Box's Aaron Levie: On Reinventing Yourself in the AI Age and Enterprise Diffusion

Starting a company is hard. Reinventing your company for AI as a public company with quarterly earnings results is even harder. Aaron Levie has pulled off the transition with Box and offers hard-won advice for founders. The cofounder and CEO of Box argues the value isn't only in the model; it's in the bridge from a model's raw capability to the actual workflow inside a bank, a law firm, or a pharma company. That's the case for the application layer, and Box is building it: an agent harness tuned so tightly to its own file system, permissions, and search that it beats handing the raw API to Claude or ChatGPT on both accuracy and latency. Aaron explains why token subsidies from the labs can't last, why you want a model-agnostic company routing your tokens rather than the one selling them, and why coding diffused fast while the rest of knowledge work won't. (There's no "give us your GitHub" for a sales rep.) His prediction: within five years, 90% of enterprise tokens go to work no human user ever initiated. Hosted by Sonya Huang, Sequoia Capital 0:00 – Introduction 1:55 – Are application companies the hottest neolabs? 6:56 – Will the labs move up the stack? 12:34 – Box and betting the company on AI 16:50 – Hero use cases: reading a million contracts and long-running agents 18:42 – Work slop: why AI code is embraced but AI content isn't 24:08 – Building Box's agentic harness and the evals that matter 27:23 – The state of the model race 29:25 – Open-weight model adoption in the enterprise 32:34 – Memory, continual learning, and what belongs in the weights 37:29 – Box Labs and systems of record in a world of agents 44:55 – Will chat be the dominant UI for enterprise AI? 48:00 – Why coding diffused fast and the rest of knowledge work hasn't 54:31 – Staying wired in, making a company AI-first, and what it takes to win

• 0:00Speaker 00

  I want to send an emergency alert to like, everybody who's like a sophomore or junior in college and just be like, follow these 20 accounts on Twitter and also join Twitter because like this will just help your career. You're either like a year ahead or a year behind, simply based on your feed. And my feed is like so, so wired in. I'll still talk to 20 year olds that are like, yeah, like, you know, I, I see some articles and I'm like, what do you mean? How do you see articles? Like, I don't even know what that means. Like, do you just like get lucky that somebody emailed you an article? You just have to be wired in. I, I enjoy it. It's a lot of fun. I play with everything.

• 0:50Speaker 01

  Today, I'm excited to welcome Aaron Levie, founder and CEO of Box. Box is building a collaboration platform and content management platform for the enterprise that has now taken on really new life with AI. And I'm excited to chat with you today about Box and AI, about your general thoughts on AI because you are such a thought leader and about how founders can reinvent themselves for this AI wave. So thanks for joining us.

• 1:10Speaker 00

  Thanks for having me. I'm a big fan of the podcast. I religiously watch every episode and so great work. I am wondering though, I know this is a different podcast, but when do you talk about root canals?

• 1:22Speaker 01

  Oh, my gosh. We're gonna call in Doug Leoni.

• 1:26Speaker 00

  Have we figured out how to weave that in yet or not? I mean, I'm happy to talk about different kind of pain and suffering that I've been through.

• 1:32Speaker 01

  We should just do this pod with a live root canal. Let's do it.

• 1:35Speaker 00

  See how it goes. Actually, it would be like Hot Ones, but you get a root canal and you have to talk about your strategy while you're in a dentist's chair. And Doug is just on the other end of it.

• 1:45Speaker 01

  Amazing, amazing. Doug is so pleased with himself, by the way, right now. Okay.

• 1:49Speaker 00

  Has he seen every tweet?

• 1:51Speaker 01

  Oh, yeah. Oh, yeah. He's very pleased with himself. Okay, let's start with, I'm curious your take on this. Application companies are the hottest Neo Labs. Agree or disagree?

• 2:02Speaker 00

  You know, two years ago, I think it would have not made that much sense as like, what does that mean? But, but very clearly, I think this is what's playing out in the market. And it's all working out mostly because of open source. But what's, what's pretty amazing is right now, you know, the, the whole concept of being a sort of L M rapper or model rapper is actually working out. Because what, what, what I think people underappreciated was that in the real world and the enterprise, what you need is some bridge from the model's capability to the actual workflow that the enterprise has. And that bridge, you basically like probably a trillion dollars has been bet on, on basically one of, of, of two outcomes.

• 2:45Speaker 00

  Either that bridge is, is very kind of limited or that bridge is actually very vast and, and needs to be able to, to, you know, take on, you know, a lot of depth within organizations. And that bet basically looks like, are you only long, you know, kind of the, the model itself and super intelligence? Or are you long this sort of application tier? Or, you know, maybe previously would have been just pure Neo Lab. But, but I think it's very clearly playing out that, that actually there's a lot of gap between the model and the workflow. And as you bridge that gap, over time, you get to a point where you realize, oh, I should also do the model. And, and then you have enough data, you have enough sort of domain expertise where that becomes its own, you know, sort of flywheel.

• 3:25Speaker 00

  So I'm very bullish on this, on this idea. And what's cool is it's opening up like multiple layers of, of, you know, sort of opportunity for startups because you could either be the actual applied company itself, i.e. the Neo Lab, or you could be the infrastructure provider to the Neo Lab and you have like multiple layers of, of, you know, going and attacking that space. But I think a huge update for, for the market and everybody's kind of views of it.

• 3:47Speaker 01

  Box Labs, let's go.

• 3:49Speaker 00

  We, we already have it. It's a little bit, it's a little bit secret, but we, we, we pay very close attention. Right now, the main focus is let's make the agent really, really good on any model. But over time, obviously, you would peel off certain use cases, either on a per customer basis or, or kind of across the, the whole data set.

• 4:06Speaker 01

  Yeah. I mean, it seems like there's two forces that are happening. One is people don't want the fox to be guarding the hen house. They don't want the seller of the token to be the one that is also metering and gating, you know, what is the best token for each use case? And then the second is, you know, there's actually a lot of work to do on that bridge to cross and there's real research involved in it. And it's pretty bespoke to the exact, the exact workflow and the exact end customer that you have.

• 4:29Speaker 00

  Yeah, I think, I think what I tend to see happen in the valley is, and for very good reason, and it's actually why these companies have been so successful, is like, everybody's so kind of research-pilled, which is again, totally awesome, big fan. It's, it's led, leads to all these breakthroughs. But the, the sort of sense that, okay, the model and the intelligence in the model is kind of the only form factor that matters. And then you go to the real world and you, you sort of see how intelligence actually gets rolled out in people's workflows. And the model could be the most intelligent, you know, super intelligence in the world, but that workflow still requires you to connect up to other data systems, still requires these moments where there's a human in a loop interaction. There's delays in the workflow and so the, the sort of agent has to sit idle.

• 5:13Speaker 00

  There's change management of the actual business process. There's legacy systems that haven't been modernized. So you kind of go through these five or 10 things that are way sort of, you know, much more operational, much more blocking and tackling than just the pure super intelligence of the model. And the last thing that I think a classic sort of research organization wants to go do is go attack every single one of those things. And this is sort of not a, there's like, this is not a sort of a one-off in history. Like, we've always had this relationship between infrastructure and application. Like, you know, obviously AWS or GCP or Azure have created, you know, trillions of dollars in value of, of kind of market cap of infrastructure. But guess what? There's also trillions of dollars of value in software that only exists because of that infrastructure.

• 5:54Speaker 00

  And if you were to go back, you know, 10 years ago and you were to look at what was happening in the data space and you looked at what, what, you know, GCP, your early kind of versions of GCP was building or AWS is building, I guarantee you would not have predicted Snowflake or Databricks existing. You would have been like, the infrastructure just already does that. Why would you pay another $10 billion of revenue to all of these other products that are just making it so you can work with your data? The same thing is going to be true for intelligence, which is the models will be insanely valuable. But the application of bringing those models into real workflows in banking and life sciences and healthcare and government, that's just going to be a lot of software. Now, the sort of challenge for the next, let's say, two to five years is how much do the model providers need to move up that stack and also try and, you know, go and compete at that layer?

• 6:38Speaker 00

  Or do they either leave open intentionally or accidentally that entire space for the application sort of ecosystem? And actually, to some extent, this is like a, a big question strategically for them because on one hand, you want to be closer to the customer. On the other hand, you also want to be able to have an ecosystem so people trust you. So, so that's going to be like a really interesting tension over the coming years.

• 6:56Speaker 01

  How do you think that'll play out? I feel like that's the question we spend every day wrestling with.

