EP 135: Aaron Levie (CEO, Box) on Enterprise AI Trends No One is Talking About Yet

Box CEO Aaron Levie joined the show to share his perspective on how AI is reshaping the enterprise landscape. He shared what his customers are actually thinking about when it comes to AI, the shift from closed to open-source models, and why the biggest opportunities might not be in flashy consumer tools but in workflow automation and data-rich enterprise applications. Aaron also shares his take on the changing business model dynamics in B2B, including the rise of usage-based pricing, and what it means for the next wave of software companies. 00:00 Introduction to AI in B2B and Consumer Worlds 00:32 Conversation with Aaron Levy: AI in the Enterprise 01:10 Defining a Model Company and AI's Role 02:19 Challenges and Opportunities in AI for Enterprises 03:44 Open Source vs. Commercial AI Models 05:40 The Future of AI Agents 11:42 AI's Impact on Business Models and Pricing 15:16 AI's Potential to Transform Industries 27:14 Comparing AI and Cloud Adoption 30:05 Strategizing AI Implementation 31:08 Customer Adoption and Resistance 32:59 Practical Considerations and Challenges 34:58 Building Flexible AI Architectures 37:57 Exploring Agentic Workflows 41:57 Future of AI in Enterprises 43:36 Rapid Fire Questions on AI Impact 53:20 Exciting AI Features at Box 55:53 Conclusion and Final Thoughts

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Logan Bartlett?

  Welcome to Logan Bartlett Show. On this episode, what you're going to hear is a conversation I had with co-founder and CEO of Fox, Aaron Levy. We covered a bunch of ground related to the state of the enterprise today and what he and his customers are thinking about artificial intelligence. We talked through a bunch of different topics related to AI, what different people should be thinking about. His views on closed source versus open source and the opportunity that presents going forward, as well as some of the considerations related to the business model change that seems to be occurring with usage-based pricing. A really fun conversation that you'll hear with Aaron now. Aaron, thanks for doing this.

• 0:39Speaker 01

  Thanks for having me.

• 0:40Logan Bartlett?

  What in your mind is a model company in today's parlance as we sit here, you know, March of 2025?

• 0:47Speaker 01

  Wow. Okay, that's just jumping right in. What is a model company? Well, I mean, I think, I don't think you actually even have many model-only companies. And so maybe the only distinction is, is you have companies that have models and then other things. So, so if I'm being like extremely technical, uh, they technical about the definition,

• 1:12Speaker 01

  there's probably not, not that many like model companies. I think you have, you have AI product companies that have research divisions that, that, you know, build models. And then they have APIs into those models. So like, I mean, probably to this point, like, it's like Mistral is probably the, the only, you know, like true model company. Where most, you know, OpenAI, I would, I, you know, I would, I would not call a model company. I wouldn't call Anthropic a model company. I'd say those are, you know, AI, you know, foundation frontier lab companies that have products and models and services and just like all the things.

• 1:49Logan Bartlett?

  Do you think that serving enterprises with some type of model offering, but ultimately selling?

• 2:00Logan Bartlett?

  Whatever the core things that enterprise is mostly by, do you think a company can build long-term value around that? Or do you think that will ultimately, whatever that is, resides within an AWS or a Microsoft Azure or a GCP or something like that?

• 2:17Speaker 01

  I think it's, I think it would be hard to underwrite that alone as a massive business, because you basically are dealing with this issue where all of the hyperscalers at a minimum, and then, and then a few other companies that aren't kind of natural hyperscalers, but they have adjacent, they're adjacent enough to the hyperscalers, like, they need to make sure their models get used at, at an unbelievable scale. And because they have to win an infrastructure war, they need the compute to, to come to them, they have other software they have to sell. So it is, it is so existential to them to have their models get used at massive

• 2:57Speaker 01

  scale, that they will constantly drive down the price of AI and the quality up in AI, that you would just get squeezed, you know, you know, into such a, such a difficult position, that I'm not sure, I'm not sure what your, what your value proposition would be. Like, we ran these, we ran these tests on Gemma from, from two weeks ago, and the new open source model from Google and-

• 3:23Logan Bartlett?

  Not to be confused with the severance character, Gemma.

• 3:26Speaker 01

  Yes, yes, exactly. So, so we ran these tests, and it actually performed only like a couple of points worse than Gemini on, on our evals for, for accurate, accurate data extraction from, from enterprise documents and data. And so the fact that you have an open weights model from Google, that is almost at par with their commercial model, that is only, was already kind of state of the art two, three months prior, as, as a set of breakthroughs, means that like, you know, you're going to, as an enterprise, you're going to have a choice between an open weights model, you're going to have a choice between a commercial model, they're going to all perform extremely well, and you're really only going to be making a choice of like, do I want all of the-

• 4:10Speaker 01

  The scaffolding managed for me, or do I want to just run this in my own data center, GPUs, or my own cloud? But like, imagine being a kind of a complete standalone company trying to do that when you have, you know, Alphabet and Meta and, you know, Microsoft and then OpenAI and Anthropic and X. Like, everybody's doing that. I don't know how you would, I don't know how you'd sort of slip in there. So, you know, there's a case to be made for obviously an extreme like deep vertical, you know, use case or some, you know, kind of of niche. But but I'm taking your question literally. I think you'd still eventually then need to build the software around it.

