The Exchanges

Q I'm going to do a quick fire round with you. Uh, you have to go and be a public company CEO, I know. Um, so what have you changed your mind on in the last 12 months most significantly?

A I do think that, that I've, I've become more convinced that software is headless in the past year than I was maybe three years ago. And it's because of the, the level of agentic capabilities on tool calling and searching across systems and the accuracy of that. Uh, and that, that has happened faster than I, I would have, uh, perceived. So two to three years ago, if you were to kind of You know, wire up an agent and tell it, hey, go work inside a box and find a document to work with and do some process. It would, it would basically almost always find the wrong document and it wouldn't be able to handle actually like cracking open the file and reading through it. And so thus, you know, going headless wasn't sort of the, the most urgent priority, uh, from an agentic standpoint. And in the past year, those capabilities have just absolutely accelerated. To the point where I'm fully convinced that you just, you have to be, you know, headless first as a software platform.

AI assessment note: “I've become more convinced that software is headless in the past year”

Q When you think about say like a box today, how do you think about leveraging LLMs? Is it a case of actually using six at the same time and switching between them for different use cases and purposes and being able to transition seamlessly between them? How do you think about that and the choice that companies have there?

A So we are building an architecture that lets our customers do what you just said. So effectively switch between models that they want to use for different purposes. Um, I think it's sort of less likely that we would be able to Kind of switch on their behalf in some kind of completely abstracted way because each model sort of, you know, is just a little bit different in, in how verbose it is, um, or how succinct it is, or what kind of style does it, does it sort of respond in? And so I don't think you're going to have complete commoditization of sort of the personality of the models for the, the sort of style and the response, uh, to the point where then, where then, you know, if you're a user, you know, any given response could come from Gemini, Versus GPT-IV versus, you know, Claude. I think that's probably less practical. Um, you're gonna be more wired into a particular model for some, some use case as a user of software. What we did basically is as, as soon as sort of this wave started, you know, let's say, 18 months ago, we basically started working on an AI platform layer that, that connects the data in box securely with any AI model, starting with, with, uh, with OpenAI's models. Over time, we will be opening that up to other AI models as well. So if you're a customer and you say, okay, I really, You know, find GPT-IV is very good at legal, you know, answers, but Gemini i…

AI assessment note: “we are building an architecture that lets our customers do what you just said”

Q When you think about say like a box today, how do you think about leveraging LLMs? Is it a case of actually using six at the same time and switching between them for different use cases and purposes and being able to transition seamlessly between them? How do you think about that and the choice that companies have there?

A So we are building an architecture that lets our customers do what you just said. So effectively switch between models that they want to use for different purposes. Um, I think it's sort of less likely that we would be able to Kind of switch on their behalf in some kind of completely abstracted way because each model sort of, you know, is just a little bit different in, in how verbose it is, um, or how succinct it is, or what kind of style does it, does it sort of respond in? And so I don't think you're going to have complete commoditization of sort of the personality of the models for the, the sort of style and the response, uh, to the point where then, where then, you know, if you're a user, you know, any given response could come from Gemini, Versus GPT-IV versus, you know, Claude. I think that's probably less practical. Um, you're gonna be more wired into a particular model for some, some use case as a user of software. What we did basically is as, as soon as sort of this wave started, you know, let's say, 18 months ago, we basically started working on an AI platform layer that, that connects the data in box securely with any AI model, starting with, with, uh, with OpenAI's models. Over time, we will be opening that up to other AI models as well. So if you're a customer and you say, okay, I really, You know, find GPT-IV is very good at legal, you know, answers, but Gemini i…

AI assessment note: “we are building an architecture that lets our customers do what you just said.”

Q Aaron, you've got to take attribution. As a venture investor, it's all about coining a term, ok? This was your original thought. In the shower, Aaron Levy's. Share it with me.

A I've been influenced by nothing I've seen online. Uh, this is all from me. Um, so there's some kind of, and who knows if this sustains as, as a full-time role or where it gets diffused into. I'm not, I'm not, uh, I'm not a hundred percent clear on that, but there is a hundred percent a role right now that there's going to be 500,000, a million jobs created for, and, and, and it's basically some kind of agent operator. And, and this person is, um, is actually going to be needing to be, uh, somewhat technical. They're going to have to like be deep in the AI world. They're going to have to understand MCPs and CLIs, and they're going to have to know how to write skills. They're going to have to understand agents.md files. The, the, it's going to be this group of people. That will know how to go into your marketing team or your legal team or your operations team or your life sciences research team. And this is the person that is basically going to enable that function to get leverage from agents. And, um, and the problem that the real world has that startups and, and frankly, many of your guests don't understand is that, is that when, when you start a company from scratch, you've got like, you know, the world is your oyster, right? You can design your workflows however you want. There's really no risk if you, if something goes wrong, cause you don't have much scale to begin with. Th…

AI assessment note: “it's basically some kind of agent operator”

Q But is this the same as the kind of classic VC blog post, which every fucking firm has written, which is like, You know, AI, it's moving from software budgets to labor budgets, and every partner goes and likes the tweet, and there's like, no fucking shit. Like, really?

