Logan: I only went to one year of high school cause I was convinced it was like a inefficient way of learning. He convinced my parents, also some of our friends who did the same thing. Our plan was to just teach ourselves the things you would learn in high school.
Logan: Welcome to the Logan Bartlett show. On this episode, what you're going to hear is a conversation I had with Jay Kreps.
Jay is the co founder and CEO of Confluent, a public infrastructure software company. Let's get the glory in these boom times where everything's like really good. Everybody will tell you all, you're such a genius. You kind of earn the glory in the bad times.
Logan: Jay and I talked about a number of different things, including hiring talent in the early days of an infrastructure business.
What do you learned about going from an individual contributor at LinkedIn to a CEO, spinning out an open source project into a business that was very commercially. Successful. If you talk to anybody who knows anything about startups, they'll tell you like, do not try and do a second product when you have one thing working.
Like that's just such a terrible idea. And yet we kind of knew we had to get this right. Literally any of the news about us was like startup destroyed by AWS. My mom at one point called me and was like, I pretty much heard you're going out of business. And I was like, what? Not dead yet, mom. You'd rather fail at the company you want to be than, you know, end up with some niche part of the problem.
That's kind of liberating. At least we're going to go down the way we want it. We're going to go down with honor. You'll hear that conversation with Jay here now.
Jay, thanks for doing this.
Jay: Yeah, I'm excited to be here.
Logan: So. Once upon a time you wanted to be a writer,
Jay: This is true. Novelist. Yeah.
Logan: You settled for public company CEO. I've read some of your blog posts before and you're still a very talented communicator. Like the original Kafka paper, it's very elegantly laid out. Like the, the, um, vision for it, the structure, all that stuff's very impressive.
Logan: Um, how do you think having aspired to be a writer has benefited you as a CEO?
Jay: Yeah, maybe in two ways. I mean, first of all, I wanted to be a writer. I also at some point wanted to be a firefighter and an astronaut and 12 other things. Uh, but, but more seriously writer. Um, and I, I CEO's job is, you're kind of putting together a lot of things. And so a little bit of diversity and interests helps.
And then, um, you know, any senior job in a company, a lot of it is communication. You know, you're ultimately trying to get some big group of people to do something together, which means convincing them to believe in it and take it on and work together and be excited. And that's a communication thing.
Logan: Have you oriented around like, uh, pre reads before meetings or anything like
Jay: Yeah, we, we do do that. Although I, you know, I actually think a lot, um. You know, for CEOs, a lot of it is live, right? It's not so much sending long emails, it's, uh, you know, talking to people directly. And so a lot of, you know, I would say when I was an engineer, I was writing a lot of design documents and then, you know, a lot of CEO stuff is live presentation.
And that was, that was part of the learning was how to do that well.
Logan: One of your board members told me that you're one of the best he's ever worked with at building out a management team. How did you go about gaining both the, the confidence and, uh, competence in bringing in people that was outside of your core domain
Jay: Yeah, I mean, nothing smart at all. Like when we started Confluent, um, you know, it was three engineers who had effectively never worked in like a enterprise software company, really. We'd worked at LinkedIn, which had, was an enterprise software company, but didn't know it. And, um, you know, so we just, the set of things we didn't know was very long.
You know, I remember when we were first meeting with investors, uh, somebody asked me, what's your strategy around services? And I just assumed they meant web services and I was like, Oh yeah, we're going to have those.
Logan: I'm pro web
Jay: yeah. And, uh, I didn't realize they meant professional services because I didn't know what that
Logan: Hmm.
Jay: So in terms of the level of like things we didn't know is very high. And yet, you know, because when we were starting the company, we actually had a very popular open source product. We were not that far from a product we could go and sell. Uh, the result was we were kind of. Taking on go to market pretty quickly for an early company.
Um, and that meant, you know, we needed to kind of scale up and do it. We were ready to do that. There was interest in what we were doing. And so it immediately became like, okay, go try and figure out how to build this and hire executives and really do the company pretty quickly. And, you know, at least when we were starting, it was, um, it was a totally unappealing proposition.
You know, I think no matter what executive in a very small company that has. minimal success so far is kind of a lukewarm proposition. And then this was a company with an open source thing that was very hard to explain around real time streaming data. And so just getting good people who wanted to do that was a, was a big challenge.
And so I just kind of made it my full time job to try and figure out, okay, what are, you know, what do these people do? And how is this kind of company structured and what is it that we need? And, you know, try and get some good people in it. I think it made, I think it made all the difference. And then Just that kind of progressive refinement each time there's an opportunity or somebody's leaving or you know, whatever, you know Trying to just really stretch on who would be the best possible person for this role we can get And kind of go all in on that and so there's no you know, I didn't think there was any real art to hiring I think it's kind of a grind, you know, the only big takeaway I had was and I think this advice You know It's the kind of advice you maybe want to listen to it Maybe don't is that I do think people develop like a pretty good gut instinct for other people And I think you have to like really listen to that and refine it and spend enough time for that to kind of kick in When you're making a decision, I always find people try and be more logical And a lot of the things that end up mattering, it's not as much a like logical equation.
It's, you know, something you figure out almost subliminally and then try to figure out how to express why is a big part of it.
Logan: So when you're when you're hiring people in the early days, how are you thinking about how long that person would do the job?
Jay: Yeah. I, you know, I, I certainly, I didn't have, I wasn't coming into it with a, Oh, this is the right person for this amount of
Logan: One to five million versus not? Yeah.
Jay: Yeah, you know, I, I, I think if you, um, have been involved in a lot of startups and seeing that you kind of have a very clear picture of, Oh, this is what this executive looks like at this stage.
And this is how long that person is going to be effective in the organization for. I of course had none of that. I, you know, I was just really trying to figure out like, what does a CMO do or what does a head of sales do? And is this person going to be a good one? And uh, how do I tell? And, uh, you know, that, that I think is inherently one of the challenges for CEOs, like whether you're founding the company or stepping in, you probably haven't run all these functions.
So you somehow have to pick that up as quickly as possible and make good decisions. And it was really compressed for Confluent because we were kind of ready to go pretty quickly. So it was like, okay, let's put together that full team and go do it as quickly as possible. Don't make any mistakes.
Logan: Did you have someone you turned to either on the board or an advisor that was kind of your go to confidant on calibration,
Jay: Yeah, you know, I I think we were lucky to have like really awesome Venture investors, so, you know, Eric Vistria did the Series A at Benchmark Mike Volpe at Index Ventures. I would say both of those are you know, they're both very good at assessing People and and I think I leaned pretty heavily on that and I think that was a huge that was a huge asset
Logan: I guess, just cause you bring it up. And, uh, Eric is, uh, is a friend and we've collaborated together. You were his first investment. What was it?
Jay: Yeah, we were both pretty green. I don't know if you would describe it that way, but you know, I think he'd really kind of been on the job for just a few weeks and, you know, I think wasn't really necessarily expecting to do something right away. Um. He had, uh, actually had some interaction with the open source project Kafka, which was kind of founded around, so he kind of knew what it was, and so he actually had a bit of a head start on some of the other people we were talking to, um, and, and we hit it off really well,
Logan: Was that just a personal dynamic feeling thing back to the earlier point about sometimes it's a gut thing because taking a chance on
Jay: yeah
Logan: venture investor.
Jay: yeah, yeah, well, you know, um, yeah, I, I think it was a number of things, um, you know, I think part of it was just building trust. You know, I, I, uh, at least our assessment was like, Hey, we should try and get, you know, a good financial deal and a good firm and then really get somebody we can kind of trust to, to be part of it.
And that last bit's kind of the hardest to assess. And it's like, sometimes you don't get there with somebody. It doesn't mean you wouldn't eventually, but you just, you just aren't sure. And, um, you know, I think that was the, that was probably the thing that did it was just. Kind of walked out of it with the most confidence.
Logan: Were there certain mistakes that you made in the early days from a hiring standpoint that you wish you could go back and tell yourself, like, hey, don't touch that stove or?
Jay: Yeah. Yeah, you know, I um This is maybe a little back to what I was saying on the kind of gut instinct I always think that people it's it's shocking the degree to which the problem that's gonna be a big problem later is Visible in some small way early on in the interactions and kind of not paying attention to that You know that that's there's always things that will go wrong that you don't foresee But the thing that you're just, it's a little nagging worry going in and then turns into a really big issue later.
Um, I, I think that kind of like, you know, having the confidence to listen to that and be like, okay, I want to spend another hour on that one subject with that one person and really put that to rest before we move any forward, you know, any further forward. I think that's really important for these, these big hires and you know, I do think that's one of the things that tends to go wrong.
In executive hiring is you just have so many stakeholders. There's board members and there's the people on their team who's going to report to them and there's the other management team and pretty soon it gets very kind of diffuse and spread out. You're, you know, there's a lot of people talking to a lot of people.
And there's not really depth from any one person. And then it kind of turns into some kind of, you know, listening to a lot of feedback from a lot of people versus really kind of going deep and like gaining a lot of confidence that this is going to work.
Logan: a potential for the lowest common denominator,
Jay: Yeah.
Logan: Just like the, the person that's least, uh, offensive to everyone of the constituents rather
Jay: That's right. That's right. And I, I think CEOs like early CEOs are often, I think, susceptible to that because you actually. Have not done that job and, and so you're, you know, you're going to listen to the marketing people and you're going to listen to the people on the board who have seen it more than you have.
And, and yet you're the person who's probably spent the most time with that individual. And those I think are the avoidable ones where you're like, Oh, I saw it and I didn't, you know, I didn't act on it.
Logan: What is your goal in an interview? Is there something that you're, you're trying to tease out in the initial conversation with someone?
Jay: Yeah, I, um, initially I actually just try and get a feel for what they're about. Um, you know, over time I'm actually kind of crazy structured about it. where I just literally write down like a theory of why this is going to be great or fail just in a very structured way and then try and assess that with each person they talk to.
I, um, you know, I don't try and go into it. I like to talk to each person and be very busy and like, I want to know everything they're worried about or excited about with the person and then kind of just factor that in. And then I just try and prove the good things are good and get comfortable with the bad, the worries.
Um, and so I, you know, I just do that through a progression of conversations. Uh, and I think it's actually, it's actually been helpful for me. You know, it's a little bit, um, it's probably overkill for certain types of hiring, but I think the executive hiring ends up mattering so much. You know, if you can get a really great executive team that works really well together.
A lot of the problems of running the company are solved. And if you can't, then that's going to be the big problem. So I think going a little crazy on that side helps.
Logan: Have you done retrospectives on those, uh, docs
Jay: No. Yeah. Um, no, not, not in like a formal way. Maybe that would be the right thing, right? Like some kind of investment hypothesis and then, no, uh, no, no, I just agonize about it all the time.
Logan: Do you have a favorite interview question to ask or?
Jay: No, you know, I, I always just kind of go where, um, you know, where the conversation takes you. I, I think you want to. You know, the, the important thing I always think is just get people's guard down and, you know, get a real sense for how they do things and how they've done things in the past and who they've hired and what they think is good and get them involved enough in what's happening in the company that they can start to talk about that to you.
You know, so I, I see it as more a progression than like, you know, the interview question I think is always when you're trying to fit something into 45 minutes and you're going to ask these three questions and grade everybody versus this kind of, hey, we're going to keep digging till we get there, uh, approach, which just takes more time.
But, um, you know, but I think it's worth it and for these kind of roles.
