It’s not all useless but most of the days I think I would be more productive if some processes were streamlined rather than if I had to throw tokens at them and still fail.
Of all the showcases I’ve seen the best are the ones written by people assuming that the token bonanza will not last so they used AI to build tools they wished they had. AI used to build the tool but by no means used by the tool, so if/when token quota gets reduced we still have a functional tool.
Trying to crank out all the tools I never had time to build because I think we’re going to get cut off eventually
I also have scripts to fetch specific database assets and forward them to slack channels so I can easily share them with a group rather than manually running a query and generating them.
I had a theory about improving a product. I asked it to build an offline simulation setup to try various implementations. The results were a bit fishy but i decided to give it a try and A/B testing is showing similar results.
And now im vibecoding a locally hosted dashboard. This one is less useful for anything specific, and more of a minor quality of life improvement, but its fun to just vibe code and see changes happen occasionally. Its not a critical thing.
Are companies using per-token billing? Why - is there some reason they can’t buy the $200/mo Claude plan for every employee?
Leadership is not being dumb, at least on this topic. If your token usage is that low, you just aren't using AI that much (even if you think you are.)
> Before the doomers come in, you get $200 in API credits every month for claude -p usage. Usage counts against those API credits.
So which is it $300/day is trivial to consume or $200/month is a completely reasonable limit, it can't be both.
"If you aren't donating at least your salary's worth of company money to another company every day, are you even working?"
I think Mitchell's point is well taken -- it's possible for these tools to introduce rotten foundations that will only be found out later when the whole structure collapsed. I don't want to be in the position of being on the hook when that happens and not having the deep understanding of the code base that I used to.
But humans have introduced subtle yet catastrophic bugs into code forever too... A lot of this feels like an open empirical question. Will we see many systems collapse in horrifying ways that they uniquely didn't before? Maybe some, but will we also not learn that we need to shift more to specification and validation? Idk, it just seems to me like this style of building systems is inevitable even as there may be some bumps along the way.
I feel like many in the anti camp have their own kind of reactionary psychosis. I want nothing to do with AI but I also can't deny my experience of using these tools. I wish there were more venues for this kind of realist but negative discussion of AI. Mitchell is a great dev for this reason.
If I ask it to me produce a design, I'll almost always end up with something unworkable or inefficient.
Though if you push it hard enough then it can sometimes give you a good description of what existing code does and how it does it (which can be easily verified).
So now the AIs will do more of that, at superhuman speed.
> will we also not learn that we need to shift more to specification and validation
We'll just quickly learn what we've been trying to do for decades, while also treading water in floods of more code than has ever been written before? And some of the motivations to write correct code are being deflated - "just vibecode it again and see if the bugs disappear, it only took a week and $200."
Currently the bugs are found by people using LLM's but aren't the developers. As more projects start getting access to compute, they can run those LLM searches for bugs themselves, and can simply prevent shipping the bugs.
I'm surprised no one has tried making any statistical analysis of bug densities, and "bug authors" in an attempt to identify untrustworthy developers, regardless of intent. Given a dataset of authors and prior bugs, it may help find more bugs by tracking their pull requests with higher scrutiny...
Some people may end up with an eternal stain if they've been taking money to submit vulnerable code to code bases...
You're using psychosis wrong. My literal reality is my entire industry trying to use Ai as an excuse to payoff hundreds of thousands, to millions of American engineers in lieu of outsourcing work overseas. It's having hostile promots to use AI that never truly go away (if you're even given an option to turn off the prompt). It's seeing an emerging generation completely stunted because AI's best use is to cheat the education system and ruin the youth's critical thinking. It's looking in apallment at proposals for data centers that take more energy than the state actually has.
And while you can try to call these exaggerations, you're falling into the very psychosis of this article if you want to deny this reality as a whole. "but the tech is making us so productive" is not a valid justification to literally collapse human society as we know it.
Right know, prompters are setting up whole company infrastructure. I personally know one. He migrated the companies database to a newer Postgres version. He was successful in the end, but I was gnawing my teeth when he described every step of the process.
It sounded like "And then, I poured gasoline on the servers while smoking a cigarette. But don't worry, I found a fire extinguisher in the basement. The gauge says it's empty, but I can still hear some liquid when I shake it..."
If he leaves the company, they will need an even more confident prompter to maintain their DB infrastructure.
I have seen people write highly complex code where all the complexity was not necessary. Think: deep unnecessary branching, pointless error handling and retries which make no sense in our context, hand-coded parsing using regexps, haphazard data flow, functions which seem purely computational but slyly make API calls, pointlessly nullable model fields, verbose doc comments which describe the implementation instead of the contract. I could go on.
The worst part is, even when "prompted" by bad coders, it works in the end. Even has tests (ostensibly mock-ridden, a pet peeve of mine which always falls on deaf ears). So I cannot reject the PR without being an asshole.
I am no luddite. I make heavy use of AI, with all the skills / AGENTS.md / style guides and clear specs, then review every line of code, prefer testing with minimal mocking. I'd even say with right prompting, it can write better low level code than me (eg: anticipating common error conditions).
But my biggest fear about AI is how it enables normies with little to no understanding of CS principles to produce code faster which looks correct but slowly poisons the codebase.
Oh man, I think you may have touched the third rail here.
My first job out of high school was as an AutoCAD/network admin at a large Civil & Structural firm. I later got further into tech, but after my initial experience with real Engineering, "software engineering" always made my eyes roll. Without real enforced standards, without consequences, it's been vibe engineering the whole time.
