I’m at a FAANG and we have $300/day token quota. Personally I don’t use that much of it but management is pushing really hard for it. “the quota has been raised for a reason, use it”. Any task: “have you tried working on it with Claude?”. Every meeting “now engineer x and y will show you what he did with AI”.
It’s not all useless but most of the days I think I would be more productive if some processes were streamlined rather than if I had to throw tokens at them and still fail.
Of all the showcases I’ve seen the best are the ones written by people assuming that the token bonanza will not last so they used AI to build tools they wished they had. AI used to build the tool but by no means used by the tool, so if/when token quota gets reduced we still have a functional tool.
I by myself use now more than 15 accounts combined of all providers + API as well for external providers, more than 50K$ equivalent a month in API tokens, my team is doing the same thing, it's not really that much once you figured out the real automation loops and workflows, solving 300 issues a day with guarantees is common.
I feel that a lot of users are still stuck on Claude code or tools like this and don't really have a real argument about why they are even following the thread at all, everything has to be async for serious automation, you shouldn't even be seeing what Claude or any other model is replying (everything has to be digested with another model to increase relevancy and accuracy of the message so you can read faster (like a bot)), it's irrelevant, only human in the loop when a decision must be made, the rest has to be loops with all model, typical e2e, regression, computer use test, video into frames into all model loop and so-on.
This seems seductive, but how do you get past the wall of "fixing XYZ or adding convenience ABC isn't on our pre-planned roadmap" so you can't get buy in from people who have to sign-off or deploy stuff?
Maybe that type of awkwardness is specific to my firm, but that's sort of what killed my drive to try to do that. We used to have one day every second week for that sort of work, but since it was scattered around, the tasks ended up disappearing-- nobody reviewed them and they didn't get merged.
So now they're trying to do a week-long internal hackathon to recover that vision, but I feel like that's going to produce a handful of big-bang ideas and not the 25 tiny tools that would actually streamline things.
Same. I've used it for debugging failed canary tests which required scripts and very specific knowledge on the canary platform that I wouldnt of ever spent time on.
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.
I don't think we will. I think this level of token cost/availability will trend cheaper and faster, long term. These companies that spent too big and too fast might try to limit it and raise the prices and they might be temporarily successful but they'll very quickly be taken over if they keep doing it.
"AI used to build the tool but by no means used by the tool" is a really good way to put it. Feels like the smart play right now is treating these credits as temporary subsidy and building stuff that still works when the bill comes due.
It is fairly easy to tokenmax by having and inefficient automation set up.
Not something I would do personally. But it is surprisingly easy to set up a claw that eats half of your token budget in a meaningless "research" task. Set it up as a cron job and you will soon be promoted for being an AI visionary
The $200/mo Claude plan is not available for every employee. You can buy the $100/mo plan for up to 150 people, and then you have to switch to API billing.
Those plans are going the way of the dinosaur, ai provider loses money on them. Most enterprise offerings are already there, Anthropic changed theirs to $20/seat plus token usage a couple weeks back
I have an unrelated question, please. I am trying to make a post and get this error: "Sorry, your account isn't able to submit this site.", you know why or have a solution for it?
I use $30 a day to produce a decent amount of code. Certainly more than we need - thinking about/designing the correct solution/distilling requirements is still the bottleneck. How can you possibly even review $300/day worth of output?
Wouldn't they save an enormous amount of money by getting rid of either you and the token quota, or a bunch of other people to continue paying your salary plus this insane quota?
How? I struggle to use the 1000 Kiro tokens I get a month, and that only costs $20. And I use it more then anyone else on my team. Maybe we're just massively behind?
I feel in a really weird position where I both really dislike what AI is doing to the experience and practice of writing code, to the point where I want a job doing literally anything else besides using the computer, but also think that these tools are extremely powerful and only getting better.
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.
I've never had more fun coding, but the key is actually still writing the code yourself. The LLM has terrible judgment but an encyclopedic knowledge and the ability to pick out important details in a sea of information. Their worse use is producing code, but somehow that gets all the energy. Being an LLM babysitter is energy draining and you feel less and less in control. No job is worth being miserable doing something that you used to enjoy.
In my experience, its the opposite. The AI is very good at writing code, but it is unreliable at any kind of design. I use it as a fancy form of autocomplete. I give the broad strokes: "add a method here and change all but this one caller to use the new method", "Apply this design pattern here for this change but don't do this other thing". It completes the task reasonably well and sometimes even remembers to run the code formatter and check that tests pass.
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).
After turning off my brain a few times and ending up in a place I don't want real fast, I am learning to ride this dragon.
And, you are right - use it as a fast typer, not a fast thinkier.
And for those who claim that AI is a good code writer - wrong. It can OUTPUT a wall of code, but it's overeager to flood you will legacy code on arrival to "solve a problem". It's harder to write LESS code, which is still the goal (even more true today).
My biggest complaint about AI is getting it to lock onto current or specific information has been darn near impossible. Its definitely there in its training data but for the life of me I cannot get it to stop bleeding long outdated or external information into its responses.
> The LLM has terrible judgment but an encyclopedic knowledge
Bingo. Claude can bang out nearly correct code when I give it an idea, but it doesn't have the idea and repeatedly misjudges both how much work remains and what kind.
On the other hand, I don't know all the ins or outs of macro expansion in yaml at compile time or when and where macros run, enabling us to conaume their results elsewhere in the yaml. Frankly, if I had time, I'd happily spend time on that and learn more about it. I don't, though. Claude knows and does the guessing and checking. So I provide the concept and it translates into a horrible soup of yaml. Clearly I'm able to press forward with ignorance, which is dangerous. There's a real risk that I'll wind up with the kinds of unhealthy work that worries the author of the tweet.
> But humans have introduced subtle yet catastrophic bugs into code forever
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."
I think the commenter was referring to formal verification with "specification and validation": have the LLM emit formal proofs about invariants etc.
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...
> I feel like many in the anti camp have their own kind of reactionary psychosis.
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.
Not FANG, but I work at a company that operates some infrastructure at scale. What I've seen is after we've rotated through a number of different tools, in different pilot groups, eventually converging on tool X (a custom, internal wrapper around opencode).
Now every "working session" like meeting, at the team or dept level, has been around how to use tool X. Tricks using tool X. Problems using tool X. I can't help feeling if we had spent the same amount of time building up core knowledge/contempencies around say, design patterns, networking, specs, we'd be in a better place for building.
Instead we are going to have a few thousand people who know a tool really well.
Maybe this is what will turn software engineering into an Engineering field.
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.
As a junior dev there is this pressure to produce code, add features, and investigate bugs within unprecedented time period. I know whole code base is fking up but i will still add that feature or do a sloppy bug fix without digging deeper.
In my experience, AI really lowered the bar for bad code in the name of delivering faster.
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.
> Maybe this is what will turn software engineering into an Engineering field.
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.
Note that other types of engineering are also often vibes based. The mechanical engineering for a rocket engine is extremely rigorous but the engineering for an injection molded housing for a cheap cell phone is a lot more about following a few heuristics and getting it out the door. Even in robotics where I work, it’s mostly about making parts that pass whatever acceptance tests you come up with. In civil engineering and aerospace failure costs human lives and millions or billions of dollars. In robotics maybe you have some machines fail in the field but in many instances you have one overarching safety system and many of the parts are irrelevant to that. The camera housing for example. So no paper trail or mathematical design validation is required to prove you designed it right. Often those are desirable but if you just manufacture it and test it a lot you’re probably fine.
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.
What makes it a profession is not just the certification, it's the burden of responsibility for consequences. Your lawyer, accountant, and real engineers carry "we need insurance for this" level of risk in their work, all the way up to "can go to prison for getting things really wrong".
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.
Eh writing software for healthcare, or aircraft or self driving cars is more rigorous than an EE working on industrial lighting or toys.
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 work at software in a medical setting. We are piloting an integration with a startup for measuring [some bodily variable relevant in ICU setting]. They are obviously vibecoding (docs are telling) and their API is failing in unexpected ways that they are not able to resolve. I am just waiting when this are going to harm somebody.
> Maybe this is what will turn software engineering into an Engineering field
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”?
Now imagine if you’re one step removed. You don’t see the cigarettes, smell the gasoline, nor see the fire extinguisher gauge. You only see the servers running business-as-usual. Those “engineering” guys are always drama queens, you think. We have processes and fire extinguishers when shit hits the fan, right?
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?
This is the pattern you will see when medium-successful ignorant people take o ver a system that was based on some kind of standard.
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
Recently I had a request come through to allow finance analysts to vibe code their apps. During a discussion one of the finance managers let the cat out of the bag. Turns out our CFO had met fellow CFOs at a get together. They talked about how each of them were using AI. Our CFO was lagging behind and felt that we need to "accelerate" our usage of AI. He wants to push it just because he lost a bragging contest.
> He wants to push it just because he lost a bragging contest.
That is an uncharitable interpretation, IMO.
The CFO heard of a novel technique used by his peers in other companies, and they reported good results. He wants to try it within his organization too. As an executive, he is paid to (among other things) keep abreast of such developments in the industry and ensure that the organization he is leading is not caught flat footed in the market.
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.
I call this Dinner Driven Development. That feeling of being Patrick Bateman when everyone is sharing their calling cards must be every C-suite's nightmare.
I don't know why you think a "real" industry would work in the most idealized way. The media heavily reports on the stupid insane crap of the tech industry, that doesn't mean every other industry is sane they're just not as vocal on Twitter.
Hate to break it to ya, but this is how most C-suites operate. Their job isn't to run a company well. It's to appear to the board/investors that they're running a good company.
It is a better play to do the popular thing in a way that measures as "ahead". Then it's hard to argue against a raise. But if you stick your neck out on your thoughtful expertise, it can take years or more for the value to come thru. You can easily be replaced by then.
The only antidote is a board that has a real working nuanced understanding of the entire industry. But this rarely happens, for many reasons.
I'm going through a mixed experience regarding this, personally.
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.
Totally agree with one shotting GUI tools. I especially have liked it to create a single-file web app, and then open it with Chromium locally (no web server needed).
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.
This will be pushed down from people, who will have no deep understanding of it. But it does check some boxes in an ISO certification.
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...
I think AI rescue consulting is going to be come a significant mode of high value consulting, similar to specialists who come in to try and deal with a security breach or do data recovery.
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!).
A non-technical friend of mine has just won some hospital contracts after vibecoding w/ Claude an inventory management solution for them. They gave him access to IT dept servers and he called me extremely lost on how to deploy (cant connect Claude to them) and also frustrated because the app has some sort of interesting data/state issues.
What concerns me about this is that as these stories multiply and circulate people will just completely stop buying software/SAAS from startups, because 90% or more will be this same thing. It will completely kill the market.
As a SWE that has only ever worked for an employer or on his own projects, this makes me wonder: how would someone even get such a contract? Did this person already have a consulting business? Do you just call up random hospitals and ask if you can demo an inventory management system for them? Did this person already know people at the hospital? I know technical folks that do independent consulting, but even with a vibecoded product, how is it that anyone can just get such a contract?
This hospital will learn some hard lessons. I hope their backup strategy is good. I'm surprised they can field software from an entity that isn't SOC2 & HIPAA certified.
As a cybersecurity IR professional as much as I hate to see this happen to a hospital this kind of thing is responsible for essentially tripling my income over the last 3 years.
This is going to happen all over. Company I'm currently contracting with has gone AI everything (aka technical debt hell), and they're gonna suffer for it. I'm glad my consulting contract ends in 2 months. I don't want to be around for the crash
I'd really like to know how he won contracts, just in general. Did he have some connections. And he doesn't even know how to get it to run on a server by himself? There's millions of people that can do that, if he can win contracts why worry about vibe coding at all, just hire someone to do it. Winning contracts is the challenge in my view.
Heh. Got a customer recently around this. Entire infrastructure and CI/CD vibecoded. They half implemented Kubernetes in Github Actions that were several thousand lines long and impossible to understand.
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.
> If you are not an expert, AI seems to create a complex solution to whatever it is you were trying to do.
I've been watching non-developers vibe code stuff, and the general failure mode seems to be ignorance of 3-pick-2 tradeoffs.
They'll spam "make it more reliable" or some such, and AI will best-effort add more intermediary redis caches or similar patterns.
But because the vibe coders don't actually know what a redis cache is or how it works, they'll never make the architectural trade-offs to truly fix things.
I don't understand this point of view at all. There's a symmetry that is going entirely unappreciated by most of the comments in the thread: just as I can give Claude X,000 words of text to use to describe the code I want it to write, I can also give it some existing code and ask for X,000 words of text explaining what it does. (Call it, oh, I don't know, a "spec," maybe.)
The explanation, in turn, can be fed back to recreate the functionality of the original code.
At that point, why care about the code at all? If it works, it works. If it doesn't, tell the model to fix it. You did ask for tests, right?
That is where we're indisputably headed. It's not quite a lossless loop yet, but those who say it won't or can't happen bear a heavy burden of proof.
I've already done a handful of these gigs for early vibecoded products that had collapsed in on themselves. The scope of work was to stabilize the product and only make existing features work.
The issues have all been structural, not local. It's easier to treat it like a rewrite using the original as a super detailed product spec. Working on the existing codebase works, but you have to aggressively modularize everything anyway to untangle it rather than attack it from the top down.
All of these projects have gone well, but I haven't run into a case where a feature they thought was implemented isn't possible. That will happen eventually.
It's honestly good, quick work as a contractor. But I do hope they invest in building expertise from that point rather than treating it like a stable base to continue vibecoding on.
> I think AI rescue consulting is going to be come a significant mode of high value consulting
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.
> Somewhere in the future, the new software engineering will be primarily about principles to avoid this in the first...
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.
This might not pan out to be the glorious victory of human craft as you’re imagining it to be.
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.
That sounds so horrible, though. It's akin to people working as COBOL devs because someone has to do it, so they'll get the big bucks. Except I've never heard of anyone who actually likes COBOL and the more I've learned about how mainframe development actually works, the more horrified I've become haha. Dealing with an LLM spaghetti codebase sounds like hell.
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.
What you're describing really isn't a new problem for organizations. Historically it's been a team of humans not using AI who gets over their skis and they have to have other more capable humans (also not using AI) to bail them out.
I'm with you on this one, having "vibe coded" some smaller internal tools on GPT 5, and then re-vibed it on Opus 4.6 and 5.5 -- they basically just fixed all of the problems without me doing much of anything other than prompting it to look at the existing code and make it "better".
Pretty much. We're intensely vibe coding something that has gone through so many requirement changes. The code has become very gnarly. I took a stab at basically one prompt rewrite of the whole thing. And it wasn't there, but it was 80% of the way there. and a hell of a lot cleaner.
> Purely AI written systems will scale to a point of complexity that no human can ever understand
But won’t those more complex systems presumably solve more complex problems than the systems that humans could build? Or within a comparable time?
I think it is reasonably safe to assume at this point in the game that these AI systems are increasingly able to reason rigorously about novel problems presented to them, of ever increasing complexity and sophistication.
Someone responded to a previous comment of mine [0] positing a Peter principle [1] of slopcoding — it will always be easier to tack on a new feature than to understand a whole system and clean it up. The equilibrium will remain at the point of near, but not total, codebase incomprehensibility.
People are often skeptical when I say this, but there's simply no guarantee that it's possible in principle to clean up a bad architecture. If your system is "overfitted" to 10,000 requirements from 1,000 customers, it may be impossible to satisfy requirements 10,001 through 10,100 without starting over from scratch.
Frankly this is what everyone is counting on whether they know it or not. The question though is not “will the models get good enough?”. The question is does the repo even contain enough accurate information content to determine what the system is even supposed to be doing.
Yes. And as the models get better, it works better. But at one point you do have to understand the code because it's also just guessing as to what your actual intentions are.
It doesn't know what mess you want to clean up. A lot of times AI just starts making up new patterns on top of other patterns and having backwards compatibility between the two. How does it know which one you actually like?
Those design principles it will take us 20 years to learn are just the principles for writing good maintainable, debug-able, understandable code today. Will just take 20 years to figure out they still apply when AI writes the code, too.
The complexity you would come to the rescue to solve, would that be from AI or from the style of programming you let the AI have? I mean, you have very different problems if you use functional style vs object-oriented. It is up to the programmer to realize they want a functional style and request that from the AI, as much as possible. Even AI cannot imagine every state transition, unless it is so smart that it should be the one telling you what to do.
Sounds like wishful thinking by a human programmer. Probably more likely tools will be rewritten from requirements or the old code will be refactored by newer models.
My company and my buddy's company, we're experiencing the same thing. We are trying to fire a SAAS vendor and it's become the hot new project. Now we to these meetings with 50 different people that are allegedly stakeholders, two or three product managers who have already vibcoded their version of something.
Ultimately, if you want to move fast, it's better just to have one engineer vibe coding something. but, that engineer is under so much pressure. Now he's got a legacy mode and another legacy mode because the requirements keep changing. And now there's a deadline in four weeks.
This all could work just fine, but the ungodly amount of attention that this world is getting puts too many cooks in the kitchen, which is always a recipe for disaster.
> 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.
Wow, it’s true, AI really is set to match human performance on large, complex software systems! ;)
Humans who have been writing systems like that for many years know how to maintain and modify them successfully. It’s just that our industry has a bias towards youth who don’t think they have anything to learn from those who came before them.
The origin of 'dark DNA' begins to make more sense through this sort of lens, except the system somehow maintained a level of compensation to fix all its flaws.
is this true because training companies have not been training AI for both performance and brevity (or some other metric like that)? If this becomes a much more serious issue surely they would adjust the training processes
It's kind of like producing code is becoming more like farming.
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.
Interesting perspective. Fundamentally at conflict with the data, science, and 20+ year trends of AI coding systems - to the point of dogmatism. But interesting from a sociological point of view.
yes, I was never so happy to work in Germany. People used to joke about the proverbial fax machine still being a thing but I've never been so glad to work in a culture where this mania doesn't exist. Reading HN is like entering Alice's Wonderland of token maxxers and AI psychotics. Genuinely don't know a single person here who is forced to work like this.
Actually, I have been wondering to which extend the AI craze has reached the DACH region. I don't work for any company and neither do my friends. HN is essentially my only peephole into the world of commercial software development and I'm aware that it's extremely biased towards Big Tech and SV startup culture.
do you mean this aesthetically or quantitatively? Are they actually outcompeting / making more money ? Or do you mean they are now looking more desirable because their competitors are racing to the bottom (though likely making money on the way down)
It is absolutely going to be a competitive advantage if it isn't already. When your competitors' products suck because they are using LLMs to write them, and yours work because you aren't, customers notice.
No offense, but if you think your using AI in the development and design of your site, voxos.ai , gave you a competitive advantage it didn't. I can instantly tell when someone used an LLM to build their whole site and lets just say... Its not a good thing.
Hard to have sober talk about this since a lot of discourse is AI psychosis vs. AI naysayers. Does software quality seem to have taken a jump in the past few years to anyone? Not to me, seems to be getting worse. Think that's a decent signal. Can tell you I'm dealing with a non-technical VP who loves blast submitting vibe-coded PRs and while there's some quick wins, overall quality is bad, and we had our first real production outage that Claude one-shot caused but could not one-shot solve.
There's an acceleration of current known processes that is being referred to as agent speed (vs human speed). But this is purely a mechanical effect. There don't seem to be augmentive cognitive effects. "AI has invented this revolutionary algorithm/workflow/architecture" is an article title you'd expect to see pop up quick, and often.
Bug reports also go down when people lose faith that they will be fixed, because reporting them is often a substantial time commitment. You see it happen pretty regularly as trust in a group/company collapses.
The last three times I filed detailed bug reports as a client, all I got back were AI replies asking the same questions I’d already answered in the original report and suggesting alternatives I’d explicitly said I’d already tried. No wonder people don’t write bug reports anymore.
Add this the real possibility that significant part of reports that get filed might be AI generated or rewritten. With high possibility of being misreported because of that. Or have incorrect parts... So attack on multiple sides.
And we do not get even get into potential adversarial tactics. If you have no morals what is better than using agents to flood your competitor with fake bug reports.
Just let AI filter out the fake reports! Then let AI work on the real ones. See, there's really no problem "more AI" can't solve (as long as you're willing to ignore all of the underlying ones). "Pay us to create the problems you'll have to pay us to fix for you" is one hell of a business model. It basically prints money.
oh i’ve definitely seen “we’re going to track the number of bugs created in jira per team” turn into “people just file things as tasks instead of bugs” or “only easy things are filed as bugs and completed right away”. It’s trivially gameable.
I agree, and I'd like to point out that this problem isn't unique to AI driven projects. I think much, if not all, of what Mitchell has been observing can readily happen without AI in the mix.
"Just use autoresearch and it will fix your app's memory leaks in an hour" is what I was nonchalantly told by someone who has never written a line of code ever.
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'm just waiting for my current company to have a Sev 1 CritSit so I can document the bejesus out of the root cause and expose our non-technical AI evangelist leadership as the sort of goons most of the senior development staff already suspect.
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.
Oh man, if only. The top brass driving this screaming frenzied MORE AI crusade will never face the firing line no matter what happens. It will either be a) "mistakes were made" and nobody is really at fault because we're all trying to change the world or fellate the future or whatever the line is, or b) James, Sam, Jesse, and the rest of Team B (none of whom are truly top brass) are getting fired out of a cannon into the sun as a warning to the rest of the plebs.
The AI psychosis is not the anti-opinion to the use of AI.
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].
> 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.
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.
It's really inconsistent though.. it takes shortcuts and leaves todos all the time without really calling it out explicitly, you have to pay close attention.
There's a lot of people writing bad code. With AI being forced top down (with the promise of turning people into 10x-ers), we're going to get a lot of people writing bad code 10x faster.
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.
Not security, but I ran into a related supply-chain issue recently. I needed a library to perform a moderately complex task, and found one in the ecosystem I was working with that had been around for a while, appeared reputable, and passed my cursory inspection. So I dropped it in, got the feature implemented, and moved on.
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'm pretty sure he's talking about companies and people outsourcing their decision making and thinking to AI and not really about using AI itself.
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.
Correct. I use AI a ton and I'm having more fun every day than I ever did before thanks to it (on average, highs are higher, lows are lower). Your characterization is all very accurate. Thank you.
I thinking that it’s quite a different experience going all Jackson Pollock with AI in your own studio on your own terms, compared to the sorry state of affairs of having 100s of Pollocks throwing paint around wildly within a corp to meet a paint quota.