• 6:59Speaker 00

  Yes. You can, yeah, you know, I don't want to speak for, for Sequoia, but you can sense some of the existential dread of a VC right now is like, should I just put another billion dollars into Anthropic? Or, you know, should I attempt to sort of see what, what the applied layer is going to, is going to play out? So nobody's, you know, that envious of, of your guys' position, having to, to figure that out. But, but obviously it's even harder for the entrepreneur. But, you know, I, I am, you know, I'm pretty long, obviously, the application layer. Like, I'm also equally very biased. Like, I'm, I'm, I have a very concentrated bet with very limited diversification on, on it working out that you still want to buy technology that, that sort of understands your workflow and can get to the, the core enterprise data.

• 7:43Speaker 00

  But I don't know that, that there's, I, I just don't see a different event happening than the, than all of history. I mean, you know, we only have like 50 or 60 years of, of, of computer software history. But basically, when you go to that law firm and you go to that, you know, pharma company and you go to that bank, they need something that bridges the core technology to their workflow and their business process. And AI has not meaningfully sort of changed the need or the shape of what that looks like. And the, and the best sort of manifestation of this is sort of this chatbot versus kind of agentic workflow, you know, demarcation. So the chatbot can be totally universal and can be totally horizontal.

• 8:23Speaker 00

  But, you know, all of a sudden, you kind of look at that and you say, well, my workflow kind of needs something to kind of ping me at the right time in the process. Or it needs access to a certain kind of data that, that just, you know, the, the chatbot can't natively get access to. So somebody has to go into that organization, like get it set up and somebody has to go and provide domain expertise to this model. So it really understands our particular business process. And, and so then unless you really just underwrite, you know, the big labs at, honestly, like, I'm not exaggerating, like 100,000 employees. If you don't underwrite that, then, then the diffusion kind of economy is going to be massive. Because every single one of those companies, whether that's a 50 person firm or certainly a multi 100,000 person firm,

• 9:04Speaker 00

  is going to need an army of people to go in and help them with that transformation, that change management. And so this is, you know, like whether it's the FDE phenomenon or just again, understanding that domain expertise, that, that's going to be a very big deal. And then you You kind of alluded to this point. Fox kind of guarding the head house. And that might actually be singularly the biggest sort of reason this has to happen.

• 9:26Speaker 01

  Yep.

• 9:26Speaker 00

  Which is even even under all sort of like complete benevolence and like, nobody's actually doing something in any kind of like, you know, sneaky way. It just stands to reason that if I'm going to sort of give a task to an agentic system, I just want that task to be cost optimized.

• 9:42Speaker 01

  Yep.

• 9:42Speaker 00

  Like with accuracy as as kind of holding constant. And so who can do that? It's the company that doesn't care among, you know, 10 different models, which model is performing that task. It's like definitionally, you would want that to be the company that does not have a sort of a preference.

• 9:58Speaker 01

  And then the force you have going against that is that the model companies can subsidize their their models or offer them at different rates.

• 10:04Speaker 00

  I don't know how long that lasts, though, because when when these companies become public, I think they will be held to basically the same laws of capitalism that everybody else's. So the subsidization is working very well up to a certain threshold of spend. And we might have we might have exceeded that spend when you're at like tens of billions of dollars of kind of capital. And in fact, if anything, it might even be worse because eventually you have to go in and sort of pay for your training runs as well. So like they like the subsidization of tokens is a, I think it just has to be a temporary phenomenon.

• 10:37Speaker 01

  You mean from a gross margin perspective or from like an antitrust perspective?

• 10:40Speaker 00

  Entirely gross margin, like if I.

• 10:42Speaker 01

  But they have such high gross margins on inference right now.

• 10:45Speaker 00

  But then what are they subsidizing? The like then it's actually then they're then they're charging actually like, you know, decent rates.

• 10:51Speaker 01

  Yeah, but they can afford to price the API higher than their own first party products, right?

• 10:56Speaker 00

  So that part totally fair. You know, there's an interesting kind of, you know, dimension, which is, well, like if API is the high margin thing that is sort of paying for the subsidization, but you've moved all your customers over to the applied product and there's no API revenue. So like, there is like an equilibrium you have to strike with this.

• 11:11Speaker 01

  Totally.

• 11:11Speaker 00

  And and so then all the while, if you have some sort of either non economic actors or people just with a totally different game theory in this, you know, Meta being one, maybe SpaceX being one, China certainly being a giant one, even Nvidia being one, like that changes the calculus as well, which is those four kind of cohorts don't necessarily need to make money on on inference in the in sort of at least the same margin structure at like what Anthropic or OpenAI needs. So they would might be fine to bring down inference to 10% margin because they just want to basically, you know, pay for massive compute clusters. And then as long as that happens, and as long as like there's not insane proprietary sort of, you know, you know, sort of closely held secrets,

• 11:55Speaker 00

  then no matter what, you're going to have kind of cost per token go down on a like for like basis, all of which means more value accrues to the application layer, which is, I think, like a very long winded way of saying, I think there's just value in kind of everybody in the stack. I just don't know that the I the only thing I probably wouldn't bet on is just, okay, one or two labs get 95% of the value creation. I think there's just going to be a much more dynamic environment. And honestly, if I were one of the, you know, two or three biggest labs, I think I'd prefer this outcome too. Because back to your antitrust point, like at some point, you'll just be nationalized if you're the only thing that is is sort of exists as intelligence. So you kind of want a little bit of healthy competition in this ecosystem anyway.

• 12:34Speaker 01

  Yep, totally. Let's let's transition talking about Box. I want to come back to talking about tokens and Jevon's paradox and China and all these all this stuff. But let's talk about Box for a second.

• 12:42Speaker 00

  I love that topic too.

• 12:44Speaker 01

  Give folks, I mean, I'm guessing a lot of people that listen to this podcast use Box, but give people a brief kind of explanation of the history of Box and how you're reinventing yourself as AI.

• 12:54Speaker 00

  We started the company as a way to be able to kind of securely store and share data in the cloud. And and it was a very simple idea, but but we we kind of just kind of cracked a nut or struck a nerve. And, you know, for Doug out there, we we were able to sort of scale up quickly. We pivoted rapidly in the enterprise. And the idea was enterprises would be moving from on premises systems to the cloud. And they would need a better way to be able to sort of store, share, collaborate, manage their all this unstructured data, their corporate documents, their financial documents, their marketing assets, their research materials in the cloud securely. So that was the kind of company.

• 13:34Speaker 00

  And we we had been sort of flirting with AI kind of products and and sort of experiences really since like 2015. If you remember like the first kind of rise of the, you know, maybe the, at least in modern times, the AI winter that happened in like the 2015 to 2018 period, which is like, we think it's going to happen now and then it didn't. And that was a period where we were like, okay, these very, you know, sort of early AI models were showing us signs that, okay, if you if you, you know, looked at an image and you could classify the image, that's pretty useful if you're in an enterprise because now maybe you'd take all of your image data and sort of label it. Or maybe you'd OCR something and you'd be able to kind of pull out, you know, kind of, you know, sort of the text in there. That's enormously helpful.

• 14:17Speaker 00

  The problem was insanely expensive and you had to have a model for every single use case required, you know, kind of a hyper trained model for each workflow that you wanted to do. So we kind of shelved it. You know, a few years later, you know, started paying attention to the GPTs. We had some hackathons where people were like, oh, we could do like, you know, type ahead in one of our kind of, you know, note taking products. And that was that was sort of early kind of versions. We did some some early work in in sort of text detection and classification, which helped with security use cases. Then obviously, ChatGPT moment sort of hits. And and that was the the big sort of, you know, head exploding moment. If for no other reason why, you know, then then they kind of figured out a form factor that opened up everybody's mind to, oh, these could be these interactive systems

• 15:01Speaker 00

  that you just like ask a question, get an answer back of of sort of, you know, kind of increasing complexity and length.

• 15:07Speaker 01

  Yep.

• 15:08Speaker 00

  So we looked at that very quickly. We jumped all in. We sort of did the whole company pivot, like, like everything was was kind of exactly like academically what you should do. We had a team carved out. We we put, you know, the best people on the team. We, you know, met every day, looked at the updates. And then slowly but surely sort of built out what what today is is kind of our, you know, our AI stack and then basically the Box agent. And and for us, you can imagine the use cases is, you know, very straightforward. We sit on hundreds of billions of files. Every single one of those files contains critical information for an enterprise. That could be their contracts, their research files, their marketing assets, their loan documents, like all of this critical information. The problem is they rarely know what's actually inside of it.

