AI Agents Could Drive 10,000x More Web Activity | Former Twitter CEO
- Agrawal sees 5,000–10,000x further growth in agent web use if his original 1,000x thesis holds. He estimates current agent usage at 10–20% of human usage, distinguishing it from crawler activity and headline bot traffic. His own agents already make several thousand searches daily: “1000x is too low.”
- Parallel’s growth benchmark is agent inference, with 10X as Agrawal’s current planning approximation. He expects the world to spend one to two GPUs on web search for every 10 GPUs spent on model inference. Capacity involves specific hardware, locations and timing; some GPUs must sit within five milliseconds of Parallel’s web index.
- Parallel sells search quality, model independence and lower inference costs. The introduction reports a $100 million Series B at a $2 billion valuation, while Agrawal says headcount grew from close to 30 to about 120 during the year. He claims higher-signal search lets models do “half the number of searches,” consume fewer tokens and finish faster.
- Consumer-agent retention depends on fulfilling an unusually simple product promise. Agrawal expects good retention because messaging an agent requires little onboarding, but acknowledges having no insider retention data. He rejects the suggestion that OpenClaw’s concept failed: setup, continuous maintenance and early model limitations constrained accessibility.
- Agrawal argues that agent browsing breaks attention-based web economics even when purchases survive. “You can’t monetize attention anymore,” so he calls advertising and per-seat subscriptions non-durable. Parallel is building payments to content owners for agent reads, with models determining compensation—a proposed “AdSense” for this web.
- Agent access requires commercial alignment as well as identity and security. Agrawal wants useful agents to identify themselves and inherit their owners’ permissions, yet says identification currently invites blocking. Security remains a separate, layered responsibility; he expects consequential cyber events “every few weeks” as attackers and defenders both spend inference.
- Agrawal’s next demand thesis is that “the web will go from pull to push.” Today’s always-on agents remain mostly reactive; he expects agents to monitor changes and act on users’ explicit and implicit needs. Across a trillion-page web with growing machine-generated noise, the constraint becomes allocating compute to actionable changes.
1. Agent web use remains early despite the traffic headlines
- Agrawal’s original pitch was that agents would use the web “1000x more than humans ever have.” Now, “1000x is too low”: an average person makes around 10 or fewer Google searches daily, while his tracked agents make several thousand. He puts himself roughly, but slightly below, 1,000x. Most people are not there; reaching that point might take a few years.
- O’Shea cites coding agents reaching 60% of traffic observed by Vercel. Agrawal cautions that automated traffic includes pretraining crawlers, and agents search more than humans to accomplish equivalent work. His best estimate puts agent web use at 10–20% of human usage. If his original 1,000x thesis is right, that implies another 5,000–10,000x from here.
- O’Shea’s pushback—will smarter agents eventually search less? Agrawal agrees, but rejects scaling old search infrastructure 1,000x. His best guess is that Google will do roughly 10 billion searches daily; multiplying that infrastructure a thousandfold is not something he thinks they can build. Parallel instead uses GPUs to underpin search indexes, historically CPU- and memory-bound, and distills ranking intelligence into smaller models.
- His conviction came from experimenting with BabyAGI and AutoGPT using models such as “GPT-3.5 or whatever.” They did not work, but the direction seemed predictable: better models running longer, with models becoming smaller and cheaper at constant capability. He did not predict coding agents would lead adoption or that the CLI would become their form factor.
2. Inference sets the growth hurdle and the capacity problem
- The introduction reports a $100 million Series B “last month” at a $2 billion valuation. Agrawal’s benchmark is inference: “for every 10 GPUs the world spends on model inference, we’ll spend one to two on web search.” His current growth approximation is 10X, higher than last year’s answer. He wants Parallel growing alongside or faster than agent inference, while questioning how many years 10X can persist.
- Parallel rents GPUs and has experienced multiple capacity scrambles over three months. Availability means the right GPU, quantity, location and timing; some must be within five milliseconds of its web index. For certain large clusters, he also asks whether the physical capacity can be committed for long enough. Agrawal calls every hyperscaler and neocloud. Availability gets established partly on iMessage, then finance teams handle paperwork. So far, GPU shortages have not forced Parallel to turn away a customer.
- Parallel’s BrowserBase integration, which shipped that day, powers search in BrowserBase. It illustrates the distinction between reading and acting: Parallel’s APIs search and access the web before agents use browsers to log in, buy or write. Agrawal’s “cloud for agents” combines inference, perhaps a sandbox, web search, browsers and API actions, personal or company-data connectors, plus observability, feedback loops and security.
