Invest Like the Best with Patrick O'Shaughnessy - Peter Reinhardt - Learning How to Sell – [Founder’s Field Guide, EP. 34]

Episode Date: May 20, 2021

My guest today is Peter Reinhardt, co-founder and CEO of Segment, the market-leading data customer data platform that was acquired by Twilio last year. In our conversation, we cover the fascinating journey of Segment from an education feedback tool to the business it is today, Peter’s sales philosophy on meeting the customer where they are and not where you think they should be, and why revenue operations, or RevOps, is underrated for any business. This was an incredibly honest conversation on company building that any builder can learn a lot from. Please enjoy my conversation with Peter Reinhardt.   For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Founder's Field Guide is a property of Colossus, Inc. For more episodes of Founder's Field Guide, visit joincolossus.com/episodes.   Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.   Follow us on Twitter: @patrick_oshag | @JoinColossus   Show Notes [00:02:47] - [First question] - What Segment currently does for its customers [00:03:37] - How this industry looked before Segment came along [00:04:30] - Overview of a simple data flow and the utility of capturing user data [00:05:55] - Insights that lead to developing the structure for a central data pipeline [00:07:52] - Why other companies don’t just build their own data collection API [00:10:04] - Early days of building the company and finding success outside their initial idea [00:12:29] - Pivoting from classroom software to providing software the world needed [00:16:17] - What Technology Wants  [00:17:06] - Sign that validated becoming a B to B software company [00:19:49] - Challenging moments trying to scale Segment after bootstrapping the startup [00:24:58] - Getting customers to articulate your value proposition helps grow your sales [00:26:42] - Deciding what would be sold and what would remain open source [00:28:05] - Structuring and developing an enterprise sales team and sales model [00:30:23] - Overview of a 2 million dollar per sales person contract and how it’s allocated [00:31:38] - How it feels to be participant in the SaaS industry [00:34:02] - Lessons learned about revenue operations and how underappreciated it is [00:36:39] - Backwards efficiency and companies who use products to bootstrap scale economics [00:38:51] - Focusing on customer acquisition cost to maximize scale efficiency [00:40:52] - Potential disruptors to economies of scale [00:42:21] - Lessons learned from achieving massive scale and being acquired by Twilio [00:45:40] - Behind the curtain view of current data use trends [00:48:49] - His perspectives on the data privacy landscape and their implications writ large [00:51:48] - Legitimate businesses that will be hurt by changes in data privacy standards [00:53:11] - How data privacy standards may affect everyday merchants [00:54:03] - The worst advice he’s heard given to new entrepreneurs [00:56:20] - Impactful advice received along the way when growing Segment [00:56:47] - What has him most excited about the future [00:57:13] - Lessons learned about leadership from Jeff Lawson and his own experience [00:58:51] - The hardest changes he’s had to make as a leader [01:00:20] - The kindest thing anyone has ever done for him  

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that I became an investor. To embrace the future of sleep and get $150 off your new mattress, go to 8Sleep.com slash Patrick or use the code Patrick. Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Founders Field Guide. Founders Field Guide is a series of conversations with founders, CEOs, CEOs, and operators building great businesses. I believe we are all builders in our own way, and this series is dedicated to stories and lessons from builders of all types. Founders Field Guide is part of the Colossus family of podcasts, and you can access all of our podcasts, including edited transcripts, show notes, and resources to keep learning at join colossus.com. Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management.

All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaunsi asset management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of O'Shaughnessy asset management may maintain positions and the securities discussed in this podcast. My guest today is Peter Reinhart, co-founder and CEO of Segment, the market-leading customer data platform that was acquired by Twilio last year.

In our conversation, we cover the fascinating journey of Segment from an education feedback tool to the business it is today. Peter's sales philosophy on meeting the customer where they are and not where you think they should be and why revenue operations or revops is underrated for any business. This was an incredibly honest conversation on company building that any builder can learn from. Please enjoy my conversation with Peter Reinhardt. So Peter, I think we need to start at the end. I'm a data person by background, so I don't want to get too off the deep end here before we orient the audience

and what segment does today. And then we'll probably rewind all the way to beginning. because the journey has been so unique and interesting in your case. Can you begin by describing what Segment does for its customers today in 2021? Yeah, so we help companies collect and manage all of their first-party customer data. So all the interactions that they have with their customers on their mobile applications, their websites, their help desks, their payment systems, all those things. All that data, they float into segment where we assemble this holistic record of their customer interactions. And then more like the pipe.

So we've sort of moved that data out to all the different marketing sales support tools where they actually then make use of that data to interact better with their customers. Can you say what existed or how this function was handled prior to Segment? Presumably you made life easier for people that are generating first party data, want to do something else with it. They probably were still doing something else with it before. What did it look like before Segment came along? Yeah, before us and frankly still most of the market today goes and builds this in-house.

So they've got engineers who get assigned to requests from the marketing team to go integrate the newest ad campaign pixel into the website, like bespoke, use the API directly alongside the other 20 or 30 marketing pixels that they've got going on, pull data out of this stripe API or out of this Salesforce API and loaded into our data warehouse, lots and lots of bespoke engineering going on to build each one of these pipes independently. And what we stumbled on us that there was a relatively clean API to be. built that would act as one giant pipe for all that stuff. Could you give a simple example of maybe just one of those data workflows just to put some flesh around the concept of how a normal company might care about a data pipeline

like this, just like a simple example? A small company that's just getting started, often the first thing they'll do when they launch their website is they'll put Google Analytics on the page. And so that's copy pasting a snippet, adding little pieces of tracking code around the website. Then they start getting a sense of like how many people are coming and viewing the page, how many people are clicking through my e-commerce flow. And then they start to notice that, hey, not everyone is actually going all the way through my checkout flow.

So I'd love to be able to send them an email. Okay, well, now I need to get that same data about people moving around the webpage into my email marketing tool so that I can send emails to the people who started but didn't complete the checkout flow. I actually also want to send blast emails once a month to my customers announcing my new product launch. So, okay, now I need a different email marketing tool

that will allow me to send these batches. that. Oops, I need to get that same email data and tracking data into the other email marketing tool. And then as the company gets a little bigger, they're like, now I actually, I really want an analyst that can sit down and run arbitrary SQL queries and build beautiful visualizations of how people are interacting with my site, what products they're using, all these things. And oops, now I need that same data in a data warehouse. Over time, the, like, the rat's nest of all these data pipelines multiplies as companies become more complex and want to make use of data in more and more places. So if we think about segment as the universal pipe, if you will, from information about

customers that's digital and getting that to the right places for analytics or product improvement or whatever they want to do with that data, what is the insight that makes, you said it's unique that it's the universal structure for that pipe versus having to rebuild it each time you have some workflow like this. Say a bit about that maybe technically or in terms of the product. What was that insight? Flesh that out for us a bit more. I think it was getting the abstraction layer of the API. And credit for this goes to my co-founder Ian. There's a really actually awesome paper by the author of the R programming language. It's called Tidy Data is the name of the paper. And he basically explains what it is that makes data in this concept tidy. Basically, what it boils down

to is a long table of observations of a thing at a time. And that has a bunch of nice properties to it. And you can aggregate all kinds of things on top of that. But you really want to start with data that is things like user A, did X at time Y. And if you start with that, you can reassemble the current state of the user, you can reassemble all these other things. We discovered that same thing by accident, I think, with our API, which is very, very simple. So the API for collecting all this data, you can say, like, well, there's so many different tools.

