Companies rein in AI usage as costs strain budgets

josefritzishere

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.

danielvaughn

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.

sroerick

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.

jakubmazanec

https://archive.ph/AKMjS

le-mark

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.

deadbabe

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.

throwaway85825

Corporate AI will likely go on prem where costs can be fixed.

noncoml

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?

sirnicolaz

https://archive.ph/z24oE

IAmGraydon

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?

simonw

> 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.

simonw

> 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.

Trasmatta

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.

nixpulvis

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.

1eieies

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.

balgaly

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.