Ex-Twitter CEO Says We’re Underestimating AI Agents by 10,000x

Paralo web systems closing a $100 million series be funding round last month volume that AI started up at $2 billion. You're at Twitter for 11 years you were CTO then you became CEO. I was also an engineer before that. We're building a collection of APIs that agents use to search and access the web. Paralo is a almost three-year-old company now. I was the lunatic walking around three years ago telling people agents will use the web 1000x more than humans ever have. Most people didn't believe me at the time. I was wrong. Why? 1000x of 2. My deck literally contained a section which is like are we too early? Turns out we were not totally. We're building a parallel definitions. We can transform the web even if you don't publish your website to be agent friendly, we'll do that for you. In such a competitive career in every stage, how do you not break from the pressure? You don't feel pressure? No. Prague, welcome to Sorcery. Thank you for having me. Well we're in indexes office in San Francisco. It is gorgeous here. They're one of your investors. I'm not sure if you've ever gone ask this question before but what have you gotten down this week? I'll tell you what I got done today. We just shipped a really exciting integration with browser base. We now power search in browser base and if you think about every AI agent that people use, what are they doing? They are looking at your internal information. They're looking at web information. They're searching a bunch more than us than they're going into a browser logging in and doing something, buying something, writing something. That's what we got done today. Congratulations. We were talking a lot before this and we were having a really great discussion. Every interview I've had recently is some sort of explosive growth of agents and it's going to be really fun to unpack all of that because from your point you were talking about read versus write, what you're doing versus browser base. I guess to start, let's talk about that. Parallel, what is parallel? How is it different? How is it evolved? How are you learning as things are just changing so fast? Yeah. Parallel is a three, almost three-year-old company now. There were no agents. They didn't work. Fortunately, some did, including index. At the time, it felt like the science fiction hypothesis that there will be a lot of agents at work and interpersonal lives and that they'll be doing web searches for some reasons. I believed it because I'd obsessed with it for a long time. But today, if you look at what's happened in the last three years, I was wrong but on the other side, 1000x is too low. I think at the time, I thought 1000x was like the crazy number to make up to talk about. At the time, my deck literally contained a section which is like, "Are we too early? Turns out we were not too early. Turns out today, if you force me 1000x is too low a number." If you just look at just some high-level method, a person and average person does like order of 10 or less Google searches a day. I was checking how many I do. Several thousand now. My agents, the ones I try. Wow. Several thousand per day. All the agents that I use today. So I am now sitting at roughly 1000x a little bit less than 1000, personally. Most of the world is not. Most of the world will in due time. It might take us a few years, but 1000 is a low number. But is that efficient? Do you think that will crawl back at the certain point because they'll just get smarter and know where to go? Yes, but it is efficient because the other part of the story is that you can't build old school search in 2000x of this. You think about like, "Okay, all the infrastructure that Google might have built to serve whatever order of 10 billion searches a day, like the best guess I have is 10 billion a day is what Google will do." There are a lot of infrastructure to pull that off. I don't think we get to build 1000x that infrastructure to do 1000x the searches. So part of what we do is build extremely efficient and optimized search, which again benefits from models getting better for us to be able to distill intelligence ranking into smaller and smaller models for us to be able to use GPUs as the underpinning of search indexes. Historically, search was done on CPUs, memory CPU bound. Now you get to architect and re-architect search for a price performance band that the world never needed until today. Of one of the things we were talking about before, I recently interviewed G from Versailles and this is, you know, maybe this is in parallel, but AI agents for coding have reached 60% of traffic on their indices. What they see, you had a hot take. Generally on the web, the statistic being shared these days in the last few months is that agent traffic is exceeding human traffic on the web. That's roughly the characterization. Now there are two things to remember. An agent for roughly doing the same work that a human does will likely do a lot more searches because it can and a human is time constrained. An agent isn't really, it's more, right now they're not even that dollar constrained because people are willing to spend on frontier. A human read on the web or a human search on the web to create equal length value. It takes way more agent traffic. A lot of agent traffic is also people crawling for pre-training and people like when you look at like if you're a website owner and you're looking at automated activity versus human activity, automated activity is ripping. But my best estimation right now is where we are. These agents are using the web 10 to 20% of humans which is to say if my thousand x number is right, we have from here at least 5000 x or 10,000 x growth left. Just another way of saying we are