The Exchanges, every show

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 do you, Just one question. I'm curious about that. So you, so you've talked to all these institutional investors. Is there room for another 10 box like IPOs? Is there enough appetite out there to do another? Is there just other check? Obviously they can write. There's huge amount of institutional capital, but for SAS, right? Is there enough appetite for 10 more IPOs like box this year? You think?

A Uh, well, that would be probably dangerous for me to answer. So what I would say is, um, what I would say is, is that there's, uh, plenty of room For, for companies that are benefiting from this, this very clearly secular change toward cloud and mobile. And if you've built a significant business on the, on the, on the back or benefiting from that, then there's, then there's maybe room, there's room for a hundred. I mean, it's not, I don't think there's any particular, um, kind of, you know, limit to the appetite. The, the investors that are betting on this thesis are realizing that Um, you know, so in our space, what we do is, and this is the, the singular most misunderstood part about our businesses that, um, is that people think we're in the storage business, and we, we buy storage, we buy hard drives, or else we can't store your data, but what we sell you is an application and a service and a platform, and so the, the, the significant difference there is actually when you look at our architecture, storage is one layer, but there's also a security layer, there's a content management layer, there's a search layer, there's a, there's a data loss prevention layer, and the big, Um, the big disruption in our particular space is that, is that maybe, you know, as an end user, you use Box as an easy way to share and collaborate around files, but actually, as an enterprise, you're sav…

AI assessment note: “then there's maybe room, there's room for a hundred... so in our space”

Q model, it's got like, 85% of the way there. I speak to many of the best early-stage and more mature, you know, West Coast-based companies, and they say, hey, we use frontier models to set where we can be, and then we use open-source Chinese models to get as close as we can to that frontier benchmark. Is Silicon Valley being funded by a generation of open, CCP-funded open models?

A I mean, that, that must be kind of empirically true. Um, I, uh, I, I, I don't have the same kind of like, uh, oh, that's so scary, you know, kind of element. Now, obviously, again, holding out some, some element of risk of, of some, some backdoor weights that, that can get triggered at some moment or some parameters, but like, like, like I'm not, I, I just like, that's not how I'm perceiving it, but, um, uh, but yeah. And, but also that's, Yeah, I would say that's kind of orthogonal to my point about like the best frontier model still will go and do the wrong thing. Uh, and so thus I have to be in the, I have to be in the workflow loop to make sure that I review its, its work.

AI assessment note: “that, that must be kind of empirically true.”

Q buffer for 15 to 20% for services, and then there's another buffer, and we'll talk about platform, 15 to 20% for the other crap I have to buy to make that product actually work, right? Integration. So, so, So I wanted easy to use product all, but it's, you're an idiot to not take that 20% or whatever the number is. What is it for box? I, I don't remember.

A Right now it is far less than that, but it's actually, it's actually been a bigger priority for us because it goes towards solving that problem, which is, which is, okay, we, we've got this, we've got this great customer. They are deploying the product, but why don't we have an active conversation and strategic conversation with every one of their IT architects at all times On how can they be using us more? And how do we make that not be a, uh, any kind of trade off decision for us? Right? Like, because normally if they're not paying for it, then you're going to have to say, well, how do we balance all of these? How do we balance all of these customer success managers time with all of these customers? If, if, if we can have a partnership with GE where they're, they're actually, um, they're actually funding the ability to find all these new use cases. That's actually a great partnership for both of us. So, so I would say that That the CEO, if you're, if you're having to be responsible for that 250 to a billion, um, uh, then, then you might, you know, might want to just also just look at investing in some other areas, but I'm gonna, I, I still am pretty balanced with my time.

AI assessment note: “Right now it is far less than that”

Q mean, if I didn't create it, the company on the receiving end of this, though, is anthropic. And, you know, you talked about these mythical Capabilities. They called the model mythos. They put in the documentation that like it broke out of its containment and wrote the engineer while he was having a sandwich in the park. Is it that surprising that this is one of the downstream impact? Yeah.