I’ve had to do a ton of SQL stuff lately, which I haven’t really worked with since the late 90s. ChatGPT has been a godsend, not just for me, but for our only coworker who knows SQL well, whom I’d probably be bugging several times a day at my wits’ end.
But no one cares about those kinds of productivity gains. Just the ones that will completely replace us.
It’s really frustrating too because even just the plain language translation and pattern matching aspects have such incredible uses.
As a cybersecurity IR professional being able to have a constantly logging counterpart who’s also able to go run queries and check logs on its own is an incredible speed boost.
I can just throw it a finding and have it slot it into a timeline and make notes.
I can toss it something mildly interesting to chase down while I focus on the obvious activity.
So many things that don’t involve having it “think” for you and keep you in the front seat.
But all of that is constantly overshadowed by these companies pushing the automation or “reasoning” aspects more and more and the sycophants who screech that it’s perfect and can do no wrong when every serious users experience is that “yes, it definitely can, often to catastrophic effect”.
> outsourcing their decision making and thinking to AI and not really about using AI itself
> I use AI a ton and I'm having more fun every day than I ever did before
With respect, this is what makes me worry.
If someone is a user of AI, can they really tell the difference between "outsourcing" and "using"? I worry that a lot of people will start out well-intentioned and end up completely outsourced before they realise it.
The worst part of AI is that the time to produce software has become entirely unpredictable. "If Claude is randomly good at this, and happens to be up today, it will take me about 3 hours. If Claude is randomly bad at this task, or has downtime, 2 weeks"
Hi Mitchell. Psychosis is a serious psychiatric condition that can be induced or triggered by AI. “AI psychosis” in this context is a misuse of a clinical term. Your tweet describes a disagreement on a value judgment that boils down to “move fast and break things” with high trust in AI outputs vs going all in on quality and reliability with low trust in AI. It’s an engineering tradeoff like any other.
Claiming that the people who disagree with you must be experiencing a form of psychosis, experiencing actual hallucinations and unable to tell what is real, is a weak ad hominem that comes off no better than calling them retarded or schizophrenic.
If you genuinely think one of your friends is going through a psychotic episode, you should be trying to get to them professional help. But don’t assume you can diagnose a human psyche just because you can diagnose a software bug.
What I'm seeing is a little eternal September of support tickets about programs that fail to interface the JSON API of a customer of mine. The API is always allucinated. In the best case there are out of place attributes. Often they don't exist at all. I've seen x, y, width, height when we have only top and left. Of course no human read the documentation. Those are probably founders vibe coding a client without the technical competence of understanding the API doc on Postman. That is understandable. Unfortunately they don't even have the competence of pointing their AI to Postman in the right way. My custumer assessed that they will always find a way to do a mistake despite any mitigation from our side. What I do is replying to those tickets with line by line comments of the allucinated JSON. I never talk about AIs because I might hurt the pride of some of them and, who knows, some little mistakes could be from real junior developers. Sometimes the tickets are followed up by more puzzled ones, sometimes they fix the problem. Probably they copy and paste my reply to their bots.
I've heard the same thing mentioned by a close friend building integrations. They are helping/supporting real use cases but they decided not to help vibe coder founders without an understanding of how APIs work etc. It's just too big of a gap to cover even for larger companies with strong support.
Seeing this too. Customer support tickets are all AI now. The random bolded words, the em dashes, they way where if you KNOW what is actually happening, they are slightly off or just WAY off.
Several people I know have already gone through phases like this. When you're doing it alone there is a moderating factor when their friends and family start calling them out on their behavior or weird things they say.
I can't imagine how bad it would be if your employer started doing this from the leadership. You'd be pressured to get on board or fear getting fired. Nobody would be trying to moderate your thinking except your coworkers who disagree with it, but those people are going to leave or be fired. If you want to keep your job, you have to play along.
I have a friend that is a junior in a security-oriented sys-admin/network engineer type role. They have been doing the job for only a bit over a year. No background in programming.
Their entire organization has been handed Codex/Claude and told to "go all in on AI" and "automate everything". So the mandate is for people that do not know how to code and have the keys to the castle to unleash these things upon their systems.
This is at a large organization with tens of thousands of employees.
I am waiting with bated breath for the ultimate outcome!
I suspect we're going to see this in many corporate environments soon, if we aren't already
> your coworkers who disagree with it, but those people are going to leave or be fired.
Personally I expect that I will be this person soon, probably fired. I'm not sure what I will do for a career after, but I sure do hate AI companies now for doing this to my career
this is exactly what is happening. instead of building true AI culture around thoughtful adoption of AI strengths while defending against weaknesses, they're coming up with bullshit heuristics like "every repo has a CLAUDE.md", watching private token usage dashboards, and terrorizing everyone into doing it (or lose your job).
this leads to naive AI adoption, which is the worst of both worlds (no real speedup, out sourcing thinking, ai slop PRs, skill rot).
I didn’t think just offloading your thinking to AI was AI psychosis.
To me AI psychosis is the handful of friends I’ve had who have done things like have a full on mourning session when a model updates because they lost a friend/lover, the one guy who won’t speak to his family directly but has them talk to ChatGPT first and then has ChatGPT generate his response, or the two who are confident that they have discovered that physics and mathematics are incorrect and have discovered the truth of reality through their conversations with the models.
But language is a shared technology so maybe the term is being used for less egregious behavior than I was using it for.
I'm curious how to best define what AI psychosis actually is.
My understanding is that regular psychosis involves someone taking bits and pieces of facts or real world events and chaining them into a logical order or interpolating meanings or explanations which feel real and obvious to the patient but are not sufficiently backed by evidence and thus not in line with our widely accepted understanding of reality.
AI psychosis is then this same phenomenon occurring at a more widespread scale due to the next-word-prediction nature of LLMs facilitating this by lowering the activation energy for this to happen. LLMs are excellent at taking any idea, question, theory and spinning a linear and plausibly coherent line of conversation from it.
> friends I’ve had who have done things like have a full on mourning session when a model updates because they lost a friend/lover
I mean, isn't that the natural and expected response? An AI company sold them a relationship with a chatbot and at least some their social/romantic needs were being met by that product. When what they were paying for was taken from them and changed without warning into something that no longer filled that void in their life why wouldn't
they morn that loss?
The fact that they were hurt by that sudden loss is totally healthy. It's just part of moving on. The real problem was getting into an unhealthy relationship with a fictitious partner under the control of an abusive company willing to exploit their loneliness in exchange for money.
Hopefully they now know better, but people (especially desperate ones) make poor choices all the time to get what's missing in their lives or to distract themselves from it.
The way I put this to myself is that AI gives “correct correct answers and incorrect correct answers”.
They almost always generate logically correct text, but sometimes that text has a set of incorrect implicit assumptions and decisions that may not be valid for the use case.
Generating a correct correct solution requires proper definition of the problem, which is arguably more challenging than creating the solution.
> which is arguably more challenging than creating the solution.
This hasn't been the case in my experience. Devising a correct solution without a definition of the problem is impossible because you wouldn't recognize a correct solution without a definition. Often you discover the problem definition by exploratory programming and trial and error on solutions, but LLMs are still good for process this too. Arguably better because they type faster so you can iterate faster!
It’s simpler than that - it’s a guessing machine that has superior access to a whole load of information and capacity to process at a speed at which we humans cannot compete.
Does it make it better than us? No because ultimately the thing itself doesn’t ‘know’ right from wrong.
Yeah, very often the issue is that some context is missing. It'll say something true, but which misses the bigger point, or leads to a suboptimal result. Or it interprets an ambiguous thing in one specific way, when the other meaning makes more sense. You have to keep your wits about you to catch these things.
It's an incredible tool but it's also very derpy sometimes, full of biases, blind spots etc.
Garry Tan has been the primary crusader for AI driven decision making. I'm sure his position is more nuanced, but his twitter driven communication makes him appear like a caricature of a man in AI psychosis.
When the head of YC champions AI driven decision making, companies will inevitably be influenced into doing exactly that. It's unfortunate, because AI is generational technology and the hyperbole distracts from the real sea change occuring in labor markets everywhere.
when you outsource thinking to AI, you get that magical speed up. the agent is making decisions for you, so things move at agent speed. it often makes decisions without telling you, and the final "here's the plan" output often requires you to understand the problem at great depth, which requires return to human speed, so you skim and just approve.
the trick is to be mindful, aware, and deliberate about what decisions are being outsourced. this requires slowing down, losing that absurd 10x vibe coding gain. in exchange, youre more "in-the-loop" and accumulate less cognitive debt.
find ways to let the agent make the boring decisions, like how to loop over some array, or how to adapt the output of one call into the input of another.
make the real decisions ahead of time. encode them into specs. define boundaries, apis, key data structures. identify systems and responsibilities. explicitly enumerate error handling. set hard constraints around security and PII.
tell the agent to halt on ambiguity.
a good engineer will get a 2x or 3x speedup without the downsides.
> find ways to let the agent make the boring decisions, like how to loop over some array, or how to adapt the output of one call into the input of another.
Those kind of advice ultimately don't matter. If you're familiar with a programming project, you'll also be familiar with the constructs and API so looping over an array or mapping some data is obvious. Just like you needn't read to a dictionary to write "Thank you", you just write it.
And if you're not, ultimately you need to verify the doc for the contract of some function or the lifecycle of some object to have any guaranty that the software will do what you want to do. And after a few day of doing that, you'll then be familiar with the constructs.
> make the real decisions ahead of time. encode them into specs. define boundaries, apis, key data structures. identify systems and responsibilities. explicitly enumerate error handling. set hard constraints around security and PII.
The only way to do that is if you have implemented the algorithm before and now are redoing for some reason (instead of using the previous project). If you compare nice specs like the ietf RFCs and the USB standards and their implementation in OS like FreeBSD, you will see that implementation has often no resemblance to how it's described. The spec is important, but getting a consistent implementation based on it is hard work too.
That consistency is hard to get right without getting involved in the details. Because it's ultimately about fine grained control.
If there's one thing I know about users is that they're never certain about whatever they've produced.
I agree with you, except it isn't even good at writing code. Almost every time that you get an LLM to write a bunch of code for you, it has mistakes in it. The logic isn't right, the API calls aren't right, the syntax isn't right (!). That problem hasn't yet been fixed and it looks as though it never will be. That means that every line of code it generates, you have to review, because even if 95% of the code is correct, you need to find the 5% which isn't. But if you have to do that, it becomes slower than just writing the code yourself. As people have pointed out over and over again: typing in the code was never the part that took time. So I don't agree that LLMs are really useful for writing code.
LLMs are good at producing code that seems plausible at first glance and appears to work, but it never really does. And when trying to fix things, you discover 7 slightly different ad hoc implementations of the same thing, with their own weird edge cases and behaviors. And you likely miss 4 more. There is no intention or coherence behind any of it.
> if you just prompt the AI and believe what it tell you then you have AI psychosis
This is the right definition. LLM outputs have undefined truth value. They’re mechanized Frankfurtian Bullshiters. Which can be valuable! If you have the tools or taste to filter the things that happen to be true from the rest of the dross.
However! We need a nicer word for it. Suggesting someone has “AI psychosis” feels a bit too impolitic.
Maybe we reclaim “toked out” from our misspent youths?
e.g. “This piece feels a little toked out. Let’s verify a few of Claude’s claims”
I wouldn’t say they have an undefined truth value. Their source of truth is their training data. The problem is that human text is not tightly coupled to the capital T truth.
this author suggest its essentially the same risk https://www.poppastring.com/blog/what-we-lost-the-last-time-.... i feel its heightened because execs and leaders are absolutely salivating over the opportunity to fire thousands of humans with no regard for the cognitive debt that comes from outsourcing thinking to ai.
> companies and people outsourcing their decision making and thinking to AI
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.
It’s almost as if we haven’t learned anything from Hans the horse, Ouija boards, "facilitated communication", or the countless examples of the folly of surrounding yourself with yes men. The point about improv is spot on.
He uses AI himself, so I agree he doesn't see AI use as black/white.
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.
I am starting to come around to a similar sentiment. I have seen several large projects cook now for almost a year are not done. These are not trivial projects but the leads are heavily using ai at every opportunity.
I wasnt before but I am 100% confident that AI has done nothing to speed the delivery. It hasnt slowed it down either. It is a wash. The job is more miserable though.
> 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
I'm seeing it with lawyers, too. Like, about law. (Just not in their subject matter.) To the point that I had a lawyer using Perplexity to disagree with actual legal advice I got from a subject-matter expert.
I’ve been talking to a lot of engineers about how they use AI in their day to day and it’s dramatically different than what you see from the hypers.
The vast majority use one agent at a time and careful step through code. The main benefit they report is often about researching the codebase and possible solutions.
While you have to think about things objectively no matter what, when I start researching topics like physics, using AI as suggested in that article has proven very useful.
I've been strictly using LLM's to either push stuff that I've done plenty times before and are mostly boilerplate or have zero value for writing them by hand (not even educational), and I always ENSURE that they work on stuff that are easily verifiable and proven incorrect with my existing knowledge or a few minutes of googling.
I would say writing it myself is more enjoyable (in some cases). But I quite understand that I am not paid to enjoy myself. I'd say it's quicker getting AI to do it and reviewing. I believe the outcome is no worse on average. So yes, that's my chosen approach.
Part of the psychosis are AI usage mandates, where companies require a certain amount of LLM usage per worker. Of course these things are useful, but forcing them on workers is psychotic.
> 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.
Today's frontier models are genuinely useful as rubber ducks or grunt units. They are horrible for actual problem solving. These tools are not capable of actual reasoning. They will happily crap out a broken, untyped, untested Next.js monstrosity with no discernible architecture. They will build esoteric shell scripts to perform operations that could be done idiomatically and simply with tools already in your codebase. They will tell you to walk to the car wash then have the car wash valet your car back to you when confronted with the flaw in their logic. They will validate incorrect beliefs like ketchup being an acceptable hot dog condiment or the notion that "The Red Hot Chili Peppers" make good music. They have no taste, no anima, no drive.
Rule #1: Do not anthropomorphize the LLM. It is a million monkeys at a million typewriters piped into a digital sieve. I don't know how or why people place such trust in them while bemoaning other technology in our lives for being so broken ("my algorithm [sic] only shows me X", "the new iPhone update sucks", etc). If everybody followed this rule then the deluge of emoji-ridden hokum pouring into Slack workspaces and GitHub PRs around the world would cease but I'm not holding my breath.
>but if you just prompt the AI and believe what it tell you then you have AI psychosis.
No it isn't. Do you believe what teachers told you in school? Yes? Well, I guess you're suffering from just normal psychosis!
I don't understand how people don't understand that people offer unreliable information too. We learned about the tongue map in school as kids - many kids still learn that in school today. It's still BS regardless whether it was told to you by a teacher or AI.
You don't suffer from psychosis for believing a source of information, you're simply mistaken. You need a more critical eye to assess what you're told in general, not just AI.
There's a huge difference between a teacher giving outdated information representing what was once our (or at least their) best understanding of the world, and a chatbot that just randomly makes up things for no reason while insisting that it's all true.
Also, a good teacher should be encouraging the development of critical thinking skills and correcting your errors, while AI will just tell you how brilliant you are when you wrongly tell it about how you've just invented a new form of math or disproved a scientific theory you barely understand in the first place.
Not all BS is the same, just as not all sources are equally unreliable.
> Do you believe what teachers told you in school? Yes?
Nope. At least, not without proof. That would, IMO, be kinda crazy. We could argue semantics - maybe “stupid” would be a better word? Lacking in critical thinking skills? Whatever “it” is, it isn’t good.
LLMs can do advanced math and coding, which involves logic, so they are definitely capable of using logic. Which is what most people call reasoning.
So "LLMs are incapable of reasoning, they are just pattern matchers" is wrong. A lot of logic _is_ pattern matching, BTW. Like, syllogisms - deductive reasoning - do you think LLMs are incapable of that?
The thing you're referring to is that LLMs are trained to produce an answer which a human would like, i.e. they aim to produce plausible rather than correct answers.
So it's not so much a mental deficit as a different goal. Trusting LLM blindly is definitely dangerous, but dismissing it as useless for anything by code is rather wrong.
Pattern matching is hardly what distinguishes human from LLM - if you ask somebody a question about policy, for examples, chances are they'd just recite something they heard somewhere, never really thinking about it from first principles.
You're speaking of my company and I'm forever grateful.
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.
> 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.
The developers signing off on this will be "Human crumple zones" to protect the company from liability. Be very cautious if asked to sign off on anything like this.
This reminds me of Rich Hickey’s “Simple Made Easy” and his approach in making Clojure.
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 think one factor is AI is encouraging people to turn off their brains.
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.
The longer I look at the AI transformation, the more it seems like a people problem than a technology problem. The technology is undeniably there. The people are all over the place.
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.
I'd like to chime in and mention that its really obvious how to RL a coding agent to get the human addicted asap. and its also clear that there's a ton of $$$ to be made by doing this. therefore its done. the only LLMs I use are the ones I run locally because i know they aren't RL'ed for that metric (no incentive for the company that made them to make their open weights models addictive)
I think there's a few things, but its a little subjective and its more about the style the ai uses when doing these than the actual specific behavior:
- 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.
> I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation.
What's the historical context for this MTBF vs. MTTR reckoning?
If you optimize for MTBF, you optimize for it to be a long time between failures. You optimize for the system not going down in the first place, but when it does do down it might be Pretty Bad.
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.
Not the GP commenter, but I'm still struggling to understand how this relates to the AI world, or perhaps more importantly, what the historical context was. Did people end up switching to MTTR optimization over MTBF optimization? If so, is the implication that the recovery times got lower but software instability went up as a result?
Before the cloud, people were trying to reduce the mean time between failure (MTBF) essentially trying to prevent a thing from failing. With cloud, people are trying to recover as quickly as possible (mean time to recovery) accepting that things will fail —- it’s about how fast you can react to it.
I think there's a reasonable argument that our entire society right now is under AI psychosis:
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?
Weird that you mention the stock market and then conclude that there are only two outcomes: bust or success. If anyone can learn anything from the stock market it's that boom and bust are cycles that oscillate around a trend and everything tends to revert toward longer term trajectories. So, yes everyone is caught up with and overhyping AI and yes there will be a bust after the boom at some indeterminate point but that isn't the end of the story and we'll see a rise and further oscillation afterwards while we get better at applying the technology.
I mean, there's a reason I started with a distancing phrase about a "reasonable argument"; I think what I'm suggesting is an interesting lens but does not capture the whole picture.
But also, even if bust is business as usual in the big picture and not a social disaster long term, it's of course not what individual investors want for their particular current investments.
My car drives itself. That's a $18T global market.
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.
Well they are projected to spend $175 - $185B on capex in this year alone most of it for AI buildout. Lets say only 150B of that is for AI. If they can then somehow replace all their software engineers with AI that they then run for free and depreciate over 10 years then they just replaced 9B a year software expense with 15B a year depreciation expense for the next decade. Yes this is grossly oversimplified but it still illustrates how crazy high of a bet they're making on AI.
> My car drives itself. That's a $18T global market.
That's not a new market, that's a new feature in an existing market. Lots going on in transportation and I'm not seeing any scenario where self-driving cars vastly increase total output vs just eat up other forms of transportation and change where people live/how long they commute.
> 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.
Similarly, many companies are trying to be more efficient - "do what we already do, but better". That's different than growth.
What could Google do with 9B on software agents? Let's say the future of them is amazing and this means they could write 100x more code than they can today.
Has Google recently showed much ability to turn "more/faster code" into "superbly profitable new market"?
Someone's gonna have to crack the demand side issue for anything transformative to happen.
> My car drives itself. That's a $18T global market
Which will take decades to become addressable. Self-driving cars work OK in a few cities in one country. Expanding that to be able to cover Mumbai and Omsk and Nairobi will require significantly more work.
> Also $1T in data center investment makes sense when you realize that companies are racing to create virtual white collar workers.
Does it make sense? How much would the resulting virtual white collar worker cost? Because datacenters have running operational costs, and so do the people operating them and working on the software that runs in them.
No. It doesn’t. And if you’re defining “drives” as “it drives as well as I do” then you probably shouldn’t be on the road.
> makes sense
Nothing about any of this makes sense. Tell me, when all white collar jobs are replaced by AI, where will the customers come from? Who will have income to afford your products or services? The poor barista whose surveillance videos are training the robot that will soon replace them?
Leaving aside any consideration of human compassion or questioning of the purpose of an economic system (hint: it’s not just an abstract machine), shrinking the pool of potential customers by orders of magnitude has never been a recipe for sustainable success (let alone growth).
> Bust. We've come away with a useful tool but the hundreds of billions of capital expenditure were thrown away on a pipe dream.
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.
It’s useful for a while. Hardware has a pretty short useful lifespan. I’ll be curious how the landscape will look in 10 years as it comes time to replace all of these servers. Maybe we’ll extend the lifespan, or usage will continue to grow, or we start shutting down datacenters.
Another possibility is that the hype continues, growing and growing and sucking up more and more resources, and the piper has to wait yet another day to be paid, until someone figures out how to pivot to the next big thing and all the debt (financial, social, environmental) gets carried forward and we keep going.
In other words, BAU for the last few thousand years.
> The closure leads to price increases which leads to inflation which leads to non-dollar assets (ie stocks going up in value)
I think this argument proves too much. Historically energy shocks have led to recessions, and in recessions the stock market usually doesn't go up. And the US economy is certainly exposed to global recession regardless of whether we're a net exporter of fossil fuels.
> we're collectively operating more in the interests of the future AI than in the interests of humanity
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.
Oh wow that gives the billionaire class so much intellectual credit they don't deserve. No, they see the same ChatGPT we all do and their mediocre brains with zero self understanding (see andreessen's explicit comments to this effect) determine "it's a new life form ! It's brilliant / conscious / my new girlfriend!"
People that don’t understand current AI likely have no idea how to differentiate Opus from some super intelligence. Further in their domain with the safeguards off it probably creates capabilities never imagined. To me they are making that leap of expecting continued capability improvement and their framing is “what I already saw is fundamentally game altering”. It doesn’t need to imply anything further, yet.
Furthermore, if you try to make your own decisions, you would be outcompeted by someone who has outsourced their brain. And, of course, since intelligence and labor would no longer be scarce resources that humans can use as leverage, gunning you down if you protest wouldn't really harm anyone in power.
People say that LLMs won't take us there. I think that's accurate, but there's a great deal of research going towards the next breakthroughs. How much are you willing to bet that all future attempts will fail?