A I mean, if you do it in that voice, it sounds, it sounds kind of like, um, you know, maybe, uh, you know, simple, but like, yeah, that, that, but like, that's just like a very big deal in technology. We've never had, there's never slash rarely been a technology that you could sell into an enterprise where you weren't capped by that company's Corporate IT budget. And so now for the first time ever, you have a technology where you can go into the line of business and you can say, I can now offer you a, a new tool in the form of an agent that will augment a workflow that will make you 50% or a hundred percent more productive. And so maybe I should be able to get five percent of your OPEX budget this year to go and do that. Like that, that is a new budget to tap into. And I don't think it like, You know, 10 X's the size of IT spend or technology spend globally, but it certainly doubles it.

AI assessment note: “yeah, that, that, but like, that's just like a very big deal in technology.”

Q Jensen very clearly said, AI won't kill software. It will explode the amount of software needed. And when I thought about that, you know, the thesis there is obviously kind of, you have this kind of core AI that crawls over 15 SaaS tools, and they really become databases that agents crawl on top of. Is that what it looks like? And are they not just valueless SaaS tools then?

A Yeah. I mean, I, I think that, that I'm, I'm sympathetic to that argument in some, in some categories. I think there's some software where, because the person was the user of the, of the software and they were clicking all the buttons that you're sort of ratio of buttons to underlying APIs was like more in favor of buttons. And I'm oversimplifying, but there, there are some tools where you open it up and there's like 93 features, um, that you're kind of clicking around on. And the, and the user has been so accustomed to exactly how to do that. That the, that the software's value proposition was correlated to, to roughly that, that sort of mass in a world of APIs and a world of agents being able to do more of the, of the work that you used to do on clicking those buttons. Then, then again, the value goes more to the API layer. So then the question is how many APIs do you have? Not, not in like a, you just need a thousand APIs, but like, like how robust and useful and proprietary and how much business logic is embedded in those APIs. Versus it's just calling a database and pulling a record. Like does the API surround a, a, a, a set of business logic of like, no, it actually secures the data or it knows exactly what person each piece of attribute should have access to inside the organization. That's, you know, at the end of the day, all software has a database behind it. So you co…

AI assessment note: “there's a lot of business logic in the layer above the database”

Q protect it. We mentioned our mutual love for Rory O'Driscoll. I do a show with Rory and Jason every week. Jason has bluntly said that this will be the golden age for cybersecurity because the security threats are going through the roof. Are you concerned with the system vulnerabilities and the security threats that are coming with AI? And what do we not know about security that we should know?

A I am concerned, but not, uh, in any kind of like new concern sense. Uh, this, this to me was kind of priced in the moment that we were generating code with AI. So if you can generate code, you have two problems. One, you're going to generate way more code than anybody's ability to review that code. So, you know, starting with GitHub co-pilot six years ago or whatever the date was five years ago, like that was just priced in, which is, which is as soon as As AI writes most of the code or, and then like 90% of the code and then 95% of the code, then by volume, we're just going to produce this unbelievable amount of code and, and any, you know, any change in a system, ah, you know, everybody kind of thinks about security as like, um, you know, is there a zero day where there was an unpatched, you know, component of your technology or somebody found a clever new packet, a package that, that, that you could kind of slip into, ah, every time you, you ship a new feature, You have a chance of a security vulnerability because the AI could have written in, oh, you know, we want to actually open up that port in, in the system because we need to do something. And maybe that was the wrong decision for the agent to go and do so. So we are going to be living in this new world of, of cyber risk, uh, in the form of, of using agents more. And then on the other side, obviously, if you have the of…

AI assessment note: “I am concerned, but not, uh, in any kind of like new concern sense.”

Q the one thing that I worry about is we see this insane demand side pull Every company in the world needs an AI story. Everyone wants to kick the tires with something, and I think we project the same demand side pull and extrapolate it continuously. Do you worry that we are in a momentary 18 month period on the demand side pull, and that may not always be lasting?

