AI's Affordability Crisis

zoobab

Spelling mistake: "a return on these invetment"

simianwords

This is basically bunk because AI costs have gone down by 50x or more (api costs) since 3 years.

jschveibinz

I don't have a crystal ball, but based on similar historical scenarios, I think that one or two of these companies will win--probably because of some unique application, delivery or trade secret that will drive 80% of their revenue. Consider Google, Apple, Amazon, etc. It's still early days...

trollbridge

The article fails to mention DeepSeek, Alibaba, Qwen, Xiaomi, MiMo, z.ai, or GLM. It's hard to take such an article seriously that doesn't do this. (Our monthly total spend is around $180 with a team of 6, about half technical; our biggest line items are for American models or subscriptions which we probably will be planning to get rid of.) And then remarks like this: Anthropic, OpenAI and Microsoft have all now transitioned customers from subscriptions to token-based pricing. Huh? I use OpenAI via a subscription, as is anyone else using GPT-5.5-Pro who isn't a multimillionaire.

knuckleheads

Shouldn't we know a better answer to these questions once Anthropic's IPO materials surface publicly? I understand, and maybe even expect, SpaceX's materials to be all over the place and skate on by any discussion of unit economics, but the nerds over at Anthropic might just be forthright enough to just tell us what their margin is on tokens as part of their IPO.

HDThoreaun

I really can’t stand when writers point to the difference in price per token on the api and subscription and use that as evidence that inference loses money. This author even says it’s implausible that the api charges 4x marginal cost when I think it’s very likely even higher than that. The entire rest of the post sits on this faulty assumption. Fixed costs don’t matter when marginal revenue is profitable and growing rapidly. The ai labs only have 2 questions. Can they prevent users from switching to open source models? Can they scale the number of users on enterprise plans the way they did for coding but in a more general way for all knowledge jobs?

tacone

My take is that Anthropic and OpenAI simply are NOT competing on price. 2 big players are often not enough to create tension on price. Chinese models and open model providers are, indeed, competing on price, and the difference shows.

fny

The unit economics might be just fine. We'll know more after IPO. The drug dealer analogy has a darker side to it, however. Once your dependent, they can drive up the price just because . It doesn't need to be for existential reasons.

steveBK123

I think the biggest problem is not necessarily the cost to develop & serve the models, but how quickly user behavior changed with token based pricing. I know a lot of people at companies where the marching orders changed on a dime end of Q1/start of Q2. These are shops that were fully on the "use AI or die (because we will fire you)" train. Now there's monitoring, reporting, alerting not just on overall cost but on "over-use" of best/priciest models based on total-or-percent tokens/dollars, etc. All of this comes with direct developer engagement & standardized management escalation for holding it wrong. To me this customer behavior does not smell like a product you can 10x the pricing on to get profitable. We have exited the exploration phase and now ROI matters.

sleepybrett

It's funny when you watch the doomscroll all these anthropic guys talking about how you should be writing self-improving loops and that's all they do. Of course that's all they do, they don't have to pay for their tokens.

Catloafdev

Affordability is not the current goal. Vendor lock-in is the current goal. Consumer prices are a drop in the bucket comparatively.

GodelNumbering

I don't see any real point being made in (or point of) the article. The author sort of just...dumped a bunch of links with the noise that is so incredibly mainstream at the moment that I doubt any of it is news to anyone even somewhat tracking the AI cycle. Most of it (except for maybe the BLS[1] stat) is just regurgitation. [1]: And this too is incorrect, should be " the number of jobs displaced would be around 32.5M" (the post says 32.5K)

raincole

> OpenAI Had $13.07 Billion In Revenue, $34 Billion In Costs and Expenses, and $20.92 Billion In Losses, with a net loss attributable to the company of $38.53 Billion This is going to be the new most misquoted/misunderstood data of the year, isn't it? The cost is mostly from a one-time accounting situation due to their pivot from a non-profit organization.[0] If we trust the leak [1] OpenAI is likely turning profitable this year. [0]: $30Bn of it is the one-time cost. https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb... [1]: I suspect OpenAI itself leaked that financial report. It's almost unbelievably healthy.