very early with agents. I think in the Bay Area and Silicon Valley, we're all living in a little bit of bubble. Yeah. I think most people's agents aren't doing the number of searches my agents are doing. But they will. So Muse just came out with Jolly and I really like Jolly the character for Muse. We see dots coming out for OpenAI. We've been seeing a lot of these agents pop up for individuals for consumers. That growth is faster than any growth ever seen. Like even chat GPT, I think maybe possibly. We'll see. But how do you expect that to grow and I guess where does retention stay? Two things. One, I have to make a note. We should call out instinct two. Yes, I am. I am. The reason is listen, I'm on the rebel alliance here. There are all these big people. Yeah. And then there are us startup founders who are running at solving really hard problems and pushing the frontier of what's possible and innovating. So we should talk about instinct. Yeah. They are growing to amazing product. So yes, the category is amazing. It's interesting. I think it's growing really fast. I don't have any insider information around retention. I am, as you can imagine, it's a professional hazard that I use every one of these agents. Yeah. And I try to deeply understand what they can and cannot do and what problems we can solve for them. We'll get into this category and how it changes our product strategy later. But I think my expectation is that these agents retention will be good. And it'll be good because for the first time, the product onboarding feels natural. Like not someone designing like a bunch of UX click targets and flows to get you to understand the product. The product is natural. All of these products are somewhat natural. And the question is can they deliver on the promise created by the UX? The UX is you can text this thing and you can do anything. That's the UX. It's not complicated. Yeah. Now retention is entirely a function of can it actually deliver on this very simple high level promise often enough to become a part of your life. Now it's going to happen. A lot of other products struggle with the onboarding UX barrier 20, but this new generation of products just, that just not a thing. And so I am feeling that retention will get solved here. Is that where you think OpenClaw maybe hit a wall? Because that was really the first. No, I think OpenClaw was right in all of the ways. Yeah, I think once someone got, it's just not for the masses. It's just like, most people don't have the ability to set it up and then to keep up a change. I think the other factor we discount, it's not just a one-time cost to set something up. Things are changing so fast that you have to adapt your instantiation of it alongside that change. And you have to adapt your use of it alongside new capabilities. So part of it might have been that the setup time because of the flexibility and you controlling it was far too much. Part of it is also that it was perhaps a little too early because in the beginning, there was like one model which was really good, like the OpenClaw model. And then as soon as you move to other models, it wasn't as great. In the very beginning, when people started using OpenClaw a little last year. And so you had to adopt alongside it. But today, as evidenced by instant and views, you kind of don't have to know or think about it. And the product will change underneath. But as the customer of it, you don't need to know what changed in the last month. You're oblivious to the capability improving underneath. I can't discount that this was really just invented. So how did you, you mentioned this at the start of the interview, but like you've been obsessed with this for a while. So what does that mean and how? So I think I got obsessed with it almost three and a half years ago. I was the, I don't know if people remember, there was this moment of this baby AGI and auto GPT speaking of oftentimes developers in the open source world being ahead of everything else. People were building with like, I don't know, what is the model of the GP35 or whatever, like old models that weren't like relative to today, they were like really bad models. They're relative to today. And people were turning all these things and I was trying all these things to try and make things work. And they didn't work. But if you believe, you spent any time with the labs back then, you internalized how models will scale and how models will improve. Like in some sense today, it feels like everything changes every month. In some sense. I don't know if it's every month. It might be every week. But if you look at it in a different way, almost nothing that is happening is diverging from what you could have predicted in a straight line three years ago. Models will keep getting bigger and better. They will run for longer, holding capability constant. Models will get way, way, way smaller and cheaper. And people will be able to productize around them to disrupt every category of work that can be done on a computer. These were very simple things that we believed and said three years ago. And we have only seen evidence week on week and month on month supporting. These very few simple things. Yeah. And almost everything follows. So now you can say, yeah, I didn't predict that like the big category would be coding agents first. Right? I didn't predict the CLI form factor, right? Those were all bits of information and news to me along the way. But the macro thesis of agents are going to get more capable. You're going to do a bunch of inference. And for all the inference you do, you're going to have to do web search along it. I've now been on the record everywhere saying like like the one invariant, I believe, is we will use a lot of GPUs for model inference