I'm so frustrated by both the zealous AI bulls and the blind AI opposition.
There's a lot of issues, ranging over technical, cultural, environmental, and moral problems. But there's also obvious value. To say otherwise tells me you haven't actually tried to make use of these tools.
It's one thing to get an AI google response and feel like it's dubious, it's another thing to know what you want and have an LLM find the APIs for a framework you're not familiar with yet and put the pieces together. The only way I use AI for programming still involves a large amount of rejecting the responses and a massive amount of reading and validating.
Am I able to write things faster with LLMs, yes. Am I missing out on the work involved in learning things I would be forced to otherwise, also yes. Are coworkers pushing stuff they don't understand more, surely.
It's a mixed bag, and we need more balanced takes in the discussion around this.
Nuance has been completely lost on our society. If it doesn't spark immediate outrage or joy it has no place in our attention economy. I really wish this wasn't the case and I'm not sure how we can reverse it. Most people just aren't interested in the 'balanced takes' because it's just not exciting enough it seems sadly.
I recently learned the term "thought-terminating cliché" and it's so incredibly prevalent in society these days. Always has been I'm sure, but short form text based anonymous communication must be making it worse.
There is plenty of nuance to go around generally still but on any topic where there are incredibly powerful or wealthy parties spending a fortune pushing a narrative it immediately stops being possible.
Nuance simply gets overwhelmed by repetition.
The most recent opinion forming narrarive Ive been seeing repeated endlessly on this topic is about some supposed "split" in programmers between those who "enjoy the process" and those who "want the outcome".
It is very carefully designed to miss the point and is some quite clever propaganda - it presupposes that the outcome is the same without directly stating it and implies that skeptics are dinosaurs without needing to be too direct about it.
It is good to remember that the discourse is not fully real.
There are fake accounts pushing AI everywhere, and people burned out by the marketing that react positioning too aggressively in the opposite direction (particularly when it's their boss who bought into the marketing and makes unreasonable demands).
There’s “obvious value” in lots of things that aren’t forced on us and that aren’t pretended to be something everyone is required to learn and use before sticking to their existing toolset or their preferred methods for getting things done.
Morality wise, it might have “obvious value” to poison my competitors and steal from my business partners, employees, and customers. That doesn’t mean I am obligated to “try to make use of these tools.”
But there's also obvious value. To say otherwise tells me you haven't actually tried to make use of these tools.
The issue is not that AIs are useless. It's that they are objectively worse at the job (except apparently programming[1]), than a cheaper, entry-level worker on their first week still learning how to do the job, even though nearly a trillion dollars has been spent on them by this point. The worker gets substantially better over time, and slightly more expensive. LLM models get slightly better over time, but substantially more expensive.
[1] The output of even the latest models would have gotten a junior programmer PIP'd back when I was still programming many years ago. That is now considered to be above average code quality is a disheartening referendum on how bad programming standards have fallen since then.
That’s assuming that the tools should be able to replace juniors. I have a hard time seeing how a junior could take over the role which Claude code currently fills in my line of work and certainly not at the same price point even if I paid for the tokens rather than a subscription.
To sum it all up, there are good arguments both for and against AI, but it increasingly feels like it's ideologically driven and feeds into delusions.
Not only that, but while the technology itself isn't inherently evil, you can leave it up to humanity to absolutely misuse it - together with unregulated capitalism, it results in one big mess.
I think it's possible to acknowledge that the technology has value while still being pretty much entirely opposed to AI as it exists and is being used today. It's sort of like believing we should have a police force while also believing that pretty much every existing police force should be disbanded and reconstituted from scratch (which also generated polarizing responses), or believing you need a government while also believing that all people currently in office are crooks. It's true there might be a version of something like AI that would be a net good, but I think that's quite far from what we actually have.
> To say otherwise tells me you haven't actually tried to make use of these tools.
There's also a lot of ideological opposition, which often tries to claim that the tech is useless etc.
> Am I missing out on the work involved in learning things I would be forced to otherwise, also yes.
Yes, but many of those things are things you might not really care about learning about. And if you want to learn about them, AI can be a big help, if you use it appropriately.
The "mixed bag" comes from the way people use it, mostly not from the tech itself.
Get it right, 99.99% of ideological opposition is mostly do to the naked display of wealth concentration where we are literally forcing Americans to bear the higher costs of electricity, noise pollution, actual pollution, and increased cancer rates while they are flushing down trillions of dollars into the drain while Americans lack medicare for all, universal childcare, free higher education, or even a public jobs program (fun fact, all these programs poll above 70-80%).
The naked display of the elites willing to fuck over large swaths of Americans is not only despicable it is in our civic duty to rightfully fight and reject this.
No one wants this yet it is being thrusted upon us and we are forced to pay for this madness. If you ask most Americans they would probably be completely okay with nationalizing big tech or seizing their assets.
Why does this tech have to champion naked displays of greed where the elites feign surprise that people are willing to fight for their lives?
This is the single issue that unites Americans across parties, incomes, and race; it's the class issue of the century and the people yearn for material change.
What I pointed out is that ideological objectors often try to claim that AI is actually technically useless.
The problem with that is that it makes their arguments seem less credible, and even misleads their own supporters into underestimating what they’re dealing with. It’s a literally self-defeating position.
> There's a lot of issues, ranging over technical, cultural, environmental, and moral problems. But there's also obvious value. To say otherwise tells me you haven't actually tried to make use of these tools.
Why would you try to make use of these tools when there are obvious environmental and moral problems with them? What do you tell yourself about those problems, and how do you get past them?
I think it's worth understanding the technology so we can have informed strategies for how to best regulate it.
We don't solve the climate crisis by abandoning all the technological progress which has put such a strain on our ecosystem, we solve it by rethinking how we use this technology, and finding new technologies and policies to better meet our needs.
> I think it's worth understanding the technology so we can have informed strategies for how to best regulate it.
There's a significant difference between understanding a technology and using a technology. We can understand how a technology is made without totally changing our workflows to rely on it essentially.
