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

25:43And he obviously put the most bold stamp of strategic decisions on that company more than I think anybody would have ever imagined. So I just think that there are executives that are super competent, well read on how disruption theory works. And the reason that they're the CEO of that company, hopefully, 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 decide that that's a moment where you have to kind of pivot the business.

26:24and I don't mean like alternatively I know lots of founders where they're reluctant to do that because the thing looks so different from the thing they created that actually they're not in a position to go and rapidly pivot in that kind of case so I just think that like we have a market right now to your exact point that understands Clayton Christensen everybody understands disruption we've sort of seen the examples we saw what happened to Blackberry when Android and iPhone emerged Obviously, everybody saw what happened to various chip makers over time as you had new disruptive architectures. We've seen what happened in the 90s to a variety of businesses as the Internet kind of went after those companies.

27:06And so I think everybody sort of 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? The thing that Google faced was actually counterintuitively they had like too much AI. So it's like, we got AI everywhere. Why do we have to go and change something as a result of this? And ostensibly from what I've read, Sundar realized, okay, no, we actually have to drive better coordination. We have to execute on the strategy better. And I would say six months later after the initial ChatGPT launch, I would say they're in a very strong position.

27:45And I don't think like, you know, 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 Chatsubut will have an approach. I think Bing will 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. 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 Levy of 2006 in 2023, and you know you have the urge to go start a company in some way, how do you even go about doing that?

28:19Like, 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 20 years old today. But how would you go about doing that? Yeah, I mean, I would definitely I think I'm pretty confident it would be an 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.

28:59We sort of attempted to assume what incumbents might do in the market. 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, you know, a product like that where 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.

29:34If we build better software for this, like then let's go and, you know, kind of create this thing. And that's the approach we took. I think AI probably requires a little bit of that as a result of the incumbent advantage that does exist in this space. So like I think if you just go and say like, oh, I built this really cool thing. And obviously it's amazing. And I'm going to pursue this as a startup opportunity. Well, if that cool thing, again, is like right head on in front of Salesforce and their AI strategy, I would just say like that's going to be very, very hard to scale because Salesforce is going to get very good at this.

30:12And the same would be true of 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 to do the whole thing or they actually reduce the seats of the software by by the virtue of using it.

30:49And so so find opportunities where there's some kind of inherent innovator's dilemma challenge with that. It feels like you've been totally like reenergized 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 excitement about Box? And absent this, 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 has this extended the window at which Aaron Levy is going to be excited as the CEO of Box? Did you ever get bored? Or was it just constantly new challenges and this is just a new, exciting one for you?

31:29I would say something about me finds excitement in kind of any hard problem. And so I would say I didn't need there to be a platform shift. Yeah. To be excited about all the things that we were doing. I'm sure this is more re-energizing the activist investors. If you were to put this... Compared to proxy battles. Yes, this is substantially more fun. But so I would say my energy level was already pretty high. But what this did was this sort of opened up a different set of areas of, you know, my brain or I think a lot of other entrepreneurs' brains that were not as activated, which is purely like on the imagination side.

32:17Like what is going to happen as AI rolls out into every enterprise on the planet? How will work change? What's the nature of a knowledge worker in five or ten years from now as AI advancements continue 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, I'm extremely annoying on this front, but it's just like we're so excited because of how much impact it can have directly on our product and experience. And that's what compels us to kind of pursue this so aggressively.

33:04One of the things you guys have done with your own AI is 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 document itself in 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 or AI.

33:42Right. And now we could have infinite content that's AI generated and misinformation. People, 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? I'll say that, yeah, percentage of my time, you know, more is going into the exciting, you know, optimistic scenarios that we're building out. However, you know, obviously it would be irresponsible to avoid, you know, what the downsides are. I mean, deep fakes, very alarming. I actually don't know the theory of how we're supposed to solve that.

34:17I think there's some really interesting 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 extra effort? 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 percent. Yeah. And this one, I mean, there's there's it's like it's hard to know exactly where you take the analogy. Like, OK, well, is it like autocorrect?

34:55Is 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 that's similar to anything else we've seen in history, because that 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 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 sort of like the trustworthiness of the information?

35:31Or, 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 did some work to to to 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 on, you know, they don't want AI writing the six page memo because the whole point of the memo was it proved that you 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.

36:10Inputs as much as the outputs. Right. It's for other people's benefit, but also for your own. Um, so it's, it's doing, it's doing two things on that dimension. So it's, it's everybody else's educational, but 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 socials, you know, system element, which is like, I now trust you, um, 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, I like when you go and execute, I know that you know those things because you literally created them.

36:43And so all of a sudden, if AI is writing that out, do I really know 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 I think there's going to be a lot of those kinds of questions in this new world of work. And I'm more fascinated by them than worry about them. But I think that's going to 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, um, you've got the election challenges, um, and disinformation.

