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

Former Twitter CEO & CTO Parag Agrawal predicts AI agents could drive another 5,000–10,000x increase in web usage, fundamentally transforming how the internet operates and makes money. Now Founder & CEO of $2 Billion Parallel Web Systems, Parag is building the search infrastructure for an internet where AI agents, not humans, become the primary users. With a reported $230M raised, customers including Harvey and Notion, and a team that has grown 4x this year, Parallel is positioning itself at the center of the AI infrastructure boom. Recorded at Index Ventures in San Francisco, we discuss the massive GPU demand behind AI search, why traditional advertising and subscription models could break, and how Parallel is developing a new way to compensate content owners as agents replace human browsing. Parag also explains why the web will shift from reactive to proactive, how he's scaled Parallel from 30 to 120 employees this year, and lessons from his 11 years at Twitter as engineer, CTO, and CEO. Parallel Web Systems was funded by Index Ventures. Parag Agrawal: https://x.com/paraga Molly O’Shea: https://x.com/MollySOShea Sourcery: ⁠https://x.com/sourceryy 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊 YouTube: ⁠https://youtu.be/iRI6gTe1wGM⁠ https://youtu.be/iRI6gTe1wGM 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 • Brex https://brex.com/sourcery—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, & travel. https://brex.com/sourcery • Harvey http://www.harvey.ai?utm_medium=paid-partner&utm_source=sourcery&utm_campaign=2026-10-brand-sourcery-partnership—The AI platform built for legal work, is helping the best firms and companies close deals faster. More than 200,000 lawyers, 80% of the Am Law 100, and over 20% of the Fortune 500 build with Harvey. Visit: http://harvey.ai/ • Zone https://zonefrontier.com/—develops next-generation data center campuses, partnering with AI companies, site developers and technology leaders to bring compute online faster and at scale. Visit: https://zonefrontier.com https://zonefrontier.com/ • Mercor http://mercor.com/sourcery—The human intelligence behind frontier AI. Frontier labs and leading enterprises use Mercor to turn the knowledge of 5M+ experts into the data, evals and benchmarks that make AI better at real work. Visit:http://mercor.com/sourcery • VCX https://fundrise.com/vcx—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at http://GetVCX.com http://getvcx.com • Deel https://www.deel.com/sourcery—Deel is the global people platform that helps startups hire, manage, pay, and equip anyone, anywhere. Trusted by more than 35,000 fast-growing companies, Deel is the people platform that just works, so teams can scale without the chaos. Visit: https://www.deel.com/sourcery • Public http://public.com/sourcery–Investing platform Public just launched Generated Assets https://public.com/generated-asset, which lets you turn any idea into an investable index with AI. With Generated Assets, you can build, backtest, refine, and invest in any thesis with AI. Gone are the days of one-size-fits-all ETFs. https://public.com/sourcery Follow Sourcery for the latest updates! https://www.sourcery.vc https://www.sourcery.vc/ Disclosure: Paid Ad. Agents are an experimental AI tool. Its output may be inaccurate and is not investment advice. Public Holding’s, Inc. makes no guarantees about its accuracy or reliability—verify independently before use. Brokerage accounts and services provided by Open to the Public Investing, Inc. See additional disclosures at https://public.com/disclosures/agenticdisclosures. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Parag Agrawal, Founder & CEO at Parallel (01:03) What is Parallel AI? (05:28) Bots took over the web & it’s only the start (07:22) Will people stick with AI agents? (11:55) Why models will get bigger, smarter and cheaper (14:47) Where AI startups actually find GPUs (19:43) Is 10x a year the new normal? (21:59) Searching the web faster than you can blink (26:02) AI that knows what you need before you do (26:57) Agents are breaking the web’s business model (31:08) Good bots vs bad bots (42:04) Why great models still give bad answers (47:56) Proactive agents and the future of search (51:30) The person who drives Parag’s ambition

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Parallel web systems closing a $100 million Series B funding round last month, valuing that AI startup at $2 billion.

• 0:07Molly O'Shea?

  You were at Twitter for 11 years, you were CTO, then you became CEO.

