The Tech Leadership Compilation: Scaling Teams, Culture & AI with Leaders from Twitter, Sonos & Datadog

Transcript

[00:00:01] Intro:  Welcome to Signal to Noise by Riviera Partners, the podcast where leading executives share how they cut through the noise and act on what matters most. We go beyond the headlines to explore the pivotal decisions, opportunities, and inflection points that define their careers and shape the future of the companies they led. It’s time to cut through the noise and get to the signal.

[00:00:24] Ali:  I’d love to hear your philosophy around leadership here. So, what models did you have early in your career? Obviously, you have an approach and a framework to leadership. How did you develop that?

[00:00:35] Mike:  I think this is gonna be probably not the normal answer because I didn’t have a mentor from day zero and whatnot. A sport that you and I both played was rugby. And, you know, I think when you play sports, it doesn’t matter what sport, but any team sport, you tend to develop skills around leadership. And I don’t know about your team, but, like, you know, our rugby team, we had quite a diverse group of people. Like, you know, we had folks from ag, and here I am, like, this nerd that’s gonna go do a PhD. Like, we’re, like, all over the map. But I learned later that, like, that was one of those valuable skills, which is, like, everyone’s different. You gotta meet people where they’re at. When I started my career, because I was doing startups, I had no idea what I was doing, and I really didn’t have a mentor. So, I just kind of made it up. And what that resulted in was certainly making more mistakes than I needed to, but I leveraged a lot of what I learned in sports. And, you know, subsequently, I remember talking to my partner, Mary Meeker, about this because we realized that at Kleiner, every partner, maybe minus one at the time, had done sports in college. And I think it’s just interesting. And I’m not saying you have to do sports. It’s just one of the areas where I think you can develop these things. For me, I didn’t really have a true mentor until Twitter, actually. It was Bill Campbell. He was part of my interview process, and, like, oh my gosh. Like, this guy is legendary. He’s one of the reasons why I went to Kline of Perkins. Like, you know, he agreed to come visit weekly as we tried to change the firm. He, by far, had the biggest impact.

[00:02:00] Josh:  How do you think about identifying the talent that you need in any context, not just Sonos? And what do you think of when you try to determine good from great?

[00:02:12] Patrick:  I think I’ve learned over time that we all get fooled. We have to be very careful because a lot of our initial impressions, we think we all can, you know, determine who somebody is in, you know, even an hour interview, right, or something like that. And so I’ve begun to rely a lot more. And I think over time, one of the other things I would highlight, Josh, is the importance of references at Sonos was higher than I’d seen everywhere. And usually, you know, yourself and myself, we divide and conquer on that. But the leader would be heavily involved in talking to both listed references and then other references we would find, truly, who this person is and how we would help make them successful if they were the right candidate. And so for me now, so much of it has been understanding somebody at a deeper level, so spending a fair bit of time with them, understanding what motivates them, what do they wanna get out of it, and really developing that kind of relationship, like you said, a really candid relationship over a period of time, but as well talking to as many people as possible that have worked with that individual to understand what they’re really about, and is this going to be a good fit. And so I still think that we, in tech in general, way underestimate the importance of references that maybe aren’t provided, but understanding, you know, that you seek out and find that ultimately tell you the strengths and weaknesses of particular individuals. Because, again, the interview process and the experience, no matter how many hours you put in, are still going to, potentially, you could fool yourself, right? And so I would say that’s the important thing to me. And then I think I found that just the humility and curiosity is the thing that really sets the leaders apart that are great versus those that are good. There are some people that are fantastic experts in their area and in their discipline, but, you know, they don’t really have an interest necessarily in learning the next thing or how AI impacts their area. And so they’re good, but the great ones are figuring out, hey, what’s my job gonna look like in five years? What’s the next step for this? Where’s this all going? And they’re humble enough to know that they don’t know, but they’re going to work towards figuring it out. Right? And they’re curious about what those answers are. And so I think that’s a really, really important dynamic.

[00:04:17] Sean:  How have you navigated fast-growing organizations in terms of the security challenges changing? And what changes as the company grows as quickly as some of yours have?