for agents, whether they be at work or consumer agents. And for every 10 GPUs, the world spends on model inference will spend 1 to 2 on web search. That's the business. So are you aggregating GPS? You've raised 230 million reported and announced, I don't know. But is are you aggregating GPS? Yeah, we are not, I wouldn't use the word aggregating, we are buying, we're renting GPUs to build the scale of search traffic that we are seeing and expecting. And as I said, we grow alongside inference. So inference is growing. So we have to do a proportionate amount. I don't know if it's like already, I don't think it's a core competency of us. Like we have been in multiple mad GPUs, crambles in the last 3 months to be like, oh shit, we need GPUs, we don't have GPUs, can someone figure it out and like you're making a bunch of calls and like. Were you calling? Sorry? Were you calling? Everyone. Everyone. Like every hyperscaler, every neoclaw. What's like the typical response back because this comes up in pretty much every conversation now, it's like, how do you get, I guess the allocation of GPUs? No, I think it's like, I think at least for us, I think it's true for many people. It's like, okay, what GPU is it? Physically where is it? Sometimes we need GPUs within 5 milliseconds of where our large web index is sitting. Okay. Other GPUs will sometimes be more flexible than the multiple GPU workloads. It's not just like, okay, do you have GPUs for me? It's like, do you have the right GPU in the right scale, in the right physical locations for me available at the right time? And that is what makes the problem much harder. I think so far we have managed to solve every problem we have encountered. We've not had to turn away accustomed because I didn't have GPUs, but we've had a bunch of GPU acquisition, Fire Dells. I mean, that's a really interesting line to go down. It's like, what is the sourcing process like for GPUs, like, what are the main questions that you're asking? What do you need? Yeah, it's like, you mostly, so I think different workloads are different. But welcome to our workload. It's like, how many GPUs, which types are you open to when do you need them, and where must they be? It's like, there isn't too much more to it. I think for certain large training clusters, there's a little bit more to it. And then for us, like, are you willing to commit enough duration to that physical thing? Yeah, but that's, it's not complicated. The reality is that I wouldn't say the market is entirely efficient right now. Like, a lot of it is on iMessage, right? Really? Yeah. But it works, the market works. Well, that's crazy. So you're doing deals on iMessage for GPUs? Not exactly. You're feeling out of availability on iMessage, and then like the finance person then calls the finance person and the paperwork gets done. Oh my god. This episode is brought to you by Brex, my favorite. You become what you spend on, and I refuse to spend my time on work that shouldn't exist. Business reports, receipt chasing, and manual closes, the company is building what's next from Versel, OpenAI, Anthropic, Granola, and Deepgram, all made the same call. They all run on Brex. Brex is the intelligent finance platform that combines cards, expenses, and banking into a single stack with a gentick finance built in. 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I want to go back to the topic of scale because we were chatting about historical scale and what that used to look like and what you would expect. So you were at Twitter for 11 years, you were CTO, then you became CEO. We're also in a few minutes. engineer before that. You've done the whole thing. You also have a PhD. I've done all this research, but I want to talk about that. So, like, can you set expectations now? Like, what would scale meaningfully look like today? If, like, back then it was a triple, triple, double, double. Now, it's like, what do you have to achieve? I don't know how somebody else could do it. For our business, turns out the answer is relatively easy. As I've said, like, we just build alongside inference. So, if you think about inference growing year-on-year, to us, that is the anchor. Like, our right goal is to grow faster than inference for agents for knowledge work or consumer agents grow. So, if we grow alongside or faster, I'm feeling pretty good, right? And that's to us the benchmark. Now, what is the right inference benchmark? That's a question. I don't know long term the answer to, but I think in the short term, it's clear that GPUs that are coming online this year or next year are going to do inference. And so, then they're going to do certain alongside it. So, the short term there is very clear, but our benchmark is just growth of inference. So, what is that numbers? I think like the easiest approximation is 10x. You have to think about like 10x goal as the very simple round number. If you think about today, like, if you'd ask me last year, I would have probably said a lower number, but the pattern here is even when I have thought I was being provocative and making up a high number, I have somehow managed to underestimate. So, now I feel like I can go to 10x. Feel a little bit uneasy. It feels crazy to grow 10x for too many years. It'll look how long can that happen? Maybe I'm wrong again. I'm curious because you get to see so many different market insights from internally, externally, all around. What are some surprises that you've come across and what are the signals you're looking for? So, I think it might be useful to tell people about the kinds of customers we end up working with. Okay. All of our insights, or at least my insights, come from working with people doing really, really interesting and unique things. We started working