• 0:10Speaker 02

  I was also an engineer before that. We're building a collection of APIs that agents use to search and access the web. Parallel is almost three-year-old company now. I was the lunatic walking around three years ago telling people agents will use the web thousand X more than humans ever have. Most people didn't believe me at the time. I was wrong.

• 0:31Molly O'Shea?

  Why?

• 0:31Speaker 02

  Thousand X is too low. My deck literally contained a section which is like, are we too early? Turns out we were not too early. We're building a parallel web for agents. We can transform the web, even if you don't publish your website to be agent-friendly. We'll do that for you.

• 0:45Molly O'Shea?

  In such a competitive career in every stage, how do you not break from the pressure?

• 0:51Speaker 02

  What pressure?

• 0:51Molly O'Shea?

  You don't feel pressure?

• 0:52Speaker 02

  No.

• 1:03Molly O'Shea?

  Prague, welcome to Sorcery.

• 1:05Speaker 02

  Thank you for having me.

• 1:07Molly O'Shea?

  Well, we're in Index's office in San Francisco. It is gorgeous here. They're one of your investors. I'm not sure if you've ever gotten asked this question before, but what have you gotten done this week?

• 1:17Speaker 02

  I'll tell you what I got done today.

• 1:19Molly O'Shea?

  Okay.

• 1:20Speaker 02

  We just shipped a really exciting integration with Browserbase. We now power search in Browserbase. 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, a bunch more than us. Then they're going into a browser, logging in and doing something, buying something, writing something. So that's what we got done today.

• 1:48Molly O'Shea?

  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 gonna be really fun to unpack all of that because from your point, you were talking about read versus write, what you're doing versus Browserbase. So I guess to start, let's talk about that. Parallel, what is parallel? How is it different? How has it evolved? And how are you learning as things are just changing so fast?

• 2:18Speaker 02

  Yeah. Parallel is a three, almost three-year-old company now. I was the lunatic walking around three years ago telling people agents will use the web thousand X more than humans ever have. And there were no agents. Agents didn't work. Most people didn't believe me at the time. Fortunately, some did, including Index. And at the time, it felt like the science fiction hypothesis that there will be a lot of agents at work and in our personal lives. And that they'll be doing like web searches for some reason. I believed it because I had obsessed with it for a lot, long time. But today, if you look at like what's happened in the last three years,

• 3:00Speaker 02

  like I was wrong, but on the other side.

• 3:05Molly O'Shea?

  Why?

• 3:06Speaker 02

  Thousand X is too low. I think at the time, I thought thousand X was like the crazy number to make up.

• 3:12Molly O'Shea?

  Okay.

• 3:13Speaker 02

  To talk about, right? And at the time, I was like, 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, thousand X is too lower number. If you just look at just some high-level math, right? A person, an 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 track.

• 3:43Molly O'Shea?

  Wow.

• 3:43Speaker 02

  Several thousand per day, all the agents that I use today. So I am now sitting at roughly a thousand X, a little bit less than thousand personally. Most of the world is not.

• 3:57Speaker 02

  Most of the world will in due time. Might take us a few years, but thousand is a low number.

• 4:04Molly O'Shea?

  But is that efficient? Do you think that'll crawl back at a certain point? Because they'll just get smarter and know where to go?

• 4:13Speaker 02

  Yes, but it is efficient because the other part of the story is that you gotta, you can't build old-school search in 2000 X of this. You think about like, okay, all the infrastructure that a Google might have built to serve whatever order of 10 billion searches a day. Like the best gift I have is 10 billion a day is what Google will do. They have a lot of infrastructure to pull that off. I don't think we get to build thousand X that infrastructure to do thousand X the searches, right? So part of what we do is build extremely efficient and optimized search, which again benefits from models getting better.

• 4:55Molly O'Shea?

  Yeah.

• 4:55Speaker 02

  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.

• 5:28Molly O'Shea?

  Of one of the things we were talking about before, I recently interviewed G from Vercel. 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.

• 5:47Speaker 02

  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. And right now, they're not even that dollar constraint 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.

• 6:32Speaker 02

  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 of where we are is 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 5,000 X or 10,000 X growth left. Just another way of saying we are very early with agents. I think in like in the Bay Area and Silicon Valley, we're all living in a little bit of bubble.

• 7:15Molly O'Shea?

  Yeah.

