This assumes any research gains last. In practice it seems like none of the frontier companies can keep much of their "secret sauce" secret for very long.

If company X has a better model than company Y, do you use it? Well, that depends: will Y take two years to catch up? If so yeah, you buy X.

But if Y is just as good a few months later, you purchase based on other factors, because you know you can live without the brilliant research X is doing ... since you'll have it soon enough.

The problem with your logic is that the history of tech is full of “good enough” eating “best” for lunch all day long. Anthropic’s plan to be the best is increasingly looking like a terrible business plan.

> Their moat is their research capability.

That doesn't seem like much of a moat. What happens when a couple researchers go on new parent leave at the same time, or someone's productivity drops for a quarter because a parent passed, or gets divorced, or whatever?

Continually staying 6 months ahead of the distilled open-weight models means you need to keep flipping heads (nobody goes on vacation) until the end of time. The geometric distribution describes that, and it ain't pretty for the expectation value of these companies.

How long till that "research capability" becomes just 5 people with many AI agents, and no other humans in the main research loop?

> Their moat is their research capability.

Research does not bring in revenue. Products or services do.

You mean, if they keep running quick enough they are save? That's like the compete opposite of a moat, in my mind at least.

That's not that big of a moat though, as we've seen with DeepSeek and even smaller teams doing edge model work.

I’m not sure I’d call maintaining the fastest research program a “durable advantage”.

If their moat is their ability to create marginal improvements every few months by releasing new models, then that is an extremely expensive moat to maintain.

You need those staggering amounts of compute to run an inference service, but you don't need it to run a powerful model.

It doesn't cost a company that much to buy a box capable of running the Qwen3.8-Flash-Next model in the GP comment and never pay Anthropic again. The cost will be even more reasonable once the supply issue is solved for that hardware.

AI-as-a-service is a unique product because when the interface is natural language, there is no lock in. VS code has a hotkey to swap between claude, openai and a local model. Compare that to the difficulty of swapping from macOS to Windows.

Sure, but anyone with money could do that.

We're the only people that can spend this money is not a moat.

I wouldn't be so sure about capital in future. I think there might be real look for return on any new capital they are getting. Be it equity or debt. And that alone will not be great for them.

How long they will have access to staggering amounts of compute is uncertain and not guaranteed, especially if they get a junk or near-junk credit status when they go public.

The capital is running out and the compute is a massive liability on their balance sheet. Your definition of a moat is rather odd.

How much of their compute is leased/rented? Last I read it was quite a lot of it.

the idea of a moat is that it's an inexpensive way to keep the invaders at bay.

If you need to buy staggering amounts of compute, spend staggering amounts in utility costs and lobbying, and keep spending that as your hardware amortizes and your operations go on, what you have isn't a moat, it's a standing army. It's extremely expensive, and the day you start having issues, mercenaries will cut your throat before you know it.

That argument seemed to have legs in 2022 when OpenAI was the only game in town.

But the fact that were even talking about Anthropic like this only a few short years later demonstrates that that was only kind of a kind of moat.

The current compute they are buying is NOT state of the art (check this year's academic research on inference hardware). There is 10x-100x inference-per-watt improvements in existing prototypes (in the industry, not just academia). Not sure what I am talking about? Ask your friendly AI agent of choice, for the details.

Check out mythic.ai for one example.

compute and debt*

Banks are going to want that "capital" to materialize into gains at some point. The water in that moat is evaporating.

Capital only means anything if you can spend it to buy resources like compute and turn those resources into additional value.

Hardware's ridiculously expensive. Then, there's no good place to put it, because the new breed of tech bro managed to propose the data center buildout in the most exploitative, alienating, and electorate-angering way possible. So compute's hard to buy.