EP 65: Aaron Levie's (CEO/Cofounder, Box) AI Takes and Advice for First Time Founders

Aaron Levie is the CEO and Co-founder of Box - a now profitable public company he founded in 2005 after dropping out of USC. In his second appearance on The Logan Bartlett Show, Aaron shares what he's most excited about in the AI world including real applications that Box is building today. He also shares his takes on who will win the AI battle between incumbents and startups, AI doom and where there are opportunities for new founders to build. As always, Aaron gives great operating advice for first-time founders and recounts some of the things that Box did well to get to where they are today. (0:00) Intro (0:54) Welcome back, Aaron Levie (4:28) Lightning bolt moment with AI (10:00) OG Customers (12:50) Describe Box's AI usage (24:13) Why have the big tech companies been able to execute as well as they have? (28:00) How would you start a company as a young enterprising founder today? (33:04) The social contracts of AI (37:21) Aaron's doom perspective (45:06) Operating profit (48:29) One lesson of operational excellence (52:59) Wrong startup advice (56:38) Reading recommendations Mixed and edited: Justin Hrabovsky Produced: Rashad Assir Executive Producer: Josh Machiz Music: Griff Lawson 🎙 Listen to the show Apple Podcasts: https://podcasts.apple.com/us/podcast/three-cartoon-avatars/id1606770839 Spotify: https://open.spotify.com/show/5WqBqDb4br3LlyVrdqOYYb?si=3076e6c1b5c94d63&nd=1 Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9zb0hJZkhWbg 🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1 Follow on Socials 📸 Instagram - https://www.instagram.com/theloganbartlettshow 🐦 Twitter - https://twitter.com/loganbartshow 🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow About the Show Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode, Logan goes behind the scenes with world-class entrepreneurs and investors. If you're interested in the real inside baseball of tech, entrepreneurship, and start-up investing, tune in every Friday for new episodes. Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA 🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1 Follow on Socials 📸 Instagram - https://www.instagram.com/theloganbartlettshow 📱 X - https://twitter.com/loganbartshow 🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow About the Show Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode of The Logan Bartlett Show, we sit down with the people behind today’s most important startups and extract the tactics, lessons, and frameworks they’ve learned the hard way. Conversations span hiring to GTM, product, growth, fundraising and everything in between - collectively forming the ultimate playbook to make you a better CEO, investor or board member. Tap follow and enable notifications to stay ahead of the game.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Logan Bartlett?

  Welcome to the Logan Bartlett Show. I am your host, Logan Bartlett, and on this episode, you're going to hear a conversation that I had with Aaron Levie. Aaron is the founder and CEO of Box, a $5 billion public company. Aaron and I go in a bunch of different directions in this conversation, including all things AI, talking about what Box is doing with AI, the opportunity that Aaron sees for net new companies to get started around this trend, as well as the opportunity that exists for existing businesses. We also talk about some of Aaron's operating lessons that he has learned over the years. Box has gone from a very unprofitable business that was growing at all costs to a very profitable one in the public markets. And so Aaron and I talk about the lessons learned along the way from here.

• 0:50Logan Bartlett?

  Trust you'll enjoy that conversation with Aaron and I here now. Thanks for doing this. I haven't seen you in a, the last time I saw you was at a party in New York and Weinberg, I think a few drinks in, referred to you as the big Jew himself when you walked in. So I, that was the last time I actually saw you, but we're now at a year, I think, since you've done this. Now you're the, you're the second, second person that I've had on.

• 1:13Speaker 01

  Oh, nice.

• 1:13Logan Bartlett?

  Yeah. Keith Roy beat you to be the first. I guess Weinberg was a recurring character, but.

• 1:18Speaker 01

  Weinberg, are you doing anything with him anymore or what?

• 1:21Logan Bartlett?

  Yeah, he comes on every so often. He canceled on me last week. I don't know why. He claims he wants to like rail on the market and explain how down rounds are going to be bad and how founders need to, I don't know, buckle up or whatever. And he'll tweet about it and then he'll say he's too busy running his company to actually hop on and talk about it. So I don't know. I don't know how that works out. Well, I went back and listened to our last episode, which was like a year ago. We talked about crypto falling off. We talked about how the market needed the correction and we talked about how technology industry needed a, a new frontier. So I, I don't know. I feel like, I feel like all of those things have sort of played off. Crypto has continued to fall off. The market actually did correct and we have a new frontier here, right? Artificial intelligence.

• 2:05Speaker 01

  Those three things kind of played out. I did not, I mean, obviously couldn't have known that the AI piece was going to happen as, with the level of force. That it did, but yeah, pretty incredible what what happens in six months in this industry right now.

• 2:19Logan Bartlett?

  I went through your tweets last night and it seems like you're you're excited about AI to say, say the least. And I know Box is, Box is innovating here now as well. What are you most excited about or what what was kind of the aha moment for you here?

• 2:34Speaker 01

  Yeah, my wife, my wife literally tells me to stop tweeting as much on the topic. So, um, so I have, I think I've expressed my feelings, but, um, well, the, the reason why, the reason why I am like, like, you know, peak level of excitement is these latest, you know, kind of wave of large language models solved for us specifically at Box is something that we could really have only dreamed of, um, you know, even a couple of years ago. Um, you know, what, what is inside of every single document in our platform is lots of language. And so for the first time ever, you can actually use AI to reason through that information in a way that was literally never possible before.

• 3:15Speaker 01

  Um, you know, so I think if you look at like a continuum of software, um, based on, based on kind of like how big of a deal are L L Ms for software, we would be like, we would be in the upper, you know, we'd be like the 97th percentile of impact to the potential of what we do.

• 3:34Logan Bartlett?

  Box specifically, not just software at large.

• 3:37Speaker 01

  Box specifically because what we deal with are documents and what L L Ms can help you reason through is lots of language. Um, it can look at a contract and, and tell you literally anything you want to know about that contract. You know, what are the risky clauses? What are the terms of the contract? What might be missing in the contract? It could look at a marketing document and give you suggestions of how to improve it. It could take an HR handbook and let you ask HR, you know, policy questions and instantly get an answer. So kind of like every use case you could dream of, like an instant expert, um, based on the data that you already have, uh, the ability to improve your content, the ability to answer any kind of question that you would otherwise, you know, maybe take hours or, or days to figure out the answer to, all of these problems can be solved by what's inside of the content that you have and the combination of AI models.

• 4:20Speaker 01

  And so, um, so for us, this is just a major breakthrough and that's why I'm like, like, you know, Quite literally obsessed, you know, diagnosably obsessed with the topic right now.

• 4:28Logan Bartlett?

  Was it like a lightning bolt hitting you that you typed something in? You were like, holy shit, this is going to change our business in a meaningful way. What was that moment that sort of sank in?

