alt Hacker NewsAI sticker shock hits corporate America
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We use GH Copilot at work and this week sat for a presentation by GH about optimizing token usage and maximizing ROI on tokens used. Anyone else get this presentation? They didn’t have time for questions because they had to run and give it to the next big enterprise on their list…
They basically said that everything is too expensive, you have to watch it like a hawk. It was as if they poured a bucket of cold water on the room. People were wondering how they could do anything faster with all these strategies. And then “sorry no questions. Bye!”
This is an important inflection point.
When tokens get correctly priced, all of the insane over-investment in capital will need to draw back: buying data centers, semiconductors, and politicians.
Even then, it won't be right-priced with regard to actual costs. The environmental impact should have been priced in from the beginning. There seems to be a parallel with subsidizing fossil fuels, under pricing them which encourages over dependence, ignoring the real costs society will pay later.
This is almost entirely on Anthropic and the stupid C suite people trying to push TokenMaxxing. GPT5.5 is much more token efficient, other models are much cheaper, and if used in moderation rather than than trying to get everyone to OpenClaw 24/7 with token leaderboards, it's much more economical.
Also ironically, a lot of GenZ and young Millenials who were already bitter at their employers have used the tokenmaxxing push to sabotage the AI rollouts by burning tokens on stupid shit. It seems to be working.
Overheard recently: "Thanks to AI we're producing more code and more MRs, faster than ever, but the milestones aren't getting hit any sooner. Actually the opposite, if anything."
I wonder how widespread that phenomenon is. Perhaps it's no wonder the prominent actors are trying to rush to IPO...
I wish I could do the same thing. Coworkers would be allowed to ask me X number of questions per month, and once they hit that limit I get the rest of the month off.
Just a week ago, Anthropic barely breaking even was hailed as AI companies being close to profitability much earlier than forecast.
In fact it is all smoke and mirrors, pure mania from C-level executives out of their depth trying to one-up each other with company money, and they aren't even close.
"An AI consultant tells Axios one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on Claude licenses for employees." Like physically, how could this even happen?
>Corporate leaders are starting to question whether soaring AI spending is delivering meaningful returns.
This isn't surprising. Ive recently run into quite a few rabbit holes where AI is bad enough that its much more efficient to do it myself. I wanted to refactor some code, gave it a design pattern to go towards, some specific classes and methods, etc. making it a well described problem. AI just couldn't do it satisfactorily. The code was ugly, overly verbose, and after multiple tries with multiple prompts saying to keep things simple. They still would introduce new classes, useless fields, etc.
I’m also detecting a vibe shift in AI content
The mood has gone quickly from “this is cool” to “screw AI and any business that wants to use it”
This is particularly clear among the taste making class
I really can't tell what is going on with AI these days. I hear AI labs claiming theyre profitable or close to it. I hear companies say they're dubious the juice is worth the squeeze. I've seen anecdotal claims of a measurable increase in productivity of 2x in PRs created and merged coming in at cost 20% of engineering employee budget. Others say they're still getting no value (which I doubt). Simon Willison's recent post went into debunking the AI sticker shock claims somewhat. Either way this seesawing between new golden era and the greatest VC money furnace is becoming exhausting.
I'd like to see real numbers at this point, and this article is just a few bullet points that link to other articles. Talk is far cheaper than tokens and I'd like to have a workflow that I can rely on being there in six months.
https://simonwillison.net/2026/May/27/product-market-fit/#th...
This is because the current AI approach relies on AI to be a glorified search engine – know everything about everything requiring enormous, ever growing models, and demanding search-engine like near instant responses requiring bigger more complex chips and sprawling data centers to run them in. This leads to a loop demanding ever bigger models, updated at a more and more expensive cost, and chipsets that become much more expensive to deploy.
If you move those things to software and utilize tools that are cheap at scale (databases, web search etc.) the hardware arms race ends and the price becomes sustainable. With the right tools preparing dynamic context for a conversation, models are used for their reasoning and not for their knowledge. And waiting even a minute or two for a model to prepare a response, evaluate it, and iterate to improve quality makes a huge difference.
"“It’s a real dollar investment,” says Tan, speaking for Claudeholics who go full blast. “You actually have to spend six to seven figures on tokens—I’m on a run rate to do seven figures this year.”"
https://www.wired.com/story/how-ai-agents-plunged-tech-world...
https://www.merriam-webster.com/dictionary/sticker%20shock oh. i thought it was about people putting up stickers like https://mastodon.social/@spellingmistakescostlives@mastodon....
> "Most people default to automating tasks they dislike rather than tasks most valuable to the company," Sophia Velastegui, CEO of Velastegui Ventures and former chief AI officer at Microsoft, told Axios. Instead, they should focus on using AI to drive revenue.
That's right! Work, slave, pain away every day! How dare you make your life a bit less miserable?!
There's a very real possibility that we end up with a bunch of companies simultaneously realizing that they just don't have any good ideas to leverage AI on and rushing more code out the door doesn't lead to tangible value. Many execs are going to realize that they're no longer growth companies and that paying $500k a year for engineers no longer makes sense...
If that's the case, then I do expect the AI bubble is going to pop spectacularly next year as token budgets are going to collapse. The damage to the tech industry is going to be catastrophic. If you think the job market is bad now, wait until data center spending goes off a cliff.
The AI fever pitch has done a great job at exposing which companies were run with a degree of sanity, versus who bought blindly into the hype train narrative of worker replacement and went all-in.
