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"The answer to every problem is a model."
Parag Agrawal shares the ideas behind Parallel: products that continuously learn, and teams designed around versatile problem solvers.
He also shares why he built an AI agent for himself, anyd why AI's biggest opportunity is expanding what people can achieve.
Guest: Parag Agrawal, founder and CEO of Parallel, and former CEO of Twitter
Connect with Parag
Parag Agrawal shares the ideas behind Parallel: products that continuously learn, and teams designed around versatile problem solvers.
He also shares why he built an AI agent for himself, anyd why AI's biggest opportunity is expanding what people can achieve.
Guest: Parag Agrawal, founder and CEO of Parallel, and former CEO of Twitter
Connect with Parag
Connect with Mamoon
Connect with Joubin
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Creators and Guests
What is Grit?
Grit explores what it takes to create, build, and scale world-class organizations. It features weekly episodes highlighting the leaders who are pushing their companies to make a difference. This series is hosted by Joubin Mirzadegan, go to market operating partner at Kleiner Perkins, a venture capital firm investing in history-making founders.
The answer to every problem is a model. You're tempted to write a bunch of code for it? No.
Instead, collect some data. Figure out what a good output would look like. Figure out what model
architecture is right. Train the model and put it in a loop so that you can keep improving the
model. That mindset just allows you to get out of that decision making process and hire people who
are very versatile and problem solvers. It's simple. Once you sort of just internalize that
worldview, agents and AI's will just use the web a lot more than humans ever have. New business
models will be needed, and now there's going to be like a second birth of what the future of the open
web is a parallel web, a parallel web, a few minutes. If we just figured out how to incentivize
being open, how to reward high quality, unique, differentiated content that we get to keep the
web open.
Welcome to grit. I'm Joubin, partner at Kleiner Perkins, a show where we go beyond the highlight
reel and explore the personal and professional challenges of building history making companies.
Today on the show, we've got Parag Agrawal, former Twitter CEO and now founder and CEO of parallel,
the company building web infrastructure for AI. Before starting parallel, Parag spent more than a
decade at Twitter, where he rose from engineer to CTO to CEO. Now he's asking a different question
what happens when AI becomes the web's second user? We're also joined by my partner, Mamoon Hamid.
Enjoy. When we were sitting in the kitchen right now. Um, I, we were talking about
fundraising, all sorts of stuff. And, uh, I said, can you take a, can you take a breath? And you're like,
dude, you take a breath, this company is dead. And I was like, okay, that's a fair and
dramatic point of view. I like, but okay, like, I understand. And he's like, do you not feel that way?
And yeah, Parag asked me like, do you feel that way? And I said, well, I definitely feel that
way, except I just want to make sure that whatever I'm running at is the right thing, because there's
so many things that are feel like they're on fire and it's full chaos. And let me ask you, can you
take a breath? No, but I just want to make sure. He's money in company. Right? I know, I know, he just
told me, I didn't know I didn't like I didn't. Know it until. That company has a whole team I know
elsewhere. It's half your team size. Yeah. At this point, I did not know that. Yeah. Walking into it. I
found out. In the kitchen and I'm on his board. Yeah. That's right, that's right. I just wanted I
you know, your point resonated. The reason I took a beat was because I was like, the temptation when
everything feels like it's so insane, where if you take a breath, you die, makes you feel like you
have to do everything all at once. And for me, I was like, okay, what am I actually doing that moves
the needle? Because otherwise I could be doing stuff, making myself feel like the most productive
human in the world. We can. We can talk about all. These things like. Yeah, we're live, we. Can just die.
We're already live. We just go. Just go. No no, no. So I am there. So
I'm the kind that is extraordinarily comfortable dropping balls. And I don't know if I should say
this in front of my board, but, like, I drop balls all the time, and so there'll be, like, a bunch of
things. I'll come at me. But every week, every day I have like a few things that I care about or that
matter. And I'm just going to do those things and there'll be a bunch of other balls that are less
important that I will drop. And I think ultimately catch them, though, right? No. Someone will. Someone
will. I am betting that my team, because I drop balls, is just built in the structure of
evolving to catch the balls I drop. And so now you build this sort of culture, this team where.
And if it's important enough, it's going to come back to me. It's going to bounce back to me. What
if the ball that is dropped is only known to you? You know that is a problem and you don't drop
those ones. So you again. You don't get to drop random balls. Yeah, you drop the balls. You can
drop partly by design. So you have to prioritize the ones that you don't drop. How many tiers of
memory do you have. So you have cash. And then the second tier I have a hard disk drive. You know, you
have a flash memory in between. Uh, I think it's like. A list of three things every week. Every day.
It's like three things. Can you give an example of what I would be surprised
that you're dropping, that you're willing to drop? Like, what are some balls that you're like, yep. No
problem. Let's see if it bounces back there hard, like in the moment that you're like, oh, I really
don't. I should respond to this, but I can't because it's not the most important thing. So it's
it's you think it would be that deliberate and intentional a process? It's not
right. It is more that like for example, I separate out reading and responding to emails,
the two different work cycles for me. But instead of sitting down and trying to respond to
emails, I read all emails. Then I sit down in the morning and I'm like, okay, what are the
three things this week? Or what are the three things today that I need to proactively do? And a
bunch of things you do reactively, but you can't let the reactive stuff swarm the proactive stuff.
