Box CEO Aaron Levie on Why AI Agents Won’t Take Your Job

[00:00] AI is filled with CEOs predicting the end of white collar work. So I talked to one of the few CEOs who has a vision of what the world will look like, where AI works for us instead of replacing us. As long as there's still a three-dimensional world out there that we have to go and participate in, [00:17] We're going to have much more signal, much more context than the AI will. And that's going to just keep us in jobs, keeps us doing things for as long as we can kind of look out right now. That's Bach CEO Aaron Levy. [00:28] He's been running the company for almost two decades. They have 2,000 employees. And almost overnight, he's made the company AI first. And that's taught him a lot about what the future is going to look like over the next few years. If you care about the skills you're going to need in an economy filled with AI agents, this is the interview to watch. [00:59] This podcast is supported by Google. Hey folks, Stephen Johnson here, co-founder of Notebook LM. As an author, I've always been obsessed with how software could help organize ideas and make connections. So we built Notebook LM as an AI-first tool for anyone trying to make sense of complex information. Upload your documents and Notebook LM instantly becomes your personal expert, uncovering insights and helping you brainstorm. [01:29] you [01:29] Aaron, welcome to the show.

[01:31] Hey, thanks for having me. [01:32] Thanks for coming on. So for people who don't know, you are the CEO of Box. You are a longtime ex-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. [01:57] 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, you know, just going to tweet about AI agents till the whole thing's over. [02:27] my lifetime. And, and so just having a lot of fun with it. [02:32] So one of the 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 agents. Do you want to talk about that? [02:43] Yeah. So, you know, I still leave open a 5% chance that I'm like totally obscenely wrong. As you should. Yeah. So this is sort of like a high confidence with room for debate and, you know, some internal doubts here and there. But for the most part, and I think it's sort of somewhat empirical when you use these tools.

[03:13] tasks, jobs are a collection of tasks and AI is very good at automating tasks. [03:18] And obviously, the definition of a task is expanding dramatically based on now what agents can do. [03:25] But at the end of the day, there still is, you know, we still need a human to incorporate whatever the task that was executed into a broader workflow, into a broader business process, into actually, you know, some form of value creation. And so because you can't ever get rid of that person, that means we eventually will still have some degree of specialization of what people, you know, end up owning from a completion of all the relevant tasks in their domain. [03:55] And so, you know, let's just go through the list. You know, even, you know, when an engineer needs to [04:01] develop some form of software, they're going to go to an AI agent and have it go work on part of the code base. But then they're going to have to make a decision of, 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? [04:16] I have to incorporate it into a broader project or a broader system. And so no matter what, you're still going to need people for all of that. So that's that sort of. [04:24] you know, why jobs as a general matter don't really go away. Then the question is, so what do you do if, you know, work, you can, you know, output 20 or 50 or 100% more in any given job? A lawyer can review double the amount of, you know, legal briefings. An engineer can generate, you know, 2x the amount of code or 5x the amount of code. Well, shouldn't that sort of, you know, reduce jobs in some kind of commensurate way? And my view on that one is really just, I think,

[04:54] doing way less work than what is actually economically useful. And we are merely just constrained by how much time we have in the day and the cost of labor to be able to go and do that work. [05:07] And, you know, and so I look at examples throughout our own company. And if I could have lawyers who, [05:13] go and review legal documents and contracts two times faster, I would not have half the number of lawyers. I would actually the throughput of our of that particular bottleneck in the organization would just go way faster and it would it would be reduced. And so we would have we would employ the same number of lawyers. [05:32] But we would be reviewing all of our contracts at 2x the speed, which would actually 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 kind of customer satisfaction rates. That would actually even in some cases lead to more revenue or better business results, which ironically then would lead to hiring more people in those functions that could drive more growth. [06:02] if we could ship two times the amount of code [06:04] What's likely going to happen is we're just going to expand the footprint of our features [06:09] We're going to build even more software that will then create even new that will create newer bottlenecks in the organization that cause us to hire more people in the areas that now are are involved in that. And so in most cases, I'm more finding that that that we are just actually not at capacity and we have not reached the point of of supply demand equilibrium where where we are doing just the perfect amount of work in the economy in any in any given function. And if we can make it more efficient, we'll actually do more of it.