How many cities do we have to blow up to understand nuclear weapons? That's a serious question, because atomic bombs were dropped on both Hiroshima and Nagasaki.
> We don't solve the climate crisis by abandoning all the technological progress which has put such a strain on our ecosystem, we solve it by rethinking how we use this technology
I don't think that's true. Shouldn't we abandon fossil fuels? Switch to cleaner energy, solar, wind, geothermal, electric cars, etc.?
If a thinking machine can find new mathematical proofs we haven't thought of yet, it might also find new medicines and other things that really do make it worth finding a way to live with. If I can ask the thinking machine to find bugs in my code and it does, that seems nice to have.
The analysis that I want is on a studied cost/value basis somehow. I'd start by trying to force tech companies to sell products at cost sooner, so it's not driving markets before it can truly be absorbed. I'd also ask for energy/resource consumers to be forced to buy climate credits which are used to help offset the impacts and fund research and development for sustainable technology.
> The analysis that I want is on a studied cost/value basis somehow.
Absolutely. The AI ROI question is the entire ballgame. Earlier today I heard the term "jagged technology" for the first time, and it's an extremely good one. Given how jagged AI appears to be, anecdotal reports are all over the place. It also doesn't help that it appears heavily using this technology can affect people psychologically. The information environment for someone who wants to know what this tool is good for and what it isn't is.. treacherous. Some clear, objective grounding would really help.
>It's one thing to get an AI google response and feel like it's dubious, it's another thing to know what you want and have an LLM find the APIs for a framework you're not familiar with
What I'm hearing is that the 0.00001% use case is great while the 99.9999% use case is shit--and we're supposed to think that's reasonable.
Feeding nonsense to pretty much the entire population is just fine with tech bros because a few programmers have an easier time cranking out some code to more effectively sell ads?
That's not a mixed bag, friend. That's a bag of horse shit with one M&M at the bottom.
Like I said, if you can't see the value in AI, I'd hedge a bet you haven't tried. Even a brief conversation with google's can help identify a gap in your understanding of many topics.
How we balance the positives with the negatives is the hard part I want to hear thoughtful and studied opinions on.
We're in a dangerous valley where AI is _just_ good enough to fool some otherwise very smart people. Similar to the old adage of "a little bit of information is a dangerous thing." Lots of CEOs got duped into thinking that model capabilities were far ahead of where they actually were. I'm actually not sure if we're going to get out of the valley without figuring out a surefire way to reliably evaluate these things.
> Lots of CEOs got duped into thinking that model capabilities were far ahead of where they actually were.
Are these CEOs the "very smart people" you're talking about? To my eyes it's mostly the same crowd that was drooling over crypto and blockchain and the metaverse just a few years ago, and it was clear that was a stupid idea at the time too.
It's not the same crowd. I know some genuinely extremely intelligent people (can't go into specifics) who have gotten bit by AI psychosis. It's been wild to see.
They got duped into it partly because of the enthusiasm and demoware shown by folks like the ones that topped HN with their exhortations about the wonders of AI for coding.
We’re not innocent bystanders here, and it’s important to recognize that. Our hype added to the hype. Our optimism added to the optimism. After layoffs due to Section 172 and interest rates going up, technologists were looking for a reason to be in-demand again, and generative AI as a platform specialization provided that.
We can’t now criticize CEOs for being taken in by the same enthusiasm we pushed for our own purposes.
I don’t claim responsibility for any of that. I’m not part of that “we”. Time and time again I’ve been sceptical of AI on places like Hacker News and I’m hardly alone. As a community I think there is a great variation in opinion.
CEOs are very well compensated for the things they do. If they were led astray by snake oil salesman that does not make Hacker News as a community complicit. They were fooled when they shouldn’t have been and it’s their own fault.
I'm not criticizing CEOs, only mentioning them because they're at the helm and the ones calling the shots at companies. But the psychosis has hit pretty much everyone, of all intelligence ranges.
Also, I'm genuinely excited about AI. It's absolutely incredible. Even with all the problems, I'm more invigorated about being a programmer than at almost any point in my career. You have to embrace the chaos.
I don’t know who you’re including in “we”, but since most CEOs are being paid more for their judgment than HN commenters are, it wouldn’t be unreasonable to hold them to higher standards.
While it's true that HN, as a collective, was participating in that hype, you need to be very careful about trying to extend that to mean that every poster on HN is complicit in it.
Many of us were skeptical from the start, and have done nothing to hype up LLMs.
What about the large contingent of resident wet rags? Can we still shit on the CEOs for not seeing through the BS and guiding their companies effectively?
I have a theory there’s a more nefarious problem that AI changes the incentives around what work is easy to do, in a way that can affect how work hits the bottom line. Aside from the slop problem- people create more documentation more people have to read, etc. it makes it easier for me to say do a bunch of bug fixes or refactors that aren’t in the critical path.
So even if in say 100 person engineering out 10 folks might get 2-3x critical path work. 50 folks
Might just add non-critical path work, and the other forty might use it in a way that they end up doing g less critical path work.
But depending on your metrics productivity could look up while the bottom line is unaffected. In which case model quality is a red herring.
AI is a bullshit artist's wet dream. It's bluffing turned up to 11, plausible enough to be believable, especially to the gullible and the greedy who are both succumbing to its psychotic effects.
No one was "duped" CEOs tend to look out 10-20 years in the future. Extrapolating with s, linear, and log curves. If they all look good then they hop on board. Any way you slice it AI will be unimaginable in 20 years, you don't want an engineering culture that focuses on retaining useless skills. Look at mega-caps like Google and Facebook, so called web native companies, are STILL shunned by "real devs" despite looking nothing like they did when they started, because in terms of "real skills" they have release product failure after product failure. They live in a completely different plane of abstraction. Similarly John von Neumann hate assembly he would have loathed C and Unix.
It's all boils down to the same problem: A smart programmer who enjoys optimizing something that's not on the hot path: developer velocity.
