The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:02:53 · Harry Stebbings, Parag Agrawal
Learn how you can get more out of your site from a Framer specialist or get started building for free today at framer.com/20vc for 30% off a Framer Pro annual plan. That's framer.com/20vc. Rules and restrictions may apply. [00:03:08] S0: You have now arrived at your destination. [00:03:11] S1: Parag, I'm so excited for this, dude. I I spoke to Vinod. I spoke to Andrew Reed. I spoke to Todd Jackson. Dude, I stalked the shit out of you, so thank you for joining me. [00:03:21] S0: Thanks for having me, and thanks for making all the calls. Not at all. [00:03:25] S1: I would love to start with, for anyone [00:03:26] S0: that doesn't know, how would you describe Parallel in sixty seconds? Parallel is the Google for agents. So agents need to search the web to do anything they do for you, whether it's a personal agent or an agent built for work. Just like humans need to go on a browser, search Google oftentimes during work or for whatever you're doing in life. Your agent needs to do the same. Turns out agents are different from humans, and the way you build web search for agents is different. Parallel is about building the technology for agents to search the web and then the business models to make that sustainable. [00:04:01] S1: Was that the original insight that you had?
00:04:01 · Harry Stebbings, Parag Agrawal
[00:04:01] S1: Was that the original insight that you had? [00:04:04] S0: Yeah. Literally, first genesis of the company was the statement that agents will use the web 1000x more than humans. I wrote that down at some point. Hence, new tech is needed and new business models are needed. Thousand x gives you a sense of scale. It changes how you think about building the tech underneath because, like, no tech built for a certain scale survives three orders of magnitude. And then when you need new business models alongside new technology, a problem becomes really interesting. [00:04:31] S1: How does the world of agents change web search in terms of technology required? [00:04:36] S0: There are many, many layers to the answer, but let's start at the first thing we mentioned, which is scale. Right? Now if you think about... Let's say agents actually do end up searching the web a thousand x more. If we spend the amount of compute we currently spend per web search, That's too much compute for web search. So you now need to make it way more efficient by perhaps, I think, 10 to a 100 x for it to make sense. [00:05:01] S1: Yeah. If you're doing a thousand x.
00:05:03 · Parag Agrawal
[00:05:03] S0: Yeah. Exactly. The second thing is humans operate in a very narrow zone. So if you think about how we use web search, we type keyword queries, which are short and underspecified. We wait for about half to one second. If web search takes more than that, we're impatient. And then we get 10 blue links, and then we random walk across them and a collection of searches to get what we're doing. Agents are not like that. Agents are gonna perhaps tell you exactly what they're looking for, not like three keywords, but like a full sentence. Like, this is what I'm trying to do. Agents will either be super impatient. Like imagine a voice agent, the agent will be like, I need an answer like now, a hundred milliseconds. I can't wait five hundred milliseconds because the human is waiting on me and they're going to wait five hundred milliseconds. So I need web search to do it in hundred. Are there going to be a background agent who's like, I don't care. Just give me the best answer possible. And so the variance of what you can do within web search changes completely. In fact, the one thing that is the least interesting is what we've built for humans. Right? You either have much time or too little time. Almost never the same amount. The output is not the same. So the input's different. The time you have is different. The output is different. The output is in blue links. The output is tokens or files on a file system depending on the type of agent. And so now all of a sudden, you say, okay. Now the problem's inputs are different, outputs are different, and constraints are different. And you get to spend very different amounts of compute on it. So imagine someone running an agent built with a LUNA model. And then imagine someone running an agent built with a Fable model. They're very different models. How you wanna optimize signal to noise and tokens for each of them is so different in terms of what you do in the web search track.
00:06:39 · Harry Stebbings
[00:06:39] S1: So what do mean optimize signal for noise? So think of it this way.
00:06:43 · Parag Agrawal
[00:06:43] S0: Let's say you took web search, which was cheap and fast and low compute. One way of conceptualizing the web search problem is you start with, like, a trillion documents that are on the web, some few trillion. Given any search, I now need to narrow it down to a thousand tokens that your model's context window should see. So the problem is going from a trillion URLs with, let's call it, a few thousand tokens each down to a thousand total tokens. So how do we do it in website? We first say, okay, we're gonna do retrieval. So for most documents, I'm gonna spend zero compute. For small number of documents, I'm gonna spend minuscule amounts of compute to figure out which 10,000 to look at. Once I get these 10,000, I'm gonna spend a little bit more compute for each of these 10,000 documents to narrow it down to 1,000 documents. Mhmm. And I'm gonna keep doing this with bigger and bigger models and rankers with more and more features features until I can narrow it down to a thousand tokens for your model. So you're essentially allocating compute to web search in order to save compute on the model. That's roughly what's going on here. So if you wanna save compute for the model, the question is how much compute should you allocate? So if your LUNA model is really cheap, you don't want to do too much compute in web search because it's okay to leak a little bit more information into LUNA's context because it's cheap. Into Fable, you wanna do the work before you waste Fable's time because that's gonna be expensive in time and money for you if web search gives you worse answers because you cheaper out on web search.
00:08:10 · Harry Stebbings
[00:08:10] S1: Can I ask, how do you deal with the ambiguity of what what agents want? And what I mean by that is for different things, an agent might want different things. And for different people, an agent might want different things. I may really care about accuracy and not at all about latency or cost. I may really care about latency, but not a tool by accuracy, really.