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I disagree on the consumer side and I honestly can't really comprehend why people aren't talking about subscription prices like they ARE the prices, as consumers (and startups), the price we pay for IS that price, that it's sustainable or not on providers side, that's another matter and not our concern.

The reality is that there is more and more subscriptions available everywhere, and for $400 you can easily get $10K of tokens from Openrouter/official API pricing, you can pile up as many subs as you want and there is virtually no constrain once you make CC an API, you don't need Claude Code, you don't need Codex, you don't need Antigravity, you don't need Kimi Code.

We use now about 20 subscriptions (mixed, around $5K) and we average $60K-100K in token a month, THAT is the price, that's what's debited from our account. There is also many providers that resell tokens for cheaper, why wouldn't that be the price for us consumers?

If that changes in 6 months, so be it, but the current price of AI is that one.

PS: Many enterprises (maybe not large scale) have at least 1-2 subscriptions for each of their employees, so it's not really only startups & consumers.

If you are using AI like you use Netflix (something you like but can live without if it gets too expensive) then you have a point. But if you are relying on AI for your business, the cost of AI sooner or later is going to be passed on to you and it's going to impact your margins. And it's probably goong to be sooner, rather than later.

The amount of money these AI companies are burning is unprecedented and there is simply not enough money in the economy to keep it going at a loss for ten years. Google (Google!) went cash flow negative and is issuing bonds, basically asking for a loan. That's the reason markets are getting so nervous.

And yes, choice is good but how many of these new models are trained from scratch vs distilled from frontier models? If OpenAI and Anthropic go down in flames how much of the cheaper choices we see today are going to keep evolving? I am not trying to make a prediction either way, I am just pointing out the uncertainty. Which, again, is fine if AI is a commodity you can easily cut, but not great if your company is betting big on AI.

It's stated in many places that inference is profitable (margin 70% to 90%), only research is expensive. Hyperscalers are burning through their cashflow training new models while inference-only providers are printing money.

If all the research went away tomorrow, people are still going to sell inference at current prices or higher. There are already open weights competitive with proprietary models. There will be no lost capability.

If businesses find inference useful today then it doesn't have to drastically improve in a short timeframe anymore. History is full of inventions which became "good enough" and didn't improve much or at all for a long time.

eg: Western society runs on radial tyres which have seen only marginal improvements for the last 50 years.

(yes there have been some small improvements in compounds, tread patterns, TPMS, etc. hardly drastic revolutionary changes to the tyre industry)

> It's stated in many places that inference is profitable (margin 70% to 90%), only research is expensive.

It's been stated by the AI companies, which are known to routinely lie to our faces, and have a vested financial interest in making people believe they will be able to turn a profit at current prices. In other words, it's one of the most unbelievable claims out there, and you shouldn't believe it for a second.

Even assuming that inference is really profitable today's models don't learn new things by themselves, except in the limited sense of temporarily storing everything they need for a conversation in their context (and maybe leaving themselves little notes in md files like the guy from "Memento").

The difference with tyres is that If today's LLMs had been invented and had become "good enough" 50 years ago, you would have a cutover in their knowledge that excludes 50 years of information. Every "write me a program in Rust" conversation would involve LLMs filling up their context trying to learn Rust programming from scratch every time and probably doing a very bad job.

An example of that was when Fable disproved that mathematical conjecture and HN was full of other people who fed that information to other models (or Fable itself) and received incredulous answers from their LLMs. If something is proven true or false in math, the world of science moves on and that new piece of information can be used to build, prove or disprove other things. But an LLM is excluded from learning even from the very thing it just helped demonstrate and needs to re-discover it over and over again. In order to have an LLM that "lives" in a world where the Jacobian conjecture is false, you need to train a new model and add that information.

I think the article is more about the cost for AI companies like OAI and Anthropic to operate relative to what they’re making (not much).

So in a way your argument that users are able to spend 10k in tokens while paying a sub of 200 is actually agreeing with his point.

Agreed, I went a bit away from the article perspective which is why I talk mostly about consumers, however on the provider side, I don't think they are in a great danger, this is at a level of economics that most people (myself included) can't really comprehend, I highly doubt leaders in Finance would invest such large sums if they didn't have a comprehensive forecast of all eventualities, let's also not forget that they do have access to many predictive modeling, data, tools... that the general public can't access. They likely can continue operating at a heavy loss for a decade.

They can also leverage their own tech to crush most businesses in an automated way if they wanted/needed to in the future. Let's be real, a good 90% of all existing techs company could be replaced and automated entirely with the right approach and unli-tokens.

> this is at a level of economics that most people (myself included) can't really comprehend, I highly doubt leaders in Finance would invest such large sums if they didn't have a comprehensive forecast of all eventualities, let's also not forget that they do have access to many predictive modeling, data, tools... that the general public can't access

I would recommend to watch the documentary „Enron: The Smartest Guys in the Room“.

Meanwhile Deepseek V4 Flash seems to be pretty solid and is a fraction of the price of most other models.
Compare the cost of raising a bachelor's degree educated human and AI is still cheap.
I didnt realize employers are paying for your education. Or have you somehow replaced having offspring with AI too?
I have seen an employer approve a degree under training budget. Just needs the right employer.
Mine is paying for my masters right now. It’s not unusual.
too expensive, so far