I always remember of the infamous Steve Jobs quote "Ideas are cheap". If execution is everything, and frontier LLMs solve execution, then ideas are the gateway to abundance now, but abundance alone does not guarantee "stickiness".
What I think is often overlooked is the human "Willingness" and "Care" of staying with the thing for the lack of a better term. What I mean by that is that a lot of people just don't care enough, or don't want to, build, maintain, and own things. Sure you can ship V1 faster, but will you remain on the grind?
I think a great example of what probably will happen is found in Suno, the AI Music thing. I don't know if y'all have tried it, but it now produces really good stuff. What's happening there? A lot of people play with their own little universe and get tired quickly, move away from it, and only a few prolific creators stay and turn it into a "job like" environment.
We may have shifted the scale and the economics of "delegation" and "execution" but I think there are still a lot of other factors to consider.
> If execution is everything, and frontier LLMs solve execution, then ideas are the gateway to abundance now, but abundance alone does not guarantee "stickiness".
They don't "solve" execution.
If you're willing to push them enough, and put in place the system that they can actually get working code, they can solve execution.
They are far from doing that by default now.
Maybe in 3 years. They're moving fast.
But you can't ask them to build you a better Rust compiler, sit back and watch, and get a result today.
Totally, I meant that more in the lenses of how folks are perceiving it. They solve the execution part of the "one shot" aspect mentioned in the post. You still need to do a lot of plumbing, orchestration, supervision, etc. I think it will get cheaper and cheaper over time, though not magical enough to one shot a Rust compiler from "write a Rust compiler make no mistakes" haha.
Could you elaborate on the AI Music tool? My impression was that it's used as a one-shot generation tool. I don't know much about music but I imagine artists need intermediary steps, track separation, instrument customization and other stuff I'm oblivious about. Without these, it's hard for me to imagine it being used for professional work.
The frontier music models, the paid/pro Opus 4.8 equivalent ones, are more capable now, and Suno has a "harness" like Claude Code on their Studio tool that lets you iterate on the generation by doing stem splitting, track separation, edits that stay within the tempo, rhythmic structure, etc.
> I always remember of the infamous Steve Jobs quote "Ideas are cheap". If execution is everything, and frontier LLMs solve execution, then ideas are the gateway to abundance now, but abundance alone does not guarantee "stickiness".
> I think a great example of what probably will happen is found in Suno, the AI Music thing. I don't know if y'all have tried it, but it now produces really good stuff. What's happening there? A lot of people play with their own little universe and get tired quickly, move away from it, and only a few prolific creators stay and turn it into a "job like" environment.
Sturgeon's law states, "Ninety percent of everything is crap". The adage was coined by American science fiction author and critic Theodore Sturgeon while defending the merits of the genre. Sturgeon observed that most works in any field were low quality. Therefore, science fiction was not uniquely inferior.
Wut? I pilot LLMs all day but there's no way in hell I'd agree to be at the helm of a finance product. That first pillar is still there. Maybe the author isn't aware of the impact they have, but I know, with the evidence of reverted PRs, that when I step outside my area of deep knowledge I can no longer call BS on the agents. Our most capable agent, with access to the same kind of distributed systems the author talks about, is regularly wrong, frequently myopic, and just outright dumb constantly. It's the expertise of engineers on the team that push it back on track.
Posting this under a burner so I don't dox myself: I work in FinTech on a regulated product. We have access to Mythos. Mythos identified part of our codebase that it confidently asserted was not complaint with a particular regulation and we were at grave risk by allowing it to operate the way it was.
Except this was not the case, it had of course hallucinated what the regulation actually required (I know this because the code in question had already been reviewed by human counsel). This is (supposedly) the most bleeding-edge model available.
We use a lot of genAI to help us write code, but there is no way in the mid-term we could ever rely on these tools to actually build compliant financial products. We'd have to be totally mad. Yes, lots of Fintech companies are using these agents to accelerate, but anyone who's using them to actually ship product without a human actually digging into it is opening themselves up to a world of risk.
Even if that's the case, I feel like accurately knowing which regulations you're in compliance with and not is would be kind of important from a risk management perspective. From a "maximize profits" perspective (which I'm not saying is good but what you're saying you thought they operated with), you'd want to know the potential gain from ignoring a given regulation and the likelihood of getting caught (along with the cost of the punishment if that's happens). This is the kind of math that I'd expect a finance company to be pretty familiar with, and giving that up for a fuzzy "idk if we're in compliance or not" check seems like a pretty huge liability (unless there's confidence in not being liable for blindly trusting the LLM, which I hope is not the future we're headed for but I guess I can never be totally confident in us not somehow ending up with rules that defy common sense).
In my experience this is not representative of most fintechs. Of course there are both cases of real intentional noncompliance, and accidental, but by and large it seems like everyone’s trying to innovate within the law.
Companies that are growing tend towards faking compliance. Many financial rules like pci onky kick in at certain scales. So a company growing very quickly will often be behind the curve but will do everything to seem like they are compliant. Then they would hire people like me to come in and make them actually compliant. More often than not, making an effort at improvement was enough to keep the ball rolling.
What I usually do when in doubt is challenge the AI. “Please quote the section of regulation the product is non compliant with”. It usually admits it hallucinated the whole thing.
I've worked on projects in the airline and health industry which are highly regulated too. The regulations can be incredibly difficult to process and implement, and make sure you adhere to everything correctly. I've been involved in multiple scenarios where people have made false assertions about compliance or lack of. I'd still place a bet that the SOA models make _far_ less mistakes than humans.
They might make fewer mistakes, but they aren't evenly distributed. They don't use logic when making mistakes, it is gaps in the training data and now large of a span they have to bridge in the latent space. Just as they aren't smart like humans, they aren't stupid like humans. Don't mistake rate for quality.
For some reason, tons of people seem to be in camps at both extremes. It's either "AI sucks don't trust it!" or "AI is so much better than humans!"
But the most reasonable take, which I'm happy to see reflected in so many comments in this thread, is… use both.
Do an AI pass, and have humans verify, and vice versa. Let the humans drive the AI. Then the unique shortcomings of each party can be covered by the other's strengths.
AI review is never going to beat a fully resourced human review.
It might beat an underresourced human review, on time, efficiency, cost metrics. But on the metric of accuracy, throwing unlimited humans at a problem will still beat throwing unlimited AI at it
>I'd still place a bet that the SOA models make _far_ less mistakes than humans.
Genuine question: your top coder seems to be producing the most error-free code from your perspective, has the deepest knowledge of the architecture and codebase, and is faster on the trigger than the others.
But your top coder has proven and verifiable dementia, where they will confidently assume the existence of apis and code that do not exist, mix up the purpose of others and forget other things, and you can't predict when and how they will introduce errors into the system or the severity of such errors.
Are you really comfortable letting this person with dementia generate most of your codebase in the airline and health industry?
I also hope you have an iron-clad agreement that prevents the model provider from doing silent updates because all your evidence of correctness you collected thus far goes out the window in that case.
Another genuine question:
You have witnessed a human coder and the AI you're using make the same important mistake. Assuming you do not have the time and resources to retrain, fine tume, and test your frontier model:
Who would you trust not to make the same mistake multiple times in the future after you have warned them that their job depends on it, the AI or the human?
Your top coder has guard rails in place to prevent him autonomously going free - right? This is how you should approach agentic development with LLMs. Like it or not, we are the final bastion, the gatekeepers. The hallucination thing I think is mostly overblown and from speaking to colleagues it seems to vary wildly depending on which model and harness you are using - always go for SOA. In the last 3 months I can count on one hand where it's done something wrong and that's primarily as I'm operating it with guard rails and giving it context.
This stupid argument again. The number of mistakes _does not matter_. Get. This. In. Your. Head. The predictability of the _type_ of error is what matters. For LLMs and machine learning in general the error distribution is not what you would expect and it is not possible to predict either.
100%. Unfortunately those not in the depths of mission critical systems or regulated products will continue to believe that producing tons of code quickly using LLMs without humans in these systems is acceptable.
Here's an example of what we will continue to see with folks fully immersed in gen AI psychosis:
"The creator of claude code said that he no longer writes code for about 6 months and now has Claude doing all his work now. He also said recently that he no longer prompts Claude and now has it running in loops and it is self-improving itself and performing better than a human!"
If the code produced by the LLM is perfect, the LLM takes the credit. But when a disaster happens, you cannot blame the LLM and it then falls on the human who did it.
I don't think SWEs heavily vibe-coding with LLMs realize the risk in not understanding what the code the LLM being produced is doing even after generating tests (lol). We will see more of this too. [0]
Why is it such a dramatic statement for Boris to claim that he no longer writes code?
Are people on HN still typing out functions by hand one character at a time?
It would be like a developer in 2020 claiming that he only writes assembly because compilers can’t be trusted. No one is taking that person seriously. If you chose a career in tech you made a decision to work in one of the fastest moving fields in human history. Now it’s time to get over it, learn the new tools and adapt.
I think that what technical people fail to understand is that a lot of the time, "compliance" is not the same as a binary compiles/does not compile. For a lot of rules/regulations, compliance means "making enough effort that legal is willing to back you up".
A system which will just randomly decide to give the legal team reasons to not back you up is:
* A system whose output will get brought up in lawsuits and make legal's job harder.
* A system that will make the dev team perpetually chase its tail while it oscillates between the several different valid interpretations of the rules.
Odd take. So if it identified 17 real gaps and helped fix them, the fact it was wrong about one gap, and the appropriate humans caught it and no harm was done, the whole thing is useless?
Not saying that is the situation, I don’t know. But if “one error is too many” is your point of view… do you think the humans in these orgs are 100% perfect 100% of the time?
>you take a spec and create tests, every little thing
A spec detailed enough and unambiguous enough to be translated into machine execution deterministically is called code.
Unlike a compiler, AI can build with a spec that is not detailed enough or unambiguous enough: It does so by filling in the gaps with educated guesses.
This is safe if and only if you take the time to later read the output, understand what its guesses were, and judge wether they were acceptable. No AI can do this for you because the truth lies in your original intentions, which it does not have access to.
The jury is out there on how reliable and time consuming this is vs writing the code yourself; it is not immediately obvious that is faster or requires a smaller cognitive load.
IME, regulatory compliance is something you are rarely able to test for in a nice little box or with well-known suite. So there's no easy "this complies" in many situations, no matter how many lawyers, compliance officers, and llm's you run it past.
If each step requires micro-steps iterating with an LLM with human review to prevent hallucinations creeping in.. at some point you might just be better off letting the human do the work.
Particularly as tokenmaxxing has ended and people are being charged more economic prices. If the pricing 5-10x the way Uber,etc did on the path to profitability.. even more so.
This is such an abstract principle that the principle itself cannot be refuted. The plan sounds fine on paper. "Just iterate bro". But it entirely depends on what rational agents you put into the system. Obviously, if I sub in a 5 year old child everywhere, this loop breaks. Humans and AI, sometimes one is better than the other at certain things, we're still learning.
The only way to test this is to test it out, in real life. Sometimes people see results, sometimes people don't. Note that yes, I am including the entire iteration process - even after iterating, people still don't see results with AI.
I have had both positive and negative experiences with AI, over multi-week projects. But apparently on hackernews, anything positive about AI is proof that AI is superhuman and taking over, and all follies about AI are lies by stupid humans who secretly have psychological dispositions to fear AI. Sometimes the AI genuinely isn't good enough. Are we not allowed to say that now? We might not know why, but it's just the truth.
The other solution is to formally analyze the entire space of possible actions the agent can take a priori. Then yes, you can definitively say whether or not the principle breaks or not. Can you, though? Can you give a formal specification for the space of possible actions for AI and show that your loop never breaks, or breaks less than humans, or any other sensible criteria? If not, then you can't just give an abstract principle and start making inferences from that.
Isn't that a net positive though? (not sure about the cost human and tech cost). I'm guessing that without using Mythos, those conversations would never have been had, and confidence in the compliance of the product would've been lower.
I love using AI tools as casinos. It's epic in helping to forge ideas and kickstart thought processes. You basically have the entirety of world knowledge at your fingertips to have a pint with.
> I'm guessing that without using Mythos, those conversations would never have been had, and confidence in the compliance of the product would've been lower.
The conversations had already been had and the product made compliant. Mythos just pulled new rules out of its ass and of course the product wasn't compliant with those. So they do a fire drill and find that to be the case at great expense.
Yeah you can frame it as "more checking is always better" if you wanted but that's just the same old "other people's resources are valueless" slight of hand we see on everything. It probably was mostly wasteful work.
> It's the expertise of engineers on the team that push it back on track.
But how are you so sure your colleagues are not more "expert" than you? Prior LLMs there was room for very good engineers and mediocre engineers to work together in 99% of the companies out there. With LLMs, only the "best" engineers will survive, because nobody needs mediocre engineers anymore.
This being HN, I imagine every engineer reading this thinks they are in top the 10-5% of their company/city/country, and therefore they think they are not "mediocre" engineers that can get affected by the introduction of LLMs. Statistically, they are probably wrong. So, it's all about ego. Chances are you are not a rockstar and LLMs will eventually take over your job.
As usual, the only winners here are corporations and executives. Most of us are the last monkeys in the chain, and so we'll get screwed.
The corporations and executives are already winning if you swallowed the concept of 'rockstar' engineer. Sure there are more and less experienced engineers, but even interns can and often do provide good input and spot mistakes made by seniors. The 'rockstar' engineer at most tech companies simply equates to the somewhat autistic guy with a brown nose who's working 15 hour days for a pat on the head from management (and making many mistakes in the process).
Even if we forget "rockstar", there are certainly different levels of engineers. More experience doesn't automatically mean better either. That is not to say experience doesn't matter. It matters quite a bit.
Sure , good interns can sometimes have good feedback or spot mistakes. But not consistently enough.
All of this to say that it's not just experience that makes one a better engineer.
Experience is one of the only objective signals we have, but you're right it's not the only one. I've seen plenty great junior engineers and interns, and plenty of incompetent staff/principal engineers.
> With LLMs, only the "best" engineers will survive, because nobody needs mediocre engineers anymore.
I don't think this is true.
A good engineer doesn't have infinite throughput. In my opinion the best engineers should be constantly bottlenecked because they solve difficult problems. They don't have time for grunt work. Every company needs less than perfect engineers, AI assisted or not.
> Wut? I pilot LLMs all day but there's no way in hell I'd agree to be at the helm of a finance product.
Dunno how much longer that is going to remain true for your specific employer - all the fintech companies I deal with personally have had some sort of AI account for their devs since last year.
Even places like jane street have employees posting blogs (one of which was on HN frontpage about 60m ago) saying they mostly direct agents.
How long do you think your specific employer is going to hold out?
Sorry if I was unclear. I don't work in finance. I do work with agents. I think expert engineers in finance who are guiding agents are adding a lot of value because of their knowledge of finance. Because I lack that knowledge of finance, even given access to agents, I would not accept a role guiding agents in a finance company because I wouldn't be able to guide the agents well and my/our output would be bad.
Unfortunately every software related industry is embracing LLM/Codegen. Your banks, fintechs, insurance. Everyone. Your concerns are the same I'm having, yet it's regularly dismissed or hand-waved away as "don't worry about it the delivery velocity/ROI is worth it"
It's not so much about velocity or quality, both of which LLM do (or will) provide.
The real question is about accountability and liability.
When a major data leak is going to happen, who will they sue or fire ? That is the value engineers provide. They understand, confirm, and take ownership.
This is what I'm wondering too. We've signed a confidentiality agreement with all the big players (as I'm sure all other companies have done), which is supposed to ensure our data is both segregated and not used for training. I don't trust these companies not to do just that; their business is in taking what we have and training their models.
This question has been easily answered by many companies.
You, the IC, the developer prompting the code extruder, are ultimately responsible for its outputted code and its behaviour.
You may feel pressured to push out thousands of lines of code a day. You may see those thousands of lines refactored several times over the lifespan of a merge request. You may be asked to do this continue this in the long term with all the mental fatigue that entails.
When it's too much for you to sustainably deal with and you turn to using LLMs to review the code, that will still, presumably, fall on you at the end of the day.
Ostensibly, due-diligence should not change. But people are lazy, just as they've always been around testing/QA/definition-of-done.
I'm not even certain that laziness gets them further along than it used to; I think it's that people have not had their overconfidence painfully corrected yet. Behaviors will re-align pretty fast when people realize that no, they're not going to get away with just pressing a button and saying everything is "good". That is happening right now.
> When a major data leak is going to happen, who will they sue or fire ? That is the value engineers provide. They understand, confirm, and take ownership.
This goes for serious incidents, disasters, outages and security breaches.
If there was an investigation and the answer was "a piece of software was vibe coded with AI" why would anyone trust the software vendor after that?
Are banks that concerned about velocity? Because moving fast and breaking things in the banking sector can get extremely expensive. It's also not a who-gives-a-shit industry like operating a taxi service or hosting images, but a very tightly regulated sector.
I might have been a bit broad with the brush. I can't speak for banks, but I can speak for the the fintech/money-movement space (e.g. Remitly, Wise, Revolut).
It's a race to get first-to-market for backend integrations/features. It's given rise to a culture of "move fast break things" where safety is only for some core features, but absolutely not for the constellation of other services we provide. Failure rates have increased almost a percentage point since Codegen/LLM adoption was mandated from up top.
You would think regulators would be on top of this, but our industry runs on all actors "self reporting" their outages. Most don't unless they can't hide it (>1h)
'Keeping up with regulations' may as well be a separate field from the core stuff. It has the same pressures as any other development effort. Managers will want the integration to the KYC service LLM'd as quickly as possible.
Reg PRs - for the ones with complex requirements what I am seeing is that time to initial PR is very short, and a ping-pong between the reviewer and developer begins, because in my cases (not all) the developer vibe-coded parts, and they didn't really understand the requirements deeply or their code, and it takes multiple iterations for them to fix it. You can argue this is a human problem but this is the net effect I'm seeing.
I am not sure but for complex cases it seems to me that the earlier sum of moderately long PR time + moderately long review time has been replaced by very short PR time + even longer review time. I am not sure if there's a net gain in these cases. Sometimes even if the code is functionally correct, it's verbose enough (e.g., too many intermediate functions) that I think they will impact future reviews.
> That first pillar is still there. Maybe the author isn't aware of the impact they have, but I know, with the evidence of reverted PRs, that when I step outside my area of deep knowledge I can no longer call BS on the agents. Our most capable agent, with access to the same kind of distributed systems the author talks about, is regularly wrong, frequently myopic, and just outright dumb constantly. It's the expertise of engineers on the team that push it back on track.
I'd posit there's another layer. You have domain knowledge, certainly. But more valuable still is the wisdom to find more.
Anthropic and OpenAI can stick financial regulations in the training data all they want, but the AI systems will never learn to anticipate the future, or reach out to clients, partners, or regulators in complicated situations.
A lot of companies are investing money on “ai factories” that are join to automate a lot of software development (that is, steer LLMs) on the basis of jira tickets (or linear/trello cards or whatever).
a year ago I would have agreed, but the gap is getting smaller all the time... these things can do 90% of the work, and how many people does a company really need for the remaining 10%? certainly not as many as they needed before
My career path is suprisingly similar to the author's. Weirdly enough, what he takes as the first pillar to fall is the one I see most undamaged currently.
LLMs routinely fail at our business specifics: Local tax regulations, particularities of the accounting process, specifics of our ledger implementations. They're great at refactoring, translating between languages, tracing bugs on existing code even, but there is always many things subtly wrong iterating and expanding our domain.
This might be because the companies I worked for happen to be tackling complex domains precisely for moat-building reasons. They stay in business explicitly because there's not a book out there you can read to build a clone, the knowhow stays inside.
Also, a fintech whose managers recommend speeding up design docs with AI sounds way too careless to be in the money handling business. It's way, way too easy to end up with millions incorrectly allocated, particularly if you deal with high volumes of small transactions. These bugs are always a bitch to deal with because correcting the logic is just step one, you then have to correct all the wrongly calculated data in immutable DBs, move around the red tape and client comms, and your fix is bound to become a gotcha that new features and observability have to take into account ("remember that there's a bump in the data in february 2 because we had incident X".)
>> LLMs routinely fail at our business specifics: Local tax regulations, particularities of the accounting process, specifics of our ledger implementations.
My company also deals with a lot of complex regulations and domain-specific system implementations, which AIs used to struggle with. We were able to solve the problem with well-organized claude.md/agents.md files. On top of that we also implemented supermemory.ai, so newly made decisions are always recalled by AI agents when starting new sessions.
It's odd to me how quickly the author devalues their own experience just because AI can do certain things well. There's a huge chasm between what AI can do when prompted by an expert software engineer vs a non-technical person. Sure the models and the tooling will get better, but it still needs to be driven by someone with an intuition for how software works and able to dig in when necessary to unpack and correct the hallucinations, misplaced assumptions, or straight up borked code that will come from the gap between what a human wants and what they can express in words.
I have no idea how things will play out, but so far I am not worried because the amount of software continues to increase, and AI only accelerates that trend. This will require the same mental modeling, first principles thinking, and relentless curiosity that already formed the foundation of the software engineer skillset.