[06:39] And that kind of ties to the Jevons paradox idea. Jevons paradox, you know, I think mostly is applied to inanimate, you know, kind of resources and and, you know, AI systems or or, you know, rail trains and, you know, energy consumption. But you could actually apply it to people, too. And the idea would be if you could have a lawyer. [07:00] that could do 30% more output or 50% more output of, again, legal reviews, the demand actually would 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... [07:22] 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 health care. We're going to have more tutoring and education. But all of those things still then drive jobs in the economy. [07:39] I agree with you. I think the lawyer example is such a good one. I have caught like hundreds of thousands of dollars worth of legal mistakes just by putting my contracts into GPT-5. 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 like 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, you know, chat GPT or whatever, or chat GPT [08:09] have bought before.

[08:10] Yeah, I think if you think about it, like 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 us an external service. Like, you know, the minimum amount of money that you can spend just to even start to talk to a lawyer is thousands of dollars, right? The minimum amount of money you can spend to prototype an idea with an ad agency is tens of thousands of dollars. [08:40] 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 then the 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. [09:10] 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 got it started before. [09:19] if AI didn't exist, because we would not have even thought that we should go, you know, sort of 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 your 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

[09:49] But that's actually going to be probably a substantial amount of work in the future. [09:53] How do you think about the future? So you're 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? And your job is to figure out where the future is 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. [10:23] And you're approaching it from a perspective that seems to, you know, you seem to have a pretty informed perspective on where it's going. And I'm curious, like how you form that yourself? How do you how do you go through all the possibilities to understand what's coming next? [10:36] Yeah, I mean, I think so to some extent, you know, I'm kind of working through analogy and working through the fact that I've seen, you know, a couple of these major shifts. And so you have that. [10:47] You have that as a background experience that informs a lot of what's going to happen next. Everyone can debate what are the most relevant platform shifts that we've experienced that AI relates to. But at a minimum, you can think about it as, okay... [11:08] We went from the mainframe to the PC. We went from PC to mobile. We went from [11:13] 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 kind of mainstream, pragmatic buyers of technology to adopt those. Then that kind of accelerates, and then you eventually have the laggards. We see this pattern every single time a new technology emerges. And

[11:43] and mobile, and then it happens at the micro technologies, like any subservice in cloud or mobile, experience the same thing. So AI actually has a very similar curve. It's going through the exact same sort of, typical bell curve of adoption patterns, [12:00] One thing that's different is it's happening in a compressed fashion. So where cloud may have taken, you know, 10 years to reach, you know, complete mainstream adoption by every company that is relevant. AI is probably doing that in two years. But each individual technology within AI still has, again, a very similar curve. So obviously, you know, those of us spending too much time on X, you know, we're seeing AI agents in coding before the rest of the world. [12:30] over the next two years. [12:32] Everybody's going to adopt AI coding agents. It's just a guarantee because because the efficiency and the productivity gain is so massive that this will ripple through the economy. And so so I think by having a lens into both what the prior trends have been. [12:47] 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 so maybe it won't be that impactful. [13:04] That ends up helping inform this.

[13:17] of a company that had to go through executing on a large technology shift that was happening. And so to some extent, I'm kind of pulling from those memories as much as possible and saying, let's [13:26] I kind of have to do that again because, [13:28] Now there's obviously more people, you know, we have, we have new risks, we have new opportunities. But it's very much like hardcore startup mode of, you know, I often just ask myself, like, quite literally, what would we do if we were starting the company from scratch? [13:42] 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? And 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 me and informing the company, I think, on what this should look like. So what are some specific things that you remember from that cloud wave? [14:12] You know, the term digital transformation was the big was the big thing, you know, like 10 years ago. And everyone, everyone needed a digital transformation strategy, a cloud strategy. And some people probably did it well. And but a lot of people, I think, they just know they need to say those words. And [14:30] 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, like what your memories are of of how that works and who who does it well and who doesn't. And then how you think that applies in this era?

[14:47] Yeah, you know, what's interesting is this is going to be much bigger than that because in the cloud transformation, the digital transformation, [14:58] 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, like it was always a little bit of an abstract concept of like, [15:09] 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 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... [15:30] Like basically nothing changed. So pre and post digital. It's probably a little worse. It might be worse. [15:50] 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 changed. [16:01] did not meaningfully change, meaningfully jump. Probably some better designed website, et cetera. And I think that's kind of probably felt across a significant portion of the economy pre and post digital transformation. Now there's more extreme examples. So if you look at Disney, [16:16] 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 movies on the movie theaters. We're going to go to direct. And that's a more significant business model disruption.