Logan: There's two competing concepts or philosophies in Silicon Valley. One is that titles are very expensive and one that titles are very inexpensive. Where did Confluent fall on the, that, that spectrum?
Jay: Yeah, yeah, a little bit. Um, you know, we're a little bit split brain where our, um, you know, our engineering team has always wanted to give themselves the lowest possible titles because they feel that just really hardly anybody deserves to be an engineer at Confluent. And if, if anything, it's really just a software engineer with, you know, very little else.
Um, in everywhere else in the company, I think it's kind of a more normal, the average of all companies is roughly where we're at. And we've tried to square it, but it's like a deeply held belief on each side. And so there still is a little bit of a schism between the two areas where, um, you know, my, my personal philosophy is, yeah, the, the titles are not that expensive compared to money, but, um, I think it's important that teams kind of hold that the most important thing is the team really believes in that system.
You know, in an early company, it doesn't matter at all, but as you start to have a slightly bigger organization, you know, humans just love progress. And so having. You know, some notion of seniority. Uh, I think it's really important in theory, people work for money, but I think in practice they work for like. which is often captured by money, but like the honor of, you know, I have achieved this and this recognition, it really matters. So if you like debase that, or it's not strongly held in the team, I think it totally doesn't work. And you know, the human nature is always, people will look at the least deserving person at that level and just fixate on it.
Like if they're. You know, if they're a senior director, then I should be BP.
Logan: There, there's a
Jay: And in reality, that one is that's the one big mistake you made that they're just literally comparing themselves to the failure of the system. And so, so I do think that's where you kind of need the team to really internalize the system and, and care about it.
Otherwise there's no real. Honor in achieving the thing.
Logan: past a certain level of, uh, Maslow's hierarchy of needs and some baseline salary. I found that like. People just lose their mind over fairness if they feel like it was treated unfairly to the ends of the earth And you see partnerships in BC break up over it. You see co founders like get into fights It's it's it's the most human nature thing that
Jay: No, it totally is. And it's funny the degree to which you see this in little kids. You know, they don't care if they got enough cookie. They just care that they got the same amount as their sister. And like, if that was not the case, you know, if one person got more cookie, it's like, not good. And so it's um, Yeah, it's a funny thing.
I think the modern society has kind of absorbed this economics worldview where everything is just kind of ultimately about money, like maximizing utility. And then you look at how people act and it's actually a little bit of that, but also like a bunch of other stuff.
Logan: Well, if you look at like and this is a little meta or even outside of but if you look at like how quality of life improvements have happened over the course of the last 50 years and like longevity and You know, access to health care, all these things that keep going up and up. But as social media is proliferated, people are even more miserable because they're comparing themselves to the optimal state of famous people.
And it's just like, it's, it's led to this discontent. I think that's a structural
Jay: Yeah, yeah, that's, that's the downside of it. And then, you know, the plus side of it is I think, um, you know, people really like honor. You know, they want to, they want to be honored, uh, by their peers. So it's like people I respect, respect me and, uh, um, you know, it's, uh, I think Napoleon is supposed to have said that, uh, you know, men, men will die for scraps of ribbon.
And so it's kind of very dismissive that quote, but it actually kind of, it's actually interesting to think about. So if you think about like, what's the motivational system. For like the bayonet charge. It's not like bonus. It's not like oh, I'm gonna get a good bonus It's not your stock compensation. You know, it's kind of a it's an honor thing.
And so You know, I think in in HR There's too little there's a little bit of acknowledgement of that But not as much as you would think and and I think it actually drives people a lot that you know They want to do Um, you know, they want to do great things and be recognized for having done it.
Logan: I want to back up a bit.
Logan: Uh, so you grew up where?
Jay: I grew up mostly, I was born in North Carolina, but I grew up mostly in California, in Sonoma County in, uh, what's now wine country, but was, uh, I think at that time, more like cow country.
Logan: What did, what did your parents do for a living?
Jay: Uh, they did a bunch of things. So, um, you know, the people there, they were both kind of maybe a little bit counterculture, um, and then a little bit blue collar.
So somewhere in between those two. Uh, you know, I think, um, my dad. Like lived on the houseboats and was a fisherman and then a kind of carpenter construction worker type and my mom Like lived on the Russian River and made quilts and then cleaned houses and then you know ran a nonprofit At different times.
So just kind of a sequence of things not real career oriented type things So definitely part of having no idea what I wanted to do was it was actually just to really have a very clear picture of what? people did Um, you know, for, for work and it wasn't necessarily the, the biggest focus. So I felt like, Oh, I want to do something great, but I have no idea what.
Logan: It's an interesting, uh, upbringing that you don't hear that often. How, how did that influence you most in terms of like who you are now as a CEO, a leader of a public company? I'm sure there's, there's a bunch of derivative things from that, but.
Jay: Yeah. Yeah. I think, um. Yeah, you know, it's actually not that uncommon in this area as a background and not that uncommon for tech people just because I think of what the California Bay Area was and who came here, you know back in the day. I think it was It's probably helpful in that there was a lot of freedom like I wasn't really expected There was no expectation that I go to college.
My parents hadn't gone to college. There was no You know, kind of do whatever I, I actually, I only went to one year of, uh, high school because I was convinced it was like an inefficient way of learning. So it was a lot of freedom. Um, now, of course, as a teenager, you know, whether or not you know how to take advantage of that, probably not, right?
That's why teenagers usually are put on these tracks of everything they have to do. But at least it gives you some confidence after you've kind of worked through things of what what you want to do
Logan: So, so you did one year of high school, you kind of dropped out to teach yourself among your
Jay: Yeah. Yeah. Yeah. So, you know, uh, some people who did one year of high school is because they were just so smart Some people it's because they were like so not successful at school. It was somewhere in between the two Yeah,
Logan: Normally it's one or the other, right? You get sent away to some like rehab facility or you like are placing out,
Jay: I think um You know, I was, I was probably a little headstrong and I was just convinced this is like a very inefficient way of
Logan: which is probably true. It's just you maybe not
Jay: It's a hundred percent true. Now, I, I would say in retrospect, there's probably some value in going through high school. Uh, and there's, you know, it's probably, it's harder than I thought to teach yourself chemistry.
Logan: So what did you actually do? So, so you, so you, did you need to convince your parents or your parents like, sure,
Jay: I, I convinced my parents, um, and you know, uh, also some of my friends who did the same thing. And, you know, our plan was to just, like, teach ourselves the things you would learn in high school. It turns out you don't have to have gone to high school to go to college. People don't realize that. Um, but basically, you can either, like, pass a test or have good SAT score.
There's a whole bunch of ways to get into college. And, um, so that was our plan. Now, it turns out it's a little harder to just Teach yourself everything. So I would say there's still a few holes in what basic high school education, you know, some areas I, I probably over indexed and some I probably did less than
Logan: So, so you took, was it three years that you actually did this?
Jay: yeah, I did it for a couple years and then took junior college classes and then went to UC Santa Cruz. Just really without having gone to most of high school.
Logan: Huh. Do you feel like that experience, it sounds like maybe some of the stuff in chemistry or whatever, you probably need some more, um, structure or products or whatever it is to, uh, to learn.
Jay: Yeah, you know, I think the, the challenge for teenagers, you kind of study what you're interested in, right? So I, I did like math, a lot of math and English and a little history and then some other subjects I was a little weaker in and then I caught up on it in college, uh, to, to the extent I could.
Logan: and so how did you, your parents didn't go to college. How did you make the decision that you wanted to go to junior college, go to college?
Jay: you know, it was a little bit process of elimination. Like I, you know, I was interested in biology. I was interested in writing. Um, and then I, but I didn't really know what any of the academic subjects were really about. And then I realized, Oh, you know, if you study English, they're not like training you to be a writer.
They're training you to analyze other people's writing and kind of critique it. And I was not as interested in that. So I, and, um, you know, in school I hung out with a bunch of, um, more science oriented people. And so I was like, I think I want to do that.
Jay: And, um, you know, just almost by process of elimination and a little bit.
Which tracks I could actually get all the classes done in I was like, okay, I'll do computer science. That seems interesting. I was interested in artificial intelligence in in kind of a weird way like the you know, what what I hadn't realized that I kind of became aware of was Digital music and so, you know, at least the way I came into I was like, wow That's really interesting if you could just take sound and turn it into something digital and then manipulate that with a computer You could probably do that with anything.
Like, you could do that with all kinds of things in reality, and there's like a computational version of a lot of things. So I was like, okay, this is fascinating, you know, what, is there a reason this AI stuff can't work that we know of? Or is it just like anything that, you know, an animal or a person can do, a computer could do?
And so I was like, okay, that's really interesting. I'll, I'll, I'll study that. Um, and, but I had no background in, really, in computers or programming or anything
Logan: You got your first computer in
Jay: Yeah, in college. Yeah, I was not, I was not like a early computer prodigy or anything like that. So then I worked really hard the last few years to get all that
Logan: so how did you, how did you. Take to it. I assume you didn't have the the the grounding that maybe some of your peers did within like I mean Simple sort of stuff. I would I would assume within computer science
Jay: that's right. Yeah. I mean, I think it's true of computer science even now is it's a mixture of people who've spent a lot of time learning to program and doing that, um, with people who are just kind of starting in the curriculum and it's harder if you're just starting. And so, so yeah, I had to work real, real hard at it.
But I was, um. You know, I was, I was interested. I actually think, uh, you know, this is probably less true now, but, um, you know, I think at that time, maybe computer science was somewhere in between, you know, kind of a professional training and an academic subject, you know, where it was a little bit more intellectual than like accounting, but not as intellectual as like biology.
But I actually think, you know, there's a lot in computer science. It's actually really interesting about, Okay. computation and how the world works. And I think that's, um, I think that's expanded a lot with the machine learning and AI stuff where it's all about reasoning. And, um, you know, so I, I thought that was fascinating.
Uh, and then I, I actually got good at programming almost in a very roundabout way where I probably took the least practical computer science classes, uh, that you could and still get a degree. Um, you know, by, by the end of it, I did, uh, I worked on this music sharing site for my friends. And so I really learned a lot more about programming from that.
And it was this fun thing where you could all share, you could share songs and comment on them. And of course it was totally illegal. So then the problem was they were all inviting their friends and I had to eventually turn it off. Uh, but, um, you know, really doing that, I think I learned more about programming than, you know, from the, the class assignments.
Logan: Is it is it weird to see artificial intelligence blow up like it is right now and to have been studying it
Jay: Yeah, I think it's awesome. You know, I, I stayed on and I was in a PhD program for, um, machine learning. I left after the master's degree. Uh, and, you know, the reasoning at the time I was like, Hey, this is. It's a fascinating subject, you know, at that time, machine learning had probably five paradigms for how to think about learning, all of which kind of worked, but not very well.
Um, and that's kind of a fascinating thing where it's like, Hey, you get to the same result via thinking from a statistical back, you know, statistical or probabilistic background, like, uh, information theory backgrounds, like a geometric. Kind of worldview, uh, those neural networks, which are more kind of like, whatever, biology, um, uh, inspired, uh, and then you kind of all end up sort of in a similar place, but as, you know, at the time I was like, hey, you know, this is not gonna make any progress till we have like a firmer grounding.
In what learning is, you know, it's like a little bit like being a physicist before Newton where somebody's gonna come in and they're gonna figure all this out and there's all the other stuff's gonna get thrown away and all we're doing is just kind of hacking. Um, and so I was like, okay, this is not, this is not a good way to spend your time.