Look, LLMs thrive when they’re given structured data that’s well annotated, clear direction, and treated as the probabilistic machines they are. Not one of those meshes with the AI narrative of “works on existing stuff, requires minimal guidance, and can behave deterministically.”
I said as much in 2024 when my employer at the time was grading folks on AI usage while my role was entirely deterministic in nature. It didn’t resonate with specific leadership then, it doesn’t seem to be doing so in the larger market now, and unfortunately not one of these dolts will suffer any consequences for their organizational myopia.
> Instead, they should focus on using AI to drive revenue.
There is a complete disconnect between wages of employees and company's revenue => Why aren't employees working towards revenue? What a mystery. Children, let's help Elmo solve this mystery.
And then random mass layoffs to make numbers for shareholders look great in quarterly reports. Surely this motivates people work to their fullest potential and to care for company's revenue.
The classic blunder of believing that there is, in fact, a "free" lunch.
Just wait until companies are dependent on on it. When their employees can't think without it. When their AI generated codebase is such a mess they'd need a rewrite to understand it without AI. When they've got AI embedded in all their internal processes and tools. Then massive price hikes will come because they've been bent over a barrel and they'll have no alternative that isn't at least as painful in the short-term as letting the AI company fuck them. The long term won't matter then because any company capable of seeing past the short term wouldn't let themselves get into that position in the first place.
These two paragraphs were paywalled for me.
If I understood correctly, a few months ago Anthropic and OpenAI both started charging per-token billing at API pricing for Enterprise customers? i.e. representing roughly a 10x price increase? That's kinda nuts.
Similar discussion yesterday:
> Corporate leaders are starting to question whether soaring AI spending is delivering meaningful returns.
This is act one the AI bear market. Yes I know everyone screams “bubble”. Let me explain the scenario I have in mind.
1. AI booms because the technology seems to actually have promise of revolutionizing how work gets done. It can do your taxes! It can drive Excel! It can act as a CEO! It can code up full apps and SaaS products! It can replace this vendor or that! You know the drill.
2. Every company must in corporate AI or be seen as obsolete. Having a bad quarter? Announce that you are “seeking to explore opportunities to develop an AI integration plan framework” for your plumbing business. Massive AI compute buying happens. While two of the three major AI houses are not publicly traded proxies like Nvidia and RAM manufacturers are so the market rips higher and higher. Nvidia trades as if it is already 10+ years from now, every company out there has adopted AI perfectly, and it is delivering huge profits to them.
3. Reality checks start pouring in. Turns out that not only is AI expensive (a problem that presumably will be taken care of with time and development), but that the technology itself just isn’t suitable for everything. (IMHO it’s great at augmenting a power user but it is terrible at interacting directly with customers). We start seeing individual companies change tone on investment. They can’t stop it due to momentum but they are starting to shift the narrative to warn of what comes next. This is where we are.
4. Numbers come in. Earnings show what the actual ROI is. Some companies do benefit, but crucially we see examples of where investing in AI destroys value. I think this happens when replace crucial parts of their workforce with agents and find that they lost in-house expertise, when customers left due to worse products, or simply when AI was roughly as expensive as human labor without being significantly more productive.
5. The market stumbles. What do you mean AI won’t take over every corporate America?! Surely that can’t be right! Nvidia and other proxies flag.
6. In a late to the game rush Anthropic and OpenAI IPO fearing that the market has noticed that the emperor has no clothes. Their internal numbers turn out to be scary: very high revenue but no path to insane profitability. They quickly get included in QQQ and maybe even S&P500 but as their IPO price is the highest they trade they drag the broad market indexes down. This is leveraged by the Nvidia proxy status.
7. Infrastructure course correction. Hyperscalers who started huge datacenter buildouts cannot justify it. They pay contract penalties and get out of some of the projects, writing down losses. The market fully melts.
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I think there is a competing market downturn fueled by the affordability squeeze. Basically while AI spend is corporate driven, the biggest investors are consumer companies. Of the hyperscalers you can maybe argue that Microsoft is not a B2C fully but it is close. If consumers don’t have money to spend, hyperscalers take a hit, investment slows from there, and AI is hit directly by it.
I think either scenario is likely, it’s just which happens first. But right now the market is sprinting down a tight rope and trading like that tight rope has no end and that the sprinter never makes a mistake regardless of wind changes. Everything has to go right for a very long time to justify valuations. One stumble can stop it all.
"Most people default to automating tasks they dislike rather than tasks most valuable to the company,"
Well, no shit, but also: suggests those tasks have questionable value? And also: this is why I learned to write code in the first place.
Playing out in company after company right now:
CEOs: “Get me some of that GenAI”
CTO: “OK, we have all the GenAI”
CEOs: “Employees, it’s AI or bust”
Employees: Tokenmax
CFO: “Um, this is costing a ton and we’re not seeing savings or efficiency materialize.”
CEO: “Are we getting any value out of this?”
COO: “Not really, and frankly I’m getting annoyed at all the AI slop turning up all over.”
CEO: “OK, well, let’s do a big layoff and then I’ll just say it was because of AI. Hopefully folks won’t blame me for the mess and I’ll just talk about how amazing AI is.”
Well, if AI has a massive sticker shock attributed, so we should target the high value roles and should save money, right?
So im looking at CEO, CTO, CFO, and all the chief-something-officer. If LLMs are that totally amazing at thinking, then we should be targeting upper management, not the workers.
That would save a LOT of money for the shareholders! /snark
We all know why they wont.
>Corporate leaders are starting to question whether soaring AI spending is delivering meaningful returns.
We should start to question whether soaring CEO salary spending is delivering meaningful results.