> The company is now hiring again for a few roles and domain familiarity is not a strong differentiator anymore. We used to list "Software Engineer - Area". Now it's just "Software Engineer" and the team assignment comes after the offer is accepted.
> Of course, this is good for brilliant engineers that never had the chance to get deep into the domain and now have better chances at getting a job, but it's also sad to think that other brilliant engineers that spent their lives collecting domain knowledge are now competing on the same lane.
If the author's vision of the future is correct, then competent software engineers are safe. Domain knowledge can be learnt much quicker than how to apply good engineering principles.
Engineers whose main competitive advantage is domain knowledge are probably not that brilliant at engineering. They might still find employment in other areas of the industry where they accumulated domain knowledge.
And I'd still question it. The experience of just… knowing how a good architecture looks like without being able to really put it in words is what makes a good engineer to me. These people can pick up relevant regulations or industry terms and deliver value quickly enough.
How is "without being able to really put it in words" a mark of experience? Surely an engineer should be able to justify why an architecture should be arranged the way it is!
It's perfectly possible to put that sort of knowledge into words, but not in a condensed "recipe" that can be explained in a meeting, that will go into a single Hacker News comment, that will cover all cases, or that will satisfy LLM users looking for the easy way out.
Pretty much every area of knowledge is full of those. That's why people publish books, that's why people go to college or get PhDs, that's why people with experience gets hired.
This same complaint comes up on the topic of generic coding interviews, although shadowed behind the bigger complaints about simply disliking them. When people develop domain expertise they want to use that as a moat around their job. They want interviews to focus on stories about the things they’ve been exposed to on their past jobs, not test their abilities.
If you’ve been lucky enough to get jobs that expose you to the right things then you have a big advantage when the interviewers are looking for those specific things instead of your generic abilities or potential. It feels nice because you’re competing against a much smaller pool of people.
Unless you are not lucky enough to have been exposed to those specific domains yet. You can be a great engineer and even someone who learns quickly, but if you can’t point to the lines on your resume that match the job description then nothing else matters when the interviewers are playing experience bingo with your resume.
The move to generic coding interviews changed that. It was no longer enough to say that you had exposure to a topic at a past job. You had to show your coding skills, too. It wasn’t enough to ride on your credentials any more, which was highly frustrating to the well-credentialed.
However if you didn’t have the exact experience then the world of job opportunities becomes much larger. The people I know who like coding interviews the most (other than the rare competitive programming enjoyer) are people who are highly talented but came from less credentialed backgrounds: They don’t have an amazing university on their resume, they had to work at some company you’ve never heard of in their small town, but they are great at programming and just want a chance to prove that so they can move up to better companies. They’re never going to be picked by a company that’s looking for exact domain experience, but as companies open up job listings to people without that exact experience they have a chance to prove themselves.
The other people who relied on that domain experience to lock other candidates out of the hiring process don’t like it at all, though.
> Domain knowledge can be learnt much quicker than how to apply good engineering principles.
Partially disagree. Broad-strokes domain knowledge can be learned quickly, but honing that domain knowledge with nuance and consideration for complexity, particularly for organisations that are unique and are not often thought of as 'software development houses', can take years if not decades.
Yet I still see (and code review) 'professional' software developers that don't follow good software engineering practice.
> Engineers whose main competitive advantage is domain knowledge are probably not that brilliant at engineering.
The same is also true of engineers without domain knowledge, certainly in my experience. Maybe we just got unlucky...
>>"Domain knowledge can be learnt much quicker than how to apply good engineering principles."
I'm not sure that's universally true. Good software engineers who are arrogant about easily acquired domain knowledge have been the downfall of many an ERP system.
There's SO much IT that's literally all about putting business rules into the system.
> Good software engineers who are arrogant about easily acquired domain knowledge
This is a problem of arrogance, not of domain expertise.
Having worked in a few different industries, I'd wager that for the vast majority of them, a competent person can probably learn 80% of the required domain knowledge in under 6 months. For the latter 20%, as long as the person is not arrogant, they will seek help from colleagues who have been around for longer.
On the other hand, solid engineering principles will take 10-15 years of actually experimenting and learning in practice what makes a system resilient and durable.
>Domain knowledge can be learnt much quicker than how to apply good engineering principles.
Can it? I'm of the opposite opinion. You can improve methodology much faster than gaining specialized knowledge.
You can enforce and fast-track the former because it's a matter of approach.
The latter is subject to the person's learning affinity, capacity and availability at the time and can't be forced beyond reasonable facilitation. It also builds on itself, with the corollary that there's a much steeper curve early on.
The development and acquisition of valuable domain knowledge is a hard, risky, expensive and slow process. Because the valuable domain knowledge isn't yesterday's, it's today's and tomorrow's. In fields where domain knowledge matters, it is also deeply intertwined with engineering - you won't task Jeff Dean to develop Unreal Engine from scratch.
With that said, there are still many SWE principles that are not fully internalized or adequately practiced by domain knowledge experts, and that will remain the case as much as domain knowledge remains valuable, because software engineering is yet but another domain.
That's a fair question. I suspect highly specialised industries are harder (rocket and space, defense, nuclear, etc), but for things like finance (most of it, anyway) and retail, which IME make the bulk of the tech jobs out there, it's certainly nothing out of this world.
That's an extraordinarily rosy view of the future.
I'm old enough to remember the dot-com crash, specifically the years afterwards. In 2002-2003, the unemployment rate of software engineers was something like 40%. In fact, the only reason it wasn't higher was because of the number of people who had permanently left the field to become plumbers (or other trades).
I think this is going to be worse. In the dot-com crash, what really happened is that non-businesses got funded and it basically the capital markets ceased to function to a large degree. That's not what's happening now. Yes, huge amounts of money are going into AI companies but the change is more structural.
Other industries have gone through this. In the 1980s a bunch of industries were intentionally destroyed or offshored in areas that have never recovered. This has continuing social, economic and political impacts. I think people are being naive here thinking this can't or won't happen in tech.
There's quite a large cushion for software salaries to decline before permanent structural unemployment were to set in.
It's not really feasible for "normal" businesses to hire developers at current salaries.
Tech companies will probably shrink in headcount, but all the non-tech kind of businesses can increase developer headcount.
Current Tech salaries are far above other fields while requiring (used to) significantly less training or time investment to get into.
Phase 1 is more likely that software comp will normalize with other professions, and more hiring will happen at the fringes rather than being concentrated in a few big companies.
There will be a handful of people who make stratospheric compensation, a bit like we have now.
Everyone else will have extreme job uncertainty, getting laid off multiple times, losing compensation as a result (ie equity vesting) with compensation that at first stagnates and then starts to slowly decline in real terms.
A lot of the big tech companies will likely spend less effort on non-core activities. Think of all the things Google does. Anything that's purely internal will be gutted staffing-wise because it's the safest testbed for shifting the engineer-AI balance on teams before rolling it out further.
If you listen to non-tech people now you hear tales of applying for hundreds of jobs and getting no response. That will become more normal. What's worse is that AI seems to be to blame here. Companies all use the same AI ATS systems and I've seen allegations that candidate scoring gets cached for upwards of a year. So if the system happens to give you a bad score, literally nobody will see your application because you'll get filtered out before any human sees you.
I was watching a VC give a talk from some conference in France and the general sentiment is that no companies are being funded with teams greater than 5. Why? AI. So don't think you can startup your way out of this slump unless you're somebody who has the connections and CV to get funded anyway, in which case you might well have some of those stratospheric options anyway, at least for now.
> I spent 10 years (even more when you account for non-profession experience) getting good at things that are becoming less and less valuable.
This is just how it is, and has always been in this industry. And it takes about 10 years to realize it.
When I started my career in software, businesses were still writing new code in COBOL. 10 years later those skills were pretty much useless, except for dwindling maintenance roles.
Then there was the client/server era. Then the web era. Then mobile. Then cloud, etc.
All the same functionality, written and re-written time and time again, using the latest popular stacks and methodologies.
I hope to be retiring in a few years and pretty much everything I have learned over nearly 40 years is no longer applicable or is at best losing relevancy to the way sofware is built today. And that's how it's always been.
I’ve been using Claude Code with Opus 4.7; it’s not that the code it produces is wrong, it simply tends to write too much of it. In my opinion it’s still worth thinking about a particular feature and finding the best way to fit it into your code because Claude will often just pick a layer of the stack (maybe presentation), and jam it in there. A couple weeks later you need this data somewhere else and Claude can’t reuse the code (maybe in the service layer) so it kind of “ports” it over. Unless a person is paying attention we now have the double the amount of code and duplicate logic. I don’t see AI tools like Claude getting better at this anytime soon.
Where I work there’s already pressure to use Opus 4.7 less to save money, someone mentioned using a smaller model for “simple bug fixes”. This might work sometimes but how often do we really know it’s a simple bug fixe ahead of time? I suspect as costs go up we’ll see interest in using these tools to write “all the code” go down. As people migrate to cheaper and less effective models I suspect we’ll see the pressure to skip reviewing that code dissipate as well.
We’ll see where we land, maybe it won’t as dramatically different as the author of this post fears.
> Maybe I should consider transforming my woodworking hobby into a profession...
Whatever your feelings on the future of the industry are, it's hard to imagine you'll find more professional success in artisan woodworking than artisan software.
Custom furniture/cabinetry is already a pretty tough market, and woodworking is such a common programmer hobby that if a significant chunk of us decided to make a go of it the market would get heavily oversupplied pretty fast :).
I’ve had people tell me I should try selling some of the furniture I make and my response is always that I made the mistake of turning a hobby into a career once, I don’t intend to make that mistake again, and at least software still pays pretty well.
I have a historic house with a hand carved/ uniquely shaped door. The jamb rotted and we paid a woodworker $4k to create a replacement. The door itself would easily cost $25k to replace. So, move to a major historic area with hand carved doors and you could make some decent money.
You assume there are enough of these jobs to go around and that you can just show up and do some extremely intricate work. Repairing historic doors and more elaborate woodworking isn’t easy to learn as the knowledge mostly doesn’t exist online anywhere, I also own a historic house and often ask the top tier LLMs for details e.g. about my staircase, they always give wrong answers as this knowledge is simply too exotic and not in their training set. And no one online talks about these things, 99 % of woodworking videos on YouTube are focus on beginners, you can’t replace a professional education watching videos and reading books. That will protect woodworkers with these skills of course but it’s wrong to assume you can just break into this market and be successful, most devs with woodworking hobbies are really shit at their craft and struggle to create even a regular elaborate cabinet, no way they will be able to compete with good craftsmen for these few lucrative projects.
I work with a guy who does decking (gardens, caravans, etc) and builds sheds, fences, things like that and he does very well indeed (he's also incredibly good at it to be fair)
look at layoffs.fyi. chances are he will be laid off pretty soon. and if not tomorrow, give it couple extra years until AI gets even better. it is one-way road, down the hill.
not woodworking. farming. get a pot of land and grow your own food. do not participate in economy at all. that's the only survival.
My comment was about the fact that even if you're laid off, you're more likely to find success in artisanal software than artisanal woodworking. That statement is not an assertion that you're guaranteed success, just that it's more likely to sustain yourself than woodworking is.
Layoffs also don't really tell you anything. Is it actually LLMs that are causing layoffs or is it deteroriating economic conditions and uncertainty amidst war, oil shocks, etc.? Is it junior employees being laid off, or seniors? If it's the former, someone with 10+ years of professional experience might not have reason to be concerned. I happen to believe that, LLMs or not, the software development field already had far too many jobs, employing a large number of clueless people who contributed somewhere between zero and negative value to their organizations, and that it was overdue for a correction anyways.
You are not allowed to have land without participating in the economy. The government forced you to acquire land by buying it, and to pay taxes in dollars.
> Whatever your feelings on the future of the industry are, it's hard to imagine you'll find more professional success in artisan woodworking than artisan software.
A small percentage of the market, maybe a fraction of a percent, are still willing to pay for hand-built goods - bonus if it's thoroughly modern but retro (steam-punk keyboards, maybe).
Exactly zero percent of the market is willing to pay for hand-built software.
We are less than a year into good-enough coding agents, and as of right now there is not a single job opening I see that offers a salary for non-AI output.
It is fairly obvious that the majority of people who buy software (>99%) don't really care how it's built. They care a lot about the outcome of using it, they care a little bit about whether there are bugs or not, and they care about the cost a lot, but beyond that nothing seems to matter to the purchaser. Even obvious things like whether or not there are tests, documentation, SLAs for fixes, or backwards compatibility between versions don't really seem to matter much.
That doesn't mean you couldn't carve out a niche providing hand built software to people it does matter to, because the software industry is large, but saying 'zero percent of the market isn't willing to pay for it' isn't really wrong. It's just a rounding error that does care.
(One massive caveat though ... the argument assumes that 'hand built' means 'higher quality than AI-assisted', and that's probably not true for >99% of developers.)
> Exactly zero percent of the market is willing to pay for hand-built software.
This is a provably false statement, given that eg. Handmade Hero exists and sold a bunch of pre-orders despite never coming close to completion, and spawned an entire community that prides themselves on handmade software. There are also content creators like Tsoding who make a living by having people watch them do handmade coding for the love of the craft.
Some non-zero percentage of people will also always be willing to pay a premium for superior-quality software. The author's thesis isn't that LLMs can produce S-grade software but that 'nobody cares' about quality and that C-grade software is good enough. While it's true that software quality isn't greatly valued at scale, I think the minority who care is larger than the minority who care about premium woodworking goods, particularly because as an artisan software developer you more or less have access to the global market of every single person who cares, while as an artisan woodworker you mostly only have access to the market of people in your town who care.
This also overlooks that LLMs are politically divisive and there are movements to boycott them and shame people for using them. There's a niche for organic, free-range, vegan, etc. products at the supermarket for conscientious objectors, there will undoubtedly be such a niche for software. All the more so if LLMs reach a point where they actually are putting everyone out of a job, they will get much more divisive. There was already an assassination attempt against Altman and his promises to destroy everyone's livelihood haven't even come to fruition.
Only if the quality is bad. And users normally can only judge this when something is not working. So maybe only badly written/tested software will get labeled ai slop.
I’m excited about the genAI future. I’m a software engineer interested in product, user experience, architecture, and entrepreneurship. After 4 years in the industry, mostly within fintech, I have gotten tired of slow organizations, company politics, nontechnical managers doing the decisions etc.
I’ve saved up a couple of months of salary, have a couple of bootstrap ideas that I believe are within reach for me equipped with a coding agent to build. Hosting can be done almost for free. What used to take entire teams and hence millions of dollars to build can now be done a lot cheaper. If I’m lucky one of those ideas can pay my bills soon. If not I’ll go back to consulting for a couple of months.
I sympathise with the author being in the same boat, largely.
I just want to emphasise a point... Calculators give 100% correct answers and yet we still hire accountants; for the simple fact that we don't want all to be accountants.
People will hire software engineers for the simple fact that they do not want to be software engineers.
With todays LLMs, yes. But if they can ever reach a level of a contractor in a reliable way and companies offering them willing to take responsibility (because confidence is high and rest is insured), then one can just hire a cheap AI agent to fulfill a contract - design, implement, deploy, run and maintain your service/website like the engineer before.
Calculators are not a replacement for accountants, online accounting services are in many cases. Which again can be run by an AI if they reach that level of reliability.
In the US, all you need to work in tax prep is a high school diploma and most individuals are not worth the cost of an audit.
I wouldn't say it's particularly brave, in fact LLMs are probably better at identifying mistakes than most tax payers. The % of Americans using a CPA to file taxes is fairly small.
There's a certain irony in masters of automation lamenting that their roles are being automated. I wonder whether the jobs their efforts eroded in the past ever got the same thoughts...
Programming, logic, etc are skills and toolkits. The optimal state of society is everybody being able to apply them, not just the enlightened compsci caste. There was a time in the past where scribes were paid nice cash for their efforts, too.
I guess the lesson to learn here is treating a toolkit as an identity and job for life. By virturee of the essence of the job itself - if the tool gets cheaper and more widespread, it's aactually success, not betrayal.
You say that the optimal state of society is for everyone to apply programming and logic etc. but the obvious final result of these developments is that no one will.
Maybe the artform will be lost, but surely humanity will inherently be more 'logical' and systems driven afterwards?
Maybe using writing as an analogy is flawed, but most of humanity having 'writing' as a core skill did enable many other things, even if oral storytelling cultures suffered at its hand.
At its core, tech is all about breaking through inefficiencies and barriers. Does it matter if people can't code python if people demand government systems be frictionless in the year 2500?
Ride the wave. You rode it when websites/webapps were the wave. I came into software industry before internet, kept changing my horse. You are never too old to learn new tricks. The new wave create new kind of work and workers. Be one of them. Ride the beast, master the tools. It's the same game again.
This feels fake/engineered but regardless of that also redundant.
It's the exact same story that we've heard countless times by now. Hosted on a blog with just a single post. Named in a way that suggests that said blog was created for this very single post.
What is there to learn from this other than LLMs seem to be bad for some people's psyches and that AI companies need these very stories to not get their funding shut down?
There are lots of positives that have resulted from using AI in software engineering. (1) No more long repetitive text editing sessions. I.e. changing namespaces or replacing deprecated APIs with the "correct" ones. AI will make nearly perfect text modifications with ease. (2) No more bike-shedding code reviewers nitpicking over every tiny coding decision. I.e, "you should use std::format instead of std::stringstream". AI will match the existing set of nitpicks so you don't have to. (3) Average Joe's and Jane's can craft applications by just talking to the computer. This might inject a freshness to the current state of software. Currently, we are all forced to use the same bloated applications like Word, Excel, Jira, and Photoshop. We are currently forced to deal with the same set of monopolistic SW companies. Now average folk can solve problems and avoid dealing with Microsoft for a spreadsheet program.
To me looks like, if we're not collectively careful, civilisation will soon be on a path to an evolutionary dead-end.
Anything that can replace a deeply experienced s/ware engineer can replace anyone in the employment stack, meaning that only the owners of capital will be left, and they too will soon fade as the economy falls off a cliff and money has no value, because the only value that money has is the value of a human backing that, with thought, with ideas, with human output.
Whether you like it or not, "Economic output" is just a different phrase for "Human output that is valuable". When all human output is valued at the fractions of a penny per month of work, there is no future.
That sounds more like a problem of close minded narrow focus on economic output instead of culture, virtue and spiritual traditions.
AI is fundamentally an equivalent to slave economy. Cheap, plentiful workforce. This time ethically neutral. You either get Greece or Rome. I’d prefer Greece but it will probably be Rome.
From the past we can predict the future.
> That sounds more like a problem of close minded narrow focus on economic output instead of culture, virtue and spiritual traditions.
I’m starting to be more sensitive to the argument that without god, people are unable to have a strong moral foundation. Not for the people expressing creativity in how they fuck, but as a check on those in power.
>i’m starting to be more sensitive to the argument that without god, people are unable to have a strong moral foundation. Not for the people expressing creativity in how they fuck, but as a check on those in power.
If this were true, why did the medieval peasant have less rights and autonomy in society than we do now?
There has been said so much about it in the past and I always believed it to be true, as an atheist.
I like to think that one of the symptoms is politics becoming really absolutist, idealistic and cultish. You do not debate followers of a different religion. But many topics really becoming kind of a mini religions.
I don’t know for sure though, there are arguments against it too and other factors.
I think substantial amount of people really need some kind of subjective spiritual experience to their life and maybe ignoring that need breeds some maladaptive tendencies
The major blocker for manual labor automation in that fashion is cheap energy. China is ahead of the pack with the States' weight behind aggressive expansion of solar tech, and still can't do that.
Nope, just a specific kind. Those who developed and cultivated only a very specific skill set at the expense of all others.
I used to think being a generalist, and having persued technical roles with a people facing element was to my detriment, but it’s turned out to be the best decision I ever made.
I've consulted with some big companies on AI strategy. I tell them there are two approaches to AI.
1) Train AI to replace human work. This gives you 50% quality for 10% cost.
2) Train AI to assist human workers. This gives you 200% quality for 110% cost.
Most companies will go with option 1, and it's a race to the bottom. Eventually, someone will go with option 2 and gather up all of the pieces and take over the market.
> All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.
I think the author downplays how much of that knowledge is used on knowing what to zoom in on, what to prompt, or what to look for.
I’m genuinely curious about what happens to pillar 3. LLMs are quite bad at this still, to their own detriment. I disagree with authors take that low grade code bases can be reasonably acceptable now as long as the agents work in it.
If relying on agentic tools to develop a codebase, The lack of architectural coherence causes code rot at an accelerated pace, in the usual tech debt like curve. There’s some prompting around this, but once established, it's really hard to undo. Your 90% test coverage will bully LLMs back into putting things in the wrong shape. Workarounds will proliferate. Left to their own devices, LLMs will keep bolting on, rather than refactoring. This will further entrench the status quo of decoherent architecture until change is impossible.
There’s one force where software engineering is being automated by LLMs, but the other force is that there isn’t really much more software that needs to be built. Even before AI coding became big, back in 2021, we were already in late stage SaaS territory where each new idea was an increasingly minor variation of an existing idea. There were no new GitHubs, Herokus, Stripes, Salesforces, Instagrams, Reddits, just variations of those for more specialized markets.
