Box CEO on Enterprise AI Trends No One is Talking About Yet

If you were running OpenAI, would you keep pushing the boundaries of frontier models? Or does that just feel like such diminishing returns? And why not redirect all that energy to the consumer product that is ChatGVT? Yeah, so I would definitely do it. And I don't have that much, I don't have that much kind of critical feedback for OpenAI or kind of any of the major players at the moment. You know, the areas of critical, you know, issue are, you know, what does Google do about core search? Like, that's a super interesting question. What does Apple do about, you know, kind of the Siri problem? And like, how much do they need to own themselves versus, you know, kind of let other players, you know, kind of, you know, power some of that functionality.

So, you know, there's some interesting strategic questions that I think emerge. But in terms of OpenAI, their execution, I think it's exactly what probably I'd be doing, which is let's kind of, you know, it's not even hedging your bets. There's a nice flywheel. So they're not like different work streams. It's like, let's have the best models in the world. Let's then incorporate that into a consumer and, you know, prosumer and then business set of products. And then there's now a new layer, which is like, we're going to let's make sure that we have like applied use cases in these agents. the rumored engineering agent and all this stuff. And so they can kind of, I think they can have their cake and eat it too in a lot of this. And, but it's, it's critical that they are always at the, at the forefront of the state of the art models, because that's the, that's the calling card into the business conversation, which is, we also have, you know, the, the, the best performing models on these, on these dimensions. And if there was only, you know, the chat GBT play, I'm not sure that would be, you know, that is not why their brand got to where they are.

And I think you want both those things to kind of be interlocked. Now, there was a there was an interesting thought experiment that somebody mentioned recently, which was, you know, for at least the consumer use case. If tomorrow you swapped in, you know, Gemini or DeepSeek, you know, just like and just, you know, had a person go to ChatGPT, would they notice the difference? The answer is, you know, obviously, probably not at this point. Like we've reached, we've reached a convergence on model quality for most consumer use cases to be satisfied for, for, for, but, but most as in like, as in like, you know, the majority, but not necessarily the most important ones.

And so, and so there's going to be an 80-20 rule, which is we probably have solved like 80% of, of like, Hey, what's the score to the sports game? Tell me about World War II. You know, what should I wear in this meeting? Like, like we, we, we, we've solved that. but the the 20 is going to still be like you know orders of magnitude more more quality that we need which is like go out and you know do the classic i need to book a flight type problem. And those were nowhere close to solving. So we kind of got to the 80% really quickly. And now the 20% is going to take years and years to kind of keep cranking

You're talking my book here. I guess one question, the business model of cloud, SaaS, if you will, I think has been historically probably underappreciated versus obviously the delivery mechanism and all that.

I think people have a lot of appreciation for, but just purely how the pricing was able to disrupt a lot of things, be it Salesforce or whoever. I'm curious, do you think that usage pricing is an opportunity with AI to have that similar effect or is it something different in your mind? for like uh the the broad transformation or disruption or or which on which axis Yeah, I guess as you think about like who your historical competitors were, I think historically people underestimated the on-prem to SaaS transition and the number of customers you could reach was probably one of the big things. But there was a lot of innovation that ended up happening around seek-based pricing and elements of fair use pricing like Slack was early in that.

And I think all of that stuff, there was a bunch of different elements of the SaaS transition that ended up being, you know, user based in some way that I think was an accelerant of this transformation and transition that ended up playing out. I guess, do you think usage has a similar opportunity for existing businesses or is it a little orthogonal and ultimately it might not be as broadly applicable as?

When you extrapolate that service budget there forward and think about the value to the end customer, totally makes sense that they'd be willing to pay. Hey, there was this person that I had to do this, and now I can get software to reach things that weren't even possible or do it more.

