[cloudbonsai]

Can I ask what is your opinion about their core CapEx, i.e. model training?

The general trend I observe is that the "shelf lives" of large language models are really short. It costs $1-10 billion to train cutting-edge models at the moment, and they only really last 6 months at best.

There seems to be very little brand loyalty too. Whenever a shiny new thing comes out, people just switch over, which implies that they constantly need to fight the time decay.

[danielmarkbruce]

It's high, really high. But, that isn't bad. In fact... they are better of with it being extremely high. Then scale matters. They need enough revenue at high enough margins to earn a decent return on that spend, but higher is, from a competitive perspective, better.

[cloudbonsai]

I understand your logic ("the high CapEx is the moat"), but on the other hand, isn't it be a bit like multiple high speed railway systems trying to connect San Francisco to Los Angeles?

And there are three internal players chasing the same goal at the moment (OpenAI, Anthropic and Google), and two others (Deepseek and Alibaba/Qwen). What will prevent them from cutting the price floor each other?

Looking from a different angle: Microsoft has been able to maintain its monopoly because it was/is a huge pain for companies to switch the operating system, but do LLMs have that stickiness?

[danielmarkbruce]

No. But stickiness isn't the only way to build a moat. Scale is a way too.

[cloudbonsai]

All right. Thank you.