How SaaS can survive the AI revolution, with Aaron Levie

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0:51Well, for us, everything's at stake. I would say that other than that sort of very early founding period, maybe the first year or so, it's never felt as much like a startup as it does today. We feel like we're very much back in that early formalization period, mostly because of the size of the opportunity. Aaron Levy is co-founder and CEO of Box, a SaaS company now worth over$4 billion. They've been around for two decades. So why is Box suddenly infused with the frenetic energy of a startup? Two letters. AI. Every single day, there's a new model release. There's some new breakthrough in how you design and build agents.

1:33So it really, really is critical that we are moving at this very, very fast-paced way. Everybody working on AI feels that type of pace and intensity right now. Even as a successful founder and CEO of a major public company, Aaron finds himself back in the trenches. His company is a leader in developing cloud-based enterprise tools, so he knows what it means to strike while the iron is hot. So on this week's episode, we ask, is SaaS dead? As one of the biggest figures in SaaS, Aaron makes the case for how companies like his can ride out the AI transformation. I'm Rana El-Khalyubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

2:30Aaron, welcome to Pioneers of AI. I'm so excited for our conversation. Thanks for having me on. I want to kind of go back to your origin story. So it sounds like, or it feels like you've wanted to do startups forever when you were an undergraduate student at USC. I guess you explored with like starting a search engine and a real estate portal and a whole bunch of ideas. And then it was an internship at Paramount that led you to starting Box. So tell us that story. And I guess were you fixated on starting a tech company or were you looking for a problem that you're excited about and wanted to solve or both?

3:04Yeah, I got enamored with technology fairly early on. So in the early years of high school, I had a couple of friends that were building software and building kind of Internet websites. That piqued my interest quite a bit. And so I started building lots of websites and through that created a bunch of projects with, again, kind of various friends, sometimes on my own, sometimes with what eventually would become one of Box's co-founders. I was just trying anything that seemed interesting and a good use of the internet. And none of those ideas really kind of panned out, but went to college and tried to explore another kind of passion area, which at the time was film, still remains a kind of an area of interest, but thought maybe I could combine digital technology and media and do something with that.

3:50So I got some internships in the entertainment industry. And then I was an intern at Paramount in 2004. And at the exact same time, I had to do a project in college to kind of identify a market opportunity. I chose this idea online storage sort of through just a random set of events of working from a corporation and sort of seeing, you know, how sluggish legacy software was. And then in school, just like emailing yourself files to move between computers. So kind of a few stars aligned and eventually decided to study the space of, okay, well, there had been companies that would let you store your data on the internet, but they all kind of died off in the late 90s.

4:30They didn't really innovate. They were sort of these zombie products and companies. And it felt like right this moment where you could finally sort of deliver on this promise of you could put your data in the cloud. It wasn't called the cloud at that point, but you can put your data online, store it securely, access it from anywhere. Browsers were getting faster. The internet was getting faster. Storage was getting cheaper. And so that was kind of the general thesis that we had. And I started building that idea out. And then we launched it in early 2005 and then just sort of started growing from there.

5:01That's amazing. So we recently had Mark Cuban on the show and you have your own Mark Cuban story. I guess he gave you a 350K check. But I'm most curious about, I guess he advised you to never hedge your bets in a startup. So I would love to hear more about like, how did you implement that advice at Box and do you still live by it? Yeah. How did you know him, by the way? Kind of probably how everybody gets to know Mark Cuban. So it usually starts with an email from you to Mark Cuban. And then from there, a relationship sort of blossoms. So it was our, between our sophomore and junior year of college, Box had been around for about four months or so.

5:41We were trying to just tell everybody about it and also raise some money as well. So Mark was sort of right at this intersection where he had a popular blog. So we wanted him just, maybe he could just tell people about it. And we also were raising money constantly. We were pitching everybody we could find. Everybody was rejecting us. So we had like a very, very low hit rate. But Mark, he is passionate about entrepreneurship. He's fine to kind of throw a Hail Mary for a crazy idea and bet on crazy founders. And we kind of qualified on all of those things. Like, you know, this idea had been tried but failed miserably through the 90s.

6:14So it was like, on paper, it seemed like a bad idea. We were brand new entrepreneurs, at least in the kind of ecosystem that real companies were coming from. So it was very much a kind of a Hail Mary for us to pitch him and for him to even invest. but he made this investment. Love that story, by the way, for all the founders who are listening, right? Yeah, I mean, the takeaway there is just pitch everybody and just don't give up. I look back and I can sort of figure out why it was Mark Cuban that invested, but we pitched another 30 people or 50 people and everybody else said no, except for a couple other people.

