The Exchanges

Q word commoditized. I'm really interested when it comes to the data itself. Sorry, my mind just jumps around. It's Friday evening. It's dark. Just roll with me on this one, Jeff. You mentioned the challenge in terms of acquiring clean data earlier. How challenging do we think it is for companies today to acquire high quality clean data? Is it as proprietary a defense mechanism as some suggest it is?

A It is extremely challenging, and what's interesting is the counter reaction, because you're seeing Reddit, Twitter, et cetera, shut off APIs, put in more strict rate limits, et cetera, et cetera. And so the whole world is starting to lock down data, which was counter to the trends over the past 20 years, when everything was being pushed more and more open and API accessible and so on. And so I think there's been a clear realization That the data is valuable. And that's one of the things like we've been really focused on at Digits is we have a proprietary data set of a hundred million financial transactions. And that's what we can train on and make sure our finance and bookkeeping eyes know what they're doing. Um, so I do think the data is really, really important.

AI assessment note: “It is extremely challenging, and what's interesting is the counter reaction”

Q Okay, so when I met you, you were at Twitter, and it is a incredibly formative experience, I think, being at Twitter, especially in the role that you were. How did it shape your operating mindset and approach today, do you think?

A Yeah, the biggest lesson I learned was empathy, honestly. So when you're in consumer software, you can't possibly begin to understand how many different people, personas, use cases, mindsets, like the human experience all comes to bear on your product. And what I saw was actually a trap. So the product managers who were super data-driven started designing and building features for the average user, because that's what the data told them. And they actually believed that there was something such as an average Twitter user. It's such a huge mistake, right? Like you're conflating all of these different populations. You have sports fans who want a live, like chronological timeline during the game. You have celebrities who want to maximize their reach. You have Japanese users who by and large want to remain anonymous. None of them is average. And so what I really learned is you have to deeply understand each population and design and build a feature for them. Don't like let the data lie to you.

AI assessment note: “the biggest lesson I learned was empathy, honestly”

Q Do you like celebrating wins? I worry that it creates complacency. We've never won. I'm always chasing someone. We both are always paranoid. I hate this, like, tap on the back. Do you celebrate wins?

A We do. It's also, it's important to do it correctly. So I agree with your mentality. Crashlytics, I never thought was like successful in any one moment. Not when we were acquired, not when we hit a billion MAU, et cetera, et cetera. Like there's always the bigger goal. But if you have that mentality with the team, it's very demotivating, right? It's like, what are we, what are we trying to go to? Like, when are we going to get somewhere? And so it's really important to celebrate small wins. And so we use this Friday, uh, show and tell, we call it basically to show off what we did each week. And champion who did what and celebrate all the small wins of the week. So people feel really connected to the company and what's happening.

AI assessment note: “We do. It's also, it's important to do it correctly.”

Q to get better at it. All right. Um, I love the way I still use this show despite its size. I still use it as like this, like merciless testing ground for my own ideas. Um, tell me, obviously I know this story of being an investor, but you know, we have Crashlytics, then we have Twitter. How did Digits come to you? What was that founding moment for you?

A Yeah. Digits really came out of the Crashlytics journey. And so, you know, we got very lucky with market timing. We scaled from zero to three hundred million phones in 12 months. Got acquired by Twitter. Today Crashlytics is on five or six billion MAU, roughly every active smartphone on earth. It's incredible. And through that journey, I was struck by this dichotomy. So on the product side, you have real time analytics, performance monitoring, live dashboards, right? Like I knew exactly what was going on with the product and who was using it. And then on the finance side, I literally had a black and white PDF of my P and L and balance sheet once a month, Two to three weeks late that I didn't understand because I didn't have a background in finance. I was an engineer and so I was like, what is happening? And so literally that is why I started Digits. The simple premise, can we make accounting real time and intuitive for startup founders? And so what's crazy is it took us five years, but we finally just launched it. Like it's actually here five years later.

AI assessment note: “I was struck by this dichotomy... literally that is why I started Digits.”

Q I've got to admit, I've got a man crush on Peter. I do. I know. I, I, he knows it. I told him. My question to you is, what's been your biggest lesson from working with Peter?

