This week, we’re excited to release a special live episode recorded during the community event portion of our 2023 annual meeting. Madrona Managing Director Matt McIlwain talks with the hosts of the Acquired Podcast and Madrona Alumni Ben Gilbert and David Rosenthal. The three reflect on Acquired Podcast episode No. 28, which dove into the Amazon IPO with the late Madrona Co-founder and original Amazon Board Member Tom Alberg, and the early days getting the show off the ground from the Madrona offices.

You can watch the live video here.

This transcript was automatically generated and edited for clarity.

Matt: I’m Matt McIlwain, I’m one of the partners at Madrona and we’ve had a very action-packed day, and some of you are here for the day and many of you have been investors and partners and friends. At the beginning of the day, we reflected back on one of our co-founders, Tom Alberg. Tom was a co-founder of Madrona. He built, helped build Perkins Coie Law Firm, sold McCaw to AT&T. He started Madrona, led the first investment in Amazon and a bunch of other exciting things over the years. And he always had this incredible ability to be curious, to be impact-oriented, and to think long-term.

As we were thinking about how to celebrate this community, this amazing ecosystem, he even wrote a book on flywheels. Actually, we were talking about the University of Washington and how Tom had helped raise the money for the first standalone computer science building 20 years ago. You think about how Madrona’s invested in well over 20 companies out of that computer science school and now, increasingly, places like the Institute of Protein Design and the intersections of those. So, we wanted to have another fun engaging way for the whole community to both share on Tom and then through that really Tom’s legacy. We thought what better way to do that than hang out with Dave and Ben.

I think for very few of the people in this room, David and Ben need an introduction. But they are the co-founders of the Acquired Podcast and Madrona alumni. A consistently top 10 in the world technology podcasts.

David: And it all began after hours in Madrona conference rooms.

Matt: There you go.

David: And maybe a few partners’ offices that we commandeered without them knowing.

Ben: Thanks, Tim and Scott.

David: And Soma a few times!

Matt: I think that’s a part of the fun aspect of this story is that David and Ben were at one point working at Madrona. But let’s even take a little bit of a small step back from that. They’re both accomplished venture capitalists, incredibly community-oriented people. But I think our good friend Greg Gottesman was the person that gets credit for bringing you two together, and I think it might have even been at a Seder dinner at his house. Tell us about that story.

Ben: Yeah, I’m originally from Ohio.

David: I’m going to cut you off even before we start.

Matt: See, they’re not used to being the ones that get interviewed.

David: We’re like an old married couple at this point. So, there were ulterior motives for both of us at that Seder, I believe. And the irony is we ended up getting together.

Ben: No. I didn’t have an ulterior, I was from Ohio. I did not know people in Seattle. Greg wanted generously invited me to his home for Passover…

David: Because he wanted to recruit you to Madrona…

Ben: It worked.

Matt: Well. Was that the whole story?

David: The other half of it was that I was just about to come back to Madrona from business school and was working on trying to close what I thought would be my first deal and court an entrepreneur. And Greg was helping me and said, “Okay, why don’t you invite him to Passover Seder?” And so, I was trying to work on a deal at this Passover…

Ben: David’s like reading the Haggadah trying to sell.

David: And there was this Ben guy there.

Matt: That eventually did lead to both of you working together at Madrona. And so maybe share a little bit about how that started to shape your friendship long before there was a gleam in your eye about doing some podcast together.

Ben: It’s interesting. I was fortunate to work at what we call then Madrona Labs now Madrona Venture Labs. And I didn’t know anything about venture capital, and so I had this sort of immense respect and looked up to everyone who was an actual investor at the firm. And I always, I have this sort of mental framework that in any business, you want to work in the core competency of the thing that the business does. I was looking around the thing here is investing and like David’s a guy that I had this relationship from a Passover Seder with, and I just want to absorb everything in his brain. And Acquired really was me attempting to find a way to get to spend more time with David.

Matt: Now the true story, now the true story.

David: The feeling is definitely mutual because of course, as a venture capitalist, you realize that it’s the entrepreneurs and the builders who create all the value. So, I just wanted to extract everything from Ben’s brain as like, ” Hey, as a Microsoft product manager who” — Ben chronically undersells himself. He was on the cover of the Seattle Times as the future of Microsoft.

Ben: The day before I joined Greg.

Matt: Hey. Timing, timing.

Ben: And it was super unintentional. I thought the story wasn’t going to run because it was like a long lead piece that I had done the interview like three months before. And I assumed because it didn’t come out yet that the story was dead. And so, I signed the offer letter with Greg to come to Madrona Labs and then I got the email from the Seattle Times, “Hey, watch the paper tomorrow morning”.

And it was only on a morning run. I was mentally preparing to go and give notice. “Hey, I signed an offer last night.” And I run past one of those newspaper bins with my face on the cover. Like a hundred percent. True story. No exaggeration.

Matt: There’s been some newspaper bins that my face has been on the cover. But that’s a different topic though. But I don’t think I’d ever heard that story. That’s really cool. And is that why you’ve never gotten to interview Satya?

David: It might be… despite repeated attempts.

Matt: Maybe we can all figure that out someday here together. Podcasts were not quite what they are today, back when you all started the Acquired Podcast. So how did this idea even come about? How did you first get started on it?

Ben: I actually do mean it when it was, I was trying to spend more time with David. So, we were at drinks and I was pitching him on two ideas. I was like there’s two podcasts that I think would be good podcasts. I was a really big into podcasts. I was like loading podcasts over FireWire onto my iPod in 2009.

Matt: You were an early adopter, an early adopter.

David: Back when podcasts were cast that you put on your iPod. You were doing that?

Ben: Yes. And so the two concepts were acquisitions that actually went well. Because I have always felt like the media narrative is around look how terrible of a deal this was. And incinerating billions of dollars and three quarters later there’s a write-down. And if what we’re doing in the world is creating companies that we want to one day go public or what the vast majority of companies do get acquired, we should understand how to work backward from when those go well. That was topic one, and topic two was let’s dive into businesses that have had multiple billion-dollar insights. Because the working hypothesis that I had at the time was most businesses that get to scale forever draft on the core insight they had when they started the business. And can never transform and come up with a second one. Occasionally you get an iPhone or an AWS, but very rarely. And David, I remember your comment to me was like, “I will 100% do the first one with you. And for the second one, I think we’ll run out after five episodes.”

David: And the first one only got us about 20 episodes or so before we ran out of that. But I think, like to me, the magic of how we started is both of those ideas were small ideas. And starting a podcast was a small idea. It really was, the feeling genuinely was mutual. It was an excuse for us to spend time together and build our friendship. We never could have imagined that podcasts would grow as big as they have, that we would grow as big as we have. We were talking with Nick from Rec Room earlier about when he started a VR company. However, many years ago. That was about the same time.

Matt: By the way, Nick, we got grilled on VR companies today. They were like, “What were you guys thinking?” We’re like, “Wait, we invested in Rec Room.”

Ben: Rec Room is an open world multi-platform experience. I don’t know…

Matt: Because Nick listened to the market.

David: Sometimes you just get really lucky in life and magic happens with the right set of people and things change and you figure them out along the way. And that’s what we’ve done since the days in the Madrona offices.

Matt: And I think you all were recording in the offices if I remember correctly.

Ben: Yeah. We’ve got a picture, I think in what is now Soma’s office, of us sharing a headphone because we didn’t buy multiple sets of good headphones. (See photo above)

Matt: It’s a good frugal startup. There’s nothing wrong with that. That’s perfect. Since you did go down the Acquired Podcast path, tell us about those first few episodes. And then, I want to get to this one that was, I think, your second break from actually the Acquired story. But you did a few before that, maybe 20, 25 before you went up in a different direction on IPOs.

Ben: Yeah. The early ones, it was interesting. We, if you look at our analytics now, what you will see is, every time we break our record and achieve the greatest episode that we’ve done to date, which is cool that happens every couple months.

Matt: Especially because they’re so freaking long. But that’s just my opinion.

Ben: Totally. It is an episode that is just David and I and the canonical wisdom for when you start a podcast is 30 to 40 minutes release weekly to set a listener habit. And have guests because then the guests can promote the show. And zero times in our entire, at least the last five years, has a guest episode been the highest ever.

And that’s forced a lot of introspection. And I think at least one of our big takeaways is to always focus on the thing that you do that is unique and differentiated. And almost everyone who’s a good podcast guest goes on multiple podcasts. And so, it’s less…

Matt: Concentrated.

Ben: Exactly. The thing that we can do that’s different than everybody else is the format that we have developed. That is just the rare thing that’s David and I doing that format.

David: And I think that is the core of the magic that was there in those first days that I think carries through to today was, it is about our friendship and about us learning together. And that’s what we were doing in the Madrona offices for, Matt, you were referring to, I think it was until the Facebook IPO episode we did. Where every episode we did, it was so tightly constrained to this has to be an acquisition of a technology company that went well. That we are going to analyze. And then whenever, this must have been 2016 when we were like, well, the Facebook story is so important. Maybe we can expand to do IPOs as well.

Ben: And the thing that we thought was true was people want to listen to this because they like us grading acquisitions and we were super wrong about the job to be done of acquired in the mind of a listener. The thing that they were actually there for is great storytelling, structured analytical thinking, and Ben and David. And over time we learned that we can apply that to like anything. There doesn’t have to be a liquidity event. It could be Taylor Swift’s music career.

Matt: When’s the food show coming out?

Ben: We should LVMH — Dave and I talked about handbags for four hours.

Matt: So eventually, and actually I’m thinking specifically of episode 28. You not only went to a second IPO, but you had a guest on to help tell the story, and that was of course the story of Amazon’s IPO and Tom Alberg was the guest. So, take us back to that episode. I’ve re-listened to it now multiple times. And just tell us a little bit about how that an idea even came about. And how you prepared for that particular moment?

Ben: First of all, I was super nervous because I didn’t really have a relationship with Tom, and he’s so unbelievably accomplished. And so, David, you were the one to like swing in his office and be like, “Hey Tom, do you think you might be willing to come on our pathetic little podcast as a guest?”

David: And Tom, of course, said, “Yes.” As anybody who knows Tom, would’ve known that immediately he would. I was also a little nervous in part because he was one of my bosses. I guess one of your bosses, too. In a sense. But Tom was just so you know, it’s funny, Ben said that guests go on multiple podcasts. Tom probably did some others, but he was just quiet and unassuming and so humble and like genuinely. And I was coming from, before Madrona, I had worked at News Corp where not my direct boss, but like my…

Ben: Little bit of a different culture.

David: The culture of the founder was a little different.

Matt: I think we’re picking up what you’re putting down.

David: Yeah, a little different than Tom was. I re-listened to the episode on the flight up here, and one of the things I didn’t even pick up on at the time, while we’re interviewing, he just casually says the line of, “Oh yeah, I was involved with Vizio, too”. Like Vizio was a multi-billion-dollar outcome. And he was just like oh yeah. Oh yeah. I think it was when Ben was introducing him.

Ben: And there were moments where we would ask a question and Tom would say, ” I don’t know. That’s a good question.” And most other people would’ve conjured some sort of answer to try to sound smart. That was of no interest to Tom.

Matt: I think the Vizio connection, if I remember correctly, was that Doug Mackenzie from Kleiner was on the board.

David: That’s how it came up. We were talking about what happened about John Doerr and Amazon. Yes. And that’s how the Vizio connection came up.

Matt: Re-tell that story. That’s a great story.

David: The John Doerr. Oh, this is so great. Re-listening to the episode, I Tweeted about it from the plane. It is so cringey. Cringeworthy to me and to Ben, too, to re-listen to it because we listen to ourselves. We’re like, we were talking too much.

We just we had this amazing person, like we were so young and green in what we were doing, and if we had the chance to redo it, we would do it differently. But Tom is amazing and one of the stories he told that we almost didn’t let him tell was when the second venture round for Amazon came together.

Ben: And Tom was on the advisory board for Amazon.

Matt: It wasn’t quite an official board yet.

Ben: Because I think Amazon had raised maybe like $1 million on a five pre. Tom was one of the angel investors. And so, he was Jeff’s maybe only advisory board member at that point.

David: I think he said there were a few.

Matt: Yeah, it was a small group, and I think Tom was the only one with substantial business experience.

David: And now putting it together because of the Vizio connection is probably how this came together. But Tom says, ” I came home from work one day and we and Jeff were starting to think about raising some more money. And my wife said, do you know some guy named John Doerr?” And Tom was like, “Well yes. I know who John is”. And she said, “You need to call him back because he’s been calling here every 15 minutes, for the last several hours. He on your home phone number and saying he needs to talk to you.” And it was all about trying to get an angle to leave the round.

Matt: And he beat out General Atlantic, which is, was in the news the last two weeks for different topics we probably won’t get into right now. But yeah, that was pretty interesting time.

David: And not only beat out General Atlantic but beat out at half the price. Yep. That was how powerful John was.

Matt: But almost lost the deal because…

David: He tried to hand off the board seat.

Matt: Ah, there you go. There you go. What else do you remember from that episode or things that stood out to you two as you looked back and listened to it, other than, ” Hey, we’ve come a long way”.

I thought you guys did a great job on that interview.

Ben: Tom did a great job at helping under, helping David and I understand when certain things got introduced into the Amazon dogma that today we just assume have been there forever. Like he set the record straight that the famous flywheel diagram and we all talk about the Amazon flywheel and everybody tries to graph their own business onto Amazon’s flywheel. But no one’s flywheel as strong as Amazon’s flywheel. That was a two years post-IPO conversation that sort of came up. And I think a lot of us try to attribute, “oh, Amazon, from the very moment that Jeff conceived of it was exactly this way.” And Tom just very graciously was both very respectful of the genius of Amazon from the very start, but also helping to unpack when did certain components of the Amazon lore actually get layered on.

David: Yeah. There was a moment, I think, Ben, when you asked him, ” When you met Jeff in that first fundraising round. Oh. Was he like something special? Was it clear to you that this was one of the most amazing entrepreneurs in history that was sitting before you.” And Tom again so graciously was like, “No.”

Ben: I think he said. ” He was very good. Definitely in the top 10 or 20% of the entrepreneurs that I meet with. But, you know, we work with a lot of great entrepreneurs.”

Matt: We do work with a lot of great entrepreneurs for on record.

David: And I think he, he just made the point so well. Yes, of course Jeff was incredibly smart, incredibly driven, had a great idea, was operating in a great market. But nobody, and I think Tom even said this, not even Jeff and his wildest dreams at that moment, as ambitious as he was, could imagine what Amazon was going to become. And I think the conversation with him was great because, and this just so reflected his style that I absorbed from working with him, of, you can’t have delusions of grandeur. You need to work every day along the way and respond to the market as it develops. And that is the story of Amazon. And Tom is such a big part of it.

Matt: And then there’s this whole thing about, as a CEO, you’re always busy, there’s always a lot of work, but there’s really just a handful of consequential decisions. I remember the Barnes & Noble story, that’s another one that really stood out from that episode to me. And he unpacked that whole, the Barnes & Noble folks. You guys know this knows better than me, but that to me, or maybe there’s another one that stood out to you about how the CEO’s job is to be super thoughtful about being the kind of the, ” aligner in chief,” but then also be there to lead those couple of decisions that are the key decisions every year to be made. And that felt like one of them.

David: It absolutely was. The Barnes & Noble, the quick story is the Riggio Brothers who were these rough and tumble, like Brooklyn, New York guys. Not Tom’s style. And at that point in time, not Jeff’s style. Jeff then was very different than Jeff today. And they basically came and said, “We’re going to kill you,” at dinner with Tom and Jeff and said we’re coming for you.

Ben: “You can either partner with us, or we can buy you on terrible terms, or we can leave dinner and we will kill you,” was effectively the message.

David: And Tom and Jeff together, decided to fight. It was Jeff’s decision, but I think Tom helped him get to the decision to fight.

My favorite non-Amazon Tom story is my last day. I will never forget this, my last day at Madrona, before I went to business school. I spent two years as an associate. I was locked with Tim and Tom, in a Wilson Sonsini boardroom, negotiating a recap of a company that had raised too much money and fallen on some challenges and other people on the cap table were unhappy. I remember the whole time thinking like, this is my last day before my summer, before business school, and I’m locked in this boardroom. What are we doing here? Ventures about focusing on your winners. What are we doing? And that was just not Tom’s style. And that company today is Impinj, which is a three-and-a-half billion-dollar public company.

Matt: Yeah, that’s that long-term mindset.

David: That is that long-term mindset.

Matt: One of the things that really impresses me and leads me to listen to your podcasts on my jogs. It’s multiple jogs for me to get through one of the episodes. Just to be clear, I don’t run that far these days. But you are so good at research and preparing. How do you process having this external research with what you want to ask and what you want to do your own analysis around on episodes? So that one may be particularly, but more, maybe more generally, too.

Ben: If there’s any metric that we watch the most carefully, this is the one that we care about the most. Which is, is it possible for us to have no inside information but for insiders to have to think we did because we understood it that well. And it’s not like it shows up in a graph, so it’s a hard thing to track over time. This has been like a superpower of David’s and a thing that sets us apart from other podcasts or Substacks. There is an immense amount of public information available on a company. And if you’re just willing to take a month and use the internet the very best way that you can, you find so much. If you scope with date operators, so you’re only looking at New York Times articles on Nintendo from 1989 to 1990, like I was last week. The public sentiment and the journalist sentiment changes so much from year to year, that five years from now, the way people talk about companies will completely overwrite how they’re talked about in the era today.

Another great example is people go to conferences all the time. Lots of times, the talks are recorded on YouTube. A lot of times, they’re like boring industry conferences that don’t get a lot of fanfare and coverage. But David will go find a talk that some mid-level executive at SpaceX gave that got like 2000 views on YouTube at an aerospace conference and have this insight on SpaceX that is super overlooked by the media and a key part of their story.

Matt: So I wasn’t planning to ask this, but can’t help but asking, have you tried using ChatGPT yet

David: Yes. And it’s wrong. Sometimes very wrong. I can’t remember what the example was, but I…

Ben: Oh, when we were doing the NFL episode, I was what was the most recent stadium that was built that didn’t have a dome on it, and it gave me the wrong answer. And I was like, that’s not true. Give me the one before that and it gave me a wrong answer again. This is, it’s like fun to talk about, right? In rooms like this and on Twitter, these things get a lot of like pickup of GPT is so wrong, it hallucinates. But do you remember talking to your friends in 2004 and being like, you can’t trust anything on the internet.

Matt: Or as Mikhail pointed out at lunch today. Can you trust your friend? Is it a hallucination or an insight? So that was really powerful moment we had earlier in the day.

What about, you’ve done a couple of more recent episodes on the history of Amazon and then the history of AWS. I enjoyed them immensely. Maybe just an extra thought or two on what you all have learned, what we all have learned in this ecosystem. We spent a lot of time talking about some of the amazing things that Microsoft’s doing and less so this year and today on Amazon. Any thoughts, big picture takeaways from all the research and the podcasts you’ve done on Amazon over the years?

David: Yeah. It’s interesting. My view on Amazon has evolved quite a bit, even up until our most recent episode that we recorded that is not out yet. We did another episode with Hamilton Helmer, who wrote the great book, “7 Powers.”

Ben: Best business strategy book out there.

David: Absolutely. Up there with Porter’s “5 Forces” and “The Innovator’s Dilemma.” Anyway, this episode that we did with him was specifically about transforming as he calls it, which is ironically Ben’s second idea for a podcast of companies that have had a second act.

And the amazing thing about Amazon and the AWS story within it, and as we talked about on the episode, there are many origin stories of AWS, all of which have some element of truth.

Ben: Except for the fact that they had excess servers, that is patently false.

David: That is false.

It seems so far afield from what Amazon retail was. But what Hamilton and his research has done on this. Is it’s actually, if you think about a two-by-two matrix for companies of your existing customer base, it’s your existing customer base on one access and your existing capabilities within the company. AWS is a completely 100% different customer base than the Amazon retail customer base. But the capabilities within the company that Amazon had to build to serve retail were the same to build AWS. They had, at the time, the world’s best internet architects, backend servers, etc. Everything that goes into building AWS, they had to build in-house. So, it actually was a very natural thing. And in Hamilton’s research companies that have done this, almost always, that is the case. Different customer set, same set of capabilities within the company to serve their customer base. I always used to think about Amazon as this like just incredible wild idea factory. And I’ve come to appreciate, at least in the AWS case, and I think in some of their other successful forays, too, there is a little more science to it than that.

Ben: Hamilton’s sort of advice to founders who are looking to figure out what’s the next S-curve to stack on top of your S-curve is what are your capabilities uniquely enable you to do versus your competitors? Or all the other companies out there, even if you’re not competing with them right now, what can you uniquely do, even if it’s serving a different need in the world?

Matt: I think back, 16 years ago, we hosted an event up on Capitol Hill with Andy and couple of startups. I think Eric Brown, who’s in the room, presented how Smartsheet was using AWS at that event. And that was the launch of AWS. And it was clearly focused on startups and developers, but it was that developer orientation. And then they were able to build a platform strategy, too. And you think about companies over time built on top of that platform, Snowflake, our portfolio companies, and many others. And that kind of platform capability is a core competency of Amazon and other areas. Of course, Prime being a prime example.

David: The other amazing piece of the AWS story that I didn’t appreciate until we did our episode. Hearing from, in that case, we did get to talk to some folks within the company. The go-to market organization around AWS that was not an existing capability within Amazon. And the story of AWS and Andy Jassy building that is one of the most incredible entrepreneurial journeys. There’s this trope in venture: in enterprise, like at the end of the day, whatever percentage, 50%, 70% of enterprise software gets sold through the four or five big enterprise software sales giants, whether it’s Microsoft or Oracle or Salesforce, or what have you. Amazon built another one of those giants from scratch, which is amazing, with a lot of Microsoft DNA.

Ben: And then retained first place. They had a five-year lead on cloud. And then they retained first place 15 years later.

Matt: And they’re always continuing to learn, which I think is, I think both Amazon and Microsoft have now built or rebuilt that muscle of continuous learning. And I think that is possibly why they’ve become both, such major forces in the technology-driven ecosystem. Speaking of ecosystems, you both, and particularly David, have exposure to the Seattle ecosystem and Silicon Valley. You and your wife, Jenny, moved down to Silicon Valley many years ago. This was long before we opened up a Silicon Valley office. How do you compare and contrast the ecosystems both from a startup perspective and a venture perspective? You’ve seen both, you’ve certainly got plenty of experience with companies you’ve worked with and built and co-invested with others, too.

David: This is just my perspective, and I’m not sure that it’s right, but it’s the lens that I think about this. Even when I was at Madrona now many years ago, I thought that this was starting to happen and now I think it’s really progressed on this journey. I don’t think of them as actually different. I think of them as the same ecosystem. And now I think with the number of people that I know that I’ve met and become close with over the past few years in the Bay Area ecosystem that have moved up to Seattle during Covid is enormous. And all of those companies are, maybe they’re Seattle companies, maybe Bay Area companies — I don’t even know how to classify them. It’s the same thing. They’re cross-border, so to speak. And so, for me, I never thought them about them as totally separate. Now, I do think, on the margins, technology workers in the Bay Area historically are more likely to start startups and on the margins, Seattle technology workers have been more likely to stay at Amazon and Microsoft. A large part of that was the Amazon share price. It was a good incentive to do that. In my investing and working with founders and getting to know folks through the podcast I, I think that’s changed as well. I don’t see the appetites as being different now. What do you think, Ben??