And so I'll write down these proactive things, and they're informed by the things that are in the
back of my mind because I've been reading emails, because I've been thinking, because I've been
doing stuff, because I've been hacking. Right. But I'll write three things down and I will make sure
those three things get done. Now, other things, almost by definition, have a risk of getting
dropped. Now I'll try to go skim at my. I reread my email all the time and if it's
important enough, it is bubble into the top three and make its cut. So I'm not making the decision
that I'm going to drop this ball. I am making the decision that it like, which is like. And I'm not
even making the decision. This is not in the top three. But then when you do the top three, you're
not looking at your inbox. If something was important enough, it better stick in your head as
being an important enough upside opportunity, downside protection opportunity. Something should
do. And so those are the three things that I must do. Now I have time beyond doing those three
things. And so I will go respond to a lot more emails. But then I'm not like some machine trying
to prioritize every decision because that's exhausting too. Okay. Can you when you were when
you were in the kitchen, you were describing how, uh, Friday afternoons you have like demos 430 to
6 and it's your way as a founder to kind of internalize the rhythm of the business on
the on the product side. And then going into Friday night, you feel like, all right, things are
happening like, I know what's happening. And then Saturday morning you'll wake up with some
existential panic that something is breaking. Can you describe that? Yeah. So this is
my rhythm weekly rhythm. So there is like a team is amazing. We're doing a bunch of
really cool things. And I am often not even fully aware of really good things that are cooking
every week, or some cool result that someone came up with, or some cool insight someone came up with
and they shipped something. And the best way I found is I like to see things which are like
raw and in progress. So our demos are designed to be not like, oh, here's a package product that's
going out to customers on Monday. That's not what Friday evening demos are for. Friday evening demos
are for. I was doing this this week. It's not done. It's raw. Here's why I'm excited about it. And you
should all know about it. And so that is the stuff that like if I'm prioritizing the three things
that matter, that is the stuff that never Bubbles up and then I just wouldn't
know. And I would find out when. It's like a packaged up thing. And that's what sort of gets
the both the job satisfaction and the ideas and the rolling for me. And so
getting that dose of infectious excitement about the work we are doing every Friday is really
rewarding because you start thinking about it. And then some of those boil into, oh, why did
I? There's so much more we could do with this thing. And so now you're paranoid about like, a
missed opportunity. Some of those are about, wait, this is so cool. If someone else figured this
out and then like, your head, like your mind goes in many, many different directions, and then you
have to boil all of those things into the one that matters. To be truly
either paranoid about or excited about, or is actionable. Right? And by Monday, you have to make
it actionable. Can we go back to the top three things? Yeah. So a few weeks ago, you showed
me this vibe coded thing that you built for yourself. Can you talk about that? Of course. Yeah.
Why not? So you showed it to me. And it's incredible, actually. And I think it was we were
having lunch together at our CEO summit, and you showed me the app, and I'm like, this is mind
blowing. What was even more mind blowing was that by 1 p.m. when we were having lunch together, you
had spent, I believe, $378 in on tokens that day. And I asked
you, like, what the heck are you doing in the background? And you're you're clearly token maxing.
And I think at the time you're saying, hey, like, I'm just trying to do as much as possible and
actually don't care right now. Two questions embedded. One is like, how does that help you with
the top three things for that day? And then how does it help you, those drop balls that come back
up. How does that help you with that? And then thirdly anxiety. How does it help you on Saturday
morning Oh, you know, the anxiety is less because I just built this amazing thing that helps me be
less anxious. So, number one, me building that wasn't one of those top three things. Just as a
clarification, it is just because I was just excited about it. I wanted to play with it and I'm
going, I was I built it with the rationale of making a two, understanding that if I built a
thing that was that could see everything I can see, and it was doing all the work proactively
that I should be doing with the most limited access that I could give it. Um, what would that
thing do? How much would it rely on all of my internal data, like slack and
granola and emails versus the open web? Like, we obsess about the open web at parallel. And I
really like I tell investors, I tell customers that when you do work, you're combining across all
the information available to you, whether it is your personal information, whether it is your
company information, or whether it is the open web. And so this project's rationale was to see what
the machine likes to spend on parallel versus everything else in terms of tokens in a in the
purest form I could while being productive on my behalf. Uh, and so this was a journey to
one be useful and discover that for my role, uh, it has ended up
being pretty useful for me, for my top three things. Like it makes me feel more secure that I
am not dropping the important balls because it is also paranoid on my behalf. It
is inferring my priorities. Uh, I write down my top three priorities so it knows what those are. Uh, it
will push back on other things. I'm balls, I'm dropping, and I'm slightly more deliberate about
dropping those balls than I was before I had this thing. It's token uses outrageously inefficient
and I wouldn't read too much into the goal. Isn't token maxing the the. Yeah, I just it was not
worth it to optimize. But I think it can be materially materially optimized. Just not. The
second part of the question was would you let everyone on the parallel team go build that
version of themselves and spend, let's say, $500 a day on things to make
people less anxious? Probably, yeah. I haven't thought it through. I haven't done the full math,
but yeah, if once useful. Totally. I think it's like the utility. It takes some real work and
effort to make it actually useful to who you are, at least today. For me. Like it like out of the box.