• 4:37Speaker 01

  It's a little embarrassing because, because I should have had that moment in like the OpenAI playground with like GPT two and GPT three. Like that's, that's when it should have initially occurred, but it just didn't at the time. It wasn't as obvious to me, you know, how kind of like this next token or next word prediction was, was going to be, you know, so powerful for our particular set of use cases like it, like, you know, the, the things that we theorized about was like, you could give a document to some GPT model and you could say, you know, please classify this document. And it would read the document and tell you, okay, it's a movie script or it's a contract. And like, that was really cool, but it didn't open up, you know, an incredible, you know, sort of wealth of opportunity.

• 5:21Speaker 01

  And what chat GPT did was it sort of just combined the right interface with an improved model with 3.5. And, and it sort of let you explore the, the sort of, you know, the, the almost limitless boundaries of now what we can do with these large language models. And so basically in early December, like a week or two after, you know, starting to play with, after the release of chat GPT, after a couple of days that I'd been playing with it, we kind of got the team together and we said, like, you know, how big of a moment is this for our business? And, and we kind of concluded it's going to be massive. So throughout December, we sort of pivoted the roadmap and, and put a team on the problem and, and started building from there.

• 6:01Logan Bartlett?

  How do you go about actually, when you recognize this moment is going to be transformative? And you have people that are used to operating at some course and speed and running kind of linearly at this problem. That's a big one, you know, documents in the cloud and all that stuff. But how do you actually shift people's mindset to like, hey, we're going to go back to? You know, reinvestment mode and thinking about how we can do everything different from a first principle standpoint. And like, how do you actually do that?

• 6:26Speaker 01

  Yeah, well, these days it's a little bit less dramatic because you're like, okay, like new Zoom meeting. And, and, and, you know, everybody gets on, on a video call. Like it used to be like, let's get in a war room. And like, you know, turn off the shades and everybody's stuck in here with pizza for, for the next month. And now it's like, okay, like who's got the, who's got the dial in? And, um, and, you know, we, we kind of look through it and, and, um, I, I think, I think there's a little bit of, of, you have to do a little bit of, of kind of like, um, you know, sort of thinking through the extrapolating, you know, on the decisions which are like, okay, um, what's the right model to bet on? Do you have to move like, is there a first mover advantage or, or can we wait and see what the rest of the market starts to do?

• 7:11Speaker 01

  You know, what, where is the level of value creation? Is it like, how deep in the model is it versus is it an abstraction layer? So you kind of have to like work through all of that. And then you sort of decide like, how important of a moment is this? And, and then you decide what's the level of, you know, people on it. And we have to go and like adjust our, our, uh, you know, roadmap appropriately. And, and, you know, so it's kind of like a lot of boring stuff like that, but, but just all, you know, kind of normal execution. With again, some kind of like strategic decisions built into it.

• 7:40Logan Bartlett?

  How'd you go about actually making those, those decisions? I assume you had your lieutenants together or whoever your executives are. And you're just sort of reasoning through all this stuff. Hey, should we bet on OpenAI? And how do we, how much, uh, failure mode are we going to be willing to tolerate in the product? Right. Or how much hallucinations are we going to be able to have in our users? Like, is it just sort of talking that stuff through out loud over a bunch of sessions?

• 8:06Speaker 01

  Yeah. And then, and then as quickly as possible, just literally seeing it. A lot of, a lot of stuff in AI is like, you're, you're wasting your time if you're, if you're talking about it versus just playing with it. And, and so within, within probably days of, of our initial sort of set of meetings that said, hey, this is a really big deal. We, we had prototypes that, that we had built out and. Of interacting with with content. And so very quickly, we we realized, you know, what's the surface area of this? What kind of things do we have to get good at? For instance, your point, you know, one of the big areas is, okay, we have to reduce hallucinations. So how do you do that? What kind of prompt do you write? How do we? How do we kind of make sure that it's not trying to make up information? You know, another big, another big scenario is like, how how much do you bet just on OpenAI?

• 8:51Speaker 01

  OpenAI versus do you build an abstraction layer that lets you plug in different models that are going to be good at different, you know, use cases? So we spent a lot of time on that.

• 8:58Logan Bartlett?

  Where'd you decide there, by the way?

• 9:00Speaker 01

  Well, we, by the virtue of our platform, we we've always been a kind of a platform neutral type of technology provider. Like, we work with Microsoft and Google and Apple and Amazon. So so it's already in our DNA to make sure that we're as neutral as possible. That that's sort of what allows us to be differentiated. And so while we're very close to OpenAI, because they've obviously exceeded, you know, most other benchmarks in terms of of just the how the models have performed. And that's our initial launch partner. We do want to make sure that we have components that that come from various providers. So so a lot of our time has been building out this abstraction layer that sort of is the in between, you know, your data and your content.

• 9:40Speaker 01

  And these AI models and what we're trying to solve for customers is otherwise you have to go and work with with you'd have to find a way to get your data in a good in a good, you know, kind of taxonomy and organization structure. You have to solve the the data permissions problem. Then you have to plug it into multiple models and you have to do so securely. So so we handle kind of all that, you know, at, you know, with the software that we're building out.

• 10:00Logan Bartlett?

  Do you have a handful of customers that like are OGs from 2006, 2007? That have sort of been along the journey and you're texting them about this? Or are you guys at the scale now that you're just kind of, you you generally are intuitive enough to sort of know what the people are going to want? And so you're you're just sort of, obviously, you need to bring it to market and get feedback on it. But initially, were you sanity checking it with people?

• 10:28Speaker 01

  Yeah, yeah, yeah. A lot of sanity checking. The only reason I laughed is like literally I was text messaging customers. And I mean, I think I probably like if I were a customer of Box and interacting with me like Aaron, I would be probably scared because it's like, who is this erratic? You know, founder that is like texting me late at night about AI like, like you'd kind of think it's a crazy person. So, but, but I just got, I got way too excited about the technology. And so we had to show it to some, some of those kind of close OG customers. We got really great feedback. We had use cases that we never would have thought of.

• 11:06Speaker 01

  You know, the way that we kind of think about it is, you know, the mental model that we have internally is, is any kind of today discreet task in the future, maybe multi, multi, you know, kind of task sequences, but any kind of discreet task that you would give out to another person who maybe was an engineer or maybe was an MBA or maybe was a marketing expert or maybe was an HR person. Any discreet tax task you would give a person that dealt with content. We, we believe AI will get to a point where we can have the AI do that task for us. And so the use cases that started to emerge as we talked to customers were, what kinds of things do you have other people do with content?

• 11:48Speaker 01

  And, and that, and once you start to kind of ask that question of an enterprise, you get, you know, hundreds or thousands of use cases. There are some organizations where they have a document come in, it's a 200 page document. They need that document to be reviewed, summarized, and they have to go through a particular checklist on that document. And they can only get to a certain amount per day. And so there's always this massive backlog. People never actually are able to kind of keep track of what was reviewed. It's too much data. And so now AI can basically do that in, in, you know, maybe 20 to 30 seconds. They could do, you can have AI do what, what normally would take maybe four or five hours for a human and you would thus never be able to really afford to have kind of humans do that across all of the documents that are coming in.