> Since the start of the year, Chinese AI models have overtaken their US counterparts in token consumption, according to data from OpenRouter, an aggregation platform that allows users to access multiple AI models.
That's a bit of a dodgy statistic. OpenRouter only tracks their own users - the vast majority of API customers for OpenAI and Anthropic presumably go straight to their APIs.
It's true that OpenRouter may be a biased sample - however, do we have reason to think that isn't a relatively stable biased sample? If not, then the trendline in OpenRouter usage is still pretty interesting.
> The ride-hailing company has introduced usage caps, limiting employees to $1,500 in monthly token spending on individual AI tools, after blowing through its entire AI 2026 budget by April.
Right, because they set their 2026 budget in 2025. And in 2025 nobody could predict how good (and token-hungry) coding agents would get after November 2025.
I'd be surprised if any company that set an AI budget for 2026 hasn't blown through it by now, assuming their staff have picked up Claude Code or Copilot or Cowork.
I think this probably has more to do with companies switching to API pricing for enterprises, no?
Regardless, the C-suite wouldn’t be performing due diligence if they weren’t at least attempting to perform the calculus of “what are we getting out of this spend?” and what we’re seeing now is them looking for the justification.
Didn’t Uber mention that they’re having a hard time tying all of that spend to any new or improved features?
I think it's both. Last year even the most AI-hungry companies had employees who were primarily using ChatGPT and Claude. It's really hard to burn a noticeable number of tokens with those tools.
The moment you have Claude Code or Codex running in a loop - or in multiple streams (something that people don't really do with chat because it returns fast enough there's no point running them in parallel) your token usage goes through the roof.
And then by May this year both OpenAI and Anthropic had migrated their enterprise pricing to API costs, not fixed subscription per month costs. That wouldn't have made much of a difference in the pre-coding-agent era, but today it means $100s or $1000s per employee per month.
If they were getting something in return (financially) they’d have no problem expanding the budget even.
You seem to be desperate. What’s wrong? Finding it hard to believe that you can have all this intelligence but it can make the firm financially worse off? Haha.
I'm consistently burning over $100/day in Claude/OpenAI tokens using Codex and Claude - but I'm on the subscriptions so I'm only paying $100/month to each vendor.
If I worked for a larger company that isn't allowed to use those subscriptions and has to pay list price I'd be costing them ~$2,000/month. Now times that by a large engineering team.
They believed and were financially incentivized to embrace the hype and operationalized it because technical leadership thought rapid PoCs would apply to the rest of the tech stack.
IMHO it’s just good old fomo. They fear that the landscape will shift overnight and they’ll be left with a ton of useless meat bags instead of a pay as you go models that you can upgrade instantly every quarter.
Out of touch leadership and management combined with a culture of self-congratulatory "innovation" where companies just plagiarize eachother in an endless loop... It doesn't surprise me at all, tbh.
Apple and Meta (and others) did the same thing with VR. In 2015 all these CEOs were looking for their next app store and, herd-like, settled on VR computing. They couldn't come up with anything more promising so they burned tens of billions on failed goggles that anyone with any sense could have told you was never going to work.
Groupthink. FOMO. Envy. Hubris. Greed. Those fuel Big Tech and we get to pay for the results.
Once you realize most execs are literal sociopaths driven by pure greed and power a lot of things start to make sense. Of course for simple minded folks like us none of it is remotely understandable
Does anyone know the inside story of some of these AI adoptions that have been downsized? The company I’m at has only only recently gotten an enterprise license.
We’ve had our AI budget per Engineer cut twice now since the peak mania in early 2026 and Fable was banned even before it was removed by Anthropic due to cost. It’s still used a lot but I think maybe they’re not really seeing the ROI especially when some people spent thousands and thousands per month on dubious AI things.
Personally I truly can’t manage that many tasks (really 1 or 2 max) in parallel with these since you need to think very hard about everything the AI spits out because they’re such natural bullshitters and you end up in places where no one on the team understands anything in the project
Since the switch to API pricing they’ve cut usage limits in half twice and are now saying that anyone with high usage is essentially going to be audited.
I know about a small company with literal competition about who spends more tokens. They had a prize for winner - not too big materially but a lot of status attached.
The competition remained after switch to token pricing and abruptly stopped at seemingly random moment.
Frankly, I want competition about who finds the most expensive work trip hotel.
The cost issue is real, but the solution isn't less AI such as the solution for cloud costs is not less cloud. You should choose the right model for each task.
lack of knowledge by most people cause them to use frontier models for basically anything.
Companies are learning that even with mass layoffs, AI isn't worth what it costs for most use cases. This is an important inflection point because none of the AI companies are profitable, i.e. they're all still charging substantially less than what it costs to actually deliver the service. With things in that intermediate state, it's hard to know what a future stable state will be like.
Matthew Prince of Cloudflare has been outspoken in saying that companies that do not go all in on AI will be left behind. There probably is some value being received in exchange for tokens but it seems quite plausible that some or even much of this spending by companies is driven by fear.
$1k/month is nothing as a hedge against a CEO being accused of causing their company to be left behind in the AI utopia of tomorrow. If you’re in a peer group that rewards risk taking and all of your peers are taking a risk, you’ve got to take that risk too. Better to burn money trying something and then failing, than to not try. Failure is acceptable, missing an opportunity is not.
Partially because they think that once employees put enough context and rules into markdown files they can be fired and replaced by an online subscription.
> Following adoption, junior employment declines in adopting firms relative to non-adopters, while senior employment trends remain largely unchanged. This decline is concentrated in occupations most exposed to GenAI and is driven primarily by slower hiring rather than increased separations.
I used to go above and beyond and take pride in writing decent/good documentation for my team and have gotten a lot of compliments about them.
But I've started to deliberately constrain myself because of what you describe. Sure, I'm not convinced that it actually come to fruition, but just the realization that this is what they're daydreaming about makes me sick so I've forced myself to only do the bare minimum or less now.
