AI Agents Could Drive 10,000x More Web Activity | Former Twitter CEO

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

You were 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.

Parallel is an almost 3-year-old company now. I was the lunatic walking around 3 years ago telling people, "Agents will use the web 1,000x more than humans ever have." Most people didn't believe me at the time. I was wrong.

Why?

1,000x 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.

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

What pressure?

You don't feel pressure?

No.

Praag, welcome to Sourcery.

Thank you for having me.

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?

I'll tell you what I got done today.

Okay.

We just shipped a really exciting integration with Browserbase.

Mm-hmm.

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.

Mm-hmm.

So 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 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?

Yeah. Parallel is a 3, almost 3-year-old company now. I was the lunatic walking around 3 years ago telling people, "Agents will use the web 1,000x 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 long, long time. But today, if you look at, like, what's happened in the last 3 years, like, I was wrong, but on the other side.

Why?

1,000x is too low. I think at the time, I thought 1,000x was, like, the crazy number to make up.

Okay.

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, 1,000x is too low a number.

If you just look at just some high-level math,right, a person, an average person does, like, an 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.

Wow.

Several thousand per day. All the agents that I use today. So I am now sitting at roughly 1,000x, a little bit less than 1,000, personally. Most of the world is not. Most of the world will in due time.

It might take us a few years, but 1,000 is a low number.

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?

Yes, but it is efficient because the other part of the story is that you got to you can't build old-school search and do 1,000x 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 guess 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 1,000x that infrastructure to do 1,000x the searches,right?

So part of what we do is build extremely efficient and optimized search, which, again, benefits from models getting better.

Yeah.

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 Vercel. And this is, you know, maybe this isn't 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 andright 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 equivalent 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 estimationright now of where we are is agents are using the web 10% to 20% of humans, which is to say, if my 1,000x number isright, we have from here at least 5,000x or 10,000x 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 a 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.

Yeah.

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

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

Yes, I almost forgot.

I am. The reason is, listen, I'm on the rebel alliance here.

Okay.

Which is 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 an 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 the my expectation is that with 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? So the UX is, you can text this thing.

Mm-hmm.

And it can do anything.

Yeah.

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. Like, a lot of other products struggle with the onboarding UX barrier to entry.

Mm-hmm.

But this new generation of products, just that's just not a thing. And so I have a feeling that retention will get solved here.

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

No, I think OpenClaw wasright in all of the ways. 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 with change.

I think the other factor we discount, it's not just a one-time cough 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,right? Because in the beginning, not there was, like, one model which was really good, like the Opus model.

Mm-hmm.

And then as soon as you moved to other models, it wasn't as great in the very beginning when people started using OpenClaw late last year. And so you had to adopt alongside it. But today, as evidenced by Instinct and Muse.

Yeah.

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.

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.

Yeah.

So what does that mean and how?

So I think I got obsessed with it now almost 3 and 1/2 years ago. I was the I don't know if people remember. There was, like, this moment of this baby AGI and Auto-GPT. Speaking of, oftentimes, developers in the open-source world being ahead of everything else.

Mm-hmm.

People were building with, like, I don't know, what is the model of the 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. And they didn't work. But if you believed 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.

Mm-hmm.

Three years ago.

Yeah.

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 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.

Yeah.

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.

Mm-hmm.

That's the business I'm in.

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

Yeah, we are not I wouldn't use the word aggregating.

Mm-hmm.

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 a I don't think it's a core competency of ours.

Like, we have been in multiple mad GPU scrambles.

Yeah.

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.

Yeah. Who are you calling?

Sorry?

Who are you calling?

Everyone. Like, everyone. Like, every hyperscaler, every NeoCloud.

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 the at least for us, I think it's true for many people. It's like, OK, which GPU is it?

Mm-hmm.

Physically, where is it? Sometimes we need GPUs within 5 milliseconds of where our large web index is sitting.

OK.

Other GPUs were sometimes more flexible.

Right.

With multiple GPU workloads. It's not just like, OK, do you have GPUs for me? It's like, do you have theright GPU in theright scale, in theright physical location for me, available at theright 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 a customer because I didn't have a GPU.

Right.

But we've had a bunch of GPU acquisition fire drills.

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, so I think you mostly so I think different workloads are different.

Mm-hmm.

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's too much more to it. I think for certain large training clusters, there's a little bit more to it.

Mm-hmm.

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 the I wouldn't say the market is entirely efficientright now. Like, a lot of it is on iMessage,right?

Really?

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

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

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

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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.

I was also an engineer before that.

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

Yeah.

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, it turns out the answer is relatively easy.

Mm-hmm.

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, ourright 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 theright 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 search alongside it. So the short term there is very clear. But our benchmark is just growth of inference.

So what is that in numbers?

I think, like, the easiest approximation is 10x. You have to think about, like, a 10x 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.

Mm-hmm.

But

the pattern here is even when I have thought I was being provocative in making up a high number, I have somehow managed to underestimate. So now I can I feel like I can go to 10x. I feel a little bit uneasy that, like, it feels crazy to grow 10x for too many years.

You know, 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 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.

OK.

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.

Yeah.

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, we ended up really pushing our systems 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 being like, let me look that up and come back to you.

Like, that was.

Yeah.

Acceptable for, like, a three-month period, I think.

Yeah.

And then very quickly today, if that happens to you.

Mm-hmm.

At least the people who've interacted with multiple voice agents, we'd be like, that is ridiculous. Like, that's a really bad product.

Mm-hmm.

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 in, like, 150 or 200 milliseconds.

Like, human web search was, like, a sub-second product, like, 600, 700 millisecond product, roughly end to end.

