I'd like to get in front of a whiteboard with someone who knows economics and the token providers businesses well enough to answer my "explain to me like I'm five" questions. But I'll start with these in here:

Is my observation correct that for the token providers this is a margins game, while for the consumers this is a quality of service/product game? If the quality:margin lines will cross at some point on the x-axis, is the race is to reach this point before running out of money? If yes: What historical examples are there where the delta between these two is huge?

I'm guessing LLM's are unique in a sense, since there's really no limit to how good a consumer of the product expects it to get? (Compared to for example email which is much easier to scale in regards to compute.)

Also extreme noob at life question: Why would you want to IPO before having a sustainable business model? What's the upside?

They maybe running at loss after all the salaries and stock comp, but tokens are in profit now.

Perhaps OpenRouter can be used as a benchmark for commodity cost to serve AI. I keep hearing it's better value than Claude, which suggests to me that either Anthropic is especially inefficient for some reason, or they're turning a profit on inference. They could be losing money on training, but I suspect that's just part of the cost of staying a leading lab. If any single one goes under due to debt etc. then companies can just switch?

Github Copilot has been doing this with business and enterprise seats, but that will be coming to a head very soon. I expect a fast follow after june when they re-align consumer pro and pro+ accounts.

OpenAi seems to be trying to throw tokens at clients to get lock in. So i'd be most worried about the rug pull that will come from open AI post IPO. Anthropic is already acting responsibly in this area and github copilot is attempting to remediate their insane subsidies in the next several months.

1. Training is expensive. Not just compute but getting the data, researchers salaries etc 2. You have to keep producing new models to ensure people use your inference and there seems to be no end to this. So they have to pour more billions to keep the cycle going on 3. People salary and other admin cost are not that high compared to 1 and 2.

--You lose control over their "salary"

--You lose control over their "schedule"

--Your company becomes reliant on another party that does not share your interests or values, and can stop working for you on a whim for any reason

But AI is definitely good and trade unions are definitely bad, apparently...

Uber raised prices some, but mostly squeezed drivers harder. When Uber started, driving for Uber was a well paid job. It isn't, now. AI companies are mostly capital cost, so they don't have the oppression option.

Hardware price/performance may not improve much near term. Graphics GPU price/performance hasn't improved much in the last decade. DRAM prices have gone up. Fabs are all booked up. NVidia says not to expect better price/performance before 2030.

More efficient, specialized models are a strong possibility. Dumping all of human knowledge into a coding tool may be unnecessary. Although this would work a lot better if the LLM crowd figured out how to get a reliable "I don't know" answer out of a small model, then call on a bigger one for help.

Many companies use models deployed on Azure/Bedrock etc are already paying based on usage (often with discounts).

Those same companies are getting sweetheart deals with the frontier AI labs in the hope that infrastructure costs go down enough in the future to invert profitability, but it's still a risky position for them to be in. (Having their own infrastructure gives the bigcos huge leverage, even if it's only 80% as good as frontier.)

If you increase the price, the value is still astronomical in comparison.

Companies need to find a way to leverage local models in tandem with frontier models to offset the costs.

It’s all about targeting specific workloads with the appropriate AI. These tools are not sentient beings they are tools that need to be properly configured to match the job at hand.

It's clearly llm-spew in its mannersims, making me wonder if there were any nuggets of wisdom in its core or if it in entirety is part of some llm-driven blog spam project?

So the frontier model companies might have crazy valuations and they might never reach that. But that might not mean they are actually unprofitable.

And some parts of most publicly traded ones.

If it’s not a bootstrapped company with a single offering, there’s a highly likely something there doing is at a loss in the name of growth (and even there, the loss might come in the form of deferred compensation)

The best course of action is to take advantage of subsidy for awhile, but not integrate is so deeply one can’t retreat. You’ll still have full productivity, just be cognizant of the reality of the situation.

Hopefully the market eventually collapses to where companies are hosting their own inference, and you simply lease a model package to run on your own (or rented ) specialty hardware.

There will be a repricing for sure as any ends of subsidies does but the world will not end

* People keep finding ways of cramming more intelligence into smaller models, meaning that a given hardware spec delivers more model capability over time. I remember not that long ago when cutting edge 70B parameter models could kinda-sorta-sometimes write code that worked. Versus today, when Qwen 27BA3B (1/23 of the active parameters!) is actually *fun* to vibe code with in a good harness. It’s not opus smart, but the point is you don’t need a trillion parameters to do useful things.

* Hardware will continue to improve and supply will catch up to demand, meaning that a dollar will deliver more hardware spec over time. Right now the industry is massively supply constrained, but I don’t see any reason that has to continue forever. Every vendor knows that memory quality and memory bandwidth and the new metrics of note, and I expect to start seeing products that reflect that in a few years.

I hope that one day we’ll look back on the current model of “accessing AI through provider APIs” the same way we now look back on “everyone connecting to the company mainframe.”

What? Anthropic's costs aren't the API rate. The article never attempts to estimate that cost, which renders its thesis tautology.

1. GenAI companies are making a loss in order to gain adoption and later lock-in

2. ???

3. They're going to cash-in soon and start milking you now that business critical systems rely on GenAI

The "???" denotes a complete failure to offer compelling arguments that link 1 and 3.

Meanwhile datacenters put out more pollution and use more electricity than all the plane rides Bill Gates took with Epstein combined, for business meetings of course.

There's plenty of sand on the planet and clever people (and AI) figuring out how to do more work with less sand and power, so any argument that AI is going to cost so much that it won't be usable, seems just preposterous.

The software world is, by and large, no longer about making products with a focus on the long-term, whether that's about the customer's well being or even the company's own long-term functioning. It's about trapping people, siphoning their money, then running away after setting the building on fire. Founder McBuilder will throw away his entire userbase and tell them "lol idk good luck" about their usage needs if it means he can make an extra dollar.

This is as true for enterprise as it is for consumers. Look at all the lamenting when a liked name gets bought by venture capital or considers an IPO.

We all know every frontier AI lab is heavily subsidizing usage, and so do all of the VCs & CEOs funding them.

But also... is this shit AI written? I'm so tired of this.

Colour me skeptical on that one. Unless the AI improves a lot so it makes sense to spend more.

Who said it was?

> Pull out the napkin. This matters.

The article wouldn't exist if you didn't think it mattered, just tell us why.

> the question is not whether they got a good deal. The question is

Who said that was the question?

> This Is Not One Company's Problem

Who said it was?

Stop telling us what thing aren't, just speak like a normal human and convey your own thoughts. It's an insult to your audience to throw constant AI slop at them.

> thousands of companies have woven AI subscriptions deep into their operations. Marketing teams draft copy through ChatGPT Plus.

Yea I bet you do..

Did OpenAI switch from fixed prices per seat to usage based? This will surprise many companies I reckon.

Personally I use Claude Code, the 200 euro plan. And am a heavy user. A few weeks ago I realized that CC shows the token usage in cli, in the bottom right. Something I never cared about because I thought paying 200 euro a month will give me „unlimited“ access.

But I guess the party is slowly coming to an end? Prices are going to increase slowly? And the flatrates will be removed eventually?

Too bad, it was nice while it lasted.

I will continue to use it as an assistant that does the menial stuff quicker than I ever could, but it's just too early to let it do stuff that would hurt if it disappeared. Enjoy it while it lasts.