It’s really unfortunate that AI hasn’t raised the ceiling on the space of possibilities as much as it’s raised the floor on how much can be automated, we’re all getting squeezed in the space between.
> there isn’t really much more software that needs to be built
Yup. Most everything we need was already built in the 1970s. Programmers have been kept busy because we've kept introducing incompatibilities into the mix, like DOS programs needing to be rewritten for Windows, and then the web, and then mobile.
And now they're being rewritten for AI platforms. It may be giving the squeeze due to being the first platform that will also help with the rewrite effort, but it is also the thing that kept the industry going. As you point out, there wasn't any work left to do until AI showed up.
> I have no domain expertise that another Sr. engineer steering an LLM cannot match. All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.
Don’t sell yourself short! Taste is not promptable, I suspect good taste is AGI-complete.
Especially in domains like fintech, there is a lot of accumulated wisdom, and that is what you’ll be handsomely paid for (for at least the next couple years :/ )
For example, architectural patterns, when you need bitemporality, immutable logs, CQRS, all these good patterns that can only be learned by owning years of system architecture - none of these feedback loops are in the training set.
And from a product design side, agents will just miss key concepts and you need a few words to prompt a fix - but that might represent a massive tree search optimization, or the agent on many cases would just fail to identify the requirement. These small steers feel small, but by evaporation our work has distilled down to just the extremely high value insights.
METR task time is still at weeks, doubling every 7 months; it’s years (assuming we keep riding this crazy exponential) until you hit multi-year tasks. I don’t see wisdom / Métis being solved in 2027.
All this said - I think it’s important to extrapolate forwards, if the trend continues, this will may all be true in 3-5 years. Now is the time to pre-register what metrics would make you worried, so that you can define your red lines. There will be a rapid consolidation of power and wealth if these tools continue on their existing growth trajectory.
Same boat here, just a couple years more experience.
Current LLMs are still kind of shit at actually programming so many jobs do still care to have professional programmers. However, I think it's evident that if things stand where they are, employers will care to have far fewer of them, at least of highly paid highly experienced programmers. If this is the state we're in with LLM adoption when they can't help but create the same helper functions 15 times, god knows we're screwed.
So we should probably work on clearing out our debts and figuring out what else we might want to do with our time, I reckon.
I'm still going to try to do a good job. I'm still trying to learn the best effective ways to apply current LLMs (Right now I still prefer to mostly write code myself but have been using LLMs to bang code into shape via iterative code review; this is a way to exploit LLMs to make better code, especially applicable if your velocity was already good.)
Yeah the writing is on the wall. Not just for knowledge work, but for jobs in general, as I've been saying in other comments. The era of wage labour, and this dominant economic system, is coming to an end. There's no way it can coexist with AI, but it also needs to continuously push for better AI, which means there's no stopping it. The only thing to do really is brace for the disruption - which will likely be pretty rough - and hope governments play their role properly to ease the transition.
I agree but I think it would take awhile. Some of us here seem to believe 2026-2027 is the end of programming jobs. At least that's Amodei seemed to be saying but then changed his mind later on?
I think that the domain knowledge still matters: if for nothing else, then at least it can make the communication both with savvy AI tools and savvy humans more effective compared to "outsiders": acquired vocabulary, truly grokked concepts in the field of target expertise etc… -- that all seem like a huge competitive advantage over folks having to learn all that "on the go", constantly struggling to pick the right nomenclature or using wrong or vague terms. It's mostly that domain knowledge what makes experts understand problems faster or at all, even.
Agents may have made 80% of your experience go to $0, but the other 20% is exponentially more valuable now. This outweighs your other losses.
The ability to orchestrate intelligence is a magnificent power that few have, and while barriers to entry will be eroded, it will take time and they won't be eroded fully. This is your edge.
He says that taste doesn't matter and it hasn't in the past. However, in an era of "extruded code product" (by analogy to https://tvtropes.org/pmwiki/pmwiki.php/Main/ExtrudedBookProd... ) automatically generated by the truckload at negligible cost, the differentiator for software developers will necessarily be the ability to create a product that doesn't reek of extruded code product, i.e. the things like quality that he labels taste.
(Whether any one reading this, myself included, survives in the industry long enough to reach the other side of that transition is a different question.)
[EDIT] The reason I use books as an example is that 4.2 million books were published in 2025 (https://ideas.bkconnection.com/10-awful-truths-about-publish...); 3.5m self published (with most likely LLM assisted or wholly generated) and the remainder traditionally published. (That's ~9,600 new self-published books a day.) Who actually still sells enough copies to make money in this paradigm and why offers hints as to where the software industry is likely headed.
Even if the model can replace a domain expert on the software side, you still need a human who can decide if the technical solution actually meets the business needs and that would require a human with domain expertise.
Many people share this sentiment, many people don't.
Who you belong to depend on at least two things: A) How knowledgable is the AI on what you are working on, B) How well do you wield these new tools to work better than before? (Better here can mean many different things).
The saying goes... first we shape our tools, then they shape us
We are now manufacturing intelligence (why it's artificial) and it shall be interesting to see how it shapes us individually and as a whole.
While marching on May Day, the woman next to me made the comment that Ai will force every human and humanity to reflect on what it means to be human, all of us at the same time over a short time period. What makes a human valuable beyond their work? Why do we go to other people when their expertise is at everyone's fingertip? What value are we giving, trading, or sharing in the time we have in this world?
Interesting Tony, you seen to have been working for the AI since long time. We others are catching up but eventually all of us will be working for it, as directly or indirectly we are already doing. The "robot" was figurative cause we are no different from a machine, but that is too much for humans to comprehend.
I work for myself and the world, not for Ai. It's a tool without identity, but a damn good one that multiplies my capabilities.
I anticipate the first bifurcation to be wheat from chaff. Ai is going to do better at a job than say half the people, those who don't care about the effort they put in or the quality of their output. These people will have to come to terms with their mediocrity or blandness.
I'm still unsure what the good ideas are for when we reach a world without labor scarcity.
<I'm still unsure what the good ideas are for when we reach a world without labor scarcity.
It should be real creativity the final goal to this life optimization. For now many of us need to fight for survival, for food, shelter... so is a bit difficult to be purely creative. But is also true that given all the benefits (taking off the survival instinct) makes creativity obsolete.
>I work for myself and the world, not for Ai.
Yourself really? Start by defining "I", "work" or "yourself"... then we may proceed to the next LOL
I still struggle to accept this when my colleagues are producing implementations with AI assistance that are consistently broken and don't do what they think they do. As yet I can't square this circle, no one is better at their job than they were before.
I feel that I am faster and better, sure, but trusting self perception would be an absurd thing to do.
It's not just our careers. In the hunks versus nerds wars, it is now clear who has won. The nerds have made themselves obsolete and put the continued evolution of homo sapiens to an abrupt halt.
> I still have one pillar standing, though: code quality and software architecture - what's now being reduced to being called "taste".
Genuine question: what exactly is "quality"?
It's something I've been trying to understand for a very long time. It seems like it's entirely contextual, and it has both subjective and objective facets (the latter only for quantifiable things, and still entirely contextual).
Ah the age old question: what makes something good? I think you’re already describing it well at a high level; context matters, and there are multiple axes to consider. But that’s extremely vague and doesn’t help you identify or measure quality, so it might be worth listing as many specific axes as you can.
Maybe ask the same question about other things. What makes a good guitar? What makes a good chair? What makes a good airplane? What makes a good book? What makes a good song? What makes good art? Each of these has a long list of very specific goals and concerns. And to help define the boundaries, also ask what makes something bad, and what makes something mediocre.
Code quality starts with functionality. Does it perform the stated requirements? Does it have testing in place to catch breaking changes in functional requirements? That’s the basic stuff that probably isn’t part of “taste”. A lot of code quality goals center around how code changes over time, and beliefs about designing to avoid functional breakage.
For example you can ask things like does the code use minimal dependencies? Is the code organized into clean classes/modules/functions that each have a single clear role? Is the API easy to read, understand, and use? Is the API hard to misuse accidentally? Is all the code easy to read? Is there documentation, and is the documentation useful, and more than a list of contents? Is the code self-documenting? Is the code efficient, both in how it executes, and in its use of code itself? Is the code designed so that it won’t fail when someone runs it with different sized types, or a different compiler or execution environment, or on a different architecture? Is the code surprisingly elegant and fun to use?
Those are just the beginning. There are of course more layers of application-specific and environment-specific and audience-specific qualities. The good news is that quality depends on your own goals, you can decide which aspects of taste matter to you, and ignore the ones that don’t. It’s fine if your taste & goals change over time.
If you're using the product, and you want to question or debug what's going on, you can:
* Jump directly to the single relevant part of the frontend responsible
* Likewise with the backend. The layout and naming of the code should scream its purpose.
* Once you're looking at the code, it should be trivial to run it, right now, instantly, in unit test, or cli. You shouldn't need to stand up a database to see whether your code rounds taxes the expected way.
The system contains its own checkability. You can, for instance, just sum up all the incoming money and outgoing money and see if your balance is correct. (It's not enough to have good tests today, if you're working on data that was incorrectly calculated and stored yesterday)
Good question indeed, I think quality matters less these days because it's trivial for an LLM to increase code quality.
Quality is usually observed from a human perspective. But in my experience, codebases that humans would judge as "low quality" are actually fine for LLMs. They don't have as much trouble as we do with spaghetti code. They don't have problems with readability or obscure syntax, it's all perfectly fine for them. They don't care about indentation either.
Also it's really easy to increase the quality of the code base. You can just prompt to add unit test coverage and it will. You can prompt the LLM to handle edge cases better and it will (you don't even have to specify which, it helps, but it's optional). If you want to have better separation of concerns, just ask the LLM to have more separation of concerns and you'll have it. Documentation lacking? Just one prompt away. More robust build pipeline? You get the idea.
> All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.
Is it really though? Access to information is quicker, but you still need to know what ‘good’ looks like to leverage it effectively. I can prompt my way to a medical diagnosis, but I’d still want to run it by a doctor.
I’ve found it extremely hard to get LLMs to exit the basin of your current knowledge.
One of my tests for new models is to ask about a concept I already know the mathematical model for, but as if I don’t. So far, they all answer the same way:
1. Convoluted explanations about how it kinda-sorta is common terms.
2. If you follow up with the correct mathematical term, it immediately claims that’s correct and the right way to model it.
3. If you ask it why it didn’t use that term for your question, the LLM gives some version of explaining that it tried to match your language.
I have no choice but to assume the model behaves similarly other times — and that I am largely trapped in a basin of my own ignorance, when using LLMs.
Realistically, what should we have done instead? Not invent LLMs? What happens when a couple thousand people invent the next disruptive technology and even more of the population loses their jobs?
It seems like new tech is something most of us have to lie down and accept as the new reality each time it's invented, barring full-scale rioting. Much as with the Cold War.
“even though you're delivering code at a good pace, you're taking too long to deliver those Design Docs. Are you using AI? You should use more AI.”
This here is the crux of it I think… it’s often promoted that AI will give us the time to do the “real” engineering work of designing systems and really serving the user, but in practice all I’ve seen is further attempts at optimizing every last process with AI - just homogenizing every product and feature into slop.
It feels like every leader has been to some talking points boot camp where they’re incentivized to apply pressure to every part of their process - sort of a desperate attempt to justify the costs they’re incurring. I think we will look back at this and see how obviously short sighted it was.
AI is beat thought of as an exoskeleton, you'll be at a huge advantage if you learn how to use it properly, and you will, unfortunately fall behind if you don't. I still think we're going to need people who can reason about code, and the amount of code to reason about is exploding in volume. Think of it as doctors having access to better drugs and techniques - they can can cure more illness, but the bar and expectation of what they can do will just raise. And doctors are still well paid, because what they do is important and needs doing well.
Engineering hasn't gone away, you're now just directing things at a higher level. You are now a architect & manager (but you're managing agents not people).
Who sometimes has to deep dive & mentor a agent on solving the right problem.
>Agents do a really bad job at keeping codebases organized.
If you do a disciplined way of development with agents by keeping all Documentation in markdown format, repo structure, Decision records and architecture, they do absolutely organized. Every new module should be documented and the editor configuration and coding patterns can be given as reference. this worked well for me. and it make enhancements, extensions development without any big troubles.
Software engineers with low self-esteem who built their entire identity as mechanical cognitive workers are having an identity crisis and spreading FUD.
Currently, LLMs are nothing more than amplification tools that require significant steering. If you think your job is mainly to take input from POs or managers, translate it into if/else statements and loops, and review PRs, then you never really understood your role. Software engineering—for those who went to university and studied it—is fundamentally about complexity management and cognitive automation. People in the field, or at least those with some math background who studied software engineering properly, understand that it's all about managing complexity; current tools are nowhere near replacing a software engineer. What they call "taste" is imagination, creativity, embodiment, a more intuitive understanding of context, and yes, superior intelligence compared to current AI. However, AI and LLMs are excellent at mechanical work and mimicking human intelligence, so use them for what they are, and stop whining.
Going forward, the world is ever-growing in complexity, and automation will become widespread everywhere. LLMs just unlocked another level. So basically, cognitive work will be automated—perhaps up to 90%—until the next breakthrough (if ever). You can sit and cry, or you can learn the tools and help shape the future.
Software engineers can automate the entire economy now, including the executives, yet they just sit there whining and crying. This is a self-esteem, confidence, and identity issue more than anything else.
Chinese Gen Zers are starting companies before graduating, people are generating music and starting their own studios, others are improving models and building harnesses, and the rest are on a mission to automate the entire knowledge economy—from healthcare to governance.
Regarding your employer's bank account: if that is all you were doing before, then that is all you will be doing after. You are just complaining about capitalism now. The irony, is that the means of production is now in the hands of millions. Those who are crying are those who paid their mortgages with for loops..well, I think they will continue doing so, with less hubris that's all. LLMs are nowhere near replacing full engineer.
I used to be in the "AI will soon do all your thinking for you" camp, but I was overlooking a scenario: sometimes the gap between what you understand and what you're trying to achieve is so wide that no prompt can bridge it. Simply asking "what's the right question to ask?" doesn't feel enough, no matter how advanced LLMs become.
I read all the posts in this thread - but no one has a good idea to avoid software developer obsolescence. My guess is this profession has 5 more years. It was a good run while it lasted.
All the other white collar workers are in the same boat. A pillar of the economy is going to be destroyed with no obvious replacement in sight.
5 more years? That’s quite pessimistic, given how much evidence we have that LLM coding has as much of a long tail problem as any other tech we’ve created.
Don't need much training data for bank/insurance/retail analyst work - it's just basic reasoning and data retrieval. If AI could crack the programming nut - one of the most intellectually challenging professions - it can handle the rest with ease. The only human role will be high level monitoring - and even this will be largely automated so fewer will be required.
Sorry to reply to myself but I just recalled the recent Apple ad that ran last year about a corporate goofball who got his Apple device to fire off well crafted email slop to his manager who looked surprised/impressed. That trick will only last for a short time. The joke is that people like him and his manager will be the first to be fired.
"Maybe I should consider transforming my woodworking hobby into a profession."
As an AI optimist, I think all forced labor should eventually be done by AI. People can then spend their time pursuing their own hobbies. Just as many people still play Go after AlphaGo appeared, because they genuinely love the game.
In the future, coding may return to being an art form. People will no longer focus on utility alone, but instead on the enjoyment of the process of writing code itself.
> As an AI optimist, I think all forced labor should eventually be done by AI. People can then spend their time pursuing their own hobbies. Just as many people still play Go after AlphaGo appeared, because they genuinely love the game.
And what sort of economic system do you imagine will be in place to support billions of people being able to just play Go all day long? How do you imagine the large capitalistic global powers transitioning into that state?
I think that huge deflation will follow for everything except land value.
If automation makes producing food so cheap that it is almost free than it is ridiculously easy to acquire it. Similarly automated construction.
The way I see it the economy will point towards outer space. That’s where most jobs and flow of economy will be.
However most people will have 10x times uplift in purchasing power compared to today so their relative poverty will be ridiculous for us to call it the poverty but they will still think they are poor and troubled.
Generally I don’t think it will be utopia for the people living in that moment but if you look from medieval times at today it looks like utopia for serfs from the past. You however wouldn’t call it an utopia because your standards grew as fast as your purchasing power.
I think that rich and poor will be separated by accessibility to anti age treatment and other bodily improvements.
The tragedy of the poors in the future will be living measly 80 year old life like a today millionaire and that will be considered lower class. Those people with wrinkles we don’t want to look at because of uncomfortable pangs of guilt.
So you believe that your work will be done by AI and you will enjoy life more? This is not a loaded question, just trying to understand what your future ideal day / week would look like as an "ai optimist".
That’s just not economically viable. Even if it becomes viable after some singularity event the path there will be 1000 the upheaval seen during the wipe out of manufacturing and mining
What work remains valuable when implementation becomes cheap? How about moving closer to ownership?
I think that in a product-centric or mission-centric perspective, effective automation is good, because it frees you up to do other important things. E.g., in gardening, time spent weeding, is time not spent surviving slug armageddon.
Businesses like a record of reliability, so devs going solo with AI is going to be a hard sell. I think we will know that AI is actually good enough when these AI providers start absorbing project management companies and hiring contractors to use their product instead of selling subscriptions.
I wonder how do people use LLMs so it does not hallucinates. Like 90% of the time the code is impeccable, but the remaining 10%... Let's say I determine the expertise by how well do people act of these 10%. For me, the first pillar is still there, but not in a good condition
Easy just add "Make no mistakes" to the proompt, clear skill issue.
In reality people who use LLMs so it does not hallucinate are the ones that just have to little knowledge to actually see when it does, because LLMs do and they always will. That is the only thing you can get with a stochastic word predictor.
I am also feeling anxious. I lucked out by having natural inclination towards software development, career which can provide good upper middle class life to anyone. But I feel like writing is on the wall. If I don’t find a way to pivot to something else, I might experience class migration, but in the opposite direction this time.
It’s a good time to save and move out to a cheaper country to buy money generating assets here. It’s not easy but if you have at least one million dollars in investment money, it’s arguably wiser than staying in US that penalises such passive lifestyle heavily. Sooner or later some medical bill will leave you bankrupt. Unlike in EU.
"Except that nobody cares anymore." Noone (from mid-management) cared about it also before. You hit the deadline, get promoted and leave the technical debt to the next one. Even if you're the one to deal with it, you set up the next project, get the budget, prioritize the issues etc. Not much changed in this regard with LLMs
I am kind of like of in the same place though roughly 5 years more than author.
I thought about going back to college, learning Math, Statistics, advanced Machine Learning and applying for research role at a frontier lab.
That's a super silly take. As much as I did math and even course on machine learning back in the days and I was making basic perceptron in code at university - to get back and be able to do so on frontier level that's years I don't have anymore.
Anthropic is doing all that also with their LLMs so that ship sailed.
Big thing is — business people are not going to spend time prompting LLM to make an application. If they do then they will become "programmers" and we all (experienced developers) know — you touch it you own it — they (business) will not bother running or taking responsibility.
Right now on r/sysadmin there was bunch of posts where admins have "vibe coded apps" requested to be "productionized". Those business types requesting don't know yet — you touch it you own it — they think they can vibe code app drop it at ops and it is all fun and games. When people will start requesting features, start nagging about bugs, start cursing on whatever changes they introduced it will be back to "hey maybe we will just get someone to do that for us".
You might not need as deep software dev knowledge but with deep software dev knowledge you still will be faster operating LLM to build systems than non-dev
It's not just about it taking the technical competence away from our job, it's taken away the joy [1] which I wrote about.
I feel like many of my peers are beating around the bush on this topic and in denial. Even if you accept it can do a large portion of the technical part of our work, we are just supervisors at this point making sure it doesn't do any stupid shit. What is the point? Where is the fun in this? Where is the challenge? At least I have enjoyed building my career over the last 20+ years and building software, but find little joy in the work I'm doing now.
I think we're going to see a massive exodus of folks leaving the profession and a huge mental health crisis, long before the folks working in other sectors realise what's hit them.
LLMs are good at general solutions but not specific solutions. As industries evolve and laws, regulations, and practices change LLMs will struggle because those things are not included in its training set yet. We'll always need humans to push companies in new directions in order to compete, unless we eradicate capitalism altogether and then we're all out of luck. No competition means no incentive to try and be better than the next guy which means no new products and services for humans to develop that AI hasn't seen already.
> when I step outside my area of deep knowledge I can no longer call BS on the agents
It's still funny that 4 years into this mania the models can hallucinate basic ground truths, humans are increasingly not reviewing the output, and misusing LLMs where simple automation would suffice.
My wife does project management and works with a lot of tech leads. They came to her with a project plan deck, and she started questioning some weird dates.
The LLM was able to pull artifacts out of their issuer tracker, but it just.. hallucinated some of the dates in the process of creating a project plan deck out of the underlying data. These guys didn't care to review and notice, and who knows what else it hallucinated content wise. They were happy to send this project plan multiple levels up the food chain with hallucinated unreviewed dates.
5 years ago they would have just written a script and had none of this mess.
That’s why I use AI more like: Write a tool for me that does this.
Instead of directly: do this.
Preferably I would interweave code and AI queries where some function waits on prompt result too I think?? To avoid too big context hallucinations
I mean that would work for my use cases.
