[nr378]

Dario has made a specific cohort argument here. His numbers (from various interviews) are: you train a model in 2023 for $100M, deploy it, and it earns $200M over its lifetime. Meanwhile you train the 2024 model for $1B, which goes on to earn $2B. Each vintage returns 2x on its training cost.

However, the GAAP P&L tells the opposite story. You book $200M revenue in the same year you spend $1B training the next model, so you report an $800M loss. Next year you book $2B against $10B in training spend, reporting an $8B loss. The business looks like it's dying when every individual model generation actually generates a healthy profit.

That's actually Dario's answer to your depreciation question. If each cohort earns back its training cost within its natural lifespan (however short that lifespan is), the depreciation schedule is already baked in. The model doesn't need to live forever, it just needs to return more than it cost before the next one replaces it. Whether that's actually happening at Anthropic is a different question, and one we can't answer without audited financials, but it's the claim Dario makes (and seems entirely reasonable from a distance).

[calvinmorrison]

GAAP doesn't work here really. the R&D treadmill means you are always betting on next year and its NOT inventory or something you can defer your cost on. It's an upfront R&D expense.

so what happens on year 10 when Anthropic hits a $10B training and only returns $8T? they're cooked

[Verdex]

Yeah, that's kind of what I'm wondering about.

It's an interesting story about how even though all metrics show massive losses actually they have massive gains.

Accounting is a rather mature field, so I figure that someone in the past has tried this stunt and there should probably be ways for dealing with it.

Or do they always flame out after losing all the money? Knowing the history here would be informative.

[elbasti]

If those numbers are correct, then my assertion that "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is incorrect.

And I admit that I made that assertion from my gut without actually knowing if it's true or not.

[lovich]

If you have to continually spend greater amounts of money to keep up with the competition on every new model then it is dying.

Every single time a company comes around and goes "Actually GAAP are wrong, look at my new math that says were good" its led to much wailing and gnashing of teeth in the future when it inevitably isnt.

[Verdex]

That's an interesting idea. I'm curious, though, are there any other industries and/or companies that have tried to pull this sort of thing off? And what ultimately happened to them?

[stusmall]

Enron had a system like this. They regularly worked on large, long term contracts that became profitable over years/decades. They wanted to push rewards forward so would estimate the total value of the contract and book the profit when it closed. Mark-to-market accounting wasn't unheard of the time but using it for assets without an active market was unique. Without the market to make against, the numbers were best guess projections.

The problem is everyone along the line is incentivized to be aggressive with estimate (commissions for sales are bigger, public financials looks better) and discouraged from correcting the estimates when they go wrong.

Estimating multi-year returns on frontier models looks harder than estimating returns on oil and gas projects in the 90s.

[yunwal]

The bar for "wildly unprofitable" has risen quite a bit since then, but Amazon basically pioneered this.

[kikimora]

Why would anyone use 200M model when 1B model is available? The company increase its bet with each iteration increasing risks. It blow up at some point because it cannot guarantee 2B return after 1B investment.

To GAAP point - 200M or 1B or 10B is not a loss but cash converted into an asset. It won’t affect the bottom line at all. Unless the company re-evaluates the asset and say it now cost 1M instead of 200M. This would hit the bottom line.

[skybrian]

If you can remember where you read it, could you share a link?

[Avshalom]

https://youtu.be/GcqQ1ebBqkc?t=1027 is on such but he doesn't actually say that each model has been profitable.

He says "You paid $100 million and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume in this cartoonish cartoon example that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model is actually, in this example is actually profitable. What's going on is that at the same time"

importantly you'll notice that he's talking revenue, and assumes that inference is cheap enough/profitable enough that 100M + Inferance_Over_Lifetime < 200M