3. Retention and enterprise adoption depend on delivering the promise
- Agrawal expects consumer agents such as Instinct and Muse to retain users well, while acknowledging no insider retention information. Messaging provides natural onboarding; the challenge becomes whether the product delivers. His test: “can it actually deliver on this very simple high level promise often enough to become a part of your life?”
- Asked whether OpenClaw hit a wall, he disagrees: “OpenClaw was right in all of the ways.” Most people cannot set it up and continuously adapt it as capabilities change. Early performance also depended heavily on a strong model such as Opus. Managed products can improve underneath users without requiring them to understand what changed.
- Early customers Harvey and Notion pushed quality; voice agents now push latency. Search needs roughly 150–200 milliseconds, versus roughly 600–700 milliseconds for human search. Expectations harden quickly: Agrawal bets disabling ChatGPT’s web search today would disappoint users on 90% of requests, even with a new model. “You will feel like a caveman.”
- Headcount grew from close to 30 at the year’s start to about 120, largely as Parallel built go-to-market capacity over the last six months. Agrawal gives three buying triggers: bundled search fails demanding work; customers need independence across proprietary and open-weight models; or scaled workloads require better economics. He says high-signal results enable “half the number of searches,” fewer tokens and faster completion.
4. Keeping the web open requires payments and inherited permissions
- Agrawal’s economic argument starts with humans “random walking the web.” Publishers monetize subscriptions, advertising, referrals or transactions along that journey. With agents, “the buying action still remains. Every other action breaks.” He calls advertising and per-seat subscriptions non-durable, making incentive alignment with content owners part of the infrastructure problem.
- Parallel has built a system that runs a model to compute what to pay each content owner whenever an agent reads its content, and is establishing content partnerships. Agrawal describes a version of “AdSense” with “easy, automatic differential pricing.” The bet is that compensation for quality content can keep the web open, replacing publishers’ incentive to block agents with a reason to welcome them.
- Asked whether bot detection still makes sense, he distinguishes crawlers, useful agents and malicious bots. He has “two contradictory feelings”: good agents should declare themselves, yet declarations can trigger blocking before incentives align. His Instinct and Muse cannot access many sites he can. He wants all agents, including those outside large labs, to inherit their owners’ permitted access and actions.
5. Security remains a continuing contest at every layer
- Agrawal calls cybersecurity risks real: agents help attackers and defenders. He thinks organizations adopting defensive agents early will be fine because attackers seek the weakest link; frontier-model weights present a separate, more sensitive problem. He expects aggressive use of coding agents for security, consequential cyber events “every few weeks,” and substantial inference spending on both attack and defense.
- His Twitter analogy is a “knife fight”: attacks can be contained, almost never eliminated, and the hardest activity is what defenders cannot distinguish. Web content can flow into agent contexts and enable injection attacks. Models have made real strides over the last two years, but “you can’t say, ‘Oh, models will solve it.’” Parallel, model builders and applications must each defend their layer; occasional failures will still escape.
6. Proactive agents change search and compute allocation
- Agrawal’s current obsession is that “the web will go from pull to push.” Instinct, Muse and Dot show glimmers, but remain more reactive than proactive despite being always on. He expects agents to understand explicit and implicit needs, set high-signal, high-precision, high-recall alerts across the web and determine when changes require action. Falling intelligence costs could make earlier alerting concepts work.
- The allocation problem is a trillion-page web, much of it junk, with many pages changing daily. “No matter how cheap you make compute, we don’t have enough compute to process everything.” Resources must prioritize actionable changes. Agent-generated publishing increases the rate of change and potentially the noise, making signal extraction more demanding.
- O’Shea distinguishes GEO for chat from AEO for agents; Agrawal initially treats them as equivalent, then accepts her distinction. Parallel has offered human and machine website views since its early stealth period. Still, he considers presentation secondary: “we can transform the web” even when publishers have not made their sites agent-friendly.
- Parallel designs documentation and onboarding around a developer accompanied by a coding agent. Customers can paste a prompt into their agent; the human authorizes access, adds a credit card and accepts terms. The agent can handle the rest. Agrawal sees designing the entire customer journey around agents as fundamental.
7. Internal expectations drive Agrawal’s operating philosophy
- Agrawal credits his wife, a physician and Andreessen Horowitz investor, with raising his ambition beyond what he thought possible. His childhood peers were children of nuclear scientists in India, surrounded by expectations of becoming the best. He connects that environment to hiring: “If you don’t hire outliers, you don’t get outliers. If you hire only outliers, you get only outliers.”
