Free with a sign-in.
The best 13 min of 61
Ex-Twitter CEO on Why AI Needs a New Internet | Parag Agrawal
Parag Agrawal argues that AI agents need new web infrastructure and incentives to keep high-quality content open.
Grit · Joubin Mirzadegan & Mamoon Hamid
- Parag Agrawal is founder and CEO of Parallel, former CEO of Twitter.
The best minutes
- 35:23AI agents need incentives that keep the web open4 min
- 22:54Agrawal wants engineers to move work into learning loops3 min
- 13:09Agrawal would rather overuse tokens than be too cautious6 min
Also worth watching
- 8:39Agrawal built a personal agent to catch dropped priorities5 min
- 2:49Agrawal protects proactive work with three daily priorities4 min
- 18:46AI productivity can increase engineering demand4 min
- 29:12A strong close rate revealed a sales pipeline problem4 min
- 58:18AI can expand the deals a PE firm can assess2 min
Skip the Friday-demo discussion, the Harvey and product walkthrough, the staffing and customer-list section, and the closing grit exchange.
Summary · 4 min read
Agrawal spent more than a decade at Twitter, rising from engineer to CTO and CEO. He is now the founder and CEO of Parallel, which builds web infrastructure for AI.
Agrawal says AI agents will use the web far more than people do, creating a need for infrastructure and incentives that keep useful content available and the web open. He argues that teams should build more work into learned models and use AI to extend what people can do, not only to speed up existing tasks. He would rather risk spending too much on tokens than be too conservative. He also says Parallel's opportunity spans AI products and enterprises, including use cases that make it practical to explore work that people previously could not do at scale.
Minute by minute
- 2:49Prioritize three proactive tasks
Agrawal says he separates reading email from responding and chooses three proactive tasks to complete each day or week, allowing less important work to drop. His Friday demos show raw, unfinished work and discoveries he might otherwise miss; by Monday, he tries to turn the most important ideas into action.
- 8:39Build an agent for yourself
Agrawal built a personal agent to see what it could do with limited access to the information he uses, including Slack, Granola, email and the open web. It infers his priorities, knows the three he writes down and can flag work he might be dropping. He says it became useful but took effort to customize and was initially inefficient with tokens.
- 13:09Measure tokens by value
Hamid says he asks his CEOs how token costs are changing. Agrawal warns that token-maxing can waste time as well as tokens, but he would rather risk spending too much than be too conservative. Mirzadegan says Roadrunner tracks spending without enforcing limits. Agrawal judges use by whether it expands people's work: for example, helping engineers take on frontend, security or optimization tasks. Mirzadegan describes trying to automate customer setup rather than hire more people to do it manually.
- 20:00AI can widen engineers' scope
Agrawal says AI does not necessarily mean hiring fewer engineers: more capacity can help a company build valuable products and serve customer demand. He encourages engineers to automate work when the results can be measured, and to put more processes into model-driven feedback loops. He sums up the approach as collecting data, defining good outputs, training a model and improving it in a loop.
- 26:16Sales revealed product pull
Mirzadegan says his operating partner Liam became interested in joining Parallel after seeing the company generate tens of millions of dollars in revenue with about four people. Agrawal says a board question about how often Parallel won deals exposed a different problem: the company was not showing up for enough opportunities.
- 30:06Recruit around the mission
Agrawal says Liam's outside perspective helped the team recognize the strength of the product's market pull and gave them more direction. He also describes building a relationship with Graham, who believed in the company and later helped scale its go-to-market work. Agrawal says exceptional people who believe in the mission can compound into a strong team.
- 35:23Keep the web open for agents
Agrawal says he started Parallel after concluding that AI agents would use the web far more than people do; he initially wrote down a 1,000-times estimate and now thinks it may be higher. He argues that agent use requires new business models and incentives for people to publish high-quality, distinctive content, or the open web could start to close.
- 39:58Models need access to the web
Agrawal says users now expect models to be both smart and up to date, making web access essential for many tasks. He says Parallel supports products such as Harvey with fresh search results and hard-to-reach public information, including content behind portals that traditional search indexes may not capture. For legal work, he says access to authoritative sources and strong ranking helps ground outputs.
