Large AI labs are not competitive threats to vertical AI companies because labs lack focus on specific industries, prioritize generalizable research over practical deployment, and don't invest in the orchestration and product layers needed to solve real-world problems.

12:48they're actually going through something and there's an human element, I do think there's quite a bit of a while for robotics to be closer in our chapter. >> And then I've kind of heard you talk about um the big labs, so open AI, anthropic, uh maybe Google, Meta, etc. Um how do you think they'll process industry or how do you think what you're doing is different from what they can

13:08do? >> Different way of asking that maybe can labs do this? Mhm. >> Um I think that's a fair question because there's a lot of startups in today's world that do that build things functionally and visually very similar to the labs key products, right? It's either a chatbot or a coding agent. Um I do chuckle at the question a little bit though because I think 10 years ago that

13:32same exact question was can Google do this and then now it became can labs do this? Sure, some of the things the core competencies they can do it but some of the other things they're not investing in it. For Ned's case, I don't see them as a competitive risk. It's actually I think the two especially the leading labs, there are amazing businesses. They're also great partners to companies

13:53like us. So I really respect that. But in terms of looking at what we provide to these industries and companies, I actually think two things are very important. one in focus in what you're building and I will say you know it would be a funny question to these enterprises right open AI builds amazing products really fast but also it kills them really fast so I don't think

14:16enterprises or at least enterprises in these industries looking for that really fast or in anthropics case you know Silicon Valley converged in the idea that you know they pulled ahead in coding agents um or like claude because they had focus but you see exactly the opposite in the enterprise case there's about like 20 products like what is really happening um and I don't really see that and a meta focused question

14:42maybe about the labs and specifically researchers they really care about solving the most generalizable way of the problem right so in this case maybe looking at the problem we're solving the answer would be well when we get the a AGI we'll ask how to solve it for essential services and I think that is both operationally and intellectually a bit lazy thinking And the finally is for to solve these

15:06type of extremely difficult problems with millions in the country that have completely different worries, different accents. How do they want to engage different contexts and also even engage them again to make them multi-time customers. There's quite a bit of lost mile that you really have to do that doesn't only come from models that has to come from your harnesses and orchestration, the software and the product that you have to build on top.

15:29So if anything actually companies like us and Netic have to be good at all three layers. >> Yeah, that makes sense. One thing I've noticed um which is more sort of a side comment on what you're saying is that one big shift I see from four years ago and now you know four years ago I was funding things like Harvey and Perplexity and uh you know a little bit