Every Agent Needs a Box — Aaron Levie, Box

Like you don't write code, you talk to an agent and it goes and does it for you and you maybe it best review it. That's even probably like largely not even what you're doing. What's happening is we are changing our work to make the agents effective in that model. The agent didn't really adapt to how we work. We basically adapted to how the agent works. All of the economy has to go through that exact same evolution. Right now it's a huge asset and an advantage for the teams that do it early and then are kind of wired into doing this 'cause you'll see compounding returns but that's just gonna take a while for most companies to actually go and get this deployed. Welcome to the Lane SpacePod. We're back in the Chroma studio with Chroma CEO, Jeff Hoover. Welcome returning guest of a now guest host. It's a pleasure. Wow, how'd you get upgraded to that? Because he's like the perfect guy to be guestfuls for you. That makes sense actually. We love context. We both really love context. We really do. We really do. And we're here with Aaron Levy. Welcome. Thank you. Good to be here. Yeah, so we've all met offline and I chatted a little bit but it's always nice to get these things in person in conversation. You just started off with so much energy. You're super excited about agents. I love agents. Yeah, open claw just got by go by OpenAI. Not bought, but you know what I mean. Some, you know, aquahirie. Exactly. Exactly fire. Hey, that's my turn. Okay. What are you pounding the table on agents? You have so many insightful tweets. Well, the thing that we get super excited about is that I think is probably, you know, should be relatively obvious is we've built a platform to help enterprises manage their files and their corporate files and the permissions of who has access to those files and the sharing and collaboration of those files. And all of those files contain really, really important information for the enterprise. It might have your contracts and might have your research materials, might have marketing information, might have your memos. All that data obviously has predominantly been used by humans. But there's been one really interesting problem, which is that humans only really work with their files during an active engagement with them and they kind of go away and you don't really see them for a long time. And all of a sudden with the power of AI and AI agents, all of that data becomes extremely relevant as this ongoing source of answers to new questions, of data that we'll transform into into something else that produces value in your organization. It contains the answer to the new employee that's onboarding that needs to ramp up on a project. It contains the answer to the right thing to sell a customer when you're having a conversation to them with them contains the roadmap information that's going to produce the next feature. So all of that data that previously we've been just sort of storing and occasionally forgetting about because we're only working on the new active stuff, all of that information becomes valuable to the enterprise. It's going to become extremely valuable to end users because now that they can have agents go find what they're looking for and produce new value and new data on that information. And it's going to become incredibly valuable to agents because agents can roam around and do a bunch of work and they're going to need access to that data as well. And sometimes that will be an agent that is sort of working on behalf of you and effectively as you as and they are kind of accessing all of the same information that you have access to and operating as you in the system. And then sometimes there's going to be agents that are just effectively autonomous and kind of run on their own and you're going to collaborate and work with them kind of like you did another person, open claw being the most recent and maybe first real sort of updating everybody's views of this landscape version of what that could look like, which is okay, I have an agent, it's on its own system, it's on its own computer, it has access to its own tools. I probably don't give it access to my entire life. I probably communicate with it like I would an assistant or a colleague and then it sort of has this sandbox environment. So all of that has massive implications for a platform that manages enterprise data. We think it's going to just transform how we work with all of the enterprise content that we work with and we just have to make sure we're building the right platform to support that. The sort of shorthand I put it is as people build agents, everybody's just realizing that every agent needs a box. It's nice to be called box and just give everyone a box. If we can make that go viral, I think that turned all into every agent needs a box. Every agent needs a box. If we can make that headline of this, I'm fine with this. That's the billboard. Exactly. I like it. Can we ship this? I like it. Okay. I got the value I needed. So the thing that we kind of think about is whether you think the number is 10x or 100x or whatever the number is, we're going to have some order of magnitude more agents than people. That's inevitable. It has to happen. So then the question is, what is the infrastructure that's needed to make all those agents effective in the enterprise? Make sure that they are well governed. Make sure they're only doing safe things on your information. Make sure that they're not getting exposed to data that they shouldn't have access to. There's going to be just incredibly, spectacularly crazy security incidents that will happen with agents because you'll prompt inject an agent and sort of find your way through the CRM system and pull out data that you shouldn't have access to. So we have good. I mean, this is going to happen all over the place, right? So then the thing is, how do you make sure you have the right security, the permissions, the access controls, the data governance? We actually don't yet exactly know in many cases how we're going to