[etaioinshrdlu]

This is wrong because LLMs are cheap enough to run profitably on ads alone (search style or banner ad style) for over 2 years now. And they are getting cheaper over time for the same quality.

It is even cheaper to serve an LLM answer than call a web search API!

Zero chance all the users evaporate unless something much better comes along, or the tech is banned, etc...

[scubbo]

> LLMs are cheap enough to run profitably on ads alone

> It is even cheaper to serve an LLM answer than call a web search API

These, uhhhh, these are some rather extraordinary claims. Got some extraordinary evidence to go along with them?

[etaioinshrdlu]

I've operated a top ~20 LLM service for over 2 years, very comfortably profitably with ads. As for the pure costs you can measure the cost of getting an LLM answer from say, OpenAI, and the equivalent search query from Bing/Google/Exa will cost over 10x more...

[johnecheck]

So you don't have any real info on the costs. The question is what OpenAI's profit margin is here, not yours. The theory is that these costs are subsidized by a flow of money from VCs and big tech as they race.

How cheap is inference, really? What about 'thinking' inference? What are the prices going to be once growth starts to slow and investors start demanding returns on their billions?

[jsnell]

Every indication we have is that pay-per-token APIs are not subsidized or even break-even, but have very high margins. The market dynamics are such that subsidizing those APIs wouldn't make much sense.

The unprofitability of the frontier labs is mostly due to them not monetizing the majority of their consumer traffic at all.

[etaioinshrdlu]

It would be profitable even if we self-hosted the LLMs, which we've done. The only thing subsidized is the training costs. So maybe people will one day stop training AI models.

[clarinificator]

Profitably covering R&D or profitably using the subsidized models?

[guappa]

He was doing neither. He was using a 3rd party API and has no idea what it costs them to actually run it.

[throwawayoldie]

So you're not running an LLM, you're running a service built on top of a subsidized API.

[haiku2077]

https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch..., also note the "objections" section

Anecdotally thanks to hardware advancements the locally-run AI software I develop has gotten more than 100x faster in the past year thanks to Moore's law

[oblio]

What hardware advancement? There's hardly any these days... Especially not for this kind of computing.

[Sebguer]

Have you heard of TPUs?

[Dylan16807]

Sort of a hardware advancement. I'd say it's more of a sidegrade between different types of well-established processor. Take out a couple cores, put in some extra wide matrix units with accumulators, watch the neural nets fly.

But I want to point out that going from CPU to TPU is basically the opposite of a Moore's law improvement.

[oblio]

Yeah, I'm a regular Joe. How do I get one and how much does it cost?

[Dylan16807]

If your goal is "a TPU" then you buy a mac or anything labeled Copilot+. You'll need about $600. RAM is likely to be your main limit.

(A mid to high end GPU can get similar or better performance but it's a lot harder to get more RAM.)

[haiku2077]

Specifically, I upgraded my mac and ported my software, which ran on Windows/Linux, to macos and Metal. Literally >100x faster in benchmarks, and overall user workflows became fast enough I had to "spend" the performance elsewhere or else the responses became so fast they were kind of creepy. Have a bunch of _very_ happy users running the software 24/7 on Mac Minis now.

[oblio]

The thing is, these kinds of optimizations happen all the time. Some of them can be as simple as using a hashmap instead of some home-baked data structure. So what you're describing is not necessarily some LLM specific improvement (though in your case it is, we can't generalize to every migration of a feature to an LLM).

And nothing I've seen about recent GPUs or TPUs, from ANY maker (Nvidia, AMD, Google, Amazon, etc) say anything about general speedups of 100x. Heck, if you go across multiple generations of what are still these very new types of hardware categories, for example for Amazon's Inferentia/Trainium, even their claims (which are quite bold), would probably put the most recent generations at best at 10x the first generations. And as we all know, all vendors exaggerate the performance of their products.