Transcript: 'How Box Is Building an AI-first Company

The transcript of AI & I with Aaron Levie is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

Introduction: 00:01:30
Why AI won’t take your job: 00:02:36
Jevons paradox and the future of work: 00:06:42
How Aaron’s experience with the cloud era shapes his view of AI: 00:10:40
Why every knowledge worker is becoming a manager of AI agents: 00:19:44
What Aaron’s learned from bringing AI into every corner of Box: 00:25:21
What’s overhyped in AI today: 00:33:57
How Aaron balances everyday execution with innovation: 00:43:31

Transcript

(00:00:00)

Dan Shipper

Aaron, welcome to the show.

Aaron Levie

Hey, thanks for having me.

Dan Shipper

Thanks for coming on. So for people who don't know, you are the CEO of Box. You are a longtime X poster — P-O-A-S-T-E-R. And you have turned yourself into, I think, a really interesting thinker on AI, which is not surprising, but I think for someone running a big company, it seems like you've really gotten your hands in it and really understand it in a deep way, which is awesome.

Aaron Levie

Yeah, no. I think my POAST days are a little bit behind me, but I'm handing that off to the next generation. And now I'm just just going to tweet about AI agents until the whole the whole thing's over. But yeah, it's been a super exciting journey for all of us that have been deep in the space probably the most exciting technology and at least my lifetime, and so just having a lot of fun with it.

Dan Shipper

So one of the places I want to start is I feel like you have a pretty particular perspective on why jobs aren't going away because of AI agents. Do you want to talk about that?

Aaron Levie

Yeah, so I still leave open a 5 percent chance that I'm totally, obscenely wrong—as you should. So this is sort of a high confidence with room for debate and some internal doubts here and there. But for the most part, and I think it's somewhat empirical when you use these tools, I'm a big believer in—and thousands of people have talked about this at this point—but jobs are not tasks. Jobs are a collection of tasks. And AI is very good at automating tasks. Obviously, the definition of a task is expanding dramatically based on what agents can now do. But at the end of the day, we still need a human to incorporate whatever task was executed into a broader workflow, into a broader business process, into some form of actual value creation. And because you can't ever get rid of that person, that means we'll eventually still have some degree of specialization of what people end up owning from a completion of all the relevant tasks in their domain.

So let's just go through the list. Even an engineer who needs to develop some form of software—they're going to go to an AI agent and have it work on part of the code base, but then they're going to have to make a decision: Do I ship that feature? Do I like that code? Do I have to go talk to a product manager to make sure it's the right thing? I have to incorporate it into a broader project or a broader system. No matter what, you're still going to need people for all of that. So that's sort of why jobs, as a general matter, don't really go away.

Then the question is, what do you do if you can output 20 or 50 or 100 percent more in any given job? A lawyer can review double the amount of legal briefings. An engineer can generate two times the amount of code, or five times the amount of code. Well, shouldn't that reduce jobs in some commensurate way?

My view on that one is, I think it turns out that we're doing way less work than what is actually economically useful. We are merely constrained by how much time we have in the day and the cost of labor to be able to go and do that work. I look at examples throughout our own company, and if I could have lawyers go and review legal documents and contracts two times faster, I would not have half the number of lawyers. Actually, the throughput of that particular bottleneck in the organization would just go way faster and be reduced. So we would employ the same number of lawyers, but we would be reviewing all of our contracts at two times the speed, which would probably, in most cases, mean that there's some feedback loop where we're growing sales incrementally faster and we're getting back to customers more quickly. So we have higher customer satisfaction rates that would actually, even in some cases, lead to more revenue or better business results, which would then ironically lead to hiring more people in those functions that could drive more growth.

Similarly, in product and engineering, if we could ship two times the amount of code, what's likely going to happen is we're just going to expand the footprint of our features. We're going to build even more software that will then create new bottlenecks in the organization that cause us to hire more people in the areas that are now involved in that. So in most cases, I'm finding that we are just not at capacity and we have not reached the point of supply-demand equilibrium where we are doing just the perfect amount of work in the economy, in any given function. And if we can make it more efficient, we'll actually do more of it.

That kind of ties to the Jevons Paradox idea. Jevons Paradox, I think, is mostly applied to inanimate resources and AI systems, or trains and energy consumption. But you could actually apply it to people too. The idea would be, if you could have a lawyer that could do 30 percent more output or 50 percent more output of legal reviews, the demand would actually go up for legal work because it becomes incrementally cheaper to then go and do that legal work, which means you open up a new tranche of use cases for that kind of work to get executed.

So I just think all of the evidence right now is pointing more toward: we're just going to do more. We're going to ship more, we're going to better serve customers, we're going to have more marketing campaigns, we're going to build more software, we're going to get better healthcare, we're going to have more tutoring and education. But all of those things still then drive jobs in the economy.