A But if you put that in your announcement blog post, you know, people might be able to kind of extrapolate and get pretty, pretty, pretty scared of things. I think it's interesting. So, um, you know, on the anthropic front, first of all, I have I have a huge amount of respect for the entire kind of stack of researchers and policy folks across AI. I happen to have disagreements with some of the, the categories, but, but I think there's a deep, let's say, if you were, if you imagined a continuum of the most, like, you know, if, if you, uh, uh, if you kind of had like, like the most, I, I, I mean, it's only in like a polite way. It will sound impolite, but like, I mean, like, like if you're the most doomer on one end of the spectrum and the most like, like accelerationist on the other end of the spectrum, here, here's kind of the, the views, the most doomer, Uh, possible is, was afraid of like GPT three and GPT three was going to like, you know, sort of accelerate and, and, you know, kind of achieve some kind of unstoppable continual improvement. Um, and, you know, the acceleration that says like, we need like fable 20 as soon as possible. Right. So that's, that's sort of the continuum. I'm probably like, I, you know, maybe two thirds up to the acceleration is kind of side of things. But if you were on the, on the Doomer and I, I, I'm trying to say the polite version of Doomer, lik…

AI assessment note: “if you put that in your announcement blog post... people might be able to kind of extrapolate”

Q Can I ask, you mentioned the bet there between kind of web-based versus P to P based. Can you tell me about a bet that Didn't go to plan and you made that was wrong. And were there any big takeaways for you from that?

A Yeah. So I would say that there's been two kinds of things, I guess, that are sort of bets that have either been wrong or, or more alternative scenarios that I would have preferred from kind of how we executed. So some bets are just, you're betting on a particular product and for whatever reason, it doesn't work. And usually a consistent pattern. And when certain features or products don't work and often they frequently have a, an element of Us as sort of product managers and people within the company brainstorming about how amazing it would be if we could do X thing with Y technology, as opposed to thinking about what are the customer problems that really need to be solved right now by our customer base at this particular time and working backwards from the customer problems and then delivering a solution to those customer problems. So usually there's a tendency where the idea itself is, is what is so interesting as opposed to the problem is so important for customers. That's sort of category one where you, you sort of can get it wrong sometimes. And it is a very subtle thing. These aren't like binary obvious moments because you can kind of squish together different data points to make different arguments when you're actually, you know, inside doing the development. And then I'd say the other scenario where, you know, I have some, you know, times where we kind of regret certai…

AI assessment note: “So I would say that there's been two kinds of things, I guess”

Q But do you think this is evidence for or against?

A I think the smartest people on the planet have two totally different views, and so I am, uh, I'm not gonna get in, in the middle of that one. I mean, clearly you have people like Ilya where, you know, it's rumored that he's working on a different architecture or, and, and, and maybe a different path, um, and then obviously you have other people that are, are, you know, let's just throw more compute and data at the problem. I think you can start to sense actually as an industry that the, the, the AGI term has actually kind of gone into the backseat and obviously more of the conversation is around super intelligence. Um, and I think there's more and more comfort around this idea that actually the race really is just how do we build intelligence that far exceeds a human and what will the economic and, and, you know, kind of societal benefits be of just even accomplishing that. Which are massive. And, um, I have always sort of found the AGI thing to be, you know, particularly squishy as a concept. Um, I, I, in the B to B world, I, I deal way more with just like utilitarian concepts. And so super intelligence and this idea of we have AI that will far exceed a human, like that, that alone is enough of a breakthrough to be shooting for. And I think what you're seeing with scaling is we will be able to certainly accomplish our collective definition of super intelligence. With the curre…

AI assessment note: “I'm not gonna get in, in the middle of that one.”

Q finish on something a bit off script, but you're a phenomenal CEO. You're a public company CEO. The pressure that you have on you is intense. You're also, like, married and have a great relationship. Biggest advice on marriage when it's super, now I'm being serious, when it's super stressful, it's hard, and you also have to show up and be a great husband. What's the advice on marriage? Uh,

A It feels dangerous if I actually acknowledge the great husband, uh, piece and other, other parts that were embedded in that. Um, that, that feels like you need like a full three 60 eval. Uh, I will, uh, I'll just say from my perspective, and I'm very lucky to have, uh, an amazing wife and family and, you know, you, you are, you're in a grind in one of these roles. And, um, and so obviously having, uh, a strong support base, um, uh, you know, helps a ton. Um, we try and make time, you know, for, for the fun, you know, side of, of life, uh, as much as possible, but, uh, obviously that gets constrained in, in the kind of window that we're in, but I've been with my wife for, I don't know, 15 years or so, uh, 16 years, and so she's seen the whole, the whole grind, uh, all, all the way, and, uh, she has her own set of grind, uh, in her business, and so it's, it's just lots of fun, so.