• 4:51Speaker 01

  You'd have to build, you'd have to get really good at all of the the technology that surrounds delivering that AI model to the customer in a way that lets them actually use it, as opposed to just selling tokens back and forth. I don't think there's a business model for us, you know, kind of an independent player at scale to just own kind of, you know, monetizing tokens.

• 5:10Logan Bartlett?

  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 ChatGPT?

• 5:30Speaker 01

  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 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 their, let other players, you know, kind of, you know, power some of that functionality? So, so, you know, there's some interesting strategic questions that 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, 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.

• 6:16Speaker 01

  Let's then incorporate that that into a consumer and, you know, prosumer and then. Business side of products. And then there's now a new layer, which is like, we're gonna, let's make sure that we have like, applied use cases in these agents, you know, 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, 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 GPT play, I'm not sure that would be, you know, that, that, that is not why their brand got to where they are.

• 6:59Speaker 01

  Um, and I think you'd 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, and just, you know, had a person go to chat GPT, 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, 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 eighty twenty rule, which is, we probably have solved like eighty percent of, of like, hey, what's the score to this sports game?

• 7:44Speaker 01

  Tell me about World War Two, um, you know, what should I wear in this meeting? Like, I like, we, we, we've solved that. But the, the twenty percent 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 you to book a flight, you know, type problem. And those, those, you know, were nowhere close to solving. So, so we kind of got to the eighty percent really quickly. And now the twenty percent is going to take years and years to kind of keep cranking through.

• 8:12Logan Bartlett?

  I, I get the feeling you're excited about agents, if I, if I read your tweets and all that. Is there an agent? That you think has product market fit today?

• 8:22Speaker 01

  Um, yes, uh, but, but, you know, a couple of things like I think that the you, um, I don't know everybody's, you know, is if everybody's equally defining agents in the same way, but I'll give you like three or four. So, um, you know, one company, uh, that I happen to be an investor in, but, but I think they, I think they just independently, you'd probably agree is this sort of space around outbound selling. Um, so one company is 11x. I think there's others that are good. Um, and they've, I think they've tapped into, you know, it may be the only, maybe the product market fit right now is like lead gen, um, and then it kind of expands from there. But, but for all intents and purposes, people are using this to augment, you know, their, their sales capacity, you know, generate more demand.

• 9:07Speaker 01

  Um, that seems to be working. I think like writ large coding agents, you know, seem to be working. Again, maybe we could define different levels of agenticness. Um, uh, but, but what you're seeing in, you know, cursor, windsurf, uh, replit, you know, levelable, et cetera. Like obviously, you know, probably different use cases, like probably 80% of the vibe coding is more fun and it's interesting to watch and not going into production. But I could see a lot of, you know, product market fit for, hey, I have this internal IT tool. It's not, you know, not the most strategic thing we have to do. I can have an agent kind of write it or modify it and, and then boom, you know, like I've just like solved that problem that, that used to maybe go to low code tools or you would have to like, you'd either never solve or you might hire a contract firm and it would take six months to go

• 9:52Speaker 01

  build. So I'd say that's product market fit. Um, on our end, you know, we, we have a, we have, um, one of our first kind of agentic use cases in, in just the, the fact that it's not kind of a, a single, you know, one shot, uh, in the model, um, is just we extract data from documents. So then you can, you know, say, hey, here's a contract, here's an invoice. I want to, you know, read the document, put that into a database and then automate a workflow around it. That's, you know, clearly got at least in our world, product market fit, like, like, like mostly our problem is we can't keep up with all the range of use cases customers want. Um, uh, and, and so like, there's like more demand than, than the ability to kind of satisfy, you know, everything that the customers are asking for. But those are, those are a few, I, you know, on the consumer side, I'd say probably not a lot

• 10:36Speaker 01

  of, uh, PMF on, on, on agents. Um, but on business, you know, it's

• 10:43Speaker 01

  My bet was this two years ago, my bet will probably be it in five years from now. We are probably, you know, we're probably

• 10:52Speaker 01

  over indexed on, on the flashiest parts of AI in the consumer world. And then under indexed on the things that will just make, you know, hundreds of billions of dollars in B2B. And like, I'm sure like Nikita is like.

• 11:06Logan Bartlett?

  No, I mean, it's good. You're talking my book here. Yeah, yeah, yeah.

• 11:12Logan Bartlett?

  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, 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, is an opportunity with AI to have that similar effects? Or is it something different in your mind?

• 11:53Speaker 01

  For like, the, the broad transformation or disruption or which, on which axis?

• 12:00Logan Bartlett?

  Yeah, I, I guess.

• 12:03Logan Bartlett?

  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 seat-based pricing and, and elements of, you know, fair use pricing, like Slack was early in that. And I think all of that stuff, there was a bunch of different elements of, 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?

• 12:45Logan Bartlett?

  Or is it a little orthogonal and ultimately it might not be as broadly applicable as?

