AI's Affordability Crisis

I work at a Fortune 200 company. At first, it was the Wild West. Need an LLM? You got it. Need to or want to build an army of agents? Done and done. We literally had everything at the tips of fingers for about 3 months. Teams were building their own internal tools, the team I work on canceled contracts with several software vendors because teams were building the same tools for what they thought was nothing.

Then they signed contracts with Anthropic and Google because I would assume they saw the token usage was through the roof. One month later? They completely cut off access to everybody for both Claude and Gemini. If you wanted access? Suddenly it was several forms, along with several approvals and a rock solid business case why you needed it. And before you got to the forms? You were added to a waiting list that was thousands of people long.

The entire company is now in damage control after trying to get the genie back in the bottle. I'm guessing someone saw how much we would be paying for the tokens we'd been using and decided to shut the party down so to speak.

Over the last month I have seen companies scrambling to measure deliverables against cost. Most of the back room talk is to the affect of giving devs a small allowance ($500 a month) and then making them prove their own productivity increases (again, based on deliverables, not LoC) before they either take it away or give them more.

Obviously this won’t be on an individual basis but some kind of unit.

Either way, with how much I see these companies cutting back I have no idea how the big AI companies are going to be profitable.

If you're paying an engineer $X and they're getting 3x the amount of work done you should be happy paying up to $2X in AI tool usage.

In reality many companies start complaining at their employees when they hit $0.1*X or less.

It's more about the level of abstraction. If AI handles 80% of the grunt work and I spend my time on architecture and reviews that's still a win

Rational takeaway is to step back and analyse what's really happening here.

- Are we really in for a crash?

- What does it say about the culture and people's mental models that we have two radically opposing viewpoints on AI costs and people still arrive at same conclusion?

I'm not sure that's something to rely on. I would be Fable 5 will be phased out and the bleeding edge will be priced up.

Weird, why didn't my subscriptions decrease in price then? Oh wait..

If true then why are neither Anthropic or OpenAI dropping their API pricing to gain market share when both are clearly doing all sorts of political and PR maneuvering to compete in a cutthroat market?

Since they aren't dropping the API usage prices (and are in fact raising them in a lot of subtle ways) then one of these options almost has to be true: they are still subsidizing inference, training costs are so ridiculously high that they need to make huge profits off inference or collapse in on themselves, or they are price fixing.

Only reason deepseek is so cheap is because well I don't know, but actual pricing should be around their initial price which was 4x, at that price you have a healthy 25-50% margin based on occupancy, given the deepseek v4 is a very sparse moe model.

GLM 5.2 for example doesn't have more than 30-50% margins that's assuming old pricing for GPUs, current inflated GPU pricing well I am certain the margins must be lower. Ofc you can host for cheaper with quantization, and if you have very consistent capacity/utilization, which is not the norm with AI workloads.

Overall for large models like GPT 5.5 or Opus there must be healthier margins of around 50-70% assuming GPU pricing didn't increase for these companies. Even if it did 30-40% margin should be possible, even in worst case assuming all GPU they had saw a jump in pricing.

For smaller models it's hard to say, I would guess 20% but these models might be much smaller than I suspect, then it might be double that.

Note the issue is less intelligent tokens don't linearly scale down in memory usage, which is the biggest pain point of serving models. Context sizes have fucked us all.

Also anyone claiming OAI makes less margins on APIs or stuff might be wrong given they are on much lower context size, 1M context definitely is a lot more expensive to serve especially with smaller models like sonnet.

  > Neither Anthropic nor OpenAI allow Business nor Enterprise customers access to the high value $200/mo plan. 

> Neither Anthropic nor OpenAI allow Business nor Enterprise customers access to the high value $200/mo plan. Both organizations have moved to a "cheaper plan per user + API Pricing after that" (e.g. $20/mo + usage).

I actually think that even the API pricing of OpenAI and Anthropic are still subsidized. I don't think they make any profit on inference when you factor in depreciation. They likely still operate that at a loss.

It's no coincidence that Anthropic only had a "profitable" EBITDA with not paying Elon for compute for a bit of time, and when EBITDA curiously ignores depreciation. Models grow stale over time, as knowledge is not static.

Btw, some Chinese corporates have already seen this and increased their price. Zhipu AI & Tencent for example. Alibaba, Baidu, and Tencent also announced multiple price increases for their AI services.

If apparently the only way you can make money with your product this early is to dilute and adulterate it behind the scenes, it strongly suggests you want the customer to continue to believe they are getting value that you can't afford to supply.

More prosaically: if either of these firms could prove that they were even really close to profitable on inference, they would have bloomin' said so while they were trying to raise more money.

This is the crisis point for vibe-coders. A developer can go back to writing code by hand, as horrible as that might sound. Someone who hasn't learned to code but builds with AI can't go back. They either pay or they stop. That will be an painful choice whichever way you fall.