Dan Shipper

I agree with you. I think the lawyer example is such a good one. I have caught hundreds of thousands of dollars worth of legal mistakes just by putting my contracts into GPT-4. And I couldn't have hired a lawyer for that before because it would have taken too much time. I already had a lawyer draft the contracts, and they're the ones that made the mistake. So the second level of legal review is a thing I couldn't have paid for before, but now is a job that either a lawyer assisted with an AI, or a legal firm could offer via ChatGPT or whatever—or ChatGPT just offers that now in a way that I couldn't have bought before.

Aaron Levie

Yeah, I think if you think about it, what are all the things today that you're not doing because the price of entry to doing that thing is I have to hire a person or go and pay and procure an external service? The minimum amount of money that you can spend just to even start to talk to a lawyer is thousands of dollars. The minimum amount of money you can spend to prototype an idea with an ad agency is tens of thousands of dollars. So what happens in a world where you can go and do that for $5 or $100 or $1,000? You're just going to do way more of those types of activities.

And then again, kind of ironically, by doing more of those activities, you might actually find scenarios where you want to now bring an expert back into that workflow that you wouldn't have had before. So there are areas within our company where we start to do something purely as a test case or a prototype, or just kind of an ideation with an AI system. And it works just enough that we say, now let's actually go do this in the real way and let's go pay somebody or hire somebody or put somebody on that project. But we wouldn't have even gotten it started before if AI didn't exist, because we would not have even thought that we should go light up that project if we couldn't have prototyped it in the first place.

And so this is sort of the part that, again, no economist has any way to estimate how much of the economy is going to grow as a result of that. It's impossible for our brains to kind of get around: well, how many new things get lit up because AI lowered the barrier to entry to cause then more people to get involved in that particular task? But that's actually going to be probably a substantial amount of work in the future.

Dan Shipper

How do you think about the future? So you're someone running a big company, you're running a company that came up in the cloud era, seeing a new technology wave come through. There's probably maybe a little bit of a sickening moment, like, oh my God, am I going to have to change everything? Or how does this affect my business? Right? And your job is to figure out where the future's going and to start to understand, okay, is this going to be good for us? Is this going to be bad for us? How is it going to change jobs? All that kind of stuff. And you're approaching it from a perspective that seems—you seem to have a pretty informed perspective on where it's going. And I'm curious, how do you form that yourself? How do you go through all the possibilities to understand what's coming next?

Aaron Levie

Yeah, I mean, I think to some extent I'm kind of working through analogy and working through the fact that I've seen a couple of these major shifts, and so you have that as a background experience that informs a lot of what's going to happen next. And everyone can kind of debate what are the most relevant platform shifts that we've experienced that AI relates to. But at a minimum, you can kind of think about it as: okay, we went from the mainframe to the PC. We went from PC to mobile. We went from on-prem to cloud. These are platform-level shifts. We kind of know how they work. You have the early adopters start to play around with new technologies. They adopt these tools. And then there are breakthrough use cases that cause more of the mainstream, pragmatic buyers of technology to adopt those, then that kind of accelerates, and then you eventually have the laggards.

(00:10:00)

We see this pattern every single time a new technology emerges, and it happens at the big macro technologies like cloud and mobile. And then it happens at the micro technologies—like any sub-service in cloud or mobile experiences the same thing.

So AI actually has a very similar curve. It's going through the exact same typical bell curve of adoption patterns. One thing that's different is it's happening in a compressed fashion. So where cloud may have taken 10 years to reach complete mainstream adoption by every relevant company, AI is probably doing that in two years. But each individual technology within AI still has, again, a very similar curve. So obviously, those of us spending too much time on X, we're seeing AI agents in coding before the rest of the world. But you kind of know exactly what's going to happen next. Over the next two years, everybody's going to adopt AI coding agents. It's just a guarantee because the efficiency and the productivity gain is so massive that this will ripple through the economy.

And so I think by having a lens into both what the prior trends have been and just by being a very active user of these technologies myself, I can kind of see where things are such a breakthrough that they will most likely, again, ripple through the economy versus which things are maybe more incremental and won't be that impactful. And that ends up helping inform this.

And then I think maybe the third factor is, because we were a startup at the early days of cloud, I felt that shift deeply inside of a company that had to go through executing on a large technology shift that was happening. So to some extent, I'm kind of pulling from those memories as much as possible and saying, let's—I kind of have to do that again. Now, there's obviously more people, we have new risks, we have new opportunities. But it's very much hardcore startup mode of, I often just ask myself quite literally, what would we do if we were starting the company from scratch and it was just 10 people? How would we operate? How would we execute? What would we be building? What features would we be creating? So if we were starting the company over in an AI-first world, what would that look like? And so again, I'm benefiting from the fact that I saw that front-row seat on the cloud wave, and we're trying to—again, that's informing the company, I think, on what this should look like.