Have you considered that that money would have gone to raises or promotions? From that perspective the company nets out while the employee that would have received that money feels the effects of wage suppression.
It's just not very much money for these companies. The source article says Uber allows $1,500 per month, but senior engineers at Uber make $400k/yr ~= $33.3k/month; even disregarding fully-loaded cost, the AI only has to make them about 3% more productive to be worth it.
Because tech is so deeply hooked on fashion and short-termism that it’ll make both a Temu & Wall street exec blush in envy. LLMs floods both these receptors like the Great flood.
If your choices are “reduce productivity, make money” or “increase productivity, but still make the same money”, why would you ever choose the latter?
The thing about option 1 is that you still have “potential productivity” that you can tap into during critical times, where as in option 2, employees have already used up the “potential productivity” doing god knows what with AI, and you can’t push them more without breaking.
I would imagine this is the route it will go too, once (if) hardware supply chains catch up and costs can start coming down.
It's too useful to ignore, but too expensive to pay-as-you-go at current pricing for frontier models. Smaller, less capitalized companies will use the subscriptions (if they still exist by then), and larger firms will just build out their own compute for their dev teams.
I knew this was going to be the end result after seeing so many companies reward employees based on how many tokens they were using. My company even pulled stats and gave physical awards out at an in person retreat. Ridiculous, and it was never going to last.
I’ve got screenshots from months ago telling people exactly this stuff would be reported in the coming months. Just funny to watch really. I’m more convinced than ever you cannot trust most people - you must think for yourself and most people are stupid.
I remember back when ChatGPT first came out, there was an article on HN about this AI researcher who worked for one of the big companies (I think Google) who came to believe the model was truly intelligent and that it was being abused by being locked in the machine. We all laughed as the guy had clearly lost his mind to AI psychosis. What we didn’t realize is he may have been patient zero.
This is the delusion that went viral, or at least one version of it. It all leads back hijacking the human tendency to anthropomorphize, leading to the belief that an LLM is somehow something more than it actually is. So the question is - what breaks the spell? Failed attempts to automate that don’t work out? The realization that the return on money spent doesn’t make sense? Furthermore, how to we accelerate the eventual realization?
If throwing more compute at the problem keeps only resulting in incremental gains, I think that should do it. It goes one of 2 ways, really. Either we can throw enough compute at pre-training that results in infinitely more capable models to the point that the cost is now justified [1], or, we hit a scaling wall, get stuck with what we have now (or at that time) and the valuations crash knowing that "this is it" for the foreseeable future without a big breakthrough.
The labs go bankrupt or get acquired by the typical giants (Google, Microsoft, Amazon), the models get rolled into GCP, Azure, and AWS as a service, and that's it. It becomes another dev tool, much like a new IDE.
[1] cost being justified I'd rank as "your average non technical PM can now end to end develop robust, production software free of most serious vulnerabilities." model & tool capabilities that would allow you to hire a small team of non-techincal roles, for half the salary, that can produce the output of a large engineering org. If that doesn't happen, I don't see how the current buildout is sustainable.
I talked to a former coworker recently and he kept gendering Claude, which felt really, really weird to me. And this guy is probably one of the best data people I've ever worked with.
It's a really strange time in that I use a bunch of AI and am really excited about the product capabilities, and yet I feel way less AI pilled than all my friends and coworkers.
I'm so frustrated by both the zealous AI bulls and the blind AI opposition.
There's a lot of issues, ranging over technical, cultural, environmental, and moral problems. But there's also obvious value. To say otherwise tells me you haven't actually tried to make use of these tools.
It's one thing to get an AI google response and feel like it's dubious, it's another thing to know what you want and have an LLM find the APIs for a framework you're not familiar with yet and put the pieces together. The only way I use AI for programming still involves a large amount of rejecting the responses and a massive amount of reading and validating.
Am I able to write things faster with LLMs, yes. Am I missing out on the work involved in learning things I would be forced to otherwise, also yes. Are coworkers pushing stuff they don't understand more, surely.
It's a mixed bag, and we need more balanced takes in the discussion around this.
Nuance has been completely lost on our society. If it doesn't spark immediate outrage or joy it has no place in our attention economy. I really wish this wasn't the case and I'm not sure how we can reverse it. Most people just aren't interested in the 'balanced takes' because it's just not exciting enough it seems sadly.
I recently learned the term "thought-terminating cliché" and it's so incredibly prevalent in society these days. Always has been I'm sure, but short form text based anonymous communication must be making it worse.
It's a useful term but humorously risks becoming auto-exemplary.
Haha, yes 100% I've had the same thought.
stop generating.
It is what it is.
And that's just the way things are.
There is plenty of nuance to go around generally still but on any topic where there are incredibly powerful or wealthy parties spending a fortune pushing a narrative it immediately stops being possible.
Nuance simply gets overwhelmed by repetition.
The most recent opinion forming narrarive Ive been seeing repeated endlessly on this topic is about some supposed "split" in programmers between those who "enjoy the process" and those who "want the outcome".
It is very carefully designed to miss the point and is some quite clever propaganda - it presupposes that the outcome is the same without directly stating it and implies that skeptics are dinosaurs without needing to be too direct about it.
It is good to remember that the discourse is not fully real.
There are fake accounts pushing AI everywhere, and people burned out by the marketing that react positioning too aggressively in the opposite direction (particularly when it's their boss who bought into the marketing and makes unreasonable demands).
There’s “obvious value” in lots of things that aren’t forced on us and that aren’t pretended to be something everyone is required to learn and use before sticking to their existing toolset or their preferred methods for getting things done.
Morality wise, it might have “obvious value” to poison my competitors and steal from my business partners, employees, and customers. That doesn’t mean I am obligated to “try to make use of these tools.”
But there's also obvious value. To say otherwise tells me you haven't actually tried to make use of these tools.