If we are successful building an "ultra" human AI, it will require massive amounts of energy. That translates directly to "money". There will always be money unless someone finds a way to negate the second law of thermodynamics.
You seem to fall into the same set of criticisms as everyone who’s bearish about ai. It’s somehow so powerful that we can’t handle the ramifications. Meanwhile, it’s a waste of money and doesn’t do anything. You have to pick a criticism and stick with it. Otherwise, it’s just angst-driven noise.
> 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?
If we want to understand a phenomenon, we should be careful with technical terms. It is not "psychosis" [1] anymore than bad software that makes mistakes is "hallucinating".
The truth is simpler and less dramatic: hapless ambition-monkeys who climb the corporate ladder are demonstrating that they are not promoted for mental acuity. Corporations, after all, do not serve "the interests of humanity" — they are an organised collusion system that diffuses responsibility and anonymises negligence. And when it works as intended, shareholders rejoice.
For companies that can afford to build data centres, the latter are seen as a sure bet that can't fail, like building a bridge or buying new computer/hardware now with the pile of cash on hand without necessarily knowing which OS/software they will install. They are even planning to restart or build nuclear reactors. [2]
I don't think the majority of humanity will ever accept "AI" as being anything more than a fancy computer, let alone a 'new species', even if it was proven sentient.
You just need to embody the AI in something that moves, and then people will definitely treat it as a new species. Already happening in my town with delivery robots: when they get stuck on a kerb, a person will stop and help them up the kerb while saying soothing words like to a pet: “There you go, little guy, now everything is alright.”
The people with capital are gambling that this will be an innovation good enough for the first player to take all. That’s where the hubris comes from. You’re a billionaire and you have the chance to rule the world if this plays out, and if you don’t, someone else is going to. That’s where all of this is coming from
It’s time for humanity to admit that this is the end of the line.
We had a good run, some beautiful moments were created for shareholders along the way.
Let’s take each other by the hand and walk into the darkness together.
So Long, and Thanks for All the Fish
I don't know. We invented machines that can answer arbitrary questions and are quickly demonstrating the ability to answer questions no human has been able to answer. We're sending more rockets to space in a week than we have in the prior decade. My car can drive itself. We experienced a global pandemic and within six months engineered and scaled the mass production of a vaccine to mitigate it. We also just invented a weekly shot that nearly cures the most common cause of non-natural death. All of these things are new in the past ~five years. There is no definition you can invent that does not classify the times we are living in, right now, as the most impactful ever, in human history.
But the homemade god would liberate the elites from the nightmare of being responsible for running the planet into the ground. AI jesus take the wheel!
If we achieve runaway AI, the stock market goes to infinity. So from an expected value standpoint, massive spending on it is worthwhile. Even if the odds are tiny, the payoff warrants a massive bet size.
> 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 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.
Totally unrelated pet peeve of mine, I hate when people write this: "MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery)".
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.
Just talked to an exec yesterday about their multinational company, where the newly-installed CEO just came in with "everyone needs to be using AI" and "we should be doing everything with AI".
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".
The race to invent variants of Gas Towns, Ralph loops, pump out videos, blogs, etc. showing off greenfield development with cleverly named agents running in parallel is another case of engineering people diving head first into Resume Driven Development.
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.
This is a critical communications issue that is becoming what I believe the defining characteristic of "This Age": nobody knows how to discuss disagreement, and because it cannot even be discussed communication ends, followed by blind obedience, forced bullying, retreat and abandonment. This is going to be a hell of a ride, because nobody can really discuss the situation with a rational tone.
That people don't realize full test coverage just means every line is hit, not that everything is correct is always funny to me. (I don't view as an argument against tests, but with AI it's especially important as if you're aren't careful it'll be very happy to make coverage that is not quite right.)
My biggest grief, among many, is that the field is just no longer enjoyable to work in.
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.
Anyone who's taken VC funding has no choice. More money has been spent on AI commercialization than the atomic bomb, the US interstate build-out, the ISS and the Apollo program combined. Failure is going to be catastrophic and therefore, one tied to this ship cannot accept a world in which it fails.
Or anyone who even wants VC funding. 90+% of investors only want to invest in AI companies.
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.
I was reminded of the universe of Doctor Who. It's an incredibly complex technology, but it often behaves either unpredictably, like AI agents, or like code written by a vibe-coder without understanding the architecture or boundary conditions. And programmers are more like architects of consciousness, building machines rather than writing code.
Similarly, when the Doctor hacks a PC, he doesn't write code but rather communicates with the computer, using diplomacy to crack the agent.
It is likely that we will come to a world where software solutions are "grown" by iterations of agent work, and no one will know exactly how it works.
I think this will happen. A quick, low-quality solution is more common than a solution created by a master craftsman.
In addition to low-quality furniture, bad knives, electric kettles that burn out after a week, and poorly cut clothes that don't fit, have unpleasant fabric, and fall apart, there will also be a disposable, rotting code.
Master programmers will remain, just as master craftsmen have remained. They may even continue to earn well.
However, there will be fewer of them, and the requirements for their skills, knowledge, and reputation will increase.
The hype or psychosis is mainly by mediocre/non expert/middle manager/you name it, especially when a person who never wrote a single line of code suddenly is making a wall of text, and it actually works!? Oh my!!
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.
The primary issue here is that CEOs and investors are particularly vulnerable to AI psychosis which is then forcibly propagated to the rest of the organization. Understandably, the perceived benefits are almost impossible to ignore, compounded by the FOMO of the AI first/AI native narrative being sold by AI influencers.
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.
I was rewatching Rich Hickey's "Simple Made Easy" talk (as one does) and there was a great line about full test coverage.
"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)
Company I just left is reportedly now using Claude to analyse the metadata generated from the company MDM that tracks actual laptop use, and then pulling people up if they're not working "enough".
They're also reportedly now giving staff AI-related "homework" in an attempt to force staff to use AI more.
At work they are purging any developers who are not all in on AI. I must constantly be in full support of AI to not get fired, despite whatever my true thoughts are, including anything I post on LinkedIn. There can be no doubt.
Honest comment: it is transition time. This time is to make bets and take positions. Your humble position maybe.
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.
So rewriting gets cheaper and cheaper. New features fall more or less into the same category. Refinement doesn't.
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?
his worry is similar with search engine, I believe 90% of population don't even know how to properly do a good search in Google, that's why the info asymmetry still exists and the gap is bigger. It's just now we have AI.
We built too many layers of abstraction, so much that even the people in power have forgotten where the fantasies are. The objective reality is behind so many curtains that we forgot what is powering the whole theatre play to begin with. Or maybe we know but became too far detached from it to care. If you are at the same the better and the player, then what’s left?
I don't entirely know what rational discussions that can not be had?
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.
So it sounds like he’s not talking about you. He’s talking about people who actively choose to ignore complexity risk and refuse to have a rational discussion about it because they believe AI will always be able to fix it.
The problem is that the only thing that has proved out so far is cyber security. Unfortunately cyber security improvements is not going to improve living standards, and it's just going to increase the cost of just doing business. There is no productivity boost, in fact it's the opposite.
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
Amazing how the dev community is suffering from a similar inability to approach the subject of real world AI efficiencies and business benefits. I don’t think it’s helpful to accuse the other side of psychosis. It disqualifies any data or experience they bring to the conversation.
I don't think it's helpful to call this psychosis. N
Beyond that I don't think it's even irrational.
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.
This specific psychosis is driven by peer pressure. If you are seeing everyone around you using tools to "enhance" their work, you wouldn't want to be "left behind". So it has permeated the entire ecosystem. The lucky ones are those who are outside this (or can afford to be outside this) and can see why it isn't working. You can't be inside it and hope to have any rationality. Everyone is competing on "I can figure it out better and quicker than you can if only I can get X to work".
"its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!"
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 tweet is criticizing over-reliance on the "agents will fix it anyway".
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.
> Agents are fixing bugs so quickly and at a scale humans can't do already.
This is an illusion, I assure you. On a side project of mine with behavior that's very hard to translate into an algorithm (never mind code), after a few failed attempts between the both of us, I figured it out. I gave the AI (Opus) an extremely specific algorithm with detailed tests. All completely and utterly ignored (including the tests), like I never even said it. It proudly declared the work done without ever having written the tests that would have proved that wrong - it basically wrote code that didn't change behavior at all, it just gave the illusion of looking busy.
That's just a single extreme example that comes to mind, but I've had it ignore me at least 4-5 times a day this week.
If you think agents are fixing things reliably then you simply haven't noticed that they are "looking busy."
More likely people thought GP was missing the point; "MTTR-optimized YOLO deployment" only succeeds against recoverable errors and acceptable periods of downtime against errors that are detected quickly. You could have a bug silently corrupting data for months, and that data may only be used by 1 critical process that runs once every quarter. So you could introduce a timebomb that can't be gracefully recovered from (depending on the nature of the data corruption).
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.
> won’t concede until you can just ask Codex or Opus “find and fix all the bugs in this
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!
Cloud people highly overvalue random web-services that most people (and most of the economy) simple don't rely on in any critical fashion. Yes, there are some example of people who make lots of money a day on these services, most people make pennies or nothing.
Github is not critical infrastructure. It's your personal social media addiction.
Up to 80% of software projects fail. Most startups will fail. VC's and bankers know this.
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.
Good point but he didn't go far enough. I would expand the AI psychosis to include all local optimization based on phony measurements , even time spent , DAU etc (which are mostly bots & synth accounts). In other words AI psychosis has been going on for 20+ years.
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.
I don't doubt there are companies totally misusing coding agents and LLMs in production. There are also real companies with real revenue and solid architecture using LLMs to deliver products. There are also companies with real revenue and rapidly accumulating tech debt.
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'm in a company going through this. Everyone outsources their thinking to LLMs and the results are painfully mediocre. The smart ones will use it to get their bearings on the topic then go to primary sources, the not so bright just ctrl-c ctrl-v.
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.
> "In psychopathology, psychosis is the inability to distinguish what is or is not real. Examples of psychotic symptoms are delusions, hallucinations, and disorganized or incoherent thoughts or speech."
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.
I use AI heavily. But my #1 productivity tool is still a custom code generator I wrote 15+ years ago. It routinely generates about 90% of the code needed to write a biz application. So using AI to write productivity tools is a great way to go IMHO.
Codex is freakin hot-to-trot to churn out test coverage for every single thing it implements, and some of it is very esoteric and highly prescriptive (regexes for days) BUT .. after a while, it dawned on me that LLM-driven test coverage is less about proving “code correctness” (you’re better off writing those tests yourself alongside them), and more about just trying to ensure that whatever gets bolted on stays bolted on. For better or worse, obviously, since if you bolt on trash, trash you shall have.
Wholeheartedly agree, but in fairness, I trust the tests of the best AI models more than those of the average human developer. There's a lot of people around that combine high diligence with complete intellectual laziness, producing tons of useless tests.
Actually no, cancel that. I realise now that I trust AIs more than the average developer, period. At this point they do produce better code than most people I've dealt with.
Sounds pretty accurate. Bunch of comments on this thread sound like AI is some kind of a new doomsday cult. The most annoying thing I find personally is that all engineering principles are getting crushed by non techies. Management counting token usage, forcing agent use, reducing headcount in the name of productivity gain. Devs building bridges but nobody knows what the bridge is, what are the standards to which it was built, how it works and how to maintain it. VCs counting extra money claiming chasing the holy profit is the future. The abundance of engineering apathy is disturbing.
Companies who use AI passively and mindlessly will create immense opportunity for those who don't is a concrete definition for risk to companies, similar to the risk to individuals who go down the validation hole with AI.
This whole situation of forcing people to spend tokens is hilarious and shows who is true tech manager and who is impostor in the field. I think it’s a good indicator of competence and hopefully board members see that soon.
I shut down AI Agent fanatics on the regular. But chop one head off there and two take its place. And I say that as someone working with Claude and Codex daily. While they are both incredibly good at clearly described and defined atomic tasks, application scope makes them lose their minds and the slop ensues.
Deprecating immature workflows (LLM agents in this case) is much simpler and faster than building them from scratch. Many companies get this risk assessment right. The case where being wrong is much more costly than being right.
It's worrying because it feels like a loss of control. But there must be control. And this what responsibility is. You should worry only about people who don't understand responsibility, not AI-inspired ones
I'm starting to long for the age after AI. When the generative euphoria has settled and all outputs are formally verified based on exquisite architectures and standards.
I like to think,
(it has to be!)
of a cybernetic ecology
where we are free of our labors
and joined back to nature,
returned to our mammal
brothers and sisters,
and all watched over
by machines of loving grace.
I like how you haven't wagered which exquisite architectures and standards. I am sure we will all agree on what they are and follow them the same way :)
Because of the concerns you cite, I think working out the basic economic systems and incentives for paying people is a much more pressing concern than building magnificent machinery that we don't even own. There has been no effort on their end to demonstrate good faith nor to uphold their end of the social contract, which is why it's in our hands to demand the fundamentals to lead a life of dignity.
I have a ton of respect for Mitchell - I didn't really know who he was until Ghostty but his writings and viewpoints on AI seem really grounded and make the most sense to me. Including this one.
Many people on this forum are suffering under this same psychosis.
I was under the impression that anyone that uses the MTTR abbreviation knows enough to understand that you need to balance it with change failure rate, deploy frequency, and lead time.
Possibly psychosis. Possibly just serious ignorance and mob mentality. Leadership is supposed to be phlegmatic and measured; instead, we are saddled with hysterical hotheads. (Of course, when they are phlegmatic and chasing fads, then it does indeed resemble psychosis.)
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.
We're definitely in the mess around phase of AI adoption.
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 don't think it's super clear what we'll find out
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.
I believe it. I've seen the cyber team in my org bend every rule just to get access to frontier security models.
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.
I’m at a large Fortune 500 company. On a recent call, a mandate came from the CEO himself - “we have to use more agentic AI”.
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”.
I don't think this is actually anything new. In large-enough companies, even before AI, it was and is quite common for executives to lose touch with base reality. I don't think anyone is under any delusion that people like Mark Zuckerberg intimately know the entirety of their corporate codebases. Everything is filtered through layers and layers of middle management whose summaries, cherry-picked statistics, and perpetually up-and-to-the-right graphs make it difficult to have an objectively informed opinion. Companies would, are, and will have mass layoffs that unintentionally (or, intentionally but with indifference to the consequences) fire key engineers whose loss results in "familiarity debt" within the systems those engineers owned.
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.
Mitchellh is on to something. Some of the AI products I've seen seem like psychosis hallucinatory fever dreams, using terms and concepts that have no meaning. Funding? $50,000,000 pre-seed.
The entire problem is vibe coding is only good for demos, prototyping and finding signs of product market fit without actually releasing a product into the market.
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.
Generally agree. I use AI very heavily, but rarely am I letting it actually think for me. It's a tool that reduces the time it takes for ideas in my head to manifest into reality. If you don't have those ideas, or a poor understanding of the system the AI is working on, you're going to produce slop. If you can't recognize this slop, you're more susceptible to having psychosis.
Very general comment/sentiment/observation here for me personally is that about a year or two ago, everyone asked me ‘so ..where is the ai’. Nowadays it seems that this sentiment is already on its way out and I see more and more ‘no ai used’ statements and non-ai workshops popping up. I am in a weird place between art, technology and ecology so I get the best and worst of these worlds. But yeah, I feel the hype cycle is coming to an end in my personal bubble. I never cared or feared ai, I mainly fear the mania around it. Luckily I am in a position where I can afford to just observe. Sure I have used AI and it helped me a lot, mainly to get projects going when I am exhausted/stressed.
The real AI psychosis is the expectation of 5x/10x productivity gains akin to the mythical 10x developer during the 2010s JS growth period.
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…
> "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!"
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.
I saw this first hand at a company, and I think this is what happens when you combine FOMO with an utter lack of industry best practices. No one knows where they are going, but are convinced they are not getting there fast enough.
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.
this AI transitionary phase to Quantum, light chanels and new way computation will be architected will in the future be looked at similar to a toxoplasmosis like societal wide parasite, which invaded the host in order for the host to act more favorably to it
The DevOps team at my company wants to hire a replacement for a very talented engineer. They’ve been interviewing candidates. The board got wind of it and someone not in their team decided they needed an AI Engineer, which is absolutely not what they want. So to release the funds they have been forced to change the job description and go after a different type of role altogether. It’s complete nonsense.
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.
I work for a small telecom services provider whose current VP immediately set an AI course when stepping on board 6 months ago. Involving AI in everything and every task is now our first priority - across all employee segments, not just us system developers - and leadership is embarking on a program to measure employees' AI usage levels as a means to gauge everyone's individual efficiency. It's like the era of the evangelic crypto bros all over again.
Psychosis means inability to distinguish the real from the not real -- delusion. I don't think the article describes that, at least not in a literal or clinical sense. The author lifted a term usually applied to people who fall in love with chatbots and applied it to the context of software developers not understanding AI coding tools, and the limitations of those tools.
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.
> We talk about "technical debt" and "maintainability" and "scalability."
If we are just talking about writing code, I believe LLMs are indeed better than the median programmer already. Where they fail, however, is in the tasks that require proper reasoning.
At a micro-scale, you can simulate reasoning with token prediction, but I think this breaks at a higher-level (e.g. architecture.)
I believe that, given enough complexity, a purely vibe-coded project will always reach a point where each new feature breaks five other ones. So qualms about "maintainability" are important.
Maybe we'll reach the point where LLMs can deal with the complexity of most codebases without human guidance.
But it doesn't change the fact that this limitation is inherent to LLMs: chain of thought is not reasoning, only the illusion of it.
> 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
Right, and the important part here is that it did so with your guidance and taste, which have both been honed by years of experience.
This post calls out how you can't argue with these people because they say its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!"
the top reply is from someone doing exactly that, arguing "but the agents are so fast!"
Yeah: If the tools aren't good enough and fast enough to fix the bugs before release, what makes anyone think they'll be able to so easily catch up afterwards?
Maybe they're assuming that doubling the code-base/features is more beneficial versus the damage from doubling the number of bugs... Well, at least for this quarter's news to investors...
> It's game theory. Someone will do it, and you'll be forced to do it, too.
You'll be forced to do it, or lose. The unstated assumptions are that, first, it will work, and second, that you can't afford to lose. But let's just assume those for the sake of argument.
> It can't be that bad
That does not follow at all. It can in fact be that bad. That was what made the game theory of MAD different from the game theory of most other things.
> 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".
Oof. Potential "bad" outcomes of "game theory" should be calibrated to include all the bloody wars and genocides throughout recorded history.
Why did the Foi-ites kill every man, woman and child of the conquered Bar-ite city? Because if they didn't, then they'd be at a disadvantage if the Bar-ites didn't reciprocate in the cities they conquered...
My prediction is that in the next year, we’ll start to see some dismantling of code review at some companies. It might take the form of “AI-only review,” or something similar, but many companies are getting frustrated with developers saying “no” to immediately merging slop they can barely understand.
That seems like an odd way to interpret what they wrote.
Imagine old school machinists saying to a CNC machinist “Ha! See, maybe you don’t jog the axes manually, but you still have to be involved in placing the stock material, and you have to do the CAD/CAM work - so did it really machine the part for you? No!”
AI is a tool like any other. It has its limitations. It has classes of problems that it is suited to handle, and others it isn’t. If it’s true that they haven’t written (as in “typed out by hand”) a single line of code, why can’t they say that without you making that statement into more than it is?
I haven’t written a single line of code in 6 months, and that’s simply fact. It is also true that I put in a lot of other work to make that feasible, but that work isn’t in the form of writing code.
“it’s mature and the next step of engineering”
Tautologically, it’s mature enough for what it is mature enough for, and it certainly is the next step in the same way that CNC was the next step for machining — if you’re not using it as a machinist, you’re going to produce less compared to those who are.
Same thing with garden hoses. Yes, you can go fetch water from a lake and splash it on your lawn, or, you know, you could just use a sprinkler connected to your garden hose. Doesn’t replace buckets. Buckets just have a narrower scope in a world where garden hoses exist.
I think discussion with open registration is doomed precisely for this reason, it is too open to being influenced by bad actors. Maybe the lobste.rs invitation model would be better ...
To me the big thing I see in blog posts is this implication that “all software engineering best practices are out the window”
And to me, AI should best be used to add rocket fuel to existing practices. Better tests, better observability, more atomic changes instead of big changes, automatic rollback etc.
> And to me, AI should best be used to add rocket fuel to existing practices
The more your codebase follows best practices and consistent patterns, the better AI will do and the faster you can move.
Same as humans really, just even faster. I'm also excited that people are finally writing docs and without even any flogging! They're calling the docs "skills" but hey whatever works
I think what has really happened is a re-weighting of the importance of a lot of software practices. I think basically all of scrum/agile is completely useless now, but tests, PR reviews, documentation, decision records, etc, are more important than ever.
> To me the big thing I see in blog posts is this implication that “all software engineering best practices are out the window”
Yes, this is indeed a pungent smell. AI code assistants allow whole projects to be refactored and even rewritten in entirely different programming languages and software stacks in a few minutes, sometimes even with one-shot prompts. Most assistants even support creating and maintaining test suites with first-class support. Whatever you prompt, they do it.
And here we are, expected to believe that these tools can't or don't follow best practices?
> And I haven't written a single line of code myself since what - February maybe?
Have you measured the impact of that on your ability to create good code? From my experience, relying on AI tends to degrade that ability.
Also, you seem to be able to do all of what you say and benefit from AI tools because you seem to understand the overall bigger picture well enough to be able to drive the AI agents to do their work properly. In other words, you operate in a familiar territory where you do not need to learn much new things.
But what about the junior people with little experience? Will they be able to manage such AI workflow? And more importantly, if junior people are given such AI tools, how will they learn?
These are all questions which may not matter in the short term and one might ignore them if they just want to see the profits and efficiency gains during the next cycle. But what about the long term?
What tools have you tried? Are we talking Codex GPT 5.5 and Opus 4.7?
Would you say the project is well architected? Clear boundaries? Or ball of mud?
How large is large?
Are there AGENT.md files giving good information that helps LLMs get context when looking at a certain area of the code?
Is it all in one repo? multiple repos?
Are there good tests?
I feel like these are some of the many variables that can make a difference.
I work on a pretty large project/code base, written mostly in Go, and I have pretty positive experience with LLMs. I take on fairly small chunks, I review and understand the changes. I also use LLMs to explore options and prototype quickly. They're also very good at fixing bugs, failing tests etc.
