The Exchanges, every show

Q What was your biggest lesson from working with him? If there was one takeaway?

A That you can overestimate what you can achieve in one year and underestimate what you can achieve in five. When I started, it was a three-year-old startup, and it was basically a glorified contact manager, you know, Salesforce automation. Um, we essentially said what you're traditionally using ACT or Goldmine or using a spreadsheet, you can use Salesforce for. But Mark had this bigger vision and he said, we're going after Siebel, SAP, Oracle, Microsoft. We didn't have the product set to go after those competitors, but he was such an incredible marketer that he created this perception in the industry that some of the largest companies in the world could adopt this technology and that we would deliver on a roadmap that would satisfy the requirements over time. And he did.

AI assessment note: “That you can overestimate what you can achieve in one year and underestimate”

Q Why football? Why English Premier League? You can sponsor F-One, you can sponsor, you know, LaGuardia Golf as well. Why football?

A Well, I love the sport, but let's put that to the side for a minute. I think the value of these sponsorships obviously come in two forms. The first is awareness. And as we saw last night against Chelsea, this is a game that's being televised globally. So you've got millions of viewers that are looking at your brand and you're getting impressions, obviously. The second, which is obviously easier to quantify, is hospitality. And we're sitting here at Craven Cottage along the Thames. I think this is arguably the best sports experience in the world, and I've been to many. We had 20 executives last night attend an intimate Michelin grade dinner. We had, you know, the C-level executive come from Paris, one of the largest banks in Europe, just to experience that. And those types of relationships are extremely important, especially as we move up market.

AI assessment note: “I think the value of these sponsorships obviously come in two forms.”

Q they're about tenders. We see them more and more for employees, and I think talent acquisition is one of the hardest things today. I actually got in a lot of trouble the other day for this. I said, if you are trying to hire A-star talent today, you can't. OpenAI and Anthropics simply pay, and they go to the front-end model providers. Is that true, or was I being glib?

A I think it's true depending on the category that you're in. I think if you're a Digital native AI startup in San Francisco. It's a very difficult employment environment because you're competing against OpenAI and Anthropic and others, um, that are extremely well capitalized and are putting offers that are extraordinarily aggressive into the market. I think if you're a infrastructure provider like ClickHouse, uh, we look for a slightly different, um, profile. You know, we're looking for database engineers, people that have experience with distributed systems. Um, slightly different than what the frontier labs are hiring for. We employ people in 27 different countries, um, which gives us a competitive advantage, so I can hire engineers in Portugal and Germany and Singapore. Um, you know, we've got single digit attrition, so we've got extraordinarily high retention. Um, we have done some structured secondaries, um, and we'll continue to do so over time, but not with the frequency that I think some of the younger companies are doing.

AI assessment note: “I think it's true depending on the category that you're in.”

Q If you could have your way, would you not have everyone be in office in some way?

A I, I wouldn't, and I'll explain why. Uh, we're a very international company, uh, by almost every measure. Over half of our revenue comes from outside of the US. 40% here in EMEA, 10% in Asia. Over half of our customers are outside of North America. And so we need to support our customers in a variety of different languages, in a variety of different time zones. I mentioned we're live in 36 different regions around the world across all three hyperscalers. There's no way that you can centrally manage that. From one location. You need to have people in every single time zone. You need to have relationships with the hyperscalers in region. We go to market with AWS. We go to market with Google Cloud. We go to market with Azure. I flew to China to launch a partnership with Alibaba. Like you're going to have, you're going to need local language speakers to maintain those partnerships. And you can't do it from one or two or three centralized hubs.

AI assessment note: “I, I wouldn't, and I'll explain why.”

Q So I am, you know, Controversial in many ways. Uh, one of them is because of my vocal, uh, expressions about remote work. Why am I wrong?