- Asked how he withstands pressure, he replies, “What pressure?” His sleep stayed fine through the Twitter acquisition; he distinguishes pressure from performing daily. “I only want jobs which I wouldn’t hire myself for”: insecurity encourages humility, a little paranoia, curiosity, effort and seeking help. Excepting his wife, he says his expectations have always exceeded others’—internal standards drive him more effectively than external ones.
Full transcript
Parallel Web Systems closed a $100 million Series B funding round last month, valuing the 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 three-year-old company now. I was the lunatic walking around three 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 that said, “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, at every stage, how do you not break from the pressure?
What pressure?
You don’t feel pressure?
No.
Parag, welcome to Sorcery.
Thank you for having me.
We’re in Index’s office in San Francisco. It’s gorgeous here. They’re one of your investors. I’m not sure if you’ve ever been 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. If you think about every AI agent people use, what are they doing? They’re looking at your internal information. They’re looking at web information. They’re searching a lot more than we do. 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 has been about some sort of explosive growth in agents, and it’s going to be really fun to unpack all of that. From your point of view, you were talking about read versus write—what you’re doing versus BrowserBase. So I guess to start, let’s talk about that. What is Parallel? How is it different? How has it evolved? And how are you learning as things are changing so fast?
Yeah. Parallel is an almost three-year-old company now. I was the lunatic walking around three years ago telling people, “Agents will use the web 1,000x more than humans ever have.”
Mm-hmm.
There were no agents. Agents didn’t work. Most people didn’t believe me at the time. Fortunately, some did, including Index. At the time, it felt like a science-fiction hypothesis: there would be a lot of agents at work and in our personal lives, and they’d be doing web searches for some reason. I believed it because I’d been obsessed with it for a long time. But today, if you look at what’s happened in the last three years, I was wrong—but on the other side.
Why?
1,000x is too low. At the time, I thought 1,000x was the crazy number to make up, to talk about.
Okay.
Mm-hmm.
Right? At the time, my deck literally contained a section that said, “Are we too early?” Turns out we were not too early. Today, if you force me, 1,000x is too low a number. If you look at some high-level math, an average person does on the order of 10 or fewer Google searches a day. I was checking how many I do: several thousand now. The agents I track—all the agents that I use today—do several thousand per day.
Wow.
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 get there 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 come back down 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 can’t build old-school search and do 1,000x of this. Think about all the infrastructure that Google might have built to serve on the order of 10 billion searches a day. My best guess is that Google will do 10 billion a day. 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?
Mm-hmm.
So part of what we do is build extremely efficient and optimized search, which benefits from models getting better and allows us to distill intelligence for ranking into smaller and smaller models, and to use GPUs as the underpinning of search indexes. Historically, search was done on CPUs.
Mm-hmm.
It was memory- and CPU-bound. Now you get to architect and re-architect search for a price-performance band that the world never needed until today.
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 coding agents have reached 60% of traffic on their indexes, from 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 2 things to remember. An agent doing roughly 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.
Yeah.
Right now, they’re not even that dollar-constrained because people are willing to spend on frontier models. For a human to read or search the web to create equivalent value, it takes way more agent traffic. A lot of agent traffic is also people crawling for pre-training. When you look at automated activity versus human activity as a website owner, automated activity is ripping. But my best estimate of where we are right now is that agents are using the web at 10% to 20% of human levels. Which is to say, if my 1,000x number is right, we have at least 5,000x or 10,000x growth left from here. That’s another way of saying we are very early with agents.
Mm.
I think 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 Joly, and I really like Joly, the character for Muse.
Mm-hmm.
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 we’ve ever seen—even ChatGPT, I think, maybe, possibly.
Yeah.
We’ll see. But how do you expect that to grow, and where does the retention stay?
Two things. One, I have to make a note. We should call out Instinct too. I am—
Yes, I almost forgot.
The reason is, listen, I’m on the rebel alliance here. There are all these big people, 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 too. 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. As you can imagine, it’s a professional hazard: I use every one of these agents.
Yeah.
I try to deeply understand what they can and cannot do, and what problems we can solve for them.
Mm.
We’ll get into this category and how it changes our product strategy later. But I think my expectation is that these agents’ retention will be good, because for the first time, the product onboarding feels natural. It’s not someone designing a bunch of UX click targets and flows to get you to understand the product. The product is natural. All of these products are somewhat natural, and the question is, can they deliver on the promise created by the UX? The UX is: you can text this thing, and it can do anything.
Yeah.
That’s the UX. It’s not complicated.
Yeah.