6:48And it could have been somebody else and not Mark Cuban. So you just have to pitch everybody. And just like, and then, you know, if you are so lucky that you have multiple choices to pick from, then pick the people most relevant to you. And Mark actually would have been very high on that list just due to his background. But we were unbelievably thankful that he made the investment. That led to actually us dropping out of college and betting on the company full time and pursuing it full time. But within about sort of six months or so of the investment around that time period, we were trying to decide a couple different business model options and even technical options.

7:21We were split between a couple different approaches that we wanted to take. and he basically said, you know, as a startup, just don't hedge your bets. Like it's either going to work or it's not going to work, but it won't work if you try and do both things. And so from even like a game theory standpoint, that's sort of the best advice. If you have a startup with very limited focus and attention and bandwidth, if you try and do two things that are of opposing sort of directions, you're guaranteed to lose. If you do one of those things, maybe you only have a 50-50 chance depending on which one you chose, but that's your only path.

7:54to having any ability to get a return because you're just going to be too diluted. Your energy is going to be split in too many directions. And so that was the advice he gave. And we live by it today. Every time that I see a situation where it feels like we're hedging, which is like we can't quite make a decision. So we want to try cool things. Channel Mark Cuban. We just fully live by it. Now that can be different from keeping your options open. And so it's important to not kind of confuse the two. So we often are keeping our options open where we will have maybe a technology framework approach that's sort of flexible.

8:26It can move in different directions depending on the trends that we see in a market. But if there's something where you really need to make a fundamental decision, we do not parallel track it. We make a bet and we generally go big on that bet. And so far, our hit rate, I think, is pretty good. And it's avoided a lot of sort of disasters that guaranteed would have happened if we had tried to go both directions. So what's an example where parallel tracking kind of multiple options would have been disastrous. Yeah, so once we pivoted into the enterprise market, which we did relatively early on, a couple of years into the business, and it's a great example of just not hedging.

9:03We were all in on enterprise. We had a lot of customers that said, okay, could you deploy an on-premises version of this software where we could just run it in our own data centers? And this was a time where the cloud wasn't 100 % obvious. There were a lot of enterprises, especially in banks, pharma companies, governments, that didn't want to have a cloud version of technology. They needed to be able to manage it and control it themselves. So a lot of companies at the time were actually giving enterprises on-premises systems at the same time as building out cloud products because they wanted to be able to win the deals and not have to convince the customer to go to the cloud.

9:35We made the bet that architecturally it was going to be way more important to be fully in the cloud because as each new innovation happened, mobile devices or eventually AI, you need the ability to have all of your customers have access to that technology on day one, the moment it exists. When you have customers on on-prem environments that are managing their own systems, you're at the mercy of their upgrade cycle and how often they can keep their technology up to date. And then what's funny is actually the end users end up blaming the vendor. Right. They don't blame their internal IT systems.

10:07They will think to themselves, oh, that box product isn't very easy to use because it doesn't work on my phone. And so what we realized was we don't want to be in a situation where we have all of these customers that have fragmented deployments of our software. We need to be able to be in control of all of the new innovation that they get. And so we basically said, we're not going to hedge our bets. We're betting fully on the cloud. It will mean, and it did mean that we will lose deals along the way. There'll be lots of banks that we don't win. There'll be lots of pharma companies. There'll be lots of government agencies that we don't win.

10:37But guess what? Years later, certainly as all of our cloud customers kind of grew. They got all of the benefits of using the most modern features and technologies that were available. But probably the vast majority of the companies that said, no, we have to stay on-prem eventually realized they have to go to the cloud. And we had now a better architecture to have them move into. And all of the players that were doing the on-prem software were the software that those companies were moving from. So yes, you had to be much more patient. But if you are right architecturally, you end up in a way better spot, assuming that you kind of stick to your principles.

11:13So the general advice there is for any startup, certainly don't hedge your bets, but absolutely don't change your technical principles because if you're right, customers eventually will move to your side. If you're wrong, the business wouldn't have worked out anyway. So it doesn't matter. Yeah, that's great. All right. I read somewhere that you were a magician in middle school. I don't know. Are you still a magician? Do you still do magic? I can occasionally produce a magic trick. Well, actually at MIT, one of my lab mates, Seth Raphael was a magician and he studied the emotions and signs of wonder, which I thought was really cool.