A I don't think you're missing anything. I'm of two minds and I contradict myself constantly about this topic because I spent 12 years at Salesforce. I was in the office every single day, five, six days a week, 10:12 hours a day. And it was during this extremely formidable time in my career. I learned so much. From those experiences being in the office amongst my colleagues and peers, learning from people with more experience than I do, than I had at the time. When I started this company, it was during COVID, and so we kind of had to be distributed by design, and I started it with some Europeans, and so people were in Europe, I was in the Bay Area, my co-founder was in Utah, and so, you know, we started the company, we grew very quickly, we Found engineers that had a very unique skill set and they weren't all in the Bay Area. They weren't all in London. They weren't all in New York. And so we built a distributed company. Fast forward to where we are today. We're almost 800 employees. We'll be a thousand by the end of the year. We are introducing, uh, in-person options for our employees. We don't have this draconian return to work mandate or return to the office mandate, but we do have offices and we have a huge office in Amsterdam. We have an office here in London, New York. The Bay Area.

AI assessment note: “I don't think you're missing anything. I'm of two minds”

Q How do you think, Nikesh at Palo Alto is a very good friend of mine, he's a very brilliant M&A. How do you think about the buy versus build versus distraction? What's that internal decision maker for you when you think about those six acquisitions?

A If I think that our product and engineering teams can innovate in a specific area that we're not in today, then I'll let that play out organically. If I see a founder or a group of founders that are building on top of ClickHouse, that are getting into a category that I think is going to be You know, a future component of what we build as an ultimate data platform. Then I think about doing something inorganically and we partnered again with six different founders, uh, actually more than that. Some of these companies have multiple founders. Our most recent one earlier this year was Langfuse out of Berlin, um, which is three incredible founders entering agent observability. Every enterprise in the world is going to need this technology.

AI assessment note: “If I think that our product and engineering teams can innovate... I'll let that play out organically.”

Q So what about, uh, self-hosted, uh, deployments? Are you, uh, strategically deciding to not support those? Are you supporting those in some way? What's the, What's this thinking?

A So we do work with a handful of companies that are using ClickHouse in a self-managed way, whether it's on-prem or they're deploying it in a public cloud provider, but they're not using our hosted service. And an example of that would be Netflix, for example, that that's using ClickHouse, um, in AWS, but they manage that environment and we work with them. We provide technical support and there's a dozen other Companies that we also provide technical support around, but it's not our primary business model. Ultimately, we want to understand their use case so that we can improve upon the technology, make them successful. Ultimately, we'd love to migrate them to our cloud offering, whether it's the current multi-tenant serverless offering or in the future deployment model that we're going to launch later this year called bring your own cloud, where the data plane will sit behind the customer's VPC. We'll still manage the control plane. And so for data residency requirements, data locality, whatever its costs, security concerns, and we think that's going to open up a big market for us, especially in the enterprise.

AI assessment note: “we do work with a handful of companies that are using ClickHouse in a self-managed way”

Q You know, which was going to be like while we're on the topic, going to be my next question, like, what license is Clickhouse under, and how do you, um, yeah, how do you plan on, on, on working with the, the hyperscaler, the cloud providers?

A So it's currently governed, governed under an Apache two license, which, you know, is extremely permissive, allows for redistribution, modification. You can build a managed service around it. It obviously aids in, in growth of the project and popularity, but it comes with obvious risks that I just described. There are a variety of different licenses that we have considered, um, that we have not yet adopted, like AGPL version three, uh, or SSPL, um, which other open source companies have, have deployed. We feel that today the right decision for the community is to stay with an Apache two license, and we're excited about that. Like I said before, you know, we're not moving away from open source, quite the contrary. We're going to double down and double the size of the team of the core contributors and recruit new people into our company that understand the technology and that have been contributing to the projects in the past. Um, and at the same time in parallel, we are going to build A multi-tenant managed service in the cloud, which will inevitably be deployed on a variety of different cloud platforms, whether it's AWS, GCP, Azure, whether we go to China, like I've done in the past and partner with companies like Alibaba and Tencent and enable them to, you know, take all of the orchestration framework that we develop, which will in some of which will be open source, some of wh…

AI assessment note: “So it's currently governed, governed under an Apache two license”

Q So I'd love to, uh, go down memory lane a bit and go back to the origins of the company. So it's, it's started, the open source project started at Yandex, right? Which is the, the, the Google of Russia. I'd love for you to, uh, tell us the story.