• 7:15Speaker 02

  I think most people's agents aren't doing the number of searches my agents are doing, but they will.

• 7:21Molly O'Shea?

  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.

• 7:43Speaker 02

  Yeah.

• 7:44Molly O'Shea?

  Even ChatGPT, I think maybe, possibly. We'll see. But how do you expect that to grow? And I guess, where does retention stay?

• 7:57Speaker 02

  Two things. One, I have to make a note. We should call out Instinct 2.

• 8:02Molly O'Shea?

  Yes, I almost forgot.

• 8:03Speaker 02

  I am. The reason is, listen, I'm on the Rebel Alliance here.

• 8:07Molly O'Shea?

  Okay.

• 8:07Speaker 02

  Which is there are all these big people.

• 8:09Molly O'Shea?

  Yeah.

• 8:10Speaker 02

  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.

• 8:22Speaker 02

  So we should talk about Instinct.

• 8:24Molly O'Shea?

  Yeah.

• 8:25Speaker 02

  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.

• 8:40Speaker 02

  I am, as you can imagine, it's a professional hazard that I use every one of these agents.

• 8:47Molly O'Shea?

  Yeah.

• 8:48Speaker 02

  Uh, 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 the, my expectation is that these agents' retention will be good. And it'll be good because for the first time, the product onboarding feels

• 9:18Speaker 02

  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? So the UX is you can text this thing and you can do anything. That's the UX. It's not complicated.

• 9:44Molly O'Shea?

  Yeah.

• 9:45Speaker 02

  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, like a lot of other products struggle with the onboarding UX barrier to entry. But this new generation of products, that's just not a thing. And so I have a feeling that retention will get solved here.

• 10:14Molly O'Shea?

  Is that where you think OpenClaw maybe hit a wall? Like, because that was really the first.

• 10:22Speaker 02

  No, I think OpenClaw was right in all of the ways. Um, yeah, I think once someone got, it's just not for the masses, right? It's just like, most people don't have the ability to set it up and then to keep up the change. I think the other factor we discount, it's not just a one-time cough to set something up.

• 10:47Speaker 02

  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, right? Because in the beginning, not, there was like one model which was really good, like the Opus 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 late last year.

• 11:30Speaker 02

  And so you had to adopt alongside it. But today, as evidenced by instinct and muse.

• 11:37Molly O'Shea?

  Yeah.

• 11:39Speaker 02

  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 like oblivious to the capability improving underneath.

• 11:55Molly O'Shea?

  I can't discount that this was really like 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?

• 12:08Speaker 02

  So I think I got obsessed with it now almost three and a half years ago. I was the, I don't know if people remember, there was like this moment of this baby AGI and AutoGPT, 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 day, GPT-3.5 or whatever, like old models that weren't, like relative to today, they were like really bad models, relative to today. And people were trying all these things and I was trying all of these things to try and make things work. Um, and they didn't work. But if you believed, you spent any time with the labs back then,

• 12:52Speaker 02

  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.

• 13:03Molly O'Shea?

  I don't know if it's every month, it might be every week.

• 13:05Speaker 02

  But if you look at it a different way, almost nothing that is happening is diverging from what you could have predicted in a straight line three years ago.

• 13:16Molly O'Shea?

  Yeah.

• 13:16Speaker 02

  Models will keep getting bigger and better. They will run for longer. Holding capability constant, models will get way, way, way smaller and cheaper.

• 13:30Speaker 02

  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.

• 13:54Molly O'Shea?

  Yeah.

• 13:54Speaker 02

  And almost everything follows. So now you can say, yeah, I didn't have, 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.

• 14:11Molly O'Shea?

  Yeah.

• 14:12Speaker 02

  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, 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, we'll spend one to two on web search. That's the business I'm in.

• 14:47Molly O'Shea?

  So are you aggregating GPUs? You've raised 230 million, reported and announced. I don't know. But is, are you aggregating GPUs?

• 14:56Speaker 02

  Yeah, we are not, I wouldn't use the word aggregating. We are buying, renting GPUs uh, to build the scale of search traffic that we are seeing and expecting. Uh, 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 a, I don't think it's a core competency of ours. Like we have been in multiple mad GPU scrambles.

• 15:30Molly O'Shea?

  Yeah.