Nothing is useful unless you mold yourself and mold it around you. And I don't know if you can
if I yet know how to productize it for everyone and all the different roles at parallel and but
yeah, anyone who does the work and can drive through value like $50 a day's fine if you get
really maximize yourself. Yeah, I remember you had an offsite coming up the week thereafter. So you
were really token maxing because you had some stuff running in the background. And I imagine
today less tokens have been used. I have since optimized it. It uses far less tokens, and it
works in certain periods more intensely and other periods less intensely. And how much do you think
you'll spend today? Like 100. Okay, a. Couple hundred bucks. Do you care? Like, if you look at your
portfolio now, like, obviously token spend is going up. Like, we're spending a lot of money
on cloud code, basically everybody. Are you starting to look at that like, uh, as you dig right
now, do you actually care or are you more curious, like, is this do you think this is a line item
that's going to show up at the board level where you're like, this is getting insane. Yeah, I ask
most of my CEOs at this point and we talk about it because I'm trying to understand the curve of
token use, like use and cost coming down. And at what point does it sort of stabilize or does it
keep going through the roof where if it's right now, maybe like one tenth of a person's salary,
does it go to 50% of someone's salary to one times a person's salary? And I don't know where it
sort of lands, but I'm trying to make a sense. And that's part of our job is to understand, like,
where do these, uh, macro trends land for, you know, for knowledge work for other types of work in
the future? I will say my I have two takes on this. Number one, I think
most token maxing a lot of token maxing that is happening isn't that valuable,
including a lot that I have done over time in the last few months. So at the same time,
if you have to. So clearly there is a failure mode to just token max. Not because it's
expensive in terms of tokens, but it's expensive in terms of like AI,
psychosis or time you spend token maxing, right? It takes some effort and
energy to token max. And sometimes it's actually not super productive. So I think it's there are
some people who are really, really good at it and actually drive themselves and accelerate
themselves forward. And not everyone is. So I think it's really important to not measure by token
maxing. The second framework that I have is like, listen, none of us know what the
perfect line is at any time on this. Which way would you rather be wrong on? Right? Would you
rather be wrong because you're too conservative and just not spending tokens? Because you're too
afraid of either spending tokens the wrong way, or spending your time on tokens the wrong way? Or
would you rather go on the other side and spend a little bit too much time with the models? Aren't
good enough. Spend a little bit too much tokens, and at least my personal decision is I'd rather
be wrong on the token maxing side than the non right, and you're never going to be right. But if
you know which way you'd rather be wrong, that tells you what you should do. You track it. And
Roadrunner. Uh, my co-founder Eugene tracks it. We first it was like,
let it rip. That was like, that's been the last six months. Was let it rip and everyone let it rip. And
then you're like, okay, this is getting expensive. And so then we started to make sure that it's all
in one credit card. So you can track like, all right, where is it actually being spent. And then
you started to ask yourself, okay, well what model is it being spent on. Okay. So then we started
figuring out like, all right, should we just let everybody use whatever model. And so we've kind of
like kept pushing in a little bit. And the short answer is we track it, but we
don't enforce anything today. Um, we're also watching to
make sure that at some point, if an engineer is spending their salary on tokens,
they better be pretty good. You know, they better be pretty good. And so we I would say we,
um, we watch it, but our best engineers are doing such unique techniques and how these agents are
working that I think limiting the ways that they're using these tokens feels like a mistake
to me, because they're on the bleeding edge of pushing the boundaries of like, sequencing five
agents together to do all these interesting things. And I would rather spend more now to let
them be on the bleeding edge. And look, they happen to also be the best engineers that we have. Do you
agree with that? Yeah, I think there are. We definitely see, I think one way
people perhaps undersell the use of tokens is like, oh, you're doing the same work that you would
do faster. I actually think the what excites me is people
like broadening what they could have done, or taking more end to end ownership of things by
using agents and models. So the example is like a a back end engineer will now do more front end
work, or a front end engineer will change more of your API ship or. Someone will
use agents to do a bunch of security reviews. Someone will use agents to push a bunch of
optimizations which they would otherwise not have been doing, and design something and do it. So it's
actually people expanding their span of influence and ownership on the product. That's
where I think there is true business value being generated in my mind. And so
people who end up sort of feeling comfortable about that and like really pushing their own sort
of learning curves really fast. That's where I think the real value is, at least for me, that I
see. Mhm. Yeah. The, the, the real life use case that came up this morning for me was every time
a customer wants to see the proof in the pudding of Roadrunner, they want to see it with their SKUs.