Now, retention is entirely a function of whether it can actually deliver on this very simple, high-level promise often enough to become a part of your life. A lot of other products struggle with the onboarding UX barrier to entry. But with this new generation of products, that’s just not a thing. So I have a feeling that retention will get solved here.
Is that where you think OpenClaw maybe hit a wall? Because that was really the first.
No, I think OpenClaw was right in all of the ways.
Mm.
It’s just not for the masses, right? Most people don’t have the ability to set it up and then keep up with change. I think the other factor we discount is that it’s not just a one-time cost to set something up.
Things are changing so fast—
Mm.
—that you have to adapt your instantiation of it alongside that change, and adapt your use of it alongside new capabilities. 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? In the beginning, there was 1 model that was really good, like the Opus model.
Mm.
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. So you had to adapt 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. As the customer, you don't need to know what changed in the last month. You're oblivious to the capability improving underneath.
I can't discount that this was really just invented. You mentioned this at the start of the interview, but you've been obsessed with this for a while?
Yeah.
So what does that mean, and how?
I think I got obsessed with it almost 3.5 years ago. I don't know if people remember, but there was this moment of BabyAGI and AutoGPT. Developers in the open-source world were often ahead of everything else.
Mm.
People were building with, I don't know, what was the model of the day? GPT-3.5 or whatever.
Yeah.
They were old models that, relative to today, were really bad. People were trying all these things, and I was trying all of them to make things work. They didn't work. But if you spent any time with the labs back then, you internalized how models would scale and improve.
In some sense, today it feels like everything changes every month. In some sense, it's—
I don't know if it's every month. It might be every week.
But if you look at it a different way, almost nothing that's happening is diverging from what you could have predicted in a straight line 3 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. 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 3 years ago, and we've only seen evidence week on week and month on month supporting these few simple things.
Yeah.
Almost everything follows. So now you can say, “Yeah, I didn't predict that the big category would be coding agents first.” I didn't predict the CLI form factor. Those were all bits of information and news to me along the way.
Yeah.
But the macro thesis is that 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 alongside it. I've now been on the record everywhere saying that the 1 invariant I believe is that we'll use a lot of GPUs for model inference for agents, whether they be at work or consumer agents. For every 10 GPUs the world spends on model inference, we'll spend 1 to 2 on web search.
Mm-hmm.
That's the business I'm in.
1. The GPU Scramble
So are you aggregating GPUs? You've reportedly raised $230 million, I don't know. Are you aggregating GPUs?
I wouldn't use the word aggregating.
Mm-hmm.
We're renting GPUs to build the scale of search traffic that we're seeing and expecting. As I said, we grow alongside inference. Inference is growing, so we have to do a proportionate amount. I don't know if it's already a core competency of ours. I don't think it is. We've been in multiple mad GPU scrambles in the last 3 months, like, “Oh, shit, we need GPUs. We don't have GPUs. Can someone figure it out?” And you're making a bunch of calls—
Yeah.
—to be like—
Who are you calling?
Sorry?
Who are you calling?
Everyone. Every hyperscaler, every neocloud.
And what's the typical response back? Because this comes up in pretty much every conversation now. How do you get the allocation of GPUs?
I think, at least for us—and I think it's true for many people—the question is, “Which GPU is it?”
Mm.
Physically, where is it? Sometimes we need GPUs within 5 milliseconds of where our large web index is sitting.
Okay.
Other GPUs, we're sometimes more flexible.
Right.
For multi-GPU workloads, it's not just, “Okay, do you have GPUs for me?” It's, “Do you have the right GPU, at the right scale, in the right physical location, available at the right time?” That's what makes the problem much harder.
Mm.
So far, we've managed to solve every problem we've 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. What's the sourcing process like for GPUs? What are the main questions that you're asking? What do you need?
Different workloads are different. For the bulk of our workload, it's how many GPUs, which types are you open to, when do you need them, and where must they be? There isn't too much more to it. For certain large training clusters, there's a little bit more to it.
For us, are you willing to commit to that physical thing for long enough? But that's not complicated. The reality is that I wouldn't say the market is entirely efficient right now. A lot of it—
No.
—is on iMessage, right?
Really?
Yeah. But it works. The market works.
Well, that's crazy. So you're doing deals on iMessage for GPUs?
Not exactly. You're figuring out availability on iMessage, and then the finance person calls the finance person, and 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, what that used to look like, and what you would expect. 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. Can you set expectations now? What would meaningful scale look like today if, back then, it was “triple, triple, double, double”? What do you have to achieve now?
I don't know how somebody else could do it. For our business, it turns out the answer is relatively easy.