• 12:50Speaker 01

  No, I think it's, I think it's, I think it's very relevant. And then, you know, so my, my characterization of SaaS and cloud from a business model disruption was it took something that you used to spend upfront, like maybe millions of dollars on to now you spent thousands of dollars on.

• 13:09Logan Bartlett?

  Capex to Opex transition.

• 13:11Speaker 01

  Capex to Opex, sort of like perpetual license to, you know, by the drip. You know, anything that was just like, okay, I have to wrap my head around this major outlay of capital, system integrator fees, infrastructure I have to build out, team, like, you know, if you, if, this is more anecdotal, but if you were like to deploy a CRM system in the 90s, you know, just think about what had to go into that, like, like data centers, system integration, like, you bought software for the CRM system, but you still had to buy hardware, you had to buy all the servers, you had to have a network. So like, think about how many steps from the moment you said, I want a CRM system,

• 13:55Speaker 01

  to the day that you're actually going to like, log into it with your data, I don't know, a year, year and a half, you know, millions of dollars. Salesforce shows up and they're like, literally like tomorrow, you can just have a CRM system. And oh, by the way, you could use it for 20 people in your company. And there's no, you don't need some kind of like critical mass to make the economics work, like, just like, start using it. And so, so, you know, you multiply that over two decades or two and a half decades, and it's like every SMB on the planet can do it, every small team can do it. And the TAMS of these markets just basically grow, because now everybody can afford a CRM system that couldn't. So, so that was, it was because it was a seat base, it was because it was a subscription, it was also just because literally like, like your day one upfront bill was just a hundredth of what it would have been,

• 14:38Speaker 01

  you know, because you don't have all the other, all the other things, you know, wrapped around it. Cloud did the same thing, obviously more on an elastic basis with compute. So I think what agents do is, is that, I think there's going to be two big, two big kind of areas of upside. One is you're doing that now for effectively a form of labor. And so that kind of has this interesting impact of it expands the TAM of software from only being able to sell the software to people on the other end. So you were kind of capped by the number of seats, to now you have kind of like, you know, like, I can think of categories where, where the category of AI agent spend will be in order of magnitude

• 15:21Speaker 01

  more than the spend of the software in a kind of a non-AI world. So, take, take, you know, a relatively simple example of like contract management software. Contract management software as a category is like a couple billion dollars, like globally in the world. Legal services is a couple hundred billion dollar category. And so, so can, can now can software take, you know, 10% of that spend, you know, through AI to and and pull from the legal services spend or expand the legal services spend? And then all of a sudden, this category that was was relatively niche, couldn't make that much money because the amount of people that wanted to buy contract managers and software was kind of low. Could that now be a $20, $20 billion category as opposed to a couple billion dollar category?

• 16:06Speaker 01

  So you could do that probably 20, 50 times across software and already underwrite master TAM expansion. After going after, you know, you know, a whole bunch of of new use cases that used to be, you know, kind of labor essentially. And then the other, the other part is, is AI, you know, has a way of just completing the things that people have kind of always wanted to do in the products themselves, but just whenever possible, which then also unlocks a whole bunch of new budget as well. So, so I think you're going to see this is, so this is the new version of disruption is, is it just will, it, it makes, you know, it lets you go and complete the outcome that the customer was looking for in a way that they are not required to go and deliver it themselves with their people.

• 16:50Speaker 01

  And then that means that you, you know, more than that value accrues to you. And so that's the, that's the usage base or outcome pricing I think we're going to see in AI.

• 16:57Logan Bartlett?

  When, when you extrapolate that service budget there forward and and think about the value to the end customer, totally makes sense that they'd be willing to pay. Hey, I, 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, 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 customer or sorry, 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 we're,

• 17:43Logan Bartlett?

  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.

• 18:06Speaker 01

  Yeah, so um I don't think that any law of of uh market dynamics, you know, changes because of AI. So so uh you know, I I but I would say that has probably been true in in a venture startup land kind of forever, which is, you know, the CRM. I bet there was a CRM market map from Gartner in ninety six. That was like forty seven companies that did CRM and somehow only Siebel and like one or two others made it to the to the other end. And so, you know, did they temporarily cause a deflationary impact on the price of maybe what Siebel could have charged if they were left alone? For sure. Did it still create a great, you know, outcome for for Siebel?

• 18:47Speaker 01

  And, you know, you know, there there's a version of the world where it doesn't sell the Oracle and they they, you know, pivot the company and now it's a, you know, hundred billion dollar company. Um, so so I I guess uh I guess it's a hundred percent true, but but nothing about AI seems to follow a different law of of how these things play out, which is competition will suppress, you know, what what you could like maximally charge. And it keeps kind of all the players in check. But eventually you get like some degree of network effects, some degree of like, you know, reference customers that tell each other, you know, that you can all use, you know, X thing and and and so on. And, you know, like, just apropos the timing of this conversation, like, um, uh,

• 19:27Speaker 01

  my my my dad was asking like, what what how did Wiz get sold for, you know, the thirty billion dollars? And we're Wiz customer and I I think the technology is fantastic. Um, but how does that happen in a world where like, obviously, there's like five hundred security startups? Like, well, how does one break out? And it's there's a little flywheel where like your product's just twenty percent better. Then you get a few more reference customers and then and then you learn faster and then your product is another twenty percent better, faster. And then you get slightly better, you know, people working on it and and it kind of spins out. And then all of a sudden you have one with escape velocity, you know, out outpacing the rest of the category. But yet probably there's still a second and third and fourth player that are pretty good and that they'll probably also grow and then they'll get acquired.