A You know, it's very possible I should be more sensitive to that. Um, uh, but, uh, I, I would take the opposite side of, of, of that particular wager at the moment because, um, partly because I already saw one diffusion cycle with cloud and actually how long that ended up taking and the, and, and the, the, the kind of spiky early nature, you would have just been like, oh my God, this is, this is on fire. It's, it's, and how could this last? And 20 years later, it lasted and got way bigger than we ever realized. If it works, the market's always larger than you ever think. And, um, and then the only, the only part why, why 18 months is like not even a relevant window to me is I think diffusion is going to take longer than Silicon Valley thinks. And it's back to the very first kind of that new role idea. When you go to most companies, they can't yet just deploy an agent to do, you know, full, uh, you know, financial proposals for all of their, their clients without a human reviewing the thing. And because the SEC will just show up and be like, Hey, like you, you just, you just gave this person bad financial advice and you're going to lose your license. Like that, that will just start to happen kind of across the board. And so, um, and, and so that that's why, you know, people take time. That's why we, we, we, there's a lot of regulatory controls and compliance teams, security teams…

AI assessment note: “I would take the opposite side of, of, of that particular wager”

Q It's, when we look back on this period and be like, what the fuck, companies trading at three times cash flow, like, way over-exaggerated or not?

A Well, three times cash flow is very much over-exaggerated. I would say that we're in a period right now where basically the market is being treated roughly as in, as the kind of indiscriminately, you know, kind of bucketed sector. And the next year, two years or whatnot, you'll start to see some separation and parsing between the companies, because as I noted in the beginning, agents will be really good for some parts of software. And agents will put pressure on other, other parts of software. So, and it'll mean some companies have to fully pivot and some companies can just sort of ride the wave. And if they respond, you know, effectively, like clearly three X free cashflow is, is like, you know, that, that seems like aggressively low territory. But I also think that at times in software, things have been aggressively overvalued, um, beyond the, the realm of, of likely what the terminal value is of, of, of a particular, you know, category or, or, or company as well. So I think we're, I think there's just a pendulum that, that needs to kind of find its equilibrium right now. Um, and, uh, and that, that'll play out over the next year.

AI assessment note: “three times cash flow is very much over-exaggerated.”

Q Can I ask a stupid question now? Is that not what RPA is? Like, you know, we, we have large RPA provides. I always thought that was what RPA was.

A Everything I just said is exactly how you would have pitched RPA for the past decade. Um, and, and everything I just said doesn't sort of negate the need for, for what RPA would be in the enterprise. The challenge with RPA is, you know, RPA is relatively frail. It's, it's often sort of, it's like looking at your computer screen and, and, and, you know, performing some kind of wrote, you know, routine actions doesn't handle variability very well. Um, because there's, there's again, doesn't have the, the level of intelligence that you now have in AI models. So, um, so I think RPA actually gives you a little bit of a, of a early preview of what becomes possible when you could apply more general intelligence, maybe not, not, not sort of full AGI, but like a general intelligent model to a large number of, of business tasks. So the big breakthrough is what if we could go from a world where software is something that you or I use To get our job jobs done, you know, faster, or it enables us to do our jobs to where software is something that you or I use to basically farm out work to AI to go do. And, and it's kind of, it's a, it's a sort of real shift of how we think about software and, and the role of, of, you know, information and intelligence in our organization. So the best, you know, examples that are emerging now are I could have an AI that is my outbound sales rep. Or I could ha…

AI assessment note: “Everything I just said is exactly how you would have pitched RPA”

Q I, you know, the show has been successful because I specialize in dumb questions from the way that you described agents. It was like the next generation of RPA. What happens to prior RPA providers? Does, does UI path just adopt an agent based model and actually move away from kind of rote learning? Or what happens?

A Yeah, I mean, actually, I think, I think being an incumbent RPA vendor is, is actually a great spot because, because if you already are talking to customers about literally automating business processes and workflows, and there's just a better way to do that, um, and, and there's nothing in conflict with their business model. In fact, if anything, actually, they, they probably were the first to have more of a consumption oriented, you know, kind of model for, for, uh, automation. Um, so I, I think, I think you could be very bullish on RPA vendors right now. Um, but I do think it means that more players kind of get in and around the space. So the, the, um, I think the, the, the thing that will, uh, 100% guaranteed happen, like, like in five years from now, this will be the most obvious thing of all time. But when you, when you look at like RPA, you had to be, you know, a relatively deep expert in, in RPA. You had to be like a midsize or large enterprise or, or kind of developer oriented, you know, kind of individual. And so the, the total size of the market was basically arbitrarily or artificially held back by, by just the, the, the complexity of the legacy approach. So if AI makes it 10 times cheaper, faster, and easier to automate workflows, then, then it stands to reason that the market will be substantially larger. It could be a hundred, a hundred times larger At, at the en…

AI assessment note: “I think being an incumbent RPA vendor is, is actually a great spot”

Q On the AI agent space. And when we look forward, what do you think it looks like in five years time?