As I've said, we just build alongside inference. If you think about inference growing year on year, to us, that's the anchor. Our goal is to grow faster than inference for knowledge-work agents or consumer agents. If we grow alongside that or faster, I'm feeling pretty good. That's the benchmark for us.
Now, what's the right inference benchmark? That's a question I don't know the answer to long term. But I think in the short term, it's clear that GPUs coming online this year or next year are going to do inference, and search is going to grow alongside it. The short-term benchmark is very clear: our benchmark is the growth of inference.
So what is that in numbers?
I think the easiest approximation is 10X.
You have to think about a 10X goal as the very simple round number.
Mm.
If you think about today, if you’d asked me last year, I would’ve probably said a lower number.
Mm.
But the pattern here is that even when I’ve thought I was being provocative and making up a high number, I’ve somehow managed to underestimate. So now I feel like I can go to 10X, and I feel a little bit uneasy that it feels crazy to grow 10X for too many years.
Mm.
How long can that happen? Maybe I’m wrong again.
2. What Customers Demand
I’m curious, because you get to see so many different market insights internally, externally, all around. What are some surprises that you’ve come across, and what are the signals you’re looking for?
I think it might be useful to tell people about the kinds of customers we end up working with.
Okay.
All of our insights, or at least my insights, come from working with people doing really, really interesting and unique things. We started out working with a lot of the people pushing the limits of building agents. Some of our customers, like Harvey and Notion, were some of the early people building agents to do real work where the quality bar was quite high.
Yep.
Everyone in the adoption chain was asking, “Oh, can we trust this agent to do our work?” That was the question being asked. By working with really demanding customers, or customers who had really demanding customers in turn, we ended up pushing our systems’ capabilities on the quality dimension.
The next phase, I would say, is that voice agents are starting to work. The interesting thing is that, initially, there was some acceptance for your voice agent pausing a little unnaturally or saying, “Let me look that up and come back to you.” That was—
Yeah.
—acceptable for a three-month period, I think.
Yeah.
And then very quickly, today, if that happens to you—
Mm.
—at least people who’ve interacted with multiple voice agents will be like, “That is ridiculous. That’s a really bad product.”
Mm-hmm.
Now you’re seeing all these voice agents that are 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 150 or 200 milliseconds. Human web search was a sub-second product, roughly a 600- or 700-millisecond product end to end.
Mm.
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, the cost dimension, and the latency dimension. You’ve got to crush all 3.
That’s something we get pushed on by the frontier customer, who says, “Listen, the product experience that we can build today is going to be unacceptable very, very quickly.” So what does every underlying infrastructure piece need to solve—
Right.
—in order to create the market expectation, which changes so fast? Going back to web search, you should at some point try turning off web search in ChatGPT, even with the new model.
Okay.
I think we all used ChatGPT for the first, I want to say, 8 months, maybe 9. 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: 1, 90% of things you ask, it will disappoint you. 2, you will feel like a caveman or cavewoman using that product. So it’s only 3 years—what? 3 and a half, 4 years ago now.
Mm-hmm.
Our implicit expectations of a product that looks the same have changed so dramatically, and that is what’s mind-boggling to me. I think that is essentially what drives all technical progress—
Yeah.
—is that it becomes table stakes.
Right. And now they might be getting some consciousness. They might have some consciousness to them and start judging us and attacking us.
I’m actually more of an optimist on this.
Okay.
I think they will actually get proactive.
Mm.
They will know us. They will know our explicit and implicit needs, and they will try to proactively do work. That’s the next phase. I think you’re starting to see glimmers of proactivity in Instinct, 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 happen? 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 makes up that base?
There are 2 ways to talk about this, I think. Before going into the internet, let’s first talk about the underlying infrastructure 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. The rest of it is all the same stuff we’ve always had: observability, feedback loops, security. And there you have a cloud for agents.
Mm.
Now you go into the web piece of the infrastructure layer. You need to do web search, but you also need to figure out—to your point about 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: What is the new business model? Previously, if you put your content out there on the web, you were either building a subscription business—you put some content out there, a bunch of users come, and 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 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—
Mm-hmm.
—opening pages and taking the next action after the next action on them, 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. The ads business model, per-seat subscription business models—these things are not durable. So what replaces them? That’s part of what gets me super, super excited about building Parallel, because I think solving just technology problems is far less interesting than solving a technology and a business model problem.
Mm-hmm.
We have a highly opinionated point of view on how we keep the web open and 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 going to block all AI agents because I don’t know how that becomes business for me.”
Right.