• 12:29Speaker 01

  And so we found all these use cases where actually people are doing these highly, you know, kind of just laborious. Often, you know, kind of just not, not really exciting tasks on content that now we can farm out to AI. And that is the engine that we're building. And but it really kind of came from talking to customers that we got all these use cases.

• 12:50Logan Bartlett?

  I won't make you live demo like other podcast hosts, but maybe describe.

• 12:56Speaker 01

  I actually, I actually enjoy it. So you don't want to, you don't want to threaten me.

• 12:59Logan Bartlett?

  Well, I mean, I don't know enough people watch this on YouTube, but I think we can describe what it is. I saw one of the things that that I saw you you demo was basically box is a content repository. And so you can some like you're going to have a document sitting there and you can summarize that document, right? It could be, hey, can you give me the five most important bullets out of this document? Right? Are there other things that sort of will will help people internalize all the different things that you guys are thinking about?

• 13:29Speaker 01

  Yeah, yeah. So so the summarization was actually like that was our initial go to. And we're like, oh, we could summarize anything. And then very quickly, you're like, OK, that's actually like very rudimentary relative to what what the technology is capable of. So, um, so, uh, you know, you, uh, you take a contract and you say, uh, pull out. What are the riskiest clauses in the contract that I really need to to make sure to be aware of? And it'll, it'll go and read through the contract and say, here are all the things that you should, you know, basically pay attention to. You can take an invoice and have it automatically read the invoice, extract all of the relevant data from that invoice, and then eventually build an automated workflow based on the data that's inside that invoice. So again, used to take a kind of human to go read through the invoice and type in the information themselves.

• 14:12Speaker 01

  You know, I bring up the kind of HR use case, but if you think about the amount of times and the amount of time that that workers spend just trying to find facts from large data sets. But the problem is they don't know what data to go ask, and they don't know where in that data the answer is going to be. That's the kind of stuff that AI can now solve across large amounts of unstructured data. So you take a 50 page, you know, HR document, and you just want to know like, what's the, what's our like, you know, jury duty policy? Like if I have to go on jury duty, like, how long does that take? Like, how long do I have? And literally like that's a that's a two second answer with AI. That might be an hour or two of finding the right information, finding the right document,

• 14:56Speaker 01

  pinging somebody to figure out what the answer, all of that, all those kinds of problems get solved, you know, instantly. And then you have the the content generation side. So, um, you know, you're looking at a a product specification document and you say, you know, please write a sales pitch to a customer in a certain industry that I can that I can go pitch this product to them and it'll read the whole document, synthesize it and then create a sales pitch based on that, the underlying kind of, you know, content that you're working with. And so anything where you want to either ask a question or generate new information off of existing information, these are the kind of use cases that we can solve.

• 15:32Logan Bartlett?

  As a public company CEO, like you have this lightning bolt aha moment and you're you're certainly not pivoting, but you're you're investing and this is going to be an important thing customers need to talk through and think about. And I assume it's important to the street as well. Like, do you wait for your next earnings call that you flag this? Do you issue a press release as kind of a warning shot? Like, how does that actually work?

• 15:54Speaker 01

  So we just lucked out. We actually had a financial analyst day in March. And so that was sort of right the window where where you couldn't really be in public with investors and not talking about AI if you hadn't, if you had an AI strategy. So we were able to kind of expose some of our our philosophy and and kind of, you know, some of the inner workings of what you're working on. So that was just well timed, totally, totally fortuitous. But, um, but, you know, I think, I mean, if you look, if you just actually, you know, we've used AI to analyze this. Um, uh, if you just look at earnings calls uh, over the past quarter, if you're in tech, you know, it is the number one topic of every single financial analyst call of of what are you going to do about it?

• 16:35Speaker 01

  And what's interesting is that, um, I actually think rapidly Wall Street has has sort of already figured out that that you can't really necessarily um, differentiate winners right now in AI in terms of who is going to uh, gain the most. Um, what you can do is is you can, you can start to assess risk um, of people that that Might be in a more troubled spot. It's hard to sort of set us have a sense for who are the big winners other than like Nvidia is going to win no matter what. Like, like the dollars are going to flow there. But I think what's happening is largely it's being priced into almost all software. That that AI is going to be a component in every single piece of enterprise software. And so it's sort of hard to then parse.

• 17:21Speaker 01

  You know, where is this a meaningful driver of growth versus it's just table stakes for competition in the future? That everybody's going to have to have, you know, 10 years ago. While maybe 15 years ago on earnings calls, you probably talked about your mobile strategy. But pretty quickly, everybody had a mobile strategy. And mobile was not was not really like a major, you know, sort of divider of of winners and losers. Unless they're just literally, for some reason, you didn't adapt to mobile, but everybody kind of figured out that you had to do mobile. I think AI is going to be the same thing. I think it's going to be a platform capability that anybody, you know, who's doing their job in software. Understands it's going to have to be embedded into your software going forward.

• 18:02Logan Bartlett?

  It's interesting because mobile is the technological shift, I guess I've drawn on as well. Trying to internalize where equity value is going to be created here. And it's clear to me that people such as yourself are going to benefit from it, right? And I get people with distribution, people that already have data like are going to be able to find the incremental gains from it. Do you think there's a bunch of net new equity value that's going to be created by startups and venture backed opportunities? When we're looking out 10 years from now?

• 18:35Speaker 01

  I do think there are, partly because partly because I just think like with the level of creativity that's happening. That like there will always be something that emerges with that. So so at a fundamental level, I believe, you know, definitely the answer is yes. I think some very obvious things, just to just to call out first, and then you can kind of get to maybe the less obvious. The very obvious things are some parts of AI require new tooling and new dev infrastructure. So like automatically, that's going to create, you know, the Mongo DBs of AI. Like you're going to have five or 10 of those, just just almost guaranteed.

• 19:10Logan Bartlett?

  So Database market has blown up overnight, right? Are you guys using vector database at all for for feeding stuff in?

• 19:18Speaker 01

  We are actively sort of deciding what our vector

• 19:22Logan Bartlett?

  Yeah, it's crazy. I mean, it's clear that's that's happening and a bunch of people are investing behind it. Right. So I agree 100%. There'll be tooling around that stuff.

• 19:31Speaker 01

  Right. And so like, let's call that a maybe a, I don't know, like a $15 billion, you know, plus, you know, category of software in terms of market cap that that kind of gets created in all the AI. There's Langchain and Pinecone and just all these things that you're going to need to be a developer and you'll pay money for that. Okay, that that one's easy. Another, I think relatively easy category are are sort of vertical or even line of business applications where there's not an obvious incumbent to solve the use case that AI can now solve. And so, and I like, we, I could kind of come up with some examples, but it's just easy to think about it as like,

• 20:14Speaker 01

  what things right now are kind of like undersolved by software? Because they kind of required human, you know, you know, kind of intellect to go and review something or look at something or answer a question. And so then there's not like an obvious incumbent winning in that market because you just couldn't do it with software previously. I think there's a whole space around there where you'll have lots of vertical plays, you know, in healthcare, in finance, and in in customer service, you know, software. Like, I think there's, there's, you know, I don't know, hundreds of billions of dollars that that'd have to get created in in dozens or hundreds of of, you know, kind of B to B software companies around those spaces.