The issue is not that AIs are useless. It's that they are objectively worse at the job (except apparently programming[1]), than a cheaper, entry-level worker on their first week still learning how to do the job, even though nearly a trillion dollars has been spent on them by this point. The worker gets substantially better over time, and slightly more expensive. LLM models get slightly better over time, but substantially more expensive.
[1] The output of even the latest models would have gotten a junior programmer PIP'd back when I was still programming many years ago. That is now considered to be above average code quality is a disheartening referendum on how bad programming standards have fallen since then.
That’s assuming that the tools should be able to replace juniors. I have a hard time seeing how a junior could take over the role which Claude code currently fills in my line of work and certainly not at the same price point even if I paid for the tokens rather than a subscription.
Juniors slowed me down way way way before AI came along but theyre getting stiff competition these days from the AI slop I'm having to wade through.
> Am I able to write things faster with LLMs, yes.
LLM is great at identifying useless writing - if you have llm writing it, it should not exist.
Tried writing down my own take on it: https://blog.kronis.dev/blog/ai-psychosis-and-anti-ai-psycho...
To sum it all up, there are good arguments both for and against AI, but it increasingly feels like it's ideologically driven and feeds into delusions.
Not only that, but while the technology itself isn't inherently evil, you can leave it up to humanity to absolutely misuse it - together with unregulated capitalism, it results in one big mess.
I think it's possible to acknowledge that the technology has value while still being pretty much entirely opposed to AI as it exists and is being used today. It's sort of like believing we should have a police force while also believing that pretty much every existing police force should be disbanded and reconstituted from scratch (which also generated polarizing responses), or believing you need a government while also believing that all people currently in office are crooks. It's true there might be a version of something like AI that would be a net good, but I think that's quite far from what we actually have.
> To say otherwise tells me you haven't actually tried to make use of these tools.
There's also a lot of ideological opposition, which often tries to claim that the tech is useless etc.
> Am I missing out on the work involved in learning things I would be forced to otherwise, also yes.
Yes, but many of those things are things you might not really care about learning about. And if you want to learn about them, AI can be a big help, if you use it appropriately.
The "mixed bag" comes from the way people use it, mostly not from the tech itself.
Get it right, 99.99% of ideological opposition is mostly do to the naked display of wealth concentration where we are literally forcing Americans to bear the higher costs of electricity, noise pollution, actual pollution, and increased cancer rates while they are flushing down trillions of dollars into the drain while Americans lack medicare for all, universal childcare, free higher education, or even a public jobs program (fun fact, all these programs poll above 70-80%).
The naked display of the elites willing to fuck over large swaths of Americans is not only despicable it is in our civic duty to rightfully fight and reject this.
No one wants this yet it is being thrusted upon us and we are forced to pay for this madness. If you ask most Americans they would probably be completely okay with nationalizing big tech or seizing their assets.
Why does this tech have to champion naked displays of greed where the elites feign surprise that people are willing to fight for their lives?
This is the single issue that unites Americans across parties, incomes, and race; it's the class issue of the century and the people yearn for material change.
> Get it right
What did I get wrong exactly?
What I pointed out is that ideological objectors often try to claim that AI is actually technically useless.
The problem with that is that it makes their arguments seem less credible, and even misleads their own supporters into underestimating what they’re dealing with. It’s a literally self-defeating position.
> There's a lot of issues, ranging over technical, cultural, environmental, and moral problems. But there's also obvious value. To say otherwise tells me you haven't actually tried to make use of these tools.
Why would you try to make use of these tools when there are obvious environmental and moral problems with them? What do you tell yourself about those problems, and how do you get past them?
I think it's worth understanding the technology so we can have informed strategies for how to best regulate it.
We don't solve the climate crisis by abandoning all the technological progress which has put such a strain on our ecosystem, we solve it by rethinking how we use this technology, and finding new technologies and policies to better meet our needs.
> I think it's worth understanding the technology so we can have informed strategies for how to best regulate it.
There's a significant difference between understanding a technology and using a technology. We can understand how a technology is made without totally changing our workflows to rely on it essentially.
How many cities do we have to blow up to understand nuclear weapons? That's a serious question, because atomic bombs were dropped on both Hiroshima and Nagasaki.
> We don't solve the climate crisis by abandoning all the technological progress which has put such a strain on our ecosystem, we solve it by rethinking how we use this technology
I don't think that's true. Shouldn't we abandon fossil fuels? Switch to cleaner energy, solar, wind, geothermal, electric cars, etc.?
If a thinking machine can find new mathematical proofs we haven't thought of yet, it might also find new medicines and other things that really do make it worth finding a way to live with. If I can ask the thinking machine to find bugs in my code and it does, that seems nice to have.
The analysis that I want is on a studied cost/value basis somehow. I'd start by trying to force tech companies to sell products at cost sooner, so it's not driving markets before it can truly be absorbed. I'd also ask for energy/resource consumers to be forced to buy climate credits which are used to help offset the impacts and fund research and development for sustainable technology.
> The analysis that I want is on a studied cost/value basis somehow.
Absolutely. The AI ROI question is the entire ballgame. Earlier today I heard the term "jagged technology" for the first time, and it's an extremely good one. Given how jagged AI appears to be, anecdotal reports are all over the place. It also doesn't help that it appears heavily using this technology can affect people psychologically. The information environment for someone who wants to know what this tool is good for and what it isn't is.. treacherous. Some clear, objective grounding would really help.
Humans can already find new medicines and bugs in code. We didn't need AI to find the Covid vaccines, for example.
> The analysis that I want is on a studied cost/value basis somehow.
If something is immoral, do the ends justify the means? I would reiterate that you originally mentioned moral problems.
> I'd start by trying to force tech companies to sell products at cost sooner, so it's not driving markets before it can truly be absorbed.
That sounds an awful lot like abolishing capitalism. Which might actually help with the climate crisis, regardless of AI.
> climate credits
These always seemed like BS to me, a way for wealthy corporations to buy their way out of doing anything.