A The power of really deep intuition and conviction and looking at a market from a very theoretical level. So one of the most interesting aspects when he originally agreed to do our A round, um, I sort of asked him why afterwards, like why he committed so quickly. And he said, well, it had flashbacks to Uber because when Uber was going against the taxi industry, the NPS scores on taxis was so bad, right? Like even if Uber was mediocre, it would still be way better. And he said accounting gave him the exact same vibes. Like the status quo is just so bad that if you can make accounting like somewhat enjoyable, it doesn't even need to be delightful. You've already won. And so his ability to distill these markets into these like very high level, crisp, understandable talking points is super impressive.

AI assessment note: “The power of really deep intuition and conviction and looking at a market”

Q But then actually, I guess, you know, the rise of the iPhone and the app store was actually really quite quick. When we think about the speed of transitions, Will this be a slow transition or a faster transition than we give credit to?

A It'll be faster for a couple reasons. So if you look back at mobile, so the iPhone came out in 2007, they opened up the app store in 2009. By 2011 to 20 12, a lot of people were using and building apps. And then enterprise adoption even lagged from there. So call it five to seven years. With AI, there's no new hardware to buy. So you don't need this huge purchase price, right? Locking out enterprises and people all around the world. You can just instantly benefit from it online and there's no new UX pattern to get familiar with, right? You don't need to be used to carrying something around in your pocket, looking at a little screen, just squinting at reading things like their chatbots. It's a very fluid interface. It does things for you. That makes sense. And so I actually think the adoption curve here will be radically faster and industries will be disrupted probably way quicker than prior tech waves just because of the barriers are so low.

AI assessment note: “It'll be faster for a couple reasons.”

Q to get better at it. All right. Um, I love the way I still use this show despite its size. I still use it as like this, like merciless testing ground for my own ideas. Um, tell me, obviously I know this story of being an investor, but you know, we have Crashlytics, then we have Twitter. How did Digits come to you? What was that founding moment for you?

A Yeah. Digits really came out of the Crashlytics journey. And so, you know, we got very lucky with market timing. We scaled from zero to three hundred million phones in 12 months. Got acquired by Twitter. Today Crashlytics is on five or six billion MAU, roughly every active smartphone on earth. It's incredible. And through that journey, I was struck by this dichotomy. So on the product side, you have real time analytics, performance monitoring, live dashboards, right? Like I knew exactly what was going on with the product and who was using it. And then on the finance side, I literally had a black and white PDF of my P and L and balance sheet once a month, Two to three weeks late that I didn't understand because I didn't have a background in finance. I was an engineer and so I was like, what is happening? And so literally that is why I started Digits. The simple premise, can we make accounting real time and intuitive for startup founders? And so what's crazy is it took us five years, but we finally just launched it. Like it's actually here five years later.

AI assessment note: “literally that is why I started Digits. The simple premise, can we make accounting real time”

Q I mean, that five-year journey is one with twists and turns, and the idea that initially was Digits on founding day one is different in terms of the product that we're releasing today. What did you learn that led to your realization of the need to pivot? Like, why pivot, and why was that enough?

A Yeah, it's definitely been quite a journey, and I know you've been on it with us for years now, so I deeply appreciate it. Obviously, trying to make accounting real-time is a lot easier said than done. Uh, when we started the company, we went heads down on R&D, And really struggled with data quality for like three years. And that was because in 2018, when we started the tech to really automate bookkeeping didn't fully exist. I think I was a little optimistic on how it could work. And so we have dozens of patents on it now, but it was like a brick wall. Um, so in 2021, we made the decision to pivot from like the pure bookkeeping automation to collaboration tools. So better financial reporting, better client portals, Better transaction review process. And, and that worked. We got a thousand accounting firms on the product, 5000 downstream businesses, like that was sort of off and running. But what bugged me is that wasn't really why we started the company. We had bigger ambitions. And so then last year, literally all of a sudden GPT three comes out, chat GPT comes out, GPT four, and we started experimenting and we're like, Whoa, hold on. We're back. Like we can actually do what we set out to do. And so literally overnight, just like we're back focused on this and spent this whole year building it.

AI assessment note: “we made the decision to pivot from like the pure bookkeeping automation to collaboration tools.”

Q I've got to admit, I've got a man crush on Peter. I do. I know. I, I, he knows it. I told him. My question to you is, what's been your biggest lesson from working with Peter?

