one line just the same as 20.
I could keep going, there's so many fundamental differences in the process that the analogy only serves to provide a false feeling of security.
I don't think its just, there's also the fact that if you're working with c or cpp or any systems level language, you typically do know how to read assembly because you've stumbled upon it for some reason, and if you're writing low level programs (which is typically what these languages are used for) you will definitely at some point need to know to read assembly and maybe even write some. But with LLMs the entire field has shifted. You don't need to know anything to write any language and you don't even need to have high level computer science knowledge nowadays to get something that works and the world increasingly just seems to want something that works.
Because you don't have to coax, trick, or guide your compiler into doing the right thing.
Clearly you are not a C++ programmer. :)
Maybe C++ compilers would benefit from asking an LLM to rewrite their messages in a way that makes the point clearer...
But the GP stands.
Compiler: "Here is an exact program. Translate it while preserving its meaning."
LLM code generation: "Here is an intent/specification. Invent code that hopefully satisfies it."
Does the compiler analogy provide value under those terms? I don't think it does. In fact, I think it provides negative value.
We don't need to use tortured analogies to express excitement over these tools.
> The career of a pro athlete has a maximum lifespan of around fifteen years. You have the opportunity to make a lot of money until around your mid-thirties,
This is pretty ageist - I'm around 40 and feel I am at my mental peak, compared to even my mid 20's. This isn't a good analogy at all, the brain doesn't "wear out" like a professional athletes' body does, it just changes its structure. The brain is a remarkable organ.
He just means: by this age you've probably found your preferred title and level, unless you want to rise to more C-level / executive positions, which are rarer in any case and most folks don't want.
This is definitely more charitable, but isn't this already the case now? It seems he's saying past your mid 30's you'd no longer be viable as a software engineer. That's never been the case, and I'm not sure why it would now suddenly be the case.
Even clearer, if you don't adapt to the changes taking place in the field there might not be a future for you. Its not about age, it is about attitude and flexibility (which are, admittedly, issues when getting older).
In other words, if you want to continue stubbornly typing out code by hand, the person right over there has already mastered agentic tooling and is doing vastly more than you, more quickly, and with greater precision, and will simply be a more fit candidate to hire. Roles for this type of legacy stubborn personality will be less and less, and you will age out as part of the old school.
I'm repeating what others have essentially said, but ask yourself what's on your resume. If it says "Software Engineer" and that's all it talks about, then yea you might not find it's a lifetime career.
But if it's a diversity of things (that include software development) then you probably have a lifetime career ahead of you.
I've been writing software for over 40 years but I've never seen myself as having a software engineering career. I've been a research assistant in geophysics, a marine technician on research ships, a game developer, an advisor to the UN, and a lot more. Yes all through that I used software, but I did a lot of other things in the process of using it.
Argument A: AI means you don't learn as much, so even though you are more effective, it inhibits your growth and you shouldn't use it. However, on a pragmatic level, it's effective to hire a bajillion people, fire them at will, and get AI to do everything. You will get so many JIRA tickets closed and so many lines of code written.
Argument B: AI means you don't learn as much, and the single most useful work product of a software engineer is knowing how the code functions, so it's depriving your company of the main benefit of your work. Also, layoffs are terrible business strategy because every lost employee is years of knowledge walking out the door, every new hire is a risk, and red PRs are derisking the business.
Institutional and personal knowledge seem similar, but the implications of each are radically different.
Nah it is
Was it ever a lifetime career? Haven't most people looked around and asked themselves where are all the 50+ engineers? They basically don't exist in large numbers. Ageism is real in this industry. You either save up enough money to retire early, switch into management, or get forced out of the industry eventually. AI is just accelerating the trend. I see very few junior engineers resisting AI. I see a LOT of staff+ engineers resisting it. Just look at the comments on HN. Anti-AI sentiment is real.
If you are lucky and got in early, then probably yes, it could be a lifetime career. It's like all careers, when you joined early, you got a lot of opportunities, you also rode the wave, you eventually rose to the top if you grit through.
It's a lot easier to be early than to be smart or quick.
> Haven't most people looked around and asked themselves where are all the 50+ engineers? They basically don't exist in large numbers.
I'm not discounting ageism in the industry, but how popular of a career was it 30+ years ago compared to now?
In 1996? Software development was the hot ticket to upper middle class in the early 80s when I was a recent CS grad, and I was already working with people who were in it for the money. By the late 90s, if you could spell “HTML”, you were making decent money as a web developer. This all came crashing down during the Dot Bomb collapse, but SW has been pretty popular for most of my career, and it just continued to get more popular, especially as salaries continued to increase.