But, but actually, no, the hacking got us pretty far. We still have no underlying theory of what learning is. It's gotten a lot better.
Logan: Yeah, it's interesting. I mean you studied this from a longitudinal perspective, but the It kind of plateaued for a while in terms of like the progress of machine learning or
Jay: yeah, that, that was, you know, at the time I was doing, it was very much in that state where, yeah, there was nothing was really getting better that, you know, for academic subjects like that, that means it gets very math heavy, you know, so it was a lot of support vector machines and complicated math around it, but in practice, nothing was working better than it had 10 years ago, really, and that, that's kind of a sad state, you know, from a practical point of view and so, yeah, it's, uh, You know, it's funny that my prediction of how it would get out of that was just dead wrong, just totally wrong. But intellectually, I remain just totally fascinated by that area. And I, it's probably the area I've just kind of read the most in, you know, um, spent the most time on. When I, when I left, you know, that, um, school, that, that was my goal was, okay, I thought, you know, hey, look, with the internet, there's all this data.
This is going to be a big thing. I want to get a job where I can kind of use some of this stuff, even if it's hacky. You know, at least you can do something with it. And, um, you know, that was how I eventually ended up at, uh, LinkedIn was, um, you know, I just thought, Oh, these social networks are going to have really great data.
Logan: so you downselected, you wanted to go join a social network, right? And so you, you Nick's Facebook because my space had already won the, uh,
Jay: Yeah. Yeah. Yeah. Yeah. So in, in terms of bad calls, I was like, yeah, it just seems like it's really hard, you know, to come from behind. And then it was also written in PHP and I was just like, yeah, that's probably not very serious technology. Uh, and so, yeah, that was probably
Logan: What year was it?
Jay: totally right. Yeah, that's a good question.
Um, you know, I joined LinkedIn in 2007, so I probably talked to Facebook probably a year before then, something around that.
Logan: And so, so, so you end up hitting it off with LinkedIn and in some way, and you were there for seven years.
Jay: Yeah.
Logan: And so what, uh, what was your role initially? And it evolved to what?
Jay: Yeah, I joined as a software engineer and, you know, that was basically what I did. I, I ran, um, you know, some small engineering teams, uh, and really focused, uh, originally on the kind of use of data. So I, I joined in the first project I had was trying to, um, you know, recommend news articles to people based on whatever they had in their LinkedIn profile, uh, which we did a terrible job of.
And, um, you know, I guess. Somehow I kind of, you know, I was aiming at this more use of data, but ended up working on infrastructure. Cause like for a growing social network, you just weren't going to do anything interesting or predictive until you had that problem solved. And so that was how I ended up working on, um, some of the open source infrastructure there that, you know, we built like a.
Live database for serving the site and built, uh, Kafka, the open source project as kind of this real time layer and some of the backend data lake stuff that was all, you know, really just trying to get some basis for using data.
Logan: Hey guys, I'm Jacob Efron, a partner of Logan's at Redpoint. I wanted to take a quick break from the episode to let you know that Redpoint's AI podcast, Unsupervised Learning, now has its own YouTube channel. We have an incredible set of guests really at the forefront of the AI revolution. So if you're interested in what's happening in AI, what it means for businesses in the world, definitely subscribe.
Now back to the show.
Logan: Open sourcing this as you were thinking about it at the time, like, how did you, you're, you're working on these projects for LinkedIn's benefit, right? And, and you make the decision to open source these because I guess, how do, how do those conversations play out internally within
Jay: Yeah. You know, I think there was a couple of goals. I mean, you know, for me, I, I just wanted to make something great. You know, I just thought it was really cool that you, there was these, you know, there's these layers in the stack and they just come from somewhere. And, um, you know, when I was in school, I really admired Linux that, you know, somebody would just come make something that would be this foundational layer, everything would run on.
So I thought that was like, I want to do that. And, um, You know, then as a practical thing for a company like LinkedIn is, you know, you're these kind of very fundamental layers. They're not really the basis of competition. You know, your user base and your data and the whole service, that's kind of how you're competing.
It's not like the better key value store is going to be the thing. And um, you know, to make those things, uh, be really good, you have to somehow be able to attract people who want to do that. And so it was a weird time, you know, at that time there was no like cloud services you could get. The commercial products were not targeting kind of a large scale data infrastructure.
Uh, and so it was really kind of do it in house or do nothing, right? And so it was, you know, Yahoo or Google had built these internal infrastructure layers that everything ran on and the other companies just didn't have it. So the question was, Hey, how can you get that? If you're some smaller tech company, you got to somehow attract people and piece something together.
And that's going to be a combination of trying to find things that are open source that we can use or building things and try to make them catch on and attract people to work on it. And I think that became more, um, prevalent. And, you know, now it's probably the opposite where there's all these products and cloud services.
Probably nobody needs to build, you know, foundational infrastructure just to have a social network or whatever the next app is. But at least at that time, there wasn't that many other ways.
Logan: So, so you launched a handful of these different projects, project Voldemort, project Kafka. Um, and so Kafka. What does Kafka do and can you take me through the, the problem you were looking to solve internally and then what happened after the launch?
Jay: Yeah, so, um, yeah, you know, Kafka probably had the biggest, uh, idea behind it, which was just, you know, in the world of data, everything's kind of been about storage, you know, you have these databases or file systems that some pile of data that's stored somewhere, you can look up little bits of it, you know, it's really kind of targeted at how do I build one application.
But you look at a big system like LinkedIn or any company now, it's, it's a bunch of pieces of software and they all have their own little piles of data and, you know, somehow it all has to come together and all the parts have to react, you know, as one thing. And so, you know, the idea was, uh, focus on that part of the problem.
Like how does not just the kind of data at rest, but the data in motion, like how does it move around? How do we react? If. Somebody joins the site, how do the 15 things that have to trigger off that and happen, happen? And, um, you know, you can view that in different ways, but one way to view it is taking, um, you know, some of the processing of data that's traditionally happened, like in batch, like the end of the day, some big thing kicks off and turns through data and spits out some results.
You know, taking that from something that happens periodically to something that happens continuously.
Jay: So that like, you're just processing data as it occurs and reacting to it as it happens. And, you know, that, that was an idea that was very appealing to us. We were kind of, you know, doing a lot of work to scrape data out of different systems and put it into search indexes and social graphs or into some kind of data warehouse or data lake.
And then all the most sophisticated use of data was something that would happen. You know, the end of the day, and by then, you know, the users gone, they've moved on to something else. There's only so much you can do. And so that, that was kind of the inspiration for it. There'd been a lot of ideas in the computer science literature about this kind of real time processing of streams of data that was, it was almost kind of a natural generalization of database ideas, but it was seen as almost kind of very researchy and not that practical.
And so, you know, our idea was, yeah, let's just try and turn all the data sources into some kind of stream. Anything can get. And, um, you know, that was a project first internally at LinkedIn, and then as we open sourced it kind of out the rest of the world.
Logan: Was there a why now as you look back and think that this was possible or that it took off in the way it did?
Jay: Yeah. I think the, the, um, you know, if you think about the problem LinkedIn was solving versus maybe the. You know, step back 10 years from that, the problem with company was solving with software. Uh, you know, the way software came into a company was there's little bits here and there. It's like, Oh, we got this app for this team.
And it's really kind of UI centric. They're going to type their things into the CRM and it's going to show them the things they typed in in different ways. It's kind of its own Island. And if you think about these kind of tech companies. It's not an island, like everything's connected, right?
Jay: It's a continent, right?
And, um, that's like a different problem and in many ways, a lot of the problems in software architecture are this, how do you build a giant connected system? You know, how do you have all these different services? How do you orchestrate them? All the cloud computing layers are kind of oriented around how you put all the parts together now.
That's one of the biggest challenges and we were trying to do that for data. And so yeah, I think, I think it was, um, you know, a little bit, people had made progress on distributed systems and how to do this kind of stuff and a little bit just. the problem itself had changed and the need was different.
Logan: So you launched it in the open source and did it take off right away or what?
Jay: Yeah. No, it was, it was originally a bit of a flop. Like our, our key value store was very popular and this like Kafka thing, nobody had any idea what we were talking about. Um, you know, which was an interesting experience for me. Cause I thought it was much more exciting. Uh, and I, I didn't realize the degree to which, you know, latching onto like a category that people already understand, uh, helps.
So if you're like, Hey, we have a database, people are like, I know what that is. I know what it's for. How is your database good?
Logan: still with you. Yeah.
Jay: And then you can answer that question and they'll use it. Right. If you're like, Hey, we have some kind of distributed streaming platform. They're like, okay, I don't know what that is.
Logan: How did you solve that?
Jay: Um, you know, uh, we were excited about it. So we were like, well, we'll, we'll go and try and explain it to people. And, um, over time, the explanations got. And they got shorter.
Jay: And, uh, it's one of the things I've developed a real appreciation for is kind of product marketing. You know, the difficulty of compressing an idea down to something that like catches and will transmit, you know, from one person to the other, um, it's a little harder than it sounds.
And so the. You know, as we were, you know, we went around and first just kind of did some tech talks and tried to explain this to people. And I think that worked, you know, given an hour with somebody was like, okay, we can explain why we did this. We're like basically instead of having 15 ways of shipping data around and a bunch of batch processing, we're going to try and just have everything be a real time stream that you can react to as it occurs.
And this is how that plays out for feeding your data warehouse. And this is how it plays out for applications driven off this. And people would be like, okay, that's great. And then, um, yeah, we tried to. write it down in a blog post. And we're like, Hey, if we can get people excited about this. And so the blog post is like 23 pages or something.
So it breaks every blog post rule. But, uh, you know, at least our thinking at the time was like, Hey, if we can get the people excited about this, uh, maybe there's something here and we should go follow this and, you know, try and make it succeed in the world. If we can't, you know, maybe not. And, uh, yeah, sure enough, that, that blog posts developed like a real kind of cult following and how to think about data and how to structure around it.
And so we thought, Hey, this is, this is great.
Jay: And it, it brought us into contact with a lot of companies kind of outside the core tech people we already knew. Um, and, and that was interesting for me just to realize like, okay, you know, these ideas that sounded pretty advanced or in some ways, like. Equally applicable in like a bank or an insurance company or a retailer.
And so the, the kind of market for this is actually quite broad. It's not just like a tech company thing.
Logan: At what point in that did you start thinking like, all right, this might, there might be a company behind it. Like when you were iterating on these different open source projects, did you think, Oh, maybe one of them will take off and I can go be the CEO of a company around it?
Jay: Yeah, um, you know, maybe in a very, uh, vague way, you know, I think one of the cool things about LinkedIn, I had a very, uh, um, entrepreneurial culture, I would credit, uh, Reid Hoffman for that. Um, and so I think everybody inside of LinkedIn and especially in this data area had a whole set of ideas for startups and concepts.
And so, um, you know, and then for me, I thought, Hey, this is like. One of the biggest paradigm shifts in, uh, in data and just how it's used, like this idea of going from something where you're processing static fixed data to something that's continuous, that's like a very fundamental change and nobody else is doing that.
So that's a good one to go after if you can get that to, if you can get people interested and get that change happening in the world, that's a big enough area that could turn into a really significant data platform. And so, you know, I think early on it was more just, hey, can we get anybody excited about that?
It wasn't, there wasn't a detailed plan. Um, but then yeah, over time, you know, it did seem like, okay, yeah, this could be. Potentially some kind of business, you know, there's certainly gonna be a lot of value around it.