At least what I learned is that the less AI itself does in the context is the better so to say as critical LLM mistakes are approaching 100% of probability over time.
The crazy thing is how many people who can write code (with or without uAI) are in fact using the LLMs in the latter "go do this" mode.
There are a lot of non-tech people using these products in this manner.
Along these lines my friend is CTO at a non-tech firm and theres vibe coding happening in one department on a project that is going to churn $1M of tokens. Head of that department told him it's OK because instead of paying a SWE annual salary, they'll just pay $1M of tokens once forever.
People don't know what they don't know about software, SDLC, support, maintenance, etc. If code was something you write once and never think about again, most tech orgs could be 75% smaller.
My job as a staff engineer has turned into just reviewing slop farm vomit from offshore devs in Pakistan making pennies on the dollar given a slop code subscription and going wild.
I’ve lately just turned to having Claude do a quick /review, spot checking it, doing my own review and the. firing up some web agents to make the needed changes and just ignoring the back and forth because they don’t give a fuck anyway.
Just waiting for someone to notice and ask the obvious question at this point.
The direction I'm given is to take humans out of the loop. As much as possible. Everything AI. Automate everything. If you are in the loop you are overhead.
You’ve already faced this the entire time with… libraries on github.
If employers knew how much you can just use a new standard library, or ask you to “use React”, that’s a lot like asking you to use an LLM to speed things up. You also benefit from the collective wisdom of a lot of people. Do you write assembly or pixel shaders by hand?
While LLM's are beyond junior level at this point, they're still just that. I don't really agree that the first two pillars have been affected.
I've shared a story before that between now and 2 years ago a developer who solely relied on AI has produced the same hot garbage instancing system within the same time period. For example back in my day in 2 years I went from writing a system that struggled with few hundred players to one that could handle thousands and far beyond that. The person using AI 2 years ago wrote a system that didn't work and wrote a system 3 months ago that doesn't work.
Everyone is saying how great AI is, but they're missing that the driver is just as important AI wouldn't be able to achieve any of this without capable (often seniors) using it and giving it guidance. It's really a difference between "it works" and "it works without flaws".
Of course AI can produce things that also "work without flaws" with solved problems and someone "recreating" something that already exists with AI is not that special, a junior developer could accomplish the same thing given the time.
But I do agree that AI becoming part of performance reviews and all that is producing more productive developers which is going to drive the cost way down. In a way AI is stealing from a developers salary and giving it to the AI companies which is pretty ironic considering how cold developers seem towards artists.
Am I the only one that has noticed the massive increase in buggy software across almost every domain? Like, EVERYTHING has so many more bugs now. Things just break constantly. AI isn’t one shotting fixing bugs, it’s one shotting making hundreds of new ones every time it writes anything.
I think this experience is universal. The answer is the same as always has been - develop skills that are becoming most important.
Right now that is (at least from what I can see):
- Data analysis, data pipelines, models, etc.
- Tacit business knowledge
- architecture and design patterns (always has been, but now the scale os larger so this is even more important)
It's harsh but nobody cares if a model or a human made a system.
The "good" bits are that now automating anything and providing value from software is much easier. If I have an idea or a nitpick somewhere, I can just do it, up to a limit (which is quickly rising).
I have always been a generalist and generally interested in a very wide array of things, and this period has been the most exciting in my engineering career (13y now).
Learning about anything is so frictionless, looking back at my first learning experience - picking up a fat C++ book and spending days/weeks debugging, while I can romanticize that, I would never go back.
I can also now write software solo or with an extremely small team at a huge scale in comparison, and that is super exciting.
A lot of skills that took sleepless nights to acquire, they are "gone", but I still don't regret anything or wouldn't go back. Their "usefulness" has degraded, true, but this has always been the case with engineering.
We are now able to spend much more time thinking about utility rather than low level implementation and imo that's great.
We have many challenges ahead of us, and there are seriously bad things, the biggest one I have experienced is the hours are increasing and mental load is vastly increasing as well. As capacity, speed and leverage increases, so do expectations and hours, and that is probably a social problem.
Sorry for the unstructured stream of thoughts, and this is just an opinion (quite an unpopular one I believe), I hope your distress decays away for a new excitement and new opportunities.
The reason that I’m looking for an out is that it’s turned everyone I work with into imbeciles.
Nobody wants to think anymore. Coworkers are now just intermediaries for their LLMs. Talking to them is just talking to the LLM - sometimes directly copied and pasted, sometimes minimal effort to conceal what they’re doing. It is so disheartening.
And the sad part is, LLMs are incredible and can enable you to do much better work if you can stay in the loop, and stop focusing only on shipping speed. But from what I have observed, very few people care to do this. Who cares about substance when middle management thinks your productivity is 10x?
> Of course, I'm still employable because someone has to review the code and steer the robot. But I'm just another off-the-shelf engineer now. I have no domain expertise that another Sr. engineer steering an LLM cannot match. All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.
Ownership and responsibility are the new currency for the engineering staff. Willingness to implement these tools and then own the consequences of their use is what leadership is looking for. They want their cake while they eat cake, and they will keep those around who enable something approaching that experience. Owning the side effects of LLM use is more challenging than our own natural output because of the radical volume increase and unfamiliarity with low level details. However, I argue it is still possible. It has always been significantly more expedient to poke holes in someone (something) else's work than it is to perform that same work. And, the executives know this. They leverage this capability too.
The relationship between the business and the development team has been tenuous at best. I've rarely seen a technology team that was properly subservient to the business that ultimately signed their paychecks. I every case I have personally experienced, it is was like a hostage situation where the business owners are in constant terror of the technology people screwing them over in some infinitely nuanced way they or their lawyers could never understand. Many business owners are looking at this technology as a way out of the hostage situation. They noticed a window that was left unlocked. They are going for it right now. Whether or not they will succeed in their escape is a separate matter. Whether or not them being held hostage was justified is also a separate matter. It really helps to keep these things in their own lanes.
LLM is a powerful tool but it still doesn’t have the context that a person would have. A million tokens is a drop in the bucket compared to the overall context that the person guiding the LLM needs to keep it on track and being productive.
If you’re not a good engineer and you don’t have the domain knowledge, your token costs will be very high for whatever gets shipped, because you won’t be able to provide the context necessary to prompt machine efficiently.
Claude will still very often hallucinate bugs, explanations, domain requirements, that have no basis in reality. It will offer fixes and improvements that are pretty standard but not optimal. This is correctable if you catch it, but you need to review every line of code and comment, because in addition to being obviously wrong, it is often very subtle in the wrongness. For every bit of “slop” there is almost microslop, the places where it just kind of confidently guesses… and doesn’t tell you… but sometimes is correct anyway.
The “problem” is there’s less low hanging fruit. You have to know a lot to add value beyond being a middleman gating the slop. You have to really pay attention to the details to find some of the errors that it’s making.
Code quality and architecture still matter, because they also make it easier for LLMs to reason about the system.
That said, Opus 4.8 and Codex 5.5 both can write code that is higher quality than your average engineer. They are not quite there yet in terms of code re-use, but I think that's a solvable problem.
You're wrong there. You are capable of judging the outcome of the llm.
> But I don't know what to think about the long-term.
Don't you think it all has taken long enough. When I look back at the beginning of my career and compare what we do now ... I cannot shake the feeling we're essentially still solving he same problems and we have accepted that as being normal. Complexity skyrocketed, (abstraction) layers got added but the needle didn't move exponentially together with that. I think the IT industry as a whole gets what it deserves, thinking that we would remain the maze masters of the mazes we create.
> Maybe I should consider transforming my woodworking hobby into a profession...
I'm looking for 8 (affordable) oak panel doors with the exact same measurements as my current doors so I can replace them. That shouldn't be too hard to find you'd think right?
I've wondered about this a lot. I am brand new to software engineering, fully powered by AI coding. Traditional software engineers have to pivot hard or the are going to be left in the dust. The slow, methodical, take two days to put a change on a production site approach are over. I'm shipping exponentially faster than a co-worker who hasn't embraced AI yet.
This anonymous article is likely more FUD from the AI industry. "Just give up,you can't beat the machine. Please go quietly, we want to take your place and it's easier for everybody if you don't resist because you believe it's pointless"
If corporations really thought LLMs were a great cost-savings tool, then the obvious target for replacement are not the lower-paid staff, but the higher-paid staff - the ‘product managers and stakeholders’. That justifies token burn, replacing the 7- and 8-figure people, right?
But that’s not the real goal, is it? The goal is to inflate the stock value, take the cream off the top, and dump the whole business on the pension funds, maybe creating a too-big-to-fail scenario where the government steps in an bails out the industry as with the airlines during Covid.
This is why all the testimonials and narratives are so suspect - nobody knows what fraction of online posts were created simply to sell the narrative that LLMs are this incredible disruptive tool that will change the world, solely in order to create FOMO in the investor class.
In this particular case, I’d like to see links to samples of LLM created codebases for “PCI compliance, double-entry ledgers, escrows, reconciliation, payment lifecycles, bank transfer idempotency”. It should be easy to put an open-source LLM-generated version up on github, right? And if not, why not?
I have no idea what you guys are up to, but it is just a job, it is just a role, it says nothing about you or who you are and it is not tied to your meaning. If you make it so and your perception is aligned with that, then you are not in control of what happens to you. What kind of slavery it is to give other people so much control over you is crazy
I'm not worried. You cannot hold a machine accountable and there's no way OpenAI, Anthropic, etc. are going to take on that kind of liability if some code resulted in a major outage or a lawsuit. Perhaps that's the signal I'd be looking for: so much confidence in the product that they put their money where their mouth is.
Besides, you can look at the websites/apps/software you use everyday and evaluate whether or not the agentic era has produced better results. Personally, there's still plenty of bugs and annoyances. Banks still using SMS 2FA, library breakages in minor version bumps, inconsistent UIs between web and mobile, etc.
If all that was a hurdle before... because humans, regulations, or something else... then surely these magical machines that can supposedly replace us and do it much faster would've handled it by now? And they wouldn't introduce more bugs[0], would they? ;)
The glory days are over. In the future, one software engineer will be able to support multiple product areas much like how one HR team can support 1,000's of employees.
LLMs have made domain knowledge and reasoning "cheap"; it doesn't matter if the output is lower quality - look around you for countless examples of where cheap wins and "cheap" continues to improve.
It's crazy the crazed anti-AI people yelling with foam with their mouth that it's useless, meanwhile Claude for me at work oneshots complex bugs in a massive project with a 95% success rate. And the customer happiness survey has never been as good as it's now btw
This was a good summary. I feel similar. At this point I think 95% of the skills I've developed over the 2 decades are basically useless. Prior to 2023 I felt like every new skill only made me more employable, but now I don't really see any software skills that are safe from AI today. Even the ones that are very likely won't be in a year or two so there's no point in learning.
I've said this in other threads, but it concerns me how little the average person is preparing for what's coming right now... It seems people are making decisions as if their jobs and income are safe when in reality their entire profession could be gone in less than a decade. People in this comment thread saying crap like "yea, but the code LLMs write still isn't that good by my standards" are totally missing the trend. The fact LLMs are even one-shotting extremely technically difficult problems was something almost no one thought they'd be able to do by now a couple of years ago. Even I as someone who pushed back against this and thought they would become extremely competent within years am genuinely amazed at just how good they are. Trust me, regardless of your opinions, your job and career is at risk.
Another thing to understand is that if AI replaces workers in a variety of fields from SWE, accounting, customer support, graphic design, etc. Then it's likely going to be hard to fine other jobs to pivot into because when unemployment increases that significantly everyone will competing for the same limited number of jobs. Some will fine something, but most will struggle to find anything.
I hear a lot of people talking about how they'll just go into 'x' field if AI comes for their job, but realistically you'll need years of reskilling and you're assuming that in a world where other people are also losing their jobs, and where AI is touching ever more forms of work, that you'll easily be able to get a job in that other field. And I'm not saying that won't happen, just that this isn't as realistic or as safe of a bet as some people seem to think it is. You're also likely deluded about how hard it is to find work because you've been in software for the last decade.
Please, please, please, start preparing for what's coming. The economy is going to get extremely rough over the next 10 years. You need to be prepared to be without income for years, if not indefinitely.
So instead of a programmer, you become a software designer. I recently came across the idea of building fantasy for the player (in context of games), but now that I think more about it. Onlyfans, is just that. Advertising, Beauty products, novels, games, TV shows, and so on. You're really just creating / selling a fantasy for vast majority of people. Most people will never lose that 30 lbs, but you can sell them all kinds of products to fuel the fantasy of them losing that weight, being beautiful, rich, healthy and so on. So an LLM replacing the need for you to write every piece of code, is actually kind of freeing. You as a a former programmer, should embrace your new creative role. Writing code, at least for me was always slow and tedious. I just want to be able to express the ideas I have, so LLMs just make it possible to build things I never could otherwise.
Maybe just maybe here in HN we are in an echo chamber that is convincing us that there is a theoretical limit to how far the LLMs can make progress. It’s not unthinkable that LLMs will make better overall architectural decisions or follow the good practices better or understand the problem in bigger picture (more access to company/product context already makes a huge difference)
Lots of jobs have been automated away and careers based on those jobs faded away in history. Maybe in near future there won’t be a ton of opportunities for software engineers in the traditional form. I’m also embracing for that future.
There were people called calculators that did manual calculations in the past. There were people hand weaving all the fabric. There were people painting cars in the factory. All those jobs are gone for the most part.
We are sitting here portending there is going to be demand for software engineers managing those engineer robots but let’s be real. The demand for software is not increasing at the rate software engineering is becoming efficient using those robots. Some (many) of us have to find new careers.
computers are made for automation. programmers were always working on automating things, making other things obsolete, and we have been killing jobs for decades. did you really think we would suddenly stop when it's your job? i'm happy this is happening, genuinely giddy
There is an element of human nature that is known as self delusion and it is extremely common. Almost everyone on HN is suffering from a form of self delusion.
Usually when a human self deludes they do it when they're identity is under threat. People would rather hold on to identity then face the truth at the cost of their identity. That is what is going on in almost every HN thread that has to do with this topic.
A good example is religion. Someone who is intelligent, but born into a religion, will have a hard time giving up that religion EVEN when presented with logical/rational/realistic arguments for why that religion is false. They will rationalize the most convenient reasoning to maintain their own identity.
I mean think about it. Even the concept of religion is obviously false. It's not science, it talks about phantasmic beings that OBVIOUSLY don't exist. It's inconsistent among different groups as in there's thousands of religions in the world and nobody thinks the obvious of the fact that if only religion can be correct, then most of the world is fundamentally believing a total lie.
Anyway, the same thing is happening with AI. AI is eroding our identity as software engineers. So you'll see rationalizations in this thread in attempt to protect that identity. The biggest excuse is LLMs are hallucinate and are often wrong and fortunately for humans... this rationalization still works because it's still very true.
However what people are not mentioning is the obvious. People are avoiding it because they are delusional. The topic of this thread is "erosion" of "software engineering career" AND that is utterly true. ADDITIONALLY the error rate of LLMs have been going down. AI in general is improving. The erosion is real and obvious.
But you will see here on this thread that people are not talking about the erosion. They are holding on to the one last rationalization that is a differentiator without ever thinking about how that differentiator is "eroding" even though "erosion" is the LITERAL topic of the conversation.
I don't see it as negatively, in that there are specific trade-offs.
For one: LLMs make a lot of mistakes. We all see that when they hallucinate search results and what not. But, possibly even more important than that, you ultimately become dependent on some big company via LLMs. Perhaps that trade-off is worth it for some companies, but I personally don't want to become dependent on these companies. I actually consider it a hostile attack from the USA, and under Trump this is even more obvious.
Another thing that sucks by LLMs is documentation. They generate a lot of crap that is useless. So that's another area where humans could be better.
Admittedly a lot of vibe-coded AI slop is also useful in some ways, but it has started to make me rather angry in general - youtube already spoiled me here. I no longer want to see ANY AI videos at all whatsoever. It just wastes my time. I am not here to empower skynet version 20.2.
I secretly wish LLMs take my job away because I'll get about two years of unprogrammed rest, which I absolutely will not take of my own accord. But it's unlikely to happen.
I always remember of the infamous Steve Jobs quote "Ideas are cheap". If execution is everything, and frontier LLMs solve execution, then ideas are the gateway to abundance now, but abundance alone does not guarantee "stickiness".
What I think is often overlooked is the human "Willingness" and "Care" of staying with the thing for the lack of a better term. What I mean by that is that a lot of people just don't care enough, or don't want to, build, maintain, and own things. Sure you can ship V1 faster, but will you remain on the grind?
I think a great example of what probably will happen is found in Suno, the AI Music thing. I don't know if y'all have tried it, but it now produces really good stuff. What's happening there? A lot of people play with their own little universe and get tired quickly, move away from it, and only a few prolific creators stay and turn it into a "job like" environment.
We may have shifted the scale and the economics of "delegation" and "execution" but I think there are still a lot of other factors to consider.
> If execution is everything, and frontier LLMs solve execution, then ideas are the gateway to abundance now, but abundance alone does not guarantee "stickiness".
They don't "solve" execution.
If you're willing to push them enough, and put in place the system that they can actually get working code, they can solve execution.
They are far from doing that by default now.
Maybe in 3 years. They're moving fast.
But you can't ask them to build you a better Rust compiler, sit back and watch, and get a result today.
Totally, I meant that more in the lenses of how folks are perceiving it. They solve the execution part of the "one shot" aspect mentioned in the post. You still need to do a lot of plumbing, orchestration, supervision, etc. I think it will get cheaper and cheaper over time, though not magical enough to one shot a Rust compiler from "write a Rust compiler make no mistakes" haha.
Could you elaborate on the AI Music tool? My impression was that it's used as a one-shot generation tool. I don't know much about music but I imagine artists need intermediary steps, track separation, instrument customization and other stuff I'm oblivious about. Without these, it's hard for me to imagine it being used for professional work.
The frontier music models, the paid/pro Opus 4.8 equivalent ones, are more capable now, and Suno has a "harness" like Claude Code on their Studio tool that lets you iterate on the generation by doing stem splitting, track separation, edits that stay within the tempo, rhythmic structure, etc.
> I always remember of the infamous Steve Jobs quote "Ideas are cheap". If execution is everything, and frontier LLMs solve execution, then ideas are the gateway to abundance now, but abundance alone does not guarantee "stickiness".
https://x.com/chamath/status/2033385903520129161
> I think a great example of what probably will happen is found in Suno, the AI Music thing. I don't know if y'all have tried it, but it now produces really good stuff. What's happening there? A lot of people play with their own little universe and get tired quickly, move away from it, and only a few prolific creators stay and turn it into a "job like" environment.
https://en.wikipedia.org/wiki/Sturgeon%27s_law
Sturgeon's law states, "Ninety percent of everything is crap". The adage was coined by American science fiction author and critic Theodore Sturgeon while defending the merits of the genre. Sturgeon observed that most works in any field were low quality. Therefore, science fiction was not uniquely inferior.
Wut? I pilot LLMs all day but there's no way in hell I'd agree to be at the helm of a finance product. That first pillar is still there. Maybe the author isn't aware of the impact they have, but I know, with the evidence of reverted PRs, that when I step outside my area of deep knowledge I can no longer call BS on the agents. Our most capable agent, with access to the same kind of distributed systems the author talks about, is regularly wrong, frequently myopic, and just outright dumb constantly. It's the expertise of engineers on the team that push it back on track.
Posting this under a burner so I don't dox myself: I work in FinTech on a regulated product. We have access to Mythos. Mythos identified part of our codebase that it confidently asserted was not complaint with a particular regulation and we were at grave risk by allowing it to operate the way it was.
Except this was not the case, it had of course hallucinated what the regulation actually required (I know this because the code in question had already been reviewed by human counsel). This is (supposedly) the most bleeding-edge model available.
We use a lot of genAI to help us write code, but there is no way in the mid-term we could ever rely on these tools to actually build compliant financial products. We'd have to be totally mad. Yes, lots of Fintech companies are using these agents to accelerate, but anyone who's using them to actually ship product without a human actually digging into it is opening themselves up to a world of risk.
It was my impression that a whole lot of products are only pretending to be compliant, and that it's much more profitable to operate like that.
Even if that's the case, I feel like accurately knowing which regulations you're in compliance with and not is would be kind of important from a risk management perspective. From a "maximize profits" perspective (which I'm not saying is good but what you're saying you thought they operated with), you'd want to know the potential gain from ignoring a given regulation and the likelihood of getting caught (along with the cost of the punishment if that's happens). This is the kind of math that I'd expect a finance company to be pretty familiar with, and giving that up for a fuzzy "idk if we're in compliance or not" check seems like a pretty huge liability (unless there's confidence in not being liable for blindly trusting the LLM, which I hope is not the future we're headed for but I guess I can never be totally confident in us not somehow ending up with rules that defy common sense).
In my experience this is not representative of most fintechs. Of course there are both cases of real intentional noncompliance, and accidental, but by and large it seems like everyone’s trying to innovate within the law.