• 20:09Speaker 01

  So I think AI, I mean, I don't I don't see a different dynamic playing out in AI for for any reason. And so and so then the things you probably want to do to like maximize your moats are. You know, again, the same lessons probably from thirty five years ago in enterprise software, which is, you know, get data, do workflows, you know, make sure you've got, make sure you've got a, you know, kind of enough happening within your ecosystem that keeps customers to have some kind of, there's like a, like a compounding value, more data that comes into the system, you know, the next run of the AI agent, you know, gets just a little bit smarter. You know, there's going to be a lot of network effects on the embeddings of your data. So,

• 20:51Speaker 01

  so the, the, you could, as a single programmer, you could probably switch between Cursor, WinSurf and, you know, Replit and a few others. But as a, as a network of five hundred engineers in a company, and I've done embeddings on the whole codebase, and I'm talking to all the different, you know, systems it's wired up to, wow, change management on that, you know, becomes a lot harder. So, so I think, I think you kind of, I think we're going to see a lot of very similar, you know, maybe this will be bad news for everybody in AI, but it's going to look a lot like SaaS, and the lessons are going to look like, a lot like SaaS. And, and so you want data, you want workflows, you want strong customer references, and you want to build a network, network, network, found your, network effect around your customers. And I think it's going to play out like that. And then maybe the

• 21:35Speaker 01

  one thing that we just have to be prepared for is, there might be some pricing dynamics right now in play in AI that won't necessarily last forever, which is like, right now, you can kind of comp it to labor in some areas. And that, that probably feels like a little bit of a temporary advantage that, that you might have, when ultimately, it'll probably be more comp to like, like typical software margin structures. And that'll just be a journey that, that, you know, the industry goes through, as, as we kind of land to the, you know, terminal business model.

• 22:05Logan Bartlett?

  Yeah, I guess we don't need to belabor this point. The two things that I do wonder about with AI specifically is, are the solutions that are maybe less workflow UI centric. In some ways, there's like the, the SaaS world, traditionally, we had all these screens and logins and, you know, the workflow that existed on top. And so maybe security in that way is actually the right analogy, because there's definitely dashboards and integration points and all that with security. But ultimately, the work. Is getting done oftentimes outside of those screens, right? It's pulling in all the, all the data in some ways. And so, um, yeah, it's just something we've been noodling internally.

• 22:46Speaker 01

  And what, what do you conclude because of that? Or what?

• 22:49Logan Bartlett?

  I think, I think the things that are just tapping into, uh, the work automation, be it, uh, customer support or SDR or whatever it is. Ultimately, because there's less workflow embedded into it, the change from one to the next, I think there's less, uh, friction associated with that change. And so you can swap it out. You see, it's almost like a meme on the internet of people going from, you know, cursor to, to codium or whatever it is. And that's not the right analogy because that actually has some workflow around it. But it does feel like people are a little more willing to switch than at least I had historically experienced where, I mean, in truth, what's the difference between Asana and Monday and Trello or whatever? Well, the difference is like, you gotta switch your entire team onto something different,

• 23:34Logan Bartlett?

  uh, in a meaningful way versus when the work is actually being done outside of the UI. It might just be one click change, which just leads to a little bit, um, I don't know, uh, more ability to price shop, more ability to do other stuff. But I guess that it ties back to the integration, similar to security.

• 23:49Speaker 01

  Yeah, yeah. Well, so that's, so that's the thing. So, so if you, so there's, so, so the question is, so it used to be the network, the, the stickiness was I had a hundred people that all learned how to use the, use the buttons and, and then there was data behind the buttons. And so, man, that's a lot of, that's a lot of change. But the question would be like, is there going to be an analogy where, you know, it's inverted where you still, the AI, it, you know, it's, it's APIs, it's APIs into your other systems, it's the APIs into your e-commerce thing. It's then the logic around when to make the decision about the e-commerce thing. So I think.

• 24:25Logan Bartlett?

  And it's self-reinforcing and learning, right? And so like, some extent, you get all the data, get all the integrations, but then there's ultimately some algorithm or whatever, some AI thing on top that's learned on top of it. So sure, you could switch, but then it's a cold start to start over.

• 24:37Speaker 01

  Cause I, you know, I could, I could underwrite the exact opposite of this point, which is that the, that when you have a learning system, uh, that, that learning system will probably perform better than your people did. And so previously, you could change out, you could swap out software. And, and there wasn't like so much IP in the heads of the people about how that software worked. Cause we're, we're just like, okay, click the next button and whatever. But like, but like the AI is going to track every event it's done in history. And, and you know, and, and just get a little bit better. And, and the signal will just get a little bit better and it'll, it'll, you know, have a little bit more fine tuning, or it'll have a little bit better instructions to the model.

