Unit economics needs to include the cost of the thing being sold, not just the direct cost of selling it.

Unit economics is mostly a manufacturing concept and the only reason it looks OK here is because of not really factoring in the cost of building the thing into the cost of the thing.

Someone might say I don’t understand “unit economics” but I’d simply argue applying a unit economics argument saying it’s good without including the cost of model training is abusing the concept of unit economics in a way that’s not realistic from a business/economics sense.

The model is what’s being sold. You can’t just sell “inference” as a thing with no model. Thats just selling compute, which should be high margin. The article is simply affirming that by saying yes when you’re just selling compute in micro-chunks that’s a decent margin business which is a nice analysis but not surprising.

The cost of “manufacturing” an AI response is the inference cost, which this article covers.

> That would be like saying the unit economics of selling software is good because the only cost is some bandwidth and credit card processing fees. You need to include the cost of making the software

Unit economics is about the incremental value and costs of each additional customer.

You do not amortize the cost of software into the unit economics calculations. You only include the incremental costs of additional customers.

> just like you need to include the cost of making the models.

The cost of making the models is important overall, but it’s not included in the unit economics or when calculating the cost of inference.

That isn't what unit economics is. The purpose of unit economics is to answer: "How much money do I make (or lose) if I add one more customer or transaction?". Since adding an additional user/transaction doesn't increase the cost of training the models you would not include the cost of training the models in a unit economics analysis. The entire point of unit economics is that it excludes such "fixed costs".

There is no marginal cost for training, just like there's no marginal cost for software. This is why you don't generally use unit economics for analyzing software company breakeven.

You can amortise the training cost across billions of inference requests though. It's the marginal cost for inference that's most interesting here.

The thing about large fixed costs is that you can just solve them with growth. If they were losing money on inference alone no amount of growth would help. It's not clear to me there's enough growth that everybody makes it out of this AI boom alive, but at least some companies are going to be able to grow their way to profitability at some point, presumably.

But what about running Deepseek R1 or (insert other open weights model here)? There is no training cost for that.