Companies that are growing tend towards faking compliance. Many financial rules like pci onky kick in at certain scales. So a company growing very quickly will often be behind the curve but will do everything to seem like they are compliant. Then they would hire people like me to come in and make them actually compliant. More often than not, making an effort at improvement was enough to keep the ball rolling.
> it had of course hallucinated what the regulation actually required
Did it do the correct job once you put the regulations doc(s) in the context?
What I usually do when in doubt is challenge the AI. “Please quote the section of regulation the product is non compliant with”. It usually admits it hallucinated the whole thing.
I've worked on projects in the airline and health industry which are highly regulated too. The regulations can be incredibly difficult to process and implement, and make sure you adhere to everything correctly. I've been involved in multiple scenarios where people have made false assertions about compliance or lack of. I'd still place a bet that the SOA models make _far_ less mistakes than humans.
They might make fewer mistakes, but they aren't evenly distributed. They don't use logic when making mistakes, it is gaps in the training data and now large of a span they have to bridge in the latent space. Just as they aren't smart like humans, they aren't stupid like humans. Don't mistake rate for quality.
For some reason, tons of people seem to be in camps at both extremes. It's either "AI sucks don't trust it!" or "AI is so much better than humans!"
But the most reasonable take, which I'm happy to see reflected in so many comments in this thread, is… use both.
Do an AI pass, and have humans verify, and vice versa. Let the humans drive the AI. Then the unique shortcomings of each party can be covered by the other's strengths.
AI review is never going to beat a fully resourced human review.
It might beat an underresourced human review, on time, efficiency, cost metrics. But on the metric of accuracy, throwing unlimited humans at a problem will still beat throwing unlimited AI at it
>I'd still place a bet that the SOA models make _far_ less mistakes than humans.
Genuine question: your top coder seems to be producing the most error-free code from your perspective, has the deepest knowledge of the architecture and codebase, and is faster on the trigger than the others.
But your top coder has proven and verifiable dementia, where they will confidently assume the existence of apis and code that do not exist, mix up the purpose of others and forget other things, and you can't predict when and how they will introduce errors into the system or the severity of such errors.
Are you really comfortable letting this person with dementia generate most of your codebase in the airline and health industry?
I also hope you have an iron-clad agreement that prevents the model provider from doing silent updates because all your evidence of correctness you collected thus far goes out the window in that case.
Another genuine question:
You have witnessed a human coder and the AI you're using make the same important mistake. Assuming you do not have the time and resources to retrain, fine tume, and test your frontier model:
Who would you trust not to make the same mistake multiple times in the future after you have warned them that their job depends on it, the AI or the human?
Your top coder has guard rails in place to prevent him autonomously going free - right? This is how you should approach agentic development with LLMs. Like it or not, we are the final bastion, the gatekeepers. The hallucination thing I think is mostly overblown and from speaking to colleagues it seems to vary wildly depending on which model and harness you are using - always go for SOA. In the last 3 months I can count on one hand where it's done something wrong and that's primarily as I'm operating it with guard rails and giving it context.
This stupid argument again. The number of mistakes _does not matter_. Get. This. In. Your. Head. The predictability of the _type_ of error is what matters. For LLMs and machine learning in general the error distribution is not what you would expect and it is not possible to predict either.
> I'd still place a bet that the SOA models make _far_ less mistakes than humans.
Well too bad, the problem is that they also produce things much faster than humans so errors will compound quicker.
100%. Unfortunately those not in the depths of mission critical systems or regulated products will continue to believe that producing tons of code quickly using LLMs without humans in these systems is acceptable.
Here's an example of what we will continue to see with folks fully immersed in gen AI psychosis:
"The creator of claude code said that he no longer writes code for about 6 months and now has Claude doing all his work now. He also said recently that he no longer prompts Claude and now has it running in loops and it is self-improving itself and performing better than a human!"
If the code produced by the LLM is perfect, the LLM takes the credit. But when a disaster happens, you cannot blame the LLM and it then falls on the human who did it.
I don't think SWEs heavily vibe-coding with LLMs realize the risk in not understanding what the code the LLM being produced is doing even after generating tests (lol). We will see more of this too. [0]
[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...
Why is it such a dramatic statement for Boris to claim that he no longer writes code?
Are people on HN still typing out functions by hand one character at a time?
It would be like a developer in 2020 claiming that he only writes assembly because compilers can’t be trusted. No one is taking that person seriously. If you chose a career in tech you made a decision to work in one of the fastest moving fields in human history. Now it’s time to get over it, learn the new tools and adapt.
Have you added "Make no mistakes" to the proompt? Mythos can't go wrong then, must be a skill issue.
its shocking people don't realize you're being ironic
AI cannot fail, it can only be failed
I realize they’re being ironic, it’s just a poor contribution to an otherwise productive conversation.
Is that all that Mythos did?
Did it find any real potential issue, optimization/simplification opportunities, or sparked any thought-provoking discussion within your organization?
Or was it purely a net negative experience?
Read their comment. It's a negative anecdote surrounded by them using genAI all the time.
You're the only one coming away thinking there was a net negative experience.
In regulated industries none of those matter if the tool invents compliance issues or breaks compliance.
The only thought-ptovoking discussion should be "why the hell do we have this stochastic parrot anywhere near out codebase"
I think that what technical people fail to understand is that a lot of the time, "compliance" is not the same as a binary compiles/does not compile. For a lot of rules/regulations, compliance means "making enough effort that legal is willing to back you up".
A system which will just randomly decide to give the legal team reasons to not back you up is:
* A system whose output will get brought up in lawsuits and make legal's job harder.
* A system that will make the dev team perpetually chase its tail while it oscillates between the several different valid interpretations of the rules.
Odd take. So if it identified 17 real gaps and helped fix them, the fact it was wrong about one gap, and the appropriate humans caught it and no harm was done, the whole thing is useless?
Not saying that is the situation, I don’t know. But if “one error is too many” is your point of view… do you think the humans in these orgs are 100% perfect 100% of the time?
what am i missing?
you take a spec and create tests, every little thing
you use another ai to verify these tests against the spec
you review the tests vs the spec (at one point human review)
you put the tests off limits to change / wall them.
you let the ai write the software that fulfills the tests.
there will be some gaps where you repeat the cycle above
if the tests fulfill the spec, the code will fulfill the spec
>you take a spec and create tests, every little thing
A spec detailed enough and unambiguous enough to be translated into machine execution deterministically is called code.
Unlike a compiler, AI can build with a spec that is not detailed enough or unambiguous enough: It does so by filling in the gaps with educated guesses.
This is safe if and only if you take the time to later read the output, understand what its guesses were, and judge wether they were acceptable. No AI can do this for you because the truth lies in your original intentions, which it does not have access to.
The jury is out there on how reliable and time consuming this is vs writing the code yourself; it is not immediately obvious that is faster or requires a smaller cognitive load.
IME, regulatory compliance is something you are rarely able to test for in a nice little box or with well-known suite. So there's no easy "this complies" in many situations, no matter how many lawyers, compliance officers, and llm's you run it past.
so, whats the difference to human engineering?
other than there are "internal micro feedback loops" during development?
If each step requires micro-steps iterating with an LLM with human review to prevent hallucinations creeping in.. at some point you might just be better off letting the human do the work.
Particularly as tokenmaxxing has ended and people are being charged more economic prices. If the pricing 5-10x the way Uber,etc did on the path to profitability.. even more so.
This is such an abstract principle that the principle itself cannot be refuted. The plan sounds fine on paper. "Just iterate bro". But it entirely depends on what rational agents you put into the system. Obviously, if I sub in a 5 year old child everywhere, this loop breaks. Humans and AI, sometimes one is better than the other at certain things, we're still learning.
The only way to test this is to test it out, in real life. Sometimes people see results, sometimes people don't. Note that yes, I am including the entire iteration process - even after iterating, people still don't see results with AI.
I have had both positive and negative experiences with AI, over multi-week projects. But apparently on hackernews, anything positive about AI is proof that AI is superhuman and taking over, and all follies about AI are lies by stupid humans who secretly have psychological dispositions to fear AI. Sometimes the AI genuinely isn't good enough. Are we not allowed to say that now? We might not know why, but it's just the truth.
The other solution is to formally analyze the entire space of possible actions the agent can take a priori. Then yes, you can definitively say whether or not the principle breaks or not. Can you, though? Can you give a formal specification for the space of possible actions for AI and show that your loop never breaks, or breaks less than humans, or any other sensible criteria? If not, then you can't just give an abstract principle and start making inferences from that.
Isn't that a net positive though? (not sure about the cost human and tech cost). I'm guessing that without using Mythos, those conversations would never have been had, and confidence in the compliance of the product would've been lower.
I love using AI tools as casinos. It's epic in helping to forge ideas and kickstart thought processes. You basically have the entirety of world knowledge at your fingertips to have a pint with.
your parent:
> the code in question had already been reviewed by human counsel
They cant read all comments they comment on...
> I'm guessing that without using Mythos, those conversations would never have been had, and confidence in the compliance of the product would've been lower.
The conversations had already been had and the product made compliant. Mythos just pulled new rules out of its ass and of course the product wasn't compliant with those. So they do a fire drill and find that to be the case at great expense.
Yeah you can frame it as "more checking is always better" if you wanted but that's just the same old "other people's resources are valueless" slight of hand we see on everything. It probably was mostly wasteful work.
> It's the expertise of engineers on the team that push it back on track.
But how are you so sure your colleagues are not more "expert" than you? Prior LLMs there was room for very good engineers and mediocre engineers to work together in 99% of the companies out there. With LLMs, only the "best" engineers will survive, because nobody needs mediocre engineers anymore.
This being HN, I imagine every engineer reading this thinks they are in top the 10-5% of their company/city/country, and therefore they think they are not "mediocre" engineers that can get affected by the introduction of LLMs. Statistically, they are probably wrong. So, it's all about ego. Chances are you are not a rockstar and LLMs will eventually take over your job.
As usual, the only winners here are corporations and executives. Most of us are the last monkeys in the chain, and so we'll get screwed.
The corporations and executives are already winning if you swallowed the concept of 'rockstar' engineer. Sure there are more and less experienced engineers, but even interns can and often do provide good input and spot mistakes made by seniors. The 'rockstar' engineer at most tech companies simply equates to the somewhat autistic guy with a brown nose who's working 15 hour days for a pat on the head from management (and making many mistakes in the process).
For the most part there aren’t 10x engineers
But there are certainly 0.1x engineers
Even if we forget "rockstar", there are certainly different levels of engineers. More experience doesn't automatically mean better either. That is not to say experience doesn't matter. It matters quite a bit. Sure , good interns can sometimes have good feedback or spot mistakes. But not consistently enough.
All of this to say that it's not just experience that makes one a better engineer.
Experience is one of the only objective signals we have, but you're right it's not the only one. I've seen plenty great junior engineers and interns, and plenty of incompetent staff/principal engineers.
> With LLMs, only the "best" engineers will survive, because nobody needs mediocre engineers anymore.
I don't think this is true.
A good engineer doesn't have infinite throughput. In my opinion the best engineers should be constantly bottlenecked because they solve difficult problems. They don't have time for grunt work. Every company needs less than perfect engineers, AI assisted or not.
Exactly. Same with tractors. Once they arrived, nobody benefited except Big Tractor.
Famously a net loss for humanity.
Well almost 70% of the developers in the industry can't write a fizz buzz.
But, besides coding skills (which some possess), the engineering, social, and business ones are close to non existent.
> Wut? I pilot LLMs all day but there's no way in hell I'd agree to be at the helm of a finance product.
Dunno how much longer that is going to remain true for your specific employer - all the fintech companies I deal with personally have had some sort of AI account for their devs since last year.
Even places like jane street have employees posting blogs (one of which was on HN frontpage about 60m ago) saying they mostly direct agents.
How long do you think your specific employer is going to hold out?
Sorry if I was unclear. I don't work in finance. I do work with agents. I think expert engineers in finance who are guiding agents are adding a lot of value because of their knowledge of finance. Because I lack that knowledge of finance, even given access to agents, I would not accept a role guiding agents in a finance company because I wouldn't be able to guide the agents well and my/our output would be bad.
Unfortunately every software related industry is embracing LLM/Codegen. Your banks, fintechs, insurance. Everyone. Your concerns are the same I'm having, yet it's regularly dismissed or hand-waved away as "don't worry about it the delivery velocity/ROI is worth it"
It's not so much about velocity or quality, both of which LLM do (or will) provide.
The real question is about accountability and liability.
When a major data leak is going to happen, who will they sue or fire ? That is the value engineers provide. They understand, confirm, and take ownership.
This is what I'm wondering too. We've signed a confidentiality agreement with all the big players (as I'm sure all other companies have done), which is supposed to ensure our data is both segregated and not used for training. I don't trust these companies not to do just that; their business is in taking what we have and training their models.
This question has been easily answered by many companies.
You, the IC, the developer prompting the code extruder, are ultimately responsible for its outputted code and its behaviour.
You may feel pressured to push out thousands of lines of code a day. You may see those thousands of lines refactored several times over the lifespan of a merge request. You may be asked to do this continue this in the long term with all the mental fatigue that entails.
When it's too much for you to sustainably deal with and you turn to using LLMs to review the code, that will still, presumably, fall on you at the end of the day.
The output is your responsibility.
Don't worry, we can throw in all in 55 gallon drums and dump it over a cliff when the time comes.
Ostensibly, due-diligence should not change. But people are lazy, just as they've always been around testing/QA/definition-of-done.
I'm not even certain that laziness gets them further along than it used to; I think it's that people have not had their overconfidence painfully corrected yet. Behaviors will re-align pretty fast when people realize that no, they're not going to get away with just pressing a button and saying everything is "good". That is happening right now.
> When a major data leak is going to happen, who will they sue or fire ? That is the value engineers provide. They understand, confirm, and take ownership.
This goes for serious incidents, disasters, outages and security breaches.
If there was an investigation and the answer was "a piece of software was vibe coded with AI" why would anyone trust the software vendor after that?
When has any company ever faced consequences from atrociously bad code leaking data or negatively impacting their customers?
Even Solarwinds is still alive.
Are banks that concerned about velocity? Because moving fast and breaking things in the banking sector can get extremely expensive. It's also not a who-gives-a-shit industry like operating a taxi service or hosting images, but a very tightly regulated sector.
I might have been a bit broad with the brush. I can't speak for banks, but I can speak for the the fintech/money-movement space (e.g. Remitly, Wise, Revolut).
It's a race to get first-to-market for backend integrations/features. It's given rise to a culture of "move fast break things" where safety is only for some core features, but absolutely not for the constellation of other services we provide. Failure rates have increased almost a percentage point since Codegen/LLM adoption was mandated from up top.
You would think regulators would be on top of this, but our industry runs on all actors "self reporting" their outages. Most don't unless they can't hide it (>1h)
'Keeping up with regulations' may as well be a separate field from the core stuff. It has the same pressures as any other development effort. Managers will want the integration to the KYC service LLM'd as quickly as possible.
> Are banks that concerned about velocity?
Yes
Reg PRs - for the ones with complex requirements what I am seeing is that time to initial PR is very short, and a ping-pong between the reviewer and developer begins, because in my cases (not all) the developer vibe-coded parts, and they didn't really understand the requirements deeply or their code, and it takes multiple iterations for them to fix it. You can argue this is a human problem but this is the net effect I'm seeing.
I am not sure but for complex cases it seems to me that the earlier sum of moderately long PR time + moderately long review time has been replaced by very short PR time + even longer review time. I am not sure if there's a net gain in these cases. Sometimes even if the code is functionally correct, it's verbose enough (e.g., too many intermediate functions) that I think they will impact future reviews.
> That first pillar is still there. Maybe the author isn't aware of the impact they have, but I know, with the evidence of reverted PRs, that when I step outside my area of deep knowledge I can no longer call BS on the agents. Our most capable agent, with access to the same kind of distributed systems the author talks about, is regularly wrong, frequently myopic, and just outright dumb constantly. It's the expertise of engineers on the team that push it back on track.
I'd posit there's another layer. You have domain knowledge, certainly. But more valuable still is the wisdom to find more.
Anthropic and OpenAI can stick financial regulations in the training data all they want, but the AI systems will never learn to anticipate the future, or reach out to clients, partners, or regulators in complicated situations.
> AI systems will never learn to anticipate the future
Citation needed. I don’t see any reason these systems shouldn’t be able to speculate; indeed some would say that’s all they do, even about the past.
> I pilot LLMs all day
Love the metaphor. Planes are sophisticated machines capable of auto-piloting, but humans are still needed to ultimately pilot the beast.
You pilot LLMs all day but that might not last.
A lot of companies are investing money on “ai factories” that are join to automate a lot of software development (that is, steer LLMs) on the basis of jira tickets (or linear/trello cards or whatever).
a year ago I would have agreed, but the gap is getting smaller all the time... these things can do 90% of the work, and how many people does a company really need for the remaining 10%? certainly not as many as they needed before
The things can do 90% of the work ... but only if used by the right people.
I've seen first hand what less experienced developers produce using the same models, your 90% accuracy suddenly drops to 50%...
My career path is suprisingly similar to the author's. Weirdly enough, what he takes as the first pillar to fall is the one I see most undamaged currently.
LLMs routinely fail at our business specifics: Local tax regulations, particularities of the accounting process, specifics of our ledger implementations. They're great at refactoring, translating between languages, tracing bugs on existing code even, but there is always many things subtly wrong iterating and expanding our domain.
This might be because the companies I worked for happen to be tackling complex domains precisely for moat-building reasons. They stay in business explicitly because there's not a book out there you can read to build a clone, the knowhow stays inside.
Also, a fintech whose managers recommend speeding up design docs with AI sounds way too careless to be in the money handling business. It's way, way too easy to end up with millions incorrectly allocated, particularly if you deal with high volumes of small transactions. These bugs are always a bitch to deal with because correcting the logic is just step one, you then have to correct all the wrongly calculated data in immutable DBs, move around the red tape and client comms, and your fix is bound to become a gotcha that new features and observability have to take into account ("remember that there's a bump in the data in february 2 because we had incident X".)
>> LLMs routinely fail at our business specifics: Local tax regulations, particularities of the accounting process, specifics of our ledger implementations.
My company also deals with a lot of complex regulations and domain-specific system implementations, which AIs used to struggle with. We were able to solve the problem with well-organized claude.md/agents.md files. On top of that we also implemented supermemory.ai, so newly made decisions are always recalled by AI agents when starting new sessions.
It's odd to me how quickly the author devalues their own experience just because AI can do certain things well. There's a huge chasm between what AI can do when prompted by an expert software engineer vs a non-technical person. Sure the models and the tooling will get better, but it still needs to be driven by someone with an intuition for how software works and able to dig in when necessary to unpack and correct the hallucinations, misplaced assumptions, or straight up borked code that will come from the gap between what a human wants and what they can express in words.
I have no idea how things will play out, but so far I am not worried because the amount of software continues to increase, and AI only accelerates that trend. This will require the same mental modeling, first principles thinking, and relentless curiosity that already formed the foundation of the software engineer skillset.
> The company is now hiring again for a few roles and domain familiarity is not a strong differentiator anymore. We used to list "Software Engineer - Area". Now it's just "Software Engineer" and the team assignment comes after the offer is accepted.
> Of course, this is good for brilliant engineers that never had the chance to get deep into the domain and now have better chances at getting a job, but it's also sad to think that other brilliant engineers that spent their lives collecting domain knowledge are now competing on the same lane.
If the author's vision of the future is correct, then competent software engineers are safe. Domain knowledge can be learnt much quicker than how to apply good engineering principles.
Engineers whose main competitive advantage is domain knowledge are probably not that brilliant at engineering. They might still find employment in other areas of the industry where they accumulated domain knowledge.
> Domain knowledge can be learnt much quicker than how to apply good engineering principles.
There was an entire thread a week ago about how domain expertise has always been the real moat: https://news.ycombinator.com/item?id=48340411
And I'd still question it. The experience of just… knowing how a good architecture looks like without being able to really put it in words is what makes a good engineer to me. These people can pick up relevant regulations or industry terms and deliver value quickly enough.
How is "without being able to really put it in words" a mark of experience? Surely an engineer should be able to justify why an architecture should be arranged the way it is!
It's perfectly possible to put that sort of knowledge into words, but not in a condensed "recipe" that can be explained in a meeting, that will go into a single Hacker News comment, that will cover all cases, or that will satisfy LLM users looking for the easy way out.
Pretty much every area of knowledge is full of those. That's why people publish books, that's why people go to college or get PhDs, that's why people with experience gets hired.
You're not wrong that a rationale is required.
But the master knowing when to break the rules because of tacit knowledge without being able to explain it is a real effect
This same complaint comes up on the topic of generic coding interviews, although shadowed behind the bigger complaints about simply disliking them. When people develop domain expertise they want to use that as a moat around their job. They want interviews to focus on stories about the things they’ve been exposed to on their past jobs, not test their abilities.
If you’ve been lucky enough to get jobs that expose you to the right things then you have a big advantage when the interviewers are looking for those specific things instead of your generic abilities or potential. It feels nice because you’re competing against a much smaller pool of people.
Unless you are not lucky enough to have been exposed to those specific domains yet. You can be a great engineer and even someone who learns quickly, but if you can’t point to the lines on your resume that match the job description then nothing else matters when the interviewers are playing experience bingo with your resume.
