Ex-Twitter CEO on Why AI Needs a New Internet | Parag Agrawal

28:10I think it's a fair way to describe it. And, and this happens all the time. Like this, I tell the team, your measurement of if you're doing a good job is if a founder tries to hire you, like that is truly the measure of greatness. That's like the highest compliment you give someone. And, you know, eventually, he, it became obvious that this was his thing. and um and so that was the first time i had like that was the first time you really caught my attention and mamoon and i talked and um we're genuinely both two things happened at once in my opinion tell me if you think i'm wrong one we were sad to see liam go but really excited and two way more bullish on parallel so i think you know to be fair we've been

28:56Parag Agrawal:And fortunately, our job is invest in great companies. And the things that Liam saw, we'd seen six months before. That's how I put it. Yeah, that's fair. But then when you see Liam and Graham and like this group of people also then see it and then Progg's ability to recruit them, that's like the vision coming to life. It is. But let me tell you the, I'll double down on the in spite of ourselves comment. So the way this whole thing happened, I think there is a real story to tell here. So we were at this board meeting at the beginning of the year. And Mamoun in his sort of simple way asked the questions, which was like, you guys like, you're in some, when a customer is looking at you, like when you show up, like how often do you win?

29:48How often do you close? And we said, we tend to always close. And I was like, so you're just not in enough deals. it's a very simple comment and the one that really hurts when you hear it and think about it right and so it really hurt me that you're like you're just not in a fight you're just not showing up so I called Mamun back the same day I was like okay I have a problem help me fix it and Mamun was gracious enough to get Liam to help us solve this problem we'd been looking and recruiting for ahead of sales for some time but these things take time and I don't think we fully internalized how well our product was being pulled by the market until Liam just showed up as an outsider on week one and looked at things objectively and it was him telling us about how unusual it is what we were seeing on the inside because we had no calibration points.

30:57He did. That actually gave us more confidence in what we were doing and more direction in what we were doing. So it was really helpful in so many ways to get to work with him. And yeah, I think we... It's amazing to get to work with Liam and I'm forever grateful. I keep telling... It was like a hard conversation to call Mamoun and ask about this.

31:24Parag Agrawal:and I knew it when you texted me you said hey can we go for a walk tomorrow I was out of the country and I said I land at 3pm on Friday how about 4pm and we met at 4pm for a walk and I sort of knew what was coming it is, listen like it was on the top 3 for the week right so then you do not waste time, you land at 3.15 we can wait at 4 why would you I did take a shower and then I met you after a 12-hour flight. But yeah. I generally also think like when you want something from someone, them being jet-lagged is usually in your favor. That's right. Crafty. Crafty. Tell me, tell Jubin, how was the conversation?

32:06No, it was great. So one, I'm glad you knew what it was because Mamoun came. Mamoun's first reaction was amazing. He's like, number one, we're all about founders. And so if it's right for you in parallel, it's right for me. second don't do it and I kind of needed to hear both of those things because if he had not said the second thing then I was like wait why is he not trying to save Liam and so then he went and went to bat to save Liam and negotiated compromises and it was clear that from that conversation that I had made the right bet both on trying to hire Liam, but more importantly, in working with Mamoon.

32:58So I felt validated on two of my big decisions that I had made over the last few months in that one walk.

33:08Parag Agrawal:And you also got Graham. Which happened right after. Yeah. Also, Mamoon's design. Mamoon first introduced me with Graham. Who ran sales at Windsurf previously. Yes. and Mamun introduced me with Graham well before you invested in the company. Graham believed in what we were doing early and I think ultimately you're building a company of believers and missionaries and no matter what you role, what function you're in. And so he, him and I like spoke quite a lot for several months and it was when we truly got to the point of being able to fully benefit from someone like Graham like really just like scaling up the GTM motion and crushing the market but that early introduction with Graham getting to know each other for several months him being enough of a believer in the company to want to invest in it those are all small things that all compound into being able to build a really powerful, really deep partnership with extremely special people.

34:27Parag Agrawal:And to be clear, he was at Codium before he was even called Winsurf. Yes. And my intent was purely for you to get to know someone who could be a really good sounding board for GTM way before you had any salespeople. And obviously, I didn't want you to hire him from Winsurf because we were on the board there too. Yeah. yeah no I think but I think once I every special person you end up talking to as a company builder you think about hiring if they are the right person like it's very hard to not think about like it's simultaneously very hard to believe that someone is exceptional at what they do and at some point I will need that kind of exceptional in my business and also not visualize trying to make that happen.

35:17Right? Like, I just don't know how to do that. So, I walk the world trying to figure out how parallel can be better because there are some amazing people that can, who are great at what they do and actually believe in the mission to join us. Can you talk about the mission? Why did you start the company? What are you trying to do? Yeah, we started because I got obsessed with this notion that I wrote down one day, which is like, agents and AIs will just use the web a lot more than humans ever have. And I wrote down a number which is like 1000x. This is like two and a half years ago. We wrote down this random number, 1000x more than humans ever have.

