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.

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.

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.

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.

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.

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

CEOs laid people off to replace them with AI, but turns out AI is more expensive, does a worse job.

If I made a blunder of that scale, would I or would I not be put on a PIP?

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?

> 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.

> 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.