• 15:30Speaker 02

  In the last three 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.

• 15:39Molly O'Shea?

  Who are you calling?

• 15:40Speaker 02

  Sorry?

• 15:40Molly O'Shea?

  Who are you calling?

• 15:41Speaker 02

  Everyone, like everyone, like every hyperscaler, every NeoCloud.

• 15:49Molly O'Shea?

  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?

• 15:58Speaker 02

  No, I think it's like, I think the, at least for us, I think it's true for many people. It's like, okay, which GPU is it?

• 16:07Speaker 02

  Physically, where is it? Sometimes we need GPUs within five milliseconds of where a large web index is sitting.

• 16:15Molly O'Shea?

  Okay.

• 16:16Speaker 02

  Other GPUs will sometimes be more flexible, with 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 location for me, available at the right time? And that is what makes the problem much harder.

• 16:41Speaker 02

  I think so far we have managed to solve every problem we have encountered. We've not had to turn away a customer because I didn't have a GPU.

• 16:48Molly O'Shea?

  Right.

• 16:49Speaker 02

  But we've had a bunch of GPU acquisition firetrails.

• 16:56Molly O'Shea?

  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?

• 17:05Speaker 02

  Yeah, so I think you mostly, so I think different workloads are different. But for bulk of our workload, it's like, how many GPUs, which types are you open to? When do you need them and where must they be? So it's not like there is too much more to it. I think for certain large training clusters, there's a little bit more to it. Uh, and then for us, like, are you willing to commit enough duration to that physical thing? Um, yeah, but that, that's, it's not complicated. The reality is that the, I wouldn't say the market is entirely efficient right now. Like a lot of it is on iMessage, right?

• 17:43Molly O'Shea?

  Really?

• 17:43Speaker 02

  Yeah, so, so yeah, but it works. The market works.

• 17:49Molly O'Shea?

  Well, that's crazy. So you're doing deals on iMessage for GPUs?

• 17:53Speaker 02

  Not exactly. You're figuring out availability on iMessage. And then like the finance person then calls the finance person and the paperwork gets done.

• 18:02Molly O'Shea?

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• 18:47Molly O'Shea?

  It's time to get Brex AF. Learn more at brex.com/sorcery. That's B R E X dot com slash S O U R C E R Y. Bye. From handshake to signature faster. Harvey, the AI platform built for legal work is trusted by eighty percent of the Am Law one hundred and over twenty percent of the Fortune five hundred. Learn more today at harvey.ai. That's harvey.ai. AI needs more than chips. It needs power, land and infrastructure. Zone develops next generation data center campuses, partnering with AI companies, site developers and technology leaders to bring compute online faster and at scale.

• 19:33Molly O'Shea?

  Zone is building the foundation of the AI frontier. Visit zonefrontier.com to learn more. That's zone frontier dot com to learn more. 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 eleven years, you were CTO, then you became CEO.

• 19:57Speaker 02

  I was also an engineer before that.

• 19:59Molly O'Shea?

  You were also an engineer before that. You've done the whole thing.

• 20:02Speaker 02

  Yeah.

• 20:02Molly O'Shea?

  Um, 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?

• 20:18Speaker 02

  I don't know how somebody else could do it. For our business, turns out the answer is relatively easy.

• 20:25Speaker 02

  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.

• 21:01Speaker 02

  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 search alongside it. So the short term there is very clear, but our benchmark is just growth of influence.

• 21:21Molly O'Shea?

  So what is that numbers?

• 21:22Speaker 02

  I think like the easiest approximation is ten X. You have to think about like a ten X goal as the very simple round number. If you think about today, like if you'd asked 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 can, I feel like I can go to ten X, feel a little bit uneasy that like it feels crazy to grow ten X for too many years. You know, how long can that happen? Maybe I'm wrong again.

• 21:59Molly O'Shea?

  I'm curious, because you get to see so many different market insights from internally, externally, all around. What are, what are some surprises that you've come across and what are the signals you're looking for?

• 22:15Speaker 02

  So I think it might be useful to tell people about the kinds of customers we end up working with.

• 22:20Molly O'Shea?

  Okay.