• 20:52Logan Bartlett?

  I'm trying to think of what the cloud analogy of that is. I guess, I mean, there are all these feature like solutions that have just built massive, like DocuSign or Calendly or whatever, that like the distribution advantage, the feedback loops, the network effects, sort of allowed something that if you were carrying a bag at Oracle, it would have never made sense to pay someone, you know, 500K, 500K OTE to go sell. But because of the internet, because of cloud, it was uniquely able to solve that problem.

• 21:24Speaker 01

  Well, so there's two ways to cut it. So I think you've gone down the Vector of of like the more like feature tactical capability and that that is totally one. There's another which is just like which is just cloud enabled on just more efficient delivery of software in verticals that otherwise like. It's just like like, how do you kind of build the the network effects fast enough and and kind of get all the customers together and just like cloud was so efficient and so like. Like a very, you know, basic example, but but I think is is the kind of vertical point of making is like Viva. You know, I don't know today's market cap, but let's say thirty forty billion dollar company doing life sciences, you know, software that fifteen years ago.

• 22:05Speaker 01

  You know, we we would not have been able to imagine a thirty forty billion dollar life sciences software company just focused on that vertical. And and so I I, you know, I can, I I have to imagine that that there's a bunch of use cases where AI for the first time. Can let you automate workflows that we just we never did through software and it and people that really deeply understand the vertical. Will be in a good position to go build software for those spaces and then you'll build, you know, kind of really strong businesses. As a result of that, because you can go and say, hey, we can make your business, you know, twenty percent more efficient by using our vertical expertise on top of an L M. With this particular workflow.

• 22:44Logan Bartlett?

  It might not be a net new company created necessarily, but it might take what was a two hundred million dollar outcome and make it a five billion dollar outcome. Because of the whatever you can do things in a different way than were possible and and address it more scalably. Because of the customer support ticketing or whatever it is, right?

• 23:01Speaker 01

  That's a fantastic point. I think there are some people who this opportunity just landed right in front of them where where, you know, maybe. They were doing, you know, some particular, you know, limited workflow. All of a sudden L M has become like the big. The big, you know, kind of market opportunity opener for them and they're in the right position with the right software. And so those are the two obvious categories where I just I just believe there will be venture, you know, kind of scale outcomes that that come from that. And then the third category, which is like the raging, you know, debate, you know, six months ago, I think. I think now would be interesting if if if the debate has changed is sort of like. You know, what are the, what are like the big Google scale and or, and or just like, like, you know, disrupting an existing incumbent type, type, type approaches?

• 23:46Speaker 01

  And I think we're very quickly realizing that incumbents are pretty, pretty fast to adapt to this new technology cycle and, and nobody's sort of sitting around, you know, you know, just sort of waiting to be disrupted. And so, so, you know, I think if you were to say, hey, we have an AI first CRM system, I think it'll be more difficult because, you know, Salesforce is not going to just like, let, let AI, you know, go and disrupt, you know, CRM. So.

• 24:13Logan Bartlett?

  It's been impressive to watch big tech execute, right? In the last, the last six months, 12 months. And it's every, I mean, Microsoft was out in front of this. Google was maybe caught slightly flat footed in terms of the LLM world. But, I mean, they've certainly thrown all their resources behind it. Right? I'm sure Amazon and Apple are figuring out their strategy. What do you think it is about this? Because none of them are founder led companies at this point. Right? They're all on at least their second, if not their third generation of leadership there, at least the big, big tech folks, with the exception of Facebook, I guess, with Zuckerberg. What do you think it is? Do you think everyone's just read Andy Grove enough? And, you know, have, have been paranoid enough about the prior generations of companies

• 24:58Logan Bartlett?

  that have, have become too fat and lazy? Or why do you think they've been able to execute as well as they have here? Or at least as paranoid?

• 25:04Speaker 01

  I would love to, I'd love to sort of officially dispel the founder, the founder concept.

• 25:11Logan Bartlett?

  Let's do it here.

• 25:13Speaker 01

  Let's just, yeah, the, where you, you, this is the breaking, this will be the breaking news of the, of the podcast. But, you know, I, like, listen, I, as a founder, I think it's like, great that, that, you know, when, when founders are sort of, you know, perceived as, as sort of having, you know, some kind of particular intuition on the product of the business. I mean, I think a lot of times that's accurate. But, but this idea that sort of like, like these big founder led moments of the company, like, I think we've sort of invented that as mythology. Like, Bob Iger was not the founder of Disney and, and he obviously, you know, put. The most bold stamp of of strategic decisions on that company, you know, more than I think anybody could have would have ever imagined.

• 25:53Speaker 01

  So so like, I just think that like, there's like, there are executives that are super competent, well, well read on how disruption theory works. And and the reason that they're the CEO of that company, hopefully, is is due to their competence. Like Satya is, you know, like unequivocally founder or not, just like one of the strongest, smartest executives on the planet. So like, so like, he can just see a technology emerging and realize how big of a deal that's going to be for Microsoft. And decided that's a moment where you have to kind of pivot the business. And and I I mean, like, like, alternatively, I I know lots of founders where where they're reluctant to do that. Because it the thing looks so different from the thing they created that actually they're not, you know, in a position to go and rapidly, you know, you know, pivot in that kind of case.

• 26:38Speaker 01

  So I actually, so I just think that, like, we have a market right now, to your exact point, that understands Clayton Christensen. They everybody understands disruption. We've we've sort of seen the examples, we saw what happened to BlackBerry when Android and iPhone emerged. You know, obviously, everybody saw what happened to to various chipmakers over time. As you had, you know, new disruptive architectures. We've seen what happened in the 90s to a variety of businesses as the Internet, you know, kind of went after those those companies. And so I think everybody sort of is is just well versed in how disruption works. And then it's just about kind of like resource alignment. How do you kind of get everybody working in the right direction? You know, the thing that Google faced was actually, you know, counterintuitively, they had like too much AI.

• 27:21Speaker 01

  So it's like, it's like, we got AI everywhere, like, like, we, you know, why do we have to go and and, you know, change something as a result of this? And and, you know, ostensibly, from what I've read, Sundar realized like, OK, no, we actually have to drive better coordination. We have to execute on the strategy better. And I would say, like, you know, six months later, after the initial chat GPT launch. I I would say, like, you know, the the they're in a very strong position. And and I don't think, like, you know, I don't, I think they aren't at risk of like major, you know, kind of like business model disruption. I think they're going to have an approach. I think chat GPT will have an approach. I think. We'll have an approach and we're just going to see a lot more innovation in this in the search space as a result of that.

