Posted by poisonfountain 5 hours ago
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.
My guess is the model makes the same mistakes as the programmers: taking 'rules' literally, unaware of sectoral joint understanding, validated interpretations and habits. (btw. this is often on the non-tech side also a difference between regulatory and legal. The former are much more result oriented while the latter are primarily risk averse.
IME this is less the fault of IT and more so bad auditors that won't consider, or just don't understand, what compensating controls are. If it doesn't meet their little checklist exactly, they fail the audit.
As an enterprise architect, these are all part of the meetings you have with compliance when you are working on major projects. I have had the privilege of working with some excellent compliance officers, and they are the opposite of the nay-saying caricature that is often painted of them. I found these people to be extremely creative and helpful, working together towards solutions rather than stalling or nixing viable progress.
It doesn't feel like we're living in the same world of regulation that existed prior to DOGE.
I'm not implying anything else. I used your own "literal" wording to refer to the "more strict than yours" interpretation.
I suppose I should have used scare quotes around "literal".
Company politics, feudal wars, fiefdom protections, backstabbing and outright sabotaging, now there's a daily occurrence and many minions are cannon fodder in those skirmishes, but they usually stay clear of regulatory issues minefields.
If the company you work for actually had such a no-fault culture, I doubt you'd be criticizing programmers so aggressively for being sticklers, but would instead be trying to understand and account for the systemic factors (including human factors) behind their behavior.
I don't see why developers should be in trouble. Developers don't make unilateral decisions on non-trivial compliance matters. A finding of non-compliance at a financial institution would typically be the result of an investigation, a disagreement with the regulator or a court ruling. It would come years after the organisation as a whole decided to adopt the interpretation in question.
Then the rules should enumerate all the ways. From your posts, you come across as if programmers don't know what they are doing which is insulting to those who work in mission critical industries like aviation where a programmer could be criminally charged if he/she didn't implement the specs STRICTLY.
Is neither what I said nor believe.
There’s a reason it’s called “judgement”
My point was simply that it's easy to scoff at someone else being careful if it's their neck and not yours.
I've seen accidental non-compliance. I've seen what I would call negligent compliance, where a company attempted to be compliant but didn't meet full, correct compliance (one example I've seen is that a company assigned resources to compliance and forgot to increase resources as workload increased, causing them to be increasingly behind on compliance work), but I've never seen a company that just decided to pretend to be compliant knowing that they were not.
Security, GDPR, backups, build pipelines, disaster recovery, most of it will be faked, half-heartedly done once or ignored entirely.
Then there's the more abstract things like scalability, idempotency when integrating with external APIs, error recovery, accessibility, UX, etc.
Almost always that sort of stuff will have been entirely ignored, or there will be a fig leaf over a real mess of misunderstood standards or manual intervention steps.
Startup developers usually have to be generalists as they often wear many hats, so things that need deeper domain knowledge get done to a bare minimum.
The problem is that sucks, even if all software engineers keep their jobs and salaries, the floor is still pulled out from under us. Imagine if a surgeons job was to supervise robot surgeons from a remote computer, or a woodworker just signs off on work before the machines do all the cutting and assembly. Sure they still have important jobs in their field but the soul & humanity of their skill is gone.
Does the woodworker who shape using a handsaw use less "soul" than the one who uses a machine?
Does the musician who use a DAW and VSTs instead of analogue tape recorders create music with less "soul"?
Does the painter who buys acryllic paint instead of synthesizing their own dye from plants use less "soul"?
As technological innovation progresses, the barrier to creation falls. The process of creating something is not to be conflated with the final piece of art itself.
This isn't like the step from hand saws to power saws, and it's disingenuous to pretend like it is. This is what the startup machine has been doing to every industry... finding... "inefficiencies" and "optimizing" them.
Compare to this to prompting an LLM: “Generate a third person where game with a view from above where you can steal cars, shoot at people, run from the police, etc.” Anybody with access to the tool can do this, and the results are just another uninspiring GTA clone that you would imagine.
The latter is more like a carpenter ordering their “work” from alibaba then it is like using a skill saw.
It's when a woodworker, musician or painter completely outsources their work and just marks what's wrong, sending those parts back. Yes, the final art piece might be the same, but the artist definitely uses less of their "soul".