A Sure. So it first came on my radar a few years ago, uh, the technology itself, um, as I started to observe its increase in popularity, uh, in the market. And earlier this year, I was introduced to Yandex, the CFO specifically, who then quickly brought in the founder and CEO of Yandex, the co-founder, Arkady Velos. And that was literally the start of the calendar year back in early January. And Arkady and I Started to romanticize about what it might look like to spin ClickHouse out of Yandex, as well as the core engineering team and the creator of the project, Alexa, and form a new company around this extremely popular open source database technology. And I immediately reached out to two investors who I had worked with in the past, specifically Mike Volpe at Index Ventures and Peter Fenton at Benchmark, two people that you, I believe, have interviewed in the same forum in the past.

AI assessment note: “Arkady and I Started to romanticize about what it might look like to spin ClickHouse out”

Q Am I glib and wrong, and that won't take into account infrastructure plays, or is that actually just the new world that we're in?

A Well, as I mentioned previously, I think the durability of revenue is the most Underestimated attribute of these companies, meaning how high are the switching costs when somebody's using your product? And for any category that goes from zero to a hundred million in a year, I worry what's the competitive moat that they have to preserve that hundred million from that customer going to something else. So ours was a bit more gradual. We went 12, 5200, and we'll finish this year north of 500. Which in the database world is faster growth than we've ever seen, including all of the competitive companies that I mentioned earlier today in terms of the first three years of revenue growth. And it's over a very broad customer base. So we've got very little concentration risk. The basket of AI companies that's using us in nearly every AI companies built on ClickHouse from Harvey, Sierra, Decagon, Anthropic, OpenAI, et cetera, represents less than 12% of revenue. And so even if half of that goes away, the winners are going to offset the loss from the losers.

AI assessment note: “durability of revenue is the most Underestimated attribute of these companies”

Q You said there about, I love this, the AI awakening. The challenge then becomes, and we had the president of Uber on the show, who was much more skeptical of the ROI that it generated internally. Um, how do you, do you think about token budgeting cost when suddenly you're incentivizing this, hey, run free, and then the bill might come at the end of the quarter?

A Well, the primary measure that we care about, as you know, is revenue growth. And if we see The sustained revenue growth that we've experienced over the last three years, and we're a very efficient company, as you know, um, some would argue we're too efficient. Then I worry less about the expense that we're incurring on coding agents, for example, because we're covering a very broad surface area and our roadmap is accelerating at a pace that we've never seen before. And as long as we can continue to ship features that satisfy this very broad set of use cases, Then I can always rein in token consumption and the cost of tokens, as we know, is going down, not up. And so they're becoming more efficient, not less efficient, but our revenue is growing faster than it ever has. So I'll take that trade any day of the week.

AI assessment note: “I worry less about the expense that we're incurring on coding agents”

Q That's actually a really interesting thought. Any, any guess why this hasn't happened as much at the application layer?

A I think the applications are so purpose built that the business logic behind them is so bespoke that, um, it's a good question. Like we at Salesforce, there is a open source product called Sugar CRM. And I remember when it emerged and I personally saw it as a potential threat, um, especially on the low end of the market. Which was a large part of Salesforce's business, small to mid-sized companies, uh, that were, that were using Salesforce and paying per seat. Well, now they had an open source alternative, but the reality was the business logic that was required That was built into Salesforce, both for Salesforce automation, but for customer service and for marketing automation, the underlying platform. We just didn't see the same pace occur, uh, with the open source alternatives and who knows what the future will hold.

AI assessment note: “applications are so purpose built that the business logic behind them is so bespoke”

Q You said that you're too efficient in some people's eyes. Where should you have spent where you didn't spend? And how do you reflect on that?

A So we've got about a hundred quota caring salespeople, for example, and I think, you know, our revenue scale is significantly more than a hundred million. And so our average, uh, rep productivity is quite high relative to the industry average. Our competitors that we're going up against some of these very large data warehousing companies, some of these very large observability companies have thousands of salespeople. They wake up every morning thinking about one specific use case. Our salespeople wake up every morning thinking about 10 different use cases. And there's only a hundred of them going up against an army of 2003 thousand sellers for some of these very large data warehousing companies. So I think the one thing, and if I look back over the last two years that I wish I had done differently was increasing sales capacity.

AI assessment note: “I wish I had done differently was increasing sales capacity.”

Q Do you think you raised aggressively enough? We're seeing a new world by capital. It's more and more moat in a lot of cases.