In Civil, Structural, and many other fields, Engineers have a path to Professional Engineer. That PE stamp means that you suffer actual legal consequences if you are found guilty of gross negligence in your field. This is why Engineering firms are a collective of actual Professional Engineer partners, and not your average corporate structure.
The issue is that in software dev, we move fast, SOC2 is screenshot theater, and actual Engineering would slow things way down. But, now that coding is fast, maybe you are correct! Maybe vibe coding is the forcing function for actual Software Engineering!
___
edit: I just searched to see if my comment was correct, and it turns out that Software PE was attempted! It was discontinued due to low participation.
> NCEES will discontinue the Principles and Practice of Engineering (PE) Software Engineering exam after the April 2019 exam administration. Since the original offering in 2013, the exam has been administered five times, with a total population of 81 candidates.
https://ncees.org/ncees-discontinuing-pe-software-engineerin...
Until and unless software is held to that standard, software will never be engineering and always just a craft that can be performed to any or no standard.
This was something I noticed in my early career in mechanical engineering and later doing PCB design and software for robotics. It’s easy to find firms that just need adequate parts without the professional certifications or ass-covering calculations of other engineering fields.
All this to say, it’s not just software versus the rest of them. From my position, civil and aerospace seemed more like the exception while much of the rest of the engineering world is more vibes based.
Im sure for the most part, engineers in physical space deal with the same kind of tradeoffs software engineers make, where you try your best based on industry standards, personal past experiences without some way to prove what youve done is right
I think it’ll be the opposite. Maybe it’ll be what will eventually cement the field as “talent” based field. Just like it was difficult to quantify what makes a flute player better than another, how good your are at endlessly prompting a blackbox machine would be the only measure. The engineers of ol’ whoe developed kernels and drivers would be thought of as the “crazy people who put the flute against their temple to tune it” LOL. we don’t need people like that. You can just buy a flute tuning device. who gives a fuck? Can you make the next “Shake it, Shake it”?
So it sounds like it was fine? Why would this prompt (haha) a change in their approach to things?
That’s basically every M2, and many if not most M1s, in the last 10 years. So fuck it. Why does any of it matters?
You can see the same approach is taken by Trump and other people.
“You have TDS!! He is actually doing good. He doesn’t follow rules because the system is rigged etc.”
These arguments border on religion because it is predicated on you believing their ignorant point of view in the first place.
Engineering and science is built on rigor and empirical evidence, it is not built by scammers/businessman/ignorant-people/politicians because that is just not how it works
I wrote a while back, Most of the executives I have met really have no clue. They just go with what is being promoted in the space because it offers a safety net. Look, we are "not behind the curve!". We are innovating along with the rest of the industry.
There are people who write important software that the world runs on, but they do it outside the 'industry'.
A real industry should be responsive to events of nature, or at least the market, not vibes.
Purely AI written systems will scale to a point of complexity that no human can ever understand and the defect close rate will taper down and the token burn per defect rate scale up and eventually AI changes will cause on average more defects than they close and the whole system will be unstable. It will become a special kind of process to clean room out such a mess and rebuild it fresh (probably still with AI) after distilling out core design principles to avoid catastrophic breakdown.
Somewhere in the future, the new software engineering will be primarily about principles to avoid this in the first, place but it will take us 20 years to learn them, just like original software eng took a lot longer than expected to reach a stable set of design principles (and people still argue about them!).
I think the problem will get worst. I dislike the marketing around AI, but I do think it is a useful tool to help those who have experience move faster. If you are not an expert, AI seems to create a complex solution to whatever it is you were trying to do.
“ These are highly complicated pieces of equipment… almost as complicated as living organisms.
In some cases, they’ve been designed by other computers.
We don’t know exactly how they work.”
Now how did that work out ;-)
Here’s a slightly different future - these AI rescue consultants are bots too, just trained for this purpose.
Plausible?
I have already experienced claude 4.7 handle pretty complex refactors without issues. Scale and correctness aren’t even 1% of the issue it was last year. You just have to get the high level design right, or explicitly ask it critique your design before building it.
Do you think people are not giving their agents specs and asking for input?
That's serious levels of circular thinking right there.
- AI Hype
- AI Psychosis
- AI keeps getting better and better until it can work around big AI slop code bases
You have not seen the spreadsheets that accounts run the firm on.
Bloody kids!
Are you sure about this? Yes, there is a stable set, but they are used in all of the wrong places, particularly in places where they don't belong because juniors and now AIs can recite them and want to use them everywhere. That's not even discussing whether the stable set itself is correct or not - it's dubious at this point.
I exaggerate only a little.
(None of above is theoretical)
I thought the same when I saw development outsourced to Indians that struggled to write a for loop.
I was wrong.
It turns out that customers will keep doubling down on mistakes until they’re out of funds, and then they’ll hire the cheapest consultants they can find to fix the mess with whatever spare change they can find under the couch cushions.
Source: being called in with a one week time budget to fix a mess built up over years and millions of dollars.
It's really nowhere near as complicated as making distributed systems reliable. It's really quite simple: read a fucking book.
Well, actually read a lot of books. And write a lot of software. And read a lot of software. And do your goddamn job, engineer. Be honest about what you know, what you know you don't know, and what you urgently need to find out next.