11:44But anyway, the whole, I guess, premise of a magic trick is that you are distracting the audience to look at the wrong thing while you're here doing the real trick. Well, first of all, you're not supposed to know that. Okay, okay. That's like our little secret. So yeah, you don't have to tell people this. But anyway, I want to apply this to AI. Oh, yeah, great. Yeah, what is everybody distracted with that is like the distraction? It's not like where the real value of AI is being created. And where do you think the real value of AI is being created? Oh, interesting segue. Because actually, we do use magic.

12:19I use some of these principles in the actual product. Cool. Okay, say more. Well, there's a lot of stuff you try and do where you need to buy yourself some time in the product experience while the system is working. It's not like classic misdirection, but it's ways of sort of the system is sort of thinking and how do you make that not very boring, but you actually give people a real experience through that process. And so I thought maybe there's a chance that that's where you're going. But yeah, on the overall space, I think that I'm such a deep kind of enterprise person that my general belief is enterprise AI is where most of the power of AI will be delivered.

13:01AI at the end of the day, for the most part, minus a couple categories, I can argue for a few categories that are kind of consumer oriented. I think really it's an enterprise industrial technology. It's much more akin to kind of our classic industrial automation systems, but for knowledge work. So if you saw a gigantic, you know, forklift for the first time ever, you wouldn't immediately be thinking that's a massive consumer market. You'd be like, yeah, this is going to be in warehouses. And so I think of AI like that. I think there's a lot of fantastic consumer tools, search and finding information and maybe therapy and a few of those things.

13:37But I think the big market is the big TAM is on the enterprise side. And then I think I'm probably just a pragmatist in the sense where I think you're going to model progress that just continues to go at an incredible rate, which will just keep happening. But model progress will always have a capability overhang effectively until you have the workflow systems that take the models and put them into the business process. And so I think the part that maybe some people have wrong is that that will take longer than maybe some of the most optimistic or bullish AI people believe. I think the change management in enterprise workflows just takes longer than most realize.

14:15And I think that will cause a little bit of cognitive dissonance because in Silicon Valley and in tech, we will see these incredible breakthroughs. And we will be using them because we'll be coding with them and we'll be changing our workflows very quickly. But then for the rest of the world to actually adopt those and transform a business process, transform their life sciences process, transform their manufacturing workflow that will still take two years, three years, five years, maybe a decade. We saw this in the cloud where everybody in tech, like within two years of like, let's say like 2007, 2008, 2009, we were all like, yeah, obviously cloud computing, that's the future, we get it.

14:50Well, guess what? It took two decades for that to actually play out across all businesses. And still, we're still going through the migration phase. I was with a customer four weeks ago. They are the biggest believers in cloud of all time. There's no question they love the cloud. And yet they still have massive systems on-prem that they are only now starting to think about how do they migrate. That is the kind of change management that actually happens in kind of large enterprise and in the corporate world. This is gonna take a little bit longer than people think. Maybe the good news is if you're on the entrepreneurial side of this.

15:24It means you have time to build great solutions for the market. If everything could just instantly be solved by the model, there wouldn't be a lot of opportunity for entrepreneurs. You know, the labs would just have all the value. So I think it's mostly a good news story. It just means that we have to be a little bit more patient with our timelines. In a minute, we tackle the elephant in the room. Is AI the end of SaaS? Why Aaron is still betting on SaaS after a short break.

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16:58Welcome back to Pioneers of AI. You can also watch this episode by heading over to our YouTube channel. So AI is definitely creating this kind of structural shift in how and where value is being created. And I want to segue next into software as a service. And I'll just name it as it is. There's this meme going around that SaaS, as we know it, is dead. And I just want to kind of unpack why I'm like, I want to position this and then, of course, get your point of view. So the idea is that SaaS produces these tools that help us do work faster and more efficiently, right? And it's mostly kind of a systems of record, whereas AI just does the work.

17:43And the example that I like to give is, you know, instead of creating a tool that helps lawyers organize their contracts, you are just creating an AI lawyer that is going to redline, review, approve the contract. What do you think about all of this? Boxes is the system of record, right? I am a believer in exactly your set of sentences. Where people get it wrong is thinking that the agent that does the contract doesn't need a system of record to work on. and that's the part that I think some of the narrative misses. So like I 100 % endorse the vision you just laid out but then the question is, well, how did the agent get the contract?

18:21How did the person interact with the agent to give them the contract or to get the output of that contract? How did the ecosystem of other enterprises interact with the agent for the redlining process that has to go outside of your firm? Well, all of that actually needs software. That needs workflows. that needs data governance, that needs access controls. My argument would be that the system of record becomes meaningfully more valuable in a world of agents. Because my general view is that what you basically have is like a thousand X increase in the number of users on these platforms. So it used to be that when we sold, let's just take that contract example.

