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
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.
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.
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.
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.
except a lot of people really do hate AI
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."
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 $.
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”.
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.
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.
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.
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.
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.
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.
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".
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.
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.
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.
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.
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.
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.
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.
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.
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 a very interesting observation. I think I'm safe for now ;)
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.
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.
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.
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.
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?
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.
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.
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.
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.
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”.
this must be one of the incredible AI innovations the folks in Seattle are missing out on
So even the creator can't decide what to call it!
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.