Dan Shipper

So what are some specific things that you remember from that cloud wave? Because I think the term "digital transformation" was the big thing like 10 years ago, and everyone needed a digital transformation strategy, a cloud strategy. And some people probably did it well, but a lot of people, I think, they just knew they needed to say those words. And there's a big difference between companies that say the words and are like, yeah, we have a cloud strategy, and companies that actually ended up effectively doing it. And I'm curious what your memories are of how that works and who does it well and who doesn't, and then how you think that applies in this era.

Aaron Levie

Yeah. You know what's interesting is, this is going to be much bigger than that. Because in the cloud transformation and the digital transformation, first of all, it was always a little bit abstract as to—and I think you're kind of getting at that in the question—it was always a little bit of an abstract concept. When United Airlines does digital transformation, what does that really mean? That means that they should probably have a really good mobile app, a really good website, the customer support should be intelligent and kind of relevant to you. But at the end of the day, and with all respect to United Airlines, if you look at your flying experience from 15 years ago to today, basically nothing changed. So pre- and post-digital—

Dan Shipper

It's probably a little worse.

Aaron Levie

It might be worse. It might be worse. So the reality is, for as much work as went into that digital transformation process—and I'm sure that behind the scenes, lots of really interesting technology got used, lots of new ways that they're operating in their data centers changed—the actual day-to-day experience as a customer did not meaningfully change or meaningfully jump. They probably have a better-designed website, et cetera. And I think that's probably felt across a significant portion of the economy, pre- and post-digital transformation.

Now, there are more extreme examples. So if you look at Disney as just a random example, I think they would probably say, well, we had to become a digital company in the form of our product has to be now fully digital. We can't rely on the movie theaters. We're going to go direct, and that's a more significant business model disruption that had to occur. Probably some banks maybe land in that category. But you were kind of on this continuum.

In the AI transformation, the reason why this is going to be so different and so much more impactful is it changes how every single employee in your company operates. Again, the daily experience of that United Airlines employee 15 years ago to today—their tools are a little bit more modern, they're probably getting real-time data feeds where it used to be a little bit more asynchronous. That was sort of a contained level of transformation in your daily experience as that employee. With AI, there's not going to be any going back to the way things used to be and how we work. It's just not possible because the efficiency gain between the company that uses AI versus the one that doesn't is just too insurmountable to try and make up for if you're not using these technologies. And the way that we will work at the end of that transition will be so different that you'll just fundamentally feel it again in your daily work, in your daily tasks.

And so maybe that would be the biggest difference between digital transformation and this sort of AI era that we're entering—the way that we work is going to be so fundamentally altered that you'll, again, just experience this in your daily life as an employee in any one of these companies. Some jobs will entirely change and be entirely shifted. And then other jobs, again, the daily activities will just be so different.

So let's take the engineering space, obviously, that we're following. If you talk to a very clued-in, online engineer right now and you say, how are you developing software today versus even one year ago? It is probably the biggest shift in any period in history of almost any knowledge worker job that's ever occurred. One year ago, you were typing into an IDE. Maybe you were having some autocomplete technology like GitHub Copilot. Maybe you were asking a question of an AI system, getting some suggestion back. That was sort of one year ago. Obviously, three years ago, none of that even existed.

Today, you're prompting an agent that's going to go off and do a large amount of work. It's going to come back with that work product, and you're going to go and review it. That is a completely different job. And within a one-year period, if not every job changes as much as that, if you kind of look at how that's going to ripple through knowledge work and you apply that to almost every form of knowledge work, it will mean that all of our daily workflows—if you're generating marketing assets and building marketing campaigns, if you are in sales and you're supporting a customer, if you're in research and life sciences—every single one of our jobs is going to look completely different in the next, let's say, five, plus or minus years. And that will be why it's so different than, let's say, even digital transformation was.

Dan Shipper

So do you think then the better metaphor, a more apt analogy, is like the shift to using computers at all? When we first started using VisiCalc and spreadsheets or something like that?

Aaron Levie

I think it would rank at that level in terms of the amount of change. So the paper-to-digital process was a fundamental form factor change in how you worked, right? Everything about the workflow of a company—well, it's probably even more significant. It's probably from paper to digital plus the internet. Because I think what we sort of did was the skeuomorphic thing in the first phase, where we just kind of took the paper-based desk worker's set of tools and we put them into a digital screen. That was a shift, but it was then an even bigger shift once you could connect those systems and you could collaborate in real time. So we're kind of compressing that level of shift in a, again, one- or two-year period. But very much akin to that.

Even the shift from kind of on-prem to cloud—it sort of impacted the aesthetics of software, and it impacted the fact that when I chatted with you, you got the response faster and it was queued up differently, and the way we collaborated, we didn't use version control. We just worked together in real time. That was a very big deal. But we already kind of understood the general structure of how we would work together and how we would communicate together, kind of pre- and post-cloud.