[16:31] 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 employer in your company operates. Right. [16:48] Again, the daily experience of that United Airlines employee 15 years ago to today, their tools are a little bit more modern. They're, you know, they're probably getting real time data feeds where it used to be a little bit more asynchronous. That that was that was sort of a contained level of transformation in your daily experience as that employee with AI. [17:08] 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 it 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. [17:38] you know, this sort of, you know, AI era that we're entering is, is the way that we work is going to be so fundamentally altered that, that, that you will, again, just experience this in your, in your daily life as an employee in any one of these, you know, 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 engineering, you know, space, obviously that we're, we're following.

[18:08] clued in online engineer right now, and you say, how are you developing software today versus even one year ago? It is the probably the biggest shift in any period in history of almost any knowledge worker job that's ever occurred. [18:22] Right. Like one year ago, [18:25] You were typing into an IDE, [18:28] Maybe you were having some autocomplete technology like GitHub Copilot. Maybe you were asking a question of an AI system, getting some suggestion back. [18:36] 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, [18:45] It's going to come back with that work product and you're going to go and review it. That is like a completely different job. [18:51] than, you know, within a one year period. If you, even 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 every, all of our daily workflows, if you're generating marketing assets and building marketing campaigns, if you are in sales and you're, you know, supporting a customer, if you're in, you know, research and life sciences, every single one of our jobs is going to look completely different. [19:19] 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. So do you think then the better metaphor or a more apt analogy is the shift to using computers at all, like when we first started using VisiCalc and spreadsheets or something like that? [19:38] I think that would be it would rank at that level in terms of the amount of change. Right. So the paper to digital process is was a fundamental form factor change.

[19:49] in how you worked, right? Everything about the workflow of a company, you know, it's probably even more significant. It's probably from paper to, [19:58] to digital plus the internet. Because I think what we-- we sort of did the skeuomorphic thing in the first phase where we just kind of took the paper-based desk workers set of tools and we put them into a digital screen. That was a shift, but it was an even bigger shift. Once you could connect those systems, you can collaborate in real time. So we're kind of compressing that level of shift in, again, a one or two year period. [20:24] But but very much akin to that, even the even the shift from kind of on prem to cloud, it sort of was was more impacted the aesthetics of software. And it impacted the fact that, you know, when I chatted with you, you got the you know, you got the response. [20:42] faster and and it was queued up differently. And the way we collaborated was we didn't use version control. We just work together in real time. That was a very big deal. But we already kind of understood the the general structure of how we would work together and how we would communicate together kind of pre and post cloud. The shift from pre and post AI is, again, fundamentally different because, you know, what I think we're seeing is, is that [21:05] The job of an individual contributor really begins to change because you are now a manager of agents. And that is a completely change in the kind of work that you do. And we don't, again, that's a very different level step function shift than what we've seen previously. Yeah.

[21:23] A hundred percent. Yeah. The way I've been writing about it for a couple of years is thinking about us moving from a knowledge economy to an allocation economy where your job is to allocate intelligence, even as an IC. [21:37] That's a key point, right? So managers' jobs are really about prioritization. They're about allocation. They're about using judgment across a set of tasks and projects that are happening. [21:53] And that effectively becomes the new IC job in the future. [21:57] Totally. One thing I want to get into, though, is you said you're in startup mode. [22:04] and you've been running box for a while um and i'm curious what that has been like for you are you like you seem energetic but are you were you like oh fuck i can't believe i have to go back to startup mode or were you like uh this is great finally i get to you know feel like i'm back to the ground floor again or some mix and you can be honest this is safe space but i'm really curious [22:34] introspective for five seconds. I'd say it's 80% to 90% [22:41] very excited. 10%, 20% anxiety. So on the 80%, 90%, I'm going to be [22:49] I just I love technology so much. And I mean,

[22:54] You have to be in this industry and to be trying to build a company for as long as we've been working on this. So I would say that this is me in my happy place, which is some major technology event is happening and you can get your hands on it. [23:24] appeals to my ADD, you know, instincts. There was probably a period, you know, three, four years ago where I was like, huh, maybe I should start to pick up some hobbies. It was kind of settling down. We kind of knew that like all the different landscape, we knew cloud, we knew mobile, we knew how everything was going to work. And so this is like way better because at 10 PM, instead of doing some arbitrary hobby, I'm on a, you know, zoom call with somebody about AI or [23:54] And that is so much better for me from an emotional excitement standpoint. I don't know. I mean, I feel like the world needs more of your pottery or woodworking. [24:04] At the very tail end of COVID, I started picking up guitar. [24:10] Just because I had some free time. And that's clearly a sign that the industry was kind of cresting and there wasn't a lot of change going on if I have time to learn guitar. Yeah.