They want us to do like hairy SKUs and show a demo of what it looks like. And so I asked our
head of solutions architecture, how long does it take you right now to like, set up this
environment? It's like hand-to-hand combat. And she's like a couple days and I'm like, what would
you need to make it a couple of hours? And so just like I started like peeling back like a few whys
and eventually it was like, well, I need an engineer and I need these skills that they have
to do these things. And then I'm like, okay. But like, can't you just embed those skills into
Claude and then start to try? By the way, you might fail, but at least start to try to automate this
away, because if we have 15 customers at the same time, this isn't going to work. So like
either we go hire another ten people or we figure out how to automate this process and we can hire
three. And I think like that to me is where I'm like, yeah, let it rip. Like go, go. Because the the
trade off is I have to go hire more hands and I'd prefer not to. That is interesting. Can I at least
I struggle with this. I think there's this. We are hiring engineers as fast as
we can. Despite all of this today. And I find that counterintuitive,
but also not because if you think about it, like if you have an extremely
interesting set of things that you can build, which are extremely valuable, and your customers
need it. Even if each engineer is more productive, buy a lot
more is better until you start saturating your opportunity space, right? So I do think what's
interesting is, and I think there is an explanation for why there is a class of
businesses growing at crazy rates. It is because you get to multiply the two effects together,
which is like how fast and well can you hire and how quickly can you channel your
capacity to build into really valuable things, and how fast your customers can adopt
these new things that you're making available? And all of these things are multiplying together into
these outrageous growth rates that we're starting to see now. And so I have
never once sat down and been like, oh, Everyone's going to be so much more
productive. So maybe I need fewer people. Yeah. And it's the exact opposite that you feel. Yeah, I
think I agree with you, except, like, uh. So we have insatiable appetite to hire as many engineers as
we can, which is. Yeah, counterintuitive. Maybe. I don't even know if it's that kind of. It's not
counterintuitive. Yeah. It's just like, you know, you guys have the tools, you know how to use them. You
know how to build more product. You have large, wide, blue oceans to go after. Uh, and yeah, like go
build more. And, uh, if you know, and you've got capital, you've got all the ingredients. Yeah, yeah.
You're welcome. Uh, to go build and sell and generate revenue. And which is why it goes back to
the point, the, the multiplicative effects of all this are that companies are growing at rates
we've never, ever seen before. And that's great. That's amazing. And so if you're not
if you're not growing up those rates uh, you're a good company. Not a great company. Mhm. But don't
you think there is certain types of work that, like the truly great engineers, just don't want to
do. Like this example of like seeding a customer environment and having to do that 20 times like
that seems to me a little bit adjacent to core product development. Or do you also
have infinite appetite for hiring hands and that type of work as well? No, but I think instead of
defining what kind of work, I think great engineers want to do
high impact work, which happens to be hard. So if you have great
engineers, you have to kind of trust their judgment and their taste that if they can
automate something away with the existing tools, they will. And so
we just we have a few simple things we talk about, which is like beyond the meta layer, if you can
like use an AI to automate something like there are big parts of our system which are loops
with models in them, which in an old world you could have thought that this was a.
Like an engineer would have done that, right. Like if you're doing quality work, like if I think back
to our time at Twitter when we were building, like these sort of recommendation systems, and you were
doing a bunch of work to improve its quality and run, build better models, improve them.
And there were engineers, a bunch of them, iterating on models by using intuition,
running a bit as finding new features, building data pipelines. You can take some of those things
and now have them be in a more automated loop, because wherever there is a nice, clean way
of measuring things, right, you can automate a lot of those things, and not a lot breaks because
they're going to go into opaque models anyways, right? So those things are in our system and we
don't even think about them as, oh, this was work that an engineer did earlier which
are being done by models now. Does that make sense? And so to me, it's like what an engineer
does become more and more meta over time as you find feedback loops that you
trust and can automate and make measurable, and you can trust on your models and then you don't,
you have to look less under the hood of how it's working. Right. And our goal is
to push more things inside learned models rather than in code.
Right. So if there is a I've been saying one sentence internally quite a lot like the answer
to every problem is a model. So and which is sort of how we work. So you have a problem.
You're tempted to write a bunch of code for it? No. Instead, collect some data. Figure out what a good
output would look like. Figure out what model architecture is right. Train the model and put it
in a loop so that you can keep improving the model. The more data you can collect and the where
do you want to point it? So it's simple. Once you sort of just internalize that worldview
and the whole debate of like, is this worth like? I can't sit there and curate what work is
worth doing versus not. And so that mindset just allows you to get out of that decision making
process and hire people who are very versatile and problem solvers. How many engineers do you
have? 30 odd. And how many did you have six months ago? 15
I want to say. Yeah. So about two months ish. Yeah, yeah. In about 6 or 7 months, we've doubled. I want
to say we. Haven't gotten the parallel. Yet. I know I was about to. Yeah, well, I guess we're kind of
talking about it, but can I tell you a story about parallel story? So I, like I haven't gotten to, um,
know you that well yet until recently. And I got a call from Liam on my
team. I was on a work trip, and this was like, gosh, eight weeks ago, not even six weeks
ago. And he was like, hey, I got to tell you something. And I'm like, what? And he's like, you
know how sometimes founders, when we work really well together, you know, they kind of want to see
if there's ways for us to work more formally together. And Liam, for those listening as their
sales operating partner, I'm like, yeah. He's like, and you know how I always like I'm, you know, I'm
not interested. I'm like, yeah. He's like, I think I'm interested in one. And I'm like, huh? Why?
And he's talking about parallel. And he was like, well, number one, they've sold like tens
of millions of dollars of revenue with like for people and like it's in spite of themselves.