• 28:00Logan Bartlett?

  One thing that I was interested, we kind of touched on this in that second bucket of equity value creation. But if you were a young enterprising founder, right, Aaron Levie of 2006 in 2023, and you know, you have the urge to go start a company in some way. How how do you even go about doing that? Like, what would you? What would you go do? Would you just think for a while, then start talking to prospective people to validate that idea? Or I assume you work in AI, I would guess, based on your enthusiasm. If you were, you know, if you were 20 years old today, but how would you go about doing that?

• 28:36Speaker 01

  Yeah, I mean, I would definitely, I think I'm pretty confident it would be in AI. And but I mean, this is how we did it for Box, actually. We were like, we were pretty, I mean, like, as rigorous as 19 and 20 year olds could be on sort of business strategy. We did like the work, like we we looked at the market, we analyzed the market, we tried to figure out our competitive advantage. We sort of attempted to assume what incumbents might do in the market and and how we would compete with them. And so I think that there is, I think there are some situations where like, you just totally, you know, capture a zeitgeist moment like, you know, a Facebook or,

• 29:18Speaker 01

  you know, a product like that where you you build something for fun and it works and it clicks and it takes off. And it, you know, sort of takes over the world, there's that category. And I would say like, at your own risk, pursue that approach. And then there's the category, which is like, hey, we analyze the market, we think there's an opportunity. If we build better software for this, like, then let's go and and and, you know, kind of create, create this thing. And that's the approach we took. I think AI probably requires a little bit of that as as a result of, as a result of the incumbent advantage that does exist in this space. So like, I think if you just, if you just go and say like, oh, I built this really cool thing. And obviously that it's amazing. And and I'm going to pursue this as a startup opportunity.

• 30:01Speaker 01

  Well, if that cool thing again is like right head on in front of Salesforce and their AI strategy. I would just say like, like, that's going to be very, very hard to scale. Because Salesforce is going to get very good at this. And the same would be true of, you know, Atlassian or ServiceNow or whatnot. That being said, I think there's a lot of spaces that are either in the blind spots of those incumbents or areas. Where the business model of those incumbents is in sort of direct conflict with pursuing that opportunity. You know, there's a lot of use cases where you have an enterprise software company that sells seats of software and they're not going to be as good at going after use cases that either are just they only you only need one seat of the software

• 30:44Speaker 01

  to do the whole thing, or they actually reduce the seats of the software by the virtue of using it. And so, so find opportunities where there's some kind of inherent innovator's dilemma challenge with that incumbent.

• 30:55Logan Bartlett?

  It feels like you've been totally like re-energized by all of this stuff. You've been on this journey for 17 years. Is that a fair characterization? Has this given you a whole new like excitement about about Box? And absent this, like, were there periods that you kind of, obviously, it's amazing, you crossed a billion in revenue and you're a big public company and all that. But like, has this extended the window at which Aaron Levy is going to be excited as the CEO of Box? Did you ever get like bored? Or was it just constantly new challenges? And this is just a new exciting one for you?

• 31:29Speaker 01

  I would say something about me finds excitement in kind of any, any hard problem. And so, so I would say I didn't need there to be a platform shift.

• 31:44Logan Bartlett?

  Yeah.

• 31:45Speaker 01

  To be excited about all the things that we're.

• 31:47Logan Bartlett?

  I'm sure this is more re-energizing than activist investors. If you were to put this.

• 31:51Speaker 01

  Compared to proxy battles. Yes, this is, this is substantially more fun. But, but so I'd say my energy level was, was already pretty high. But what this did was this sort of opened up a different set of, of areas of, of, you know, you know, my brain, or I think a lot of other entrepreneurs' brains that, that were not as activated, which is purely like on the imagination side. Like, like what is, what, what is going to happen as AI rolls out into every, you know, enterprise on the planet? How will work change? What are, what's the nature of, of a knowledge worker in five or 10 years from now as AI advancements continue to?

• 32:33Speaker 01

  To dramatically improve, what are all the ways that this can be exposed inside of our software? So I think that has a lot of incredibly exciting components to it and is very re-energizing. And again, like, and again, I'm like extremely annoying on this front, but like, but it's just like, we are, we are, we're so excited because of how much impact that can have directly on our our product and experience. And, and that's what, what compels us to, to kind of pursue this, you know, so aggressively.

• 33:04Logan Bartlett?

  One of the things you guys have done with your own AI is like making the phrasing pretty clear according to this document. And we touched on a little bit like hallucinations and all of that. I assume, I know there's some other use cases you guys are thinking about that involve a broader knowledge set than just the self-contained element of it, right? And there's definitely some applicability that exists outside of the, the document itself and what, what you're able to do with it. But do you worry about the world of misinformation that we could be entering into with just like, I mean, there's all these dystopian elements of the things about people, their girlfriends and their best friends are AI, right? And now we could have infinite content that's AI-generated and misinformation,

• 33:48Logan Bartlett?

  people's, you know, deep fakes and all that. Like, does it concern you? Or are you just so focused on what you have going on right now that you don't really give it much thought?

• 33:55Speaker 01

  Um, I, I'll say that, yeah, percentage of my time, um, you know, more is going into the exciting, you know, optimistic scenarios that, that we're building out. However, you know, obviously it's, it would be irresponsible to avoid, you know, what, what the downsides are. I mean, deep fakes, very alarming. I actually don't know the theory of, of, of how we're supposed to solve that. Um, I think, um, I, I think the, I think there's some really interesting, like, like sociological questions, which is like, if I receive an email from you that was AI-generated, like, is there something happening in my brain that sort of is like, is like different from Logan emailing that, like, and like, you know, that you put in that little ounce of, of extra effort?

• 34:40Logan Bartlett?

  Totally. I'm sure we went through with the handwritten note versus the email as well, right? There's like, still elements of both, but I agree, yeah, 100%.

• 34:48Speaker 01

  Yeah, and this one, I mean, there's, there's, it's like, it's hard to know exactly where, where you take the analogy like, okay, well, is it like autocorrect? Is it like spell check? But you know, by the point that something's producing like multiple sentences, it's like hard to argue that that's similar to anything else we've seen in history because that's, that's almost the equivalent of I copied and pasted a block of text and sent it to you as my own. And so I think there's some really interesting like elements of like, what's the social contract on, on information that we produce? I have, I've, I've asked a lot of people like, hey, if you found out that like, I wrote that internal memo with AI, does that change your, your, you know, sort of like the trustworthiness of the information?

• 35:31Speaker 01

  Or, you know, because we use, we use the creation of information as, as, not to use like a crypto term, but like, almost like proof of, proof of knowledge or proof of work, that I, I did some work to, to, to, you know, create evidence that I know the subject that I'm talking about. And that work was like, I wrote the, I mean, the Amazon, you write the six-page memo. Um, you know, they don't want AI writing the six-page memo because the whole point of the memo was it proved that you've thought this through and that I, and that you've really had to like actually like game out what the strategy is. And now, and like, it's not about the document as much as now I-

• 36:09Logan Bartlett?