- 46:08Build for the next scale
Agrawal says Parallel's system is intended to reach a scale meaningfully larger than Twitter's because it covers a wider slice of public information. At Twitter, he says, systems had to be rebuilt as the service reached new orders of magnitude, without trying to build so far ahead that nothing shipped. He also says Parallel can move more directly toward end-to-end learned systems.
- 49:54Recruit beyond existing networks
Agrawal says Parallel started quietly, and he decided that more than half the team should be people he had not worked with before. He asked people he respected to name the best colleagues they had worked with, then met some of those candidates through multiple referrals. He says this helped the founding team reach beyond his Twitter network.
- 52:14Build the company around the product
Agrawal says Parallel is hiring across functions, with engineering, go-to-market, marketing and developer relations among its priorities. He says the company first built its technology and product, and is now adding the functions needed to serve customers. He values working with products his team uses and says demanding customers push Parallel's product and technology forward.
- 56:04Make more work possible
Agrawal says any model used for work may need the web, and Parallel aims to offer the right way to access it across products and enterprise workflows. He argues that AI can help people do work beyond what they previously attempted, not just do the same work faster. As an example, he describes a private equity firm using models to examine a much broader set of potential buyouts before deciding which to pursue.
- 1:00:28Grit needs meaning
Asked what grit means to him, Agrawal says it is doing hard things for something meaningful. He adds that the work also has to be fun.
Published Jul 27, 2026.
Transcript · 43 min read
Grit, hosted by Joubin Mirzadegan and Mamoon Hamid, with Parag Agrawal.
From the transcript Grit publishes with the episode.
The answer to every problem is a model. You're tempted to write a bunch of code for it? No. Instead, collect some data. Figure out what a good output would look like. Figure out what model architecture is right. Train the model and put it in a loop so that you can keep improving the model. That mindset just allows you to get out of that decision making process and hire people who are very versatile and problem solvers. It's simple. Once you sort of just internalize that worldview, agents and AI's will just use the web a lot more than humans ever have. New business models will be needed, and now there's going to be like a second birth of what the future of the open web is a parallel web, a parallel web, a few minutes. If we just figured out how to incentivize being open, how to reward high quality, unique, differentiated content that we get to keep the web open.
Welcome to grit. I'm Joubin, partner at Kleiner Perkins, a show where we go beyond the highlight reel and explore the personal and professional challenges of building history making companies. Today on the show, we've got Parag Agrawal, former Twitter CEO and now founder and CEO of parallel, the company building web infrastructure for AI. Before starting parallel, Parag spent more than a decade at Twitter, where he rose from engineer to CTO to CEO. Now he's asking a different question what happens when AI becomes the web's second user? We're also joined by my partner, Mamoon Hamid. Enjoy. When we were sitting in the kitchen right now. Um, I, we were talking about fundraising, all sorts of stuff. And, uh, I said, can you take a, can you take a breath? And you're like, dude, you take a breath, this company is dead. And I was like, okay, that's a fair and dramatic point of view. I like, but okay, like, I understand. And he's like, do you not feel that way?
And yeah, Parag asked me like, do you feel that way? And I said, well, I definitely feel that way, except I just want to make sure that whatever I'm running at is the right thing, because there's so many things that are feel like they're on fire and it's full chaos. And let me ask you, can you take a breath? No, but I just want to make sure. He's money in company. Right? I know, I know, he just told me, I didn't know I didn't like I didn't. Know it until. That company has a whole team I know elsewhere. It's half your team size. Yeah. At this point, I did not know that. Yeah. Walking into it. I found out. In the kitchen and I'm on his board. Yeah. That's right, that's right. I just wanted I you know, your point resonated. The reason I took a beat was because I was like, the temptation when everything feels like it's so insane, where if you take a breath, you die, makes you feel like you have to do everything all at once. And for me, I was like, okay, what am I actually doing that moves the needle? Because otherwise I could be doing stuff, making myself feel like the most productive human in the world. We can. We can talk about all. These things like. Yeah, we're live, we. Can just die.