For me, AIs have actually made the job more soulful, not less. For one thing, it lets me use the part of my mind that is good at human language, not just the part of my mind that is good at software. This makes the job feel a bit less one-dimensional in terms of what parts of me are engaged while doing it. For another, I find it liberating to no longer have to think much about boilerplate code or to spend time roaming around the Internet looking up documentation of various language syntax and API details, the vast majority of which are arbitrary rather than being based on any kind of mathematical beauty. For me it makes the job more soulful that I can think of the job on a higher level instead of having to spend effort on arbitrary and tedious details.
Of course there is still the question of "will the job even exist in a few years, at least for more than a relatively small number of people?". But that's a separate question. For now at least, I am finding that for me AIs have brought a lot more soul and humanity to the job than it ever had before.
However, if I were just having to do things for the man, I might have a rather different take on all this.
When industrialization hit, we definitely lost a ton of craftsmanship and craftsman, but a standard Ikea chair is less likely to wobble than the average chair at a much better price (for a random example). Yes, we traded artistry for convenience, but what we really did was bifurcate our needs between "some place stable to sit" from "a beautiful chair for my home". Most people wanted the former more than the latter, and the same applies to software.
If we split the roles into buckets, many woodworkers disappeared, some became artisans, some became designers for industrially-produced products, and some catered to Luddites for a long transitional period. Despite Anthropic's claims, SWEs won't disappear in a year but over a generation or two, no matter how good LLMs become.
Obviously software is much more complicated and integrated into other elements of business, which in a way makes it more vulnerable to AI taking over and in another way will be at the mercy of larger shifts to how businesses organize human roles and responsibilities. What we call "taste" comes down to "intent" - what the hell does a company do? What should it be doing and how should it operate? These will be the only questions that matter and the one thing LLMs can't replace since they will always choose the most default path. So I think human's roles will be to inject intent/taste at different levels of abstraction throughout an organization.
But really that particular issue could have been solved by literally just telling it in a markdown file or instructions something like "verify all facts or compliance requirements with web search and include citations in responses".
“Verify all facts and compliance requirements” leaves enormous holes even if you assume the LLM has a concept of facts and requirements (it does not).
What facts? What requirements? For what industry? For what subset of that industry? For what country or countries that you will be doing business in? Are these current “facts” and “requirements” or is the LLM referencing a dusty article from 1992 for which the subject matter has been radically overhauled?
In my job I regularly see small but incredibly important mistakes like this lead to major issues. Some of those are human driven but increasingly the defense of the person responsible has turned into “Claude said it was fine though!”
Additionally, using a specific tool does not suddenly give the model common sense enough to say “this piece of information doesn’t answer the question of whether this solution fits in this specific industry at this time in this place”.
IME people would benefit greatly from the process, albeit tedious and time-consuming, of testing out the same prompt sequence/session with the exact same model multiple times. It becomes clear extremely quickly how capable but unreliable and inconsistent a model can be even when given the same context. If you have ever completed a long, complicated task with an agent and then lost the session and tried doing the same thing again from scratch you may have had the experience of seeing the subtle changes that come up in the model's thinking which lead it to accept or reject certain paths and ignore or incorporate prompt instructions like the one you've provided.
We have long historical experience and innate tools for detecting and mitigating errors made by humans. If we can't apply those to automation, then even fewer total mistakes may end up being a worse outcome.
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.
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
You can do that, sure. But doing so negates any improvements in speed the LLM brought. And at that point, you may as well just do it yourself to begin with.
I use GenAI tools when coding a lot, but I do not vibe code. I go through everything it generated, and we iterate. And yes, it doesn't save me a lot of time. But what it does do is free up mental capacity in a similar manner. But instead of syntax, it's more complicated patterns. Maybe I don't remember how to stitch something together, but i know it can be done. Instead of spending the time to look it up and then code it, I just tell it to do it for me.
Or are current AIs too similar for that to be fruitful?
regulation questions. even the simple ones, AI gets all the time wrong. it wasn't Mythos, but other models like opus.
I can adjust the view on this topic if/when we get access to mythos.
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?
The parent is implying they would prefer an AI when working in the airline and health industry because it makes less errors. Read the comment again.
They have not said, "Hey, I work in the airline and health industry and I'd love to use AI for a couple of the bullshit IT UIs we have as long as we can put guardrails on the AI to stay in its lane."
I asked a yes or no question. The guardrails you can put to mitigate errors are the same guardrails pre-AI for the humans (tests, regressions, reviews). If you were wary of employing a top lead engineer with verifiable dementia prior to AI for a mission critical system, logic implies you should think twice giving that much responsibility to an AI as well.
> The hallucination thing I think is mostly overblown
Can you predict when and how the SOTA model will hallucinate? Yes or no. Can you predict the severity impact of that error beforehand? Yes or no.