• 25:21Speaker 01

  And then that becomes some, you know, opaque, you know, data source to you, the customer that is inside this, you know, AI product that just makes the thing always perform five or 10% better in a way where you can't just go to people and say, here's the new sheet of paper for how to use this new product we just implemented. Like, like you almost have now created your own, you know, for all intents purposes, model for, for doing customer support or, or coding in your product. So, so I, you know, it's, so to, to, the more black box or obfuscated or non UI oriented it is, to me, doesn't necessarily correlate to its stickiness. And, and in fact, maybe it could be the opposite.

• 26:03Logan Bartlett?

  I heard you say that, uh, cloud was, um, pure efficiency. And maybe there goes back to one talk you were giving at some big bank that, uh, everyone's eyes were glazing over. I think it was like a conference or something that maybe the, the most boring or least interested you've had a crowd. Uh, but it wasn't like tangibly output based, uh, in, in some way that like they, the rank and file within an organization didn't care about the backend infrastructure. Uh, and ultimately you're speaking about some elements of it. Uh, can, 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?

• 26:44Speaker 01

  Yeah, so if I recall the anecdote properly, it was, I think I was just referencing that like in the peak of cloud adoption, I did this keynote on like the future of, you know, running your bank or, you know, financial services and it was, it was mostly just the pitch cloud. And, and it was, you know, the, the, I found it very exciting, but the audience didn't because it was, because it was just like, yep, cool. We get it, man. Like the servers are not in our data center anymore. They're in now a cloud data center like really like super cool. And, and so, so that, that, that to me was kind of the, it kind of captured the cloud transition, you know, at an industry level in a nutshell,

• 27:26Speaker 01

  which was like, it was, it made your, it made your IT operations faster. It meant you could store more data. So you can make better decisions. It makes your employees more efficient because they have better software. But it didn't radically transform like entire industries or sectors. And AI, you know, conversely, has the opportunity to actually radically transit, you know, transform how you run your business, how you make decisions, you know, how you, you know, serve your customers, how you build products. And, and, you know, I think it's, it's, I think it's always useful to learn, you know, figure out like what things will be similar to a prior technical, you know, technology transition, which things will be different. I think the thing that will be similar is that, that adopting AI versus cloud will take all the same change management.

• 28:10Speaker 01

  It'll take all the same meetings. It'll take all of the same, you know, privacy and policy and governance and compliance review people. Everybody like it's, it's going to be all of that. But I, but I do notice and see one big distinction in, in the two waves, having kind of been at the similar timeframes for both. In the very early days of cloud, you would go into a meeting with a bank, a pharma, a government agency, and you'd say, you know, I'd love to talk to you about, you know, moving your data to the cloud. And it was sort of like, like not, not interested, shocked.

• 28:45Speaker 01

  Like definitely not gonna happen, not gonna happen here. Maybe, maybe there'll be some small little workload that's like a dev test workload. Maybe some, you know, department or fringe, you know, group will use it. And, and so it was mostly like years and years of just like selling and selling and selling and in trying to get you to understand what was so much better. And all this. Conversely, and so we're only about two and a half years into chat GPT, let's say, so that would, that would be, that would easily describe two and a half years in the cloud. If I go back to, to the late 2000s, let's say. If I look at AI right now, that exact same meeting with a customer or set of, of peer customers. Is like, is like, we know this is gonna change everything. Everybody's clamoring for it. Every employee needs it.

• 29:28Speaker 01

  The new generation coming into the workforce is asking for it. My boss is asking for it. The board is asking for it. I need to figure out a strategy. And, and like, it's not when, it's, it's how. It's, I'm sorry, it's not if, it's, it's when, it's how, it's like, what do we, what can we do? I need to do more yesterday. And so totally different universes. And, and it is, uh, it is so, so, so. Basically what that means is that you've bypassed. All of the normal, like, resistance to this happening. That normally, you know, happens with one of these big changes. And, and now it's purely in the mode of like, how do we make it happen? Which, which functions are gonna use it? What, what, what is the impact to, you know, how do we make decisions when an AI generated the response?

• 30:13Speaker 01

  How much human in the loop do we need? So we've jumped all into just now that mode.

• 30:19Speaker 01

  You know. Five to seven years faster than we did with cloud. Now, that doesn't mean that the adoption will follow a different trajectory because again. It's really people, it's change management, it's systems. But the, the reluctance and emotional kind of consternation is very different this time.

• 30:37Logan Bartlett?

  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?

• 30:56Speaker 01

  Yeah, I would say actually, the maybe also this might abuse the analogy too much. But if I compare to cloud, cloud could do more than 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, you know, hundreds of percent more. Maybe actually the reverse is true, which is I'd say most conversations now, the customers 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.

• 31:35Speaker 01

  Or, you know, you know, we're still waiting on some reasoning model breakthroughs from, you know, OpenAI or Claude or something to like, just get that extra 3% of accuracy that you're going to need. So in this case, I think customers have have pretty much jumped ahead to way closer to the end state, which is like, yeah, great. Like, let's give all the employees a productivity boost. Let's figure out where we can deploy agents to the organization. Now, you know, I want to, I want to do a master of asterisks. 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, some of the anecdotes I'm sharing are from those types of customers. I think there's, there's still an 80% of customers that are, that are like, whoa, like my head's exploding. Where should I start?

