Confluent, Inc. (CFLT) Earnings Call Transcript & Summary

Okay. So we're just delighted to have Confluent joining us at the JMP Securities Technology Conference here in San Francisco. And we're even more delighted that Founder and CEO, Jay Kreps, was able to come and join us. Tall gentleman sitting to my left. How tall are you?

I'm 6'5".

Yes. Excellent. And so I'm going to ask Jay how's business, and we're going to talk a little bit about his background and then we'll do some more sort of standard questions. [indiscernible] how's business, Jay?

Business is pretty good.

Pretty good. Yes, it's pretty good.

Yes, it's -- for any of these cloud services, definitely the last year plus, there's been, I would say a lot of just optimization and focus on cost and fewer new software projects. But we called this out in our earnings. It seems like that's stabilized a bit. I would call it a reversion back to where we were a few years ago, but there's some stability in that. And then the area that we're in remains really hot. It's an area of investment. It's technology around streaming and real-time data is even more relevant than it was a few years back and the rise of AI and use cases around that kind of helped push that along even further. So this remains a big focus for our customers, and that's a great place for us to be.

Yes. And we're one of them. So I -- 2 sessions before this, I hosted -- Barb was in there. I hosted our CIO, Chief Analytics Officer and Head of Consumer Tech, right? And you guys came up. [indiscernible] a little like -- so when you say Kafka [indiscernible].

That's right. That's right.

Okay. And where are you from originally?

I was born in North Carolina, but I grew up actually north of the Bay Area in Sonoma County.

Really.

Yes.

[indiscernible].

It was probably more Cow country at that point. I think NAPA started with the wine country and it was just kind of becoming that as I was leaving.

And then you were...

My knowledge of wine is kind of woefully poor.

My tequila knowledge is excellent. My wine knowledge is [indiscernible].

I don't think I [indiscernible] with that either. So I don't know [indiscernible] I guess.

And then -- so we were just talking earlier, so Jay went to Santa Cruz, you did Majors in Computer Science. But this, I did not know. Here's a new tidbit. He was in the PhD program for machine learning.

That's right. Yes. That was -- I was originally going to [indiscernible] research. And then I realized that at least at that time, there wasn't as many breakthrough machine learning, but there was really interesting stuff happening out with the Internet. And Google and all these companies that were using data and had data. And so it was an exciting time to kind of get out in the industry.

Yes, it is basically just a bunch of linear algebra.

Well, at that time, actually, there was a whole array of -- yes that's right. There was a whole array of techniques. Interestingly, the stuff that generative AI has built on the neural networks have become very popular and they have become very unpopular because they were too hard to work with. And there had been a whole set of kind of more mathematical models and kernel embeddings and support vector issues, all these other techniques built on kind of other paradigms almost -- and sadly, I guess, between all of that, there wasn't a lot of progress on predictive accuracy, right? So there was a lot of development, but it wasn't really getting better. And then meanwhile, some of the companies that had more data, we're just applying more data to problems and getting better results. And so I think that was a bit of the mindset shift kind of started around then, probably in the next 5 years is when you saw companies kind of going back to neural networks and trying out some of these really interesting things out of Stanford, where they're learning to fly helicopters and stuff that we're not -- it's not the kind of problem you could have taken on before and the use of GPU started around. That was interesting times. Some of the very early academic work was kind of just after I got out of it. So it was a funny -- I had a kind of strong belief that for machine learning and AI to really evolve, it needed a stronger theoretical ground. So I was like, hey, you know like in physics, there was all these people who had all these little hacks, the Newton came and kind of gave -- kind of all those hocks kind of went away, they weren't work worth much. And so I thought, well, either you're the Newton in this area or you're kind of one of the people who stuff gets replaced. Interestingly, the field advanced quite significantly with no change in the understanding of what learning is. It actually was just more computation of the same hacks. So it turns out, it's hard to call these things, I guess, when you start out.

So [indiscernible] was up here earlier. And do you know this guy Stephen Wolfram?

Yes, he was a mathematician [indiscernible] Mathematica.

Really I didn't know. So it's 100 and -- it's 101 page is long. I'm on Page 2. Yes, And I've already learned something incredibly which I didn't understand what temperature was before this. And so anyways, what is ChatGPT doing? Had another thought I wanted to go after on this point. Totally off-topic, I can do it anyways, do you read the Elon Musk letter OpenAI or the complaint?

I didn't read the complaint. I heard the kind of headline summary.

Yes. What are your thoughts on the headline summary?