>from speaking to colleagues it seems to vary wildly depending on which model and harness you are using
You have partially answered my question it would seem.
No, but the same can be said for your colleagues. You might call what the LLM does hallucinations, I'd call them mistakes. I think we have totally forgotten that humans make them all the time and are confidently wrong too.
Your original question, doesn't really get to the bottom of the point I'm trying to make, and I don't really feel it fairly represents the issue we are talking about here. They are not the same things.
Also, if a human does this, you can replace them and get a human who will not do it. The default for an LLM is to generate plausible-looking text that may or may not be completely incoherent. That is not the default for a human. Again, if you find that your colleague consistently fabricates APIs, you can hire someone who isn't crazy instead, but you cannot do the same with LLMs.
That's absolutely false. My collegues don't routinely and confidently invent apis that are not there, or spectacularly and repeatedly misunderstand the purpose of certain functions or exhibit extreme forgetfullness. Especially when I've warned them. Hallucinations and confabulations in otherwise healthy individuals are mental disorders. When I ask them why they made an certain kind of error, I can expect to get a reasonable answer. No one has uttered the phrase "Bob hallucinated again while writing those tests" when the Bob in question is a human.
Calling hallucinations simply mistakes does not seem to me to be a healthy way to reason about LLMs. I can ask a collegue how well they can program in Ada and adjust my expectations on productivity and bug rates. I can't ask an LLM how well they can code in Ada (just a throwaway example), or even how much of Ada was in its training data. I have to actually spend money and spend time code reviewing before I can even formulate any expectations at all.
Well too bad, the problem is that they also produce things much faster than humans so errors will compound quicker.
And this is fine. Developing new software with a really smart intern is the same, you, as an expert, need to bring your experience/expertise on the table to have everything right. Because experience needs time.
Did it do the correct job once you put the regulations doc(s) in the context?
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...
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.
Well I use tab completion, of course. And I copy-paste snippets from LLM more often than from SO now. But otherwise not much has changed in my career in the last 5 years. Is this different for you?
I'm not fundamentally opposed to code generation, and I use LLMs for some taks, but I don't see myself vibecoding whole pages of production code. I vibecoded a throwaway note-taking app for myself though.
If the AI is producing what you tell it to, why are you needed?
No, thank you. I have used the new tools, determined that they aren't helpful to me, and set them aside as I would with any other bad tool. I don't feel the need to let hype take the steering wheel.
Exactly. You are free to use openclaw or a coding agent to build a competing bank, hedge-fund, hospital or even a new airliner because the previous ones were built by humans. Surely an AI can do it better by itself.
So why haven't you done it yet?
Yes, me. Yes, I tried LLMs for what I am doing and will try again in few months. No, there was no noticeable or clear improvement over doing it manually.
Yes, I am using some LLMs for some purposes but Claude Code had slight improvement, if any, not worth introducing proprietary dependency.
I work at a big tech company and I don't know a single person that still hand writes code. Most people haven't hand written code for at least half a year now.
I do wonder what sort of bug is making its rounds on HN that people here find this so shocking and unbelievable.
Because we can actually see the disjointed slop that Anthropic produces. And when issues happen, they can't fix them for weeks on end because no one understands what code does anymore, and all of their "hard problems causing issues" they blog about are literally "if we had actual engineers this wouldn't even be an issue to begin with". Like this bullshit they had in spring: https://www.anthropic.com/engineering/april-23-postmortem
> It would be like a developer in 2020 claiming that he only writes assembly because compilers can’t be trusted.
LLMs are not compilers. For a few very obvious reasons I'll leave as an exercise to figure out
The original Mythos release used ASan to filter false-positives so it was able to maintain a good FPR, but when Mythos moves into domains that don't have a readily available oracle to help filter hits, the result is a deluge of false bullshit.
"Make it better" with no additional or reasonable previous explanation of what better might mean.
"AI will figure it out" not for pattern extraction, but for a full blown analysis with equally generic prompt all confidently stated by an executive telling people working it how it works
So the question remains if non-programmers will adapt, the LLMs will accept wider range of input styles, or .. its just another abstraction layer for devs to use.
I've observed this in the wild where someone is iterating with an LLM and giving it only negative feedback. For example responding to edits with "don't make it blue" rather than "keep the existing button shape, and change the color back to green".
The LLM doesn't really come back the way a human would and say "so what color do you want?".. it just, guesses. Now abstract that to more complex tasks.
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
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.
As for whether or not LLMs can write unit tests. The answer is yes.
