According to Dario, each model line has generally been profitable: i.e. $200MM to train a model that makes $1B in profit over its lifetime. But, since each model has been more and more expensive to train, they keep needing to raise more money to train the next generation of model, and the company balance sheet looks negative: i.e. they spent more this year than last (since the training cost for model N+1 is higher), and the model this year made less money this year than they spent (even if the model generation itself was profitable, model N isn't profitable enough to train model N+1 without raising — and spending — more money).

That's still a pretty good deal for an investor: if I give you $15B, you will probably make a lot more than $15B with it. But it does raise questions about when it will simply become infeasible to train the subsequent model generation due to the costs going up so much (even if, in all likelihood, that model would eventually turn a profit).

> if I give you $15B, you will probably make a lot more than $15B with it

"probably" is the key word here, this feels like a ponzi scheme to me. What happens when the next model isn't a big enough jump over the last one to repay the investment?

It seems like this already happened with GPT-5. They've hit a wall, so how can they be confident enough to invest ever more money into this?

I mean, this is how semiconductors have worked forever. Every new generation of fab costs ~2x what the previous generation did, and you need to build a new fab ever couple of years. But (if you could keep the order book full for the fab) it would make a lot of money over its lifetime, and you still needed to borrow/raise even more to build the next generation of fab. And if you were wrong about demand .... you got into a really big bust, which is also characteristic of the semiconductor industry.

This was the power of Moore's Law, it gave the semiconductor engineers an argument they could use to convince the money-guys to let them raise the capital to build the next fab- see, it's right here in this chart, it says that if we don't do it our competitors will, because this chart shows that it is inevitable. Moore's Law had more of a financial impact than a technological one.

And now we're down to a point where only TSMC is for sure going through with the next fab (as a rough estimate of cost, think 40 billion dollars)- Samsung and Intel are both hemming and hawing and trying to get others to go in with them, because that is an awful lot of money to get the next frontier node. Is Apple (and Nvidia, AMZ, Google, etc.) willing to pay the costs (in delivery delays, higher costs, etc.) to continue to have a second potential supplier around or just bite the bullet and commit to TSMC being the only company that can build a frontier node?

And even if they can make it to the next node (1.4nm/14A), can they get to the one after that?

The implication for AI models is that they can end up like Intel (or AMD, selling off their fab) if they misstep badly enough on one or two nodes in a row. This was the real threat of Deepseek: if they could get frontier models for an order of magnitude cheaper, then the entire economics of this doesn't work. If they can't keep up, then the economics of it might, so long as people are willing to pay more for the value produced by the new models.

Do they have a function to predict in advance if the next model is going to be profitable?

If not, this seems like a recipe for bankruptcy. You are always investing more than you're making, right up until the day you don't make it back. Whether that's next year or in ten or twenty years. It's basically impossible to do it forever - there simply isn't enough profit to be had in the world if you go forward enough orders of magnitude. How will they know when to hop off the train?

if you're referring to https://youtu.be/GcqQ1ebBqkc?t=1027 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"

notice those are hypothetical numbers and he just asks you to assume that inference is (sufficiently) profitable.

He doesn't actually say they made money by the EoL of some model.

That can only be true if someone else is subsidizing Anthropic's compute. The calculation is simple: Annualized depreciation costs on the AI buildout (hundreds of billions, possibly a trillion invested) are more that the combined total annualized revenue of the inference industry. A more realistic computation of expenses would show the each model line very deeply in the red.

> According to Dario, each model line has generally been profitable: i.e. $200MM to train a model that makes $1B in profit over its lifetime.

Surely the Anthropic CEO will have no incentive to lie.

Well how much of it is correlation vs causation. Does the next generation of model unlock another 10x usage? Or was Claude 3 "good enough" that it got traction from early adopters and Claude 4 is "good enough" that it's getting a lot of mid/late adopters using it for this generation? Presumably competitors get better and at cheaper prices (Anthropic charges a premium per token currently) as well.

What about inference costs?