• 22:21Speaker 02

  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, they 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, oh, can we trust this agent to do our work? That was the question being asked. And we, by working with like really demanding customers or customers who had really demanding customers in turn,

• 23:07Speaker 02

  we ended up really pushing our systems capabilities on the quality dimension, right?

• 23:14Speaker 02

  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 being 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.

• 23:53Speaker 02

  And so now you're seeing all these voice agents which are like supernatural, but also super fast. So what does that mean for web search? Now you've got to be able to search the web in like a hundred fifty or two hundred milliseconds. Like, human web search was like a sub second product, like six hundred, seven hundred millisecond product roughly end to end. Because we don't notice it getting any faster, right? For a voice agent, that latency constraint on web search is unacceptable. So now you have to push yourself on the quality 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

• 24:39Speaker 02

  is going to be unacceptable very, very quickly. So what does every underlying infra piece need to solve?

• 24:47Molly O'Shea?

  Right.

• 24:48Speaker 02

  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,

• 25:02Speaker 02

  you should some, at some point, try, even with the new model, turning off web search in chat GPT ones.

• 25:09Molly O'Shea?

  Okay.

• 25:10Speaker 02

  I think we all use chat GPT 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, ninety percent of things you ask, it will disappoint you.

• 25:31Speaker 02

  Number two, you will feel like a caveman or a caveman in using that product. So it's only three year, what, 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.

• 25:59Molly O'Shea?

  Yeah.

• 26:00Speaker 02

  Is because it becomes table ticks.

• 26:02Molly O'Shea?

  Right. And now they might be getting subconscious, you know, they might have some consciousness to them and start judging us and attacking us.

• 26:14Speaker 02

  I actually, I don't know, I'm more of an optimist on this.

• 26:19Molly O'Shea?

  Okay.

• 26:19Speaker 02

  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 trying to, you're starting to see glimmers of proactivity in instinct and muse and dot.

• 26:41Speaker 02

  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.

• 26:57Molly O'Shea?

  I guess, uh, 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?

• 27:18Speaker 02

  There are two ways to talk about this. I think before going into the internet, let's first talk about the, the underlying infra components for building an agent. You need a 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, your company's data.

• 27:50Speaker 02

  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, right? Now you go into the, the web piece of the infra layer.

• 28:11Speaker 02

  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's 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.

• 28:52Speaker 02

  Other internet businesses were monetized by saying, I'm going to put ads and I will get paid based on human visitors seeing ads. Other business models were like either affiliate business models or you drive a transaction of some sort.

• 29:08Speaker 02

  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.

• 29:31Speaker 02

  The buying action still remains. Every other action breaks.

• 29:37Speaker 02

  So you can't monetize attention anymore.

• 29:40Speaker 02

  So ads business model, per seat subscription business models, these things are not durable. So what replaces it?

• 29:50Speaker 02

  That's part of what gets me super, super excited about building parallel. Because 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 align content owners with the AI app or AI agent. So that a content owner isn't sitting there saying, I'm gonna block all AI agents because I don't know how to, how that becomes business for me.

• 30:26Molly O'Shea?

  Right.

• 30:27Speaker 02

  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've 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 partnership with content owners. And the bet we are 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 content.

• 31:10Molly O'Shea?

  That's a really good point. 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 LLMs 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.

• 31:41Molly O'Shea?

  In another interview I did at Browserbase with John Collison 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 bots, these agents, like, what is the point?

• 31:59Speaker 02

  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 in that like, it's not serving anyone.

• 32:42Molly O'Shea?

  Yeah.

• 32:42Speaker 02

  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, the bot detection market is doing well right now. And it's 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.

• 33:26Speaker 02

  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.

• 33:46Molly O'Shea?

  One of the biggest topics behind growth is cybersecurity 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?

• 34:02Speaker 02

  No, I think the cybersecurity risks are real.

• 34:10Speaker 02

  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 attackers like to find the weakest link. And there will always be a weakest link.

• 34:39Speaker 02

  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.

• 34:52Molly O'Shea?

  Yeah.

• 34:53Speaker 02

  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. But yeah, we're gonna spend a bunch of influence on both attack and defense.

• 35:21Molly O'Shea?

  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 that creates a lot of vulnerabilities.

• 35:35Speaker 02

  No, all of the, there are obvious parallels, right? Not to just keep calling out my company's name again and again.