I worry about the deflationary element a little bit of like the competition. And at the end of the day, are you beholden to what your next competitor is willing to charge? And so we might not be able to actually tap into the service budget quite as much, not because the opportunity is not there, but because there's tons of venture companies popping up and competing with one another. So I guess, one, we'd love to get your reaction to that. Two, how do you think about durable value creation? Because I think everyone agrees, for example, Zoom, I would pay anything for it, basically, to work if there was no other alternative. But there's a lot of other alternatives. So there's an anchor on what I'm actually willing to pay. Yeah, so I don't think

one talk you were giving at some big bank that everyone's eyes were glazing over. I think it was like a conference or something that maybe the most boring or least interested you've had a crowd. But it wasn't like tangibly output based in some way that like the rank and file within an organization didn't care about the backend infrastructure. And ultimately, you're speaking about some elements of it uh can you give that quick anecdote but then also talk about how ai is is different and how that translates to the enterprise and the conversations you're having

And so what is the resistance when you're going into the organization or how would you describe beyond the enthusiasm and trying to figure out the how and the when? Like, what are the long poles in the tent? How much are on these organizations versus how much are on the commercial vendors?

Yeah, I would say, actually, maybe also this might abuse the analogy too much, but if I compare to cloud, cloud could do more than what customers were asking for because they were resisting it. So you could have always implemented cloud 50% more than you actually were at the time or hundreds of percent more. Maybe actually the reverse is true, which is I'd say most conversations now, the customer is actually like, they're jumping like three steps ahead once they see the demo and they're like, oh, that's great. Like, we'll just like totally automate that entire thing. And you're like, okay, well, well, let's, let's, your data is not yet in the environment that it's going to need to be to do that use case. Or, you know, you know, we're still waiting on some reasoning model breakthroughs from OpenAI or Cloud or something to just get that extra 3% of accuracy that you're going to need. So in this case, I think customers have pretty much jumped ahead to way closer to the end state, which is like, yeah, great. Let's give all the employees a productivity boost. Let's figure out where we can deploy.

to the organization. Now, you know, I want to do a massive asterisk. I'm talking to the people that are talking to us, which means that like, by definition, they're leaning in, they're a little bit more forward thinking. So some of the anecdotes I'm sharing are from those types of customers. I think there's still an 80% of customers that are like, whoa, like my head's exploding. Where should I start? Where should I go first? But there's a lot of, there are a lot of customers where they're now ahead of the technology based on the set of use cases that they have. Um, and, uh, and so I wouldn't say it's, it's, it's like the hurdles unfortunately are, are much more like practical considerations. Like literally like I can't just, you know, turn on an AI thing that looks at all my data. And then an employee can ask the question because guess what? It's all of a sudden going to reveal, you know, secrets in the organization that they shouldn't have had access to, but there were over provision for access like five years ago.

Or, hey, you know, AI agents are really good at doing code, you know, auto-completion, but, like, am I ready to put an AI agent in production for, like, generating a full, you know, multi-file, you know, output? And then how do I review that when the human actually wasn't along the ride for the actual, you know, editing process? Are they going to really understand the code? Are they allowed to press submit and that goes into production? like like that's going to be that like these are like the big questions that people are dealing with i mean imagine imagine how unhinged we are online where we're like oh man we just made a video game in three you know three hours and it's like and it does all this stuff and then you're like you're you're an engineer in like a banking system like are you supposed to go implement that right now like like is that like are we ready for like are we ready for like your bank account to be driven by vibe coding? We don't know. So these are the kind of things that I think every enterprise is trying to figure out like, okay, so what pace should I implement this stuff? Is my data ready?

Are my systems ready? A lot of it is about, wow, the industry is moving so fast. Ironically, the industry is moving so fast. How do I make decisions right now that I'm going to be stuck with in a fast changing space? So a lot of it is actually like, can you get your architecture set up for like optionality in the future, because maybe the vendor you work with today is not going to be the one that's the best in the future. You need to swap out different parts. These are the kind of conversation that we have. To that end, you cautioned, I forget if it was a blog post or

Klarna, Sebastian, who we've had on before and a good, a good guy, great CEO has been, I think he tweeted something recently. I think he accidentally made news in his telling of like consolidating SaaS vendors and moving more things to a Gentic workflow. And I'll tell you, It set off a little bit of a firestorm internally to see if this is like a long term thing that we need to be existentially worried about investing in software because there was going to agentic workflows and systems were just going to do away with the need for the interface. And therefore, everything could be in a CRUD database in some ways.