[00:04:29] Emilio:  So, the one thing about working for a company that’s a hyper scale mode is that I joke about this internally. And in fact, Alexei, our CTO and cofounder, that was one of the things that we aligned pretty early in my tenure at Datadog is strategy, we call it the S word, and that is because I can write in a Confluence or a Google Doc or whatever, like, where I think we’re gonna be in three years, and I will spend so much time editing that document that is it really a strategy document, or is it just my journal as I learn new things. So, I toss all that out. And second to it is, which is very abnormal because security tends to be very top down, I’m one of the ones that hire smart people to lead and dictate what we should do. So, a lot of the things that we do at Datadog is very bottoms up. That was another thing where I think as a hyperscaler has worked for me. Same thing at Hulu, where I didn’t tell my teams what to do. If something was important enough for me to define priority, they would have already known about it, or it was just something that was decided that they had no clue that we were even in conversations about. But you can ask my teams, well, Google and Datadog is a lot of the planning comes from them. They’re the ones that say, hey, based on what we know, we’re deep in the fire. These are the things that we need to do. And then where I get involved is in the how we get done, what sort of feedback loops that we build, how do we know things are successful, like, all of that. Like, how are we thinking about it from, like, a big picture standpoint, but then what needs to be done? And this is a, I find that a lot of people struggle with it when we get hired because they’re used to working in environments where the planning is very top-down. So, they’ll come to me and be like, hey, what do you think I should do? And I’m like, I don’t know. You told me what you should do. I don’t know. I don’t know your world, right? I’m not the one doing what you do. Well, what do you think we’re gonna be in two years? And like I said, that’s like, yeah, good luck. So, I think being open to the fact that you just don’t know. And then you start looking for what you should do is start looking for the signals that inform you of what you should be aware of and what you should be focusing on.

[00:06:23] Adam:  When you look at the organizations and you’ve got an interesting window in. When you look at those organizations that are AI-ready, you know, Riviera, through our work, figured that only about 2% are really hitting the AI readiness button well. What makes those 2%? What have they done? Is it structural? Is it leadership? Is it fill in the blank? What have they done to get themselves AI-ready, where the other organizations, enterprises, maybe are still struggling with things?

[00:06:50] Bill:  Yeah. I think it comes down to will, and that usually comes down to leadership. Now, having the will at the top level to take this seriously and really treat it as something that’s business, you know, required and have that position cascade all the way down your organization takes a ton of energy and leadership. Now, once you do that, you also get the foundational things right, and you know, you run some experiments, and you figure out what works. So, they’re sort of like they progress down that, but it always starts with the leadership saying, this is super important for the company. We need to invest significant resources to do it, and we are going to treat it like a first-class citizen. This is not, you know, a little experiment off to the side that doesn’t matter, and maybe it’ll hit. This is fundamental to who we are as a company, and we’re gonna do it excellently. So, you know, there’s a quote from one of my companies. The CEO said that if the CEO is not the chief AI officer, then they should be fired. Now, perhaps that’s a little aggressive, but I think that is why his organization is 

completely on their front foot, related to every other competitor in their industry.

[00:07:57] Michael:  What do you think makes a great data scientist? If you could put the metrics around that. 

[00:08:01] Jon:  So, we’re in an interesting time, right, where thanks to built-in Gen AI models and a lot of people’s favorite tools like Claude Code, like Cursor. This means that the data scientist no longer needs to be incredible at writing accurate Python code, for example. Data scientists still need to have a lot of experience with experimentation. So, being able to come up with ideas for teasing some signal from noise, for example, to be able to run multiple different experiments and be able to make sure that there aren’t going to be confounders, you know, unexpected things in the data that are really leading to some result. It’s these kinds of, like, methodological questions that if somebody does a PhD in a quantitative science like you have, then you will invariably kind of develop this skill set of working with a lot of quantitative data and figuring out the kinds of things that can go wrong in interpreting results and making sure that you’re looking out for the biggest likely issues.

[00:09:16] Eoin:  Talk to me about when you’re thinking about opportunities that you wanna spend your time on because I know you’re incredibly dedicated when you’re in. You’re all in on what you’re doing. It’s a huge investment for you. What are some of the factors that go to your mind when you’re evaluating any opportunity, and then specifically how you think about making this leap to be a cofounder?