Ben: I’ve had to redefine my lens. For PSL Ventures, I’m a Seattle, Pacific Northwest-focused venture capitalist. What does that mean? That used to mean we invest in butts and seats here, but that’s stupid. What I care about and the reason that this is our fund’s thesis, and part of Madrona’s thesis, is the talent pools that get trained at these institutions. The University of Washington that come out of Amazon, that come out of Microsoft. I don’t care what physical location people are in when they’re starting companies. I care that they have unfair access to the talent networks that are coming out of these institutions. That is where there’s alpha generation.

David: One thing that’s interesting, we’ve gotten to know a bunch of Stripe folks over the past couple of years through the Acquired Podcast. A huge percentage of those people are now in Seattle for various reasons.

Matt: Yeah. I was with a bunch of Stripe senior execs the weekend of SVB, and a lot of them live here, to your point. That is an interesting point, was one, we didn’t explore as much today at our annual meeting this notion of what’s the hybrid. We still think that having a nucleus of the team close by, especially at the early stage, matters a lot. But a lot of these teams are increasingly hybrid, increasingly distributed teams, and you want to have the best talent in the best roles in the world. So, it is evolving and yeah, I’d like to think that Seattle’s got a little bit of a different culture than the Valley based on my experiences. I don’t know if you guys would agree with that or not.

Ben: Totally. I think we are for better or for worse, and the answer is both, insulated from height. You’re not going to see as many companies go raise four consecutive funding rounds with very little revenue growth or product development advancement, but also when the market falls apart and you’re like, “Oh no, where’s the intrinsic value,” the companies that are in the portfolio of Seattle venture funds tend to be correctly valued. I don’t know. It’s a double, double-edged sword.

David: I do think, though, there is a tremendous demand among Silicon Valley venture capitalists to invest in Seattle companies. I’m sure you both see this every day. Still, I haven’t lived here since 2016, but I get asked all the time, what are the best companies in Seattle?

Matt: And I’m sure you just point them our way.

David: Exactly. I say, have a great venture firm, two great venture firms I can introduce you to.

Matt: That’s awesome. Coming back, building on this ecosystem. Of course, Tom’s book about flywheels, thinking about that episode and Tom’s impact on really all of our lives. We sat next to each other for 20 years and he was a total amazing human being and friend, mentor. Is there anyone last thought you might share about how you think about him and maybe his legacy and something that might leave as a thought around inspiration or legacy for the rest of the group?

Ben: Every conversation I ever had with Tom — he was a very curious person. And I hope that if I have a fraction of the success that Tom had in life, that I stay as curious as he did.

Matt: Love it.

David: I completely agree that. I was chatting with someone earlier about our interactions with Tom, and just how he approached things and I was reminded he was so much older than us. And I don’t mean that in a bad way, but just like a factual way. But from talking to him, you would never think that. He had a young mind always. And I hope that I can be that same way when I’m further up there in years.

Matt: Guys, you’ve been really kind to let me turn the tables a little bit on you and do this interview and have this discussion.

You’re great people, great friends of the firm. We wish you all the continued successes as investors and ecosystem builders and, of course, with the Acquired Podcast. So, thanks so much for being here today.

Ben: Thanks, Matt.

Thanks again for listening to this week’s live episode of Founded & Funded. Tune in in a couple of weeks for our next episode of Founded & Funded with the CEO of Credo AI.

Madrona Managing Director Soma dives into the world of intelligent applications and generative AI with Common Room Co-founder and CTO Viraj Mody. Madrona first invested in Common Room in 2020 — and we had the pleasure of having the founders join us on Founded & Funded the following year.

Common Room is an intelligent community growth platform that combines engagement data from platforms like LinkedIn, Slack, Twitter, Reddit, GitHub, and others with product usage and CRM data to surface insights from across an organization’s entire user community. Customers like Figma, OpenAI, and Grafana Labs use Common Room to better understand their users and quickly identify the people, problems, and conversations that should matter most to those organizations.

Soma and Viraj dive into the importance of deeply understanding the problem you’re trying to solve as a startup — and how that will feed into your product iterations — why organizations need a 360-degree profile of their user base, how Common Room has utilized foundation models to build intelligence — not just generative intelligence — into its platform — and so much more. So I’ll go ahead and hand it over to Soma to dive in.

This transcript was automatically generated and edited for clarity.

Soma: Hi, everyone. My name is Soma and I’m a managing director at Madrona Ventures. Today, I’m excited to have Common Room co-founder and CTO Viraj Mody, here with me. I’ve been fortunate enough to have been a part of the Common Room journey right from day one when we were co-leads in the series seed round the Common Room did a couple of years ago. So it’s been fantastic to see the company come from start to where they are today. Viraj, welcome to the show.

Viraj: Thank you for having me, Soma, and thanks for partnership from the early days.

Soma: Absolutely. So Viraj, why don’t we start with you giving us a quick overview of the genesis of the idea and where you guys decided this is the problem space you’re going to go after?

Viraj: So one of my co-founders and our CEO, Linda Lian, led product marketing at AWS and did a lot things by hand. So AWS has a phenomenal champion development program called AWS Heroes, and Linda was involved in that and that planted the seed for her in terms of the power of unlocking community and champions out there to help. And then independently myself, my previous experience was at Dropbox, which was pretty early in the product-led growth journey. And so we spent a bunch of time at Dropbox building internal tools that essentially helped unlock a lot of the insights like Common Room.

So Tom, one of our other co-founders, him and I worked at Dropbox, and then our fourth co-founder, Francis, was one of the early designers for Facebook groups, which was a very community-led, powered by the people-type surface within Facebook. So all of us had various perspectives on the same problem, and so it was a very natural fit when we all started chatting and exploring how to convert all of our various experiences into a product that we can help other companies leverage their community and customer base.

Soma: People say that hindsight is always 2020. Today, you’ve got a product out in the market. You’ve got a lot of great logos as your customers. S o you can say like, you know, “Hey, based on the traction, I can sort of project what the future could look like.” But when you started at Common Room, how did you decide at that time that this is a bet that was worth taking, and why you decided to spend the next chunk of your life working on this company, building this set of products, and going and doing something fantastic in the process.

Viraj: I think it comes from really understanding the problem space, and I think that’s why having co-founders with complementary skills is really important. Each of us brought a unique perspective but had a pretty unifying vision for where we want to see this product and the company go. We were pretty early in terms of seeing some of the motions that were being unlocked by community, both based on our experiences, but more importantly, talking to customers who are already doing this as part of their product journey. Some of our early customers, like Figma and Coda, were great partners in helping us think through how they would like to shape their business growth engine. And then that spurred a bunch of ideas for us.

One thing we did a lot early on that I would suggest everybody spend time if they’re in an early team, is talk to customers, not with the idea of helping them give you solutions, but really deeply understanding their problem. And then using your unique perspectives and experiences, plus your context of what’s going on in the ecosystem. We’re pretty well connected in terms of not just our networks but also a lot of peer companies. And so connecting the dots of the problems customers face, especially progressive customers who kind of see where the world is going, and then partnering with them in order to build that vision of the future. I’d say that’s been a key ingredient in the early days. And then, for me personally, I think I have a lot of experience and confidence in my ability to build this.

Back when Common Room started, there were a few fads going on. There was crypto and there was FinTech, and those were all exciting marketplaces, very exciting. But for me personally, my strength and experience and scale lined up really nicely with building business enterprise-scale SaaS software. And that also beautifully coincided with some of these leading customers we were talking to who had real problems that we thought we could uniquely solve that no one else was paying attention to.

Soma: Love to hear that confidence, Viraj. Both you having in yourself as well as your co-founders and what signals you’re seeing in the market. I don’t know if you remember this, but we had you and Linda on this show a little while ago. And at the time, we were talking a bunch about what goes into making a great founding team and how do you find people that are aligned or bound by a common mission and vision and there was a great story that you guys talked about what you were looking for, what were some of the mishaps you had along the way, and how you ended up with the founding team that you have now and, and, and all that fun stuff. If you fast forward two years from then, how do you think that journey is going? Do you still feel the same level of excitement and energy around the founding team, or do you wish you had done anything differently?

Viraj: There are plenty of things I could have done differently, but I feel like, all in all, I feel fortunate having the founding partners, but also, the rest of our team has just been phenomenal working with all of the Roomies. I wouldn’t change the team for anything. I think we have a great crew. One thing that I think summarizes how we’ve operated over the last few years is just relentlessness and focus on executing with velocity. I feel like these two have been pretty consistent. As with every step of scale, we’ve had to change our approach, but we haven’t changed both of these just focusing relentlessly on customers and their problems and then internally focusing relentlessly on speed of execution. And I think both of those together have been really impactful in helping us get to where we are. So I definitely wouldn’t change those. Anyone who says they wouldn’t change anything about the past is generally not seeing the whole picture. So obviously, there are things, but all in all, I feel like we’re positioned to do well as long as we continue focusing on the things that matter.

Soma: I always say, Viraj, that having a great founding team is sort of half the battle won. And I have a variety of companies that I work with and I look at like the forwarding team that you guys are and have put together — I feel really good about what you guys have done. People talk about ICP, or Ideal Customer Profile, but I want to take a step back and ask you what kinds of companies do you think need engagement with their communities. And how do you think it impacts the business? Is it all around customer satisfaction, or it does go beyond and say like, “Hey, I can help you with the top line, I can help you with the bottom line, I can help you with product adoption. I can help you with this, I can help you with that.” How do you think about that?

Viraj: Broadly speaking, this is important for every company out there because it all starts with the definition of community. I think it’s very easy to try and paint a very narrow picture of what a community is, but really you think about your community as existing users, future users, people who engage with your brand, people who have heard about your company, but not really used it. You can sort of encompass all of these and then build a bottom-up community strategy. And I think the answer goes way beyond, you know, just the customer satisfaction part of it. That’s kind of the bottom of the funnel in many ways. I think every company in the world really needs to think about how they can accelerate their own growth with data and insights unlocked from their broad community of users, not just like a very narrow definition of social media community or forum community, or Slack community. Companies that do this right unlock all sorts of superpowers, not just in terms of growing their top line and bottom line and absolute dollar numbers, but also getting really high-quality signal from people out there about problems that need to be solved that they may not have on their radar. Or identifying some of your most active champions in different parts of the world, who exhibit behaviors that are not easily spotted using conventional tools.

Soma: I’ll tell you from my vantage point, one of the things that I thought you guys did a great job, even in the last 12 or 18 months, is with the kinds of customers you’ve been able to sign up to start using Common Room and to see the benefits of Common Room community engagement. You go down that list, it’s literally the who’s who of technology customers and logos. And I would say that’s a fantastic place to be in because it’s one of these things where you get the leading companies then others follow fast. Was that a strategic imperative that you guys took or how did you guys end up with literally a phenomenal set of logos and customers, even in as early stage of a company as you are?

Viraj: That’s been a key focus for us from the early days, making sure that we have some of the best thought leaders in our space. And I think that’s been key. When partnering with early customers, it’s important to identify companies that are seeing the future the same way you do. So many of the logos you see on our website and using Common Room embody that — they are at the bleeding edge of how to engage with their community, how to leverage their community, and how to grow and cultivate champions. So it was a very deliberate decision on our end to identify who we think these companies are. And then it’s been almost a “practice what you preach,” right? Once you work with people who are really aligned with your vision, they then help sort of promote your product and your vision to their peers. Who, by definition, are other leading logos. So it’s been really helpful for us to sort of use what we’re helping our customers do on our own to grow our customer base. So between that and the networks we’ve been able to unlock, obviously from the team here, but also from our investors and partners, it is been very deliberate and I think it’s been paying off, at least so far.

Soma: Can we now go one step further and talk about a couple of specific examples? I know for example that Figma and OpenAI are a couple of your customers. Can you specifically talk about what they do with Common Room?

Viraj: We’ve been fortunate to work with some of the best companies out there and some incredibly well-known logos. Each one obviously has a different focus, but there’s a pretty common overlap of use cases across all of these. Since you mentioned Figma and OpenAI, I can chat a bit more about those companies. Both of these are very community-first in terms of not just a community of users but also a community of practice, where, you know, “Hey, we are building a product and obviously we want users to use and champion our product. But independently of that, we also want to build a very robust community of designers for Figma who speak with each other, bounce ideas, and help each other grow and nurture. And then for OpenAI, you know, a bunch of researchers and people on the forefront of AI practice. And then tying that back out to business outcomes.

So when Figma launches a new feature, how do they go and reach out to their champions who’ve been requesting that feature to help them spread the word and generate content? How do they host the best events geographically across the world, bringing together in real life or online people who are their biggest champions, people who’ve been generating content independently and talking about Figma features?

Similarly for OpenAI, as they’ve gotten to where they are, they’ve had several versions — GPT-2, GPT-3. Along the way, they’ve had communities they’ve developed on Discord and forums, where they discuss best practices about how they take this really nascent technology and then help unlock powerful use cases amongst each other. But then also having the company collaborate with them. So how do you make sure that people at the company are paying attention to these conversations going on?

Both of these companies have the fortunate problem of just having so much engagement that one thing that’s been powerful for them is being able to unlock signal from the noise, right? When you have something as powerful as GPT-3 and ChatGPT unleashed in the world, everybody’s talking about it and we have some of the most significant community growth we’re seeing amongst any of our customers in these companies. But from a company’s perspective, how do you take all of this great activity going on everywhere and extract the things that actually matter to you, which is where we use a lot of machine learning models of our own, but also foundational models from OpenAI itself to help.

Soma: One of the things that I’ve heard you and Linda talk a lot about recently is the intelligence layer that you guys are building as part of Common Room. Tell us a little bit about why do you think that is critical to what you’re building. And, more importantly, how do you go about building it into your platform?

Viraj: Once you start collecting information about all of the conversations happening across various digital channels. For some of the best companies out there, the volume of conversations happening is pretty overwhelming. The typical company will have social media presence with Twitter and Facebook and so on, conversations on Reddit. Then they’ll have closed forums, for example, where people are asking usage questions or questions about product, or open and or closed conversational communities like a Slack or a Discord server. Then you have technical conversations in GitHub and Stack Overflow. Plus, you have a lot of your internal systems with your CRM and your product usage data.

When you start thinking about each of these merging with the other, the amount of data you have starts to exponentially grow. And then being able to convert that into meaningful signal is where some of the most impactful outcomes happen. So where we’ve invested a lot in the intelligence aspects of Common Room are on the axis of community members. Once you have members in your community, really understanding who they are and building a 360 profile for them across all of these various channels, that’s one layer. The other one is around the activity that they represent going on. So someone publishes a YouTube video, someone else has a post on a forum, and someone else has a GitHub pull request or a GitHub issue — how do you take all of these as part of that 360 profile and then help paint a very clear picture of what sentiment is this member expressing? What are the key frustrations? What topics are they talking about? What are the different categories of conversations they’re having on all of these platforms?

So, intelligence about the members, intelligence about their activities. And then on a third axis, you can think about intelligence about businesses who are likely to buy your product or the propensity of businesses who are either existing customers or future customers — and how that interacts with the previous two. So, a business entity is made up of members who are having conversations. How do you build a model that helps you see the propensity of conversion or propensity of churn or propensity of upsell? And all of this can be derived from various signals for each channel there are different signals. So from day one, our focus has been not just on collecting data, because collecting data is a starting point. But how do you unlock key insights and outcomes from that data and then drive actions based on those insights and outcomes?

Soma: I really like how you framed it, Viraj. Talking about it as intelligence on people, intelligence on activities, and intelligence on outcomes. Now, switching to how did you decide what approach you are going to take with your models or with the models that you’re going to use? How do you approach training, you know, tuning of these models, are you using any of the currently really popular foundation models or are you thinking of building your own foundation models? How, how, how are you thinking about all of this?

Viraj: Yeah, it’s a combination of both. We have certain layers of intelligence that leverage custom ML models built with a pretty standard tech stack — you know, XG boost on SageMaker and feature engineering in-house. And then we also go leverage some cutting-edge foundation models, for example, OpenAI’s Da Vinci model but then help fine-tune it to perform ideally for our use cases and then also help us scale across our production data.

So from a custom ML capability perspective, we have a bunch of features we’ve built around the ability to auto merge members and organizations across various signals that we have about them. So, Common Room integrates with Slack and Twitter, Discord, GitHub, Stack Overflow, or for LinkedIn, Meetup, and dozens more. And the same person may have different profiles across all of these. The same person may have different conversations on each of these, plus internal systems like Salesforce and HubSpot. So we’ve built custom ML models that use signals from all of these different sources that we’ve trained in-house, using some of those technologies I mentioned earlier. Then we’ve built out a scoring that allows us to go say, “Hey, look, with a high degree of confidence, we think that this person is the same regardless of having a different name here or a different avatar image there, or a different email address”. So there’s an aspect to custom machine learning models that we’ve built for the use cases of merging members or merging organizations.

Then there is another use for custom models around propensity. Once you see a community-qualified lead of some sort, either through your CRM or either through community activity — how do you go build a model that helps predict a propensity of certain outcomes? Like, “Hey, you know, this organization is ripe to adopt your technology based on their champion behavior”. Or here’s one that’s likely to churn, so please go invest some time in making sure they don’t. So these two are examples where we’ve built a bunch of in-house models. But where the world is really exciting now with some of these foundation models is NLP and LLMs, providing a capability that just didn’t exist until recently, where you can go and quickly extract sentiment or extract conversational topics that are not necessarily keyword search. Or even categorize conversations as, “Hey, here’s a conversation about a feature request, or here’s somebody asking for support, or here’s somebody complimenting your product, you know, maybe you want to use that in marketing material”. So this is where we use foundation models by companies like Amazon or OpenAI. But in order to scale them for production use cases, we have to be able to fine-tune them. So, you know, OpenAI has fine-tuning capabilities, so we’ve been able to take the Da Vinci foundation model and fine-tune it for our use case, both as a performance optimization for better performance for our specific customer base, but also from a cost optimization so that we can actually go apply these models in a scalable way across our entire user base. Because without these, it can get really costly. It’s very easy to put on an exciting demo that leverages the hot new foundational model, which is great for a weekend project or with like toy data. But the minute you want to scale it to the kind of customer base that we have, or beyond that, you have to start worrying about the practicalities of, you know, downtime. If these hosted models have downtime, you don’t want to have downtime yourself. Or cost, if you are going to just simply pass through all of that cost, it’s going to become really expensive for you or for your customers.

The other one obviously around is precision and recall, right? A lot of the foundational models are built for general-purpose use cases, and they do a phenomenal job for them. At the end of the day, your specific use cases are going to be slightly more nuanced, and so how do you tune those so that your precision and recall are both even better for your customers? That’s where we’ve spent a bunch of time investing. I know generative AI is sort of the buzzword of the day, and we have some like, pretty clever ideas. But even before you go there, there are so many powerful things you can do with just unlocking capabilities that don’t need generative AI capabilities is just extracting signal from noise in interesting and meaningful ways. There’s some huge opportunity there as well.

Soma: Whenever you talk about Open AI today, most people immediately jumped either thinking about GPT-3 or ChatGPT kind of thing. And the fact that you are sort of not necessarily using that, but you’re fine-tuning a model to make it work for what you are looking for and do it in a cost-effective way that’s great to hear.

It’ll be interesting to hear how is OpenAI helping startups like yourselves. There are people who use ChatGPT. There are people who use GPT-3, and that’s sort of one set of people. And then, for people like you, has OpenAI been helpful?

Viraj: Yeah, absolutely. We’ve been partnering with them since the early days. We’re fortunate enough to have worked with some of the early OpenAI team, and it’s been really interesting to explore how to take some of these research and exploratory models and help commercialize them. OpenAI has different tiers of models internally. There’s Ada, Da Vinci, and several others. Each of them have a different cost, they have different performance characteristics. They have different use cases that they’re optimal for. And we’ve had a pretty open channel with them just in terms of trying new things before they are available to the general public or giving feedback both ways on what’s working, what’s not working. On pricing models, etc. So it’s been, it’s been extremely collaborative for us since the early days. And part of it also is walking the walk. We are OpenAI’s community, along with every other developer out there who’s dabbling in their technology. So making sure that they have the ability to get feedback at scale is pretty important to them. And so I’m, I’m glad we’re able to make that happen

Soma: You did mention a little bit about cost, and in today’s economic climate, managing the burn rate is super critical for every startup of every company for that matter. There is so much hype and buzz and excitement and craze around this generative AI and everybody’s experimenting with that in one way, shape, or form. And the cost could add up pretty quickly before you realize what’s going on. Do you guys feel like you are encountering that, or do you feel like your approach has enabled you to sort of stay ahead of the curve?

Viraj: One example of one of the models we use is it’s 10 x cheaper than off-the-shelf models that we could just pass through to. And a lot of that is the result of fine-tuning that I mentioned earlier. It helps us not just get higher quality results than just basic prompt design, but it helps us train the models so that it can optimize for our use cases without all of the extra cost. And then, from a deployability perspective, this just helps us deploy it in a way that takes a lot of the critical dependencies in our control as well. So monitoring cost, I think, is super important, especially as you are making some of these foundational capabilities available to customers. Because for some of the problems we solve, activity can change wildly. So, if a customer has a conference, you’ll get a week where there is a huge spike in activity, which will obviously drive a whole bunch of additional cost if it’s not built in a way to sort of foresee that event happening already. So if you build a company and then actually deploy it to production where you’re simply passing everything down to some foundational model, be it OpenAI or whatever else, you are likely to be in for a surprise if there’s a variance in volume that you’re driving, which is where some of the lessons to focus on our, like, “Hey, how can we keep our costs under control while still making sure you leverage some of the most exciting capabilities out there”.

Soma: Before we wrap up, Viraj, are there some hurdles you’ve run into when getting the company off the ground into where it is today? And what did you do to get over those hurdles, that might be helpful lessons for other people who are coming from behind?

Viraj: I think there is a level of paralysis that can happen if you try and game theory out every potential outcome. Even in your product — you could hypothesize till the end of the world around what customers actually want and what they’re saying, what they’re not saying, but nothing beats shipping product and watching customers use it or not use it. I think being comfortable shipping things at extremely high velocity with high quality, and that’s, that’s a hard one to balance. So my advice would be to have strong conviction within the team, not just the founding team but the broader team, around your expectations for what it means to ship. What it means to ship quality software, right? You don’t always want to throw stuff over the fence and say, we ship a lot of code. But also, you have to have some ability to ship an MVP. And so develop a consistent understanding internally within your team of what is and isn’t acceptable for who you are as a company. And then live that day in, day out. Many companies will say, “Oh, we should embrace failure,” but then they don’t actually embrace failure. Or many companies will say, “Hey, we should like ship MVPs.” But then when you ship an MVP, they point out all the a hundred things that are broken. And so, clearly defining how you want to operate as a company and then backing it up with the actual execution of how you work, I think, is important. There’s no right answer. There’s no single answer that works for every company. But I feel like each company needs to have a well-understood definition of what, how they ship, and what they ship.