• 32:21Speaker 01

  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. And, 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 a 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 at doing code, uh, you know, auto completion.

• 33:01Speaker 01

  But like, am I ready to put an AI agent in production for like generating a full, you know, multi-file, you know, um, you know, output? And then, and then how do I, how do I review that when the human actually wasn't along the ride for the, for the actual, you know, editing process? Are they going to really understand the code? Are they allowed to press submit and that goes in production? Like, like that's going to be that, that like, these are like the big questions that, 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's 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?

• 33:43Speaker 01

  Like, like, is that like, are we ready for, like, are we ready for like your bank account to be, you know, driven by vibe coding? Like, I, we don't know. So, so these are, these are the kind of things that I think every enterprise is, is trying to figure out like, okay, so what, what pace should I implement this stuff? You know, is my data ready? Are my systems ready? A lot of it is about, wow, the industry is moving so fast. How do I make, um, 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 page, you know, 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 conversations that we have.

• 34:28Logan Bartlett?

  To that end, you cautioned, I forget if it was a blog post or a tweet or something, but that being early to AI in some ways can be a liability if you don't set up the architecture with the right abstractions in some ways. I guess, as you think about the applicability of this for Box as an organization, and therefore the advice you might have for others, how do you think about being flexible with this pace of change? And is it more about being model agnostic or modular in elements of the design? What would you share on that?

• 35:05Speaker 01

  Yeah, all the above. So there's how we build and then how we even implement AI internally. How we build is basically, we try and figure out the parts of the stack that, there's probably a few different categories, and I'm making up some of them, maybe the nomenclature on the fly, but like, there'll be parts of the stack that just like will never change. You know, a model breakthrough will happen, and it doesn't matter, like, we're not going to change that particular interface, you know, with our system. So that, you can just like lock and load and start executing. There's other parts where you're like, you know, we think there's a best-in-class technology now, you know, maybe we're kind of like, we're

• 35:47Speaker 01

  shaky on it, you know, we probably prefer there to be an open source version. So that's like a spot where you're, you know, you probably need to be really thoughtful about, you know, how much do you lean into a particular, you know, part of the architecture? I'm thinking like, you know, your vector search, your, you know, your RAG, you know, kind of, you know, execution, you know, these are spaces where there's a bunch of first movers, you know, people started adopting them. It's not exactly clear where it all ends up in five years from now, so you kind of want to be, you know, thoughtful about that. And then there's the final part on your modular point, which is like, you know, this space

• 36:28Speaker 01

  is changing every three minutes. You need to decide, do you want like maximum flexibility? So these are the model providers. Do you want to be able to have a way of, you know, swapping in different parts? There's multiple parts of Box where we use multiple model providers for the same workflow. And so you just need to then have like a high degree of optionality, a high degree of flexibility in that part of the stack. And so that, you know, so you and then you have different like abstraction layers between, between those, those different interfaces. But I think that's, I think basically anybody in AI at this point has been building with that pattern. You know, you go to perplexity, you change your model, you go to cursor, you change your model. I think you can, I think we're all realizing like,

• 37:12Speaker 01

  okay, you probably want to let the user change their model, or at least you as the developer have some, some degree of optionality there because we know how fast that space moves. And then you have to decide like which other parts of the stack do you just kind of, you know, lock in on and don't worry about again.

• 37:27Logan Bartlett?

  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 agentic workflow. And I'll tell you, it set off a little bit of a firestorm internally to see if this is like a, you know, 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, the need for the interface and therefore everything could be in a crud database.

• 38:09Speaker 01

  Right.

• 38:10Logan Bartlett?

  In some ways. I'm curious your perspective on, on that as we zoom out and maybe is, 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?

• 38:27Speaker 01

  Yeah, I mean, I found it very fascinating and I, I enjoyed that it was happening and I, I mean, I enjoy that it's still theoretically happening. I think some of his updates were that like they didn't actually do as much on the customer support side, or they have some more, you know, kind of premium agent, you know, human agents that they now have. But, but so I, I thought it was great from a provocativeness standpoint. I think we do need people testing the boundaries. I think it's the kind of thing which is, it didn't cause me to rethink all of SaaS because, because the, the, you know,

• 39:03Speaker 01

  Uh, I, I don't remember the first time I heard this, but maybe, maybe it was like fifteen years ago or something. I was, uh, I was meeting with, um, an IT, you know, leader at Tesla. And, and they were like building, I'm gonna make up eighty percent of the story, but they were building their own ERP system. And I was like, holy crap, I cannot believe that, that you're doing this. And like, what, like, maybe this is the future. And then you like quickly wake up that like, okay, like, no, like, like, you know, this is sort of an Elon special where he needed to control end to end, the whole thing, and they needed their own, you know, they, they didn't want any of the off the shelf stuff. And the amount of fortitude and just, you know, sheer entrepreneurialness you, you need to have to even wanna attempt that is like kind of insane.

