A short conversational video discussing employment dynamics in AI: contrasts mature/public companies (focused on ARR efficiency and headcount reduction) with two types of private AI firms — capital-intensive model builders (large compute spend, small headcount) and capital-efficient apps-layer startups (rapid product-driven revenue with minimal hiring). The speaker argues all three categories reduce demand for people, raising concerns about employment.
Chapters
In fact, as we think about it, Jason, it's giving me an insight on your eternal question on employment, because big picture divided up into AI startups and mature companies. I think the comment on ARR efficiency for ARR is very much a mature company comment. If you're a public company and you only grow in 10 or 15%, you better be kicking off cash or you'll be in trouble real quickly. Even if you are kicking off cash, you'll still get grief. But those are the companies that are optimizing like their AOR per employee and are just focused on FCs free cash flow. Right. And that's a very different qu. Different set of people with a very set of dynamics than the AI startups. And we'll come to them in a second. So public companies grinding on efficiency, which means, as you say, lower employment and all that stuff. Now come to the private AI companies. I think there's two categories. There's a small number of companies which are taking huge amounts of capital because they need it for model development. And primarily it's not humans, it's they got to spend it with Nvidia. Right? So look, no one's telling OpenAI be efficient or if they are, he's clearly not listening. Right? Yeah, to a rounding error. Those companies are attract, as Jason said, able to get all the money they want, spend it on compute, have relatively small headcount relative to their size. And no one's saying be efficient, they're just saying grow quickly. Then separately at the apps layer, you think something slightly different. And Gamma's a good example of that. The interesting thing is because of this amazing new capability, for lack of a better word, called the foundation models, there are people in apps land building a product, shipping it, and they're getting such traction that the traction is ahead of their ability to hire. I mean, they're literally there's no way to spend the money. Right. So we're seeing some of these apps companies be astonishingly capital efficient, especially at the early stages. I mean, Gamma's a great example of that. It's like you ship the product, it's fricking amazing. People buy it, they give you credit cards. By the time you get around to hiring a salesforce, you're doing so much money already that you're kicking off cash. So at the apps level, not all the time. I think some companies at scale are spending, but we're seeing the combination of hyper growth, even reasonable margins, not as good as SaaS land, but still 60%. If you have that, then you have quite attractive profile. Not all of them. In that case, obviously the coding companies have margin issues, but a lot of the companies are getting a long way and not a lot of. Not a lot of capital and definitely not a lot of employees. So I think those are the three categories. And the interesting thing, Jason, this goes back to something you've been talking about, and I've been trying to figure out the answer to what's going on, on employment. Right. And what's the consequence? And I've been more, it'll all be fine in the end. And I still stand by that. But the interesting thing, when I listed those three categories, the one thing they all have in common is they all don't need people. The big companies can't have people because they gotta be efficient. The model companies don't need people because they just need geniuses and gpu. And the small AI app startups are going so damn quickly they can't hire people. Hmm, that's not great if you're people.