I'm actually not sure. A lot of these -- I think this is around the kind of open sourcing should it be a private company, should it be open development, I think it's super interesting. I think we just don't know. I mean the way technology plays out, it usually takes a couple of decades for it to really get fully adopted. And so there's a lot of thought about risks and adoption and all the pace -- I think it's very hard to cost them that I'm just a dumb technologist. So I'm just watching it. I think we'll find out.

Yes. So maybe -- so on the other side of the story, then maybe there is some benefit to not immediately open sourcing everything. Is that...

I think we just don't know -- there's definitely technologies that were dangerous that we did manage to kind of smother and the refining uranium is kind of the example everybody would go to. And I think accurately, that was probably good that, that was held back. I think with AI stuff, I don't know that that's a great analogy, but I think we have to really see how this stuff gets used before we know.

Okay. So when you -- when did you join LinkedIn?

2007.

Okay. So in 2007, you joined LinkedIn, [indiscernible] developer for LinkedIn, right? Where does the beginning of Confluent come from?

Yes. I joined LinkedIn really kind of with a similar charter where I wanted to use data. I'd come out of this. It's kind of machine learning, had learned a lot of use of data. And as I got into the company, that was my intention was to focus on that. But what I found was before you can really build a lot of fancy algorithms that use data, like a lot of the infrastructure that just lets you get it is it was the first challenge. And for social network at that time that was growing. That was a pretty big challenge in that the website had to scale. It was getting more complex. You needed more interfaces and products and mobile applications and just bits of software. And then to really use the data as kind of the [indiscernible] challenge. So I ended up working a little bit lower.

How big was LinkedIn in 2007?

I think it was around 100-and-something people. It was pretty small when I joined and then it grew pretty rapidly from there. Yes. So I ended up working a little lower in the stack than I had intended in some of these infrastructure areas and just building out kind of some next-generation distributed databases for the [indiscernible] some of the back end kind of data lake analytics capabilities. And what I noticed was, hey, we have all these great technologies for storing data, kind of data at rest -- but how it flows between systems and how we can kind of act on it as it happens. We have almost nothing there. And it was a little bit weird to me that some of the most sophisticated things we did with data, we would do in these big batch processes where it's like, oh, the end of the day, this big scheduled job runs and processes all the data and spits out some new results and -- it just seemed like a little bit of something out of the era of mainframes. You kind of run the big batch job and when it's done, it spits out the result, whereas if you think about what LinkedIn is, it's not very digital business that's continuing interacting with people and continuously generating data. So why wouldn't you process that data and react to it as it happens, where you could still benefit the users that are on the site, rather than hoping they come back some time...

It won't be a really good specific LinkedIn type of example.

Yes. Some of the stuff I worked on, if you've ever been on the site and it kind of predicts people you should connect to, I hope to build the first version of that was kind of based on real machine learning techniques to make those recommendations. Some of the people who have viewed your profile. This is people who've got a little -- you go, they really like this one. I look at everybody who's viewing me, all the profiles that [indiscernible] a lot of the data-driven features that was kind of where we started was, hey, how can you make something that's richer that's more interactive, and a lot of that really puts heavy demands on the use of data. And I think that became particularly relevant because I think for a lot of companies, that digital interaction went from something that was a little bit on the side to something that's really kind of a first-class citizen, they all have to do similar things.

Yes. And then you had a couple -- so when did you -- how did you come out of LinkedIn and start Confluent?

Well, internally, we created this layer, Kafka. It was all about harnessing real-time data and -- at the time, we thought this was really revolutionary. And we open sourced it, and it caught on in Silicon Valley, some of the tech companies really started to use at the Ubers and Airbnbs and Pinterest, all the kind of tech companies that were at that time, relatively small, but growing rapidly and needed to harness data in similar ways. And so we knew that it was a really big deal for that type of company. But we weren't quite sure if it made sense in the rest of the world or not. And then it just came about that we started to get contacted by just random organizations from big banks to media companies, to insurance companies that were all looking at similar problems and have somehow stumbled into this open source. And then they had this long list of new features that they needed and help that they needed, and we were like, okay, realistically, there's this very big transition that's going to happen in the world from moving from kind of batch use of stored data to something that's continuous in real time. That's like as big a transition as any use of data that's happened, and we're kind of right at the heart of that transition. But we're not going to accomplish that sitting in the back room of some social network in our spare time with just an open source project, like there really needs to be a company that will invest in this that will kind of build out software products and cloud services that will make this accessible to this type of company if it's going to really work. And so then we really decided to go after that full time and left and started Confluent. And the thesis was exactly that, and that's effectively what confluent has done since and has really build a suite of products around this kind of real-time data, real-time processing that are based around Kafka, which has become the standard in this area.

