Presumably your new company isn't building AI tools, so they don't care what you use.
Imagine a developer in 1990s Microsoft saying "I want to use Borland C++ because it's better than the Microsoft IDE". Maybe it is, maybe it isn't, but that's not the point.
People with fiefdoms don’t like criticism. Microsoft pays their vassal dependent companies to use their products, no users actually like or would choose the products (Teams? 365 copilot? Azure?), and the whole enclosed ecosystem is pretty awful.
The main point is that the tools need to be of a certain quality/maturity for dogfooding to be effective.
Regarding dog fooding, Project Reunion was also a victim of all engines AI, now the damage is done and only the Windows team cares, because their job depends on using it.
As J. R. "Bob" Dobbs once said, "I don't practice what I preach because I'm not the kind of person I'm preaching to." ( see https://en.wikiquote.org/wiki/J._R._%22Bob%22_Dobbs )
Maybe the engineers complaining about dogfooding vibe-coding tools aren't the kind of developers you should have vibe-coding.
Being forced to use a shit tool because <some other department somewhere in the company wants your feedback>, while your deadlines haven't been adjusted for all this wasted time is not acceptable behaviour. It's the kind of authoritarian horseshit that's that's so often pushed by unproductive parasites onto people who do actual work.
I work at Google, and I am of the overall opinion that it doesn't matter what you deliver from an engineering perspective. I've seen launches that changed some behavior from opt-in to opt-out get lauded as worth engineering-years of investment. I've seen demos that were 1-2 years ahead of our current product performance get buried under bureaucracy and nitpicking while the public product languishes with nearly no usage. The point being, what you objectively deliver doesn't matter, but what ends up mattering is how the people in your orbit weave the narrative about what you built.
So if "leadership" wants something concretely done, they must mandate it in such a way that cuts through all the spin that each layer of bureaucracy adds before presenting it to the next layer of bureaucracy. And "leadership" isn't a single person, so you might describe leaders as individual vectors in a vector space, and a clear eigenvector in this space of leadership decisions in many companies is the vector of "increase employee usage of AI tools".
Hang around old Microsofties and you'll encounter a phrase: "The Deal." The Deal is this informal agreement: Microsoft doesn't pay amazingly but you're given the time to have work-life balance, you can be relatively assured that upper leadership gives a shit about the ICs, there's space for "... So I was thinking..." to become real "... and that's our next product" discussions and that it's okay to fall so long as you can get back up and keep walking afterwards.
The Deal is dead.
People fired for performance after a bad review their manager didn't give them. The constant slimming of orgs and the relentless gnawing at budgets. I watched as a team went from reasonable to gutted because it got the short straw in "unregretted attrition quotas"
AI is driving this, and I want to see the chat logs between executives and copilot. What sycophantic shit is it producing that is driving them to make horrible decisions?
Funnily, Apple also has an unspoken "deal" (pay a bit low but treat really well) and they stuck to it even through the layoff era.
Thankfully I am technology mercenary, polyglot, and use whatever the clients need, regardless of my point of view, but it is sad to see the human part behind those decisions being affected.
That is kind of insane right? They are practically mining their own people for data, one wonders what they would not do to their customers.
I feel fatigued by AI. To be more precise, this fatigue includes several factors. The first one is that a lot of people around me get excited by events in the AI world that I find distracting. These might be new FOSS library releases, news announcements from the big players, new models, new papers. As one person, I can only work on 2-3 things at a given interval in time. Ideally I would like to focus and go deep in those things. Often, I need to learn something new and that takes time, energy and focus. This constant Brownian motion of ideas gives a sense of progress and "keeping up" but, for me at least, acts as a constantly tapped brake.
Secondly, there is a sentiment that every problem has an AI solution. Why sit and think, run experiments, try to build a theoretical framework when one can just present the problem to a model. I use LLMs too but it is more satisfying, productive, insightful when one actually thinks hard and understands a topic before using LLMs.
Thirdly, I keep hearing that the "space moves fast" and "one must keep up". The fundamentals actually haven't changed that much in the last 3 years and new developments are easy to pick up. Even if they did, trying to keep up results in very shallow and broad knowledge that one can't actually use. There are a million things going on and I am completely at peace with not knowing most of them.
Lastly, there is pressure to be strategic. To guess where the tech world is going, to predict and plan, to somehow get ahead. I have no interest in that. I am confident many of us will adapt and if I can't, I'll find something else to do.
I am actually impressed with and heavily use models. The tiresome part now are some of the humans around the technology who participate in the behaviors listed above.
Even said fundamentals don't have much in the way to foundations. It's just brute forcing your way using a O(n^3) algorithm using a lot of data and compute.
"broo it's so dangerous let me tell you how dangerous it is! you don't want to get this out! we have something really dangerous internally!"
Those are the worst, Dario included there btw, almost a worse grifter than Altman.
The models themselves are fine except Claude that calls the police if you say the word boob.
the AI just an LLM and it just does what it is told to.
no limit to human greed though
AI is about centralisation of power
For me AI is just a natural language query model for texts. So if I need to find something in text, make join with other knowledge etc. things I'd do in SQL if there was an SQL processing natural language, I do in LLM. This enhances my work. However other people seem to feel threatened. I know a person who resigned CS course because AI was solving algorithmic exercises better than him. This might cause global depression, as we no longer are on the "top". Moreover he went to medicine, where people basically will be using AI to diagnose people and AI operators are required (i.e. there are no threats of reductions because of AI in Public Health Service)
So the world is changing, the power is being gathered, there is no longer possibility to "run your local cloud with open office, and a mail server" to take that power from the giants.
I do not believe this is the main reason at all.
The core issue is that AI is taking away, or will take away, or threatens to take away, experiences and activities that humans would WANT to do. Things that give them meaning and many of these are tied to earning money and producing value for doing just that thing. As someone said "I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes".
Much of the meaning we humans derive from work is tied to the value it provides to society. One can do coding for fun but doing the same coding where it provides value to others/society is far more meaningful.
Presently some may say: AI is amazing I am much more productive, AI is just a tool or that AI empowers me. The irony is that this in itself shows the deficiency of AI. It demonstrates that AI is not yet powerful enough to NOT need to empower you to NOT need to make you more productive. Ultimately AI aims to remove the need for a human intermediary altogether that is the AI holy grail. Everything in between is just a stop along the way and so for those it empowers stop and think a little about the long term implications. It may be that for you right now it is comfortable position financially or socially but your future you in just a few short months may be dramatically impacted.
I can well imagine the blood draining from peoples faces, the graduate coder who can no longer get on the job ladder. The law secretary whose dream job is being automated away, a dream dreamt from a young age. The journalist whose value has been substituted by a white text box connected to an AI model.
There are open source models and these will continue to keep abreast of new features. On device only models are likely to be available too. Both will be good enough especially for consumer use cases. Importantly it is not corporations alone that have access to AI. I for-see whole countries releasing their versions in an open source fashion and much more. After all you can't stop people applying linear algebra ;-)
There doesn't appear to be a moat for these organisations. HN users mention hopping from model to model like rabbits. The core mechanic is interchangeable.
There is a 'barrier to entry' of sorts that does exert some pressure or centralisation particularly at scale. It conveniently aligns well for large corporations and it is that GPU's are expensive and AI requires a lot of processing power. But it isn't the core issue.
You're absolutely right, this is another face of this AI coin... We people are taught to do things and love doing them and we're scared it's going to be taken away from us. This is what I thought when writing about the man who cancelled CS course. He apparently predicted that learning algorithms solving won't make him happy because AI will do it for him
AI is just not that good. If it really made me more productive, why wouldn't I use it all the time? I'd get everything done before lunch and go home. Or I'd use it all day to do the work of 3 people and be on the fast track to promotions.
The problem is simply that it gets in the way. For things I know nothing about, AI is excellent. For things that I'm good at and have literally been doing for a decade+, I can just do it better and faster myself, and I'm tired of people who know nothing about my profession gaslighting me into thinking that LLMs do the same thing. And I'm really tired of people saying "oh AI is not good today, but it'll be good tomorrow so just start using them" -- fine, wake me up when it's good because I've been waiting and patiently testing every new SOTA model since 2023.
Just get the facts right, that's all I ask of tech execs. Why has AI become a religion?
Or, like execs want, you do work of 3 people, so we can fire two and get the bonus, plus maybe a 5% pay increase for you. "If someone is good at digging, give him a bigger shovel".
Most people I've worked with that were already some of the most productive before AI took off are still at the top, and AI didn't move the needle much for them. There's simply no way for them to do 3x the work.
As an average consumer, I actually feel like i'm less locked into gemini/chatgpt/claude than I am to Apple or Google for other tech (i.e. photos).
It was already tough to run flagship-class local models and it's only getting worse with the demand for datacenter-scale compute from those specific big players. What happens when the model that works best needs 1TB of HBM and specialized TPUs?
AI computation looks a lot like early Bitcoin: first the CPU, then the GPUs, then the ASICs, then the ASICs mostly being made specifically by syndicates for syndicates. We are speedrunning the same centralization.
The hardest part with that IMO will be democratizing the hardware so that everybody can afford it.
Once the market either absorbs that demand (if it's real) or else over-produces for it, RAM prices are going to either slowly come back down (if it's real) or plunge (if it isn't).
People are already running tiny models on their phones, and there's a Mistral 3B model that runs locally in a browser (https://huggingface.co/spaces/mistralai/Ministral_3B_WebGPU).
So we'll see what happens. People used to think crypto currencies were going to herald a new era of democratizing economic (and other) activity before the tech bros turned Bitcoin into a pyramid scheme. It might be too late for them to do the same with locally-run LLMs but the NVidias and AMDs of the world will be there to take our $.
If it's ever to be economically viable to run a model like this, you basically need to run it non-stop, and make money doing so non-stop in order to offset the hardware costs.
Creating a search engine index requires several orders of magnitude less computing power then creating the weights of an LLM model. Like it is theoretically possible for somebody with a lot of money to spare to create a new search index, but only the richest of the rich can do that with an LLM model.
And search engines are there to fulfill exactly one technical niche, albeit an important one. LLMs are stuffed into everything, whether you like it or not. Like if you want to use Zoom, you are not told to “enrich your experience with web search”, you are told, “here is an AI summary of your conversation”.
Everyone I know accepts AI for the things it is good at, and rejects it for things it sucks at. The dividing line varies by task and the skill of the operator (both "how good at persuading the AI" and "how easy would it be to just do the job by hand").
In some companies the problem is management layers with thin understanding trying to force AI into the organization because they read some article in CIO Magazine. In other companies (like Microsoft) I suspect the problem is that they're forcing the org to eat their own dogfood and the dogfood kinda sucks.
[1] Yet.
I wish people would stop spreading this as if it were the main reason. It’s a weak argument and disconnected from reality, like those people who think the only ones who dislike cryptocurrencies are the ones who didn’t become rich from it.
There are plenty of reasons to be against the current crop of AI that have nothing to do with employment. The threat to the environment, the consolidation of resources by the ones at the top, the spread of misinformation and lies, the acceleration of mass surveillance, the decay of critical thinking, the decrease in quality of life (e.g. people who live next to noisy data centres)… Not everything is about jobs and money, the world is bigger than that.
AI meeting notes are great! After you spend twice as long editing out the errors, figuring out which of the two Daves was talking each time, and removing all the unimportant side-items that were captured in the same level of detail as the core decision.
AI summaries are great - if you're the sort of person that would use a calculator that's wrong 10% of the time. The rest of us realize that an hour spent reading something is more rewarding and useful than an hour spent double checking an AI summary for accuracy.
AI as Asbestos isn't even an apt comparison, both are toxic and insidious, but Asbestos at least had a clear and compelling use case at the time. It solved some problems both better and cheaper than available alternatives. AI solves problems poorly and at higher cost, and people call you "threatened" if you point that out.
AI, on the other hand? Seems like we're mostly getting cancer.
The problem is that the wrong summary will be treated as the truth, as the original recording will of course have been deleted after a grace period. Oh, you're looking into a way to clean up hanging child processes spawned by your CI worker? Guess it's now on the record that "rvba mentions looking into the best way to kill his children without leaving a trace"! There's no way that could possibly be misinterpreted a few years down the line, right?
About the only problem here is the increase of surveillance and you can avoid that by running your own models, which are getting better and better by the day. The fact that people are so willing to accept these criticisms without much scrutiny is really just indicative of prior bias
> The threat to the environment, the consolidation of resources by the ones at the top, the spread of misinformation and lies, the acceleration of mass surveillance, the decay of critical thinking
My question was that how many people are actually concerned about those things? If you think about it it's kind of obvious but it takes conscious effort to see it and I suspect not many people do.
When I turn off my browser, video player, and ebook reader, outside it's a bit of a hellscape really, I really can't wait to get back online where people care about the real things, such as systemic collapse. But while I'm disconnected I do notice how the only thing that people seem to actually be enjoying right now are those self-same glass beads and plague blankets of Big Tech that we're dissing while trapped within them.
Source: My ass.
Would it make their concerns less valid however if it wasn't?
Of course not. I think more people should be aware of this especially talking about the majority who are outside of our own bubble. If you go to a random place and interact with a random person, you will likely encounter the dominating group, and that, I think, directly corelates to what is going to happen next.
While for programming task I do use Claude currently, local models can be tuned to serve 80% of the time reduction you win by using AI. Depends a bit on the work you do. This will improve probably, while frontier models seem to hit hard ceilings.
Where I would disagree is that joining concepts or knowledge works at all with current AI. It works decently bad in my opinion. Even the logical and mathematical improvements of the latest Gemini model don't impress too much yet.
The parent didn't say that though and clearly didn't mean it.
Smaller SaaS providers have a problem right now. They can't keep up with the big players in terms of features, integrations and aggressive sales tactics. That's why concentration and centralisation is growing.
If a lot of specialised features can be replaced by general purpose AI tools, that could weaken the stranglehold that the biggest Saas players have, especially if those open weights models can be deployed by a large number of smaller service providers or even self hosted or operated locally.
That's the hypothesis I think. I'm not sure it will turn out that way though.
I'm not sure whether the current hyper-competitive situation where we have a lot of good enough open weights models from different sources will continue.
I'm not sure that AI models alone will ever be reliable enough to replace deterministic features.
I'm not sure whether AI doesn't create so many tricky security issues that once again only the biggest players can be trusted to manage them or provide sufficient legal liability protection.
Sorry, I don't see this happening, at least not for the majority. Even if it does, it would still be arguably centralizing.
This is the absolute opposite to using an LLM. Please stop using this comparison and perhaps look for others, like for example, a randomised search engine.
And he's right. LLMs are fancy text query engines and work very well as such.
The problem is when people try to shoehorn everything into LLMs. That's a disaster yet being perused vigorously by some.
In practice they seem to work well for that at a surface level, most of the time. The complaint is not that LLMs are not a tool for the job of "fancy text query engine", the complaint is that at scale and in the long run, LLMs are not a good tool for that.
And there are applications where you don’t have/wouldn’t pay another human, and the job that an AI does for mere cents is good enough most of the times. Like doing an analysis on a legacy codebase. I’ll read and verify, but running that “query” then saved me a lot of time.
Not everything needs to be deterministic to be of value.
There's obviously a value in practical tools, deterministic or not. It's just worth making the distinction that a practical tool is not always fit for purpose as the "right" tool if you really are seeking the (most) right tool for the job.
1. You were a therapy session for her. Her negativity was about the layoffs.
2. FAANG companies dramatically overhired for years and are using AI as an excuse for layoffs.
3. AI scene in Seattle is pretty good, but as with everywhere else was/is a victim of the AI hype. I see estimates of the hype being dead in a year. AI won't be dead, but throwing money at the whatever Uber-for-pets-AI-ly idea pops up won't happen.
4. I don't think people hate AI, they hate the hype.
Anyways, your app actually does sound interesting so I signed up for it.
And I read a lot of articles about games that seem to love throwing a dig at AI even if it's not really relevant.
Personally, I can see why people dislike Gen AI. It takes people's creations without permission.
That being said, morality of the creation of AI tooling aside, there are still people who dislike AI-generated stuff. Like, they'd enjoy a song, or an image, or a book, and then suddenly when they find out it's AI suddenly they hate it. In my experience with playing with comfy ui to generate images, it's really easy to get something half decent, it's really hard to get something very high quality. It really is a skill in itself, but people who hate AI think it's just type a prompt and get image. I've seen workflows with 80+ nodes, multiple prompts, multiple masks, multiple loras, to generate one single image. It's a complex tool to learn, just like photoshop. Sure you can use Nano-Banana to get something but even then it can take dozens of generations and prompt iterations to get what you want.
[0] https://www.theverge.com/entertainment/827650/indie-develope...
That's a big aside
>Like, they'd enjoy a song, or an image, or a book, and then suddenly when they find out it's AI suddenly they hate it.
Yes, because for some people its about supporting human creation. Finding out it's part of a grift to take from said humans can be infuriating. People don't want to be a part of that.
Way back me and my friends played a lot of starcraft. We only played cooperatively against the AI. Until one day me and a friend decided to play against each other. I can't tell put into words how intense that was. When we were done (we played in different rooms of house), we got together, and laughed. We both knew what the other had gone through. We both said "man, that was intense!".
I don't get that feeling from an amalgamation of all human thoughts/emotions/actions.
One death is a tragedy. A million deaths is a statistic.
Of course knowing the provenance of something you enjoy, and learning that it has dark roots, can certainly tarnish your enjoyment of said thing, like knowing where your diamonds came from, or how sausage is made. It's hard to make a similar connection to AI generated stuff.
I listen to a lot of EDM. Some of the tracks on my playlist are almost certainly AI generated. If I like a song and check out the artist and find that it's a bot then I'm disappointed because that means I can never see them live, but I can still bop my head to the beat.
Anecdotally its almost like they see them like mad scientists who are happy blowing up themselves and the world if they get to play with the new toy; almost childlike usually thinking they are doing "good" in the process. Which is seen as a sign of a lack of a type of intelligence/maturity by most people.
Lets say I'm a small business and I want to produce a new logo for some marketing material. In the past I would of paid someone either via a platform or some local business to do it. That would of just been the cost of business.
Now since there is a lower cost technology, and I know my competition is using it, I should use it too else all else equal I'm losing margin compared to my competition.
It's happening in software development too. Its the reason they say "if you don't use AI you will be taken over by someone who does". It may be true; but that person may of wished the AI genie was never let out of the bottle.
Negative sentiment also comes through in opinion polling in the US.
It's clearly not that straightforward.
That is not the only reason to use a tool you think is bad. "good enough" doesn't mean "good". If you think it's better to generate an essay due in an hour then rush something by hand, that doesn't mean it's "good". If I decide to make a toy app full of useless branches, no documentation, and tons of sleep calls, it doesn't mean the program is "good". It's just "good enough".
That's the core issue here. "good enough" varies on the context, and not too many people are using it like the sales pitch to boost the productivity of the already productive.
I'd wager that most people would find both as "good" depending on how you framed the question.
> in what way is the opinion "generally negative"
I'm just trying to tell you what people outside your bubble think, that AI is VERY MUCH a class thing. Using AI images at people is seen as completely not cool, it makes one look like a corporate stooge.
In polling japan and sweden are very similar in terms of sentiment though: https://www.pewresearch.org/global/2025/10/15/how-people-aro...
"Region of the world" correlation looks a lot stronger than that.
I wonder if these feelings are what scribes and amanuenses felt when the printing press arrived.
I do enjoy programming, I like my job and take pride on it, but I actively try for it not to be the life-mean giving activity. I'm a just mercenary of my trade.
Cool, you "made" that image that looks like ass. Great, you "wrote" that blog post with terrible phrasing and far too many words. Congrats, I guess.
And I will not be replying to anyone who trots out their personal AI success story. I'm not interested.
That's probably me for a lot of people. The reality is a bit finer than this namely :
- I hate VC funded AI which is actually super shallow (basically OpenAI/Claude wrappers)
- I hate VC funded genuine BigAI that sells itself as the literal opposite of what it is, e.g. OpenAI... being NOT open.
- I hate AI that hides it's ecological cost. Generating text, videos, etc is actually fascinating, but not if making the shittiest video with the dumbest script is taking the same amount of energy I'd need to fly across the globe.
- I hate AI that hides it's human cost, namely using cheap labor from "far away" where people have to label atrocities (murders, rape, child abuse, etc) without being provided proper psychological support.
- I hate AI that embodies capitalist principles of exploitation. If somehow your entire AI business relies on an entire pyramid of everything listed above to capture a market then hike the price once dependency is entrenched you might be a brilliant business man but you suck as a human being.
etc... I could go on but you get the idea.
I do love open source public AI research though. Several of my very good friends are researchers in universities working on the topic. They are smart, kind and just great human beings. Not fucking ghouls riding the hype with 0 concern for our World.
So... yes maybe AI haters have a slightly more refined perspective but of course when one summarize whatever text they see in 3 words via their favorite LLM, it's hard to see.
I get your overall point, but the hyperbole is probably unhelpful. Flying a human across the globe takes several MWh. That's billions of tokens created (give or take an order of magnitude...).
My point is mobility, especially commercial flight, is extremely energy intense and the average westerner will burn much more resources here than on AI use. People get mad at the energy and water use of AI, and they aren't wrong, but right now it really is only a drop in the ocean of energy and water we're wasting anyways.
That's not what I heard. Maybe it was in 2024 but now data centers have their own categories in energy consumption whereas until now it was "others". I think we need to update our collective understanding in terms of actual energy consumed. It was all fun & games until recently and slop was kind of harmless consequence ecologically speaking but from what I can tell in terms of energy, water, etc it is not negligible anymore.
In real life, I don't know anyone who genuinely wants to use AI. Most of them think it's "meh", but don't have any strong feelings about using it if it's more convenient - like Google shoving it in their face during a search. But would they pay for it, or miss it if it's gone? Nope, not a chance.
AGI? No, although it's not there. LLMs? Yes, lots. The main benefit they can give is to sort-of-speed-up internet search, but I have to go and check the sources anyway so I'll revert back to 20+ years of experience of doing it myself. Any other application of machine learning such almost instant speech to text? No, it's useful.
I think there is no "her", the article ends with saying:
> My former coworker—the composite of three people for anonymity—now believes she's [...]
I think it's just 3 different people and they made up a "she" single coworker as a kind of example person.
I don't know, that's my reading at least, maybe I got it wrong.
- At it's inception in 1955 it was "learning or any other feature of intelligence" simulated by a machine [1] (fun fact: both neural networks and computers using natural language were on the agenda back then)
- Following from that we have the "all machine learning is AI" which was the prevalent definition about a decade ago
- Then there's the academic definition that is roughly "computers acting in real or simulated environments" and includes such mundane and algorithmic things as path finding
- Then there's obviously AGI, or the closely related Hollywood/SciFi definition of AI
- Then there's just "things that the general public doesn't expect computers to be able to do". Back when chess computers used to be called AI this was probably the closest definition that fits. Clever sales people also used to love to call prediction via simple linear regression AI
Notably four out of five of them don't involve computers actually being intelligent. And just a couple years ago we still sold simple face detection as AI
1: https://www-formal.stanford.edu/jmc/history/dartmouth/dartmo...
Many people claim it's doing great because they have driven hundreds of kilometers, but don't particularly care whether they arrived at the exact place, and are happy with the approximate destination.
Is the siren song of "AI effect" so strong in your mind that you look at a system that writes short stories, solves advanced math problems and writes working code, and then immediately pronounce it "not intelligent"?
Same for short stories, it doesn’t actually write new stories, it rehashes stories it (probably illegally) ingested in training data.
LLMs are good at mimicking the content they were trained on, they don’t actually adopt or extend the intelligence required to create that content in the first place.
They weren't finding a lot of matches. That was odd.
That was in the days of GPT-2. That was when the first weak signs of "LLMs aren't just naively rephrasing the training data" emerged. That finding was controversial, at the time. GPT-2 couldn't even solve "17 + 29". ChatGPT didn't exist yet. Most didn't believe that it was possible to build something like it with LLM tech.
I wish I could say I was among the people who had the foresight, but I wasn't. Got a harsh wake-up call on that.
And yet, here we are, in year 20-fucking-25, where off-the-shelf commercially available AIs burn through math competitions and one shot coding tasks. And people still say "they just rehash the training data".
Because the alternative is: admitting that we found an algorithm that crams abstract thinking into arrays of matrix math. That it's no longer human exclusive. And that seems to be completely unpalatable to many.
And, maybe that is the difference. Non coders can use AI to help build MVPs and tooling they could otherwise not do (or take a long time to get done). On the other hand, professional coders see this as an intrusion to their domain, become very skeptical because it does not write code "their way" or introduces some bugs, and push back hard.
If you want to use concrete to anchor some poles in the ground, great. Build that gazebo. If it falls down, oh well.
If you want to use concrete to make a building that needs to be safe and maintained, it's critical that you use the right concrete mix, use rebar in the right way, and seal it properly.
Civil engineers aren't "threatened" by hobbyists building gazebos. Software engineers aren't "threatened" by AI. We're pointing out that the building's gonna fall over if you do it this way, which is what we're actually paid to do.
The flip of this is to understand and appreciate what the new tooling can help you do and adopt. Sure, junior coders will face significant headwinds, but I guarantee you there are opportunities waiting to get uncovered. Just give it a couple of years...
I legit don't know any professional SWE who feels "threatened" by AI. We don't get hired to write the kind of code you're writing.
I’m tempted to propose a new law—like Poe’s or Godwin’s—that goes something like: “Any discussion about AI will eventually lead to someone insisting it can’t match human programmers.”
Seeing an AI casually spit out an 800 lines script that works first try is really fucking humbling to me, because I know I wouldn't be able to do that myself.