[24:24] Um, and, uh, and now, now it's just, yeah, we're, we're in kind of full, full crank mode and it's incredibly exciting because something changes every single day that you have to respond to. Um. [24:35] And that's definitely, you know, exactly where I like to be. [24:38] We are so back. We're so back. We're so back, yes. [24:43] One thing that I think you've done really impressively is you were one of the first CEOs to really start the where AI first wave. You sent the memo and you were like, we're transitioning to being a company that takes us really, really seriously. And if you're not in, you're out. And I'm curious, I think a lot of companies are thinking about doing this right now or trying to do it with varying levels of success. [25:08] gone for you and what you've learned about the way to do this well and to really be able to start from ground zero inside of an existing company like that's so fucking hard so yeah i'm curious uh what you've learned [25:20] Yeah, it's extremely hard. And I would I would say still still totally on that journey. And we are not you know, we're not yet. [25:31] I can't put the mission accomplished flag in the ground and say that we are the case study. [25:37] We're cranking on this every single day. A few quick lessons. One thing that we tried to do was just be very clear that this is not about replacing jobs or spending less money as a company. This is purely about how do we get output to increase? How do we move faster as a company? How do we do more? And how do we better serve customers? So the first thing that I wanted to do is just make sure that this was not some threatening technology.

[26:04] But this is something actually that we should be on the forefront of because it's going to let us work better. It's going to let us actually do more as an organization. That was kind of, I think, important to lay out up front. The next is just making sure that everybody's using it every single day in some capacity and then constantly showing each other how we're all using it. So we do this thing every single Friday. It's our internal all hands. We have somebody demo it. [26:28] how they're using, in our case, BoxAI for different use cases. So they created an agent to automate some sales workflow. They created an agent to automate a compliance workflow. And so we want to constantly just have everybody learn from each other on the, [26:41] on what, you know, what, what this technology is, how it works, how it helps, uh, in your, in your daily work and then how you can go off and do it yourself. Um, you know, we, we've, we haven't quite systematized this, but, but I think increasingly I'm, I'm at least asking, and I'm hearing more people ask, Hey, why can't we do that faster? Um, you know, you look at a project timeline that comes back and it's three weeks or, you know, four weeks and you say, well, you know, I don't know why we can't do that in two days. Like, like, if you really just thought [27:11] just trying to create this thing or build that. Like, why can't we dramatically compress that timeline? And that's causing people to say, okay, maybe actually I should relook at this [27:19] Maybe there is a technology out there that that we can go and leverage to make that happen. And and so that's starting to kind of create a flywheel. But I think, again, the end result is companies are just going to move far faster. They're going to get way more done. They're going to be able to better serve their customers as a result of this. And I'm seeing.

[27:35] plenty of examples of all that happening. The one asterisk, [27:39] That'll say is the one thing we can't yet do, [27:43] And this is maybe me being defensive. Maybe we actually could if I was just like so like burn the bridges kind of right now. When I talk to five or 10 or 20 person startups with no existing process, with complete clean sheet of paper and how to operate, I am seeing them be so differently wired than you can be once you have existing workflows or processes that it is causing me to think like, [28:07] Do I have to start to maybe go and find areas where from a completely fresh start, we go and re-engineer something? And it's because these startups start with, again, nothing that they can kind of think about their engineering workflows more in this modern way, which is the workflow is actually you're prompting an agent. It's operating the background. You're reviewing the code of that agent. You're very documentation driven. You're very spec driven. You're prompt driven. [28:37] do lots of the work and then you're reviewing all of that. And that you can afford to do when you're not putting AI into an existing workflow, but when you're again kind of reinventing the workflow from scratch. And I do think there's areas where I want to do that much more in the organization. [28:52] Yeah, I mean, we have that. We run four software products internally, and we're 15 people. And I commit code to those, which I should not be able to. [29:02] And yeah, it would be totally impossible without Cloud Code and Codex CLI and all those kinds of things. And it just totally changes the engineering process because it's about the plan and if the plan's up to date. And who's reviewing the plan and what work has been done and the actual code doesn't matter as much.