I’m also in a big tech company and a lot of the team hasn’t written any lines of code by hand for awhile and it’s causing a whole lot of tech debt and frustrations are beginning to boil.
I’m not sure it’s possible to force someone to read every line of AI generated code and understand it. People generate code faster than they take time to read it.
Pressure from C-suite to AI AI AI AI AI MORE AI AI AI AI doesn’t help.
> I feel like I'm in a different field compared to the rest of hacker news.
That should be my line. My new employer does not use LLMs at all. Software development, marketing, hardware development, nothing. Maybe too little, but whatever.
The problems the company is facing are entirely unrelated to "throughput".
> Now Claude is writing great commit messages but since I'm no longer looking at code - I never see them.
Let it be a learning opportunity for us, folks. This is why you shouldnt take comments on the internet too seriously. People (or bots) will say anything just to get attention.
p.s. Offtopic, but this is why I believe the ability to hide post history was the tipping point of Reddit's downfall.
I'm in a big tech company everyone has heard of and we have seen a huge spike in incidents which correlates with how much new code is shipped due to AI. Perhaps it's to AI's credit or our engineers' credit that the spike is relatively 1:1 with the spike in new code.
It's causing problems in all parts of the business and leadership's answer is that we must use AI to make fixing incidents faster and automated rather than assess whether we should be shipping enormous amounts of buggy code every day...
It may be the case. I've been around in the industry for 25 years and I barely code. I babysit multiple instances of Claude and we were very purposeful and deliberate in altering our workflows for it; we made our local dev environments capable of spinning up multiple instances to work from parallel worktrees. We added MCP servers to let LLMs observe our CI, Jira and deployments.
Most of our time is spent doing spec work, planning, and injecting the proper context into LLMs. Like the OP, our metrics have drastically improved the time for delivery of new features, slightly improved bug resolution times, and now we're bottlenecked by needing more code review and manual QA to handle the workload.
Microservices in big companies where you have to first write the spec and then fully understand the changes is maybe among the least benefiting use cases yet.
When you work on just a new mobile app, this is where I find AI is making the biggest difference.
On mobile you don't need specs and you don't need to understand every detail of the implementation. You can QA test the app on a real device. It gives me more confidence than just having written the code myself, and it's much faster. You can implement multiple major features in a single day.
This kind of e2e testing is just not possible with backend services.
> And I haven't written a single line of code myself since what - February maybe?
And how many lines of Markdown have you written? Pointless metric. I think I type more now because I don't get any helpful autocomplete for... English.
I feel the same and don’t get the extreme AI is inherently evil vs. AI is the best thing ever invented discussions. For me it’s all just emacs vs vi or tabs vs spaces kind of discussions.
It’s a tool and the good old sh* in sh* out principle applies.
People might take Mitchell’s comment as some kind of anti-AI stance, but it’s not he uses it regularly and makes a point in the X comments: “use AI, but think”
That comment sums it up best, because right now it’s hard to talk to either side, which separates at the comma.
I believe your anecdote. I am also agree with what you wrote below: "Tautologically, it’s mature enough for what it is mature enough for"
What programming language are you using? It seems like some programming languages are more mature in LLMs, e.g., Python, Java, C#, maybe Golang. (Oh yeah, and definitely JavaScript/TypeScript.) Rust, Zig, C++: I have a harder time believing you can manage a large project using only an LLM to write code.
The magnitude of negative responses to this comment is very encouraging.
Not because I agree with my sibling comments, but because I strongly agree with the parent, making me think my org and I are much earlier than I thought. :)
It's a two months account hyping AI (look at the comments).
And to answer your question: No. I am yet to see a product made by AI or a product that used to require a dozen engineer and a few years being made by a single engineer in a month. Anything demoed is always a UI/functionality clone of the same thing LLMs regurgitates.
Our experience is very similar except we didn't really have a review process before, and now LLMs find bugs before PRs get merged in main.
We had 5x-100x speedups in some legacy but important pipelines, with no regressions (validated after extensively by humans).
It's not that the code was actively bad. It's just only 1-5% people in the local SWE market would be able to write code that runs so fast and efficient and benchmark it correctly.
We found a subtle correctness bug that was in production for half of the decade (both GPT-5 and Claude Opus were able to find it), confirmed by human after.
And we keep finding subtle bugs that have been introduced by humans before (despite the human reviews, the particular domain is just difficult no matter how many docs and comments and tests one writes)
I am convinced human reviews are overhyped in the industry. We've done it in my company since we started it, and bugs keep happening. People are just terrible at spotting them in the middle of 100 lines of correct code.
Machines, OTOH, are very good at it. I am currently trying to make the code review experience better for humans by not just having the AI review the code, but interact with the human, pointing out potential problems, bad patterns, perhaps hiding some code (e.g. renamings, formatting changes).
Developers still want to review the code, despite provably being bad at spotting bugs, because they want to actually keep knowledge of what's being modified in the code base, so I think this is the best approach.
If you actually have time to read all your code, understand it, and are willing to be bottlenecked by human understanding, then yes, you are living in a different world.
In my world, that is far too slow, and you will be seen as a low performer who just can't keep up with the tech.
I think this divide has something to do with the way people are using these tools. I do a lot of planning in my documents and I rarely use conversations accept to interate on something I wrote instructions for.
> I haven't written a single line of code myself [...] I need to understand the code
What's the difference? I don't think anybody get paid by how efficiently they type on a keyboard. If you to use a die or raise a crow to get your next keypress I honestly don't think your PM cares as long as the actual output you contribute to the project is something you are responsible for.
I'm not saying it has no implications on how you think or no costs socially, ecologically, politically, solely that nobody cares HOW you get the code, only in your ability to keep on making it increasingly work better, closer to the evolving needs of the project.
Some programmers are gardeners. It sounds like you're one too. Your job is to maintain a large existing codebase. You probably didn't understand the entire codebase before AI, nobody did, so it doesn't matter that you don't understand it now. AI is very good at gardening, nobody doubts that.
Other programmers are painters. Their job is to start with a blank canvas and create something that others will value. When AI tries to paint, it tends to produce slop: a facsimile of everything it's ever seen.
The right metaphor isn't painting, though, it's molding clay. That first pass is slop, but it's raw clay that the agent is very good at molding given a modicum of direction and "not this, do that" comments. The combined first-pass and reshaping time is still far less than writing by hand from scratch. And increasingly, that first pass is ... not bad?
I think you're mixing up "psychosis" with fads, trends, or perhaps executive excuses to do layoffs.
A feature of psychosis is being unable to distinguish between external ideas and internal ones. For example, if a brown-nosing Yes-Man machine keeps reflecting your own leading questions back at you, laundering them into "independent" wisdom.
In contrast, I'm pretty sure COVID and the invasion of Ukraine are actual external phenomena that affect businesses and economies.
The lists of who's, what's, why's, and when's always change but when the decades pass it's never one narrow type of people or the "not me's" which are gullible - it's just human nature + regional timing. The targeted groups are the only ones who are really easy to break out.
The tone of the twitter post feels very personal, and emotional, and I am sorry for the author. I hope he can find peace and calm with the pace of change to put forward his best self without needing to act like he needs to defend or fight something.
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.
yes, the tone feels personal, and I feel happy for the author for expressing it on a platform that is desgned for it.
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.
It sounds like you know a lot more about him and the context than I do, the angle I am coming from is mass audience. This reached far, to the point I have no clue who he is and what else is going on. That’s why sometimes messages like those can be misunderstood, so I like to err on the side of caution over personal. Didn’t mean to say personal = bad, but that if you wish to change a broad status quo and raise awareness then communication is tricky!
Assuming he’s right, I don’t see how that constitutes “psychosis”, as opposed to this beyond yet another of a billion examples of companies jumping on a bandwagon / cargo cult, and then learning they took it too far.
And also, he might not be right. But the good news is, we’ll all get to find out together!
Do you believe that an AI can write persuasive text? Do you believe that an AI can be trained to elicit a specific user reaction?* Can we agree that AI companies are strongly incentivized to make money, and they can do so by making their systems addictive? Because AI psychosis can be a byproduct of that.
*Il outline how briefly: mutate the model 500 times, give 1/500 of your user base a mutated version of the model, and save the top 5 of these model, ranked by how often the users did something, over the course of a week. Repeat for a year, passing the top 1% of these models onto the next round. This is the simplest way to do this and I can think of better ways to do this. I don't even work on this sorta thing; its 100% obvious to the AI labs how to do this better
Yes, no. Yes, they want to make money. No, addiction is physical compulsion and nothing else. Despite the effort to redefine that term to cover all kinds of things, an app is not equal to heroin.
That's a study. I can link you studies that say violent video games cause aggression, that porn causes rape, etc. Studies are products of the biases of the researchers.
It seems the diagnosis of psychosis is too quick: it seeks to reestablish the frame of expert for the developer identity that is being replaced by it.
“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.
Mitchell aches because his career has been solving broadly scoped problems by building a collection of thoughtful primitives for others to extend. LLMs seem to do the opposite but at great speed, and it hurts to watch.
Reading more, it seems part of his point is “if you’re making these primitives, it’s up to adopters to deploy, so mean-time-to-recovery isn’t that relevant.” Which is valid I guess.
But equally, like, do people need Terraform if they can just tell codex “put it live”, and does that hurt to see?
Honestly, I don't get this argument. In my opinion, "a collection of thoughtful primitives for others to extend" is more valuable now, not less. From LLM assisted engineering standpoint a nicely put reusable box with thoughtful interface is an easy win, more so if it is also easily extensible.
This doesn’t constitute AI psychosis. His argument is that we need to retain understanding of the systems we use, but there’s no compelling argument as to why that is the case. (I get that people are going to be offended by that statement, but agents are already better than the average software engineer. I don’t see why we need to fight this, except for economic insecurity caused by mass layoffs.)
It all just feels like horse drawn carriage operators trying to convince automobile drivers to stop driving.
If you want to draw that line of argument - it's more like horse riders being convinced to give up their horses in favour of trains: You're travelling faster, don't have to navigate yourself, or think about every boulder on the way; but there are destinations you can't go, overcrowded trains slowing down the journey, hefty ticket prices, and instead of enjoying the freedom, you're degraded to a passive passenger.
Very funny, this. Did we need forward deployed engineers to convince people that they absolutely need to use the trains in order to "not be left behind"? Or otherwise hype? Or was it sort of obvious and did not need to explained so much - like a bad joke called LLMs ?
> there’s no compelling argument as to why that is the case.
I'm not sure that's true. We've actually seen several open source projects that were vibe coded literally fold up and disappear because they ran into issues that the AI couldn't solve and no one understood them well enough to solve.
There's a reason openai/anthropic and friends are hiring shitloads of software engineers. You still need people that can understand and fix things when the AI goes off hte rails, which happens way more often than any of those companies would like to admit. Sure, "fixing things" often involves having the AI correct itself, but you still have to understand the system enough to know how/when to do that.
I am sure you will feel that this is missing the point of your analogy, but we would not have gotten very far with automobiles if we didn't know how they worked.
You are breaking the analogy because automobiles are machines for transportation, and understanding them is important to make them move. LLMs are machines to understand, and well, if they do the understanding you don't need to.
I find talking about X psychosis (or generally using mental illness metaphors) unproductive. It sets up the conversation to be "nothing else to do with this person".
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.
I have respect for Mitchel and I’ve spent a good deal of time trying to think of ways to justify his message. I can’t. Either I am missing a big piece or he is worrying about something that comes naturally as more software gets developed (and sooner).
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.
Yeah. The same processes that allow corporations to outsource their software to barely qualified 3rd-world body shops are the processes that allow you to deploy AI-generated code of unknown quality.
I’m at a FAANG and we have $300/day token quota. Personally I don’t use that much of it but management is pushing really hard for it. “the quota has been raised for a reason, use it”. Any task: “have you tried working on it with Claude?”. Every meeting “now engineer x and y will show you what he did with AI”.
It’s not all useless but most of the days I think I would be more productive if some processes were streamlined rather than if I had to throw tokens at them and still fail.
Of all the showcases I’ve seen the best are the ones written by people assuming that the token bonanza will not last so they used AI to build tools they wished they had. AI used to build the tool but by no means used by the tool, so if/when token quota gets reduced we still have a functional tool.
300 a day?? 7K dollars a month? No wonder they need to lay people off!
At Nvidia, we have no limit for Anthropic or Open AI models (for now) and are heavily encouraged to use them as much as possible.
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I by myself use now more than 15 accounts combined of all providers + API as well for external providers, more than 50K$ equivalent a month in API tokens, my team is doing the same thing, it's not really that much once you figured out the real automation loops and workflows, solving 300 issues a day with guarantees is common.
I feel that a lot of users are still stuck on Claude code or tools like this and don't really have a real argument about why they are even following the thread at all, everything has to be async for serious automation, you shouldn't even be seeing what Claude or any other model is replying (everything has to be digested with another model to increase relevancy and accuracy of the message so you can read faster (like a bot)), it's irrelevant, only human in the loop when a decision must be made, the rest has to be loops with all model, typical e2e, regression, computer use test, video into frames into all model loop and so-on.
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> No wonder they need to lay people off!
He clearly works at Apple, and they aren't laying people off.
I'm not aware of a limit in my current role. There is, however, a leaderboard.
Well, presumably (hopefully) they aren't expected to work weekends.
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Yes, the cost of AI is a big contributing factor.
The unsubsidised costs can't be revealed soon enough.
That’s funny I’ve been doing that too
Trying to crank out all the tools I never had time to build because I think we’re going to get cut off eventually
This seems seductive, but how do you get past the wall of "fixing XYZ or adding convenience ABC isn't on our pre-planned roadmap" so you can't get buy in from people who have to sign-off or deploy stuff?
Maybe that type of awkwardness is specific to my firm, but that's sort of what killed my drive to try to do that. We used to have one day every second week for that sort of work, but since it was scattered around, the tasks ended up disappearing-- nobody reviewed them and they didn't get merged.
So now they're trying to do a week-long internal hackathon to recover that vision, but I feel like that's going to produce a handful of big-bang ideas and not the 25 tiny tools that would actually streamline things.
Same. I've used it for debugging failed canary tests which required scripts and very specific knowledge on the canary platform that I wouldnt of ever spent time on.
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.
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I don't think we will. I think this level of token cost/availability will trend cheaper and faster, long term. These companies that spent too big and too fast might try to limit it and raise the prices and they might be temporarily successful but they'll very quickly be taken over if they keep doing it.
May I ask what tools did you make so far? And what is on your roadmap?
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"AI used to build the tool but by no means used by the tool" is a really good way to put it. Feels like the smart play right now is treating these credits as temporary subsidy and building stuff that still works when the bill comes due.
Seems like people are spending more time building tools than doing actual work. Lots of overlap too
In all fairness, doing actual work in this current slice of time is not what componies are prioritizing as of now.
It is fairly easy to tokenmax by having and inefficient automation set up.
Not something I would do personally. But it is surprisingly easy to set up a claw that eats half of your token budget in a meaningless "research" task. Set it up as a cron job and you will soon be promoted for being an AI visionary
Innovation signalling.
> $300/day token quota
Are companies using per-token billing? Why - is there some reason they can’t buy the $200/mo Claude plan for every employee?
The $200/mo Claude plan is not available for every employee. You can buy the $100/mo plan for up to 150 people, and then you have to switch to API billing.
Max 20x is for individuals only. (could probably have emps get it themselves, and reimburse)
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Those plans are going the way of the dinosaur, ai provider loses money on them. Most enterprise offerings are already there, Anthropic changed theirs to $20/seat plus token usage a couple weeks back
I’m curious what FAANG is actually doing per-token billing? I’m guessing not google or amazon (since my wife and I aren’t aware of that).
Compliance
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How do you even use that much daily?
I have an unrelated question, please. I am trying to make a post and get this error: "Sorry, your account isn't able to submit this site.", you know why or have a solution for it?
>we have $300/day token quota.
Unless other FAANG have the exact amount this is going to be Apple.
And no wonder why the quality of Apple software has gone downhill.
Apple in software development and design used to be very conservative. BSD like. Especially the lower end of the stack.
Now it is no different to other Silicon Valley companies.
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Also at a FAANG here. Surprised you don't manage to use $300 in a whole day. It's almost trivial to productively use that much in under an hour.
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.)
I use $30 a day to produce a decent amount of code. Certainly more than we need - thinking about/designing the correct solution/distilling requirements is still the bottleneck. How can you possibly even review $300/day worth of output?
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Also you regarding Claude usage limits:
> 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.
Do you even realize how insane your comment is?
"If you aren't donating at least your salary's worth of company money to another company every day, are you even working?"
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Wouldn't they save an enormous amount of money by getting rid of either you and the token quota, or a bunch of other people to continue paying your salary plus this insane quota?
If you are burning through $2400 a day, you’re just wasting tokens on idiotic tasks.
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How are you able to get to $300/hr productively? (I’m assuming this isn’t fast mode tax).
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I am glad I am not on your team, the amount of slop they have to deal with coming from you must be overwhelming
How? I struggle to use the 1000 Kiro tokens I get a month, and that only costs $20. And I use it more then anyone else on my team. Maybe we're just massively behind?
300 an hour, that's insane
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You must be using a really bad harness or just writing very vague prompts. 20 Million tokens is a lot.
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I feel in a really weird position where I both really dislike what AI is doing to the experience and practice of writing code, to the point where I want a job doing literally anything else besides using the computer, but also think that these tools are extremely powerful and only getting better.
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.
I've never had more fun coding, but the key is actually still writing the code yourself. The LLM has terrible judgment but an encyclopedic knowledge and the ability to pick out important details in a sea of information. Their worse use is producing code, but somehow that gets all the energy. Being an LLM babysitter is energy draining and you feel less and less in control. No job is worth being miserable doing something that you used to enjoy.
In my experience, its the opposite. The AI is very good at writing code, but it is unreliable at any kind of design. I use it as a fancy form of autocomplete. I give the broad strokes: "add a method here and change all but this one caller to use the new method", "Apply this design pattern here for this change but don't do this other thing". It completes the task reasonably well and sometimes even remembers to run the code formatter and check that tests pass.
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).
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After turning off my brain a few times and ending up in a place I don't want real fast, I am learning to ride this dragon.
And, you are right - use it as a fast typer, not a fast thinkier.
And for those who claim that AI is a good code writer - wrong. It can OUTPUT a wall of code, but it's overeager to flood you will legacy code on arrival to "solve a problem". It's harder to write LESS code, which is still the goal (even more true today).
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My biggest complaint about AI is getting it to lock onto current or specific information has been darn near impossible. Its definitely there in its training data but for the life of me I cannot get it to stop bleeding long outdated or external information into its responses.
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What do you use an LLM for then? Or you’re saying just don’t use it at all?
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> The LLM has terrible judgment but an encyclopedic knowledge
Bingo. Claude can bang out nearly correct code when I give it an idea, but it doesn't have the idea and repeatedly misjudges both how much work remains and what kind.
On the other hand, I don't know all the ins or outs of macro expansion in yaml at compile time or when and where macros run, enabling us to conaume their results elsewhere in the yaml. Frankly, if I had time, I'd happily spend time on that and learn more about it. I don't, though. Claude knows and does the guessing and checking. So I provide the concept and it translates into a horrible soup of yaml. Clearly I'm able to press forward with ignorance, which is dangerous. There's a real risk that I'll wind up with the kinds of unhealthy work that worries the author of the tweet.
> But humans have introduced subtle yet catastrophic bugs into code forever
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."
I think the commenter was referring to formal verification with "specification and validation": have the LLM emit formal proofs about invariants etc.
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...
> I feel like many in the anti camp have their own kind of reactionary psychosis.
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.
> It's looking in apallment at...
"looking on appalled at..."
>You're using psychosis wrong
Also "reactionary" haha
Not FANG, but I work at a company that operates some infrastructure at scale. What I've seen is after we've rotated through a number of different tools, in different pilot groups, eventually converging on tool X (a custom, internal wrapper around opencode).
Now every "working session" like meeting, at the team or dept level, has been around how to use tool X. Tricks using tool X. Problems using tool X. I can't help feeling if we had spent the same amount of time building up core knowledge/contempencies around say, design patterns, networking, specs, we'd be in a better place for building.
Instead we are going to have a few thousand people who know a tool really well.
Maybe this is what will turn software engineering into an Engineering field.
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.
As a junior dev there is this pressure to produce code, add features, and investigate bugs within unprecedented time period. I know whole code base is fking up but i will still add that feature or do a sloppy bug fix without digging deeper.
In my experience, AI really lowered the bar for bad code in the name of delivering faster.
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.
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> Maybe this is what will turn software engineering into an Engineering field.
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...
Note that other types of engineering are also often vibes based. The mechanical engineering for a rocket engine is extremely rigorous but the engineering for an injection molded housing for a cheap cell phone is a lot more about following a few heuristics and getting it out the door. Even in robotics where I work, it’s mostly about making parts that pass whatever acceptance tests you come up with. In civil engineering and aerospace failure costs human lives and millions or billions of dollars. In robotics maybe you have some machines fail in the field but in many instances you have one overarching safety system and many of the parts are irrelevant to that. The camera housing for example. So no paper trail or mathematical design validation is required to prove you designed it right. Often those are desirable but if you just manufacture it and test it a lot you’re probably fine.
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.
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What makes it a profession is not just the certification, it's the burden of responsibility for consequences. Your lawyer, accountant, and real engineers carry "we need insurance for this" level of risk in their work, all the way up to "can go to prison for getting things really wrong".
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 is actually not a bad exam to administer: https://ncees.org/wp-content/uploads/2025/01/FINAL_PE-Electr...
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Perhaps this will make a comeback when the need arises to distinguish between actual software programmers and prompters.
Eh writing software for healthcare, or aircraft or self driving cars is more rigorous than an EE working on industrial lighting or toys.
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
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I work at software in a medical setting. We are piloting an integration with a startup for measuring [some bodily variable relevant in ICU setting]. They are obviously vibecoding (docs are telling) and their API is failing in unexpected ways that they are not able to resolve. I am just waiting when this are going to harm somebody.
Don’t worry, some medical professionals are also delegating their thinking to LLMs. No need for the software middleman to cause harm.
> Maybe this is what will turn software engineering into an Engineering field
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”?
>He was successful in the end
So it sounds like it was fine? Why would this prompt (haha) a change in their approach to things?
Now imagine if you’re one step removed. You don’t see the cigarettes, smell the gasoline, nor see the fire extinguisher gauge. You only see the servers running business-as-usual. Those “engineering” guys are always drama queens, you think. We have processes and fire extinguishers when shit hits the fan, right?