You're going to need such a complex API to support all this different explosion or advertising tools and email marketing tools and analytics and data warehouses and so on, so on. But in reality, you actually only need basically two API methods. One is the identified call, which is who is the customer. And the other is the track call, which is an observation that user X did Y at times E. That's it. That is the entire API. We've since added a little bit to that, but that simplification down to this abstraction of these two API methods, I think, has allowed it to be a single pipeline and what allowed a lot of engineers to look at it. And so, like, out that is an elegant solution to this massive spaghetti code that I've been writing.

What, given the simple, elegant architecture of that concept makes it hard for others to build their own version of a universal pipe. So if I was a huge organization and I had 200 of these workflows, why wouldn't I just dedicate some engineering resources to build a segment cloned and manage it in-house? Many have tried. It turns out that there is devils underneath in the details, specifically in a few different places. One is how do you translate those two API calls, simple abstraction, how do you translate that then to how the data should show up in Google Analytics? We have been building our Google Analytics integration and refining it for almost a decade now, and it shocks me that we still find aspects of that integration that still need to be better.

I guarantee you that when you have 20,000 customers using an integration, we're still finding things that can be improved about it. There's just depth within the quality that one of these integrations needs to have, and then we've got hundreds of them. So if you think about as a company like, oh, I'll just go build this myself, that is a giant tarpet of time

that you can waste going and rebuilding these integrations that are already super high quality. So that's one. The other is, turns out data deliverability is no joke. So, okay, great, you've got the data point in. Sure, you send it off to the downstream provider. But what if that provider has downtime? It happens all the time.

It's not like a what-if scenario. It's like we've got 300 partners. I guarantee at least one of them is down right now or having significant deliverability issues. So how then do you queue that data and then re-deliver it at a time when the API is up? We have a cool status page at status.com where you can actually see the deliverability to all of those partners in real time. And there's like substantial losses along the way that we patch over by re-delivering the data when they're up or when they're ready to accept it. So there's like probably 100 things in this realm of just like, yeah, sure, you can do it. it'll be okay.

Whenever it'll be done high quality, unless you're sort of are amortizing all of this R&D over thousands of customers. And it's a huge maintenance burden. So all of these integrations change their APIs. They change how they want to accept. They change what features they have. They launch new things. They need new types of data.

So that is just a huge workload to keep up as well. I want to rewind time. Then there's a million other things I'll ask about current state of the business and customers and everything you've learned. But your story to get to hear is quite whine. and interesting, especially in terms of how segment began. I think it'll be surprising to people to hear what the business did at the beginning, given everything you just described. Can you bring us back to the earliest days or day one, day zero, what you and your co-founders set out to do and

the lesson you learned in finding or building a product that the market actually wanted? In 2011, I was studying aerospace engineering at MIT. My two co-founder is Phillya and Calvin were studying computer science at MIT and, of course, co-founder was studying design at Rhode Island school design. And naturally, we were really excited about things to help students. The idea that we had and that we applied to I Combinator with and that we got into the program with in 2011 was a classroom lecture tool where students could push a button to say, I'm confused. And the professor would see this graph over time about how confused their students were. And we had talked to some professors who seemed to be excited about it, including Professor Morris at MIT, who's also on the YC committee.

and we built a little product where people could do this thing. It turns out that this is an incredibly distracting thing to put into the classroom. Basically, as soon as you put this in front of a student, they go straight to Facebook, they go straight to Twitter, they go everywhere except focusing on the lecture.

By having a thing where the professors went and said, like, everyone, please get out your laptops and open up, it's called Class metric, open up Class metric and we'll use it as a way to like engage better during the lecture. Total disaster. I remember standing in the back of this one classroom at Boston University. I think it was an anthropology class.

And we started counting screens just standing in the back. Like, what are people doing? How many of them are actually using our tool? I think at the beginning of class, 60% of students were not using class metric at any given time. And by the end, it was about 80%. We're not using class metric. It was basically, yeah, the most horrible thing you could ever put into the classroom, pretty quickly realized that that was not going to work.

But we'd raise 600K at a demo day. on the back of kind of professors trying it out in the fall. And so we called back all the investors that we had raised money from a few weeks prior and said, hey, we just tried this in the classroom. It was a disaster. We should do something else. What do you want us to do with the money? All of them said, well, all of them except a couple said, we invested for the team,

so go find something else. And then there were two that we paid back. This is a fascinating transition from a classroom piece of software to something very, very different. Describe that bridge. You've talked to me before about the difference between Zickickickicketts, to talk about it, founders with a vision of the future versus founders that sort of recognize something the world technological machines sort of wants or needs. So talk us through that concept and how that

applies to switching from a classroom software to what segment became. Yeah, we were very excited about the classroom electric tool because we thought that it was the way that the world should work. You know, we were like, hey, you should be able to give feedback on a lecture and a professor should go and improve their lecture. And, like, people should care about how good these things are. And this should be a great way to go get all of this learning and maybe extract it and build it into, like, some kind of massive online research or learning opportunity.

But, yeah, the world just doesn't really care. How you think it should work. It just doesn't. It just doesn't. The world has its own problems. And we actually made this mistake, again, after class metric failed, which is we were like, hey, we should have been able to look in our analytics and see that the students were distracted.

And so we then spent a year trying to build an analytics tool to compete with Mix Panel or Google Analytics or any of those sort of players in that space. That was also a disaster because, again, we had this mentality that, like, we knew it was right and we knew how it should work, and we're going to go build that maybe later in life. You've only seen enough that you can have an opinion like that,

but certainly for some college kids, like not a good strategy. And I think about a year and a half later, we were basically realizing that our analytics tool was also failing. So we're a year and a half in, basically failed at everything. We've got 100K left in the bank of the 600K that we've raised. We realized we have about four to six months of runway left. That was pretty intense. We realized we got like one more shot on goal.