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.
other than there are "internal micro feedback loops" during development?
Doing the above doesn't actually make the model smarter, so, if it couldn't get to correct code with fewer steps, then the light you see at the end of the tunnel is an oncoming train.
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.
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?
You're the only one coming away thinking there was a net negative experience.
The only thought-ptovoking discussion should be "why the hell do we have this stochastic parrot anywhere near out codebase"
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.
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?
How many gaps have humans not caught?
> But if “one error is too many” is your point of view
Yes, in regulated industries "one error is too many" is the only right approach.
Yes, humans also make errors, and there you have a range of options: from tracing and finding the causes of the error (and tightening processes) to literally jailing those responsible. Your hallucination machine will happily "identify" 17 gaps, and create 34 more. And no, there are no processes to make it better. The "make no mistakes" incantation will happily be ignored for obvious reasons, regardless of how many forms of it you throw at it.
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.
> the code in question had already been reviewed by human counsel
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.
So, in this case, the LLM's behavior was equivalent to the behavior of the resistance during WWII.
I think that book should be required reading for all engineering students.
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.
But there are certainly 0.1x engineers
All of this to say that it's not just experience that makes one a better engineer.
This is giving too much credit to LLM. I think LLMs are great and it is incredibly useful both in personal and professional settings. However, it exist on a separate plane than human workers in the tools category.
Sooner or later, people will find out that LLMs only overlaps with existing human hierarchy (e.g. junior dev X%, senior dev Y%, etc), but almost never 100%. If it was 100% to a certain position, you are probably using the humans wrong to begin with there - since humans have one of the most priced thing that I don't see an single ounce out of LLMs: initiative
LLMs are going to show that there's a huge divide in "engineers" between people who love "coding" and people who like "engineering".
The group of people kicking and screaming the most are the people who love code and don't want to see their coding go away.
These are typically the build vs buy folks. "We can't use anything anyone else wrote, I can do it better..."
What do you think Staff level engineers do? They don't sit around coding all day.
Writing the code is just something you had to do in the past to get the job done.
What you get paid to do is "engineer" and the two are separate. Coding is very small part of the average engineer's job.
And yet the vast majority of engineers think that the world is going to end if they aren't spending most of their time "coding".
The majority of my time is an engineering manager has been teaching “engineers” how to actually do engineering with any kind of rigor
The number of engineers who have an absolutely no theoretical structural or system basis for what they’re doing is the vast vast majority
Famously a net loss for humanity.
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.
But, besides coding skills (which some possess), the engineering, social, and business ones are close to non existent.
There was also another study I cannot find where 56% of engineering graduates struggled to write a fizz buzz.
I think people highly underestimate how long is the average developer, closed in their bubbles of mostly well established software teams that forget that for each of them there's 10 software consultants in southern Europe glueing APIs with trial and error on Java 8 monstrosities.
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?
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.
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.
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.
If a nurse does something incorrectly, they can lose their license. Ensuring that nurse will never be a nurse again. There is a very clear path of accountability and very clear ways to mitigate it.
For instance, if a nurse is drunk and you recognize there is a pattern of people showing up drunk, you institute drug tests and breathalyzers and move on.
While we probably won't have LLM's autonomously performing procedures, they are 100% parsing documentation, reading lab results, making suggestions, etc. And right now, the burden has been placed squarely on the clinicians themselves. It'll feed them them the data, ask if they approve/agree, and then essentially wash their hands of accountability. Let's say an LLM starts incorrectly reading lab results, how is that fixed/remedied? A prompt update? Additional safeguards? Adjusting the temperature? Changing a model?
This is a far different type of engineering that still feels pretty new. Granted, I'm still an amateur in this space (I use Claude Code a decent bit), but it feels really opaque to me right.
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?
Even Solarwinds is still alive.
So that is starting to dig deeper than a plain mistake. I guess we will soon-ish witness the first AI slop trial going on, this will be interesting to follow
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)
Yes
I understand the frustration of spending years nurturing a skill and then seeing its value decline.But this isn’t really an LLM problem. The same thing happened to factory workers, typists, draftsmen, and many others before. The technology changes, but the underlying issue is the economic system we live in, where the market can suddenly decide that something you’ve spent years mastering is worth much less than before.
LLMs are not creating that dynamic. They’re just accelerating it.