827a

> Zitron's numbers don't tell us the real cost of generating tokens but, subject to the assumption that the platforms are not subsidizing the token price, that means Anthropic is subsidizing their enterprise customers by up to 40 times, and OpenAI up to 70 times Neither Anthropic nor OpenAI are subsidizing enterprise customers. 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). The $100/$200/mo plans are for individuals only (of course, many individuals use these plans at work, but that's beside the point; they aren't selling this plan to enterprises). > SemiAnalysis also analyzed the platform's gross margins, implausibly assuming that tokens were priced at 4 times the cost of generating them and: With the current subsidies, all it takes for a user to have a gross margin of at best negative 25% is for them to use as little as 25% of their rate limit. The article's source for this claim is not SemiAnalysis; its Zitron. But once you dig through his article, Zitron links to a SemiAnalysis tweet [1] where they, as the paragraph states, implausibly assume gross margins of 75% to come up with their weird analysis of the subscription plans. Citing this for anything is weird, because afaik that 75% number is a total shot in the dark. We have no clue what their margins are. My take is that the only reason that 75% number is implausible is because it may underestimate the inference margins of Ant/OAI's API pricing. [1] https://x.com/SemiAnalysis_/status/2064815045767213400?ref=w...

avereveard

> Anthropic is subsidizing their enterprise customers by up to 40 times, and OpenAI up to 70 times might as well be the other way around with non subscribed token being 50x overpriced, or any combination thereof also uber was non profitable for the longest time, raking up 31b in losses, on the bet of capturing the market worldwide. scale here is different, but it's also 10 years later, with a lot more volatility and floating cash in the market (voo grew 327% over that period, not unreasonable that round size grew on the same trajectory)

cmiles8

The math doesn’t add up and the wheels are starting to come off the bus. The conversation in a lot of wealth management offices has shifted dramatically in the last few month from “how do I get in on this AI thing?” to “how do I protect my assets when this AI stuff blows up.” There’s little question now if this will all implode, just when and who’s going to lose their shirt and be left without chairs when the music stops. What’s playing out now is the scene from The Big Short where the banks wouldn’t mark down the value of bonds until they secured a short position. Once the big money has their helmets on it will stop providing fuel for the bubble and then look out below!

holyknight

Most of the "affordability" and "pricing" discussion is pointless because we don't have any real numbers on their margins per token. So, yes, they are subsidizing their subscription plans compared to the API prices, but the API prices could already be stupidly inflated, so the relative price comparison is a nothing burger. Until we know (or at least get a hint) on their margins on API prices, any pricing discussion is pointless.

titzer

The coming AI enshittification is going to be epic. For those of us who have been on the web for more than five minutes, we can see this a mile away. If you think search ads are annoying, pre-roll YouTube ads are annoying, streaming ads are annoying, or basically ads-on-any-screen-anywhere-at-any-time are annoying, just wait until every stupid thing is powered by AI and is subtly trying to manipulate you to buy/watch/believe some crap all the time .

evrydayhustling

The willingness to throw capital at AI is definitely doing some crazy things, but this article has some bad takes on the data. > [Ratio of per-token cost to subscription cost] means Anthropic is subsidizing their enterprise customers by up to 40 times, and OpenAI up to 70 times Actually, they could be subsidizing by more (if they are taking a loss on API), or not at all (if they are soaking API customers by a massive margin). Separately, these subscriptions get sold to large groups with varying usage, so it's crazy to model assuming every subscription is maxed out. Banks, gyms, and many other businesses work this way, offering consumers flexible access to services that they will realistically use in bursts. It's not always worth the complexity to prevent overuse by a small minority. You can feel like this kind of business model isn't as transparent, but it's silly to pretend it can't work. > OpenAI spent 44% of their revenue [$5.3B] on sales and marketing! The hype needed to keep the AI bubble inflated is incredibly expensive. Over that same period (2025), OpenAI added $10B in realized revenue and $14B in run-rate. Sounds like they're getting >2X return within 12 months of those go-to-market dollars. Compare that to like, any other business. > Thus in recent weeks the idea that Generative AI (LLMs for short) is too expensive has been all over mainstream business media. Would it be smarter for these companies never to test customers' price tolerance? The quotes following this make it seem like the companies are getting important information about the nature of that price tolerance, and preparing to react. This is the work markets do on both sides to understand the value of a new product. There are lots of good arguments about AI overinflation, but in order for them to be useful, they have to be rigorous and targeted.