And so we had a big discussion over a couple of days and it ended with a huge fight where my 500 Ian had this idea that we had built an open source library a bit over a year before that for our own use, which was just to take data from our website and send it through to these analytics tools and so on downstream. It's called Analyticsjs. He was like, you know, I think that little analytics data abstraction could be a really big business.

And I was like, that is the worst idea I've ever heard. It is 300 or 500 lines of JavaScript. It's already open source. I don't understand the business opportunity here. And so we got in a huge fight about this. And I went home and I was like racking my brain. Like, how do I kill this thing? This is terrible.

Finally figured it out, came in the business. the next day and I was like, all right, guys, here's what we're going to do. We all trust hacker news. Hackernus is full of developers. It's clearly a developer product. It's built a beautiful landing page. We'll put it up on hacker news and we'll see what happens. I was like, done, dead. And so we built the landing page. We put it up on hackery news. Went straight to the top. Got a few hundred up votes on hacker news. Sat at number one for most of the day. Got a few thousand stars on GitHub. Got thousands of email signups. People are reaching out to us on these back channels like LinkedIn demanding access to a hosted version of this library.

the whole thing basically blew up in a good way in like 24 hours. And I was like very one excited, but two, like self-confidence or like self-work or like judgment was just like, I've been wrong three times now. You know, I had two ideas. They were wrong that I thought were good and the world told me they were wrong. And then this one that I thought was right, the world told me the opposite. I was like so baffled. It took me like a month and a half to recover from that as we started getting more traction

with the library. But yeah, the real learning for me was like the world. world could not give a shit, how you think it should operate. It really has problems that it once solved. If you go and humbly solve those problems, it rewards you. And if you try to tell it how it should be done, like, just get your butt kicked. How broad do you think that observation is? Do you think that describes, I love Kevin Kelly's idea of the technium. I think you're the one that turned me on to his book, what technology wants, where it's like this independent thing that has needs. And if you, like you just described, if you serve its needs, great things will happen. But you probably can't change.

the whole technium. Do you think that that describes most successful technology businesses versus this romantic notion of a visionary founder that wants to change how everything is? I think it describes most B2B businesses. And I'm the last person on earth that you should ask about B2C. Self-admitted, deep nerd. In B2B, like we're under contract to solve a business problem. I think in B2B, it's pretty cut and dry. B2C might be different. The Steve Jobs of the world's maybe attracted to that market where like maybe there's a little more room for that kind of creative here's what you should want. Say a bit more about, so you have the hacker news experience. So obviously there's interest in the problem space, even though it's 500 lines of code,

open source already. Describe more of what that felt like in the early days. You realize you were wrong. I'm sure there were more data points that started coming that said, okay, we might be on to something. This is sort of like a product market fit question. For those that might be operating in the similar, like give the B to B machine what it wants versus try to change the world, to say more about what that felt like or what the data points were, like, what were some of the early signs that were continued to validate that? I would say it was very explosive interest. And I think the same is true of a lot of other B-to-B companies and their founding stories. Dropbox, another great example. They blew up on Reddit. I think the feeling, it was like every metric was going, hey, wire.

We'd had like a trickle of like 10 visitors per day or whatever to our website before that, for our analytics tool. And then all of a sudden it was like tons of people submitting issues. tweeting about it on Hacker News, emailing us, LinkedIn us, like, just everything. People were just trying to get the thing. Part of it actually felt like loss of control. We were going from this mindset of, hey, we know what's right. Actually, we don't know what's right. People are going to tell us what's right.

And there was this like big letting go of, whoa, this thing is going to move on its own. I'll try to clear the ground in front of it, but it was just going to go. And that loss of control that like actually we're handing the roadmap in many ways to our customers and their needs was a big adjustment for us. We kind of overcorrected on it, actually, because we were like, this is new and it's really working. And so let's lean into that mentality. And so for six months, until we fundraised again, we literally just said, we are not going to drop a vision. We've discovered this open-starts library that seems to solve people's problems. We're going to spend the next six months just trying to solve their adjacent problems. We're not going to drop what we think

this could be in the future. We built tons of adjacencies. We actually killed a lot of those initial directions because we knew how to build at that point as a team. And so we built stuff, killed stuff again and again and again and again. And then after six months, we kind of looked back at what we had built and what was working or wasn't. And what people had wanted was more integrations, more destinations to send data. They wanted more sources, like more places to pull data from. And that was pretty much it. And so we started to get a sense, though, that it was all centric on customer data. And we started to get a sense that that problem was actually pretty deep.

And so after six months of exposure to just solving adjacent problems and solving these requests and getting more contact with customers, then we started to write down kind of what the vision might be that like, hey, maybe we could sort of back our way into being the customer record that a business has internally. What was the most challenging thing you and the team faced? So if we call that first chapter maybe through that six month period, the early product chapter or something, whatever you want to call that next chapter, early building this into a proper business and scaling the team, et cetera, scaling the product. What was the most challenging

part of that second chapter of segment? Maybe I'll actually add one thing also, which is the most challenging aspect of the first chapter, I think, was actually keeping the team together. I think most teams actually would have split in that first chapter. I mean, it was a year and a half of misery. I ended that year and a half with went to the hospital multiple times for panic attacks. I lost like 10 pounds in two weeks. By any health measure, like we were not doing well. And we were fighting, it was bad. But ultimately what kind of kept us together was that we've been roommates for a long time. We were great friends for years beforehand. We were not particularly interested in working anywhere else. At the end of the day, knew that we wanted to work with each other.

The idea mattered less than the people around the table. And I suspect that many other companies kind of die without that going through the same struggles, but they just die and split up before they maybe get to their breakthrough if well. That was the first big challenge. The second challenge after we had product market fit, and it was like obvious that this was a thing and people were adopting it. We had just like a very steady growth curve, despite not really marketing beyond kind of the launch on Hacker News and a few blog posts here and there. The real challenge became sales. And that's because we were all college students. We'd never been exposed how companies operated before. We'd certainly never sold anything in our life, aside from equity

in the company. And we were really scared to ask for money. We sent out this apologetic email to like the top 15 customers or something by volume. And we basically said like, hey, like we're thinking we need to raise the price to about like $15 a month. Would that be okay with you? And bless his heart, this guy, Eduardo from Brazil wrote back. And he said, guys, this price is so low that I may have to stop using your products because there's no way you're going to be around in a year. So don't charge me more, but you better raise your prices. And that was obviously a wake-up call. Then we got a sales advisor.

I'll never forget the first sales meeting that I had with him. Our first sales meeting, this is with NAAP, who's the CEO of Zammar and now CEO of GitHub. They were existing using us on the pre-plant and everything, walking over to this sales meeting in the financial district in San Francisco with Mitch. Mitch sort of stops and says, Peter, in this meeting, you need to ask for $120,000 contract. And I was like, Mitch, that is a thousand times more expensive than our public price listing.