I like your comment, want to try to expand on it
Comment long but there is a TL;DR at the bottom
My theory is that there are 4 areas to domain knowledge worth taking about here - there may be more but I like 2*2 matrices
1) explicit internal requirements - core of how the how the app should work towards achieving your business objectives - code expresses what should be done and to a pretty large extent, why it should be done - from business unit requirements - we are building a tool to do “X”
2) implicit internal requirements - core of how the how the app should work towards achieving your business parameters and constraints
eg profit = selling price - ( total of costs )
- code expresses what should be done but really can’t express why. At best it is in the comments 3) implicit internal requirements - core of how the how the app should work towards achieving your business constraints - code expresses what should be done but really can’t express why. At best it is in the comments
eg if item is “rocket” , shipping = $1m ( we only make rockets in Antarctica and shipping from there is $1m)
4) implicit external requirements - core of how the how the app should work towards achieving your business constraints - code expresses what should be done but really can’t express why. At best it is in the comments
eg if item is “rocket” , add a 3 month gating stage to get approval from government to sell the item and do not collect payment till gating approved - AI can see the code but has no idea why it has to be done
These come from partners, regulation, compliance, auditability etc
So, my theory
AI can be good at the explicit stuff trivially (1, 2) but cannot be good at the implicit stuff (3,4)
It might be able to figure out implicit stuff needs to be done but will probably not be able to figure out why it needs to be done and it will definitely not be able to definitively figure out edge cases for when to do it / not do it
As long as you focus on implicit stuff, you will be fine for a little bit
TL;DR - become good and keep being good at being the person who understands the implicit external drivers of software dev
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.
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.
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.
Most surprising to me about the article was the desire for OP's company to use AI for design docs. I feel like AI-generated design docs are some of the worst -- basically treating English as a programming language. They aren't enjoyable to read, and they often miss the forest for the trees. A human written sketch explaining why we're here and what we're working towards is still meaningful and important. If you want code-level details of every decision and algorithm, we have code for that.
I have mixed feelings on whether these documents are useful LLM inputs. I did a project where I carefully paired with Claude Code on producing a specification that another model would actually implement. I'm not sure it saved me any time, and it was very un-fun. (I kind of blame Opus 4.7 xhigh for this. It ain't speedy.) I feel like I can nitpick code to get exactly what I want, but defining exactly what I want an auto-mode LLM to go and do, in English, is much more difficult. I don't think the PLAN.md I generated would have been useful for a human trying to understand the system (too verbose), and Claude Code still made its usual mistakes that I have reminded it a billion times not to make (t.Context() in tests, not context.Background()!), so I'm just not sure it was worth it. I would say I probably wouldn't do it again in the near future. A rough sketch to get humans on board and to get the high level details worked out, written by hand, and then pairing with the LLM on actually typing in the code seems the most productive to me. But I do try to go outside my comfort zone once in a while to test the edges of these tools. They are very impressive and are worth a lot of the hype. (I know I will never write a YAML file again. I hate it more than anything, and Claude is amazing at it. But I worry I wouldn't feel the same way if I hadn't already had 8 years of k8s experience.)
Love the metaphor. Planes are sophisticated machines capable of auto-piloting, but humans are still needed to ultimately pilot the beast.
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).
I've seen first hand what less experienced developers produce using the same models, your 90% accuracy suddenly drops to 50%...
We had a PoC in place to get fabric, it had like 500 hours allocated for what I did in a week with cowork, and my product is actually on secure vnet network with Azure identity security with both a test and a production environment delivering actual data.
Cowork even made the damn powerpoint slideshows for decision makers.
The single saving grace right now is that it apparently isn't easy for everyone to do this yet. But I didn't use a whole lot of my knowledge on software engineering to make any of it happen, not even the pandas and arrow code that moves the data behind the scenes. I mainly used my knowledge of NIS2 compliance and general data architecture in a step-by-step process. To me anyone with common sense should be able of doing this, and I really don't think I'm special... but then I teach other people AI at our company and they can barely get it to create a running program. Which is fine for now, but I have to work another 20ish years before I retire, and by then a lot of young people will have grown up with AI, and like I said, I'm not special. I think the only thing that differentes me is that I mash the buttons until it works but also have decades of security and compliance hammered into me.
I learnt a lot about the domain and how to effectively write programs for it: PCI compliance, double-entry ledgers, escrows, reconciliation, payment lifecycles, bank transfer idempotency, etc.
It was, then, obvious that I should focus my career on becoming an expert on that domain to stand out as a professional and differentiate myself in a field that showed signs of an increasing need for domain specialists."
He said "Last year, I got hired by a company in the finance workspace.".
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".)
This is the case perhaps 95% of the time.
Oversight is very important, and architectural thinking cannot yet be outsourced, only execution.
