“The industry's gotten much more competitive, but I think all these new use cases too, and some of these verticalized use cases are really where we're going to see the growth going forward because it's obvious coding isn't going to power us to even triple from here.”
sputnikintelligence.com
Nilay Patel asked where Anthropic would find a trillion dollars of revenue, given competition from other labs and open-weight models. Brookhart had just said switching costs in coding are low and that Anthropic's share of new revenue shifted over the year.
…and then running either their inference off of that or even their products for internal or external use cases. So there's that. And then obviously DeepMind at Google and MSL with Meta are very well-funded labs that have pretty similar training compute capacity. And they're bringing on a ton of,
…in the first half of the year, Anthropic ran away with it. They were 90% of the industry's net new ARR. And then we saw in Q3, OpenAI was probably actually the majority of industry net new ARR. And then open source has grown from probably a billion-dollar ARR in Q1 to 10 billion plus here in Q3.
So yes, the industry's gotten much more competitive, but I think all these new use cases too, and some of these verticalized use cases are really where we're going to see the growth going forward because it's obvious coding isn't going to power us to even triple from here.
It's interesting. The concept is called tokenomics. You run that practice alongside a co-lead at Seventalysis. Explain what it is because to me, it's such a smart way of thinking about, okay, we're going to put some money in and maybe we'll get some money out.
I think of it as like
the unit economics or even just the broader everything from a macro to microeconomics of running an AI business or even looking at AI demand. So we cover the hyperscalers, now some of the NeoClouds as well and kind of a separate product. But then two, just the AI labs. And I think for investors or…