• 35:46Speaker 02

  So at Twitter, right, like, there was a real incentive to attack, whether it were crypto spams.

• 35:52Molly O'Shea?

  Yeah.

• 35:52Speaker 02

  Those were popular for a period, right?

• 35:56Speaker 02

  Or whether it was some state-sponsored influence campaign, 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.

• 36:19Speaker 02

  You have to contain it because it is a knife fight and the tactics change. Anytime you control some vectors, another new vector will emerge and then you'll 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 bot, it's harmless. Because you could see that's a bot. So the real problem is going to be in the margins when we lose our ability to differentiate.

• 36:56Molly O'Shea?

  Yeah.

• 36:56Speaker 02

  Either as people or as the technical people building these systems, when you can't differentiate a human from a bot or an agent. That's when you're like, okay, now I don't know what I'm up against.

• 37:14Speaker 02

  And I think that is very similar today, that we used to deal with back then.

• 37:21Molly O'Shea?

  Do you think bots should address themselves?

• 37:24Speaker 02

  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 we keep using the word bots, I would like to use the word agent.

• 37:45Molly O'Shea?

  Okay, well, every time we use bots, we're so polite.

• 37:49Speaker 02

  I really think bot has a more negative connotation, while an agent is a more appropriate word. I think agents are perhaps the good bots, mostly.

• 37:59Molly O'Shea?

  Yeah.

• 38:00Speaker 02

  The, 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 the other side is like,

• 38:18Speaker 02

  This is a conceptual question. Should your agent have the same access as you? Should it 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

• 38:59Speaker 02

  on many websites because they got blocked there. And they will get even more blocked if they started declaring themselves.

• 39:06Molly O'Shea?

  Yeah.

• 39:06Speaker 02

  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 and they started declaring themselves everywhere, like, we don't participate in that space, but if you started doing that, a bunch of people today, because we haven't done the work to do business model alignment, are going to start blocking them.

• 39:30Molly O'Shea?

  Yeah.

• 39:31Speaker 02

  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?

• 39:44Molly O'Shea?

  Do you think everyone will need alignment and safety on their team? Is that the next higher class?

• 39:49Speaker 02

  Yeah, I think how do you not deal with the problem, right? Like, we, if you think about us, we are the web layer. The web is the, is a dangerous place. Like, anyone can post anything malicious anywhere, right? A lot of that content will flow into an agent's context. And so you can do injection attacks. Now, to the credit of model builders, in the last two years, models have made very real strides on not being very vulnerable. But I think this whole will, whole world will rely on defense in many, many layers. So almost every layer has to obsess about this problem.

• 40:32Molly O'Shea?

  Hmm.

• 40:33Speaker 02

  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.

• 40:56Molly O'Shea?

  How good is AI at real professional work? Merckor's AI Productivity Index, or APEX, tracks it with every major frontier model release. Built with partners like Harvey and Cognition, see where the frontier actually stands at mercor.com/apeX. Today's episode is sponsored by V C X 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

• 41:40Molly O'Shea?

  or defense tech companies growing revenue over 25% year over year. Public's AI then dispatches a swarm of agents that scan every single U.S. 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 backtest your idea against the S and 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.

• 42:25Molly O'Shea?

  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 dot com slash sorcery. How did you recruit for your team, especially as this is moving so fast? So you're, you have global offices now.

• 42:47Speaker 02

  Yes.

• 42:49Molly O'Shea?

  How big is the team and how, how have you recruited? What are the main roles?

• 42:52Speaker 02

  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 web search. So we're finding really large,

• 43:35Speaker 02

  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.

• 43:46Molly O'Shea?

  Hmm.

• 43:47Speaker 02

  So a lot of our growth is the sales team, which has been sort of this.

• 43:50Molly O'Shea?

  How do they know to buy you alongside the tokens?

• 43:54Speaker 02

  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.

• 44:06Molly O'Shea?

  Okay.

• 44:07Speaker 02

  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 model being great, just causing it not to be capable enough to solve the problem they're solving. Which is perhaps how we started first working with some of the AI legal, AI finance, AI science kind of customers.

• 44:30Molly O'Shea?

  Right.

• 44:32Speaker 02

  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 applications. 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.

