00:08:03 · Parag Agrawal
[00:08:03] S2: I am. Listen, I'm on the rebel alliance here. Okay. There are all these big people. Yeah. And then there are us, startup founders, are running at solving really hard problems, and pushing the frontier of what's possible, and innovating. So we should talk about instinct. Yeah, they are growing to amazing product. So yes, the category is amazing. It's interesting. I think it's growing really fast. I don't have any insider information around retention. I am, as you can imagine, it's a professional hazard that I use every one of these agents. Yeah. And I try to deeply understand what they can and cannot do and what problems we can solve for them. We'll get into this category and how it changes our product strategy later. But I think my expectation is that these agents retention will be good. And it will be good because for the first time, the product onboarding feels natural. Like not someone designing like a bunch of UX click targets and flows to get you to understand the product. The product is natural. All of these products are somewhat natural. And the question is, can they deliver on the promise created by the UX? So the UX is you can text this thing and it can do anything. That's the UX. It's not complicated. Yeah. Retention is entirely a function of can it actually deliver on this very simple high level promise often enough to become a part of your life. Now it's going to happen. Like a lot of other products struggle with the onboarding UX barrier to entry, but this new generation of products, just that's just not a thing. And so I have a feeling that retention will get solved here.
00:10:14 · Molly O'Shea, Parag Agrawal
[00:10:14] S1: Is that where you think Open Claw maybe hit a wall? Because that was really the first. [00:10:22] S2: No, I think Open Claw was right in all of the ways. Yeah, I think once someone got it, it's just not for the masses, right? It's just like most people don't have the ability to set it up and then to keep up with change. I think the other factor we discount. It's not just a one time cough to set something up. Things are changing so fast that you have to adapt your instantiation of it alongside that change and you have to adapt your use of it alongside new capabilities. So part of it might have been that the setup time because of the flexibility and you controlling it was far too much. Part of it is also that it was perhaps a little too early. Right? Because in the beginning, there was like one model which was really good, like the Opus model. And then as soon as you move to other models, it wasn't as great in the very beginning when people started using OpenCLORE late last year. And so you had to adopt alongside it. But today, as evidenced by Instinct and Muse, You kind of don't have to know or think about it, and the product will change underneath. But as the customer of it, you don't need to know what changed in the last month. You're oblivious to the capability improving underneath. [00:11:55] S1: I can't discount that this was really just invented. So, how did you... You mentioned this at the start of the interview, but like, you have been obsessed with this for a while. So, what does that mean and how?
00:11:55 · Molly O'Shea, Parag Agrawal
[00:11:55] S1: I can't discount that this was really just invented. So, how did you... You mentioned this at the start of the interview, but like, you have been obsessed with this for a while. So, what does that mean and how? [00:12:08] S2: So I think I got obsessed with it now almost three and a half years ago. I was the... I don't know if people remember, there was like this moment of this baby AGI and auto GPT speaking of oftentimes developers in the open source world being ahead of everything else. People were building with like, I don't know what is the model of the day, GP three point or whatever, like old models that weren't like relative to today. They were like really bad models, Relative to today. And people were trying all these things, and I was trying all of these things to try and make things work. And they didn't work. But if you believed you spent any time with the labs back then, you internalized how models will scale and how models will improve. Like in some sense today, it feels like everything changes every month. In some sense, I [00:13:03] S1: don't know if it's every month, it might be every week.
00:13:03 · Molly O'Shea, Parag Agrawal
[00:13:03] S1: don't know if it's every month, it might be every week. [00:13:05] S2: But if you look at it in a different way, almost nothing that is happening is diverging from what you could have predicted in a straight line three years ago. Models will keep getting bigger and better. They will run for longer. Holding capability constant, models will get way way way smaller and cheaper. And people will be able to productize around them to disrupt every category of work that can be done on a computer. These were very simple things that we believed and said three years ago. And we have only seen evidence week on week and month on month supporting these very few simple things. Yeah. And almost everything follows. So now you can say, yeah, I didn't have, I didn't predict that like the big category would be coding agents first. Right. I didn't predict the CLI form factor. Right? Those were all bits of information and news to me along the way. But the macro thesis of agents are going to get more capable. You're going to do a bunch of inference and for all the inference you do, you're going to have to do web search along it. I've now been on the record everywhere saying like, like the one invariant I believe is we will use a lot of GPUs for model inference for agents, whether they be at work or consumer agents. And for every 10 GPUs, the world spends on model inference will spend one to two on web search. That's the business I'm in. [00:14:47] S1: So are you aggregating GPUs? You've raised $230,000,000 reported and announced. I don't know. But are you aggregating GPUs?
00:14:47 · Molly O'Shea, Parag Agrawal
[00:14:47] S1: So are you aggregating GPUs? You've raised $230,000,000 reported and announced. I don't know. But are you aggregating GPUs? [00:14:57] S2: Yeah, we are not. I wouldn't use the word aggregating. We are buying, renting GPUs to build the scale of search traffic that we are seeing and expecting. And as I said, we grow alongside inference. So inference is growing. So we have to do a proportionate amount. I don't know if it's like already a, I don't think it's a core competency of ours. Like we have been in multiple mad GPU scrambles in the last three months to be like, oh, shit, we need GPUs. We don't have GPUs. Can someone figure it out? Like, you're making a bunch of calls. [00:15:39] S1: Who are you calling? [00:15:40] S2: Sorry? [00:15:40] S1: Who are you calling? Everyone. Everyone. [00:15:45] S2: Like every hyperscaler, every neo cloud. [00:15:49] S1: What's like the typical response back? Because this comes up in pretty much every conversation now. It's like, how do you get, I guess the allocation of GPUs?
00:15:49 · Molly O'Shea, Parag Agrawal
[00:15:49] S1: What's like the typical response back? Because this comes up in pretty much every conversation now. It's like, how do you get, I guess the allocation of GPUs? [00:15:58] S2: No, I think it's like, I think the, at least for us, I think it's true for many people. It's like, okay, which GPU is it? Physically, is it? Sometimes we need GPUs within five milliseconds of where a large web index is sitting. Other GPUs which sometimes more flexible, right? There are multiple GPU workloads. It's not just like, okay, do you have GPUs for me? It's like, do you have the right GPU in the right scale in the right physical locations for me available at the right time? And that is what makes the problem much harder. I think so far we have managed to solve every problem we have encountered. We've not had to turn away a customer because I didn't have a GPU. [00:16:49] S1: Right. [00:16:49] S2: But we've had a bunch of GPU acquisition fire drills. [00:16:56] S1: I mean, that's a really interesting line to go down. It's like, what is the sourcing process like for GPUs? Like, what are the main questions that you're asking? What do you need?