• 15:51Speaker 00

  So they they unless you literally look at the document and and kind of search and find it, you just don't know what's inside of it. So now agents can go and basically be farmed out to go and answer questions about that data. They can pre process it and extract metadata from those documents and turn it into structured data. You can use agents to automate sort of steps and workflows. So we built a platform that basically lets you deploy agents against all of that unstructured data. And that's been the kind of core focus.

• 16:17Speaker 01

  Are you using agents to create new content?

• 16:20Speaker 00

  We are. There's a couple of modalities where that shows up. One is we have, again, an online sort of collaborative product that that an agent can just like generate any amount of content in it. And then and then we've done most of the, I think, probably more exciting work with with OpenAI and Anthropic on just how do you do like advanced document creation, PowerPoint creation? We've we've decided that their tech is, you know, at this point can always be frontier. So so we have an agent that goes and interacts with those systems to produce, you know, a high quality PowerPoint, et cetera.

• 16:50Speaker 01

  Awesome. Your tagline, your business lives in content, unleash it with AI.

• 16:54Speaker 00

  Yes.

• 16:55Speaker 01

  What are the hero home run use cases for how people are unleashing it today?

• 16:58Speaker 00

  Yeah.

• 16:58Speaker 01

  And then if you had to fast forward a few years, what do you think people will be doing with AI in your product in a few years?

• 17:04Speaker 00

  Yeah, so the probably the easiest hero for, again, more of a traditional enterprise to think about is is just as simple as you have a million contracts, why don't you find out what's inside them? Or you have a million research documents, be able to go and pull out all of the critical structured data, put that into a database and then be able to query, analyze, automate workflows around that. So that's kind of the thing that that just knocks it out of the park every single time because it's been a longstanding problem that people have never been able to go and and and sort of apply human, you know, sort of labor to because it's just too expensive to read every every contract, every research document. You know, maybe you could do it if you had like a loan document process, but most other data just never gets read at that at that scale. And then I think the the stuff that that we're probably, you know,

• 17:48Speaker 00

  as much if not more excited by is is really the equivalent of what we see with, let's say, coding agents or other other other complex agents, which is you have long running agents that are just executing your entire kind of workflow or process. And and this would be in the form of you go to a bank and you're onboarding at a bank and they've basically like automated every step that is possible to automate and then sort of jumps out to a person in the in the steps in the process for extra review or extra verification. But now instead of that sort of one or two week back and forth, it just happens in like an hour. Like, that's the dream state of most of these enterprise workflows is what if we can onboard a client faster? What if we could discover kind of critical data inside of our research much more quickly? What if we can alert to a security event much more much more quickly?

• 18:33Speaker 00

  So to do that, you need these sort of background agents or workflows that are that are sort of pre established for those processes.

• 18:39Speaker 01

  Totally. It's the year of the long running agents.

• 18:41Speaker 00

  It is, it is.

• 18:42Speaker 01

  Yeah. I'm curious, like you made the analogy to Claude Code. It seems to me that in the coding domain, using AI is like not only accepted, it's embraced.

• 18:51Speaker 00

  Yes.

• 18:52Speaker 01

  In the content domain, which is, I think, where a lot of the content in Box sits.

• 18:57Speaker 00

  Yep.

• 18:58Speaker 01

  Using AI to produce content at least, it's just like there's this almost this allergic reaction to it, like all the Pangram stuff on Twitter, there's the, you know, it's like this concept of work slop. I'm curious what you think about work slop and like, will this still be a thing in a few years?

• 19:13Speaker 00

  I'm going to sort of separate the Box corporate hat and just now kind of maybe riff as a as a as a as a consumer of of, gosh, I wish there was a better term, but work slop as, you know, inside of an enterprise context.

• 19:25Speaker 01

  I get board decks that are entirely written by AI these days and it kills me.

• 19:29Speaker 00

  So here's the difference, I think, on the acceptability. So there's probably like more symbolism to this actually topic than than than just like the the slop element, but like actually like diffusion of AI in general is sort of almost ties to this. Code, like other than, you know, the the top engineers that we hang out with that like are like, they have, you know, deep taste in the code, like, like, you know, and and the judgment is incredible. And like, it is them as much an art as it is a science.

• 20:00Speaker 01

  Yep.

• 20:00Speaker 00

  So take that group aside. For most of the world, code is a utility.

• 20:04Speaker 01

  Yep.

• 20:04Speaker 00

  It is just trying to accomplish something. You're you're just trying to automate something. You're just trying to put a sort of interface up there that somebody presses a button and moves to the next step. So for most of the world, the value creation of code has been to automate things and to be able to have it as a utility. So so at the end of the day, like, like, and we'll probably still use the term slop for a while because because there's taste in kind of front and design and there's taste in in sort of systems and you don't want to have vulnerabilities in your code. So that's going to exist for for a while. But at the end of the day, if you can tell an agent, like, please go and generate my entire backend system or my frontend system, like, it's just, it's not only acceptable,

• 20:46Speaker 00

  it's preferable, because it's just like, that was the thing that was blocking us from moving forward. So we need to go do that. At least the way society functions and the way the world works and our brains work at the moment. Maybe this changes. You know, when you when you get a presentation from somebody, there's still this association, which is like, I'm trying to decide if I can trust that person to go and execute on that thing, or deliver that result, or understand that topic. And so if when you see work slop, you're like, I'm losing my ability to to sort of know for a fact that like, like, how much of the of the thought process was them versus how much was the AI? How much did I even care about that? Because I myself am doing the same thing. So like, we have this weird, like, it's this very weird sort of like collective issue that we have, which

• 21:31Speaker 00

  is like, which is like, I'm doing work slop for some of my, you know, brainstorms and decisions. But when I get it from somebody else, I'm like, hmm, should I trust you? And and I don't know, I, you know, I mean, it just might be a thing that as a society, we have to kind of keep cranking through over the next kind of three to five years and and end up at the other at the other side. Like, like, you know, I hate to use like these like totally busted analogies, but, you know, obviously, you don't care when you see somebody's financial model. You're like, yeah, that was generated clearly by like a macro, or, you know, that was like not, you did not personally go and compute all of that. But but you're showing it to me and we're talking about it. So why can't the same exist for a strategy deck or whatnot? But I think right now, we're going through

• 22:13Speaker 00

  this evolution of like, what is the person's role? What is the content a proxy for? Is it supposed to be a proxy for how much that person knows? Is it a proxy for what we think they can go and execute on? I think I think we're just in this very messy period where we have to kind of figure that out.

• 22:28Speaker 01

  Totally. Did you read the Stan Druckenmiller Wall Street Journal article?

• 22:32Speaker 00

  I read the

• 22:33Speaker 01

  Read the discussion about it?

• 22:35Speaker 00

  Yeah, the the reaction to it. But actually, I didn't, I didn't read it. But was it like very sloppy?

• 22:40Speaker 01

  I didn't think it was sloppy. I loved it. And so to me, it was just a nice counter example of I have this like allergic reaction.

• 22:46Speaker 00

  How many, how many it's not X, it's Y's were in there?

• 22:49Speaker 01

  I don't think there were any.

• 22:49Speaker 00

  Okay, okay, okay.

• 22:50Speaker 01

  But it but it does show up as 100% AI in in Pangram.

• 22:53Speaker 00

  Okay. And dashes?

• 22:55Speaker 01

  I think there are dashes.

• 22:56Speaker 00

  Okay, you can't do that. Yeah.

• 22:58Speaker 01

  But it's like, it was a nice counter example to me, because normally, I read something that's clearly written by AI and I just have this allergic reaction. Whereas with the Stan piece, I didn't.

• 23:06Speaker 00

  Yes.

• 23:07Speaker 01

  I'm not sure how much of that was just, you know, it's Stan, therefore I trust in Stan versus

• 23:10Speaker 00

  Yeah, I know. But it's it is psychologically kind of like weird, because I'll read these like, you know, X articles. And I'm like, now, like, doing 2X the amount of work to read these.

• 23:20Speaker 01

  Yeah.

• 23:20Speaker 00

  I'm reading it, one for the substance. I'm also reading it, two for the calculation of like, did the person write it? Or am I just literally reading like a Claude prompt? And then like, I'm like, I'm literally like, my mental processing is now like, should I now, does that up weight or like lower my my sort of judgment of the person or the post? And I think we're, yeah, we're in for some weird times because of this. I would hate to be a college professor. I would just, I would totally quit.

• 23:43Speaker 01

  All right.

• 23:43Speaker 00

  Because you're just like, I you're like, I don't, I don't know anymore what you did. I like, what's going on?