I don't know what you're smoking, but like, I cannot go in and ask for a thousand price increase. And he's like, well, then I quit as your sales advisor. Literally our first thing. I was like, okay, I guess I'll ask for 120K. So we go into this. He's like, just trust me. That's the like base rate for any enterprise contract, which in retrospect is true.

So we go into this thing and that's like, okay, well, what's the price? And I was like, well, and I turned beat red. And I was like, now the price is $120,000. And I was like, not believable in any fashion. And he looks at me and he's like, how about 12K a year? And I was like, how about 18? So he agrees to 18K and became our first contract, I think, or second contract once we finally got around the signing.

So he was like victorious. He saved 85%. I was completely mind blown because here we were charging 150x. Literally 150x. That was a second big, awakening moment from me. The value that we were attaching in our mind's eye was on a cost basis rather than a value basis, and then we had no understanding the value that we were actually providing to customers. But in B&B sales, value is what you should sell on and value is what matters

to the customer. They couldn't care less what your cost basis is. So that was a huge learning. Later on, we were starting to scale the sales team. We had an amazing first sale person named Rath. I sort of gave him free reign to go build a team. And the first person that he brought in, I was like, there's no way. There's no way that this guy is going to be successful in selling to VPs of engineering. He was from Jersey, slicked back hair, not the vibe that you expect an engineer to be like really excited to work with, kind of heavy sales intense vibe. And so I actually said no, and then Rath overrode me. He said, no, you're wrong and I'm going to hire him. You're going to see. I was wrong. He was our most successful

sales rep for the entirety of segment's history and his customers loved him. What I learned going out in the field with him over the years was that he was extremely intense about extracting information from the customer about what the value was to them. Many cases, I think, clarified their own understanding in the process. And so by clarifying what the value was going to be, then he could walk back to, well, and here's how the product is going to solve that value. It's very simple, very clear, exceptionally methodical, and customers love them for it. That last piece of like what makes a great sale, yeah, sure.

Sure, relationship building, being a Smith talk, or maybe, but like what actually matters is, can you get the customer to actually tell you what value is that the product? And then can you connect that to what the product does? Could you say any more about what that feels or looks like, whether that's specific questions that are asked or sort of like a routine with a new customer that helps you triangulate on what part of it might be valuable or how valuable it might be? It sounds intuitive, but it also sounds hard. So I'm just curious what literally works when trying to get to that endpoint. What's hard about it is that it's awkward. I remember we were in this one media company in New York. The person was like, well, we're having some challenges with our data. So that's why we

want to do here to walk through your pitch. And so he was like, well, why do you have problems with your data? And then the person responded like, oh, we have trouble integrating this, that, and the other things like, well, but why do you have trouble integrating those things? And it's like, dude, you're supposed to be telling me that it's hard. Why are you asking me? And the customer was like so baffled by this. But then just kind of like, kept answering his questions, and we're articulating the pitch. We're self-articulating the pitch back to Surrey. And all he was asking was why.

And every other segment person in the room, there were like four of us. Every time he asked why, we're like cringing with the awkwardness of this. Can you believe he asked that? Like, we're supposed to be explaining that. And it was extremely effective. You just get the customer to articulate it. They're fine for them. It's terrifying for you.

The whole thing moves faster. But it's very awkward. I think that is actually the reason why most people come to it. It was just awkward. And so it sounds like you have to almost invert the entire way of thinking you go in with a pitch deck telling people stuff and instead go in like a truffle pig basically trying to sniff out where the actual pain point is, but have them say it not you. Is that a fair summation of the strategy? Any great enterprise sales organization actually knows this, but it was completely mind bending and mind altering for me as a 22 year old or whatever coming out of engineering school. So when this all started, maybe you can orient us in time. So now you've got this open source thing. that was an overnight sensation, if you will, lots of penetration into the developer community.

What else was happening that you were selling? Literally, what were you selling? How did you decide what product meant that you would charge for versus source code that anyone could access on GitHub or somewhere like that? Yeah, so we got lucky in this regard, actually, that the open source product, quote unquote, never really competed with the hosted product. And the reason is that the open source product, by nature of being open source, doesn't solve the problem. And that's because you would have to deploy it yourself as a customer. But the entire point is to get the engineer out of the loop and empower a marketer or some business person to turn on an integration to send data data back.

We got very lucky in like the structure of the product and the structure of the business and the structure of the whole value proposition that the open source library and engineer if it look at it and be like, it's well designed, it's well written, it's well architected, it's the appropriate abstraction. I like it. And then they're like, okay, but I'm not going to deploy this open source library because that doesn't solve the problem. Where's the hosted version? You can go to segment, buy the hosted version. So the structure of the thing, I think, was pure luck that we never really had to compete with open source. And all of the value that we added on later was fundamental nature had to be inside the core data processing pipeline, which we host.

We never faced the sort of tension between those team. Can you describe what you learned in this process about the relationship between the structure of the sales team, the model for pricing, and the level of pricing. So I'm assuming that those three things all work in this recipe that makes or breaks a successful enterprise sales distribution effort. What did you learn about that? What you charge for, how you charge, how much you had to generate per salesperson, like those kinds of details about building a successful enterprise sales team would be fascinating. You have to be able to get to a quota for salesperson of one point. 5 to 2 million a year very quickly.

If you look at a salesperson's time allocation, the only way they're going to get there, on the enterprise side at least, is if the deals are easily in the six figures. So you pretty quickly back into what the price point has to be. And then the question is, do you deliver enough value to be at that price point? The other sales models, right,

are like super high velocity tiny sales with very junior reps who are just closing inbound interest. We have some of that too. But by and large, every time, it is in the enterprise where honestly it has more to do with the buying cycle, which is you can't make it through the buying cycle unless you put it in enough resources such that you will then have to charge six figures because you need to be able to pay a sales

rep and a sold in there and so on. I think you end up being more constrained by the buying process that your customers want to go through. If your customers, say like Twilio today, has a very different buying process, which is that customers love to self-serve as developers. They love to come in. They love to start sending an approach to SES. they let's do this, that, and the other thing, and then eventually they get to significant volume, and then a silver person have a conversation with them. That's how those customers want to buy. So again, it's almost like flipping the mentality of how do I want to go to market with this thing to like, nobody cares. How do your customers want to buy? That is what matters here.

