The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:00:00 · Parag Agrawal
[00:00:00] S0: We're gonna see the biggest or the frontier models be bigger and bigger over time. It appears there is no end to the scaling law that we can perceive so far. I think today we don't know how to pay for unique valuable insight or data. Ads don't work with agents in their current form. You need to find a replacement to the ads business model. I do think there should be some disparity in wealth. But I don't know what's too much.
00:00:24 · Harry Stebbings
[00:00:24] S1: This is 20 VC with me, Harry Stebbings. Now what do Andrew Reid, Vinod Khosla, and Josh Koppelman all have in common? Well, they all believe in Parag Agrawal to change the future of agentic search. Parag is the founder of Parallel, as I said, changing the future of how agents do web search efficiently. This is an incredible discussion on the future of Vagantic search, the future of income and wealth inequality, and so much more. Panag rarely does shows, and so it was very special to sit down in person with him in London. But before we dive into the show today, MongoDB has always been the database developers love. Well, now is the data platform AI agents need. Agents need accurate context, fast. MongoDB stores, searches, and reasons over your data in real time. JSON native database, vector search, and Voyage AI embeddings all in one place. One system instead of 10. No data pipelines to maintain. Ugh. This sounds too good to be true. Build and scale from your first user to billions of vectors. Run on any cloud, on prem, or your laptop even. That's why 75% of the Fortune one hundred run their most critical apps on MongoDB, moving trillions of dollars every single day. And it's why AI native companies like Eleven Labs run 40,000,000 agents on MongoDB. So if you're building an AI, MongoDB for startups helps you move faster with Atlas and Voyage AI credits and dedicated support. Don't build agents that answer once and forget. Build agents that remember and learn from your real time data. Go to mongodb.com/agents. That's mongodb.com/agents. While MongoDB scales the product, Framework scales the story. When a new landing page turns into a pile of tickets and handoffs, Framer helps your team move faster. Framer is the AI website builder that helps creators, teams, and businesses ship production ready sites faster than ever while getting every detail right. Prompt, inspect, edit, and publish in one place at a whole new pace. Agents and humans work in tandem. Agents bring speed and scale. You bring taste, judgment, and control. The work lands on the canvas and stays editable. Build custom code components, manage CMS content, optimize SEO, and audit for issues all in one place. Enterprise grade hosting, security, and 99.99% uptime SLAs trusted by leading brands like Perplexity and Miro.
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.