[29:22] You know, highly, you know, tuned tribal knowledge on how to build things and, you know, in-person code reviews and all of the internal workflows. You know, it's sort of harder to do the let's just start. Let's invent this whole thing from scratch. But. [29:37] We are going through that journey. The way we build software already looks very different than it did two years ago. And I think it will look vastly more different in a year from now than the combination of the past couple of years. [29:52] Yeah, I'm totally with you. One thing that's been on my mind is, and I agree on the, there's way more demand than can be served. And you're generally just going to want to do more work as a company. [30:04] We've been in a non-recessionary environment for a long, long, long time. I'm curious, do you think that changes in a recession? [30:11] I think it changes, but again, we don't know the counterfactual where usually in a recession, you unfortunately have job reduction regardless. So now you would probably still have job reduction in a recession. [30:25] But the companies are still able to drive more output because they can, again, they get more leverage from AI. So, you know, maybe optimistically, I'm totally making this up. Maybe you get out of the recession faster because you haven't totally decimated your productivity levels as an organization. But I would say that, you know, I could totally contemplate a scenario where you have a very bad economic environment that would lead to job reductions, as it kind of always has.

[30:55] probably be blamed on AI because that would be, you'd still see people doing work with AI. When again, it's one of these, it's a counterfactual, which is, well, again, in a recession, you know, prior to AI, you also had, unfortunately, job cuts that you have to make in those situations. So you can't really know what would have happened in the non-AI scenario. So I do think we have to kind of watch out for that. But I really think about this as, you know, [31:20] Again, I'm not seeing any evidence to the contrary. I think about this as [31:25] as just the next kind of era of of knowledge, work, technology that we have always had these boosts in capability and productivity. And and you you sort of you're when you're in the moment of that transformation, it's easy to kind of look at it kind of myopically and say, well, oh, my gosh, this is going to be you know, this is going to totally reduce the number of jobs in this area or impact us in this area. And then you look at it 10, 20, 30 years later and you realize, wow. [31:55] Actually, it turned out the demand for that type of work was way bigger than we had imagined. And if we had never made it more efficient, we would never have actually gone and been able to capitalize on that. I mean, if you just look at like I'm totally this is fan fiction, but like if you look at probably, you know, what a graphic designer is. [32:14] 30 or 40 years ago would have said when they saw Photoshop, right? And you're like, wait a second, right now, this project takes like a week to go in and design this poster for this client. And you're going to make it take five hours. Well, you know, how is it not going to reduce graphic design jobs by, you know, 10x? And today we have vastly more graphic designers in the world than we did, you know, 30 or 40 years ago. Or, you know, you look at, you know, all of the,

[32:44] counting when we went from any kind of paper-based methods to the PC and Excel and VisiCalc and Intuit. And we have way more accountants today than ever before. So what is it about digitization that actually causes increases in [33:02] in these jobs. It's because we finally make the function efficient enough that way more people can actually go acquire those services. And AI is, again, for everything that I'm seeing, is just going to do the same thing again for a number of fields. [33:18] I'm hoping that the one negative impact of technology is more accountants than ever before. So, I'm hoping that doesn't continue. I love my accountant. [33:29] Well, one thing's for sure. You can imagine, again, in your example of AI reviewing your contract, now imagine when you start doing that to everything in your organization from a legal standpoint, you're going to be hiring way more lawyers as a result because you're going to say, [33:48] And [33:49] And it's still going to be bottlenecked by that human. So I don't see a lot of these, like in these large job categories getting reduced. [33:57] What do you think is overhyped in AI right now? [33:59] This is going to certainly show my bias on this topic. I don't know if I can think of something. [34:11] I think if we look at where we are, and this is, again, this is maybe one of the examples of having been through the cloud wave. And again, I might extrapolate too much, but I remember 15 years ago, you know, being like, gosh, I can't imagine this ex-SaaS category getting 10 times larger or five times larger.