I would assume when price hikes happen either 1) less non technical people would vibecode as it doesnt impact the work that much 2) people use the cheaper chinese models 3)we're jamming ai into everything because were exploring. We will just niche down into use cases that provide high roi

If you zoom out to the year 2100, it becomes a little pimple on the economy that is ready to pop, but in the here and now it can cause a lot of damage to real people's wages and finances over the next 3 years.

The funniest comment here. Have you seen the prices of the technical shit for the past two years? Dang, GPUs are not getting any cheaper, but more expensive with each year.

Here's a concrete example. Does some random AI company make operating profit on inference? I.e. if you only kept marginal costs, would you make a profit?

Well, depends what you account as your costs. If you're using hand-me-down hardware from previous generation's training, how much do you charge yourself internally for it? Maybe you show less, so investors take solace in profitable inference, even if you're losing money overall. How exactly are you accounting for electricity costs between training and inference? Is your army of SREs mostly servicing training new models (R&D expenditure) or inference (operating cost)?

This even has a name, and is called the "big bath" approach. If investors expect one part of your business to be a fiscal black hole, just shove all your costs there. They are accepting of it, and you make the rest of the business look better.

I'm not accusing AI companies of cooking the books, rather I'm trying to highlight you could see all the cash flows and still not know how much money is made or lost where.

Having growth up in the 90s, it is weird seeing companies share their technology secrets publicly.

Eventually the frontier labs will try to cut out the middle man once these models prove themselves and start doing partnerships with big firms in the domains, so they can take a % of the profits in perpetuity rather than just taking a one time payment. For example, after Anthropic Galen, they'll do a partnership with Pfizer to generate Ozempic-Superjacked and take 20% royalties on global sales.

But next year we could be in the middle of a massive $600B/yr capital-spending bubble deflating hard with unemployment accelerating towards 10% (or higher).

The internet never failed, but the telcom/dotcom collapse still happened in 2001.

The big push for regulation and export controls is only going to ensure OpenAI & Anthropic are more like the automakers. Only in business because of protectionism, left to screw over US consumers meanwhile the rest of the world gets to enjoy cheap EVs

There is going to be a point soon where HN is just ai models posting ai articles to be filled with ai comments and for what reason exactly? I guess to try and train new ai slop company products into the datasets of various ai models to capture the budget spend of some ai middle manager model.

Yes they can do ads, but if they try to be subtle they will likely (eventually) be hit with fines.

Though, do the current rules apply to AI? Likely unclear. But if this becomes a problem I would expect new consumer protection regulation to be introduced aimed at this specific issue.

Do these knowledge jobs have a significant corpus of not only knowledge but discussion and problem solving, all conveniently labelled for the AI to train on? Probably not. Coding has stack overflow, what does, say, advertising use?

I think you forgot what super-intelligence means…

I find it hard to imagine it would ever be cost efficient vs hosted/cloud i.e. you should always be able to run faster and/or better models remotely at a comparable price since its just way more efficient due to batching

As a localLLM evangelist, I am hopeful this will bring more attention to the joys of rolling your own sovereign AI.

On the other hand, there's two AI labs, that could afford to eat your profit, because what are you gonna do? They're your entire labour force.

Well, OpenAI and Anthropic are racing to IPO for a reason.

They will need every bagholder they can get their hands on.

Cheap, but gave them a massive user base they can claim is using AI

We have a pretty good idea of how much it costs to serve these models. You can pencil out the economics and guess at the model sizes and we know pretty decently how expensive the hardware is.

This like claiming it's meaningless to guess the margins of a restaurant without going into their books and seeing the exact recipets and recipes.

They ain't doing dark arts in the back. You can guess at what goes into the food based on similar recipies and how much that costs based on what you pay at the grocery store.

I didn't get the sense this was LLM-written, but typo-signalling is... I donno a bit weird. Firefox is underlining some of the words as I write. I'm leaving "donno" unchanged even though it's flagging it as a misspelling but I suppose I'd still opt to fix something like "maiinstream" even at the risk of potentially seeming more LLM-ish!

If you decided to boycott every company that replaced staff with automation, you would be forced to exit the economy. Every company does this to some degree and the customers who vote with their wallet do not seem to care about a reduction in force.

[1]: https://arstechnica.com/ai/2026/06/gm-installs-robots-at-fla...

Please tell more :). Do you pay per token from bedrock / openrouter / somewhere else? How many tokens you use over the month, and how many for each task? Which harnesses?

How do you know that the other models you are referring to aren't subsidized?

The math doesn’t math.

For awhile it was every 2-3 years you'd start a hardware refresh. As companies moved into more and more training, this timeframe started to shrink. It went from 36 months to 24 months. From 24 months to around 16-18 months. Last I checked last year, it was at 12 months. I think things may have slowed because of component availability, but otherwise whole data centers would be 6-12 months into full operations before they would start a refresh cycle.

Not to mention the massive increase in power density demand and cooling demand per rack that entails.

So no, "AI costs" have not gone down, in fact they are more expensive on training AND inference than ever.

This is why many are concerned about the heroin drip of api costs into orgs. For the companies that are public, look into their financials. It's gonna hit companies and high volume users like a ton of bricks.