  Inputs as much as the outputs, right? It's for other people's benefit, but also for your own.

• 36:14Speaker 01

  If, yeah, so it's, it's doing, it's doing two things on that dimension. So it's, it's everybody else's educational benefit. It's your own, it's your own introspection and thinking. And but then there's a third thing, which is like, which is just the, you know, human social, you know, system element, which is like, I now trust you. Because I know that you did that and I know what, I know, I can believe what, what I believe you know. And that means that you, like, when you go and execute, I know that you know those things because you, you literally created them. And so, so all of a sudden, if AI is writing that out, do I really know that, that, that you know how to execute on that strategy in the same way that I used to know because you had written the whole document yourself? And so there's like, I think there's like, gonna be a lot of those kinds of questions.

• 36:59Speaker 01

  In this new world of work and I'm more fascinated by them than worry about them. But I think it's going to like be really, really interesting to think through what, how does, how do you like, you know, prove that you know, something in a world where you have AI generating so much information? Obviously, you've got the, you've got the election challenges and disinformation. And this is not even touching on, you know, the, you know, Eliezer, you know, sort of vector of risk. Brendan the pod. Yeah, exactly. And, and so what's your doom perspective? I had, I do one podcast with Eliezer and, and now I've had more people ask me like what my

• 37:39Speaker 01

  actual opinion is on this, right? And I, I don't know, I'd be curious on yours, like where you are in all of this, or how far down that rabbit hole you've gone. Yeah, I've gone like, I've gone like the shallowest part of the rabbit hole. The, I was, I didn't get into sci fi, uh, growing up, like everybody's got their favorite sci fi book. And like, I'm like, I'd never heard of that. Um, so I'm like a very boring, like practical technology person, like you build software to like, help people like with CRM and ERP and writing emails. So like, that's how I think about technology, not like robots, you know, taking over the world. Um, and so, um, and so I think my brain isn't wired down the, down some of this, the more sci fi,

• 38:23Speaker 01

  dystopian, you know, you know, kind of scenarios. I, I totally, you know, trust and appreciate that people are, you know, thinking through those things, worried about them. Um, I think that they sometimes underestimate the, the, you know, our ability to go and actually like correct for those risks, um, whether that's directly in the things that we control or within the AI models themselves. Um, so, so sometimes I think that like, there's an incredible imagination on the downside and a very limited imagination on how you would then go and mitigate those things if, if those, if those were starting to occur. Um, and I also think they, they sometimes underestimate like all the risks that already exist today in the, in the world that we've, we've found ways to put guardrails around.

• 39:08Speaker 01

  Half the scenarios that that people sort of, you know, say that AI will do to us, you could literally do tomorrow with one bad guy if you if you just if you just wanted to. And the world somehow has found ways to prevent those things from happening. And so I don't know, I I'm it's not intuitive to me why AI all of a sudden, you know, sort of becomes the unlock for those things to now occur at a at a greater scale. But, you know, clearly, clearly, this is a a space that is going to warrant a ton of work and and capability around. But but I think all that said, I would I would just say like more regulation, more auditability, you know, more controls being put in place around, you know, AI models.

• 39:53Speaker 01

  Those are only good things because at some point there is this is a superpower that we need to have have guardrails around.

• 40:00Logan Bartlett?

  Yeah, I I think we have the same person in both of our ears on this AI doom topic. And I I'm not totally sure where I net out on it. It's a I just know there's a lot of smart people that like I trust their intuition. I too didn't grow up with sci fi. I I was like reading baseball cards as a kid, so I sort of missed this whole this whole thing, but there are a lot of smart people that believe. And I've realized I have a hard time internalizing probabilities after the Trump election and COVID. Right? I sort of realized low probability tail events. I just I don't have the right calibration around that stuff. And so, yeah, there are a lot of smart people that are concerned. And so I guess it makes me very curious, but I don't know if I have the intellectual horsepower

• 40:44Logan Bartlett?

  to go totally down the rabbit hole and think about all the different permutations of this stuff.

• 40:49Speaker 01

  The only concern I have is that is that those those a lot of those fears are adjacent to also the belief that AI sort of takes over all aspects of life as as well. And and I I tend to think that's sort of like not as not giving humans as much credit or and or just maybe even a more basic level, not appreciating that humans just like humans more than than robots and and that we're a species that is going to Continue to want to find human connection. Um, and so like a lot of the things that we believe like wow, like AI will totally replace X category of thing. Um, is like, no, because actually like I want to be able to call a lawyer.

• 41:31Speaker 01

  And and just like, I just need to, I need somebody to patiently walk me through. This contract at the end of the day and what risk I'm taking on. And so. The most knowledgeable, the friendliest, you know, AI bot in the world will never replace. What, why you call a lawyer at the end of the day, or, or why you, you talk to a therapist, or why you go to a restaurant and, and want to, you know, have, have really great service like. And so, so I think a lot of, a lot like, these, these things are, are all under the, usually under the. Umbrella of like AI takes over everything. And it's just like, no, actually, I think that, that like, like the, the base case is. We use AI as a productivity boost.

• 42:13Speaker 01

  It acts as a way to connect dots on information at a thousand times faster than a human can. Um, and, um. Uh, and then, and then we just all get this sort of, you know, new capability that we can leverage. Um, and so that, that's the, that's where I think the base case, and I think we will put the right kind of guardrails. Like the market will, will end up creating guardrails around this technology, whether it's through government action or just. Um, you know, self-regulation, uh, I, I, I, I think, I think people are all equally understand that we don't want the risky scenarios to, to be. High probability. And so, uh, ultimately, I think that, that we will begin to, to solve a lot of the, the potential risk on that.

• 42:53Logan Bartlett?

  I sure hope you're right. I, every time I think I understand something, I hear a story like, I guess one of these, I forget if it's character AI or replica or one of these things, but like their subreddit. There's an entire section dedicated to, uh, the emotional trauma when you go through, when they go through an upgrade. And like how to emotionally deal with the connection you built around the, the AI and then like what you do restarting that relationship. And I'm like. I don't know what's going on here. So I, I, I agree. I hope you're right in, in all of this, but I, uh. The human connection thing, I, I, I mean, you look at some of the adoption of these, these tools of the chat, it's just crazy how, how many people are just communicating only with. Building friends with these different characters and all that.

• 43:37Speaker 01

  But now I'm really freelancing on opinions on this. I've like thought this through exactly like now six seconds.

• 43:45Logan Bartlett?

  Perfect. That's what we're here for. Yeah.