Soma: That’s awesome. That’s, that’s a great answer as, as people think about getting off the ground kind of thing and, and sort of going through their execution environment and, more importantly, the culture. Because I think sometimes these things all come together, and you really need to think about these different pieces of the puzzle and how they come together as you build and scale a team. So with that, Viraj, I do want to say thank you for being with us, it’s fun talking to you. As much as I’ve been part of the Common Room journey from day one, just hearing it, and some of it is rehearing, it just gives me a lot of energy and excitement for what you guys are and what you guys are doing. Thank you again for being here.

Viraj: Absolutely. It’s been great so far. I’m looking forward to more fun times ahead.

Coral: Thank you for listening to this week’s episode of Founded & Funded. If you’re interested in learning more about Common Room, please visit commonroom.io. Thank you again for listening, and tune in in a couple of weeks for an IA40 Spotlight Episode of Founded & Funded with the founders of the Acquired Podcast.

In this episode of Founded & Funded, partner Aseem Datar talks with Leaf Logistics Co-founder and CEO Anshu Prasad. Leaf is applying AI to the complexities of the freight and transportation industry, connecting shippers, carriers, and partners to better plan, coordinate, and schedule transportation logistics. This enables network efficiencies and unlocks a forward view of tomorrow’s transportation market while simultaneously reducing carbon emissions.

Leaf Logistics was founded in 2017, and Madrona joined Leaf’s $37 million series B in early 2022. From the beginning, Leaf has been fighting the one-load-at-a-time way that trucking has historically been conducted. The company analyzes shipping patterns to make sure that when a truck is unloaded at its destination, another load is located to return to the city of origin. Identifying these patterns allows Leaf to coordinate shipments across shippers at 1000x the efficiency and effectiveness typical in the industry.

Aseem and Anshu dive into the story behind Leaf, what makes logistics so complex, and how AI can continue to improve it. And Anshu offers up great advice for founders that he’s learned on his own journey.

So, I’ll go ahead and hand it over to Aeem to take it away.

This transcript was automatically generated and edited for clarity.

Aseem: Hi, everybody. My name is Aseem Datar, and I’m a partner at Madrona Ventures. And today, I’m really excited because I have here with me Anshu Prasad, who’s the CEO of Leaf Logistics and also the founder. Anshu, welcome, and glad that you’re spending time with us today.

Anshu: Thank you, Aseem. This is great to chat with you.

Aseem: So, you know, Anshu as they it all starts with the customer. Tell us a little bit about your journey and the problem space and what you observed talking to customers in this space, and how you narrow down the problem that you’re solving.

Anshu: I’ve had the benefit of working in the space for some time before starting Leaf. And in that journey, what I got to observe was what, when I started in the space, seemed like a winnable game slowly and undeniably became an unwinnable game. It was hurting both the buyers of transportation and the providers of transportation. And being able to sustainably do something for their business to make sure that they had healthy returns and they had some reliability on a day-to-day basis. It’s a big part of our economy, as we all appreciate, and if anything, the pandemic shown a nice bright light on the essential nature of a well-functioning supply chain — and what happens when it doesn’t function all that well. But in the in-between times, in between crises, transportation and logistics is something that we’d all just, frankly, wish we could ignore because it would just work in the background. But if it doesn’t work for the participants, the customers who are invested in the supply chain, it doesn’t really work.

And over the last couple of decades, it’s become clear that even big sophisticated companies for whom transportation is a big deal are finding it to be less reliable and less planable than they’d like it to be. So, that was really the core problem, seeing some very smart, very hardworking people that I had a chance to work alongside and serve struggling with a critical part of their business. And it became clear that it was a time for us, and many of the folks working at Leaf Logistics now who’ve also spent similar amounts of time poking at this problem, to do something differently rather than just wade into the same fight and try to do more of the same and hope for a different outcome.

Aseem: Anshu, maybe just double-click into this a little bit and give us a sense of the kind of problems both the shippers and the carriers face on a day-to-day basis.

Anshu: At the fundamental level, this is a very transactional industry. A truckload from point A to B is seen as a snowflake. And for anyone outside the industry, it seems really alarming that it would be that way. Because we have all had the experience of driving down the highway and seeing every color of truck you can imagine on the road — how is this being done one load at a time? But that’s really, for a host of reasons, the way that this industry has evolved. So, the core problem as it gets felt by shippers is transportation becomes a one-load-at-a-time execution challenge. And if you’re a big shipper, you might have 500,000 or 600,000 loads a year that you need moved, and you’re treating each of them as an individual OpEX transaction.

And on the flip side, the carriers are responding to a demand signal that is very fleeting. It is, again, just one load at a time. I’m getting a request 48 hours in advance to go pick up a load in LA and then drop it off the next day in Phoenix. But I don’t know anything else beyond that. I don’t know what I’m supposed to do once I unload in Phoenix. Where is that next load going to come from? And I’m supposed to get up and start to play this game again, one load at a time, tomorrow. So, the ability to keep my truck utilized or my driver paid, maybe even return the driver back home, which is very important for the driver, becomes really hard for me to manage. So it becomes a constant challenge of trying to catch up with the transactional intensity but not really solving the traveling salesman problem. We think that should be solvable, but it’s not really what the data on the table allow us to do.

Aseem: Yeah, I mean, it’s, it’s just fascinating to understand and learn and, you know, as we’ve sort of worked together, just get educated every day on the complexity of this industry. I have been curious about this for quite some time — how did your background, your consulting mindset, set you up for tackling this huge problem and for ultimately achieving success in this industry?

Anshu: When I entered the startup world in the late ’90s, the flavor of the month was to apply technology to old problems. And we lucked upon an area where freight buying is a big deal for many companies in CPG, for example. We helped them buy their freight using basic technology that allowed them to automate a process that they’d been running for decades, using floppy disks and in web 1.0 kind of ways. And that helped to streamline some of the basic procurement processes. But it gave us an appreciation for the centricity of this purchasing decision to their core business operations. At the end of the day, everyone obsesses over their customer, and transportation is often the last point of interface with your customer, and yet we’re buying it as if it was just this free for all transactional auction. And so, what was getting lost was that sort of customer engagement, the customer entanglement from a well-serviced, well-structured supply chain to something that is very much ephemeral.

And the way that I kind of landed up in the space was a little bit by circumstance and by happenstance. We ended up helping companies like a Unilever or Bristol Myers negotiate their transportation rates. But what really drew me in was working with the people who had to do this work on a day-in and day-out basis and just empathize with them for a moment. You come to your desk at a Coca-Cola every day, and you’ve got a stack of shipments that need to get covered off, and you work your way through that stack as much as you can, and you get home and come back again tomorrow, and again, you have exactly the same Groundhogs Day problem. The only thing that shifts is outside of your control, ie. what is the market doing? I mean, if you’re a company like Coca-Cola, you’ve hedged your exposure to things like aluminum prices or sugar or high fructose corn syrup, and yet the second or third largest cost in your business, which is your transportation and logistics, is a bit of a guess and a gamble, and it shouldn’t be. And so as we, over and over again, and as I got into consulting, I saw this problem around the world, as we confront sort of the unreliability, not just of service, but of just the exposure that we have in our core businesses to this big cost item getting out of control. And last year was a good example. There were several top-fold of the Wall Street Journal shippers who all do a great job managing their transportation, just being subject to the whims of the market and being tens of millions of dollars over budget to the point where their earnings were depressed.

That is something that needs to get solved better with today’s data. And why I and many of the folks working at Leaf Logistics focused our energies on solving for something different was this is a remaining sort of big risk that looms in people’s business operations. And I’ve talked a lot about the shippers. If you think about the million or so trucking companies that are registered in the country operating at razor-thin margins, the roller coaster of the freight industry hurts them just as badly. And so the idea that no one is really winning and people are paying, and people in specific terms, layoffs, and bankruptcies are impacting this industry adversely. And it’s happening with increasing frequency over the last several years. Something should be done differently. As opposed to more of the same.

Aseem: I think the meta takeaway for me is that you have an asymmetric advantage, having spent so much time in this industry and really understanding the business processes like you described. And it’s amazing to me that the biggest spend has kind of often gone ignored, and you guys are doing a killer job in trying to build, I would say smart systems and intelligent applications around it. You know, one question that comes to my mind, Anshu, is that you’ve been steeped in this industry for quite a bit. You’ve been on the consulting side, you’ve been on the advising side. And starting a business is no small task. Especially in an age-old industry like this, where things are often done the way they’re done. And that’s the same way has been going on for many years. What headwinds did you face in, you know, tackling this problem in this industry, like landing your first big customers, can you just tell us a little bit about that journey?

Anshu: So there were three areas that I really focused on. One was customers. If we built something fundamentally different, would they be willing to take a risk and try something different? And let’s face it, you know, trucks are moving around the country, and they have been, somehow, people are muscling it through. So would there be a case for change? One test was talking to 50 perspective customers and saying if we built something, would they be willing to test it? Second was what is that earned insight that we had for so many years poking at this problem? What are we seeing that other people are not seeing because they’re caught up in the day-to-day fray? And that was fundamentally that much of this problem is planable. And if you apply a different sort of analytical approach to this, you could understand and uncover the planable bits and, at minimum, take the planable bits off the table to allow people to focus their creative energies on the stuff that just needs to be triaged through brute force.

And then the third was that you had to put the pieces together. And for me personally, the third piece was the most important. Finding other people who had worked with, who had seen this problem from different perspectives and angles, who are all kind of seeing the possibility of solving the problem differently enough that they would drop their current work and come do this. And the personal conviction from individuals that I had a ton of respect for, and I knew that they brought special skills to the table, come and jump into the boat and start rowing, gave me the most momentum of anything.

And so getting that first customer was as much about having built something off of a particular understanding with a set of folks who had special skill sets as it was about convincing that customer. To be honest, I think some of the early customers said, I understand what you’re describing. I think you guys will figure it out. They were betting on us as much as anything, and it was as much a partnership around sniffing through the common problem that we saw and working and iterating on that to solve for a different outcome. We were as invested as the customers were. Those early customers saw as much in the promise of what we were trying to build as we did. They just didn’t have necessarily the same sleepless nights as we did.

Aseem: I had the privilege of talking to a few of your customers, and I could say that they were not just fans, but they were raving fans. And I remember one comment where one of them said to us that I think Anshu understands and the team understands the problem more than we do. Which is a testament to your empathy and your, you putting yourself out in your customer’s shoes. Anshu, was there a moment when you started talking to these 50 and going deeper into the problem where you thought that I’m onto something, right? Was there a turning moment, or did it just happen at a consistent pace that built your conviction?

Anshu: I think what started to build conviction the most was how quickly we could arrive at a common understanding of the problem. It became very crisp and clear. So, if we just basically said, you know, and this past year is a good example. This past year can never be allowed to happen again, says the budget holder at a big shipper. That acknowledgment that something is fundamentally broken. It may be incredibly complex to solve, but just the common understanding that there’s a problem here versus the things are happening. Things are getting done. There isn’t a compelling case for change — that would’ve been a warning sign.

So, there were a couple of ideas that I’d been chatting to folks about, and they all agreed that there was some value to be delivered, but it wasn’t clear that it was a problem compelling enough to go take a risk. And in this particular case, the risk was give us data that you’ve never given to anybody else. A brand-new startup that’s starting out building the technology. Give us data you’ve never given to anybody else and trust us to be good stewards of those data is a big ask. And I was surprised and really encouraged by how many people were willing to part with these data in such a transparent way. Because, you know, it signaled to me that they appreciated the importance of a potential solution. They didn’t know what the potential solution was quite yet, but they were invested in trying to work toward one.

Aseem: Great point. I think often, people look at their data and say, hey, this is data that I’ve collected. It’s sort of my crown jewel. And it’s a huge testament to you and the team where, you know, customers came to you and said, look, I’ve got all this historics, but in some senses, I don’t know what to do with it. And if we can find a meaningful way to mine that data, not just look at it as mere flat files, but derive insights and then take action and complete the loop, like that’s the holy grail, which I think Leaf Logistics is doing so beautifully.

How are you thinking about building intelligence into your solutions? Tell us a little bit about your vision around the smart applications, the ML/AI-infused things. How are you thinking about next-generation technology as an enabler to solve this unique problem?

Anshu: Yeah, it’s actually an interesting area to apply that branch of analytical thinking and algorithmic decision-making. So we apply machine learning to large longitudinal data sets as sort of a starting point for the work that we do. So we understand that there are some patterns that will hold, and we can plan and schedule freight against those patterns. Just doing that, using some of the technology we’ve built, allows us to coordinate those shipments across shippers at a thousand times the efficiency and effectiveness that people in the industry do. So that confers a pretty significant advantage. Just planning and scheduling with the benefit of machine learning, pointing us to where we know that patterns will hold.

Where we’re starting to see some decision-making get enhanced is there are way too many inputs, right? So just with two or three shippers, the numbers of decision variables you might need to consider, too, for example, we’re working a fleet right now that works across multiple shippers in eastern Pennsylvania. It keeps 10 trucks and drivers and 32 trailers busy on a continuous basis. But on load by load level, that could mean there’s a load that is being fit into a, a standard plan that the pattern identified by machine learning holds over and over again. But it could also mean that there’s a load that needs to be taken to Long Island or to Ohio, and you need to be able to solve for that. And the consequences of stretching the fleet to Ohio needs to be factored in. And that sort of supervised learning based on those different inputs so that the algorithm is smarter the next time that an Ohio load pops up on the board becomes important. And building the technology to think about that because we know that those problems exist, I, we see that in the historical data. And so, how can we train the algorithms to do that? To kind of give you an example- optimizing for those decisions on a weekly basis as opposed to annual basis, which is what the industry typically does, confers anywhere between six and 16% advantage. So just literally taking sort of the learnings from week one and applying it to week two. Week two, applying it to week three, can have that kind of an impact. You know, let’s call it 10% on a $500 million spend — that is an enormous impact for a company.

What we don’t know is what shape it will take in the future sort of working of this industry. There are something like 5 million white-collar workers in U.S. logistics. Do we arm them with better decision-making tools so that the transactional work that they do now they have better data at their fingertips so they can execute smarter decisions? Or do we do what media buying and ad buying have done, where the algorithms take some of the rote decision-making and figure that out and execute that so that the creative brain power of the humans can be focused on up and downstream decisions that are impacted by transportation? I don’t know which way the industry will evolve and at what pace, but there are significant opportunities for bringing some of these technologies into this industry.

Suffice it to say that the millions of man-hours that are spent doing transactional work. I think, for most people outside the industry, it’d be alarming the level of manual intensity that transportation still requires. It will be rung out of the system. Exactly how that will be rung out of the system so that people can work on, hey, if I now know the rate from Dallas to these two locations three months in advance, how would I structure my production scheduling and my manufacturing processes differently? You just can’t answer that question today because those data don’t exist. But when those data exist, there are some very interesting problems for humans to spend their energies on versus what the machine or the algorithm can take off their plate.

Aseem: That’s fascinating. And you know, I, I, I think this is really unique as to how you guys are thinking about the problem and bringing the technology of today to solving a very well-known complex problem.

Anshu: It is fundamentally something we’ve all bashed our heads against the wall at for a long enough time. We talk a lot about waste in the industry and in terms of empty miles and emissions associated with them, but there’s just the waste of human capital. Today, a truck driver in our industry, if he or she’s driving empty to pick up the next load, they don’t get paid. They’re paying for diesel out of their pocket in a lot of cases. And then there are, of course, the man-hours wasted. On average, over four hours are wasted at pickup and at delivery, loading, and unloading. And so many inefficiencies that we’re all paying the tax for. If we can free up the human capital to work on more interesting, more valuable problems, we’re all going to be better off.

Aseem: You know, wanted to sort of just pop back up for a bit to get back to the 30,000 feet view. How are you thinking about scaling, and what are the challenges in front of you?

Anshu: It’s a very interesting problem, and in some weird ways, because of the complexity of the problem, there are multiple areas to pursue. So, one of the main things for scaling is to continue to have a disciplined focus on the few things that we think will make the most difference to our customers over the next handful of stages of our growth.

That focus and discipline becomes a really important thing for the management team to focus on, which brings me to maybe the most important point. One of the things that we’ve been very clear-eyed about is the team that it took to muscle through and get from zero to one may not be, and likely isn’t, the team that scales from where we are to where we’re trying to go. There just different skillsets. The obsession with the problem. The ability to iterate and think in first principles was essential for us to get off the starting block. Now we have to take the pieces of product-market fit and repeatability and drive toward scalability by looking at patterns and executing against those patterns with discipline. And hiring and upgrading our talent and challenging each other to make sure that we’re not settling for the status quo have been really important. Culturally we have a very transparent and open culture. Many of us have had the opportunity to work with other people on the team in past lives. So, there’s built-up trust, and yet we’re all trying to do things that scaling is an interesting word to use in the context of a startup, but human beings don’t scale very well. There are certain things that we do remarkably well, this is an organizational culture challenge to build something that scales, and that might mean that myself and others need to give up things that we used to get our hands dirty with to allow other people to pick them up and do a better job with. And that is, frankly, a big challenge. Hiring for and building the organizational muscle to genuinely scale as opposed to just doing a few of the things that we’ve been successful at a few more times. It’s really, it’s fascinating. And what’s been interesting is the learning that we, almost on an individual level, you can palpably feel are going through. This idea of letting something go that you used to obsess all night over because somebody else can come pick that up and, within a couple of hours, have a different solution than you, just because they look at the problem and frankly at the world differently than you, is a learning experience, a growth opportunity for many of us.

Aseem: Well said. So much of it is, building the right team, hiring folks that are coming from different backgrounds, different points of view, looking at the problem differently, but also world-class at what they do, right? And oftentimes, that’s probably not the existing team that’s there because they have different domain expertise or they come from various stages of a company’s life cycle as you scale fast. Another question on that front is, how do you think about repeatability and understanding patterns on what to invest behind? The challenge with scale is often prioritization because you can’t scale if you are focused on too many things. I mean, Yeah, you can scale horizontally, but that often doesn’t make you best-in-class in certain areas. So, what’s your guidance to founders or companies who are just slightly behind you on prioritization and being maniacally focused on a few core areas?

Anshu: I think that’s one of the toughest things for us to do and, honestly, to challenge ourselves to ensure that we’re applying the same strict filter continuously. Because sometimes we fall in love with our own ideas. And one of the challenges for an experienced team like ours is we do have so much familiarity with the problem, but we might be a little too close to it. So sometimes, our hypotheses are are tough to let go of. And similarly, even our most high-conviction customers might not be able to tell you what is it that they want next. So this is, oftentimes, when we’re talking about something different, we’re skipping a few logical steps in the solution design. So asking the customer for a set of features might lead us down the wrong path. And being able to really understand that requires that we have more than a handful of data points. And so we have this sort of ethos here that zero to one is very hard. One to 10 gives you data. And one to 10 is just, we’re going to be disciplined in making sure that we get enough data points so that we are not getting skewed perspectives that we double down on, and we don’t scale until we’ve got that repeatability understood. So repeatability and scalability are seen as distinct. And oftentimes, the people who are invested in innovation are not the people to take it to repeatability. And the people who are in the repeatability motion of sort of new ideas that we germinate with our customers are not the people that are responsible for or charged with scaling it.

And that sort of baton handoff has been helpful because I think that’s something we struggled with. Just switching gears as the founding team was very hard to do because, to your point, each idea deserves a ton of scrutiny and attention.

The other sort of lens that we apply is we have north star metrics that we look at, and we look at the differential impact of those north star metrics from each idea. So, it’s almost like a mini PNL ROI-based argument per idea. An exercise that we go through, which is different from the standing sort of reviews of the project planning and the metrics, it’s stepping back and saying, if I had to draw the line at three, which ones would be above and below the line, and forcing a debate. So, people are then debating the data that they have, the ideas, as opposed to any sort of custodianship for, you know, the work that they’ve been doing. It’s learning from great companies who will put competing teams to work on the same feature because they learn so much from looking at the same problem but with different diverse teams tackling the problem independently. We’re trying to just borrow from those pages. And that means that reprioritization is as important as prioritization in the face of new data. But it just becomes the way we work, and we’re trying to develop that muscle as we scale because we start to get to be a larger company, to be able to reset priorities doesn’t seem like what big companies do very well.

Aseem: The one thing that I’ve observed in partnering with the team, which is just amazing, is you all think in terms of 10x, you all think in terms of the outsized impact that an effort or a project or an idea could have relative to the metrics. Does it improve it by 5%, 10%, and is it worth doing it, or does it actually have its own leapfrog moment where it’s having an outsized impact if you go funded or if you go execute on that idea? That’s a good sort of framework for somebody to have as they think about scale.

Anshu, let’s talk a little bit about your experience and your principles around hiring and adding to the team. What tenants do you keep in mind when you hire people, especially at this stage in attacking this scale challenge? What roles are you adding, what should founders in your position know and learn from you on how you are thinking about the right kind of team and shaping the right kind of, I would say family, to go after this problem?

Anshu: Yeah, I think family is a great term for it. I think one fundamental thing is that the needs shift. Early on, one of the most important things we look for are people who have demonstrated grit. That they’ve had to go out and find a way through. And that can come in multiple disciplines, but there’s something to be said for finding a way through. And the shift between that and the scaling phase now is the thing that we spend a lot of time doing, even in panel interviews and group discussions with some final round sort of leadership candidates are filtering for the ability to distinguish between the things to pursue and which things to leave behind. And that honestly is very hard for that founding grit-based team. Cause that grit-based grinding team, they can’t leave any stone unturned. You just keep working. And the problem with scaling is you can’t afford to have every detail consume your time because it gets you in the way. So, the ability to put the blinders on and make sure that the blinders get tighter with each iteration is a skill that people who’ve scaled before seem to demonstrate, and they can prove that to you. We can even look at our current set of priorities that people who are working at Leaf Logistics right now are struggling with force ranking or prioritizing. Put that in front of someone who’s had scaling experience, and they’ll ask the right essential questions to be able to distinguish and at least relatively prioritize those items on the list. That’s the clear-eyed kind of perspective that I think at the scaling stage is distinct from that sort of grind it out and find a way type person or people you’re looking for at that early stage.

Aseem: just following through on that thought. Leading into 2023, Anshu, I know you’re growing, you’re adding to the team. Tell us a little bit about what would ‘great’ look like for you this year?

Anshu: There are three things, and we try to make sure that at this point, for everybody on the team, we all know what those three things are. The first is we are seeing the coordination thesis that we’ve started with actually playing out, and that’s driving an improvement in our net revenue picture. And so, as we get more scaled, frankly, people who haven’t spent as much time as you and your team have, Aseem, understanding what we’re doing and why can look at the business from the outside and understand the progress that’s being made in just pure sort of financial statement terms. That’s an area of focus. So we’re just trying to get those clear financial metrics to jump from our performance. And that is, through doing some things that are pretty cool and pretty distinct in terms of being able to build circuits and continuous moves and even deploy fleets and parts of the geography that others aren’t able to do. And we, frankly, put a lot of effort in being able to get to this point but to go execute those things and show that impact on the bottom line is job one.