Sure, it's an area of AI advantage, and I still crush AI in complex codebases or embedded code. But AI is not strictly worse than me, clearly. The fact that it already has this area of advantage should give you a pause.
Most of it is because there's little that ties actual output to organizational outcomes. AI mandates after all are just a way to bluntly for e engineers to use AI, where if you were at a startup or smaller company you would probably organically find how much an LLM helps you where. It may not even help your actual work even if it helps your coworkers. That market feedback is sorely missing from the Big Techs and so hamfisted engineering mandates have to do in order to for e engineers to become more efficient.
In these cases I always try to remind friends that you can always leave a Big Tech. The thing is, from what I can tell, a lot of these folks have developed lifestyle inflation from working in Big Tech and some of their anger comes from feeling trapped in their Big Tech role due to this. While I understand, I'm not particularly sympathetic to this viewpoint. At the end of the day your lifestyle is in your hands.
Close. We're in a recession and they are using AI as an excuse for another wave of outsourcing.
>I don't think people hate AI, they hate the hype.
I hate the grift. I hate having it forced on me after refusing multiple times. That's pretty much 90% of AI right now.
What about the complete lack of morality some (most?) AI companies exhibit?
What about the consequences in the environment?
What about the enshitification of products?
What about the usage of water and energy?
Etc.
What about diverting funding from much more useful and needed things?
What about automation of scams, surveillance, etc?
I can keep going.
There are plenty of reasons to hate on AI beyond hype.
It's a bit more expensive. It's not the end of the world. Production will likely increase if the demand is consistent.
> What about diverting funding from much more useful and needed things?
And who determines that? People put there money where they want to. People think AI will provide value to other people and those people will, therefore, pay money for AI. So the funding that AI is receiving is directly proportional to how useful and needed people think AI is. I disagree, but I'm not a dictator.
> What about automation of scams, surveillance, etc?
Technology makes things easier, including bad things. This isn't the first time this happened and it won't be the last. It also makes avoiding those things easier though but that usually lags a bit behind.
> I can keep going.
Please do because it seems like you're grasping at straws.
It's the closing trash compactor of soullessness and hate of the human, described vividly as having affected Microsoft culture as thoroughly as intergranular corrosion can turn a solid block of aluminum to dust.
Fuck Microsoft for both hating me and hating their own people. Fuck. That. Shit.
That's a great way to describe it. There's a good article that points out AI is the new aesthetic of fascism. And, of course, in Miyazaki's words, "I strongly feel that this is an insult to life itself."
This article assumes that AI is the centre of the universe, failing to understand that that assumption is exactly what's causing the attitude they're pointing to.
There's a dichotomy in the software world between real products (which have customers and use cases and make money by giving people things they need) and hype products (which exist to get investors excited, so they'll fork over more money). This isn't a strict dichotomy; often companies with real products will mix in tidbits of hype, such as Microsoft's "pivot to AI" which is discussed in the article. But moving toward one pole moves you away from the other.
I think many engineers want to stay as far from hype-driven tech as they can. LLMs are a more substantive technology than blockchain ever was, but like blockchain, their potential has been greatly overstated. I'd rather spend my time delivering value to customers than performing "big potential" to investors.
So, no. I don't think "engineers don't try because they think they can't." I think engineers KNOW they CAN and resent being asked to look pretty and do nothing of value.
A lot of us tried it and just said, "huh, that's interesting" and then went back to work. We hear AI advocates say that their workflow is amazing, but we watch videos of their workflow, and it doesn't look that great. We hear AI advocates say "the next release is about to change everything!", but this knowledge isn't actionable or even accurate.
There's just not much value in chasing the endless AI news cycle, constantly believing that I'll fall behind if I don't read the latest details of Gemini 3.1 and ChatGPT 6.Y (Game Of The Year Edition). The engineers I know who use AI don't seem to have any particular insights about it aside from an encyclopedic knowledge of product details, all of which are changing on a monthly basis anyway.
New products that use gen AI are — by default — uninteresting to me because I know that under the hood, they're just sending text and getting text back, and the thing they're sending to is the same thing that everyone is sending to. Sure, the wrapper is nice, but I'm not paying an overhead fee for that.
"Engineers don't try" doesn’t refer to trying out AI in the article. It refers to trying to do something constructive and useful outside the usual corporate churn, but having given up on that because management is single-mindedly focused on AI.
One way to summarize the article is: The AI engineers are doing hype-driven AI stuff, and the other engineers have lost all ambition for anything else, because AI is the only thing that gets attention and helps the career; and they hate it.
Worse, they've lost all funding for anything else.
That doesn't mean investors have gotten smarter, they've just become more risk averse. Now, unless there's already a bandwagon in motion, it's hard as hell to get funded (compared to before at least).
> now believes she's both unqualified for AI work
Why would she believe to be unqualified for AI work if the "Engineers don't try" wasn't about her trying to adopt AI?
You touched on some of the reasons; it doesn't take much skill to call an API, the technology is in a period of rapid evolution, etc.
And now with almost every company trying to adopt "AI" there is no shortage of people who can put AI experience on their resume and make a genuine case for it.
Then things don't turn out as they expected and you have to deal with a dude thinking his engineers are messing with him.
It's just boring.
But now, to your point: they can vibe-code their own "mockups" and that brings us back to that problem
There's a lot of disconnected-from-reality hustling (a.k.a lying) going on. For instance, that's practically Elon Musk's entire job, when he's actually doing it. A lot of people see those examples, think it's normal, and emulate it. There are a lot of unearned superlatives getting thrown around automatically to describe tech.
If you haven’t had a mind blown moment with AI yet, you aren’t doing it right or are anchoring in what you know vs discovering new tech.
I’m not making any case for anything, but it’s just not that hard to get excited for something that sure does seem like magic sometimes.
Edit: lol this forum :)
I AM very impressed, and I DO use it and enjoy the results.
The problem is the inconsistency. When it works it works great, but it is very noticeable that it is just a machine from how it behaves.
Again, I am VERY impressed by what was achieved. I even enjoy Google AI summaries to some of the questions I now enter instead of search terms. This is definitely a huge step up in tier compared to pre-AI.
But I'm already done getting used to what is possible now. Changes after that have been incremental, nice to have and I take them. I found a place for the tool, but if it wanted to match the hype another equally large step in actual intelligence is necessary, for the tool to truly be able to replace humans.
So, I think the reason you don't see more glowing reviews and praise is that the technical people have found out what it can do and can't, and are already using it where appropriate. It's just a tool though. One that has to be watched over when you use it, requiring attention. And it does not learn - I can teach a newbie and they will learn and improve, I can only tweak the AI with prompts, with varying success.
I think that by now I have developed a pretty good feel for what is possible. Changing my entire workflow to using it is simply not useful.
I am actually one of those not enjoying coding as such, but wanting "solutions", probably also because I now work for an IT-using normal company, not for one making an IT product, and my focus most days is on actually accomplishing business tasks.
I do enjoy being able to do some higher level descriptions and getting code for stuff without having to take care of all the gritty details. But this functionality is rudimentary. It IS a huge step, but still not nearly good enough to really be able to reliably delegate to the AI to the degree I want.
In the end you can save like 90% of the development effort on a small one-off project, and like 5% of the development effort on a large complex one.
I think too many managers have been absolutely blown away by canned AI demos and toy projects and have not been properly disappointed when attempting to use the tools on something that is not trivial.
The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time.
It feels like a gigantic win when it carves through that first 90%… like, “wow, I’m almost done and I just started!”. And it is a genuine win! But for me it’s dramatically less useful after that. The things that trip up experienced developers really trip up LLMs and sometimes trying to break the task down into teeny weeny pieces and cajole it into doing the thing is worse than not having it.
So great with the backhoe tasks but mediocre-to-counterproductive with the shovel tasks. I have a feeling a lot of the impressiveness depends on which kind of tasks take up most of your dev time.
Or your job isn't what AI is good at?
AI seems really good at greenfield projects in well known languages or adding features.
It's been pretty awful, IME, at working with less well-known languages, or deep troubleshooting/tweaking of complex codebases.
This is precisely my experience.
Having the AI work on a large mono repo with a front-end that uses a fairly obscure templating system? Not great.
Spinning up a greenfield React/Vite/ShadCN proof-of-concept for a sales demo? Magic.
Well, there’s your problem. You should have selected React while you had the chance.
When you work with a large codebase which have a very high complexity level, then the bugs put in there by AI will not worth the cost of the easily added features.
I know I don't. I have never been paid to write anything beyond a short script.
I actually can't even picture what a professional software engineer actually works on day to day.
From my perspective, it is completely mind blowing to write my own audio synth in python with Librosa. A library I didn't know existed before LLMs and now I have a full blown audio mangling tool that I would have never been able to figure out on my own.
It seems to me professional software engineering must be at least as different to vibe coding as my audio noodlings are to being a professional concert pianist. Both are audio and music related but really two different activities entirely.
The code is split between a backend in Java (no GC allowed during trading) and C++ (for algos), a frontend in C# (as complex as the backend, used by 200 traders), and a "new" frontend in Javascript in infinite migration.
Most of the code was made before 2008 but that was the cvs to svn switch so we lost history before that. We have employees dating back 1997 who remembers that platform already existing.
It's made of millions of lines of code, hundreds of people worked on it, it does intricate things in 10 stock markets across Asia (we have no clue how the others in US or EU do, not really at least - it's not the same rules, market vendors, protocols etc)
Sometimes I need to configure new trading robots for random little thing we want to do automatically and I ask the AI the company is shoving down our throat. It is HOPELESS, literally hopeless. I had to write a review to my manager who will never pass it along up the ladder for fear of their response that was absolutely destructive. It cannot understand the code let alone write some, it cannot write the tests, it cannot generate configuration, it cannot help in anything. It's always wrong, it never gets it, it doesn't know what the fuck these 20 different repos of thousands of files are and how they connect to each other, why it's in so many languages, why it's so quirky sometimes.
Should we change it all to make it AI compatible, or give up ? Fuck do I know... When I started working on it 7 years ago coming from little startups doing little things, it took me a few weeks to totally get the philosophy of it all and be productive. It's really not that hard, it's just really really really really large, so you have to embrace certain ways of working (for instance, you'll do bugs, and you'll find them too late, and you'll apologize in post mortems, dont be paralized by it). AIs costing all that money to be so dumb and useless, are disappointing :(
The latter codebase doesn’t tend to be in github repos as much.
Results are stochastic. Some people the first time they use it will get the best possible results by chance. They will attribute their good outcome to their skill in using the thing. Others will try it and will get the worst possible response, and they will attribute their bad outcome to the machine being terrible. Either way, whether it's amazing or terrible is kind of an illusion. It's both.
There are some exceptions where AI is genuinely useful, but I have employees who try to use AI all the time for everything and their work is embarrassingly bad.
Yes, this is better phrased.
LLMs are great in their own way, but they're not a panacea.
You may recall that magic is way to trick people into believing things that are not true. The mythical form of magic doesn't exist.
Much of this boils down to people simply not understanding what’s really happening. Most people, including most software developers, don’t have the ability to understand these tools, their implications, or how they relate to their own intelligence.
> Edit: lol this forum :)
Indeed.
There are many valid critiques of AI, but “there’s not much there” isn’t one of them.
To me, any software engineer who tries an LLM, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems. Maybe AI isn’t the right tool for the job, but that kind of shallow dismissal indicates a closed mind, or perhaps a fear-based reaction. Either way, the market is going to punish them accordingly.
I've been around long enough that I have seen four hype cycles around AI like coding environments. If you think this is new you should have been there in the 80's (Mimer, anybody?), when the 'fourth generation' languages were going to solve all of our coding problems. Or in the 60's (which I did not personally witness on account of being a toddler), when COBOL, the language for managers was all the rage.
In between there was LISP, the AI language (and a couple of others).
I've done a bit more than looking at this and saying 'huh, that's interesting'. It is interesting. It is mostly interesting in the same way that when you hand an expert a very sharp tool they can probably carve wood better than with a blunt one. But that's not what is happening. Experts are already pretty productive and they might be a little bit more productive but the AI has it's own envelope of expertise and the closer you are to the top of the field the smaller your returns in that particular setting will be.
In the hands of a beginner there will be blood all over the workshop and it will take an expert to sort it all out again, quite possibly resulting in a net negative ROI.
Where I do get use out of it: to quickly look up some verifiable fact, to tell me what a particular acronym stands for in some context, to be slightly more functional than wikipedia for a quick overview of some subfield (but you better check that for gross errors). So yes, it is useful. But it is not so useful that competent engineers that are not using AI are failing at their job, and it is at best - for me - a very mild accelerator in some use cases. I've seen enough AI driven coding projects strand hopelessly by now to know that there are downsides to that golden acorn that you are seeing.
The few times that I challenged the likes of ChatGPT with an actual engineering problem to which I already knew the answer by way of verification the answers were so laughably incorrect that it was embarrassing.
And for the better. I've honestly not had this much fun programming applications (as opposed to students stuff and inner loops) in years.
I'm happy that it works out for you, and probably this is a reflection of the kind of work that I do, I wouldn't know how to begin to solve a problem like designing a braille wheel or a windmill using AI tools even though there is plenty of coding along the way. Maybe I could use it to make me faster at using OpenSCAD but I am never limited by my typing speed, much more so by thinking about what it is that I actually want to make.
Another very useful trick is to think in terms of vertices of your object rather than the primitives creates by those vertices. You then put hulls over the vertices and if you use little spheres for the vertices the edges take care of themselves.
This is just about edges and chamfers, but the same kind of thinking applies to most of OpenSCAD. If I compare how productive I am with OpenSCAD vs using a traditional step-by-step UI driven cad tool it is incomparable. It's like exploratory programming, but for physical objects.
"There's not much there" is a totally valid critique of a lot of the current AI ecosystem. How many startups are simple prompt wrappers on top of ChatGPT? How many AI features in products are just "click here to ask Rovo/Dingo/Kingo/CutesyAnthropomorphizedNameOfAI" text boxes that end up spitting out wrong information?
There's certainly potential but a lot of the market is hot air right now.
> Either way, the market is going to punish them accordingly.
I doubt this, simply because the market has never really punished people for being less efficient at their jobs, especially software development. If it did, people proficient in vim would have been getting paid more than anyone else for the past 40 years.
The skeptics are the ones that have tried AI coding agents and come away unimpressed because it can’t do what they do. If you’re proudly proclaiming that AI can replace your work then you’re telling on yourself.
That's asking the wrong question, and I suspect coming from a place of defensiveness, looking to justify one's own existence. "Is there anything I can do that the machine can't?" is the wrong question. "How can I do more with the machine's help?" is the right one.
That's a very interesting observation. I think I'm safe for now ;)
Yeah, stock prices, unregulated consolidation, and a chance to replace the labor market. Next to penis enhancement, it's a CEO's wet dream. They will bet it all for that chance.
Granted, I think its hastiness will lead to a crash, so the CEO's played themselves short term.
In fact, it tends to be the opposite. You being more efficient just means you get "rewarded" with more work, typically without an appropriate increase in pay to match the additional work either.
Especially true in large, non-tech companies/bureaucratic enterprises where you are much better off not making waves, and being deliberately mediocre (assuming you're not a ladder climber and aren't trying to get promoted out of an IC role).
In a big team/org, your personal efficiency is irrelevant. The work can only move as fast as the slowest part of the system.
I think this means a lot of big businesses are about to get "disrupted" because small teams can become more efficient because for them sheer generation of somtimes boilerplate low quality code is actually a bottleneck.
Its why unions, associations, professional bodies, etc exist for example. This whole thread is an example -> the value gained from efficiency in SWE jobs doesn't seem to be accruing value to the people with SWE skills.
The other day Claude Code correctly debugged an issue for me, that was seen in production, in a large product. It found a bug a human wrote, a human reviewed, and fixed it. For those interested the bug had to do with chunk decoding, the author incorrectly re-initialized the decoder in the loop for every chunk. So single chunk - works. >1 chunk fails.
I was not familiar with the code base. Developers who worked on the code base spent some time and didn't figure out what was going on. They also were not familiar with the specific code. But once Claude pointed this out that became pretty obvious and Claude rewrote the code correctly.
So when someone tells me "there's not much there" and when the evidence says the opposite I'm going to believe my own lying eyes. And yes, I could have done this myself but Claude did this much faster and correctly.
That said, it does not handle all tasks with the same consistency. Some things it can really mess up. So you need to learn what it does well and what it does less well and how and when to interact with it to get the results you want.
It is automation on steroids with near human (lessay intern) capabilities. It makes mistakes, sometimes stupid ones, but so do humans.
If the stories were more like this where AI was an aid (AKA a fancy auto complete), devs would probably be much more optimistic. I'd love more debugging tools.
Unfortunately, the lesson an executive here would see is "wow AI is great! fire those engineers who didn't figure it out". Then it creeps to "okay have AI make a better version of this chunk decoder". Which is wrong on multiple levels. Can you imagine if the result for using Intellisense for the first time was to slas your office in half? I'd hate autocomplete too?
I would argue that the "actual job" is simply to solve problems. The client / customer ultimately do not care what technology you use. Hell, they don't really care if there's technology at all.
And a lot of software engineers have found that using an LLM doesn't actually help solve problems, or the problems it does solve are offset by the new problems it creates.
This feels like a mentality of "a solution trying to find a problem". There's enough actual problems to solve that I don't need to create more.
But sure, the extension of this is "Then they go home and research more usages and see a kerfluffle of legal, community, and environmental concerns". Then decides to not get involved in the politics".
>Either way, the market is going to punish them accordingly.
If you want to punish me because I gave evaluations you disagreed with, you're probably not a company I want to work for. I'm not a middle manager.
To me, any software engineer who tries Haskell, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems.
To me, any software engineer who tries Emacs, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems.
To me, any software engineer who tries FreeBSD, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems.
We're getting paid to solve the problem, not to play with the shiniest newest tools. If it gets the job done, it gets the job done.
AI is terrible at anything it hasn’t seen 1000 times before on GitHub. It’s bad at complex algorithmic work. Ask it to implement an order statistic tree with internal run length encoding and it will barely be able to get off the starting line. And if it does, the code will be so broken that it’s faster to start from scratch. It’s bad at writing rust. ChatGPT just can’t get its head around lifetimes. It can’t deal with really big projects - there’s just not enough context. And its code is always a bit amateurish. I have 10+ years of experience in JS/TS. It writes code like someone with about 6-24 months experience in the language. For anything more complex than a react component, I just wouldn’t ship what it writes.
I use it sometimes. You clearly use it a lot. For some jobs it adds a lot of value. For others it’s worse than useless. If some people think it’s a waste of time for them, it’s possible they haven’t really tried it. It’s also possible their job is a bit different from your job and it doesn’t help them.
Or, and stay with me on this, it’s a reaction to the actual experience they had.
I’ve experimented with AI a bunch. When I’m doing something utterly formulaic it delivers (straightforward CRUD type stuff, or making a web page to display some data). But when I try to use it with the core parts of my job that actually require my specialist knowledge they fall apart. I spend more time correcting them than if I just write it myself.
Maybe you haven’t had that experience with work you do. But I have, and others have. So please don’t dismiss our reaction as “fear based” or whatever.
something I enjoy about our line of work is there are different ways to be good at it, and different ways to be useful. I really enjoy the way different types of people make a team that knows its strengths and weaknesses.
anyway, I know a few great engineers who shrug at the agents. I think different types of thinker find engagement with these complex tools to be a very different experience. these tools suit some but not all and that's ok
I think a big mistake junior managers make is that they think that their nominal subordinates should solve problems the way that they would solve them, without recognizing that there are multiple valid paths and that it doesn't so much matter which path is chosen as long as the problem is solved on time and within the allocated budget.
I have solved more problems with tools like sed and awk, you know, actual tools, more than I’ve entered tokens into an LLM.
Nobody seemed to give a fuck as long as the problem was solved.
This it getting out of hand.
This is almost by definition not really true. LLMs spit out whatever they were trained on, mashed up. The solutions they have access to are exactly the ones that already exist, and for the most part those solutions will have existed in droves to have any semblance of utility to the LLM.
If you're referring to "mass code output" as "a new class of problem", we've had code generators of differing input complexity for a very long time; it's hardly new.
So what do you really mean when you say that a new class of problems became solvable?
Writing code via a LLM feels like writing with a wet noodle. It’s much faster and write what I mean, myself, with the terse was and precision of my own thought.
Hehe. So much for precision ;)
I personally use it, I find it helpful at times, but I also find that it gets in my way, so much so it can be a hindrance (think losing a day or so because it's taken a wrong turn and you have to undo everything)
FTR The market is currently punishing people that DO use it (CVs are routinely being dumped at the merest hint of AI being used in its construction/presentation, interviewers dumping anyone that they think is using AI for "help", code reviewers dumping any take home assignments that have even COMMENTS massaged by AI)
I don't understand why people seem so impatient about AI adoption.
AI is the future, but many AI products aren't fully mature yet. That lack of maturity is probably what is dampening the adoption curve. To unseat incumbent tools and practices you either need to do so seamlessly OR be 5-10x better (Only true for a subset of tasks). In areas where either of these cases apply, you'll see some really impressive AI adoption. In areas where AI's value requires more effort, you'll see far less adoption. This seems perfectly natural to me and isn't some conspiracy - AI needs to be a better product and good products take time.
We're burning absurd, genuinely farcical amounts of money on these tools now, so of course they're impatient. There's Trillions (with a "T") riding on this massive hypewave, and the VCs and their ilk are getting nervous because they see people are waking up to the reality that it's at best a kinda useful tool in some situations and not the new God that we were promised that can do literally everything ever.
This takes all the joy away, even traditional maintenance projects of big corps seems attractive nowadays.
PC, Web and Smartphone hype was based on "we can now do [thing] never done before".
This time out it feels more like "we can do existing [thing], but reduce the cost of doing it by not employing people"
It all feels much more like a wealth grab for the corporations than a promise of improving a standard of living for end customers. Much closer to a Cloud or Server (replacing Mainframes) cycle.
I was doing RPA (robotic process automation) 8 years ago. Nobody wanted it in their departments. Whenever we would do presentations, we were told to never, ever, ever talk about this technology replacing people - it only removes the mundane work so teams can focus more on the bigger scope stuff. In the end, we did dozens and dozens of presentations and only two teams asked us to do some automation work for them.
The other leaders had no desire to use this technology because they were not only fearful of it replacing people on their teams, they were fearful it would impact their budgets negatively so they just quietly turned us down.
Unfortunately, you're right because as soon as this stuff gets automated and you find out 1/3rd of your team is doing those mundane tasks, you learn very quickly you can indeed remove those people since there won't be enough "big" initiatives to keep everybody busy enough.
The caveat was even on some of the biggest automations we did, you still needed a subset of people on the team you were working with to make sure the automations were running correctly and not breaking down. And when they did crash, since a lot of these were moving time sensitive data, it was like someone just stole the crown jewels and suddenly you need two war rooms and now you're ordering in for lunch.
Or hiring a mathematician to calculate what is now done in a spreadsheet.
"You should be using AI in your day to day job or you won't get promoted" is the 2025 equivalent of being forced to train the team that your job is being outsourced to.
I think there is a broader dichotomy between the people-persuation-plane, and the real-world-facts plane. In the people-persuation plane, it is all about convincing someone of something, and hype plays here, and marketing, religion and political persuation too. In the real world plane, it is all about tangible outcomes, and working code or results play here, and gravity and electromagnetism too. Sometimes there is a reflex loop between the two. I chose the engineering career because, what i produce is tangible, but I realize that a lot of my work is in the people-plane.
This right here is the real thing which AI is deployed to upset.
The Enlightenment values which brought us the Industrial Revolution imply that the disparity between the people-persuasion-plane and the real-world-facts-plane should naturally decrease.
The implicit expectation here is that as civilization as a whole learns more about how the universe works, people would naturally become more rational, and thus more persuadable by reality-compliant arguments and less persuadable by reality-denying ones.
That's... not really what I've been seeing. That's not really what most of us have been seeing. Like, devastatingly not so.
My guess is that something became... saturated? I'd place it sometime around the 1970s, same time Bretton Woods ended, and the productivity/wages gap began to grow. Something pertaining to the shared-culture-plane. Maybe there's only so much "informed" people can become before some sort of phase shift occurs and the driving force behind decisions becomes some vague, ethically unaccountable ingroup intuition ("vibes", yo), rather than the kind of explicit, systematic reasoning which actually is available to any human, except for the weird fact how nobody seems to trust it very much any more.
likely not. Our natural state tuned by evolution is one of an emotional creature persuaded by pleasing rhetoric - like a bird which responds to another bird's call.
I always figured, unlike human speech, bird song contained only truth - 100% real-time factual representation of reproductive fitness/compatibility, 0% fractal bullshitting (such as arguing about definitions of abstract notions, or endless rumination and reflection, or command hierarchies built to leak, or...).
Although who knows, really! I'm just guessing here. Maybe what we oughtta do is ask some actual ornithologists to ask an actual parrot to translate for us the songs of its distant relatives. Sounds crazy enough to work -- though probably not in captivity.
Overall I see your point, and I see many people sharing that perspective; personally, I find it rather disheartening. Tbh I'm not even sure what would be a convincing argument one way or the other.
I worked building tools within the Microsoft ecosystem, both on the SQL Server side, and on the .NET and developer tooling side, and I spent some time working with the NTVS team at Microsoft many years ago, as well as attending plenty of Microsoft conferences and events, working with VSIP contacts, etc. I also know plenty of people who've worked at or partnered with Microsoft.