And he and he has spent a lot of time in your company at this point and he's talked to all the
reps and so he can like see it. Like, you know, sales has a really good nose for like, can you
stick it to this product to sell. And so that was one. And then he was like um to
Parag and, and your co-founder Shivers, they're the real deal. Like they're the real deal. And
I said, um, handicapped for me. Like, how serious are you? And he was like
at least 50%. And within a week we went from 50%. Mamoon and I
talked, we talked to Liam. It was very obvious, like this was like his calling was materializing in
front of us. I think it's a fair way to describe it. And and this happens all the time. Like this I,
I tell the team, your measurement of if you're doing a good job is if a founder tries to hire
you like that is truly the measure of greatness. That's like the highest compliment you give
someone. And, you know, eventually, uh, he it became obvious that this was his thing.
And, um, and so that was the first time I had like, that was the first time you really caught my
attention. And Mamoon and I talked and, um, were genuinely both two things happened at
once, in my opinion. Tell me if you think I'm wrong. One, we were sad to see Liam go, but really excited
and two, way more bullish on parallel. So I think, you know, to be fair, we've
been fortunately, uh, our job is invest in great companies. And the things that Liam saw, we'd seen
six months before, that's that's how I put it. Yeah. That's fair. But then when you see Liam and Graham
and like, this group of people also then see it and then Parag's ability to recruit them, that's
like the, the vision coming to life. It is. But let me tell you the the I'll double down on the in
spite of ourselves comment. So the way this whole thing happened, I think there is a real story
to tell here. So we were at this board meeting at the beginning of the year, and
Mamoon, in his sort of simple way, asked a question, uh, which was like,
you guys like you're in some when a customer is looking at you, like when you show up, like,
how often do you win? How often do you close? And we said we tend to always close
and once like so you just not enough deals. It's a very simple comment. And the one that really hurts
when you hear it and think about it. Right. And so it really hurt me that you're like you just not
enough fights. You're just not showing up. Mhm. Uh, so I called Mamoon back the same day. I was like,
okay, I have a problem. Help me fix it. And Mamoon was gracious
enough to get Liam to help us solve this problem. We'd been looking and recruiting for a head of
sales for some time, but these things take time, and I don't think we fully internalized
how well our product was being pulled by the market. Until
I can. Liam just showed up as an outsider on week one and looked at things
objectively, and it was him telling us about how
unusual it is, what we were seeing on the inside, because we had no calibration points. He
did. That actually gave us more confidence in what we were doing and more direction in what we were
doing. So it was really helpful in so many ways to get to work with him. Uh, and yeah, I think
we. It's amazing to. To get to work with Liam and I'm like forever grateful. Like
keep telling. It was like a hard conversation to call Mamoon and ask about this. And I knew it
when you texted me and said, hey, can we go for a walk tomorrow? As I was out of the country and I
said, I land at 3 p.m. on Friday, how about 4 p.m.? And we met at 4 p.m. for a walk. And
I, I sort of knew what was coming. Yeah. It is. Listen, like it was on the top three for the week.
Right. So so then you do not waive time. You're like, yeah, you land at 315. We can leave at four.
Yeah. Like, why would you. I did take a shower and then I met you after a 12 hour flight. But yeah. I
generally also think like when you want something from someone, then being jet lagged is usually in
your favor. That's right. Crafty. Crafty. And tell me. Tell you. How was the
conversation? No, like I was it was it was great. So one I'm glad you knew what it was because the
Mamoon came. Mamoon's first reaction was amazing. He's like number one. We're
all about founders. And so if it's right for you and parallel, it's right for me. Second, don't do
it. And I kind of needed to hear both of those things. Uh, because if you had not said the
second thing, then I'd be like, wait, why is he not trying to save Liam? And so then he went and
went to bat to save Liam and negotiated compromises. And it was
clear that from that conversation that I had made the right bet,
both on trying to hire Liam, but more importantly in working with Mamoon. So
I felt validated on two of my big decisions that I had made over the last few months
in that one walk. And then you also got Graham. Which happened right after. Yeah. So
also Mamoon first introduced me with Graham. Who ran sales at Windsurf
previously. Yes. And Mamoon introduced me with Graham. Well, before
you invested in the company. Graham believed in what we were doing early. And I think
ultimately you're building a company of believers and missionaries, and no matter what you roll,
what function you're in. And so he, him and I like spoke
quite a lot for several months. And it was when we truly got to the
point of being able to fully benefit from someone like Graham. Like really just like
scaling up the GTM motion and crushing the market.