And so we have different parts of our market. Some parts of our market want to self-serve. And so we've got a self-serve thing where they can come in and use it and start getting started and our sales trip will kind of help along the way, especially in the back half of that. But most of our market is companies that want to turn the crank on the same SaaS buying process that everyone else has and run a proper sales process with multiple vendors and salespeople and sales engineers and so on. If that's how they want to buy, we got to show up. What's magic about that quota number, let's call it $2 million just to keep it round? Never thought about this before. You might hear $2 million per salesperson sounds really high thinking about the cost of the salesperson's

time and whatever. So what else is in that equation? That doesn't. seem to be kind of the standard you hear from other companies that are selling into enterprises. Like what's beneath that two million that, you know, as you think about your company's margin or whatever, maybe it would surprise people. Yeah, if you have normal software margins of 80% or whatever, then that's kind of where you end up in terms of once you pay for all the other marketing acquisition costs and pay your gross margin, your cost of goods sold in terms and data processing and stuff like that. And then once you pay commissions, not only for the AEE, but for the sales development rep and for the sales engineer.

and for a couple of people on the way, once you pay it for their customer success manager, like all these things, those economics close at $1.5 to $2 million. If your margin starts coming down, you know, 70%, 60%, 50%, 40%, your quota fundamentally has to go higher because you've got less contribution margin

for the 80% or whatever you're getting out. You've got less to play with there in terms of paying out all the commission and so on along the way. So therefore you need to drive more volume for sales rep. But those are sort of the fundamental. mental constraints that you're planning with. The consensus has been that certainly in the last 10 years, software as a business model, certainly SaaS as a business model, is like the holy grail.

On average, really high gross margin, scales incredibly beautifully. The market has been enormous and growing really, really, really fast. What's it felt like to be an investor, but a participant in that ecosystem? And how do you think that changes or evolves into the future? Do you think that that will continue to be the case? Or do we have sort of the Cambrian explosion behind us? 2012, it felt like a Cambrian explosion, which is when we got started with segment. It felt like every week there was at least one interesting new SaaS company launching on Hacker News, just constantly. It doesn't quite feel that way anymore.

It feels more like derivatives or like in between concepts launching on Hacker News. So I do kind of feel like a Cambrian explosion of SaaS might be behind us. I don't know if investors disagree with that, but certainly on Hacker News, it feels like a lot of the V-1s of everything are out there. And so one other interesting thing to me was that when we launched in 2012, the number of startups was also exploding. So our initial go-to-market was startups. They understood the value proposition and they were just building and so on and so on.

What we noticed is that it was a great starting market for us. Our growth was extremely fast because we were selling into a cohort of companies who were growing in their own right and the number of them was growing. and our market penetration within that group was growing. So you just had like three growing things stacked on top of each other, and so you just saw this explosive growth. In one year, we went from two and a half million error to 10 and so on. The thing just took off.

Something weird did start happening around 2016, and I've seen these charts from a few different companies, but the number of properly funded startups seems to have flatlined or slightly started declining around like 2016-2017. And so that same triple experience, engineering market force in startups. Something changed in like 2016, 2017. And that was around the time that we transitioned upmarket or started going more of market into the enterprise. So it kind of didn't affect our ability to grow. But I wonder if it affects new startups ability to grow because

there isn't like a cohort of peers that they can sell their SaaS into each other basically. But yeah, there's a few different companies where I've seen their internal reports on their startup programs and like something funky happened starting in 2016, 2017. Before we leave the topic of what you learned in scaling the sales side of the business, we had talked before too about this concept of revenue operations that in that you kind of started to walk through with the $2 million on down and why that quota exists. Say a bit about what you've come to appreciate about RevOps. You don't hear anyone talk about this as a system that gets optimized over time.

But I think you learned hands on that there's a lot of value to be extracted there if you do a good job building. Any other thoughts on that topic? Yeah, revenue operations, I think, is deeply underappreciated. If you're going to build a large sales team, which you're going to do if you're doing an enterprise software, then the question is, like, how do you design that? The commission plan or quota is like one aspect of a very complex machine of like territories, who do you, what companies do put in those territories? How do you classify those? Like, are there tier one territories that they should really be outbounding into and then different territories that they'll accept inbound leads from, but they're less likely to close unless they have the inbound energy. How do you think about

which industry verticals to target? Those are all the targeting questions. Well, there's a question of ratios. For every account executive, how many sales engineers should there be? Does one sales engineer support two or four or six AEs, for example? What about sales development representatives? Inbound and outbound, are they the same? And how many SDR should there be for how many AEs? So all these ratios, what about customer success managers to AEs? Like, it's one thing when you have this kind of scrappy team that's like getting it done in the early days. But when you break through like 20, 30, 40, 50 million error, like, it's a big team. And these ratios matter a lot for the overall economics and overall accountability structure and so on. And it's very complicated to design.

And there are people who are absolute wizards at this. And what I think is also interesting is it's not like a question of, it's not like RebOps earns their key. or whatever by like extracting more from a sales team. In many cases, it's actually smoothing the entire sales pipeline, such that everyone is actually happier, maybe even doing less work, but actually getting more results. It's not like RebOps comes in and squeezes 10%, and squeezes 10%, and squeezes 10%. It's like they come in and they make sure that at all points in the year, we're going to have the right ratio of all these people so that flow of customers, inbound interest coming in, actually makes it all the way through to being a closed

one successful customer that's going to renew next year. And that is not a 10% improvement. That's like a 2x or 5x potential improvement. Because if you start getting that stuff right, it also shows you how you should deploy the capital that you've just raised and keeps your growth right high. And probably it starts in the way you described before with the revenue event and the method through which people want to buy. Like you have to respect that first and then just constantly work your way backward toward the team members, I guess would be the very beginning of the whole process and then just constantly do that on a loop. This conversation on sort of backward efficiency makes me think of another conversation we had last time we talked about companies like Tesla and

business models that almost through their products bootstrap good scale economics and some underlying piece of technology, whether it's batteries or whatever. I love your thinking on this sort of thing. Could you talk through that concept for us? Because I do think we'll see a lot of hardware and other kinds of technology innovations in the next decade. Yeah, I think people think of Tesla as a car company, which is why everyone was so upset when they bought the solar company. Tesla is really a battery company. And for them, it's a question of how do they achieve scale in battery production?

World is literally not producing enough batteries for them, and so they are pushing the envelope battery production. So the question that I think Elon and team figured out, which is really impressive, is they were like, well, we need a way to sell a car that's super premium because the batteries are expensive to start with. And so we're going to start with the Roadster and then we'll go to the Model S and X and then the Model 3 and then so on down into utility goods scale storage. But like this concept of premium markets, I think is very interesting and is something that I think pretty much every hardware company should copy.