35:59And I said like, I've built systems before, at 1000x the scale, none of, no system works. No system is designed for it from an architecture perspective, from infrastructure perspective. I also wrote down that new business models will be needed. for how agents use the web. All the business models that have existed on the web for, and have been built and evolved for like the last three decades, let's call it, are about to evolve and change completely. And that seems like an opportunity, and it seems like an opportunity that is like really close to me, because like the open web is like for where I grew up and how I grew up.

36:39Open web is like this generational change in the world that I have experienced and lived through. and now there's going to be like a second birth of what the future of the open web is.

36:51Parag Agrawal:A parallel web. A parallel web if you may. That came to us later by the way. Not at the founding moment.

37:01So this notion of like this observation that the business model will change and like there is this existential threat right that the whole contract, the whole bargain of the open web is that people put content out in the open because there are amazing ways of getting distribution, driving monetization, selling goods. There are many incentive systems built in which keep things open. Now, the risk is if we don't innovate enough that the web starts closing up and we're starting to see that already. Right? And so there is some missing incentive alignment today in terms of agents using the web and people putting content out on the open web.

37:53That needs solving. Now the good news is if agents are going to use the web 1000x more and I actually think 1000x is undershot. I'm already using the web with this token maxing 1000x more. I think it's going to be way more than 1000x. That's It's not work that is just a pure waste. I actually think we will generate real value, satisfaction, all kinds of things as a result of these tokens over time. And if we do that, there is enough value generated so that if we just figure out how to incentivize being open, how to reward high quality, unique, differentiated content that we get to keep the web open and prospering and becoming like a whole new thing like we can't imagine.

38:44And so that's the core mission of the business, to keep the web open, to create incentive alignment between all of the plenty we will create with agents having access to more high quality information, and the people who curate, create,

39:05Parag Agrawal:produce that information. So when we, I think, first spoke about this two and a half years ago. I remember it was like Thanksgiving of 2023. 23? 23, right? Yeah. And the notion of a web for AI agents completely, 100 % made sense. And a lot has happened since then in terms of models that are actually utilizing the web in ways that at the time, you maybe not had foreseen. I certainly hadn't foreseen that. But I knew that AI agents would exist and they would need a web or a parallel version of the web that they would use a lot more than humans use the web presently today. And so maybe take us through what that inflection point was in the significant tailwinds that Parallel has gotten as a business.

40:01So two or three things, and some of this is like fitting to what we've observed over the last couple of years, right? If we all go back in time and think about, like, the initial version of ChatGPT, it did not use the web. And we still thought it was illegal, right? For people who've now been using any of these products, ChatGPT, Claude, for a period, if you go back and just use a model without giving it access to the web, for 75 % of what you do with them, you will find it unusable. Now, why is that? We should observe. Why did we suddenly reorient ourselves? It's because these models present interfaces and a mental model, which is that they're both smart and all-knowing.

40:57Once you build that mental model into a product, you must use the web. Because otherwise, you're not all-knowing. it feels entirely stupid that the thing doesn't know the date of the last Super Bowl it just it's so confusing to people, the same thing happened with coding agents, right, coding agents, I remember every coding agent, like I would be pasting links to docs into a UX to have it fetch and index it locally so that it could build in an API that was not in the pre-training data set. Now we'll feel like cavemen doing it. And so really what's happening is every, almost every application we build with models creates the customer expectation of that application that this is all knowing.

41:54And that intuition has gone from, oh a model is smart to a model plus the web. and now over time it's evolving into and it has access to all of my data

42:05Parag Agrawal:right? But the web is going to remain in that expectation. So we really think of parallel as a adjacency, infrastructure adjacency to every model to inference for all kinds of work, right? So if you think of how we evolve, it's when different categories of work, when people derive value from models in the work context we take off and that really started happening in my observation last summer in some, like 9 months ago in some areas and has been exploding since then and we show up with like the highest quality way which is extremely agent native to help an agent do more do it faster and do it cheaply when you're building an agent for any kind of knowledge work end to end and so since last summer we're now seeing like across categories the people who are at the the tip of the spear the frontier of using agents to do really cool things incorporate our APIs and make their own products even better than they already were and we write that and then as sort of So we've seen these models being really useful in finance, really useful in sales.

43:34Can you give some examples? Maybe like the Harvey use case. You guys just announced a big partnership with Harvey. Like, what are they using it for? We have a pretty big product suite at that point. So they use it for multiple things. One of the things they do is just use our search products so that all engagements with Harvey, the product always knows what's out there. and it knows it without burning too many of its tokens, it knows it with fresh data, it knows it with hard to reach data. Now, there is a second element to this, which is there is a lot of data that over time, let's call it like, I think in 2000, this was called the deep web, which is content that is extraordinarily hard to get to on the web.