A The power of really deep intuition and conviction and looking at a market from a very theoretical level. So one of the most interesting aspects when he originally agreed to do our A round, um, I sort of asked him why afterwards, like why he committed so quickly. And he said, well, it had flashbacks to Uber because when Uber was going against the taxi industry, the NPS scores on taxis was so bad, right? Like even if Uber was mediocre, it would still be way better. And he said accounting gave him the exact same vibes. Like the status quo is just so bad that if you can make accounting like somewhat enjoyable, it doesn't even need to be delightful. You've already won. And so his ability to distill these markets into these like very high level, crisp, understandable talking points is super impressive.

AI assessment note: “The power of really deep intuition and conviction and looking at a market”

Q But then actually, I guess, you know, the rise of the iPhone and the app store was actually really quite quick. When we think about the speed of transitions, Will this be a slow transition or a faster transition than we give credit to?

A It'll be faster for a couple reasons. So if you look back at mobile, so the iPhone came out in 2007, they opened up the app store in 2009. By 2011 to 20 12, a lot of people were using and building apps. And then enterprise adoption even lagged from there. So call it five to seven years. With AI, there's no new hardware to buy. So you don't need this huge purchase price, right? Locking out enterprises and people all around the world. You can just instantly benefit from it online and there's no new UX pattern to get familiar with, right? You don't need to be used to carrying something around in your pocket, looking at a little screen, just squinting at reading things like their chatbots. It's a very fluid interface. It does things for you. That makes sense. And so I actually think the adoption curve here will be radically faster and industries will be disrupted probably way quicker than prior tech waves just because of the barriers are so low.

AI assessment note: “It'll be faster for a couple reasons.”

Q Yeah, no, listen, I totally agree. I think taking out the office is the biggest headache in the world. There are always challenges there, especially when you're doing it wholesale and also for the first time. What were some of the biggest challenges or maybe teething problems in the early days? And I guess, how did you overcome them?

A Yeah, so from our experiences at Twitter, we had teams across half a dozen offices that I was running, and we were very convinced engineering would be fine. We knew how to run remote engineering teams. That was okay. The unknown, honestly, to us was the product side. We had a lot of doubts of how do you do product management remotely? How do you brainstorm? How do you whiteboard? How do you collaborate in a way that we felt would be productive? And so we struggled, I'd say quite a bit with this for the first six months of digits. And Wayne and I even had to finally come to an agreement where either I would fly down to visit him in LA, or he would come up to San Francisco And we'd do that every other week so that we could get enough product time together. And then crazily enough, we solved it. So we found out about this app called Pixelboard, and we both bought iPad Pros. And this was 18 months ago, and I don't think I've been to LA since. And it's really crazy. And so what it is, is it's a real-time whiteboarding app. You jump on an audio chat together, fire up the app, you can draw live, et cetera. But that part's obvious. I think the core realization that they had Is that most of the time you're collaborating around a whiteboard, you're not actually drawing, right? You're standing there, you're thinking, you're gesturing, and remotely, it's the gesturing that gets lost. And w…

AI assessment note: “The unknown, honestly, to us was the product side.”

Q I mean, wow, I love that idea of giving away phones for free and what that enables for Google. Jeff, I think that you're quite pessimistic about Google's future in terms of the next wave of AI. Jeff, Cybert, obviously from Digits, how do you think about the next few years for Google in this respect?

A I think Google's by far the most vulnerable because, again, their business model is pretty binary, right? Search is all their revenue, and so if that gets damaged, they're in a huge problem, and they've been slow to react. They combined two different ML AI teams. They've just punted Gemini into Q one, which tells me it's not doing very well. Uh, so I would be nervous. They need to go all in on it. I don't think they have a choice. I agree with you. I think it's existential for them because if AI replaces search, their golden goose has been killed. It is way more effective to kill your own golden goose than let and watch someone else do it. And again, I mean, going back to Apple, it reminds me of the iPod Nano. Apple killed their most popular product. Because they knew there was better tech coming. I think Google needs to get bold and do the same.

AI assessment note: “I think Google's by far the most vulnerable because, again, their business model is pretty binary”

Q You mentioned accountability within CEO ship and white, like no one really can, who could do it with the visibility they have. And then, you know, the, those that could won't because they don't have the visibility. So how do you create that accountability as a CEO?