• 43:46Speaker 01

  I think that's another element, though, where where our relationship with AI is still being figured out. So so as an example, you know, like, like that set of scenarios represents like the beta testers of of of humans interacting with AI at a more personal level. And what we're finding out is like, actually, there are real risks of developing that deep of a connection with something that somebody controls a one computer, one line of code in a computer and can upgrade for everybody. And so like, so like, like, probably in a few years from now, we will have to all evolve to either decide, do you build those kinds of relationships with AI? And what's the what's the social contract with the enterprise that creates those AI bots?

• 44:31Speaker 01

  Or actually, is that actually too risky to build that kind of relationship because they do get upgraded? And and we actually have to treat these things as more ephemeral, you know, kind of interaction tools. And I would just say that these are the like, that set of scenarios is the untested, uncharted water of of this versus like the like, the stable equilibrium that's going to exist in 10 years from now.

• 44:52Logan Bartlett?

  That's the episode title, by the way. Aaron Levie encourages incels to touch grass. I just go outside, go make some human connections, stop doing this. I, you know, so one other thing, I guess I'm curious, as we sort of put the AI topic to bed. So operating profit, like what what would you encourage companies that are now sort of finding this for the first time? And having to slam on the brakes or at least, you know, cool off the gas a little bit?

• 45:17Speaker 01

  You know, it's hard to be too generic because some people are like, it depends on how like, how much is the

• 45:23Logan Bartlett?

  No, you're a VC now. I want you to be a VC for a second. Just give platitudes that aren't rooted in substantive information, just like talk in broad strokes.

• 45:32Speaker 01

  Okay, so my Twitter thread on the topic then would be would be, I mean, it's just, it's hard because it's so trite. Because I think everybody now fully understands it, but like. But you know, we're in a world where I think that the I think that the complete sort of shift from revenue multiple to more of a balance of profitability and growth is probably somewhat temporary and and and sort of somewhat just driven by the the economic, you know, macroeconomic circumstances. I think that, but I think some of it is not transitory and is going to be with us forever, which is which is, you know, the kind of like, like, you know, you know, 18 to

• 46:14Speaker 01

  to 22 period of of revenue multiples, I think are probably just done, you know, they're fully, they're fully over because because I think we figured out that you can't sustain, you you literally can't sustain those in the exit scenario in the public market. And so by definition, all of the subsequent rounds can't can't sort of require that level of of multiple to be maintained. Because, you know, if you're investing and if you believe that your valuation is sort of 30 or 50x revenue, like, the public market eventually will converge on some discounted free cash flow way of measuring your business, whether it's, you know, the first year of going public or the fifth year, who cares, like, like, at some point, that is what you're going to converge on.

• 46:59Speaker 01

  And so all of your subsequent rounds need to assume that, which means that all of your subsequent investors need to also get a return, which means that they can't be paying for two rounds from now's, you know, sort of valuation. So so if you kind of assume all of that, then if you're an entrepreneur and a builder, like, you need to be just thinking about, like, like, all of the, all of the, you know, kind of like, normal boring business things, like, like, what kind of gross margin will likely produce a, you know, a five or 10x revenue multiple company in the public market? Well, that gross margin probably needs to be like You know, 70 to 85%, let's just say to make up kind of large numbers. What is the rough operating profit?

• 47:41Speaker 01

  What's the rough, you know, kind of efficiency you need from your sales and marketing teams? How much can you invest in R and D as a percentage of revenue? Like all these kinds of things like, like you should be working backwards from like, what is a public company likely going to get valued on? And, and making sure that you're always within, I don't know, the ability to like toggle the business. So you could get there in one or two or three years versus building a business model where it's always going to take you five to 10 years. And you're going to keep kind of like kicking that can down the road. Those days I think are fully over. And so, so I think that ability to be in a position where you can always sort of see cash flow positive. You can always see having like, like real operating profit to the business. Doesn't mean you have to run the business that way like tomorrow,

• 48:25Speaker 01

  but having those kinds of, of capabilities and levers, I think is now increasingly important.

• 48:29Logan Bartlett?

  When I was crowdsourcing questions, and this is from an executive friend of mine, he said, he feels like you're an operator's operator in some ways that like you get just managing expectations and execution and all of that. And your market, I feel like you guys have had a bunch of tailwinds, like, like macro cloud being the big one, tailwinds at your back. But I also, competition's probably been fierce at different points in time. And you've, you've executed well to get to, to where you are. Are there things that you look back on and you feel like, gosh, I'm really glad we had the operating dashboard as tied out as it was. And there were KPIs across the business that everyone knew to go after.

• 49:12Logan Bartlett?

  Really, if you were to leave one lesson of like operational excellence, what you feel like something Box did really well?

• 49:19Speaker 01

  I, I think we do a lot of this well. I think we're always kind of improving many dimensions of it. So, so it's sort of like, you know, there's like Apple like level discipline. And then there's like some large gap and then there's us. And so, so there's a lot of, you know, still, still, you know, distance from, from let's say that level of intensity. But, but I, you know, I think if you distill it down to the component parts and you sort of say like, okay. You know, there's KPIs that that are like the important KPIs to the business like like cost per employee, um, uh, you know, your new, your, your, your pipeline, um, your, how much you're spending on R and D as a percentage of revenue like all like there's, you know,

• 50:00Speaker 01

  thirty, fifty KPIs like that. And like we, we have a very good handle, I believe, on all of them, uh, to, to the ones that we can forecast and understand. And so like having very, very good people in finance and, and kind of financial planning and analysis is like, that's a function that when we started the company, we, we wouldn't have been able to, you know, kind of spell. And, and now it's like we run the business with, with that kind of discipline. Um, uh, so having a very, very, very strong finance team is incredibly important for literally any company, probably a past like ten million in revenue, um, that can understand all these KPIs. I think, um, being extremely rigorous on your investment decisions, um, and, and, and, you know, that, that there's only like, I think

• 50:44Speaker 01

  there's really only two major problems financially that, that companies really can get into, um, i- i- you know, in, in sort of like startup software land that has any remote, you know, this kind of success. It's either you over invest, um, and that, that could be like, like too many growth initiatives, too many markets you've entered, um, too many, uh, product priorities, like any, any form of over investing, you know, sort of with the hope or bet that like it'll all work out. Like that's like one big category of mistake. And then another big category mistake is just like the thing you're doing is just literally not working. Like you may, maybe like just the product is, is, is failing, or you have to pivot, or the market has changed. So, so, and then like, and then what will happen is like your revenue goes lower, your costs have maintained, and then you're kind

• 51:28Speaker 01

  of screwed. So like, those are the, those are the main failure modes past, past like product market fit. And so, and so then like operational discipline, I, I think is just like, is some version of, of, of avoiding those two outcomes. Like, like, how do you not over invest? Well, like you have a sequence to, or, or a, or a set of heuristics of like, when will we enter a new market? Like, how profitable does our, our core business have to be before we enter new product? Categories or new GOs. How many new product categories can we afford to manage in any one time that are net new? How do we make sure that we don't dilute our our efforts by being in five, you know, product, you know, categories instead of two? Like, it's all those kind of things. And that's why it's like hard to be generic, because every business has a different version of that.