[00:09:35] Toufic:  For me, at the risk of sounding arrogant, I am at a point in my life and my career where it has to be fun. What I do next, it has to be fun, and it has to be impactful. So these are, in terms of opportunities, these are the two major criteria that I use to say, hey, is this something that I want to do? It has to be, to me, means something to me and has impact, and it has to be fun. I hope that doesn’t sound too arrogant. So, that’s in terms of the opportunity. In terms of being a cofounder and finding the right cofounder, you’re gonna hear me use this word a lot. It’s culture, culture, culture. That’s really the major headline here. So, a lot of people go into finding a partner or a cofounder for someone who they look at it a lot more from a number or logic perspective. Some skills matrix. I have these skills. I need somebody who completes the skills. That’s really good and important. But for me, what trumps all that is the culture because I’ve been through it, as you mentioned, a few times, and you’re going to be deep in the trenches with this person. You’re going to argue with this person. You’re gonna have big fights with this person. You’re going to deal with really stressful situations, whether from a value perspective or dealing with external circumstances, dealing with people, and so on. If your values and your cultures are not really, really truly in alignment, for me, that’s the recipe for disaster. That’s why I put that above anything else.

[00:11:06] Glenn:  I’m curious how you identify a great talent as well on that theme of kind of you’ve obviously coached a lot of people and I’m sure mentored a lot of people, but how do you identify great talent, maybe especially from diverse backgrounds or different skill sets, and identify those kind of characteristics you want?

[00:11:22] Tiama:  I think, well, first of all, I know what type of leader I am. So, I know that there’s certain people who are gonna enjoy working with me, and there’s certain people who are not. So, I always try to be really upfront and honest about that. Because if I pick the people who are gonna thrive, as I always tell the teams, I’m looking for someone who’s gonna thrive, not survive. So, if I pick the people who thrive, they’re gonna get the best out of it. I’m gonna get the best out of it. And as a result, our clients, our shareholders, our investors, etc, they’re gonna get the best out of it. But there’s three signals that I know work very well for me. The first is the ability to abstract and clearly communicate. So, I need people who are able to, those eight legs I’ve got going, right, like, I need to be able to contact switch quickly. I need people to be able to keep up with me as we’re going. And I also need to be able to learn. I don’t have any desire to be the person that knows everything. In fact, everybody I hire needs to be better than me in at least one area. Otherwise, I’m not hiring them. What’s the point? They’re better than me in all eight areas. That might be scary. But I need people to be better than me in at least one. So, what I’m usually looking for when I meet people is the ability to abstract, and we can go into that more if we need to. But they need to be able to very clearly communicate something to me without jargon, and they need to be able to break it down to the smallest pieces and explain why whatever it is that they’re saying matters. Because that’s gonna be a signal that they’re gonna be able to be influential. They can organize information well. They can communicate well. You need that in most roles, but you certainly need that if you’re working in product and technology. The second one is, I wanna make sure that the people I hire are self-aware. They are growth-minded. They are looking to evolve. And usually, the way that I see that is they can answer a question with pretty quick specificity about a time that has really evolved the way that they work. And it doesn’t always have to be a positive. It can be a negative. They don’t have to sit there and say, oh, gosh, you know, it’s a tough one. Let me think about it. Like, it’s fine. That’s totally fine. But it shows me that they haven’t really self-reflected on the areas that had really been set changes for them in their career. And I’m looking for self-awareness because I’m looking for people who wanna be the best. They wanna lean into that coaching. They wanna lean into that growth together. And then the third one is the org fit, or the stage of business fit. I’ve worked for businesses that are a billion dollars, and I’ve worked for businesses that were sub 1 million. And the thing that matters most is, are you right for that stage of business? Because what it takes to win in the sub-1-million business versus what it takes to win in a billion-dollar-plus business are totally different. And you could hire someone who is amazing at the distribution phase. But if they’re in a 0 to 1, like, it’s not to say that they can’t be successful. There’s just gonna be so much context to build and so much competency to build.

[00:14:06] Outro:  Signal to Noise is brought to you by Riviera Partners, leaders in executive search and the premier choice for tech talent. To learn more about how Riviera helps people and companies reach their full potential, visit rivierapartners.com. And don’t forget to search for Signal to Noise by Riviera Partners on Apple Podcasts, Spotify, or anywhere you listen to podcasts.