Logan: Was there a point at which you, you thought to yourself, uh, okay, now it's crossed the tipping point that I, I should, I need to go step outside the walls of LinkedIn and turn this into a
Jay: Yeah, you know there was I I think at one point. Um, Yeah, I think I forgot what the organization was. I think it was somebody from like ESPN or something had like tracked us down and called us and they were like, we need this Kafka thing, but we need all these other, like need all these security features and other stuff.
And like, can you do that? And we were like, no, like, like we're, we're just running this for LinkedIn. Like we're not doing stuff for you. And we, but we were like, but you should get that stuff. Uh, and so You know, and that, that was enough of a kind of brand name that was far enough. I feel that we're like, Oh, that's, that's real.
And you know, at that point it, it did, it was kind of clear. It was like, Hey, look, this is a team of like six people in the basement of a social network. Like, we're probably not going to. Totally revolutionized how people think about data. Just throwing something out on GitHub kind of in our spare time.
You know, it's going to take a focused effort to make that happen. And yeah, I think that's, that's a little bit how it came about.
Logan: I want to talk about the transition from, you were an individual contributor within LinkedIn. Did you have a team?
Jay: Yeah. Yeah. I also managed a team and then I, you know, I guess I was in kind of a lead architect role for a while too, where I kind of had some broader responsibility, but yeah, it's totally different from being a CEO.
Logan: what was that transition that, that occurred? Was it you step outside the four walls, you raise money and
Jay: yeah that that that was right.
Jay: Yeah, it was awful. Um, the You know, I really underestimated actually how jarring it was gonna be to be in a totally different job In in a whole bunch of ways. I think part of it was, you know If you start in one career you're like not very good at it and then you learn and then you get very comfortable being good and at least for me I kind of mastered a lot of the kind of engineering leadership, you know, technical system design programming.
So I felt good. And so you're just kind of in your comfort zone. You're doing a bunch of things where you're just good at all the parts. Um, and you can kind of hone your craft and try and get better, but it's basically very comfortable. And then you do something totally different with a totally different skillset.
Um, and you know, the early part of a company, you know, it's always kind of. You know, valorized or idealized. But, um, you know, it's actually pretty hard. You're, you're pitching kind of an idea to people. It's definitely kind of whether you're trying to get customers or people to join the company, it's kind of a sales role as much as anything.
And the reception is often not that. Great. I mean, several people, I think, took us aside as we were starting the company and we're like, this is not a very good idea and you should not do this. And, uh, and, um, we were like, oh, that's, that's not, that's not good. So, and, uh, you know, so that made it hard. And then if you think about who, who's a good CEO, you know, I don't know, maybe it's Steve Jobs or whoever it is, right?
There's some list. You know, you're pretty far down that totem pole in your five person startup with zero customers and no product yet. And so it's just pretty humbling if you're, if your day job is being told, no, the skill set that you built is not that useful for the thing you're trying to do. And, uh, you're kind of pure set.
Is vastly better accomplished, you know, it's I think startup CEO is a very humbling role.
Logan: That's interesting. Um, was there a particular functional area that you found more daunting than, than others to kind of get up to
Jay: think part of why it was harder For me was like, you know, we basically had this very successful open source project and we had You know three technical co founders were able to hire good engineers. So we're kind of making progress What we needed to do was go figure out how to turn that into a product and sell it So the go to market came very early, and we had zero expertise in how to do that.
So there was a lot of, um, you know, just bumbling around trying to figure it out. Um, you know, probably in the best of circumstances, because people are excited about what we were doing, but just not, you know, not in a put together way.
Logan: If someone's listening to this and debating if they can or should, or want to be a founder, CEO, or are there questions you would advise them to ask themselves or things that you wished you would go back and tell Jay leaving LinkedIn, like. Hey, you should really validate that you enjoy this because that's going to be a large portion of the job.
Jay: Yeah, I, I think it's a good question. I mean, I think, um, What is it? This is not quite the question you're answering, but somebody described parenting. They said, Oh, you know, um, when you ask parents, they're actually less happy than people who don't have kids, but they're more satisfied. I thought, Oh, that's, that's interesting.
I think it's similar. Uh, if you start a company, you'll, you'll, you'll probably be less happy, but more satisfied. Is that what you want? Um, the. And, and it may be right, like, uh, I, you know, I wouldn't, I wouldn't take it back. So the, you know, and I don't know, I think it's very hard to predict who's going to be good at it or who's going to enjoy it.
I do think there's a lot of, um, hyping up of entrepreneurship and of entrepreneurs, which is sort of helpful, you know, creates an environment where people will try stuff, but it can be a little misleading where, you know, maybe people may not know exactly what they're getting into. Uh, as a result. So, so yeah, I, I don't know.
Jay: I think the, um, you know, I, I, it was certainly a growth experience. I mean, whenever, I always feel like whenever you try and do something hard that you're not good at is when you learn the most. And I think that was, that was really good for me. I, and I certainly benefited from it.
Logan: You can't say now, but is there a point in time other than that, that you look back with most nostalgia on over the course of, I mean, you guys have had a fairly serendipitous run, I would say, at least for starting in 2014, went public in 2021. That's pretty fast path. But is there a point in that journey that you look back with, with some level of fondness?
Jay: Yeah. You know, um, You know, I, I think a lot of the early parts of a company are, um, really cool, you know, I think, um, with probably the first time we did a user conference, um, we were just really worried. No one was going to show up. And there was like 800 people and we were so pumped.
Logan: user
Jay: Yeah. Yeah. Um, and it was, it went really well and we were, it was just very energizing.
The company had people kind of all over. So everybody got together and it was like a big, high energy thing. So that was, uh, yeah, that was, that was a big deal. And then, yeah, you know, I mean, um, to some extent, all these companies from the outside, it looks good, but of course, yeah, there's lots of ups and downs within it.
Jay: So, you know, whether it's serendipitous or not, I don't
Logan: you, you would, I'm sure you would dispute the moments along the way, but from the outside in, it was at least in the, uh, in a rarefied air of, uh, of startup journey, at least from, from my perspective of success, at least the, the metrics valuation, you know, all, all of that stuff.
Logan: So, um, I guess on the flip side of that coin, you made a few strategic decisions, I think mostly around cloud that were real.
Uh, fork in the road kind of considerations and we were talking before you said, uh, one of them, I think they've ultimately worked out, but one you described as almost killing the company. Can you go back to these decisions and what were the layout for people, the different consideration sets and then like, how did you go about making the decision?
Jay: Yeah. You know, it was, uh, one of the big challenges for us was, okay, we didn't really have a complete solution around streaming as we started. We just had this one popular open source thing. So we needed to kind of complete that package and make it usable, but we were also in a time where we were 100 percent sure, as we were starting the company, that cloud was going to be like the way people consumed this type of infrastructure over time, but the market wasn't quite there yet.
You know, it was very, um, you know, Amazon was successful. It was unclear that there would be other successful cloud providers. And, um, there was no examples of like a third party company selling you. Operational cloud infrastructure and, um, a lot of resistance
Logan: This is 2014, 15, 16 in that time
Jay: yeah. And so the people who were able to succeed early on, I think it was like, you know, if you're snowflake kind of analytics, that's maybe further away, you can kind of get there.
But these kind of operational databases, real time streaming, that was like a little bit harder. And so that was the dilemma for us is how do you, how do you square that?
Logan: so for people's benefit that aren't, uh, infrastructure walks, you basically have one, you have this open source product that now you've built a, or, uh, you have this open source projects that now you've built a product around, but it's not cloud. The core initial product was not cloud, and so it was existing beyond your own
Jay: Yeah. Yeah. And even, even before we started that, like as we started the company, we basically were very torn about whether to do like a SaaS offering in the cloud first for which there wasn't much of a market, but we were like, that could be very good over time. Or should we do a software product or should we try a new both?
And you know, we kind of made the call as we were starting the company, we'll do both. We'll start with a software product. We have a bunch of people who will pay us now for that. We'll add the cloud offering. Um. You know, I think probably the, whatever we, uh, pitched benchmark, it was probably all in the first year where like, Hey, we're going to do this.
This isn't this. And of course, you know, it took us many years, but, um, yeah. So, so as we were starting, then we just felt this pressure of like, okay, you have to, we have to get this cloud thing going, you know? So we started it, I think. Maybe it was a year and a half or two years into the company. We said, okay, we're going to build a little team of people.
They'll build a, a cloud offering, you know, in AWS first, but we'll take it to the other clouds over time. And the, the, the challenge is it's, it's really hard to do that. And, you know, we, we, we got feedback from people. So one of the, uh, Kind of cloud companies, somebody there told me directly, they're like, there are zero examples of a company that has an on premise product creating a cloud offering.
You know, this was at that time and really succeeding with it. Like you will 100 percent fail. You know, if you talk to anybody who knows anything about startups, that's how you like, do not try and do a second product. When you have one thing working, like that's just such a terrible idea. Uh, and yet we kind of knew we had to, like, we had to get this right.
Um, and so the, the challenge was how to do it. So we started building a product and what happened was, it was the side product and it just wasn't that good. Like, we had a mediocre offering and as we were doing this, um, we started to hear rumors that Amazon was going to launch a, you know, something around the open source Kafka in our space.
And we're like, okay. Big problem, right? Like it's there, you know, this is like their grocery store, uh, that where we have shelf space and our product is okay, but it's not great. Uh, how are we going to be successful at this? And you know, the, there was a big debate at the time about whether to just kind of pull back and get the enterprise business really working, kind of double down on that, make the big customer successful, come back at this later.
You know, we went the other way, which was just put the whole team on the cloud thing. It's very difficult to do because of course you've, you know, built a fast growing business already off the software offering. Um, but we thought it was just really important. We thought like, look, the way we're going to be successful is.
Kind of cover all the environments. These big companies work in, you know, since ultimately our area is about tying all the parts together, you have to be in all the places and that's going to be the big differentiator, you know, against any of the competitors that doesn't have it. Um, but of course, uh, it was very difficult to do and the.
You know, the challenge is basically kind of sustaining the momentum in that core business as you're kind of building out the second thing. And then, you know, what we didn't really understand at the time was just the degree to which kind of a SaaS cloud go to market was, um, different from selling on premise infrastructure and how much.
Effort would be involved in really kind of digesting that and..
Logan: What were the learnings of, of that? Like, why is it so different to sell SaaS versus on prem?
Jay: the people are different. Um, the kind of security and networking requirements are very different.
Logan: are the people in this case are the
Jay: yeah. All of it, all of it. So the target customers, the, you know, just what your sales team has done, um, the process by which it works, you know, there's much more of a try and then buy. Uh, you know, with kind of a cloud SaaS offering, um, the fundamental business model is different.
Like they have a kind of usage model where you pay as you use it and you may commit to some amount. So effectively everything is changing. And, um, you know, for me, at least it seemed very logical. I was like, okay, we, you know, it's kind of similar things at the very high level. Of course we can sell both of these.
And of course the reality of getting a second thing going is much, much harder. And so, um, yeah, that, that was a huge stress point. You know, I, I think, uh, you know, it was a, it was a. topic of discussion in the press, how Amazon was going to crush all these startups. So like literally any of the news about us was like another, another, you know, startup destroyed by AWS.
Uh, my mom at one point called me and was, you know, she read some article and she was like, Oh, I, I pretty much heard you're going out of business. And I was like, well, You know, we're not, we're not dead yet, mom, like hang in there. Uh, but, but it made it, you know, of course that makes it very hard because your employees are like, why they're either, why are we doing this cloud thing or why are we not succeeding?