There is no magic. Hard work is hard. If you don't like it get the fuck out of this profession and find a different one to ruin.
We all need to get a hell of a lot more hostile and unwelcoming towards these lazy assholes.
Wow, it’s true, AI really is set to match human performance on large, complex software systems! ;)
We didn't create the dna we rely on to produce food and lumber, we just set up the conditions and hope the process produces something we want instead of deleting all the bannannas.
Farming is a fine an honorable and valuable function for society, but I have no interest in being a farmer. I build things, I don't plant seeds and pray to the gods and hope they grow into something I want.
plot twist: it's Starbuck
Management is really pushing AI. It's obnoxious, and their idea on how it fits into my team's job specifically is completely, hilariously detached from reality. On the off chance someone says something reasonable, unless it fits the mold, it's immediately discarded. The mold being "spec driven development". We're not even a product team for crying out loud. I straight up started skipping these meetings for the sake of my sanity. It's mindwash, and it's genuinely dizzying. The other reason I stopped attending is because it ironically makes me more disinterested in AI, which I consider to be against my personal interests on the long run overall.
On the flipside, I love using Claude (in moderation). It keeps pulling off several very nice things, some of which Mitchell touched on in this post (the last one):
- I write scripts and automation from time to time; Claude fleshes them out way better with way more safety features, feature flags, and logging than I'd otherwise have capacity to spend time on
- Claude catches missed refactors and preexisting defects, and does a generally solid pass checking for defects as a whole
- Claude routinely helps with doing things I'd basically never be able to justify spending time on. Yesterday, I one-shotted an entire utility application with a GUI to boot, and it worked first try; I was beyond impressed.
- Claude helped me and a colleague do some partisan cross-team investigation in secret. We're migrating <thing> and we were evaluating <differences>. There was a lot of them. Management was in a limbo, unsure what to do, flip-flopping between bad options. In a desperate moment, I figured, hey, we kinda have a thing now for investigating an inhuman amount of stuff in detail - so I've put together a care package for my colleague with all our code, a bunch of context, a capture of all the input data for the past one week, and all the logs generated. Colleague put his team's side of the story next to it, and with the help of Claude, did some extremely nice cross-functional investigation. Over the course of a few weeks, he was able to confirm like a dozen showstopper bugs, many of which would have been absolutely fiendish if not impossible to fix (or even catch) if we went live without knowing about them. One even culminated in a whole-ass solution re-architecturing. We essentially tore down a silo wall with Claude's help in doing this.
So ultimately, it really is a mixed bag, with some really deep lowpoints and some really nice higlights. I also just generally find it weird that a technical tool [category] is being pushed down people's throats with a technical reasoning, but by management. One would think this goes bottom up, or is at least a lot more exploratory. The frenzy is real.
In my case, it built a tool for splitting sounds and a tool for defining hitboxes for a game. Tools made exactly for more workflow. Wild times.
Well, now you must to work with a confusing tool which slows you down. You are not allowed to use claude directly anymore, because someone heard that mythos is really bad for security. But hey, the tool integrates well with Jira!
You hate every second working with this thing. All the joy you had with explorative coding is forever gone, which was the sole reason you entered this field.
Deep inside you know that you can't change your job, because every other employer will cut its workforce as AI removes all manual labor of a software engineer and reduces risk to a minimum.
Oh, now we can finally move all those jobs to india without risk and shareholders will love it! How awesome is that! Wait, do we still need that guy in cubicle 42, who bitches and moans about AI every day? Nah...
Show HN here: https://news.ycombinator.com/item?id=48151287
Only by walking us into some revenue or customer impacting failure - through inappropriately having junior devs doing senior level things - will some sense of sanity start to prevail again.
It sometimes feels like AI chatbot use is like the doomscrolling of work - it's always easier just to dump something into the chatbot than think about it.
The real question is: what's the fallout going to be after the dust settles? My guess is that the explosion of codebase entropy now underway from this is going to make for an interesting future - once it reaches the point where AI agents are spinning constantly despite progress grinding to a halt.
And they're be no veterans who know the codebase deeply to step in and fix things because it was all vibecoded - and then what are companies going to do?
I think that's the point where they turn back to the thinkers for help.
I guess what I relate to the most is how dismissive people get about real software engineering work.
I may have skill issues, but I am yet to reach the level of autonomous engineering people tend to expect out of AI these days.
I use AI coding tools every day, but AI tools have no concept of the future.
The selfish thinking that an engineer has when they think "If this breaks in prod, I won't be able to fix it. And they'll page me at 3AM" we've relied on to build stable systems.
The general laziness of looking for a perfect library on CPAN so that I don't have to do this work (often taking longer to not find a library than writing it by hand).
Have written thousands of lines of code with AI tool which ended up in prod and mostly it feels natural, because since 2017 I've been telling people to write code instead of typing it all on my own & setting up pitfalls to catch bad code in testing.
But one thing it doesn't do is "write less code"[1].
[1] - https://xcancel.com/t3rmin4t0r/status/2019277780517781522/
Maybe it's just my prompt or something but my coding agent (Opus 4.7 based) says things like "this is the kind of thing that will blow up at 2am six months from now" all the time.
The stock market keeps going up in the face of the indefinite closure of Hormuz. We're investing in datacenters at a scale that only makes sense if AI capabilities continue to advance to the point where they surpass most humans at most white collar tasks, if not reach superintelligence.
And what are the possible outcomes?