And to me this all reads like classic Microsoft. I mean, the article even says it: whatever you're doing, it needs to align with whatever the current key strategic priority is. Today that priority is AI, 12 years ago it was Azure, and on and on. And, yes, I'd imagine having to align everything you do to a single priority regardless of how natural that alignment is (or not) gets pretty exhausting, and I'd bet it's pretty easy to burn out on it if you're in an area of the business where this is more of a drag and doesn't seem like it delivers a lot of value. And you'll have to dogfood everything (another longtime Microsoft pattern) core to that priority even if it's crap compared with whatever else might be out there.
But I don't think it's new: it's simply part and parcel of working at Microsoft. And the thing is, as a strategy it's often served them well: Windows[0], Xbox, SQL Server, Visual Studio, Azure, Sharepoint, Office, etc. Doesn't always work, of course: Windows Phone went really badly, but it's striking that this kind of swing and a miss is relatively rare in Microsoft's history.
And so now, of course, they're doing it with AI. And, of course, they're a massive company, so there will be plenty of people there who really aren't having a good time with it. But, although it's far from a foregone conclusion, it would not be a surprise for Microsoft to come from behind and win by repeating their usual strategy... again.
[0] Don't overread this: I'm not necessarily saying I'm a huge fan. In fact I do think Windows, at is core, is a decent operating system, and has been for a very long time. On the back end it works well, and I have no complaints. But I viscerally despise Windows 11 as a desktop operating system. That's right: DESPISE. VISCERALLY. AT A MOLECULAR LEVEL.
My assumption detector twigged at that line. I think this is just replacing the dichotomy with a continuum between two states. But the hype proponents always hope - and in some cases they are right - that those two poles overlap. People make and lose fortunes on placing those bets and you don't necessarily have to be right or wrong in an absolute sense, just long enough that someone else will take over your load and hopefully at a higher valuation.
Engineers are not usually the ones placing the bets, which is why they're trying to stay away from hype driven tech (to them it is neutral with respect to the outcome but in case of a failure they lose their job, so better to work on things that are not hyped, it is simply safer). But as soon as engineers are placing bets they are just as irrational as every other class of investor.
One interesting take away from the article and the discussion is that there seem to be two kinds of engineers: those that buy into the hype and call it "AI," and those that see it for the fancy search engine it is and call it an "LLM." I'm pretty sure these days when someone mentions "AI" to me I roll my eyes. But if they say, "LLM," ok, let's have a discussion.
The wealthiest person in the world relies entirely on his ability to convince people to accept hype that surpasses all reason.
I understood “they think they can’t” to refer to the engineers thinking that management won’t allow them to, not to a lack of confidence in their own abilities.
Spot. Fucking. On.
Thank you.
But despite all that, for writing, refactoring, and debugging computer code, LLM agents are still completely game changing. All of these things are true at the same time. There's no way someone that works with real code all day could spent an honest few weeks with a tool like Claude and come away calling it "hype". someone might still not prefer it, or it's not for them, but to claim it's "hype", that's not possible.
I've tried implementing features with Claude Code Max and if I had let that go on for a week instead of just a couple of days I would've lost a week's worth of work (it was pretty immediately obvious that it was too slow at doing pretty much everything, and even the slightest interaction with the LLM caused very long round-trips that would add additional time, over and over and over again). It's possible people simply don't do the kind of things I do. On the extreme end of that, had I spent my days making CRUD apps I probably would've thought it was magic and a "game changer"... But I don't.
I actually don't have a problem believing that there are people who basically only need to write 25% of their code now; if all you're doing for work is gluing together libraries and writing boilerplate then of course an LLM is going to help with that, you're probably the 1000th person that day to ask for the same thing.
The one part I would say LLMs seem to help me with is medium-depth questions about DirectX12. Not really how to use it, but parts of the API itself. MSDN is good for learning about it, but I would concede that LLMs have been useful for just getting more composite knowledge of DX12.
P.S.:
I have found that very short completions, 1-3 lines, is a lot more productive for me personally than any kind of "generate this feature", or even function-sized generation. The reason is likely that LLMs just suck at the things I do, but they can figure out that a pattern exists in the pretty immediate context and just spit out that pattern with some context clues nearby. That remains my best experience with any and all LLM-assisted coding. I don't use it often because we don't allow LLMs for work, but I have a keybind for querying for a completion when I do side projects.
> The one part I would say LLMs seem to help me with is medium-depth questions about DirectX12. Not really how to use it, but parts of the API itself. MSDN is good for learning about it, but I would concede that LLMs have been useful for just getting more composite knowledge of DX12.
see there you go, I have things like this I have to figure out many times per week. so many of them are one-off things I really dont need to learn deeply at the moment (like TypeScript). It's also very helpful to bounce off ideas, like when I need to achieve something in the Go/k8s realm, it can sanity check how I'm approaching a problem and often suggest other ways that I would not have considered (which it knows because it's been trained on millions of tech blogs).
My company is basically writing blank cheques for "AI" (aka LLM, I hate the way we've poisoned AI as a term))tooling so that people can use any and all tooling they want and see what works and doesn't. This is a company with ~1500ish engineers, ranging from hardware engineers building POS devices to the junior frontenders building out our simplest UIs. There's also a whole lot more people who aren't technical, and they're also encouraged to use any and all AI tooling they can.
Despite the entire company trying to figure out how to use these effectively precisely because we're trying to look at things objectively and separate out the hype from the reality, the only people I've seen with any kind of praise so far (and this has been going on since the early ChatGPT days) have been people in Marketing and Sales, because for them it doesn't matter if the AI hallucinates some pure bullshit since that's 90% of their job anyway.
We have spent god knows how much time and resources trying to get these tools doing anything more useful than simple demos that get thrown out immediately, and it's just not there. No one is pushing 100x the code or features they were before, projects aren't finishing any faster than they were before, and nobody even bothers turning on the meeting transcription tools either anymore because more often than not it'll interpret things said in the meeting just plain wrong or even make up entire discussion points that were never had.
Just recently, like last week recently, we had some idiotic PR review bot from coderabbit or some other such company be activated. I've never seen so many people complain all at once on Slack, there was a thread with hundreds of individuals all saying how garbage it was and how much it was distracting from reviews. I didn't see a single person say they liked the tool, not 1 single person had anything good to say about it.
So as far as I'm concerned, it's just a MASSIVE fucking hype bubble that will ultimately spawn some tooling that is sorta useful for generating unimportant scripts, but little else.
Basically if people are producing code or documentation that looks like an LLM wrote it, that's not really what I see as the model that makes these tools useful.
so, people with experience?
In hindsight it makes sense, I’m sure every major shift has played out the same way.
It also turns out that experience can be what enables you to not waste time on trendy stuff which will never deliver on its promises. You are simply assuming that AI is a paradigm shift rather than a waste of time. Fine, but at least have the humility to acknowledge that reasonable people can disagree on this point instead of labeling everyone who disagrees with you as some out of touch fuddy-duddy.
Get over yourself, and try to tone down the bigotry and stereotyping.
Cryptocurrency solves the money-printing problem we've had around the world since we left the gold standard. If governments stopped making their currencies worthless, then bitcoin would go to zero.
Bitcoin is probably unkillable. Even if were to crash, it won't be hard to round up enough true believers to boost it up again. But it's technically stagnant.
Many other cryptocurrencies are popular enough to be easily tradable and have features to make them work better for trade. Also, you can speculate on different cryptocurrencies than your friends do.
The only thing that MIGHT kill it is if governments stopped printing money.
The values of bitcoin are:
- easy access to trading for everyone, without institutional or national barriers
- high leverage to effectively easily borrow a lot of money to trade with
- new derivative products that streamline the process and make speculation easier than ever
The blockchain plays very little part in this. If anything it makes borrowing harder.
how on earth does bitcoin have anything to do with borrowing or derivatives?
in a way that wouldn't also work for beanie babies
There are actually several startups whose pitch is to bring back those innovations to equities (note that this is different from tokenized equities).
The whole cryptocurrency world requires evangelical buy-in. But there is no directly created functional value other than a historic record of transactions and hypothetical decentralization. It doesn’t directly create value. It’s a store of it - again, assuming enough people continue to buy into the narrative so that it doesn’t dramatically deflate when you need to recover your assets. States and other investors are helping make stability happen to maintain it as a value store, but you require the story propagating to achieve those ends.
That people are greedy and ignorant and bid up BTC doesn't prove anything about its value.
AI is not both of these things? There are no real AI products that have real customers and make money by giving people what they need?
> LLMs are a more substantive technology than blockchain ever was, but like blockchain, their potential has been greatly overstated.
What do you view as the potential that’s been stated?
LLMs are not an intelligence, and people who treat them as if they are infallible Oracles of wisdom are responsible for a lot of this fatigue with AI
Please don't do this, make up your own definitions.
Pretty much anything and everything that uses neural nets is AI. Just because you don't like how the definition has been since the beginning doesn't mean you get to reframe it.
In addition, if humans are not infallible oracles of wisdom, they wouldn't be an intelligence in your definition.
I also don't understand the LLM ⊄ AI people. Nobody was whining about pathfinding in video games being called AI lol. And I have to say LLMs are a lot smarter than A*.
Also it's funny how they add (supervised) everywhere. It looks like "Full self driving (not really)"
Look I don't like the advertising of FSD, or musk himself, but we without a doubt have cars using significant amounts of AI that work quite well.
In those cases the actual "new" technology (ie, not the underlying ai necessarily) is not as substantive and novel (to me at least) as a product whose internals are not just an (existing) llm.
(And I do want to clarify that, to me personally, this tendency towards 'thin-shell' products is kind of an inherent flaw with the current state of ai. Having a very flexible llm with broad applications means that you can just put Chatgpt in a lot of stuff and have it more or less work. With the caveat that what you get is rarely a better UX than what you'd get if you'd just prompted an llm yourself.
When someone isn't using llms, in my experience you get more bespoke engineering. The results might not be better than an llm, but obviously that bespoke code is much more interesting to me as a fellow programmer)
Way better than AI jammed into every crevice for no reason.
In reality, the US is finally waking up to the fact that the "golden age" of capitalism in the US was built upon the lite socialism of the New Deal, and that all the bs economic opinions the average american has subscribed to over the past few decades was completely just propaganda and anyone with half a brain cell could see from miles away that since reagonomics we've had nothing but a system that leads to gross accumulation to the top and to the top alone and this is a sure fire way (variable maximization) in any complex system to produce instability and eventual collapse.
The root problem is nepo babies.
Whether it's capitalism or communism or whatever China has currently - it's all people doing everything to give their own children every unfair advantage and lie about it.
Why did people flee to America from Europe? Because Europe was nepo baby land.
Now America is nepo baby land and very soon China will be nepo baby land.
It's all rather simple. Western 'culture' is convincing everyone the nepo babies running things are actually uber experts because they attended university. Lol.
We're conditioned to do so, in large part because this kind of work ethic makes exploitation easier. Doesn't mean that's our natural state, or a desirable one for that matter.
"AI-based economy" is too broad a brush to be painting with. From the Marxist perspective, the question you should be asking is: who owns the robots? and who owns the wealth that they generate?
I do believe that the product leadership is shoehorning it into every nook and cranny of the world right now and there are reasons to be annoyed by that but there are also countless incredible use cases that are mind blowing, that you can use it every day for.
I need to write about some absolutely life changing scenarios, including: got me thousands of dollars after it drafted a legal letter quoting laws I knew nothing about, saved me countless hours troubleshooting an RV electrical problem, found bugs in code that I wrote that were missed by everyone around me, my wife was impressed with my seemingly custom week long meal plan that fit her short term no soy/dairy allergy diet, helped me solve an issue with my house that a trained professional completely missed the mark on, completely designed and wrote code for a halloween robot decoration I had been trying to build for years, saves my wife hundreds of hours as an audio book narrator summarize characters for her audio books so she doesn't have to read the entire book before she narrates the voices.
I'm worried about some of the problems LLMs will create for humanity in the future but those are problems we can solve in the future too. Today it's quite amazing to have these tools at our disposal and as we add them in smart ways to systems that exist today, things will only get better.
Call me glass half full... but maybe it's because I don't live in Seattle
Is it going to deliver on even 1% of the hype any time soon? Unlikely.
I think our tooling is holding us back more than the actual models, and even if they never advance at all from here (unlikely), we'll still get years of improvement and innovation.
Yes strong AI is always about 10 years off.
But yes any new tech takes time to work itself out. No question that LLMs are useful but they will wildly under-deliver by current hype standards. They have their own strengths and weaknesses like everything, but they can be very misleading, thus the hype.
Yep.
I feel like actually, being negative on AI is the common view now, even though every other HN commenter thinks they’re the only contrarian in the world to see the light and surely the masses must be misguided for not seeing it their way.
The same way people love to think they’re cooler than the masses by hating [famous pop artist]. “But that’s not real music!” they cry.
And that’s fine. Frankly, most of my AI skeptic friends are missing out on a skill that’s helped me a fair bit in my day to day at work and casually. Their loss.
Like it or not, LLMs are here to stay. The same way social media boomed and was here to stay, the same way e-commerce boomed and was here to stay… there’s now a whole new vertical that didn’t exist before.
Of course there will be washouts over time as the hype subsides, but who cares? LLMs are still wicked cool to me.
I don’t even work in AI, I just think they’re fascinating. The same way it was fascinating to me when I made a computer say “Hello, world!” for the first time.
These aren't meant to be gotcha rhetorical questions, just parts of my professional life where AI _isn't_ desirable by those in power, even if they're some of the only real world use cases where I'd want to use it. As someone said upthread, I want AI to do my dishes and laundry so I can focus on leisure and creative pursuits (or, in my job, writing code). I don't want AI doing creative stuff for me so I can do dishes and laundry.
I have mostly seen people on HN criticizing the few people in tech who have attached themselves to the hype and senselessly push it everywhere, not "the masses." The masses don't particularly like AI. It seems like it's only people hyping it that think everyone but Luddites are into it.
You're both painting a narrative that anti-AI sentiment is a popular bandwagon everyone is doing to be cool, as well as not that big actually and everyone is loving AI. Which is it?
What I feel is people are denouncing the problems and describing them as not being worth the tradeoff, not necessarily saying it has zero use cases. On the other end of the spectrum we have claims such as:
> countless incredible use cases that are mind blowing, that you can use it every day for.
Maybe those blow your mind, but not everyone’s mind is blown so easily.
For every one of your cases, I can give you a counter example where doing the same went horribly wrong. From cases being dismissed due to non-existent laws being quoted, to people being poisoned by following LLM instructions.
> I'm worried about some of the problems LLMs will create for humanity in the future but those are problems we can solve in the future too.
No, they are not! We can’t keep making climate change worse and fix it later. We can’t keep spreading misinformation at this rate and fix it later. We can’t keep increasing mass surveillance at this rate and fix it later. That “fix it later” attitude is frankly naive. You are falling for the narrative that got us into shit in the first place. Nothing will be “fixed later”, the powerful actors will just extract whatever they can and bolt.
> and as we add them in smart ways to systems that exist today, things will only get better.
No, they will not. Things are getting worse now, it’s absurd to think it’s inevitable they’ll get better.
As for the other points, are the LLMs wrong sometimes, yes. But so are humans so it's not really a novel thing to point out. The question is, are they more correct than humans? I have seen they can be more accurate, less biased, etc... and we are driving toward higher accuracy and other ways to make them right.
And the fix later attitude is not great toward everything and I was referring to the accuracy issues that people often point out as why AI is hype. The things you mention are side effects and those should be controlled because the cat is out of the bag. You can spend your time yelling at the clouds or try to do something to make it better. I assure you, capitalism is a tough enemy. This is no different than another type of combustable engine that was created that has negative consequences on the environment in different ways.
I'm not disagreeing with you... mostly just saying: the hype is warranted
The thing with humans is that you can build trust. I know exactly who to ask if I have a question about music, or medicine, or a myriad of other topics. I know those people will know the answers and be able to assess their level of confidence in them. If they don’t know, they can figure it out. If they are mistaken, they’ll come back and correct themselves without me having to do anything. Comparing LLMs to random humans is the wrong methodology.
> This is no different than another type of combustable engine that was created that has negative consequences on the environment in different ways.
Combustible engines don’t make it easy to spy on people, lie to them, and undermine trust in democracy.
AI pushed down everywhere. Sometimes shitty-AI that needed to be proved at all cost because it should live up to the hype.
I was in one of such AI-orgs and even there several teams felt the pressure from SLT and a culture drift to a dysfunctional environment.
Such pressure to use AI at all costs, as other fellows from Google mentioned, has been a secret ingredient to a bitter burnout. I’m going to therapy and under medication now to recover from it.
What I don't understand is where the AI irrationality is coming from: the C-suite (still in B37?) are all incredibly smart people who must surely be aware of the damage this top-down policy is having on morale, product-quality, and how the company is viewed by its own customers - and yet, they do.
I'm not going to pretend things were being run perfectly when I was at MS: there were plenty of slow-motion mistakes playing-out right in front of us all[1] - and as I look back, yes, I was definitely frustrated at these clear obvious mistakes and their resultant unimaginable waste of human effort and capital investment.
Actually, come to think about it... maybe perhaps things really haven't changed as much? Clearly something neurotoxic got into the Talking Rain cans sometime around 2010-2011 - then was temporarily abated in 2014-2015; then came back twice as hard in 2022.
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[1]: Windows 8 and the Start Screen; the SurfaceRT; Visual Studio 2012 with SHOUTY MENUS and monochrome toolbar icons; the laggy and sluggish Office 2013; the crazy simultaneous development of entirely separate new reimplementations of the Office apps for iOS, Android, WinRT, the web. While ignoring the clear market-demand for cloud-y version of Active Directory without on-prem DCs (instead we got Entra, then InTune).
FWIW: I realized this year that there are whole cohorts of management people who have absolutely zero relationship with the words that they speak. Literal tabula rasas who convert their thoughts to new words with no attachment to past statements/goals.
Put another way: Liars exist and operate all around you in the top tier of the FAANGS rn.
> I wanted her take on Wanderfugl , the AI-powered map I've been building full-time.
I can at least give you one piece of advice. Before you decide on a company or product name, take the time to speak it out loud so you can get a sense of how it sounds.
In English, I’d pronounce it very similar to “wonderful”.
So even the creator can't decide what to call it!
this must be one of the incredible AI innovations the folks in Seattle are missing out on
Now I want to know how you pronounce words like: through, bivouac, and queue.
That must be fun any time you talk about Worcestershire (the sauce or the place).
I personally thought it was wander _fughel_ or something.
Let alone how difficult it is to remember how to spell it and look it up on Google.
I thought ‘wanderfugl’ was a throwback to ~15 years ago when it was fashionable to use a word but leave out vowels for no reason, like Flickr/ /Tumblr/Scribd/Blendr.
Among actual people (i.e. not managers) there seems to be a bit of a generation gap - my younger friends (Gen Z) are almost disturbingly enthusiastic about entrusting their every thought and action to ChatGPT; my older friends (young millennials and up) find it odious.
Or, drive through Worth and Bridgeview in IL, where all the middle eastern people in Chicago live, and notice all the AI billboards. Not billboards for AI, just, billboards obviously made with GenAI.
I think it's just not true that non-tech people are especially opposed to AI.
That seems more like a canary than anything. This is the demographic that doesn't even know which tech company they're talking to in congress. That's not the demographic in touch with tech. They have gotten more excited about even dumber stuff.
For people under 50, it's a wildly common insult to say something seems AI generated. They are disillusioned with the content slop filling the internet, the fact that 50% of the internet is bots, and their future job prospects.
The only people I've seen liking AI art, like fake cat videos, are people over 50. Not that they don't matter, but they are not the driver of what's popular or sustainable.
[1] https://www.pewresearch.org/science/2025/09/17/ai-in-america...
One look at the dystopian billboards bragging about trying to replace humans with AI should make any sane human angry at what tech has done. Or the rising rents due to an influx of people working on mostly useless AI startups, 90% of which won't be around in 5 years. Or even how poorly many in tech behave in public and how poorly they treat service workers. That's just the tip of the iceberg, and just in SF alone.
I say all this as someone living in SF and working in tech. As a whole, we've brought the hate upon ourselves, and we deserve it.
There's maybe a slow trend towards "that's not true, you should know better than to trust AI for that sort of question" in discussions when someone says something like "I asked AI how [xyz was done]" but it's definitely not enough yet to keep anyone from going to it as their first option for answering a question.
At least, that's my wife's experience working on a contract with a state government at a big tech vendor.
It makes a lot of sense that someone casually coming in to use chatgpt for 30 minutes a week doesn't have any reason to think more deeply about what using that tool 'means' or where it came from. Honestly, they shouldn't have to think about it.
I think actually a lot about it is the sort of crass, unthinking, default-American-college-student manner about the way ChatGPT speaks. It's so American and we can feel it. But AI generated art and music is hugely unpopular, AI chatbots replacing real customer service is something we loathe.
Generally speaking I would say that AI feels like something that is being done to us by a handful of powerful Americans we profoundly distrust (and for good reason: they are untrustworthy and we can see through their bullshit).
I can tell you that this is so different to the way the internet was initially received even by older people. But again, perhaps this is in part due to our changing perspectives on America. It felt like an exciting thing to be part of, and it helped in the media that the Web was the brainchild of a British person (even if twenty years later that same media would have liked to pretend he wasn't at a European research institution when he did it).
The feeling about AI is more like the feeling we have about what the internet eventually did to our culture: destroying our high streets. We know what is coming will not be good for what makes us us.
Personally, I’m in-between the opinions. I hate when I’m consuming AI-generated stuff, but can see the use for myself for work or asking bunch of not-so-important questions to get general idea of stuff.
That's the presumption. There's no data on whether this is actually true or not. Most rational examinations show that it most likely isn't. The progress of the technology is simply too slow and no exponential growth is on the horizon.
The world is not at all about fairness of benefits and impacts to all people it is about a populist mass and what amuses them and makes their life convenient, hopefully without attending the relevant funerals themselves.
Honestly I don’t really know what to say to that, other than it seems rather relevant to me. I don’t really know what to elaborate on given we disagree on such a fundamental level.
It's lovely that you care about health, but I have no idea why you think you are relevant to a society that is very much willing to risk extinction to avoid the slightest upset or delay to consumer convenience measured progress.
I’m not sure what this question is addressing. I didn’t say it needs to “stop” or the industry has to respond to me.
> It's lovely that you care about health,
1) you should care too, 2) drop the patronizing tone if you are actually serious about having a conversation.
The members of HN are actually a pretty strongly biased sample towards people who get the omelet when the eggs get broken.
No not the devil, but years ago I stopped finding it funny or useful when people "played" the part of devil's advocate because we all know that the vast majority of the time it's just a convenient way to be contrarian without ever being held accountable for the opinions espoused in the process. It also tends to distract people from the actual discussion at hand.
To be honest I’m kind of surprised I need to explain what this means so my guess is you’re just baiting/being opaque, but I’ll give you the benefit of the doubt and answer your question taken at face value: There have been plenty of high profile incidents in the news over the past year or two, as well as multiple behavioral health studies showing that we need to think critically about how these systems are deployed. If you are unable to find them I’ll locate them for you and link them, but I don’t want to get bogged down in “source wars.” So please look first (search “AI psychosis” to start) and then hit me up if you really can’t find anything.
I am not against the use of LLM’s, but like social media and other technologies before it, we need to actually think about the societal implications. We make this mistake time and time again.
Search for health and safety and see how many results are about work.
If you don't care about that so be it - just say it out loud then. But I do not feel like getting bogged down in justifying why we should even discuss it as we circle what this is really about.
If your concerns are things like AI psychosis, then I think it is fair to say that the tradeoffs are not yet clear enough to call this. There are benefits and bad consequences for every new technology. Some are a net positive on the balance, others are not. If we outlawed every new technology because someone, somewhere was hurt, nothing would ever be approved for general use.
I do not feel they are but also I was primarily talking about the AI-evangelists who shout people asking these questions down as Luddites.
I think the same thing of the precautionary movements today, including the AI skeptic position you are advocating for here. The comparison is valid, and it is negative and pejorative because history is on the side of advancing technology.
I can assure you, living in Seattle I still encounter a lot a AI boosters just as much as I encounter AI haters/skeptics
So between the debugging and many times you need to reprompt and redo (if you bother at all, but then that adds debugging time) is any time actually saved?
I think the dust hasn’t settled yet because no one has shipped mostly AI generated code for a non-trivial application. They couldn’t have with its current state. So it’s still unknown whether building on incredibly shaky ground will actually work in real life (I personally doubt it).
Yup. Ai is so fickle it’ll do anything to accomplish the task. But ai is just a tool it’s all about what you allow it to do. Can’t blame ai really.
These were (formerly) not the kinds of humans who regularly made these kinds of mistakes.
Those slops already existed, but AI scales them by an order of magnitude.
I guess the same can be said of any technology, but AI is just a more powerful tool overall. Using languages as an example - lets say duck typing allowed a 10% productivity boost, but also introduced 5% more mistakes/problems. AI (claims to) allow a 10x productivity boost, but also ~10x mistakes/problems.
A tool is something designed by humans. We don't get to design electricity, but we do get to design the systems we put in place around it.
Gravity isn't a tool, but stairs are, and there are good and bad stairs.
I see how the use of AI is useful, but I feel that the practitioners of AI-as-coding-agent are running away from the real work. How can you tell me about the system you say you have created if you don't have the patience to make it or think about it deeply in the first place?
You don’t get to fix bugs in code by simply pouring it through a filter.
Anecdote: In the 2 months after my org pushed copilot down to everyone the number of warnings in the codebase of our main project went from 2 to 65. I eventually cleaned those up and created a github action that rejects any PR if it emits new warnings, but it created a lot of pushback initially.
Who are we speeding up, exactly?
It's about as productive as people who reply to questions with "ChatGPT says <...>" except they're getting paid to do it.