What's your roadster market? Because if you are in the hardware business, we're in the economies of scale market. Interestingly in SaaS, and maybe that that's actually sales. I think into a very significant degree, it may be that you're trying to achieve economies of scale and sales. And I think that's why you end up with these big conglomerate. honestly of Adobe salesports, other big software companies that purchase and acquire their way

into large product suites and the key to their success is a large sales team. I think they've achieved this sort of like customer account lock-in effects where they have great relationships and all the big accounts that matter. Companies like to buy from someone

they already know and understand a single threat to choke do well. And I do wonder in SaaS if actually the scale economy or note that gets created, is actually the sales team. I love the idea. And if you think about the sources of competitive advantage, kind of the famous ones, I think the ones that you often find your way back to steady state in the long term. So there's things like counter positioning early on that can be really effective. But

long term, huge companies, what's their defensibility? It's scale. It's either supplier demand side scale, a network effect maybe in a consumer business or something like what you're describing, a supply side scale effect. I think people get the roadster model S, model 3. idea that by selling something higher, you can sort of fund the scale production of batteries and work your way down. Maybe just say a little bit more about understanding it could be sales and software that that's the scale advantage. What's the roadster equivalent then? Like if you're a new company trying to get to that scale, does that mean that you should be going after the biggest customers first and kind of going all in on a fewer number of customers and then letting that

system work its way down? Is there enough of an analogy there to draw on? Yeah, I think rather than thinking about it as the cost to manufacture the product. In a SaaS context, you have to think about it as the cost to acquire the customer. You need to start with the customers that have the lowest cost to acquire, like if your sales price is kind of constant, the way you get that biggest premium is by minimizing your cost to acquire, which means that the business models that have, or the companies that have inbound flow of deals are going to have the most efficient sales cycles. And that's what's going to allow you to build out a sales team efficiently and kind of achieve scale over time. Actually, venture capitalists look for this. Venture capitalists specifically go and

look for the most promising companies being the ones that have a lot of inbound interest that have a fairly passive, high quota sales team that can plug and chug on inbound interest coming in. Knowing full well that at scale, it will of course have to transition to an outbound sales model, maybe the next sales force, that actually has sort of account locks on all those big customers. But that transition, I think, is from the most efficient sales model to one that has that moat established. Any of the thoughts on how with this maturity, these couple of maids like Salesforce, you know, great example, just it's in the name, literally unbelievable scaled sales operations. And it makes them naturally a buyer for great software products that can be pushed through

those distribution. Arguably Twilio is the same story, right? That the relationships that it has built with developers means like now they can just keep solving more. problems for developers, and they're sort of the natural winner of continuing innovation in this space because it's so hard to compete against. Do you think that story just runs for a long time, or do you think we'll see other attacks on these big scale winners in sales? I think there may be limits to those economies of scale. First, and just like the sheer number of products that you can ask the sales team to that to cross-sell. The second is in what is the actual customer experience of using those products? And so I think when you look at a lot of these big suites,

The products that they've purchased that they've acquired in, this is not integrated. They're basically separate products that have like a loss layer and branding slapped on top. In the end, that's not that compelling to customers. I think there is a little bit of an embedded lifetime in there. At some point, someone comes along with the solution that's more integrated or solve the problem in a different way, restructures it, such that it gets disrupted. I do think for a lot of these big suites that that is likely to eventually happen and maybe a deeper force that undercuts the kind of sales modes. The biggest complaint about these marketing and sales suites is that they're not integrated and

customers drives customers nuts. I love the lessons from these chapters of the business. And I think maybe there's two more chapters to ask what lessons stand out from your memory of those chapters. The first is, I'll call it chapter three, the later more mature version of segment as an independent company up through the acquisition by Twilio. And then chapter four will be what you've learned at Twilio so far about business, about any of this stuff, product, et cetera. What lessons stand out from those two separate chapters of the segment journey?

I think from the chapter that was basically scaling from 10 on up, 10 million era on up, it was probably what we talked about around revops. Frankly, I think I missed the boat there. I actually still kept spending most of my time focused on products. That's what had gotten me there or gotten us there. I think that was probably actually the wrong choice. I think it probably should spend a lot of that time focused on revenue operations

and figuring out how do we actually build for scale in terms of going out and winning his customer. customers and understanding our target audience and everything. That was the big lesson for me from that period. In terms of post-Tuilio, man, there's a lot. There's a lot for people and that's actually one of the things I'm most excited about. Personally, being at Tullio now, is George Hue, the chief operating officer, unbelievable. He was CEO at Salesforce before this and just like an incredible depth of operating experience. It's next level getting inspected by George or getting inspected by Cozeman, the CFO at T T Tioio, both just wicked short.

sharp in terms of isolating what the actual business problem is and driving accountability. I think for me, one of the things that I've been really trying to learn over the last year and that I think has been accelerated is how to drive accountability in a larger organization. It's one thing when you're like working with your friends or working with a small team, but the second organization is now 600 people, quite a bit bigger than that by the end of the year because we're hiring very aggressively. I think they have like a few hundred open racks right now. how do you hold accountable and like operate at the right level of altitude in that kind of

organization is something that I'm really excited to be learning from from georg and co what have you learned so far what are the key early lessons there one lesson that's really interesting is this concept of hierarchical unpacking for example george will ask like what's your plan for next year okay well like our plan is to do x amount of revenue like okay what's new bookings on that like all right well new bookings is is x and like okay how many reps do you need to go do that in each quarter. Like, okay, well, X, Y, Z. He's like, okay, how many regional directors do you have to support those AEs today? And how many regional directors are you going to need to hire? When are you going to need to hire them? Like, uh, well, yeah, we need to hire a whole bunch in Q1.

And then like, it's like, okay, do you have the recruiting resources to hire those in Q1? Is you just like, we're like seven levels deep now, 40 seconds in this like unpacking of one, an intuition for like where the bottleneck is likely to be. Two, being very crisp about whether we really have solved that bottleneck or not. So I guess part of it is kind of the concept of like a bottleneck theory of business, which was like there was one bottleneck somewhere that's constraining growth. And so isolating what those bottlenecks are and making sure that we're executing against changing them. It's super important trying to build this intuition and inspection model of how do you go and verify

that all these levels, it's actually going to close that the plan is in the pipe dream, that it actually goes all the way to ground truth. It's like, yeah, all right, we can hire those recruiters in time to do all these things and like stage it out timeline wise and like, yeah, it's actually going to work. That I think is probably my number one. Absolutely fascinating. And I'm sure key to any company of that size. Like you have people that can drill that quickly and identify lever points quickly. Like that just seems like the skill that would matter in such a big organization. What have we not touched on around segment specifically? Like one interesting area we really haven't talked about at all is the importance of data for

companies. The reason that you're going to grow is more companies are using more data in more places to affect their business, they're marketing, their product, whatever. Is there anything interesting there, since you're the pipes, you get cool seat to see how and why and when data is being used? What have you noticed there? What's been the evolution of how companies use this stuff? We do see how data flows to these hundreds of different integrations and each of those integrations is really a whole company that does something interesting with customer data.