37:21Um, and this is not even touching on, you know, the, you know, Eliezer, you know, uh, sort of vector of, of, of risk. Friend of the pot. Yeah, exactly. Um, and, uh, uh, and so what's your doom perspective? I do one podcast with Eliezer, and now I've had more people ask me what my actual opinion is on this. And I don't know. I'd be curious on yours, where you are in all of this or how far down that rabbit hole you've gone. Yeah, I've gone the shallowest part of the rabbit hole. I didn't get into sci-fi growing up. Everybody's got their favorite sci-fi book, and I'm like, I've never heard of that.

38:01Um, 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, I think my brain isn't wired down the, down some of this, the more sci-fi dystopian, you know, you know, kind of scenarios, I totally, you know, trust and appreciate that people are, you know, thinking through those things, worried about them. I think that they sometimes underestimate the, you know, our ability to go and actually like correct for those risks, whether that's directly in the things that we control or within the AI models themselves.

38:46So 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 those were starting to occur. And I also think they sometimes underestimate all the risks that already exist today in the world that we've found ways to put guardrails around. Half the scenarios that people say that AI will do to us, you could literally do tomorrow with one bad guy if you just wanted to. And the world somehow has found ways to prevent those things from happening. And so I don't know, 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 greater scale.

39:32But, you know, clearly, this is a space that is going to warrant a ton of work and capability around. But I think all that said, I would just say like more regulation, more auditability, you know, more controls being put in place around, you know, AI models. Those are only good things because at some point there is, this is a superpower that we need to have, have guardrails around. Yeah. 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, I just know there's a lot of smart people that like, I trust their intuition.

40:14I too didn't grow up with sci-fi. I was like reading baseball cards as a kid. So I sort of missed 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 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 to go totally down the rabbit hole and think about all the different permutations of this stuff.

40:49The only concern I have is that a lot of those fears are adjacent to also the belief that AI sort of takes over all aspects of life as well. And I tend to think that's sort of like not giving humans as much credit 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. And so like a lot of the things that we believe like, wow, like AI will totally replace X category of thing is like, no, because actually like I want to be able to call a lawyer 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.

41:40And so the most knowledgeable, the friendliest, you know, AI bot in the world will never replace 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 things are all under the usually under the umbrella of like, AI takes over everything. And it's just like, no, actually, I think that the base case is we use AI as a productivity boost. It acts as a way to connect dots on information a thousand times faster than a human can. And then we just all get this sort of new capability that we can leverage.

42:26And so that's sort of, 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, you know, self-regulation. I think I think people all equally understand that we don't want the risky scenarios to to be high probability. And so ultimately, I think that that we will begin to solve a lot of the potential risk on that. I sure hope you're right. 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 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 AI and then like what you do restarting that relationship.

43:17And I'm like, I don't know what's going on here. So I agree. I hope you're right in all of this. But I the human connection thing. I mean, you look at some of the adoption of these these tools and these chat. It's just crazy how many people are just communicating only with building friends with these different characters and all that. But now I'm really freelancing on opinions on this. I've thought this through exactly like now six seconds. Perfect. That's what we're here for. Yeah. 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.

44:05And what we're finding out is like, actually, there are real risks of developing that deep of a connection with something that somebody controls 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? Or actually, is that actually too risky to build that kind of relationship because they do get upgraded? And we actually have to treat these things as more ephemeral, you know, kind of interaction tools.

44:40And I would just say that set of scenarios is the untested, uncharted water of this versus the stable equilibrium that's going to exist in 10 years from now. That's the episode title, by the way. Aaron Levy encourages incels to touch grass. I just thought, go outside, go make some human connections, stop doing this. So one other thing, I guess I'm curious as we sort of put the AI topic to bed. So operating profit, like 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? You know, it's hard to be too generic because some people are like, it depends on how, like, how much is the...

45:23No, 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. Okay. So my Twitter thread on the topic then would be, I mean, it's just, it's hard because it's so trite because I think everybody now fully understands it. But we're in a world where 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 sort of somewhat just driven by 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 22 period of, of revenue multiples, I think are probably just done for, you know, they're, they're fully, they're fully over because, because I think we figured out that you literally can't sustain those in the exit scenario in the public market.

46:32And so by definition, all of the subsequent rounds can't sort of require that level of multiple to be maintained. Because if you believe that your valuation is sort of 30 or 50x revenue, the public market eventually will converge on some discounted free cash flow way of measuring your business, whether it's the first year of going public or the fifth year, who cares? At some point, that is what you're going to converge on. 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 sort of valuation.