A Yeah, it is. It's certainly not easy because you can constantly fall into a trap of thinking you're getting feedback and you're not. It's really how you set the culture of the company. So one of the things we do at Digits is we run the entire company on a weekly sprint. As part of that, every Friday, we end every week with a full team retro. And so, and we call it anchors and breezes. Anchors are what slowed you down, what didn't go well, what you need help with, like sort of feedback on the week. And then breezes are what went well, shout outs to people who helped you, things you learned, et cetera, et cetera. And you create this culture of just constant iterative improvement, which then allows sort of feedback conversations and one-on-ones and so on to be widely recognized by the company. It's like, that's what we want. The whole mindset is just how do we get one percent better each week?

AI assessment note: “It's really how you set the culture of the company.”

Q I totally agree with you. So when we think about like founders today building in this environment, who's vulnerable then? Like, and what I mean by that is like, is it incumbents like Zendesk? Is it like high growth companies? Like, I don't know your notions of the world, or is it your startups, or is it all of them?

A It's probably more startups. The one thing I would say is I view open AI probably evolving more into an infrastructure company like AWS. Like they will host these models that allow you to fine tune them. They'll allow you, they'll give you all these base capabilities, uh, like you get with EC two and S three and so on and so on. The big companies, the big incumbents that will be able to leverage that tech. I would doubt if opening eye goes and builds like an ocean competitor or an HR or Salesforce competitor or so on, they probably want to stay at the more generic level from the startup side. A lot of startups are getting killed. And it's funny. I use the term Sherlocked. I'm an old school Mac programmer. Back in 2002, Apple killed Watson with its Sherlock tool. And so the name sort of stuck. Um, there's a lot of companies getting Sherlocked because they're pretty incremental and they're filling gaps in OpenAI's current product without realizing that like, yes, they're just on the roadmap. They haven't gotten there yet. And so if you're working on a use case that's pretty horizontal, that like OpenAI is going to need to solve within five years in order to scale. That's not a great investment, and that's not a good use of your time as a founder.

AI assessment note: “It's probably more startups.”

Q word commoditized. I'm really interested when it comes to the data itself. Sorry, my mind just jumps around. It's Friday evening. It's dark. Just roll with me on this one, Jeff. You mentioned the challenge in terms of acquiring clean data earlier. How challenging do we think it is for companies today to acquire high quality clean data? Is it as proprietary a defense mechanism as some suggest it is?

A It is extremely challenging, and what's interesting is the counter reaction, because you're seeing Reddit, Twitter, et cetera, shut off APIs, put in more strict rate limits, et cetera, et cetera. And so the whole world is starting to lock down data, which was counter to the trends over the past 20 years, when everything was being pushed more and more open and API accessible and so on. And so I think there's been a clear realization That the data is valuable. And that's one of the things like we've been really focused on at Digits is we have a proprietary data set of a hundred million financial transactions. And that's what we can train on and make sure our finance and bookkeeping eyes know what they're doing. Um, so I do think the data is really, really important.

AI assessment note: “It is extremely challenging, and what's interesting is the counter reaction”

Q Okay, so when I met you, you were at Twitter, and it is a incredibly formative experience, I think, being at Twitter, especially in the role that you were. How did it shape your operating mindset and approach today, do you think?

A Yeah, the biggest lesson I learned was empathy, honestly. So when you're in consumer software, you can't possibly begin to understand how many different people, personas, use cases, mindsets, like the human experience all comes to bear on your product. And what I saw was actually a trap. So the product managers who were super data-driven started designing and building features for the average user, because that's what the data told them. And they actually believed that there was something such as an average Twitter user. It's such a huge mistake, right? Like you're conflating all of these different populations. You have sports fans who want a live, like chronological timeline during the game. You have celebrities who want to maximize their reach. You have Japanese users who by and large want to remain anonymous. None of them is average. And so what I really learned is you have to deeply understand each population and design and build a feature for them. Don't like let the data lie to you.

AI assessment note: “the biggest lesson I learned was empathy, honestly.”