The trends are interesting. There are basically five categories of tools that people. people really depend on. One of them is basic analytics. So what's happening on the website, what's happening on the mobile app. The second is email marketing. The third is pushing application and SMS marketing. The fourth is advertising, so think Facebook or Twitter or LinkedIn. And the fifth is an advancing analytics use case, which is data warehousing. After that, it falls off dramatically. Some people use customer data for to like improve support experiences and stuff like that, but it's a little bit rare, although I think probably growing very quickly. But those are the top use cases. I think one of the

things that we are excited about at Twilio is Twilio has always been very focused on communicating with customers, enabling these communication channels, SMS, voice, video, email with SIM grid, push applications, et cetera. So I've always been very interested in enabling those communications, but never had the customer data to then improve. Who should you be sending those to? At what time? why was what message? And Segment does like literally the inverse of all the data which should inform what message you send to who when and none of the communication channels to actually send that. I think we're really excited about it's bringing these two things together and all of a sudden you have both the world's best customer data platform and the world's best APIs for communication.

That opens a lot of very interesting possibilities. I also think that there's a future where today you have these applications that are very role-specific. You have a support desk for your support team. You have a CRM for your salespeople and you have the marketing tool for your email marketing tool for your email marketing, your advertising tool for your advertising. It's so role-specific. I do think machine learning is going to make the blending amongst these different engagement layers and tools very confusing, actually, for the next 10, 20 years.

I don't know how long it will take to come to full fruition of machine learning during most of it. It's going to be confusing, though, because you already have the blending of support and sales and a lot of business models and how that then interacts with like, oh, well, we're tweeting at customers. That whole sort of pipeline, I think is going to get very messy, and those applications are going to blend, especially as machine learning starts to make more recommendations for what should happen to where. It's going to be super interesting to see how that plays out. Seems like a really key trend in the world, especially around customer data, is privacy. And I know there's a lot of changes happening maybe with Apple and another platform.

that's been in the news a lot. I'd love you to describe your perspective on what those changes are, maybe even describe them, what they mean for the system writ large? What changes in privacy preferences and rules might do to how customer data matters or is used? Yeah, so I'm actually super stoked about how the privacy landscape has changed over the last three years. Back in 2013, I think right after we launched Anal XGS and we're getting all these requests for integrations. I think the only integrations that we turned down or started rejecting, because remember, we were very customer-led at this point. We were like, no vision, we're going to do what customers

ask. That wasn't quite true. There was one exception to that. And that's when customers would ask us to integrate a tool that was about sharing third-party data. We like integrated them. You can find it in our blog. Two weeks later, we were like, that was a terrible idea. And we booted them off the platform. Like in many ways, they were legal at the time. Now it's probably illegal to do what they did. but it just didn't feel right. It didn't feel like the end customer was going to expect that their data was being shared in this fashion, regardless of whether they clicked through some legal document. We brought a random person on the street and asked them,

are you aware that your credit card data is connected to cookies in your online browsing history? I guarantee you it's a zero percent rate. No one thinks that that's what's happening. Or maybe they do, but they're pissed off about it. And to me, it was like, or to all of us at second, it was like, okay, great. There's like some companies that are pretty sleazy. and they're going to capture some arbitrage window here where, like, this is legal.

This is going to close. First of all, it's horrible. It's not what people actually expect. And eventually that will end up in regulation. So one, it's the wrong thing to do. And two, in the long arc of time, it's not going to pay off. That was right around the time that data management platforms or DMPs were exploding, which are literally giant warehouses of data trading for third-party sales.

There was a lot of, like, investor confusion that were like, well, I don't understand. of so much data flowing through. Like, shouldn't you just be a DMP too? And we were like, nope. At the time, it was a very contrarian bet, I guess, we get the hip term to use. And so it's actually very vindicating now in 2021 that the last three years basically saw the complete demise of DMPs,

like their worthless assets. The one that Salesforce acquired is now being sued, like a $10 billion class action lawsuit or something, and it's a mess. And you are starting to see things like CCPA and California. You're starting to see GDPR in Europe. some hints maybe that something will happen in the U.S. federal and U.S. federal level, things are interesting things are happening in Brazil and India and so on.

Of course, Apple is making moves or killing off third-party cookies, the one with Google. So there's a lot of really, like, awesome things. I think happening now and focusing people on first-party data and helping companies just do first-party data stuff to engage better with their customers. And that's where we've been pointed for a long time. So it's actually really awesome seeing a lot of the other noise coming to get trimmed off by these changes. Who do you think it hurts the most that are still legitimate? business. You mentioned businesses that are sort of useless now, but I think there are plenty of businesses

that benefit from the ability to target marketing or do whatever that changes in preferences or standards may affect. What do you think there? Is there a category that this is good for versus obviously it's good for segment? This is kind of what you do. Who would it be bad for? How does it affect the business landscape that way? I would add that it's good by choice. But yes, it is good for segment now. Having made that choice many years ago, seemingly at the time, kind of suffered for it. It's great for Google. It's great for Facebook. It's great for Twitter. It's great for any company that has their own consumer, probably great for LinkedIn. Any company that has their own rights and reasons to actually have legitimate data, unique data on a person

of our company, because that person wants to be a part of that platform benefits from this. The people or companies who are really hurt by it are the companies that exist in the middle, purely for the purpose of connecting third-party datasets. The gazillions of out. Ad networks that have existed in Sleasy, sketchy ad data deals in the background for a long time. Those are the companies that are converting. But yeah, it will cause a centralization of ads revenue on the Googles and the Facebooks and LinkedIn's and Twitters and Pinterest and Snapchats of the world, the people who have their own consumer network effects that lock in their audience and give them unique data about their

audience. Do you think it affects the users of that data much? So if I'm like a DDC brand selling whatever flowers on Facebook or something, Do you think that it changes the landscape for the end merchant all that much? It does. It increases the cost to acquire slightly. It's harder to target. If you sort of play through with the ramifications of that,

it probably means that if your cost to acquire goes up, it probably means your lifetime value needs to go up in equal measure, which means you actually need to spend more time making sure your customers are happy. I'm not sure that it's a net negative for the end consumer in that sense. That's, okay, now my bank actually needs to make sure that I'm happy with me. the airline actually needs to make sure that I'm happy as opposed to just going and acquiring the next person who goes to Google search. The externalities are a little complicated to play out, but I think there's actually a pretty good world on the other end of it. But it does involve

customer or companies going and properly engaging post-acry. We live in this cool era of so much educational content. We're certainly trying to do our part to bootstrap as much knowledge into the world open source style, honestly, as possible for others to build on top of lots of conversations like this one floating out there that wouldn't exist it five years ago even. The downside of that is like anything. There's a lot of good advice. There's a lot of bad advice.