- Bust. We've come away with a useful tool but the hundreds of billions of capital expenditure were thrown away on a pipe dream.
- Success! We're the dog that's caught the car. Then what? Currently the political debate is, to caricature only slightly, between "oh no the datacenters will use more water than golf courses" and "lol what are you going to do, regulate matrix multiplication?". How the hell are we going to cope with introducing a new intelligent species?
Either way, it sure seems like we're collectively operating more in the interests of the future AI than in the interests of humanity. What is this, if not a sort of psychosis?
Also $1T in data center investment makes sense when you realize that companies are racing to create virtual white collar workers. Google spends $9B a year on software engineers.
heh this is the trick. The tech companies will angle for a bailout and they'll benefit from all this speculative data center building. Compute is generally useful.
Why wouldn’t it? The closure leads to price increases which leads to inflation which leads to non-dollar assets (ie stocks going up in value)
Second from a US perspective the strait matters the least it has since world war 2. If the price stays high a bunch of fracking will come back online.
In other words, BAU for the last few thousand years.
IMO, what's happened is a few richest investors in the world had access to the uncensored tier of AI, talked to it and came out with impression that they've witnessed something so dark, so much beyond anything we can imagine, that the only course forward is towards the transcendent abyss. Call it AI psychosis or demonic inspiration, but they are the people who control the economy, so they are dragging everyone with them. "Operating in the interest of the future AI" is a neat way to put it.
Why wouldn't it? The value of the USD is decreasing, the value of the companies to the world stays the same => stock price in USD increases.
The real thing to analyze is "amount of VOO shares you need to buy a Chipotle meal / Uber ride / 1 month's rent in SF / etc."
If there is a psychosis, what is it? It is not an AI psychosis - modern AI started in the 1940s, or by some definitions before, and made progress up until 15 years ago to where deep neural networks became viable. And it has been progress on every front since then. No psychosis, it is doing well.
You mention the stock market, and that is another story. Cryptocurrencies, sub-prime loans, dot-com crash, Asian financial crisis. The economy has veered from crisis to crisis, overproduction and overproduction to crashes and bailouts.
AI is doing just fine - the past 15 years are a success for it we did not see in the decades before. If the economy as constituted is dealing with this in a "psychotic" fashion, it would not be the first time.
I really do worry - I especially worry about security. You thought supply chain security management was an impossible task with NPM? Let me introduce to AI - you can look forward to the days of AI poisoning where AIs will infiltrate, exfiltrate, or just destroy and there's no way of stopping it because you cannot examine the internals of the system.
AI has turbo charged people's lax attitude to security.
God help us.
Some time down the line, I discover CPU being maxed out, which is showing up in degraded performance in other parts of the system. I investigate, and I trace the issue to a boneheaded busy loop in this library that no human with the domain expertise to implement the library would have written. Turns out I'd missed one deeply-buried mention in the README that maintenance was being done via AI now, and basically the whole library had been rewritten from the ground up from the reliable tool it used to be to a vibecoded imitation.
Yeah, yeah, sure, bad libraries existed before all this. But there used to be signals you picked up on to filter the gold from the dreck. Those signals don't work anymore.
I don't think using AI to write code is AI psychosis or bad at all, but if you just prompt the AI and believe what it tell you then you have AI psychosis. You see this a lot with financial people and VC on twitter. They literally post screenshots of ChatGPT as their thinking and reasoning about the topic instead of just doing a little bit of thinking themselves.
These things are dog shit when it comes to ideas, thinking, or providing advice because they are pattern matchers they are just going to give you the pattern they see. Most people see this if you just try to talk to it about an idea. They often just spit out the most generic dog shit.
This however it pretty useful for certain tasks were pattern matching is actually beneficial like writing code, but again you just can't let it do the thinking and decision making.
Here's some other topics I've written on it:
- https://mitchellh.com/writing/my-ai-adoption-journey
- https://mitchellh.com/writing/building-block-economy
- https://mitchellh.com/writing/simdutf-no-libcxx (complex change thanks to AI, shows how I approach it rationally)
Hard agree about ideas, thinking, advice. AI's sycophancy is a huge subtle problem. I've tried my best to create a system prompt to guard against this w/ Opus 4.7. It doesn't adhere to it 100% of the time and the longer the conversation goes, the worse the sycophancy gets (because the system instructions become weaker and weaker). I have to actively look for and guard against sycophancy whenever I chat w/ Opus 4.7.
It's so interesting how easy it is to steer the LLM's based on context to arriving at whatever conclusion you engineer out of it. They really are like improv actors, and the first rule of improv is "yes, and".
So part of the psychosis is when these people unknowingly steer their LLM into their own conclusions and biases, and then they get magnified and solidified. It's gonna end in disaster.
I'm afraid to say this out loud internally because I'm afraid of the next round of layoffs and I want to keep my job. So I just keep on shipping at a high pace, building massive cognitive debt and hoping the agents will get so good in near future, that there won't be the need for understanding the codebase.
Agents might get better. But who will own the code and take responsibility for it? The AI agent? The company who created the AI agent?
If e.g. a car crashes and does not deploy its airbags because the AI agent made a mistake in the airbag code, will the manufacturer be able to shift the blame to OpenAI or Anthropic?
I do not think so.
And therefore I believe that no matter how good the AI agents will ever become, the ultimate responsibility for the code will always remain with the companies that create the code. Regardless of which AI tools they use.