I remember last year or even earlier this year feeling like the models had plateau'd and I was of the mindset that these tools would probably forever just augment SWEs without fully replacing them. But with Opus 4.5, gemini 3, et al., these models are incredibly powerful and more and more SWEs are leaning on them more and more—a trend that may slow down or speed up—but is never going to backslide. I think people that don't generally see this are fooling themselves.
Sure, there are problem areas—it misses stuff, there are subtle bugs, it's not good for every codebase, for every language, for every scenario. There is some sloppiness that is hard to catch. But this is true with humans too. Just remember, the ability of the models today is the worst that it will ever be—it's only going to get better. And it doesn't need to be perfect to rapidly change the job market for SWE's—it's good enough to do enough of the tasks for enough mid-level SWEs at enough companies to reshape the market.
I'm sure I'll get downvoted to hell for this comment; but I think SWEs (and everyone else for that matter) would best practice some fiscal austerity amongst themselves because I would imagine the chance of many of us being on the losing side of this within the next decade is non-trivial. I mean, they've made all of the progress up to now in essentially the last 5 years and the models are already incredibly capable.
I'm extremely skeptical of the argument that this will end up creating jobs just like other technological advances did. I'm sure that will happen around the edges, but this is the first time thinking itself is being commodified, even if it's rudimentary in its current state. It feels very different from automating physical labor: most folks don't dream of working on an assembly line. But I'm not sure what's left if white collar work and creative work are automated en masse for "efficiency's" sake. Most folks like feeling like they're contributing towards something, despite some people who would rather do nothing.
To me it is clear that this is going to have negative effects on SWE and DS labor, and I'm unsure if I'll have a career in 5 years despite being a senior with a great track record. So, agreed. Save what you can.
Exactly. For example, what happens to open source projects where developers don't have access to the latest proprietary dev tools? Or, what happens to projects like Spring if AI tools can generate framework code from scratch? I've seen maven builds on Java projects that pull in hundreds or even thousands of libraries. 99% of that code is never even used.
The real changes to jobs will be driven by considerations like these. Not saying this will happen but you can't rule it out either.
edit: Added last sentence.
That's what Im' crossing my fingers at, makes our job easier, but doesn't degrade our worth. It's the best possible outcome for devs.
Most people do not dream of working most white collar jobs. Many people dream of meaningful physical labor. And many people who worked in mines did not dream of being told to learn to code.
Your point stands that many people like physical labor. Whether they want to artisanally craft something, or desire being outside/doing physical or even menial labor more than sitting in an office. True, but that doesn't solve the above issue, just like it didn't in reverse. Telling miners to learn to code was... not great. And from my perspective neither is outsourcing our thinking en masse to AI.
As you're also saying this is the worst it will ever be. There is only one direction, the question is the acceleration/velocity.
Where I'm not sure I agree is with the perception this automatically means we're all going to be out of a job. It's possible there would be more software engineering jobs. It's not clear. Someone still has to catch the bad approaches, the big mistakes, etc. There is going to be a lot more software produced with these tools than ever.
This is the ultimate hypester’s motte to retreat to whenever the bailey of claimed utility of a technology falls. It’a trivially true of literally any technology, but also completely meaningless on its own.
In general I think -> if I was not personally invested in this situation (i.e. another man on the street) what would be my immediate reaction to this? Would I still become a software engineer as an example? Even if it is doesn't come to past, given what I know now, would I take that bet with my life/career?
I think if people were honest with themselves sadly the answer for many would probably be "no". Most other professions wouldn't do this to themselves either; SWE is quite unique in this regard.
I'm also of the mindset that even if this is not true, that is, even if current state of LLMs is best that it ever will be, AI still would be helpful. It is already great at writing self contained scripts, and efficiency with large codebases has already improved.
> I would imagine the chance of many of us being on the losing side of this within the next decade is non-trivial.
Yes, this is worrisome. Though its ironic that almost every serious software engineer at some point in time in their possibly early childhood / career when programming was more for fun than work, thought of how cool it would be for a computer program to write a computer program. And now when we have the capability, in front of our eyes, we're afraid of it.
But, one thing humans are really good at is adaptability. We adapt to circumstances / situation -- good or bad. Even if the worst happens, people loose jobs, for a short term it will be negatively impactful for the families, however, over a period of time, humans will adapt to the situation, adapt to coexist with AI, and find next endeavour to conquer.
Rejecting AI is not the solution. Using it as any other tool, is. A tool that, if used correctly, by the right person, can indeed produce faster results.
I have to challenge this one, the research on natural language generation and machine learning dates back to the 50s, it just it only recently came together at scale in a way that became useful, but tons of the hardest progress was made over many decades, and very little innovation happened in the last 5 years. The innovation has mostly been bigger scale, better data, minor architectural tweaks, and reinforcement learning with human feedback and other such fine tuning.
I sure hope so. But until the hallucination problem is solved, there's still going to be a lot of toxic waste generated. We have got to get AI systems which know when they don't know something and don't try to fake it.
> there's still going to be a lot of toxic waste generated.
And how are LLMs going to get better as the quality of the training data nosedives because of this? Model collapse is a thing. You can easily see a scenario how they'll never be better than they are now.
I've been reading this since 2023 and yet it hasn't really improved all that much. The same things are still problems that were problems back then. And if anything the improvement is slowing down, not speeding up.
I suspect unless we have real AGI we won't have human-level coding from AIs.
pretty sure the process I've seen most places is more like: one junior approves, one senior approves, then the owner manually merges.
so your process seems inadequate to me, agents or not.
also, was it tagged as generated? that seems like an obvious safety feature. As a junior, I might be thinking: 'my senior colleague sure knows lots of this stuff', but all it would take to dispel my illusion is an agent tag on the PR.
Yeah that’s what I think we need to enforce. To answer your question, it was not tagged as AI generated. Frankly, I think we should ban AI-generated code outright, though labeling it as such would be a good compromise.
I don't think it is too outrageous to believe that LLMs can do a lot of what all those armies of corporate bureaucrats do.
SWEs could do so as well, if only we were unionized.
-206dev
I still think there’s a third path, one that makes people’s lives better with thoughtful, respectful, and human-first use of AI. But for some reason there aren’t many people working on that.
I am thinking about this third path a lot, but the reality is that it wouldn't make AI more interesting than any other tool humans use to go about their daily lives. Does one get this obsessed about screwdrivers?
The issue is that a human-first world where technology is subservient to our needs is very incompatible with our current society. The AI hype is part and parcel of the capitalistic mode of production where humans are ultimately judged for their ability to produce more commodities; the goal has always been to improve productivity to make more for cheaper — this time, the quest for efficiency has found a viable replacement for many humans activities.
I haven't escaped this mindset myself. I'm convinced there are a small number of places where LLMs make truly effective tools (see: generation of "must be plausible, need not be accurate" data, e.g. concept art or crowd animations in movies), a large number of places where LLMs make apparently-effective tools that have negative long-term consequences (see: anything involving learning a new skill, anything where correctness is critical), and a large number of places where LLMs are simply ineffective from the get-go but will increasingly be rammed down consumers' throats.
Accordingly I tend to be overly skeptical of AI proponents and anything touching AI. It would be nice if I was more rational, but I'm not; I want everyone working on AI and making money from AI to crash and burn hard. (See also: cryptocurrency)
I'm the only person at my current company who has had experience at multiple AI companies (the rest have never worked on it in a production environment, one of our projects is literally something I got paid to deliver customers at another startup), has written professionally about the topic, and worked directly with some big names in the space. Unsurprisingly, I have nothing to do with any of our AI efforts.
One of the members of our leadership team, who I don't believe understands matrix multiplication, genuinely believes he's about to transcend human identity by merging with AI. He's publicly discussed how hard it is to maintain friendship with normal humans who can't keep up.
Now I absolutely think AI is useful, but these people don't want AI to be useful they want it to be something that anyone who understands it knows it can't be.
It's getting to the point where I genuinely feel I'm witnessing some sort of mass hysteria event. I keep getting introduced to people who have almost no understanding of the fundamentals of how LLMs work who have the most radically fantastic ideas about what they are capable of on a level I have ever experienced in my fairly long technical career.
But my opinions about what LLMs can do are based on... what LLMs can do. What I can see them doing. With my eyes.
The right answer to the question "What can LLMs do?" is... looking... at what LLMs can do.
You should be doubly skeptically ever since RLHF has become standard as the model has literally been optimized to give you answers you find most pleasing.
The best way to measure of course is with evaluations, and I have done professional LLM model evaluation work for about 2 years. I've seen (and written) tons of evals and they both impress me and inform my skepticism about the limitations of LLMs. I've also seen countless times where people are convinced "with their eyes" they've found a prompt trick that improves the results, only to be shown that this doesn't pan out when run on a full eval suite.
As an aside: What's fascinating is that it seems our visual system is much more skeptical, an eyeball being slightly off created by a diffusion model will immediately set off alarms where enough clever word play from an LLM will make us drop our guard.
One key thing that people prefer not to think about is that LLMs aren't created by humans. They are created by an inhuman optimization algorithm that humans have learned to invoke and feed with data and computation.
Humans have a say in what it does and how, but "a say" is about the extent of it. The rest is a black box - incomprehensible products of a poorly understood mathematical process. The kind of thing you have to research just to get some small glimpses of how it does what it does.
Expecting those humans to understand how LLMs work is a bit like expecting a woman to know how humans work because she made a human once.
I work in a space where I get to build and optimise AI tools for my own and my team's use pretty much daily. As such I focus mainly on AI'ing the crap out of boring & time-consuming stuff that doesn't interest any of us any more, and luckily enough there's a whole lot of low hanging fruit in that space where AI is a genuine time, cost and sanity saver.
However any activity that requires directed conscious thought and decision making where the end state isn't clearly definable up front tends to be really difficult for AI. So much of that work relies on a level of intuition and knowledge that is very hard to explain to a layman - let alone eidetic idiots like most AIs.
One example is trying to get AI to identify security IT incidents in real time and take proactive action. Skilled practitioners can fairly easily use AI to detect anomalous events in near real time, but getting AI to take the next step to work out which combinations of "anomalous" activities equate to "likely security incident" is much harder. A reasonably competent human can usually do that relatively quickly, but often can't explain how they do it.
Working out what action is appropriate once the "likely security incident" has been identified is another task that a reasonably competent human can do, but where AIs are hopeless. In most cases, a competent human is WAAAY better at identifying a reasonable way forward based on insufficient knowledge. In those cases, a good decision made quickly is preferable to a perfect decision made slowly, and humans understand this fairly intuitively.
Example: many people created websites without a clue of how they really work. And got millions of people on it. Or had crazy ideas to do things with them.
At the same time there are devs that know how internals work but can’t get 1 user.
pc manufacturers never were able to even imagine what random people were able to do with their pc.
This to say that even if you know internals you can claim you know better, but doesn’t mean it’s absolute.
Sometimes knowing the fundamentals it’s a limitation. Will limit your imagination.
> “In the beginner’s mind there are many possibilities, but in the expert’s there are few”
Experts do tend to be limited in what they see as possible. But I don't think that allows carte blanche belief that a fancy Markov Chain will let you transcend humanity. I would argue one of the key concepts of "beginners mind" is not radical assurance in what's possible but unbounded curiosity and willingness to explore with an open mind. Right now we see this in the Stable Diffusion community: there are tons of people who also don't understand matrix multiplication that are doing incredible work through pure experimentation. There's a huge gap between "I wonder what will happen if I just mix these models together" and "we're just a few years from surrendering our will to AI". None of the people I'm concerned about have what I would consider an "open mind" about the topic of AI. They are sure of what they know and to disagree is to invite complete rejection. Hardly a principle of beginners mind.
Additionally:
> pc manufacturers never were able to even imagine what random people were able to do with their pc.
Belies a deep ignorance of the history of personal computing. Honestly, I don't think modern computing has still ever returned to the ambition of what was being dreampt up, by experts, at Xerox PARC. The demos on the Xerox Alto in the early 1970s are still ambitious in some senses. And, as much as I'm not a huge fan, Gates and Jobs absolutely had grand visions for what the PC would be.
Few years ago I had this exact observation regarding self driving cars. Non/semi engineers who worked in the tech industry were very bullish about self driving cars, believing every and ETA spewed by Musk, engineers were cautious optimistically or pessimistically depending on their understanding of AI, LiDAR, etc.
(BTW, if you're an engineer who thinks you don't understand AI or are not qualified to work on it, think again. It's just linear algebra, and linear algebra is not that hard. Once you spend a day studying it, you'll think "Is that all there is to it?" The only difficult part of AI is learning PyTorch, since all the AI papers are written in terms of Python nowadays instead of -- you know -- math.)
I've been building neural net systems since the late 1980s. And yes they work and they do useful things when you have modern amounts of compute available, but they are not the second coming of $DEITY.
Curiously some Feynman chap reported that several NASA engineers put the chance of the Challenger going kablooie—an untechnical term for rapid unscheduled deconstruction, which the Challenger had then just recently exhibited—at 1 in 200, or so, while the manager said, after some prevarications—"weaseled" is Feynman's term—that the chance was 1 in 100,000 with 100% confidence.
Correlations are interesting but when examined only individually they are not nearly as meaningful as they might seem. Which one you latch onto as "the truth" probably says more about what tribe you value or want to be part of than anything fundamental about technology or society or people in general.
I don't know shit about the math that makes it work, but my mental model is basically - "A LLM is an additional tool in my toolbox which performs summarization, classification and text transformation tasks for me imperfectly, but overall pretty well."
Probably lots of flaws in that model but I just try to think like an engineer who's attempting to get a job done and staying up to date on his tooling.
But as you say there are people who have been fooled by the "AI" angle of all this, and they think they're witnessing the birth of a machine god or something. The example that really makes me throw up my hands is r/MyBoyfriendIsAI where you have women agreeing to marry the LLM and other nonsense that is unfathomable to the mentally well.
There's always been a subset of humans who believe unimaginably stupid things, like that there's a guy in the sky who throws lightning bolts when he's angry, or whatever. The interesting (as in frightening) trend in modernity is that instead of these moron cults forming around natural phenomena we're increasingly forming them around things that are human made. Sometimes we form them around the state and human leaders, increasingly we're forming them around technologies, in line with Arthur C. Clarke's third law - that "Any sufficiently advanced technology is indistinguishable from magic."
If I sound harsh it's because I am, we don't want these moron cults to win, the outcome would be terrible, some high tech version of the Dark Ages. Yet at this moment we have business and political leaders and countless run-of-the-mill tech world grifters who are leaning into the moron cult version of AI rather than encouraging people to just see it as another tool in the box.
I use it for throwaway prototypes and demos. And whenever I’m thrust into a language I don’t know that well, or to help me debug weird issues outside my area of expertise. But when I go deep on a problem, it’s often worse than useless.
To management (out of IC roles for long enough to lose their technical expertise), it looks perfect!
To ICs, the flaws are apparent!
So inevitably management greenlights new AI projects* and behaviors, and then everyone is in the 'This was my idea, so it can't fail' CYA scenario.
* Add in a dash of management consulting advice here, and note that management consultants' core product was already literally 'something that looks plausible enough to make execs spend money on it'
If you ask for an endpoint to a CRUD API, it'll make one. If you ask for 5, it'll repeat the same code 5 times and modify it for the use case.
A dev wouldn't do this, they would try to figure out the common parts of code, pull them out into helpers, and try to make as little duplicated code as possible.
I feel like the AI has a strong bias towards adding things, and not removing them. The most obviously wrong thing is with CSS - when I try to do some styling, it gets 90% of the way there, but there's almost always something that's not quite right.
Then I tell the AI to fix a style, since that div is getting clipped or not correctly centered etc.
It almost always keeps adding properties, and after 2-3 tries and an incredibly bloated style, I delete the thing and take a step back and think logically about how to properly lay this out with flexbox.
I suspect this is because an LLM doesn't build a mental model of the code base like a dev does. It can decide to look at certain files, and maybe you can improve this by putting a broad architecture overview of a system in an agents.md file, I don't have much experience with that.
But for now, I'm finding it most useful still think in terms of code architecture, and give it small steps that are part of that architecture, and then iterate based on your own review of AI generated code. I don't have the confidence in it to just let some agent plan, and then run for tens of minutes or even hours building out a feature. I want to be in the loop earlier to set the direction.
I don’t think this is an inherent issue to the technology. Duplicate code detectors have been around for ages. Given an AI agent a tool which calls one, and ask it to reduce duplication, it will start refactoring.
Of course, there is a risk of going too far in the other direction-refactorings which technically reduce duplication but which have unacceptable costs (you can be too DRY). But some possible solutions: (a) ask it to judge if the refactoring is worth it or not - if it judges no, just ignore the duplication and move on; (b) get a human to review the decision in (a); (c) if AI repeatedly makes wrong decision (according to human), prompt engineering, or maybe even just some hardcoded heuristics
Basically human junior engineers start by writing code in a very procedural and literal style with duplicate logic all over the place because that's the first step in adapting human intelligence to learning how to program. Then the programmer realizes this leads to things becoming unmaintainable and so they start to learn the abstraction techniques of functions, etc. An LLM doesn't have to learn any of that, because they already know all languages and mechanical technique in their corpus, so this beginning journey never applies.
But what the junior programmer has that the LLM doesn't, is an innate common sense understanding of human goals that are driving the creation of the code to begin with, and that serves them through their entire progression from junior to senior. As you point out, code can be "too DRY", but why? Senior engineers understand that DRYing up code is not a style issue, its more about maintainability and understanding what is likely to change, and what will be the apparent effects to human stakeholders who depend on the software. Basically do these things map to things that are conceptually the same for human users and are unlikely to diverge in the future. This is also a surprisingly deep question as perhaps every human stakeholder will swear up and down they are the same, but nevertheless 6 months from now a problem arises that requires them to diverge. At this point there is now a cognitive overhead and dissonance of explaining that divergence of the users who were heretofore perfectly satisfied with one domain concept.
Ultimately the value function for success of a specific code factoring style depends on a lot of implicit context and assumptions that are baked into the heads of various stakeholders for the specific use case and can change based on myriad outside factors that are not visible to an LLM. Senior engineers understand the map is not the territory, for LLMs there is no territory.
But, senior engineers can supervise the AI, notice when it makes suboptimal decisions, intervene to address that somehow (by editing prompts or providing new tools)… and the idea is gradually the AI will do better.
Rather than replacing engineers with AIs, engineers can use AIs to deliver more in the same amount of time
AI operating at all levels needs to be constantly supervised.
Which would still make AI a worthwhile technology, as a tool, as many have remarked before me.
The problem is, companies are pushing for agentic AI instead of one that can do repetitve, short horizon tasks in a fast and reliable manner.
If your AI reliably generates working code from a detailed prompt, the prompt is now the source that needs to be maintained. There is no important reason to even look at the generated code
The inference response to the prompt is not deterministic. In fact, it’s probably chaotic since small changes to the prompt can produce large changes to the inference.
So? Nobody cares.
Is the output of your C compiler the same every time you run it? How about your FPGA synthesis tool? Is that deterministic? Are you sure?
What difference does it make, as long as the code works?
Yes? Because of actual engineering mind you and not rolling the dice until the lucky number comes up.
https://reproducibility.nixos.social/evaluations/2/2d293cbfa...
Nobody else cares. If you do, that's great, I guess... but you'll be outcompeted by people who don't.
In the meantime: no, deterministic code generation isn't necessary, and anyone who says it is is wrong.
How weird would it be if something like this worked perfectly out of the box, with no need for further improvement and refinement?
Yeah the business surely won't care when we rerun the prompt and the server works completely differently.
> Is the output of your C compiler the same every time you run it
I've never, in my life, had a compiler generate instructions that do something completely different from what my code specifies.
That you would suggest we will reach a level where an English language prompt will give us deterministic output is just evidence you've drank the kool-aid. It's just not possible. We have code because we need to be that specific, so the business can actually be reliable. If we could be less specific, we would have done that before AI. We have tried this with no code tools. Adding randomness is not going to help.
Nobody is saying it should. Determinism is not a requirement for this. There are an infinite number of ways to write a program that behaves according to a given spec. This is equally true whether you are writing the source code, an LLM is writing the source code, or a compiler is generating the object code.
All that matters is that the program's requirements are met without undesired side effects. Again, this condition does not require deterministic behavior on the author's part or the compiler's.
To the extent it does require determinism, the program was poorly- or incompletely-specified.
That you would suggest we will reach a level where an English language prompt will give us deterministic output is just evidence you've drank the kool-aid.
No, it's evidence that you're arguing with a point that wasn't made at all, or that was made by somebody else.
LLMs do not and cannot follow the spec of English reliably, because English is open to interpretation, and that's a feature. It makes LLMs good at some tasks, but terrible for what you're suggesting. And it's weird because you have to ignore the good things about LLMs to believe what you wrote.
> There are an infinite number of ways to write a program that behaves according to a given spec
You're arguing for more abstractions on top of an already leaky abstraction. English is not an appropriate spec. You can write 50 pages of what an app should do and somebody will get it wrong. It's good for ballparking what an app should do, and LLMs can make that part faster, but it's not good for reliably plugging into your business. We don't write vars, loops, and ifs for no reason. We do it because, at the end of the day, an English spec is meaningless until someone actually encodes it into rules.
The idea that this will be AI, and we will enjoy the same reliability we get with compilers, is absurd. It's also not even a conversation worth having when LLMs hallucinate basic linux commands.
Essentially all of the drawbacks to LLMs you're mentioning are either already obsolete or almost so, or are solvable by the usual philosopher's stone in engineering: negative feedback. In this case, feedback from carefully-structured tests. Safe to say that we'll spend more time writing tests and less time writing original code going forward.
I guess one thing it means is that we still need extensive test suites. I suppose an LLM can write those too.
This is a common intuition but it's provably false.
The fact that LLMs are trained on a corpus does not mean their output represents the median skill level of the corpus.
Eighteen months ago GPT-4 was outperforming 85% of human participants in coding contests. And people who participate in coding contests are already well above the median skill level on Github.
And capability has gone way up in the last 18 months.
https://mikelovesrobots.substack.com/p/wheres-the-shovelware...
Basically, if the tools are even half as good as some proponents claim, wouldn't you expect at least a significant increase in simple games on Steam or apps in app stores over that time frame? But we're not seeing that.
Charts I'm looking at show a mild exponential around 2024 https://www.statista.com/statistics/552623/number-games-rele...
Also theres probably a bottleneck in manual review time.
https://docs.google.com/spreadsheets/d/1Uy2aWoeRZopMIaXXxY2E...
The shovelware software is coming…
GitHub repos is pretty interesting too but it could be that people just aren't committing this stuff. Showing zero increase is unexpected though.
App stores have quality hurdles that didn’t exist in the diskette days. The types of people making low quality software now can self publish (and in fact do, often), but they get drowned out by established big dogs or the ever-shifting firehose of our social zeitgeist if you are not where they are.
Anyone who has been on Reddit this year in any software adjacent sub has seen hundreds (at minimum) of posts about “feedback on my app” or slop posts doing a god awful job of digging for market insights on pain points.
The core problem with this guy’s argument is that he’s looking in the wrong places - where a SWE would distribute their stuff, not a normie - and then drawing the wrong conclusions. And I am telling you, normies are out there, right now, upchucking some of the sloppiest of slop software you could ever imagine with wanton abandon.
Or even solving problems that business need to solve, generally speaking.
This complete misunderstand of what software engineering even is is the major reason so many engineers are fed up with the clueless leaders foisting AI tools upon their orgs because they apparently lack the critical reasoning skills to be able to distinguish marketing speak from reality.
Coding contests are not like my job at all.
My job is taking fuzzy human things and making code that solves it. Frankly AI isn’t good at closing open issues on open source projects either.
First, I don't know where those human participants came from, but if you pick people off the street or from a college campus, they aren't going to be the world's best programmers. On the other hand, github users are on average more skilled than the average CS student. Even students and beginners who use github usually don't have much code there. If the LLMs are weighted to treat every line of code about same, they'd pick up more lines of code from prolific developers (who are often more experienced) than they would from beginners.
Also in a coding contest, you're under time pressure. Even when your code works, its often ugly and thrown together. On github, the only code I check in is code that solves whatever problem I set out to solve. I suspect everyone writes better code on github than we do in programming competitions. I suspect if you gave the competitors functionally unlimited time to do the programming competition, many more would outperform GPT-4.
Programming contests also usually require that you write a fully self contained program which has been very well specified. The program usually doesn't need any error handling, or need to be maintained. (And if it does need error handling, the cases are all fully specified in the problem description). Relatively speaking, LLMs are pretty good at these kind of problems - where I want some throwaway code that'll work today and get deleted tomorrow.
But most software I write isn't like that. And LLMs struggle to write maintainable software in large projects. Most problems aren't so well specified. And for most code, you end up spending more effort maintaining the code over its lifetime than it takes to write in the first place. Chatgpt usually writes code that is a headache to maintain. It doesn't write or use local utility functions. It doesn't factor its code well. The code is often overly verbose. It often writes code that's very poorly optimized. Or the code contains quite obvious bugs for unexpected input - like overflow errors or boundary conditions. And the code it produces very rarely handles errors correctly. None of these problems really matter in programming competitions. But it does matter a lot more when writing real software. These problems make LLMs much less useful at work.
It does, by default. Try asking ChatGPT to implement quicksort in JavaScript, the result will be dogshit. Of course it can do better if you guide it, but that implies you recognize dogshit, or at least that you use some sort of prompting technique that will veer it off the beaten path.