• 39:49Speaker 01

  And so I think the Klarna example is more in that category. Um, uh, like, it's just like, if you go to the average company and you're like, do you want to be responsible for running your own HR system? Like, the average enterprise is, is not gonna say yes to that. And no matter how agentic, you know, something is, you, you, it does mean now you're responsible for the thing. Like, like this, the, the, the AI provider that had made the agent that wrote your, you know, your HR system is not taking responsibility for the AR HR system code that was generated. So that means by definition, you, the customer, are now responsible for whatever the thing is that it produced. Um, it's not gonna be the AI models, you know, Claude is not, Anthropic is not, uh, taking any responsibility for how your HR system works.

• 40:32Speaker 01

  And if it leaks data and you get sued by the EU because there's some kind of, you know, data privacy thing, like, that's not Claude. Like, uh, that's Workday. Workday will do that, not, not Anthropic. So I think you'd have to be like, you know, which parts of software are people so unhappy about that they would do themselves? Or which parts of software are just like, are so needed to be customized? Um, or which parts of software are so unstrategic that you don't mind whipping up these like micro apps for like one-off things? And so, so you don't, you, you're, you're fine that it is built by this, you know, an, an agent that you can just deploy quickly and, and turn it off. And it's kind of, you know, it's a three month, you know, it needs to live in the world for only three months.

• 41:13Speaker 01

  Those for the foreseeable future, next two years, let's say, that would be the only thing I think could be disrupted. But I don't think your HR system is going away. I don't think your CRM system is going away. I don't think that really changes. To your point about deflationary impact, I do think that there's a chance that you could see, you know, you could see a new crop of like, I'm gonna just like, like YOLO it and build like the AI, you know, like, like I'm gonna build Oracle with AI agents and just like give it a shot. But that would still be a software company that emerges from that. And you're really using AI purely to just, you know, add engineering augmentation. I don't think the customer wants to be responsible for that software at the end of the day.

• 41:57Logan Bartlett?

  That was my conclusion as well, which I think is good for people investing in software companies.

• 42:02Speaker 01

  Yeah, I know, big asterisks, like in 10 years, do I want to, you know, be on the record as saying this, I'd be like, let's see, but like, but like, just, I just don't think companies want to be responsible for every problem in the world. Like, like you want, like, the, the, Jeffrey Moore came up with this idea, I think it was Jeffrey Moore that came up with it, but, but maybe he credited somebody else. Of course, context and, and like context is just like shit that like, like you just want done for you. You don't want, you don't want to worry about it. Core obviously being like, this is like existential to my business. And for most people, like, like their core business is not their HR system. That, and so thus you hire other people to do your HR system. And so that's why you don't really want, you know, you don't, you don't

• 42:45Speaker 01

  want your internal IT team being responsible for rebuilding something that you can get off the shelf and just it's done for you.

• 42:51Logan Bartlett?

  That's the way I feel whenever technical founders want to reinvent sales comp. And it's just like, you're not, it's hard enough to differentiate on one vector. You know, this isn't going to be the make or break. Yeah, we, we, you know, we can go through all the books and talk about it. All right, I'm gonna give you some quick ones and you give me your, 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?

• 43:24Speaker 01

  Well, how many years?

• 43:26Logan Bartlett?

  Five to 10.

• 43:29Speaker 01

  I'm gonna, I'm gonna stick with no for now.

• 43:31Logan Bartlett?

  Yeah, I think, I think I agree. Um, do you think AI will flatten org structures in any meaningful way? Example, fewer middle managers, faster approval, more self-service, knowledge work?

• 43:41Speaker 01

  This, this one I could buy. Um, I, I could buy. I think actually layers have gotten pretty crazy over the past couple of decades. Um, so, so I, I think, I, I think yes. Um, uh, I think yes. And, and, and part of it is because I think that AI kind of lets you, it, 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 AI, the front end code. It lets the backend developer write the front end, you know, code. It lets the copywriter, you know, generate the full white paper.

• 44:22Speaker 01

  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, uh, uh, uh, 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.

• 44:56Logan Bartlett?

  Did you ever read the David Epstein book Range, by Generalist Triumph?

• 45:00Speaker 01

  No, uh, would it relate to this?

• 45:02Logan Bartlett?

  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 in 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, new breakthroughs come outside.

• 45:31Speaker 01

  I think, I think work has probably gotten like a little bit too Adam Smith. Um, 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.

• 45:46Logan Bartlett?

  Yeah, I think 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.

• 45:55Speaker 01

  Yeah, 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, 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. Uh, that, that I think has a lot of upside.

• 46:28Logan Bartlett?

  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.

• 46:40Speaker 01

  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.

• 46:52Logan Bartlett?

  It's like the hedonic treadmill in some ways, right? You're just like, I, there's the, I, I always reference now with AI, the Louis C K bit about like, uh, airplane and wifi. And you're just like, this is, you're flying through the sky like, and you have access to the internet. And that's the way I feel about a lot of the AI things where, you know, deep research like elucidates a little bit. And I'm like, well, this is bullshit. Like,

• 47:16Speaker 01

  It only saved me seven hours.

• 47:18Logan Bartlett?

  Was that 15 hours of work you would have done otherwise prepping for this interview? Like, yes, you know?