• 23:47Speaker 01

  It does seem like the calculator is the closest analogy, though.

• 23:50Speaker 00

  Yeah, except it's just like, that was like more like finite in terms, and you still had to piece together so many more things.

• 23:57Speaker 01

  Yeah.

• 23:57Speaker 00

  Like, we like, and some of these analogies are breaking down of like, it's just a task because it's like, well, at some point, like, this thing is doing like at least 10 tasks at once.

• 24:05Speaker 01

  Yeah.

• 24:06Speaker 00

  But, but yeah.

• 24:08Speaker 01

  Let's talk about harnesses. How does the how does the box agents work?

• 24:11Speaker 00

  Great transition to harnesses. Okay. Speaking of calculators, let's talk about harness. Okay. This is back to the kind of NeoLab, kind of applied, applied layer. There's, there's a bunch of things that that we know about our file system, permission structures, our search, our search engine, that, that, you know, certainly, and by all means, we actually would love the, we'd love all of the labs to train on our understanding of this, because it would only make external agents, you know, as good as possible.

• 24:39Speaker 01

  Using box, yeah.

• 24:40Speaker 00

  Yeah, like, like, we, we always talk to labs, like, hey, we'll give you as much data as you want about about kind of how the system works. But but let's just say, like, that aside, we have a lot of depth of understanding of what do people do in box? How do they search box? How do they decide when they look through 10 files, which is the one to go pick? How did, what is the in their internal kind of calculus or heuristic on on sort of figuring out the most relevant document to look at? So we know all of that. And we basically, you know, have built an agentic harness that that, you know, attempts to kind of understand that set of domain understanding about our system. It, it obviously has access to our search system, our file system. It has a bunch of kind of mechanisms for just pulling out just the text of a document, just pulling out chunks from the document,

• 25:25Speaker 00

  doing embeddings on the document on a fly. So there's a set of kind of tools that it can use. And effectively, it's a harness for asking questions of a large data set. So in my box account, I have, I don't even know the latest number, but on the order of tens of millions of files, just because like, it's every, everything that has ever kind of accumulated over over 20 years. But I can now ask any question of of all that data set using the box agent, and it goes around, it does, it does multiple searches in one, it reranks it, it then very quickly sort of pulls out the most relevant information, then it'll in some cases, read the full document, you know, does all the steps. And then we compare that against like, well, what if we just gave, you know, Claude our API or gave OpenAI our API? And we see like meaningfully better results on accuracy and

• 26:08Speaker 00

  latency, because again, we kind of know exactly how to how to tune it for our workflows. So that's effectively the harness that we built out.

• 26:15Speaker 01

  And then what evals matter the most to you?

• 26:18Speaker 00

  So I have a couple, I have a couple, like, you know, just funny, personal ones that I just keep track of, of like my own use cases. But we do, we have, I don't know, hundreds of different tests that we do on every single model. We actually have two evals at the moment. One is, we put out a thing called the complex work eval, which is a, which is a set of domain specific work in life sciences, financial services, public sector, tech, etc. And it's, it's kind of exactly what you'd think of as a, as a document centric eval. So given these five documents and this set of problems, like, what would your answers be? And we test every single model against those with our agent. And then we have a holdback eval, which is, actually, the first one is holdback also. But but the second one is just like, then our box instance, and how box employees use, use their data.

• 27:01Speaker 00

  And then we eval every model again on that. So we're able to kind of roughly keep track of of all the incremental progress. Like, we see when things move by half a point in terms of model improvement. And then we roll out sort of default models, based on different kind of costs and accuracy thresholds. And then we let customers also choose any model they want from effectively our model garden.

• 27:23Speaker 01

  What's your current view of the race and like where all the horses are in terms of model performance on your use case?

• 27:28Speaker 00

  They more or less closely correlate code with one exception, which is actually in some of our use cases, Gemini is disproportionately better than than what you would see from coding. And and it might be, you know, sort of just better tool use, you know, given, given kind of, you know, the Gemini ecosystem and what they need to build for. It solves, you know, kind of a, a strong set of sort of general knowledge work use cases as well. But I think mostly correlating to kind of code. So Fable 5.1 was clearly kind of, you know, state of the art, and the best model that that we've seen. There's obviously rumors about other models. And so we'll see how the kind of race.

• 28:08Speaker 01

  Yep.

• 28:08Speaker 00

  You know, kind of, you know, continues on this, on this front. But but basically, by and large, like, when you look at GDP Val, Merckor has their Apex Eval, these things will all generally follow the coding models. And so I think it's, we're just neck and neck on like Grok, Muse, the Fable class, and, you know, GPT-56 slash whatever they're building next. Like, these are just like, it's a total race right now.

• 28:35Speaker 01

  And do your customers typically express a preference on which model they want to use? Or do they just use your default?

• 28:41Speaker 00

  So they, they, you know, kind of by volume, they use our default, because it's just easy and it works extremely well. And it's tuned for, there's a few ways our, our kind of agent manifests. So like, the way that you'd most commonly experience in as an end user is you would just be searching and kind of, you know, asking questions of your data. The, but by volume, the the volume of tokens tends to go through more of our workflow agents or data extraction. That's where actually you have customers actually doing evals. And they're basically saying, okay, I want to really make sure that at this cost profile, I can get 98% accuracy on data extraction. And that's a place where like, we'll have an FDE that goes in and helps you, you know, helps understand your data environment, tests against five different models.

• 29:21Speaker 01

  Yeah.

• 29:21Speaker 00

  And then you're just basically at the mercy of of the eval.

• 29:25Speaker 01

  Yeah. What are you seeing in terms of the adoption of open weight models in your customer base?

• 29:29Speaker 00

  So probably higher than people think.

• 29:33Speaker 00

  Lower than what enterprises actually want and much, much, much, much, much lower than what it'll be in five years. So, so like some mix of that would be the kind of message like.

• 29:44Speaker 01

  And it's primarily cost that's driving that decision?

• 29:46Speaker 00

  I, I, you know, I have to probably attribute 30 plus percent to just kind of the, the sexiness of, of like.

• 29:53Speaker 01

  I want to try GLM.

• 29:54Speaker 00

  Yeah, I think, I think there's that. Like I've heard, I've heard, you know, CIOs of Fortune 500 companies say, we're, we're playing with open source here. And, and I look at that and be like, well, I know for a fact like, like Gemini or Muse would have been just fine at that particular cost profile that you're trying to do. Or probably even like, you know, you know, five, six Luna or Terra or whatever, whichever, you know, one had the, had the crazy discounting they just did. Like it probably would have been totally fine, but you want to be able to be like, okay, I'm a little hedged. Like it's cool. Like, like we're at that phase still. Over time, I think it stands to reason that, that you'll see meaningful different costs because you'll be able to peel off workloads that, that just only makes sense at, at sort of, you know, grinding down to the cost of inference. In which case open, open weights will have the sort of economic advantage.

• 30:38Speaker 00

  Right now there's, you know, this challenge of like, sometimes it's more token inefficient. You know, sometimes like randomly, like I've heard stories like randomly it'll just like speak Chinese like, like mid, mid chain. So you're like, okay, well that'll be weird for a bank. So.

• 30:52Speaker 01

  Funny.

• 30:52Speaker 00

  So like, we need to like probably work on some of those things. But long-term, I think it has to be, you know, the case that, that you're gonna, you're gonna peel off those workloads. You know, one, one of the more interesting posts, I think on this that I totally subscribe to is Jesse at, at Decagon. Do you probably read that post of like, of like this paradox of like, we're gonna have, like, you're gonna see close, just, you know, continue, you know, go exponential. But what's gonna happen is each use case that kind of matures, you can peel off to open source. And once you have kind of stability in that use case, it starts to make sense to veer it toward an open weights model. Assuming one of two things is true. One, that, that it's actually literally cheaper or two, having some post-training gets you X, X percent more performance.

• 31:32Speaker 00

  And, and so I think you will just be in a reality where we will, we will, and this is gonna be very confusing probably for like the press more than people in the valley. Cause you'll be like, wait a second, like the revenue of anthropic opening, I et cetera are like off the charts. But somehow open weights is like also growing exponentially. You know, like, how is this like?

• 31:49Speaker 01

  The pie is growing so fast.

• 31:51Speaker 00

  Yeah. And it's like, the pie is growing so fast.

• 31:52Speaker 01

  Yeah.