Second, is the only way or the best way that we think that we’re going to get there is to double down with some of our key customers who are growing very rapidly with us, but there’s still yet another gear that we can hit together. And so, account management becomes incredibly important as a discipline to not just further build out but to enhance. And the amazing thing is that there’s just as much appetite from our customers. We’re finding engagement at such different levels and across so many different personas that it’s an incredibly intellectually stimulating exercise to find those different perspectives because many of these customers, this matters a lot. So just earlier today, we had one of our logistics service providers, CEO and CFO, in the office, specifically talking about their 2023 plans and how much the work that we are doing together could impact that trajectory. And that’s the kind of partnership we’re really looking for from an account management perspective.

And then the third thing for us is to make sure that we are prioritizing the 10x moonshots that are coming next. You know, how do we build upon some of the early advantages that we’ve established to continue to do things that other people just don’t have the established foundation to do?

So, we’re really excited about some of the payment and lending-type solutions that we can bring to market right now in an economy where those types of solutions are pretty few and far between. This is still a massive industry with huge inefficiencies and a recognized need for change. A lot of innovation needs to be brought here to mitigate the significant amount of waste that we have in the industry. That hurts both people and all of us indirectly as the environmental impact of an inefficient supply chain is experienced across the economy and climate. How do we make those investments possible? That’s going to require innovation and the 10x ideas that we’ve been of working on. Making sure that those ideas see the light of day that they’ve germinated, but also that we talk about some of those things to pull some of the next set of customers and prospects into the journey with us. There’s a fair bit of growth for us this year, Aseem, but we will sacrifice top-line growth for growing with the right people at the right pace, with the right level of innovation to set us up for the future potential that we see for the company and for the impact that we can have on this industry.

Aseem: One of the things I admire about you and the team, Anshu, is just this notion of growing right versus growing in an inflated way. And I love the fact that you’re looking at 2023 from a, Hey, what’s today? What is one year out, and what is the 10-year out change in this industry look like? And aligning yourself with that. Anshu, it’s amazing to me how the team has come together and how you’re hiring, and how you’re growing. You know, you talked a lot about you’re so focused, and you’re so close to the problem, but who is your sounding board? Who do you go to for advice? Tell us a little bit about that.

Anshu: You know, as I said before, I think people don’t scale, and that goes for founders. I think a lot about people who can work on the business as opposed to in the business. And that’s where our literal board folks at Madrona, but also just generally people from outside of Leaf Logistics, can look in and see what they see. So, I make it a point to start my week with external advisers and to bookend the back half of the week with the same because, at the end of the day, I’m not building this company for anybody aside from the problem. And the problem needs to be solved furiously, sort working on the problem from the inside is insufficient. It needs to translate. And so that external perspective is really important. I think, for me personally, making sure that the blinders aren’t on too tight in terms of the narrowness of scope. I probably read more widely than I did at the earlier parts of the company’s growth. And I’m always on the lookout for things that will just be absorbing and bring different perspectives. Understanding what’s going on in other fields, being able to speak with people who’ve just done outlandish things in other disciplines, and understanding what types of models of leadership there are. Entrepreneurs who are further along on the journey are incredibly helpful to learn from because they have run through some of the roadblocks, and they’re incredibly generous with their advice. So I think those are the three areas. Really making sure that the personal growth at least tries to keep pace because it’s not realistic that any of us evolve that rapidly. But it’s a lot of fun. And it’s a lot more interesting and multifaceted than it might feel on a day-to-day basis.

Aseem: Anshu, any parting thoughts for founders who are new or first-time founders or thinking about walking in your shoes and are just maybe a year or 18 months behind?

Anshu: I, I asked that question of founders 18 months ahead of me, and I benefited a lot. So, hopefully, this is of use to some of you. Really two things. One is to ask for advice and ask for people to pressure test your problem. And funding and fundraising will come with that. Asking for advice will bring you money. Asking for money will bring you advice, as somebody told me early on in my journey. And that was really helpful. And every single partner that we have today, we engaged in a conversation outside of, and well before, there was a fundraising opportunity. So, it was really about understanding do we see the problem the same way or do we see pieces of the problem that we could be helpful to each other before it was time for fundraising.

The second is, clearly, there are a lot of exciting things about the fundraising process itself, but if anything else, it’s a learning opportunity. You’re not just pitching your company, you’re getting an understanding of who else is out there, what perspectives they have, and what can you learn from. If you really love the problem you’re trying to solve, it’s not about winning the argument or getting your point of view across. As fun as it is to watch Shark Tank, it is not about trying to convince people to just follow along with your way of thinking. It’s about making your thinking better, so you can solve the problem you came here to solve. And the best thing I think I can say about working with Madrona and other key investors and partners, and advisers that we have around the table is they love the problem just as much as we do.

I will get pings, texts, and phone calls with ideas around why does this or why not this as much from people that are not working the problem day to day as those who are, which gives me a lot of confidence that I think we have the right team assembling to really solve something that matters over time.

Aseem: You know, I will say that having known the team for quite a bit now, I have had a deep level of appreciation for this industry and the challenges that your customers face on a daily level. And I often find myself wondering, where is this person driving the truck going from point A to point B? Where are they going? Like, how much load are they carrying? And it’s fascinating. If you’re in that job, you’re powering the economy, and yet you have a suboptimal experience as a person who’s doing a very tough job, and I think I, I feel nothing but empathy on that front, but Anshu, thank you so much for taking the time. And we couldn’t be more thankful to have you on this podcast and for sharing your words of wisdom. Good luck in 2023, and we are excited to be on this journey.

Anshu: Thank you, Aseem. Thanks for the continued partnership. It’s going to be an exciting year, but there is much more to do.

Coral: Thank you for joining us for this episode of Founded & Funded. If you’d like to learn more about Leaf Logistics, please visit their website at leaflogistics.com. Thanks again for tuning in, and we’ll be back in a couple of weeks with our next episode of Founded & Funded with Common Room Co-founder and CTO Viraj Mody.

 

Welcome to Founded & Funded. My name is Coral Garnick Ducken — I’m the digital editor here at Madrona. Today, investor Elisa La Cava is talking with Josh Crossman, CEO of Magnify, which was incubated at Madrona Venture Labs. Josh was actually recruited by Madrona Venture Labs to help launch a business that would bring AI to the customer lifecycle. He was quickly persuaded and signed on in 2021, spinning out of MVL in less than three months with a $6 million seed round.

In today’s episode, Josh and Elisa discussed the incubation process and how Magnify’s intelligent application to improve customer retention, expansion, and adoption is solving one of the biggest pain points of the customer experience — focusing on and understanding the needs of every single user. They also dive into finding product-market fit and the importance of incorporating AI/ML into a product from the very beginning. I’ll go ahead and hand it over to Elisa to dive into all this and so much more.

This transcript was automatically generated and edited for clarity.

Elisa: Well I’m so excited to be here today I’m Elisa La Cava — I’m an investor on the Madrona team, and I have the distinct pleasure of having a conversation with Joshua Crossman. Josh and I have known each other now for a little over a year, since we invested in Magnify’s seed round. And today’s topic is all around very early-stage company building. Josh, you have been in the trenches, literally going from zero to one, building a company from ideation to launch and there are so many incredibly meaty topics we can cover, and I’m excited to dive in. But first I just want to give you a chance to introduce yourself.

Josh: Yeah. Thanks Elisa. So I’m the CEO and Co-founder of Magnify, the operating system for the customer lifecycle, and I’m really excited to be here and — let’s dive into it.

Elisa: So the customer life cycle, that’s a like a big statement, right? Everything, especially in today’s economy, it’s all about how do we make sure our customers are happy. In software that means are they using our product the way that we wanted them to? Can we get more folks to use the product over time? What does it look like years down the road when you want them to continue to be happy, thriving customers and utilizing all of the new products and features that you’re building? Sometimes we call that process post-sales orchestration, which can be a mouthful — how do you think about what that space is?

Josh: Yeah. The whole concept is exactly what you’re talking about. The challenge is that we’ve built these really amazing technologies in the software industry — particularly in B2B software, and enterprise software — but most enterprise software companies have real significant challenges with adoption and retention. And so what Magnify is doing is bringing automation, bringing AI/ML, bringing software into a problem that software faces it in itself. It automates and personalizes that customer life cycle. And fundamentally, that’s about understanding the needs of the user — what are their challenges and how do we automate and connect with them in ways that can transform their experience in really delightful ways? It’s a real shift from the way that traditional enterprise software thinks about working with their customers and working with their users. And we think it’s one the industry’s really ready for.

Elisa: Let’s talk about that shift. What is the status quo? Why does enterprise not get this today, and why is this a huge pain point that is worth solving?

Josh: Let’s start with kind of the current state of the state. Let’s say you buy a piece of business software — what typically happens is that you’re assigned a human being called a customer success manager. And this customer success manager is this wonderful human that is going to work with you over the course of your onboarding, your adoption of the product, ongoing usage, retention, your experience. This is the account manager for you that is really centered on making sure that you get value out of your software product. And I’ve run some of the largest and most highly regarded customer success teams in the industry. And I have nothing but love for customer success.

Elisa: Well, and it’s important, right? You are the voice and the face of that company and that software. You are the point person they go to when something goes wrong.

Josh: That’s right.

Elisa: You know, when they have questions about things they can do better. It’s like, this is the one person on your team that is always a go-to.

Josh: That’s right. Sometimes we call it “The one throat to choke,” when things are going bad. Usually, we like to say it’s your single point of contact that’s here to make sure you are delighted by our piece of software. And so this human being is doing great work, and we’ll call her “Sally CSM” and Sally CSM is talking to you every day maybe every week or maybe every month, depending on the nature of the engagement. The challenge is that Sally’s only talking to Elisa, and Sally has another 15, 20, 30 other accounts that she’s managing. Sally just doesn’t scale. Really the model and the industry has really been relying on that customer success manager working with a single point of contact and then that point of contact in turn driving adoption and usage out into the rest of that business, into that enterprise. And Sally never talks to the thousands of users at Elisa’s company. She can’t. She’s a human being with like finite time and finite capacity. And that just doesn’t scale. You can’t scale the personalization.

And with the rise of product-led growth, with the rise of user adoption, we think there’s a real change needed in the way that we think about it. We’re still going to have CSMs. We still need that. That’s absolutely essential. And we think that we need to start centering on the user. So much of everything we do in, in software and enterprise software is centered to the account level. And we have to get to a place where we’re thinking about every single user, no matter the size of the account, and understanding their needs, understanding their behaviors, understanding where they are and their adoption journey, understanding what are their excited and thrilled, and where they’re frustrated and understand how can we automate and personalize that experience for each and every user.

Elisa: This is exciting. I love this space. I love what you’re building. Let’s talk about what building a day-one startup feels like. You co-founded this company literally day zero to now with product launch. And you also had an interesting path that I want to talk about in how you did that, which was through a venture lab. When you started working with Madrona Venture Labs to build the idea for Magnify, what was your goal?

Josh: So, I’ve been in tech for over 20 years now. I’ve been a senior executive at a bunch of different companies, scaled companies, taken companies public, and as a GM I took a company from single-digit millions to almost a hundred million and profitable. The last one we scaled and then it was acquired by a public software company. So, where I was in my process after we sold the last company, I took some time off. I sort of poked my head up and said, “What am I going to do?” And I was in conversations with a late-stage data observability company doing some really interesting stuff. They’d just taken down a big round and they wanted to bring in sort of a COO/ president type to help run the company and work with the CEO founder. And I thought, oh, that’s great. It’s a perfect kind of role for me and let’s go do that. And then I get this phone call, and this phone call is from Madrona, and I know some of the partners and folks and, and they have this phone call says, “Hey, we’ve got this really interesting idea that we’d love to talk to you about.”

And I think that’s where the conversation with MVL (Madrona Venture Labs) starts. So, MVL has two models. One model is that you are a CEO/founder type that wants to come in and you’ve got this concept, you’ve got this passion. And if it aligns with what MVL is thinking about and how they think about the world, they can help bring you along on that journey. There’s a second type of idea though, that they have, and that is ideas that they’ve been thinking about and saying, Hey, we want to bring AI to finance, or we want to bring AI to travel, or we want to bring AI and just take AI for a second. We want to bring AI to the post-sales world. And so Moderna Venture Labs has been thinking hard about this problem, and then what they say is, I think there’s something here. We need to bring in a co-founder to help solve this problem, to help create the idea. We’ve sort of developed it, we’ve tested it with other companies, depending on where they are in that process can be relatively far along and the idea, and then they’re trying someone to help take it all the way.

And that was what happened to me. So they said, “Hey, we’ve got this thesis around bringing AI automation into the customer experience, into the post-sales world. Sounds like it’d be a fit given your background, Josh, why don’t we talk?” And I said, you know, “Tell me about the company.” And they said, “It’s pre-revenue…”

Elisa: it’s not a company, yet.

Josh: It’s not a company yet, it’s an idea.

And I said, oh, no way. That’s crazy talk. You know, that’s a young man’s game. What are you talking about? Uh, you know, I’m not that old. And I’ll tell you what, once I talked to them, I absolutely fell in love with the idea, fell in love with MVL and what they’re doing there, and got so excited. And, and you know, I think the process took about three weeks.

Elisa: Wow, that’s fast. That’s a quick turn. So you were instantly hooked. So how, how was that process? Tell me more!

Josh: So, in my case, you know, it moved really quickly. They already had a relatively strong idea so we took that idea, and you know, I’d say I put my 25 to 30% spin on the idea. Like, okay, let’s change this. We’re going to modify the product concept this way. Think if we add this capability or that capability, it could make a real difference. And then, MVL really just worked with me. So the way that Madrona Venture Labs works is that they have a set of functional capabilities and functional experts on the team.

So you may have a CTO type and they’ve got, uh, in my case, a guy named Keith Rosema. And Keith is amazing and super smart and talented technically. So, they’ve got sort of the engineering side. Then they may have a product side, they may have a marketing and sales and GTM side, they have a design capability and branding, and then some general management experience and all those things. And so in my case, what happened was we took the different functional capabilities and used them as we needed to refine the product concept to get the pitch right. I probably needed a little less of that and needed more help on the product side. So I’ve got a lot of product perspectives on what the product needed. But I’m not an engineer. I’m not a VP of product. And so what I really needed was the help, like, let’s get the mocks right, let’s get the designs right. Let’s talk about the backend architecture. How are we going to think about data stores? That allowed us to accelerate and move on the build right away rather than maybe being gated until I had a technical co-founder, until we had made some hires, until we had a bunch of other stuff. So we could accelerate that process in some really important ways and allow us to get to market much more quickly.

Elisa: Okay. Switching gears — I want to talk about product-market fit. In your case with Magnify, you had an idea, you spun that out, you did fundraising, you found a co-founder, you started building your initial team, you really went from zero to one. Like you had mock-ups that you were thinking about, and then trying to build kind of three products in one to go to market in a really strong way. And that’s a hard and long process. So, I would love to hear kind of what’s your approach or what has been your philosophy over the past year as you have gone from napkin to bits flowing, product launching software company.

Josh: First of all, it happened over the course of about a year, but it feels like it’s happened really quickly. Like, just to help put context, I think we started talking with MVL in July like is 2021. I came on board in August. We pitched to Madrona as the primary pitch and then to Decibel as the second, you know, as the other investor, in September. We funded October. That was a very quick process. And then you’re, you’re kind of like, oh, great, now I’ve got this check for $6 million. You’re kind of like the proverbial dog that caught the car. It’s like, now what? I’ve got this big check. I got to go build a product. Holy smoke.

So, I think a couple of things that, at least for our journey were specific to us. We had already, MVL specifically, Madrona Venture Labs, had already talked to a bunch of customers and had a thesis on the market. And so there was a bunch of research that had been done that we could immediately plow into it. I’ve led large customer success and, and customer-facing teams for many, many years now. And so I had a point of view on the market as well. So, in some ways, we could immediately accelerate the process of, okay, what do we want to build? What do we want to design? Because I can say, I want this, not that, I want this, not that. And that’s really dangerous for myself to be like, Hey, I know all the answers and all of that because that becomes very quickly a segment of one. But I want to be clear, like, if you are a founder and have a vision for the product, yes, you need to get external points of view, and also, my council is to build what you think you need to build. I don’t think it’s going to be crazy off the mark. If you’re in the space and you know what you’re doing or you know what you want to build and understand and have empathy for the users of your product, then there’s a lot of real estate that that covers. And so I think that was kind of the first moment, or first few months of the process was like, Hey, we know we’re going to need this thing, that thing, that thing. Let’s go work on those and, and design the process.

Now, once we got into that, then you very quickly go and get a set of design partners. So, we talked to a number of folks in my network and other networks and saying, Hey, I’m going to test the positioning, the concept with you. And so there was a sort of quasi pitch deck that we built, that we tested with them, and a bunch of questions and some really helpful scripts that actually Decibel had given us around ways to ask questions and do focus groups and things like that. There were some really good things about product that we learned during that process and learned by getting input and counsel from some subject matter experts into the design. Then you start putting mocks in front of your customers. And, you know, and mock we’re talking like stuff in Figma that we’ve, we designed and, you know, it’s actual clickable demos, but it’s, there’s no code. It’s just all, all slideware. I think. Here’s one of the things that, you know, sometimes you’ll hear, was that “nos” were much more helpful than “yeses.” People will often say things like, yeah, yeah, yeah. Or, I like that. Because people generally don’t want to offend other people.

Elisa: Said another way, if someone’s saying “yes” maybe they’re just not engaging. Maybe they’re not into it, so, if you get someone saying “no,” it means some part of them cares enough to give you that feedback, and some part of them thinks the problem is annoying enough to talk about.

Josh: That’s right. And so what would happen is sometimes we test product concept. And I’d be like, oh, this is like the best idea ever. And I, I remember this one call with this one woman who is a chief customer officer at a, a fairly large company, and she’s like, I don’t get it. This doesn’t make sense. I’ve already got the solution — it is a solved problem. I’m like, here are the differences. Here’s why this isn’t a solved problem for you. And she’s like, yeah, I’m not compelled. And I remember getting so pissed at the end of that call. I was externalizing my failure to persuade and convince and you know, you ask all the why, how, what questions in that conversation, trying and unpack, well, what would make it compelling? How do you solve this problem today? So, we got a ton of data, but I remember the whole time just sort of seething inside being like, how dare you not believe in my vision? And like…

Elisa: That’s the fire you need because you’re thinking back to, I mean, you’re talking about building these mockups in Figma, which is basically a, a pretty picture for prospective customer to look at and react to, but they can’t really click through get genuine insights. But it gives them enough context for the conversation that they can give you feedback. And so I can imagine that kind of a conversation with a number of prospective customers, kind of put some fire in your belly. Like, how do I translate my belief and conviction, and what my product can solve onto a page and do it in the flow that resonates with my prospective customers?

Josh: That’s right. So after we did, you know, a bunch of these mocks, we did proof of concepts. And this is, you know, this is not very revolutionary, but we did a lot of stuff manually — some in code, some with a little bit of mechanical turk, like human beings sort of doing the work to show that yes, indeed this would work and that we can solve these problems. In one case we worked with a company and they took a year to build basically an equivalent product with us and generate some insights. They had sort of built something homegrown and we have a product that can replace their homegrown solution. And we were able to do something that took almost a year for them to do in the span of a couple of weeks. And that is awesome. Like when you can do that sort of proof of concept and get somebody excited. And in particular, I’d say a great indicator of product-market fit is when you start running into multiple customers that have built something similar as a homegrown solution. Because that means it’s important enough for them to invest dollars. It’s important enough for them to invest time. They think it’s a real problem. And so even if you can’t displace a homegrown solution immediately because maybe you just are not far enough along and your development process or whatever it is, that’s still great validation of product-market fit.

And so anyway, . We did a bunch of proof of concepts, got sort of good validation, good fit. And then ultimately we get to a place where we are today, which is releasing the product to getting to minimum viable product. And what we like to talk about a lot is MDP, which is minimum delightful product.

Elisa: You brought up an interesting point, and I’m curious if this has been a part of your discovery with your prospective customers in selling and figuring out if there is a space in the market and budget in the market for me to build this incredible solution that a homegrown product couldn’t fix, for example. And what we see with a number of our other companies we work with is that decision some companies have between buy versus build and how do you make that decision? Engineering resources are scarce. Time is always a really critical factor. And so what it sounds like with that discussion you had is, Hey, what you guys, took you a year to do with part-time resources, that we could do in a number of months.

Josh: Weeks days, you can’t sell me short.

Elisa: Okay. Weeks to days. Plus, even more importantly, what you plan to do and what’s on your roadmap for the year ahead in creating value and helping them drive effectively top-line revenue to the business is a very strong value proposition.

Josh: And I think the way that you get there is you have to demonstrate sort of competency, and I think you have to create transparency and trust. It’s a two-part process because first, the product has to work or you have to be able to show that you can actually do something. And, and all three of our initial customers that we did, uh, some of our initial proof of concepts with, in every single instance, we had to demonstrate competency and trust and the product work, that our ML insights were significant and superior, all of that sort of stuff. And if we didn’t do that, I’m not sure we’d be in the place we are today with the product. Because that pushes you to excellence, it pushes you to build something that’s very compelling. From our point of view, it’s not enough just to meet a minimum bar. It’s like, no, we have to demonstrate real and significant value. Then I think at the same time, the whole time I’m in conversation with the executives that are sponsoring projects saying, Hey, I’m going to be really transparent about where we are, here’s what we’d like to do. We’d like the opportunity to earn your business. We’d like the opportunity to partner with you. You know, is that a ridiculous question? Like, For you, a big company to work with, a small company, you know, and then they say, no, it’s not ridiculous. That’s great.

And I say, okay, good now what would you need to see from us to feel comfortable to do that? That then also shapes your product roadmap. That shapes your deployment model. It shapes a bunch of different things around what they need to see from you. And I think that that becomes really important when you’re moving from kind of zero to one. When you’re moving from some proof of concepts to actual revenue. What are those components that you need to have? And it could, by the way, it could not just be product features. It could be, well, am I going to get a customer success manager to help me manage or, what are your service level agreements? Like, there are a bunch of things that sit in there that you have to understand, and if you’re open early days in that process and you’re clear and transparent about what your priorities are and what their priorities are, you can usually get to a place where that translates to revenue in a relatively short period of time.

Elisa: Okay, one last topic.

Josh: We still haven’t talked about the product, by the way.

Elisa: Okay. Let’s get into the product. Uh, well this is almost a segue into that, and it’s thinking about how do you build intelligence. You have effectively multiple different products that you’re building right now inside of Magnify. And one of the beautiful and magical things is how do you build artificial intelligence, machine learning, deep learning into the data that you’re collecting and pulling, to create these incredible insights for your customers. So let’s hear more about what those multiple products are, and then I would love to hear how are you thinking about building an intelligent application.

Josh: So, Magnify automates and personalizes the customer life cycle for every single user. So, the way to think of it is it’s taking all of the data in your systems, across all the disparate disconnected systems, taking that in, generating business insights on them, and then automatically engaging every single user to drive product adoption and satisfaction. And if you can do that, you’re going to grow your revenue, you’re going to improve your retention rates, you’re going to get your upsell, and your customers are going to feel better for it, which is, which is arguably the most important thing. And we do that through three components. The first component is actually, that we have to ingest all this data. So, there’s user behavior from product telemetry, and from other systems. There’s sales revenue data, there’s customer success data, there’s marketing data, there’s all this stuff. So, you have to pull all these different disconnected systems into a single repository.