In retrospect the Dot Bomb was a bump in the road. Yes, some people who only knew enough HTML to be a "Webmaster" might have been filtered out, but pretty quickly anyone who could really build software had opportunities greater than before.
There’s a hierarchy amongst knowledge work and AI hasn’t yet been able to do the work that is rare and valuable.
Over the past two decades, there have been lot of solved problems like building boring scalable web apps, UX design etc and AI is fairly good at this, enough so that good prompting can get you very far. This shouldn’t be a surprise, there’s a lot of publicly available data for this (GitHub repos etc).
On the other hand, there are rarer Computer science problems like designing efficient Datacenters, GPUs, DL models. Think about problems that someone of Jeff Dean’s or James Hamilton’s (AWS SVP) or a skilled Computer Architecture researcher like David Paterson’s ability would solve. These are incredibly hard and rare problems and AI hasn’t been able to make much progress in these areas. That’s true for other sciences as well.
If you’re a regular Joe like me who builds boring CRUD apps, AI is coming for you.
What I mean is if you are working on incredibly hard and rare problems that require rare skills and also those problems don’t have publicly available data that LLMs can be trained on, you’re safe from being “automated” away. If not, you must plan accordingly. Also if you’re a skilled manager (in any field) AI cannot replace you, highly skilled managers that can get the best out of their teams have rare skills that aren’t easily replicable even amongst humans much less AI. Although, if going forward we need fewer developers we will need fewer managers too.
>I don’t think there’s compelling evidence that using AI makes you less intelligent overall1.
That statement is enough of an evidence
The differentiator is augmenting reasoning with AI versus replacing reasoning with AI. But those who choose to replace their reasoning with AI probably weren't good at it to begin with; cause if they were, they'd choose to not replace it. Exception is that AI can actually replace reasoning (which it can't, yet) - then it's game over with a career in software engineering anyway.
I take issue with the premise that "Using AI means you don’t learn as much from your work" With AI assistance, I tackle far more tasks than I would without it. Learning per task goes down, but cumulative learning does not.
>If the models are good enough, you will simply get outcompeted by engineers willing to trade their long-term cognitive ability for a short-term lucrative career
> (2) AI-users thus become less effective engineers over time, as their technical skills atrophy
Wouldn't (2) imply that if everyone just used AI there eventually would come a time when there aren't engineers who will outcompete you (because their skills are so atrophied)?
Seems the solution here is the same it's actually always been if you want career progression: be more than just a code jockey. The true value of an engineer is to be plugged into overall roadmaps, broader thinking around product, how to achieve company goals, etc etc.
Yes, LLMs might dramatically reduce the amount of code we write by hand. But I'm a lot less convinced they'll solve all of the amorphous, human-interacting aspects of the job.
My exprience has been that companies actively work to prevent people from becoming more than just code jockeys. For example, most of the places I've worked have viewed code delivered as the ONLY metric used to evaluate performance. Attempts to contribute to roadmaps or strategy are ignored at best and punished at worst.
Yeah, 95% of the available advancement in computing is in people management, not technical mastery. Businesses much prefer to hire externally to serve any non-core capabilities, especially to minimize internal culpability should anything go wrong. That leaves little opportunity to think outside the box technically.
80% of my day to day job has never been pumping out lots of code. it is a complicated career is it? we do a lot of alignment, design and thinking. i can't even agree the idea of outsourcing thinking, i think AI is very good at helping us to think clearly, but it doesn't really "think" for us.
if you do that then... likely very replacable.
If AI becomes good enough to easily produce maintainable and high quality software, then I really can't see how demand for software engineers would not plummet. Even lots of non-coding work that software engineers do, such as accurately capturing what client actually wants, will become much less valuable - e.g. currently misunderstanding of client's requirements is catastrophic and can lead to waste of months of labour; with AI it could become matter of max few hours lost. So I can understand argument that software engineering careers might be safe because AI may plateau and we might never reach level where it's actually capable of producing good software. But I absolutely don't buy that software engineering will be safe even if such AI exists. Even if your current work is just 20% actually coding, you must remember about second order effects that will take place once quality code generation is 1000 times faster.
AI can also do alignment and pull from its vast training dataset for design and "thinking" -- because 99% of the problems in this world were already solved, multiple times, maybe not in the exactly same format, but in a very similar format.
I also see that in the future humans will adapt to AI, instead of the opposite. Why? Because it's a lot easier for humans to adapt to AI, than the opposite. It's already happening -- why do companies ask their employees to write complete documentation for AI to consume? This is what I called "Adaption".