Q You mentioned accountability within CEO ship and white, like no one really can, who could do it with the visibility they have. And then, you know, the, those that could won't because they don't have the visibility. So how do you create that accountability as a CEO?

A Yeah, it is. It's certainly not easy because you can constantly fall into a trap of thinking you're getting feedback and you're not. It's really how you set the culture of the company. So one of the things we do at Digits is we run the entire company on a weekly sprint. As part of that, every Friday, we end every week with a full team retro. And so, and we call it anchors and breezes. Anchors are what slowed you down, what didn't go well, what you need help with, like sort of feedback on the week. And then breezes are what went well, shout outs to people who helped you, things you learned, et cetera, et cetera. And you create this culture of just constant iterative improvement, which then allows sort of feedback conversations and one-on-ones and so on to be widely recognized by the company. It's like, that's what we want. The whole mindset is just how do we get one percent better each week?

AI assessment note: “we end every week with a full team retro. And so, and we call it anchors and breezes.”

Q Do you like celebrating wins? I worry that it creates complacency. We've never won. I'm always chasing someone. We both are always paranoid. I hate this, like, tap on the back. Do you celebrate wins?

A We do. It's also, it's important to do it correctly. So I agree with your mentality. Crashlytics, I never thought was like successful in any one moment. Not when we were acquired, not when we hit a billion MAU, et cetera, et cetera. Like there's always the bigger goal. But if you have that mentality with the team, it's very demotivating, right? It's like, what are we, what are we trying to go to? Like, when are we going to get somewhere? And so it's really important to celebrate small wins. And so we use this Friday, uh, show and tell, we call it basically to show off what we did each week. And champion who did what and celebrate all the small wins of the week. So people feel really connected to the company and what's happening.

AI assessment note: “We do. It's also, it's important to do it correctly.”

Q I totally agree with you. So when we think about like founders today building in this environment, who's vulnerable then? Like, and what I mean by that is like, is it incumbents like Zendesk? Is it like high growth companies? Like, I don't know your notions of the world, or is it your startups, or is it all of them?

A It's probably more startups. The one thing I would say is I view open AI probably evolving more into an infrastructure company like AWS. Like they will host these models that allow you to fine tune them. They'll allow you, they'll give you all these base capabilities, uh, like you get with EC two and S three and so on and so on. The big companies, the big incumbents that will be able to leverage that tech. I would doubt if opening eye goes and builds like an ocean competitor or an HR or Salesforce competitor or so on, they probably want to stay at the more generic level from the startup side. A lot of startups are getting killed. And it's funny. I use the term Sherlocked. I'm an old school Mac programmer. Back in 2002, Apple killed Watson with its Sherlock tool. And so the name sort of stuck. Um, there's a lot of companies getting Sherlocked because they're pretty incremental and they're filling gaps in OpenAI's current product without realizing that like, yes, they're just on the roadmap. They haven't gotten there yet. And so if you're working on a use case that's pretty horizontal, that like OpenAI is going to need to solve within five years in order to scale. That's not a great investment, and that's not a good use of your time as a founder.

AI assessment note: “It's probably more startups.”

Q now? Because I speak to some of the largest enterprises in the world, and they're like, dude, I'm not sending my customer data, my transaction data, and I'm not picking on OpenAI here, but to a model that is outside of our bounds. How do we think about enterprise control of very sensitive data in this world where they want to get the benefits, but don't want to lose control?

A Yeah, two different thoughts here. This is a really good question. So they had the exact same reaction to cloud. If you go back 10 years, they were like, I would never put stuff on AWS or Google Cloud or Azure or whatever. That's ridiculous. Why would I share my data with those companies? Right? And now it's just like, not only do they all do it, but it's actually better because those companies core competency is running data centers. Most enterprises have no idea how to operate a data center. And so I think this will go in the same direction. Um, and like you'll, you'll have very clear guidelines around how the companies use the data for model training and it's off limits and so on. And sort of that trust will be overcome. The other angle though, is there's also the danger of every enterprise jumping on AI because it's hot and cool and sort of like they all jumped on blockchain for zero reason, even though it did nothing. Right. And like IBM's at fault. IBM was consulting, like charging for services to consult on how to adopt blockchain into your enterprise. That's ridiculous. And so, like, again to me, focus on your customer, your market, your product need, and view this as a tool, not a panacea, and adopt it strategically on, like, what makes sense and where it's going to push the product forward.