Now, some folks have a CDP, customer data platform or sort of other data warehouses that grab all that information, which is great, and that makes our life a lot easier. Most do not. And so in our experience, we take all that data and we connect and stitch it all together and we create what we call the lifecycle graph. And that is where every single user is in their adoption journey. And how that connects to other signal like revenue or retention or churn or growth. Okay. Then what you do is you do the second piece of our product, which is we then apply user insights on top of that. So, we can then say, ah, we now have looked at every single user, we can understand where they are in their adoption journey and what is the next best step for them. So, user 258 is in this place in their adoption journey, and here’s what they need to do next. And that’s different than user 259 and different than user 260, and so on and so forth. And what that allows you to do is actually create a real level of personalization for every single user. That’s fundamentally important.

What matters there is that we can also then say, how does that correlate or connect with things like churn, revenue growth, trial conversion. So, we can then say, great, here are the risk indicators. Here’s revenue prediction. Here’s how we think about the world both at the account level, and at the user level, and this set of insights, we can literally predict revenue quarters out now for our customers. Then, okay, cool. Great that you’ve got these insights, but frankly, insight without execution is kind of worthless. And so what we ultimately do is we then connect and drive orchestration/ execution. Most companies have lots of different digital touchpoint systems. So maybe they have an email system, maybe they’ve got some sort of end product platform — there’s this laundry list, this Cambrian explosion of tools: Slack, text, chat all of that stuff that sits out there. And each of those are disconnected. And so what we do is we then can connect via API to every single one of those different systems. And then drive the next best touchpoint. So, let’s say user 259, they need to use this feature. The next time they log into the product, that feature pops up. Did they use it? No. Oh, maybe we send them a text reminder. Maybe we send them a Slack reminder. Maybe we open a support ticket because they could only get a third of the way through and they seem stuck somewhere. And we execute and drive each of those actions in those different existing digital touchpoint systems, which allow our customers, the users of Magnify, to ultimately get more value out of their go-to-market investments.

Elisa: And I love this because it’s personalization and almost magic at scale. Those three product pillars that you’re building inside of Magnify to deliver that, that is the hardest technical challenge and the most beautiful and magical solution when you can deliver it. So that’s the vision for Magnify today.

Josh: That’s right. And so you’d asked the question like, Hey, how does AI connect to this? And so here’s what I think some companies do that is the anti-pattern, which is you can’t just sprinkle some sort of user, magic pixie dust on something and it will all of a sudden fix these problems. The way that we’ve thought about AI and ML is that it has to be incorporated from the very beginning. We had applied scientists on our team from day one, and they were influential in understanding what are the product requirements, and how you think about that. For instance, we pull in all this data to create the customer lifecycle graph, but you’re pulling data from all these different systems and each of those data fields has different contexts. There’s different metadata around that that the AI systems need to understand in order to be able to correlate and compare signals across different systems. So if I just said, well, I’ve just created this AI insight engine, but our applied science team isn’t talking to our product and engineering teams around their requirements upstream for data ingest, and the work that we’re going to have to do and to our customer success teams on what is needed for customer onboarding and onboarding wizards and things like that, all of that needs to be incorporated into the product. So, when you think about AI – AI is delivering value because it’s giving these insights, and it can optimize the journeys, the next best steps for every user. There’s all the stuff that it can do, but In order for it to do it well, it has to be woven into the very fabric of your product from day one.

Elisa: We should talk about this in a part two because this is exciting and I know that the journey for Magnify is just beginning. But I have a couple of lightning round questions I do want to end on.

Josh: Bring it on.

Elisa: Okay. So the first one, what do you think will be the greatest source of technological disruption in the next five years?

Josh: Hmm, I’m really torn. I’d say the short answer is the generative AI I think will be a significant source. If you asked me 15 to 20, I think it’s going to be something in biotech.

Elisa: Second question, what is an important lesson you’ve learned becoming a first-time CEO?

Josh: I think how important it is to constantly reinforce the vision and the messages you’ve already communicated over and over and over.

Elisa: It’s so funny hearing you say that because every single CEO I talk with says the same thing. It’s like half my job is just reinforcing the vision, repeating the vision, making sure that what we are doing and our go forward is ingrained with all of our people. Alignment is huge, especially at these early stages when you’re building your first product. Okay. Finally, what’s one thing you’re watching or reading right now?

Josh: Ooh. I just finished binging “Welcome to Wrexham” with Ryan Reynolds and Rob McElhenney, which is a story of them buying a tiny little English football slash soccer team in the middle of nowhere North Wales, and them kind of going this turnaround journey. And neither of them are business types. That’s very clear. And it is gripping television in a fun, lighthearted way. I cannot, it’s like, it’s like a real, it is not just like it is a real-life Ted Lasso. If Ted Lasso were the owner of the football team. It’s a great show.

Elisa: Well, Josh, thank you for spending so much time with us so thoroughly enjoyed the conversation, love working together, and it’s exciting to get more of the Magnified story out into the world.

Josh: Yep. Fantastic. It was great to see you, Elisa, and I hope you have a wonderful next few days.

Coral: Thank you for joining us for this week’s episode of Founded & Funded. If you want to learn more about Magnify visit Magnify.io. That’s M-A-G-N-I-F-y.io. Thanks again for listening, and tune in in a couple of weeks for another episode of Founded & Funded.

In this week’s episode, which is leading up to our Intelligent Applications Summit on November 2nd, Soma speaks with Bob Muglia. Bob has thought deeply about the Modern Data Stack, and they speak about it here — what is needed in the data stack to enable Intelligent Applications (or data-driven apps as Bob calls them) and the opportunities for new companies to innovate. Bob is also well known as the CEO that took Snowflake from a promising application for the public cloud to success by focusing on the problem of scaling a data warehouse in the cloud, and building product and sales teams that could win the hearts and minds of their loyal customers. Bob talks here about the early days after he joined Snowflake and what he did to get a product to market, how partnering with the big public cloud providers worked- and had its challenging moments. It’s a great view into how both Soma and Bob are thinking about the future of enterprise data and intelligent applications.

Note: Bob is on the board of IA40 company, Fivetran, which was the focus of a recent podcast, and he is chairman of the board of FaunaDB and RelationalAI, Madrona portfolio companies. Madrona holds shares in Snowflake,

This transcript was automatically generated and edited for clarity.

Soma: Bob, good afternoon. It’s fantastic to have you here with us. I’m very excited to talk to you about the future of data and data-driven and intelligent applications

Bob: Good to be here with you, Soma.

Soma: Absolutely Bob. And as you know, we at Madrona have been longtime believers in ML/AI and, more importantly, how do we apply ML/AI to different enterprise use cases and to different scenarios to be able to build what we refer to as next-generation intelligent applications.

And I was thinking about this, and as I was getting ready for the session, I couldn’t think of a better person to have this conversation with — and let me tell you why I say that. First of all, let me introduce you. Here with me is Bob Muglia, the former CEO of Snowflake, and prior to that, a long-term senior executive at Microsoft. He has done a variety of incredible things in his career — a lot of it data-driven, and this is where I come back to why I think you are the best guy for this conversation. Bob. Ever since I’ve known you, and I’ve known you for almost 30 years now, I think about you as a data guy first and foremost.

You go back to when you started your career at Microsoft, you were a Product Manager in SQL server. And through the following decades, I’ve seen you do something or other with data in one way, shape, or form. After leaving Microsoft, you decided to take on the range of Snowflake when it was a pre-product and a pre-revenue company. You spent over six years at Snowflake, growing it literally from zero to hundreds of millions of dollars of revenue. And I think you laid a lot of the foundation for Snowflake to be the leader that it is today in the cloud data platform world. After your stint at Snowflake, you’ve been working with half a dozen or more companies, startups, I should say, private companies as an investor, adviser, and board director.

The one common thread among all these companies is they all are doing something or other with data. I just look at the body of work behind you, and I say, “What a fantastic opportunity for us, and by extension, our audience to be able to hear from you about the future of data and how you see the world of intelligent applications evolving.” With that as a backdrop, I thought let’s just dive into some questions to kickstart this conversation. Let’s first go back to your days at Snowflake. As I just finished mentioning, when you started at Snowflake the team was still working on a product.

The product wasn’t in the market, and you went through this sort of what I call the “growing pains” of birthing a product and bringing it to market, thinking about the business model and getting it to scale. But along the way, I’m sure there were a handful of what I call “defining moments.”

Where you had to make a decision, or you had to think about something that literally laid the foundation for why Snowflake is who is today and what it is today. Can you think about a couple of those defining moments and just share with us what those are and how you navigated through those?

Bob: Sure. There were a couple of things early on that happened in the early time of Snowflake. You’ve got to go back to the period we’re talking about, which is 2014, 2015, where it was the early days of the cloud. Really, AWS was the most viable cloud at the time. Azure was still very early, and GCP was in some ways even earlier. it was a very different time. And a lot the of the focus of Snowflake was really about changing that. But a big part of it was also getting the product to the market because we were fortunate in the sense that we could scale to data of any size basically and as many users as you wanted to throw on it. And only having one copy of data for the whole organization instead of having to have copies scattered hither, tither and yon, which was the default at the time. So, it was a revolutionary product, but it still had to come to market.

And it was funny because when I started at Snowflake, the founders, said to me that their plan was to make the product generally available to enterprises in about six months. That was in June of 2014. I knew that somewhat unlikely, frankly, from all of our experience. You’re smiling, Soma, so you know what this is like in terms of developers in the early days. And I watched them for a period as they went through a couple of these two months’ milestones that were doing. And I had this observation that during that two months, they said they were going to do a bunch of things, and basically, none of them got finished during that period of time.

Other things did get done. They were working hard. They were certainly working hard, but it wasn’t like they were really working towards some well defined goals. One of the things I focused on was really trying to help bring some rigor and discipline to what it means to be an enterprise class product , , over the period of the next year or so, a little bit less than that, we went through a process whereby we really defined what general availability meant, and we went through a process of, getting, focusing on getting those tasks done.

I literally turned weekly team meetings into a project review only thing the sales people cared about was the status of the product as we don’t everybody cared about. And, we went through focused effort and got the product shipped in the middle of June. And that was really the beginning the beginning of the Snowflake experience.

The another thing that, we had, there’s always these sort of things that happen to companies in their early days that they, that, that happened to them and they survive or not. one of the more challenging things we went through early on was inside Snowflake, the transactional heart of the product is a technology called Foundation DB.

And Foundation DB the time in 2014 was a company, it was actually a sister company of one of our VCs, Sutter Hill. So we knew the company well. But I was in the process of negotiating agreement and I was able to negotiate an agreement. If anything happened to that technology, if it went off the market, that we had access to it through a code escrow, of course we hoped never happened, but it turns out it did happen.

And in seven, eight months later, Apple bought Foundation DB and immediately took the product off the market and made it unavailable to which is our worst nightmare. And, fortunately we were running it and we’re a bunch of database people and the source code escrow actually worked and we got the product through that we actually had to do what it took to actually learn how to be good at bugs and Foundation DB.

And pretty much had to do that all from scratch. And that was a very big deal. Had that, we not had access to Foundation DB, there really was no other good choice. Not at that time. Products like Fawn and Cockroach didn’t exist. And I don’t know what we would’ve done, to be honest with you.

I don’t know. I honestly don’t know what we would’ve done. But we survived that and now, fortunately, Foundation to be as open sourced and that’s very healthy. It’s actually healthy and Snowflake’s a major, actually a major contributor to it. So it’s actually a really good story, but it was a tough one.

So there’s, things like that happen in your process. I would say the other thing is just customers, I focused on all the time and being successful with customers. And, we didn’t lose customers basically speaking because didn’t take on things we couldn’t do, times would turn people down because we couldn’t do the work they wanted us to do occasionally.

And and really focused on the success of working with costumers.

Soma: That’s super helpful, Bob. I also remember that there was a lot of talk during the initial days of Snowflake about hey, we should think about like, you separating out computer and storage and that could enable us to get to the level of scale and economics, right?

That would be good for our customers and hence for us kind of thing. Any sort of color on, on, like how that came about.

Bob: think architecturally that was always part of the idea of separating compute and storage was always part of the design demo. And Terry had, the architecture at Snowflake has something called Global Services, that manages the metadata and does the query and does the query planning.

And then they have an execution processor that runs the actual, is the virtual warehouse and runs the actual SQL jobs. And now I believe it’s running the Python jobs too and, other languages. So it’s become multi-language really. And, the evolution of that whole thing changed dramatically over time and how we stored the metadata and everything and that separation of the metadata was a fundamental component of Snowflake.

But the way we did it certainly changed over time and I think, we were able to stay ahead from a scale perspective. I always said was interesting because, we were just ahead of our customers. In the early days, they were chasing our tail and scale in a variety of ways. And we, it was always, we were always working hard to stay ahead of customers, so customers had a great experience.

Soma: Bob, I do want to take this opportunity to say thank you to allowing both Madrona and I to be able to invest in Snowflake and be a part of the journey along with you and see it come to scale

Bob: …you guys were super helpful too. At the time we were opening Bellevue, the Bellevue office, which was very, I think, very pivotal office for Snowflake. of course Madrona has such strength in the Seattle area,

Soma: . I’m glad it worked out all well. But when I got involved in Snowflake, one of the things I heard, a fair bit is you got all these like big cloud platform providers, whether it’s AWS or Azure or GCP, all wanting to have their own solution in the space and how they’re going to , you crush Snowflake and how a fledgling startup can just not compete with any of these, at scale, massive cloud providers , but somehow Snowflake navigated through that and just reached a level of scale and success and is literally a leader in the cloud data platform world today.

, two questions that I want to ask you. In that context, how did you feel about it when everybody was probably telling you, or you heard the same thing about “Hey, all these big guys are there, they got their own, data warehousing solution, the cloud”, and how did you feel like confident that Snowflake was going to be able to navigate through it?

But then the second part, which I want to focus on is the partnership that Snowflake had with all the cloud providers. Because on the one hand, you could argue like, if if there is a customer that know goes with Snowflake on Azure, it is still a win for Azure. On the other hand, if you think about Snowflake running on AWS, Snowflake is competing with Redshift on AWS, right?

So you got this what I call a cooperative, in mind, midway, you are, partnering with the platform, but you’re competing with the service. How did that whole landscape, work out for you?

Bob: Yeah. To, so in terms of how we competed with the big cloud vendors, we had a better product. It was really that simple. If the product has, I said many times, if Snowflake was 10% or 20% better than Redshift, snowflake wouldn’t have gained any material share, but it was many times better.

I It worked in situations where Redshift didn’t work and Redshift is a very good product, it, paved the roads for cloud data warehouse. And for that I’m eternally grateful.

By bringing out Redshift very early in the marketplace, which they did. It was, the thing is it was a on-premises product brought to the cloud, so it didn’t really take advantage of the cloud. And what it was cheaper. It was definitely cheaper than anything you could buy. It on premises.

, but it didn’t ultimately scale.

People in the cloud world particularly wanted it to scale. And we saw, I wonder, in fact, one of my earliest, my earliest first salespeople, Vince Trada, I remember vividly, I was on the subway with him in New York in February or so of 2015. And we’re seeing customers who were talking about adopting Redshift.

And Vince said to me, “Bob, don’t worry about this because every one of those customers that adopt Redshift are going to come to us in the next 18 months when they run out of gas”. And he was right. That’s essentially what happened. A lot of Snowflake’s early business was Redshift conversions, well as working with semi-structured data, which we did a good job on and nobody else did.

Certainly we were better than Hadoop, which is what people were using at the time. And and so that was a major, the major part of the success force. So we were just better and frankly, particularly in AWS, we had a much better product. We’re lucky that we were in the timeframe we were establishing, and that’s the other thing, it was the right time. That was the time for establishing , the position in that, in the data space in the cloud. Because it was all pretty new.

Terms of our relationships with the vendors, they were challenging to say the least. We certainly had, many challenging times with Amazon, who we were competing with in Redshift. What I would say is first of all, is Amazon did an, incredibly good job of supporting Snowflake at all times and they were great at support and

AWS is a great product to build on top of. But they were brutally fought against us in the business marketplace in the early days and, it was pretty challenging it sometimes, but we were winning. We, the thing is we won those challenges partially because again, we had a better product and frankly we had them much a better trained sales team.

Our sales team. Was able to, to outsell Amazon’s. And so that was the early days of Amazon. And then, when we moved to Azure and we established Azure as our second cloud in part because of my relationships with Microsoft people, we were able to build good partner relationships there and actually had some amazing, very positive going to market motions with Microsoft in the early days where they did a bunch of joint selling with us and really discovered, whole different business.

What we discovered was that Azure, it was a whole set of customers we’d just never seen before. It’s just a whole, it’s a whole different market almost, really customers and we always said this, choose your cloud first and then choose your data warehouse.

And Snowflake ran on all of them, so it makes it a little easier. But at the time we were just running on AWS and then Azure and so it was positive it was a win-win situation in some senses for Microsoft and Snowflake to go together. I think that about the time I left Snowflake in 2019, Snowflake was probably becoming more competitive in a number of ways.

And in some senses the strength of the partnerships in Snowflake, I think flipped really. And they had a rough time with the Azure folks for a while and they actually built some very strong relationships with AWS, .

There’s lot of good things happening there. I think Google is still tough, if I’m not mistaken. , Google has, generally speaking, not the most partner-centric company on the planet. And I know that’s been a little bit more challenging for Snowflake, in part because they really love Big Query and they have the same feelings about Big Query that, the folks used to have about Redshift.

Only time will tell. These are challenging because they’re competitive they’re definitely complimentary and competitive.

Soma: Yeah the thing that was interesting for me to watch is, there would be a face off time when you would think now, hey, this particular cloud provider is the best partner. And then things will change and then things will change back. Just the volatile team, the partnership as Snowflake went from strength to strength and depending on where the other cloud providers were all, it was just fascinating to see how it was a very interesting and ever changing landscape.

Bob: It It just proves my first rule of partnership. Soma, partnerships are tactical. You know when it’s win-win, they work. When not, they start to falter a bit

Soma: But I think no Snowflake could be a sort of a good good, what should I say? An inspiration or a good role model or a good case study. For a lot of the other new startups that are coming up and saying “Hey, am I competing with the cloud providers or am I partnering? How do I navigate this tough thing?”

And depending on what space they are in and what the cloud provider’s aspirations are, it, many companies could be in a similar situation. That’s why I want to make sure that we talked a little bit…

Bob: That’s very true in fact. I have this conversation with a number of the companies that I talked to about potential, about their potential conflicts with cloud vendors. A lot of the stuff people are working on these days is complimentary to it’s new things that I don’t think have the same kind of conflicts that we had with Snowflake.

I do think in general though, that Snowflake is a good role model. Building a partner-centric company in general. In addition to really working with the strategic, the strategic cloud vendors and spending a lot of energy there, we spend a massive amount of time working to build an ecosystem and working with partners all around, whether they be, partners, like BI partners, ML partners, I mean, ATL partners, whatever it might be there, as size, and I feel very good about what Snowflake has done in that space.

And I think that, I definitely felt like I had something to do with that. And the history our shared history together at Microsoft, is the lessons that I learned from

Soma: Great, Bob. I thought it’d be good to take a step back now and think about hey you’ve been, as I mentioned, you’ve been working in data in one way, shape, or form for the last 30 years. How do, how have you seen the..

Over 30 years,

Bob: Been over 30 years. It was Windows NT Summit. It was Windows NT.

Soma: Over 30 years. But during this period of time, Bob, how have you seen the world of data evolve? New platforms, new computing paradigms, new devices, new, everything has happened. But the importance of data seems to have only gone from strength to strength and has exponentially gone up in the last 10, 15 years.

Now, I wanted to get your quick thoughts on “Hey, where do you see data today and where do you see data moving forward?”

Bob: It was just over 30 years ago that Bill did his information at your fingertips. Saw talk, which really, I was working on, I was a program manager when I started at Microsoft and SQL Server. So I was working and I had been working on database things as a, as really building applications inside a company before I joined Microsoft.

So I’d been focusing on data pretty much my whole career. And so I, while I’ve been focusing on SQL in the business side, but I still feel in some senses, like the beginning and the perspective began with information at your fingertips and all the focus that we had on information of all kinds at Microsoft and building out businesses and enabling people to work with data.

In the early days, I was found in, involved in SQL Server from the, from very early on. And then I watched as other folks at Microsoft built SQL Server and built it into the business that it really became. And I watched that transform. I watched these kinds of data systems together with the applications that sit on top of it, transform businesses of all sizes.

And, Microsoft contribution was the of all sizes really. You know, if you were a big company you could buy from IBM, Digital or Sun. A big, expensive set of systems, but Microsoft made servers that were quite inexpensive and brought computing to literally millions of small businesses around the world, maybe tens of millions never had it before.

And that was really, data was a centric part of that. We’ve watched, now we’re clearly head to head, we’ve gone through the internet era and the evolution of that has been new types of data that have become important, in particular, semi-structured data, that’s generated in large quantities by machines.

In some ways is some of the most important data we analyze today. We’re now living in a cloud centric world, which allows us to do things that we never could do before. I am a big believer that, data’s generated everywhere, but you need to centralize it to a certain extent to do analysis around it, to bring different types of data together so that you can, you can perform the relationships, but look at the relationships between them and perform the kinds of and dashboarding that people want to do, as well as deeper analysis with machine learning. So things have changed so dramatically from, a fairly simple environment where literally people worked on pencil and paper.

Literally we’re, Excel was a massive step forward and in or 1, 2, 3, was a massive step forward in dealing with information now this world of the cloud where we have this vast amount of data available to us. It’s pretty amazing really.

Soma: You just summed it up Bob. It’s pretty amazing actually. What in the world, how far we’ve come along, but for all the progress we’ve made, I feel like there is still a turn more that is waiting to happen. And it’s just that the rate of innovation is only getting faster as opposed to slower as we move forward here.

Today, like now you can’t have a conversation about data without know, talking about the modern data stack. That’s that’s a sort of buzzwords or a new concept or whatever you want to think about it kind of thing. But everybody talks about like the modern data stack. In your mind, how do you define the modern data stack?

Bob: So I people have been trying to work with data in a variety of ways and fundamentally the cloud and the ability for companies to work together to provide a complete solution for organizations on the cloud, has never been as strong as it is today. And that’s really what the modern data stack is about.

Really enabling the industry to work together to provide solutions to companies. And those companies take on a cer, those solutions take on a certain shape in the modern data stack. And there’s three defining characteristics that I think exemplify that the modern data stack is really building modern, building data analytics.

That is delivered as a SaaS cloud service first and foremost, which means that building these components, you’re purchasing them from third parties that are providing the service for you and means that a lot of the things are taken care of for the customer. So the first thing is that it’s a SaaS service.

The second thing is that it runs in the cloud and it takes advantage the scalability, that the cloud provides to allow you to work. All of your users and any kind of data, and I mentioned earlier that data, is both structured data that people work with SQL and semi-structured data that comes out of machine generated systems.