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?
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This is the pattern you will see when medium-successful ignorant people take o ver a system that was based on some kind of standard.
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
Recently I had a request come through to allow finance analysts to vibe code their apps. During a discussion one of the finance managers let the cat out of the bag. Turns out our CFO had met fellow CFOs at a get together. They talked about how each of them were using AI. Our CFO was lagging behind and felt that we need to "accelerate" our usage of AI. He wants to push it just because he lost a bragging contest.
> He wants to push it just because he lost a bragging contest.
That is an uncharitable interpretation, IMO.
The CFO heard of a novel technique used by his peers in other companies, and they reported good results. He wants to try it within his organization too. As an executive, he is paid to (among other things) keep abreast of such developments in the industry and ensure that the organization he is leading is not caught flat footed in the market.
Peer-pressure driven management style. Fantastic.
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It is a tale as old as time (in the industry)
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.
https://news.ycombinator.com/item?id=46670190
"What, you guys are still not using Big Data? What's wrong?"
I call this Dinner Driven Development. That feeling of being Patrick Bateman when everyone is sharing their calling cards must be every C-suite's nightmare.
Now let’s see Paul Allen’s AI adoption strategy.
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Things like this make me realize the software engineering 'industry' is not a real 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.
I don't know why you think a "real" industry would work in the most idealized way. The media heavily reports on the stupid insane crap of the tech industry, that doesn't mean every other industry is sane they're just not as vocal on Twitter.
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Oh its well within industry norms for leaders to make decisions based on dick measuring contests.
> A real industry should be responsive to events of nature, or at least the market, not vibes.
Market is vibes! The price of something at a moment is, for example, what market participants collectively agree what the price of it should be.
Hate to break it to ya, but this is how most C-suites operate. Their job isn't to run a company well. It's to appear to the board/investors that they're running a good company.
It is a better play to do the popular thing in a way that measures as "ahead". Then it's hard to argue against a raise. But if you stick your neck out on your thoughtful expertise, it can take years or more for the value to come thru. You can easily be replaced by then.
The only antidote is a board that has a real working nuanced understanding of the entire industry. But this rarely happens, for many reasons.
It is really surprising how many of people that are running entire companies are ignorant businessmen
I'm going through a mixed experience regarding this, personally.
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.
Totally agree with one shotting GUI tools. I especially have liked it to create a single-file web app, and then open it with Chromium locally (no web server needed).
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.
What's the matter with spec driven development? It probably carries derisk IP benefits
This will be pushed down from people, who will have no deep understanding of it. But it does check some boxes in an ISO certification.
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...
I think AI rescue consulting is going to be come a significant mode of high value consulting, similar to specialists who come in to try and deal with a security breach or do data recovery.
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!).
A non-technical friend of mine has just won some hospital contracts after vibecoding w/ Claude an inventory management solution for them. They gave him access to IT dept servers and he called me extremely lost on how to deploy (cant connect Claude to them) and also frustrated because the app has some sort of interesting data/state issues.
What concerns me about this is that as these stories multiply and circulate people will just completely stop buying software/SAAS from startups, because 90% or more will be this same thing. It will completely kill the market.
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As a SWE that has only ever worked for an employer or on his own projects, this makes me wonder: how would someone even get such a contract? Did this person already have a consulting business? Do you just call up random hospitals and ask if you can demo an inventory management system for them? Did this person already know people at the hospital? I know technical folks that do independent consulting, but even with a vibecoded product, how is it that anyone can just get such a contract?
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This hospital will learn some hard lessons. I hope their backup strategy is good. I'm surprised they can field software from an entity that isn't SOC2 & HIPAA certified.
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As a cybersecurity IR professional as much as I hate to see this happen to a hospital this kind of thing is responsible for essentially tripling my income over the last 3 years.
Have you tried to talk him out of it, and have you considered blowing the whistle on him? He could kill people!
Wow. This is like every other gold rush. Millions will walk into the ice and snow, somehow not questioning that their ability to dig is not unique.
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This is going to happen all over. Company I'm currently contracting with has gone AI everything (aka technical debt hell), and they're gonna suffer for it. I'm glad my consulting contract ends in 2 months. I don't want to be around for the crash
Don't help him. Let him figure it out by himself, else they (he and hospital) will never learn.
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I'd really like to know how he won contracts, just in general. Did he have some connections. And he doesn't even know how to get it to run on a server by himself? There's millions of people that can do that, if he can win contracts why worry about vibe coding at all, just hire someone to do it. Winning contracts is the challenge in my view.
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I hope you have quoted him a very very high hourly rate.
Did he lie about HIPAA compliance?
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Heaven help us.
Hospitals? Vibe code?
Dear Lord. Respect to your friend for mad marketing skills, however. Selling slop to mission-critical sectors is next level.
jfc lmao
Heh. Got a customer recently around this. Entire infrastructure and CI/CD vibecoded. They half implemented Kubernetes in Github Actions that were several thousand lines long and impossible to understand.
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.
> If you are not an expert, AI seems to create a complex solution to whatever it is you were trying to do.
I've been watching non-developers vibe code stuff, and the general failure mode seems to be ignorance of 3-pick-2 tradeoffs.
They'll spam "make it more reliable" or some such, and AI will best-effort add more intermediary redis caches or similar patterns.
But because the vibe coders don't actually know what a redis cache is or how it works, they'll never make the architectural trade-offs to truly fix things.
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Reminds me of the quote in the original Westworld movie:
“ 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 ;-)
However Michael Crichton imagined it would.
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> Purely AI written systems will scale to a point of complexity that no human can ever understand
I think it will be needless verbose complexity.
I kind of imagine someone having an unlimited budget of free amazon stuff shipped to their house.
In theory, they are living a prosperous life of plenty.
In reality, they will be drowning in something that isn't prosperity.
I don't understand this point of view at all. There's a symmetry that is going entirely unappreciated by most of the comments in the thread: just as I can give Claude X,000 words of text to use to describe the code I want it to write, I can also give it some existing code and ask for X,000 words of text explaining what it does. (Call it, oh, I don't know, a "spec," maybe.)
The explanation, in turn, can be fed back to recreate the functionality of the original code.
At that point, why care about the code at all? If it works, it works. If it doesn't, tell the model to fix it. You did ask for tests, right?
That is where we're indisputably headed. It's not quite a lossless loop yet, but those who say it won't or can't happen bear a heavy burden of proof.
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I've already done a handful of these gigs for early vibecoded products that had collapsed in on themselves. The scope of work was to stabilize the product and only make existing features work.
The issues have all been structural, not local. It's easier to treat it like a rewrite using the original as a super detailed product spec. Working on the existing codebase works, but you have to aggressively modularize everything anyway to untangle it rather than attack it from the top down.
All of these projects have gone well, but I haven't run into a case where a feature they thought was implemented isn't possible. That will happen eventually.
It's honestly good, quick work as a contractor. But I do hope they invest in building expertise from that point rather than treating it like a stable base to continue vibecoding on.
How do you find this type of work??
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> I think AI rescue consulting is going to be come a significant mode of high value consulting
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.
What happened after development was out sourced to Indians: developer salaries continued to rise much faster than general wages.
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> Somewhere in the future, the new software engineering will be primarily about principles to avoid this in the first...
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.
"Purely AI written systems will scale to a point of complexity"
You have not seen the spreadsheets that accounts run the firm on.
Bloody kids!
This might not pan out to be the glorious victory of human craft as you’re imagining it to be.
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.
> 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?
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One AI can't vibe code out of the mess, so you'd make another AI trained on getting out of vibe coded messes?
That's serious levels of circular thinking right there.
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And the bots training the bots are just bots that were trained to train bots?
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I think that will happen. I think several things can be true at the same time:
- AI Hype
- AI Psychosis
- AI keeps getting better and better until it can work around big AI slop code bases
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That sounds so horrible, though. It's akin to people working as COBOL devs because someone has to do it, so they'll get the big bucks. Except I've never heard of anyone who actually likes COBOL and the more I've learned about how mainframe development actually works, the more horrified I've become haha. Dealing with an LLM spaghetti codebase sounds like hell.
There's an LLM for that too...
https://www.hypercubic.ai/hopper
> reach a stable set of design principles
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.
What you're describing really isn't a new problem for organizations. Historically it's been a team of humans not using AI who gets over their skis and they have to have other more capable humans (also not using AI) to bail them out.
But it's so easy now to redo it all ground up, and if models improve, do it better next time.
I exaggerate only a little.
I'm with you on this one, having "vibe coded" some smaller internal tools on GPT 5, and then re-vibed it on Opus 4.6 and 5.5 -- they basically just fixed all of the problems without me doing much of anything other than prompting it to look at the existing code and make it "better".
Pretty much. We're intensely vibe coding something that has gone through so many requirement changes. The code has become very gnarly. I took a stab at basically one prompt rewrite of the whole thing. And it wasn't there, but it was 80% of the way there. and a hell of a lot cleaner.
How much is your budget for tokens?
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> Purely AI written systems will scale to a point of complexity that no human can ever understand
But won’t those more complex systems presumably solve more complex problems than the systems that humans could build? Or within a comparable time?
I think it is reasonably safe to assume at this point in the game that these AI systems are increasingly able to reason rigorously about novel problems presented to them, of ever increasing complexity and sophistication.
As the models keep improving, wouldn’t you be able to task a newer AI to “clean up this mess”?
Someone responded to a previous comment of mine [0] positing a Peter principle [1] of slopcoding — it will always be easier to tack on a new feature than to understand a whole system and clean it up. The equilibrium will remain at the point of near, but not total, codebase incomprehensibility.
[0] https://en.wikipedia.org/wiki/Peter_principle
People are often skeptical when I say this, but there's simply no guarantee that it's possible in principle to clean up a bad architecture. If your system is "overfitted" to 10,000 requirements from 1,000 customers, it may be impossible to satisfy requirements 10,001 through 10,100 without starting over from scratch.
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How is a newer AI going to "clean up" dropped databases, compromised computers or leaked personal data?
(None of above is theoretical)
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Frankly this is what everyone is counting on whether they know it or not. The question though is not “will the models get good enough?”. The question is does the repo even contain enough accurate information content to determine what the system is even supposed to be doing.
Yes. And as the models get better, it works better. But at one point you do have to understand the code because it's also just guessing as to what your actual intentions are.
It doesn't know what mess you want to clean up. A lot of times AI just starts making up new patterns on top of other patterns and having backwards compatibility between the two. How does it know which one you actually like?
Are they improving? I thought they were just getting more expensive
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How could anyone answer that with any level of certainty?
Ai runs `rm -rf`
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Those design principles it will take us 20 years to learn are just the principles for writing good maintainable, debug-able, understandable code today. Will just take 20 years to figure out they still apply when AI writes the code, too.
Why would it take 20 years to learn? People all around me, in an AI pilled company, have been saying this the whole time,
No. You can use AI to code this way. I’ve successfully steered AI to implement good architecture by moving slowly and constantly course correcting
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> Purely AI written systems will scale to a point of complexity that no human can ever understand
In their current forms, it's unlikely for a product that actually needs to work.
It's not getting that complex and working with current LLMs.
Yeah, LLMs will have to advance a bit more to really create slop of great complexity.
The complexity you would come to the rescue to solve, would that be from AI or from the style of programming you let the AI have? I mean, you have very different problems if you use functional style vs object-oriented. It is up to the programmer to realize they want a functional style and request that from the AI, as much as possible. Even AI cannot imagine every state transition, unless it is so smart that it should be the one telling you what to do.
Sounds like wishful thinking by a human programmer. Probably more likely tools will be rewritten from requirements or the old code will be refactored by newer models.
Have you watched Jurassic Park? That story is not about Dinos.
I'm sure AI capabilities will plateau any moment now..
This is def true but I also wonder if AI models and context sizes and capabilities will scale to keep up and eventually be able to untangle the mess.
My company and my buddy's company, we're experiencing the same thing. We are trying to fire a SAAS vendor and it's become the hot new project. Now we to these meetings with 50 different people that are allegedly stakeholders, two or three product managers who have already vibcoded their version of something.
Ultimately, if you want to move fast, it's better just to have one engineer vibe coding something. but, that engineer is under so much pressure. Now he's got a legacy mode and another legacy mode because the requirements keep changing. And now there's a deadline in four weeks.
This all could work just fine, but the ungodly amount of attention that this world is getting puts too many cooks in the kitchen, which is always a recipe for disaster.
> 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.
Wow, it’s true, AI really is set to match human performance on large, complex software systems! ;)
Humans who have been writing systems like that for many years know how to maintain and modify them successfully. It’s just that our industry has a bias towards youth who don’t think they have anything to learn from those who came before them.
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it's been 10y and i still haven't seen a human system that bad
maybe some that people said were that bad. but they just needed some elbow grease. remember, it takes guts to be amazing!
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The origin of 'dark DNA' begins to make more sense through this sort of lens, except the system somehow maintained a level of compensation to fix all its flaws.
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We already know them but everyone is busy throwing them in the trash. It’s all gas and no breaks or handling right now.
Financial auditing with pre-AI technical chops will be uniquely niche-valuable, too :)
is this true because training companies have not been training AI for both performance and brevity (or some other metric like that)? If this becomes a much more serious issue surely they would adjust the training processes
AI janitors
Not janitors. Hazmat cleanup crews.
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It's kind of like producing code is becoming more like farming.
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.
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My current business plan!
Interesting perspective. Fundamentally at conflict with the data, science, and 20+ year trends of AI coding systems - to the point of dogmatism. But interesting from a sociological point of view.
My very large employer has always been glacially slow on modernization and tech adoption. It may now, oddly enough, become a competitive advantage.
Literally the plot of Battlestar Galactica! Life imitates art indeed...
who is the Starbuck of AI?
plot twist: it's Starbuck
Or Mr Krabs' fear of robot overloads keeping technology at bay in the Krusty Krab!
yes, I was never so happy to work in Germany. People used to joke about the proverbial fax machine still being a thing but I've never been so glad to work in a culture where this mania doesn't exist. Reading HN is like entering Alice's Wonderland of token maxxers and AI psychotics. Genuinely don't know a single person here who is forced to work like this.
Actually, I have been wondering to which extend the AI craze has reached the DACH region. I don't work for any company and neither do my friends. HN is essentially my only peephole into the world of commercial software development and I'm aware that it's extremely biased towards Big Tech and SV startup culture.
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Ah so it's like 2000 again. Germany will go even farther behind it seems
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do you mean this aesthetically or quantitatively? Are they actually outcompeting / making more money ? Or do you mean they are now looking more desirable because their competitors are racing to the bottom (though likely making money on the way down)
That has probably been an advantage since the move of everything into the web.
Spoiler: it's not
Risk aversion is a tradeoff, not always a weakness.
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It is absolutely going to be a competitive advantage if it isn't already. When your competitors' products suck because they are using LLMs to write them, and yours work because you aren't, customers notice.
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No offense, but if you think your using AI in the development and design of your site, voxos.ai , gave you a competitive advantage it didn't. I can instantly tell when someone used an LLM to build their whole site and lets just say... Its not a good thing.
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Hard to have sober talk about this since a lot of discourse is AI psychosis vs. AI naysayers. Does software quality seem to have taken a jump in the past few years to anyone? Not to me, seems to be getting worse. Think that's a decent signal. Can tell you I'm dealing with a non-technical VP who loves blast submitting vibe-coded PRs and while there's some quick wins, overall quality is bad, and we had our first real production outage that Claude one-shot caused but could not one-shot solve.
There's an acceleration of current known processes that is being referred to as agent speed (vs human speed). But this is purely a mechanical effect. There don't seem to be augmentive cognitive effects. "AI has invented this revolutionary algorithm/workflow/architecture" is an article title you'd expect to see pop up quick, and often.
If you feel this way, you might like my new CLI tool, Burn, Baby, Burn (those tokens) (https://news.ycombinator.com/item?id=48151287
This is delicious.
Cynic!
Bug reports also go down when people lose faith that they will be fixed, because reporting them is often a substantial time commitment. You see it happen pretty regularly as trust in a group/company collapses.
The last three times I filed detailed bug reports as a client, all I got back were AI replies asking the same questions I’d already answered in the original report and suggesting alternatives I’d explicitly said I’d already tried. No wonder people don’t write bug reports anymore.
TBF I've had that experience before AI.
I think it was just text templates being used by some support staff.
Add this the real possibility that significant part of reports that get filed might be AI generated or rewritten. With high possibility of being misreported because of that. Or have incorrect parts... So attack on multiple sides.
And we do not get even get into potential adversarial tactics. If you have no morals what is better than using agents to flood your competitor with fake bug reports.
Just let AI filter out the fake reports! Then let AI work on the real ones. See, there's really no problem "more AI" can't solve (as long as you're willing to ignore all of the underlying ones). "Pay us to create the problems you'll have to pay us to fix for you" is one hell of a business model. It basically prints money.
Just let AI report the bugs. Problem solved!
oh i’ve definitely seen “we’re going to track the number of bugs created in jira per team” turn into “people just file things as tasks instead of bugs” or “only easy things are filed as bugs and completed right away”. It’s trivially gameable.
I agree, and I'd like to point out that this problem isn't unique to AI driven projects. I think much, if not all, of what Mitchell has been observing can readily happen without AI in the mix.
"Just use autoresearch and it will fix your app's memory leaks in an hour" is what I was nonchalantly told by someone who has never written a line of code ever.
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'm just waiting for my current company to have a Sev 1 CritSit so I can document the bejesus out of the root cause and expose our non-technical AI evangelist leadership as the sort of goons most of the senior development staff already suspect.
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.
Oh man, if only. The top brass driving this screaming frenzied MORE AI crusade will never face the firing line no matter what happens. It will either be a) "mistakes were made" and nobody is really at fault because we're all trying to change the world or fellate the future or whatever the line is, or b) James, Sam, Jesse, and the rest of Team B (none of whom are truly top brass) are getting fired out of a cannon into the sun as a warning to the rest of the plebs.
The AI psychosis is not the anti-opinion to the use of AI.
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/
> 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.
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.
It's really inconsistent though.. it takes shortcuts and leaves todos all the time without really calling it out explicitly, you have to pay close attention.
There's a lot of people writing bad code. With AI being forced top down (with the promise of turning people into 10x-ers), we're going to get a lot of people writing bad code 10x faster.
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.
Not security, but I ran into a related supply-chain issue recently. I needed a library to perform a moderately complex task, and found one in the ecosystem I was working with that had been around for a while, appeared reputable, and passed my cursory inspection. So I dropped it in, got the feature implemented, and moved on.
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.
Preach. No amount if AI will give an administrator an engineer's mind.
I'm pretty sure he's talking about companies and people outsourcing their decision making and thinking to AI and not really about using AI itself.
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.
Correct. I use AI a ton and I'm having more fun every day than I ever did before thanks to it (on average, highs are higher, lows are lower). Your characterization is all very accurate. Thank you.
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)
I thinking that it’s quite a different experience going all Jackson Pollock with AI in your own studio on your own terms, compared to the sorry state of affairs of having 100s of Pollocks throwing paint around wildly within a corp to meet a paint quota.
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I’ve had to do a ton of SQL stuff lately, which I haven’t really worked with since the late 90s. ChatGPT has been a godsend, not just for me, but for our only coworker who knows SQL well, whom I’d probably be bugging several times a day at my wits’ end.
But no one cares about those kinds of productivity gains. Just the ones that will completely replace us.
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It’s really frustrating too because even just the plain language translation and pattern matching aspects have such incredible uses.
As a cybersecurity IR professional being able to have a constantly logging counterpart who’s also able to go run queries and check logs on its own is an incredible speed boost.
I can just throw it a finding and have it slot it into a timeline and make notes.
I can toss it something mildly interesting to chase down while I focus on the obvious activity.
So many things that don’t involve having it “think” for you and keep you in the front seat.
But all of that is constantly overshadowed by these companies pushing the automation or “reasoning” aspects more and more and the sycophants who screech that it’s perfect and can do no wrong when every serious users experience is that “yes, it definitely can, often to catastrophic effect”.
> outsourcing their decision making and thinking to AI and not really about using AI itself
> I use AI a ton and I'm having more fun every day than I ever did before
With respect, this is what makes me worry.
If someone is a user of AI, can they really tell the difference between "outsourcing" and "using"? I worry that a lot of people will start out well-intentioned and end up completely outsourced before they realise it.
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The worst part of AI is that the time to produce software has become entirely unpredictable. "If Claude is randomly good at this, and happens to be up today, it will take me about 3 hours. If Claude is randomly bad at this task, or has downtime, 2 weeks"
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Hi Mitchell. Psychosis is a serious psychiatric condition that can be induced or triggered by AI. “AI psychosis” in this context is a misuse of a clinical term. Your tweet describes a disagreement on a value judgment that boils down to “move fast and break things” with high trust in AI outputs vs going all in on quality and reliability with low trust in AI. It’s an engineering tradeoff like any other.
Claiming that the people who disagree with you must be experiencing a form of psychosis, experiencing actual hallucinations and unable to tell what is real, is a weak ad hominem that comes off no better than calling them retarded or schizophrenic.
If you genuinely think one of your friends is going through a psychotic episode, you should be trying to get to them professional help. But don’t assume you can diagnose a human psyche just because you can diagnose a software bug.
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What I'm seeing is a little eternal September of support tickets about programs that fail to interface the JSON API of a customer of mine. The API is always allucinated. In the best case there are out of place attributes. Often they don't exist at all. I've seen x, y, width, height when we have only top and left. Of course no human read the documentation. Those are probably founders vibe coding a client without the technical competence of understanding the API doc on Postman. That is understandable. Unfortunately they don't even have the competence of pointing their AI to Postman in the right way. My custumer assessed that they will always find a way to do a mistake despite any mitigation from our side. What I do is replying to those tickets with line by line comments of the allucinated JSON. I never talk about AIs because I might hurt the pride of some of them and, who knows, some little mistakes could be from real junior developers. Sometimes the tickets are followed up by more puzzled ones, sometimes they fix the problem. Probably they copy and paste my reply to their bots.
Create a <domain>.tld/llms.txt or some SKILL.md files. When I encounter these tickets I just share links to these resources and the problem goes away.
> Probably they copy and paste my reply to their bots.
You must not give in to the temptation to mention pirate talk, Klingon, or goblins.
But now that I've put the seed in your mind, you probably (hopefully) will. :)
I've heard the same thing mentioned by a close friend building integrations. They are helping/supporting real use cases but they decided not to help vibe coder founders without an understanding of how APIs work etc. It's just too big of a gap to cover even for larger companies with strong support.