Yes. And where is it going from here? Where is this business headed? Don't worry about not this year, next year, not time frame. Just where can this thing end up?

Yes. Well, the -- from when we started the company, we had a very clear picture of what we thought was going to happen with software. And you could see this as an evolution from maybe the early days of software where you really have these kind of disjoint applications that you've adopted that it really kind of acts mostly independently. They don't really interact with each other that much. And that was certainly true at a point in time. And then you can imagine there's more and more bits of software. And now the requirements for software, it has to integrate. Big parts of the customer experience, big parts of how products and services are built and deployed and managed out in the world, the real drivetrain of the business is now all run through bits of software that all have to act in concert. And so our view is that the role for this kind of streaming technology is really take on, it's almost like the central nervous system in an animal or something that connects it all that kind of brings the real-time impulses of what a business is doing that lets you act on it. And much as the central nervous system is in an animal, that's an incredibly important platform. And we believe that whoever gets that, that's probably the most strategic position around data that you can add to over time. And so our view is every company is going to end up like that architecturally, you can see that happening out in the world now. And our goal as an organization is really build the product that enables that and capitalize on all the opportunities around it. And you can see that progress out in the world in virtually every industry. There's been pretty rapid progress in the most sophisticated companies towards this kind of real-time architecture. And there's been adoption really up and down in the not-so sophisticated companies who are kind of starting on that path. And so the world is indeed moving in that direction.

Awesome. Right. So it was 2 quarters ago that you guys had a hiccup, right?

Yes.

That was your first hiccup, right, as a public company.

For as a public company...

Your first hiccup as the CEO.

No. I mean, look, Confluent is a young company that grew quickly in a domain that is evolving rapidly. We went through a bunch of big changes from really aggressively building out a cloud product while we were very early as a company and running both [indiscernible] a whole bunch of hard things that we did. So yes, it was by no means the first difficult thing. Not to mention the fact that the whole sector we were in went through a bit of an adjustment.

Everybody.

Yes.

Everybody. So tell us what happened 2 quarters ago, what it feels like is the -- as the quarter like when did you -- so there used to be this company called [indiscernible] going way back with. Okay. And it has this founder and this guy, the founder's name is -- the founder's name is this guy [indiscernible] and he told me when he said, Walravens always asks the wrong questions. Because when a company misses, you always ask the wrong question. And I go, "What do I ask?" And he goes, you ask why they missed. And he said, I go [indiscernible] he goes, you should ask them when did you know? Because often, when you know the answer to when did you know, you no longer need to ask why. So 2 quarters ago, as you're going through -- and by the way, very nice recovery from Confluent for anyone who hasn't looked at the chart, okay? So it's all good. But -- but when you have the hiccup, when did you know?

Yes. Well, the -- I think the stock reaction was largely relative to our guidance, not our results.

Yes. But the guidance comes off of what you booked in the quarter, right?

That's right. And the -- what we -- there were several things that happened in parallel. It's been a difficult period of time to operate, particularly in the kind of cloud infrastructure space overall. One of the things we felt was the customer behavior had changed fairly significantly from, say, a 2021 environment where there was a lot of buy ahead type behavior to something where people were really conservative in kind of still adopting, but more hesitant in their forward commitments. And so one of the decisions we made was really accelerate the internal orientation of our go-to-market to consumption. Externally, we've charged customers on a consumption basis for our cloud product for a number of years well before we went public. But internally, like a lot of other cloud companies, our go-to-market was really oriented around kind of booking upfront commitments from customers for that pay-as-you-go usage. And the peer set of companies that we would be similar to, whether it's the different offerings in the cloud providers or the Snowflakes and MongoDBs of the world. They all made this transition a year or so ahead of us with different kind of pace. But for any company that has both a software business and a cloud business, you can't kind of jump right to the end state because you're a software business, you are indeed booking subscriptions upfront. But one of the things we felt was, yes, this is not really where the market is now, and it's certainly not where it's going. Customers really appreciate the flexibility and you want to have your go-to-market team really driving the adoption of new use cases, not just trying to book ahead everything that the customer might do in the next 3 years. And that's a relatively big change internally. It may not sound like it, but you're kind of changing your compensation model, you're changing how you think about pipeline. And we had done approximately 20% of that change already, but we wanted to do the rest of it really on an accelerated time frame [indiscernible] and we knew that would have some impact, but we felt like over even a reasonable time period, it would end up being positive for us. And that did mean a little bit lower guidance for the first half of the year. I think the reaction was partially that lower guidance. And I think it was partially just, well, what does it really mean, right? So I think the -- I think as we came into Q1 and reported our kind of Q4 results, I think there was a pretty solid recovery, which I think was more or less what we said would happen, as what happens.