19:01When we sold a document management system to a law firm or a company with a legal team, you could only sell to as many lawyers or as many people were in the legal operations of that company. Now with agents, that law firm might be doing 2x the amount of work or 5x the amount of work with agents. So now all of a sudden, the system that has to maintain what agents are allowed to do what on which data and which people can see the output of that data and how do you manage and govern that whole workflow, that actually increases in value in a world where there's more agents or humans on those systems because the stakes are now higher, right?

19:36An agent getting the wrong piece of data, answering the wrong question, combining the wrong information, leaking data to the wrong person, the stakes of that now have gone up by an order of magnitude. And we are only in the earliest stages of seeing what AI security is going to look like. What happens when you prompt inject an agent to pull out data from a system you shouldn't have access to? All of a sudden, people are going to care a lot about their systems of record to ensure that they're up to date, they're maintained, they have the right data, their access controls are correct, their security is good.

20:08that they connect all of the agents in the right way. So that's sort of why I think the premise is a bit off when people talk about, well, the SaaS sort of get consolidated into agents. When was the moment you realized that you, Box, have to go from this, like being the system of record to implementing these agentic workflows? And what did you then do to bring that to fruition? And perhaps also then give us an example of how you've worked with an organization to make this happen? Yeah, I mean, we realized it basically probably within 10 days of ChatGPT. And I kind of look back at that period and honestly, it's a little bit like, like I probably just should post more to my own thinking process because I don't know why it had to take ChatGPT to make it so obvious what we should be doing with LLMs.

20:59But the ChatGPT moment was like just the right packaging, the right user experience that kind of made it super obvious what we were all sitting on. So within about 10 days, we were like, okay, if people start asking questions of an AI system, then obviously I shouldn't just ask questions of a trained model. I should ask a question of my enterprise information. So like that was obvious. Now, again, to be clear, like six years prior, we actually had a project to attempt that. It just was never going to work because until you had like a 200, 300 billion parameter model that could be run efficiently, Like you just wouldn't be, it wouldn't generate the right answer.

21:36It'd be very domain specific. So, so that was kind of like very shelved and we were not even like, it wasn't even our headspace. So it really required that the ChatVT, you know, experience to manifest it again. So then we pivoted. It started small. It was like a 10, 15 person team. And, and we were, we were focused on the experience you just said, which is like ask documents questions. That was great. And, and then the other very obvious use case that emerged was, well, if I have an AI model can read a document. You can then pull out structured data from documents. So that becomes, that lets you take unstructured data and turn it into structured data.

22:10That's very powerful for a wide variety of use cases. So we were kind of cranking on that. And then I think the really big like 10x has just been agents in the past year. And we started really getting our arms around this idea of, well, what happens when you can just deploy an agent to go and do work for you? And what you give them is access to any of the most important data for that work. So I want to go and review a bunch of financial documents and have an agent go and do a due diligence process on that financial data. That's obviously a highly valuable activity in the economy. And so can agents sort of loop through data hundreds or thousands of times for a long running task that could take hours?

22:49That's the kind of technology that we're now building. So that's the early stuff. The stuff that's live right now that is a meaningful breakthrough for us is just give us your contracts, your invoices, your healthcare documents, your research files, and we have agents that go through all of that data and extract all of the structured data from that. So then you can automate a broader workflow, or you can get business intelligence on all of this content that you're sitting on, which is great for if you're a real estate firm and you need to know, you know, what are the clients that maybe could be saving money on their next renewal at the site that they're at?

23:23How do you have data across all of your clients, across all of your contracts and lease agreements that give you that kind of insight? Let's say you're a talent agency and you want to know new ways that you can either package up talent or projects or monetize in new ways. Well, what if you could read through, again, all of the artist information you have, all of the contracts that you have, and be able to just in natural language query all that data? Those are the kind of things that our customers are now starting to do. And it's either automating workflows or delivering new kinds of intelligence and insights into their business process.

23:54Yeah. So I want to talk about business models because traditionally, again, SaaS companies, the business model was a per-seat pricing model, right? But increasingly with a lot of AI companies and agentic AI workflows, it's outcome-based. Like, did you complete the task? I'm also an investor, for example, a number of companies where it's almost like they're leasing a digital coworker or an AI agent or agentic workflows. How do you think about business models in this scenario where you're not just like the organization system, but you have all these tasks that you're helping your customers complete?