Right? So we need to solve this problem for people: Give me an incentive to make my content available to AI agents. What is that? We built a system that runs a model, not surprisingly, to compute how much to pay each content owner every time an agent reads their content. We’ve now been doing a bunch of partnerships with content owners, and the bet we’re 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, automatic differential pricing that actually provides content owners 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’s traditional business is being cannibalized by AI, LLMs, agents. It just is. So Gemini is like the Hail Mary to pull it in, and they’re emphasizing that and taking advantage of that.
I’m curious to go deeper into this monetization standpoint. In another interview I did at BrowserBase with John Collison of Stripe, he posed the question: Do we need bot detection anymore? There are obviously good bots and bad bots, but if all the traffic becomes essentially bots—these agents—what is the point?
Yeah. No, I think today there are a lot of people doing a lot of bot detection. The term “bot detection” refers to a few different things: crawlers, actual agents working on behalf of a human or a business, and malicious bots that are trying to do harm.
I think we have to use that word a little more carefully now than we have in the past, because historically people have mostly had negative connotations with that word, as if it isn’t serving anyone.
Yeah.
Today there is a category that is actually highly useful for you or for me.
I would like my agents to inherit the privileges I have. I think we should be careful. The bot detection market is doing well right now because we haven’t figured out how to align incentives.
Hmm.
There’s a separate kind of incentive alignment needed on the action side. Earlier, I spoke about the incentive alignment needed on the content side: why do I let you read my content? The other side is, what happens if I let your agent buy from my store?
You’re starting to see divergent strategies there. I’m of the belief that we need to solve these problems so that all agents—not just the ones built by a large lab—can access all content and take all actions their owner is allowed to take. But we’re not there right now.
One of the biggest topics around growth is cybersecurity and active threats. I know you said you’re an optimist, but realistically, how do you see the biggest concerns around this?
The cybersecurity risks are real. Agents help both defenders and attackers. The people who rush ahead to adopt agents for defending themselves will be fine, because attackers like to find the weakest link, and there will always be a weakest link. Broadly, that’s what I think.
The security risk for something highly sensitive, like the model weights of a frontier model, is a whole different—
Mm.
—conversation.
Yeah.
By and large, my intuition is that we will use coding agents aggressively to secure things. Along the way, there’s going to be a consequential cyber event every few weeks, which will drive action and urgency around getting ahead on defense. We’re going to spend a bunch of inference on both attack and defense.
Do you see any of this as analogous to your time at Twitter in consumer internet? Obviously, it’s one of the largest—
Yeah.
—social media platforms, and so—
Yeah.
—that creates—
No.
—a lot of vulnerabilities.
There are obvious parallels. Not to keep calling out my company’s name again and again, but at Twitter, there was a real incentive to attack, whether it was crypto spam—
Yeah.
—which was popular for a period—or some state-sponsored influence campaign—
Mm-hmm.
—which we used to disclose publicly a lot. Those things always happened, and it took real energy and focus to control them. The thing most people don’t understand is that it’s almost impossible to take them to zero. You have to contain them because it’s a knife fight, and the tactics change. Any time you control some vectors, another new vector will emerge, and then you’ll control that.
The other thing people don’t understand is that most of the bad stuff doesn’t show up on the surface. If you saw an account and thought it was a bot, it was harmless because you could see that it was a bot. 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 think, “Okay, now I don’t know what I’m up against.” I think that’s very similar to what we used to deal with back then.
Do you think bots should address themselves?
I have 2 contradictory feelings about this.
Okay.
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’d like to use the word “agents.”
Okay. Well—
Right?
Every time we use “bot,” we just overlay it.
Because I really think “bot” has a more negative connotation, while “agent” is a more appropriate word.
Mm-hmm.
I think agents are perhaps the good bots, mostly. So, yeah, I think they should declare themselves. I know there are a bunch of protocols being developed around declaring identity to the world at large.
The core challenge on the other side is a conceptual question: should your agent have the same access as you? Should it inherit your access, or should it have only part of your access? 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 them that privilege.
Today, it’s strange. I can’t get my Instinct[?] or Muse[?] to have the access I have on many websites because they got blocked there, and they’ll get even more blocked if they start declaring themselves.
Yeah.
That’s why I think we’re in this moment of transition, where these products have rough edges partly because we haven’t aligned incentives. If we started declaring them everywhere, and they started declaring themselves everywhere, a bunch of people would start blocking us because we haven’t done the work to align business models.
Yeah.