[34:41] point. And I was like in shock that AWS was still doubling 10 years ago. And it's probably two orders of magnitude bigger today than it was. And so I think there, I don't know of a category where if I look at it, I say it's not going to be 10 times larger in five years from now. I don't think I can find that. [35:03] What would you argue? What do I think is the most overhyped thing in AI right now? I mean, I think that we just... People tend to... Just generally with AI, they tend to oscillate between like it's utopia and we're going to... Everything is going to be solved and it's free room service and teleportation for everybody. Yeah, yeah, yeah. Or we're all going to die. [35:32] And... [35:33] That's why I like talking to people like you, because I think you have a more grounded perspective on what's going to change. [35:39] It's going to change a lot of the way that we work. And also the world is going to continue more or less, you know, like we're still going to have problems. The bottlenecks just move somewhere else. And I think that's actually a much more interesting perspective and much more, a much better way to talk about the future. [35:54] I'm totally fine with the utopian people, assuming they're, you know, driving positive progress in the technology on that. I don't think you end up in the scenario that probably people imagine on that front, simply because every step along the way, some problem emerges that the AI is not good enough to handle, that humans have to play the kind of stopgap on. And that, I think, is a rolling process.

[36:23] As far as far as the eye can see. And and I think that each new technology breakthrough just leads to a new bottleneck somewhere else that people have to kind of, you know, we play the role of of the duct tape on. [36:53] rest. And, and I don't, I, again, I don't see AI meaningfully changing that. [37:00] There's just so much, and this is sort of back to this thing of like, why does the engineering job still exist in the future, et cetera. [37:08] There's so much signal and so much context that you get [37:11] by still operating in the outside world that is necessary for these AI systems to know about, but they can't glean on their own. And there's no breakthrough that we have any example of that replaces that. My ability to talk to a person down the hall that gives me an idea because they just talk to a customer, we can't replicate that in an AI system right now. We don't know how. [37:41] technological breakthrough that we know of that will replicate that so as long as as there's still a three-dimensional world out there that we have to go and participate in we're going to have much more signal much more context than than the ai will um and that's going to just keep us keep us in jobs keeps us doing things again for for as long as we can we can kind of look out right now yeah i agree with that i think um

[38:04] Mwah. [38:05] Even if we get there and people are working in robotics and continue learning and all that kind of stuff, it's easy to forget that there are all these weird – like people collect experiences and you learn from experiences. And so if you've spent a long time in an industry, for example, for the last 15 years, you have a lot of this tacit knowledge in your neural networks that you can't – you get feelings about. And even if we have an AGI, if it hasn't been there, it's not going to have the same perspective. [38:35] But there's still just your own personal experience thinking about you versus other humans is mad matters. [38:42] Yes. And, and I don't think we want, I don't think it'd like be that fun if you have to just prompt engineer everything in your life. [38:50] Like, [38:50] Like if I have to, if you had to prompt me when we're getting on this podcast and like, okay, you are doing a podcast right now and here's what you're going to, it's like, no, that wouldn't, it would be like, it would be so draining to, [39:01] That we just – we would completely halt the – you know, all of our interactions. So you do need – like – but people don't have to be prompted in that way constantly, right? We can kind of pick up on the cue that lets us be like, okay, I can put this in this part of my memory and I'm not going to – [39:17] I'm not going to accidentally talk to you about health care right now because that's not what we're talking about. And so people are just going to be better at that until – you kind of look at the – if you watch the Richard Sutton podcast with Dorkesh, it's kind of – it's exactly that, which is these systems are not – they do not have any context.

[39:47] And it's insanely valuable. There's an incredible amount of economic value in that, but they don't replace people because people, we can go around the world and we can build a tremendous amount of context that the AI will never be able to get. [40:01] No amount of humanoid robots in the world will be able to still replicate that. And we will deploy these as as utilities for us. As again, we always have. We continue as a as a species to be able to deploy technology. [40:17] as a utility, so we can get better things in life, better healthcare, better life expectancy, better food production, better entertainment. And I think this is, again, one of those technologies. Why do you think that more AI CEOs don't talk like this? [40:35] When you say AI CEOs, are you saying like Dario or? Yeah, I think a lot of the CEOs of the big labs don't talk publicly with this perspective. [40:47] I think, well, there's a reason I chose B2B software. I'm probably on the more boring end of this ecosystem. I've read your Twitter. You're not on the boring end. But there's a reason that I landed in enterprise software and not building an AI lab. So I live in reality with the practical implications and limitations of these tools. [41:17] or others either talk in the way they do or have the ambition in the way that they do, I believe by the time the technology hits the real world, it will manifest just a bit differently. And so thus the implications are a bit different. But I think it's kind of fun that we have different approaches to this. I think it would be actually very boring if everybody was like hyper pragmatic and practical. But,