A I can sort of sufficiently say, hey, you know, AI generate leads for my business or answer my support tickets or review my contracts or process my invoices. If I can do that, then, you know, realistically you will have sort of, I think you'll have kind of category, you know, winners in, in a large portion of sort of job functions that today exist. And there'll be AI versions of those functions. Um, and, and this is a, this is again, one of these windows Where 10, 20, 30, 50 companies will get started that, that were like the window in two, in the mid 2000 where like all of today's SaaS companies basically emerged in like a five year period essentially. And we'll have that for, for basically AI jobs where you'll have the AI security engineer, the AI, you know, customer support agent, the AI, you know, marketer. And, um, you know, lots of companies won't work just for, for the, the, the, the kind of typical reasons. But we will have a landscape of, of basically labor that you can get from AI. Then there'll be like really interesting kind of second derivative effects, which is, okay, you know, how do you manage all those different, that like AI labor? Like right now, you know, when you want to manage lots of software, you, you have, you know, you implement Okta or you implement a security tool. Well, it's kind of a crazy world where all of a sudden I have, I have digital labor, uh…

AI assessment note: “we will have a landscape of of basically labor that you can get from AI”

Q What did you not do with Box that you wish you'd done? You know, everyone has to prioritize. Everyone has strategic decisions on, we'd focus here. What decision did you decide not to do that you wish you had done?

A Earlier in our journey, I would have focused more on cash flow. I have become a little bit of a, of, of religious around cash flow. Um, uh, I think, I think owning your own destiny as a company is important. I think, um, you know, caring about every, and inspecting every single dollar of spend, In the business is very important. Um, I think, um, uh, I, I think these things, uh, you know, in, in, in sort of very loose capital environments, it, it sort of gets forgotten about or people don't really care about it. Um, but actually I think it helps you build a better business because you, you kind of apply constraints that force better decisions, better strategy, better execution. Um, so I would have done that, you know, years earlier than, uh, when we ultimately, uh, focus on cashflow.

AI assessment note: “Earlier in our journey, I would have focused more on cash flow.”

Q Do you think new juggernaut companies will be created both in the foundation model layer and in the application layer, or just in the application layer with incumbents gobbling up the foundation model layer?

A I would say that, that there will be some foundation model companies, um, but not, not nearly the, uh, the magnitude of, of the application layer companies. And, um, and much of that is, is due to the trends we've already seen, which is, you know, the moment you have, you know, people like Zuckerberg that are, are literally willing to spend billions and billions of dollars commoditizing the, the, the model layer, It becomes very hard to sort of figure out, well, how do you, how do you differentiate in that space enough where, where you won't, you know, kind of be taken out by, by one large training run from an open AI or a Google or, or a Zuck. There will be like niche or, or maybe, you know, industry specific, you know, sort of approaches you could take or some very, you know, you know, kind of specific domains you could go after. Like, I think there could be categories where, You know, maybe the big incumbents are more nervous about going after audio, um, because, because there's obviously going to be lots of, of interesting conversations around copyright and whatnot. But I think for like the, the pure play horizontal LLMs, uh, we, I think those, those will largely be subsumed by the, the, the big players with maybe room for one, two, three independent companies at scale that are not in the, in the hyperscalers, but there will not be room for 50 companies. Um, that, that's ju…

AI assessment note: “there will be some foundation model companies, um, but not, not nearly the”

Q Can I ask a stupid question now? Is that not what RPA is? Like, you know, we, we have large RPA provides. I always thought that was what RPA was.

A Everything I just said is exactly how you would have pitched RPA for the past decade. Um, and, and everything I just said doesn't sort of negate the need for, for what RPA would be in the enterprise. The challenge with RPA is, you know, RPA is relatively frail. It's, it's often sort of, it's like looking at your computer screen and, and, and, you know, performing some kind of wrote, you know, routine actions doesn't handle variability very well. Um, because there's, there's again, doesn't have the, the level of intelligence that you now have in AI models. So, um, so I think RPA actually gives you a little bit of a, of a early preview of what becomes possible when you could apply more general intelligence, maybe not, not, not sort of full AGI, but like a general intelligent model to a large number of, of business tasks. So the big breakthrough is what if we could go from a world where software is something that you or I use To get our job jobs done, you know, faster, or it enables us to do our jobs to where software is something that you or I use to basically farm out work to AI to go do. And, and it's kind of, it's a, it's a sort of real shift of how we think about software and, and the role of, of, you know, information and intelligence in our organization. So the best, you know, examples that are emerging now are I could have an AI that is my outbound sales rep. Or I could ha…

AI assessment note: “Everything I just said is exactly how you would have pitched RPA for the past decade.”