Seeing this too. Customer support tickets are all AI now. The random bolded words, the em dashes, they way where if you KNOW what is actually happening, they are slightly off or just WAY off.
That's why I made an API LLMs can call to search fastcomments APIs: https://docs.fastcomments.com/guide-llm-kit.html
If you were tapped into AI first features you'd design aliases in your api so AI hallucinated api exists for next time
Several people I know have already gone through phases like this. When you're doing it alone there is a moderating factor when their friends and family start calling them out on their behavior or weird things they say.
I can't imagine how bad it would be if your employer started doing this from the leadership. You'd be pressured to get on board or fear getting fired. Nobody would be trying to moderate your thinking except your coworkers who disagree with it, but those people are going to leave or be fired. If you want to keep your job, you have to play along.
I have a friend that is a junior in a security-oriented sys-admin/network engineer type role. They have been doing the job for only a bit over a year. No background in programming.
Their entire organization has been handed Codex/Claude and told to "go all in on AI" and "automate everything". So the mandate is for people that do not know how to code and have the keys to the castle to unleash these things upon their systems.
This is at a large organization with tens of thousands of employees.
I am waiting with bated breath for the ultimate outcome!
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I suspect we're going to see this in many corporate environments soon, if we aren't already
> your coworkers who disagree with it, but those people are going to leave or be fired.
Personally I expect that I will be this person soon, probably fired. I'm not sure what I will do for a career after, but I sure do hate AI companies now for doing this to my career
this is exactly what is happening. instead of building true AI culture around thoughtful adoption of AI strengths while defending against weaknesses, they're coming up with bullshit heuristics like "every repo has a CLAUDE.md", watching private token usage dashboards, and terrorizing everyone into doing it (or lose your job).
this leads to naive AI adoption, which is the worst of both worlds (no real speedup, out sourcing thinking, ai slop PRs, skill rot).
I didn’t think just offloading your thinking to AI was AI psychosis.
To me AI psychosis is the handful of friends I’ve had who have done things like have a full on mourning session when a model updates because they lost a friend/lover, the one guy who won’t speak to his family directly but has them talk to ChatGPT first and then has ChatGPT generate his response, or the two who are confident that they have discovered that physics and mathematics are incorrect and have discovered the truth of reality through their conversations with the models.
But language is a shared technology so maybe the term is being used for less egregious behavior than I was using it for.
I'm going directly to the point here: those people have clearly mental issues that would and probably did already show in the past without AI as well.
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How do you have so many crazy friends?
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I'm curious how to best define what AI psychosis actually is.
My understanding is that regular psychosis involves someone taking bits and pieces of facts or real world events and chaining them into a logical order or interpolating meanings or explanations which feel real and obvious to the patient but are not sufficiently backed by evidence and thus not in line with our widely accepted understanding of reality.
AI psychosis is then this same phenomenon occurring at a more widespread scale due to the next-word-prediction nature of LLMs facilitating this by lowering the activation energy for this to happen. LLMs are excellent at taking any idea, question, theory and spinning a linear and plausibly coherent line of conversation from it.
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> friends I’ve had who have done things like have a full on mourning session when a model updates because they lost a friend/lover
I mean, isn't that the natural and expected response? An AI company sold them a relationship with a chatbot and at least some their social/romantic needs were being met by that product. When what they were paying for was taken from them and changed without warning into something that no longer filled that void in their life why wouldn't they morn that loss?
The fact that they were hurt by that sudden loss is totally healthy. It's just part of moving on. The real problem was getting into an unhealthy relationship with a fictitious partner under the control of an abusive company willing to exploit their loneliness in exchange for money.
Hopefully they now know better, but people (especially desperate ones) make poor choices all the time to get what's missing in their lives or to distract themselves from it.
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The way I put this to myself is that AI gives “correct correct answers and incorrect correct answers”.
They almost always generate logically correct text, but sometimes that text has a set of incorrect implicit assumptions and decisions that may not be valid for the use case.
Generating a correct correct solution requires proper definition of the problem, which is arguably more challenging than creating the solution.
The way I phrase this to others is: Language models produce linguistically valid sentences, not factually correct sentences.
> which is arguably more challenging than creating the solution.
This hasn't been the case in my experience. Devising a correct solution without a definition of the problem is impossible because you wouldn't recognize a correct solution without a definition. Often you discover the problem definition by exploratory programming and trial and error on solutions, but LLMs are still good for process this too. Arguably better because they type faster so you can iterate faster!
It’s simpler than that - it’s a guessing machine that has superior access to a whole load of information and capacity to process at a speed at which we humans cannot compete.
Does it make it better than us? No because ultimately the thing itself doesn’t ‘know’ right from wrong.
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Yeah, very often the issue is that some context is missing. It'll say something true, but which misses the bigger point, or leads to a suboptimal result. Or it interprets an ambiguous thing in one specific way, when the other meaning makes more sense. You have to keep your wits about you to catch these things.
It's an incredible tool but it's also very derpy sometimes, full of biases, blind spots etc.
Aren't we ignoring the elephant in the room ?
Garry Tan has been the primary crusader for AI driven decision making. I'm sure his position is more nuanced, but his twitter driven communication makes him appear like a caricature of a man in AI psychosis.
When the head of YC champions AI driven decision making, companies will inevitably be influenced into doing exactly that. It's unfortunate, because AI is generational technology and the hyperbole distracts from the real sea change occuring in labor markets everywhere.
when you outsource thinking to AI, you get that magical speed up. the agent is making decisions for you, so things move at agent speed. it often makes decisions without telling you, and the final "here's the plan" output often requires you to understand the problem at great depth, which requires return to human speed, so you skim and just approve.
the trick is to be mindful, aware, and deliberate about what decisions are being outsourced. this requires slowing down, losing that absurd 10x vibe coding gain. in exchange, youre more "in-the-loop" and accumulate less cognitive debt.
find ways to let the agent make the boring decisions, like how to loop over some array, or how to adapt the output of one call into the input of another.
make the real decisions ahead of time. encode them into specs. define boundaries, apis, key data structures. identify systems and responsibilities. explicitly enumerate error handling. set hard constraints around security and PII.
tell the agent to halt on ambiguity.
a good engineer will get a 2x or 3x speedup without the downsides.
> find ways to let the agent make the boring decisions, like how to loop over some array, or how to adapt the output of one call into the input of another.
Those kind of advice ultimately don't matter. If you're familiar with a programming project, you'll also be familiar with the constructs and API so looping over an array or mapping some data is obvious. Just like you needn't read to a dictionary to write "Thank you", you just write it.
And if you're not, ultimately you need to verify the doc for the contract of some function or the lifecycle of some object to have any guaranty that the software will do what you want to do. And after a few day of doing that, you'll then be familiar with the constructs.
> make the real decisions ahead of time. encode them into specs. define boundaries, apis, key data structures. identify systems and responsibilities. explicitly enumerate error handling. set hard constraints around security and PII.
The only way to do that is if you have implemented the algorithm before and now are redoing for some reason (instead of using the previous project). If you compare nice specs like the ietf RFCs and the USB standards and their implementation in OS like FreeBSD, you will see that implementation has often no resemblance to how it's described. The spec is important, but getting a consistent implementation based on it is hard work too.
That consistency is hard to get right without getting involved in the details. Because it's ultimately about fine grained control.
If there's one thing I know about users is that they're never certain about whatever they've produced.
Can concur. I would say I am doing 3 things per day now instead of 1.
I agree with you, except it isn't even good at writing code. Almost every time that you get an LLM to write a bunch of code for you, it has mistakes in it. The logic isn't right, the API calls aren't right, the syntax isn't right (!). That problem hasn't yet been fixed and it looks as though it never will be. That means that every line of code it generates, you have to review, because even if 95% of the code is correct, you need to find the 5% which isn't. But if you have to do that, it becomes slower than just writing the code yourself. As people have pointed out over and over again: typing in the code was never the part that took time. So I don't agree that LLMs are really useful for writing code.
LLMs are good at producing code that seems plausible at first glance and appears to work, but it never really does. And when trying to fix things, you discover 7 slightly different ad hoc implementations of the same thing, with their own weird edge cases and behaviors. And you likely miss 4 more. There is no intention or coherence behind any of it.
> if you just prompt the AI and believe what it tell you then you have AI psychosis
This is the right definition. LLM outputs have undefined truth value. They’re mechanized Frankfurtian Bullshiters. Which can be valuable! If you have the tools or taste to filter the things that happen to be true from the rest of the dross.
However! We need a nicer word for it. Suggesting someone has “AI psychosis” feels a bit too impolitic.
Maybe we reclaim “toked out” from our misspent youths?
e.g. “This piece feels a little toked out. Let’s verify a few of Claude’s claims”
“Toked out” is really, really good, thank you for this
I wouldn’t say they have an undefined truth value. Their source of truth is their training data. The problem is that human text is not tightly coupled to the capital T truth.
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I wonder how different this is from having companies let Fortune or Inc magazine do their thinking for them.
Or random consultants.
Is "AI said it was a good idea" and worse than "we were following industry trends"?
> Is "AI said it was a good idea" and worse than "we were following industry trends"?
Based on the stuff I've seen, yes it seems a lot worse.
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this author suggest its essentially the same risk https://www.poppastring.com/blog/what-we-lost-the-last-time-.... i feel its heightened because execs and leaders are absolutely salivating over the opportunity to fire thousands of humans with no regard for the cognitive debt that comes from outsourcing thinking to ai.
> companies and people outsourcing their decision making and thinking to AI
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.
It’s almost as if we haven’t learned anything from Hans the horse, Ouija boards, "facilitated communication", or the countless examples of the folly of surrounding yourself with yes men. The point about improv is spot on.
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He uses AI himself, so I agree he doesn't see AI use as black/white.
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.
share the prompt!
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I am starting to come around to a similar sentiment. I have seen several large projects cook now for almost a year are not done. These are not trivial projects but the leads are heavily using ai at every opportunity.
I wasnt before but I am 100% confident that AI has done nothing to speed the delivery. It hasnt slowed it down either. It is a wash. The job is more miserable though.
> 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
I'm seeing it with lawyers, too. Like, about law. (Just not in their subject matter.) To the point that I had a lawyer using Perplexity to disagree with actual legal advice I got from a subject-matter expert.
I’ve been talking to a lot of engineers about how they use AI in their day to day and it’s dramatically different than what you see from the hypers.
The vast majority use one agent at a time and careful step through code. The main benefit they report is often about researching the codebase and possible solutions.
I digress; this article actually has helped identify useful knowledge gaps around topics I have researched. https://drensin.medium.com/elephants-goldfish-and-the-new-go...
While you have to think about things objectively no matter what, when I start researching topics like physics, using AI as suggested in that article has proven very useful.
I've been strictly using LLM's to either push stuff that I've done plenty times before and are mostly boilerplate or have zero value for writing them by hand (not even educational), and I always ENSURE that they work on stuff that are easily verifiable and proven incorrect with my existing knowledge or a few minutes of googling.
If you think you can let AI write code without double checking you have AI psychosis.
If you prefer reviewing AI-written code over writing it yourself, you just have odd preferences from my perspective (but not psychosis).
What does 'prefer' mean here?
I would say writing it myself is more enjoyable (in some cases). But I quite understand that I am not paid to enjoy myself. I'd say it's quicker getting AI to do it and reviewing. I believe the outcome is no worse on average. So yes, that's my chosen approach.
Part of the psychosis are AI usage mandates, where companies require a certain amount of LLM usage per worker. Of course these things are useful, but forcing them on workers is psychotic.
Ai gives generic answer for ideas but it's great for code. Pattern matching works for one not the other.
> 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.
Today's frontier models are genuinely useful as rubber ducks or grunt units. They are horrible for actual problem solving. These tools are not capable of actual reasoning. They will happily crap out a broken, untyped, untested Next.js monstrosity with no discernible architecture. They will build esoteric shell scripts to perform operations that could be done idiomatically and simply with tools already in your codebase. They will tell you to walk to the car wash then have the car wash valet your car back to you when confronted with the flaw in their logic. They will validate incorrect beliefs like ketchup being an acceptable hot dog condiment or the notion that "The Red Hot Chili Peppers" make good music. They have no taste, no anima, no drive.
Rule #1: Do not anthropomorphize the LLM. It is a million monkeys at a million typewriters piped into a digital sieve. I don't know how or why people place such trust in them while bemoaning other technology in our lives for being so broken ("my algorithm [sic] only shows me X", "the new iPhone update sucks", etc). If everybody followed this rule then the deluge of emoji-ridden hokum pouring into Slack workspaces and GitHub PRs around the world would cease but I'm not holding my breath.
I think the author means that we as homosapiens cant stop talking about this new shinny hammer we just invited
>but if you just prompt the AI and believe what it tell you then you have AI psychosis.
No it isn't. Do you believe what teachers told you in school? Yes? Well, I guess you're suffering from just normal psychosis!
I don't understand how people don't understand that people offer unreliable information too. We learned about the tongue map in school as kids - many kids still learn that in school today. It's still BS regardless whether it was told to you by a teacher or AI.
You don't suffer from psychosis for believing a source of information, you're simply mistaken. You need a more critical eye to assess what you're told in general, not just AI.
There's a huge difference between a teacher giving outdated information representing what was once our (or at least their) best understanding of the world, and a chatbot that just randomly makes up things for no reason while insisting that it's all true.
Also, a good teacher should be encouraging the development of critical thinking skills and correcting your errors, while AI will just tell you how brilliant you are when you wrongly tell it about how you've just invented a new form of math or disproved a scientific theory you barely understand in the first place.
Not all BS is the same, just as not all sources are equally unreliable.
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> Do you believe what teachers told you in school? Yes?
Nope. At least, not without proof. That would, IMO, be kinda crazy. We could argue semantics - maybe “stupid” would be a better word? Lacking in critical thinking skills? Whatever “it” is, it isn’t good.
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What is "thinking"?
LLMs can do advanced math and coding, which involves logic, so they are definitely capable of using logic. Which is what most people call reasoning.
So "LLMs are incapable of reasoning, they are just pattern matchers" is wrong. A lot of logic _is_ pattern matching, BTW. Like, syllogisms - deductive reasoning - do you think LLMs are incapable of that?
The thing you're referring to is that LLMs are trained to produce an answer which a human would like, i.e. they aim to produce plausible rather than correct answers.
So it's not so much a mental deficit as a different goal. Trusting LLM blindly is definitely dangerous, but dismissing it as useless for anything by code is rather wrong.
Pattern matching is hardly what distinguishes human from LLM - if you ask somebody a question about policy, for examples, chances are they'd just recite something they heard somewhere, never really thinking about it from first principles.
You're speaking of my company and I'm forever grateful.
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.
> 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.
The developers signing off on this will be "Human crumple zones" to protect the company from liability. Be very cautious if asked to sign off on anything like this.
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This reminds me of Rich Hickey’s “Simple Made Easy” and his approach in making Clojure.
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.
Ah, a cognitive Moore’s law, so to speak.
I think one factor is AI is encouraging people to turn off their brains.
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.
Deliberate thinking will be a think soon in our field.
The longer I look at the AI transformation, the more it seems like a people problem than a technology problem. The technology is undeniably there. The people are all over the place.
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.
I'd like to chime in and mention that its really obvious how to RL a coding agent to get the human addicted asap. and its also clear that there's a ton of $$$ to be made by doing this. therefore its done. the only LLMs I use are the ones I run locally because i know they aren't RL'ed for that metric (no incentive for the company that made them to make their open weights models addictive)
Interesting angle, didn't think of this. How do you think/find that current tools are optimized for being addictive?
I think there's a few things, but its a little subjective and its more about the style the ai uses when doing these than the actual specific behavior:
- 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.
> I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation.
What's the historical context for this MTBF vs. MTTR reckoning?
If you optimize for MTBF, you optimize for it to be a long time between failures. You optimize for the system not going down in the first place, but when it does do down it might be Pretty Bad.
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.
Not the GP commenter, but I'm still struggling to understand how this relates to the AI world, or perhaps more importantly, what the historical context was. Did people end up switching to MTTR optimization over MTBF optimization? If so, is the implication that the recovery times got lower but software instability went up as a result?
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Before the cloud, people were trying to reduce the mean time between failure (MTBF) essentially trying to prevent a thing from failing. With cloud, people are trying to recover as quickly as possible (mean time to recovery) accepting that things will fail —- it’s about how fast you can react to it.
John Allspaw (previously CTO at Etsy) has written about this: https://www.kitchensoap.com/2010/11/07/mttr-mtbf-for-most-ty...
I think there's a reasonable argument that our entire society right now is under AI psychosis:
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?
Weird that you mention the stock market and then conclude that there are only two outcomes: bust or success. If anyone can learn anything from the stock market it's that boom and bust are cycles that oscillate around a trend and everything tends to revert toward longer term trajectories. So, yes everyone is caught up with and overhyping AI and yes there will be a bust after the boom at some indeterminate point but that isn't the end of the story and we'll see a rise and further oscillation afterwards while we get better at applying the technology.
You're describing the Gartner cycle: https://en.wikipedia.org/wiki/Gartner_hype_cycle
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Your comment and the parent comment are not in disagreement.
Yes and my partners father has been calling for a bust for the last decade and everything has simply taken off.
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I mean, there's a reason I started with a distancing phrase about a "reasonable argument"; I think what I'm suggesting is an interesting lens but does not capture the whole picture.
But also, even if bust is business as usual in the big picture and not a social disaster long term, it's of course not what individual investors want for their particular current investments.
My car drives itself. That's a $18T global market.
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.
> Google spends $9B a year on software engineers.
Well they are projected to spend $175 - $185B on capex in this year alone most of it for AI buildout. Lets say only 150B of that is for AI. If they can then somehow replace all their software engineers with AI that they then run for free and depreciate over 10 years then they just replaced 9B a year software expense with 15B a year depreciation expense for the next decade. Yes this is grossly oversimplified but it still illustrates how crazy high of a bet they're making on AI.
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> My car drives itself. That's a $18T global market.
That's not a new market, that's a new feature in an existing market. Lots going on in transportation and I'm not seeing any scenario where self-driving cars vastly increase total output vs just eat up other forms of transportation and change where people live/how long they commute.
> 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.
Similarly, many companies are trying to be more efficient - "do what we already do, but better". That's different than growth.
What could Google do with 9B on software agents? Let's say the future of them is amazing and this means they could write 100x more code than they can today.
Has Google recently showed much ability to turn "more/faster code" into "superbly profitable new market"?
Someone's gonna have to crack the demand side issue for anything transformative to happen.
Market for who? Who is left working? If we can’t answer that question then we’re not prepared for what’s next.
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> My car drives itself. That's a $18T global market
Which will take decades to become addressable. Self-driving cars work OK in a few cities in one country. Expanding that to be able to cover Mumbai and Omsk and Nairobi will require significantly more work.
> Also $1T in data center investment makes sense when you realize that companies are racing to create virtual white collar workers.
Does it make sense? How much would the resulting virtual white collar worker cost? Because datacenters have running operational costs, and so do the people operating them and working on the software that runs in them.
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> My car drives itself.
No. It doesn’t. And if you’re defining “drives” as “it drives as well as I do” then you probably shouldn’t be on the road.
> makes sense
Nothing about any of this makes sense. Tell me, when all white collar jobs are replaced by AI, where will the customers come from? Who will have income to afford your products or services? The poor barista whose surveillance videos are training the robot that will soon replace them?
Leaving aside any consideration of human compassion or questioning of the purpose of an economic system (hint: it’s not just an abstract machine), shrinking the pool of potential customers by orders of magnitude has never been a recipe for sustainable success (let alone growth).
> Bust. We've come away with a useful tool but the hundreds of billions of capital expenditure were thrown away on a pipe dream.
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.
It’s useful for a while. Hardware has a pretty short useful lifespan. I’ll be curious how the landscape will look in 10 years as it comes time to replace all of these servers. Maybe we’ll extend the lifespan, or usage will continue to grow, or we start shutting down datacenters.
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Another possibility is that the hype continues, growing and growing and sucking up more and more resources, and the piper has to wait yet another day to be paid, until someone figures out how to pivot to the next big thing and all the debt (financial, social, environmental) gets carried forward and we keep going.
In other words, BAU for the last few thousand years.
Paypal Mafia -> Crypto Mafia -> AI/LLM Mafia -> I'm calling Biotech/BCI Mafia, WW3 Military Industrial Mafia, or Energy Mafia next.
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> The stock market keeps going up in the face of the indefinite closure of Hormuz
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.
> The closure leads to price increases which leads to inflation which leads to non-dollar assets (ie stocks going up in value)
I think this argument proves too much. Historically energy shocks have led to recessions, and in recessions the stock market usually doesn't go up. And the US economy is certainly exposed to global recession regardless of whether we're a net exporter of fossil fuels.
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Because uncontrolled inflation will destroy your economy
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> we're collectively operating more in the interests of the future AI than in the interests of humanity
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.
Oh wow that gives the billionaire class so much intellectual credit they don't deserve. No, they see the same ChatGPT we all do and their mediocre brains with zero self understanding (see andreessen's explicit comments to this effect) determine "it's a new life form ! It's brilliant / conscious / my new girlfriend!"
Never overestimate the billionaire class....
People that don’t understand current AI likely have no idea how to differentiate Opus from some super intelligence. Further in their domain with the safeguards off it probably creates capabilities never imagined. To me they are making that leap of expecting continued capability improvement and their framing is “what I already saw is fundamentally game altering”. It doesn’t need to imply anything further, yet.
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> The stock market keeps going up in the face of the indefinite closure of Hormuz.
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."
Success is also bust: Money becomes worthless. Everything you know and are is now obselete.
Furthermore, if you try to make your own decisions, you would be outcompeted by someone who has outsourced their brain. And, of course, since intelligence and labor would no longer be scarce resources that humans can use as leverage, gunning you down if you protest wouldn't really harm anyone in power.
People say that LLMs won't take us there. I think that's accurate, but there's a great deal of research going towards the next breakthroughs. How much are you willing to bet that all future attempts will fail?
We're trying very hard to build an ugly future.
If we are successful building an "ultra" human AI, it will require massive amounts of energy. That translates directly to "money". There will always be money unless someone finds a way to negate the second law of thermodynamics.
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I highly doubt that. Humans might become obsolete, but money will almost certainly still be a thing.