Yes. So the -- how are the salespeople handling being [indiscernible]?

Yes, that's going well. I mean the -- it was a little bit rougher, I think, for some of the infrastructure companies...

Do you have any sales people in the audience, by the way.

Yes, they all come to these.

No, I invite them. I invite them. Yes, I invite my distribution list. Usually you get a couple. Anyone want to -- no one wants to do it. [indiscernible] is that you back there? Right there. Well, one of the best software sales recruiters in the industry. So do people care, like does this come up? Like do candidates go like, "Oh, I don't want to be on a plan where I'm paid on consumption."

No. It all depends on what [indiscernible].

Yes. Will I make money? That's a not what the metric is. Yes. Yes, I think this is an evolution that's happened in our space. I think it makes sense from the customer's point of view. Ultimately, the thing that's valuable to them is these new use cases and applications. And it's actually what makes sense to Confluent like our revenue is again driven by these applications. And so it's a little bit more complicated to drive your go-to-market off for a whole set of reasons, right? It can fluctuate hour-to-hour and day-to-day. But it ultimately lines up the company results, the valuable thing for the customer and your go-to-market apparatus. So you are indeed driving these new use cases, helping unblock things, making sure that comes successfully to production.

Really different for a salesperson. I mean it's great but it's a really different. Yes.

It's a little bit different. I think one of the things that's happened was as a lot of companies did this, I do think the larger set of enterprise reps saw that change. Some probably flat those companies. But I think the ones who stated, if you were at MongoDB through this, you did okay, you did okay, right? And that's true for our team as well. So I think we benefited from 2 facts. One, a number of companies that have already done this. Two, if you look at the behavior of customers in 2023, this was very pronounced, right? Customers did not want to buy ahead. It was a conservative buying environment. It doesn't mean that there was no new use cases happening. It doesn't mean that there was no deals to be had, but let's go around the organization and round up everything that might happen over the next 3 years or commit to it upfront, that was a hard motion to execute. And so I think as a result, the message is probably a little easier for us than it was for some of the peer companies who went earlier. Nonetheless, it's a significant change to make. We feel like it's gone well so far as we've rolled this out and switched over our systems. Obviously, this doesn't impact anything to our customers. It's not like they're paying us differently or anything in our offering would show up differently. But it definitely changes the motion that we execute.

All right. What's the most important thing for you to get right over the next year?

I think that, that consumption transformation is very important. The second thing that we're very focused on is the expansion of our offering and the usage of the non-Kafka components. So Kafka was kind of the first thing we started with. This is that kind of raw stream of data, but the connectors that plug it into your organization, the capabilities of governing that data, and then the ability to process it in real time, we have something called Flink, which is part of our offering that will just be going GA this quarter. And that set of components, we think each have really huge potential, and they're all kind of earlier on that S curve ramp than our Kafka offering is. So when we think about, hey, what's important for us to execute this year, that consumption transformation and then the adoption by our customer base of those additional components and starting to see that ramp. Those are 2 of the most critical things.

Yes. And what's going to be the biggest challenge that you have to overcome with the adoption of Flink?

I think there's a fair amount of customer demand. A lot of it for Flink comes down to just any new cloud service, there's a lot to get right. A lot of the iceberg is beneath the water in these cloud infrastructure offerings to really get something that works at scale across every cloud that checks all the boxes. We've done that with our Kafka offering. I think we're on a very good trajectory with what we've done for Flink, but there's obviously a lot of work to do.

So this quarter, what are we talking about? We're kind of getting there?

Yes, that's right. You want to know the results ahead of time. Is that the...

All right. We have 44 seconds. Any questions from the audience?

[indiscernible].

Yes. It depends a little bit on the type of use case. A smaller portion of our business would be kind of analyzing data where maybe you could down-sample and just look at a fraction. But a lot of the application use cases are just software that runs the business. Like in a payment system, you can't just submit 80% of the [indiscernible] and still serve the customer the right way. And so yes, there's less of that type of optimization. I guess our use cases tend to be these production use cases that come out relatively well optimized. The thing that tends to drive us either to accelerate or not in the market is more the pace of new software application development, right? If that slows a bit, that's a headwind for us. If that accelerates, that's a tailwind. There's typically less ability to kind of dynamically turn on and off individual applications. You build some app, it does what it does. By and large, there may be some way to optimize it, but you probably would have done that in development, if you could.

All right. Jay, we're honored to have you here. Thanks so much. We really appreciate.

My pleasure. Great.

It's great to have you.