Q On the AI agent space. And when we look forward, what do you think it looks like in five years time?

A I can sort of sufficiently say, hey, you know, AI generate leads for my business or answer my support tickets or review my contracts or process my invoices. If I can do that, then, you know, realistically you will have sort of, I think you'll have kind of category, you know, winners in, in a large portion of sort of job functions that today exist. And there'll be AI versions of those functions. Um, and, and this is a, this is again, one of these windows Where 10, 20, 30, 50 companies will get started that, that were like the window in two, in the mid 2000 where like all of today's SaaS companies basically emerged in like a five year period essentially. And we'll have that for, for basically AI jobs where you'll have the AI security engineer, the AI, you know, customer support agent, the AI, you know, marketer. And, um, you know, lots of companies won't work just for, for the, the, the, the kind of typical reasons. But we will have a landscape of, of basically labor that you can get from AI. Then there'll be like really interesting kind of second derivative effects, which is, okay, you know, how do you manage all those different, that like AI labor? Like right now, you know, when you want to manage lots of software, you, you have, you know, you implement Okta or you implement a security tool. Well, it's kind of a crazy world where all of a sudden I have, I have digital labor, uh…

AI assessment note: “we will have a landscape of, of basically labor that you can get from AI”

Q I mean, also just for, you know, it's, uh, 10:30 here and I'm working. So, I mean, it's like, I literally had a reference call before this and they were like, dude, are you, Are you European? I'm like, yeah, I know, I know. I'm weird. Uh, but, uh, so are you nervous about regulation preventing progression in AI?

A I'm increasingly less nervous only because of what we've actually seen these bills sort of, um, come up with. They don't seem as, um, they don't, they don't seem as sort of progress halting as maybe what would have been rumored about a year ago. You know, the, the scariest moment to me was the pause AI kind of moment, which was, which was, okay, we need to, we need to stop the, the development of advanced AI for six months until we kind of, you know, figure something out. And, and it was like, it's been, let's say a decade of, of us all as a community talking about AI. If you think that in an extra six months is all it takes for us to have some kind of alignment on, on like, what, what is the doomsday AI going to look like? What is, What is sort of dangerous AI? What is less dangerous AI? Six months is not going to solve this. There are very fundamentally different philosophies in, in the land of AI that, that, that are irreconcilable. They will not, they will never fit together. It's just, it's okay. It's great to have actually a dynamic set of perspectives. They will not be able to ever be fully unified. And so I thought that that was going to really kind of, you know, gum up the, the, the, the, the advancements if You know, government started looking at pause AI, and they're like, oh, even the tech community wants us to, to stop this thing. Um, and that, that was what I was …

AI assessment note: “I'm increasingly less nervous only because of what we've actually seen these bills”

Q So I actually tweeted that AI services companies are going to make more revenue Than, um, uh, any foundation model providers. And then Accenture posted 2.4 billion in revenue and OpenAI posted two billion in revenue. So absolutely right. Um, how do you think about that? Do you agree with me?

A Services are almost the leading indicator to compute, uh, because you need services to implement the thing that then eventually is sort of on autopilot, you know, not, not, not fully, but, but more or less. So, so I think I would take the bet on, on AI services for the next five years, unquestionably. Um, the amount of dollars that will go into the change management of systems, the implemented, the implementation of the technology is, is going to continue to be massive. On the other hand, the other thing that goes along with that though, is, is, you know, a bunch of the AI stack. So the actual GPUs, the, the data center build out that as well. So I think the, the, the, the, at some point though, the curve, if, if AI is, is as, You know, meaningful as, as I believe it is, and I think, you know, so much of tech believes it is. At some point, the, the actual AI, sort of, the software services of AI, this, the infrastructure services of AI will eventually exceed the human services on the implementation simply because now once it's in production, you don't need that same change management 10 years later. Like, it's just literally running. Like, the amount of money we spend today on our cloud infrastructure vastly exceeds the amount of money we spend maintaining our cloud infrastructure. Versus, you know, five years ago when we were first moving more into the cloud, our services were…

AI assessment note: “I think I would take the bet on, on AI services for the next five years”

Q What did you not do with Box that you wish you'd done? You know, everyone has to prioritize. Everyone has strategic decisions on, we'd focus here. What decision did you decide not to do that you wish you had done?