Mm-hmm.

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

Right.

In order to create the market expectation, which changes so fast,right? Like, I think I don't know. Going back to web search, one of my

you should, at some point, try, even with the new model, turning off web search in ChatGPT once.

OK.

I think we all used ChatGPT 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 or cavewoman in using that product. So it's only three what, 3 and 1/2, four years ago now? But our implicit expectations of a product that looks the same have changed so dramatically.

And that is what's sort of mind-boggling to me. And I think that is essentially what drives all technical progress.

Yeah.

Is because it becomes table stakes.

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

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

OK.

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.

Glimmers. I think they're still more reactive than proactive.

Mm-hmm.

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 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 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,right?

Mm-hmm.

Now you go into the web piece of the infra 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, OK, 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. 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. All of these business models have 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.

So you can't monetize attention anymore. So ads business model, per-seat subscription business model, these things are not durable. So what replaces it? 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.

Mm-hmm.

Problem. So we have a highly opinionated point of view on how we keep the web open, on how we incentively align 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.

Right? So we need to solve this problem for people, which is, like, OK, 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 partnerships 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.

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. In another interview I did, AppBrowser base, with John Carlson of Stripe.

Mm-hmm.

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?

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, the bot detection market is doing wellright now.

And it's doing wellright now because we haven't figured out how to do incentive alignment.

Mm-hmm.

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 thereright now.

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?

No, I think

cybersecurity risks are real. I think agents help both defenders and attackers. The 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. 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.

Mm-hmm.

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 andurgency in getting ahead on defense.

But 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 obvious parallels,right? Not to just keep calling out my company's name again and again. So at Twitter,right, like, there was a real incentive to attack, whether it were crypto spams.

Yeah.

Those were popular for a period,right? Or whether it was some state-sponsored influence campaign.

Mm-hmm.

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 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 an account and thought, this is a bot, it's harmless, because you could see that it's a bot. 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 an agent, that's when you're like, OK, now I don't know what I'm up against. And I think that is very similar today that we used to deal with back then.

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

OK, well.

Right?

Every time we use bot.

I really think bot has a more negative connotation, while an agent is a more appropriate word.

Mm-hmm.

I think agents are perhaps the good bots, mostly.

Yeah.

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 to the world at large. The core challenge there, why the other scientists like,

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 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, and they've started declaring themselves everywhere, like, we don't participate in that space.

But if we 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.

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,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.

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.

Mm-hmm.

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.

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Visit deel.com/sourcery. That's D-E-E-L dot com slash sourcery. How did you recruit for your team, especially as this is moving so fast? So you're you have global offices now.

Yes.

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

The team is about 120 people now.

Mm-hmm.

I think we've 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.

Mm-hmm.

We were a 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, 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.

Mm-hmm.

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

Well, 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.

OK.

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, is 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.

Right.

The second point at which they really need us is they decide to use multiple models. So we're like, OK, 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.

Correct. 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.

Wow. So when we talked to Gia Vercel, we were talking about the shift of GTM. It was SEO. Then it was GEO. And now it's AEO. So like.

I think GEO and AEO are the same.

A little bit, but not really.

OK.

Because agents now.

Are more than chat.

Yeah.

GEO was like chat.

Exactly.

OK.

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.

Right.

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.

Yeah. No, I think it's 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 weren't in stealth. We had a machine version.

Until this date, 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, we were 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. And it's like a cute thing to do. 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.

Oh, wow.

We don't do AEO or GEO, but I think your your website will become consumable by agents with us.

Mm-hmm.

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. Like, one click, just like you just need the human need to just 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 the agent first is, I think, pretty fundamental. Vercel, like, I know, is very agent built. So people who live that world have an 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, our primary way of searching the web was just us going and searching on Google.

For the rare few, you used Google Alerts. But they don't really work. People try to build, like, what, 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. And so now, if you imagine an instinct or a muse implicitly understanding your world and your life 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'm currently obsessed with.

Mm-hmm.

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 going to dramatically increase,right? You're starting to see that, like, people are publishing with agents. Like, you can, like. The AEO/SEO business is, like, churning out agent-generated.

Yeah.

Content on the web.

Yeah.

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.

Mm-hmm.

I I hope they work.

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

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

Yeah.

I have gone down a little bit of the how sovereigns will participate in the data center.

Mm-hmm.

And that is fascinating to me about how that'll play out.

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?

My wife is her name is Benita.

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. 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. 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, yeah, 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 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. She does health care. She does biotech. She works

really hard and 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?

Oh, all the time. Like, 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 callout. 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. And, like, you as you said,right? Like, you your peer group. Like, I was fortunate growing up to have, like, the really crazy, weird outlier of a peer group,right? Like, all of my friends were essentially kids of nuclear scientists in India.

And everyone was like, OK, 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.

Mm-hmm.

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 was the most competitive. 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.

Mm-hmm.

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 been 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, your partner selection.

Yeah.

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

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

What pressure?

You don't feel pressure?

No.

No? Why?

Why would I?

A lot of people do.

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

I really believe that. Like, I think I

through the Twitter acquisition, I used to track my sleep score.

Really?

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

You can you don't have to feel pressure. Like, 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, 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.

Really?

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 word 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.

Mm-hmm.

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 of of me, even from, like, true believers, like, who believed in me.

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

OK.

For such a fantastic conversation.

Yeah.

It was really awesome to learn more about Parallel and then your personal life too.

Yeah.

If only we started with this.

Yeah.

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

Yeah, that's that's fun.

Yeah.

Yeah.

Well, thank you so much, Parag.

Amazing.

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