regulate some of these agents, right? If you think about an agent in financial services, does it have the exact same financial sort of requirements that a human did or is it the risk fully on the human that was interacting or created the agent, all open questions? But no matter what, there's going to need to be a layer that manages the data they have access to, the workflows that they're involved in, pulling up data from multiple systems. This is the new infrastructure opportunity in the era of agents. You have a piece on agent identities, which I think was today, which I think a lot of security people are talking about, right? Basically, I always think of this as like, well, you need the human you and then you need to agent you. Yes. And well, I don't know if it's that simple, but is the box going to have an opinion on that or you're just going to be like, well, we're just the sort of the source layer. Let's octa of cereal handle that. I think we're going to have an opinion and we will work with generally wherever the contours of the market end up. And the reason that we're going to have an opinion more than other topics probably is because one of the biggest use cases for why your agent might need it and identity is for file system access. So thus, we have to kind of think about this pretty deeply. And I think unless you're like in our world thinking about this particular problem all day long, it might be, you know, like, why is this such a big deal? And the reason why it's a really big deal is because sometimes sort of say, well, just give the agent an account on the system and it just treats treat it like every other type of user on the system. The problem is that I as Aaron don't really have any responsibility over anybody else's box account in our organization. I can't see the box account of any other employee that I work with. I am not liable for anything that they do. And they have, I have a strict privacy requirements on everything that they are able to, you know, that they work on. Agents don't have that, you know, don't have those properties. The person who creates the agent probably is going to further foreseeable future, take on a lot of the liability of what that agent does. That agent doesn't deserve any privacy because it's, you know, it can't fully be autonomously operated. It doesn't have any legal kind of responsibility. So thus, you can't just be like, oh, I'll just create a bunch of accounts and then I'll kind of work with that agent and I'll talk to it occasionally. Like you need oversight of that. And so then the question is, how do you have a world where the agent, sometimes you have oversight of what if that agent goes and works with other people and that person over there is collaborating with the agent on something, you shouldn't have access to what they're doing. So we have all of these new boundaries that we're going to have to figure out of, you know, it's really, really easy. So far we've been in easy mode. You hit the easy button with AI, which is the agent just is you. And when you're in cloud code and you're in cursor and you're in codex, you're just, the agent is you, if you're offing into your services, it can do everything you can do. That's the easy mode. The hard mode is agents are kind of running on their own, people checking with them occasionally, they're doing things autonomously. How do you give them access to resources in the enterprise and not dramatically increase the security risk and the risk that you might expose the wrong thing to somebody. These are all the new problems that we have to get solved. I like the identity layer and identity vendors as being a solution to that, but we'll need some opinions as well because so many of the use cases are these collaborative file system use cases, which is how do I give it an agent a subset of my data and give it its own workspace as well, because it's going to need to store off its own information that would be relevant for it. And how do I have the right oversight into that one thing which I'm kind of just getting as well as how humans work. I might sit next to you and scroll to this one part of the file and just show you that one part in partial file access. I'm just saying, I think our back does seem to be dead. You want to say something is dead. Probably our back is dead. And the off story to me seems incredibly unsolved and unaddressed by an existing state of AI vendors. Yeah, I think we're, I mean, you're taking obviously really to love the limit that we probably need to solve for. And we built an access control system that was kind of like, you know, its own little world for a long time. And the idea was this, it's a many, many collaboration system where I can give you any part of the file system. And it's a waterfall model. So if I give you higher up in the system, you get everything below. And that kind of created immense flexibility because I can kind of point you to any layer in the tree, but then you're going to get access to everything below it. And that mostly is working in this world, but you do have to manage this issue, which is how do I create an agent that has access to some of my stuff and somebody else's stuff as well? And which parts do I get to look at as the creator of the agent? And these are just brand new problems. And humans, when there was a human there, that was really easy to do. Like, if the three of us were all sharing, there would be a vent diagram where we'd have an overlapping set of things we've shared, but then we'd have our own ways that we shared with each other. But in an agent world, somebody needs to take responsibility for what that agent has access to and what they're working on. These are like some of the most probably boring problems for 98% of people on the internet, but they will be the problems that are the difference between, can you actually have autonomous agents in an enterprise context that are not leaking your data constantly? No, like, I mean, I run a very, very small company from a conference and we already have data sensitive