I'm curious your perspective on that as we zoom out and maybe is what they're doing, you think, broadly applicable to other organizations? Or is this a one of one, maybe exceptional case that they're testing something and on the early frontier?

You know, this isn't going to be the make or break. Yeah, we, you know, we can go through all the books and talk about it. All right, I'm going to give you some quick ones and you give me your gut reaction on some of these things. Ready? Do you think the net of AI in some reasonable period of time, maybe five to 10 years, decreases the number of hours worked by your average knowledge worker? Well, how many years? Five to 10. I'm going to stick with no for now.

Yeah, I think I agree. Do you think AI will flatten org structures in any meaningful way? Example, fewer middle managers, faster approval, more self-service, knowledge work. This one I could buy. I could buy. I think actually layers have gotten pretty crazy over the past couple of decades. So so I think I think, yes, I think, yes. And part of it is because I think that. AI kind of lets you, it lets functions, my latest kind of conclusion is that it kind of lets functions sometimes do their adjacent function. And so it lets the designer write the front end code, it lets the back end developer write the front end, you know, code, it lets the copywriter, you know, generate the full white paper.

Or so, so I think what that means is that you will have, have sort of more full stack workers almost like, like we don't really think about this outside of engineering, but I think you'll probably, I think we've gotten like maybe overly disciplined in, in job functions in some parts of, of the economy. And I think AI kind of lets you have a little bit more expansive responsibility. So then that probably means that that's a little bit of a flattening when you don't have to hop through and around as many orgs to get things done. So, but we'll see. Did you ever read the David Epstein book, Range, Why Generalists Triumph?

No. It's a good one. It's a good one. It's a good one. Yeah, kind of. It sort of talks about, like, they give the example of Roger Federer growing up and playing multiple sports versus Tiger Woods being specialized in one. And how many breakthroughs in society have occurred from people outside of a core field or domain, leveraging the knowledge they have of that and going into another one. And so it goes through scientific breakthroughs or company foundings or all this, where when you get too verticalized in some ways, you just don't have the context of the way other people do things. And so oftentimes breakthroughs come outside.

I think I think work has probably gotten like a little bit to Adam Smith. And we've we've like, you know, we're all just doing our little part of the paperclip factory or whatever the, you know, or the needle. I think it was needle factory or. Yeah, the paperclip. We've all Andrew, what is it, Aaron Ross or whatever, predictable revenue. We've done that across every element of society at this point. Yeah. So, so I think, I think probably AI relieves us a little bit of that, like division of labor, maybe, maybe got just a little bit too granular over, over time. And so I think maybe we get, we get a little bit of a chance of a reset, which is like, no, I can like just throw a little bit more of a general problem at somebody and they've got the ability to kind of go and fully solve it, which, which frankly, I think is a huge, hugely exciting thing and much more fulfilling probably for, for the knowledge worker kind of class.

um, uh, that, that I think has a lot of upside. What's the coolest, uh, or most mind blowing demo you've seen in the last, call it three to six months where, where you were like, wow, I can't believe that's actually possible that we can do this. Yeah. Um, I'm having a, a funny journey because like, it's like, it's like you do, you do start to now, unfortunately, like build such high tolerance and desensitization. It's like the hedonic treadmill in some ways, right? You're just like, I always reference now with AI, the Louis C.K. bit about airplane and Wi-Fi. And you're just like, you're flying through the sky and you have access to the internet.

And that's the way I feel about a lot of the AI things where deep research hallucinates a little bit. And I'm like, well, this is bullshit. It only saved me seven hours. Was that 15 hours of work you would have done otherwise prepping for this interview? Yes. you know? Yeah. Um, so, so it's, it's tough because I, like, I do wish I could, I wish I could have my pre chat to BT, like wiring for every individual breakthrough, because I think I would just be like, holy shit, this thing is witchcraft like every day. Um, like I remember, I remember like yelling at a friend who was like deep in LLMs, like, you know, a week into chat to BT. And I was like, I was like, I'm sorry, I literally don't understand how did it write the business strategy on a thing it's never scanned from the internet and and like i was just like obviously an idiot at that point on on on all this stuff and um And like, but I, I want that, I want that like, like energy again, where I'm just like surprised by everything.