You know, your customers for the cloud offering don't exist yet. You have a bunch of customers for the other thing who have a long list of desires. And, um, you know, just the kind of enthusiasm among people you would hire. There's just kind of an open question. So really kind of getting that working was a big deal for the company, just kind of grinding it out.
There was, you know, there was nothing, I don't know that we did any of the individual steps perfectly. Um, but we just kind of stuck it out until we had something really good. technically and kind of could sell it. Um, and I think that was a big thing. I think if I look at the peer companies at that time, you know, it's maybe about a 30 percent success rate of kind of getting, if you had a good open source thing, getting to a, you know, high quality cloud SaaS offering that can be like 50 percent of the business or, you know, a substantial portion of the business.
Logan: Was that just a strategic decision that that you made and felt the conviction around? Did you, did you decide by consensus? Because I assume there's some sample bias or some survivorship elements of like, well, no, all our customers are telling us this is great. Yeah, but you don't know the customers that we're not seeing in the future that won't, will want this.
Jay: I, I think it helped that we had, um, you know, our job at LinkedIn had been running this system. And so we understood like, Hey, the delivery of it is just a really deep problem that you could create a lot of value in. So I think, I think that helped. Um, but what really helped was actually. You know, we realized the world was kind of moving like these cloud providers were going to have streaming offerings of different sorts.
So we were either going to like really commit and do it now, or we were going to give that up. And so we were like, Hey, if you give it up, you know, is that even a company you want? Like, let's say you win in just the on premise thing. Is that even the company you want to have? And it was like, no. And so then it was like, well, you know, even if we're, we were just kind of failing at the cloud thing, but we knew like, okay, you'd rather, you'd rather fail at the company you want to be, then, you know, end up with some niche part of the problem.
And so then that, that's kind of liberating. I think whenever you're like, well, you know, at least we're going to go down the way we want to, you know, we're going to go down with honor, uh, then, then it's easier. So then, then we really jumped on it and, um, you know, I think that was very helpful. I, I do think, you know, always for companies that if you can have, you don't want to do it too often, but those kind of burn the boats moments, it's just very clear what has to be done.
And so for the, for everybody in the company, it's very clear, okay, we're going to do this. And, um, you know, so we did that for the engineering team. We're like, okay, look, everybody now works on the cloud thing. Everything will get released there first. You know, we are going to lead with that as we sell. Um, you know, effectively nothing was quite set up for it, but at least it was clear that we were a hundred percent committed.
Logan: The open source project, you were, you were in the early ish days of what the, the kind of second generation of open source businesses were kind of after red hat, uh, and, and the stuff that they did. Um, Did you feel, was there tension internally between people that joined because they were zealots of the Kafka Open source project and they just wanted to work on that and we're totally hey, why do we want to build this business?
This is going cloud first is gonna shut off access to different people that might otherwise want the product or
Jay: Yeah. There really wasn't a lot. You know, the, the, um, Yeah, the challenge we had was more external, which is, you know, there was kind of a real trough of excitement about anything related to open source from an investor point of view, roughly as we were starting the company, I think Cloudera had been very exciting and was kind of not doing as well and literally no, nobody wanted any part of any open source thing for about four more
Logan: Hmm.
Jay: uh, until really the cloud models I think started to show success.
And then people are like, okay, that, That makes sense. Um, you know, so there was an external problem. Internally, I think we benefited, you know, the early open source, um, things were almost a little bit religious in how they approached it. You know, it was kind of a alternative to capitalism in some ways, right?
And I, I think that did tend to create these companies that were just very divided in what it was they wanted to do. And um, I think we were more clear in, you know, how both things could be successful. And I think starting that way made it easier to kind of keep the alignment internally that everybody's kind of rowing in the, you know, we're all trying to make something great.
Logan: How many people does Kafka have today? Or Confluent? 2, 700. What's something unintuitive that you've learned about? Operating a business at that scale, leading that many people managing.
Jay: Yeah, you know, there's nothing shocking. I, I think a lot of it's, um, communication. I think the, you know, a lot of problems just get a little bit more abstracted over time. Uh, but at the end of the day, it's still kind of a bunch of people's stuff, the same as it was when it was a small company, which is, you know, Hey, do we have the right person running each thing?
You know, do we have a way of kind of measuring what's happening there? Um, the. You know, I think the biggest change is it does become a little more difficult for senior people to get Quick unfiltered information about everything because there's a couple hops and it becomes easier To not be aware of what's going on.
And I think kind of building that Sensory system that keeps you informed about all the important things I think for me, but also for everybody on the leadership team I think that that ends up being really important.
Logan: Did you set up any structure, like skip level meetings or anything
Jay: Yeah. Yeah, I think it's all of that, right? So I think you know you you need to have some kind of idea of what metrics matter in some way of measuring things from without and that will tell you Like, just empirically, things are not going well, it won't tell you why, right? And it's often a lagging indicator.
And then, you know, some way of getting truth out of people and a culture where people have some interest in speaking up about things, um, you know, I think that's kind of the other side of it. And you have to kind of, I think, push on both of that, both sides of that. You know, the, um, you know, the nature of any company, but I think particularly as the company gets bigger, you know, there's always hesitancy to bring bad news, right?
So you have to somehow really make that normal. Otherwise people stop giving the bad news until it's quite apparent. And that's usually, uh, at that point, it's a little hard to fix.
Logan: Have you set up certain processes, uh, or, or hiring structures or anything to keep the talent bar high
Jay: Yeah. You know, I, I think the, um, yeah, there's, there's definitely a set of processes. I think the most important thing is actually, um, you know, the training of managers. Um, you know, this is something that it's not taken very seriously, I think, in Silicon Valley. Um, I think because the tenure of employees is so short that it's like, well, how much can we invest?
But, um, you know, I think it's very easy in a fast growing company for managers to get really lazy and kind of take what comes their way. And it's like, I'm too busy. So I, you know, I'm too busy to stretch. And so I think just really, really, you know, getting people to value the quality of hiring and um, you know, I, I, I tried to, you know, really take our, you know, the executive team and lay down everything we do for hiring our most critical hires.
I would say 20 percent of that doesn't apply because it's really about executive hiring. But about 80 percent of it is what you should do hiring anybody. And you want to be like a little bit more crazy about it than seems normal and put more time into it. And, um, you know, I think if you can get people to take that on and value it, it's just very valuable for them.
Like if you want to succeed as a manager, you succeed by having a good team and, you know, a big part of that has to be hiring great people. And so figuring out how to get people to put that time in. And I think there's just no substitute. You know, no matter how talented your recruiting team is, they can't fill in for, you know, passive or lackluster managers that aren't really out there trying to find great talent or convince people to join.
Um, uh, you know, so, so I, I think that kind of, that will side of it really, really matters. Uh, and then, you know, a ton of just techniques and stuff, but, but yeah, I, I think that's a big deal. You know, I think the other side of it is the, you know, kind of expectations on performance. I do think over the last few years, that's one of the things that kind of fell to the wayside.
You know, the thing everybody sees is kind of the inflation in comp in tech. But, um, you know, one way of giving the comp is of course, kind of promotions and rewards and what is really expected of people. And that, that all came out of just having a human capital shortage relative to the number of companies that people wanted to have and the headcount that was available.
And so, you know, I do think it's also the case that a lot of companies just got lazy. About performance management and having high expectations and, you know, where the bar is for the people who have been hired and I think that's getting corrected, but it's almost like a whole learning thing for people who built a management career just in that time period, they may have been very sloppy about how they were hiring.
They may have been very sloppy about what the bar was internally. You know, making sure people are good at that, I think is one of the most important things.
Logan: as the market shifted you, you all in public in 2021. The, uh, the, the public markets definitely, uh, have moved to value more. Uh, profitability or efficiency metrics versus, versus growth. Um, how has that belt tightening, and maybe it alludes to the performance management point that you were just making as well.
How do you think about that as you sit here today versus, you know, the, the learnings that have
Jay: it's been a huge project. I mean, you know, it's hard to know in retrospect, like we went public in 2021. We were probably a little green as we were going public, but it was a good time to go public. Um, and then of course the stock went way up and then the stock went way down and it went up and then it's gone down.
So it's been a, like a real rollercoaster for the whole employee base. And so, you know, the question, of course, is like, Hey, did we do this at the right time? You know, did we do it right? The, um, you know, one of the things we had to do was get a lot more efficient. And, you know, one of the questions you could ask yourself is, well, maybe we should have been more efficient to start with.
But we were definitely going after it as we started the company. I think each year we were like, Hey, we will like plan to grow aggressively. We will invest heavily. We will go after it. We had a set of early competitors that we just out executed. And so I think to some extent it made sense that we were just kind of pushing, but then yeah, that put us in a position where, um, you know, really coming into this year, we had to get significantly more efficient.
And so the, you know, over the last six quarters, you know, we will have, um, improved operating margins by about 40 points. Which is like quite significant. If you just think about like, hey, for every dollar that we spend, Um, You know, we're getting 40 percent more out of it. So yeah, it's a pretty significant retooling of how the company works.
And totally not valued by Silicon Valley, which is very growth oriented, of course, even, you know, even now, uh, but it's a huge accomplishment and, you know, it's a whole set of things of just like, Hey, what are people doing? What is all this software we bought? Which go to market tactics actually work? Um, you know, like to what extent are we hiring in different locations?
There's just a whole list of things that roll up into it. And so it's been an interesting thing, um, you know, I think for a lot of the leadership folks just to learn how to do that. Some of them had done this, you know, done a lot of this before some hadn't, um, you know, but, but certainly a lot of newer managers just hadn't dealt with this, you know, hadn't looked at the business with this lens at all.
And you know, I, I think it's a healthy thing, you know, I think it's a very healthy thing for the business. I think on the whole, the operations is probably better as a result. Um, you know, I think we're definitely getting more per dollar spent. So yeah, it's, it's been an interesting exercise, but it was definitely important, I think, for us to do that because we were on the, you know, we were on the far side of leaning in and spend, you know, as we went public in 21.
And, you know, we just did not want to be in that position the rest of this year.
Logan: Were there any specific things you all did in that? It sounds like a lot of lead bullets and no silver bullet, but anything you did, if someone was listening to this saying, well, my company really should have another 20 basis points of, uh, of, of operation improvement. And it was, it was a. Did you issue a mandate?
Hey, let's find the dollars in the couch cushions that we can or how did it kind
Jay: yeah. I mean, it was the set of things you would expect, right? We had about an 8 percent layoff at the beginning of the year. Um, and then a lot of structural things, um, you know, the obvious ones are just, uh, kind of zero based budget planning where you really look at the existing money and where's that going and is this working?
You know, having, I think, a higher expectation for programs, uh, that are new to kind of prove themselves at a small scale before they, you know, go any further in scaling up. The, you know, a real kind of stack ranking on the go to market side of what's working and what's not working and a significant shifting of investment between segments that we're performing and not, you know, those were all big things.
And then, yeah, if you look, you know, um, uh, another CEO that had been through this recommended me effectively a private equity playbook. And it sounds like a funny thing, but you know, there's a whole group of people that buy inefficient companies and make them more efficient. And so it was like, well, you should probably know what they do.
Um, and it's, it's exactly what you would expect. You know, basically look at areas where every function has built some analytics team and see would it be possible to share the analytics team, uh, or look at where we've bought the same piece of software 10 times. And so you can kind of just like literally take that and be like, yeah, like half this stuff doesn't apply to us because we're just a younger business.