in the beginning with all of the people pushing the limits of building agents. So, some of our customers like Harvey Notion, there were some of the early people building agents for doing real work where the quality bar for that work was quite high and everyone in the adoption chain was like, how can we trust this agent to do our work? That was a question we asked. And we, by working with like really demanding customers or customers who had really demanding customers in turn, we ended up really pushing our system's capabilities on the quality dimension. Right? The next phase I would say is like voice agents are starting to work and the interesting thing there is initially there was some acceptance for your voice agent pausing a little bit unnaturally or like, let me look that up and come back to you. Like that was acceptable for like a three month period I think. And then very quickly today, if that happens to you, at least the people who've interacted with multiple voice agents, you'd be like, that is ridiculous, like that's a really bad product. And so now you're seeing all these voice agents which are like super natural, but also super fast. So what does that mean for web search? Now you've got to be able to search the web and like 150 or 200 milliseconds, like human web search was like a sub-second product like 600, 700, millisecond product roughly into it because we don't notice it getting any faster. Right? For voice agent, that latency constraint on web search is unacceptable. So now you have to push yourself on the colleague dimension, on the cost dimension and the latency dimension. And you've got to crush all three. And that's been something like we get pushed by sort of the frontier customer who is like, listen, the product experience that we can build today is going to be unacceptable, very, very quickly. So what does every underlying infra piece need to solve in order to create the market expectation, which changes so fast, right? Like I think, I know, going back to web search, one of my, you should at some point try even with a new model, turning off web search in chat GPD once. Okay. I think we all use chat GPD for the first, I want to say eight months, maybe nine, it did not have web search and we were just talking to a model. Try to do that today. And see what happens. My bet is one, 90% of things you ask, it will disappoint you. Number two, you will feel like a caveman. I'm using that product. So it's only three, but three and a half, four years ago now. But our implicit expectations of a product that looks the same have changed so dramatically. And that is what sort of mind boggling to me. And I think that is essentially what drives all technical progress. Yeah. It's because it becomes table ticks. Right. And now they might be getting subconscious, you know, they might have some consciousness to them and start judging us and attacking us. Actually, I know I'm more of an optimist on this. Okay. I think they will actually get proactive. So they will know us. They will know our explicit and implicit needs. And they will try to proactively do work. That's the phase, the next phase. I think you're starting to see glimmers of proactivity in instinct and muse and not glimmers. I think they're still more reactive than proactive. They're always on, but they're not really proactive. I think the next era, they will be hyper proactive, which I'm really excited about. I guess to peel back because this is a brand new economy and layer of the internet and what could, I don't know, the internet might not exist anymore. Maybe we'll just use agents. This is what people are saying. What are those components of the underlying infrastructure? You're one of them. What are like, what makes up that base? There are two ways to talk about this. I think before going to the internet, let's first talk about the underlying infrastructure components for building an agent. A little model, which is inference. You need perhaps a sandbox. I think you need web search like we provide. You need an action space which includes a browser and a few other API hooks. And you need access to a bunch of connectors to access your personal data or your company's data. You build these together and you can build an agent. And then the rest of it is all of the same stuff we've always had, which is observability, feedback loops, security. And there you have a cloud for agents. Now you're going to the web piece of the info layer. You now need to do web search, but also you need to figure out to your point around the internet, shutting down. I do think the primary consumer of the internet is going to be an agent, which means the internet will get re-architected. The first thing that's happening is agents are reading the web, which is what we do. What we haven't figured out is, okay, what is the new business model? Like previously, if you put your content out there on the web, you were either building a subscription business, which is like you put some content out there, a bunch of users come. Some fraction of them subscribe for a monthly subscription to a newspaper. Other internet businesses were monetized by saying, I'm going to put ads and I will get paid based on human visitors, seniors. Other business models were like either affiliate business models or you drive a transaction or some sort. All of these business models are fundamentally relied on us, random walking the web as humans, opening pages and taking the next action after the next action on it, some of which are monetizable actions for these participants. If agents are doing this, the buying action still remains. Every other action breaks. You can't monetize attention anymore. So ads business model, per-seed subscription business models, these things are not durable. So