A You know, I'm trying to build a generational company that outlives me. And I think right now a lot of people look at fundraising As a very short term exercise, and they want to have this consistent and steady step up in valuation. It's good for your employees. You give them liquidity through tender offers. It's good for recruiting. It minimizes dilution. It bolsters your balance sheet. Uh, it lets you forward invest. All of those things are true, but I'm thinking about a company 20 years from now, not two years from now. So whether or not we raise that fifteen billion or twenty five billion in 10 years is irrelevant. Right? We need to have the right investors involved. We need to build a very durable, long-lasting, sustainable company. We need to get to a billion dollars of ARR as quickly as possible. The most important metric for the company is new customer acquisition. We add hundreds every month. Our gross retention is north of 99%. Our net dollar retention is north of 200%. The addressable market we're going after is absolutely enormous. So I think less about valuations perhaps than I should.

AI assessment note: “whether or not we raise that fifteen billion or twenty five billion in 10 years is irrelevant”

Q I do want to weird transition back, but when we said about kind of agent preferences and agent, you know, Anthropik using Anthropik to choose ClickHouse, does that mean like developer relations, developer community becomes less important? And how does the future of brand change when agents become decision makers?

A So I talked about you're building an application, like that you're building a dating app, right? So you can build it over the weekend. You can vibe code it, right? And in theory, that agent can select the stack, right? It can select a managed Postgres service because you want to support transactions. It can a managed ClickHouse service because you've got analytics, uh, and everything in between. You still have an enterprise buyer. You still have a huge data warehousing project at a top bank or a telco that's going to be driven by an engineer or a developer. And there's still going to be a human that's making that architectural decision on, are they going to use ClickHouse? Are they going to use Snowflake? Are they going to use Databricks? You've got an observability workload. Are they going to use something off the shelf like Splunk or Datadog? Or are they going to embrace open source and use something like ClickHouse? Those, for the next decade, there will still be a human involved in that decision loop.

AI assessment note: “there will still be a human involved in that decision loop”

Q Are there any trade-offs? We was using ClickHouse, like things that, um, you know, there's a lot of things that ClickHouse is excellent at. Are there areas where, um, it's not the right choice or not the right choice yet?

A Of course. Right. I mean, if you've got a very, of course, there's going to be technologies that are better suited for certain use cases. And, you know, that's another thing that we're excited about is the diversity of the use cases we're seeing. We talked a bit about business analytics. We can talk about observability and metrics, for example, APM, right? If you are looking to deploy Um, a hosted APM solution, and you asked me objectively, my recommendation, I'd say use Datadog. I'd say that the product, the maturity, the experience is, is considerably better than trying to stitch together something using ClickHouse, for example. Now, perhaps ClickHouse could integrate with Datadog, and we see that in the market as well for analytics, but for the actual instrumentation of the application, deploying the agents, I would not recommend ClickHouse and today for that specific use case. So there's obvious exceptions where there's going to be a better suited technology in the market. I think one thing that we're equally excited about is the amount of integrations that we're seeing be developed with ClickHouse. We talked a little bit about, you know, ingestion and we're going to be investing heavily to make getting data from Kafka to ClickHouse or Kinesis or an integration on Data transformation with dbt, you know, a much more seamless experience than it is today. We talked a little bi…

AI assessment note: “for the actual instrumentation of the application, deploying the agents, I would not recommend ClickHouse”

Q was going to be, and obviously Kafka and Confluent, uh, Confluent You know, built a, a, a multi-billion dollar, uh, company, uh, doing this. Um, but, uh, so what, what, for that first use case, what do you see, um, is, is, are we in the world of real time now? Is that an accelerating trend? Uh, and, uh, do you think that bit by bit everything becomes real time?