[41:40] you know, I think on the margin, I'd rather have the more sort of, you know, this is going to be a crazy utopian future than the dystopian, you know, angle that some take. But again, I think it's cool. We have a marketplace of ideas. There's lots of different opinions. But, you know, when when you see things like, let's say Dario says, you know, we're going to see this massive job dislocation or 50 percent of jobs or whatever, the exact steps. I don't want to misattribute, you know, what he said. I think the thing that that that. [42:10] That just, you know, where, where, where ends up being a little bit different than that point of view is, again, these jobs are a collection of these tasks and, and we have figured out how to automate tasks that. [42:21] And the tasks are getting bigger. [42:23] But there still is an endpoint where a person has to come in, intervene, review something and execute something. And even in the cases and again, this is where this is where. [42:33] I'm informed by having now, Box has a few thousand employees, so I'm informed by this as a result of that. But even in the places where we say, you know what, we probably could have... [42:44] you know, a smaller total number of folks in the company doing password reset emails, because we can just automate that. [42:52] The way as a resource allocator that I respond to that, though, is we put more resources in a different area of customer success than [43:01] That has always been underfunded chronically because we didn't have enough budget to go apply there. And so if I can make one job function more efficient... [43:09] I gladly will take those dollars and reapply them into an area that is more strategic, that we have not been able to automate and that I see no sort of plans to be able to automate. And that's the much more dynamic nature of these organizations and of the economy generally that I think sometimes doesn't get sort of brought into the big economic dislocation conversation.

[43:30] How are you splitting your time and your focus between, I have these two buckets that I try to think about. So one is just removing the current bottleneck for any product I'm working on or any company I'm working on. It's like, we have a funnel, like where is the biggest bottleneck in the funnel? And like, how do we make that better? How do you split your time between that and like magic moments? It's like, wow, we have, there's this new technology that does this crazy wizard shit. And we can do something fundamentally new here. [44:00] time and attention between those two things. [44:02] And to clarify, do you mean internally, operationally, or for the product experiences we're building? [44:07] I think both because I assume the internal operations like lead to the building like magic, magical product stuff. [44:15] Yeah, sometimes they can be decoupled, but I think, again, as a pragmatist, [44:24] probably 80% of the time is going into the incremental bottleneck that we can de-block. Even from a product roadmap standpoint, if you look at the AI that we are delivering, [44:34] A lot of it is just hyper practical, like [44:37] You have a lot of contracts. You have a lot of invoices. You have a lot of research papers. I want to extract data from that so I can put it into a database to automate a workflow. That's where we're putting a lot of our energy because that's just a major pain point in the economy. It turns out there's like trillions of documents that all have important data in them that you would like to be able to extract automatically. And we could then power workflows more efficiently as a result of that. That's where we spend a lot of our time.

[45:07] not a number, we have a small number of initiatives that are much more like, okay, how do I go and automate the entire workflow of [45:14] of, you know, generating a loan agreement or, or of doing a due diligence on an M&A, you know, transaction. And that's a long running agent. It's going to, it's going to do, you know, hours worth of work. It's going to read lots of documents. It's going to collate them. It's going to generate a report. And that's much more like a 10 X step function change in how that workflow works today. But again, we're going to pay the bills because we're going to do a very practical [45:44] as well, which is 80% of our time is, let's reduce that bottleneck, let's make that more efficient, let's improve how we respond to customers there. And then 20% is experimenting, saying, what would this workflow look like if we had a [45:58] Cloud Code go and just run it as a background agent and do a lot more of the work as opposed to, you know, a more of just a, you know, a basic way of interacting with the agent and ID. [46:10] Tell me about this automated due diligence research agent. Is it working? What have you learned from building it? Do you think it's going to work? [46:18] I'm still very early and it will work simply because as we're finding, it's just all a trade off on how much compute you want to apply to the problem. You know, we always we have a lot of internal debates, which are like, you know, [46:37] Like, man, we can make that thing happen in 10 seconds, but we know that the hit rate is 50% or, you know, 70, 30. Or we can make it happen in one minute and we know it'll be 93%. I'm making up all the numbers. And then, you know, at some point the customer is going to be like, wow, that wasn't very magical because it took a minute.