what replaces it? That's part of what gets me super, super excited about building parallel, with like I think solving just technology problems I think is far less interesting than solving a technology and a business model problem so we have a highly opinionated point of view on how we keep the web open on how we incentive a line content owners with the AI app or AI agent so that a content owner isn't sitting there saying I'm going to block all AI agents because I don't know how to how that becomes business for me right so we need to solve this problem for people which is like okay give me an incentive to make my content available to AI agents what is that we built a system which runs a model not surprising to compute how much to pay each content owner every time an agent reads their content and we've been now doing a bunch of partnerships with content owners and the better way of making is that we'll be able to keep the web open as a result of our version of we can call it AdSense which is easy auto differential pricing actually provides content owner value for high quality money that's a really good point um I was interviewing usv union square ventures not too long ago and Fred Wilson was talking about how google the traditional business there is being cannibalized by AI LLM agents it just is and so Gemini is like the Hail Mary to pull it in and they're emphasizing that and they're taking advantage of that I'm curious to go deeper into this monetization standpoint um in another interview I did app browser base with John Carlson of Stripe he posed the question do we need bot detection anymore there's obviously good bots there's bad bots but if everything all the traffic becomes essentially bot these agents like what is the point yeah no so I think today there's a lot of people doing a lot of bot detection the word bot detection I think is a few different things it is crawlers it is actual agents working on behalf of a human or a business it is malicious bots which are trying to do harm so I think we have to use that word now a little bit more carefully than we have in the past because historically people have mostly had negative connotations with that word and that like it's not serving anyone yeah today there's a category which is actually highly useful for you or for me like I would like my agents to inherit privileges I have so I think we should be careful I do think there is like a bot detection market is doing well right now and doing well right now because we haven't figured out how to do incentive alignment and there's a separate kind of incentive alignment needed let's call it on the action side so earlier I spoke about the incentive alignment needed on the content side which is like how why do I let you read my content the other side is what happens if I let your agent buy on my store and you're starting to see divergent strategies there I'm of the belief that we need to figure out how to solve problems so that all agents not just the ones built by a large lab can both access all content and take all actions that their owner is allowed to right but we're not there right now one of the biggest topics behind growth is cyber security and active threats how do you I know you said you're an optimist but realistically like how do you see the biggest concerns around this no I think the cyber security risks are real I think agents help both defenders and attackers problem always is that so the people who rush ahead to adopt agents for defending themselves I think will be fine because the attackers like to find the weakest link and there will always be a weakest link so broadly that's what I think now the security risk for something highly sensitive like the model weights of a frontier model are a whole different conversation yeah but by and large my intuition is that we will use coding agents aggressively to secure but along the way there's going to be a cyber event every few weeks of consequence which will drive action and urgency in getting ahead on defense yeah we're going to spend a bunch of inference on both attack and defense do you see any of this analogous to your time at Twitter on consumer internet obviously that's a huge one of the largest social media platforms and so yeah that creates a lot of vulnerabilities no all of the there are obviously parallels right not to just keep calling out my company again and again this so Twitter right like there was a real incentive to attack whether it worked with those ramps yeah those were popular for a period right or whether it was some states sponsored influence campaigns which we used to disclose a lot publicly those things always happened it required real energy focus to control it is the thing most people don't understand is it is almost impossible to take it to zero you have to contain it because it is a unknife fight and the tactics change anytime you control some vectors another new vector will emerge and then you control that the other thing people don't understand is most of the bad stuff is stuff that doesn't show up on the surface so if you saw a account and thought this is a part is how this because you could see that's what so the real problem is going to be in the margins when we lose our ability to differentiate yeah either as people or as the technical people building these systems when you can't differentiate a human from a bot or a agent that's when you're like okay now I don't know what I'm up against and I think that is very similar today that we used to deal with better do you think bots should address themselves I have two contradictory feelings about this I do think good bots should declare themselves so that others can recognize them as good bots and treat them differently from bots that might be adversarial