I can also imagine that in the near future, when employment plummets, when basic income become general, when governments build massive condos for social housing -- everything new will be required to adapt to AI. The roads, the buildings, everything physical is going to be built with ease-of-navigation by AI in consideration. We don't need a Gen AI -- that is too expensive and too long term for the Capitalist class to consider. We only need a bunch of AI agents and robots coordinated in an environment that is friendly to them.
Software engineering today is almost nothing like the role it was 30 years ago.
Maybe if you somehow stick with the same company for your entire career, it could feel somewhat similar... but I doubt it, as 'best practices' and many other things cause it to change.
The days of 'lifetime career' had already gone for most people, way before AI arrived.
I'm a software engineer and architect, I love my job, I love diving into the small details, I love the grand overview.. I love identifying concepts and applying them to achieve elegant high-performing systems.
I love thinking about what kind of assembler the compiler may generate (though honestly, I haven't got a chance), I love thinking about how languages should be more dynamic (Who's got actually-first-class functions? Like, ones that you can build, compose, combine and manipulate to the same degree you can a string or a JSON object, no LISP, you're cheating, close no point).
And yet.. I don't care that much. Not because I'm late in my career (I'm 40, there's still some years left in me), but because I want to make computers do things, and what I enjoy doing is thinking up ways the things can happen, and sometimes the particulars that matter when making a lot of different things happen in a coherent system.. And yea, LLMs are trained on peoples output, and from what I'm seeing everywhere, is that people are overall fairly terrible at that, and most of the plumbing-type glue being written is not worth anyones time..
And I'm not saying I don't care because LLMs can't do my job (heck, even after hours of back-and-forth spec building and refining every little nook and cranny, the stupid coding agent still cheats or gets it wrong (even after it's beautifully explained, proven even, by reasoning and example alone, and on first try even) that the words coming after the previous words makes sense, as soon as the plan is put into motion, it'll mess it up on some scale so fundamental I should just have done it myself.. And I hope that changes, I hope that I don't have to go into such detail.. I hope to become a steward of taste rather than a code-reviewer.. I hope that I will eventually not be needed for that anymore.. I want it to replace me, so I can move to telling what I want, and have it made that way..
I hope I won't need to steward good taste, and that nobody will.. I hope the applications I use in 5 years will be a collection of one-offs, and gradually improving tools that was written _just_ for me, for my way of working, and my way of thinking.. I want to prompt the damn program to change itself as I discover new ways to do things, until it can eventually figure out how to automate the last bit of my task away.. And then I'll go do something else exciting.
Is anything today a lifetime career? I’ve had at least five or six job descriptions over my time, and at least a few of them pretty much don’t exist anymore, or are changed beyond recognition.
So, permeant underclasses those billionaires talking about are actually just juniors that never get a chance to become a senior.
Why are we upvoting this?
Virtually, the entire blog is about AI with a ridiculous publishing rate (https://www.seangoedecke.com/page/5), funny how I can look at this site HTML and know right away it was done with AI.
Can we stop upvoting vibe published articles? The arguments are flawed and don't even make sense to anyone who does software
I think you're being a bit harsh here.
Yes, the blog is mostly about AI, and yes, he publishes very frequently. But his articles don't read like AI and he claims not to have used it in his writing (https://www.seangoedecke.com/avoid-ai-writing/). And regardless of how you feel about the content, the community has clearly decided it's worthwhile as a discussion point.
> Why are we upvoting this?
Because people want to discuss about the topic of the headline.
> Construction workers don’t say that being a good construction worker means not lifting heavy objects. They say “too bad, that’s the job”
I dont know, maybe in your part of the world, but where I'm from we have a series of robust worker protection laws that try to limit the damage the work does to you. We generally consider it a bad thing for workers to damage their bodies, and if we could build houses without it, we'd prefer that.
In this specific case we do have a techniques to build software without causing damage, so why change that?
This post is arguing that maybe software enginnering should start being harmful, even though we know it doesn't have to. It's a post of a guy begging to be fed into the capitalist meat grinder. Meaningless self sacrifice.
It never was a lifetime career, if you don't get the dough by 35 you just failed.
i'm about 35 and i have made good money but not enough to quit. i plan on just sitting around cashing checks for another decade. with a few liquidity events along the way to sweeten the deal. should pay for my mortgage, some home renos, and fund my 401k etc. i don't foresee myself being out of work (and i don't even use AI to code! i'm just Actually Good!)
Absolutely untrue, you could have a solid career writing back office or internal software in financial services, insurance, higher ed, any number of industries. Would they make you a millionaire? No. But they'd pay for a nice house in the suburbs and raising a family.