• 45:16Speaker 02

  Because our search is so high signal.

• 45:19Molly O'Shea?

  Right.

• 45:19Speaker 02

  And token efficient, the models do half the number of searches, use a 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?

• 45:37Molly O'Shea?

  So it's not just routing, you also help with token optimization?

• 45:41Speaker 02

  If a lot, for a lot of categories of knowledge work, where a lot of work happens on the web, we help a lot in token efficiency.

• 45:49Molly O'Shea?

  Wow. So when we talked to Gia Vercel, we were talking about the shift of GTM. It was SEO, then it was G E O, and now it's A E O. So.

• 46:00Speaker 02

  I think G E O and A E O are the same.

• 46:02Molly O'Shea?

  A little bit, but not really.

• 46:03Speaker 02

  Okay.

• 46:04Molly O'Shea?

  Because the agents now.

• 46:05Speaker 02

  Are more than chat.

• 46:06Molly O'Shea?

  Yeah.

• 46:07Speaker 02

  G E O was like chat.

• 46:07Molly O'Shea?

  Exactly. So it's like Vercel has optimized itself for the agents coming to its website. So the agents can read the website and know exactly what products they offer, how they can use it. And so because of that, Vercel is like one of the top uses for building out your products and that kind of coding and all of that environment.

• 46:28Speaker 02

  No, I think it's interesting you bring that up. Three years ago, when we put up our first website up, when we were still kind of, it didn't 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.

• 46:47Molly O'Shea?

  Really?

• 46:48Speaker 02

  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.

• 46:54Molly O'Shea?

  Yeah.

• 46:55Speaker 02

  And so we have always carried this metaphor, which now the world is coming around to. And 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. We'll do that for you. Like, we can, we can understand what the human version is and how to help it become agent-friendly.

• 47:24Molly O'Shea?

  Oh, wow.

• 47:26Speaker 02

  We don't do A E O or G E O, 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 a 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.

• 48:06Speaker 02

  Like one click, just like, you just need, the human needs to authorize, slash, add a credit card, slash, accept terms. And the rest of it, the agent can rip. So really rethinking how you design every experience to be agent first is, I think, pretty fundamental. Vercel, like I know, is very agent-built. So people who live that world have a advantage.

• 48:33Molly O'Shea?

  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?

• 48:47Speaker 02

  I spend a fair amount of time thinking about today proactive agents and always-on agents, how that changes the compute world

• 49:02Speaker 02

  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.

• 49:16Speaker 02

  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, our primary way of searching the web was just us going and searching on Google. For the rare few you used Google Alerts, they didn't really work. People tried to build like Google Now and a bunch of these things which would send you notifications. 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.

• 49:50Molly O'Shea?

  Yeah.

• 49:51Speaker 02

  And so now if you imagine an instinct or muse implicitly understanding your world and your life.

• 49:59Speaker 02

  And being able 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.

• 50:40Speaker 02

  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.

• 50:55Speaker 02

  So that more compute can be spent on things which are actually actionable rather than junk changes.

• 51:05Speaker 02

  The rate of change of the web is also going to dramatically increase, right? You're starting to see that, like people are publishing agents, like you can like the A O S E O businesses like churning out agent generated.

• 51:17Molly O'Shea?

  Yeah.

• 51:18Speaker 02

  Content on the web.

• 51:19Molly O'Shea?

  Yeah.

• 51:20Speaker 02

  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.

• 51:34Molly O'Shea?

  How do you think about data centers in space?

• 51:38Speaker 02

  I wish I knew more about it. I, I, I hope they work.

• 51:43Molly O'Shea?

  Have you gone down any of these rabbit holes in this compute?

• 51:47Speaker 02

  I haven't. I haven't gone down the data center since space rabbit hole. I do think we need compute.

• 51:54Molly O'Shea?

  Yeah.

• 51:55Speaker 02

  I've gone on a little bit of the how sovereign will participate in the data center. And that is fascinating to me about how that will play out.

• 52:06Molly O'Shea?

  Well, one of our partners is Brex and they're all about performance and spending smarter, 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?

• 52:34Speaker 02

  My wife is, her name is Venita. Uh, 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 about. It's mostly her. Uh, she is a superwoman. She's 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 X in the world at anything I do.