and I and we keep using the word bots I would like to use the word agent okay well every time he's bought has a more negative connotation while an agent is a more appropriate word I think agents are perhaps the good bots mostly yeah so yeah I think they should declare themselves I know there's like a bunch of protocols now being done around declaring identity or the world at large the core challenge there why or the other side is like this a conceptual question should your agent have the same access as you should inherit your access or should it have only a part of your access now I would really it would be so much better if we could figure out a way to get rid of the bad bots but live in a world where we could enable our agents to have the access we have and grant that privilege today it's strange right like I can't get my instinct or muse to have the access I have on many websites because they got blocked there and they will get even more blocked if they started declaring themselves yeah and that is why I think we're in this moment of transition where these products have these rough edges partly because we haven't done incentive alignment but if we started just declaring them everywhere they started declaring themselves like we don't participate in that space but we started doing that a bunch of people today because we haven't done the work to do business model alignment I'm gonna start blocking them yeah and so these products then will look worse and be less useful and that sucks so to me the existential problem is how do we drive alignment between agents and everything on the web do you think everyone will need alignment and safety on their team is that the next higher class yeah I think how do you not deal with the problem like we if you think about us we are the web layer the web is the is a dangerous place like anyone give post anything malicious anywhere, right? A lot of that content will flow into an agent's context. So you can do injection attacks. Now to the crate of model builders in the last two years, models have made very real strides on not being very vulnerable. But I think this whole world will rely on defense in many, many layers. So almost every layer have to obsess about this problem. And you can't say, "Oh, models will solve it." We have to really focus on it. To solve it at our layer before we get into the models, then models have to really focus on it. Then the app builders who are building with models and with us have to really focus on it at their layer. So we have to deal with adversaries at every layer. And that is actually what gives you true defense. And still the occasional thing will leak out. How good is AI at real professional work? Merchor's AI productivity index or Apex tracks it with every major frontier model release. Bill with partners like Harvey and Cognition, see where the frontier actually stands at Merchor.com/Apex. Today's episode is sponsored by VCX by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. Learn more at getvcx.com. Some of you may not have heard this yet, but our sponsor public just launched something called generated assets. And it brings AI into investing in a way I've honestly never seen before. Here's how it works. You type in an idea like AI powered supply chain companies with positive free cash flow or defense tech companies growing revenue over 25% year over year. Publix AI then dispatches a swarm of agents that scan every single US stock, evaluates them, and instantly builds a custom index around your thesis. What really stands out is how clearly it explains why each stock is included. And before you invest, you can even back test your idea against the S&P 500. So you're making decisions with real context, not just guessing. And beyond generated assets, public lets you invest in stocks, bonds, options, crypto, all in one place. They'll even give you an uncapped 1% match when you transfer your investments over from another platform. If you want to build a portfolio that actually reflects your thesis, visit public.com/sorcery, paid for by public investing, full disclosures in the description. Founders scale faster on deal. Set up payroll for any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit deal.com/sorcery. That's D-E-E-L.com/sorcery. How did you recruit for your team, especially as this is moving so fast? So you're global offices now. Yes. How big is the team and how have you recruited what are the main roles? The team is about 120 people now. I think we started the year close to 30. So this has been a year of explosive growth. A lot of that growth has been because we had no GTM at the beginning of the year. We were pure tech play. Now the last three months, six months, we've built a pretty sizable GTM team and that's been done because we're seeing all of these, almost every business that buys inference needs to buy a web search. So we're finding really large like whatever, the top 100, 500 companies in the world starting to really spend on tokens and starting to really buy us alongside those tokens to make them more effective. So a lot of our growth is the sales team, which is- How do they know to buy you alongside the tokens? They run into problems. So one of the common things people run into is people start using a model. That model perhaps bundles itself with a web search and they start building an application with that. Now sometimes it's not good enough and the team realizes that it's the web search component which is causing it, despite the modeling rate, it's causing it not to be capable enough to solve the problem they're solving. This is perhaps how we started first working with some of the AI legal, AI finance, AI science kind of customers, right? The second point at which they really need us is they