Then these products would look worse and be less useful, and that sucks. 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. How do you not deal with the problem? If you think about us, we’re the web layer. The web is a dangerous place. Anyone can post anything malicious anywhere, and a lot of that content will flow into an agent’s context. So you can do injection attacks.
To the credit of model builders, in the last 2 years, models have made very real strides in not being very vulnerable. But this whole world will rely on defense in many, many layers. Almost every layer has to obsess about these problems.
Mm-hmm.
You can’t say, “Oh, models will solve it.” We have to focus on solving it at our layer before we get into the models. Then models have to focus on it, and the app builders who are building with models and with us have to focus on it at their layer. We have to deal with adversaries at every layer, and that’s what gives you true defense. Still, the occasional thing will leak out.
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3. Building Parallel's Next Phase
How did you recruit for your team, especially as this is moving so fast? You have global offices now.
Yes.
How big is the team, and how have you recruited? What are the main roles?
The team is about 120 people now. I think we started the year close to 30, so this has been a year of explosive growth. A lot of that growth has been because we had no GTM at the beginning of the year.
We were a pure tech play. Over the last 6 months, we’ve built a pretty sizable GTM team, because we’re seeing that almost every business that buys inference needs to buy web search. We’re finding really large companies—the top 100 or 500 companies in the world—starting to spend on tokens and buy from us alongside those tokens to make them more effective.
A lot of our growth is the sales team, which has been for the past—
Well, how do they know to buy you alongside the tokens?
They run into problems. One of the common things people run into is that they start using a model.
That model perhaps bundles itself with a web search.
Okay.
And they start building an application with that.
Hmm.
Sometimes it’s not good enough, and the team realizes that the web search component is causing the problem. Despite the model being great, the search is making it incapable of solving the problem they’re trying to solve. That’s perhaps how we started working with some of the AI legal, AI finance, and AI science customers.
Right.
The second point at which they really need us is when they decide to use multiple models. They might say, “Okay, we want to switch across multiple proprietary models on a dime,” or, “We want to start adopting open-weight models.” At that point, having the highest-quality web search that works with every model enables model independence and gives you the flexibility to build your application. That’s the second reason people buy from us.
The third reason is when people have really scaled use cases and start optimizing for measuring ROI and end-to-end costs. Because our search is so high-signal—
Right.
—and token-efficient, the models do half the number of searches, use far fewer tokens, and are cheaper and faster end to end. That’s the third reason people buy from us. They’re spending too much on tokens, the product works and is useful, and they want us to help them be more efficient.
So it’s not just routing. You also help with token optimization.
Right. For a lot of categories of knowledge work where a lot of work happens on the web, we help a lot with token efficiency.
Wow. When we talked to Gia Vercel, we were talking about the shift in GTM. It was SEO, then GEO, and now it’s AEO.
I think GEO and AEO are the same.
A little bit, but not really.
Okay. Okay.
Because it’s agents now—
They’re more than chat.
Yeah.
GEO was like chat.
Exactly.
Okay. Fair. Fair.
So Vercel has optimized itself for agents coming to its website—
Yeah.
—so agents can read the website and know—
Yeah.
—exactly what products they offer—
Right. Right.
—and how they can use them.
Yeah.
And because of that, Vercel is one of the top tools for building out your products and that kind of coding environment.
Yeah, yeah. No, I think it’s interesting you bring that up. Three years ago, when we put up our first website, we were still in stealth. It didn’t say anything. We had a machine version. To this day, if you go to our website, you’ll see the human version, and then there’s a toggle to switch to the machine version.
Really?
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.
We’ve always carried this metaphor, which the world is now coming around to. It’s a cute thing to do because the truth is, the presentation layer almost doesn’t matter. We can transform the web even if you don’t publish your website to be agent-friendly. We’ll do that for you. 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 website will become consumable by agents with us.
Hmm.
We even think about our onboarding, 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 alone. On our website, you’ll see prompts like, “Put this into your agent,” and then it’ll know exactly how to use Parallel to solve whatever problem you’re trying to solve. With one click, the human just needs to authorize, add a credit card, and accept the terms. The agent can rip through the rest.
Really rethinking how you design every experience to be agent-first is pretty fundamental. Vercel is very agent-built, so people who live in that world have an advantage.
We have probably 10 or 15 minutes left, but I’m curious. You’re doing a lot of press. You’re out there, and you’re used to getting asked a bunch of questions. What are the questions you want to get asked?
I spend a fair amount of time thinking about proactive agents and always-on agents, and how they change the compute world and the web. The question for me is about this notion of a proactive web. I’ve been out there saying the web will go from pull to push.