----
function quickSortInPlace(arr, left = 0, right = arr.length - 1) { if (left < right) { const pivotIndex = partition(arr, left, right); quickSortInPlace(arr, left, pivotIndex - 1); quickSortInPlace(arr, pivotIndex + 1, right); } return arr; }
function partition(arr, left, right) { const pivot = arr[right]; let i = left;
for (let j = left; j < right; j++) {
if (arr[j] < pivot) {
[arr[i], arr[j]] = [arr[j], arr[i]];
i++;
}
}
[arr[i], arr[right]] = [arr[right], arr[i]]; // Move pivot into place
return i;
}- It always uses the last item as a pivot, which will give it pathological O(n^2) performance if the list is sorted. Passing an already sorted list to a sort function is a very common case. Good quicksort implementations will use a random pivot, or at least the middle pivot so re-sorting lists is fast.
- If you pass already sorted data, the recursive call to quickSortInPlace will take up stack space proportional to the size of the array. So if you pass a large sorted array, not only will the function take n^2 time, it might also generate a stack overflow and crash.
- This code: ... = [arr[j], arr[i]]; Creates an array and immediately destructures it. This is - or at least used to be - quite slow. I'd avoid doing that in the body of quicksort's inner loop.
- There's no way to pass a custom comparator, which is essential in real code.
I just tried in firefox:
// Sort an array of 1 million sorted elements
arr = Array(1e6).fill(0).map((_, i) => i)
console.time('x')
quickSortInPlace(arr)
console.timeEnd('x')
Uncaught InternalError: too much recursion
Secondly, if a standard implementation was to be used, that's essentially a No-Op. AI will reuse library functions where possible by default and agents will even "npm install" them for you. This is purely the result of my prompt, which was simply "Can you write a QuickSort implementation in JS?"
In any case, to incorporate your feedback, I simply added "that needs to sort an array of a million elements and accepts a custom comparator?" to the initial prompt and reran in a new session, and this is what I got in less than 5 seconds. It runs in about 160ms on Chrome:
How long would your team-mate have taken? What else would you change? If you have further requirements, seriously, you can just add those to the prompt and try it for yourself for free. I'd honestly be very curious to see where it fails.
However, this exchange is very illustrative: I feel like a lot of the negativity is because people expect AI to read their minds and then hold it against it when it doesn't.
Lol of course! The real requirements for a piece of software are never specified in full ahead of time. Figuring out the spec is half the job.
> Firstly, how often do you really need to sort a million elements in a browser anyway? I expect that sort of heavy lifting would usually be done on the server
Who said anything about the browser? I run javascript on the server all the time.
Don't defend these bugs. 1 million items just isn't very many items for a sort function. On my computer, the built in javascript sort function can sort 1 million sorted items in 9ms. I'd expect any competent quicksort implementation to be able to do something similar. Hanging for 1 minute then crashing is a bug.
If you want a use case, consider the very common case of sorting user-supplied data. If I can send a JSON payload to your server and make it hang for 1 minute then crash, you've got a problem.
> If you have further requirements, seriously, you can just add those to the prompt and try it for yourself for free. [..] How long would your team-mate have taken?
We've gotta compare like for like here. How long does it take to debug code like this when an AI generates it? It took me about 25 minutes to discover & verify those problems. That was careful work. Then you reprompted it, and then you tested the new code to see if it fixed the problems. How long did that take, all added together? We also haven't tested the new code for correctness or to see if it has new bugs. Given its a complete rewrite, there's a good chance chatgpt introduced new issues. I've also had plenty of instances where I've spotted a problem and chatgpt apologises then completely fails to fix the problem I've spotted. Especially lifetime issues in rust - its really bad at those!
The question is this: Is this back and forth process faster or slower than programming quicksort by hand? I'm really not sure. Once we've reviewed and tested this code, and fixed any other problems in it, we're probably looking at about an hour of work all up. I could probably implement quicksort at a similar quality in a similar amount of time. I find writing code is usually less stressful than reviewing code, because mistakes while programming are usually obvious. But mistakes while reviewing are invisible. Neither you nor anyone else in this thread spotted the pathological behavior this implementation had with sorted data. Finding problems like that by just looking is hard.
Quicksort is also the best case for LLMs. Its a well understood, well specified problem with a simple, well known solution. There isn't any existing code it needs to integrate with. But those aren't the sort of problems I want chatgpt's help solving. If I could just use a library, I'm already doing that. I want chatgpt to solve problems its probably never seen before, with all the context of the problem I'm trying to solve, to fit in with all the code we've already written. It often takes 5-10 minutes of typing and copy+pasting just to write a suitable prompt. And in those cases, the code chatgpt produces is often much, much worse.
> I feel like a lot of the negativity is because people expect AI to read their minds and then hold it against it when it doesn't.
Yes exactly! As a senior developer, my job is to solve the problem people actually have, not the problem they tell me about. So yes of course I want it to read my mind! Actually turning a clear spec into working software is the easy part. ChatGPT is increasingly good at doing the work of a junior developer. But as a senior dev / tech lead, I also need to figure out what problems we're even solving, and what the best approach is. ChatGPT doesn't help much when it comes to this kind of work.
(By the way, that is basically a perfect definition of the difference between a junior and senior developer. Junior devs are only responsible for taking a spec and turning it into working software. Senior devs are responsible for reading everyone's mind, and turning that into useful software.)
And don't get me wrong. I'm not anti chatgpt. I use it all the time, for all sorts of things. I'd love to use it more for production grade code in large codebases if I could. But bugs like this matter. I don't want to spend my time babysitting chatgpt. Programming is easy. By the time I have a clear specification in my head, its often easier to just type out the code myself.
That's where we come in of course! Look into spec-driven development. You basically encourage the LLM to ask questions and hash out all these details.
> Who said anything about the browser?... Don't defend these bugs.
A problem of insufficient specification... didn't expect an HN comment to turn into an engineering exercise! :-) But these are the kinds of things you'd put in the spec.
> How long does it take to debug code like this when an AI generates it? It took me about 25 minutes to discover & verify those problems.
Here's where it gets interesting: before reviewing any code, I basically ask it to generate tests, which always all pass. Then I review the main code and test code, at which point I usually add even more test-cases (e.g. https://news.ycombinator.com/item?id=46143454). And, because codegen is so cheap, I can even include performance tests, (which statistically speaking, nobody ever does)!
Here's a one-shot result of that approach (I really don't mean to take up more of your time, this is just so you can see what it is capable of): https://pastebin.com/VFbW7AKi
While I do review the code (a habit -- I always review my own code before a PR), I review the tests more closely because, while boring, I find them a) much easier to review, and b) more confidence-inspiring than manual review of intricate logic.
> I want chatgpt to solve problems its probably never seen before, with all the context of the problem I'm trying to solve, to fit in with all the code we've already written.
Totally, and again, this is where we come in! Still, it is a huge productivity booster even in uncommon contexts. E.g. I'm using it to do computer vision stuff (where I have no prior background!) with opencv.js for a problem not well-represented in the literature. It still does amazingly well... with the right context. It's initial suggestions were overindexed on the common case, but over many conversations, it "understood" my use-case and consistently gives appropriate suggestions. And because it's vision stuff, I can instantly verify results by sight.
Big caveat: success depends heavily on the use-case. I have had more mixed results in other cases, such as concurrency issues in an LD_PRELOAD library in C. One reason for the mixed sentiments we see.
> ChatGPT is increasingly good at doing the work of a junior developer.
Yes, and in fact, I've been rather pessimistic about the prospects of junior developers, a personal concern given I have a kid who wants to get into software engineering...
I'll end with a note that my workflow today is extremely different from before AI, and it took me many months of experimentation to figure out what worked for me. Most engineers simply haven't had the time to do so, which is another reason we see so many mixed sentiments. But I would strongly encourage everybody to invest the time and effort because this discipline is going to change drastically really quickly.
Who determined this? How?
But to the untrained eye the AI did everything correctly.
Its easy to recall information, but something entirely different to do something with that information. Which is what those subject ares are all about - taking something (like a theory) and applying it in a disciplined manner given the context.
Thats not to diminish what LLMs can do. But lets get real.
I'm having a dejavu of yesterday's discussion: https://news.ycombinator.com/item?id=46126988
When something threatens a thing that gives you value, people tend to hate it
AI is optimized to solve a problem no matter what it takes. It will try to solve one problem by creating 10 more.
I think long time/term agentic AI is just snake oil at this point. AI works best if you can segment your task into 5-10 minutes chunks, including the AI generating time, correcting time and engineer review time. To put it another way, a 10 minute sync with human is necessary, otherwise it will go astray.
Then it just makes software engineering into bothering supervisor job. Yes I typed less, but I didn’t feel the thrill of doing so.
I'm pretty sure this is the entire enthusiasm from C-level for AI in a nutshell. Until AI SWE resisted being mashed into a replaceable cog job that they don't have to think/care about. AI is the magic beans that are just tantalizingly out of reach and boy do they want it.
... except they didn't. In fact most AI tech were good for a nice demo and little else.
In some cases, really unfairly. For instance, convnet map matching doesn't work well not because it doesn't work well, but because you can't explain to humans when it won't work well. It's unpredictable, like a human. If you ask a human to map a building in heavy fog they may come back with "sorry". SLAM with lidar is "better", except no, it's a LOT worse. But when it fails it's very clear why it fails because it's a very visual algorithm. People expect of AIs that they can replace humans but that doesn't work, because people also demand AIs never say no, never fail, like the Star Trek computer (the only problem the star trek computer ever has is that it is misunderstood or follows policy too well). If you have a delivery person occasionally they will radically modify the process, or refuse to deliver. No CEO is ever going to allow an AI drone to change the process and No CEO will ever accept "no" from an AI drone. More generally, no business person seems to ever accept a 99% AI solution, and all AI solutions are 99%, or actually mostly less.
AI winters. I get the impression another one is coming, and I can feel it's going to be a cold one. But in 10 years, LLMs will be in a lot of stuff, like with every other AI winter. A lot of stuff ... but a lot less than CEOs are declaring it will be in today.
Ultimately they are mostly just clueless, so we will either end up with legions of way shittier companies than we have today (because we let them get away with offloading a bunch of work to tools they rms int understand and accepting low quality output) or we will eventually realize the continued importance of human expertise.
Last one at work: "Hey, here are the symptoms for a bug, they appeared in <release XYZ> - go figure out the CL range and which 10 CLs I should inspect first to see if they're the cause"
(Well suited to AI, because worst case I've looked at 10 CLs in vain, and best case it saved me from manually scanning through several 1000 CLs - the EV is net positive)
It works for code generation as well, but not in a "just do my job" way, more in a "find which haystack the needle is in, and what the rough shape of the new needle is". Blind vibecoding is a non-starter. But... it's a non-starter for greenfields too, it's just that the FO of FAFO is a bit more delayed.
But unfortunately the nuances in the former require understanding strengths and weaknesses of current AI systems, which is a conversation the industry doesn't want to have while it's still riding the froth of a hype cycle.
Aka 'any current weaknesses in AI systems are just temporary growing pains before an AGI future'
I see we've met the same product people :)
That's when I realized how far down the rabbit hole marketing to non-technical folks on this was.
When I started at my post-Google job, I felt so vindicated when my new TL recommended that I use an LLM to catch up if no one was available to answer my questions.
Working on our mega huge code basis with lots of custom tooling and bleeding edge stuff hasn't been the best for for AI generated code compared to most companies.
I do think AI as a rubber ducky / research assistant type has been overall helpful as a SWE.
From the outside, the AI push at Google very closely resembles the death march that Google+ but immensely more intense from the entire tech ecosystem following suit.
It’s the mid-range with pretensions that gets squeezed out. I absolutely do not need a $40 bottle of wine to accompany my takeout curry, I definitely don’t need truffle slices added to my carbonara, and I don’t need to hand-roll conceptually simple code.
See both sides can be pithy.
I talked to someone who was in denial about this, until he said he had conflated writing code with solving problems. Solving problems isn’t going anywhere! Solving problems: you observe a problem, write out a solution, implement that solution, measure the problem again, consider your metrics, then iterate.
“Implement it” can mean writing code, like the past 40 years, but it hasn’t always been. Before coding, it was economics and physics majors, who studied and implemented scientific management. For the next 20 years, it will be “describe the tool to Claude code and use the result”.
It works great. I have a faster iteration cycle. For existing large codebases, AI modifications will continue to be okay-ish. But new companies with a faster iteration cycle will outcompete olds ones, and so in the long run most codebases will use the same “in-distribution” tech stacks and architecture and design principles that AI is good at.
Who determined this? How?
I find people mostly prefer what they are used to, and if your preference was so superior then how could so many people build fantastic software using the method you don't like?
AI isn't like that. AI is a bunch of people telling me this product can do wonderful things that will change society and replace workers, yet almost every time I use it, it falls far short of that promise. AI is certainly not reliable enough for me to jeopardize the quality of my work by using it heavily.
Rinse and repeat for many "one-off" tasks.
It's not going away, you need to learn how to use it. shrugs shoulders
I work as the non-software kind of engineer at an industrial plant there is starting to emerge a trend of people who just blindly trust the output of AI chat sessions without understanding what the chat bot is echoing at them which is wasteful of their time and in some cases my time.
This not not new in the past I have experienced engineers who use (abuse) statistics/regression tools etc. Without understanding what the output was telling them but it is getting worse now.
It is not uncommon to hear something like: "Oh I investigated that problem and this particular issue we experienced was because of reasons x, y and z."
Then when you push back because what they've said sounds highly unlikely it boils down to. "I don't know that is what the AI told me".
Then if they are sufficiently optimistic they'll go back and prompt it with "please supply evidence for your conclusion" or some similar prompt and it will supply paragraphs of plausible sounding text but when you dig into what it is saying there are inconsistencies or made up citations. I've seen it say things that were straight up incorrect and went against Laws of Thermodynamics for example.
It has become the new "I threw the kitchen sink into a multivariate regression and X emerged as significant - therefore we should address x"
I'm not a complete skeptic I think AI has some value, for example if you use it as a more powerful search engine by asking it something like "What are some suggested techniques for investigating x" or "What are the limitations of Method Y" etc. It can point you to the right place assist you with research, it might find papers from other fields or similar. But it is not something you should be relying on to do all of the research for you.
The lesson to learn is that these are "large-language models." That means it can regurgitate what someone else has done before textually, but not actually create something novel. So it's fine if someone on the internet has posted or talked about a quick UI in whatever particular toolkit you're using to analyze data. But it'll throw out BS if you ask for something brand new. I suspect a lot of AI users are web developers who write a lot of repetitive rote boilerplate, and that's the kind of thing these LLMs really thrive with.
You get the AI to generate code that lets you spot-check individual data points :-)
Most of my work these days is in fact that kind of code. I'm working on something research-y that requires a lot of visualization, and at this point I've actually produced more throwaway code than code in the project.
Here's an example: I had ChatGPT generate some relatively straightforward but cumbersome geometric code. Saved me 30 - 60 minutes right there, but to be sure, I had it generate tests, which all passed. Another 30 minutes saved.
I reviewed the code and the tests and felt it needed more edge cases, which I added manually. However, these started failing and it was really cumbersome to make sense of a bunch of coordinates in arrays.
So I had it generate code to visualize my test cases! That instantly showed me that some assertions in my manually added edge cases were incorrect, which became a quick fix.
The answer to "how do you trust AI" is human in the loop... AND MOAR AI!!! ;-)
There’s so much evidence out there of people getting real value from the tools.
Some questions you can ask yourself are “why doesn’t it work for me?” and “what can I do differently?”.
Be curious, not dogmatic. Ignore the hype, find people doing real work.
You know where this is going. I asked Claude if audio plugins were well represented in its training data, it said yes, off I went. I can’t review the code because I lack the expertise. It’s all C++ with a lot of math and the only math I’ve needed since college is addition and calculating percentages. However, I can have intelligent discussions about design and architecture and music UX. That’s been enough to get me a functional plugin that already does more in some respects than the original. I am (we are?) making it steadily more performant. It has only crashed twice and each time I just pasted the dump into Claude and it fixed the root cause.
Long story short: if you can verify the outcome, do you need to review the code? It helps that no one dies or gets underpaid if my audio plugin crashes. But still, you can’t tell me this isn’t remarkable. I think it’s clear there will be a massive proliferation of niche software.
In other words you can’t vibe code in an environment where evaluating “does this code work” is an existential question. This is the case where 7k LOC/day becomes terrifying.
Until we get much better at automatically proving correctness of programs we will need review.
For the record, there are examples where human code review and design guidelines can lead to very low-bug code. NASA published their internal guidelines for producing safety-critical code[1]. The problem is that the development cost of software when using such processes is too high for most companies, and most companies don't actually produce safety-critical software.
My experience with the vast majority of LLM code submitted to projects I maintain is that it has subtle bugs that I managed to find through fairly cursory human review. The copilot code review feature on GitHub also tends to miss actual bugs and report nonexistent bugs, making it worse than useless. So in my view, the death of the benefits of human code review have been wildly exaggerated.
[1]: https://en.wikipedia.org/wiki/The_Power_of_10:_Rules_for_Dev...
You're right, human review and thorough design are a poor approximation of proving assumptions about your code. Yes bugs still exist. No you won't be able to prove the correctness of your code.
However, I can pretty confidently assume that malloc will work when I call it. I can pretty confidently assume that my thoroughly tested linked list will work when I call it. I can pretty confidently assume that following RAII will avoid most memory leaks.
Not all software needs meticulous careful human review. But I believe that the compounding cost of abstractions being lost and invariants being given up can be massive. I don't see any other way to attempt to maintain those other than human review or proven correctness.
"Not all software needs meticulous careful human review" is exactly the point. The question of exactly what software needs that kind of review is one whose answer I expect to change over the next 5-10 years. We are already at the point where it's so easy to produce small but highly non-trivial one-off applications that one needn't examine the code at all -- I completely agree with the above poster that we're rapidly discovering new examples of software development where output-verification is all you need, just like right now you don't hand-inspect the machine code generated by your compiler. The question is how far that will be able to go, and I don't think anybody really knows right now, except that we are not yet at the threshold. You keep bringing up examples where the stakes are "existential", but you're underestimating how much software development does not have anything close to existential stakes.
This is the game changer for me: I don’t have to evaluate tens or hundreds of market options that fit my problem. I tell the machine to solve it, and if it works, then I’m happy. If it doesn’t I throw it away. All in a few minutes and for a few cents. Code is going the way of the disposable diaper, and, if you ever washed a cloth diaper you will know, that’s a good thing.
What happens when it seems to work, and you walk away happy, but discover three months later that your circular components don't line up because the LLM-written CAD software used an over-rounded PI = 3.14? I don't work in industrial design, but I faced a somewhat similar issue where an LLM-written component looked fine to everyone until final integration forced us to rewrite it almost entirely.
The original code "looks" fine, and it works pretty well even, but an LLM cannot avoid critical oversights along the way, and is fundamentally designed to its mistakes look as plausibly correct as possible. This makes correcting the problems down the line much more annoying (unless you can afford to live with the bugs and keep slapping on more band aids, i guess)
At one point you might take over, ask it for specific refactors you'd do but are too lazy to do yourself. Or even toss it away entirely and start fresh with better understanding. Yourself or again with agent.
And then people create non-throwaway things with it and your job, performance report, bonus, and healthcare are tied to being compared to those people who just do what management says without arguing about the correct application of the tool.
If you keep your job, it's now tied to maintaining the garbage those coworkers checked in.
I think the throwaway part is important here and people are missing it, particularly for non-programmers.
There's a lot of roles in the business world that would make great use of ephemeral little apps like this to do a specific task, then throw it away. Usually just running locally on someone's machine, or at most shared with a couple other folks in your department.
Code doesn't have to be good, hell it doesn't even have to be secure, and certainly doesn't need to look pretty. It just needs to work.
There's not enough engineering staff or time to turn every manager's pet excel sheet project into a temporary app, so LLMs make perfect sense here.
I'd go as far to say more effort should be put into ephemeral apps as a use case for LLMs over focusing on trying to use them in areas where a more permanent, high quality solution is needed.
Improve them for non-developers.
If you don't know how to analyze data, and flat out refuse to invest in learning the skill, then I guess that could be really useful. Those users are likely the ones most enthusiastic about AI. But are those users close to as productive as someone who learns a mature tool? Not even close.
Lots of people appreciate an LLM to generate boiler plate code and establish frameworks for their data structures. But that's code that probably shouldn't be there in the first place. Vibe coding a game can be done impressively quick, but have you tried using a game construction kit? That's much faster still.
It's infinitely worse when your PM / manager vibe-codes some disgusting garbage, sees that it kind of looks like a real thing that solves about half of the requirements (badly) and demands engineers ship that and "fix the few remaining bugs later".
There's a shit-ton of bad and inefficient code on the internet. Lots of it. And it was used to train these LLMs as much as the good code.
In other words, the LLMs are great if you're OK with mediocrity at best. Mediocrity is occasionally good enough, but it can spell death for a company when key parts of it are mediocre.
I'm afraid a lot of the executives who fantasize about replacing humans with AI are going to have to learn this the hard way.
And its tricky because I'm trying not to appeal to emotion despite being fascinated with how this tool has enabled me to do things in a short amount of time that it would have taken me weeks of grinding to get to and improves my communication with stakeholders. That feels world changing. Specifically my world and the day-to-day roll I play when it comes to getting things done.
I think it is fine that it fell short of your expectations. It often does for me as well but it's when it gets me 80% of the way there in less than a day's work, then my mind is blown. It's an imperfect tool and I'm sorry for saying this but so are we. Treat its imperfections in the same way you would with a JR developer- feedback, reframing, restrictions, and iterate.
Well… That's no longer true, is it?
My partner (IT analyst) works for a company owned by a multinational big corporation, and she got told during a meeting with her manager that use of AI is going to become mandatory next year. That's going to be a thing across the board.
And have you called a large company for any reason lately? Could be your telco provider, your bank, public transport company, whatever. You call them, because online contact means haggling with an AI chatbot first to finally give up and shunt you over to an actual person who can help, and contact forms and e-mail have been killed off. Calling is not exactly as bad, but step one nowadays is 'please describe what you're calling for', where some LLM will try to parse that, fail miserably, and then shunt you to an actual person.
AI is already unavoidable.
My multinational big corporation employer has reporting about how much each employee uses AI, with a naughty list of employees who aren't meeting their quota of AI usage.
The fact that companies have to force you to use it with quotas and threats is damning.
“Why don’t you just make the minimum 37 pieces of flAIr?”
It's mostly a sign leadership has lost reasoning capability if it's mandatory.
But no, reporting isn't necessarily the problem. There are plenty of places that use reporting to drive a conversation on what's broken, and why it's broken for their workflow, and then use that to drive improvement.
It's only a problem if the leadership stance is "Haha! We found underpants gnome step 2! Make underpants number go up, and we are geniuses". Sadly not as rare as one would hope, but still stupid.
All of this predates LLMs (what “AI” means today) becoming a useful product. All of this happened already with previous generations of “AI”.
It was just even shittier than the version we have today.
This is what I always think of when I imagine how AI will change the world and daily life. Automation doesn't have to be better (for the customer, for the person using it, for society) in order to push out the alternatives. If the automation is cheap enough, it can be worse for everyone, and still change everything. Those are the niches in ehich I'm most certain will be here to stay— because sometimes, it hardly matters if it's any good.
If you're lucky. I've had LLMs that just repeatedly hang up on me when they obviously hit a dead end.
AI's not exactly a step down from that.
I'd argue that's not true. It's more of a stated goal. The actual goal is to achieve the desired outcome in a way that has manageable, understood side effects, and that can be maintained and built upon over time by all capable team members.
The difference between what business folks see as the "output" of software developers (code) and what (good) software developers actually deliver over time is significant. AI can definitely do the former. The latter is less clear. This is one of the fundamental disconnects in discussions about AI in software development.
I'm going to say this next thing as someone with a lot of negative bias about corporations. I was laid off from Twitter when Elon bought the company and at a second company that was hemorrhaging users.
Our job isn't to write code, it's to make the machine do the thing. All the effort for clean, manageable, etc is purely in the interest of the programmer but at the end of the day, launching the feature that pulls in money is the point.
Maybe I'm not understanding you're point, but this is the kind of thing that happens in software teams all the time and is one of those "that's why they call it work" realities of the job.
If something "seems right/passed review/fell apart" then that's the reviewer's fault right? Which happens, all the time! Reviewers tend to fall back to tropes and "is there tests ok great" and whatever their hobbyhorses tend to be, ignoring others. It's ok because "at least it's getting reviewed" and the sausage gets made.
If AI slashed the amount of time to get a solution past review, it buys you time to retroactively fix too, and a good attitude when you tell it that PR 1234 is why we're in this mess.
No, it's the author's fault. The point of a code review is not to ensure correctness, it is to improve code quality (correctness, maintainability, style consistency, reuse of existing functions, knowledge transfer, etc).
If everyone on your team is doing that, it's not long before huge chunks of your codebase are conceptually like stuff that was written a long time ago by people who left the company. Except those people may have actually known what they were doing. The AI chatbots are generating stuff that seems to plausibly work well enough based on however they were prompted.
There are intangible parts of software development that are difficult to measure but incredibly valuable beyond the code itself.
> Our job isn't to write code, it's to make the machine do the thing. All the effort for clean, manageable, etc is purely in the interest of the programmer but at the end of the day, launching the feature that pulls in money is the point.
This could be the vibe coder mantra. And it's true on day one. Once you've got reasonably complex software being maintained by one or more teams of developers who all need to be able to fix bugs and add features without breaking things, it's not quite as simple as "make the machine do the thing."
I mean this in sincerity, and not at all snarky, but - have you considered that you haven't used the tools correctly or effectively? I find that I can get what I need from chatbots (and refuse to call them AI until we have general AI just to be contrary) if I spend a couple of minutes considering constraints and being careful with my prompt language.