decide to use multiple models. So we're like okay we want to switch across multiple proprietary models on a dime or we want to start adopting open-weight models. And at that point having the highest quality web search which works with every model actually enables model independence for you to have flexibility to build your application. So that's the second reason people buy us. The third reason is when people have really scaled use cases and now start optimizing for measuring ROI optimizing for end-to-end costs because our search is so high signal right and token efficient. The models do half the number of searches use up far fewer tokens end-to-end are cheaper faster. That's the third reason people buy us which is like I'm spending too much on tokens. It works. It's useful. Can you help us be more efficient? So it's not just routing. You also help with token optimization. If a lot for a lot of categories of knowledge work with a lot of work happens on the web we help a lot in token efficiency. Wow. So when we talked to Giaversell we were talking about the shift of GTM. It was SEO then it was GEO and now it's AEO. So GEO and AEO are the same. A little bit but not really. Because the agents now are more than chat. GEO was like chat. Exactly. So it's like Versell has optimized itself for the agents coming to its website. Agents can read the website and know exactly what products they offer how they can use it. And so because of that Versell is like one of the top uses for building out your products and that kind of coding and all that. No, I think it's interesting you bring that up three years ago when we put up our first website up. Why don't we have to kind of take it and say anything. We were in stealth. We had a machine version. Until they've did if you go to our website you'll see like the human version and then there's a toggle to transition into the machine version. Really? We've had it as far as I know you're the first website to take on this metaphor because we're building a parallel web for agents. Yeah. And so we have always carried this metaphor which now the world is coming around to. It's like a cute thing to do because the truth is the presentation layer almost doesn't matter because we can we can transform the web even if you don't publish your website to be agent friendly will do that for you. Like we can we can understand what the human version is and how to help it become agent friendly. Oh wow. We don't do AOGO but I think your your website will become consumable by agents with us. But I do think like we for example even think of our onboarding and our documentation and every part of the developer or customer journey with the primary assumption that it's a human with a coding agent or a developer with the coding agent rather than a developer. And so you'll see a bunch of on our website you'll see like put this prompt into your agent and then it'll exactly know how to use parallel to solve whatever problem you're trying to solve. Like one thing just like you just need the human needs just authorize slash add a credit card slash acceptums and the rest of the agent can rip. So really rethinking how you design every experience to be agent first is I think pretty fundamental versus like I know this is a very agent build. So people who live that world have a advantage. We have like probably like 10 or 15 minutes left but I'm curious I mean you're doing a lot of press like you're out there you're used to getting asked a bunch of questions like what are the questions you want to get asked. I spend a fair amount of time thinking about today proactive agents and always on agents how that changes the compute world and how that changes the web. So the question I think there to me is and we've now built a bunch of systems which some people are starting to use around this notion of a proactive web or I've been out there saying the web will go from pull to push. So far your primary as humans are primary way of searching the web was just us going and searching on Google. For the rare few you used Google alerts. So in theory work people try to build like what Google now and a bunch of these things which send you notification nothing really worked on the web in terms of being proactive. I think with agents and the cost of intelligence going down it is going to work. And so now if you imagine the instinct of views implicitly understanding your world and your life and being able to to set up essentially really, really high signal, high precision, high recall, alerts on everything that changes on the entire web to know when your agent needs to act on something on your behalf. So, something changes in the world or something might be happening. What do you need to do as a result or does that change your plans or does that change your actions? How we allocate compute in that world is a problem I am currently obsessed with because the web is really large, right? Like there's a trillion pages out there, a bunch of them are changes changing every day. Most of it is junk. No matter how cheap you make compute, we have not enough compute to compute everything. So, now we need to be really smart at prioritizing our compute over everything that is changing on the web. So, that more compute can be spent on things which are actually actionable rather than junk changes. The rate of change of the web is also dramatically increased, right? You started to see that, like people are publishing reagents, like you can, like the AUSEO business is like churning out agent-generated content on the web, which just means the rate of change on the web is increasing, which means the job of a search engine is to pull the signal from the noise as potentially more noise gets created. How do you think about data centers in