Comment by lelanthran
3 days ago
> The current requirements are more org-scale, more payoff from scale, more moat.
What moat? None of the AI providers have a moat at the moment, and the trend doesn't indicate that any of them will in the near future.
I made 2 posts in this thread regarding why I think they have a moat. Was there anything ambiguous or that you disagreed with?
> I made 2 posts in this thread regarding why I think they have a moat. Was there anything ambiguous or that you disagreed with?
I'm afraid I don't see those posts; I see 2x posts from you asserting they have a moat, but not why you think they have a moat.
I distinguish between "They have a moat." and "This is why $FOO, $BAR and $BAZ forms a moat."
Maybe you think brand recognition is a moat, but that didn't work out for incumbents before (too many examples to list).
It was kind of buried in my second post:
> They have internal scale and scope economies as the breadth of synthetic data expands.
These frontier labs will have a hundred or a thousand teams of people+AI working in parallel generating synthetic data to solve different niches. A few teams solve computer use. A few teams solve math. A few teams solve various games. So the org is basically a big machine that mints data, and model research is only a small part of it. Scale then is the moat.
The second leg of the moat thesis is that open weights competition will die off soon because the cost to keep up with the scale will be too excessive.
The third leg of the moat thesis is that customers are happy to pay big margins for differences that appear small if the benchmark is the measuring stick.
If the paradigm was still scrape internet -> train model, I'd agree that there is no moat.
3 replies →