• 31:53Speaker 00

  But what's happening is, is actually there's, there's an interesting duality. It's, it's, it's not even just like, like rising tide lifts all boats. It's like, no, no, like we either use Fable or, or, or five, six for orchestration. And then we farm out all these long-tail tasks to a cheaper model. Or the opposite is true. Like you use, you have some kind of orchestration agent that like by default does the cheaper stuff. But occasionally sort of see some, something that is just way too hard. And then it pops it out to, to one of these heavier models. And so you might have blended 50% spend on each, but 10 times the amount of tokens, you know, on the open weights model. And so like, everybody's kind of winning, but there's an, there's an interplay between why they're winning.

• 32:34Speaker 01

  I'm curious about how you think about memory and customization or personalization and where that's gonna go. It seems like today the dominant architecture is kind of like rag-based system still. And like you can get fancy on the rag, but it's still context lookup where the weights themselves aren't fundamentally changing.

• 32:50Speaker 00

  Yeah.

• 32:51Speaker 01

  It seems like, I mean, if I listen to my friends at the labs, like continual learning, this idea of like, the model's weight should adapt as it gets to know you.

• 32:58Speaker 00

  Yeah.

• 32:59Speaker 01

  We had Engram on the podcast. I don't know if you know, Dan, like.

• 33:02Speaker 00

  I, I just got introduced to him. I listened to the podcast.

• 33:04Speaker 01

  Oh, amazing.

• 33:05Speaker 00

  I would have loved to been the fourth person in the room.

• 33:07Speaker 01

  Amazing. Yeah. They, they, I think they're working with customers that to, to help basically bake in some of the context into the weights themselves. What direction do you think is gonna go?

• 33:18Speaker 00

  You know, you're catching me at a time right before I'm actually doing my call with Dan. So, so I, I wish I, I could have talked to him first and then I'll have like a way more eloquent answer.

• 33:27Speaker 00

  I'm like extremely fascinated by, by the approach. Like I, I have no reason for not wanting to, it to work and, and exist. We live in a world at Box where we see these sort of, the, the, the high degree of complexity on permissions and access controls and data. That, that tends to be sort of the rub on a lot of these types of, of approaches. And I'm, I'm gonna put Engram aside because like, I'm sure they've already thought this through. So I'll, I'm gonna talk more generic philosophically. I think sometimes you will talk to a researcher that, you know, sort of imagines the world working the way they work. Which is like, I'm a researcher, I have access to everything. And so if I had a model that was trained just on my world, this would be amazing. And then you're like, let me introduce you to a lawyer.

• 34:10Speaker 00

  And the lawyer like has this tiny little, you know, access point of just like the five projects they're working on. Because somebody right, you know, one door over is working on the competitive project to, to a, a, another company in the space. That they can't have any sort of overlap with, with what they see or what they know. And there can't be a single document that passes between those two walls. And they have to be these kind of hard barriers. So, you know, so like, sure, like you could still, you could still now train a model just for that one user. But like, what happens if every single day they get added or removed from something that, that adds important context to, to sort of what they need to understand? And, and, and, you know, again, like, I think there's gonna be probably breakthroughs in continual learning that, that sort of all resolve this.

• 34:54Speaker 00

  But like, this is why previously it was just like, you know, there was no way you could pull this off five years ago. Cause it would be insanely expensive, impossible to kind of wrap your head around, around how those access controls are supposed to work. But, you know, obviously with like, as the cost curve goes down, as open weights, you know, get, you know, cheaper, smaller, faster, better. I think this becomes super interesting. One thing on, on the podcast that I found very fascinating, and I just need like a T-chart honestly, is just like, you know, like what is the decision point of what goes in context? And what goes in the weights?

• 35:23Speaker 01

  Yep.

• 35:23Speaker 00

  Like you, you have to be a little bit thoughtful about like, where is the, the massive performance gain that you get by, by baking in the weights? And there's probably some like incredible like, like calculation of like, like when the rate of change of the data is not, you know, so far. But the upside of the, of the weights, you know, you know, dramatically change the accuracy of the model. Like, you know, you'd have to kind of land on some sort of, you know, rubric like that.

• 35:49Speaker 01

  I mean, if you could wave a magic wand, it almost seems like, and I think Karpathia has said this in some prior interviews, like, if you could almost remove all the memorized information from the models and just have, have it encapsulate the specific reasoning capabilities. Like the ethos of how we do things, for example, at Sequoia.

• 36:05Speaker 00

  Yes.

• 36:06Speaker 01

  And then you have all the, you know, actual content.

• 36:08Speaker 00

  Yes.

• 36:09Speaker 01

  And a lookup system that almost feels like the, if you could wave a magic wand, that's what the system would look like.

• 36:12Speaker 00

  And so, so that one's super interesting. The, I, I think the, the, the question will be like, how much are enterprises different at that level? Versus it's actually their, their literal IP that is what makes them different. Like how many different types of styles of execution are there in the world?

• 36:28Speaker 01

  Yeah.

• 36:28Speaker 00

  Versus, no, it's just like the depth of knowledge about that particular legal case. And, and how do I apply it to this other project I'm working on? That's where so much of the value sits. So, so, but again, if you can just like wait till my Zoom call with Dan and then I'll really know the answer. But like, I'm a fan because no matter what, there's gonna be like, like I've jumped right into like the individual, you know, but like no matter what, like, at a firm level, there's probably ways to take this approach. Like I'm a big sort of fan of what trajectory or applied computer doing or prime intellect. Cause, cause I think there's like, there's, there's no question that if you're Eli Lilly, you want a model for how you do drug discovery. And that probably does need to go like farther or deeper or more sort of specific than what you're getting off the shelf.

• 37:10Speaker 00

  And there's not a lot of sort of, you know, kind of church and state problems for, for drug discovery, you know, workflows. They, they probably want as much of that information available to as many people as possible. So I think it's gonna be like domain specific. And, you know, you're just gonna have different outcomes based on which vertical or, or, you know, type of use case and where the firewalls need to be in that process.

• 37:28Speaker 01

  Makes sense. Okay. So you hinted at the beginning that there's a Box Labs. What, what type of work is Box Labs doing?

• 37:34Speaker 00

  So it's the equivalent of Box Labs. Like I don't know if we've used capital L yet. But, but basically, you know, it's our, it's our applied sort of AI team and.

• 37:43Speaker 01

  What research areas are most interesting to your, your team right now?

• 37:45Speaker 00

  Yeah. So, so the, the biggest areas that, that the most sort of research kind of, you know, of the continuum of engineers. There's, you know, some cluster that is sort of more in the research bent. And of that cluster, the things that we've spent time on still again is at the kind of applied layer. But, but it's a lot around how do you, how do you take agents and make them, you know, another 10 points of accuracy improvement given X problem? So what, what is the, well, you know, how do you build a map of the, of the problem set, you know, with a given set of data to sort of best execute on that task? So we spend a lot of time on, on that style of work. We have a team, for instance, working on, how do you do effectively at the agent level, at the harness level, you know, some form of kind of auto research on,

• 38:29Speaker 00

  on sort of hill climbing on accuracy of answering questions or sets of problems on, on given a kind of a, a set of client data. So if you're a bank, you have a bunch of loan documents coming in and these are like a hundred page documents. Like whether you're getting 70% accuracy with an off-the-shelf model or like 97% is like basically, obviously a world of difference. And can you actually go and automate that process? So somehow you have to hill climb from the base model to the 97%. And there's a lot of work going into the system to, to basically pull that off.

• 39:02Speaker 01

  Maybe zooming out, what do you think of as, and this can be a box question or a non-box specific question. The role of systems of record in a world with agents? And I'm sure you saw some of the Twitter discourse on like, you know, every software company is trying to sell me their own agent right now. I don't want another agent from them. I want, I want their system record to work well with my agent. And so like, how do you think about that?

• 39:21Speaker 00

  Hashtag Claude Force. So.

• 39:24Speaker 01

  Catchy name, by the way. Very catchy.

• 39:25Speaker 00

  I, I mean, literally it's like one of those things where, where like the first three minutes, you're like, man, that seems funny. And then four minutes later, especially when you see the stock, you're like, ah, brilliant move. Like, like this is great. We're doing this. And then when you, I think somebody, somebody actually said this the best there, like when they heard Matthew McConaughey say it out loud. That was like really the, that sealed the deal.

• 39:46Speaker 01

  The aha moment?

• 39:47Speaker 00

  That was the aha moment. And it's like, man, he can sell software. Like it's actually incredible. Like his voice is so good for selling systems of record and agents. If you're in like, like our.