44:27Let me give you an example. There are many portals where there's content not in the typical search index which is optimized on 10 blue links. Why isn't it there? Because a lot of this content isn't reachable by just clicking on a link. You kind of have to end up at a portal. You have to either navigate somewhere or search somewhere and it doesn't have a URL. for that content. Now, historically, people, one, it's hard to crawl. Two, it's less valuable to crawl because you can't actually land a person on the content. In a world of AIs, our job is to bring the content into the context window of an AI.

45:10So we now go crawl, we build crawlers for things that historically search engines haven't been crawling. And when you're building Harvey when you're optimizing for having access to authoritative source documents across the world so that every bit of output in Harvey is well grounded with an authoritative document you care about one having completion on these hard to crawl information that is in the public domain and two you care about really good search ranking and search quality on that And these are two things we do really, really well. And so through this partnership, we're able to push and stretch our capabilities because they're a very demanding customer.

46:05And they're able to have access to extremely hard to reach, high quality public data with great ranking on top.

46:14Parag Agrawal:Do you want to talk about just having run one of the largest web scale system at Twitter? and the parallels that you draw to what you have to ultimately build at parallel or what you may have already built in terms of supporting the scale, latency, obviously latency being so critical and important for a real-time social network. Yeah. No, I think so. Parallel is fundamentally a technical product. It's an extraordinarily large-scale, system. I would say over time its scale is meaningfully larger than Twitter. Mostly because Twitter is like amazing and influential and I'm really proud of what we built.

47:05It's a small slice of the web. It's a small slice of public content out in the world. The opportunity with parallel feels like outrageously larger in terms of keeping the web open. The mission is the same right? Twitter was trying to keep more high quality, unique content out in the open versus it disappearing into closed worlds. Giving direct access to the best minds to everyone in the world at the same time, instead of via intermediaries or via closed off groups. that is the same mission at parallel to keep the web open and having more information out in the public at all times but it feels materially larger, now going to the infrastructure though I think having great having known great engineers having known how to build and design systems pragmatically for a level of scale, knowing when to rebuild them for the next level of scale.

48:19Having done that several times over at Twitter. At Twitter, we went from this ride-off, trying to grow systems, and every year, we would have to rebuild entire systems, because they weren't built for the next order of magnitude of scale. But if you build for four years out, you don't ship anything. And so you have to constantly keep evolving for scale. And we're running the same-ish set of lessons at parallel. The other I think really interesting thing that I carry from the ML infrastructure part of Twitter is, you know how at Twitter when you're building these recommendation systems or these models, they initially start as these sort of cobbled up things which are like you train a bunch of models and then you have a bunch of heuristics on top.

49:10And over time as you learn about your problem space more, things become more end to end trained. This is the same story that's played out with like self-driving cars in Waymo. Started with like a few perception models and this model and over time things become more end to end as you know how to collect more data. Right? I think today, you don't necessarily have to go down that journey that slowly, the way we used to do 5, 7, 10 years ago in building these systems. And you can shoot more end to end from the very beginning at scale. and I think doing so puts you on this path of rapid self-improvement and so in some sense we saw the journey at Twitter and at Parallel we're sort of taking a leap to not have to go through the three steps and jumping to the fourth step.

50:00Parallel started, you had an amazing run at Twitter CEO, start Parallel was everybody like clamoring to come work with you? Like you had this vision and this mission did you come out of the gates like tell me about the very very early days was it obvious to was it obvious because you are like pretty bona fide to go work with as a founder so we were very quiet and stealthy so we literally no one knew that i was starting a company i had worked with a bunch of people. I called the people I wanted to join me at Parallel. And then there was this mission alignment problem. So I didn't get an inbound call because no one knew what I was going to do.

50:55The one thing I did early on was decided that we would keep more than half the team new and not people I had already worked with. And I had sourced really talented people through the almost a year before I truly started hiring for Parallel where I'd met a lot of people exploring lots of things and ask them a standard question which is like who are the best three people you've ever worked with and I would go meet some of them over time and so I ended up hiring and working with the initial founding team of me plus six other people and half of them were people I had not previously known but met via this sort of multi hop dance of like them being some among the top three of someone I thought was already exceptional and so this way I think we got to build a team that was beyond my in starting network from twitter and would ultimately we've done a lot of hiring via this kind of channel of like extraordinarily unique and talented people, but built via this sort of network hiring approach of exceptional people to tap outside of my personal network.

52:15And I think that's been really, really good for us. 40 people today? 50? Yeah. What do you think you'll be at the end of the year? I don't... Are you hiring? I am hiring as fast as we can. Across every function? We would hire across every function. But we proactively put our energy towards a few. Which are? Always engineering. Now, GTM. And the new ones are marketing, developer relations, or the top new ones on my list where we just need to do more. Yeah, it's like time to build the company around the product. Exactly. So we started with building the technology. when we launched the product last year, we're building the product.