I see no other way to bear that responsibility by the company than to have people internally who will be responsible. And those people, if they actually want to own that responsibility, would need to understand that code themselves, in my opinion. Because relying on a non-deterministic AI agent's vetting is fundamentally unreliable, in my opinion.
Even before LLMs generating entire programs, complex frameworks allowed developers to write the initial versions of programs very quickly, but at the cost of being hard to understand and thus hard to debug or modify.
Some of us are betting that the AIs will always be smart enough to debug, maintain and modify the programs written by AI, no matter how convoluted or complex. I’m not so sure.
I am watching a 10 person company try to run 3 different AI initiatives in parallel. Everyone wants to be "the guy" on this one. I cannot imagine there will ever be a bigger opportunity to ego trip as a technology person. This is it. This is the last call before it's all over. There are many businesses out there that are beyond traumatized by human developers taking them on bad rides. The microsecond they think this stuff will work they are going to fire everyone.
The psychosis comes from the tension here. We effectively have The Empire vs the rebel alliance now. I know how the movies go, but in real life I think I'd rather be working on the Death Star than anywhere else.
What's the historical context for this MTBF vs. MTTR reckoning?
If you optimize for MTTR, you don't care how often you go down and instead optimize your recovery time to be as short as possible.
The concepts are pre-computing.
John Allspaw (previously CTO at Etsy) has written about this: https://www.kitchensoap.com/2010/11/07/mttr-mtbf-for-most-ty...
- Nuggesting improvements to the code after finishing the task you gave it, very irritating when the improvements were obvious and the ai didn't implement them on its own
- Not trying very hard when implementing something, leading to bugs, which leads to more tokens used (this behavior can be incentivized and learned with RL)
Since its a known fact if a user continues a session after the LLM says something, its not hard to train against this. The least efficient way to do this would be to GPRO directly against the user base and try to get as many people talking to the AI, and with OAI having a billion monthly active users the least efficient method would work really well for them.
Sure there are industry changing things going on. What if you're working on an app thats a decade old and has had different teams of people, styles, frameworks (thanks to the JS-framework-a-week Resume Driven Development)? Some markdown docs and a loop of agents isn't going to help when humans have trouble understanding what the app does.
They're also reportedly now giving staff AI-related "homework" in an attempt to force staff to use AI more.
What we need is automated research that leads to real results. This is possible, but it has yet to prove out. I am concerned that unless the AI companies focus entirely on this, it may be a while before we actually see true benefits from this.
What's worse, is there is an urgent and desperate need for automated research, as we have been seeing diminishing returns in human produced research for some time now: https://web.stanford.edu/~chadj/IdeaPF.pdf
And I found it really funny, because for what? Use it for what? It’s a tool. Imagine a guy coming down to a construction site where everything is progressing fine and saying “We need to use more screwdrivers”.
It's a tool; not the second coming.
Cars replaced horses.
But AI is poised to replace a large chunk of brain labour.
Where's the ceiling?
The question is: Will we live in the world of breathless re-implementation, new features every week, rebranding every quarter or will we eventually discover the value of stability, software that does its thing more or less optimally for decades?
Recent examples of things like curl or Firefox are interesting in that regard. Will we end up with a nearly perfect HTTP user agent and stick with it for decades?
Sounds like we prefer stability for stuff we use but not for stuff we sell.
at least at my BigCo, AI is being used for everything - writing slop, writing tests, code reviews, etc.
it would make sense to use AI for writing code, but human code review. or, human code, but AI test cases... or whatever combination of cross-checking, trust-but-verify, human in the loop, etc. people prefer.
i think once it gets used for everything, people have lost the plot, it's the inmates running the asylum.
"What's true about all bugs in production? (pause for dramatic effect) They all passed the tests!" (well, he said typechecker but I think the point stands)
I already took a couple of decisions. It will go wrong or well. But is was decided a year and a bit ago.
If you think the future will be different, stop doing the same you used to do the same way you used to do it.
My analysis is that the labour market will increasingly bargain salaries and will make pressure on you. So how safe is that compared to before? Maybe working for someone as an employed full time person is not the best thing you can do anymore.
It seems like he is pointing out that Ai will increase the complexity of a system oblivion, and that this is the discussion that can not be had.
Bit I am more than happy to talk about how I am using Ai to reduce complexity and remove architectural debt that I otherwise could not justify spending time on.
Rewriting in rust does makes things faster but if an algorithm is O(n²), the improvement won't take us much farther.
Similarly with AI, if complexity is not structurally adressed, the velocity gains are but temporary.
Maybe the problem is you, but you won't figure that out if you think the other person has psychosis.
For example, maybe you need to do a better job explaining, changing your language, simplifying things, being more concrete with consequences.
Or maybe you aren't understanding that the other person has different objectives/ loss function that makes them make seemingly weird conclusions.
“very resilient catastrophe machine”
Everyone has become like petulant children. Senior leaders want access to every shiny tool (CoWork/Codex/etc) that has some buzz around it. No one seems to care about the cost or whether we are actually realising benefits.
It's sheer madness, and you can't push back. I think AI can significantly help people be more productive, and I can see a future where they safely take on more autonomy. But we are far from that world.
Does using AI increase or lower that failure rate?
Does seeing a project that uses AI fail mean it wasn't going to fail if it didn't use AI?
To try to answer it with my gut: I imagine that we could see more projects failing, but the percentage that fail would be the same. Most projects that use AI will fail because most projects generally will fail, but the time and cost to get a successful project will lower.