When I've come across people in my real life who say they get no value from chatbots, it's because they're asking poorly formed questions, or haven't thought through the problem entirely. Working with chatbots is like working with a very bright lab puppy. They're willing to do whatever you want, but they'll definitely piss on the floor unless you tell them not to.
Or am I entirely off base with your experience?
I prefer to use LLM as a sock puppet to filter out implausible options in my problem space and to help me recall how to do boilerplate things. Like you, I think, I also tend to write multi-paragraph prompts repeating myself and calling back on every aspect to continuously hone in on the true subject I am interested in.
I don't trust LLM's enough to operate on my behalf agentically yet. And, LLM is uncreative and hallucinatory as heck whenever it strays into novel territory, which makes it a dangerous tool.
The problem is that this comes off just as tone-deaf as "you're holding it wrong." In my experience, when people promote AI, its sold as just having a regular conversation and then the AI does thing. And when that doesn't work, the promoter goes into system prompts, MCP, agent files, etc and entire workflows that are required to get it to do the correct thing. It ends up feeling like you're being lied to, even if there's some benefit out there.
There's also the fact that all programming workflows are not the same. I've found some areas where AI works well, but a lot of my work it does not. Usually things that wouldn't show up in a simple Google search back before it was enshittified are pretty spotty.
Then there’s people like me, who you’d probably term as an old soul, who looks at all that and says, “I have to change my workflow, my environment, and babysit it? It is faster to simply just do the work.” My relationship with tech is I like using as little as possible, and what I use needs to be predictable and do something for me. AI doesn’t always work for me.
This is almost the complete opposite of my experience. I hear expressions about improvements and optimism for the future, but almost all of the discussion from active people productivly using AI is about identifying the limits and seeing what benefits you can find within those limits.
They are not useless and they are also not a panacea. It feels like a lot of people consider those the only available options.
It can't reason from first principles and there isn't training data for a lot of state-of-the-art computer science and code implementations. Nothing you can prompt will make it produce non-naive output because it doesn't have that capability.
AI works for a lot of things because, if we are honest, AI generated slop is replacing human generated slop. But not all software is slop and there are software domains where slop is not even an option.
I think I have a good idea how these things work. I have run local LLMs for a couple of years on a pair of video cards here, trying out many open weight models. I have watched the 3blue1brown ML course. I have done several LinkedIn Learning courses (which weren't that helpful, just mandatory). I understand about prompting precisely and personas (though I am not sold personas are a good idea). I understand LLMs do not "know" anything, they just generate the next most likely token. I understand LLMs are not a database with accurate retrieval. I understand "reasoning" is not actual thinking just manipulating tokens to steer a conversation in vector space. I understand LLMs are better for some tasks (summarisation, sentiment analysis, etc) than others (retrieval, math, etc). I understand they can only predict what's in their training data. I feel I have a pretty good understanding of how to get results from LLMs (or at least the ways people say you can get results).
I have had some small success with LLMs. They are reasonably good at generating sub-100 line test code when given a precise prompt, probably because that is in training data scraped from StackOverflow. I did a certification earlier this year and threw ~1000 lines of Markdown notes into Gemini and had it quiz me which was very useful revision, it only got one question wrong of the couple of hundred I had it ask me.
I'll give a specific example of a recent failure. My job is mostly troubleshooting and reading code, all of which is public open source (so accessible via LLM search tooling). I was trying to understand something where I didn't know the answer, and this was difficult code to me so I was really not confident at all in my understanding. I wrote up my thoughts with references, the normal person I ask was busy so I asked Gemini Pro. It confidently told me "yep you got it!".
I asked someone else who saw a (now obvious) flaw in my reasoning. At some point I'd switched from a hash algorithm which generates Thing A, to a hash algorithm which generates Thing B. The error was clearly visible, one of my references had "Thing B" in the commit message title, which was in my notes with the public URL, when my whole argument was about "Thing A".
This wasn't even a technical or code error, it was a text analysis and pattern matching error, which I didn't see because I was so focused on algorithms. Even Gemini, the apparent best LLM in the world which is causing "code red" at OpenAI did not pick this up, when text analysis is supposed to be one of its core functionalities.
I also have a lot of LLM-generated summarisation forced on me at work, and it's often so bad I now don't even read it. I've seen it generate text which makes no logical sense and/or which uses so many words without really saying anything at all.
I have tried LLM-based products where someone else is supposed to have done all the prompt crafting and added RAG embeddings and I can just behave like a naive user asking questions. Even when I ask these things question which I know are in the RAG, they cannot retrieve an accurate answer ~80% of the time. I have read papers which support the idea that most RAG falls apart after about ~40k words and our document set is much larger than that.
Generally I find LLMs are at the point where to evaluate the LLM response I need to either know the answer beforehand so it was pointless to ask, or I need to do all the work myself to verify the answer which doesn't improve my productivity at all.
About the only thing I find consistently useful about LLMs is writing my question down and not actually asking it, which is a form of Rubber Duck Debugging (https://en.wikipedia.org/wiki/Rubber_duck_debugging) which I have already practiced for many years because it's so helpful.
Meanwhile trillions of dollars of VC-backed marketing assures me that these things are a huge productivity increaser and will usher in 25% unemployment because they are so good at doing every task even very smart people can do. I just don't see it.
If you have any suggestions for me I will be very willing to look into them and try them.
More precisely:
In one side, it's the "tools that build up critical mass" philosophy. AI firmly resides here.
On the other, it's the "all you need is brain and plain text" philosophy. We don't see much AI in this camp.
One thing I learned is that you should never underestimate the "all you need is brain and plain text" camp. That philosophy survived many, many "fatal blows" and has come up on top several times. It has one unique feature: resilience to bloat, something that the current smart tools camp is obviously overlooking.
I think the feeling stems from the exaggeration of the value it provides combined with a large number of internal corporate LLMs being absolute trash.
The overvaluation is seen in effect everywhere from the stock market, the price of RAM, the cost of energy as well as IP theft issues etc etc. AI has taken over and yet it still feels like just a really good fuzzy search. Like yeah I can search something 10x faster than before but might get a bad answer every now and then.
Yeah its been useful (so have many other things). No it's not worth building trillion dollar data centers for. I would be happier if the spend went towards manufacturing or semiconductor fabs.
It feels awkward living in the "LLMs are a useful tool for some tasks" experience. I suspect this is because the two tribes are the loudest.
Simple. The company providing the tool needs actual earning suddenly. Therefore, they need to raise the prices. They also need users to spend more tokens, so they will make the tool respond in a way that requires more refinement. After all, the latter is exactly what happened with google search.
At this point, that is pretty normal software cycle - try to attract crowd by being free or cheap, then lock features behind paywall. Then simultaneously raise prices more and more while making the product worst.
This literally NEEDS to happen, because these companies do not have any other path to profitability. So, it will happen at some point.
It’s going to definitely be crappy, remember Google in 2003 with relevant results and no endless SEO , or Amazon reviews being reliable, or Uber being simple and cheap, etc. once growth phase ends monetization begins and experience declines but this is guard railed by the fact that there are many players.
They are competing themselves into massive unprofitability. Eventually they will die or do the above in cooperation. Maybe there will bw minor snandal about it, but that sort of collution is not prosecuted or seriously investigated if done by big companies.
So, it will happen exactly as it always happens with tech.
To my mind, the 'only just started' argument is wearing off. It's software, it moves fast anyway, and all the giants of the tech world have been feverishly throwing money at AI for the last couple of years. I don't buy that we're still just at the beginning of some huge exponential improvement.
no, they are currently losing money on inference too
... but maybe not in the way that these CEOs had hoped.[0]
Part of the AI fatigue is that busy, competent devs are getting swarmed with massive amounts of slop from not-very-good developers. Or product managers getting 5 paragraphs of GenAI bug reports instead of a clear and concise explanation.
I have high hopes for AI and think generative tooling is extremely useful in the right hands. But it is extremely concerning that AI is allowing some of the worst, least competent people to generate an order of magnitude more "content" with little awareness of how bad it is.
that is a real issue and yet a normal problem and so has an obvious response.
oh wow that PR
I don't recognize that because it isn't true. I try the LLMs every now and then, and they still make the same stupid hallucinations that ChatGPT did on day 1. AI hype proponents love to make claims that the tech has improved a ton, but based on my experience trying to use it those claims are completely baseless.
One of the tests I sometimes do of LLMs is a geometry puzzle:
You're on the equator facing south. You move forward 10,000 km along the surface of the Earth. You are rotate 90° clockwise. You move another 10,000 km forward along the surface of the earth. Rotate another 90° clockwise, then move another 10,000 km forward along the surface of the Earth.
Where are you now, and what direction are you facing?
Anecdotes are of course a bad way to study this kind of thing.
Unfortunately, so are the benchmarks, because the models have quickly saturated most of them, including traditional IQ tests (on the plus side, this has demonstrated that IQ tests are definitely a learnable skill, as LLMs loose 40-50 IQ points when going from public to private IQ tests) and stuff like the maths olympiad.
Right now, AFAICT the only open benchmarks are the METR time horizon metric, the ARC-AGI family of tests, and the "make me an SVG of ${…}" stuff inspired by Simon Willison's pelican on a bike.
FWIW, Claude Opus 4.5 gets this right for me, assuming that is the intended answer. On request, it also gave me a Mathematica program which (after I fixed some trivial exceptions due to errors in units) informs me that using the ITRF00 datum the actual answer is 0.0177593 degrees north and 0.168379 west of where you started (about 11.7 miles away from the starting point) and your rotation is 89.98 degrees rather than 90.
(ChatGPT 5.1 Thinking, for me, get the wrong answer because it correctly gets near the South Pole and then follows a line of latitude 200 times round the South Pole for the second leg, which strikes me as a flatly incorrect interpretation of the words "move forward along the surface of the earth". Was that the "usual error they all used to make"?)
Or anything close to it so long as the logic is right, yes. I care about the reasoning failure, not the small difference between the exact quarter-circumferences of these great circles and 10,000km; (Not that it really matters, but now you've said the answer, this test becomes even less reliable than it already was).
> FWIW, Claude Opus 4.5 gets this right for me, assuming that is the intended answer.
Like I said, now the best ones sometimes don't [always get it wrong].
For me yesterday, Claude (albeit Sonnet 4.5, because my testing is cheap) avoided the south pole issue, but then got the third leg wrong and ended up at the north pole. A while back ChatGPT 5 (I looked the result up) got the answer right, yesterday GPT-5-thinking-mini (auto-selected by the system) got it wrong same way as you report on the south pole but then also got the equator wrong and ended up near the north pole.
"Never" to "unreliable success" is still an improvement.
(use-modules (geo vincenty))
(let walk ((p '(0 0 180))
(i 0))
(cond ((= i 3)
(display p)
(newline))
(else
(walk (apply vincenty
(list (car p) (cadr p) (+ 90 (caddr p)) 10000000))
(+ i 1)))))
(0.01777744062090717 0.16837322410251268 179.98234155229127)
How does that bridge get built? I can provide tangible real life examples but I've found push back from that in other online conversations.
I do also try and use Claude Code for certain tasks. More often than not, i regret it, but I've started to zero in on tasks it's helpful with (configuration and debugging, not so much coding).
But it's very easy then for me to hear people saying that AI gives them so much useful code, and for me to assume that they are like my boss: not examining that code carefully, or not holding their output to particularly high standards, or aren't responsible for the maintenance and thus don't need to care. That doesn't mean they're lying, but it doesn't mean they're right.
Well, some non-zero amount of you are probably very financially invested in AI, so lying is not out of the question
Or you simply have blinders on because of your financial investments. After all, emotional investment often follows financial investment
Or, you're just not as good as you think you are. Maybe you're talking to people who are much better at building software than you are, and they find the stuff the AI builds does not impress them, while you are not as skilled so you are impressed by it.
There are lots of reasons someone might disagree without thinking everyone else is lying
This will cause bankruptcies and huge job losses. The argument for and against AI doesn't really matter in the end, because the finances don't make a lick of sense.
With AI I can... generate slop. Sometimes that is helpful, but it isn't yet at the point where it's replacing anything for me aside from making google searches take a bit less time on things that I don't need a definitive answer for.
It's popping up in my music streams now and then, and I generally hate it. Mushy-mouthed fake vocals over fake instruments. It pops up online and aside from the occasional meme I hate it there too. It pops up all over blogs and emails and I profoundly hate it there, given that it encourages the actual author to silence themselves and replaces their thoughts with bland drivel.
Every single software product I use begs me to use their AI integration, and instead of "no" I'm given the option of "not now", despite me not needing it, and so I'm constantly being pestered about it by something.
It has, thus far, made nearly everything worse.
I think this is probably the disconnect, this seems so wildly different from my experience. Not only that, I’ll grant that there are a ton of limitations still but surely you’d concede that there has been an incredible amount of progress in a very short time? Like I can’t imagine someone who sits down with Claude like I do and gets up and says “this is crap and a fad and won’t go anywhere”.
As for generated content, I again agree with you and you’d be surprised to learn that _execs_ agree with you but look at models from 1, 2, 3 years ago and tell me you don’t see a frightening progression of quality. If you want to say “I’ll believe it when I see it” that’s fine but my god just look at the trajectory.
For AI slop text, once again agree, once again I think we all have to figure out how to use it, but it is great for e.g. helping me rewrite a wordy message quickly, making a paper or a doc more readable, combining my notes into something polished, etc, and it’s getting better and better and better.
So I disagree it has made everything worse but I definitely agree that it has made a lot of things worse and we have a lot of Pets.com ideas that are totally not viable today, but the point I think people are maybe missing (?) is that it’s not about where we are it’s about the velocity and the future. You may be terrified and nauseated by $1T in capex on AI infra, fine but what that tells you is the scale is going to grow even further _in addition_ to the methodological / algorithmic improvements to tackle things like continual learning, robustness, higher quality multimodal generation with e.g. true narrative consistency, etc etc etc. in 5 years I don’t think many people will think of “slop” so negatively
A similar thing played out a bit with IoT and voice controlled systems like Alexa. They've got their places, but nobody needs or wants the Amazon Dash buttons, or for Alexa to do your shopping for you.
Setting an alarm or adding a note to a list is fine, remote monitoring is fine, but when it comes to things that really matter like spending money autonomously, it completely falls flat.
Long story short, I see a fad that will fall into the background of what people actually do, rather than becoming the medium that they do it by.
It’s because they know it works better every day and the people controlling it are gleefully fucking over the rest of the world because they can.
The plainly stated goal is TO ELIMINATE ALL HUMAN EMPLOYEES, with no plan for how those people will feed, clothe, or house themselves.
The reactions the author was getting was the reaction of a horse talking to someone happily working for the glue factory.
Example: you might spend less time on initial development, but more time on code review and rework. That has been my personal experience.
This falls in the category of life skills or maybe just "adulting." Sure, maybe ChatGPT can be considered a life skill, but you need others compiled into your brain to fall back on when it fails. If ChatGPT is the only skill you have, what do you do if your phone gets stolen?
Would you say the same to someone using Google?
"Sure, maybe Google can be considered a life skill, but you need others compiled into your brain to fall back on when it fails. If Google is the only skill you have, what do you do if your phone gets stolen?"
It's a sad commentary on the state of search results and the Internet now that ChatGPT is superior, particularly since pre-knowledge-panel/AI-overview Google was superior in several ways (not hallucinating, for one, and being able to triangulate multiple sources to tell the truth).
In e.g. the US, it's a huge net negative because kids aren't probably taught these values and the required discipline. So the overwhelming majority does use it to cheat the learning process.
I can't tell you if this is the same inside e.g. China. I'm fairly sure it's not nearly as bad though as kids there derive much less benefit from cheating on homework/the learning process, as they're more singularly judged on standardized tests where AI is not available.
Promoting dependency is the problem. Replacing effort is the problem. Making self-discipline be a thing only for suckers is the problem.
That's not to say that a chatbot couldn't emulate a tutor. I don't know how successful it would be, but it seems like a promising idea. In actual practice, that is not how students are using them today. (And I'd bet that if you did have a tutor chatbot, that most students would learn much more about jailbreaking them to divulge answers than they would about the subject matter.)
As for this idea that replacing effort not being a problem, I suggest you do some research because that is everywhere. Talk to a teacher. Or a psychologist, where they call it "depth of processing" (which is a primary determinant of how much of something is incorporated, alongside frequency of exposure). Or just go to a gym and see how many people are getting stronger by paying 24/7 brilliant private weightlifters to do the lifting for them.
My pushback is its very easy to tell a chatbot to give you hints that lead to the answer and to get deeper understanding by asking follow up questions if that's what you want. Cheating vs putting in work has always been something students have to choose between though and I don't think AI is going to change the amount of students making each choice (or if it does it won't be by a huge percentage). The gap in skills between the groups will grow, but there will still be a group of people that became skilled because they valued education and a group that cheated and didn't learn anything.
An LLM's job is not to give the child the answer (implying "the answer to some homework/exam question"), it's to answer the question that was asked. A huge difference. If you ask it to ask a question, it will do so. Over the next 24 hours as of today, December 5th 2025, hundreds of thousands of people will write a prompt that includes exactly that - "ask me questions".
> Learning can still happen, but only if the child forces it themselves.
This is literally what my original comment said, although "forcing" it is pure negative of a framing; rather "learning can still happen, if the child wants to". See this:
>In e.g. the US, it's a huge net negative because kids aren't probably taught these values and the required discipline. So the overwhelming majority does use it to cheat the learning process.
I never claimed that replacing effort isn't necessarily a problem either, just that such a downside has never been brought up in the context of access to a brilliant tutor, yet suddenly an impossible-to-overcome issue when it comes to LLMs.
# The truly good teachers were primarily motivation agents, providing enough content, but doing so in a way that meant I fully engaged.
LLMs were great for getting started though. If you've never tried writing before, then learning a few patterns goes a long way. ("He verbed, verbing a noun.")
Kids growing up today are using AI for everything, whether or not that's sanctioned or if it's ultimately helpful or harmful to their intellectual growth. I think the jury is still out on that. But I do remember growing up in the 90s, spending a lot of time on the computer, older people would remark how I'll have no social skills, I won't be able to write cursive or do arithmetic in my head, won't learn any real skills, etc, turns out I did just fine and now those same people always have to call me for help when they run into the smallest issue with technology.
I think a lot of people here are going to become roadkill if they refuse to learn how to use these new tools. I just built a web app in 3 weeks with only prompts to Claude Code, I didn't write a single line of code, and it works great. It's pretty basic, but probably would have taken me 3+ months instead of 3 weeks doing it the old fashioned way. If you tried it once a year ago and have written it off, a lot has changed since then and the tools continue to improve every month. I really think that eventually no one will be checking code just like hardly anyone checks the assembly output of a compiler anymore.
You have to understand how the context window works, how to establish guardrails so you're not wasting time repeating the same things over and over again, force it to check its own work with lots of tests, etc. It's really a game changer when you can just say in one prompt "write me an admin dashboard that displays users, sessions, and orders with a table and chart going back 30 days" or "wire up my site for google analytics, my tag code is XXXXXXX" and it just works.
Well either it's bad at it, or everyone on my team is bad at prompting. Given how dedicated my boss has been to using Claude for everything for the past year and the output continuing to be garbage, though, i don't think it's a lack of effort on the team's part, i have to believe Claude just isn't good at my job.
Not attempting to claim anything against your company, but I've worked for enterprises where code bases were a complete mess and even the product itself didn't have a clear goal. That's likely not the ideal candidate for AI systems to augment.
I have been mostly been paid to work on AI projects since 1982, but I want to pull my hair out and scream over the big push in the USA to develop super-AGI. Such a waste of resources and such a hit on society that needs resources used for better purposes.
That said, game engine documentation is often pretty hard to navigate. Most of the best information is some YouTube video recorded by some savant 15 year old with a busted microphone. And you need to skim through 30 minutes of video until you find what you need. The biggest problem is not knowing what you don't know, so it's hard to know where to begin. There are a lot of things you may think you need to spend 2 days implementing, but the engine may have a single function and a couple built in settings to do it.
Where LLMs shine is that I can ask a dumb question about this stuff, and can be pointed in the right direction pretty quickly. The implementation it spits out is often awful (if not unusable), but I can ask a question and it'll name drop the specific function and setting names that'll save me a lot of work. And from there, I know what to look up and it's a clear path from there.
And gamedev is a very strong case of not needing a correct solution. You just need things to feel right for most cases. Games that are rough around the edges have character. So LLM assistance for implementation (not art) can be handy.
Don't people learn from imperfect teachers all the time?
AI can be effective for learning a new skill, but you have to be constantly on your guard to prevent it from hacking your brain and making you helpless and useless. AI isn't the parent holding your bicycle and giving you a push and letting go when you're ready. It's the welded-on training wheels that become larger and more structurally necessary until the bike can't roll forward at all without them. It feeds you the lie that all you need is the theory, you don't ever need to apply it because the AI will do that for you so don't worry your pretty little head over it. AI teaches you that if something requires effort, you're just not relying on the AI enough. The path to success goes only through AI, and those people who try to build their own skills without it are suckers because the AI can effortlessly create things 100x bigger and better and more complex.
Personally, I still believe that human + AI hybrids have enormous potential. It's just that using AI constantly pushes away from beneficial hybridization and towards dependency. You have to constantly fight against your innate impulses, because it hacks them to your detriment.
I'd actually like to see an AI trained to not give answers, but to search out the point where they get you 90% of the way there and then steadfastly refuse to give you the last 10%. An AI built with the goal not of producing artifacts or answers, but of producing learning and growth in the user. (Then again, I'd like to see the same thing in an educational system...)
That was true in chess for a long time, but since at least 20 years or so, approximately anytime the human deviates from what the AI suggests, it's a mistake.
Even things that AI has gotten best at, like coding, are nowhere near that category yet. AI-written text and code is still crap compared to what humans can write. Both can often superficially look better, but the closer you look and the less a human guided it, the worse you discover it is.
Chess bots could beat the vast majority of humans at their game long before they could beat the world champion.
Similarly, AI generated code and text and images etc are getting more and more competitive with what regular humans can produce. Especially if you take speed and cost into account.
But the shockwave will cause a huge recession and all those investors that put up trillions will not take their losses. Rich people never get poorer. One way or another us consumers will end up paying for their mistakes. Either by huge inflation, job losses, energy costs, service enshittification whatever. We're already seeing the memory crisis having huge knock on effects with next year's phones being much more expensive. That's one of the ways we are going to be paying for this circus.
I really see value in it too, sure. But the amount of investment that goes into it is insane. It's not that valuable by far. LLMs are not good for everything and the next big thing is still a big question mark. AI is dragged in by the hair into usecases where it doesn't belong. The same shit we saw with blockchains, but now on a world crashing scale. It's very scary seeing so much insanity.
But anyway whatever I think doesn't matter. Whatever happens will happen.
This includes IME the initial stages of art creation (the planning, not generating, stage). It's kind of like having someone to bounce ideas off of at 3am. It's a convenient way of trigging your own brain to be inspired.
> none of it had anything to do with what I built. She talked about Copilot 365. And Microsoft AI. And every miserable AI tool she's forced to use at work. My product barely featured. Her reaction wasn't about me at all. It was about her entire environment.
She was given two context clues. AI. And maps. Maps work, which means all the information in an "AI-powered map" descriptor rests on the adjective.
LLMs are always going to give you the most plausible thing for your query, and will likely just rehash the same destinations from hundreds of listicles and status signalling social media posts.
She probably understood this from the minimal description given.
I tried this in Crotone in September. The suggested walking tour was shit. The facts weren't remarkable. The stops were stupid and stupidly laid out. The whole experience was dumb and only redeeming because I was vacationing with a friend who founded on the of the AI companies.
> if you asked anyone knowledgable about travel in that region, the counter questions would be 'Why Venice specifically?
In the region? Because it's a gorgeous city with beautiful architecture, history and festivals?
That would be a great answer to continue from. Would you come for the Biennale specifically? Do you care greatly about sustainability? Would you enjoy yourself more in a different gorgeous city without the mass-tourism problem if that meant you would feel more welcome? Is there a way you can visit Venice without contributing to the issue as much? Off-season perhaps?
Venice is unique, but there are a lot of gorgeous places in the region, from Verona to Trieste.
No, you don't have to avoid Rome — it's not as bad as Venice, and can support more people — but plan ahead and don't just do a tour of all the 'must see' highlights. Look into the off season if you are a history buff with a hyperfocus on Rome — you won't be able to finish your list otherwise due to all the pointless waiting around.
And yes, visit provincial villages and eat in an authentic Italian restaurant where tourists are mostly other Italians. Experience the difference. But you are not limited to villages. Italy is huge, and there are a lot of cities with remarkable museums, world-renowned festivals, great cuisine, and where your money is more than welcome and your stay won't be marred by extreme crowds and pushy con artists in faux Roman gladiator gear.
I don't know who first uses the asbestos analogy, but it's 1000% on point.
I think Cory Doctrow says it best,
"AI is the asbestos we're shoveling into the walls of our society — and our descendants will be digging it out for generations."
I believe that's exactly the language to combat AI hype.
It hits weirdly close to home. Our leadership did not technically mandate use, but 'strongly encourages' it. I did not even have my review yet, but I know that once we get to the goals part, use of AI tools will be an actual metric ( which is.. in my head somewhere between skeptic and evangelist.. dumb ).
But the 'AI talent' part fits. For mundane stuff like data model, I need full committee approval from people, who don't get it anyway ( and whose entire contribution is: 'what other companies are doing' ).
I think it makes some amount of sense if you've decided you want to be "an AI company", but it also makes me wary. Apocryphally Google for a long period of time struggled to hire some people because they weren't an 'ideal culture fit'. i.e. you're trying to hire someone to fix Linux kernel bugs you hit in production, but they don't know enough about Java or Python to pass the interview gauntlet...