But half of the stuff is like stuff we probably should have done, but we were moving really quick and didn't. And it's just, you know, kind of healthy cleanup. So, so yeah, that is very much that kind of. you know, lead bullets, not silver bullets. But I think it's a good muscle, you know, for, um, you know, if you think about what the demands on like an HR or finance team have been in the last year, it's very different than kind of the years before and looking at the stuff and kind of driving efficiency.
Logan: remote work?
Logan: Uh, you all have always done it, which has moved from contrarian to mainstream to contrarian again. How do you think about remote work?
Jay: Yeah. Yeah. We're, we're kind of sticking it out. Like we started, I think for the first, probably three months and we were like, Oh, we're all going to be in office. That's what we've done. But then we were like, well, okay, we, we ran this open source project, which was quite distributed. Um, The people enthusiastic about the open source project are all over the field sales team is going to be where the customers are.
So it's like, well, if the engineering team and the sales team is going to be all spread out, then like, how much value do you get out of centralizing GNA functions, a few other things, probably not that much. And so from pretty early on, we kind of built the company that way. And, um, you know, it was interesting as we were kind of entering the pandemic, there was all this enthusiasm about remote work.
And we're like, well, you know, it does have some drawbacks. And then now there's a bunch of people who are just like, oh, this is trash. It doesn't work. Um, and we're like, well, you know, it can work. Uh, so, so I think it's, it's probably neither as good or bad as people say. I mean, the, the advantages basically.
the fundamental ingredient, like the input to an enterprise company is people. And so having access to a much larger market of people all over the world, you know, um, it's just better. Like you're, you're going to have access to more talent, um, or a better cost, you know, either better talent for the same cost or lower costs for the same talent, depending on how you think about it.
Right. But. I think any company, as you get to scale, you have to look at where you're hiring because there are great people in the Bay Area, but there's great people in India and there's great people in Europe. And you have to figure out some strategy that allows you access to different pools of talent, no matter what.
And then you have to figure out how do those people all communicate and work effectively together. And, um, I think. To some extent, many of the things that you have to do in a larger company where you're very clear on what the strategy is, what are we trying to do this quarter, et cetera, those are kind of the things you have to do to make a remote company work effectively.
Plus some tactical stuff and how people communicate and work asynchronously. But, um, you know, it's been interesting. I, I think that that big advantage people kind of lost. I think part of the reason is most of the advantages of remote work are, they're about individual contributors, you know, it's about how do you hire the best people or how can those people kind of sit and do programming and be very effective.
Um, you know, the engineers never liked working in a big open plan office. The salespeople were never that much in the office anyway. Um, and most of the disadvantages kind of fall on managers where it's about coordination or how do I get, you know, how do I get things done across. And so I, I think you see a lot of the friction there where one, one group wants one thing and the other group wants the other thing.
Logan: Anyone that's not an IC always says I'm more productive at home, or anyone that is an IC always, and it's like, yeah, of course, you're more productive at home. It's not about your productivity. It's about the company's
Jay: That's right. Yeah. Yeah.
Jay: So I think, so there's no question that like remote work is a worst technology for coordination than having everybody in the same place, but you get access to then better people. Uh, and so, you know, if you can make, if you can compensate for the deficiencies.
Then that's great. Then you get kind of the best of both worlds.
Logan: You touched on this, though. How have you guys compensated for the deficiencies? Any
Jay: Yeah. Yeah, there's a there's a ton of tactical things I mean, I think what people are trying to do now is kind of this 50 50 thing, which I think is not great Right, like you basically like hey, well people and work remote a lot But they'll come into the office one or two days a week. It's kind of like then you have this very half assed in office culture And a very half assed remote culture.
And you still probably have to eventually build out, you know, international presence all over and figure out how those parts come together. So it's kind of the worst of all worlds. The, um, you know, I think at this point it's pretty well known how to do it, right? It's mostly you got to have a good culture of people writing stuff down in some ability to kind of work Asynchronously, so everything doesn't turn into some zoom meeting people have to get together and those get togethers have to be high quality, right?
You like you have to build a sense of belonging and team and etc And that has to carry through the kind of day to day work. So there's kind of periodic off sites or gets together. So there's different things we do at each level of the company. I think those become really essential, um, in a way that they wouldn't be if you were all in the same building the rest of the time.
Um, I think those are the biggest things. And then a bunch of little minor kind of tooling and practice stuff.
Logan: Politics at work, uh, was a topic, has been a topic over the course of the last couple years. You've seen Brian Armstrong, Toby Lukey, a bunch of people kind of come out on. Hey, we're going to, no social justice issues are inbounds within the workplace. Where have you guys landed on?
Jay: Yeah, we were always probably a little bit on that side. I think the, um, you know, it's, it. Like starting in 2014, you kind of are right immediately into a Trump presidency and every other issue. Um, the, I think maybe in part because we early on had people geographically all over, it was just clear. It was like, Hey, like what's the company's position on Brexit going to be?
And it's like, well, I don't, I, you know, I guess we're opposed to it cause it's really inconvenient for us. But like, other than that, we're not weighing in on each thing. I think that's worked out fine. We tried to do it in a low key way just so that that doesn't become the brand of the company itself. Um, but, you know, I, I think it's actually probably the best thing.
I certainly, as you, you know, the, the challenge I think has been, um, you know, especially for a global company. if you're kind of very centered around what I would call like California's political orientation, it's kind of weirdly alienating for people who are like, in other places, you know, you're in India, it's just not the same concerns.
Like it's whatever the thing that happened in the Bay Area, it's just not the immediate thing. And you feel like you're not really part of it. And, um, and it becomes a big distraction. So I, I think it's served us well. You know, I think it's, I think, I think it's becoming more mainstream where, um, you know, it can be okay.
And in fact, it's important. That the employees participate in the political system. Like we live in a democracy. A lot of these questions are really important. It's just actually kind of lazy to try and do it at work. Like. Convincing your coworkers is just not the fast path to success at these things.
It's not what the company is really set up to do. And so I think separating those two things, I think it's probably a good tune up that I think now a lot of companies are doing, you know, whether explicitly or implicitly, yeah, it's, it's become a bigger topic.
Jay: Um, I've thought a lot more about it since we went public because, um, you know, I, I kind of love it and hate it.
You know, the, I think people, I think people forget in Silicon Valley, how awesome it is that to some extent, the entire company is on the same page and making the company successful. Like a lot of the management labor tension that you have in many industries, you know, it comes out of the fact that, um, you know, if you're in a car company, it can be totally rational.
For all the employees to unionize and try and freeze in place the way the car is made to use the most labor. And they don't really need to care if that sets the company up to be successful with electronic, you know, electric vehicles or whatever because that's not their problem. And that puts then management in a position of trying to, you know, squeeze and bully the most out.
And it becomes this very adversarial thing where nobody can really progress. And I, I think the degree to which, um, tech companies kind of skirted that with equity is actually awesome, right?
Jay: Like, you know, the employees, uh, of course, everybody wants more money and less work, but when you think about it, you're like, yeah, I want this to be a great company.
I want it to be a good business. I want it to be successful. Um, And you're, there's a direct incentive to that. But the flip side is to get that, you know, especially for a public company, you now have people's compensation tied to, uh, a stock market. It's been incredibly volatile and it turns out a lot of people don't like that.
They don't like the stock price going down. You know, they don't want to bear the risk of something that can go up and down like that. It's very uncertain. And so it's interesting. I don't know what the right answer is. You know, I think for Confluent, the right answer is follow the market, right? Like you ultimately have to.
Pay it competitively in line with everybody else. Yeah. Yeah. For Silicon Valley, I'm not sure, you know, like, um, people loved, uh, equity compensation in tech companies when it was going up, up, up, people didn't like it as much when it went down, but that was kind of the. Point was you're kind of bearing some of the risk of the company.
I don't know if there's a magical solution that fixes it You know, my hope is we don't Throw the the baby out with the bathwater There's something really nice about having everybody kind of rowing in the same direction and being able to hang on to that You know even even later in life, you know, I think I do think there was an extent in which the it was Ignored to some extent the valuation of companies and we're kind of working that through the system where I liked out.
Okay It's ultimately money if you're giving people equity, but the, uh, um, you know, I, I hope we end up in a place that still keeps the good part of it. I actually think it's something that should probably go a little bit more mainstream where maybe, uh, you know, a small portion of people's compensation, you know, in many roles, at many levels of the hierarchy and many companies in many industries should have some ownership in that thing.
And there's probably no immediate payoff from doing that versus paying them in cash. But I feel like over time that alignment matters a lot, where everybody kind of ultimately wants the company to be successful in creating an environment where that's important. And maintaining it, even when the company goes from like a small rowboat, where it's like very clear if it tips over, like you're not going to cross the river to like some large container ship where you can kind of forget that you're on a ship at all, right?
That's where you kind of need that alignment of incentives.
Logan: Have you developed any guidance for employees on stock price and checking versus not anything like that? Or is it just human nature? And you just, you just try to guide
Jay: Yeah. Yeah. I mean, you can, you can tell people not to look at it. People are going to look, um, the, you know, we've had huge ups and downs. And so I think there it's kind of back to the communication. Like people need to know how the company is going to be successful over time. It's a lot easier in a private company.
Like that setup is much easier where you're like, Hey. You know, you own a portion of this thing. You don't really know what it's worth, but this is the big thing we're going to go do is definitely harder when every day somebody is telling you something about what that's worth and, you know, it does matter over time.
Like the day to day movements, maybe not as much, but like. You know over time kind of building the equity value of the company. That's a pretty good measurement of what you've done um But the challenge yeah The challenge is is getting people who are Kind of in the business and know all the good and bad things and all the opportunities to value that knowledge uh equally um with whatever happened today in the stock market Which is not that easy to do right because one is kind of a vague sense and the other is a crisp thing Um, and you know, I I think on the whole it's probably good You know, we've had like big ups and downs and each one comes with some pain, the, uh, the ups and the downs as well, both.
Um, but,
Logan: what's a pain on and off versus a down?
Jay: um, you know, I, I, I think it's a hard thing. You know what, what happened to us as we went public was, you know, the stock price shot way up and it wasn't like the company from the time it went public to like six months later had gotten dramatically better. But of course, if the stock price goes up, everybody feels like, yeah, we earned, you know, 100%, we did that, right?
And, and we, we hadn't done anything, like nothing had really changed, you know, we delivered what we said we were going to deliver, but it wasn't, you know, some sea change in what the business was or the opportunity or anything like that. And I think if you, you know, if you anchor to that on the way up, then of course, if it goes away, you feel very bad.
Um. And, you know, you don't, you don't want people, um, you know, you don't want people treating that as the sense of self worth or accomplishment of the company. And it's, I think it's very hard not to on the way up or the way down. Right. And I, I think the way up tends to make you, uh, overly confident and whatever in the way down tends to make you, you know, everybody's more unhappy or there's more infighting or there's more whatever.
Um. You know, the combination is probably okay. I do think it kind of builds a certain type of resilience. Uh, I always feel like the companies where there's some harder things or there's a little bit of chaos, uh, I think over time they kind of end up better. I always think about like maybe early Amazon versus early Google, where Google just kind of nailed it.