It is definitely factual that there is a complete paradigm shift in the prioritization of quality in software. It's beyond just AI side effects, and now its own stand alone thing.
There have always been many industries, companies, and products who are low on quality scale but so cheap that it makes good business sense, both for the producer and the consumer.
Definitely many companies are explicitly chosing this business strategy. Definitely also many companies that don't actually realize they are implicitly doing this.
Wether the market will accept the new software quality paradigm or not remains an open question.
Hmm, I agree with the point OP is making, but I'm not so sure this is the best supporting argument. The bottleneck is finding the bugs and if he'd criticized people saying AI will be the panacea to that I'd be with him, but people saying agents are fast and good at fixing human found bugs is nothing I'd object to.
Agents are fixing bugs so quickly and at a scale humans can't do already.
The metric is how many defects are introduced per defect fixed. Being fast is bad if this ratio is above one.
The fact that we can fix things faster now doesn't mean that we should throw away caution and prevention. The specific point of his tweet is that we're seeing a lot of people starting to skip proper release engineering.
Agents are quick to fix bugs, yes, but it doesn't mean that users will tolerate software that gets completely broken after each new feature is introduced and takes a certain number of days to heal each time.
So the point is not that agents cannot find bugs (they certainly can), it's whether you can shirk reviewing for bugs if MTTR is fast enough. There are circumstances where YOLO is appropriate, but they aren't the production environment of a mature application.
But this is just holding the Slop Companies to the standard they declared themselves! Just recently, the CEO of OpenAI babbled some nonsense on twitter about how he hands over tasks to Codex who according to him, finishes them flawlessly while he is playing with his kid outside.
> but soon we will be.
Ah yes, in the 3-6 months, right? This time next year Rodney, we'll be millionaires!
Eventually the companies that can't cope with undisciplined engineering will succumb to unacceptable reliability and be outcompeted, just like in the "move fast and break things" era.
I think the use of the word here is meant to invoke the vision of someone under heavy delusions or hallucinations, such as (what Hashimoto percieves as) the delusion that shipping more bugs is fine if AI can resolve them faster. To what extent this counts as delusion (and thereby psychosis) would depend on how deeply you believe that this and related opinions are wrong.
Never mind code, what happens when the CEOs, or the investors, listen to the sycophantic voices of their LLMs?
I think it looks like every product becomes the next Juicero of its field.
i don't have enough fingers (and toes) to count how many times i've demonstrated that "100% coverage" is almost universally bullshit.
Can someone please remind and refresh my memory what this whole debate was with what arguments?
and we all live in a green utopia of flying cars and peace upon the world.
...and it also needs more so-called AI companies present in the wreckage in this crash.
AI psychosis is undeniably real.
At the end of the day robots can do the vast vast majority of jobs better and faster. If not now, very soon.
I only worry our economic systems won’t keep up
But I only see mass layoffs and those who are working - are working longer and harder then before.
You first use the full words and then introduce the acronym that you're going to use in the rest of the text: "Mean Time Between Failures (MTBF) vs. Mean Time to Recovery (MTTR)".
With the latter, readers understand the term immediately, even if they don’t know the acronym. And they don't have to read these weird letters before getting the explanation.
Calling this "psychosis" is maybe a neologism but it's apt in perspective.
All that's actually new with "AI psychosis" is an acceleration of that phenomenon. The agents will summarize status faster than any middle manager. Claude will happily draw you any "up-and-to-the-right" graph you please, with the most common contemporary examples being "tokens burned" and "lines of code written". And vibe coding doesn't even require paying the cost of a mass layoff to get the "familiarity debt".
There have always been both good and bad engineering leaders. No tool will magically make a bad leader into a good leader overnight. There is nothing new under the sun.
I cautioned them that this a terrible idea -- you have business people who don't know what they're talking about, and all they know if "if we don't 'do AI' we'll be left behind because our competitors are 'doing AI'" (whatever tf "doing AI" means).
Yes, LLMs are a great tool. But they're not like some magic bullet you stick into everything. Use it where it makes sense, and treat it like you would other tools.
You make "doing AI" some kind of KPI in your org, and you're going to have people "doing AI" amazingly (LOC counts! tokens burned! tickets cleared!) while not actually being more productive, and potentially building something that is going to come down on your head for the next team to "clean up the AI mess".
I don't think it's super clear what we'll find out.
We've all built the moat of our careers out of our expertise.
It is also very possible that expertise will be rendered significantly less valuable as the models improve.
Nobody ever cared what the code looked like. They only ever cared if it solved their problem and it was bug free. Maybe everything falls apart, or maybe AI agents ship code that's good enough.
Given the state of the industry were clearly going to find out one way or the other, hah!
I think some companies will find out that their senior engineers were providing more value and software stability than they gave them credit for!
Corporate feedback loops are very slow though, partly because management don't like to admit mistakes, and partly because of false success reporting up the chain. I'd not be surprised if it takes 5 years or more before there is any recognition of harm being done by AI, and quiet reversion to practices that worked better.
If you're not doing AI there's an incredibly limited pool of people who will give you $$$ ... and you're competing with EVERY OTHER NON-AI COMPANY for their attention.
“It feels like entire companies are deluded into thinking they don’t need me, but they still need me. Help!”