Quite the opposite: LLMs reduce productivity, they don't increase it. They merely give the illusion of productivity because you can generate code real fast, but that isn't actually useful when you spend time fixing all the mistakes it made. It is absolutely insane that companies are stupid enough to require people use something which cripples them.
It doesn't matter how much I use it. It's still just an annoying tool that makes mistakes which you try to correct by arguing with it but then eventually just fix it yourself. At best it can get you 80% there.
---------
"If you could classify your project as "AI," you were safe and prestigious. If you couldn't, you were nobody. Overnight, most engineers got rebranded as "not AI talent." And then came the final insult: everyone was forced to use Microsoft's AI tools whether they worked or not.
Copilot for Word. Copilot for PowerPoint. Copilot for email. Copilot for code. Worse than the tools they replaced. Worse than competitors' tools. Sometimes worse than doing the work manually.
But you weren't allowed to fix them—that was the AI org's turf. You were supposed to use them, fail to see productivity gains, and keep quiet.
Meanwhile, AI teams became a protected class. Everyone else saw comp stagnate, stock refreshers evaporate, and performance reviews tank. And if your team failed to meet expectations? Clearly you weren't "embracing AI." "
------------
On the one hand, if you were going to bet big on AI, there are aspects of this approach that make sense. e.g. Force everyone to use the company's no-good AI tools so that they become good. However, not permitting employees outside of the "AI org" to fix things neatly nixes the gains you might see while incurring the full cost.
It sounds like MS's management, the same as many other tech corp's, has become caught up in a conceptual bubble of "AI as panacea". If that bubble doesn't pop soon, MS's products could wind up in a very bad place. There are some very real threats to some of MS's core incumbencies right now (e.g. from Valve).
There will absolutely some cases where AI is used well. But probably the larger fraction will be where AI does not give better service, experience or tool. It will be used to give a cheaper but shittier one. This will be a big win for the company or service implementing it, but it will suck for literally everybody else involved.
I really believe there's huge value in implementing AI pervasively. However it's going to be really hard work and probably take 5 years to do it well. We need to take an engineering and human centred approach and do it steadily and incrementally over time. The current semi-religious fervour about implementing it rapidly and recklessly is going to be very harmful in the longer term.
This is a product of hurt feelings and not solid logic.
My first reaction was "replace 'AI' with the word 'Cloud'" ca 2012 at MS; what's novel here?
With that in mind, I'm not sure there is anything novel about how your friend is feeling or the organizational dynamics, or in fact how large corporations go after business opportunities; on those terms, I think your friends' feelings are a little boring, or at least don't give us any new market data.
In MS in that era, there was a massive gold rush inside the org to Cloud-ify everything and move to Azure - people who did well at that prospered, people who did not, ... often did not. This sort of internal marketplace is endemic, and probably a good thing at large tech companies - from the senior leadership side, seeing how employees vote with their feet is valuable - as is, often, the directional leadership you get from a Satya who has MUCH more information than someone on the ground in any mid-level role.
While I'm sure there were many naysayers about the Cloud in 2012, they were wrong, full stop. Azure is immensely valuable. It was right to dig in on it and compete with AWS.
I personally think Satya's got a really interesting hyper scaling strategy right now -- build out national-security-friendly datacenters all over the world -- and I think that's going to pay -- but I could be wrong, and his strategy might be much more sophisticated and diverse than that; either way, I'm pretty sure Seattleites who hate how AI has disrupted their orgs and changed power politics and winners and losers in-house will have to roll with the program over the next five years and figure out where they stand and what they want to work on.
Satya mentioned recently that computer use agents use like 5x the windows license time on azure over a single person - they see a lotttt of inference growth coming and its multiplicative in that it uses their compute and azure infra.
And it turns out that there are some embarrassingly solved problems, like rudimentary multiplayer games, that look more impressive than they really are when you get down to it.
More challenging prompts like "change the surface generation algorithm my program uses from Marching Cubes to Flying Edges", for which there are only a handful of toy examples, VTK's implementation, and the paper, result in an avalanche of shit. Wasted hours, quickly becoming wasted days.
1) "AI" is in many ways like the unreliable coworker so many of us have had in the past - maybe someone who talked a good game in interviews, but after you'd worked with them for a while you realize that you have to double-check everything they do for stupid/careless problems. In the worst case, you also have to do some hand-holding as they ask you for help with things that they should know how to do. They can produce good output but they can't be trusted to produce good (or even marginal) output so they're a net time sink.
2) In a frightening number of companies right now, that problem coworker is the owner's or manager's relative and cannot be avoided.
So boom, there you go, bad coworkers and a toxic culture that not just protects but promotes them.
I live in Seattle, and got laid off from Microsoft as a PM in Jan of this year.
Tried in early 2024 to demonstrate how we could leverage smaller models (such as Mixtral) to improve documentation and tailor code samples for our auth libraries.
The usual “fiefdom” politics took over and the project never gained steam. I do feel like I was put in a certain “non-AI” category and my career stalled, even though I took the time to build AI-integrated prototypes and present them to leadership.
It’s hard to put on a smile and go through interviews right now. It feels like the hard-earned skills we bring to the table are being so hastily devalued, and for what exactly?
So what's different between Seattle and San Francisco? Does Seattle have more employee-workers and San Francisco has more people hustling for their startup?
I assume Bali (being a vacation destination) is full people who are wealthy enough to feel they're insulated from whatever will happen.
Seattle has more “normal” people and the overall rhetoric about how life “should be” is in many ways resistant to tech. There’s a lot to like about the city, but it absolutely does not have a hustle culture. I’ve honestly found it depressing coming from the East Coast.
Tangent aside, my point is that Seattle has far more of a comparison ground of “you all are building shit that doesn’t make the world better, it just devalues the human”. I think LLMs have (some) strong use cases, but it is impossible to argue that some of the societal downsides we see aren't ripe for hatred - and Seattle will latch on to that in a heartbeat.
Edit: are -> aren't. Stupid autocorrect.
Anyway. I think you're spot on with the "you all are building shit that doesn't make the world better, it just devalues the human" vibe. Regardless of what employers in WA may force folks to build, that's the mentality here, and AI evangelists don't make many friends... nor did blockchain evangelists, or evangelists of any of the spin-off hype trains ("Web3", NFTs, etc). I guess the "cloud" hype train stuck here, but that happened before I moved out west.
Working for a month out of Bali was wonderful, it's mostly Australians and Dutch people working remotely. Especially those who ran their own businesses were super encouraging (though maybe that's just because entrepreneurs are more supportive of other entrepreneurs).
> I wanted her take on Wanderfugl, the AI-powered map I've been building full-time.
this seems to me like pretty obvious engagement-bait / stealth marketing - write a provocative blog post that will get shared widely, and some fraction of those people will click through to see what the product is all about.
but, apparently it's working because this thread is currently at 400+ comments after 3 hours.
I think its definitely stronger in MS as my friend on the inside tells me, than most places.
There are alot of elements to it, one being profits at all costs, the greater economy, FOMO, and a resentment of engineers and technical people who have been practicing, what execs i can guess only see as alchemy, for a long time. They've decided that they are now done with that and that everyone must use the new sauce, because reasons. Sadly until things like logon buttons dis-appear and customers get pissed, it won't self-correct.
I just wish we could present the best version of ourselves and as long as deadlines are met, it'll all work out, but some have decided for scorched-earth. I suppose its a natural reaction to always want to be on the cutting edge, even before the cake has left the oven.
For now, the human dissenters have a lot of leverage because AI still makes very clear and obvious errors sometimes, and it would be a political nightmare for a decision-maker to be accused of erring on the side of AI by recognized human experts who dissented. I wonder if there will come a time that AI opinion would be on par or even favored over the human expert because the human would be considered more fallible. This doesn't even have to be true - it only has to be sufficiently perceived to be sufficiently true.
I'm not sure why. I don't think it's access to capital, but I'd love to hear thoughts.
Microsoft is the same, a generally very practical company just trying to practical company stuff.
All the guys that made their bones, vested and rested and now want to turn some of that windfall into investments likely don't have the kind of risk tolerance it takes to fund a potential unicorn. All smart people I'm sure, smart enough to negotiate big windfalls from ms/az but far less risk tollerant than a guy in SF who made their investment nestegg building some risky unicorn.
I'm being course, but like... it is though.
Second, engineering and innovation are two different categories. Most of engineering is about ... making things work. Fixing bugs, refactoring fragile code, building new features people need or want. Maybe AI products would be hated less if they were just a little less about being able to pretend they are an innovation and just a little more about making things works.
Trying hiring and retaining that solid group of engineers if you are a small/mid sized company without FAANG-level resources to offer.
So what if "everyone in Seattle hates AI"? What gives The Author the right to simultaneously invalidate Seattle's comparatively immeasurably larger advantage in experience, qualification, and education? If even the ludicrously biased title had even the barest hint of truth to it, they've stacked the deck against themselves in credibility unless they've already mentally biased themselves to blindly dismiss anyone that doesn't mirror their own now blatant fanaticism. Which we've already established now includes all of Seattle.
So put this out on the curb with the rest of the garbage meant to inflame and divide, because on it's face it is neither reasonable nor factual.
Visual Studio is great. IntelliSense is great. Nothing open-source works on our giant legacy C++ codebase. IntelliSense does.
Claude is great. Claude can't deal with millions of lines of C++.
You know what would be great? If Microsoft gave Claude the ability to semantic search the same way that I can with Ctrl-, in Visual Studio. You know what would be even better? If it could also set breakpoints and inspect stuff in the Debugger.
You know what Microsoft has done? Added a setting to Visual Studio where I can replace the IntelliSense auto-complete UI, that provides real information determined from semantic analysis of the codebase and allows me to cycle through a menu of possibilities, with an auto-complete UI that gives me a single suggestion of complete bullshit.
Can't you put the AI people and the Visual Studio people in a fucking room together? Figure out how LLMs can augment your already-really-good-before-AI product? How to leverage your existing products to let Claude do stuff that Claude Code can't do?
I expect it to settle out in a few years where: 1. The fiduciary duties of company shareholders will bring them to a point of stopping to chase AI hype and instead derive an understanding of whether it's driving real top-line value for their business or not. 2. Mid to senior career engineers will have no choice but to level up their AI skills to stay relevant in the modern workforce.
Again a somewhat positive term (if you focus on "back to nature" and ignore the nationalist parts) is taken, assimilated and turned on its head.
AI is being forced down peoples' throat. Millions want to but cannot disable Gemini from Gmail. Many SWEs don't want to use AI tools but managers are forcing them to do so.
How do you know if something is really liked/needed/wanted when there is no opt-out?
"I said, Imagine how cool would this be if we had like, a 10-foot wall. It’s interactive and it’s historical. And you could talk to Martin Luther King, and you could say, ‘Well, Dr, Martin Luther King, I’ve always wanted to meet you. What was your day like today? What did you have for breakfast?’ And he comes back and he talks to you right now."
Oddly, the screenshots in the article show the name as "Wanderfull".
But admittedly, if one had tried to productize their stuff in the 1980s it would have been hilarious. So the rewards here are going to go to the people who read the right tea leaves and follow the right path to what's inevitable.
In the short term, a lot of not so smart, people are going to lose a lot of money believing some of the ludicrous short-term claims. But when has that not been the case?
This is not the right time of year to pitch in Seattle. The days are short and the people are cranky. But if they want to keep hating on AI as a technology because of Microsoft and Amazon, let them, and build your AI technology somewhere else. San Francisco thinks the AGI is coming any day now so it all balances out, no?
Here's the deal. Everyone I know who is infatuated with AI shares things AI told them with me, unsolicited, and it's always so amazingly garbage, but they don't see it or they apologize it away [1]. And this garbage is being shoved in my face from every angle --- my browser added it, my search engine added it, my desktop OS added it, my mobile OS added it, some of my banks are pushing it, AI comment slop is ruining discussion forums everywhere (even more than they already were, which is impressive!). In the mean time, AI is sucking up all the GPUs, all the RAM, and all the kWH.
If AI is actually working for you, great, but you're going to have to show it. Otherwise, I'm just going to go into my cave and come out in 5 years and hope things got better.
[1] Just a couple days ago, my spouse was complaining to her friend about a change that Facebook made, and her friend pasted an AI suggestion for how to fix it with like 7 steps that were all fabricated. That isn't helpful at all. It's even less helpful than if the friend just suggested to contact support and/or delete the facebook account.
To be fair, pretty much all advice in life is less helpful than 'delete the facebook account'
Specifically I was using Gemini to answer questions about Godot specifically for C# (not gdscript or using the IDE, where documentation and forums support are stronger), and it was mostly quite good for that.
I just picked up an old gamecube. it's refreshing to play purely offline content from an age without any AI art of any kind. some games, like animal crossing, will break in 2031 though, so there's only a good 5 more years left to enjoy it.
I know Animal Crossing is sensitive to the RTC, but could you set the clock back 28 years and go from there? You'll have the same days of the week and what not, just the year number will be wrong.
Also, at the bottom, the beta is opened but it closes the November 15th, so it's close and open at the same time :) (else it's November 2026?)
I consider it divine intervention that I departed shortly before LLMs got big. I can't imagine the unholy machinations my former team has been tasked with working on since I left.
The distinction between "real products" (solving actual problems) and "hype products" (exciting investors) reflects a pragmatic engineering perspective.
The situation seems less about AI itself and more about corporate dysfunction using AI as cover for broader organizational failures.
My team has relied on the Microsoft stack for over a decade (dotnet, GitHub Actions, VS Code, MS extensions), and I can say that the overall quality and “polish” of their releases has declined.
I try to help where I can—filing issues for outdated docs, contributing to dotnet/core, joining discussions about .NET 10 still not being available in Ubuntu APT feeds, reporting and helping resolve issues with MSSQL drivers and SqlClient on GitHub, etc.
But every time I interact with someone at Microsoft, I can’t help but read between the lines: they seem slightly demotivated by the company's shift toward an AI-first focus.
It's sad.
And not just for travel by the way... I love just exploring maps and seeing a place.. I'd love to learn more about a place kind of like a mesh between Wikipedia and a map and AI could help
I see what the author is saying here, but they're painting with an overly broad brush. The whole "San Francisco still thinks it can change the world" also is annoying.
I am from the Seattle area, so I do take it a bit personally, but this isn't exactly my experience here.
A few months ago, a friend of mine showed a poem she wrote for her newborn. Or more specifically, she asked ChatGPT to write for her newborn.
I almost acted like this ex-Microsoft senior. Tbh if I didn't know it was for her own child, I would have acted this way.
I (thought that I) managed ignoring my opinions about whether writing poems is a good use of AI and steering the topic to baby formula milk instead.
They should focus more on data engineering/science and other similar fields which is a lot more about those, but since there are often no tests there, that's a bit too risky.
(Protip: if you're going to use em—dashes—everywhere, either learn to use them appropriately, or be prepared to be blasted for AI—ification of your writing.)
Having em-dashes everywhere—but each one or pair is used correctly—smacks of AI writing—AI has figured out how to use them, what they're for, and when they fit—but has not figured out how to revise text so that the overall flow of the text and overall density of them is correct—that is, low, because they're heavy emphasis—real interruptions.
(Also the quirky three-point bullet list with a three-point recitation at the end with bolded leadoffs to each bullet point and a final punchy closer sentence is totally an AI thing too.)
But, hey, I guess I fit the stereotype!—I'm in Seattle and I hate AI, too.
IIRC (it's been a while) there are 2 cases where a semi-colon is acceptable. One is when connecting two closely-related independent clauses (i.e. they could be two complete sentences on their own, or joined by a conjunction). The other is when separating items in a list, when the items themselves contain commas.
But introductory rhetorical questions? As sentence fragments? There I draw the line.
>>>
For me, the issue is that they’re misused in this piece. Em dashes used as appositives carry the feel of interruption—like this—and should be employed sparingly. They create a jarring bump in the narrative’s flow, and that bump should only appear when you want it. Otherwise, appositives belong with commas (when they’re integral to the sentence) or parentheses (when they’re not). Clause breaks follow the same logic: the em dash is the strongest interruption. Colons convey a sense of arrival—you’ve been building up to this: and now it’s here. Semicolons are for those rare cases when two clauses can’t quite stand alone as separate sentences; most of the time, a full stop is cleaner. Like this. Which is why full stops should be your default splice when revising.
Sprinkling em dashes everywhere—even if each one is technically correct—feels like AI writing. The system has learned what they are, how they work, and when they fit, but it hasn’t learned how to revise for overall flow or density. The result is too many dashes, when the right number should be low, because they’re heavy emphasis—true interruptions.
(And yes, the quirky three-point bullet list with bolded openers and a punchy closer at the end is another hallmark of AI prose.)
But hey, I guess I fit the stereotype—I’m in Seattle, and I hate AI too.
I understand why people are irritated by this.
However, recently I tried the GitHub Copilot agent with VS Code using Claude Opus 4.5. It literally implemented, tested and fixed entire new features in minutes, that otherwise would have taken days or even weeks of routine repetitive work from me. All while mimicking style and patterns in my existing codebase which made me instantly understand exactly what it was doing. I found it to be an insane productivity boost and I can see how it might be affecting hiring processes in numerous industries, especially in software engineering space.
Now we're using the same logic again: "Well, you just need to learn to use the AI before someone else does."
And if anyone doubts that the world can move on without the software engineer, remember that it moved on just fine after eliminating the toothpaste tube fillers. The world kept turning, just a little colder and more indifferent each time another role disappeared.
Maybe instead of pretending this time is different, we should focus on writing the best epitaph we can.
Most people in Seattle "tech" are middle management with no discernible skills other than organizational deckchair arrangement. It is a place to optimize for work-life balance, and not take risk - this is why the region, despite its technology density, has such a disproportionately small startup scene.
AI IS a huge threat to a place like this and I am not optimistic about the ability for people to adapt.
Look, good engineers just want to do good work. We want to use good tools to do good work, and I was an early proponent of using these tools in ways to help the business function better at PriorCo. But because I was on the wrong team (On-Prem), and because I didn’t use their chatbots constantly (I was already pitching agents before they were a defined thing, I just suck at vocabulary), I was ripe for being thrown out. That built a serious resentment towards the tooling for the actions of shitty humans.
I’m not alone in these feelings of resentment. There’s a lot of us, because instead of trusting engineers to do good work with good tools, a handful of rich fucks decided they knew technology better than the engineers building the fucking things.
You know who's NOT divided? Everyone outside the tech/management world. Antipathy towards AI is extremely widespread.
An opinion I've personally never encountered in the wild.
The only non-technical people I know who are excited about AI, as a group, are administrator/manager/consultant types.
Still useful but certainly not PhD-level when it imports X, you remind it's instructions are to use Y, it apologizes, imports Y but then immediately imports X again.
So when your project gets cancelled for AI and haven't gotten a raise while AI researchers in the same company are getting generational wealth- it does feel pretty bad.
In comes Wanderfugl. A tool for traveling that I will never need, where just trying to figure out what it does used more time than I wanted to spend on it. Now with AI, there will be several shiny new travel apps like Wanderfugl for you to learn and choose from literally every time you go on another vacation.
Wanderfugl may be wonderful, and an achievement. But the reaction of this Seattleite is "What's the point anymore?" This is why I am uninterested in the AI coding trend. It's just a part of a lot of new stuff I don't need.
The people prompting don't seem to realize what's coming out the other end is boilerplate dreck, and you've got to think - if you're replaceable with boilerplate dreck maybe your skills weren't all that, anyway?
The hate is justified. The hype, is not.
I think the SEA and SF tech scenes are hard to differentiate perfectly in a HN comment. However, I think any "Seattle hates AI" has to do more with the incessant pushing of AI into all the tech spaces.
It's being claimed as the next major evolution of computing, while also being cited as reasons for layoffs. Sounds like a positive for some (rich people) and a negative for many other people.
It's being forced into new features of existing products, while adoption of said features is low. This feels like cult-like behavior where you must be in favor of AI in your products, or else you're considered a luddite.
I think the confusing thing to me is that things which are successful don't typically need to be touted so aggressively. I'm on the younger side and generally positive to developments in tech, but the spending and the CEO group-think around "AI all the things" doesn't sit well as being aligned with a naturally successful development. Also, maybe I'm just burned out on ads in podcasts for "is your workforce using Agentic AI to optimize ..."
I'm not sure they're as wrong as these statements imply?
Do we think there's more or less crap out now with the advent and pervasiveness of AI? Not just from random CEOs pushing things top down, but even from ICs doing their own gig?
"I made this half-pony, half-monkey monster to please you
But I get the feeling that you don't like it
What's with all the screaming?
You like monkeys, you like ponies
Maybe you don't like monsters so much
Maybe I used too many monkeys
Isn't it enough to know that I ruined a pony
Making a gift for you?
When you are working on the AI map app, you are mapping your new idea to code.
When people are working with legacy code and fixing bugs, they are employing reasoning and validation.
The problem is management doesn't allow the engineers to discern which is which and just stuff it down their throats.
When people talk about it like this (this author is hardly the only example) they sound like an evangelist proselytizing and it feels so weird to me.
This thing could basically read “people in Seattle don’t want to believe in God with me, people in San Francisco have faith though. I’m sad my friends in Seattle won’t be going to heaven.”
People are fed up and burned out from being forced to try useless AI tools by non-technical leaders who do not understand how LLM works nor understand how they suck, and now resent anything related to AI. But for AI companies there is a perverse incentive to push AI on people until it finally works, because the winner of the AI arms race won't be the company that waits until they have a perfect, polished product.
I have myself had "fun" trying to discuss LLMs with non technical people, and met a complete wall trying to explain why LLMs aren't useful for programming - at least not yet. I argue the code is often of low quality, very unmaintainable, and usually not useful outside quick experimentation. They refuse to believe it, even though they do hit a wall with their vibe-coded project after a few months when claude stops generating miracles any more - they lack the experience with code to understand they are hitting maintainability issues. Combine that with how every "wow!" LLM example is actually just the LLM regurgitating a very common thing to write tutorials about, and people tend to over-estimate its abilities.
I use claude multiple times a week because even though LLM-generated code is trash I am open to try new tools, but my general experience is that Claude is unable to do anything well that I can't have my non-technical partner do. It has given me a sort of superiority complex where I immediately disregard the opinion of any developer who thinks its a wondertool, because clearly they don't have high standards for the work they were already doing.
I think most developers with any skill to their name agree. Looking at how Microsoft developers are handling the forced AI, they do seem desperate: https://news.ycombinator.com/item?id=44050152 even though they respond with the most "cope" answers I've ever read when confronted about how poorly it is going.
There are quite a few things they can do reasonably well - but they mostly are useful for experienced programmers/architecs as a time safer. Working with a LLM for that often reminds me of when I had many young, inexperienced Indians to work with - the LLM comes up with the same nonsense, lies and excuses, but unlike the inexperienced humans I can insult it guilt free, which also sometimes gets it back on track.
> They refuse to believe it, even though they do hit a wall with their vibe-coded project after a few months when claude stops generating miracles any more - they lack the experience with code to understand they are hitting maintainability issues.
For having a LLM operate on a complete code base there currently seems to be a hard limit of something like 10k-15k LOC, even with the models with the largest context windows - after that, if you want to continue using a LLM, you'll have to make it work only on a specific subsection of the project, and manually provide the required context.
Now the "getting to 10k LOC" _can_ be sped up significantly by using a LLM. Ideally refactor stupid along the way already - which can be made a bit easier by building in sensible steps (which again requires experience). From my experiments once you've finished that initial step you'll then spend roughly 4-5 times the amount of time you just spent with the LLM to make the code base actually maintainable. For my test projects, I roughly spent one day building it up, rest of the week getting it maintainable. Fully manual would've taken me 2-3 weeks, so it saved time - but only because I do have experience with what I'm doing.
If i really wanted to go 100% LLM as a challenge I think I'd compartmentalize a lot and maybe rely on OpenAPI and other API description languages to reduce the complexity of what the LLM has to deal with when working on its current "compartment" (i.e the frontend or backend). Claude.md also helps a lot.
I do believe in some time saving, but at the same time, almost every line of code I write usually requires some deliberate thought, and if the LLM makes that thought, I often have to correct it. If i use English to explain exactly what I want it is some times ok, but then that is basically the same effort. At least that's my empirical experience.
That's probably the worst case for trying to use a LLM for coding.
A lot of the code it'll produce will be incorrect on the first try - so to avoid sitting through iterations of absolute garbage you want the LLM to be able to compile the code. I typically provide a makefile which compiles the code, and then runs a linter with a strict ruleset and warnings set to error, and allow it to run make without prompting - so the first version I get to see compiles, and doesn't cause lint to have a stroke.
Then I typically make it write tests, and include the tests in the build process - for "hey, add tests to this codebase" the LLM is performing no worse than your average cheap code monkey.
Both with the linter and with the tests you'll still need to check what it's doing, though - just like the cheap code monkey it may disable lint on specific lines of code with comments like "the linter is wrong", or may create stub tests - or even disable tests, and then claim the tests were always failing, and it wasn't due to the new code it wrote.
Or should I say... Everybody thinks that titles using the format "everybody thinks X" would be more honest if they instead said: "I believe X."
There is zero push in my org to use any of these tools. I don't really use them at all but know some coworkers who do and that's fine. Sounds like this is a rare and lucky arrangement.
It’s possible that no matter what he asked, the people of Seattle would respond negatively.
That’s probably the worst name for an app I’ve ever heard.
Big tech workers might be perceiving writing on the wall sooner - there have already been some layoffs.
I also find a lot of technpeollemsurprisinglynhabent spent as much time with AI over the past 3 years compared to other techs.