[46:57] But you're like, but you got the right answer. [47:00] And the customers obviously would be way happier if they were in the 70% success rate in 10 seconds, but they're not going to be happy if they're in the 30%. So all of these things are just product tradeoffs, which is how long is the customer willing to wait? [47:13] Do you give them the knobs to tune, you know, those decisions? If you give them the knobs, can a regular person outside of Silicon Valley understand what those knobs even mean? Or are they just now super confused and we're doing techno speak? And so those are that's I mean, but it's it's so much fun because we have we have spent. [47:36] This I can guarantee. We have spent more time on UX patterns that are completely... [47:44] unprecedented UX patterns in the past year and a half than probably the past 20 years of building a company. Because for the most part, over the past 20 years, software didn't really change, again, pre-post cloud. We had buttons, we had tabs, we named the things, you could have a [48:09] you know, we're exactly the same. We just got better at designing pixels in the in the past kind of 10, 15 years. Figma just, you know, made it so we could actually make everything look modern and have a good design system. But like nobody really reinvented like fundamental, you know, there were drop down menus. There was there was a, you know, you know, Windows and inside of each other, et cetera. In AI.

[48:31] Man, like every single couple of weeks, we're like, shit, like, like, how should we expose the idea that this is going to be a longer running agent to the person? And how much should be anthropomorphized as what you would do when you're interacting with a human versus no, this should be actually like kind of behind the scenes and it's just software doing stuff. And that actually obviously makes it so much fun because like we're designing things. [48:53] a new form of software. The software is actually labor [48:57] you know, that you're interacting with in some form. And that doesn't have the classic patterns of software. And so we get to invent a whole new style of how we build these tools. [49:06] One of the other interesting trade-offs there on the product end is which model to use. So, you know, newer models, more expensive, usually faster, but like you get much better results. Older models, less expensive, faster. [49:22] you know, maybe worse results, but there's always that, there's always that trade-off there where it's like, yeah, we could serve this to everyone and it'd be amazing, but it would bankrupt us. So how do you think about that? [49:31] Um, in general, right now I'm in the, in the, uh, you should just always be using the best that there is. Um, and you know, in our case, we're, we're, you know, fortunate enough where we, we can, we can afford to sort of say, okay, you know what, we'll, we'll spend a little bit less in that area to fund the subset, you know, some, some of the compute on this area, we can move things around. I'm, I'm certainly sympathetic to smaller startups, maybe that don't have, you know, venture in them. You can't make those decisions as easily, but

[50:01] Generally speaking, right now, I think we're in a part of the curve where you kind of just want to always be betting on the better technology. And and and mostly because you will have a competition that does and you will not be able to be the company that has an inferior product right now. And and then equally, any company that is doing. [50:21] work to mitigate the quality issues of an inferior model relative to what you could be getting from a better model. That work is totally wasted relative to, again, real productive value creation. [50:44] Every three or six months, you're kind of like moving up the stack from a scaffolding standpoint because you built scaffolding two years ago that mitigated context window length. [50:54] And now that's not an issue. You know, for instance, we built a lot of features. This is just to give you an example. We built not a lot. We built a couple services internally because chat GPT, GPT 3.5 had a context window of, I don't know, like 6,000 tokens or 8,000 tokens or whatever. Well, that scaffolding is irrelevant in a world of 100K tokens or 200K tokens, you know, effectively. And so we could have like been like, no, let's, you know, let's really keep betting on that thing and the old model. [51:24] Screw the software that you built. Now let's just benefit from the model's capabilities. [51:28] Similarly, as you have better reasoning capabilities, better multimodal experiences, more of these features will be compressed into one single model. And so I think you kind of have to just bet on the best-in-class models that are out there.

[51:42] Unfortunately, cost be damned. If you can, if you can make it more efficient with like intent routing and whatnot on your own, great. But but you cannot have you cannot afford to have an inferior experience on any dimension right now and be competitive based on how fast the space is moving. [51:58] That's great. I totally agree. Aaron, this is a pleasure. Thank you so much for joining. [52:02] Yeah, thanks for having me. [52:32] that will leave you on the edge of your seat. [52:35] craving for more. It's not just a show, it's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor, hit like, smash subscribe and strap in for the ride of your life. [52:48] And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.