But it’s also more and more other types of data that are, is quite rich in terms of its content. People sometimes refer to this as unstructured data. I really tend to think of it as complex data. Data types such as video, audio, photos, turns out to be a rich source of complex data.

All of these things that exist in business in the form of documents of all kinds and recordings of all kinds are essentially sources of data for the modern data stack. And with the cloud, it needs to scale for all, to work with as much as many users as you want, and so the final point is that when you’re doing the analysis against it the data is modeled for a SQL database.

And that’s, that I think is a distinguishing element of the modern data stack. Is when the data comes into the system the way you actually transform it, there are multiple techniques for doing it. And so let’s put that aside. But the target environment you’re modeling it for is a sequel database and you use relational commands.

Relational algebra, basically to operate against that, that data in a relational form. So three things. Data analytics is a service that leverages the cloud for scale and models data for SQL.

Soma: That’s great. And today, Bob, if you look at it and this is know I don’t know whether I would’ve predicted that. Maybe in hindsight it’s easy to say I thought about it this way kind of thing. But today you could say that there are like, key or big technology vendors that are providing like, vast parts of the modern data stack.

, you got the three cloud platform guys in Microsoft, Amazon, and Google. And then you got Snowflake and Data Bricks. And the fact that Snowflake and Data Bricks have literally come out from nowhere in the last, let’s say, eight years or so is fantastic because hey, that shows that hey, you can innovate, you can get to scale, you can get to a level of success even outside the biggest platform guys kind of thing.

And that I think is just goodness for the whole innovation ecosystem. Do you feel like, five is too many? Do you feel, five years going to become eight or any sort of thoughts on.

Bob: It’s about right. The database market has always been somewhat fragmented. It’s never been a winner take all. I Oracle has classically been the largest winner in the database market, but even they’re only like 40%. It’s a, it’s, it is a market that has a number of vendors and I think that’ll change.

My guess is you’ll actually see some more vendors appear. We see some smaller players coming in trying to on these five vendors in a variety of ways. I think it’s hard. There may be some, we may see six or seven having some small percentage share because for some more niche markets, I think these are the big five.

I think that, there’s going to be a big dog. There is a big dog fight happening between Snowflake and Data Bricks, and we’ll watch that get fought out for the next year or two. And meanwhile, the cloud vendors will just do what the cloud vendors do and their products will all get better. I do believe that the cloud vendor products, what clearly behind things like Snowflake, are getting better.

Google is probably the furthest along. And this micro, I know a fair amount of what the Microsoft team is doing. There’s a lot of actually great work happening there. I see some good stuff coming in the future.

Soma: That’s excellent as much as all these five players, and there are a whole host of other companies that are talking about “Hey, I’m building this for the modern data stack, “. As you see what is happening in the world today, do you see any big gaps in terms of what needs to happen in the modern data stack to make it really more complete and more robust for the next set of applications?

Bob: Yeah, I think there’s a number of really major gaps. I’m fairly sure that the platforms people are using for machine learning are fairly nascent and will evolve. I mean, I’m fairly sure that’s true. That, Spark is, has a lot of adoption, I don’t think it’s the end answer to every problem

and I think we’ll see evolution in that space. There are data types, , problem characteristics that are very poorly solved today, like graph. Problems are really situations where you have a lot of relationships between things and and if you look at the data model it’s a very large number of relationships that need to be managed more than a sequel database can handle.

And in general, the graph problem is poorly solved by today’s products. Meanwhile, there’s other things, that are critical to business logic. Like reasoning, which are still done pretty separate from the modern data stack. And you have bits and pieces of code all over the place and I think that’ll converge into more model based things over time.

I think a lot of the future is really around the evolution of model based development. And I think we’re in the early stages of that.

Soma: You talked about know, SQL systems and you talked about graph databases. Bob my perspective, and I’ll share that with you and tell me if that makes sense or not, is historically, and even today, the world is bifurcated into “Hey, you can go deal with relational database systems”.

And or you can go deal with the knowledge graph systems. Those two words are what I call two silos. They don’t, they really haven’t come together.

kind of thing.

Bob: You mean? relational systems or procedural systems today.

Soma: Or procedural systems. Yeah.

Bob: Yeah, like you’re working writing code in Python the one side and then sequel on the other side. Is that what you mean?

Soma: Do you think they’ll come together? They should come together? Do you think there is an opportunity?

Bob: I do. And I think that’s, as you said, a knowledge graph, that’s what a knowledge graph really can do. It and really the idea behind a knowledge graph is that you can encode of the attributes of the business into the database, the logic associated with the business.

And, that makes it, the idea then is it becomes a complete model. The organization is actually executable, where the model is the code itself. And in a way this has been a dream of computer science since I was a kid, when I was just not far out of school. I did work in early models based things where you model stuff with these diagram stuff and they spit out coball code at the bottom, which of course didn’t really was unmaintainable and didn’t work and all kinds of issues.

But, and because it never worked those sorts of ideas of modeling became more of a whiteboard effort, and I will argue that people always model business. Every, when you’re working at anything you’re doing, you’re modeling. But in today’s world, we do it implicitly. Implicitly, and we do it, with, might write a model of something that’s relatively thought through on a whiteboard, but then that gets implemented as bits and pieces of code all over the place, implicitly within the systems.

And I think we’ll move to a world that’s much more explicit in our, in what we’re defining. And that will happen when the knowledge graph comes about and when we think about implementing a knowledge graph. I’m pretty clear that they will be relational and they will leverage relational algebra and relational mathematics.

Partially because the industry has moved forward significantly in the last 10 years in terms of understanding algorithmic changes. New algorithms that allow you to work with large numbers of relationships sufficiently, and actually do things that you could never do previously with a SQL database because we just didn’t have the sophistication of the algorithms.

That are now appearing. So it’s pretty exciting actually. But it’s also early.

Soma: That’s true but as data becomes, what I call more democratized. One of the things that you talk to enterprise , CIOs, they’ll tell you that “Hey, we are really putting a lot of energy and effort into consolidating and standardizing our data infrastructure”.

But along with all these huge volumes of data and what can you do meaningfully with the data kind of thing. One issue that keeps coming up pretty much in every customer conversation today is data governance. This is also an area where, particularly in the last two years I would say, there have been a ton of new startups emerging.

All addressing one part of data governance or the other part of data governance kind of thing. How do broadly aid the space of data governance, and the kinds of companies that are coming up? Are there any specific companies that particularly catch your attention in the space?

Bob: Not really. They’re good companies. There are some good companies. They’re doing pieces of, solving pieces of the problem. But when I think about, you the issue with the modern data stack, governance is a very real problem. I It was always going to emerge as a major issue when we took data that was scattered everywhere and we put it together that it creates a certain risk profile where, which makes access control to that very important.

And in particular, that’s the aspect. I There are many aspects of governance data modeling. Data observability all sort, many things but the one that, that I think is that sort of at the top of people’s list is access control. And while there are products in the market that, that address some elements of that, I don’t think we’ve really reached the pinnacle of where we need to be.

And I, I don’t feel like we’re well served, that our customers are well served here. I believe that while there are some ways to, there are different ways to solve the problem and perhaps there are some shortcuts that people can take. I think in the long run, the right way to solve that is by having, establishing a semantic model that understands what the business is, which is essentially a knowledge graph.

And then from that you can derive the rules, for the policies that you want to establish on your data have a very much of a policy based approach, that’s based on the business data itself. And I think we’re still away from having a standardized platform to enable that. And that’s what we really need.

You know, one of the challenges we have, and I think one of the reasons why there we’re not seeing as much success in governance and modern data stack as customers might want, is that all of these tools that are coming out don’t use the modern data stack as their database. And the reason they don’t is cause they can’t. Because doesn’t solve the problems for them.

So they all use some sort of operational database of their own. They take different approaches, but none of them inter operate. I think what we need is a common platform for a semantic model, that will become the basis for modern data stack governance. I believe that platform will be a relational knowledge graph.

It’s still early. But that’s where I think it’ll go. In the meantime, I hope we can get customers and get some answers out there if it helps to solve their problems.

Soma: True. Let’s move up the stack a little bit. You’ve seen like open AI, do some great work and in the last many years on large scale machine learning models. You’ve got all kinds of face recognition and other kinds of machine learning models that are coming up at scale.

What do you think this, the situation is today in terms of these machine learning models? Do you feel , the right amount of innovation is happening there? How do you think these models are going to be evolving over time? Any perspective on where we are with models and where we are going.

Bob: It’s very exciting, I have to say. I mean it, we’ve seen incredible progress in the last five years even. I would say it’s accelerating progress. I recently had a conversation with Xuedang Huang who runs the, all the artificial, the machine learning team at Microsoft and is working with open AI and working on foundation models it, and they’re doing a lot of work on combinatorial foundation models where they bring multiple different types of data together into one large model.

These foundation models, let’s talk about that for just a second. What they, sometimes they’re called large language models. Which is fine, but it only speaks for one domain, which is the language oriented ones, because some foundation models also apply to photos and other domains besides written language.

What they really are is world scale trained machine learning models that are trained on a corpus that approaches, global scale. And so you know, what they become essentially is incredible concentrators human knowledge. Into a model. Now the models are statistically driven.

They’re not perfect. There’s still advances that need to be done in these. But this idea of of using machine learning to take the expertise of a given domain in the world and distill it into a model is fairly incredible in my opinion.

I don’t, I can’t think of a domain that it won’t effect. Honestly, I think it affects everything. I think it affects every single element of everywhere we go. So I think that’s a very exciting, element of what’s happening. We see some incredible stuff. This DALL-E stuff is interesting. Now people are doing videos against it.

This model that, that came to OpenAI, that was one of the early rewrite code, writing models code X. You know, has done some amazing things. GitHub copilot has been an incredible success for Microsoft and is really doing dramatic things to improve developer productivity.

And, I’m seeing people use that for different purposes. Can take and improve it writing in and do things around running sequel as an example. And very powerful ideas can come from that. On the other end of the sort of spectrum I think that there’s an opportunity, where you’re trying to use artificial inte machine learning AI to improve the business workflow in a given organization where the domain is actually the terminology of that organization itself.

And it, it’s much smaller and it’s, there’s no global model to look at. There’s some local set of content you can look at. And in that case, the interesting thing is how do you inform the model more and more about the business? I think what we’re going to see is, user assisted, interactive training models appearing, which are really applications they look like, applications are working with a given domain and then leveraging machine learning to really improve business process.

And the company I’ve been involved in that’s been working on this, that I’m pretty excited about is Docugami our friend, Jean Paul of Microsoft is CEO. And they’re really focusing on taking business documents, and other high value business documents and inverting them and in turning them into data that can be processed by data systems.

And in order to do that, you really need to understand the semantics of that document. And that requires user assistance. And that’s why this interactive development is important. So that’s a real, a UI kind of experience in that. That’s two different ends of the spectrum in some senses but both examples are pretty interesting to me.

Soma: . Bob you heard me talk about this quite a bit in in the last many years. We we are absolutely convinced that every application that gets built today and moving forward is what we refer to as intelligent application. You like to call it data driven applications but it’s basically hey, taking a corpus of data that is a available to the application.

Being able to, build a continuous learning system using some machine learning models, and then continue to get better and better. Deliver a better service, you get more data. The process just, it creatively makes the application, the service get better. That’s the world we see happening today, and as we move forward. (A) do you agree with that viewpoint? And then (B), what are some of the core things that you think are happening that is going to drive the world, getting there?

Bob: If I didn’t agree with that viewpoint, I wouldn’t be continuing to do this work. I Let’s face it, that’s the reason I continue to do the work. Look, my, my whole purpose basically, in, in my business career has been to build infrastructure components that make people’s lives easier in business in one sense or another.

And data has been a huge part of that. But I I worked on Windows Server for a lot of years and I built System Center and helped with Visual Studio and all those sorts of things where we’re not databases, they were all, they’re all about making it easier for people to build.

Systems to help them more effective in their lives. And in particular in business, I’ve mostly focused on the business side, not the consumer side. It’s interesting because, because when we think about this world today, we’re seeing a world where machine learning is transforming. I think pretty much every application category I talked about, foundation models, essentially distilling the world’s knowledge into a model in some sense or another.

It’s not perfect. And even though I would say it, it’s it it is a great learn model. It might not it probably isn’t what one would call fully intelligent. It doesn’t reach the point of saying this is intelligent. However, it is an incredible source of information and can be used as a base as many things.

But there’s things that are missing machine learning and that are missing in a more full intelligence things. And in particular, that’s things like reasoning. The ability to to reason over something that says, is this because I know that this is something else. And these systems, these models have a very hard time with that today.

They have a very hard time with that. And they can, sometimes that’s when they go off into wacko things, it’s because they haven’t got the ability of adding reasoning to that. Now, I’m sure that’ll change. I mean, I’m very confident that we’ll see reasoning get added to these models in a variety of ways.

I think of this problem is what’s the infrastructure that would actually solve this problem in a more generalized sense? I mean, give somebody a Python compiler or a C compiler. In a hundred nodes they can do an awful lot. But, to me it’s what sort of infrastructure you can build, can you build to make these systems more available to a larger number of companies.

And that’s why I think it all consolidates ultimately into a relational database that will take the form of a knowledge graph. And I do think ultimately these things will come together that where you can take all of the components of intelligence, and let me to somewhat define that, is a program that can sense.

It can reason, it can plan, act, and adapt. And we see these components coming together in different systems today, in different parts of intelligence systems today. But the idea of them coming together in a cohesive platform, we’re still some distance away from that. And to some extent where I’m thinking, how does the world, lots smarter people than me are going to build these models that do these amazing things. But to me, I’d like to figure out how I can help facilitate the creation of platforms to enable all these things to be created cohesively by mere mortals. Not just the great, smartest minds on the planet.

Soma: That, that’s awesome, Bob. That’s great to hear. And I’m so glad you’re continuing to be fully focused on that mission because I think the world needs that kind of an infrastructure and the kinds of innovation that the infrastructure can provide. Like you say, that makes what I call building an intelligent application, something that every developer can do and not just the rocket scientist in the world kind of thing.

So that I’m a big believer in democratizing access to all the developers and so kudos to everybody who’s working on the infrastructure that’s going to enable that to happen. In a, I know that we are coming up on time before we wrap up. There is one thing that keeps coming up quite a bit.

When I talk to all these, what I call modern data companies, are startups, right? There are two things that they ask about “Hey, how should I think about open source?” and the other thing is, how should I think about in a product led growth, these are two things that every startup founder or CEO’s thinking about, Hey when does it make sense for me to think about open source?

When do I not think about this? When do I think about this? Particularly given your sort of, experience with a variety of sort of an proprietary and open source work and product led growth versus enterprise sales kind of thing. Are there any sort of parting words of wisdom that you want to share with the next generation entrepreneurs?

Bob: What I would say is, the biggest advantage to open source is the potential path to rapid adoption of particularly a developer focused technology. And the, the ability to get more end users, using it more, developers using it more quickly. It’s appropriate if the component runs in the infrastructure of the customer.

And it, I would say today it’s may even be essential. If you expect a component to run as a core integral part of an infrastructure, Kaftka is a good example of it, right? I Kaftka’s a perfect sort of example of something like this where that thing’s going to be sitting in, know all over the place inside customer’s infrastructure and they’re going to, they just want it to be open source for their ability to choose vendors and all sorts of stuff.

Those are good reasons to, to do open source. You know, the, the challenge with open source is that you essentially have to abandon it. To build to build a business. I’m not going to say it’s a rouse, but it’s a, it, it’s it’s a, you’ve got to do an extended focus at the very least, where you’ve got open source and then you have something that’s commercial because that’s the only way to monetize.

I In the old days, people monetized open source with services and that was Red Hat’s business model. That’s gone away with the cloud. There’s no, the cloud doesn’t help that. You can’t take what you just put in open source classically and just run it in the cloud because the cloud vendors can do the same thing and they have infinite distributions.

And their cogs are lower than you, so you’re screwed from day one. But if you differentiate and start with an open source integral component, then build on top of it. In some ways it can be very successful. There are, there are certainly examples of that. But again, what is these companies are going off and innovating in, in, in non-open source ways right now.

Soma: Bob, fantastic to chat with you as always, fun conversation and really appreciate you taking the time to be with us here and do this podcast. Thank you.

Bob: Great. It’s good to talk to you again, Soma. Thanks.

Coral: Thanks for joining us for this week’s episode of Founded & Funded, and don’t forget to check out our Intelligent Application Summit event page if you’re interested in these types of discussions. Thanks again for joining us and tune in a couple of weeks for our next episode of Founded & Funded with dbt Labs Founder and CEO Tristan Handy.

This week, Investor Aseem Datar is talking with Will Rush, co-founder and CEO of Stack, a teen crypto platform to educate a young crypto-curious generation — and their parents — about responsible investing. After working at a company, building a banking app for teens, Will took his decade-long experience in securities and finance and applied it to crypto and Web3. He saw the excitement and curiosity that teens had for crypto and knew he could apply his experience to create a world of responsible teen crypto investing.

Stack has managed to balance education and access for both parents and their teens in a way that the younger generation is actually appreciating and connecting with. And Will is maniacally focused on the education portion. Not only because he is a soon-to-be parent himself, but because in what he calls the Wild West of Web3 and crypto, he says somebody needs to be the good guy. To help continue that project, Stack just landed $2.7 million in funding to continue developing its platform. And Will’s advice for every founder — “You need to have slime on your face” — a reference most of us millennials will understand, but you’ll have to listen to see how Double Dare relates to launching a startup.

This transcript was automatically generated and edited for clarity.

Aseem: Hi, everybody. My name is Aseem Datar, I’m a partner at Madrona Ventures, and I’m excited to have Will Rush, the CEO of TryStack.io. Will, welcome to the show, and I know that congratulations are in order.

Will: Thanks so much for having me, Aseem. I’m really excited to be here. It’s been amazing working with you guys and the Madrona Venture Labs team as well. We’re very fortunate to have you in our corner.

Aseem: I’m excited to talk a little bit more about Stack and make people aware of what you’re building. I’m very excited, and it’s amazing to partner with founders like yourselves who have a big, bold vision. But before we get into Stack, I wanted to understand a little bit about your background and where you are coming from. Tell us a little bit about your story.

Will: you know, I’ve been in securities and finance at some level for over a decade. In the early part of my career, I worked with companies like Charles Schwab, E-Trade, TD Ameritrade, and a lot of the old brokerages and traditional finance guard. And then I reached that point in my career that I think a lot of people do where I’d been doing one thing for a long time, and I wanted to lean a little bit more into the early side of things and startups. So, I went back to school. I got a master’s degree up here at UW. And while I was getting my master’s degree, I worked part-time at another venture firm now called Fuse Venture Partners — at the time, it was still Ignition Partners. And that was a really fun company and environment to work with — and all of their portfolio companies and founders were a great way to get my feet wet with this startup world. And then, at some point, I met who would become one of my favorite mentors ever in my professional life, which was Eddie Behringer. He was coming off of a recent win with Snap! Raise, which had just done its private equity exit. And he just started Copper Banking — I think they had three or four team members at that point. It was himself and his co-founder from Snap! Raise and one or two others. But he was also thinking, at that time, what it would be like to build a brokerage and stock product for teenagers. The thesis of Copper was to build the finance and banking app for teenagers that was built for them instead of their parents or somebody else. And so, it was really fun. It was a great product to be a part of, and I learned so much working directly under him. And then eventually discovered this thesis for Stack.

Aseem: You know, this leads us to the obvious next question, which is the inception of the idea around Stack and really trying to understand what patterns you saw while at Copper while being at the VC firm. I want to dig deep into understanding how that idea came to you. What are the macros in the world that you saw? I want to dig into “your why,” as they call it. Why did you build Stack?

Will: Oh, man. I mean, I think it really started from understanding the last 10 years in finance and FinTech. We saw companies like Robinhood outside of the 2008 financial crisis do an amazing job of democratizing access. And they did that by allowing you to trade $1 of any stock you wanted with fractional shares and do it commission free. And so, of course, that introduced a lot of new people to the stock market. But with democratized access, I think there was a huge lag in education. And so, being at Copper, being able to impact young people who were at the very start of their financial journey, the impact that you can make and the habits that people form at that age, you know, they absorb things like a sponge, and it was cool to see and be a part of those stories. While I was at Copper and I was researching our stock product, it became abundantly clear to me just through some of the AB tests that we would do where we would literally change one word “stock” to “crypto,” and all of a sudden, our engagement on things like Instagram would just fly off the radar. And so, I think there was a story there. There was a story about this zeitgeist behind crypto, this excitement, this curiosity with the youngest generation. And could you be the good guy in that world?

That was really like, where the thesis for Stack started and how we got to do what we’re doing now. I think there’s so much to unpack, which is how you educate successfully. I think a lot of parents hear teen crypto, and their first thought is, “Whoa! My teen doesn’t even have a stock account.” Or, ” I don’t know if I want to get my teenager involved in Web3 or crypto.” But the reality is that a lot of the teenagers we’ve found are hacking their way into crypto. They’re using their parent’s driver’s licenses to get a Coinbase account. They’re going over the law in a lot of ways to do this. And if we could create a bumper lane, if you will, that to us was a really compelling solution.

Aseem: Three things that you said stood out for me. One is this whole notion of democratizing access, right? If you go back a few decades, not everybody was trading in securities. Not everybody understood what it meant to be, for lack of a better term, owner in a company and bet on the growth of that company or that enterprise. And that’s changed over, I would say, even the past couple of decades. I think the second thing that you really mentioned, which was powerful, was this wave of learning and educated investing and guidance. And in the world of crypto, I think it’s more true than ever that people are just trying to figure this out. And especially teens, who want to latch onto this wave, but at the same time, kind of don’t know where to go. They’re discovering it as most people are. It’s fascinating that you are the on-ramp or the guardrail like you mentioned, to educated investing. Because this is going to happen with or without, I would say guides or mentors. And the third thing that you mentioned, which is fascinating to me, is even parents are learning. This is where I love the theory and the thesis behind what you guys are building, which is educated access, educated investing, and providing a friction-free on ramp to growth. So, tell us a little about what’s on offer today with Stack and where people can go to try it out.

Will: Well said, on so many fronts, there is this huge opportunity with what we’re building to, I think, tie two wants of our two users. There’s the parent user, and the want is education. The other user is a teenager, and the want is access. And one is hacking their way, and the parent who wants both themselves and their teenager to be educated really doesn’t have a lot of places to go right now. Because there really just isn’t that much education in crypto in general. We’re really living on the edge of the most cutting-edge area of the financial sector. The offering that we have now — our app is live on Google Play — it is set up so that a teenager opens a UTMA account, which is the Uniform Transfer to Minors Act. That requires a parent to be a cosigner on the account. And it allows teenagers to have legal access because if they access Coinbase or Robinhood or any of those other platforms that are out there right now, they’re actually living above the wall, meaning they only allow 18 or older users. But we allow legal access for teens. And then there are a lot of other really cool things about the app. So, for one, a parent can set approval limits. That means they can say, “I want to review everything,” “I want to review trades that are above $50 or a hundred dollars,” or “I want to give my teen the keys, and I trust them to go make good decisions.”