A I think a large number of these workloads are driven on, Latency requirements and getting these queries to perform in hundreds of milliseconds versus, you know, seconds or minutes. And we're seeing that in terms of the pull to our cloud service and why people are adopting this type of technology. And that didn't just start a few quarters ago. That started a few years ago. I think in the requirement to have this very rich, immersive, analytical experience where your data is being displayed almost simultaneously as it's being streamed in. And if you think about some real world use cases, like usage data, for example, a lot of the customers that we have are storing data in Clickhouse cloud. That is the use of their service and their customers need to be able to analyze how they're using that service. Use billing. For example, you, you prepay a certain amount of credits to a leading AI company. Well, that AI company, you need to be able to see your use of that data to make sure that you're Your costs are controlled. That needs to be extremely performant. So you need to be able to see that and get the results of that billing usage data in several hundred milliseconds. And that's the power of ClickHouse. And those types of use cases, companies originally selected technologies like Postgres, for example, for that type of use case, and then realize that as these data volumes exploded, …

AI assessment note: “we're seeing that in terms of the pull to our cloud service”

Q a clear plan in mind, um, anything that really led to, uh, being able to do this faster? Was it a question of, uh, resources and more people, or was there like a certain angle or, I don't know, product building strategies? Uh, cause like you said, uh, that's pretty much record time. Like, you know, looking around at the industry typically takes It's a lot longer, a lot more.

A I mean, it ultimately comes down to the team. We're not building a mobile app, for example, like this is a pretty complex piece of technology, and so we knew that it was going to take a lot of resources, and that's partially why we raised as much capital as we did, um, back in the second half of, uh, twenty-twenty-one to get the company started, is we knew that we were going to invest heavily in R&D, and both in terms of hiring engineers, but also just the core infrastructure, the, the testing And the dev and the environment for CICD pipelines was going to be expensive and it, uh, and it, and it proved to be the case. So, um, but then frankly, you know, the team that we were able to assemble, Alexei and his core team of engineers that joined us in their based in Amsterdam. Currently, um, my co-founder Uri has been building distributed systems on top of open source for 20 years. So it's not his first time doing this. And then Tanya Bragan, who joined us from

AI assessment note: “it ultimately comes down to the team. We're not building a mobile app”

Q thought, right? Like no customer success, uh, department. Like I, I, um, I found that, uh, fascinating. So the, the way that works, Mechanically, is that, so the reps are in charge of, as you said, what was the expression? Cradle to grave or, um, so the reps are in charge of the selling and the upselling. If there's a problem that goes into support, and support is with product?

A No, supports its own function inside of the company, reports directly to me, and it's technical support. It's what you'd expect. Now, what's different about how we're approaching it is our support engineers are as involved in pre-sales As they are in post sales. And so, cause a lot of the cases that come in is when somebody's trying the product out to determine whether or not they're going to use it. And those technical cases are sometimes not that dissimilar from the cases that come after the sale. And so that team as well is also helping us not need to hire a hundred solution architects or what are called, what I used to call sales engineers. And that was my first job at Salesforce was an SC, um, by leveraging our, our support team. Yeah. We've made a couple, Organizational decisions that I think are a little bit unique, um, and sometimes controversial, and you could argue they could sit in certain areas. Product marketing is a great example. Should that sit in corporate marketing? Should that sit in product management? Because of the technical nature of our sale, we felt that that should sit in product management.

AI assessment note: “No, supports its own function inside of the company, reports directly to me”

Q as, okay, well, this is, uh, you know, a fantastic, um, Tool for, uh, you know, all out, but like more specifically like real time analytics. And I, I think what you're, um, saying is that the ultimate goal for the company is to be, um, much more than that. I mean, basically be the, the data store for all, all things. Is that a fair way of putting it?

A Why? I point to how the market is, is reacting and we're seeing this convergence today. So, you know, traditional OLAP tools where you're running analytical workloads on top of aggregated data. There's a lot of options in the market. There's nothing new. It's been around for 20 years plus. The, the, the transactional database systems that we've talked about in the past, you know, uh, are very effective for, let's say financial transactions, credit card transactions, et cetera. And we're starting to see those two converge. And I don't think it's just click house. So I'm not going to go on record and saying that we're going to be the be all end all for every analytical workload on-prem or in the cloud. I think we're very, very attractive. Piece of technology for the majority of those use cases. And I described just a subset of those around observability, but we've got banks using us for fraud detection. There's purpose-built solutions in the market for anti-money laundering and fraud detection, specifically the financial services. You wouldn't necessarily turn to a general purpose column or data store like ClickHouse, but companies are. And so I think it's more of a trend in the industry than it is specific to ClickHouse that you're going to see this convergence occur. And again, I'm going to, I'll bring it all the way back. To, to Steven O'Gretti's article that was posted. It's …

AI assessment note: “I'm not going to go on record and saying that we're going to be”

Q competing with a bunch of different, uh, competitors. So all of this is super impressive, obviously, but also very complex. I'm curious, um, you know, especially with your background as a former head of sales about like how, uh, you, you, you, How, you know, how you sell and how you have, uh, equip a team to be successful selling a product in different verticals, different countries, different use cases?