The move to generic coding interviews changed that. It was no longer enough to say that you had exposure to a topic at a past job. You had to show your coding skills, too. It wasn’t enough to ride on your credentials any more, which was highly frustrating to the well-credentialed.
However if you didn’t have the exact experience then the world of job opportunities becomes much larger. The people I know who like coding interviews the most (other than the rare competitive programming enjoyer) are people who are highly talented but came from less credentialed backgrounds: They don’t have an amazing university on their resume, they had to work at some company you’ve never heard of in their small town, but they are great at programming and just want a chance to prove that so they can move up to better companies. They’re never going to be picked by a company that’s looking for exact domain experience, but as companies open up job listings to people without that exact experience they have a chance to prove themselves.
The other people who relied on that domain experience to lock other candidates out of the hiring process don’t like it at all, though.
> Domain knowledge can be learnt much quicker than how to apply good engineering principles.
Partially disagree. Broad-strokes domain knowledge can be learned quickly, but honing that domain knowledge with nuance and consideration for complexity, particularly for organisations that are unique and are not often thought of as 'software development houses', can take years if not decades.
Yet I still see (and code review) 'professional' software developers that don't follow good software engineering practice.
> Engineers whose main competitive advantage is domain knowledge are probably not that brilliant at engineering.
The same is also true of engineers without domain knowledge, certainly in my experience. Maybe we just got unlucky...
>>"Domain knowledge can be learnt much quicker than how to apply good engineering principles."
I'm not sure that's universally true. Good software engineers who are arrogant about easily acquired domain knowledge have been the downfall of many an ERP system.
There's SO much IT that's literally all about putting business rules into the system.
> Good software engineers who are arrogant about easily acquired domain knowledge
This is a problem of arrogance, not of domain expertise.
Having worked in a few different industries, I'd wager that for the vast majority of them, a competent person can probably learn 80% of the required domain knowledge in under 6 months. For the latter 20%, as long as the person is not arrogant, they will seek help from colleagues who have been around for longer.
On the other hand, solid engineering principles will take 10-15 years of actually experimenting and learning in practice what makes a system resilient and durable.
>Domain knowledge can be learnt much quicker than how to apply good engineering principles.
Can it? I'm of the opposite opinion. You can improve methodology much faster than gaining specialized knowledge.
You can enforce and fast-track the former because it's a matter of approach.
The latter is subject to the person's learning affinity, capacity and availability at the time and can't be forced beyond reasonable facilitation. It also builds on itself, with the corollary that there's a much steeper curve early on.
The development and acquisition of valuable domain knowledge is a hard, risky, expensive and slow process. Because the valuable domain knowledge isn't yesterday's, it's today's and tomorrow's. In fields where domain knowledge matters, it is also deeply intertwined with engineering - you won't task Jeff Dean to develop Unreal Engine from scratch.
With that said, there are still many SWE principles that are not fully internalized or adequately practiced by domain knowledge experts, and that will remain the case as much as domain knowledge remains valuable, because software engineering is yet but another domain.
> Domain knowledge can be learnt much quicker than how to apply good engineering principles.
What kind of domains did you have in mind?
That's a fair question. I suspect highly specialised industries are harder (rocket and space, defense, nuclear, etc), but for things like finance (most of it, anyway) and retail, which IME make the bulk of the tech jobs out there, it's certainly nothing out of this world.
That's an extraordinarily rosy view of the future.
I'm old enough to remember the dot-com crash, specifically the years afterwards. In 2002-2003, the unemployment rate of software engineers was something like 40%. In fact, the only reason it wasn't higher was because of the number of people who had permanently left the field to become plumbers (or other trades).
I think this is going to be worse. In the dot-com crash, what really happened is that non-businesses got funded and it basically the capital markets ceased to function to a large degree. That's not what's happening now. Yes, huge amounts of money are going into AI companies but the change is more structural.
Other industries have gone through this. In the 1980s a bunch of industries were intentionally destroyed or offshored in areas that have never recovered. This has continuing social, economic and political impacts. I think people are being naive here thinking this can't or won't happen in tech.
> I think people are being naive here thinking this can't or won't happen in tech.
What would this future look like? Software developer salaries burrowing into the ground?
There's quite a large cushion for software salaries to decline before permanent structural unemployment were to set in.
It's not really feasible for "normal" businesses to hire developers at current salaries.
Tech companies will probably shrink in headcount, but all the non-tech kind of businesses can increase developer headcount.
Current Tech salaries are far above other fields while requiring (used to) significantly less training or time investment to get into.
Phase 1 is more likely that software comp will normalize with other professions, and more hiring will happen at the fringes rather than being concentrated in a few big companies.
There will be a handful of people who make stratospheric compensation, a bit like we have now.
Everyone else will have extreme job uncertainty, getting laid off multiple times, losing compensation as a result (ie equity vesting) with compensation that at first stagnates and then starts to slowly decline in real terms.
A lot of the big tech companies will likely spend less effort on non-core activities. Think of all the things Google does. Anything that's purely internal will be gutted staffing-wise because it's the safest testbed for shifting the engineer-AI balance on teams before rolling it out further.
If you listen to non-tech people now you hear tales of applying for hundreds of jobs and getting no response. That will become more normal. What's worse is that AI seems to be to blame here. Companies all use the same AI ATS systems and I've seen allegations that candidate scoring gets cached for upwards of a year. So if the system happens to give you a bad score, literally nobody will see your application because you'll get filtered out before any human sees you.
I was watching a VC give a talk from some conference in France and the general sentiment is that no companies are being funded with teams greater than 5. Why? AI. So don't think you can startup your way out of this slump unless you're somebody who has the connections and CV to get funded anyway, in which case you might well have some of those stratospheric options anyway, at least for now.
> I spent 10 years (even more when you account for non-profession experience) getting good at things that are becoming less and less valuable.
This is just how it is, and has always been in this industry. And it takes about 10 years to realize it.
When I started my career in software, businesses were still writing new code in COBOL. 10 years later those skills were pretty much useless, except for dwindling maintenance roles.
Then there was the client/server era. Then the web era. Then mobile. Then cloud, etc.
All the same functionality, written and re-written time and time again, using the latest popular stacks and methodologies.
I hope to be retiring in a few years and pretty much everything I have learned over nearly 40 years is no longer applicable or is at best losing relevancy to the way sofware is built today. And that's how it's always been.
I’ve been using Claude Code with Opus 4.7; it’s not that the code it produces is wrong, it simply tends to write too much of it. In my opinion it’s still worth thinking about a particular feature and finding the best way to fit it into your code because Claude will often just pick a layer of the stack (maybe presentation), and jam it in there. A couple weeks later you need this data somewhere else and Claude can’t reuse the code (maybe in the service layer) so it kind of “ports” it over. Unless a person is paying attention we now have the double the amount of code and duplicate logic. I don’t see AI tools like Claude getting better at this anytime soon.
Where I work there’s already pressure to use Opus 4.7 less to save money, someone mentioned using a smaller model for “simple bug fixes”. This might work sometimes but how often do we really know it’s a simple bug fixe ahead of time? I suspect as costs go up we’ll see interest in using these tools to write “all the code” go down. As people migrate to cheaper and less effective models I suspect we’ll see the pressure to skip reviewing that code dissipate as well.
We’ll see where we land, maybe it won’t as dramatically different as the author of this post fears.
> Maybe I should consider transforming my woodworking hobby into a profession...
Whatever your feelings on the future of the industry are, it's hard to imagine you'll find more professional success in artisan woodworking than artisan software.
Custom furniture/cabinetry is already a pretty tough market, and woodworking is such a common programmer hobby that if a significant chunk of us decided to make a go of it the market would get heavily oversupplied pretty fast :).
I’ve had people tell me I should try selling some of the furniture I make and my response is always that I made the mistake of turning a hobby into a career once, I don’t intend to make that mistake again, and at least software still pays pretty well.
I have a historic house with a hand carved/ uniquely shaped door. The jamb rotted and we paid a woodworker $4k to create a replacement. The door itself would easily cost $25k to replace. So, move to a major historic area with hand carved doors and you could make some decent money.
You assume there are enough of these jobs to go around and that you can just show up and do some extremely intricate work. Repairing historic doors and more elaborate woodworking isn’t easy to learn as the knowledge mostly doesn’t exist online anywhere, I also own a historic house and often ask the top tier LLMs for details e.g. about my staircase, they always give wrong answers as this knowledge is simply too exotic and not in their training set. And no one online talks about these things, 99 % of woodworking videos on YouTube are focus on beginners, you can’t replace a professional education watching videos and reading books. That will protect woodworkers with these skills of course but it’s wrong to assume you can just break into this market and be successful, most devs with woodworking hobbies are really shit at their craft and struggle to create even a regular elaborate cabinet, no way they will be able to compete with good craftsmen for these few lucrative projects.
You paid $4k because it's a niche task that isn't much in demand.
Depends what you mean by woodworking
I work with a guy who does decking (gardens, caravans, etc) and builds sheds, fences, things like that and he does very well indeed (he's also incredibly good at it to be fair)
Most people would just call that construction
Specifically a carpenter.
Construction working entirely with wood
If only there was another word for that...?
look at layoffs.fyi. chances are he will be laid off pretty soon. and if not tomorrow, give it couple extra years until AI gets even better. it is one-way road, down the hill.
not woodworking. farming. get a pot of land and grow your own food. do not participate in economy at all. that's the only survival.
My comment was about the fact that even if you're laid off, you're more likely to find success in artisanal software than artisanal woodworking. That statement is not an assertion that you're guaranteed success, just that it's more likely to sustain yourself than woodworking is.
Layoffs also don't really tell you anything. Is it actually LLMs that are causing layoffs or is it deteroriating economic conditions and uncertainty amidst war, oil shocks, etc.? Is it junior employees being laid off, or seniors? If it's the former, someone with 10+ years of professional experience might not have reason to be concerned. I happen to believe that, LLMs or not, the software development field already had far too many jobs, employing a large number of clueless people who contributed somewhere between zero and negative value to their organizations, and that it was overdue for a correction anyways.
You are not allowed to have land without participating in the economy. The government forced you to acquire land by buying it, and to pay taxes in dollars.
> get a pot of land and grow your own food
Rejecting industrialized society is actually very expensive
> Whatever your feelings on the future of the industry are, it's hard to imagine you'll find more professional success in artisan woodworking than artisan software.
A small percentage of the market, maybe a fraction of a percent, are still willing to pay for hand-built goods - bonus if it's thoroughly modern but retro (steam-punk keyboards, maybe).
Exactly zero percent of the market is willing to pay for hand-built software.
> Exactly zero percent of the market is willing to pay for hand-built software.
You took this statistic out of your rear end?
> You took this statistic out of your rear end?
We are less than a year into good-enough coding agents, and as of right now there is not a single job opening I see that offers a salary for non-AI output.
It is fairly obvious that the majority of people who buy software (>99%) don't really care how it's built. They care a lot about the outcome of using it, they care a little bit about whether there are bugs or not, and they care about the cost a lot, but beyond that nothing seems to matter to the purchaser. Even obvious things like whether or not there are tests, documentation, SLAs for fixes, or backwards compatibility between versions don't really seem to matter much.
That doesn't mean you couldn't carve out a niche providing hand built software to people it does matter to, because the software industry is large, but saying 'zero percent of the market isn't willing to pay for it' isn't really wrong. It's just a rounding error that does care.
(One massive caveat though ... the argument assumes that 'hand built' means 'higher quality than AI-assisted', and that's probably not true for >99% of developers.)
> Exactly zero percent of the market is willing to pay for hand-built software.
This is a provably false statement, given that eg. Handmade Hero exists and sold a bunch of pre-orders despite never coming close to completion, and spawned an entire community that prides themselves on handmade software. There are also content creators like Tsoding who make a living by having people watch them do handmade coding for the love of the craft.
Some non-zero percentage of people will also always be willing to pay a premium for superior-quality software. The author's thesis isn't that LLMs can produce S-grade software but that 'nobody cares' about quality and that C-grade software is good enough. While it's true that software quality isn't greatly valued at scale, I think the minority who care is larger than the minority who care about premium woodworking goods, particularly because as an artisan software developer you more or less have access to the global market of every single person who cares, while as an artisan woodworker you mostly only have access to the market of people in your town who care.
This also overlooks that LLMs are politically divisive and there are movements to boycott them and shame people for using them. There's a niche for organic, free-range, vegan, etc. products at the supermarket for conscientious objectors, there will undoubtedly be such a niche for software. All the more so if LLMs reach a point where they actually are putting everyone out of a job, they will get much more divisive. There was already an assassination attempt against Altman and his promises to destroy everyone's livelihood haven't even come to fruition.
> Exactly zero percent of the market is willing to pay for hand-built software.
People are increasingly associating “AI art” with cheap slop. I wonder if the same will ever happen to programming.
Only if the quality is bad. And users normally can only judge this when something is not working. So maybe only badly written/tested software will get labeled ai slop.
People can’t even reliably recognize AI art anymore.
The classic “AI images were everywhere in 2023, but I rarely see them now” phenomenon.
I see a lot more bad art now. I suspect most of it is AI, but I can’t prove it.
What are you talking about? They’re so ubiquitous.
I’m excited about the genAI future. I’m a software engineer interested in product, user experience, architecture, and entrepreneurship. After 4 years in the industry, mostly within fintech, I have gotten tired of slow organizations, company politics, nontechnical managers doing the decisions etc.
I’ve saved up a couple of months of salary, have a couple of bootstrap ideas that I believe are within reach for me equipped with a coding agent to build. Hosting can be done almost for free. What used to take entire teams and hence millions of dollars to build can now be done a lot cheaper. If I’m lucky one of those ideas can pay my bills soon. If not I’ll go back to consulting for a couple of months.
I sympathise with the author being in the same boat, largely.
I just want to emphasise a point... Calculators give 100% correct answers and yet we still hire accountants; for the simple fact that we don't want all to be accountants.
People will hire software engineers for the simple fact that they do not want to be software engineers.
"calculator" ("computer") was a profession. not anymore.
https://en.wikipedia.org/wiki/Computer_(occupation)
With todays LLMs, yes. But if they can ever reach a level of a contractor in a reliable way and companies offering them willing to take responsibility (because confidence is high and rest is insured), then one can just hire a cheap AI agent to fulfill a contract - design, implement, deploy, run and maintain your service/website like the engineer before.
Calculators are not a replacement for accountants, online accounting services are in many cases. Which again can be run by an AI if they reach that level of reliability.
Today with LLMs this is still sci-fi, though.
I mean, if they can do that, then game over Terminator style.
Accountants have specialized domain knowledge (laws, regulations, procedures, bureaucracy etc.) that goes well beyond what a calculator can do.
If we apply the same argument to software engineering I think it's a good point... just maybe not the one you intended to make.
funny i was able to do all my taxes this year with ai help and not needing accountant.
Lol thats brave on your part, given that a mistake can cost thousands and you have no accountability (punch!) from an LLM
In the US, all you need to work in tax prep is a high school diploma and most individuals are not worth the cost of an audit.
I wouldn't say it's particularly brave, in fact LLMs are probably better at identifying mistakes than most tax payers. The % of Americans using a CPA to file taxes is fairly small.
Your account has no accountability. Do your taxes wrong and the tax payer is liable.
Ask me how I know.
At least he has a reputational/commercial risk. LLM has none
Are you in the USA? Brave person if so.
There's a certain irony in masters of automation lamenting that their roles are being automated. I wonder whether the jobs their efforts eroded in the past ever got the same thoughts...
Programming, logic, etc are skills and toolkits. The optimal state of society is everybody being able to apply them, not just the enlightened compsci caste. There was a time in the past where scribes were paid nice cash for their efforts, too.
I guess the lesson to learn here is treating a toolkit as an identity and job for life. By virturee of the essence of the job itself - if the tool gets cheaper and more widespread, it's aactually success, not betrayal.
You say that the optimal state of society is for everyone to apply programming and logic etc. but the obvious final result of these developments is that no one will.
Maybe the artform will be lost, but surely humanity will inherently be more 'logical' and systems driven afterwards?
Maybe using writing as an analogy is flawed, but most of humanity having 'writing' as a core skill did enable many other things, even if oral storytelling cultures suffered at its hand.
At its core, tech is all about breaking through inefficiencies and barriers. Does it matter if people can't code python if people demand government systems be frictionless in the year 2500?
Sincerely, how is prompting an AI to build software for you building "logic and systems thinking"?
The thing many people are ringing the alarms over is the offloading of critical thinking and knowledge work to LLMs.
This doesn’t read to me as someone who is sincerely impressed or rather surprised what ai is capable off.
This reads like someone is trying to convince me, that ai is just this good, and that the author is telling me to use more ai.
To me this sounds like: Trust me, it’s really bad, i know what I’m talking about. Just lean into it, or change profession.
I was thinking maybe the author really likes Datadog MCP or has some kind of conflict of interest. It's weird to see this content in HN.
> I don't know what to do.
Ride the wave. You rode it when websites/webapps were the wave. I came into software industry before internet, kept changing my horse. You are never too old to learn new tricks. The new wave create new kind of work and workers. Be one of them. Ride the beast, master the tools. It's the same game again.
This feels fake/engineered but regardless of that also redundant.
It's the exact same story that we've heard countless times by now. Hosted on a blog with just a single post. Named in a way that suggests that said blog was created for this very single post.
What is there to learn from this other than LLMs seem to be bad for some people's psyches and that AI companies need these very stories to not get their funding shut down?
It's 100% fake, and half the comments here are from 20 year olds working at AI companies.
I mean I kinda get that it sucks being a junior now, but otoh, it might also not?
It might be easier to adapt to this new tech when you're 19 compared to when you're 59.
But honestly, this discussion _also_ has happened ad-nauseam by now. Everything that was worth saying has been said. And then some.
People don't actually want to talk about LLMs. They want a hug. And that's fine, human and all.
But could you please just start asking for hugs instead of encoding that into vaguely profound sounding takes on AI? I'm tired of this play pretend.
There are lots of positives that have resulted from using AI in software engineering. (1) No more long repetitive text editing sessions. I.e. changing namespaces or replacing deprecated APIs with the "correct" ones. AI will make nearly perfect text modifications with ease. (2) No more bike-shedding code reviewers nitpicking over every tiny coding decision. I.e, "you should use std::format instead of std::stringstream". AI will match the existing set of nitpicks so you don't have to. (3) Average Joe's and Jane's can craft applications by just talking to the computer. This might inject a freshness to the current state of software. Currently, we are all forced to use the same bloated applications like Word, Excel, Jira, and Photoshop. We are currently forced to deal with the same set of monopolistic SW companies. Now average folk can solve problems and avoid dealing with Microsoft for a spreadsheet program.
To me looks like, if we're not collectively careful, civilisation will soon be on a path to an evolutionary dead-end.
Anything that can replace a deeply experienced s/ware engineer can replace anyone in the employment stack, meaning that only the owners of capital will be left, and they too will soon fade as the economy falls off a cliff and money has no value, because the only value that money has is the value of a human backing that, with thought, with ideas, with human output.
Whether you like it or not, "Economic output" is just a different phrase for "Human output that is valuable". When all human output is valued at the fractions of a penny per month of work, there is no future.
No lelanthran, software engineering and plumbing are not the same job. No lelanthran, LLMs can't be plumbers.
This is so blinkered and egocentric.
Just because LLMs are good at translating English to code, doesn’t imply they are good at many other jobs.
Coding isn’t that hard, it’s just not enjoyable to most people. The enjoyment has always been the barrier to entry.
That sounds more like a problem of close minded narrow focus on economic output instead of culture, virtue and spiritual traditions.
AI is fundamentally an equivalent to slave economy. Cheap, plentiful workforce. This time ethically neutral. You either get Greece or Rome. I’d prefer Greece but it will probably be Rome. From the past we can predict the future.
> That sounds more like a problem of close minded narrow focus on economic output instead of culture, virtue and spiritual traditions.
I’m starting to be more sensitive to the argument that without god, people are unable to have a strong moral foundation. Not for the people expressing creativity in how they fuck, but as a check on those in power.
>i’m starting to be more sensitive to the argument that without god, people are unable to have a strong moral foundation. Not for the people expressing creativity in how they fuck, but as a check on those in power.
If this were true, why did the medieval peasant have less rights and autonomy in society than we do now?
Most people already have god.
There has been said so much about it in the past and I always believed it to be true, as an atheist.
I like to think that one of the symptoms is politics becoming really absolutist, idealistic and cultish. You do not debate followers of a different religion. But many topics really becoming kind of a mini religions.
I don’t know for sure though, there are arguments against it too and other factors.
I think substantial amount of people really need some kind of subjective spiritual experience to their life and maybe ignoring that need breeds some maladaptive tendencies
> Anything that can replace a deeply experienced s/ware engineer can replace anyone in the employment stack
Nope, just knowledge workers. We’re decades away from automating many manual labor professions, even “unskilled” ones.
Turns out brains just aren’t as special as we thought.
> Nope, just knowledge workers. We’re decades away from automating many manual labor professions, even “unskilled” ones.
How do you figure? We’ve already automated away way more manual labor jobs than we currently have.