The second thing that we do a lot of is, like we’ve talked about a lot — education. How do you balance the education element and teen who is on TikTok and a lot of trends that they see are evolving in a matter of minutes or hours. How do you get their attention? How do you actually win in this education world that obviously very few people in finance have been successful with? And I think the way that we have chosen to win and really what we’ve learned about teenagers over the last 18 months is — it has to be bite size. It has to be tied to just-in-time learning. So that means, if you’re about to buy Bitcoin for the first time, you want to know about Bitcoin right then, and how can we win that moment? And then there’s the third piece of it that has proven successful in a few other startups, which is the learn-to-earn model. And that means you actually get benefits, and for us, you get fractional amounts of crypto if you teach yourself about it.

To a parent user, I think that third element is really important because we are doubling down on education from day one — by saying, we will give you free benefits, rewards, whatever you want to call it, via crypto, just for educating yourselves. There is a new offering that we’re going to release later this fall that I think will be by far the most exciting offering that we have to date. And it really plays on both a parent wanting education and a teen wanting access, and really how to balance the two of those as thoughtfully as possible. And also from a lot of the early engagement statistics that we’ve seen from our user base on our app.

Aseem: I think one of the things that stands out for me is the UTMA license you guys were able to get, and that’s really the foundation of how you’re approaching responsible investing. That’s awesome because it’s all about providing that guided education path for somebody to take control of their own destiny in a responsible way. I was curious what are you hearing from the teens — or, as I call it, the entrepreneurial generation — what are you hearing as they’re using your platform?

Will: We’ve learned an immense amount from teens working with them. I mean, we literally work side by side with them and in a lot of capacities — we’ve physically shown up to high schools. We’ve physically sponsored events. We’ve digitally sponsored events. What they’ve taught us over that period is you’re only going to command a very short period of time, and that includes education. That includes even if you reward them through something. And so, how can you win small moments that have a big impact? So, we’ve built that into our education content. We expect our team users to be spending an hour between 30 seconds and 90 seconds on the educational module of our app. But if we can do that consistently a few days a week or even every day of the week, I think that’s where you win.

The second thing that we’ve learned from teens is, in this world of technology, they’re highly capable. And I think many products built for kids and teens infantilize them a little bit, and this generation is different. As you said, they’re the entrepreneurial generation. They identify as entrepreneurs. A lot of them have side hustles. High school is different, and we need to recognize that, and we need to lean into that because they’re highly capable. And if you give them the right tools and the right guardrails, I think they’ll shock the heck out of you with what they’re capable of doing.

Aseem: Which is amazing. I think there’s so much to learn with this new way of consuming content, consuming education. You’re kind of on the bleeding edge of constant evolution from the product side of things. Shifting gears a little bit Will, tell us a little bit about the recent economic changes and the impacts that the markets are having. Have you seen any changes in terms of how people are interacting with Stack, and how they’re thinking about it? I mean, you guys just launched, but I was curious about how that’s playing into the minds of your customer?

Will: Well, I’ll say this, A) we’re incredibly fortunate to have launched. And we did because we launched after the down cycle. If you believe in the thesis that crypto is going to go back up, which I think, you know, anybody that’s getting involved in it likely does. There’s a good chance that our user base is getting in closer to the bottom of the market. I mean, I look at somebody like Robinhood, that was born in the shadow of 2008. And what Robinhood did well was they built with a diehard user base. And then, when the market started to catch fire again, that’s when you saw their user base take off. Not saying that we’re waiting around for the market to catch fire again. I think there will be another one of those natural upswing moments when there’s a lot of curiosity, crypto’s back in the news all the time, and people are talking about new coins, and they’re talking about Bitcoin or Ethereum and everything else. But in between now and then, we are really living and breathing the customers that want to be doing crypto right now. And they want to do crypto for, I think, a few reasons. Number one, they know that not only is there obviously crypto the investing product, but there’s the blockchain, and there’s the technology behind crypto. And there are so many other use cases, so many other industries and companies that are building on the blockchain as a technology that can be learned on our platform and can be learned through crypto. Because crypto is the currency of this technology. So, I think we’re really leaning into that. That user base tends to be gamers or highly tech-oriented. They certainly have a certain customer profile that we found over the past few months, but that’s really kind of where we’re living and breathing right now.

Aseem: Right. That’s so awesome on so many levels. Wanted to ask you a little bit more about the product itself. So, what can teens do with Stack today that no other platform offers them?

Will: So, number one is just legal and safe access for teens and their parents. I mean, we are one of the very few investing platforms — even if you open this up to stock products as well, it’s a small niche game, the under-18 finance and mobile app game. And we were the first ones to open in this under-18 arena specific to crypto and Web3. Number two, something that you get out of it just indirectly through a UTMA account is you get tax advantages. You can earn up to $2,300 tax-free on our platform because you have a UTMA account. Whereas you go earn on Coinbase under your parent’s account, and you’re going to get charged 30% or whatever their tax rate is. And then lastly, it goes down to a product built specifically for teen crypto rather than a Coinbase or a Robinhood. What we think about there is we limit the coins on our platform. We do a lot of vetting of what currencies are on there. We vet based on market capitalization, we vet based on trading volume, we vet based on a lot of characteristics and nefarious activity. And so, we’re tracking so that the top-of-the-funnel decisions we make about our product and app is making it safer to trade in. And then lastly, we also don’t do crypto on and off ramps, and I think that is an essential decision for us. Crypto on and off ramps is where all the fraud and scams happen. It is somebody tricking you into or hacking your wallet password and transferring assets on an immutable ledger that you’ll never get back. So just by disabling that, I mean, there’s no reason for our users to be sending crypto assets off platform right now in our use cases. And so, we can just guarantee safety there. I think that’s obviously important.

Aseem: I mean, I remember the days when, when you first got a bank account, when you first got a credit card, and like the world opened up. Right. And with that comes a lot of nefarious activity and risks. What’s amazing, as I’ve learned working with you, is you guys are trying to minimize all risk that there is out there from a fraudulent activity perspective. Now, yes, there’s always risk in trading, but at the same time, you’re giving them the tools and the platform to do it in a responsible manner.

Will: Totally. It is the Wild West, but it’s in need of a good guy. And we hope to be one of those good guys that can create a safe place for you to trade. And for you to learn.

Aseem: So, shifting gears a little bit, tell us a little bit about how you’re reaching teens. I mean, so much of what teens do is driven by social proof, right? I mean, yeah, if my friends do it, then I’m going to latch onto it. I’m going to learn how to do it. How are you approaching the community angle of your focus — is there a community angle?

Will: Oh my gosh. It is essential it’s everything that we do. We have an ambassador program, which you’ll see right on our website. And that ambassador program now has a little over 200, 250 teens representing 200, 250 high schools. Most often, they are the president of their finance or investing club at their school. And what they do is they work directly with our team, and there are two aspects to it. One is we come in and do a financial literacy workshop with them, which is incredibly powerful for us, whose mission is to educate this next generation of kids. Meanwhile, the curriculum in their own high school certainly cannot keep up with crypto and digital assets in this world.

And so, we can do a lot of cool education there through this extracurricular environment. And then the second thing that we do with them is give them opportunities to earn money on the app. And they can do it in a lot of ways. So, they create some of our TikTok content. We did a challenge where we sponsored a group of teens’ side hustles for the summer. And so, we had teenagers that created designs for iPhone cases and sold them on eBay. And we had teenagers that very entrepreneurial collected golf balls, you know, they lived on a golf course, and packaged them and sold them. And so, there were just a lot of cool stories that came out of that. And they video blogged the entire thing. Just creating those powerful stories of this teen community so that teens can see their peers, see the amazing things they’re doing, and understand that Stack, as a brand and a platform, is a place that is celebrating that. And I think that’s fundamental to who we are.

Aseem: Yeah, that’s so cool on so many levels this ambassador program can just be the beachhead of what you’re trying to achieve. And I think with that comes social proof and with that comes interest and awareness. It’s so cool on so many levels. You know, as a parent of a 9 year old, soon to be entering teenage years. And it’s almost scary to think of. So tell us what’s on the mind of every parent, tell us more about what your journey has been, how you’re discovering that. And frankly, is that going into how you’re thinking about product?

Will: I’ll admit right now that we have a lot of work to do with parents. We are starting to be good listeners of parents. I’m actually entering the parenting arena in a few weeks myself.

Aseem: Yeah, congratulations!

Will: Thank you. Thank you. I’m constantly thinking about what type of world I want to create for my own children. And I know that’s on every parent’s mind. So being the good guys and sharing the message that we are the good guys that, we’ve taken a lot of steps to create the best place for a teen and a parent to go be a part of this kind of crazy world of crypto and Web3, I think is a huge message to carry. We have started doing some more bespoke programs similar to our ambassador program but for parents. And then the second place that I think we’re spending a lot of time, which I think is just as important as parents and carrying the baton as teachers. Educators can be, I think, a really interesting part of our strategy.

I’ll say this, we didn’t really hit this light bulb until recently, but we had probably, I’m going to say 10 to 12 educators specifically send our website’s email to reach out about us educating on crypto and whether they could get their class involved in our platform and wanting to know how it worked. And from that, we’ve realized that there really is this cool opportunity to share stories through the educational mode of the world to parents because who do parents trust the most? They trust teachers. And so, if we can find a way to create some really cool storytelling of teachers being a part of our platform. And using it to educate kids in a place that curriculum just can’t, I think that could be a powerful way to celebrate parents and their needs and wants, which obviously fall into education.

Aseem: Yeah, I really like it. I mean, I really like the use of the word curriculum. Because at the end of the day, your kind of giving them a very curated set of learnings that you can then apply to real-world scenarios and start trading. And, you know, you’re bang on when you talk about teachers and in some ways, you know, parents are teachers, but in, in this scenario and in, in this new world, parents are also on the educational journey, and they’re learning alongside. Talking about learning, your first-time founder, you’ve had amazing experiences at a different company, you’ve been at a VC firm — what’s been different so far as an entrepreneur? What have you learned? And I love asking this question — what has been the “aha moment” — if there is one?

Will: Oh my gosh. I mean the founder journey, I tell, friends and, and even like early founders as they’re starting their own journeys — I say, I think being a founder either spits you right back out. Or just lets you fall into this ocean of loving every single day of what you do. And it, and it really takes, a certain set of wants and needs out of the founder journey to really embrace it and love the job. It has made my career come alive in so many ways. The six months that I both created every strategy and then executed that strategy, I think, is one of those unique moments in your career because we all don’t get enough time to just spend on ourselves, especially when you work at bigger companies and you’re part of the machine in a lot of ways. And so, that six months, for me, to really go back and be honest with myself and not tell myself any stories, say, “what are you great at?” And “what do you need other team members to come and do a really great job of?” It tells you lessons about yourselves, you know, just in that honesty that I think is beneficial, not only to your career, obviously, but just to me as a person. I find myself grateful for a lot of those lessons early on and celebrating a lot of those things, the bigger we get and the more people we add to the team and all of those good things.

And, and shout out to, you know, my two co-founders who round out my skill sets incredibly well. We had a discussion earlier today, in fact, about some areas that each of us feels particularly strong in and how we can continue to celebrate those in all of the strategies that we’re running.

Aseem: We’ve had the fortune of working with, you know, both Natalie and Angela as well, and I think we can see the team coming together and the sum of parts making the whole so much better. Right? Because at the end of the day, I think you’re complementing each other well. You know, you’ve got your strengths, and the team has theirs, and I think it’s so amazing to see the humbleness, the drive, and frankly, the passion for the space all get married with your skills that then take you along the journey of achieving something really, really great. The common knowledge around talking to founders is that there’s always one thing on their mind that they’re maniacally focused on — what is it for you when it comes to Stack?

Will: The number one thing, which I’ll just reiterate like a thousand times probably, is how can we more meaningfully educate? And so, we’re constantly obsessed. I mean, where my skillset falls, a lot of the time is actually in our product and designing it thoughtfully and listening to the data and listening to users and, and really celebrating those users.

We had a fun conversation about our marketing approach right now. And we talked about how we need to create the Stack feed to mirror the exact profile of our user. And how do we make the Stack feed look like a 16 year old that’s curious about finance and has certain behaviors and patterns so we can really live and breathe the content that they’re seeing and create meaningful educational content, create meaningful marketing content, create meaningful product content, all of that stuff. I think just celebrating a lot of that in high-quality content where I think a lot of finance apps send you to a blog or send you, you know, off app to something else. We can do a lot of good stuff there. So that’s the one laser focus that I have is winning that specific space of our app.

Aseem: And so much of what you’re seeing is actually reflected in the approach that you’re taking. The way different people consume content has changed so much over the years. Like we’ve gone from news feeds to RSS feeds to blogs to articles to Twitter to TikTok now, and I think it’s amazing, you know, how meeting people where they are is the approach that you’ve taken. It’s the natural way of how teens will learn. It’s exciting to have seen the work the team has done and in working closely with you seeing how much we’ve learned as a firm in terms of the approach you’ve got to take and you’ve got to change. It’s both enlightening and fascinating at the same.

So, kudos to you and your team for always being hungry and always being on the lookout for what’s around the corner and how you can make it better and how you can strive to achieve that curated approach in a way that is very natural.

Will: I appreciate that. It means so much.

Aseem: Yeah. Advice for young founders, Will. Like a lot of people who are in big companies, have ideas, you know, going after a bold vision, thinking about solving it in a meaningful way — what advice do you have for them, having just walked that journey and, and still walking that journey.

Will: Well, number one, I would say is the metaphor I’ve used from time to time. It’s an old show I watched in the ‘90s is called “Double Dare 3000,” and there’s a part of that show where you are going through this obstacle course. You’re given no instructions. And in each obstacle, you have to find a flag, and it’s like stuck in slime sometimes, or it’s in the middle of a slide, or it can be anywhere.

Aseem: It sounds very teenager focused.

Will: I mean, it was a great show. I loved watching it when I was a kid. And why I think it’s the perfect metaphor for early founders of a company is because you’re given very little instruction as a founder because you’re your own boss, all of a sudden. And you don’t have a lot of structure. And all you’re trying to do is find the flag at every obstacle. And that changes every day. Your job changes every day. One day, you’re trying to find money. The next day, you’re trying to find product-market fit. The next day, you’re trying to interview customers. The next day, you’re trying to keep your vendor expenses down. It is a new and different challenge every day that you enter the game. For founders who love it, that’s what they love about the job. But you need to be finding a way to just find the flag. The quote that I throw out there is, “You need to have slime on your face.” You just need to go for it. You’ll be told a thousand times that you’re not going to make it. You’ll get told “No” a thousand times. And to be resilient enough to get through that and to find it in yourself, to get to the next obstacle. I think that’s the biggest thing.

Aseem: I will tell you this, Will — in the context of “slime and your face,” we were enamored by your vision. We are enamored by your tenacity, your passion, and I don’t think that there was ever a question of “No” from our side. You know, without further ado, any fun fact or any fun story that you want to share with our listeners as we bring this to a commencement,

Will: Oh, man. I mean, there’s so many things I, I think there was a moment in the second month of being a founder — and this is when I had taken the plunge, this is my full-time job, you know, I was fully locked in — and I had one of those days where I got a lot of “no,” you know, I was getting a lot of doors closed in my face. And I had sat in my chair for a long enough time that my screen had went black, which meant it had been 15 or 20 minutes or something. And I was staring at my own reflection in my computer, and I just looked at myself, and I said, “oh my God, what are you doing?”

Aseem: Yeah.

Will: And I think to pick yourself up from those moments, which every founder has them. And, you know, one of my favorite books is “The Hard Thing About Hard Things” because it talks a lot about those moments that he talks about the struggle in that book and how every founder has been through the struggle. I think there’s so much knowledge, and just knowing that others have gone through it, and talk to a good founder, have a coffee with a founder that’s a step ahead of you. The amount of knowledge that you’ll learn and the empowerment that you’ll get. I mean, every time I have one of those conversations, I rip off the warmups, and I get back in the game. And so, I think that’s incredibly powerful, and I’m happy to be that for, for any entrepreneurs out there listening.

Aseem: That’s awesome. Hey, will I really want to, you know, congratulate you on the announcement and on the raises and, it’s fascinating to be able to back someone like yourselves and partner with you and the team in, in really going after a big, bold vision. Because I think this is truly transformative. If you think of it like 20 years down the line, but looking back and saying, “Gosh, we wrote the first line of code and had the first product on the market that actually helped teens invest in a responsible way. And that really created not just good habits, but it created futures.” I think it’s just fascinating that there is so much out there that we could be enabling in a responsible way. And we are fortunate at Madrona to be in this journey alongside you and to continue building or laying the foundational elements of this. So, thank you for your partnership, and I’m even more excited about doubling down and getting to work because the real work starts now in, in trying to scale the product and, you know, deliver a stellar experience.

So, thanks for being on the show and good luck in the future, and I’m sure we’ll be looking back and saying, wow, that that first line occurred, that first staring at the screen that moment was surreal.

Will: I love it. I thank you so much for having me, Aseem. I’m humbled and honored to be a part of the amazing Madrona portfolio. And I know my team is incredibly fired up to go solve this problem. And, as you said, the hard work starts now. And I think we’re really excited to be a part of it.

Aseem: Awesome. Thanks. We’ll talk to you soon.

Will: Thanks, Aseem.

Coral: Thank you for listening to this week’s episode of Founded and funded. If you’re interested in learning more about Stack, please visit TryStack.io. Please tune in in a couple of weeks for our next episode of Founded and Funded with FiveTran CEO George Fraser.

Welcome to Founded and Funded. My name is Coral Garnick Ducken, and I’m the Digital Editor here at Madrona Venture Group. This week, Investor Maria Gilfoyle talks to Brad Van Vugt, founder and CEO of Battlesnake, a multiplayer programming game for experienced web developers where your code is your controller. Battlesnake builds games for programmers that encourage self-directed learning and are challenging to master. In this episode, Maria and Brad dive into where the idea for the game came from, how they get the developer community so involved, and how they’re able to combine aspects of gaming, eSports, and traditional sports to create an engaging experience not just for developers, but for anyone to watch and enjoy. You won’t want to miss Brad’s stories and advice in this one, so with that – I’ll hand it over to maria to kick it off.

This transcript was automatically generated and edited for clarity.

Maria: Hi everyone. I’m Maria. I’m an investor at Madrona. I am honored to be here today with Brad, the founder and CEO of Battlesnake, one of our portfolio companies. Brad, I’m excited to be here with you today.

Brad: Hi Maria. It’s good to be here.

Maria: So, I thought we could start with the story of how Battlesnake got started. You have this incredibly engaged community with over 800 developers in a Discord channel, actively playing and coding games on Battlesnake. What was the initial vision, and how has that evolved from where it is today?

Brad: It’s a good question. It’s an interesting story. Battlesnake started a few years back primarily as a developer recruiting event. I had co-founded another tech startup, and we were at a stage where we needed to hire lots of developers. We were like, okay, let’s do something interesting, specifically for intermediate, senior developers, which are historically challenging to retain, challenging to recruit, and we were like, let’s do something really interesting. Let’s see what we can do to maybe make some buzz locally.

And so, we wanted to hold the developer event, and we wanted to do something different than what you’d normally see. And at the time, hackathons were really cool. It was getting all your friends together, staying awake for 48 hours, programming on our laptops, drinking lots of coffee and energy drinks, and seeing what you could make. We thought that hackathons were fun, but they’re incredibly accessible to students or younger developers or folks that have free time on their hands. But hackathons are inaccessible when you start thinking about anyone with kids, anyone with full-time jobs, and other commitments outside of just coding. And so, we wanted to do something different from that and hold a developer event that felt different. We wanted to accomplish two goals with it. The first one was to have everyone work on the same problem. Hackathons generally — everyone goes off in their own little silos and corners and builds something, and then everyone presents on stage, and nobody cares what’s going on. But what if everyone worked on the same problem? What does that look like? And does that encourage collaboration? But then also, can we make it really fun for non-participating folks to show up and watch? And so, can we hold some sort of tournament, show, or challenge at the end that would be engaging and fun to watch? And we came up with the idea of Battlesnake. The core premise was to spend some time building a web server that plays a game autonomously on your behalf, and then we’ll hold a challenge or a competition at the end and see who wins, and we’ll celebrate this. And it took off in an interesting way — to a point where we had thousands of developers show up and try to participate and win first place.

We had hundreds of parents, kids, grandparents, and colleagues show up just to watch the tournament. And that triggered something for us where we were like, okay, this is really interesting. We haven’t seen anything like this before. We started to get inbound interest from much larger tech companies that were struggling to recruit developer audiences that were more senior and more experienced. And that pushed us over the edge of like, okay, let’s see what we can do in the space. Let’s see if we can grow this and see what that would look like. That was a decision we made in 2020, and we started working on Battlesnake full-time. And now, we consider ourselves a global game platform. We have developers all over the world. We have more than 20,000 developers that have played Battlesnake at this point. We host monthly competitions and challenges, and we do a bunch of different stuff with a bunch of different developers, but that was how we got started in the sort of humble beginnings of let’s try to do something that was better for more experienced developers specifically.

Maria: You have this incredible story of being the founder of another company, seeing a problem internally and then trying to develop the solution, which became your next startup, Battlesnake, which is such a cool story. Is there anything you learned from being a founder of a prior company that you’ve taken with you as a second-time founder?

Brad: Yeah, there are a couple of things. So, we were a B2B SaaS company. We required an API integration, so a lot of our product engineering went into like API design and being thoughtful around how the API was presented and documented. So, we learned a lot about how to speak to the developers, how to engage more senior developers, and how to build an interesting developer experience, but also like no developer really wants to integrate with a SaaS product, so it was like, okay, how do we make this as quick as possible? Those learnings led to, okay, how do we grow Battlesnake in the early stages? Like how do we build an interesting developer experience? How do we get developers to fund fast, to use the cliche term?

But there’s also an interesting number of things that didn’t transfer to the developer gaming space. Fixed roadmaps are a big one. When you’re developing for a B2B SaaS, there is a problem your solving, you have phenomenal ideas for how to solve it, and that’s what you are going to do. That’s what you’re going to build. But, for consumers or gaming spaces, you have to be much more open to your audience and the community. And this is something that we learned early on, which was like, okay, it’s not just talking to the developers, but it’s listening to their feedback. It’s engaging with them directly. What ideas do you have? What would make this more interesting for you? How could we take the platform to the next level? Where do you see Battlesnake in the next five years? And how do we help you get there? That was, I don’t want to say, painful, but it was a bumpy shift in mindset between enterprise SaaS to developer-facing consumer gaming product. The other thing that helped is I partnered with a really strong CTO who has done lots of stuff in consumer gaming. He helps balance out that background and that previous knowledge, that previous experience with how we need to think about engagement on a daily basis or a weekly basis. And he helps a lot in that regard.

Maria: You’ve been incredible at listening to your customer and keeping them engaged. But before we dive into that, I would love to dive into how you think about the journey of the user in Battlesnake. So, you started with this new version of a hackathon, and it’s now evolved into this platform where anyone from anywhere in the world can log on and participate in Battlesnake. You’ve thought a lot about how you should focus on building an open-source community with extreme flexibility. So, anyone can participate with any programming language, any cloud provider. How do you think about that flexibility and user experience from the first time someone goes to the Battlesnake website to participating in a tournament?