• 40:00Speaker 00

  Sort of, you know, contemporary group of like, you built a SaaS platform and you have some set of data and workflow that that, you know, your customers kind of operate in. There's, there's effectively two things you just have to do. And I, I think anybody attempting to do one over the other is just gonna lose. You have to build an agent that is insanely great at your product. Like that agent has to be, you have to provably be like 10 or 20 points better than an off the shelf agent using your system. Not because it's hobbled the other side. It's just like, you are so eval maxed and you're like so tuned to your particular workflow that, that you can improve your system. You have to have that. And you probably, because you understand your domain, unless you're like totally asleep at the wheel,

• 40:44Speaker 00

  you probably have use cases that no one has, has sort of thought to build products around. Because you, you talk to customers every day and you see what they run into. And you're like, oh, we could just like have our agent go do that for you. Like I've had at least a dozen, I mean, so I've probably talked to a couple hundred customers a year in, in variety capacities. I've had at least two dozen times where the customer has a use case that is like a breakthrough moment for me of like, shit, that would be actually totally insane.

• 41:11Speaker 01

  What's an example?

• 41:12Speaker 00

  Unfortunately, since you put me on the spot, I don't know if my example will pay off the, the level of excitement I just had.

• 41:19Speaker 01

  Sorry to put you on the spot.

• 41:20Speaker 00

  No, no, I, it's just like, I think, like the thing I was thinking of is just like, I think it's gonna be a womp womp for the podcast. But there was, there was basically, a customer had this idea of, they wanted an agent in the background, trying to sort of figure out when documents sort of met their governance policies. And like, does something need to go into some kind of archive or something into some kind of legal hold or whatnot?

• 41:42Speaker 01

  Oh, cool.

• 41:43Speaker 00

  And, and, see, exactly, see, that's exactly the voice. That was exactly the voice I was worried about. Yes. Yeah, no, you couldn't even, you couldn't even pull it off. Okay. So, so, but in our world, this is awesome because you're like, oh.

• 41:55Speaker 01

  Yeah.

• 41:55Speaker 00

  Like, no, because you think about it. Every company has a head of governance.

• 41:59Speaker 01

  I meant cool sincerely.

• 42:00Speaker 00

  Okay, I know, I believe you. I mean, listen, you do enterprise, so like, I think that it was at least half serious. Imagine you're in enterprise, you have a head of compliance and a head of governance, okay? They can only be like overseeing the whole sort of enterprise. They've never been able to be everywhere at once. Now imagine if they could sit next to the employee and, and basically be able to be like, oh, you're about to go do something that breaks our governance policy. So, so like, the idea was like, oh, what if there was just an ongoing agent that just like automatically was just like, nah, that's gonna break your governance policy. Instead of the user having to like try and predict or understand this stuff. So anyway, those are the kind of things where if you have an agent within your product, you're gonna be able to identify sort of sooner and better than, than the rest of the market.

• 42:44Speaker 00

  And, or just like do things that maybe would be impossible to do off platform. On the other hand, it's just like, obviously you have to go headless. You literally have to make sure that your APIs are exposed to Claude and ChatGPT and, and, you know, all the different platforms. And you have to make sure that you have a, either a direct way into deterministic APIs. So that those agents can use your, your APIs, make calls via MCP or whatever. Or at least make your agent be headless and be exposed in those systems. And then the only reason maybe this is like a, like a, even remotely a hard debate, is you have to just make sure as a, as a system of record, that you can find a way where commercially, it sort of makes sense on the other side and, and is sort of valuable and interesting. And the reason why I think a lot of people got that wrong, that, that weren't in these companies was

• 43:29Speaker 00

  just underestimating the amount of, of sort of new use cases that are just total upside. There are like complete white space opportunities for these systems of record. So like, in the Salesforce example, I use Salesforce more today, probably by an order of magnitude than I ever have. Because I MCP into it via Claude or ChatGPT. And so, and so I just am always asking questions about the data inside of our CRM system.

• 43:51Speaker 01

  And do you think that means the systems of record become more toll booth businesses then to make sure that they're capturing the opportunity?

• 43:56Speaker 00

  I don't love that term. Because like, no one's had a good experience at a toll booth.

• 44:00Speaker 01

  I love toll booths.

• 44:01Speaker 00

  Yeah, exactly. You love it more than governance agents. So, uh, so, so I would say that, that because they, they sort of have a, have a depth of purpose of organizing the workflow, managing the data, securing the data, providing guardrails. Then it's really just, yeah, you need to, you'd have to have some kind of volume oriented business model on that other side. And, uh, and I just think there's like, if you're solving real problems for customers, it'll just like make money. I like, like, this is so cheesy, but like I've, I've told like LinkedIn product managers, I'd probably pay 10x more for LinkedIn if I just could MCP into it. If I just had a way of just like always understanding like, like, okay, the CIO is doing this thing and I need to reach out or whatever. I'll, I'll like, take my money.

• 44:43Speaker 01

  Yeah.

• 44:44Speaker 00

  So, so there's, these systems actually have a tremendous amount of value based on the data that they, that they have. And customers will absolutely sort of find some way to reward you for that value creation if you're doing a job.

• 44:55Speaker 01

  Super interesting. Um, maybe related, let's talk about product UI.

• 44:59Speaker 00

  Yep.

• 45:00Speaker 01

  Big, you know, generic chatbot, chat box agent. Is that gonna be the dominant UI for how people are using AI in the future, especially when it comes to the application layer?

• 45:11Speaker 00

  This is why I think the applied layer, you know, has so much room to run is because probably the, the sort of, you know, universal chat system that you ask a question to, you get an answer back or it does sort of some work in the background. I think it's gonna be, obviously like, that's gonna be a mainstay. That, that, that UI will always exist. It'll be incredibly powerful. The horizontal products will have it, the vertical products will have it, everybody will have it. It's just like, your product has a search box, obviously it does.

• 45:38Speaker 01

  Yeah.

• 45:39Speaker 00

  So, so that, that's always gonna be here for these sort of one-off asks of an agent or go find this thing or answer this question or produce something for me on demand. But most of the enterprise is sort of made up of these processes and workflows that are kind of just happening behind the scenes. Sometimes they're happening with computers and computers are running these things. Or sometimes they're happening with other people that are doing these things. Or sometimes they should be happening with people, but you could never afford to have them happen with people, so they just didn't happen. And so that's, that sort of is a slightly different kind of, you know, metaphor than, than a chatbot where you ask a question and it comes back with, with an answer. That's like, okay, I, I kind of want agents in the background to do things for me. Read every contract, look at every log, triage every security incident, incident.

• 46:24Speaker 00

  And then instead of me chatting, maybe I'll chat as a, as a means of doing kind of a catch up. I want a dashboard, I want a workflow, I want a queue, I want a task list. So then the, the, the challenge becomes, well, does the horizontal product kind of take on every one of those, those components? And, and manifest every one of those experiences in one? In which case, I think you'll start to be like, man, that thing is like really like a, that's pretty heavy. And like, and then we'll start to like, be like, oh, this is no longer this simple, easy, delightful thing anymore. So then the vertical players actually like, like, they actually sort of understand the process and can manifest all the right buttons and tabs and the names of the things for that particular workflow.

• 47:06Speaker 00

  So I think it puts, I think as you have agents that are doing more work in the background, doing more, you know, async work that is just like, I farmed out a bunch of agents to review things as they happen or whatnot. That leans more toward the applied companies that can understand those workflows, that can understand those processes. I think you're gonna have these in every field, you're gonna, certainly, we already know that, you know, how they're gonna look in legal with Harvey Global, et cetera. We are seeing them start to emerge in areas like security. We've seen them start to emerge in the long running kind of coding agents with cognition and factory. So I think that, that will be, you know, one of the bigger kind of applied AI sort of use cases.

• 47:46Speaker 00

  And ultimately, like in five years from now, I would bet like 90% of all tokens in the enterprise are things that a user never kicked off. And they just see a result. They just, they see a task show up and they have to go review it. And it's just like, it's just happening.

• 48:00Speaker 01

  Yeah, yeah, makes sense. Okay, let's talk about AI diffusion. Coding agents, it was like, you know, boom, January 1st, 2026 happened. And like the fastest diffusion of anything into the economy we've ever seen has happened. The diffusion of, you know, the rest of the AI magic into the rest of our jobs seems like it's been a lot slower.

• 48:19Speaker 00

  Yeah.

• 48:19Speaker 01

  What are your thoughts on that? And where are the areas where you think we're gonna see faster diffusion and how's that gonna happen?