So far, as humans, our primary way of searching the web has been to go and search on Google. A rare few used Google Alerts, but it didn’t really work. People tried to build things like Google Now and a bunch of other things that 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.
Yeah. Mm-hmm.
Now imagine an Instinct or a Muse implicitly understanding your world and your life, and being able to set up really high-signal, high-precision, high-recall alerts on everything that changes across the entire web. Then your agent can know when it needs to act on something on your behalf. If something changes in the world or something might be happening, what do you need to do as a result? Does that change your plans or your actions? How we allocate compute in that world is a problem I’m currently obsessed with.
Hmm.
The web is really large, right? There are a trillion pages out there. A bunch of them are changing every day. Most of it is junk. No matter how cheap you make compute, we don’t have enough compute to process everything. So now we need to be really smart about prioritizing our compute over everything that is changing on the web, so more compute can be spent on things that are actually actionable—
Hmm.
—rather than junk changes. The rate of change on the web is also going to increase dramatically. You’re starting to see that. People are publishing with agents. The AEO and SEO businesses are churning out agent-generated content on the web.
Yeah.
Which just means the rate of change on the web is increasing. 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 hope they work.
Have you gone down any of these rabbit holes around compute?
I haven’t gone down the data centers in space rabbit hole. I do think we need compute.
Yeah.
I have gone down the rabbit hole a little bit on how sovereigns will participate in the data center.
Mm-hmm.
And that’s fascinating to me—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 a personal way. I do think performance is a matter of who you spend your time around. Some people say it’s your closest 5 people. Some people think it’s your mentor or the people who inspire you. Your career has been tremendous and very interesting, and I’m curious: who is that person or those people for you?
My wife 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 that people know about, where I’ve done well, it’s mostly her. She’s a superwoman. She’s hyper-ambitious, and I was ambitious, but I think she has driven my ambition even higher.
I’ve always aspired to be in the top X in the world at anything I do. That’s the only way. I was driven to do that for math, for physics, and for computer science research. 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. Then I was like, “Yeah, I think if you care enough about something, you can become top 10 in the world at anything.” The top 1 is a whole different field.
For me, the standard-setter is my wife. She’s a physician and still practices. She’s an investor at Andreessen Horowitz. She’s done a startup. She does healthcare and 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’m privileged that when I go to a lot of things, people use me to access her.
Really?
Oh, all the time. People will come to me to figure out how to get me to forward an email to her.
Oh my gosh. That’s amazing. Well, that’s a really great call-out. I wasn’t expecting that. Why do you think partner selection, or who you choose as a partner, is so important?
I think it’s probably the most important decision in life. And, as you said, your peer group…
I was fortunate growing up to have a really crazy, weird, outlier peer group. All of my friends were essentially kids of nuclear scientists in India. Everyone was like, “Okay, I’m going to be the best at whatever we try to be.” There was an ambition, a competitive expectation, all around.
Mm.
Then I went to college, and my college was the most competitive. Then I went to grad school, and that was the most competitive. When you surround yourself with that, you kind of surprise yourself by being around high expectations. This is what everyone’s talking about now, right? When you build a team, high talent density is a superpower.
Mm.
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. 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. That percolates into your partner selection—
Yeah.
—which then further drives things. So it’s kind of a self-fulfilling prophecy of sorts.
In such a competitive career, at 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. I really believe that. Through the Twitter acquisition, I used to track my sleep score.
Really?
I was sleeping fine. If you know what you must do, you do it. You don’t have to feel pressure, right? The world can come at you, but if you know what you’re capable of and you have a slightly long-term view, 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. The core question you have to ask is: are there moments where you must feel pressure to perform? I really like feeling insecure. That’s my word.
Really?
I’ve always told people that I only want jobs I wouldn’t hire myself for. I want to feel that somehow 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. It makes you curious, work really hard, and seek help. If you feel secure in what you’re doing, it doesn’t drive any of the most important behaviors, in my experience. Pressure is a different word, because it suggests that you’re trying to meet somebody else’s expectations instead of your own. I think internal expectations are way, way, way better drivers of success—
Mm.
—than external ones. At least for me, other than my wife, my expectations have always been higher than the world’s expectations of me, even from true believers who believed in me.
What a fascinating turn. I did not expect that. This is like—we… I will say thank you so much—
Okay.
—for such a fantastic conversation.
Yeah.
It was really awesome to learn more about Parallel and your personal life, too.
Yeah.
If only we’d started with this.
Yeah.
Your philosophy and how you think about things is really impressive.
Yeah, that’s fun.
Yeah.
Yeah.
Well, thank you so much, Parag.
Amazing.