Read the whole transcript
“But I'll write three things down and I will make sure those three things get done.”
you were in the kitchen, you were describing how, uh, Friday afternoons you have like demos 430 to 6 and it's your way as a founder to kind of internalize the rhythm of the business on the on the product side. And then going into Friday night, you feel like, all right, things are happening like, I know what's happening. And then Saturday morning you'll wake up with some
existential panic that something is breaking. Can you describe that? Yeah. So this is my rhythm weekly rhythm. So there is like a team is amazing. We're doing a bunch of really cool things. And I am often not even fully aware of really good things that are cooking every week, or some cool result that someone came up with, or some cool insight someone came up with and they shipped something. And the best way I found is I like to see things which are like raw and in progress. So our demos are designed to be not like, oh, here's a package product that's going out to customers on Monday. That's not what Friday evening demos are for. Friday evening demos are for. I was doing this this week. It's not done. It's raw. Here's why I'm excited about it. And you should all know about it. And so that is the stuff that like if I'm prioritizing the three things that matter, that is the stuff that never Bubbles up and then I just wouldn't know. And I would find out when. It's like a packaged up thing. And that's what sort of gets the both the job satisfaction and the ideas and the rolling for me. And so getting that dose of infectious excitement about the work we are doing every Friday is really rewarding because you start thinking about it. And then some of those boil into, oh, why did
I? There's so much more we could do with this thing. And so now you're paranoid about like, a missed opportunity. Some of those are about, wait, this is so cool. If someone else figured this out and then like, your head, like your mind goes in many, many different directions, and then you have to boil all of those things into the one that matters. To be truly either paranoid about or excited about, or is actionable. Right? And by Monday, you have to make
“it makes me feel more secure that I am not dropping the important balls because it is also paranoid on my behalf.”
“Which way would you rather be wrong on? Right? Would you rather be wrong because you're too conservative and just not spending tokens?”
“if you have an extremely interesting set of things that you can build, which are extremely valuable, and your customers need it.”
do. Like this example of like seeding a customer environment and having to do that 20 times like that seems to me a little bit adjacent to core product development. Or do you also have infinite appetite for hiring hands and that type of work as well? No, but I think instead of defining what kind of work, I think great engineers want to do
“Figure out what a good output would look like. Figure out what model architecture is right.”
I want to say. Yeah. So about two months ish. Yeah, yeah. In about 6 or 7 months, we've doubled. I want to say we. Haven't gotten the parallel. Yet. I know I was about to. Yeah, well, I guess we're kind of talking about it, but can I tell you a story about parallel story? So I, like I haven't gotten to, um, know you that well yet until recently. And I got a call from Liam on my team. I was on a work trip, and this was like, gosh, eight weeks ago, not even six weeks ago. And he was like, hey, I got to tell you something. And I'm like, what? And he's like, you know how sometimes founders, when we work really well together, you know, they kind of want to see if there's ways for us to work more formally together. And Liam, for those listening as their
sales operating partner, I'm like, yeah. He's like, and you know how I always like I'm, you know, I'm not interested. I'm like, yeah. He's like, I think I'm interested in one. And I'm like, huh? Why? And he's talking about parallel. And he was like, well, number one, they've sold like tens of millions of dollars of revenue with like for people and like it's in spite of themselves. And he and he has spent a lot of time in your company at this point and he's talked to all the reps and so he can like see it. Like, you know, sales has a really good nose for like, can you stick it to this product to sell. And so that was one. And then he was like um to Parag and, and your co-founder Shivers, they're the real deal. Like they're the real deal. And I said, um, handicapped for me. Like, how serious are you? And he was like at least 50%. And within a week we went from 50%. Mamoon and I talked, we talked to Liam. It was very obvious, like this was like his calling was materializing in front of us. I think it's a fair way to describe it. And and this happens all the time. Like this I, 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, uh, 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, were 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 fortunately, uh, our job is invest in great companies. And the things that Liam saw, we'd seen six months before, that's 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 Parag's ability to recruit them, that's like the, the vision coming to life. It is. But let me tell you the the I'll double down on the in
“I don't think we fully internalized how well our product was being pulled by the market.”
also Mamoon first introduced me with Graham. Who ran sales at Windsurf previously. Yes. And Mamoon 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 roll, 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. And to be clear, he was at Codeium before it was even called Windsurf. 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 Windsurf because we were on the board there too. Yeah. Uh, 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, right? Like, I just don't know how to do that. So I walked the world trying to figure out how parallel can be better, because there are some amazing people that can who
“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.”