• 48:26Speaker 00

  Yeah, so you always have to kind of compare and contrast coding versus everything else to really understand the, the dissimilarity. So in coding, essentially, and this is back to the kind of utility point on, on, you know, slop. Like, the utility of code is almost 100% represented by the amount of text that you can generate. Like, like, obviously an insane amount of value went into the text. But, but like, and then like knowledge and expertise and meetings and everything. But like, ultimately, like the text is the thing that produces the, the program that is actually the thing that you're trying to do. You know, if you could have the world's greatest programmer. Like, and it never had to sleep, never had to eat. They, they could intuit what to build and they could just sit on a computer all day long.

• 49:11Speaker 00

  Your value creation would be 100% correlated with how many hours they could sit at that computer and type code. Like, lines of code, ideally, ideally, good code, is the most thing that will be correlated to whether you produced software that, that people wanted. So, it's all text. The models are hyper-trained on these. Everybody in AI labs treat coding as, as a competitive benchmark to constantly try and exceed. They, they get to do their own evals on it every single day because they are the ones coding the, the models themselves. And it's the most technical audience of all time, where when they deploy it in a Gentic system and they run into a, either a bug or a problem, or like some MCP server comes back with like connection invalid.

• 49:51Speaker 01

  They fix it themselves.

• 49:52Speaker 00

  They know how to triage the problem. They don't call IT. They're just like, oh, yeah, no, I didn't open up that port. Sorry, I'll go fix it. So, that's like five things. Oh, and maybe like the sixth, like, it's just like a very, very high paying vertical that, that like, it's just like automatically valuable. If you could get 10% or 20% productivity gain, let alone 5X productivity gain. So, so, so take those five or six things that coding has as, as sort of beneficial properties to sort of automation. Then compare that to, you know, you know, every other form of knowledge work. And you'd probably have like a histogram, and I don't know if anybody's published this, maybe you can, like, the similarity to coding.

• 50:30Speaker 00

  And like, like, what, what are the domains that like, like, start to sort of, sort of, you know, look closer, like, like, less and less like coding as you, as you kind of scale out? And it's like, okay, well, lo and behold, legal is kind of interesting because, because like, there's a lot of value creation to somebody sitting at a computer, reviewing legal documents, writing legal documents, like, processing large amounts of information. Okay, so that's kind of blowing up. And then you kind of go down the list. Now, let's take something like, like a sales rep. Okay, so much farther down the list in terms of, of sort of likeness. The sales rep's value creation is basically convincing an external customer to buy software or technology or a caterpillar truck, you know, uh, from them. That is the value creation to the, to the economy of the sales rep.

• 51:14Speaker 00

  And so, let's say we brought the world's best automation to them. Like, first of all, again, they'd have to like, figure out how to technically wire it up. They'd have to make sure they give it all their data, all these kind of things. But no matter what, the, they're, they're still rate limited and constrained by like, did the customer respond to them? Do they want to meet? Can they meet next Tuesday or can they meet today? Like, does the customer have budget? All of these other things. So that, that's maybe the entire continuum right there is like, one is like, I mean, of knowledge work. Like, obviously, this is not even, you know, touching the, the sort of working with, with atoms. So, that's a continuum, which is, on one end, you have somebody rate limited by so many external factors. On the other end, you have somebody who could sit at a computer all day long and just type, type text. And that is your ability to basically automate things is, is that continuum.

• 51:57Speaker 00

  So, for the real world, we have to basically bring intelligence to these workflows in ways that are, that sort of somewhat feel like the shape of their workflow, somewhat feel like the shape of their work. And then, find a way to deliver the change management, deliver the implementation, get data into a format and into an environment that actually works with these systems. Like, asterix, like, one of the other big things is like, if you go to most engineers in 2026, like, maybe like minus two months ago, given the, given the latest phenomenon, but like, like, the codes in GitHub, you just like, connect it to GitHub. Like, like, remember there's this period where like, you're, when you launched a coding agent? Like, there was no like signup or register, it was just like, give us your GitHub. That doesn't exist in knowledge work. There's no like, give us your GitHub for knowledge work. There's like-

• 52:42Speaker 01

  Those give us your box.

• 52:43Speaker 00

  Well, with Box customers have a, have a much easier time with all of this. Unfortunately, like, we're only 1.3 billion in revenue run rate. So like, like, that means there's a lot of people not using Box. And so, what are they using? Their data is in on-premises systems, legacy file shares, legacy infrastructure, you know, enterprise environments that don't talk to agents particularly well. So just think about that sort of distinction between, you know, implementing coding agents versus everything else. Even, even something, again, you'll sort of fall asleep about is like, access controls in the enterprise are totally different. I actually totally forgot that, that point about coding. In coding, you get access to basically most of the stuff ever relevant for your job. In knowledge work, you're like, you're like, hey Sally, can you open up that, that sort of file share for me?

• 53:27Speaker 00

  Can you open up that, that, you know, sort of project? Because I didn't get access to it. How do you make sure the agent has access to those set of things? All of that work has to get done. So, the thing I think we have to prepare for is two things. One, Silicon Valley has to prepare for diffusion taking a lot longer than they think. Or the, then, then I'll say we, but I really think they, because, because I, like, I know how long it'll take. And then the second thing is, is, the good news is, this is sort of all correlated to the applied layer, value creation. Like, the, because the, the companies that will just have the patience, the, the sort of, the, the full sort of domain expertise, the, the, just the sheer work ethic, because it's not like everybody's just from the floodgates.

• 54:09Speaker 00

  Like, you have to go and, and just, you know, pound pavement and get out there. That will be the applied layer. So I think this actually represents, you know, a trillion dollars of applied layer AI value is, is actually, how do you go get the technology to the lawyer? Or to the sales rep, or to the life sciences researcher, or to the person that runs the customer support team? That's all sort of opportunity right now that exists.

• 54:31Speaker 01

  Awesome. I'm going to close by asking some advice for other founders. Maybe let's start with founder advice and then company building advice. On the founder's side, seems like you are in every AI cap table, you know, every cool new company, like, you know, you know, Engram, you know? How did you kind of get yourself in the middle of the AI conversation?

• 54:50Speaker 00

  There's probably two, two parts. One, I was just very well primed for it. Like, working with unstructured data for 20 years, you just like, can instantly see the, the, the benefit of agents on that. So like, it honestly like, took longer than I would have wanted that, that we, we got to have this conversation. Because we, we tried to have this conversation, you know, eight, nine, 10 years ago.

• 55:08Speaker 01

  Yep.

• 55:08Speaker 00

  And now it's finally happened. So, so first of all, just super well primed. Our product sort of, you know, shape and, and what people do with our product already lends itself extremely well to agents. So like, obviously, we, we had to bet the company on that. And, and then, lo and behold, we had the positive feedback loop of customers actually saying, yeah, that would actually be very powerful if I could go read every document and answer any question. So that, that's the first and certainly by, you know, biggest by kind of a factor of 10. And then the other is just like, I'm extremely fascinated by the technology. Um, and it's just fun. It's like-

• 55:37Speaker 01

  Where do you learn about it?

• 55:39Speaker 00

  Your podcast. Dorkesh's podcast.

• 55:45Speaker 01

  Twitter?

• 55:45Speaker 00

  Yeah, I mean, I'm, probably unfortunately for brain cells, like, it's probably 95% Twitter. I have a routine where like, at the end of each night, I just go through the feed. And it's just like, I, like, I, I look like some sad meme probably of just like, I'm just scrolling and scrolling and scrolling and like, attempting to like triangulate all the information.

• 56:04Speaker 01

  It's amazing, because it is, it is the global town square for AI.

• 56:07Speaker 00

  It is. And, um, I want to send an emergency alert to like, everybody who's like a sophomore or junior in college and just be like, follow these 20 accounts on Twitter.

• 56:16Speaker 01

  Yeah.

• 56:16Speaker 00

  And, and also join Twitter, because like, this will just help your career. You're either like, a year ahead or a year behind.

• 56:21Speaker 01

  Yeah.

• 56:22Speaker 00

  Simply based on your feed. And my feed is like, so, so wired in.

• 56:25Speaker 01

  This is the number one advice I give to people when they're asking how to get current on AI. It's like, follow, follow these 100 accounts.

• 56:30Speaker 00

  It's, it like, but, I mean, virtually first step, join Twitter.

• 56:33Speaker 01

  Yeah.

• 56:34Speaker 00

  Like, you'll still talk to, to 20-year-olds that are like, yeah, like, you know, I, I, I see some articles. And I'm like, what do you mean? How do you see articles? Like, I don't even know what that means. Like, do you just like, get lucky that somebody emailed you an article? Like, just join Twitter. Like, what are you talking about? So, so, uh, so I would, I, you know, you just have to be wired in. I, I enjoy it. It's a lot of fun. I play with everything.