In such a way that it almost kind of grew up as like the rich kid in tech And amazon just had such a brutal upbringing where you're in retail and you're just you know fighting for this thin margin and you compete with like walmart, which is a bmf and your stock drops your public in through the dot com bubble and your huge stock drop and layoffs and then the You know, the, there's plenty of things not to like about Amazon, but there's a certain kind of grittiness to it that, um, I think serves somebody like if you just look at how they approached the cloud business, the willingness to just kind of keep pushing on something that was probably very natural and hard, but obviously could be successful.
Um, you know, I think that comes from having done some harder stuff. So I don't know, you know, I think, I think it's probably okay. I think it's probably similar to people where you want enough. Small traumas that you end up, you know, kind of resilient and capable, but not such big ones that you end up, uh, you know, in, in therapy and unable to function.
Um, and it's just kind of figuring out what's, what's too much.
Logan: Yeah, I think about, um, it's like the childhood actor syndrome when things come. So easily to you in the early days, I think of Twitter as like a canonical example of like the product market fit from day one was just so strong and they ever built a culture of iteration and, you know, learning and, and talking to users and all that.
And now it's manifested itself in a bunch of different ways. Um, so you went public in 2021. What was that process? Like, were you able to actually go on a physical roadshow traveling around? Or was it zoom
Jay: No, it was all zoom with investors. Um, you know, we did actually go and stand on the roof of the NASDAQ. They wouldn't let us inside, but it was actually pretty cool on the roof. So it was, it was an amazing process. I thought it was, um, you know, it was one of the few moments in the company that was just kind of like unadulterated, like, yeah, we did it.
Um. And not for any, I mean, it's a financing event. So there's many reasons that that shouldn't be the case, but, um, but I thought it was super exciting and, um, uh, but obviously kind of a weird, a weird way of doing it. Uh, so, so yeah. And then, you know, as I said, in retrospect, a really bizarre time to take a company public.
Logan: at a market level?
Jay: That's right. That's right. And I, you know, again, I, I don't know, I don't know if it's better now to be a early public company, like we are, or a late stage private company, like both have their challenges, you know, we're, we're both just all kind of working through the environment
Logan: Yeah, I, I think public probably the dynamic stock price and the resiliency, but what you're building and there's no, I mean, sunlight's the best disinfectant in some ways. And I just, I just think that, uh,
Jay: think that's probably right. I mean, that's what we're telling ourselves anyway, is like all the efficiency work in the whatever, and the ups and downs are, you know, making us better, uh, But obviously I, you know, the friends I have running public companies, it's like, they just say, you know, don't worry, company's worth exactly the same amount.
It's only these crappy companies that have gone down and they can get away with
Logan: that's a bench.
Jay: maybe until, until the next fundraiser.
Logan: get to put it in a jar and forget about it. So all our companies are worth what we say. Um, Transitioning from a private company to a public company, was there anything that you all do now as a public business that you wished you had started earlier or done differently as a private company?
Jay: You know, I, I, I didn't think that many things changed. Um, you know, I, I, as we were doing this, I talked to a bunch of CEOs and got just like wildly different feedback on how much of a burden and. How much process was in it? Yeah, you know, I think there's some unfortunate things where it's it's there's areas We can't be quite as transparent internally, which I think is a drag But on the whole it's you know, it's not a huge.
It's not a huge systemic change and how the company operates You know other than these moments where there's you know swings in the stock price where suddenly you you know You're gonna spend a day Talking to people about that one.
Logan: I want to talk, uh, talk through some different philosophies, uh, now one of the things that we discussed prior to this was the concept of DCF valuations as a framework for thinking about business construction. Can you elaborate on that?
Jay: Yeah. Yeah. I think this is an interesting one, or at least it was for me. Like I didn't go to business school, so I had actually not really thought much about what the value of companies was until we were literally like fundraising. And it was like, where do these numbers come from? And
Logan: air is the answer, at least with
Jay: yeah, that's right.
Well, to some extent, right. Um, you know, so, so for me, I thought it was helpful, uh, really learning, um, you know, this kind of GCF framework that, you know, if we knew. What the future cashflow of the company was going to be. And how many shares there were going to be, we would actually more or less agree on what it would be worth.
Logan: Bonds, as an example, are fairly easy to value
Jay: And it's, you know, it's just this very logical model where it's like, Hey, if you, you know, right now, you know, maybe you're an early startup, you're not making any money in the future. At some point you're going to make money and you're going to hopefully continue growing and making more money over time.
And this is how you would value that flow of cash. And the reason, you know, when you hear about that, you mostly hear about it from an investor point of view, like, Hey, this is how to take a P and L and turn it into a value. But what I think isn't talked about is the interest from the entrepreneur side.
So like, you'll hear all these really interesting pieces of advice where people will tell you, um, Oh, investors don't like a lot of services. The reality is, I think that's not really true. So for a fixed amount of revenue. If you then learn, Oh, half of it was, you know, professional services revenue, then of course the company is worth less because professional services is not generating any free cashflow at all.
Um, but those things are not independent, right? It's not like you start with a certain amount of money. And then some is taken away by professional services to the extent that you can grow the kind of core product business. You should be very happy to add professional services to the extent that it doesn't grow the core product business.
It's not adding much to the market capitalization of the company because it generates no free cashflow. Um, I think similar trade offs between kind of the, you know, the investments that are going to produce, um, you know, efficiency or gross margin, which tend to be very important for these kind of cloud infrastructure, which has.
You know, real cost associated with it and growth. Like, how would you think about that? You know, how much should you value that? Um, you know, I, I think it actually gives a very clear picture, which decouples from the kind of whims or thoughts or trends in your industry, if you're just like, Hey, you know.
Over time, how are we going to build something on the go to market side and something on the product side that can capture a lot, you know, that can generate a lot of dollars of free cashflow. That's going to require us to have some kind of moat that defends us from competition. Keep a price point. It's going to, you know, force us to create efficiency to capture that.
And then it's going to be per share. So we got to think about how many shares we're issuing. And I think, um, you know, at least like having a model of that, I think is actually kind of clarifying when you're going through a planning process or just trying to think, it's not like you're going to calculate your way through these because there's a lot of uncertainties.
But when you think about product design questions, when you think about pricing questions, there's often a lot of things that just really don't matter. Um, or that matter quite a lot where having some kind of true north of what's important is, you know, will totally change things.
Logan: Yeah. It's interesting. I mean, the, the, to your earliest point or your earlier point on, um, the recurring revenue of the cash or the future cash flows, like one of the big components of that is the recurrence of the cash flows as well. And so when you talk about professional services, the margin structure is often different.
The recurrence is often different. Uh, therefore the cash flow is often different. Different and or at least the predictability of the cash flow is often different. And so it gets valued differently. You're right. Like the deconstruction of all of these components once upon a time, there was. One big open source company, and it was Red Hat, and there were only a handful of sass companies, and there were sales force, and there was a dopey, and there was service.
Now there weren't a lot of, um, and so everything was kind of thought from a first principle standpoint, right? Because it was like, well, what is this worth? And that often happens with consumer companies and how to think about X, Y, Z marketplace when the take rates different and the recurrence of purchases different and all that.
But for sass, we sort of got. Circular in our, in our benchmarks against one another and not first principles of why we do it. And so when you are like, well, you could answer this question as snowflakes, gross margin are 65 percent and therefore that's a good target. Or you could say, well, actually, here's what the cost of service, here's the amount of gross margin it takes.
Here's what
Jay: Yeah, I think that's exactly right. And so for, as a practical method for valuing companies, it's probably better just to go with revenue multiples and put them in categories. Cause you. You don't want to make like 57 assumptions, but the, you know, if you're running the business, there's no assumptions. Like you kind of, I mean, there are assumptions, but you're, there's no hidden information.
You have a clear idea of what things are going to play out as, you know, so it clarified a lot of things for us in terms of, you know, how should we approach the cloud product, the on premise product, the relationship between those two, you know. How much, how would we approach, um, the kind of info associated infrastructure cost?
Does it matter whether those costs are borne by the customer or by us? You know, a lot of things become much simpler when you have some kind of like underlying reality versus what? You know, what you see people talk about is trying to You know trying to satisfy a shorter term version of that which they think will make the business look good But actually will not play out over time.
And you see that with some of these kind of, you know, I would call it like fake cloud products, where it's kind of like, you know, or fake ARR, like it looks like it's going to recur, but it doesn't really at the rate that you hope, or it looks kind of like a SaaS product, but it's not really, and it's not going to have the attributes of that over time.
And I think, yeah, in a very short time duration, that may work. But over any longer period of time, it's not going to fulfill the expectations that are set around that. So it really doesn't help you much.
Logan: I always find it interesting when people like, uh, talk about, uh, revenue multiples and there's. I was just thinking about this as we were talking, but I feel like growth rate is probably the, the biggest one that like looking at a trailing multiple, some companies growing 2%, some companies growing 200%, it's going to be very different.
And then. the free cash flow profile or the operating income to your point earlier about increasing 40 basis points. And I was trying to think about the third one. I guess maybe the recurrence of the revenue, like the net retention or something, just because that gives the future predictability of what the go fetch is to sustain that growth rate.
I think those are probably the three that I would pick in terms of how to not just look at a. Revenue multiple and be like, all right, this is what it is. Like there's different characteristics on businesses.
Jay: Yeah. Yeah. I think it's, I mean, that's why I think the framework is actually more interesting for, it's more useful for entrepreneurs because you're constructing the things and you have full information about the business and no incentive to lie to yourself. Um, whereas, you know, something communicated to investors, the investor always has to worry like, okay, sure, they say it's going to, you know, they say this, but is that really true?
Um, and, and, you know, that's where I think trying to, you know, sketch out that construction of the business over time, you know, I think it's actually just a clarifying way of thinking. Um, and one that can kind of help the team get clarity about what matters and doesn't matter. Uh, yeah. And, you know, these, these kind of questions come up internally all the time about trade offs between efficiency and growth and smaller number of customers at higher price or larger number of customers, you know, and this gives you a way of kind of answering all those questions without saying like, Oh yes.
We, you know, it's all about, uh, whatever breadth of customers, or it's all about whatever, you know, you can actually have something that gives you like a trade off between three or four different things.
Logan: Hmm. It's interesting.
Logan: Um, how do you think about the opportunity for artificial intelligence today?
Jay: I think it's exciting. You know, I like, like, yeah, yeah, yeah. Probably like everybody else. I, um, you know, I, I think we're at kind of an interesting time where one of the things that makes it possible to reason about tech is having a really good mental model of what's. possible to do and what's not possible to do.
I always think about this with product managers, like, you know, what makes it really possible to be a not super technical product manager for, you know, kind of a kind of SAS web app type company is it's really easy to have a mental model of what you can do with a database and a piece of software and turning it into a web interface.
And so you can imagine all the possibilities, right? And, you know, that was even. Uh, quite true for the early machine learning efforts where, you know, if you worked in that domain, you could say, yeah, this is the kind of thing we can build a model for this is what it will predict accurately. And this is what it won't.
And then what's happened in the last few years is actually a lot of those limits have kind of fallen apart. And now we don't really know what you're going to be able to do. And, um, you know, it's not really an expertise thing. Um, It's actually just, uh, it is unclear where things are going to be in five years and the most optimistic version of that is so far afield.
From the current state that you can imagine all kinds of possibilities. And, uh, it's, it's very unclear what the possible, what that means internally for, you know, the construction of a company, like a enterprise software company that has a lot of humans doing stuff, like how efficient can those be, um, and where can it have impact?