So, um, so I, you know, I'm, I'm, I'm like, I think from a pure utilitarian standpoint, I still love the, you know, my, you know, it's the exact example you just did, which is, which is, I try and force myself as many times as possible to be like, okay, deep research exists, deep research exists. Like, like, don't, don't do the thing where you open seven tabs and you go through all this stuff. it's just like, just send the query, go back to it. I had a, I had a thing 48 hours ago. It saved me an hour and a half. Um, and, um, and like, I, but I had to catch myself cause I was like about to just go to do my research, my normal, like, you know, I've been doing this for 20 years.

You know, I know how to research things, did deep research and lo and behold, like as far as I can tell, no hallucination. I, I, I checked a couple of the answers. Fantastic. Because it's just like, boom, solve the problem for me. Like, so, you know, I'm now doing that probably five to 10 times a week on something, um, which is just an awesome productivity boost. I, um, I still am, um, I'm, I'm still probably incrementally, uh, surprised by and excited by front end design creation with AI. So, so, you know, I'll, I'll pop in a screenshot into cursor and it codes it, or I'll pop in a prompt into, you know, V0 Replit and it produces something that I would have like had to ping an engineer or a designer about and be like, can you go work on this?

And so like, you know, unfortunately like my browser history kind of looks a little unhinged because it's like 11.30 PM on Saturday. I'm like on V0 doing designs. So that's, you know, but that still is pretty exciting, I think, to me. Yeah. What do you think is more likely to be impactful in the enterprise for AI, multimodal capabilities like image, text, code, voice, or the agentic workflows that we kind of talked about earlier? If you have perfectly executing agentic workflows, that's the holy grail. So the amount of the, you know, my workshop analogy is like, you know, in data science as an example, and anything obviously that you put into a structured database, you know, we've had, we have, we've always had this ability to be like, okay, I'm going to go compute some large set of things, like go run the analysis on this customer base in this region to do a thing.

And like, we used to, you know, we still do, but we like, we would talk about it, like, okay, I have a job running and, and I'm going to, you know, I'll check back in in an hour and figure out what the answer was. Like imagine in, in all forms of unstructured knowledge work, a world where you just have jobs running, where you're just like, I just sent these 10 agents to go and like, figure out and synthesize all of this clinical drug trial research to give me insights like you're it's like, we were only wired to think about sending off jobs and compute tasks for like, like 3% of all corporate work. Now imagine if you do that for the other 97% or at least 80% of the 90%. And you're just like, yeah, like I sent out a job to go research this new region I want to enter as a business. I sent out a job to go and, and, you know, figure out what, what all of my, you know, product feedback was, and then build me a roadmap that I should go look at and decide if it's, you know, validated or right. Like, like, it's just going to be crazy when you can actually do full agentic workflows, whether it's deep research or executing a task or automating a customer experience, that continues to probably be the biggest area of upside.

Two years, five years, 10 years. Do you think the best model in those periods will be closed source, proprietary, or open source? Maybe go through each, like in two years, you say closed source? open source? I think for lack of having any imagination on this, I think that I would mostly be in the camp of, it almost doesn't matter because within three months of the breakthrough, you'll have an open source version. So I think it kind of doesn't matter whether the open source version did it first, you know, wow, China did it, or that OpenAI did it, Because I think within three months, we can expect that you'll have the open source version of the closed source breakthrough.

So then almost by definition, you can almost still expect then a large amount of the traffic and the use will be open source because you know you're not trading off that much because you'll still get the open weight version of what just emerged. What's the most exciting Box AI feature, application, workflow that you've rolled out that you think is underappreciated by your existing user base and everyone should really be thinking more about adopting?

Thank you, Logan. Good luck on the valuations. Thank you. Yeah, it's good. We'll be right back.