> I hope that this isn’t true. It would be really unfortunate for software engineers. But it would be even more unfortunate if it were true and we refused to acknowledge it.
More AI Soothsaying. Not so hard on the Inevitabilism this time.
https://news.ycombinator.com/item?id=47362178
On the contrary, in an efficient economy, every business operations manager (MBA) would be a skilled software engineer, able to comfortably manage data flows and design custom automated processes. There's so much potential energy there in unlocking this technical literacy.
Less "pure" programming, but lots more programming in general.
Was it ever? It's always seemed weird to me that people even think 'software engineering' is a career.
It's a tool for knowledge work.
No carpenter is a specialist in drills.
It seems to me that the best way to navigate a long term career is to have another specialty and use software engineering as a tool within that specialty.
It most certainly was a lifelong career.
I’m kind of confused how you might think it wasn’t. Going through a career as a software dev until retirement was very common.
Software engineers didn’t just disappear after age 40.
> Software engineers didn’t just disappear after age 40.
At the end of the 90th and beginning of the 00th ("dotcom bubble") it was a common saying that if as a programmer, when you are 30 or 40, you don't have a very successful company (and thus basically set for life), you basically failed in life; exactly because "everybody" knew that programming is a "young man's game" (i.e. you likely won't get a programming job anymore when you are, say, 35 or 40 years old).
So,
> Software engineers didn’t just disappear after age 40.
is rather a very recent phenomenon.
Enter the carousel. This is the time of renewal.
> At the end of the 90th and beginning of the 00th ("dotcom bubble") it was a common saying that if as a programmer, when you are 30 or 40, you don't have a very successful company (and thus basically set for life), you basically failed in life; exactly because "everybody" knew that programming is a "young man's game"
That seemed commonly held among folks participating in the dot-com bubble. Plenty of people had been doing it for decades even as the bubble was growing.
> Software engineers didn’t just disappear after age 40.
>> is rather a very recent phenomenon.
Not really. It's not that they disappeared, it's that they're a small fraction of the overall SWE population as a side-effect of how much that population has grown.
Software is wood, not drills, and if we somehow invented bacteria that gradually built an ugly but saleable house when fed on water and nutrients and nudged into shape, I bet carpenters (well, framers or whatever they're called in the US) would have an identity crisis too.
I kind of disagree. You are describing a kind person who is extremely valuable, a person who is proficient in SWE but also has domain specific skills in some niche.
That's great, but its nowhere near the norm, and people have been doing generalist software engineering for decades. There has been a sufficient amount of work for a long time to be performed by generalists that it has been a very reasonable career.
IMO AI is the first thing that has ever actually challenged that.
Id disagree with this analogy: "No carpenter is a specialist in drills." and i think its an interesting lens through which to look at the evolution of our tools.
I think there are trades where tool (or process if i may be allowed to extend the analogy) specialists exist and are highly valued. My dad is a plumber, so ill use that example but id trust similar is true for carpentry. there are specialists by task/output (new construction, repairs, boilers etc) but also tool specialist plumbers and companies for example drain clearing equipment or certain kinds of pipe for handling chemicals other than water are very specialised, and there are roles for them because the thing they enable, and the criticality of the task, and often the cost and complexity of using the tool are high enough to make specialisation valuable.
IMO software has, for the 10 years ive been working in it, been in an unusual position where the tools (languages, engineering practices, tech stacks) were super technical and involved, but also could be applied to a large number of problems. That is the perfect recipe for tool specialists: complex tool with high value and broad domain/problem space applicability.
Because of that tool specialisation, we've separated the application of the tool to a problem/domain from the tool use. reduction of complexity of applying these tools to many problems, means all domain specialists will use them, relying less on tool specialists.
imaging a mcguffin tool for attaching any two materials together, but which took a degree to figure out (loose hyperbole here), that sudenly you could use for 5 bucks and a quick glance at the first page of the manual. An industry that used to have lots of mcguffin engineers, would be mega disrupted, and you could argue that those tool specialists would have to identify more with what they were building than the mcguffin they were using.
I tell my boys (both in HS now), the combination of a specialized skill/knowledge + competent computer programming is the sweet spot. For example, my oldest wants to go into Petroleum Engineering which is great but I told him to still learn software development and get comfortable solving problems with code. Having specialized Petroleum Engineering knowledge combined with being a competent software developer is a powerful combination.
I think the logical next step is that "XYZ knowledge worker" will become a software engineer of sorts. Not literally writing code, but at minimum encoding processes/workflows into some language.
If you're a paralegal or an accountant who can't manage their workflows with AI, you're going to be way less productive than someone who can.