AI assessment note: “you'll have very clear guidelines around how the companies use the data”

Q Now, can you get the ball rolling today by telling us a little backstory about you and how you made your way into the world of tech?

A Yes. Uh, so it starts actually quite a while ago. I grew up in Baltimore, Maryland, which is not a bastion of computer technology. It's, it's very much Dominated by Hopkins. Everyone was a doctor growing up. It was like, oh, you should go be a doctor. And that just wasn't something that interested me. I was very into sort of science and nature, uh, more so than technology growing up. And in sixth grade, my parents, uh, sort of out of the blue gave me the book, Mac programming for dummies. And I was never really into computers. I read the book, didn't fully understand it. In fact, I think it took me about six months to realize that I had to copy the files off of the CD. In order to start modifying them. Um, but when I, I finally figured that out and, uh, it was, it was just incredible from there. So I, of course, followed the hello world example in the first chapter, but because this was Mac programming and because this was, uh, a 1997, roughly, I think this was the age of Mac OS eight and it's entirely graphical. There's no command line interface. So hello world was an actual double clickable app that opened the window, had an about box, had a menu bar. It looked like a real Mac app, and I changed the text to print Hello World in orange, and literally, I remember it vividly to this day. The moment that text turned orange, a light bulb went off in my head, and I thought to mysel…

AI assessment note: “a light bulb went off in my head, and I thought to myself, oh, wow”

Q And then talk to me about in Creo and the origin story behind in Creo. How did that whole journey come about for you?

A In Creo, yes. Finished up high school in Maryland, went out to Stanford for college. I, by that point, was very into computer science and software. And so coming out of Stanford, uh, basically put a group of friends together to think about startups, um, not with the explicit goal of starting a company, but But with the goal of, Hey, let's train ourselves, uh, to talk through business ideas, evaluate them and just see like what we would want to do, what is interesting. And so we met up, uh, twice a week for two hours, debated all sorts of ideas. And every time we'd met, we'd come up with this thing that we're like, oh man, this is fantastic. This is going to kill Google. Uh, cause that was the stated goal. And, um, and we went back and so we'd be so excited. We'd go back to our dorm room, start doing a little more research and, Quickly realized that whatever we had come up with was perhaps the stupidest idea that had ever been articulated. Um, either it had already been done, or it was actually impossible, or we didn't have the skill set to do it, whatever it might be. And so we did this for a couple months and, uh, got frustrated with sort of the, you know, the, the perfect idea, the game changing idea. And I think that was the first big lesson I learned is not to sort of hold out for this perfect concept that's going to blow the world away. Um, very, very few businesses start …

AI assessment note: “Why don't we build something to help people share and iterate and improve on their ideas?”

Q wildly successful and experienced some incredible growth under yours and Wayne's tutelage. But one of the problems that you did say that you encountered within the acquisition itself with Twitter was a breakdown of the reporting structure. So how can founders in a negotiation and in acquisition talks, how can they ensure that they're speaking to the VP of engineering, the CTO, the people who are really making the decisions?

A Yeah, I think this is also a super important aspect, and we thought we were set up for success here because we did come in and we're reporting directly to the VP of engineering. What we didn't anticipate as a startup was that large companies also go through periodic reorgs. Then, of course, during that, there's different priorities for the company. They maybe put you off in some other location because you're not their focus at the time. And so what I would have done in retrospect is sought some commitment Uh, to continue to report to that person for X period of time. I don't think it's fair or actually good to say, Hey, I want to indefinitely report to this person. You want to move around as the company changes structure. But for us, it was very short term. It was sort of a month in that Twitter happened to do a reorg and we just got unlucky. And so I would have sought some sort of maybe six or nine month commitment to stay at that level so that we could really Integrate into the company, understand what was going on, and then identify the best place to report into.

AI assessment note: “sought some sort of maybe six or nine month commitment to stay at that level”

Q And I have to ask, with a fantastic team, a fantastic backstory, fantastic metrics, and funding to accompany you along this journey that you didn't maybe have within Creo, why did you decide to sell to Twitter at that point then? Was it the platform?