• 47:24Speaker 01

  Yeah. Uh, so, so it's, it's tough because I, like, I, I do wish I could, I wish I could have my pre chat GPT, 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 L M's, like, you know, a weekend to chat GPT. 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, This is 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.

• 48:06Speaker 01

  So, um, so I, you know, I'm, I'm, I'm, I like, I think from a pure utilitarian standpoint, I still love the, you know, my, 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 I, 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 forty-eight hours ago. It saved me an hour and a half. Um, and, um, and like, I, but I had to catch myself because I was like about to just go to do my research, my normal, like, you know, I've been doing this for twenty years.

• 48:45Speaker 01

  You know, I, I know how to research things, did deep research, and lo and behold, like, as far as I could tell, no hallucination. I, I, I checked a couple of the answers. Fantastic, because it's just like, boom, solved the problem for me. Like, so, you know, now doing that probably five to ten times a week on something, um, which is just an awesome productivity boost. I, um, I still am, um, I'm, 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, V zero replit, and it, it produces something that I would have like had to ping an engineer or a designer about,

• 49:29Speaker 01

  and be like, can you go work on this? And so like, like, you know, unfortunately, like, my, my browser history kind of looks a little unhinged because it's like eleven thirty PM on Saturday. I'm like, on, on V zero doing designs. So, um, uh, so that's, that's, uh, you know, but that, that, that still is pretty exciting, I think, to me.

• 49:47Logan Bartlett?

  Yeah. Um, what do you think's more likely to be impactful in the enterprise for AI? Multi-modal capabilities like image, text, code, voice, or the agentic workflows that we kind of talked about earlier?

• 50:02Speaker 01

  If you have perfectly executing agentic workflows, that's the holy grail. So the amount of, 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've always had this ability to be like, okay, I'm gonna 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 I'm gonna, you know, I'll check back in in an hour and figure out what the answer was. Like, imagine in all forms of unstructured knowledge work,

• 50:46Speaker 01

  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, 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, 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.

• 51:30Speaker 01

  Like, it's just gonna be crazy when you can actually do full agentic workflows, whether it's deep research, or executing a task, or, you know, automating a customer experience. That's the, that continues to probably be the biggest area of upside.

• 51:45Logan Bartlett?

  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 or open source?

• 52:02Speaker 01

  I I think um for for lack of having any imagination on this, I think that I would mostly be in the camp of there will it it almost doesn't matter because within three months of the breakthrough, you'll have an open source version. So so I I I think it's it's 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 of the closed source breakthrough. So you almost that that so then almost by definition, you can almost still expect then a large amount of the the the traffic and the use will be open source because you know you're not trading off that much.

• 52:45Speaker 01

  Because you'll still get the open open weight version of of of what just emerged.

• 52:50Logan Bartlett?

  What's the most exciting box AI feature, application, workflow that that you've that you've rolled out that you think is underappreciated by your existing user base and everyone should really be thinking more about adopting?

• 53:10Speaker 01

  Uh, there's there's two features we're most excited about. The first is um we we have this product called hubs. And what hubs does is if you go to box and you go to any file system, you create a folder, you put files in the folder, you share that with people. Um, uh, you have this problem, which is well, what like like folders are are kind of like unintuitive for the recipient because they're like, I don't know like which what you know, which third subfolder down did you put the, you know, the the sales material that I'm looking for to to use for my my, you know, customer pitch. So we created hubs, which is an overlay onto folders. So you can put as many of your files and folders into a hub. And then you can go and just ask questions of the hub.

• 53:52Speaker 01

  And so this solves actually a very, very nuanced, highly important thing, which was if you go to a general purpose, just AI chat window. You have to do a lot of work to guess what what stuff is on the other end of of this chat window. If you go to a hub, in our case, you kind of know by by hub, what what does it have access to? So we have a sales hub, which means by definition, that has all the sales information. We have an HR hub, which means that it has all your HR information. So you go to the HR hub and you ask the HR question. And then we're basically doing RAG on your documents for all of your HR documents. And we're doing RAG on on all your sales, you know, you know, presentations. We're doing RAG on all your customer support, product documentation.

• 54:34Speaker 01

  So that basically gives you these micro knowledge portals for every topic that you want. And so that that's a game changer because because you just, again, the user instantly knows what topic is going to be covered in this thing. So you you dramatically reduce the hallucination. You dramatically increase the authoritative content that will be in the hub. So that's pretty cool. And then the final one that that is is just working out kind of way better than we we had hoped initially, like, you know, a year and a half ago, let's say, is just AI data extraction from documents. It sounds like way too straightforward for anybody listening, but it's insanely powerful if all your job is is like, review contracts, review invoices, pull out data from resumes, you know, standardize my my financial reporting documentation.

• 55:18Speaker 01

  We we could just now automate, you know, basically most of that.

• 55:22Logan Bartlett?

  That's great. Well, Aaron, thanks for doing this. I I think we covered a lot of ground, so I really appreciate you in there.

• 55:29Speaker 01

  Thank you, Logan. Good luck on the valuations.

• 55:31Logan Bartlett?

  Thank you. Yeah, it's good.