53:09Now we're building the company around it which includes GTM but includes all the other things you need to actually show up for customers.

53:21Parag Agrawal:So maybe just jump on that. So some of my favorite products that I use, AI native products, use Parallel and I'm not sure they're all fully announced in terms of as customers or you have some public statement with them, but maybe just help us. What are these products? Rogo, Harvey, Profound, Granola, Clay. There's more. It's a good list. Yeah. We love, listen, I... It's a good list. I love... So one of our core beliefs is that you have to experience your product in the context of every customer. So we are particularly interested in being a part of the journey of products that we personally love.

54:18Right? So it gives me deep, deep, deep personal satisfaction. I think at Twitter, I was a heavy-duty Twitter user before I even started working there as an engineer. I was an addict before I went to work at Twitter. And the reason I ended up there was because I was in love with the product. And it's the same thing. My team derives extraordinary satisfaction to have built something that is useful for products that we love using ourselves for our work. And it is truly the most rewarding thing to be able to solve problems that these have. The other side effect is I believe we have good taste in the products we like to use.

55:04And to be able to those products are often built by people with really good taste. And so to have them as customers we put ourselves in a place where we are demanding, extraordinarily demanding in terms of what products they would use. they push us and our technology and our product forward and my core belief is that if we can be a product that they love putting into their products to power them there's a lot more that follows from that and so it's really nice that over the last several months we've sort of built our product up to the place where the best products we like are starting to use us.

55:54Parag Agrawal:So with that, I imagine more AI native products will use Parallel. I think you'll blanket sort of that world. What else can we dream about where Parallel is deeply embedded? And so in other words, how does your market expand and how does this company really rise to the level of one of the most exciting companies that is being built right now? So, one, I don't think we have to expand the market. Like, we are like, if you use a model for building anything, it's a product, a workflow, automation, for anything related to work that you do, I almost believe you must give it the web. And if you must give it the web, the question is through which of our products right and we have and must have the best way no matter what your circumstances for the product or the workflow that you're doing to use us now this need is broad based if you're a large enterprise that is now trying to make your operations more streamlined and you're starting to use a model for it you could there are many ways you could use panel you could have an internal team buy an LLM API and parallel APIs and hook it up to your internal data via a bunch of MCPs or CLIs and build workflow automation for example like I've built my own app for my productivity right or you could go and you can say no no I don't want to do it myself I actually want someone who's productizing it for others like me for this function.

57:45And then we must be working with whoever it is that solves the end problem. All we care about is when agents access the web, we have the best products for them, no matter who they are. What you're going to see over the next year is there'll be a class of the best AI native agent products that will incorporate parallel if we do our jobs right. there'll be a class of large enterprises which will take all kinds of differentiated work that they do themselves and supercharge them with parallel. And the biggest thing that excites me is people often frame this as like, oh, we were already doing this work.

58:29Now we're going to make it more efficient and we're going to do the same work, but it's going to be faster and it's going to be cheaper and more automated. No, people do work beyond what was previously happening. I'll give you a very simple example. Let's say you're a semi-hypothetical, a PE firm thinking about buyouts. And in a previous world, you'd say, okay, I could buy out these categories of businesses and these kinds of neighborhoods and these kinds of cities. And you'd use some gut and human judgment meant to narrow down to like four or five interesting opportunities and then you'd go do extra work into them.

59:10Right? Their biggest limited resource is how many of these they can do through capital and human capital in a year. And the biggest job is to figure out which ones to do and prioritize that well. Now they can use parallel and models to really run what feel like almost simulations on various criteria that they set. And do a lot of compute to exhaustively come up with a rational list of opportunities to actually explore and then pick the ones they do better. They were previously pruning the space of what they would explore to this tiny top-down intuitive thing. And now they get to see real information, real data about what it could be.

59:55And if it makes them pick one better, there's so much value created that it's worth token maxing in that context, right? And so when you see these use cases about like just entirely new ways of operating businesses that have existed forever, that's really exciting to me. And I think that's the next year of growth, which isn't just like replace what we're doing today. It's going back to what does it take to make it 1000x, right? It is work that was not happening yesterday. Well said. Thank you for doing this. Well said. When you hear the word grit, I ask everybody the same question. What do you think of?

1:00:39The conversation we were having earlier, when you asked me in the kitchen about taking a breath. I think grit is just doing hard things. And the only way you can do it is if it's for something meaningful to you. And it's fun. Thank you. Thank you. That's it for now. If you liked the episode, please leave us a review or go back into the archives where we've done more than 200 episodes with some fantastic folks. This podcast is a Kleiner Perkins production and I'm Juven. Thanks for listening.