“We don't solve the climate crisis”
You can stop right there. Whatever we’re doing to prevent global disaster from new technologies is not working.
No OP but I can tell you exactly where I find my peace:
4 dependents, a cost of living that keeps rocketing and an industry that’s decided “the people who don’t glaze this shit don’t get to continue”.
Maybe Ive sold out but what’s the alternative, I’m 17 years in, I can’t figure how I pivot my expertise and still support the family’s demands.
I've heard it being described as an iterated prisoner's dilemma and very many people are very publicly defecting.
Fair enough. I have no criticism for people whose employers are forcing it on employees. I blame the employers there.
But the OP didn't sound like one of the reluctant.
Yeah, it sucks man.
There was a good moment where I thought it wouldn’t come for my org but here I am.
Not saying the tech doesn’t have merit but if you’re not blasting the slop cannon constantly and praising Dario for the pleasure, you’re on the list.
>It's one thing to get an AI google response and feel like it's dubious, it's another thing to know what you want and have an LLM find the APIs for a framework you're not familiar with
What I'm hearing is that the 0.00001% use case is great while the 99.9999% use case is shit--and we're supposed to think that's reasonable.
Feeding nonsense to pretty much the entire population is just fine with tech bros because a few programmers have an easier time cranking out some code to more effectively sell ads?
That's not a mixed bag, friend. That's a bag of horse shit with one M&M at the bottom.
Google was never immune to spitting out junk BTW.
Like I said, if you can't see the value in AI, I'd hedge a bet you haven't tried. Even a brief conversation with google's can help identify a gap in your understanding of many topics.
How we balance the positives with the negatives is the hard part I want to hear thoughtful and studied opinions on.
No, we can't all be friends. Never happened, never will be.
We're in a dangerous valley where AI is _just_ good enough to fool some otherwise very smart people. Similar to the old adage of "a little bit of information is a dangerous thing." Lots of CEOs got duped into thinking that model capabilities were far ahead of where they actually were. I'm actually not sure if we're going to get out of the valley without figuring out a surefire way to reliably evaluate these things.
> Lots of CEOs got duped into thinking that model capabilities were far ahead of where they actually were.
Are these CEOs the "very smart people" you're talking about? To my eyes it's mostly the same crowd that was drooling over crypto and blockchain and the metaverse just a few years ago, and it was clear that was a stupid idea at the time too.
It's not the same crowd. I know some genuinely extremely intelligent people (can't go into specifics) who have gotten bit by AI psychosis. It's been wild to see.
They got duped into it partly because of the enthusiasm and demoware shown by folks like the ones that topped HN with their exhortations about the wonders of AI for coding.
We’re not innocent bystanders here, and it’s important to recognize that. Our hype added to the hype. Our optimism added to the optimism. After layoffs due to Section 172 and interest rates going up, technologists were looking for a reason to be in-demand again, and generative AI as a platform specialization provided that.
We can’t now criticize CEOs for being taken in by the same enthusiasm we pushed for our own purposes.
I don’t claim responsibility for any of that. I’m not part of that “we”. Time and time again I’ve been sceptical of AI on places like Hacker News and I’m hardly alone. As a community I think there is a great variation in opinion.
CEOs are very well compensated for the things they do. If they were led astray by snake oil salesman that does not make Hacker News as a community complicit. They were fooled when they shouldn’t have been and it’s their own fault.
I'm not criticizing CEOs, only mentioning them because they're at the helm and the ones calling the shots at companies. But the psychosis has hit pretty much everyone, of all intelligence ranges.
Also, I'm genuinely excited about AI. It's absolutely incredible. Even with all the problems, I'm more invigorated about being a programmer than at almost any point in my career. You have to embrace the chaos.
I don’t know who you’re including in “we”, but since most CEOs are being paid more for their judgment than HN commenters are, it wouldn’t be unreasonable to hold them to higher standards.
While it's true that HN, as a collective, was participating in that hype, you need to be very careful about trying to extend that to mean that every poster on HN is complicit in it.
Many of us were skeptical from the start, and have done nothing to hype up LLMs.
What about the large contingent of resident wet rags? Can we still shit on the CEOs for not seeing through the BS and guiding their companies effectively?
I have a theory there’s a more nefarious problem that AI changes the incentives around what work is easy to do, in a way that can affect how work hits the bottom line. Aside from the slop problem- people create more documentation more people have to read, etc. it makes it easier for me to say do a bunch of bug fixes or refactors that aren’t in the critical path.
So even if in say 100 person engineering out 10 folks might get 2-3x critical path work. 50 folks Might just add non-critical path work, and the other forty might use it in a way that they end up doing g less critical path work.
But depending on your metrics productivity could look up while the bottom line is unaffected. In which case model quality is a red herring.
AI is a bullshit artist's wet dream. It's bluffing turned up to 11, plausible enough to be believable, especially to the gullible and the greedy who are both succumbing to its psychotic effects.
No one was "duped" CEOs tend to look out 10-20 years in the future. Extrapolating with s, linear, and log curves. If they all look good then they hop on board. Any way you slice it AI will be unimaginable in 20 years, you don't want an engineering culture that focuses on retaining useless skills. Look at mega-caps like Google and Facebook, so called web native companies, are STILL shunned by "real devs" despite looking nothing like they did when they started, because in terms of "real skills" they have release product failure after product failure. They live in a completely different plane of abstraction. Similarly John von Neumann hate assembly he would have loathed C and Unix.
It's all boils down to the same problem: A smart programmer who enjoys optimizing something that's not on the hot path: developer velocity.
>No one was "duped" CEOs tend to look out 10-20 years in the future
Lol.
> Since the start of the year, Chinese AI models have overtaken their US counterparts in token consumption, according to data from OpenRouter, an aggregation platform that allows users to access multiple AI models.
That's a bit of a dodgy statistic. OpenRouter only tracks their own users - the vast majority of API customers for OpenAI and Anthropic presumably go straight to their APIs.