47:12So if you kind of assume all of that, then if you're an entrepreneur and a builder, you need to be just thinking about all of the kind of like normal, boring business things, Like, what kind of gross margin will likely produce a 5 or 10x revenue multiple company in the public market? Well, that gross margin probably needs to be like 70 to 85%, let's just say, to make up kind of large numbers. What is the rough operating profit? What's the rough kind of efficiency you need from your sales and marketing teams? How much can you invest in R &D as a percentage of revenue? All these kind of things, you should be working backwards from what is a public company likely going to get valued on and making sure that you're always within, I don't know, the ability to 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 kicking that can down the road.

48:10those days I think are fully over. And so I think that ability to be in a position where you can always sort of see cashflow positive. You can always see having like real operating profit to the business. It doesn't mean you have to run the business that way like tomorrow, but having those kinds of capabilities and levers I think is now increasingly important. 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 in your market.

48:44I feel like you guys have had a bunch of tailwinds, 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 executed well to get 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. Really, if you were to leave one lesson of operational excellence, what you feel like something Box did really well? I think we do a lot of this well. I think we're always improving many dimensions of it.

49:25So it's sort of like there's Apple-level discipline. And then there's some large gap. And then there's us. And so there's a lot of, you know, still, you know, distance from, let's say, that level of intensity. But, 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 are like the important KPIs to the business. Like cost per employee, you know, your new, your pipeline, how much you're spending on R &D as a percentage of revenue. Like there's, you know, 30, 50 KPIs like that. And like we have a very good handle, I believe, on all of them to the ones that we can forecast and understand.

50:10And so like having very, very good people in finance and kind of financial planning and analysis is like that's a function that when we started the company, we wouldn't have been able to kind of spell. And now it's like we run the business with that kind of discipline. Um, uh, so having a very, very, very strong finance team is incredibly important for literally any company probably have passed like 10 million in revenue, um, that can understand all these KPIs. I think, um, being extremely rigorous on your investment decisions. Um, and, and, and I, you know, the, the, there's only like, I think there's really only two major problems financially that, that companies really can get into, um, you know, in, in sort of like startup software land that has any remote, you know, kind of success, it's either you over invest.

50:57And that that could be like, like, too many growth initiatives, too many markets you've entered, too many 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 be 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 of screwed.

51:29So 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 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 a set of heuristics of like, when will we enter a new market? Like how profitable does our core business have to be before we enter new product categories or new geos? 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 efforts by being in five, you know, product, you know, categories instead of two, like it's all those kinds of things.

52:09And that's why it's like hard to be generic because every business has a different version of that. Um, 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 this level instead of 50 engineers going off and building all those other things. And we don't get any leverage from that kind of investment. So we're thinking a lot about kind of where are the points of leverage, what things require the smallest kind of investment to the greatest output.

52:53And we're always sort of squishing all that information together and then sort of driving that as our strategy, basically. What's a piece of startup advice that you hear out there that makes you want to pull your hair out because you just think it's so wrong. Okay. That's really funny. I like that one. So I think probably the one that I hear more often and then I get confused by is sort of, but I don't know, because I think this stuff goes both ways, depending on literally who's sending the advice. But like, you know, there's all these like, I think there's a lot of like fake, not fake. I'll give you two categories.

53:33I think there's a lot of ways that we sort of simplify these sort of points of scale, like zero to 10 million, 10 million to 100 million, or like, and there's always this thing of like, this is definitely going to change at this. And it's like, no, we've had employees 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 a lot, 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.

54:14And so I tend to disagree with a lot of that. 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, I mean, maybe not. Right. Like it's sort of, well, so that, 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 a hundred 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.

54:51And 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 very quickly, I realized I shouldn't be in any sales conversation because I was literally 22 years old and I would have been a liability for the deal, if anything. And so I think more of those kinds of things where you try and oversimplify and overgeneralize is just like, I'm kind of like, I don't know. There's a lot of different scenarios. You said two. Was there another one or was that both of them?

55:25I think the secondary element of like founders, like just like anything that feels like, you know, sort of this overarching. Got it. Overarching. Yeah, I can see, I sense you reject broad-based advice in general. Like as I've tried to get you to, you know, you're like, well, I mean, every situation is different, right? 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.

55:58And, and it's not working. And so it's just like, you just can't, you know, I think, I think actually, ironically, 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 can 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. 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.

56:38What book would you recommend 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 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 High output management, all these things just end up helping you.

57:20Yeah. Last one. 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 is 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. 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, OK, our strategy is we're going to have salespeople all out in these cities everywhere in the country.

58:01And 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 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 1000s of experiments, like we've proven out like many best practices at this point. I mean, generally, it's something sales related that I feel like founders want to reinvent in some way.

58:33And 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 actually I would say don't innovate on HR though. Again, I would prefer neither of them legal as well. Like you don't, you're not going to reinvent how a corporate council works. I think, I think there's no reason to innovate on any function in the GNA 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.

59:10But it's just like, just don't change that. Yeah, exactly. Cool. Well, Aaron, thanks for doing this. Thanks, man.