What do you think now having kind of done the whole lifecycle thing from early pivots to scaling to acquisition to growing inside of a bigger firm? What do you think are the worst bits of advice that you hear doled out to other would-be entrepreneurs? I hate the Airbnb story. The Airbnb story is we had this idea that people. could sleep on couches. We just thought that that is the way the world should be. It's literally the opposite of my story. We thought that this just like makes sense for how the world should work.

We just hung on for like years until it took off. We did all this crazy stuff like cap and crunch cereal and this stuff and the other thing. And eventually these hacks got it going and like, we were right. All right. For every like 10,000 segments out there, there will be an Airbnb, but like do not bank on that. If you just like hang on for your life on how you think the world should work, you're going to get one shot on goal and it's almost certainly not going to be. I don't know. I think that Airbnb story is maybe one of the, I don't know if it's advice. If taken as advice, their story is the wrong takeaway. Is the heuristic that emerges from that story demand traction? Is it just to really insist on clear

evidence that something is working and be pretty impatient about that? I think so. And actually, Justin Kahn with founders of Twitch, it was like first time founders care about product or spend all their time on product and second time founders spend all their time on go to market. One, I think that's true. Two, it's maybe slightly misleading. That's not that they're necessarily spending their time on sales or sales just to generate revenue.

It's spending time on go to market because it's actually the best signal about whether your product is really the right thing because you're actually spending time with the customer and actually getting down to what is the real value that you're delivering with them. So I think it's pithy, but it's also, as he worded it, but I think it is indicative of this go out into the world and understand what the world really wants and really needs, as opposed to like your vision of the product. And that outside in perspective, I think is super critical by far the most important thing. Rather than ask you for generic good advice, I'll ask, was there any bit of advice that you received,

and if so from whom, in the segment journey that you think was especially impactful? Mitch's advice to raise prices 1,000 X was pretty impactful. I like that one. Yeah, at the end of the day, wouldn't have a company if we hadn't raised prices a thousand X. Not everyone is happy with our pricing for the record. There's problems there for us to fix. But, yeah.

What has you most excited for the future? I think there's a very deep and compelling roadmap for a Twilio and Segment combined, spending a bunch of my time on, as you can imagine, over the last six months since the acquisition closed. And yeah, this is just going to be super exciting. It's like crazy stuff that we can go build there. I also spend a reasonable amount of time in climate tech and both charm and on the philanthropic side. and there are good things happening there after a bit of a dark period over the last decade. So I'm really excited about that as well.

I remember meeting Jeff for the first time. And when he walks into the room, he's kind of this affect. You can just tell that this is kind of a unique human. And it's an excuse to ask a bit about leadership, both what you've learned yourself, running a company and also working with some great leaders like Jeff. What do you think matters there? And it can be about him specifically or taken as a more general question. What works in leadership in this kind of modern world?

And I think Jeff embodies that word modern is used intentionally here. It seems like he has his finger on the pulse of what matters. What have you learned about leadership from him or from your own experience? Three observations that I am trying to learn from, specifically from Jeff. One, deeply, deeply cares about the culture. It is the top topic. And that level of dedication over many years, 12 years, like it shows. People love to work there.

The second is it doesn't take no for an answer. like if something needs to get done, it needs to get done. And like, it is what it is. You can kind of try to avoid something for a little while, but like, he's going to wear you down. Ultimately, that's what happened with our acquisition, too. Like, he and I chatted once a quarter for 18 months. And like at the end, I was like, yeah, all right, Jeff, I buy the vision.

It's exciting. It's not going to take no. It might take a while, but he's not going to take no. And the same is actually true inside now, too. And it has its tradeoffs, but it's effective. And the last is just like the level of energy that he brings to, and this is a cultural piece, I guess. with the level of energy that he brings to all hands, the level of energy that he brings to

everything that he does, and how much momentum that imparts to a large team is very impressive. There's a lot to learn there. I started out as a very, very quiet nerd, would not get up and present in front of 15 people. So I've got a lot to learn there from Jeff. You make me think of literally the pictures he posts after the quarterly earnings calls. You could feel the energy through the pictures. It's not even a video, like just the smiles and the thumbs up and the picture held up. Like, it definitely is notable and unique.

questions for you. One more on leadership, which is you mentioned maybe starting out as a quieter leader. In what ways do you think you've changed the most personally across the segment journey for the better? What was maybe the hardest one to do? The hardest one came from that first two years where I was very used to being right. I was a star student. I was an A plus student, past everything all the way through MIT. And then I got destroyed. I got destroyed in a year and a half. Like, I was just wrong. No way around it. And it was very eye-opening, very humbling, I would say. I think that you don't know the right answer. You've got to go find the right answer, and you'll be rewarded for getting the right answer fast. But, like, you do not know the right

answer until you go ask someone else. That's just not how school works, but that is how the real world works. So that, I think, was the very tough and good lesson from the first two years. another hard lesson, I think, as someone who studied academically very hard is you do the work yourself. That's the wrong lesson for leaving a company. Like your work is to inspire other people to do the work and then explain why it's important, which is a fundamentally different skill than doing the work yourself. And that's actually what I'm still trying to get better at, get it better explaining why and why it matters and why we should go do it.

I've loved this conversation. I learned a lot from you just in the couple times we've talked. And I think yours is an incredibly frank and honest perspective. on company building that's a little bit more squared with reality than grand visions of the future. And I've loved learning from you. I think, you know, my traditional closing question for everybody, which is to ask, what is the kindest thing that anyone's ever done for you? I guess it would be kind of a tough love moment. I don't think my dad remembers this. But when I was about 13 or so, I was picking up like a new project every two weeks. And I would never finish the

previous one. I was like, oh, I'm going to build a go card. Oh, I'm going to build like a super sonic weed whacker. Yeah, it was just like, you know, crazy project, crazy brand. And I would never finish me. It's just like an offhand comment as he walked by one day. He's like, well, you know, eventually you're going to have to learn to finish something and passed by. I was totally floored at the time. And yet 20 years later, it's still like stuck with me as like finishing things matters a lot. Anyway, those kind of like tough love lessons, I think in retrospect are probably some of the kindest things that can happen to one. Well, it's been so much fun, Peter.

I really appreciate the time all that you've shared with the audience. Thank you. Thank you. It's good to be here. This episode of Founders Field Guide was brought to you by Dell Technologies. Dell Technologies and Windows can help you upgrade your business tech with these small business month specials. Save up to 45% on PCs with Windows 10 Pro, plus business docs, monitors, and more. Work anywhere with Windows 10 Pro.

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