Like its only focused on solving the local problem as easy as possible
This is domain expertise - software engineers are not needed for that. Ofc often senior sws are expert in it, but they aren't necessary.
Traditionally its been useful for frictionless production to have engineers to be able to do maybe 90% of their work without consulting the business experts but this is the whole crux of the moment TFA discusses - "tradition" is over.
In this new world its now the job of a senior engineer not to have this domain expertise themselves, but to know how to ensure the agents have it, or can acquire it and it be verifiably correct.
Senior engineers who hang on to the idea that their advanced business domain expertise makes them safe will soon be as dead in the water as juniors who haven't pivoted.
They are very good at writing code and debugging visible errors- but that's like 50% the harness.
Would a skill which forces you and LLM to reach a shared understanding of the product features and the regulations those features are supposed to capture be of help here? The main idea is we provide documents to the LLM and it asks lot of questions which clear ambiguity and possible misconceptions the LLM might have. I would suggest please take a look at skills. They are really helpful.
This kind of works but the difficulty is that you have to be very explicit about everything. It was mentioned in a spec document that a particular excel file is treated as a source of truth throughout the whole company and it is treated as an append only database. The agent still decided to add a check to see if a previous row was modified. It pushed back on its decision when asked why it decided to do so. "What if someone entered it wrong and had to correct it"? Valid question but it's not my teams responsibility to check for it
This check makes sense from a traditional development view point and that's why the agent did it. I would say it's good practice too but it's beyond the scope of the project it was working on. If what you are doing is beyond the norm you have to watch out for things like this
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.
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.
I played with it a bit, and no, it doesn't! And I am talking as someone with limited music culture, musicians are likely to be even more critical.
For the first few tries, it sounds impressive and the tunes are catchy. It used to sound wrong in the background but they mostly (but not completely) fixed that. However, after a few dozen songs, it starts to always sound the same. It is all generic stuff, the songs tell no story, it is a bit like the kind of music that accompany corporate advertisement. You can try to be more precise in your prompt, but I never had any success, it will just ignore most of the details that could make your song interesting.
The most interesting result I had was actually when I managed to get it off rails, a bug more or less. I asked it to mix two very different genres together, and it made something unsettling in a way I don't remember hearing before. But as always, further working on it proved extremely difficult, as it always tried to go back to making generic stuff, ignoring the details you give it.
Suno can do remixes though. And it is a bit like with code. LLMs are very good at porting, when you already have something that works, it can make it work in another language. But if you just have an idea, it will screw up at anything original. If you want a LLM to implement your idea properly, you have to give it so much guidance that it amounts to writing the code yourself, while struggling with the ambiguousness of natural languages.
i actually was discussing that with a guy i met the other day, an old school producer, did succesful stuff 30 years ago. He used SUNO to reinterpret old and ideas of his, in his judgement it did an excellent job and lets him create many songs daily if he want.
Sounds familliar? the good old "let AI be steered by experienced X and boost productivity".
All in all, gun to the head, i think i am so critical because to use these tools is surrendering to big corpos. It is not a democratic tool. If it was i would probably be using it. I have finally given up and started messing with local models (well, i did already with images) but general local models are useless.
OR maybe it's me? i cannot for one moment let go and converse with the machine. I can give order to the machine.
The tech is fantastic, but the fact that it's in the hand of corpos with all interests in never letting us be able to do shit without them, makes me one hundred and one percent against it.
By giving up that control, you do get to a quality end result sooner, but that end result can only be an approximation to your original vision, since you're giving up the control required to shape the sound to that granular level.
It's like any LLM, it's not a tool for if you know exactly what you want with all these knobs and fine grained controls.
> The most interesting result I had was actually when I managed to get it off rails, a bug more or less. I asked it to mix two very different genres together, and it made something unsettling in a way I don't remember hearing before.
I don't think that's a bug or unexpected, it's what AI is good for. I do these (very) old Blues covers of modern songs and it's terrific at that sort of conversion thing.
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 - but that IS engineering!!
They are far from doing that by default now (replacing engineering).
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.
Good ideas are expensive. They're expensive because you have to weed through all the bad ones to identify them, find a market, and turn them into a product. You don't know that from the start, which is why the landscape is littered with millions of dead projects from thousands of dead companies.
Even if the execution were cheap and implementation were perfect, if the starting idea was bad, it's all been a waste.
Ideas aren't cheap, because bad ideas are expensive and good ideas cost money to vet.