AI assessment note: “We try and make time, you know, for, for the fun”

Q your thoughts on this vibe shift, this complete pivot where we've gone from my Lord, everybody's saying, I got to get mine. You got yours. I'm getting mine to name and shame. They're naming and shaming now very specific pieces of pork in these bills, you know, including stadiums for the NFL. And people are like, why is the NFL getting this if they're worth 20, 30, forty billion dollars?

A Two, just quick thoughts. One, Patrick Carlson had a tweet yesterday that basically said this sort of this, the, this big misinformation kind of created by, by people that want to be slow is that you, you, you have to sort of choose two of fast, good, and cheap. And, and I think basically, you know, Elon's companies have sort of always effectively kind of proven the opposite, which is, which is actually, if you just like start to ask the question, like, why does that thing have to cost as much? You know, if you're building a rocket or, or designing a car or developing batteries, like what, why, you know, if you just do ground up, why does it have to cost as much? And so, so what's interesting is, is that, that probably if most people looked at what the government was spending on, they wouldn't even feel like, like, you know, it's not even helping them in like the disaster relief sense of, of, you know, I think like that there are probably actually people that actually do experience the benefits of disaster relief. It's actually just all of the, All of the overhead that we've created to getting anything done in the government that could actually make the government better serve that all of the constituents. I was talking to, you know, sort of a nameless individual in the government the other, uh, the other week where by Congress, they have to hire contractors to do work and the …

AI assessment note: “all of the overhead that we've created to getting anything done in the government”

Q Contraint leads to great art. Aaron, your thoughts?

A No, I mean, I can't add that much more to this. I think it's, there, there's probably a little bit of a, a disconnected times from the, let's say the, the voting public and, and, you know, broad constituents from then those that have sort of seen this in real life. Being inside of a company, having to, you know, do a startup and scaling up and, and just this, um, the, the perverse incentives to build bigger teams, spend more, your project then is more important than more dollars it gets. We have all of these systems in place, which is the stuff that gets attention are the things that you spent more on. So you have all these weird incentives to actually have your thing literally cost more to have, you know, more overhead because you've brought in more contractors into the project that then You know, you're gonna get some, you know, kind of future benefit from in some way. So you, you have a lot that, that is sort of fully broken in this and, um, and there's no, there's, there's, you know, it's hard to imagine any other way to veer off from that path other than something that does shake things up, you know, as, as much as Doge is doing.

AI assessment note: “I can't add that much more to this.”

Q other side. So it actually leads to acceleration because people feel comfortable in doing that and you need to invest ahead of that, right? Um, and, and oftentimes to really get ahead of it, You have to be buying when there is, you know, the proverbial blood in the streets when there is that doom and gloom, and people say it can't ever, you know, it can't ever be different.

A Well, yeah, uh, yes. Uh, I mean, I can only imagine that there is some timing element to that because you have to know when you've reached the bottom of the doom and gloom, but that aside, which I don't, I don't, uh, I don't actually want to like veer this politically at all, but like, as just like a, a bookmark, this is, uh, I get very confused why we are so passionate about changing the government right now. Like they're like, they're nailing it. Like why, like, like, why, like why pull out a Jenga piece out of nowhere and then see what, what changes. Um, but again, you know, different, different podcasts, but, uh, Uh, like, like we, we are so lucky right now that we somehow landed this thing and ensure there's, you know, a bunch of, of incremental issues on the margin, uh, that we're dealing with, but like, wow, we should like be, you know, you know, not, uh, not taking this for granted, um, too much.

AI assessment note: “but that aside, which I don't... want to like veer this politically”