The major blocker for manual labor automation in that fashion is cheap energy. China is ahead of the pack with the States' weight behind aggressive expansion of solar tech, and still can't do that.
> Nope, just knowledge workers.
Nope, just a specific kind. Those who developed and cultivated only a very specific skill set at the expense of all others.
I used to think being a generalist, and having persued technical roles with a people facing element was to my detriment, but it’s turned out to be the best decision I ever made.
Owners of capital, yes, but also owners of the means of production (which now means AI companies). See https://blog.oberbrunner.com/blog/ai-risks-taxonomy/#economi...
That's what capital means.
I've consulted with some big companies on AI strategy. I tell them there are two approaches to AI.
1) Train AI to replace human work. This gives you 50% quality for 10% cost. 2) Train AI to assist human workers. This gives you 200% quality for 110% cost.
Most companies will go with option 1, and it's a race to the bottom. Eventually, someone will go with option 2 and gather up all of the pieces and take over the market.
> All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.
I think the author downplays how much of that knowledge is used on knowing what to zoom in on, what to prompt, or what to look for.
I’m genuinely curious about what happens to pillar 3. LLMs are quite bad at this still, to their own detriment. I disagree with authors take that low grade code bases can be reasonably acceptable now as long as the agents work in it.
If relying on agentic tools to develop a codebase, The lack of architectural coherence causes code rot at an accelerated pace, in the usual tech debt like curve. There’s some prompting around this, but once established, it's really hard to undo. Your 90% test coverage will bully LLMs back into putting things in the wrong shape. Workarounds will proliferate. Left to their own devices, LLMs will keep bolting on, rather than refactoring. This will further entrench the status quo of decoherent architecture until change is impossible.
There’s one force where software engineering is being automated by LLMs, but the other force is that there isn’t really much more software that needs to be built. Even before AI coding became big, back in 2021, we were already in late stage SaaS territory where each new idea was an increasingly minor variation of an existing idea. There were no new GitHubs, Herokus, Stripes, Salesforces, Instagrams, Reddits, just variations of those for more specialized markets.
It’s really unfortunate that AI hasn’t raised the ceiling on the space of possibilities as much as it’s raised the floor on how much can be automated, we’re all getting squeezed in the space between.
> there isn’t really much more software that needs to be built
Yup. Most everything we need was already built in the 1970s. Programmers have been kept busy because we've kept introducing incompatibilities into the mix, like DOS programs needing to be rewritten for Windows, and then the web, and then mobile.
And now they're being rewritten for AI platforms. It may be giving the squeeze due to being the first platform that will also help with the rewrite effort, but it is also the thing that kept the industry going. As you point out, there wasn't any work left to do until AI showed up.
> I have no domain expertise that another Sr. engineer steering an LLM cannot match. All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.
Don’t sell yourself short! Taste is not promptable, I suspect good taste is AGI-complete.
Especially in domains like fintech, there is a lot of accumulated wisdom, and that is what you’ll be handsomely paid for (for at least the next couple years :/ )
For example, architectural patterns, when you need bitemporality, immutable logs, CQRS, all these good patterns that can only be learned by owning years of system architecture - none of these feedback loops are in the training set.
And from a product design side, agents will just miss key concepts and you need a few words to prompt a fix - but that might represent a massive tree search optimization, or the agent on many cases would just fail to identify the requirement. These small steers feel small, but by evaporation our work has distilled down to just the extremely high value insights.
METR task time is still at weeks, doubling every 7 months; it’s years (assuming we keep riding this crazy exponential) until you hit multi-year tasks. I don’t see wisdom / Métis being solved in 2027.
All this said - I think it’s important to extrapolate forwards, if the trend continues, this will may all be true in 3-5 years. Now is the time to pre-register what metrics would make you worried, so that you can define your red lines. There will be a rapid consolidation of power and wealth if these tools continue on their existing growth trajectory.
Same boat here, just a couple years more experience.
Current LLMs are still kind of shit at actually programming so many jobs do still care to have professional programmers. However, I think it's evident that if things stand where they are, employers will care to have far fewer of them, at least of highly paid highly experienced programmers. If this is the state we're in with LLM adoption when they can't help but create the same helper functions 15 times, god knows we're screwed.
So we should probably work on clearing out our debts and figuring out what else we might want to do with our time, I reckon.
I'm still going to try to do a good job. I'm still trying to learn the best effective ways to apply current LLMs (Right now I still prefer to mostly write code myself but have been using LLMs to bang code into shape via iterative code review; this is a way to exploit LLMs to make better code, especially applicable if your velocity was already good.)
Yeah the writing is on the wall. Not just for knowledge work, but for jobs in general, as I've been saying in other comments. The era of wage labour, and this dominant economic system, is coming to an end. There's no way it can coexist with AI, but it also needs to continuously push for better AI, which means there's no stopping it. The only thing to do really is brace for the disruption - which will likely be pretty rough - and hope governments play their role properly to ease the transition.
I agree but I think it would take awhile. Some of us here seem to believe 2026-2027 is the end of programming jobs. At least that's Amodei seemed to be saying but then changed his mind later on?
Amodei has a track record of saying blatantly false shit in order to drive hype. At this rate, I see him as a snake oils salesman.
I think that the domain knowledge still matters: if for nothing else, then at least it can make the communication both with savvy AI tools and savvy humans more effective compared to "outsiders": acquired vocabulary, truly grokked concepts in the field of target expertise etc… -- that all seem like a huge competitive advantage over folks having to learn all that "on the go", constantly struggling to pick the right nomenclature or using wrong or vague terms. It's mostly that domain knowledge what makes experts understand problems faster or at all, even.
Agents may have made 80% of your experience go to $0, but the other 20% is exponentially more valuable now. This outweighs your other losses.
The ability to orchestrate intelligence is a magnificent power that few have, and while barriers to entry will be eroded, it will take time and they won't be eroded fully. This is your edge.
He says that taste doesn't matter and it hasn't in the past. However, in an era of "extruded code product" (by analogy to https://tvtropes.org/pmwiki/pmwiki.php/Main/ExtrudedBookProd... ) automatically generated by the truckload at negligible cost, the differentiator for software developers will necessarily be the ability to create a product that doesn't reek of extruded code product, i.e. the things like quality that he labels taste.
(Whether any one reading this, myself included, survives in the industry long enough to reach the other side of that transition is a different question.)
[EDIT] The reason I use books as an example is that 4.2 million books were published in 2025 (https://ideas.bkconnection.com/10-awful-truths-about-publish...); 3.5m self published (with most likely LLM assisted or wholly generated) and the remainder traditionally published. (That's ~9,600 new self-published books a day.) Who actually still sells enough copies to make money in this paradigm and why offers hints as to where the software industry is likely headed.
Even if the model can replace a domain expert on the software side, you still need a human who can decide if the technical solution actually meets the business needs and that would require a human with domain expertise.
This person was hired, from the beginning, to be a meat shield. To be responsible for decisions they won't be allowed to make.
I think this is the first time I saw someone describe so clearly my concerns and experience with LLMs.
I have little to add to it, except that I agree completely. Not sure what’s next
Many people share this sentiment, many people don't.
Who you belong to depend on at least two things: A) How knowledgable is the AI on what you are working on, B) How well do you wield these new tools to work better than before? (Better here can mean many different things).
>Of course, I'm still employable because someone has to review the code and steer the robot...
We will work for the robots, steering them to steer us.
The saying goes... first we shape our tools, then they shape us
We are now manufacturing intelligence (why it's artificial) and it shall be interesting to see how it shapes us individually and as a whole.
While marching on May Day, the woman next to me made the comment that Ai will force every human and humanity to reflect on what it means to be human, all of us at the same time over a short time period. What makes a human valuable beyond their work? Why do we go to other people when their expertise is at everyone's fingertip? What value are we giving, trading, or sharing in the time we have in this world?
Interesting Tony, you seen to have been working for the AI since long time. We others are catching up but eventually all of us will be working for it, as directly or indirectly we are already doing. The "robot" was figurative cause we are no different from a machine, but that is too much for humans to comprehend.
I work for myself and the world, not for Ai. It's a tool without identity, but a damn good one that multiplies my capabilities.
I anticipate the first bifurcation to be wheat from chaff. Ai is going to do better at a job than say half the people, those who don't care about the effort they put in or the quality of their output. These people will have to come to terms with their mediocrity or blandness.
I'm still unsure what the good ideas are for when we reach a world without labor scarcity.
<I'm still unsure what the good ideas are for when we reach a world without labor scarcity. It should be real creativity the final goal to this life optimization. For now many of us need to fight for survival, for food, shelter... so is a bit difficult to be purely creative. But is also true that given all the benefits (taking off the survival instinct) makes creativity obsolete.
>I work for myself and the world, not for Ai. Yourself really? Start by defining "I", "work" or "yourself"... then we may proceed to the next LOL
I still struggle to accept this when my colleagues are producing implementations with AI assistance that are consistently broken and don't do what they think they do. As yet I can't square this circle, no one is better at their job than they were before.
I feel that I am faster and better, sure, but trusting self perception would be an absurd thing to do.
It's not just our careers. In the hunks versus nerds wars, it is now clear who has won. The nerds have made themselves obsolete and put the continued evolution of homo sapiens to an abrupt halt.
> I still have one pillar standing, though: code quality and software architecture - what's now being reduced to being called "taste".
Genuine question: what exactly is "quality"?
It's something I've been trying to understand for a very long time. It seems like it's entirely contextual, and it has both subjective and objective facets (the latter only for quantifiable things, and still entirely contextual).
Ah the age old question: what makes something good? I think you’re already describing it well at a high level; context matters, and there are multiple axes to consider. But that’s extremely vague and doesn’t help you identify or measure quality, so it might be worth listing as many specific axes as you can.
Maybe ask the same question about other things. What makes a good guitar? What makes a good chair? What makes a good airplane? What makes a good book? What makes a good song? What makes good art? Each of these has a long list of very specific goals and concerns. And to help define the boundaries, also ask what makes something bad, and what makes something mediocre.
Code quality starts with functionality. Does it perform the stated requirements? Does it have testing in place to catch breaking changes in functional requirements? That’s the basic stuff that probably isn’t part of “taste”. A lot of code quality goals center around how code changes over time, and beliefs about designing to avoid functional breakage.
For example you can ask things like does the code use minimal dependencies? Is the code organized into clean classes/modules/functions that each have a single clear role? Is the API easy to read, understand, and use? Is the API hard to misuse accidentally? Is all the code easy to read? Is there documentation, and is the documentation useful, and more than a list of contents? Is the code self-documenting? Is the code efficient, both in how it executes, and in its use of code itself? Is the code designed so that it won’t fail when someone runs it with different sized types, or a different compiler or execution environment, or on a different architecture? Is the code surprisingly elegant and fun to use?
Those are just the beginning. There are of course more layers of application-specific and environment-specific and audience-specific qualities. The good news is that quality depends on your own goals, you can decide which aspects of taste matter to you, and ignore the ones that don’t. It’s fine if your taste & goals change over time.
Off the top of my head:
If you're using the product, and you want to question or debug what's going on, you can:
* Jump directly to the single relevant part of the frontend responsible
* Likewise with the backend. The layout and naming of the code should scream its purpose.
* Once you're looking at the code, it should be trivial to run it, right now, instantly, in unit test, or cli. You shouldn't need to stand up a database to see whether your code rounds taxes the expected way.
The system contains its own checkability. You can, for instance, just sum up all the incoming money and outgoing money and see if your balance is correct. (It's not enough to have good tests today, if you're working on data that was incorrectly calculated and stored yesterday)
Good question indeed, I think quality matters less these days because it's trivial for an LLM to increase code quality.
Quality is usually observed from a human perspective. But in my experience, codebases that humans would judge as "low quality" are actually fine for LLMs. They don't have as much trouble as we do with spaghetti code. They don't have problems with readability or obscure syntax, it's all perfectly fine for them. They don't care about indentation either.
Also it's really easy to increase the quality of the code base. You can just prompt to add unit test coverage and it will. You can prompt the LLM to handle edge cases better and it will (you don't even have to specify which, it helps, but it's optional). If you want to have better separation of concerns, just ask the LLM to have more separation of concerns and you'll have it. Documentation lacking? Just one prompt away. More robust build pipeline? You get the idea.
> All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.
Is it really though? Access to information is quicker, but you still need to know what ‘good’ looks like to leverage it effectively. I can prompt my way to a medical diagnosis, but I’d still want to run it by a doctor.
I’ve found it extremely hard to get LLMs to exit the basin of your current knowledge.
One of my tests for new models is to ask about a concept I already know the mathematical model for, but as if I don’t. So far, they all answer the same way:
1. Convoluted explanations about how it kinda-sorta is common terms.
2. If you follow up with the correct mathematical term, it immediately claims that’s correct and the right way to model it.
3. If you ask it why it didn’t use that term for your question, the LLM gives some version of explaining that it tried to match your language.
I have no choice but to assume the model behaves similarly other times — and that I am largely trapped in a basin of my own ignorance, when using LLMs.
What I tell my team to do is to drop using so many cloud saas apps, and build more themselves using LLMs.
I’m not planning on firing people, but I am planning on building more, using more tokens, and less app subscriptions.
One aspect of building that doesn’t erode is human values.
LLMs don’t create software with zero direction and although I do have 12 agents building constantly, I run out of attention to increase that to 100.
How strange or at least unintuitive. Buying should be cheaper than creating for a customer of 1
Think about the worst enterprise SaaS apps you have used…
you dont need to vibe code shitty apps. you just need to learn how to use apps like codex, claude desktop.
I don’t get it. That’s what I am using.
Realistically, what should we have done instead? Not invent LLMs? What happens when a couple thousand people invent the next disruptive technology and even more of the population loses their jobs?
It seems like new tech is something most of us have to lie down and accept as the new reality each time it's invented, barring full-scale rioting. Much as with the Cold War.
“even though you're delivering code at a good pace, you're taking too long to deliver those Design Docs. Are you using AI? You should use more AI.”
This here is the crux of it I think… it’s often promoted that AI will give us the time to do the “real” engineering work of designing systems and really serving the user, but in practice all I’ve seen is further attempts at optimizing every last process with AI - just homogenizing every product and feature into slop.
It feels like every leader has been to some talking points boot camp where they’re incentivized to apply pressure to every part of their process - sort of a desperate attempt to justify the costs they’re incurring. I think we will look back at this and see how obviously short sighted it was.
AI is beat thought of as an exoskeleton, you'll be at a huge advantage if you learn how to use it properly, and you will, unfortunately fall behind if you don't. I still think we're going to need people who can reason about code, and the amount of code to reason about is exploding in volume. Think of it as doctors having access to better drugs and techniques - they can can cure more illness, but the bar and expectation of what they can do will just raise. And doctors are still well paid, because what they do is important and needs doing well.
Engineering hasn't gone away, you're now just directing things at a higher level. You are now a architect & manager (but you're managing agents not people).
Who sometimes has to deep dive & mentor a agent on solving the right problem.
>Agents do a really bad job at keeping codebases organized. If you do a disciplined way of development with agents by keeping all Documentation in markdown format, repo structure, Decision records and architecture, they do absolutely organized. Every new module should be documented and the editor configuration and coding patterns can be given as reference. this worked well for me. and it make enhancements, extensions development without any big troubles.
Software engineers with low self-esteem who built their entire identity as mechanical cognitive workers are having an identity crisis and spreading FUD.
Currently, LLMs are nothing more than amplification tools that require significant steering. If you think your job is mainly to take input from POs or managers, translate it into if/else statements and loops, and review PRs, then you never really understood your role. Software engineering—for those who went to university and studied it—is fundamentally about complexity management and cognitive automation. People in the field, or at least those with some math background who studied software engineering properly, understand that it's all about managing complexity; current tools are nowhere near replacing a software engineer. What they call "taste" is imagination, creativity, embodiment, a more intuitive understanding of context, and yes, superior intelligence compared to current AI. However, AI and LLMs are excellent at mechanical work and mimicking human intelligence, so use them for what they are, and stop whining.
Going forward, the world is ever-growing in complexity, and automation will become widespread everywhere. LLMs just unlocked another level. So basically, cognitive work will be automated—perhaps up to 90%—until the next breakthrough (if ever). You can sit and cry, or you can learn the tools and help shape the future.
Software engineers can automate the entire economy now, including the executives, yet they just sit there whining and crying. This is a self-esteem, confidence, and identity issue more than anything else.
>You can sit and cry, or you can learn the tools and help shape the future.
What exactly are you helping shape? The volume of your employers bank account?
Chinese Gen Zers are starting companies before graduating, people are generating music and starting their own studios, others are improving models and building harnesses, and the rest are on a mission to automate the entire knowledge economy—from healthcare to governance.
Regarding your employer's bank account: if that is all you were doing before, then that is all you will be doing after. You are just complaining about capitalism now. The irony, is that the means of production is now in the hands of millions. Those who are crying are those who paid their mortgages with for loops..well, I think they will continue doing so, with less hubris that's all. LLMs are nowhere near replacing full engineer.
So get a grip fellow engineers.
I used to be in the "AI will soon do all your thinking for you" camp, but I was overlooking a scenario: sometimes the gap between what you understand and what you're trying to achieve is so wide that no prompt can bridge it. Simply asking "what's the right question to ask?" doesn't feel enough, no matter how advanced LLMs become.
I read all the posts in this thread - but no one has a good idea to avoid software developer obsolescence. My guess is this profession has 5 more years. It was a good run while it lasted.
All the other white collar workers are in the same boat. A pillar of the economy is going to be destroyed with no obvious replacement in sight.
5 more years? That’s quite pessimistic, given how much evidence we have that LLM coding has as much of a long tail problem as any other tech we’ve created.
Which other profession has the same amount of training data freely available for the taking?
Don't need much training data for bank/insurance/retail analyst work - it's just basic reasoning and data retrieval. If AI could crack the programming nut - one of the most intellectually challenging professions - it can handle the rest with ease. The only human role will be high level monitoring - and even this will be largely automated so fewer will be required.
Sorry to reply to myself but I just recalled the recent Apple ad that ran last year about a corporate goofball who got his Apple device to fire off well crafted email slop to his manager who looked surprised/impressed. That trick will only last for a short time. The joke is that people like him and his manager will be the first to be fired.
The last sentence in the article is correct:
"Maybe I should consider transforming my woodworking hobby into a profession."
As an AI optimist, I think all forced labor should eventually be done by AI. People can then spend their time pursuing their own hobbies. Just as many people still play Go after AlphaGo appeared, because they genuinely love the game.
In the future, coding may return to being an art form. People will no longer focus on utility alone, but instead on the enjoyment of the process of writing code itself.
> As an AI optimist, I think all forced labor should eventually be done by AI. People can then spend their time pursuing their own hobbies. Just as many people still play Go after AlphaGo appeared, because they genuinely love the game.
And what sort of economic system do you imagine will be in place to support billions of people being able to just play Go all day long? How do you imagine the large capitalistic global powers transitioning into that state?
I think that huge deflation will follow for everything except land value.
If automation makes producing food so cheap that it is almost free than it is ridiculously easy to acquire it. Similarly automated construction.
The way I see it the economy will point towards outer space. That’s where most jobs and flow of economy will be.
However most people will have 10x times uplift in purchasing power compared to today so their relative poverty will be ridiculous for us to call it the poverty but they will still think they are poor and troubled.
Generally I don’t think it will be utopia for the people living in that moment but if you look from medieval times at today it looks like utopia for serfs from the past. You however wouldn’t call it an utopia because your standards grew as fast as your purchasing power.
I think that rich and poor will be separated by accessibility to anti age treatment and other bodily improvements.
The tragedy of the poors in the future will be living measly 80 year old life like a today millionaire and that will be considered lower class. Those people with wrinkles we don’t want to look at because of uncomfortable pangs of guilt.
So you believe that your work will be done by AI and you will enjoy life more? This is not a loaded question, just trying to understand what your future ideal day / week would look like as an "ai optimist".
That’s just not economically viable. Even if it becomes viable after some singularity event the path there will be 1000 the upheaval seen during the wipe out of manufacturing and mining
Shoemakers and horseback messengers complaining while Nike and FedEx deliver a million shoes or packages per day
We won't miss them
I'm thinking about taking a plumbing course.
This is good. We want less barrier to entry and more competition in software.
What work remains valuable when implementation becomes cheap? How about moving closer to ownership?
I think that in a product-centric or mission-centric perspective, effective automation is good, because it frees you up to do other important things. E.g., in gardening, time spent weeding, is time not spent surviving slug armageddon.
Businesses like a record of reliability, so devs going solo with AI is going to be a hard sell. I think we will know that AI is actually good enough when these AI providers start absorbing project management companies and hiring contractors to use their product instead of selling subscriptions.
I wonder how do people use LLMs so it does not hallucinates. Like 90% of the time the code is impeccable, but the remaining 10%... Let's say I determine the expertise by how well do people act of these 10%. For me, the first pillar is still there, but not in a good condition
Easy just add "Make no mistakes" to the proompt, clear skill issue.
In reality people who use LLMs so it does not hallucinate are the ones that just have to little knowledge to actually see when it does, because LLMs do and they always will. That is the only thing you can get with a stochastic word predictor.