A Earlier in our journey, I would have focused more on cash flow. I have become a little bit of a, of, of religious around cash flow. Um, uh, I think, I think owning your own destiny as a company is important. I think, um, you know, caring about every, and inspecting every single dollar of spend, In the business is very important. Um, I think, um, uh, I, I think these things, uh, you know, in, in, in sort of very loose capital environments, it, it sort of gets forgotten about or people don't really care about it. Um, but actually I think it helps you build a better business because you, you kind of apply constraints that force better decisions, better strategy, better execution. Um, so I would have done that, you know, years earlier than, uh, when we ultimately, uh, focus on cashflow.

AI assessment note: “Earlier in our journey, I would have focused more on cash flow.”

Q and you being young at the founding, My question is, I had Joe Fernandez from Joy Mode and Klaus on the show a long time ago now, actually, but he said that serial entrepreneurship was overrated, which I thought was a very interesting, quite contrarian statement, given the praise that we lord on serial entrepreneurs. Given that Box was your first company, would you agree that serial entrepreneurship's overrated? Yeah.

A Well, you know, I guess to be fair, it was my first company that maybe had, you know, some form of success, but had definitely tried lots and lots of other ideas prior. So I tend to not get too sucked into, you know, some of the maybe kind of quote unquote best Best practices or pattern recognition that I think we tend to pontificate about it. You know, there's amazing entrepreneurs that have done it once. There's amazing entrepreneurs that have done it five times, and it all comes down to the idea, the team, and do you choose the right market at the right moment? Those tend to be the factors more than whether it's your first or, or end company.

AI assessment note: “Those tend to be the factors more than whether it's your first or, or end”

Q How do you think it impacts, like, traditional enterprise and storage?

A You know, realistically, it largely doesn't. You know, the way to think about it is any design of a decentralized system has to have a certain amount of redundancy built in to obviously, you know, ensure against Censorship resistance and whatnot. And so whether that's storing something a hundred times or a thousand times or 10,000 times, you are storing something at an order of magnitude or two or three or four orders of magnitude more than what the direct value proposition requires. So for most things that you do on the web, for most software you use, you don't have a censorship resistance sort of use case or an immutability sort of oriented use case. And when you do, you have a lot of choices of what to do with your data. You can back it up. You can Put it somewhere else if you'd like. So I think there's a small, narrow fraction of data that could be very relevant on a blockchain, but that data is likely going to be limited in size, and it might be, you know, things like, let's say we do an e-signature. Maybe we want that e-signature recording to go on a blockchain, so that way in 50 years from now, we know that we can look it up and sort of, you know, check the hash of our e-signature and be able to validate it. So I think use cases like that, where we're talking about sort of like kilobytes of information, absolutely could have very relevant use cases, but the idea of Takin…

AI assessment note: “realistically, it largely doesn't.”

Q Does that increase the value of your business? When I think about that, I, I asked Aaron from Monday, if you become, you know, a data repository, which agents crawl on top of, how do you retain value in that? I would ask the same to you with respect.

A It's, it's, it's the question on the mind of every investor on the planet right now. So we're, we're used to it and it's not a, it's not, it's not a scary question. Um, one thing that, that helps us is we've always had a, a, an API sort of, maybe not first, but equal strategy. Um, uh, if, if I told you the number of API calls we did last year, you'd, or you guessed first, you'd probably be off by an order of magnitude. Um, uh, so, so the volume of, of API usage on our system is already enormous and already, you know, is outsized relative to any of the end user interactions in the system. Um, and that's just a virtue of you use content in a variety of applications and workflows that, that, you know, far exceed what, what people, you know, kind of, you know, Open up their finder and upload a document to like, like an ERP system generates files, a, a wealth management portal. You have clients uploading documents into the portal and they never see box. Um, you have workflows of invoice processing that's happening behind the scenes. So the headless version of box has been alive and well for, you know, almost since the day we started the company. And so agents to me, just again, represent a force multiplier on that. So it's not a, it's, it's, it's actually an exciting proposition for us. We already know how to monetize it. The question is like, will the exact dollar and cents be the …

AI assessment note: “agents to me, just again, represent a force multiplier on that. We already know how to monetize it.”

Q No, I get you totally. Is that anything that you think we don't spend enough time talking about, or there's not enough light shone on in the AI discussion and that we haven't discussed today?