Brad: I think it’s really challenging. And it’s something that we’ve worked very hard to improve. And I think we’ve done some great things in that regard, but there’s always more space for improvement. Battlesnake as a platform is all about exploring the technology on your own terms, and letting that mean whatever you want that to mean to you is good for us. And we want to support you in that. A lot of developers that get involved in Battlesnake, especially more experienced developers, are coming to Battlesnake already with an idea in their mind of something they want to do — they read a blog post six months ago, they heard a podcast about this new stack or this new technology or this new cloud platform. And it’s something that’s been in the back of their mind as like, “Oh, I’d really love to learn Rust,” for example. I use Rust, for example, because a lot of our players are using Battlesnake right now as an onboard ramp to learn Rust specifically. But if you’d speak to most experienced developers, they have a laundry list of things that they would like to explore more and things that they would like to try. And really, that’s what we’ve learned to latch on to. When you come to Battlesnake, we’re not going to teach you AI. We’re not going to walk you through AI. It’s not an ML competition. That’s not what this is. This is about giving you an interesting way to finally learn that thing you’ve wanted to learn forever. And most developers, especially experienced developers, have that list already. So, it’s about tapping into that and speaking to that directly.

The other interesting thing is — and this is something that we observed prior to making the jump to work on Battlesnake full time — there’s not a lot of opportunity for developers to go deep on specific technologies. If you are working professionally as a developer, the scope of things you get to work on is very small. You might be able to go deep on something that’s very specific, very siloed, but you don’t have a lot of say in how you explore outside of that or beyond that. What attracts senior developers to Battlesnake specifically is the ability to explore that over long periods of time. We’ve had developers play Battlesnake for multiple years now, and they’ve used it as a jumping-off platform to learn new languages, tech stacks, and cloud platforms. We’ll have an elite competitor do well in one particular tournament and then show up the next tournament with a snake that’s completely rewritten in a different tech stack of “Hey, I really wanted to try it this way, this time and see what that was like.” And that started to happen early on without our involvement, and that’s the core piece of growth, and that’s what keeps developers coming back.

Maria: That’s awesome. And how would you describe the profile of a super user on Battlesnake — the person you’re trying to target? You’re open to a lot of different profiles, but who ends up being the most engaged?

Brad: The most engaged tend to be senior developers with ideas about things they want to learn. But don’t have the time for a side project or are sick and tired of being told to do side projects. A lot of developers get told, “You want to learn React, or you want to learn Rust, or you want to learn this new technology, go do a side project on your own or go join open source.” And both of those things are very like lonely and intimidating, with huge barriers to entry. And our long-term core users right now — our power users — are folks that have these lists of technologies and things that they’d want to learn and things that they’d want to explore, and they just haven’t found a venue to do that yet. And those are the ones that stick around for a long time, and they help the community, and they get involved and continue playing for quite a while.

Maria: So, you’ve built this engaged community, and you’re great at listening to your users, and there are people from all over the world that are tuning into Battlesnake tournaments. How do you think about managing that community and growing it, and how the concept of community contributes to the overall growth of Battlesnake as a company?

Brad: I think it’s challenging. It’s hard to do, and we’re constantly learning. We’re constantly listening, we’re constantly learning, and we’re pretty good at acknowledging what we don’t know. But also, more recently, we’re learning that multiple channels are incredibly important — like highly engaged, multiple channels. So, it’s not enough to have a Discord. It’s not enough to have a GitHub discussion board. It’s not enough to be on Twitch. It’s all of those and more. One of the things that has worked out well for us is being on Twitch regularly. It puts real people behind Battlesnake. You can see me, or you can see someone on the team talking about how we think about the game, how we want to extend the game, the problems that we see, and what we think we’re hearing the community say. But then it also gives incredibly real-time and incredibly interactive opportunities to community members.

It’s a lower barrier for someone to show up on a Twitch stream and ask a question in real time. And I can answer right now. And that has been incredibly valuable early on and continues to be valuable even as we expand across other channels. I think that the idea of developer brands doing more live streaming or live engagement — it’s incredibly undervalued in the industry just in general. And it’s been one of our core advantages in growing and managing the community early on.

Maria: Is there anything that surprised you about growing and managing this community besides the importance of Twitch that you didn’t realize at the beginning? Or you’re continuing to explore as you build the community out.

Brad: Yeah. Developers as a community are going to do what they want, whether you give them permission or not. That’s the nature of this. Because we do so much open source, many developers will, if they have an idea, just act on it. And they’ll self-organize, and they’ll start building communities around this. A good example of this is that we didn’t have a Discord early on. We just hadn’t thought of it. I think we had a public Slack or something. And it wasn’t working, and we didn’t pay attention to it, and nobody joined it. And then what happened was someone tipped us off that a Battlesnake Discord had been started. And we joined, and there were like a hundred people in it, and they were talking about Battlesnake all the time. They just did it, they just self-organized, and they just did it.

Another good example this is more technically specific, but like, we didn’t have a CLI. There was no Battlesnake CLI to run games or run your own commands in a shell. And someone just built it and showed up on Discord one day and said, “Hey, I built the CLI.” And suddenly, hundreds of other developers started using it. We had no plans to build a CLI. We didn’t intend for that to happen. We didn’t think that was a thing we needed to work on, but the community just did it. And so now it’s like, do we fight that, or do we encourage that and engage with that? And obviously, the answer is the latter. And that’s surprising, but we’re working with makers, we’re working with builders, and it’s going to happen. And there’s a lot of lessons that we’ve learned around — rather than show a roadmap of where we want to go, or rather than build a feature and think this is the thing we need to exist. Our community’s going to build for Battlesnake whether we like it or not. So how do we encourage that? How do we leverage that and use that going forward? And we’ve got a good sense of that now, but we’re still very much learning how to treat the community like a living organism as we grow.

Maria: That’s incredible. It’s almost like you built this base platform and product, but then your communities decided the product roadmap versus you as the founder, which I think is a big difference between often consumer and enterprise companies — you have a bigger focus on listening to your community and then building the product with them. And so that leads me to your focus on building in public. So Battlesnake is great at building in public. What does open-source development contribute to the growth of Battlesnake? And how do you think about the focus on open source?

Brad: Open source is really important to us. We made a very conscious decision early on to do as much in public as we possibly can. The entire game engine is open source. The visualizations we show during streams and that you’re seeing during competitions are all open source so that people can extend those. And also, we provide a lot of onboarding content through open source. For example, if someone wants to learn Rust or AWS SageMaker, we provide some code to get them started. And we can provide a lot of jumping-off points. But it initially served two purposes for us. One was like the game itself is technical, and the mechanics are interesting, so we had a lot of community members that had interesting and great questions around how the core game mechanics worked. Like, why did I lose in this particular situation? Or how does turn resolution work in this case? Or I thought of this edge case; what happens here? And so, we found ourselves fielding these questions regularly and thought, what if we just open sourced the entire game engine? And then the conversation becomes not — here’s our justification for it behaving this way. It’s here’s the code that runs your games. Here’s the code that runs your turn resolution; go check it out and explore it. And that became part of the early onboarding experience for most developers. It’s not just playing the game; it’s exploring how the engine works. It’s exploring how it’s deployed. It’s exploring how timeouts are calculated. It’s exploring how turn resolution happens. And you’re doing that through open-source projects on GitHub. You’re browsing the code. You’re compiling the code. You’re making changes. The other major way that open source has given us an advantage is obviously contributions back. And I want to be very clear because it’s interesting how this has evolved. It’s not like fixing bugs. We’re not like, “Oh, can you please fix this bug?” Or “Can you increase the documentation on this repo?” It really is like, — how can we extend the game to make it more interesting? How can we do more things with Battlesnake? My CLI example, previously being a good example of this, right? Because everything is open source, someone was able to develop this whole other way to engage with Battlesnake through a terminal that didn’t exist before. And that was because the game engine was open source. And now we’re starting to see developers build modifications and extensions and their own game maps on top of the engine itself. We see open-source contributions as the pinnacle of our power users being engaged. They’re going to get involved. They’re going to have ideas. If we’re doing this right, they’re going to want to give back to the community. And they’re starting to do that now through open source specifically. So that’s something we really had to figure out how to lean into and encourage more. And there’s a lot of stuff we can continue to do there as well.

Maria: And as developers use the open-source opportunity to build game engines on top of Battlesnake, how does that impact your product roadmap, and where do you see Battlesnake evolving in the future?

Brad: We’ve learned that our role is not necessarily to build for the players. Our role is to build for the developers and to encourage them to contribute and be engaged. Our players want that. Our players want to give back. They want to build modifications; they want to build extensions. And so rather than us being like, okay, this game mechanic needs to exist. Let’s build an engine for players to create and release their own game mechanics, right? That’s the mental shift that’s gone on, and that’s the impact that that open-source development, that’s the impact that the community has had on how we think about building the game and building the product. And I’m not sure of many other products or games that are built that way — where our core goal as a dev team, especially long term, is enablement. Rather than what can I deploy today that’s cool. It’s what can I deploy today that will let someone else make something cool six months from now. And that requires a very concerted effort to make that happen. But that’s where we’re at right now. And I think that’s incredibly compelling.

Maria: You’ve been so great as a founder about listening to your community of developers that are participating in Battlesnake. But you have a whole other customer that we haven’t even talked about yet, which is your partnerships with companies like New Relic and AWS, who are partnering to help engage their developers at the company but also recruit developers into those companies. How do you think about these partnerships in satisfying those customers as well?

Brad: I think that right now, our core focus as a company, as a team, is our player base. It’s experienced developers. It’s getting them engaged. It’s keeping them engaged. It’s doing things that are fun and interesting — things that allow them to include their friends and their family, and their colleagues. On the partner side, we’re specifically working with large brands that understand that — that understand what developer marketing looks like. What do developer relations look like? What it means to recruit long term. If you’re DigitalOcean, you might be trying to make some hires this month, but really what DigitalOcean is trying to do is increase platform usage, increase customer base and make hires for the next three years. And that’s a much different play than, say, traditional recruiting, so we work with them because they understand our longer-term vision. They understand how we’re able to attract and retain players and more experienced developers. And they’re willing to work with us to figure out how to make that scale. One of the challenges, when you’re working with developers specifically, is you have to think long term. This works for recruiting but also for product usage. If you want someone to adopt your technology, they’re not going to show up one day and then just start using your API. You have to think long term, it has to be, you know, a 12-month onboarding experience or more, and you also have to be authentic. Developers are incredibly good at seeing through marketing tactics. That doesn’t mean you can’t market to them. It just means you have to be really authentic in how you do it. We’re able to work with a handful of really great tech brands that understand the long-term value of that. And are willing to work with us as we build the developer-facing side of this game.

Maria: So, if we think about this category, you’re building at the intersection of a lot of different spaces. You’re building at the intersection of ed tech in a way that there’s an education component to it. Gaming and eSports because of the community around it and the way you engage Twitch creators. And you’re also building up the intersection of dev tools, and you’re defining this new category. It’s a dev recruiting tool for companies like DigitalOcean, but at the same time, you’re helping developers explore new cloud providers and coding languages that they haven’t explored before. And you’re also targeting not just new developers but experienced developers. How do you think about this category and how you’re defining it, and how this kind of niche between these different spaces will continue to evolve?

Brad: I think we’re very early in this space. And my best go-to example of this is that Twitch just created a software development category this year. And there are thousands of folks that are live coding and building games and interacting with developer audiences on Twitch, but it’s just starting to get large enough now that folks are starting to notice. In terms of how we think about this intersection. We think about it from a bunch of different angles. We think about it from the eSports angle, from the traditional sports angle as well, but also from the gaming angle. Something that’s core to our mission and beliefs as we approach this space is making something that is innately incredibly technical and challenging but also incredibly accessible to most people. And I don’t know if that comes through, but an example is, say you’re watching competitive StarCraft, StarCraft being a very large eSport. Most folks watching a StarCraft game, especially if you play it at a very high level, have absolutely no idea what’s going on. You’re not going to casually watch competitive StarCraft. You have to be a player. You have to understand the strategy that happens at a high level, and you have to be available at that time in order to view that. And I think that needs to change. Gaming is kind of figuring out different ways to do that by adding different spectator modes and adding different on-ramps to different things.

But if you look at more traditional sports — most people can watch professional basketball. And they might not understand what’s going on in the strategy, but they can go home, or they can go outside and play basketball. They can try it, and they can understand what’s going on. And it gives them a sense of, when that shot is made, or that play is made, that was really hard to do. And that was impressive. And that was fun to watch as a result of that. And what we’re trying to do is how do we bring that spectator angle? How do we bring that accessibility to something that is more developer-focused or something that is more gaming-focused or eSports-focused. And this really leans into the game itself. The way that we’re building the game is universally recognizable to most people. I can spend time building an incredibly complex, incredibly competitive Battlesnake, and then when I go to play competitively, my parents can watch, and they can understand it’s just a game of snake — my options are to move around the board and try to outmaneuver my opponents. Or my kids can watch with me, and they can cheer, and they can understand what’s going on. If I’m live coding or if I’m playing StarCraft at a high level, no one’s watching me. I’m not bringing anyone along for the ride on that.

I think that the sort of intersection of the accessibility of traditional sports and the deep complexity of eSports — we work hard to combine those in an interesting way. And I think that’s where this industry is going for sure.

Maria: Let’s dive further into this focus on accessibility. How did your creative vision in terms of the visual parts of Battlesnake and this concept of using a snake? People know battle and game and snakes are understandable to any user. How do you think about the visual components of what the snake should look like? What the user experience should look like that makes Battlesnake so fun, visually appealing, and accessible at the same time?

Brad: Part of the choices around snake and the, especially the visual aspect of snake, we got lucky early on. We didn’t set out to build competitive multiplayer snake — that wasn’t the goal. The goal was to do something for developers that was fun. And the sort of core game mechanic was just chosen at random. And then we learned that it was universally recognizable. The way that we learned that was — like I mentioned, early on, Battlesnake started as this recruiting event. But people started to bring their kids. We have photos early on of 2,500 people watching this live Shoutcast Battlesnake game, and there are kids in the crowds that are cheering, and there are parents that are standing in the back, and they’re clapping, and they don’t understand how these things are built, but they understand that someone is doing well. And that’s impressive because they’re playing a game that they’ve played before at a much higher level than they’re able to play it. And that really tipped the scale for us. You know, we sort of have a third user that we pay attention to, which is the spectator. Someone who is not necessarily going to build a snake or isn’t interested or isn’t capable of building an incredibly competitive snake but still has a lot of fun watching and wants to follow the storylines and wants to cheer for their favorite.

I guess the other angle of that is we look to more traditional sportscasts, and we also bring an element of analysis and Shoutcasting and live entertainment that you wouldn’t necessarily see it at a developer event. And the way that we approach speaking about the game at a very technical level, at a high level. We try to be very accessible with that. Our target audience is roughly a 16-year-old that might be watching and might be interested in what’s happening. And we make a very focused effort to make this more interesting and tell storylines that sort of like overarch the tech itself.

Maria: How do you think about converting that spectator into someone that ends up coding in Battlesnake?

Brad: Oh, this is awesome. And this is happening already. I’ll go back to the basketball example. So, let’s say you’ve never heard of basketball. You’ve never watched the game. You watch a game for the first time, and then your friends are like, “Hey, you want to go try that?” It’s incredibly accessible for you to just go play. How do we enable that for programming? Like, how do we get to a point where you can watch this game? You can understand that someone is doing well. You can understand what it means to have an interesting strategy. How do we capture that interest and be like, try it, go home and try it, just try it right now and see what we can do? And like enter a beginner league or enter a beginner ladder and see how well you do. Our goal isn’t to build you into a competitive Battlesnake player. Our goal is to introduce you to programming — introduce you to the core concepts of what’s happening. And again, sort of marrying these two concepts of like traditional sports has this incredible accessibility to it that programming typically doesn’t, and so how do we bring those two things together?

Maria: I love the sports analogy because Battlesnake fills two things that are more native to traditional sports like basketball versus coding, which is that it’s accessible, you can watch it, you can then try it. And in Battlesnake, someone could watch and then decide to try learning to code and coding their own snake. But you could also become a professional basketball player, and you could play at a very high-level Battlesnake also great and appealing for someone who is an experienced developer but wants to continue to improve their skills. And so, there’s this broad range of users and ways that people can engage with Battlesnake.

Brad: Yeah. And I think it’s important that we acknowledge and serve both of those personas as we build. Our focus right now is on experienced developers. Like how do we get high, complex, strategically interesting games that we can watch that we can analyze because that gives us content to show on Twitch, to have on YouTube, to do live casts around, to do live analysis around — that can then expose many more folks down the road to what we’re doing and get them involved in the game itself. But it very much is — let’s tell the stories of these programmers and make it interesting to make it really compelling for anyone to watch.

Maria: So, you’ve built these different levels within Battlesnake, but you’ve also mentioned potentially another game is on the horizon. So, what are you most excited about in Battlesnake’s future?

Brad: I think that the thing that we’re most excited about is the community contributions back to the core engine. And going back to what we were speaking about before and our primary avenue for growth, we see the pinnacle of power user engagement is actually building your own game or game mode or game engine on top of Battlesnake itself. And snake being one implementation of what we built, but like what else could we do with it? I see our role in that as not necessarily becoming a game studio and being like, here’s the next iteration of Battlesnake. It’s more like, how do we enable the community to do interesting things and build their own renditions and build their own modifications and build their own communities around that. We look a lot to Minecraft. We look a lot to Roblox and these sorts of larger, contribution-based communities. When I was growing up, I was heavily involved in the early Counter-Strike scene, and I produced mods and got communities built and working behind those. I love that sort of natural extension to gaming in general of, you know, great we’ve we built a community rather than let’s go build a second community, how can we inspire folks to start their own communities?

How do we show core values around accessibility? How do we show core values around learning and collaboration and open source? How do we push that out through the communities rather than try to have a heavy hand and just dictate from the top? Let’s build a series of sub-communities and enable developers to do their own things and spread these values organically.

Maria: That leads me to the next question, which is, as a former founder and going into building Battlesnake and continuing to build Battlesnake and seeing where the future may go, what has inspired you that is dictating where you want the product to evolve to in the future?

Brad: We look to a couple of different places for inspiration. But the largest one above and beyond is the community. It’s listening to their ideas. Being active, being engaged, giving feedback, working on enablement rather than features. We also look to traditional sports. We look to things like basketball, or Formula 1 is another place that we look to regularly. Also, we look at other communities that are doing this sort of in a more niche sense. Kaggle is an example of very AI, very ML-centric, very monetarily driven developer communities. But I think they’re doing a lot of interesting things in terms of community growth. And we look at what they do and like, how do we do that bigger? How do we do that at a much broader scale at a wider spectrum of technologies? We also look at products like DeepRacer. They have a very Shoutcasty sort of live analysis, live show angle to it. And then we also looked at eSports very much so in like League of Legends and Overwatch League and MTGA (Magic: The Gathering Arena) and all this sort of stuff.

Maria: We have other early-stage founders that listen to this podcast for inspiration. So as a founder of an early-stage company, what do you wish you knew sooner or had implemented at an earlier date?

Brad: Oh, geez. We should have made our own Discord a lot sooner. We should have started streaming on Twitch a lot sooner. I talked about this before, but I cannot emphasize this enough. It’s not about live streaming. It’s not about content production. It’s about live interaction with your users — in our case, our players. Finding interesting ways to engage with them live and put a face behind it. Like it’s way different to have, you know, me write a blog post about where I think we’re headed versus see me answer questions live in real time on stream and be like, “Oh, that’s Brad” or “That’s the other member of the Battlesnake team. And this is what they think about this. And they’re a real person, and they’re incredibly accessible.” I say Twitch, but it doesn’t have to be Twitch. Obviously, go where your players are, or your users are. But I think live engagement is incredibly underutilized and undervalued by most early-stage companies, especially with developers.

Maria: Yeah. And I think that can be transferred in any consumer company, which is the importance of interacting with your users, like face to face, as early as possible and seeing where they are, learning from them, connecting with them. But also, this focus on, maybe it’s not creating a Discord channel right away, but it’s just testing different platforms that users may be engaging and getting involved, and creating a channel and seeing how people interact within it. And is it a place that you should buckle down and prioritize, or is there another space, like a Slack channel, better for your customer?

Brad: I think that’s a really good insight and a good clarification. It’s not about creating a Discord channel and being like, “All engagement will now happen on Discord. Please do so in real time with your real name attached, such that we can have an engaged community.” It’s about finding natural feedback channels, natural congregation points for your community, especially in consumer-facing, obviously. And also being open to the solutions being things you haven’t thought of. Again, using Discord as a very small or specific example of that. But like we had a Slack, and turns out more people were talking about Battlesnake on Discord behind our backs than we realized. And we could’ve just been like, “Please come to Slack.” But okay, how do we make this work? How do we not adopt this? How do we engage with it? We still don’t own the Discord server. Like it’s some community member that admins it and monitors it.

Maria: And so, speaking to that community engagement, as we wrap up, one of my favorite things about your email updates to investors is you always have these community highlights of someone talked about Battlesnake during their job interview with Google or someone got a job through participating in a Battlesnake tournament. Do you have any favorite community stories you want to highlight?

Brad: Oh yeah. Oh, so many. Okay. So, I think this happened earlier this year in the competitive spring play. We had — I’m not going to say their name because I wouldn’t want to draw unwanted attention to this particular player — but they had done incredibly well. It was like their first-ever competition, and they were just figuring things out, and they’d realized a unique strategy. And their partner baked them a Battlesnake-shaped cake. And the cake was their customized Battlesnake color and like their head and their tail. And there were like little cupcakes that were the food on the grid. And had this viewing party to celebrate the success that this one developer had in this competition. And again, that speaks to the accessibility of it and the fun behind it and that third sort of spectator persona. But when you see that kind of thing, like that just blows our mind. That is awesome. How do we do more of that? How do we encourage more of that? We want more of that to happen. That’s incredible. I could go on with community examples for hours.

Maria: That’s an incredible example of just how much your users love you to a point that they’re baking a cake around it, and they’re getting their friends and family involved, and it’s this significant part of their lives. And people are making friends through it, getting jobs as a result of Battlesnake, and developing new skills. There’re so many great outcomes. Brad, this has been so awesome. As we wrap up, where can people find Battlesnake, and how can they get involved in a tournament?

Brad: Everything you need to know is at Play.Battlesnake.com. You can also get involved on Twitter. You can get involved in GitHub. You can join our Discord channel, and multiple people will be happy to help you and answer any questions you might have. If you are a developer looking to get started, check it out. If you’re interested in just watching some Battlesnake games and seeing what this is about, our YouTube channel or our Twitch channel has a lot of great videos of what competitive Battlesnake looks like in the fun we have during more of the live tournaments.

Maria: Awesome. Thank you so much for being here today.

Brad: Yeah. Thank you for having me.

Coral: Thank you for listening to Founded & Funded. Like Brad said, if you’re interested in learning more, visit play.battlesnake.com. Thank you again for listening, and tune in for our next episode in a couple of weeks with Stack Co-founder Will Rush.