[vessenes]

I don't completely agree. Brand value is huge. Product culture matters.

But say you're correct, and follow the reasoning from there: posit "All frontier model companies are in a red queen's race."

If it's a true red queen's race, then some firms (those with the worst capital structure / costs) will drop out. The remaining firms will trend toward 10%-ish net income - just over cost of capital, basically.

Do you think inference demand and spend will stay stable, or grow? Raw profits could increase from here: if inference demand 8x, then oAI, as margins go down from 80% to 10%, would keep making $10bn or so a year in FCF at current spend; they'd decide if they wanted that to go into R&D or just enjoy it, or acquire smaller competitors.

Things you'd have to believe for it to be a true red queen's race:

* There is no liftoff - AGI and ASI will not happen; instead we'll just incrementally get logarithmically better.

* There is no efficiency edge possible for R&D teams to create/discover that would make for a training / inference breakaway in terms of economics

* All product delivery will become truly commoditized, and customers will not care what brand AI they are delivered

* The world's inference demand will not be a case of Jevon's paradox as competition and innovation drives inference costs down, and therefore we are close to peak inference demand.

Anyway, based on my answers to the above questions, oAI seems like a nice bet, and I'd make it if I could. The most "inference doomerish" scenario: capital markets dry up, inference demand stabilizes, R&D progress stops still leaves oAI in a very, very good position in the US, in my opinion.

[dabockster]

The moat, imo, is mostly the tooling on top of the model. ChatGPT's thinking and deep research modes are still superior to the competition. But as the models themselves get more and more efficient to run, you won't necessarily need to rent them or rent a data center to run them. Alibaba's Qwen mixture of experts models are living proof that you can have GPT levels of raw inference on a gaming computer right now. How are these AI firms going to adapt once someone is able to run about 90% of raw OpenAI capability on a quad core laptop at 250-300 watts max power consumption?

[vessenes]

I think one answer is that they'll have moved farther up the chain; agent training is this year, agent-managing-agents training is next year. The bottom of the chain inference could be Qwen or whatever for certain tasks, but you're going to have a hard and delayed time getting the open models to manage this stuff.

Futures like that are why Anthropic and oAI put out stats like how long the agents can code unattended. The dream is "infinite time".