A It was, yes. So our goal, when we started Crashlytics, was to solve bugs in mobile, because apps crash over a billion times a day, and it's endlessly frustrating for people around the world. And so we were growing incredibly quickly. We were on hundreds of millions of devices at that point. We had tens of thousands of customers. Uh, everything was fantastic. What Twitter offered the opportunity to do is to leverage their name, which we thought was critically important and would make us sort of the de facto standard in the space and the ability to offer the product for free. And so rather than go the startup route and Build out a sales team and build out a freemium product in an enterprise tier and have to end up doing enterprise sales ultimately in order to make the big accounts happen. Um, we believed that, Hey, this was the opportunity to make Crashlytics absolutely free for everyone. And there would be zero reason for anyone not to use it. And so going back to our mission, our mission was to universally make bugs go away. And the best way to do that is to be in every app and to have total distribution. The best way to do that is to be free. And so as we looked at sort of our goals as a company, this was actually the perfect opportunity. Um, and it's, it's worked out. So today Crashlytics is installed on well, well over a billion devices. We're on substantially every active m…

AI assessment note: “What Twitter offered the opportunity to do is to leverage their name”

Q into three chapters. The acquisition by Box, the acquisition by Twitter, and now life at Twitter. So let's start with the acquisition of InCrayo. I know we've jumped a few steps and it's been the shortest journey ever in startup land. So leading up to the acquisition by Box, what was it like going to Sandhill Road to raise in 2009 with the obvious financial turmoil only the year before?

A Yeah, it was fascinating. So of course, um, Sequoia had put out their RIP good times presentation, and that definitely put a chill over the valley. And in the course of the spring of 2009, so we had to back up for a second, we had raised our seed round in early 2008. And so we had raised 500 K. Uh, we believe that would last our small team about 18 months. Um, so it was roughly the right amount for us to raise. And now as 2009 started, We had about six months to raise funding. And so, and that we thought, okay, this is great. We thought it'd be more than enough time. Um, of course, we weren't anticipating the macroeconomic conditions, and so over the course of the winter and spring of 2009, it became increasingly difficult to raise money, and many, many of the investors were pulling back from making any investments at all, and so we were going up and down Sandhill. I think we met with something like 34 or 36 firms over the course of three months. It was just incredible. Um, in retrospect, that's too many. I would have stopped sooner, uh, knowing what I know now. But just talking with these firms, they were all taking meetings. They just weren't actually investing. And so we would go in, do our pitch, get some good feedback. And then there would just never be any follow up. Um, very, very few of them ever told us, no, they just sort of stopped responding. And that got pretty fru…

AI assessment note: “we met with something like 34 or 36 firms over the course of three months.”

Q And you said there that you were potentially overly transparent with your team about the acquisition, but in today's tech age, we're led to believe that transparency is always a great thing with the likes of Ben Horowitz, who is proclaiming it. So what do you think the benefits are of being and then not being transparent? What's your take on this?

A Yeah, transparency is a fascinating thing, and this is something I've spent quite a while thinking about, because many people believe, oh, the healthiest company culture is full transparency, and you should strive for as much as that as possible. I tend to think of this as, as one of the primary jobs as the CEO. We all know that doing a startup is this intense roller coaster, and you have these crazy highs, you have these crazy lows, and the ideal is to actually moderate that somewhat. Like, at the peak of your highs, You and your team are probably not as brilliant as everyone thinks you are. And of course at the peak of your lows, you're, you're definitely not as stupid as everyone thinks you are. By putting the entire team through that cycle, it's a real stress. It's a real strain on productivity because it's your job as the founder to absorb that and set your team up for success and allow them to focus on what's important. And it's the team's job to, of course, build the business. And so as we were going through this We were very, very transparent with the team, um, not only about the runway, which I think you should be transparent about, but exactly what the different options were and how they were going. And so we let the team go through every up and down that we as the founders did. And so, oh, this VC meeting went well. Oh, they haven't gotten back to us. Oh, this partne…

AI assessment note: “it's your job as the founder to absorb that and set your team up”

Q Who actually suggested a question for you for today's interview. Jeff wanted to know, how did you select Wayne as your co-founder? He also wanted to let you know that you're a superstar.