space? I wish I knew more about it. I hope they work. Have you gone down any of these rabbit holes in this compute? I haven't gone down the data centers in space rabbit hole. I do think we need compute. Yeah. I've gone down a little bit of the how sovereigns will participate in the data center. That is fascinating to me about how that will play out. Well, one of our partners is Brex, and they're all about performance and spending's water moving faster. I take this topic in the personal way, so I do think performance is a matter of who you spend your time around. Some people say it's your closest five people. Some people think it's like your mentor, people who inspire you. For your career, tremendous career, very interesting. I'm so curious, who is that person or those people for you? My wife is her name is Venita, I was an engineer at Twitter when we got to know each other. So if you think of the part of my career where I've done well, that people know out. Yes, mostly her, she is a superwoman, she is hyper ambitious, and I was ambitious, but I think she has driven my ambition even higher. Like I have always aspired to be the top ex in the world at anything I do. That's like the only way. Like I was driven to do that for math, I was driven to do that for physics, I was driven to do that for computer science research, and I think I got there for all of those. But I don't think I would have thought I was capable of this until she pushed me. And then I was like, I think if you care enough about something, I think you can go become top 10 in the world at anything. Now the top one is a whole different field. But I think like for me it's like the standard setter is my wife, she is a physician, she practices still, she is an investor at education highway, she's done a start-up, she does healthcare, she does biotech, she works really hard, and this is an amazing mom and wife. That's amazing. So you guys have a nice little competitive dynamic? Yes, yes, yes. I think I am privileged that when I go to a lot of things like people use me to access her. Really? People will come to me to figure out how to get me to forward an email to her. Oh my gosh, that's amazing. Well, that's a really great call out, I wasn't expecting that. Why do you think partner selection or who you choose as a partner is so important? I thought probably the most important decision in life. As you said, your peer group, I was fortunate growing up to have a really crazy veered outlier of a peer group, all of my friends were essentially kids of nuclear scientists in India and everyone was like okay, I am going to be the best at whatever we try to be. So there was like this ambition/competitive expectation all around, and so if you surround yourself and then I mean to call it in my college, it's the most competitive and I mean to crack school, that's the most competitive. So you surround yourself in that and like you kind of surprise yourself by being around high expectations. So when you, this is what now everyone is talking about, right? Like you build a team, you have high talent and city, it's super power. If you don't hire outliers, you don't get outliers. If you hire only outliers, you get only outliers. There's a lot of self-selection that happens. I've been fortunate to go through that journey and like I got started in life with that kind of peer group and then you kind of won't ever let yourself not be in that peer group and that percolates into like partner selection, which then further drives things. So it's like kind of a self-fulfilling prophecy of sorts. What pressure? No. No. Why? Why would I? A lot of people do. Yeah, I think it's kind of made up thing, right? I really believe that, like I think I, through the Twitter acquisition, I used to crack my sleep score really and I was sleeping fine. I think if you know what you must do, you do it. You don't have to feel pressure, like the world can come at you, but if you know what you're capable of and you have a slightly long term view, like everything is a week or a month. So you don't have to feel pressure. You have to perform every day, which is a completely different thing. So now the core question you have to ask is there are moments where you must feel pressure to perform. So I really like feeling insecure is my word, which is I have, I've always told people that I only want jobs which I wouldn't hire myself for. So I want somehow to feel that someone's thrown me into a challenge that on paper, I'm not supposed to be able to accomplish. One that drives you to be slightly humble, two, it makes you a little bit paranoid. It makes you want to learn things and makes you curious and it makes you work really hard and it makes you seek help. If you feel secure in what you're doing, it doesn't drive any of the most important behaviors in my experience. So pressure is a different world because it suggests that you are trying to meet somebody else's expectations instead of your own and I think internal expectations are way, way, way better drivers of success than external ones. And at least for me, I would say, other than my wife, my expectations have always be higher than the world's expectations of me. Even from true believers, who believed in me. What a fascinating turn. I did not expect that. This is like, I will say thank you so much for such a fantastic conversation. It was really awesome to learn more about parallel and then your personal life too, if only we started with it. You're philosophy and how you think about things is really, that's what that's fun. Well thank you so much, Prague. Amazing. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.bc, where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple or wherever you listen. Link in description to sign up.