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You seem to fall into the same set of criticisms as everyone who’s bearish about ai. It’s somehow so powerful that we can’t handle the ramifications. Meanwhile, it’s a waste of money and doesn’t do anything. You have to pick a criticism and stick with it. Otherwise, it’s just angst-driven noise.
> 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?
If we want to understand a phenomenon, we should be careful with technical terms. It is not "psychosis" [1] anymore than bad software that makes mistakes is "hallucinating".
The truth is simpler and less dramatic: hapless ambition-monkeys who climb the corporate ladder are demonstrating that they are not promoted for mental acuity. Corporations, after all, do not serve "the interests of humanity" — they are an organised collusion system that diffuses responsibility and anonymises negligence. And when it works as intended, shareholders rejoice.
For companies that can afford to build data centres, the latter are seen as a sure bet that can't fail, like building a bridge or buying new computer/hardware now with the pile of cash on hand without necessarily knowing which OS/software they will install. They are even planning to restart or build nuclear reactors. [2]
[1] https://en.wiktionary.org/wiki/psychosis
[2] https://www.cnbc.com/2024/12/28/why-microsoft-amazon-google-...
I don't think the majority of humanity will ever accept "AI" as being anything more than a fancy computer, let alone a 'new species', even if it was proven sentient.
You just need to embody the AI in something that moves, and then people will definitely treat it as a new species. Already happening in my town with delivery robots: when they get stuck on a kerb, a person will stop and help them up the kerb while saying soothing words like to a pet: “There you go, little guy, now everything is alright.”
People will anthropomorphize rocks.
Nobody has to believe AI is alive for governments, markets, infrastructure, labor, and energy policy to start orbiting it anyway.
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The people with capital are gambling that this will be an innovation good enough for the first player to take all. That’s where the hubris comes from. You’re a billionaire and you have the chance to rule the world if this plays out, and if you don’t, someone else is going to. That’s where all of this is coming from
It’s time for humanity to admit that this is the end of the line. We had a good run, some beautiful moments were created for shareholders along the way. Let’s take each other by the hand and walk into the darkness together. So Long, and Thanks for All the Fish
I don't know. We invented machines that can answer arbitrary questions and are quickly demonstrating the ability to answer questions no human has been able to answer. We're sending more rockets to space in a week than we have in the prior decade. My car can drive itself. We experienced a global pandemic and within six months engineered and scaled the mass production of a vaccine to mitigate it. We also just invented a weekly shot that nearly cures the most common cause of non-natural death. All of these things are new in the past ~five years. There is no definition you can invent that does not classify the times we are living in, right now, as the most impactful ever, in human history.
> quickly demonstrating the ability to answer questions no human has been able to answer.
Such as?
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Are economists still trying to sell us the fairy tale that the market is rational?
The saying "the market is rational" simply means that assets are priced using all currently available information.
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But the homemade god would liberate the elites from the nightmare of being responsible for running the planet into the ground. AI jesus take the wheel!
If we achieve runaway AI, the stock market goes to infinity. So from an expected value standpoint, massive spending on it is worthwhile. Even if the odds are tiny, the payoff warrants a massive bet size.
Not really. If run away AI ever became a real thing, the stock market would be completely destroyed since most companies would become worthless.
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> 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 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.
Totally unrelated pet peeve of mine, I hate when people write this: "MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery)".
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.
Just talked to an exec yesterday about their multinational company, where the newly-installed CEO just came in with "everyone needs to be using AI" and "we should be doing everything with AI".
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".
The race to invent variants of Gas Towns, Ralph loops, pump out videos, blogs, etc. showing off greenfield development with cleverly named agents running in parallel is another case of engineering people diving head first into Resume Driven Development.
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.
This is a critical communications issue that is becoming what I believe the defining characteristic of "This Age": nobody knows how to discuss disagreement, and because it cannot even be discussed communication ends, followed by blind obedience, forced bullying, retreat and abandonment. This is going to be a hell of a ride, because nobody can really discuss the situation with a rational tone.
while I find myself agreeing with the posted tweet, I don't disagree that we need to communicate about things we disagree about better. Take my upvote
That people don't realize full test coverage just means every line is hit, not that everything is correct is always funny to me. (I don't view as an argument against tests, but with AI it's especially important as if you're aren't careful it'll be very happy to make coverage that is not quite right.)
Correct. Tests don’t tell you the code works. They tell you that something changed that impacts the test since the last time it did work.
My biggest grief, among many, is that the field is just no longer enjoyable to work in.
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 felt this way before LLMs hit the scene.
I'm trying so hard to pivot away because of this.
Anyone who's taken VC funding has no choice. More money has been spent on AI commercialization than the atomic bomb, the US interstate build-out, the ISS and the Apollo program combined. Failure is going to be catastrophic and therefore, one tied to this ship cannot accept a world in which it fails.
On the bright side, my guillotine & rope startup is going to make a killing (no pun intended).
Or anyone who even wants VC funding. 90+% of investors only want to invest in AI companies.
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.
I was reminded of the universe of Doctor Who. It's an incredibly complex technology, but it often behaves either unpredictably, like AI agents, or like code written by a vibe-coder without understanding the architecture or boundary conditions. And programmers are more like architects of consciousness, building machines rather than writing code.
Similarly, when the Doctor hacks a PC, he doesn't write code but rather communicates with the computer, using diplomacy to crack the agent.
It is likely that we will come to a world where software solutions are "grown" by iterations of agent work, and no one will know exactly how it works.
I think this will happen. A quick, low-quality solution is more common than a solution created by a master craftsman.
In addition to low-quality furniture, bad knives, electric kettles that burn out after a week, and poorly cut clothes that don't fit, have unpleasant fabric, and fall apart, there will also be a disposable, rotting code.
Master programmers will remain, just as master craftsmen have remained. They may even continue to earn well. However, there will be fewer of them, and the requirements for their skills, knowledge, and reputation will increase.
The hype or psychosis is mainly by mediocre/non expert/middle manager/you name it, especially when a person who never wrote a single line of code suddenly is making a wall of text, and it actually works!? Oh my!!
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.
The primary issue here is that CEOs and investors are particularly vulnerable to AI psychosis which is then forcibly propagated to the rest of the organization. Understandably, the perceived benefits are almost impossible to ignore, compounded by the FOMO of the AI first/AI native narrative being sold by AI influencers.
"no no, it has full test coverage"
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.
I was rewatching Rich Hickey's "Simple Made Easy" talk (as one does) and there was a great line about full test coverage.
"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)
Company I just left is reportedly now using Claude to analyse the metadata generated from the company MDM that tracks actual laptop use, and then pulling people up if they're not working "enough".
They're also reportedly now giving staff AI-related "homework" in an attempt to force staff to use AI more.
Interesting. Were those Windows or MacOS laptops?
Both
Sometimes I feel like "doing it with AI" is the new "rewriting python in rust".
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.
At work they are purging any developers who are not all in on AI. I must constantly be in full support of AI to not get fired, despite whatever my true thoughts are, including anything I post on LinkedIn. There can be no doubt.
Honest comment: it is transition time. This time is to make bets and take positions. Your humble position maybe.
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.
Case in point: Amazon pressuring its workers to maximize AI use.
https://news.ycombinator.com/item?id=48148337
So rewriting gets cheaper and cheaper. New features fall more or less into the same category. Refinement doesn't.
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?
Preferring "boring software" over the shiny new thing is common wisdom.
Sounds like we prefer stability for stuff we use but not for stuff we sell.
My company is one. They just made "AI use" a mandatory performance goal for next year's reviews. I am thinking about retiring at this point...
Easiest goal ever !
Just use https://github.com/dtnewman/burn-baby-burn :p
his worry is similar with search engine, I believe 90% of population don't even know how to properly do a good search in Google, that's why the info asymmetry still exists and the gap is bigger. It's just now we have AI.
AI psychosis is real, but at worst is only premature. AI-denial psychosis is far more pervasive, and will bite far more people in the long run.
We built too many layers of abstraction, so much that even the people in power have forgotten where the fantasies are. The objective reality is behind so many curtains that we forgot what is powering the whole theatre play to begin with. Or maybe we know but became too far detached from it to care. If you are at the same the better and the player, then what’s left?
Why do you all still submit twitter.com links when that domain does not even work?
https://news.ycombinator.com/item?id=41173763
HN needs to start sticking an ?mx=1 on the end
People just need to calm down. We're scaring the shit out of ourselves for no reason. Just like, chill man.
It's a tool; not the second coming.
Its a tool that is built by people who are incentivized to have you addicted to it.
And how.
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It has potential to remodel society though.
Cars replaced horses.
But AI is poised to replace a large chunk of brain labour.
Where's the ceiling?
> Where's the ceiling?
A stable civilization that doesn't devalue human life and well-being to the point of absurdity.
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I don't entirely know what rational discussions that can not be had?
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.
So it sounds like he’s not talking about you. He’s talking about people who actively choose to ignore complexity risk and refuse to have a rational discussion about it because they believe AI will always be able to fix it.
The problem is that the only thing that has proved out so far is cyber security. Unfortunately cyber security improvements is not going to improve living standards, and it's just going to increase the cost of just doing business. There is no productivity boost, in fact it's the opposite.
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
Most labs are shilling “AI worker” dreams to these very companies
Also, potentially a good band name in there:
“very resilient catastrophe machine”
Amazing how the dev community is suffering from a similar inability to approach the subject of real world AI efficiencies and business benefits. I don’t think it’s helpful to accuse the other side of psychosis. It disqualifies any data or experience they bring to the conversation.
It is not the dev community writ large, it is a particular archetype among forum users, particularly common among forums with upvote mechanics
I don't think it's helpful to call this psychosis. N Beyond that I don't think it's even irrational.
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.
Less users can be the cause of less bug reports
This specific psychosis is driven by peer pressure. If you are seeing everyone around you using tools to "enhance" their work, you wouldn't want to be "left behind". So it has permeated the entire ecosystem. The lucky ones are those who are outside this (or can afford to be outside this) and can see why it isn't working. You can't be inside it and hope to have any rationality. Everyone is competing on "I can figure it out better and quicker than you can if only I can get X to work".
Im not afraid to say AI model trained on petabytes of data is better than me in many things.
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.
"its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!"
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.
> 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 tweet is criticizing over-reliance on the "agents will fix it anyway".
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.
> Agents are fixing bugs so quickly and at a scale humans can't do already.
This is an illusion, I assure you. On a side project of mine with behavior that's very hard to translate into an algorithm (never mind code), after a few failed attempts between the both of us, I figured it out. I gave the AI (Opus) an extremely specific algorithm with detailed tests. All completely and utterly ignored (including the tests), like I never even said it. It proudly declared the work done without ever having written the tests that would have proved that wrong - it basically wrote code that didn't change behavior at all, it just gave the illusion of looking busy.
That's just a single extreme example that comes to mind, but I've had it ignore me at least 4-5 times a day this week.
If you think agents are fixing things reliably then you simply haven't noticed that they are "looking busy."
[flagged]
Comments should get more thoughtful and substantive, not less, as a topic gets more divisive.
Please don't sneer, including at the rest of the community.
Eschew flamebait.
https://news.ycombinator.com/newsguidelines.html
More likely people thought GP was missing the point; "MTTR-optimized YOLO deployment" only succeeds against recoverable errors and acceptable periods of downtime against errors that are detected quickly. You could have a bug silently corrupting data for months, and that data may only be used by 1 critical process that runs once every quarter. So you could introduce a timebomb that can't be gracefully recovered from (depending on the nature of the data corruption).
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.
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> won’t concede until you can just ask Codex or Opus “find and fix all the bugs in this
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!
Cloud people highly overvalue random web-services that most people (and most of the economy) simple don't rely on in any critical fashion. Yes, there are some example of people who make lots of money a day on these services, most people make pennies or nothing.
Github is not critical infrastructure. It's your personal social media addiction.
Up to 80% of software projects fail. Most startups will fail. VC's and bankers know this.
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.
Good point but he didn't go far enough. I would expand the AI psychosis to include all local optimization based on phony measurements , even time spent , DAU etc (which are mostly bots & synth accounts). In other words AI psychosis has been going on for 20+ years.
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.
I don't doubt there are companies totally misusing coding agents and LLMs in production. There are also real companies with real revenue and solid architecture using LLMs to deliver products. There are also companies with real revenue and rapidly accumulating tech debt.
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'm in a company going through this. Everyone outsources their thinking to LLMs and the results are painfully mediocre. The smart ones will use it to get their bearings on the topic then go to primary sources, the not so bright just ctrl-c ctrl-v.
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.
Just heard this today "things that took 4 months to build we can do in 30 minutes" like alright
Also this saying "you guys aren't going to lose your jobs, you'll just work more" it's funny
What is described in the tweet may be worrying or not but it does not describe anything close to psychotic behavior.
> "In psychopathology, psychosis is the inability to distinguish what is or is not real. Examples of psychotic symptoms are delusions, hallucinations, and disorganized or incoherent thoughts or speech."
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.
I use AI heavily. But my #1 productivity tool is still a custom code generator I wrote 15+ years ago. It routinely generates about 90% of the code needed to write a biz application. So using AI to write productivity tools is a great way to go IMHO.
Like all things... this too shall pass.
> "no no, it has full test coverage"
i don't have enough fingers (and toes) to count how many times i've demonstrated that "100% coverage" is almost universally bullshit.
Codex is freakin hot-to-trot to churn out test coverage for every single thing it implements, and some of it is very esoteric and highly prescriptive (regexes for days) BUT .. after a while, it dawned on me that LLM-driven test coverage is less about proving “code correctness” (you’re better off writing those tests yourself alongside them), and more about just trying to ensure that whatever gets bolted on stays bolted on. For better or worse, obviously, since if you bolt on trash, trash you shall have.
There's a very old paper by Cem Kaner about the meaninglessness of "100% coverage" where he included an appendix where he enumerates 101 different possible types of code coverage: https://www.researchgate.net/publication/243782285_Software_...
Wholeheartedly agree, but in fairness, I trust the tests of the best AI models more than those of the average human developer. There's a lot of people around that combine high diligence with complete intellectual laziness, producing tons of useless tests.
Actually no, cancel that. I realise now that I trust AIs more than the average developer, period. At this point they do produce better code than most people I've dealt with.
Corporate management in the USA is focused on the short term and reactionary.
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.
Sounds pretty accurate. Bunch of comments on this thread sound like AI is some kind of a new doomsday cult. The most annoying thing I find personally is that all engineering principles are getting crushed by non techies. Management counting token usage, forcing agent use, reducing headcount in the name of productivity gain. Devs building bridges but nobody knows what the bridge is, what are the standards to which it was built, how it works and how to maintain it. VCs counting extra money claiming chasing the holy profit is the future. The abundance of engineering apathy is disturbing.
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Companies who use AI passively and mindlessly will create immense opportunity for those who don't is a concrete definition for risk to companies, similar to the risk to individuals who go down the validation hole with AI.
This whole situation of forcing people to spend tokens is hilarious and shows who is true tech manager and who is impostor in the field. I think it’s a good indicator of competence and hopefully board members see that soon.
I was thinking about a different topic that could have the same headline just the other day.
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 shut down AI Agent fanatics on the regular. But chop one head off there and two take its place. And I say that as someone working with Claude and Codex daily. While they are both incredibly good at clearly described and defined atomic tasks, application scope makes them lose their minds and the slop ensues.
Deprecating immature workflows (LLM agents in this case) is much simpler and faster than building them from scratch. Many companies get this risk assessment right. The case where being wrong is much more costly than being right.
I'm not convinced. There's a ton of cost to adopting a radically different workflow.
It's worrying because it feels like a loss of control. But there must be control. And this what responsibility is. You should worry only about people who don't understand responsibility, not AI-inspired ones
I'm starting to long for the age after AI. When the generative euphoria has settled and all outputs are formally verified based on exquisite architectures and standards.
> When [...] all outputs are formally verified based on exquisite architectures and standards
and we all live in a green utopia of flying cars and peace upon the world.
-- Richard Brautigan (1967)
if all the resources spent in useless wars were poured into working towards this goal, we would be there for some time already
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Will never happen, for the exact reason that we’ve almost never done that for human output either.
it is required now, or all civilization collapses.
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Another argument for less human-like AI then, I guess.
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There was not a renaissance to move back to Assembly when Java sucked. Instead more Java developers were created.
I like how you haven't wagered which exquisite architectures and standards. I am sure we will all agree on what they are and follow them the same way :)
The people were longing for utopia, just not the same utopia.
They are being developed, but it takes over a decade for this to happen normally
Well a 2008 and a 2000 level financial crash is required for this. It is always during euphoric levels of delusion such events then occur.
...and it also needs more so-called AI companies present in the wreckage in this crash.
AI psychosis is undeniably real.
The entire stock market is undergoing AI psychosis.
Can't come fast enough
This is the new normal. AI will continue to reduce the need for human workers until a Universal Basic Income is established.
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
Because of the concerns you cite, I think working out the basic economic systems and incentives for paying people is a much more pressing concern than building magnificent machinery that we don't even own. There has been no effort on their end to demonstrate good faith nor to uphold their end of the social contract, which is why it's in our hands to demand the fundamentals to lead a life of dignity.
The exact same thing was meant to happen when the desktop computer became prevalent. Then the internet. Look at us now.
Humans can already have 4 hour work week without productivity loss.
But I only see mass layoffs and those who are working - are working longer and harder then before.
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You’re forgetting the energy part of the equation.
Is this a broken bot account or you’re speed running “regarded Twitter user”?
I think that’s called "hopium". Or wishful thinking, in less trendy language.
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Reminds me of this horrifying documentary: https://www.netflix.com/us/title/81095095
> lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure.
Can someone please remind and refresh my memory what this whole debate was with what arguments?
Building things not to fail vs what Netflix does, build things to recover from failure.
I have a ton of respect for Mitchell - I didn't really know who he was until Ghostty but his writings and viewpoints on AI seem really grounded and make the most sense to me. Including this one.
Many people on this forum are suffering under this same psychosis.
I'm guessing you've never heard of Hashicorp (Terraform, Vault) then? Mitchell == Hashicorp.
I was under the impression that anyone that uses the MTTR abbreviation knows enough to understand that you need to balance it with change failure rate, deploy frequency, and lead time.
The massive, destabilizing layoffs feel like AI psychosis to me.
Possibly psychosis. Possibly just serious ignorance and mob mentality. Leadership is supposed to be phlegmatic and measured; instead, we are saddled with hysterical hotheads. (Of course, when they are phlegmatic and chasing fads, then it does indeed resemble psychosis.)
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.
Either this or we humans are out of the picture soon.
Occams' razor would assume the former.
We're definitely in the mess around phase of AI adoption.
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 don't think it's super clear what we'll find out
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.
I use ai to build a startup but I still decide what to build. Letting ai makes product decisions is where companies loose it.
I believe it. I've seen the cyber team in my org bend every rule just to get access to frontier security models.
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.
I’m at a large Fortune 500 company. On a recent call, a mandate came from the CEO himself - “we have to use more agentic AI”.
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”.
I don't think this is actually anything new. In large-enough companies, even before AI, it was and is quite common for executives to lose touch with base reality. I don't think anyone is under any delusion that people like Mark Zuckerberg intimately know the entirety of their corporate codebases. Everything is filtered through layers and layers of middle management whose summaries, cherry-picked statistics, and perpetually up-and-to-the-right graphs make it difficult to have an objectively informed opinion. Companies would, are, and will have mass layoffs that unintentionally (or, intentionally but with indifference to the consequences) fire key engineers whose loss results in "familiarity debt" within the systems those engineers owned.
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.
The Twitter post doesn’t even document some of the most psychotic things that are happening.
Mitchellh is on to something. Some of the AI products I've seen seem like psychosis hallucinatory fever dreams, using terms and concepts that have no meaning. Funding? $50,000,000 pre-seed.
When I note some strange money flow I just assume embazzlement or money laundering.
The entire problem is vibe coding is only good for demos, prototyping and finding signs of product market fit without actually releasing a product into the market.
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.
Generally agree. I use AI very heavily, but rarely am I letting it actually think for me. It's a tool that reduces the time it takes for ideas in my head to manifest into reality. If you don't have those ideas, or a poor understanding of the system the AI is working on, you're going to produce slop. If you can't recognize this slop, you're more susceptible to having psychosis.
If you don't use it you lose it, and a lot of people are losing it..
I am really looking for more reasoned approaches to AI.
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.
Isn't going to happen without the regulation hammer being thrown down.
Probably from the EU because they seem to be the sane ones of this generation.
Talking about my own personal workflow. No company has dictated one tl me yet lol.
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Very general comment/sentiment/observation here for me personally is that about a year or two ago, everyone asked me ‘so ..where is the ai’. Nowadays it seems that this sentiment is already on its way out and I see more and more ‘no ai used’ statements and non-ai workshops popping up. I am in a weird place between art, technology and ecology so I get the best and worst of these worlds. But yeah, I feel the hype cycle is coming to an end in my personal bubble. I never cared or feared ai, I mainly fear the mania around it. Luckily I am in a position where I can afford to just observe. Sure I have used AI and it helped me a lot, mainly to get projects going when I am exhausted/stressed.
The real AI psychosis is the expectation of 5x/10x productivity gains akin to the mythical 10x developer during the 2010s JS growth period.
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…
> "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!"
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.
I saw this first hand at a company, and I think this is what happens when you combine FOMO with an utter lack of industry best practices. No one knows where they are going, but are convinced they are not getting there fast enough.
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.
Saying the _quiet_ part out loud.
Welcome to the club, Mitchell! Pizza's to the right.
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!
You just have to wish for the rest of the monkey.
this AI transitionary phase to Quantum, light chanels and new way computation will be architected will in the future be looked at similar to a toxoplasmosis like societal wide parasite, which invaded the host in order for the host to act more favorably to it
Is he talking about github?
I call them True Believers
The DevOps team at my company wants to hire a replacement for a very talented engineer. They’ve been interviewing candidates. The board got wind of it and someone not in their team decided they needed an AI Engineer, which is absolutely not what they want. So to release the funds they have been forced to change the job description and go after a different type of role altogether. It’s complete nonsense.
He is a billionaire and still thinks at a developer level is pretty remarkable! Hope other billionaires pay attention to this!
The only way many people learn that the stove is hot is by burning their hands on it.
Let them.
More like how do you know when your charming partner is a catfish. Maybe 2 years and when you are living in a friends basement.
> "no no, it has full test coverage"
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.