But that early introduction with Graham getting to know each other for several months. Him being
enough of a believer in the company to want to invest in it. Those are all small things that all
compound into being able to build a really powerful, really deep partnership with extremely
special people. And to be clear, he was at Codeium before it was even called Windsurf. And my intent
was purely for you to get to know someone who could be a really good sounding board for GTM way
before you had any salespeople. And obviously I didn't want you to hire him from Windsurf because
we were on the board there too. Yeah. Uh, yeah. No, I think but I think once I
every special person you end up talking to as a company builder, you think about
hiring if they are the right person. Like, it's very hard to not think about. Like, it's
simultaneously very hard to believe that someone is exceptional at what they do, and at some point
I will need that kind of exceptional in my business and also not visualize trying to make
that happen, right? Like, I just don't know how to do that. So I walked the world trying to figure
out how parallel can be better, because there are some amazing people that can who
are great at what they do and actually believe in the mission to join us. Can you talk about the
mission? Why did you start the company? What are you trying to do? Yeah, we started because
I got obsessed with this notion that I wrote down one day, which is like, agents and AI's will just
use the web a lot more than humans ever have. And I wrote down a number which is, like 1000 x, and
this is like two and a half years ago, we wrote down this random number 1000 x more than humans
ever have. And I said, like, I have built systems before at 1000 x the scale, none of no system
works and no system is designed for it from an architecture perspective. From infrastructure
perspective. I also wrote down that new business models will be needed
for how agents use the web. The all the business models that have existed on the web for and have
been built and evolved for like last three decades, let's call it, are about to evolve and
change completely. And that seems like an opportunity. And it seems like an opportunity that
is like really close to me because like the open web is a like for
where I grew up and how I grew up. Open web is like this generational change in the world that I
have experienced and lived through, and now there's going to be like a second birth of what
the future of the open web is. A parallel web. A parallel web, if you may, that came to us later, by
the way, not in the founding moment. Uh, the so, so this notion of
like this observation that the business model will change. And like there is this existential
threat, right? That the whole contract, the whole bargain of the open web, is that
people put content out in the open because there are amazing ways
of getting distribution, driving, monetization, selling goods. There are many incentive systems
built in which keep things open. Now, the risk is, if we don't innovate
enough that the web starts closing up and we're starting to see that already, right. And so there
is some missing incentive alignment today in terms of agents using the web
and people putting content out on the open web that needs solving. Now, the good
news is, if agents are going to use the web 1000 x more, and I actually think 1000 x, did we under
shoot? I'm already using the web with this token maxing. Uh, 1000 x more.
I think it's going to be way more than 1000 x. That's not work. That is just a pure waste. I
actually think we will generate real value, satisfaction, all kinds of things as a result of
these tokens over time. And if we do that, there is enough value generated so
that if we just figure out how to incentivize being open, how to reward high
quality, unique, differentiated content that we get to keep the web open and prospering and becoming
like a whole new thing, like we can't imagine. And so that's the core mission of the business, to
keep the web open, to create incentive alignment
between all of the plenty we will create with agents having access to more high quality
information, and the people who curate create, produce that information.
So when we, I think, first spoke about this two and a half years ago. I remember it was like
Thanksgiving of 20, 23, 23, 23, 23. Right.
Yeah. And the notion of a web for AI agents completely, 100% made sense. Um,
and a lot has happened since then, uh, in terms of models that are
actually utilizing the web in ways that at the time, you maybe not had foreseen.
I certainly hadn't foreseen, but I knew that AI agents would exist, and they would need a web or a
parallel version of the web that they would use a lot more than humans use the web presently today.
And so maybe take us through what that inflection point was in the significant
tailwinds that parallel has gotten as a business. So 2 or 3 things. And some of this is
like fitting to what we've observed over the last couple of years, right? If we all go back in time
and think about like the initial version of ChatGPT, it did not use the web and we still
thought it was really cool, right? For people who've now been using any of these products
ChatGPT cloud for a period, if you go back and just use a model without giving it access to the
web for 75% of what you do with them, you'll find it
unusable. Now, why is that? We should observe. Why did we suddenly reorient
ourselves? It's because these models present interfaces
and a mental model, which is that they're both smart and all knowing Once you build
that mental model into a product, you must use the web because otherwise you're not following. It
feels entirely stupid that the thing doesn't know the date of the last Super Bowl. It just it just
it's so confusing to people. The same thing happened with coding agents, right? Coding agents. I
remember every coding agent. Like I would be pasting links to docs
into a UX to have it fetch and index it
locally so that it could build in an API that was not in the pre-training data set.
Now we'll feel like cavemen doing it. And so really what's happening is
every almost every application we build with models creates the
customer expectation of that application that this is all knowing. And the that
intuition is gone from oh, a model is smart to a model plus the web. And now over time it's
evolving into an it has access to all of my data. Right. But the web is going to remain in that
expectation. So we really think of parallel as a adjacency
infrastructure, adjacency to every model to inference for all kinds of work.
Right. So if you think of how we evolve, it's when different categories of work,
when people derive value from models in the work context, we take off. And that really started
happening in my observation last summer in some like nine months ago
in some areas, and has been exploding since then. And we
show up with like the highest quality way, which is extremely agent native to help
an agent do more, do it faster, and do it cheaply. When you're building an agent for
any kind of knowledge, work end to end. And so since last summer, we're now seeing like across
categories, the people who are at the, the tip of the spear, the frontier of using agents
to do really cool things, incorporate our APIs and make their own products
even better than they already were. And we write that and then, um, as sort of
so we've seen these models being really useful in finance, really useful. Can you give some examples.
Maybe like the Harvey use case. So you guys just announced a big a big partnership with Harvey.