So when we, I think, first spoke about this two and a half years ago. I remember it was like Thanksgiving of 20, 23, 23, 23, 23. Right. Yeah. And the notion of a web for AI agents completely, 100% made sense. Um, and a lot has happened since then, uh, 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, 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. So 2 or 3 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 really cool, right? For people who've now been using any of these products ChatGPT cloud 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'll 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 Once you build that mental model into a product, you must use the web because otherwise you're not following. It feels entirely stupid that the thing doesn't know the date of the last Super Bowl. It just 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. And the that intuition is gone from oh, a model is smart to a model plus the web. And now over time it's evolving into an it has access to all of my data. 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 nine 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, um, as sort of so we've seen these models being really useful in finance, really useful. Can you give some examples. Maybe like the Harvey use case. So you guys just announced a big a big partnership with Harvey. Like what are they like what are they using it for. We have a pretty big product suite at that point. So they use it for multiple things. Uh, one of the things they do is just use our search products so that All engagements with Harvey. The 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 the in 2000 this was called the deep web, which is content that is extraordinarily hard to get to on the web. Let me give you an example. There are many portals where they're not there's content not in the a typical search index which is optimized on ten 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 a URL for that content. Now, historically people, when 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 eyes. Our job is to bring the content into the context window of an AI. So we now go crawl. We built crawlers for extra like 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 in search quality on that. And these are two things we do really, really well right. And so through this partnership we are able to push and stretch our capabilities because they're a very demanding customer. And they are able to, uh, have access to extremely hard to reach high quality public data with great ranking on top. Do you want to talk about just having run one of the largest web scale systems at Twitter and, uh, the the parallels that you draw to what you have to ultimately build at parallel or what you may have already built, uh, in terms of the supporting the scale latency. Obviously, latency being so critical and important for, you know, 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 it. It'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, uh, 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 a disappearing into closed worlds. Right. 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. Having done that like several times over at Twitter. At Twitter. Like we went from this ride of trying to grow systems and like 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 like 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 the ML infrastructure part of Twitter is, you know, how at Twitter, when you're building these recommendations 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. And 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 model in 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 five, seven, ten 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 parallel started. You had an amazing run at Twitter CEO start parallel. Was everybody, like clamoring to come work with you like you had this this vision in 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 It 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. The 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 asked them a standard question which is like, what 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 a 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 off 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 a channel of like extraordinarily unique and talented people, uh, but built via this sort of network hiring approach of exceptional people to tap outside of my personal network. And 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? Are you hiring?
I am hiding 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 are 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 had we're building the product. Now we're building the company around it with includes GTM, but includes all the other things you need, uh, to actually show up for, uh, customers.
So maybe just, uh, jump on that. Uh, 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, uh, maybe just help us. One of these products. Uh, Rogo Harvey. Profound granola. Uh, Clay. There's more. It's good list. Yeah. We love. Listen, I could 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. Right. So.
It gives me deep, deep, deep personal satisfaction, like I think at Twitter, like I was a heavy duty Twitter user before I even started working there as an engineer. I was an addict, uh, before I went to work at Twitter. And the reason I ended up there was because I was in love with the product. Right? And it's the same thing. Like, 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 and 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 the 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. So with that, uh, I imagine more AI native products will use parallel. You know, 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 are is being built right now? So one, I don't think we have to expand the market like we are. Like if you use an 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 parallel. You could have an internal team by
an API and parallel APIs, and hook it up to your internal data via a bunch of MCPs or clients, 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 prioritizing it for others like me for this function. And 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 are you 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 will 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
“Now people do work beyond what was previously happening.”
grit, I ask everybody the same question. What do you think of. The conversation we were having earlier when you asked me in the kitchen about taking a breath. Uh, 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 Joubin. Thanks for listening.