A To me, there's just interesting downstream, um, kind of consequences if everything plays out as, as it should on like on paper. So, so, you know, I, I'm fascinated by like, I'm fascinated by the idea of, You know, what SAS did was it made it so I have a friend who has, uh, he sells balloons online. Um, and it's actually like not a bad business. It's like a, like it makes real money. Um, and he sells balloons online and, um, and I don't think he would have started the business if Shopify didn't exist. Um, like, I, like, I think like the existence of Shopify made it so he could like be like, oh, well I had this like random idea. Let's just see if it works. It lowered the barrier. To then going out and, and basically starting a business. And I, I know that Toby has, you know, thousands or tens of thousands of these types of stories. So, you know, if you think about it, you know, if Shopify caused businesses to get started because it lowered the barrier to being able to sell online, if AWS caused applications to get started because, because I was like, oh, I could just build an app and, and run it in the cloud. I don't have to think about servers anymore. Um, if, uh, if Stripe, you know, cause businesses to get started, cause I don't think about payments anymore. Then in a, in a land of AI agents, you can almost similarly be like, well, you know, somebody literally one day could ju…

AI assessment note: “there's just interesting downstream, um, kind of consequences if everything plays out”

Q No, I get you totally. Is that anything that you think we don't spend enough time talking about, or there's not enough light shone on in the AI discussion and that we haven't discussed today?

A To me, there's just interesting downstream, um, kind of consequences if everything plays out as, as it should on like on paper. So, so, you know, I, I'm fascinated by like, I'm fascinated by the idea of, You know, what SAS did was it made it so I have a friend who has, uh, he sells balloons online. Um, and it's actually like not a bad business. It's like a, like it makes real money. Um, and he sells balloons online and, um, and I don't think he would have started the business if Shopify didn't exist. Um, like, I, like, I think like the existence of Shopify made it so he could like be like, oh, well I had this like random idea. Let's just see if it works. It lowered the barrier. To then going out and, and basically starting a business. And I, I know that Toby has, you know, thousands or tens of thousands of these types of stories. So, you know, if you think about it, you know, if Shopify caused businesses to get started because it lowered the barrier to being able to sell online, if AWS caused applications to get started because, because I was like, oh, I could just build an app and, and run it in the cloud. I don't have to think about servers anymore. Um, if, uh, if Stripe, you know, cause businesses to get started, cause I don't think about payments anymore. Then in a, in a land of AI agents, you can almost similarly be like, well, you know, somebody literally one day could ju…

AI assessment note: “To me, there's just interesting downstream, um, kind of consequences”

Q might kill me for this, because he gets a little bit more sensitive about when I quote him or misquote him more appropriately, but like, he basically says, It, and this is Jason again, if you can't, like, charge way more for your agent product, That Wall Street doesn't give a shit that you have to re-accelerate revenue with agent products. Can you charge significantly more for an agent product?

A The answer is yes, but, but there's a little bit of nuance, which is our, our business model is we have a new plan tier that we just introduced last year that basically houses our, you know, best workflow capabilities, our business automation, you know, uh, our app application development capabilities. And then the agent is sort of central to that. Because it's going to help you automate the work that you're actually doing with your content. So it'll read a document and extract metadata from it. It'll process information inside of a, of a, of a workflow. So that is actually causing a real reacceleration of our revenue growth. Last year, we, we, we saw an inflection in our revenue growth. And so that, that it's already happening in our business. Um, and, and so we are, we're doing the thing that I think Rory is sort of probably saying is the new benchmark. Now to be Fair to what's, what's happening though, is I think Wall Street still is sort of saying, we kind of need to just step back and see where everybody lands in this because of how much change there is. So, um, so this is very much a year where if you're in software or infrastructure or building agents, you just, it's a year of complete unrelenting execution.

AI assessment note: “The answer is yes, but, but there's a little bit of nuance”

Q Final one. We've talked about regulation, we've talked about model quality, um, that you've tweeted before about kind of the job displacement concern. If there was one thing that did worry you, what would that be?

A I think we have ways of defending against this, but like, you know, you can, You can, it doesn't take like that much imagination to be like, ah, you know, one of those like robot dogs with like a gun and like, you know, and a multimodal AI, like, wow, wouldn't want that thing running around. So, so I, I think there are real reasons we should, you know, sort of pay attention to some, some of the, the, uh, more, um, uh, you know, uh, dangerous use cases of AI. I just think we have in many cases sufficient, uh, Legal frameworks for addressing those things. And, and then for any net new one that we come up with, let's, let's actually, you know, have, have regulation to go and support that. But what I'm, I'm less worried about at the moment is probably the more fringe extreme things of the AI sort of self-replicates. It, it sort of, you know, jumps over outside the data center to another data center and then self-propagates. And I'm, I'm less nervous that that is, is sort of on the, on the docket of events. And so.