Um, you know, it's very unknown what that will mean for the customer base and the adoption. So it's just a, you know, I, I think it makes it exciting. Um, but I, I, I do think we're kind of at it. You know, we don't know stage where, you know, the, the cool things that people have done with LLMs, there's several more leaps of imagination to get everything people are imagining.
You know, there's, there's other innovation that has to happen, but there's a lot of people working on it. And we just recently made a bunch of big jumps. So you can't say we won't make more
Logan: Yeah.
Jay: and it's unclear how much you can get out of scale and people debate it. And so what that means is just like the, um, where this.
The ceiling on what you could do with a database and a web app was just very well understood. Like it can do X and it can't do Y. The ceiling on this stuff is just not at all understood. Um, the current state is kind of understood, but like where that will be in a few years, isn't. So I think that leaves everybody in a tizzy.
I think, uh, you know, I think there'll be a lot of, um, wasted investment, both internally in companies and buying things and, you know, companies trying to productize things and so on. But I think. There's clearly a lot of value. So it's a, it's an exciting time. Uh, I, I, I think it's cool to see it. I think it's also just like intellectually cool to see it.
The, um, you know, if you think about the kind of limits of computation and what computers can do, you know, we, we, we kind of stalled out for a while there, you know, the early If you read the writing from, um, like an Alan Turing or a von Neumann, they were imagining a lot more progress in this area much quicker.
And there was a long, and then that would come back, you know, every 10 or 20 years, people would think, Oh my God, we're going to be able to solve all these problems. Vision will be a soft problem and this will be a soft problem. And we kind of didn't get it. And now we're kind of starting to get it. And that's.
Pretty cool. That's like amazing. Uh, so, so yeah, we'll, we'll see what it turns into.
Logan: Open source, um, we talked a little bit about this earlier, but, um, there's been this evolution. Uh, I think that, um, you all were a part of initially, maybe it was easier tech that was open sourced and now it's harder tech. I don't know if that's a fair characterization, but how you sort of think about the evolution of open
Jay: Yeah. You know, I would characterize it a little differently. I would say the early open source projects were clones. And so like, you know, um, Linux was a clone of a Unix kernel and the tools around Linux that comprise a lot of the operating system were a clone of those tools and MySQL and Postgres were clones of Oracle.
Uh, and so there was, you know, there was like innovation, it was effectively like a. Software development and a go to market innovation, but it was not a technical innovation, like no technical innovation was happening. It was a worse version for less money. Um, and what happened was people realized like, oh, if you want to get something out in the world, that's like A good way to start, that's a good way to get people to try it and get attention.
And so, um, you know, it, it became something where innovation was happening there and like new technical products were being developed there. And I, I think that that, um, yeah, I think it was really helpful for that because, um, it's much harder to stop an open source thing from getting adoption than it is to stop a commercial product.
You know, a lot of people have to agree to buy something, but, um, to adopt something that's not bought can often go through many routes in an organization. And so that was, you know, that was a very important thing for us with, uh, Kafka, you know, where you have like kind of a very new idea about data. And just, you know, if you imagine Confluent, um, in a purely closed source way, it would be very difficult.
Like we'd be. You know, kind of, I don't know, knocking on the doors of CIOs and trying to evangelize some new way of thinking about data and that hops from even convincing that person of that to some practical project. I mean, it's just many leaps of imagination before you would get there, um, versus something that can kind of find its way out there in the world organically and connect to something real that's happening.
And I, I think that's not unusual, you know, I think, um, the other really interesting aspect with open source is kind of the. Use of commodification, like anything that is open source is almost by definition a kind of commodity, right? Like it's a standardized good. And so like, I do think the computer industry, it likes to build around these standard layers, you know, whether it's like x86, which is not open source or something like Linux, which is.
And so I think open source became this way of kind of creating a standard. And I do think it's good to be a standard. You know, you want to be, uh, you know, you want to be Linux, not Solaris or, you know, whatever the thing everybody's betting on. And so the, um, you know, it, it then becomes an interesting dynamic for the companies where you want to have differentiation.
You want to have something better, but you also want to build against a standard that everybody's betting on. And, you know, I think that's where you're seeing kind of the models for this type of company evolve where. There's kind of a lot of innovation in these cloud services and how it works, but that kind of front facing thing people are building against is this very standard thing.
And you know, I think the independent companies have kind of evolved towards that in many ways the cloud providers have evolved towards that. It kind of seems to be the thing that makes sense. And I think it kind of makes sense from the customers point of view as well, right? So on one hand, You want something that's a commodity where you're not stuck or locked in like you were with Oracle.
But then on the other hand, you also want. Innovation and differentiation, if you're going to buy something. So you, you know, those two things have to like find a balancing point where there's enough differentiation and innovation, but there's not so much that you're completely locked in and stuck forever.
And so I think that's a little bit, what's happened in this area is that kind of, maybe the first iteration of data technologies, like Oracle, they could do something that was just. All lock in and then the customers are smart. So then after that, there's a little bit of resistance to that. And you kind of find some, you know, intermediate balance between, um, you know, kind of Uh, innovation and differentiation and something that's kind of a standard that, you know, is widely available and you have alternatives.
And I think that's a little bit what's happened in these kind of core infrastructure layers that are so important in cloud, uh, computing.
Logan: You've discussed the shift from companies in a variety of industries, not just tech, going from using software to becoming software. Can you talk about the practical implications of this or how
Jay: Yeah. Yeah. You know, I, I, I touched on this a little bit, but like maybe the early days of adoption of software was, you know, the company is really kind of a collection of. you know, people and maybe documents. That's kind of the flow of work is, you know, I do some work and I send you some document and then you do some work or I call you and you do your part.
And then, um, you know, the early adoption of software is like, Oh, this application will help this little group of people here and it'll help this little group of people here. But the kind of superstructure is still people. talking to each other. And there's, there's really no software modeling of it. And, uh, you know, I think what that's turning into is now something where, okay, now all the parts are kind of connected, right?
Like there's some flow, these systems all connect in some way. When this happens here, it triggers a bunch of action. Some of that's happening purely in software. Some of it's happening out in the real world with people, but there's some modeling of that process. Um, you know, in the digital world as well.
And so you can kind of think of it being the, the idea that there's like a people in, you know, machine, you know, people in real world version of a company and then little bits of software up here. And eventually you have kind of the full exoskeleton in software that's right there with it. And, um, I think the implication of that is like, you know, a few folds.
So like one, if you look at where the. kind of new innovations in the kind of software infrastructure world are there a lot about putting things together. So it's like Kubernetes, a framework for running many applications, right? Not one, you know, um, Kafka and streaming is about connecting data between many things, not.
You know, a database that's a backend for one application, the, um, kind of Terraform and HashiCorp is about orchestrating and deploying lots of stuff. You know, the, it's all about, um, putting all the parts together, not about how to build one part. And, um, you know, that, that, that's kind of the impact on the technology.
And then for companies, I think you finally get the full benefit of some of the digit digitalization, you know, the, the. Weak version of this is like, yeah, I type my stuff into documents. I send it the document to you. It's digital, right? But it's like, well, how much better was it than the paper version where you know, you had pretty large Bureaucracies that ran pretty effectively very complicated problems that were just run with paper You know, you don't really have a huge benefit just because you had Microsoft Office And then as you start to have more of these processes built in software, as you start to have technology that kind of closes the loop in some of them, you're really kind of getting more of the efficiency benefit of it.
And, um, you know, you're kind of seeing that in companies where, you know, big parts of kind of the drive train of, uh, you know, many companies where whether it's the customer interaction, like how people Buy things from you, the kind of support and how you could help the, like actual delivery or manufacturer of the thing.
There's now a significant software component kind of through all of that, um, in a way that there probably wasn't 20 years ago.
Logan: I want to read a quote that I heard attributed to you.
Logan: Uh, the goal of a poker player is to make good moves. Sometimes you lose a hand because you got bad cards. How does this apply to how you operate Confluent or think about the
Jay: Yeah. Yeah. I think, I think it's an idea that it's become more commonplace. Um, you know, when I was in school I was just very interested in like decision theory and probability. Um, you know, maybe back to that kind of. AI and machine learning interests, just because it's a model for thinking. And, you know, I think it's helpful, um, it was helpful for me as I was transitioning from an engineer to a CEO, because, you know, as a software engineer, you're often making decisions where all the inputs are kind of knowable.
Like we know how computers work. We know what the performance characteristics are. It may be a very hard problem to solve, but to some degree, if you like, gather all the input and think really hard, you'll get to the right answer. And then a lot of business decisions are just a bunch of vague stuff where you just don't know.
And, uh, the early days of a company, of course, those decisions are really pivotal. Like it's just the whole direction of the company will be set based on some of your choices. And so, um, You know, so how can you kind of make peace with that, uh, you know, I think that ability to separate out like, Hey, what's the information I have, what makes sense based on that, that allows you to like, kind of move a little faster with less hesitation and regret than you would, um, if you were just agonizing over the resulting uncertainty and it also allows you to kind of grade yourself.
Um, in a way that's a little bit independent of the specific outcome in that time period. And I, I think that's really important. I think, you know, one of the things I've observed, I, I, anybody who went through, anybody who's running a company or investing in companies through 2021 and, and beyond, um, kind of felt this, you know, you, you kind of, you get the glory in these boom times where everything's like really good, everybody will tell you, Oh, you're, you know, you're such a genius.
This is such an amazing company. Everything is so great. Transcribed Um, you kind of earn the glory in the bad times where, um, you know, that something is not so good. Like, it's like, okay, it looks like the company is not going to make it. You know, that cloud transition point where it's like, okay, we're, you know, we're putting all our resources into something that may or may not succeed.
You know, that those are kind of the things where, um, uh, you actually get no positive feedback. Nobody's telling you this is good. But the, um, but the, you're kind of in many ways doing your best work. And maybe later people will think it's great, maybe not, but the, you know, I think if you have that idea that like, Hey, you know, kind of making the right moves off of what I know now.
I'm going to be able to say I, I kind of played this as well as I could have, um, then that, that gives me maybe enough peace of mind to do it. And then also some way of kind of decoupling from just the ups and downs that are external, um, you know, external to me that are basically either luck or the environment or whatever it is, things outside of, you know, the direct control that you have.
Logan: Is there a book that you've read that's particularly impacted your, your thinking on decision theory or business operations or some of these things that you would recommend for people?
Jay: Yeah, you know, I, I, I don't know. I came into this kind of, um, you know, just through, uh, stats. I think there's been some business books that kind of dive into this. Um, the, the book I actually really love is a little bit more, um, it's a little bit more math heavy, but it's called, I think it's called Rational Decisions.
And it's a It's, it's basically this guy who's, um, an academic who's walking through, um, a little bit of how decision theory works and how to think about it. And then also where it does not work and why you can't just calculate your way to everything. And it's, it's, it's a very entertaining and short book.
Um, uh, I don't know if it would be for everybody, but I, I. I think it's delightful, um, and, you know, I think that's a good one.
Logan: I think about the, the score takes care of itself is the one that, uh, the Bill Walsh book about like focusing on
Jay: Yeah, yeah.
Logan: not, and the output will be what.
Jay: I think that's probably right. I haven't read that book, but I, I've, you know, the sports teams have the worst version of this, you know, especially football. You know, I think basketball is like statistically significant. There's so many points, and there's so many games, and you know, it's tournaments, not one off things, whereas football is just like, there's 17 games.
It's basically a lot of luck. You can do everything right and still lose.
Logan: Cool. Jay, thank you for doing this.
Jay: Yeah, I'm really happy to do it.
Logan: is great. Really fun.