A Yeah, it's, well, we could spend an hour on that question alone, honestly, because it's very complex, as you know, in terms of how you take a product to market and how you distribute it, and both in application side, but also in infrastructure. And then you add the dimension of open source, um, which, uh, which adds its own sets of challenges. And so, fortunately, I think that's, Potentially one of the most unique aspects about our company is the three of us started the company, myself, Yuri, and Alexei, and we couldn't be more different in terms of our respective backgrounds. Yeah, Alexei created ClickHouse. It's his life's work. He named it. It's short for Clickstream data warehouse. So he was thinking about data warehouse use cases when he formed this technology over a decade ago. Um, Yuri's been building distributed systems on open source for 25 years at companies like Netflix and Google and Yahoo, and I've been a student of distribution, and with most of that time being spent at Salesforce and then Elastic, and about a third of my career has been spent outside of the U.S. I lived in Singapore for a number of years. I've lived in Europe on two different occasions, and really there's no way to shortcut knowledge of how to go into those markets unless you live there, and so I understand The challenges and complexities with doing business internationally and the enormous oppor…

AI assessment note: “we could spend an hour on that question alone, honestly, because it's very complex”

Q Can I ask how much do you spend on that topic?

A A significant amount. You know, I, we weren't doing enough at the turn of the calendar year, so I sent an email to the company, and the subject was the AI awakening, and I said, I think we're moving too slowly, and I'm not seeing the adoption of these, uh, coding applications, for example, that I would expect to see for a leading database provider like ClickHouse. And the company rallied, uh, to the call, and we've seen just this explosive growth In terms of our use of these, of these applications. Um, and we're now looking at adopting more open weights models for us, just the traditional frontier labs, but I think Anthropic specifically and open AI have the benefit of being both a model provider and the harness provider that the open weights models right now are behind in.

AI assessment note: “A significant amount. You know, I, we weren't doing enough”

Q process, trust and security are so important. Everyone is saying that we're at this golden age in terms of cybersecurity, and we see more and more security hacks. How do you feel about the security vulnerabilities that come with the agentic future that you are planning for in three years out? More on the enterprise side. We obviously see it on the personal side, but more on the enterprise side.

A Well, I think it's going to require multiple deployment models. So you need to be able to consume services via a cloud offering through any one of the three major hyperscalers. You're going to need the ability to manage that data on-prem behind your VPC in your firewall. So you're going to need to have the flexibility to deploy these applications as you best see fit, especially in the end in the enterprise. Where you've got highly regulated industries, a lot of data privacy concerns, a lot of compliance requirements. And so as a supplier to these customers, we think about how do we support them dependent on their deployment preference? And, you know, that's a tricky roadmap to maintain because most companies pick one of those avenues. You know, I mentioned Snowflake and Datadog. They're primarily cloud services. You know, you look at more traditional technologies that run on prem. And so if you force your customer into a specific lane, you know, you're limiting the addressable market that you can go after.

AI assessment note: “I think it's going to require multiple deployment models.”

Q And how does that work? Uh, so you, you, you mentioned the, the, the technical aspect and the sales aspect of, of, of building a global company, but culturally, so you're in Silicon Valley or the Bay Area, uh, you have a big presence in Amsterdam. That's nine hours of, of time difference. Um, how do you all make it work, uh, logistically, but more importantly, culturally?

