The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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
00:38:23 · Parag Agrawal
[00:38:23] S0: Our crawl would be. So we have a product. It's called the Monitor API. What does this API do? It's... Think of it as Google alerts, except smart. With... In the world of LLMs. So you you tell it your query, which is when a founder under 25 pops up anywhere, give me a call. Now one way of doing this is the default implementation is run an agent, which is somewhat smart to go search the web. Did someone pop up and remember who previously existed? See if anyone new popped up and then find a delta, send it out. So now every six hours you're spending some money. Now what we can do is flip it into an event triggered system. We are crawling the web all the time. Now every time we see change in the web, we can try to spend very little compute on it to know should you get a call or does this trigger a more expensive compute Just like I described earlier. Now we are able to do this way, way, way cheaper if you push the context. And so the search system only finding out every six hours, a new query popped up. It having long lived context in the search system. What you can do is spend most of the time, the world doesn't change. A new founder does not pop up every hour or every six hours. So we don't need to spend compute every six hours. We only need to spend compute every five days, and perhaps we have one false positive. And then four days later, we spend more compute, and at that time, there's a real person that you should know about. Then you get a notification on average every nine days or whatever, but you didn't burn a lot of compute every six hours to miss out. Does that make sense? It totally makes sense. And so you save 10, 50 x compute to still get the same answer. And so