The centralization of 'power' in AI will be the entities that want to run the bigger, more general-purpose models. Fine. So be it. Knock yourself out. Good luck building the data centers and finding power.
After that, AI now becomes a 'field leveler', and I say this with the utmost of sincerity and confidence. Need a supply chain system? Goodbye big boys. Goodbye vendor lock. Now there will be dozens, if not hundreds of small teams that can provide this for you at a fraction of the cost. Accounting you ask? Goodbye Intuit. We'll whip up what you need and you'll be off and running and you can kiss the global monsters goodbye. You get the idea.
This is a defining moment and it's awesome. Sure there will be some initial pain. Mindsets will have to change. Priorities will shift. My fundamental point is, all of the big boy/fat cats rushing in the AI race are literally rushing to completely undermine their own leverage and power. Each and every day I am amazed at what I can do, after a lifetime of staring at screens, with a $25.00/month Claude Code license. I am reaching out and lifting my neighbors, who have no technical experience at all, into a completely new playing field where they compete against entities they had no chance of competing with ever before.
Forgive my optimism, but it's hard not to be as I can now use my experience, with a tight group of trusted friends and colleagues, and a little bit of coding help (which goes a long way) to go wherever we want to go now.
When I see apps like Wanderfugl, I get the same sense of disgust as OPs ex coworker. I don‘t want to try this app, I don’t want to see it, just get it away from me.
I don't see how the author can believe that quitting their job to work on an AI startup is NOT contributing to the problem of "AI products being shoved down everyone's throats."
Except, of course, that their financial bottom line depends on not believing this.
Then it says: "Engineers don't try because they think they can't." They don't try AI is what I understand, but that contradicts the whole article, that every engineer in Seattle is actively using AI, even forced too.
Then it says: "now believes she's both unqualified for AI work", why would they believe that? She's supposedly has been using AI constantly, has not been part of those "layed off", so must be a great AI talent.
Finally it says: "now believes she's both unqualified for AI work and that AI isn't worth doing anyway. She's wrong on both counts, but the culture made sure she'd land there." Which is completely usubstantiated and also coming from a person trying to grift us with their AI product which they want to promote and sell.
I don't know, it read like a shill article from a grifter.
Yep, big orgs doing big org things. Don't miss it a bit.
He's above the 10 year mark, which is a long time for fortune 500 ceos.
> It felt like the culture wanted change. > > That world is gone.
Ummm source?
> This belief system—that AI is useless and that you're not good enough to work on it anyway
I actually don't know anyone with this belief system. I'm pretty slow on picking up a lot of AI tooling, but I was slow to pick up JS frameworks as well.
It's just smart to not immediately jump on a bandwagon when things are changing so fast because there is a good chance you're backing the wrong horse.
And by the way, you sound ridiculous when you call me a dinosaur just because I haven't started using a tool that didn't even exist 6 months ago. FOMO sales tactics don't work on everyone, sorry to break it to you.
When the singularity hits in who knows how many years from now, do you really think it's one of these llm wrapper products that's going to be the difference maker? Again, sorry to break it to you but that's a party you and I are not going to get invited to. 0% chance governments would actually allow true super intelligence as a direct to consumer product.
But yea, AI companies should pay into a UBI fund. The value of collective creative output of humanity should go back to the living not the select few.
It is called the Alaska Permanent Fund Dividend.
Think of it this way: the entire world pays Alaska residents for the use of their oil, as a sort of tax that is worked into every energy intensive step of industry or petroleum-derived material.
Alaska's oil is only ~1% of world oil production, but its population is approximately 0.01% of world population, so Alaska residents get approximately 100x what the global per capita oil dividend would be. Oil industry is approximately 2.5% of global GDP. Stack all these multipliers together and we could expect a global per-capita total-GDP dividend of between $525 - $1325 per person per year. Exceeding this (as we did with PPP "loans" during COVID) would have compounding economic effects that lead to hyperinflation.
This is napkin math with spherical cow assumptions. Other factors would further limit UBI dividends to be less than this. But it shows that with existing national dividend systems as model, we can't even get within an order of magnitude of the low end of what UBI proponents are advocating.
I think some of the reasons that they gave were bullshit, but in fairness I have grown pretty tired of how much low-effort AI slop has been ruining YouTube. I use ChatGPT all the time, but I am growing more than a little frustrated how much shit on the internet is clearly just generated text with no actual human contribution. I don’t inherently have an issue with “vibe coding”, but it is getting increasingly irritating having to dig through several-thousand-line pull requests of obviously-AI-generated code.
I’m conflicted. I think AI is very cool, but it is so perfectly designed to exploit natural human laziness. It’s a tool that can do tremendous good, but like most things, it requires people use it with effort, which does seem to be the outlier case.
[1] basically the hall of shame for bad threads.
> This belief system—that AI is useless and that you're not good enough to work on it anyway—hurts three groups
I don't know anyone who thinks AI is useless. In fact, I've seen quite a few places where it can be quite useful. Instead, I think it's massively overhyped to its own detriment. This article presents the author as the person who has the One True Vision, and all us skeptics are just tragically undereducated.
I'm a crusty old engineer. In my career, I've seen RAD tooling, CASE tools, no/low-code tools, SGML/XML, and Web3 not live up to the lofty claims of the devotees and therefore become radioactive despite there being some useful bits in there. I suspect AI is headed down the same path and see (and hear of) more and more projects that start out looking really impressive and then crumble after a few promising milestones.
By my reading, there are several people on this discussion thread right now who think it (in the form of LLMs) is useless?
Electricl engineering? Garbage.
Construction projects? Useless.
But code is code everywhere, and the immense amount of training data available in the form of working code and tutorials, design and style guides, means that the output as regards software development doesn't really resemble what anybody working in any other field sees. Even adjacent technical fields.
I'm working on a harness but I think it can do some basic revit layouts with coaxing (which with a good harness should be really useful!)
Let me know what you've experienced. Not many construction EE on HN.
I use to draft in AutoCad and Revit before switching to software.
Saw your comment around using Gemini. I’d love to chat with you. I started building something for the build side of the electrical world, but eventually want to make the jump to the design side of the house.
That said, AI resistance is real too. We see it on this forum. It's understandable because the hype is all about replacing people, which will naturally make them defensive, whereas the narrative should be about amplifying them.
A well-intentioned AI mandate would either come with a) training and/or b) dedicated time to experiment and figuring out what works well for you. Instead what we're seeing across the industry is "You MUST use AI to do MORE with LESS while we layoff even more people and move jobs overseas."
My cynical take is, this is an intentional strategy to continue culling headcount, except overindexing on people seen as unaligned with the AI future of the company.
That's a recurring argument, and I don't believe it, especially in large tech companies. They have no problem doing multiple large non-quiet lay-offs, why would they need moustache-twirling level schemes to get people to quit.
I don't believe companies to be well intentioned, but the simplest explanation is often the best:
1. RTO are probably driven by people in power who either like to be in the office, believe being in the office is the most efficient way to work (be that it's true or not), or have financial stakes in having people occupy said offices.
2. "AI" mandate is probably driven by people in power who either do see value in AI, think it's the most efficient way to work (be that it's true or not), have FOMO on AI, or have financial stakes in having people use it.
So the thing about all large layoffs is that there is actually some non-obvious calculus behind them.
One thing for instance, is that typically in the time period soon after layoffs, there is some increased attrition in the surviving employees, for a multitude of reasons. So if you layoff X people you actually end up with X + Y lower headcount shortly after. There are also considerations like regulations.
What this means is that planning layoffs has multiple moving parts:
1) The actual monetary amount to cut -- it all starts with $$$;
2) The absolute number of headcount that translates to;
3) The expected follow-on attrition rate;
4) The severance (if any) to offer;
5) The actual headcount to cut with a view of the attrition and severance;
6) Local labor regulations (e.g. WARN) and their impact, monetary or otherwise;
7) Consideration, impact on internal morale and future recruitment.
So it's a bit like tuning a dynamic system with several interacting variables at play
Now the interesting bit of tea here is that in the past couple of years, the follow-on (and all other) attrition has absolutely plummeted, which has thrown the standard approaches all out of whack. So companies are struggling a bit to "tune" their layoffs and attrition.
I had an exec frankly tell me this after one of the earliest waves of layoffs a couple years ago, and I heard from others that this was happening across the industry. Sure enough, there have been more and more seemingly haphazard waves of layoffs and the absolute toxicity this has introduced into corporate culture.
Due to all this and the overal economy and labor market, employee power has severely weakened, so things like morale and future recruitment are also lower priorities.
Given all this calculus, a company can actually save quite some money (severance) and trouble if people quit by themselves, with minimal negative repercussions.
Not quite moustache-twirling but not quite savory either.
? For the better, or for the worse ?
- The entire community @ https://seattlefoundations.org
A key part of today's AI project plan is clearly identifying the dump site where the toxic waste ends up. Otherwise, it might be on top of you.
Its an infinite moving goalpost of hate, if its an actor, "creative", writer, AI is a monolithic doom, next its theoretical public policy or the lack thereof, and if they have nothing that affects them about it then it's about the energy use and environment
nobody is going to hear about what your AI does, so don't mention anything about AI unless you're trying to earn or raise money. Its a double life
Copilot for Word. Copilot for PowerPoint. Copilot for email. Copilot for code. Worse than the tools they replaced. Worse than competitors' tools. Sometimes worse than doing the work manually.
This is revolting. Three years ago I’d have said this is a terrible black mirror plot
My buddies still or until recently still at Amazon have definitely been feeling this same push. Internal culture there has been broken since the post covid layoffs, and layering "AI" over the layoffs leaves a bad taste.
It might just be an ESL issue on my end, but I seriously feel some huge dissonance between the explanations of "how the tech was made the main KPI, used to justify layoffs and forced in a way that hinders productivity", and the conclusion that seems to say "the real issue with those people complaining is that they just don't believe in AI".
I don't understand this article, it seems to explain all the reasons people in Seattle might have grievances, and then completely dismisses those to adopt the usual "you're using it wrong".
Is this article just a way to advertise for Wanderfugl? Because this reads like the usual "Okay your grievances are fine and all, but consider the following: it allows me to make a SaaS really fast!" that I became accustomed to see in HN discussions.
I've also heard complaints about the mandatory use of the tools in the office and the pageantry involved.
I've seen people in love with garbage they produced with AI.
I'm annoyed by the way they are being pushed in my face but hate is really too strong. I've tried using them and gotten total garbage. I think that's because my prompting sucks because I know people that love the tools and have shared great output from them. Those people are a minority in my opinion.
Trying to over simplify the experiences of humanity is a fool's game.
The cult of AI maximalists aren't helping the situation.
An opposing force is corporate momentum. Its unfortunately true that people are beholden to what companies create. If there are only a few phones available, you will have to pick. If there are only so many shows streaming, you'll probably end up watching the less disgusting of the options.
They are clashing. The ppl's sentiment is AI bad. But if tech keeps making it and pushing it long enough, ppl will get older, corporate initiatives will get sticky, and it will become ingrained. And once its ingrained, its gonna be here forever.
iykyk
I hate the entire premise even though some of it has been useful but at worst you're creating code and/or information that's just wrong and "can get someone killed" (metaphorical, but also probably literal), you're creating absolutely unrealistic expectations
Zuck said they'd be able to replace engineers with AI. Well, that tells you everything you need to know, doesn't it? With all of the scandals Facebook properties have had over the years. A real engineer/competent CEO wouldn't say that
It's not about their careers. It's about the injustice of the whole situation. Can you possibly perceive the injustice? That the thing they're pissed about is the injustice? You're part of the problem because you can't.
That's why it's not about whether the tools are good or bad. Most of them suck, also, but occasionally they don't--but that's not the point. The point is the injustice of having them shoved in your face; of having everything that could be doing good work pivot to AI instead; of everyone shamelessly bandwagoning it and ignoring everything else; etc.
That's the thing, though, it is about their careers.
It's not just that people are annoyed that someone who spends years to decades learning their craft and then someone who put a prompt into a chatbot that spit out an app that mostly works without understanding any of the code that they 'wrote'.
It's that the executives are positively giddy at the prospect that they can get rid of some number their employees and the rest will use AI bots to pick up the slack. Humans need things like a desk and dental insurance and they fall unconscious for several hours every night. AI agents don't have to take lunch breaks or attend funerals or anything.
Most employees that have figured this out resent AI getting shoved into every facet of their jobs because they know exactly what the end goal is: that lots of jobs are going to be going away and nothing is going to replace them. And then what?
I'm one of these people. So is everyone I know. The grievance is moral, not utilitarian. I don't care about executives getting rid of people. I care that they're causing obviously stupid things to happen, based on their stupid delusions, making everyone's lives worse, and they're unaccountable for it. And in doing so they devalue all of the things I consider to be good about tech, like good software that works and solves real problems. Of course they always did that but it's especially bad now.
It doesn't matter how much astroturf I read, I can see what's happening with my own eyes.
> The grievance is moral, not utilitarian.
Nope, it's both.
Businesses have no morals. (Most) people do. Everything that a business does is in service of the bottom line. They aren't pushing AI everywhere out of some desire to help humanity, they're doing it because they sunk a lot of resources into it and are trying to force an ROI.
There are a lot of people who have fully bought in to AI and think that it's more capable than it is. We just had a thread the other day where someone was using AI to vibe code an app, but managed to accidentally tell the LLM to delete the contents of his hard drive.
AI apologists insist that AI agents are a vital tool for doing more faster and handwave any criticism. It doesn't matter that AI agents consume an obscene amount of resources to do it, or that pretend developers are using it to write code they don't understand and can't test that they're shoving into production anyway. That's all fine because a loud fraction of senior developers are using it to bypass the 'boring parts' of writing programs to focus on the interesting bits.
If you are a utilitarian person and you try to parse a scrupulous person according to your utilitarianism of course their actions and opinions will make no sense to you. They are not maximizing for utility, whatsoever, in any sense. They are maximizing for justice. And when injustices are perpetrated by people who are unaccountable, it creates anger and complaining. It's the most you can do. The goal is to get other people to also be mad and perhaps organize enough to do something about it. When you ignore them, when you fail to parse anything they say as about justice, then yes, you are part of the problem.
Yeah, that's weird. Why would anyone think that? /s
My main problem is that at this point, the value of entire collective creative output of humanity should go to the living not the select few.
IMHO AI companies should pay into some kind of UBI fund/ Sovergeign fund.
Time for capitalism to evolve, yo!
As to the point of the article, is it just to say "People shouldn't hate LLMs"? My takeaway was more "This person's future isn't threatened directly so they just aren't understanding why people feel this way." but I also personally believe that, if the CEOs have their way, AI will threaten every job eventually.
So yeah I guess I'm just curious what the conclusion presented here is meant to be?
As somebody who has lived in Seattle for over 20 years and spent about 1/3 of it working in big tech (but not either of those companies), no, I don't really think so. There is a lot of resentment, for the same reasons as everywhere else: a substantial big tech presence puts anyone who can't get on the train at a significant economic disadvantage.
If you are a writer or a painter or a developer - in a city as expensive as Seattle - then one may feel a little threatened. Then it becomes the trickle down effect, if I lose my job, I may not be able to pay for my dog walker, or my child care or my hair dresser, or...
Are they sympathetic? It depends on how much they depend on those who are impacted. Everyone wants to get paid - but AI don't have kids to feed or diapers to buy.
SF embraces tech and in general (politics, etc) has a culture of being willing to try new things. Overall tech hostility is low, but the city becoming a testbed for projects like Waymo is possibly changing that. There is a continuous argument that their free-spirited culture has been cannibalized by tech.
Seattle feels like the complete opposite. Resistant to change, resistant to trying things, and if you say you work in tech you're now a "techbro" and met with eyerolls. This is in part because in Seattle if you are a "techbro" you work for one of the megacorps whereas in SF a "techbro" could be working for any number of cool startups.
As you mentioned, Seattle has also been taken over by said megacorps which has colored the impressions of everyone. When you have entire city blocks taken over by Microsoft/Amazon and the roads congested by them it definitely has some negative domino effects.
As an aside, on TV we in the Seattle area get ads about how much Amazon has been doing for the community. Definitely some PR campaign to keep local hostility low.
(.. and exactly how is boeing doing since it was forced to move away from 'engineering culture' by moving out of the city where their workforce was trained and training the next generation. Oh yeah planes are falling out of the sky and their software is pushing planes into the ground.)
I think most people in Seattle know how economics works, logic follows: while "techbro" don't work is true: if "techbro" debt > income: unless assets == 0: sellgighustle else sellhousebeforeforeclosure nomoreseattleforyou("techbro") end else "gigbot" isn't summoned and people don't get paid. "techbro" health-- due to high expense of COBRA. [etc...] end end
Anecdotally, I work at a different FAANMG+whatever company in Seattle that I feel has actually done a pretty good job with AI internally: providing tools that we aren't forced to use (i.e. they add selectable functionality without disrupting existing workflows), not tying ratings/comp to AI usage (seriously how fucking stupid are they over in Redmond?), and generally letting adoption proceed organically. The result is that people have room to experiment with it and actually use it where it adds real value, which is a nonzero but frankly much narrower slice than a lot of """technologists""" and """thought leaders""" are telling us.
Maybe since Microsoft and Amazon are the lion's share (are they?) of big tech employment in Seattle, your point stands. But I think you could present it with a bit of a broader view, though of course that would require more research on your part.
Also, I'd be shocked if there wasn't a serious groundswell of anti-AI sentiment in SF and everywhere else with a significant tech industry presence. I suspect you are suffering from a bit of bias due to running in differently-aligned circles in SF vs. Seattle.
Wrt. AI specifically, I guess we are simply a) not using AI as an excuse to lay off scores of employees (at least, not yet) and b) not squeezing the employees who remain with arbitrary requirements that they use shitty AI tools in their work. More generally, participation in design work and independent execution are encouraged at all levels. At least in my part of the company, there simply isn't the same kind of miserable, paranoid atmosphere I hear about at MS and Amazon these days. I am not aware of any rigidly enforced quota for PIPing people. Etc.
Generally, it feels like our leadership isn't afflicted with the same kind of desperate FOMO fever other SMEGMAs are suffering from. Of course, I don't mean to imply there haven't been layoffs in the post free money era, or that some people don't end up on shitty teams with bad managers who make them miserable, or that there isn't the usual corporate bullshit, etc.
If it was about the technology, then it would be no different than being a java/c++ developer and calling someone who does html and javascript their equal so pay them. It's not.
People get anxious when something may cause them to have to change - especially in terms of economics and the pressures that puts on people beyond just "adulting". But I don't really think you explained the why of their anxiety.
Pointing the finger at AI is like telling the Germans that all their problems are because of Jews without calling out why the Germans are feeling pressure from their problems in the first place.
As a customer, I actually had an MS account manager once yelled at me for refusing to touch <latest newfangled vaporware from MS> with a ten foot pole. Sorry, burn me a dozen times; I don't have any appendages left to care. I seriously don't get Microsoft. I am still flabbergasted anytime anyone takes Microsoft seriously.
Presumably the account manager is under a lot of pressure internally...
Do they repeatedly yell at you?
Do you know how your <vaporware> usage was measured - what metrics was the account manager supposed to improve?
It has all the telltale signs: lots of em-dashes but also "punched up" paragraphs, a lot of them end with a zinger, e.g.
> Amazon folks are slightly more insulated, but not by much. The old Seattle deal—Amazon treats you poorly but pays you more—only masks the rot.
or
> Seattle has talent as good as anywhere. But in San Francisco, people still believe they can change the world—so sometimes they actually do.
Once or twice can be coincidence, but a full article of it reads a tiny bit like AI slop.
I'm not sure why you needed it for edits though, since you seem good at writing generally.
I feel bad for people who work at dystopian places where you can't just do the job, try to get ahead etc. It is set up to make people fail and play politics.
I wonder if the company is dying slowly but with AI hype qaand old good foundations keeping her stock price going.
> Bring up AI in a Seattle coffee shop now and people react like you're advocating asbestos.
can you please share the methodology you used to reach this conclusion?
in other words - what is the sample size? how many Seattle coffee shops did you walk into and yell out "hey, what do people think about AI?" (or did you gather the data in a different way, such as by approaching individual people at the coffee shop?)
what is your control group? in other words, how many SF coffee shops did you visit and conduct the same experiment?
Just stop lying about AI. Thank you.
The author’s AI app looks like something that drains all the fun and challenge out of travel planning.
Seattle has been screwed over so many times in the last 20 years that its a shell of itself.
Satya has completely wasted their early lead in AI. Google is now the leader.
I wouldn't shit talk you to your face if you're making an AI thing. However I also understand the frustration and the exhaustion with it, and to be blunt, if a product advertises AI in it, I immediately do treat it more skeptically. If the features are opt-in, fine. If however it seems like the sort of thing that's going to start spamming me with Clippy-style "let our AI do your work for you!" popups whilst I'm trying to learn your fucking software, I will get aggravated extremely fast.
Except it didn't stick? https://news.ycombinator.com/item?id=43088369
I'm all for shaming people who just link to ChatGPT and call their whatever thing AI powered. If you're actually doing some work though and doing something interesting, I'll hear you out.
Well, it's not the fault on a random person doing some project that may even be cool.
I'll certainly adjust my priors and start treating the person as probably an idiot. But if given evidence they are not, I'm interested on what they are doing.
What's gonna be super interesting is that I'm going to have an rpi zero 2 power up my machine when I press the controller's ps-button. That means I might need to solder and do some electrical voodoo that I've never tried. Crossing my fingers that the plan ChatGPT has come up with won't electrocute me.
Believe me, the same reflexive, critical, negative response is true for most of Europe too
Of course, you could also go online and sulk, I suppose. There are more options between "ZIRP boomtimes lol jobs for everyone!" and "I got fired and replaced with ELIZA". But are tech workers willing to expore them? That's the question.
It just feels like it's in bad taste that we have the most money and privilege and employment left (despite all of the doom and gloom), and we're sitting around feeling sorry for ourselves. If not now, when? And if not us, who?
[…]
Seattle has talent as good as anywhere. But in San Francisco, people still believe they can change the world—so sometimes they actually do.”
Nope, still completely fucking tone deaf.
But also, it's not just my own. My wife's a graphic designer. She uses AI all the time.
Honestly, this has been revolutionary for me for getting things done.
AI the manual algorithm to generate code and analyze images is quite an interesting underlying tech.
Not only because it's destroying creator jobs while also ripping off creators, but it's also producing shit that's offensively bad to professionals.
One thing that people in tech circles might not be aware of is that people outside of tech circles aren't thinking that tech workers are smart. They haven't thought that for a long time. They are generally thinking that tech workers are dimwit exploiter techbros, screwing over everyone. This started before "AI", but now "AI" (and tech billionaires backing certain political elements) has poured gasoline on the fire. Good luck getting dates with people from outside our field of employment. (You could try making your dating profile all about enjoying hiking and dabbling with your acoustic guitar, but they'll quickly know you're the enemy, as soon as you drive up in a Tesla, or as soon you say "actually..." before launching into a libertarian economics spiel over coffee.)
1) A third party app simply cannot compete with Google Maps on coverage, accuracy and being up to date. Yes, there are APIs you can use to access this, but they're expensive and limited, which leads us to the second problem:
2) You can't make money off them. Nobody will pay to use your app (because there's so much free competition), and the monetization opportunities are very limited. It's too late in the flow to sell flights, you can't compete with Booking etc for hotel search, and big ticket attractions don't pay commissions for referrals. That leaves you with referrals for tours, but people who pay for tours are not the ones trying to DIY their trip planning in the first place.
So many products are like this - it sounds good on paper to consolidate a bunch of tasks in one place but it's not without costs and the benefit is just not very high.
If they become popular they'll have to move to OSM, Google's steep charging for their Maps API at high usage that has brought companies to their knees is well known [1].
For now, they do use Google Maps and I’m happy with it. If they stop and it’s no longer as useful to me, maybe I’ll stop using it.
But I use it as a glorified notes app to keep track of flights, reservations, rental cars, confirmation numbers, etc, in one place, not for trip planning.
Similar to "made for everyone" social networks and video upload platforms.
But there are niches that are trip planning + there are no one solving the pain! For example Geocaching. I always dreamed about an easy way to plan Geocaching routes to travel and find interesting caches on the way. Currently you gotta filter them out and then eyeball the map what seems to be nearby, despite there, maybe, not being any real roads there, or the cache is probably maybe actually lost or has to be accessed at specific time of day.
So... No one wants apps that are already solved + boring.
No shit. But that's hardly everyone is Seattle. I'd imagine people at Amazon aren't upset about being forced to use Copilot, or Google folks.
Oh yeah, call out a tech city and all the butt-hurt-ness comes out. Perfect example of "Rage Bait".
People here aren't hurt because of AI - people here are hurt because they learned they were just line items in a budget.
When the interest rates went up in 2022/2023 and the cheap money went away, businesses had to pivot their taxes while appeasing the shareholder.
Remember that time when Satya went to a company sponsored rich people thing with Aerosmith or whomever playing while announcing thousands of FTE's being laid off? Yeah, that...
If your job can be done by a very small shell script, why wasn't it done before?
The difference between the two with regards to AI tool usage couldn’t be more different- at Microsoft, they had started penalizing you in perf if you didn’t use the AI tools, which often were under par and you didn’t have a choice in. At the new place, perf doesn’t care if you use AI or not- just what you actually deliver. And, shocker, turns out they actually spend a lot building and getting feedback on internal AI tooling and so it gets a lot of use!
The Microsoft culture is a sort of toxic “get AI usage by forcing it down the engineer throats” vs the new “make it actually useful and win users” approach at that new place. The Microsoft approach builds resentment in the engineering base, but I’m convinced it’s the only way leadership there knows how to drive initiatives.