That is what takes determination and why you have to really care about the thing you are trying to sell to people. You have to stick to it before they will stick to it.
https://play.google.com/store/apps/details?id=com.sixteenam....
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.
As an information architect I find it amazing it works so good, but is useless to me except being a great think to play with… a toy really. I’m much more fascinated by Strudel.cc and LLMs do a great job to educate me into it, myself being mostly an autodidact.
As a dev I struggle to maintain coherence with Claude Code even though I’ve piped more than 10b tokens since Jan. Certain trivial stuff is easily remedied but even more devil lives in abundance of details now. So the task moves one level above in terms of abstraction, but is not solved.
If guys were good at typing one and the same thing in one and the same lang, which is nothing wrong about given how crafts went for ages, then they will be struggling to compete with the GPTs. But if they are in the architectural and operational perspective … well - work and demand just increased, so please stop whining.
It's great that people find joy in it, but as someone that is critical of both music production and fidelity, the current offerings fall incredibly short of anything I would ever want to listen to.
Does it? It produces passable stuff that is fine. However the lack of passion and care completely disinterests me.
It is the whole business flow chain of value to the end user what is valuable.
No. I assumed that at best it will be not better than average human-made music available to listeners.
> but it now produces really good stuff.
Does it? Do you have examples?
(note: I actually do not care about all "hand-made" and have no preference for once-off over serially made products)
The future is going to be different.
Right now, people effectively spend ~0% of their time entertaining themselves with their own music, art, writing, film, etc.
In the future, it's going to be >0%.
Will it be >10%? Who knows.
The high watermark of what can be "solved" (read: one shotted) is rising, and will continue to rise. Look at the gig economy (Fiver etc) for simple programming/design tasks, LLMs have taken over completely with their execution.
I work in DevOps at a firm that has been very enthusiastic about using LLMs (in the good sense).
The phases were basically:
- try out having the LLM do "a lot"
- now even more
- now run multiple agents
- back to single agents but have the agents build tools
- tools that are deterministic AND usable by both the humans (EDIT: and the LLMs)
The reasons:
1. Deterministic tools (for both deployments and testing) get you a binary answer and it's repeatable
2. In the event of an outage, you can always fall back to the tool that a human can run
3. It's faster. A quick script can run in <30 seconds but "confabulating" always seemed to take 2-3 minutes.
Really, we are back to this article: https://spawn-queue.acm.org/doi/10.1145/3194653.3197520 aka "make a list of tasks, write scripts for each task, combine the scripts into functions, functions become a system"
-- END of original post --
What I would add:
if you let LLMs do whatever they want, they will happily make code. You can add tests to confirm that the tests work (which you used to do with human code, right?). You can also read the code.
When you read the code, you'll find that they sometimes do totally bananas things that still produce working code (I've seen humans do this too but that's another story).
In other words, you still need to make sure the system being built makes sense.
More succinctly:
Coding may be dead but software engineering is alive and kicking.
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.
Overall society feels more turbulent, but this is otherwise all the same song and dance all over again.
The 90s and 00s had this wave of "object oriented programming changes everything". Hey we're doing this thing that's been done successfully 100s of times before, but now it's OO. Writing some code in involving an airplane? Just purchase this omni-airplane object that does everything for airplanes (an actual thing I was told in college).
That's weird OO isn't the be all end all? Code gen, get this Ruby on rails running. Look at me building this website in two seconds. Code gen everywhere.
Huh, that's going to a funny place... TDD. If you aren't TDDing then you're such a bad engineer that you should be locked in prison (real conversation I observed). Oh wait, not TDD, BDD. That fixes it.
Lean, no Agile, no agile like with a small a ... but it was first, no scrum, no xml wait that was last decade, json, and finally SAFe.
Hey, have you seen this chat bot thingy?
Every iteration brings good stuff if you're paying attention. But it also brings a lot of hype and anxiety. Experiment and learn.
The one thing that's remained constant for me is that nearly everyone would rather die than to think carefully about the consequences of their dreams coming true. And as long as that remains true they'll continue to pay for someone else to ride the hype dragon on their behalf.
The thing is... everything you mentioned had only brought the need to retrain.
This new hotness AI? It's bringing actual layoffs, and not just of the boom bust cycle kind, but permanent, industrial-revolution kind that lasts for decades.
Covid overhiring, no more 0% interest rates, that one accounting change, and companies needing a "growth" sounding way to announce layoffs. Maybe that's bringing actual layoffs in the name of AI?
> 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.
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
I think this is true in some things and less true in others.
