[tskj]

Dario has publicly claimed each model has been profitable, even accounting for its training costs; it's just that each new model is exponentially more expensive to train than the last, so the income lags and it looks like the company is losing money overall.

Now, we can't know if this is true unfortunately, but it's not directly contradicted by anything that's known publicly at least. I thought it was an interesting way to frame it and makes the whole situation look marginally less bad.

[NorwegianDude]

A common extreme misconception is that inference is expensive and that providers are loosing a lot of money. Inference is extremely lucrative and profitable.

[drob518]

Inference is the phase where they make money. But the question is whether they can be profitable overall as training continues to balloon.

[tskj]

I think the case for this is pretty strong actually. Last year my company was maybe willing to pay $100 a month to Anthropic (per developer). Today we're all on the $300 plan without any hesitation. If Fable ever becomes available as the default model, I imagine my company would be willing to pay in the $500-$1000 range per month per developer.

[drob518]

Okay, but that still has a limit, right? Do training costs have a limit? Everyone is in the frothy stage of this technology wave and they continue to buy more, but training the next model requires exponential increases in model sizes to get the same sorts of model performance increases, which suggests exponential cost increases, too (even ignoring temporary cost factors such as RAM price increases). You say your company will double or triple what they are paying today; how far are they willing to go? At some point they are going to have to cut developers to fund it (e.g., cut half the developers and give the survivors each an AI assistant with $180k in token budget, captured from the salary savings), but that also presupposes the productivity gains are there to support it.

[tskj]

First of all, yes cutting developers to fund AI spend budgets, is the entire operating idea behind these AI companies; and most companies would love doing that. I'm not saying this is a good thing, my heading is on the chopping block like everyone else's.

But isn't this like a Jevon's paradox thing, also? If I'm able to become vastly more productive, and that value produces more sellable output for my company, there's no reason to cut anywhere to fund it. This is the same reason a company like Microsoft can hire 80 000 developers, it's because each dev pays for themselves in value (on average). I guess the same can be true for AI spend?

[4rf]

why are you listening to these idiots who have every incentive to spin the story as much as possible

FCFF = EBIT(1-t)-Reinvestment

I dont care about your gross profit - this kind of cash profit determines the value of operating assets.

[tskj]

Well yeah obviously they have to stop reinvesting more than they make at some point to become profitable. To be clear, I think what Dario was saying was that if you consider each model training + deployment as a company, meaning all expenses and taxes, it was still profitable.

Whether he's lying is another question, but seems unlikely.

[rimliu]

Wow, you actually think any of those AI companies are profitable? Would you be interested in some bridges?

[tskj]

No, but if you'd actually read what I wrote, you'd see that what I said (that Dario said) was: if you consider each model as its own company, they've all been individually profitable. However, they keep re-investing that (and then some) into even bigger, more expensive models each time, causing the company to look unprofitable.

That being said, Anthropic did report being profitable this review quarter (Q2), so it's not as unreasonable as you claim.

Also Google is a pretty major AI company, and they're _insanely_ profitable.