And if you're a paralegal or an accountant who can manage a lot of your workflows with AI, you don't need custom software (hence less dedicated software engineers).
> No carpenter is a specialist in drills.
There's no category difference between being an expert in carpentry vs masonry and being an expert in drills vs hammers. They are both just areas of expertise.
Going down the path of trying to define what is expert functions and what is "merely" a tool using anything but descriptive technique is nonsense.
Expert functions are just those areas where using a tool is sufficiently difficult to require expertise.
Software engineering isn't a tool, it's the task.
Are people seriously thinking that you can make yourself dumber by using a chat UI?
If talking to an AI makes me dumber and a limited career, then all the customer support people that ever existed were in the same or worse position talking to dumb humans on chat all day answering tickets always about the same topics and linking the same docs over and over. This makes no sense.
You're misrepresenting the potential problem. It's more along the lines of using AI stops you exercising the cognitive processes you would doing things yourself and those encompass skills, knowledge and brain function that can atrophy. For an extreme example you can look at cognitive decline in the elderly which can be mitigated by taking part in activities that are cognitively stimulating.
Can you comment on other jobs though? The large majority of jobs require no big mental effort? Even switching from programming to management would go through that. Under that light it'd be impossible for a manager to ever become technical again because they'd atrophy so quickly?
I think you're probably castrophizing the impact with statements like "it'd be impossible for a manager to ever become technical again" because that's not the likely outcome as I understand things. But yes people who stop programming for an appreciable amount of time do find it harder to pick back up again.
The longer the manager is out of the game, the harder it is to return to the game. Returning to the game takes time. Depending on age and income, returning to the game may be impossible for some people over time.
I can't answer for the other guy, but my answer would be that talking to a clanker is LESS mental effort than being a manager, and that's why your reasoning atrophies so quickly.
Managers can go back to being technical, because they are still interacting with problems that require human thinking. Token farmers don't.
If you constantly pawn a task or cognitive load onto someone else (AI or not), you'll eventually get worse and worse at that particular type of thinking. Your overall mind doesn't necessarily get weaker, but you definitely start to get worse at anything you don't regularly practice.
I think you need to read the studies linked in the footnotes. This is a well-studied issued.
You can definitely feel it when you talk against an AI vs doing the churn yourself. It's comfortable, simple, it doesn't aggravate you.
Pretty much every study says so, so I guess?
> The career of a pro athlete has a maximum lifespan of around fifteen years. You have the opportunity to make a lot of money until around your mid-thirties, at which point your body just can’t keep up with it.
If you believe this about your software career, how do you think your going to switch into another career as a junior and keep up?
Easy, I made the switch in my 30s, now I manage software engineers :)
Software managers are being replaced by vibe coders.In the age of AI managers are irrelevant
terribly written article that failed to make any point. anyone whise read ai generated code from the best models and who understand how llms work, knows this statement is complete bs.
It will be for those fixing AI slop software. (In fact, they might need several lifetimes.)
Why do people think there will be fixing AI slop software? I see that opinion here and there on HN. The cost of codegen is next to nothing. It makes no sense to spend large sums of money having an engineer fix something that could be generated over and over until gods of stochasticity come in your favour.
We've entered a period of single-use-plastic software, piling up and polluting everything, because it's cheaper than the alternative
When everything is generated on-demand - each exploit has to be discovered anew. No more conveniences like common libraries.
This is sarcasm, but it's probably also going to get sold as a feature at some point.
The problem partially is that AI can also fix AI slop. At this point I am in doubt whether code quality matters anymore in most non-critical software. You can ask an LLM if the code has quality issues and refactor to a _better_ version. It will reason through, prepare a plan and refactor. So now with this "better" code you can expect that your LLM will be able to deliver higher quality results but that's all the quality that is needed.
Actually, at this point I feel that the value in software engineering is moving from coding to testing and quality assurance.
In my experience, an LLM "refactoring" autonomously doesn't actually improve code quality, it simply reorganizes the mess into a new mess.
This is my experience with human developers too so I'm not sure if there's a meaningful difference.
Sure, but also, AI will always find issues. It will never be mildly satisfied with the codebase and say so.
All the frontier models tell me when there are no issues. After implementing a feature I will ask it to identify issues in my implementation, list them, and support each item they identified with technical argumentation and reasoning as to why it's an issue.
If it doesn't find anything it says I didn't find anything.
Not from my experience. It's true that it will always find new issues in a new session but it is happy to say so when the code is good.
> AI can also fix AI slop
No it can't.
AI knows nothing about software engineering, all it can do is generate code.