A Well, the company was born during COVID. So we were kind of distributed a bit out of necessity. Um, and it was intentional. And when we started the company, Alexei and his team, uh, moved to Amsterdam. And so we had a hub there and my head of Europe, Arno is also based there as well. So it was obvious for us to use that as a bit of a hub. Um, the reality is because of how much importance I place on the international markets, we're going to be a global company very, very early. Plus when you're Supporting a managed service around the world. You're going to have SREs in, you know, almost every major time zone. Um, so that was, that was an obvious decision for me, how it's working out, uh, logistically, like practically day to day is because I think if you took a look at our management team, and even if you went down, you know, a layer or two, I would say on average, we've got more years of experience than your typical Early stage startup is how I would diplomatically state the generation of the people that are making decisions inside of the company. And, um, and so we've had the benefit of working for other companies in an office setting in the past, and we've been able to take, I think what's best about that and leave what, what didn't work necessarily, both about an office setting and a remote setting. And I've worked in both of those environments. Honestly, I'm getting ready t…

AI assessment note: “on average, we've got more years of experience than your typical Early stage startup”

Q How do you think about the open source project these days as you become more and more of a successful commercial company?

A It's interesting. My background is not in open source, to be clear. I mean, I, my career started at Sun Microsystems in the, in the late nineties, and then I went to PeopleSoft, and then I spent 12 years at Salesforce, and then it wasn't until Elasticsearch in 2014, 10 years ago. So I guess I've been in it for 10 years, which may be longer than most or many. Did I really understand the power of open source and the pace of innovation and the benefits of the distribution model and understand the licensing complexity that goes into those types of decisions? And I think that in just how the community comes together, And, uh, how you can have hundreds of contributors, uh, around the world, uh, that improve upon the quality of the software. Um, how users, uh, and companies like Instacart can actually also be contributors, uh, to the technology that they use, further driving a faster innovation. So it's a, it's a highly disruptive, uh, technology model, I think, as everybody knows. Um, I think it does represent really the future of, of software, especially in infrastructure. I don't think that's necessarily played out On the application layer, um, as people thought it might, and who knows whether or not that'll, that'll occur, but look what's happening right now in, in the AI category in terms of the disruption of open source and how things are just changing overnight, frankly, with t…

AI assessment note: “My background is not in open source, to be clear.”

Q Is there a real-time data stack? Is that like how you think about it? Uh, I don't know, maybe Kafka to ClickHouse to something or not?

A You know, I try not to, I don't want to call it a buzzword, this modern data stack, but If you've been in the industry as long as I have, you see a lot of things come and go pretty quickly and get disrupted even faster is what we're seeing right now with Gen AI. And so the reality is ClickHouse has been used for in AI applications or to power AI applications as a feature store for years. This isn't something that just emerged over the past year. So that's a use case that's been very common. And now people are storing embeddings in In ClickHouse and it's used for vector search, for example. So we're seeing that grow. The way I describe it simply is kind of picks and shovels for this AI movement where a lot of these AI companies need an analytical database, um, or they want, and they don't want to have a bunch of bespoke databases for each one of these use cases. They don't want to have a vector database and then an analytical database and a transactional database. They want to have one data store where they can put all of this information and query in real time. And that database needs to be extremely resource efficient based off the volume of data that they're ingesting and extremely performant based off the latency. And so ClickHouse, I think is the clear winner in that category for both of those dimensions. And so, you know, LangChain is a great example. They recently spoke a…

AI assessment note: “I don't want to call it a buzzword, this modern data stack”

Q So maybe, uh, going in a different direction. Um, I'm curious about your, Personal experience. So you have this, uh, very, uh, interesting and illustrious background as a sales leader, Salesforce and Elastic. Um, how has the transition to being a CEO been? What was surprising, different, challenging, or perhaps not so challenging?

A Well, I wouldn't say I have this illustrious background. Let's start there. I grew up in a very modest middle-class Uh, environment, the son of an immigrant, um, went to public school all the way through university. Um, was just very lucky with some of the, the decisions that were presented to me in terms of my career and surrounded myself with, with what I think are very smart people that helped me make those decisions. The reality is the only reason I went to work at Salesforce, 22 years ago was cause I did not get into business school. I always wanted to go to a leading business school. So I applied to Stanford and Harvard and MIT where I got waitlisted and I didn't get in. It was back in 2002 when a lot of people thought that it was a good idea to go back to school because the dot com boom had just occurred and bust. And so, uh, I met with Marc Benioff and a few others and I got offered a job. That's really otherwise.

AI assessment note: “Well, I wouldn't say I have this illustrious background. Let's start there.”