A few problems for Anthropic or OpenAI:

1. Model Build/Training costs are escalating if anything

2. They are in an arms race with competitors and can't afford to stop building new models - if they lose the perception of leading, they lose lots of customers overnight.

3. Inference margin is under attack from more specialised models and open weights models.*

4. They have no moat given how simple the interface to the products is - a bunch of text files and text prompts, and little prospect of building a moat.

So those all run counter the possibility of coasting on previous research. To me the most promising avenue for making money for these companies is to replace Google, not AGI or taking over the world's knowledge work. That would mean they are overvalued. Even coasting on previous research, how long would it take to pay off the money they have spent?

* https://strandsagents.com/blog/amazon-saves-millions-replaci...

> What they don't understand is that the gross profit margins on AI model inference are very high.

Can we be sure of this? The second they tried to hike the prices to be profitable, they lost a lot of clients, or a lot of clients lowered they token expenditure. In any market margins tend to go to zero. I am using frontier models, but only because they are dirty cheap if not free. If they raise the price, I just switch to a cheaper model, start exploring the option of renting a GPU-VPS or buying my own card. Or reducing my AI usage, that currently includes stupid useless things like "translate this cooking recipe from french... for free".

> But if training becomes less costly and inference margins don't compress too much, it's a totally different story.

They are being priced as the "ifs" are certainties, and not only a possibility. If anything goes a millimetre below perfect for them, their finances collapse. High margins attract competitors, and unless you have a huge moat (and their only moat is expensive training), your high margins vanish.

> gross profit margins on AI model inference are very high.

the gross margins on growing potatoes is very high, if you exclude all the other costs needed to get the potatoes to market.

Think about it, you take some potatoes you had left over from last yeah, dump them in the ground and wait 9 months. Boom pure profit.

This of course excludes the cost of the land, machinery needed to plant, till and harvest, storage, pesticide, fungicide and herbicide.

Then the transport, cleaning and packaging.

If you arbitrarily cut out all the bits of the business needed to make revenue, everything is a profit.

> It's not exactly laziness. It's that most people don't understand basic financial concepts like fixed versus operating costs.

> Companies like Anthropic and OpenAI have invested huge sums in building/training models. Those are the headline numbers everyone talks about. What they don't understand is that the gross profit margins on AI model inference are very high.

Only when you ignore that the models they provide inference for have an extremely high cost and pretend they don't depreciate. They keep having to train new models. I don't think they recovered the cost for a model when a new one releases.

It is kind of cute to say other people "don't understand basic financial concepts like fixed versus operating costs" when you seem to not understand basic financial concepts such as depreciation and amortization.

Also, and this is important, we don't really know their true compute cost. We don't know if their compute with the likes of Google and Amazon were initially subsidized, and for how long it might be. In Q2 they also got to not pay SpaceX for compute for a while.