The broad sentiment across statements of this “AI psychosis” type is clear, but I think the baseline reality is simpler. How can you be so certain it’s psychosis if you don’t know what will unfold? Might reaching for the premature certainty of making others wrong, satisfying that it might be to the ego, be simply a way to compensate the challenges of a changing work environment, and a substitute for actually considering the practical ways you could adapt to that? Might it not be more helpful and profitable to consider “how can I build windmills, ride this wave, and adapt to the changing market under this revolution” than soothing myself with the delusion that all these companies think they don’t need me now, but they’ll be sorry.
The developer role is changing, but it doesn’t have to be an existential crisis. Even though it may feel that way — but probably it’s gonna feel more that way the more you remain stuck in old patterns and over-certainty about how things are doesn’t help, (tho it may feel good). This is the time to be observant and curious and get ready to update your perspective.
You may hide from this broad take (that AI psychosis statements are cope) by retreating into specific nuance: “I didn’t mean it that way, you’re wrong. This is still valid.” But the vocabulary betrays motive. Resorting to clinical derogatory language like “AI psychosis” invokes a “superior expert judgment” frame immediately, and in zeitgeist context this is a big tell. It signifies a need to be right, anda deeply defensive pose rather than a clear assay of what’s real in a rapidly changing world. The anxiety driving the language speaks far louder than any technical pedantry used to justify it, and is the most important and IMO profitable thing to address.
You should not release a product into the market unless you have a good enough product that can keep you and your client compliant, safe and secure - including not leaking their customer info all over the place.
Prompt injection risk, etc. are massive for agentic AI without deterministic guardrails that actually work in practice.
Stop testing in production if you're shipping in a regulated industry. Ridic!
If you're not technical, you can get someone who is after signs of p-m fit, demos, but BEFORE deployment. This is common sense and best practices but startup bros dgaf because they're just good at sales and marketing & short term greedy.
Comical.
At the end of the day, we can only read so much and take on so much work before we bottleneck ourselves. Cognitive overload leads to burnout. Rumplestiltskin vibes with this AI stuff…
I cannot deny the impact of AI for my daily tasks at this point.
But I just don't enjoy the field anymore. With increased productivity, also coming from my stellar coworkers, it feels like we're rat racing who outputs more.
The quality is good, and having very strong rails at language and implementation level, strong hygiene, etc helps tremendously.
But reality is that the pace of product vastly outpaces the pace at which I can absorb it's changes (I'm also in a very complex business logic field), and the same might be true about my understanding of the systems which are changing too fast for me to keep up.
I feel mentally fatigued from a long time, I don't enjoy coding no more bar the occasional relaxing personal project where I can spend the time I want without pressures on architectural or implementation details.
I'm increasingly thinking of changing field, this one is dying right under our eyes.
I often read comments about HN users still delving at their place with technical details or rewriting AI code to their liking.
I'm increasingly sure that these people live in happy bubbles where this luxury still exists. But this methodology of work is disappearing across the industry, team by team.
Of course SE will not disappear over night, but the productivity expectations, the complexity ballooning are raising the bar where only incredibly skilled and productive engineers will be still able to practice SE properly, and as long as they meet stakeholders expectations or keep living in those bubbles.
I'm trying so hard to pivot away because of this.
Thankfully most of those things are a very small percent of my overall work.
If its a big percent of your work -> you are in trouble friend.
What's more, the only people they talk to about it are others at the same company. There is no external touchstone. There are power dynamics from hierarchy. No new ideas other than what is generated within the company. In other circumstances, this is a textbook environment for radicalization.
I would encourage all leadership to take a deep breath. You have time to think slow.
In all seriousness...well, yeah. AI is a monkey's paw, and that's how monkey paws work. So many movies and books warned us!
But in reality, anyone who knows their field and are going after certain specific issue, they will find soon how AI is nothing but an assistant, sure it can help and automate some stuff, but that’s it, you need to keep it leashed and laser focused on that specific issue. I personally tried all high end ones, and I found a common theme, they are designed to find a solution or an answer no matter what, even if that solution is a workaround built on top of workarounds, it’s like welding all sort of connections between A and B resulting in a fractal structure rather than just finding a straight path, if you keep it going and flowing on its own, the results are convoluted and way over complicated, and not the good complexity, the bad kind.
Changing this focus is not easy but one thing that will usually do the trick is economic issues.
In other words; in order to get any serious consideration, something has to be broken.
AI is perfectly capable of doing this given enough time.
There’s this delusion that if we somehow write enough tests that we’ll expunge every defect from software. It’s like everyone forgets that the halting problem exists.
The only reason it worked has been expansive money policy and a larger share of the cost of goods being dumped into marketing value while manufacturing costs dropped abroad. so no one bothered to check.
Let them.
Many people on this forum are suffering under this same psychosis.
Have you ever been in an HN thread where you're an SME on the thread topic and just been horrified by the confidently incorrect nonsense 90% of the thread is throwing around? Welcome to the training set motherfuckers.
LLMs do the same thing for what should be obvious reasons. If you search things that have some depth and you know the answer you'll be flooded by how often the models will just vomit confident half truths and misrepresented facts. They're better than they used to be, not just lying whole cloth most of the time, but truth is an asymptotic thing, not an exponential one.
I am very close to using it as a pair programmer, but with me actually coding. I am just so tired of fixing its mistakes.
Worth also noting is that while there is plenty to criticize about AI use — especially any cultish behavior surrounding it — plenty of naïveté about the quality of its results, there is a also a strain of categorical opposition to it among some tech people that is equally off and that has all the hallmarks of the chickens coming home to roost.
