The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:31:25 · Parag Agrawal, Harry Stebbings
[00:31:25] S0: I think it's possible. I think we are gonna have to execute well, and a couple of chips have to fall our way. [00:31:31] S1: What would be the reason why you don't? Let's map out the... What do we have to get gnarly about these problems? [00:31:37] S0: One reason would be that we see agent don't work. Like, there is, like, a tail risk that agents don't deliver on the profits and we overshoot at the society, which is extraneous to us. If agents work and deliver value and we spend all the money on GPUs that we plan to right now, then the main question is, did we execute well enough to have a 33% share that you bet on. And that comes down to in my mind, did we build the best tech? Did we partner with all of the content providers to have their content available? Because without it, it's not very useful if you want to rely on it. And did we go on the draft of the customers that end up having a decent chunk of that inference in four years from today? And then again, there's like large cone of uncertainty. Right? Like the... I'd say there's a five uncertainty in Fireworks share at that point. If open models crush it like based on Fireworks model, all of them will have a huge chunk of revenue. So did we end up selling alongside them? Did we find all the customers which are spending money on them to spend money on our website by making it the best website in the world?
00:32:49 · Harry Stebbings
[00:32:49] S1: What about commoditization? If you have a read of semi analysis where they did the benchmarking and then, like, you were, like, number one. Woo hoo. And then, like, a week later, you weren't number one. Sad. And there were, like, three or four providers within very close proximity. You're, like, oh, well, if it's commoditized and I take a second layer of thoughts to that, we'll see a race to the bottom on price. And then actually, the available rev... No. Why why am I going down the wrong pathway here?
00:33:18 · Parag Agrawal
[00:33:18] S0: One, there should be a raise to the bottom on price to get to the thousand x scale. I I think the pricing on web search today is just off. You think customers pay you too much? Not pay off too much. I think the market is mispriced. Let me tell you why. Let's say you used the LUNA model with OpenAI's built in web search today or with anyone's web search, forgetting us, And you ran any kind of deep research. Let's say you you built a a instinct using a LUNA class model and at some point the LUNA class model will be able to do 60, 70, 80% of your personal agent and will do a lot of searches. In that moment, you 'll be spending 80 to 90% of your dollars on web search and 10% on the model. Seems entirely silly. I was working off of five to 20% assumption earlier. So I think at that point, web search have to drop prices by one order of magnitude or more because in my compute allocation dance that I was doing earlier, you need to spend less compute on web search than you spend on the model itself that's consuming its results. And so people have built web search the wrong way so far. Web search in the market is, it doesn't matter for an Opus. For an Opus, win on quality and not on price because web search is such a miniscule portion of your... Like, in fact, you should spend even more on web search. So we're going to ship even more expensive web search for bigger and bigger models over time. And at the same time, we'll ship really cheap web search for the cheap models. But my rough intuition is that in an end to end agent doing a bunch of web work, you spend more on the agent than on web search for all agents. And so the market today on web search is totally off in pricing. Like, why are you paying $10 for... You know where the $10 for a thousand searches roughly comes from? Historically, Google's ads CPMs, which is like how well does web search with humans monetize. It's higher than that. So people build technology to the point of like, oh, now if I spend a few dollars for a thousand searches and I make 30 to 50 or whatever depending on the market and depending on who I am. It doesn't matter. We are in the right zone in terms of cogs of infra, and so I don't need to optimize it. Now I'm telling you that I can do it 50 x cheaper while keeping the quality. And now there's a bunch traffic of Luna class models where it's silly to monetize that way.
00:35:35 · Parag Agrawal, Harry Stebbings
And so I do want to raise to the bottom. I want the best technology to win, and that's the only way you [00:35:41] S1: push web search to a thousand x. But right now, we're not in a world where, like, there's much differentiation. Correct? Sorry. I'm really dumb, which is why I'm a podcaster first, not a not a founder. Like, when you look at the benchmarks, they present a quite clear view of everyone being similarly capable. Is that wrong? [00:36:00] S0: I think so. So one, I think these are all public benchmarks Yeah. Which are all weirdly saturated. Like, for example, right, like, think I would not spend a moment looking at BrowseComp because half the models are memorized. Like models even memorize, you don't even like it's not really learning very much. So I think there's a lot of public benchmarks that I don't give too much merit to, but more importantly, our web search costs $1 for a thousand for producing that quality while almost every other web search in the market available to you right now will cost you 7 or 10 or 14. So you'd... We're delivering this at like one tenth of the price. [00:36:38] S1: What will that be in three years? [00:36:41] S0: I think there is another another 10 x possible. That's extraordinary. So you're gonna pay 10¢. Yeah. But you'll do it more than 10 x as much.
00:36:41 · Parag Agrawal, Harry Stebbings
[00:36:41] S0: I think there is another another 10 x possible. That's extraordinary. So you're gonna pay 10¢. Yeah. But you'll do it more than 10 x as much. [00:36:50] S1: So I think, like, Jevan's proud of us to say, this concept obviously very well, like Instinct, I do so much more than a 100 x. When I hear something I do on Instinct, which is absolutely bizarre, every single country in Europe has a company register that you have to register your new company with. I have an automatic alerting system built for every single company registered that has an 25 year old founder who went to a top university through instinct. [00:37:15] S0: Amazing. Do you know how much that requires [00:37:19] S1: for them to do?
00:37:19 · Harry Stebbings, Parag Agrawal
[00:37:19] S1: for them to do? [00:37:20] S0: Yes. But you're on to exactly what I think the future of the web is. Today, if you think of web and web search, we all think of it like what is web search? An agent gives human or agent gives... Search engine a query gets results. So you're pulling information out of the web. What's next for the use case you highlighted? If you're an always on agent that's working on your behalf, it's kinda silly for instinct to wake up every six hours and go do a bunch of searching and bunch of inference to figure out if you need to get pinged about the the the 25 founder that popped up somewhere. You know what's a better way of doing this? I am sitting here crawling all of the web all day, every day at scale. I'm allocating compute every time I find a change in the web, every time something new happens. If I know that Harry wants to know when this happens, I can do it at 100 to 1,000, the compute that Instinct probably uses today to solve that problem for you. [00:38:19] S1: Alright. And how is that? Because they would be constantly monitoring in real time across all these