I am also feeling anxious. I lucked out by having natural inclination towards software development, career which can provide good upper middle class life to anyone. But I feel like writing is on the wall. If I don’t find a way to pivot to something else, I might experience class migration, but in the opposite direction this time.
It’s a good time to save and move out to a cheaper country to buy money generating assets here. It’s not easy but if you have at least one million dollars in investment money, it’s arguably wiser than staying in US that penalises such passive lifestyle heavily. Sooner or later some medical bill will leave you bankrupt. Unlike in EU.
The market still seems to be hot for roles that provide leverage like platform engineers and Staff+ engineers
Software engineers are fungible commodities, in the wake of the LLM.
Reads like “AI is inevitable” propaganda to me
"Except that nobody cares anymore." Noone (from mid-management) cared about it also before. You hit the deadline, get promoted and leave the technical debt to the next one. Even if you're the one to deal with it, you set up the next project, get the budget, prioritize the issues etc. Not much changed in this regard with LLMs
I am kind of like of in the same place though roughly 5 years more than author.
I thought about going back to college, learning Math, Statistics, advanced Machine Learning and applying for research role at a frontier lab.
That's a super silly take. As much as I did math and even course on machine learning back in the days and I was making basic perceptron in code at university - to get back and be able to do so on frontier level that's years I don't have anymore.
Anthropic is doing all that also with their LLMs so that ship sailed.
Big thing is — business people are not going to spend time prompting LLM to make an application. If they do then they will become "programmers" and we all (experienced developers) know — you touch it you own it — they (business) will not bother running or taking responsibility.
Right now on r/sysadmin there was bunch of posts where admins have "vibe coded apps" requested to be "productionized". Those business types requesting don't know yet — you touch it you own it — they think they can vibe code app drop it at ops and it is all fun and games. When people will start requesting features, start nagging about bugs, start cursing on whatever changes they introduced it will be back to "hey maybe we will just get someone to do that for us".
It's not just about it taking the technical competence away from our job, it's taken away the joy [1] which I wrote about.
I feel like many of my peers are beating around the bush on this topic and in denial. Even if you accept it can do a large portion of the technical part of our work, we are just supervisors at this point making sure it doesn't do any stupid shit. What is the point? Where is the fun in this? Where is the challenge? At least I have enjoyed building my career over the last 20+ years and building software, but find little joy in the work I'm doing now.
I think we're going to see a massive exodus of folks leaving the profession and a huge mental health crisis, long before the folks working in other sectors realise what's hit them.
[1] https://deanclatworthy.com/2026/02/09/the-joy-of-programming...
LLMs are good at general solutions but not specific solutions. As industries evolve and laws, regulations, and practices change LLMs will struggle because those things are not included in its training set yet. We'll always need humans to push companies in new directions in order to compete, unless we eradicate capitalism altogether and then we're all out of luck. No competition means no incentive to try and be better than the next guy which means no new products and services for humans to develop that AI hasn't seen already.
> when I step outside my area of deep knowledge I can no longer call BS on the agents
It's still funny that 4 years into this mania the models can hallucinate basic ground truths, humans are increasingly not reviewing the output, and misusing LLMs where simple automation would suffice.
My wife does project management and works with a lot of tech leads. They came to her with a project plan deck, and she started questioning some weird dates.
The LLM was able to pull artifacts out of their issuer tracker, but it just.. hallucinated some of the dates in the process of creating a project plan deck out of the underlying data. These guys didn't care to review and notice, and who knows what else it hallucinated content wise. They were happy to send this project plan multiple levels up the food chain with hallucinated unreviewed dates.
5 years ago they would have just written a script and had none of this mess.
That’s why I use AI more like: Write a tool for me that does this.
Instead of directly: do this.
Preferably I would interweave code and AI queries where some function waits on prompt result too I think?? To avoid too big context hallucinations
I mean that would work for my use cases.
At least what I learned is that the less AI itself does in the context is the better so to say as critical LLM mistakes are approaching 100% of probability over time.
The crazy thing is how many people who can write code (with or without uAI) are in fact using the LLMs in the latter "go do this" mode.
There are a lot of non-tech people using these products in this manner.
Along these lines my friend is CTO at a non-tech firm and theres vibe coding happening in one department on a project that is going to churn $1M of tokens. Head of that department told him it's OK because instead of paying a SWE annual salary, they'll just pay $1M of tokens once forever.
People don't know what they don't know about software, SDLC, support, maintenance, etc. If code was something you write once and never think about again, most tech orgs could be 75% smaller.
My job as a staff engineer has turned into just reviewing slop farm vomit from offshore devs in Pakistan making pennies on the dollar given a slop code subscription and going wild.
I’ve lately just turned to having Claude do a quick /review, spot checking it, doing my own review and the. firing up some web agents to make the needed changes and just ignoring the back and forth because they don’t give a fuck anyway.
Just waiting for someone to notice and ask the obvious question at this point.
I will be happy if Claude lets me eliminate my offshore Genpact team. I don’t need slop from slop.
The direction I'm given is to take humans out of the loop. As much as possible. Everything AI. Automate everything. If you are in the loop you are overhead.
this is the exact WRONG approach. AI is a power tool not a fucking human replacement.
Though I doubt I'm telling you anything YOU didnt know...
Like I said, this is the direction I'm given. :|
don't worry, soon there will be no "software engineering" careers anymore.
Think of it like this
You’ve already faced this the entire time with… libraries on github.
If employers knew how much you can just use a new standard library, or ask you to “use React”, that’s a lot like asking you to use an LLM to speed things up. You also benefit from the collective wisdom of a lot of people. Do you write assembly or pixel shaders by hand?
While LLM's are beyond junior level at this point, they're still just that. I don't really agree that the first two pillars have been affected.
I've shared a story before that between now and 2 years ago a developer who solely relied on AI has produced the same hot garbage instancing system within the same time period. For example back in my day in 2 years I went from writing a system that struggled with few hundred players to one that could handle thousands and far beyond that. The person using AI 2 years ago wrote a system that didn't work and wrote a system 3 months ago that doesn't work.
Everyone is saying how great AI is, but they're missing that the driver is just as important AI wouldn't be able to achieve any of this without capable (often seniors) using it and giving it guidance. It's really a difference between "it works" and "it works without flaws".
Of course AI can produce things that also "work without flaws" with solved problems and someone "recreating" something that already exists with AI is not that special, a junior developer could accomplish the same thing given the time.
But I do agree that AI becoming part of performance reviews and all that is producing more productive developers which is going to drive the cost way down. In a way AI is stealing from a developers salary and giving it to the AI companies which is pretty ironic considering how cold developers seem towards artists.
Am I the only one that has noticed the massive increase in buggy software across almost every domain? Like, EVERYTHING has so many more bugs now. Things just break constantly. AI isn’t one shotting fixing bugs, it’s one shotting making hundreds of new ones every time it writes anything.
I think this experience is universal. The answer is the same as always has been - develop skills that are becoming most important. Right now that is (at least from what I can see): - Data analysis, data pipelines, models, etc. - Tacit business knowledge - architecture and design patterns (always has been, but now the scale os larger so this is even more important)
It's harsh but nobody cares if a model or a human made a system.
The "good" bits are that now automating anything and providing value from software is much easier. If I have an idea or a nitpick somewhere, I can just do it, up to a limit (which is quickly rising).
I have always been a generalist and generally interested in a very wide array of things, and this period has been the most exciting in my engineering career (13y now). Learning about anything is so frictionless, looking back at my first learning experience - picking up a fat C++ book and spending days/weeks debugging, while I can romanticize that, I would never go back.
I can also now write software solo or with an extremely small team at a huge scale in comparison, and that is super exciting.
A lot of skills that took sleepless nights to acquire, they are "gone", but I still don't regret anything or wouldn't go back. Their "usefulness" has degraded, true, but this has always been the case with engineering.
We are now able to spend much more time thinking about utility rather than low level implementation and imo that's great.
We have many challenges ahead of us, and there are seriously bad things, the biggest one I have experienced is the hours are increasing and mental load is vastly increasing as well. As capacity, speed and leverage increases, so do expectations and hours, and that is probably a social problem.
Sorry for the unstructured stream of thoughts, and this is just an opinion (quite an unpopular one I believe), I hope your distress decays away for a new excitement and new opportunities.
Thanks for the article .
The reason that I’m looking for an out is that it’s turned everyone I work with into imbeciles.
Nobody wants to think anymore. Coworkers are now just intermediaries for their LLMs. Talking to them is just talking to the LLM - sometimes directly copied and pasted, sometimes minimal effort to conceal what they’re doing. It is so disheartening.
And the sad part is, LLMs are incredible and can enable you to do much better work if you can stay in the loop, and stop focusing only on shipping speed. But from what I have observed, very few people care to do this. Who cares about substance when middle management thinks your productivity is 10x?
> Of course, I'm still employable because someone has to review the code and steer the robot. But I'm just another off-the-shelf engineer now. I have no domain expertise that another Sr. engineer steering an LLM cannot match. All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.
Ownership and responsibility are the new currency for the engineering staff. Willingness to implement these tools and then own the consequences of their use is what leadership is looking for. They want their cake while they eat cake, and they will keep those around who enable something approaching that experience. Owning the side effects of LLM use is more challenging than our own natural output because of the radical volume increase and unfamiliarity with low level details. However, I argue it is still possible. It has always been significantly more expedient to poke holes in someone (something) else's work than it is to perform that same work. And, the executives know this. They leverage this capability too.
The relationship between the business and the development team has been tenuous at best. I've rarely seen a technology team that was properly subservient to the business that ultimately signed their paychecks. I every case I have personally experienced, it is was like a hostage situation where the business owners are in constant terror of the technology people screwing them over in some infinitely nuanced way they or their lawyers could never understand. Many business owners are looking at this technology as a way out of the hostage situation. They noticed a window that was left unlocked. They are going for it right now. Whether or not they will succeed in their escape is a separate matter. Whether or not them being held hostage was justified is also a separate matter. It really helps to keep these things in their own lanes.
LLM is a powerful tool but it still doesn’t have the context that a person would have. A million tokens is a drop in the bucket compared to the overall context that the person guiding the LLM needs to keep it on track and being productive.
If you’re not a good engineer and you don’t have the domain knowledge, your token costs will be very high for whatever gets shipped, because you won’t be able to provide the context necessary to prompt machine efficiently.
Claude will still very often hallucinate bugs, explanations, domain requirements, that have no basis in reality. It will offer fixes and improvements that are pretty standard but not optimal. This is correctable if you catch it, but you need to review every line of code and comment, because in addition to being obviously wrong, it is often very subtle in the wrongness. For every bit of “slop” there is almost microslop, the places where it just kind of confidently guesses… and doesn’t tell you… but sometimes is correct anyway.
The “problem” is there’s less low hanging fruit. You have to know a lot to add value beyond being a middleman gating the slop. You have to really pay attention to the details to find some of the errors that it’s making.
So blog with single post hyping LLMs. Oh and the domain name "human-in-the-loop". Call me suspicious.
Code quality and architecture still matter, because they also make it easier for LLMs to reason about the system.
That said, Opus 4.8 and Codex 5.5 both can write code that is higher quality than your average engineer. They are not quite there yet in terms of code re-use, but I think that's a solvable problem.
Running a couple of "scan for potential refactors"/"any duplicated code" prompt threads is already a long way there.
> I'm just another off-the-shelf engineer now
You're wrong there. You are capable of judging the outcome of the llm.
> But I don't know what to think about the long-term.
Don't you think it all has taken long enough. When I look back at the beginning of my career and compare what we do now ... I cannot shake the feeling we're essentially still solving he same problems and we have accepted that as being normal. Complexity skyrocketed, (abstraction) layers got added but the needle didn't move exponentially together with that. I think the IT industry as a whole gets what it deserves, thinking that we would remain the maze masters of the mazes we create.
> Maybe I should consider transforming my woodworking hobby into a profession...
I'm looking for 8 (affordable) oak panel doors with the exact same measurements as my current doors so I can replace them. That shouldn't be too hard to find you'd think right?
I can't write what I really think because my name is attached to my account.
Let me just say AI is not nearly as good as the billions of dollars in marketing spend say.
We are months away from catastrophic bed shitting and the tech industry will pay the piper.
I've wondered about this a lot. I am brand new to software engineering, fully powered by AI coding. Traditional software engineers have to pivot hard or the are going to be left in the dust. The slow, methodical, take two days to put a change on a production site approach are over. I'm shipping exponentially faster than a co-worker who hasn't embraced AI yet.
This anonymous article is likely more FUD from the AI industry. "Just give up,you can't beat the machine. Please go quietly, we want to take your place and it's easier for everybody if you don't resist because you believe it's pointless"
'Maybe I should consider woodworking' - Fuck off.
resistance is futile
If corporations really thought LLMs were a great cost-savings tool, then the obvious target for replacement are not the lower-paid staff, but the higher-paid staff - the ‘product managers and stakeholders’. That justifies token burn, replacing the 7- and 8-figure people, right?
But that’s not the real goal, is it? The goal is to inflate the stock value, take the cream off the top, and dump the whole business on the pension funds, maybe creating a too-big-to-fail scenario where the government steps in an bails out the industry as with the airlines during Covid.
This is why all the testimonials and narratives are so suspect - nobody knows what fraction of online posts were created simply to sell the narrative that LLMs are this incredible disruptive tool that will change the world, solely in order to create FOMO in the investor class.
In this particular case, I’d like to see links to samples of LLM created codebases for “PCI compliance, double-entry ledgers, escrows, reconciliation, payment lifecycles, bank transfer idempotency”. It should be easy to put an open-source LLM-generated version up on github, right? And if not, why not?
move yourself to regenerative ag. take a look.
I have no idea what you guys are up to, but it is just a job, it is just a role, it says nothing about you or who you are and it is not tied to your meaning. If you make it so and your perception is aligned with that, then you are not in control of what happens to you. What kind of slavery it is to give other people so much control over you is crazy
I'm not worried. You cannot hold a machine accountable and there's no way OpenAI, Anthropic, etc. are going to take on that kind of liability if some code resulted in a major outage or a lawsuit. Perhaps that's the signal I'd be looking for: so much confidence in the product that they put their money where their mouth is.
Besides, you can look at the websites/apps/software you use everyday and evaluate whether or not the agentic era has produced better results. Personally, there's still plenty of bugs and annoyances. Banks still using SMS 2FA, library breakages in minor version bumps, inconsistent UIs between web and mobile, etc.
If all that was a hurdle before... because humans, regulations, or something else... then surely these magical machines that can supposedly replace us and do it much faster would've handled it by now? And they wouldn't introduce more bugs[0], would they? ;)
0: https://www.0xsid.com/blog/meta-account-takeover-fiasco
The glory days are over. In the future, one software engineer will be able to support multiple product areas much like how one HR team can support 1,000's of employees.
LLMs have made domain knowledge and reasoning "cheap"; it doesn't matter if the output is lower quality - look around you for countless examples of where cheap wins and "cheap" continues to improve.
Good luck out there; we will all need it.
This has been said millions of times but yet you felt the need to say this again. maybe our jobs are safe :)
Yes this has been my experience as well.
It's crazy the crazed anti-AI people yelling with foam with their mouth that it's useless, meanwhile Claude for me at work oneshots complex bugs in a massive project with a 95% success rate. And the customer happiness survey has never been as good as it's now btw
This was a good summary. I feel similar. At this point I think 95% of the skills I've developed over the 2 decades are basically useless. Prior to 2023 I felt like every new skill only made me more employable, but now I don't really see any software skills that are safe from AI today. Even the ones that are very likely won't be in a year or two so there's no point in learning.
I've said this in other threads, but it concerns me how little the average person is preparing for what's coming right now... It seems people are making decisions as if their jobs and income are safe when in reality their entire profession could be gone in less than a decade. People in this comment thread saying crap like "yea, but the code LLMs write still isn't that good by my standards" are totally missing the trend. The fact LLMs are even one-shotting extremely technically difficult problems was something almost no one thought they'd be able to do by now a couple of years ago. Even I as someone who pushed back against this and thought they would become extremely competent within years am genuinely amazed at just how good they are. Trust me, regardless of your opinions, your job and career is at risk.
Another thing to understand is that if AI replaces workers in a variety of fields from SWE, accounting, customer support, graphic design, etc. Then it's likely going to be hard to fine other jobs to pivot into because when unemployment increases that significantly everyone will competing for the same limited number of jobs. Some will fine something, but most will struggle to find anything.
I hear a lot of people talking about how they'll just go into 'x' field if AI comes for their job, but realistically you'll need years of reskilling and you're assuming that in a world where other people are also losing their jobs, and where AI is touching ever more forms of work, that you'll easily be able to get a job in that other field. And I'm not saying that won't happen, just that this isn't as realistic or as safe of a bet as some people seem to think it is. You're also likely deluded about how hard it is to find work because you've been in software for the last decade.
Please, please, please, start preparing for what's coming. The economy is going to get extremely rough over the next 10 years. You need to be prepared to be without income for years, if not indefinitely.
So instead of a programmer, you become a software designer. I recently came across the idea of building fantasy for the player (in context of games), but now that I think more about it. Onlyfans, is just that. Advertising, Beauty products, novels, games, TV shows, and so on. You're really just creating / selling a fantasy for vast majority of people. Most people will never lose that 30 lbs, but you can sell them all kinds of products to fuel the fantasy of them losing that weight, being beautiful, rich, healthy and so on. So an LLM replacing the need for you to write every piece of code, is actually kind of freeing. You as a a former programmer, should embrace your new creative role. Writing code, at least for me was always slow and tedious. I just want to be able to express the ideas I have, so LLMs just make it possible to build things I never could otherwise.
> But now the market is shaping everyone into becoming a generalist.
This is interesting because in my field of VC everyone says generalists are dying.
These are the last days of software. Use the AI's and build cool shit NOW.
Maybe just maybe here in HN we are in an echo chamber that is convincing us that there is a theoretical limit to how far the LLMs can make progress. It’s not unthinkable that LLMs will make better overall architectural decisions or follow the good practices better or understand the problem in bigger picture (more access to company/product context already makes a huge difference)
Lots of jobs have been automated away and careers based on those jobs faded away in history. Maybe in near future there won’t be a ton of opportunities for software engineers in the traditional form. I’m also embracing for that future.
There were people called calculators that did manual calculations in the past. There were people hand weaving all the fabric. There were people painting cars in the factory. All those jobs are gone for the most part.
We are sitting here portending there is going to be demand for software engineers managing those engineer robots but let’s be real. The demand for software is not increasing at the rate software engineering is becoming efficient using those robots. Some (many) of us have to find new careers.
The issue is that the people evaluating you don't know the difference between legit domain expertise and pure bullshit.
computers are made for automation. programmers were always working on automating things, making other things obsolete, and we have been killing jobs for decades. did you really think we would suddenly stop when it's your job? i'm happy this is happening, genuinely giddy
There is an element of human nature that is known as self delusion and it is extremely common. Almost everyone on HN is suffering from a form of self delusion.
Usually when a human self deludes they do it when they're identity is under threat. People would rather hold on to identity then face the truth at the cost of their identity. That is what is going on in almost every HN thread that has to do with this topic.
A good example is religion. Someone who is intelligent, but born into a religion, will have a hard time giving up that religion EVEN when presented with logical/rational/realistic arguments for why that religion is false. They will rationalize the most convenient reasoning to maintain their own identity.
I mean think about it. Even the concept of religion is obviously false. It's not science, it talks about phantasmic beings that OBVIOUSLY don't exist. It's inconsistent among different groups as in there's thousands of religions in the world and nobody thinks the obvious of the fact that if only religion can be correct, then most of the world is fundamentally believing a total lie.
Anyway, the same thing is happening with AI. AI is eroding our identity as software engineers. So you'll see rationalizations in this thread in attempt to protect that identity. The biggest excuse is LLMs are hallucinate and are often wrong and fortunately for humans... this rationalization still works because it's still very true.
However what people are not mentioning is the obvious. People are avoiding it because they are delusional. The topic of this thread is "erosion" of "software engineering career" AND that is utterly true. ADDITIONALLY the error rate of LLMs have been going down. AI in general is improving. The erosion is real and obvious.
But you will see here on this thread that people are not talking about the erosion. They are holding on to the one last rationalization that is a differentiator without ever thinking about how that differentiator is "eroding" even though "erosion" is the LITERAL topic of the conversation.
"Except that nobody cares anymore."
:-(
I don't see it as negatively, in that there are specific trade-offs.
For one: LLMs make a lot of mistakes. We all see that when they hallucinate search results and what not. But, possibly even more important than that, you ultimately become dependent on some big company via LLMs. Perhaps that trade-off is worth it for some companies, but I personally don't want to become dependent on these companies. I actually consider it a hostile attack from the USA, and under Trump this is even more obvious.
Another thing that sucks by LLMs is documentation. They generate a lot of crap that is useless. So that's another area where humans could be better.
Admittedly a lot of vibe-coded AI slop is also useful in some ways, but it has started to make me rather angry in general - youtube already spoiled me here. I no longer want to see ANY AI videos at all whatsoever. It just wastes my time. I am not here to empower skynet version 20.2.
I secretly wish LLMs take my job away because I'll get about two years of unprogrammed rest, which I absolutely will not take of my own accord. But it's unlikely to happen.