• 56:56Speaker 01

  What's, what's your favorite new AI product? Please say Instinct.

• 56:59Speaker 00

  So, um, okay, uh, full, full disclaimer, I've not done the Instinct invite code yet. Simply because I have a backlog of like, three other personal assistant products.

• 57:09Speaker 01

  You gotta try Instinct.

• 57:10Speaker 00

  I, I know, I know, everybody's rage, I, I, I'm very excited, I'm on-

• 57:14Speaker 01

  And I'm not an investor, so.

• 57:15Speaker 00

  Oh, you're not? Okay, so this is like, totally genuine. Okay, so I, I 100% will have, I don't know when this is going to run, but I'm sure I will have played with it by the time it runs. I, I'm in pre-release in a couple right now that I'm spending some time with. I think the personal assistants hub is super exciting. At least in some of my use cases, it's still showing some of the limits of like browser use as an example. Like, man, we still have some work to do there. Like, probably the entire internet needs just like a CLI for their product. So we still have, like, there's still some blocking and tackling at the infrastructure level for these things to be totally awesome. And then, and then I kind of look like your average, probably, you know, AI-pilled knowledge worker on, you know, every day I'm asking one of five different AI systems,

• 57:56Speaker 00

  you know, 20 to 30 questions, um, on just like, doing research, looking for talent, looking for, what is a competitor doing? What's happening in this market? How do you expand there? So, so kind of that.

• 58:07Speaker 01

  What about on the company building side?

• 58:09Speaker 00

  Yeah.

• 58:09Speaker 01

  How do you, you know, 20-year-old company at this point? What's your advice for other people that are trying to reinvent their companies? To make sure they have, you know, max adoption of AI, not just at the individual level, but also at the kind of company level?

• 58:21Speaker 00

  Yeah.

• 58:21Speaker 01

  How do you make your business legible for AI?

• 58:23Speaker 00

  Yeah. Again, some of this we have as a, as a byproduct of how we've always thought about information systems in the company. So, like, like, to, no exaggeration, if you have a question that you would like to ask about the, the, the business that has ever been documented in a form of unstructured data. So, a meeting note, a project plan, a roadmap, a presentation, a financial document, a, a financial planning session, it's 100% in Box. So like, we benefit from a data architecture that already is insanely sort of tuned for, because this is how we've run the company. We didn't let anybody use anything else. And, and so, the data is very easy for us to be able to work with at scale. And then, of course, we have Salesforce and, and all the other kind of core canonical systems.

• 59:08Speaker 00

  We've been able to kind of- Have, I think, pretty good data hygiene. So agents running on top of that makes it a little bit easier to be kind of AI first in how we operate. Maybe a couple best practices or things that we've seen. You know, first of all, trying to figure out where the highest leverage impact workflows are going to be. And, and, you know, trying to kind of target those. So we have, you know, our CIO is very AI-pilled. We have a little bit of like a center of excellence on AI. We've hired some internal AI FDEs to kind of help with these processes.

• 59:35Speaker 01

  Do you have leaderboard?

• 59:36Speaker 00

  We don't token max. We do actually have like, I mean, like, I guess, like literally, we have a list of people buy a number of tokens. But, but it's usually to like, go and inspect like, okay, do we, do we think that's useful? Or is there a learning there that we should take back to some other function? So like, probably our top, you know, in the top three, all AI users at Box, like, probably one of them is wasting, you know, half the tokens. And then two of them are like, oh shit, whatever they're doing, we need to go like, do like an internal training session for everybody else. So, you know, we had this thing like two weeks ago where I was like, can you just get everybody in a room? You know, on this particular team and just like, like show them how this one person is using AI.

• 1:00:16Speaker 00

  And then like, you know, six hours later, they were like in a room and the guy was doing like a full demo of what he was doing. Shout out to Mick. And, and so like, that's the, that's the kind of stuff that we're trying to do, which is just like, how do we show everybody what it looks like to work in this way? But, you know, for as fast as we're moving and we are shipping at some parts of the stack, two or three times more sort of, you know, actual customer facing products. So like, I don't care about how much code, but like, did we actually deliver more functionality that customers are asking for? Some parts of the stack were doing that. Then you'll go talk to a friend in Anthropic and they're just like, and you're like, oh my God, we still have, we still have ways to go. Like, like, you know, the particular meta kind of constantly changes, which is like, you know, two years ago, you'd be like,

• 1:00:59Speaker 00

  you're just using a plugin in your IDE and you're like, man, that's, we're not going to be that like, we're not ready to fully, you know, be AI first. And then you're like, okay, everybody roll out, roll out cursor. And then you're, everybody rolls out cursor. And then you're like, and then that finally happens. And then you're like, you know, you're walking around, you're like, you're not only working from Slack, just at mentioning bots doing your code. Like, what are you doing? Like, like, it's like, we're constantly just changing what are the, what are the workflow paradigms on this?

• 1:01:25Speaker 01

  Do you guys have like a Slack coworker agent, for lack of a better term?

• 1:01:29Speaker 00

  We have a few that have that shape. I'm pretty excited about like, like Claude Tag as like a form factor. You know, you still have to again, kind of get the, the team construct right and the data right. But we have a variety of ways that people, you know, kind of work with agents in Slack. I don't know if it's as, as sort of Slack-pilled as Benioff would like us to be, or as like Anthropic or OpenAI is. But like, we're, we're heading in that direction.

• 1:01:51Speaker 01

  Yeah, I think that history books will be written about, you know, the art of business in this time. Like, I would love to read the new The Art of War with like, everything that is happening here. Cause I think it's pretty extraordinary stuff we're seeing. What do you think it takes to win in AI versus pre-AI? And like, what does it feel like to be a founder right now versus when you started Box?

• 1:02:10Speaker 00

  I am both jealous of and also not jealous of, like, like the young founders you meet. Cause you're just like, like, oh, to be young again. And, and the whole world is your oyster and you can go in, you know, any direction and the leverage you have, you'll meet with them. And they're, and you're like, oh my God, like I saw a product a week and a half ago. And they, they did a demo of the product and I was like, I was like, absolutely, this would have been a 40-person project. Five years ago, like, or, you know, especially when we were starting out, easily 40-person project. And it was, it was two people. And you're just like, how do you have so many tabs that work? And they all seem to have stuff behind the tabs that all seem very functional, like this is not fake. And, and so I'm very jealous of that.

• 1:02:51Speaker 00

  Like, it's like incredible because you could just like, you can start your company from scratch with that as the design principle. Now, we will, we will get there because we're just going to muscle through it. There's a couple of things that we just can't do, which is like, we're not like, we're very uncomfortable with the idea of like, you sort of, you know, removing the code review. And like, some of these things get talked about because our customers can't possibly entrust us with their data security and compliance if we don't take that seriously. So we're always going to have a little bit of a discount on the productivity because of, of where we are in the stack, what we, what we do as a business. But so jealous of, of being able to kind of be fresh in that. And then on the other hand, like, it's like, man, like, at the same time, for every great idea, it's like instantly five competitors. We didn't have that, you know, problem.

• 1:03:30Speaker 00

  We had a, we had a good kind of couple of years where we just could like, we could just grind on our product and our, and our sort of experience. And it wasn't like every three days you were like, oh, like Sequoia funded this thing and Benchmark funded this thing. And like, you know, we weren't going kind of nuts like with that. Now, we, we had our own version of that. So like, at the time, I probably was going, going nuts, but it was like, like in retrospect, it was like not worthy of going nuts. Now it's like, oh man, this is like a real race in every one of these markets.

• 1:03:58Speaker 01

  Totally.

• 1:03:59Speaker 00

  So I think I'm probably pretty consensus on this, which is in a world where AI builds things so much faster, then probably the shift moves to whoever can actually get it to the customer is in the best position. So, you know, it's, it's fun talking to founders that are pretty kind of pilled on that. You know, Scott or Matan are like, they, they get the mandate. They're just like, this thing is going to be an enterprise diffusion play. And, and so you have to get it to the enterprise. And, and anybody who kind of mistakes the mandate right now, you're just going to lose. It's just like, it's game over, sorry. Like, there is quite literally trillion to trillions up for grab at the applied layer. And the companies that, that know how to build the teams and get to the enterprise will be the ones that win.

• 1:04:44Speaker 00

  It's just like, obviously guaranteed.

• 1:04:45Speaker 01

  Well said. Aaron, this is a very fun conversation. Thank you so much for joining.

• 1:04:49Speaker 00

  Thanks for having me.