For years, many in tech gladly “automated away” all sorts of jobs. Large salaries were showered on them for doing so, or at least promising to do so (there was and is plenty of bullshit here, too). Now, AI appears to threaten to derail the tech gravy train, especially for SWE work that’s run-of-the-mill (which is most of it). Now automation is bad. It’s a delicious juxtaposition.
The groundwork for that was laid long ago with the idea of constant updates. It's been fine for years to ship bugs and rely on a rapid release cycle and constant pressure on users to upgrade everything all the time. To roll that back requires a lot more than toning down AI psychosis; it requires going back to a go-slow mindset where you actually don't release things until they're ready. It still needs to be done, but it's harder than just laying off the AI kool-aid.
AI coding swept over the software industry faster than most previous trends. OOP and its predecessor "structured programming" took a lot longer. Agile and XP got traction fairly quickly but still took longer than AI -- and met with much of the same kind of resistance and dire predictions of slop and incompetence.
AI tools have led to two parallel delusions: The one Mitchell Hashimoto describes, and the notion that we (programmers) knew how to produce solid, reliable, useful, maintainable code before AI slop came along. As always with tools that give newbs, juniors, managers some leverage (real or imagined) we -- programmers -- get upset and react to the threat with dire warnings. We talk about "technical debt" and "maintainability" and "scalability."
In fact the large majority of non-trivial software projects fail to even meet requirements, much less deliver maintainable code with no tech debt. Most programmers don't know how to write good code for any measure of "good." Our entire industry looks more like a decades-long study of the Dunning-Kruger effect than a rigorous engineering discipline. If we knew how to write reliable code with no tech debt we could teach that to LLMs, but instead we reliably get back the same kind of mediocre code the LLMs trained on (ours), only the LLMs piece it together faster than we can.
With 50 years in the business behind me, and several years of mocking and dismissing AI coding whenever someone brought it up, I got dragged into it by my employer. And then I saw that with guidance and a critical eye, reasonably good specs, guardrails, it performed just as well and sometimes more throroughly than me and almost all of the people I have worked with during my career. It writes better code and notices mistakes, regressions, edge cases better than I can (at least in any reasonable amount of time).
AI coding tools only have to perform better -- for whatever that means to an organization -- than the median programmers. If we set the bar at "perfect" they of course fail, but so do we. We always have. Right now almost all of the buggy, insecure, ugly, confusing software I use came from teams of human programmers who didn't use AI. That will quickly change and I can blame the bugs and crashes and data losses and downtime on AI, we all can, but let's not pretend we're really losing ground with these tools or that we could all, as an industry, do better than the LLMs, because all experience shows that we can't.
the top reply is from someone doing exactly that, arguing "but the agents are so fast!"
The answer I got is "It's game theory. Someone will do it, and you'll be forced to do it, too. It can't be that bad".
I mean, yes, logic is useful, but ignorance of risks? Assuming that moving blazingly fast and pulverizing things will result in good eventually?
This AI thing is not progressing well. I don't like this.
A lot of companies have been under AI psychosis for years and will be forever.
And also, he might not be right. But the good news is, we’ll all get to find out together!
Sorry, I don't buy your argument
The energy feels misdirected and maybe also a communication issue, I think spreading awareness needs to come not from attacking and also not from attempts to change people’s perception. It’s also quite challenging to distill a concept when it’s new, we learn both from our experiences and experiences of others; but, so far, these alleged systems that will eventually collapse, haven’t done so yet and it makes it sound like you’re preaching and predicting, condemning even, rather than raising awareness and education.
Not trying to sound hopelessly optimistic either, just that the other extreme isn’t also helpful, and that a spectrum is not what we want it to be but what the collective shapes it, so saying psychosis is rejecting the harsh reality that they’re far removed from your worldview and not working towards an understanding.
EDIT: Maybe I'm old and I don't get twitter, I also don't know much about the challenges he faced communicating his concerns, I sort of had a meta comment with the intent of "try listening more first, some people are difficult to reason with but respond better if you just let them speak and look for a teachable moment during the conversation". Anyways, I'm in agreement that there's too much unsupervised AI in the wild, I'm not saying he's wrong more like saying that doubling down on "stop doing that" will likely be ignored by those that are already ignorant to it, hence what I wrote above.
He is clear in pointing out the hard earned lessons we have learned before and how the current actions are essentially undermining it. This is dumb (i agree) and he expects better from people whom he respects.
it's clear, personal, logical. I don't understand what your criticism is.
But equally, like, do people need Terraform if they can just tell codex “put it live”, and does that hurt to see?
It all just feels like horse drawn carriage operators trying to convince automobile drivers to stop driving.
In any case, this is what blue-green deployments and gradual rollouts are for. With basic software engineering processes, you can make your end user experience pretty much bullet proof. Just pay EXTRA attention when touching DNS, network config (for core systems) and database migrations.
Distributed systems are a bit more tricky but k8s and the likes have pretty solid release mechanisms built-in. You are still doomed if your CDN provider goes down. You just have to draw a line somewhere and face the reality head on (for X cost per year this is the level of redundancy we get, but it won’t save us from Y).
The one thing I hadn’t mentioned - one I AM worried about - is security! I’ve been worried about it from before Mythos (basic prompt injection) and with more powerful models now team offence is stronger than ever.