It's a pretty high moat getting into stuff like simulation software because the people working on numerical methods overwhelmingly have PhDs and it's a mixed skill set. Domain expertise here requires you to know maths to a high level. Even mechanical engineers often struggle here; it's often applied mathematicians and physicists turned devs that work on this stuff.
I worked on a fairly gnarly signal processing thing a while back that required bringing together knowledge of physics and software and maths and I found explaining it to people was tricky as their eyes glazed over at some point because their knowledge typically only covered one part of those.
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.
But the master knowing when to break the rules because of tacit knowledge without being able to explain it is a real effect
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.
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.
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...
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.
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.
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.
What kind of domains did you have in mind?
Not like a webdev entering game engine design or a database engineer entering computer vision research, or someone working in embedded hard-realtime systems switching to making video editing GUIs.
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.
What would this future look like? Software developer salaries burrowing into the ground?
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.
Maybe in some markets but in many places around the world software salaries already weren't that high. Or at least not really much higher than other white collar professions
The reality is this all the standard lump of labor fallacy. I am not a software engineer but it is obvious to me at some point I will be using claude code or whatever to automate tasks. I won't be taking software engineering jobs, I will be using code to do what is done manually today that you wouldn't bother paying a software engineer to handle.
Today's software engineers will just be higher up the stack from me the same way they are today.
In 20 years, many of us will be working in sectors of the economy that don't exist today.
The idea we get something as powerful as AI and it doesn't create new businesses and sectors is just stupid.
Imagine telling someone in 1997 they are going to be getting deliveries from Amazon all the time in the mail. What kind of idiot would believe this? I don't even read that many books!
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.
The best people I've worked with were the people who learned the ins and outs of the business they were making software for, not the people who learned how to write code really well or read logs or learn software architecture patterns. Those people (and I've been one of those people) often go around looking for nails for their hammers rather than really focusing on the customer need.
It takes a really sharp brain to pick up and learn an area of expertise that has nothing to do with software development, and figure out how software development makes that domain better.
Applied to real world complex businesses good luck.
1. most jobs are created by small to medium size enterprise
2. the throttle for new SMEs has been people, money and ideas, in that order
3. with LLMs being a force multiplier, fewer technical people are needed but some people still ARE needed
4. with less throttling, MORE SMEs will be created with more jobs - they will be able to do more, faster, but still need some human oversight.
Also, what is the point of software if it is not to serve human needs?
Also, in open source, community building and tending is a very human enterprise that will not be replaced by bots any time soon. So, as coding becomes commoditised, perhaps the soft skills backed by technical knowledge will be the complementary skill that increases in value.
Or, maybe it's time for me to become an itinerant folk musician.
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.
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.
Parallels and interests overlap everywhere between programming and woodworking; decisions about tooling, tolerances, sequencing, and what can be easily fixed later.
The models get rectangles pretty well and has been fun exploring a parametric casework planner for my own shop.
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)
If only there was another word for that...?
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.
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.
but for "woodwork" / personal-farm still belive he is better off than software. at least he will be employed and have food on the table.
Rejecting industrialized society is actually very expensive
However, it's a risky business so I'd only recommend getting started if you either (!) are FIRE already even after sinking 3 million bucks into purchasing land and machinery as well as constructing all the buildings or if you join a cooperative/union or if you got experienced farmers in your family.
Everything else - especially following "prepper" influencers shilling books and holding more public speeches to shill for said books than they are actually working on their farm - is a recipe for certain disaster.
If in doubt... first try raising a few dozen chickens in your yard as a starting point.
No. Just try to make a 5x8 plot to grow vegetables and realise how ridiculously hard it is.
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.
You took this statistic out of your rear end?
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.)
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.
My experience of job postings advertised is exactly the same as everyone else's for the same filters.
This is not a "my personal feeling is that...", this is "I can't find an advertisement, posting or role that doesn't demand, instruct or promise that the successful candidate would be working closely with AI".
We're less than a year in, and I do not see dev jobs advertised on (for example) indeed.com with any sort of criteria omitting AI.
Imagine what it would look like in 5 years.
Says the guy with a pseudonym, active only since 2022.
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.
People are increasingly associating “AI art” with cheap slop. I wonder if the same will ever happen to programming.
This is a small part of the whole users, but.. why not. People who value hand-by wood goods are also a small part.
Also, there are also communities which slow down AI integration - like Zig. Maybe they will alive
Virtually nobody has their favourite app developer.
The classic “AI images were everywhere in 2023, but I rarely see them now” phenomenon.
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.
None of this comes out of the box atm, but it's not clear that it's not possible.