Like what are they like what are they using it for. We have a pretty big product suite at that
point. So they use it for multiple things. Uh, one of the things they do is just use our search
products so that All engagements with Harvey. The the product always knows what's out there
and it knows it without burning too many of its tokens. It knows it with fresh data. It knows
it with hard to reach data. Now there is a second element to this, which is there is a lot
of data that over time, let's call it like I think in
the in 2000 this was called the deep web, which is content that is
extraordinarily hard to get to on the web. Let me give you an example. There are many portals where
they're not there's content not in the a typical search index which is optimized on
ten blue links. Why isn't it there? Because a lot of this content isn't reachable by just clicking
on a link. You kind of have to end up at a portal. You have to either navigate somewhere or search
somewhere and it doesn't have a a URL for that content. Now,
historically people, when it's hard to crawl two it's less valuable to crawl because you can't
actually land a person on the content. In a world of eyes. Our job is to bring the content into the
context window of an AI. So we now go crawl. We built crawlers for
extra like things that historically search engines haven't been crawling. And
when you're building Harvey, when you're optimizing for having access
to authoritative source documents across the world
so that every bit of output in Harvey is well grounded with an authoritative document you care
about one having completion on these hard to crawl information that is in the public domain.
And two, you care about really good search ranking in search quality on that. And these are two
things we do really, really well right. And so through this partnership we are able to push
and stretch our capabilities because they're a very demanding customer. And they are able to, uh,
have access to extremely hard to reach high quality public data with great ranking on top. Do
you want to talk about just having run one of the largest web scale systems at
Twitter and, uh, the the parallels that you draw to what you have to ultimately
build at parallel or what you may have already built, uh, in terms of the supporting the scale
latency. Obviously, latency being so critical and important for, you know, a real time social network.
Yeah. No, I think so. Parallel is fundamentally a technical Product. It's
an extraordinarily large scale system. I would say over time,
its scale is meaningfully larger than Twitter, mostly because Twitter is,
like amazing and influential. And I'm really proud of what we built it. It's a small slice of the web.
It's a small slice of public content out in the world. The opportunity with parallel feels like
outrageously larger, uh, in terms of keeping the web open. The mission is the same, right? Twitter
was trying to keep more high quality, unique content out
in the open versus a disappearing into closed worlds. Right. Giving
direct access to the best minds to everyone in the world at the
same time, instead of via intermediaries or via closed off groups. That is the same mission
at parallel. To keep the web open and having more information out in the public at all times,
but it feels materially larger. Now going to the infrastructure, though, I think having
great having known great engineers, having known how to
build and design systems pragmatically for a level of scale, knowing when
to rebuild them for the next level of scale. Having done that like several times over at
Twitter. At Twitter. Like we went from this ride of trying to grow systems and like every year we
would have to rebuild entire systems because they weren't built for the next order of magnitude of
scale. But if you build for like four years out, you don't ship anything. And so you have to
constantly keep evolving for scale. And we're running the same ish
Set of lessons at parallel. The other, I think, really interesting thing that I carry from the
the ML infrastructure part of Twitter is, you know, how at
Twitter, when you're building these recommendations systems or these models, they
initially start as these sort of cobbled up things which are like you train a bunch of models
and then you have a bunch of heuristics on top. And over time, as you learn about your problem
space, more things become more end to end trained. This is the same story that's played out with
like self-driving cars in Waymo. Started with like a few perception model in this model. And over
time, things become more end to end as you know how to collect more data, right? I think today
you don't necessarily have to go down that journey that slowly, the way we used to do
five, seven, ten years ago in building these systems. And you can shoot more end to end from
the very beginning at scale, and I think doing so puts you on this path of rapid
self-improvement. And so in some sense, we saw the journey at Twitter and at parallel, we're sort of
taking a leap to not have to go through the three steps and jumping to the fourth step parallel
started. You had an amazing run at Twitter CEO start parallel. Was everybody,
like clamoring to come work with you like you had this this vision in this mission? Did
you come out of the gates? Like, tell me about the very, very early days. Was
it obvious to was it obvious because you are like pretty bona fide to go work with as a
founder. So we were very quiet and stealthy. So we literally
no one knew that I was starting a company. I had worked with a bunch of people. I It
called The People. I wanted to join me at parallel. And then there was this mission
alignment problem. So I didn't get an inbound call because no one knew what I was going to do. The
one thing I did early on was decided that we would keep more than half the team new, and not
people I had already worked with, and I had sourced really talented people through the almost
a year before I truly started hiring for parallel, where I'd met a lot of people exploring lots of
things and asked them a standard question which is like, what are the best three people you've
ever worked with? And I would go meet some of them over time. And so I ended up hiring a
and working with the initial founding team of me, plus six other people, and half of them
were people I had not previously known, but met via this sort of multi
hop dance off like them being some among the top three of someone I thought
was already exceptional. And so this way I think we got to build a team that was
beyond my in starting network from Twitter and would ultimately we've done a lot of
hiring via this kind of a channel of like extraordinarily unique and talented people,
uh, but built via this sort of network hiring approach of exceptional people to tap outside of
my personal network. And I think that's been really, really good for us. 40 people
today. 50. Yeah. What do you think you'll be at the end of the year? Are you hiring?