AI assessment note: “one of those like robot dogs with like a gun and like, you know”

Q Can you not micromanage at scale? Jensen Huang has told us with 60 direct reports. Who fucking needs to delegate?

A I, I cannot wait until we're a two trillion dollar company and I will be able to, I will be able to go back into that mode. Um, I'm in a brief period where I have to delegate to keep people happy, but, um, uh, but eventually we will, we'll be back to, uh, to, to that land. Um, I, I think, I think that the real lesson is now is, is you choose the areas that you, you need to exert that level of involvement. So, um, and in places like, um, you know, let's say critical areas like AI or, Or end user experience and, and some product, you know, strategy, you know, you, I, I still kind of revert back to, you know, early startup self, but there are many areas where just honestly, either because of the amount of hours that the job takes, um, or just the fact that like, you want to be able to bring on great people that, that are motivated to go execute, uh, delegation is actually extremely important.

AI assessment note: “you choose the areas that you, you need to exert that level of involvement”

Q There's many points I want to touch on in that one. Do you think we're still in the experimental budget phase for enterprises? We mentioned there about how we can change functions and optimize them. Do you think we're still in the experimental budget phase and how will the best and the worst enterprises engage and adopt with AI?

A I think there's two types of spend happening. Um, there's probably the bulk of the dollars that you see in the headlines. I would argue are likely experiments, uh, where, you know, you'll have some kind of hit rate success rate, um, that, that happens. And then those experiments graduate into production spend. And I think we just don't have a, an accurate kind of pie graph yet of what's in the production category versus what's in the experiment category, but it would be, it would be probably too generic to say it's all experimental and it's certainly not accurate that it's all production. Um, and so the exact sort of split of those two things is, is sort of hard to diagnose at this point. Um, I've just been on the, the road. Uh, we've done maybe about a dozen or so AI events, um, uh, throughout the US, um, you know, in the past quarter, the vast majority of companies have, have a meaningful number of AI experiments happening with, with lots of different, you know, kind of areas of their business, lots of different applications. Um, uh, but the vast majority also have, you know, uh, areas that are in production already. So unfortunately it all would get lumped into the same Kind of category of AI spend by the time the CFO, you know, gets the AI bill, but we, we are seeing real production and, and lots of experimentation.

AI assessment note: “we are seeing real production and, and lots of experimentation.”

Q Can you not micromanage at scale? Jensen Huang has told us with 60 direct reports. Who fucking needs to delegate?

A I, I cannot wait until we're a two trillion dollar company and I will be able to, I will be able to go back into that mode. Um, I'm in a brief period where I have to delegate to keep people happy, but, um, uh, but eventually we will, we'll be back to, uh, to, to that land. Um, I, I think, I think that the real lesson is now is, is you choose the areas that you, you need to exert that level of involvement. So, um, and in places like, um, you know, let's say critical areas like AI or, Or end user experience and, and some product, you know, strategy, you know, you, I, I still kind of revert back to, you know, early startup self, but there are many areas where just honestly, either because of the amount of hours that the job takes, um, or just the fact that like, you want to be able to bring on great people that, that are motivated to go execute, uh, delegation is actually extremely important.

AI assessment note: “you choose the areas that you, you need to exert that level of involvement”

Q always you who takes them. It's sometimes team members. My question to you is how do you create an environment of safety, especially as a public company in a sizable organization now where people have the ambition to take big bets and aren't afraid of what happens if it goes wrong, but also don't feel no accountability. It can't be free reign to fail. How do you bridge that balance?

A A huge challenge, and I don't know of a company that has sort of perfectly optimized, you know, this particular variable. I would say, you know, a couple things to keep in mind. One, whenever possible, have as ambitious of a goal as possible, and so goal setting should be sort of ambitious and idealistic in terms of what you're trying to achieve. That is, needs to be separate, though, from sort of like what maybe we would consider like harsh accountability, i.e. you're going to get fired or down-leveled if we don't achieve that goal. So, so that sort of job number one is ambitious goals, but reasonable sort of accountability, you know, around achieving those goals. So that way people are willing to sign up for them and aren't sort of afraid of putting ambitious, you know, statements out there. You know, obviously you need a certain degree of autonomy to ensure that people can actually go and execute on the ambitious goal. You know, we've had a lot of situations where you might have an incredibly ambitious individual, an incredibly ambitious goal, but there's still a sort of a morass of, of complexity that ends up slowing them down. So they Could not have even achieved that goal in any perfect scenario because of the bureaucracy or interdependence on other kind of, you know, functions in the business. So, so you had to also, you know, make sure that you've wired up the team and …

AI assessment note: “ambitious goals, but reasonable sort of accountability, you know, around achieving those goals”