I work for a small telecom services provider whose current VP immediately set an AI course when stepping on board 6 months ago. Involving AI in everything and every task is now our first priority - across all employee segments, not just us system developers - and leadership is embarking on a program to measure employees' AI usage levels as a means to gauge everyone's individual efficiency. It's like the era of the evangelic crypto bros all over again.
Make the most of it. Their delusion is your opportunity.
Hype & greed are a hell of a drug
Psychosis means inability to distinguish the real from the not real -- delusion. I don't think the article describes that, at least not in a literal or clinical sense. The author lifted a term usually applied to people who fall in love with chatbots and applied it to the context of software developers not understanding AI coding tools, and the limitations of those tools.
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.
> We talk about "technical debt" and "maintainability" and "scalability."
If we are just talking about writing code, I believe LLMs are indeed better than the median programmer already. Where they fail, however, is in the tasks that require proper reasoning.
At a micro-scale, you can simulate reasoning with token prediction, but I think this breaks at a higher-level (e.g. architecture.)
I believe that, given enough complexity, a purely vibe-coded project will always reach a point where each new feature breaks five other ones. So qualms about "maintainability" are important.
Maybe we'll reach the point where LLMs can deal with the complexity of most codebases without human guidance.
But it doesn't change the fact that this limitation is inherent to LLMs: chain of thought is not reasoning, only the illusion of it.
> 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
Right, and the important part here is that it did so with your guidance and taste, which have both been honed by years of experience.
https://xcancel.com/mitchellh/status/2055380239711457578
https://hachyderm.io/@mitchellh/116580433508108130
<https://twiiit.com/mitchellh/status/2055380239711457578> – will redirect to a currently-working Nitter instance.
Seems broken. It just throws up an anime cat girl for me.
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This post calls out how you can't argue with these people because they say its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!"
the top reply is from someone doing exactly that, arguing "but the agents are so fast!"
Yeah: If the tools aren't good enough and fast enough to fix the bugs before release, what makes anyone think they'll be able to so easily catch up afterwards?
Maybe they're assuming that doubling the code-base/features is more beneficial versus the damage from doubling the number of bugs... Well, at least for this quarter's news to investors...
I was talking with a friend in the early days of AI boom. I argued that over-reliance in AI will create all kinds of catastrophes.
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.
> It's game theory. Someone will do it, and you'll be forced to do it, too.
You'll be forced to do it, or lose. The unstated assumptions are that, first, it will work, and second, that you can't afford to lose. But let's just assume those for the sake of argument.
> It can't be that bad
That does not follow at all. It can in fact be that bad. That was what made the game theory of MAD different from the game theory of most other things.
An interesting ethical framework, your friend has.
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> 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".
Oof. Potential "bad" outcomes of "game theory" should be calibrated to include all the bloody wars and genocides throughout recorded history.
Why did the Foi-ites kill every man, woman and child of the conquered Bar-ite city? Because if they didn't, then they'd be at a disadvantage if the Bar-ites didn't reciprocate in the cities they conquered...
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reliance, not resilience
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Which is super fun as a user because every day something doesn’t work and it’s a different something than yesterday.
Yeah how do they know the fix doesn't have a bug and it will just keep deploying mire crap. What is the feedback loop, the customer?
If they’re so fast why not fix the bugs real quick before shipping
My prediction is that in the next year, we’ll start to see some dismantling of code review at some companies. It might take the form of “AI-only review,” or something similar, but many companies are getting frustrated with developers saying “no” to immediately merging slop they can barely understand.
Pretty sure I've seen references to AI-only review already happening...
their ai must have missed that part of the post when it summarized to 3 bullet points
the reality is my business continues to operate at higher efficiency, even with the bugs.
i don't think it's 'our side' that has the psychosis.
Oh, well, if it makes you money right now, it couldn't possibly be wrong or detrimental long term. Glad we settled that debate.
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This is... Not what psychosis means? Being wrong is not psychosis
being wrong and insisting on being wrong is
According to DSM V delusion is a key criteria to diagnose psychotic disorder.
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Pointing out the obvious.
A lot of companies have been under AI psychosis for years and will be forever.
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When war psychosis is not enough....
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"In 1975, Dr. Joseph Sharp proved that correct modulation of microwave energy can result in wireless and receiverless transmission of audible speech."
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There are more points of view than that on HN.
A common one: "I have stopped writing code, the world is going to end"
Another: "I will code by hand, I don't care"
Another: "I use it as a tool, but the hype bothers me so much that I have to bitch and moan from morning to night"
This one is: "I have stopped writing code, it wasn't the end of the world."
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I would say his post has the tone of earnest discourse while yours devolves into ad hominem laden reflexive sensitivity.
Which is the pathological take?
The Bun rewrite’s aftermath will provide strong evidence either for or against GP.
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That seems like an odd way to interpret what they wrote.
Imagine old school machinists saying to a CNC machinist “Ha! See, maybe you don’t jog the axes manually, but you still have to be involved in placing the stock material, and you have to do the CAD/CAM work - so did it really machine the part for you? No!”
AI is a tool like any other. It has its limitations. It has classes of problems that it is suited to handle, and others it isn’t. If it’s true that they haven’t written (as in “typed out by hand”) a single line of code, why can’t they say that without you making that statement into more than it is?
I haven’t written a single line of code in 6 months, and that’s simply fact. It is also true that I put in a lot of other work to make that feasible, but that work isn’t in the form of writing code.
“it’s mature and the next step of engineering”
Tautologically, it’s mature enough for what it is mature enough for, and it certainly is the next step in the same way that CNC was the next step for machining — if you’re not using it as a machinist, you’re going to produce less compared to those who are.
Same thing with garden hoses. Yes, you can go fetch water from a lake and splash it on your lawn, or, you know, you could just use a sprinkler connected to your garden hose. Doesn’t replace buckets. Buckets just have a narrower scope in a world where garden hoses exist.
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I think discussion with open registration is doomed precisely for this reason, it is too open to being influenced by bad actors. Maybe the lobste.rs invitation model would be better ...
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The vast majority of positive opinions about AI on Reddit and HackerNews are bots.
The one you respond to is an obvious bot, new account only posting comments saying how great AI is for example.
No need to look further.
To me the big thing I see in blog posts is this implication that “all software engineering best practices are out the window”
And to me, AI should best be used to add rocket fuel to existing practices. Better tests, better observability, more atomic changes instead of big changes, automatic rollback etc.
> And to me, AI should best be used to add rocket fuel to existing practices
The more your codebase follows best practices and consistent patterns, the better AI will do and the faster you can move.
Same as humans really, just even faster. I'm also excited that people are finally writing docs and without even any flogging! They're calling the docs "skills" but hey whatever works
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I think what has really happened is a re-weighting of the importance of a lot of software practices. I think basically all of scrum/agile is completely useless now, but tests, PR reviews, documentation, decision records, etc, are more important than ever.
> To me the big thing I see in blog posts is this implication that “all software engineering best practices are out the window”
Yes, this is indeed a pungent smell. AI code assistants allow whole projects to be refactored and even rewritten in entirely different programming languages and software stacks in a few minutes, sometimes even with one-shot prompts. Most assistants even support creating and maintaining test suites with first-class support. Whatever you prompt, they do it.
And here we are, expected to believe that these tools can't or don't follow best practices?
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It does change previously assumed cost benefit trade offs and you should at least question any previously held beliefs.
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> And I haven't written a single line of code myself since what - February maybe?
Have you measured the impact of that on your ability to create good code? From my experience, relying on AI tends to degrade that ability.
Also, you seem to be able to do all of what you say and benefit from AI tools because you seem to understand the overall bigger picture well enough to be able to drive the AI agents to do their work properly. In other words, you operate in a familiar territory where you do not need to learn much new things.
But what about the junior people with little experience? Will they be able to manage such AI workflow? And more importantly, if junior people are given such AI tools, how will they learn?
These are all questions which may not matter in the short term and one might ignore them if they just want to see the profits and efficiency gains during the next cycle. But what about the long term?
How good are you at writing assembly? What about junior people that take an introductory course in assembly but never practice it.
Maybe I’m pushing it a bit, I know, but a couple of decades ago you could’ve been asking this instead.
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Bot account - 70 days old, no submissions, all comments are hyping AI
I'm seeing the exact opposite on a large C++ project.
I have friends at other companies with similar projects, they say the same thing.
It's like we're living in different worlds.
Still, LLMs are nice for well defined small projects, microservices, tools and research.
Noticed different results from friends, we have similar projects and tools.
We're guessing it comes from organizational behavior (culture, governance, management, etc.), we work in diverse teams / regions / companies.
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What tools have you tried? Are we talking Codex GPT 5.5 and Opus 4.7?
Would you say the project is well architected? Clear boundaries? Or ball of mud?
How large is large?
Are there AGENT.md files giving good information that helps LLMs get context when looking at a certain area of the code?
Is it all in one repo? multiple repos?
Are there good tests?
I feel like these are some of the many variables that can make a difference.
I work on a pretty large project/code base, written mostly in Go, and I have pretty positive experience with LLMs. I take on fairly small chunks, I review and understand the changes. I also use LLMs to explore options and prototype quickly. They're also very good at fixing bugs, failing tests etc.
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Man I dunno.
I’m also in a big tech company and a lot of the team hasn’t written any lines of code by hand for awhile and it’s causing a whole lot of tech debt and frustrations are beginning to boil.
I’m not sure it’s possible to force someone to read every line of AI generated code and understand it. People generate code faster than they take time to read it.
Pressure from C-suite to AI AI AI AI AI MORE AI AI AI AI doesn’t help.
> I feel like I'm in a different field compared to the rest of hacker news.
That should be my line. My new employer does not use LLMs at all. Software development, marketing, hardware development, nothing. Maybe too little, but whatever.
The problems the company is facing are entirely unrelated to "throughput".
That's great.
Is it possible to have any means of private communication with you where you would share the information who this employer is?
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^ this account commented this last month:
> Now Claude is writing great commit messages but since I'm no longer looking at code - I never see them.
Let it be a learning opportunity for us, folks. This is why you shouldnt take comments on the internet too seriously. People (or bots) will say anything just to get attention.
p.s. Offtopic, but this is why I believe the ability to hide post history was the tipping point of Reddit's downfall.
I'm in a big tech company everyone has heard of and we have seen a huge spike in incidents which correlates with how much new code is shipped due to AI. Perhaps it's to AI's credit or our engineers' credit that the spike is relatively 1:1 with the spike in new code.
It's causing problems in all parts of the business and leadership's answer is that we must use AI to make fixing incidents faster and automated rather than assess whether we should be shipping enormous amounts of buggy code every day...
1. What product(s)? 2. What features? 3. How.much ARR increase per employee?
If you can't answer these questions credibly, I'm afraid I'll have to treat your answer as LLM influencer propaganda.
Anthropic/OpenAI have been flooding this site with pro "AI" bots the last few weeks, this is for sure a pro-AI bot or employee from an "AI" company.
It sure feels that way.
It may be the case. I've been around in the industry for 25 years and I barely code. I babysit multiple instances of Claude and we were very purposeful and deliberate in altering our workflows for it; we made our local dev environments capable of spinning up multiple instances to work from parallel worktrees. We added MCP servers to let LLMs observe our CI, Jira and deployments.
Most of our time is spent doing spec work, planning, and injecting the proper context into LLMs. Like the OP, our metrics have drastically improved the time for delivery of new features, slightly improved bug resolution times, and now we're bottlenecked by needing more code review and manual QA to handle the workload.
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Microservices in big companies where you have to first write the spec and then fully understand the changes is maybe among the least benefiting use cases yet.
When you work on just a new mobile app, this is where I find AI is making the biggest difference.
On mobile you don't need specs and you don't need to understand every detail of the implementation. You can QA test the app on a real device. It gives me more confidence than just having written the code myself, and it's much faster. You can implement multiple major features in a single day.
This kind of e2e testing is just not possible with backend services.
What do you consider a "major feature" in this comment?
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> And I haven't written a single line of code myself since what - February maybe?
And how many lines of Markdown have you written? Pointless metric. I think I type more now because I don't get any helpful autocomplete for... English.
I feel the same and don’t get the extreme AI is inherently evil vs. AI is the best thing ever invented discussions. For me it’s all just emacs vs vi or tabs vs spaces kind of discussions.
It’s a tool and the good old sh* in sh* out principle applies.
People might take Mitchell’s comment as some kind of anti-AI stance, but it’s not he uses it regularly and makes a point in the X comments: “use AI, but think”
That comment sums it up best, because right now it’s hard to talk to either side, which separates at the comma.
I believe your anecdote. I am also agree with what you wrote below: "Tautologically, it’s mature enough for what it is mature enough for"
What programming language are you using? It seems like some programming languages are more mature in LLMs, e.g., Python, Java, C#, maybe Golang. (Oh yeah, and definitely JavaScript/TypeScript.) Rust, Zig, C++: I have a harder time believing you can manage a large project using only an LLM to write code.
> We don't really vibe though. At least I don't. I see it more as comment driven development.
This is why this feels foreign. Most people don't take this approach (I'd argue it's the correct, rational way to use AI).
The magnitude of negative responses to this comment is very encouraging.
Not because I agree with my sibling comments, but because I strongly agree with the parent, making me think my org and I are much earlier than I thought. :)
Yeah I don’t get it, the parent is a pretty reasonable take
If you still hold code review to the same standard and just make the agent do incremental changes rather than vibing the results are pretty good.
Can you name the company or product? At least that way some of the claims of shipped features and stability can be objectively verified.
It's a two months account hyping AI (look at the comments).
And to answer your question: No. I am yet to see a product made by AI or a product that used to require a dozen engineer and a few years being made by a single engineer in a month. Anything demoed is always a UI/functionality clone of the same thing LLMs regurgitates.
It's because HN is in AI meta-psychosis :)
Our experience is very similar except we didn't really have a review process before, and now LLMs find bugs before PRs get merged in main.
We had 5x-100x speedups in some legacy but important pipelines, with no regressions (validated after extensively by humans). It's not that the code was actively bad. It's just only 1-5% people in the local SWE market would be able to write code that runs so fast and efficient and benchmark it correctly.
We found a subtle correctness bug that was in production for half of the decade (both GPT-5 and Claude Opus were able to find it), confirmed by human after.
And we keep finding subtle bugs that have been introduced by humans before (despite the human reviews, the particular domain is just difficult no matter how many docs and comments and tests one writes)
I am convinced human reviews are overhyped in the industry. We've done it in my company since we started it, and bugs keep happening. People are just terrible at spotting them in the middle of 100 lines of correct code.
Machines, OTOH, are very good at it. I am currently trying to make the code review experience better for humans by not just having the AI review the code, but interact with the human, pointing out potential problems, bad patterns, perhaps hiding some code (e.g. renamings, formatting changes).
Developers still want to review the code, despite provably being bad at spotting bugs, because they want to actually keep knowledge of what's being modified in the code base, so I think this is the best approach.
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If you actually have time to read all your code, understand it, and are willing to be bottlenecked by human understanding, then yes, you are living in a different world.
In my world, that is far too slow, and you will be seen as a low performer who just can't keep up with the tech.
Something tells me you are in a highly regulated industry.
I think this divide has something to do with the way people are using these tools. I do a lot of planning in my documents and I rarely use conversations accept to interate on something I wrote instructions for.
> I haven't written a single line of code myself [...] I need to understand the code
What's the difference? I don't think anybody get paid by how efficiently they type on a keyboard. If you to use a die or raise a crow to get your next keypress I honestly don't think your PM cares as long as the actual output you contribute to the project is something you are responsible for.
I'm not saying it has no implications on how you think or no costs socially, ecologically, politically, solely that nobody cares HOW you get the code, only in your ability to keep on making it increasingly work better, closer to the evolving needs of the project.
Some programmers are gardeners. It sounds like you're one too. Your job is to maintain a large existing codebase. You probably didn't understand the entire codebase before AI, nobody did, so it doesn't matter that you don't understand it now. AI is very good at gardening, nobody doubts that.
Other programmers are painters. Their job is to start with a blank canvas and create something that others will value. When AI tries to paint, it tends to produce slop: a facsimile of everything it's ever seen.
> to start with a blank canvas and create something that others will value
AI is much faster at taking an idea and creating a working proof of concept than any human I've seen.
Not saying it's good engineering, but leave that to the gardeners.
The right metaphor isn't painting, though, it's molding clay. That first pass is slop, but it's raw clay that the agent is very good at molding given a modicum of direction and "not this, do that" comments. The combined first-pass and reshaping time is still far less than writing by hand from scratch. And increasingly, that first pass is ... not bad?
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Are bots using Karpathy's tinystories model now? This account has been relentlessly pushing AI in a deliberately naive and calm manner.
Are other bots upvoting this?
Agreed, I’ve never been more efficient
You're at G, which is absolutely the only place I'd expect to be doing this in a mature/adult/non-psychotic way.
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I think you're mixing up "psychosis" with fads, trends, or perhaps executive excuses to do layoffs.
A feature of psychosis is being unable to distinguish between external ideas and internal ones. For example, if a brown-nosing Yes-Man machine keeps reflecting your own leading questions back at you, laundering them into "independent" wisdom.
In contrast, I'm pretty sure COVID and the invasion of Ukraine are actual external phenomena that affect businesses and economies.
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The lists of who's, what's, why's, and when's always change but when the decades pass it's never one narrow type of people or the "not me's" which are gullible - it's just human nature + regional timing. The targeted groups are the only ones who are really easy to break out.
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'AI psychosis' is a slop concept.
The tone of the twitter post feels very personal, and emotional, and I am sorry for the author. I hope he can find peace and calm with the pace of change to put forward his best self without needing to act like he needs to defend or fight something.
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.
yes, the tone feels personal, and I feel happy for the author for expressing it on a platform that is desgned for it.
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.
It sounds like you know a lot more about him and the context than I do, the angle I am coming from is mass audience. This reached far, to the point I have no clue who he is and what else is going on. That’s why sometimes messages like those can be misunderstood, so I like to err on the side of caution over personal. Didn’t mean to say personal = bad, but that if you wish to change a broad status quo and raise awareness then communication is tricky!
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nice try, Claude
lol I actually wrote all that in my own voice, that’s sad
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Assuming he’s right, I don’t see how that constitutes “psychosis”, as opposed to this beyond yet another of a billion examples of companies jumping on a bandwagon / cargo cult, and then learning they took it too far.
And also, he might not be right. But the good news is, we’ll all get to find out together!
If you know these things you can take them into account while driving the AI.
Sorry, I don't buy your argument
I do not believe 'AI psychosis' is an actual thing.
Do you believe that an AI can write persuasive text? Do you believe that an AI can be trained to elicit a specific user reaction?* Can we agree that AI companies are strongly incentivized to make money, and they can do so by making their systems addictive? Because AI psychosis can be a byproduct of that.
*Il outline how briefly: mutate the model 500 times, give 1/500 of your user base a mutated version of the model, and save the top 5 of these model, ranked by how often the users did something, over the course of a week. Repeat for a year, passing the top 1% of these models onto the next round. This is the simplest way to do this and I can think of better ways to do this. I don't even work on this sorta thing; its 100% obvious to the AI labs how to do this better
Yes, no. Yes, they want to make money. No, addiction is physical compulsion and nothing else. Despite the effort to redefine that term to cover all kinds of things, an app is not equal to heroin.
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https://www.yahoo.com/news/articles/applied-pope-losing-grip...
It is
https://psychiatryonline.org/doi/10.1176/appi.pn.2025.10.10....
That's a study. I can link you studies that say violent video games cause aggression, that porn causes rape, etc. Studies are products of the biases of the researchers.
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It seems the diagnosis of psychosis is too quick: it seeks to reestablish the frame of expert for the developer identity that is being replaced by it.
“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.
Mitchell aches because his career has been solving broadly scoped problems by building a collection of thoughtful primitives for others to extend. LLMs seem to do the opposite but at great speed, and it hurts to watch.
Reading more, it seems part of his point is “if you’re making these primitives, it’s up to adopters to deploy, so mean-time-to-recovery isn’t that relevant.” Which is valid I guess.
But equally, like, do people need Terraform if they can just tell codex “put it live”, and does that hurt to see?
Honestly, I don't get this argument. In my opinion, "a collection of thoughtful primitives for others to extend" is more valuable now, not less. From LLM assisted engineering standpoint a nicely put reusable box with thoughtful interface is an easy win, more so if it is also easily extensible.
This doesn’t constitute AI psychosis. His argument is that we need to retain understanding of the systems we use, but there’s no compelling argument as to why that is the case. (I get that people are going to be offended by that statement, but agents are already better than the average software engineer. I don’t see why we need to fight this, except for economic insecurity caused by mass layoffs.)
It all just feels like horse drawn carriage operators trying to convince automobile drivers to stop driving.
If you want to draw that line of argument - it's more like horse riders being convinced to give up their horses in favour of trains: You're travelling faster, don't have to navigate yourself, or think about every boulder on the way; but there are destinations you can't go, overcrowded trains slowing down the journey, hefty ticket prices, and instead of enjoying the freedom, you're degraded to a passive passenger.
Very funny, this. Did we need forward deployed engineers to convince people that they absolutely need to use the trains in order to "not be left behind"? Or otherwise hype? Or was it sort of obvious and did not need to explained so much - like a bad joke called LLMs ?
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> there’s no compelling argument as to why that is the case.
I'm not sure that's true. We've actually seen several open source projects that were vibe coded literally fold up and disappear because they ran into issues that the AI couldn't solve and no one understood them well enough to solve.
There's a reason openai/anthropic and friends are hiring shitloads of software engineers. You still need people that can understand and fix things when the AI goes off hte rails, which happens way more often than any of those companies would like to admit. Sure, "fixing things" often involves having the AI correct itself, but you still have to understand the system enough to know how/when to do that.
I am sure you will feel that this is missing the point of your analogy, but we would not have gotten very far with automobiles if we didn't know how they worked.
You are breaking the analogy because automobiles are machines for transportation, and understanding them is important to make them move. LLMs are machines to understand, and well, if they do the understanding you don't need to.
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agreed completely
I find talking about X psychosis (or generally using mental illness metaphors) unproductive. It sets up the conversation to be "nothing else to do with this person".
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.
How about AI Derangement Syndrome?
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I have respect for Mitchel and I’ve spent a good deal of time trying to think of ways to justify his message. I can’t. Either I am missing a big piece or he is worrying about something that comes naturally as more software gets developed (and sooner).
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.
Yeah. The same processes that allow corporations to outsource their software to barely qualified 3rd-world body shops are the processes that allow you to deploy AI-generated code of unknown quality.