• 52:13Speaker 01

  It's it's it's usually stuff that is just like a deep, a deep understanding of value creation in your particular category. Like, like where where we think a lot about, like, where are our actual literal points of leverage? As in like, as in like, 10 engineers building this thing enables enables all of the business to improve at at this level. Instead of of 50 engineers going off and building all those other things. And and we don't get any leverage from from that kind of investment. So we're thinking a lot about kind of where the points of leverage. What things require the the the smallest, you know, kind of investment to the greatest output?

• 52:53Speaker 01

  And we're always sort of squishing all that information together and then and then sort of driving that that as our strategy, basically.

• 53:00Logan Bartlett?

  What's a piece of startup advice that that you hear out there that makes you want to pull your your hair out? Because you just think it's so wrong.

• 53:07Speaker 01

  Okay, that's really funny. I like that one. I I so I think probably the one that that I I hear more often. And then I get confused by is is sort of, but I don't know, because I think this stuff goes both ways, depending on literally who's the who's the who's sending the advice. But like, you know, there's all there's like, there's all these like, I think there's a lot of like fake, not fake. I'll give you two categories. I think there's like, there's, there's a lot of ways that we sort of simplify. Like, these sort of like points of scale, like, oh, zero to 10 million, 10 million to 100 million. Or like, and like, and there's always like this, like, thing of like, this is definitely going to change at this.

• 53:48Speaker 01

  And it's like, it's like, no, like, like, we've had employees that that went from exactly zero to like 500 million in revenue. We've had processes that went from zero to 500 million in revenue. We've had processes that went from zero to a billion revenue. And then we've had things that have had to change at like 50 different steps along the way. So like. So I think like there's like all these like magical, you know, sort of like, like really like very simple, neat, you know, sort of like step functions that we kind of simplify advice in. So I tend to disagree with a lot of that.

• 54:16Logan Bartlett?

  Yeah. But your VP of Sales that gets you from zero to one isn't going to be the guy to get you from one to 10. Or and it's like, well, maybe not, right?

• 54:23Speaker 01

  Like, well, so that's a classic one. So our first, our literal first person we hired, literally the first human that sold software at Box took us from zero to 100 million as the head of sales. And, and so it's just like, I so like, yeah, like that advice is actually just not like, it's just not true. Like, and, and then you get into like, some people will say things like, you know, founders need to, you know, be responsible for the first X number of million in revenue or whatever. And you have to, you, you're, you're going to like, know how to, you're going to do all the selling yourself or whatever. And it's like, well, we actually had a head of sales that was way better at it than I was. And, and very quickly I realized I shouldn't be in any sales conversation

• 55:04Speaker 01

  because I was, I was literally 22 years old and I would have been a liability for the deal. Um, if anything, and so like, I, so, so I think more of those kinds of things where you're trying to oversimplify and over generalize. Um, is, is, is just like, I'm kind of like, you know, eh, I don't know. Like, there's a lot of different scenarios.

• 55:22Logan Bartlett?

  You said two. Was there another one? Or was that, uh, was that both of them?

• 55:25Speaker 01

  I think, I think the secondary element of like, like founders, like just like anything that feels like, like, uh, you know, sort of this, like overarching.

• 55:33Logan Bartlett?

  Of, of, uh, yeah, it feel, I can see, I sense you reject broad-based advice in general. Like, as I've tried to give you, get you to, to, you know, you're like, well, I mean, every situation's different, right?

• 55:44Speaker 01

  I've just seen too many different ways of succeeding and failing. And so not like, like just from friends. And you're just like, you're like, wow, like, like that person has never been on a sales call in their life and that business is billions of revenue. And then that person sells all day long and, and it's not working. And so it's just like, you just can't, you know, I think, um, I think actually that ironically, um, my, my solution is, is, would be to tell everybody to read everything possible because you might as well at least like train your human LLM. On like everything possible because you might see, you know, sort of a scenario emerge where you could be like, oh, like that's when Salesforce decided to invest in this strategy versus that's when, you know, Twilio or Stripe decided to do this approach.

• 56:29Speaker 01

  Like, like learn everything you can, but, but like the idea that like one of the paths is the right one is, is, or like at least a priori is, is, is not likely.

• 56:38Logan Bartlett?

  What book would you recommend?

• 56:40Speaker 01

  For, for this particular moment in time, I'm actually recommending this book called Fit for Growth, way too frequently. And what it is, it's a book about how to spend less and grow. And it's like, you know, you need probably a budget of at least 20, 50 million dollars a year annually for it to be impactful. But like, if you're at that scale or beyond, like I, I think it's a very fascinating book on just like all the ways to drive efficiency. And then separate from that, I think just all the classics, Innovator's Dilemma, Only the Paranoid Survive, how I, how high output, how high output management, like all these things just like end up helping you.

• 57:20Logan Bartlett?

  Yep. Last one. You, you, you said once as a startup, some of the best decisions come by doing the exact opposite of what is done before. And equally, some of the best mistakes come from trying to reinvent things that won't change. What's one thing that you tried to reinvent that you would, again, every situation's unique, but that you would say, hey, guys, just don't do that. Just I went down that path and it's just not worth reinventing XYZ thing.

• 57:48Speaker 01

  Our, our head of enterprise sales joined and we had like four enterprise sales reps. We were only doing inside sales prior to that. And he joined and he was like, okay, our strategy is we're going to have salespeople all out in these cities, everywhere in the country. And I was like, no, no, no. Like, like our whole, our whole, you know, expertise is that like, is like, we're all in this headquarters and we just drive efficiency and like, we can talk to everybody over the phone and it's way more efficient. And like, you know, and like, obviously that was like, like done. So like, but like, do stuff like that. Like, like, we're like, we're like, you think that you're like, you're going to kind of like invent some new thing. And it's like, no, no. Like after. Thousands of experiments like we've proven out like many best practices at this point.

• 58:29Logan Bartlett?

  I mean, generally it's something sales related that I feel like founders want to reinvent in some way. And it's just like, don't innovate on sales. I promise innovate on everything else, right? It's just like, everything else is great, but don't do sales. Like, I would prefer HR innovation than sales innovation. So.

• 58:43Speaker 01

  Actually, I would say don't innovate on HR, though.

• 58:46Logan Bartlett?

  Yeah.

• 58:46Speaker 01

  That is.

• 58:49Logan Bartlett?

  Again, I would prefer neither of them. Legal as well. Like, you don't, you're not going to reinvent how account, corporate council works.

• 58:56Speaker 01

  I think, I think there's no reason to innovate on any function in the G and A section of the business. And, and then sales is like a go-to founder, like reinvention, because, because I think like, we were like, oh, we don't want to do all the same classic sales stuff that Oracle did. But it's just like, just don't, don't change that.

• 59:12Logan Bartlett?

  Yeah, exactly. Cool. Well, Aaron, thanks for doing this.

• 59:15Speaker 01

  Thanks, man.