I am hiding as fast as we can. Across every function. We would hire across every function,
but we proactively put our energy towards a few. Which are. Always
engineering. Now GTM and the new ones
are marketing. Developer relations are the top new ones on my list
where we just need to do more. Yeah, it's like time to build the company around the product.
Exactly. So we started with building the technology. When we launched the product last year,
we had we're building the product. Now we're building the company around it with includes GTM,
but includes all the other things you need, uh, to actually show up for, uh, customers.
So maybe just, uh, jump on that. Uh, so some of my favorite
products that I use, AI native products use parallel, and I'm not sure they're all fully
announced in terms of as customers or you have some public statement with them, but, uh, maybe just
help us. One of these products. Uh, Rogo Harvey. Profound
granola. Uh, Clay. There's more. It's good list.
Yeah. We love. Listen, I could list. I love so one of
our core beliefs is that you have to experience your product in the context
of every customer. So we are particularly interested in being a part of
the journey of products that we personally love. Right. So.
It gives me deep, deep, deep personal satisfaction, like I think at Twitter, like I was a heavy duty
Twitter user before I even started working there as an engineer. I was an addict, uh, before I went
to work at Twitter. And the reason I ended up there was because I was in love with the product.
Right? And it's the same thing. Like, my team derives extraordinary satisfaction to have
built something that is useful for products that we love, using ourselves for our work.
And it is truly the most rewarding thing to be able to solve problems that
these have. The other side effect is. I believe we have good taste in the
products we like to use and to be able to. Those products are often built by people with really
good taste, and so to have them as customers, we put ourselves in a place where we are
the demanding, extraordinarily demanding in terms of what products they would use. They push
us and our technology and our product forward. And my core belief is that if we can be
a product that they love putting into their products to power them.
There's a lot more that follows from that. And so it's really nice that over the last several
months, we've sort of built our product up to the place where the best
products we like are starting to use us. So with that, uh, I imagine
more AI native products will use parallel. You know, I think you'll blanket sort of that world.
What else can we dream about where parallel is deeply embedded. And so, in other words,
how does your market expand and how does this company really rise to the level of one
of the most exciting companies that are is being built right now? So one, I don't think we have to
expand the market like we are. Like if you use an A model for building
anything, it's a product, a Workflow automation for anything related to work that you
do. I almost believe you must give it the web and if you must, give it the
web. The question is through which of our products right? And we
have and must have the best way, no matter what your circumstances for the product or the
workflow that you're doing to use us. Now, this need is broad based. If you're a large enterprise
that is now trying to make your operations more streamlined and you're starting to use a model
for it, you could. There are many ways you could use parallel. You could have an internal team by
an API and parallel APIs, and hook it up to your internal data via a bunch of
MCPs or clients, and build workflow automation, for example, like I've built my own app for my
productivity. Right. Or you could go and you can say, no, no, I don't want to do it myself. I actually
want someone who's prioritizing it for others like me for this function. And then
we must be working with whoever it is that solves the end problem. All we care
about is when agents access the web. We have the best products for them, no matter who they are.
What are you going to see over the next year is there'll be a class of the
best AI native agent products that will incorporate parallel. If we do our jobs right,
there will be a class of large enterprises which will take all kinds of
differentiated work that they do themselves and supercharge them with parallel. And the biggest
thing that excites me is people often frame this as like, oh, we were already doing this work, now we
are going to make it more efficient and we can do the same work, but it's going to be faster and
it's going to be cheaper and more automated. Now people do work beyond what was
previously happening. I'll give you a very simple example. Let's say you are a semi hypothetical,
a PE firm thinking about buyouts. And in the previous world you'd say, okay,
I could buy out these categories of businesses in these kinds of neighborhoods and these kinds of
cities, and you'd use some gut and human judgment to narrow down to like 4 or 5 interesting
opportunities. And then you'd go do extra work on them. Right. Their biggest limited
resources, how many of these they can do through capital and human capital in a year? And
the biggest job is to figure out which ones to do and prioritize that well. Now they can use
parallel and models to really run what feel like almost simulations on various criteria. Did they
set and do a lot of compute to exhaustively come up with
a rational list of opportunities to actually explore, and then pick the ones they do better?
They're previously pruning the space of what they would explore to this tiny, top down, intuitive
thing. And now they get to see real information, real data about what it could be and if
it makes them pick one better. There's so much value created that it's worth token maxing in
that context. Right. And so when you see these use cases about like just entirely new ways
of operating businesses that have existed forever, that's really
exciting to me. And I think that's the next year of growth, which isn't just like replace what
we're doing today. It's going back to what does it take to make it 1000 x, right? It is work that was
not happening yesterday. Well, sir, thank you for doing this. Well said. Uh, when you hear the word
grit, I ask everybody the same question. What do you think of. The conversation we were having
earlier when you asked me in the kitchen about taking a breath. Uh,
I think grit is just doing hard things. And the only way you can do it is if
it's for something meaningful to you. And it's fun.
Thank you. Thank you. That's it for now. If you liked the episode, please leave us a
review or go back into the archives where we've done more than 200 episodes with some fantastic
folks. This podcast is a Kleiner Perkins production, and I'm Joubin. Thanks for listening.