• 53:19Speaker 02

  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. Uh, 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, yeah, I think if you care enough about something, I think you can go become top.

• 53:46Speaker 02

  Ten in the world at anything. Now the top one is a whole different field. Uh, but I think like for me, it's like the, the standard setter is my wife. She is a. She's a physician. She practices still. She is an investor at Andreessen Horowitz. She's done a, she's done a startup. Uh, she does healthcare, she does biotech, uh, she works. Really hard and is an amazing mom and wife.

• 54:23Molly O'Shea?

  That's amazing. So you guys have a nice little competitive dynamic?

• 54:26Speaker 02

  Yes, yes, yes. I think I am privileged that when I go to a lot of things like. People use me to access her.

• 54:35Molly O'Shea?

  Really?

• 54:36Speaker 02

  Oh, all the time. Like people will come to me to figure out how to get me to forward an email to her.

• 54:41Molly O'Shea?

  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?

• 54:53Speaker 02

  Um, I thought probably the most important decision in life.

• 54:59Speaker 02

  And like you, as you said, right, like you. Your peer group, like I was fortunate growing up to have like a really crazy, weird outlier of a peer group. Like all of my friends were essentially kids of nuclear scientists in India. And everyone was like, okay, I'm going to be the best at whatever we try to be. And so there was like this ambition slash competitive. Expectation all around. And so if you surround yourself and then I went to college and my college was the most competitive. And I went to grad school, that's the most competitive.

• 55:38Speaker 02

  So you surround yourself in that and like you kind of surprise yourself by being around high expectations. So when you, like this is what now everyone's talking about, right? Like you build a team, if you have high talent density, it's a superpower. 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. Uh, I've been fortunate to go through that journey and been like I got started in life with that kind of a peer group. And then you kind of won't ever let yourself not be in that peer group. And that percolates into like the partner selection.

• 56:20Molly O'Shea?

  Yeah.

• 56:20Speaker 02

  Which then further drives things. So it's like kind of a self fulfilling prophecy of sorts.

• 56:26Molly O'Shea?

  In such a competitive career in every stage, how do you not break from the pressure?

• 56:35Speaker 02

  What pressure?

• 56:36Molly O'Shea?

  You don't feel pressure?

• 56:37Speaker 02

  No.

• 56:38Molly O'Shea?

  No?

• 56:40Molly O'Shea?

  Why?

• 56:42Speaker 02

  Why would I?

• 56:43Molly O'Shea?

  A lot of people do.

• 56:45Speaker 02

  Yeah, I think it's kind of a made up thing, right?

• 56:50Speaker 02

  I really believe that, like I think I. Uh, through the Twitter acquisition, I used to track my sleep score.

• 57:00Molly O'Shea?

  Really?

• 57:00Speaker 02

  And I was sleeping fine. I think if you know what you must do, you do it.

• 57:11Speaker 02

  You can, you don't have to feel pressure, right? Like the, the world can come at you. But if you know what you're capable of and you have a slightly long term view.

• 57:23Speaker 02

  Like everything is a week or a month.

• 57:26Speaker 02

  Uh, 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.

• 57:42Molly O'Shea?

  Really?

• 57:42Speaker 02

  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.

• 58:26Speaker 02

  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 been higher. Than the world's expectations off of me. Even from like true believers, like who believed in me.

• 59:03Molly O'Shea?

  What a fascinating turn. I did not expect that. This is like. We, I will say thank you so much.

• 59:12Speaker 02

  Okay.

• 59:12Molly O'Shea?

  For such a fantastic conversation.

• 59:15Speaker 02

  Yeah.

• 59:15Molly O'Shea?

  Um, it was really awesome to learn more about parallel and then your personal life too. It's only we started with this.

• 59:22Speaker 02

  Yeah.

• 59:22Molly O'Shea?

  Your, your philosophy and like how you think about things is really.

• 59:25Speaker 02

  Yeah, that's, that's fun.

• 59:26Molly O'Shea?

  Yeah. Well, thank you so much, Prag.

• 59:28Speaker 02

  Amazing.

• 59:29Molly O'Shea?

  Cool. Hey, it's Molly. If you enjoy our interviews, check out our newsletter sorcery dot V C. 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.