A Excellent. Yes. Um, so I met Wayne at a Boston startup event, and one of the strengths of Boston is, as we all know, it's a smaller ecosystem than Silicon Valley, but it's very, very tight knit and deeply supportive. And so there are tons of startup events. I mean, you can go to more than one a night if you want to. Um, but there's a few big ones, uh, year round that lots of people go to. And so we were at this startup dinner that a friend of mine had invited me to, uh, Wayne happened to be there. We happened to be seated at the same table and we just started talking about side projects. And this was right after I had come up with the idea for crash. I started coding. It started, uh, getting a prototype working. And Wayne was telling me about his side project. I was telling him about Crashlytics, and we agreed to meet up, uh, in a few weeks and just grab a coffee and chat further. And what really struck me was, as I was talking about Crashlytics, Wayne had a really deep understanding of technology, despite being more on the business side. He had done a bunch of tech stuff in the past, um, and an intuition for what mobile developers might want. And so, as we were talking about it, he was like, hey, this is amazing. This is not a side project. This is a startup. And I want to be involved. And I was, I was skeptical. I was taken aback. I was like, okay, I mean, this is my side pro…

AI assessment note: “Wayne had a really deep understanding of technology, despite being more on the business side”

Q I mean, wow, I love that idea of giving away phones for free and what that enables for Google. Jeff, I think that you're quite pessimistic about Google's future in terms of the next wave of AI. Jeff, Cybert, obviously from Digits, how do you think about the next few years for Google in this respect?

A I think Google's by far the most vulnerable because, again, their business model is pretty binary, right? Search is all their revenue, and so if that gets damaged, they're in a huge problem, and they've been slow to react. They combined two different ML AI teams. They've just punted Gemini into Q one, which tells me it's not doing very well. Uh, so I would be nervous. They need to go all in on it. I don't think they have a choice. I agree with you. I think it's existential for them because if AI replaces search, their golden goose has been killed. It is way more effective to kill your own golden goose than let and watch someone else do it. And again, I mean, going back to Apple, it reminds me of the iPod Nano. Apple killed their most popular product. Because they knew there was better tech coming. I think Google needs to get bold and do the same.

AI assessment note: “I think Google's by far the most vulnerable because, again, their business model”

Q So when you advise founders on the right way to pivot, what would you advise them knowing all that you do now?

A Yeah. So the key is getting your team on board. If your team loses trust in you, you literally have no one to pivot. So it doesn't matter. And they can really sense the uncertainty. And so my, my other advice, as I've said, is like be very intentional and very decisive. And so both times we've pivoted digits. I gathered our core leadership team laid out like, what are the challenges? What am I seeing? What are the options? And we knew within 24 hours what the new path was and what the priorities were. We were all in on that new direction. And so I, it comes back to conviction. Like it is still a bet, but it's like, Hey, here is the information we have on the field. We need to make a decision right now because what kills companies is uncertainty. And if you sort of muddle your priorities and have one team, try this and another team, try this, no one's heart is in it. And both are going to be mediocre. I would rather see founders take like one, To the moon bet on one new direction and it either works or doesn't.

AI assessment note: “key is getting your team on board. If your team loses trust in you”

Q you know, when we think about non-obvious things, we mentioned that two commonly said tropes, obviously speed, and then solve a problem that you know deeply. I, I think there's a lot of things that aren't well known about entrepreneurship. Given the fact that you've done Crashlytics, you've now been in Twitter, you've now founded Digis. What do you think is the most like misunderstood Or non-obvious element of entrepreneurship?

A This sounds silly, but honestly, pure execution. And what I mean by that is people know the vast majority of managers are terrible managers, right? The vast majority of founders are simply bad at running companies. And I'm sorry, but it's true. And so what I mean by that is like most founders aren't intentional about how they go through and operate the business, intentional with their time, with their decisions, with who they hire, with what they say no to. And so if you don't have conviction, you're really going to struggle as a founder because you need this like deep seated obsession of what's right and what's wrong and what you believe in and how that informs every decision you make and your decisions may still be right or wrong. They're not going to be perfect, but if you were intentional about them, at least you can trace that back and learn from it versus I see too many founders just sort of going on a random walk. And then when it turns out they were wrong, what do they have to learn? There's not, there's nothing to trace it back to.

AI assessment note: “honestly, pure execution. And what I mean by that is”