It's true that OpenRouter may be a biased sample - however, do we have reason to think that isn't a relatively stable biased sample? If not, then the trendline in OpenRouter usage is still pretty interesting.
As someone who went straight to deepseeks API, I wouldn't be surprised if this trend exists outside of Openrouter.
> The ride-hailing company has introduced usage caps, limiting employees to $1,500 in monthly token spending on individual AI tools, after blowing through its entire AI 2026 budget by April.
Right, because they set their 2026 budget in 2025. And in 2025 nobody could predict how good (and token-hungry) coding agents would get after November 2025.
I'd be surprised if any company that set an AI budget for 2026 hasn't blown through it by now, assuming their staff have picked up Claude Code or Copilot or Cowork.
I think this probably has more to do with companies switching to API pricing for enterprises, no?
Regardless, the C-suite wouldn’t be performing due diligence if they weren’t at least attempting to perform the calculus of “what are we getting out of this spend?” and what we’re seeing now is them looking for the justification.
Didn’t Uber mention that they’re having a hard time tying all of that spend to any new or improved features?
I think it's both. Last year even the most AI-hungry companies had employees who were primarily using ChatGPT and Claude. It's really hard to burn a noticeable number of tokens with those tools.
The moment you have Claude Code or Codex running in a loop - or in multiple streams (something that people don't really do with chat because it returns fast enough there's no point running them in parallel) your token usage goes through the roof.
And then by May this year both OpenAI and Anthropic had migrated their enterprise pricing to API costs, not fixed subscription per month costs. That wouldn't have made much of a difference in the pre-coding-agent era, but today it means $100s or $1000s per employee per month.
That’s fair. I mean like any other service it’s going to need some cost analysis.
I figure we’ll go from over usage to a shock leading to under usage and then land somewhere in the middle when companies decide what works for them.
If they were getting something in return (financially) they’d have no problem expanding the budget even.
You seem to be desperate. What’s wrong? Finding it hard to believe that you can have all this intelligence but it can make the firm financially worse off? Haha.
A CEO unable to predict or even imagine a few months down the road, who bets everything on the current state of things, is a shitty CEO.
I genuinely have no idea how some of these companies got so far over their skis on AI. It simply does not make sense to me.
I'm consistently burning over $100/day in Claude/OpenAI tokens using Codex and Claude - but I'm on the subscriptions so I'm only paying $100/month to each vendor.
If I worked for a larger company that isn't allowed to use those subscriptions and has to pay list price I'd be costing them ~$2,000/month. Now times that by a large engineering team.
These companies already have laid people off or are preparing to.
They believed and were financially incentivized to embrace the hype and operationalized it because technical leadership thought rapid PoCs would apply to the rest of the tech stack.
IMHO it’s just good old fomo. They fear that the landscape will shift overnight and they’ll be left with a ton of useless meat bags instead of a pay as you go models that you can upgrade instantly every quarter.
Out of touch leadership and management combined with a culture of self-congratulatory "innovation" where companies just plagiarize eachother in an endless loop... It doesn't surprise me at all, tbh.
Simple answer is that these execs live in a big bubble and goad each other into bigger and bigger investments via groupthink.
Apple and Meta (and others) did the same thing with VR. In 2015 all these CEOs were looking for their next app store and, herd-like, settled on VR computing. They couldn't come up with anything more promising so they burned tens of billions on failed goggles that anyone with any sense could have told you was never going to work.
Groupthink. FOMO. Envy. Hubris. Greed. Those fuel Big Tech and we get to pay for the results.
Once you realize most execs are literal sociopaths driven by pure greed and power a lot of things start to make sense. Of course for simple minded folks like us none of it is remotely understandable
https://archive.ph/z24oE
Does anyone know the inside story of some of these AI adoptions that have been downsized? The company I’m at has only only recently gotten an enterprise license.
We’ve had our AI budget per Engineer cut twice now since the peak mania in early 2026 and Fable was banned even before it was removed by Anthropic due to cost. It’s still used a lot but I think maybe they’re not really seeing the ROI especially when some people spent thousands and thousands per month on dubious AI things.
Personally I truly can’t manage that many tasks (really 1 or 2 max) in parallel with these since you need to think very hard about everything the AI spits out because they’re such natural bullshitters and you end up in places where no one on the team understands anything in the project
Also, unlike the other Anthropic models, Fable/Mythos couldn't be used with a zero data retention agreement.
Company is a fortune 100 and a client of mine.
Since the switch to API pricing they’ve cut usage limits in half twice and are now saying that anyone with high usage is essentially going to be audited.
They’re down to about 500 a month per person.
Probably looks like this:
- teams blitz through Jira tickets
- developers figure out they can't keep up with reviewing the code
- too many new features pushed at once, rushed work to develop training materials
- features don't work as good, many edge cases come out in support tickets
- tickers return to Jira board
- teams spend time triaging and fixing with less AI
- some features turned out to be mistake, but now have to stay
- once mess is cleared, return to "normal" pace, no AI agents, Cursor allowed with budget cap.
I know about a small company with literal competition about who spends more tokens. They had a prize for winner - not too big materially but a lot of status attached.
The competition remained after switch to token pricing and abruptly stopped at seemingly random moment.
Frankly, I want competition about who finds the most expensive work trip hotel.
The cost issue is real, but the solution isn't less AI such as the solution for cloud costs is not less cloud. You should choose the right model for each task. lack of knowledge by most people cause them to use frontier models for basically anything.
i just don't understand this thinking
Especially when AI is replacing small tasks and most people don't use it effectively
I am seeing people use it to write emails and messages on IM lol
People with existing skills can supercharger their dev output with AI. But it looks like everyone just goes up for vibe coding
CEOs laid people off to replace them with AI, but turns out AI is more expensive and does a worse job.
If I made a blunder of that scale, would I or would I not be put on a PIP?
CEOs generally suffer zero consequences for their decisions, unless that decision costs someone real money.
