You have LLM derangement syndrome, and don’t understand.

Say the first model cost $2 to make. On metered sales, they’ve made $10 on it.

They then decide to make a $20 model, raising more money. It turns out, that model made $100.

They then decide to make a $1,000 model. That model made $5,000.

There are two possible paths for their shiny new $10,000 model: either it will be a better market fit than the 1k model, or it will not.

If it is a better market fit than the 1k model, then it seems very likely that at some point it will make more than $10,000 (2x the prior model’s utility).

If it does not provide better value, then you can scrub that model, and keep selling the $1k model. Eventually it will likely provide the additional $5k of investor capital back through profits.

What we have seen is this above scenario, with a couple twists: first, the training (capital investment) decisions overlay the useful life of the prior model, so you have to tease out the profitability when you think strategy. Second, it turns out there’s quite a lot of money to be made distilling models the market likes into models that give like 90% better profit.

So, these businesses paying billions of dollars to train frontier models are absolutely rational actors. They are aggressive actors, engaged in an arms race, and not all of them will survive. But right now, with current inference demand, if all the global training capital dried up, (and therefore we are stuck with current models for some time), they would become highly, highly profitable companies during the period where fast followers tried to come in and compete on price.

OpenAI claims that each GPT generation has sold enough inference at high enough margin to recoup the cost of training it.

The company overall is still not profitable because these proceeds are being used to fund training the next GPT generation.