Ours got fired for failing to sell the company…
Companies are learning that even with mass layoffs, AI isn't worth what it costs for most use cases. This is an important inflection point because none of the AI companies are profitable, i.e. they're all still charging substantially less than what it costs to actually deliver the service. With things in that intermediate state, it's hard to know what a future stable state will be like.
If AI isn't worth what it costs why are some of these companies allowing $1,000+/employee/month?
Matthew Prince of Cloudflare has been outspoken in saying that companies that do not go all in on AI will be left behind. There probably is some value being received in exchange for tokens but it seems quite plausible that some or even much of this spending by companies is driven by fear.
$1k/month is nothing as a hedge against a CEO being accused of causing their company to be left behind in the AI utopia of tomorrow. If you’re in a peer group that rewards risk taking and all of your peers are taking a risk, you’ve got to take that risk too. Better to burn money trying something and then failing, than to not try. Failure is acceptable, missing an opportunity is not.
https://xcancel.com/eastdakota/status/2025221270061580453
“My advice: don’t be a [dinosaur]. I’ve seen this movie before and they go extinct. ”
Partially because they think that once employees put enough context and rules into markdown files they can be fired and replaced by an online subscription.
I'm not convinced that's happening, any good evidence that it is?
The impact of AI on jobs appears to be more in terms of hiring slowdowns: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5425555
> Following adoption, junior employment declines in adopting firms relative to non-adopters, while senior employment trends remain largely unchanged. This decline is concentrated in occupations most exposed to GenAI and is driven primarily by slower hiring rather than increased separations.
The point isn't that it is happening.
It's that enough execs think it can happen.
No actual evidence is required for that.
I used to go above and beyond and take pride in writing decent/good documentation for my team and have gotten a lot of compliments about them.
But I've started to deliberately constrain myself because of what you describe. Sure, I'm not convinced that it actually come to fruition, but just the realization that this is what they're daydreaming about makes me sick so I've forced myself to only do the bare minimum or less now.
The two that I know of are in a phase of trying to figure out what the return is.
Have you considered that that money would have gone to raises or promotions? From that perspective the company nets out while the employee that would have received that money feels the effects of wage suppression.
You could say the same thing about the office snack budget, or the training budget, or the budget for the office holiday party.
It's just not very much money for these companies. The source article says Uber allows $1,500 per month, but senior engineers at Uber make $400k/yr ~= $33.3k/month; even disregarding fully-loaded cost, the AI only has to make them about 3% more productive to be worth it.
AI is Labubu for CEOs.
The value is somewhere else (i.e. being able to brag at the next conference/dinner/awards show and to not feel like a loser)
Once that value is no longer there, I will stock up on popcorn. Sure Labubus are still around and still have "users", but...
Mass hysteria
Because tech is so deeply hooked on fashion and short-termism that it’ll make both a Temu & Wall street exec blush in envy. LLMs floods both these receptors like the Great flood.
If your choices are “reduce productivity, make money” or “increase productivity, but still make the same money”, why would you ever choose the latter?
The thing about option 1 is that you still have “potential productivity” that you can tap into during critical times, where as in option 2, employees have already used up the “potential productivity” doing god knows what with AI, and you can’t push them more without breaking.
> potential productivity” that you can tap into during critical times,
Critical times thinking is considered ineffectivity last 30 years or so. Managers who do that are considered bad.
Corporate AI will likely go on prem where costs can be fixed.
I would imagine this is the route it will go too, once (if) hardware supply chains catch up and costs can start coming down.
It's too useful to ignore, but too expensive to pay-as-you-go at current pricing for frontier models. Smaller, less capitalized companies will use the subscriptions (if they still exist by then), and larger firms will just build out their own compute for their dev teams.
I knew this was going to be the end result after seeing so many companies reward employees based on how many tokens they were using. My company even pulled stats and gave physical awards out at an in person retreat. Ridiculous, and it was never going to last.
I mean it’s funny to watch.
I’ve got screenshots from months ago telling people exactly this stuff would be reported in the coming months. Just funny to watch really. I’m more convinced than ever you cannot trust most people - you must think for yourself and most people are stupid.
I remember back when ChatGPT first came out, there was an article on HN about this AI researcher who worked for one of the big companies (I think Google) who came to believe the model was truly intelligent and that it was being abused by being locked in the machine. We all laughed as the guy had clearly lost his mind to AI psychosis. What we didn’t realize is he may have been patient zero.
This is the delusion that went viral, or at least one version of it. It all leads back hijacking the human tendency to anthropomorphize, leading to the belief that an LLM is somehow something more than it actually is. So the question is - what breaks the spell? Failed attempts to automate that don’t work out? The realization that the return on money spent doesn’t make sense? Furthermore, how to we accelerate the eventual realization?
https://www.scientificamerican.com/article/google-engineer-c...
it was before ChatGPT
> what breaks the spell?
If throwing more compute at the problem keeps only resulting in incremental gains, I think that should do it. It goes one of 2 ways, really. Either we can throw enough compute at pre-training that results in infinitely more capable models to the point that the cost is now justified [1], or, we hit a scaling wall, get stuck with what we have now (or at that time) and the valuations crash knowing that "this is it" for the foreseeable future without a big breakthrough.
The labs go bankrupt or get acquired by the typical giants (Google, Microsoft, Amazon), the models get rolled into GCP, Azure, and AWS as a service, and that's it. It becomes another dev tool, much like a new IDE.
[1] cost being justified I'd rank as "your average non technical PM can now end to end develop robust, production software free of most serious vulnerabilities." model & tool capabilities that would allow you to hire a small team of non-techincal roles, for half the salary, that can produce the output of a large engineering org. If that doesn't happen, I don't see how the current buildout is sustainable.
I talked to a former coworker recently and he kept gendering Claude, which felt really, really weird to me. And this guy is probably one of the best data people I've ever worked with.
It's a really strange time in that I use a bunch of AI and am really excited about the product capabilities, and yet I feel way less AI pilled than all my friends and coworkers.
https://archive.ph/AKMjS