activity issues. Yes. And some of my team members cannot see the others. And like, I can't imagine when it's like to run a Fortune 500 and you have to worry about this. I'm just kind of curious. You talk to a lot of 70, 80% of your customers. The Fortune 500 are your customers? Yep. 67% just boring. Yeah. I see. Sorry. Yeah. Yeah. Okay. Something. I'm running up. I appreciate the round. I'm projecting for the government. I'm projecting to the end of the year. Okay. There we go. Yeah. You make it sound like we've got to be honest. We're taking way too long to get to 80%. They are pushing it, right? Yes. You don't have a final answer yet. Yes. Well, okay. So this is actually, this is the stark reality that like unfortunately is the kind of like pouring the water on the party a little bit. Yes. We all in Silicon Valley are like have the absolute best conditions possible for AI ever. And I think we all saw the Dworkhash, you know, kind of Dario podcast and this idea of AI coding. Why is that taken off? And we're not yet fully seeing it everywhere else. Well, like if you just like enumerated the list of properties that AI coding has and then compared to other knowledge work, let's just, this is go through a few of them. Generally speaking, you bring on a new engineer, they have access to a large swath of the code base. Like there's like very, like you just like new engineer comes on, they can just go and find the stuff that they need to work with. It's a fully text in text out, you know, medium. It's only, it's just going to be text at the end of the day. So it's like really great from just, you know, kind of what the agent can work with. Obviously the models are super trained on that data set. The labs themselves have a really strong kind of self reinforcing positive flywheel of why they need to do, you know, agent coding deeply. So then you get just better tooling, better services. The actual developers of the AI are daily users of the thing that they're working in versus like the, you know, probably it's only like seven clawed co work legal plug-in users that inthropic any given day, but there's like a couple thousand clawed code and, you know, users every single day. So just like think about which one are they getting more feedback on all day long. So you just go through this list. You have a, you know, everybody who's developed by definition, this technical thing, go install the latest thing. We're all generally online or at least, you know, kind of the weird ones are and we're all talking to each other, sharing best practices. Like that's like already eight differences versus the rest of the economy. Every other part of the economy has like like six to seven headwinds relative to that list. You go into a company, you're a banker in financial services. You have access to like a tiny little subset of the total data that's going to be relevant to do your job. And you have to start to go and talk to a bunch of people to get the right data to do your job because Sally didn't add you to that deal room, you know, folder and that, you know, the information is actually in a completely different organization that you now have to go in and sort of run into. And it's like you have this endless list of access controls and security as you talked about. You have a medium, which is not, it's not just text, right? You have, you have a Zoom call that you're getting all of the requirements from the customer. You have a lot of in-person conversations and you're doing in-person sales and like, how do you ever digitize all of that information? You know, I think a lot of people got upset with this idea that the code base has all the context that I don't know if you follow, you know, did you follow some of that conversation that when viral is like, you know, it's not that simple that the code base doesn't have all the knowledge. But like, it's a lot, you're a lot better off than you are with other areas of knowledge work. Like you, we like, we like to have documentation practices. You write specifications. Those things don't exist for like 80% of work that happens in the enterprise. That's the divide that we have, which is, which is AI coding has just fully, you know, where we've reached escape velocity of how powerful this stuff is. And then we're going to have to find a way to bring that same energy and momentum, but to all these other areas of knowledge work where the tools aren't there, the data is not set up to be there. The access controls don't make it that easy. The context engineering is an incredibly hard problem because again, you have access control challenges. You have different data formats. You have end users that are going to need to kind of be kind of trained through this as opposed to they're adopting these tools in their free time. That's where the Fortune 500 is. And so we, I think, you know, have to be prepared as an industry where we're going to be on a multi-year march to be able to bring agents to the enterprise for these workflows. And I think probably the thing that we've learned most in coding that the rest of the world is not yet, I think, ready for, I mean, they'll have to be ready for it because it's just going to inevitably happen is I think encoding what's interesting is if you think about the practice of coding today versus two years ago, yeah. It's probably the most changed workflow in maybe the history of time, from the amount of time it's changed, right? Yeah. Like, like, has any workflow in the entire economy changed that quickly in terms of the amount of change? I just, you know, at least in any knowledge worker workflow, there's like very rarely been an event where one piece of technology and work practice has so fundamentally, you know, changed what you do. Like, you don't write code. You talk to an agent and it goes and does it for you and you maybe at best review it. Even that's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agent's effective in that model. The rest of the economy is going to have to update its workflows to make agents effective and to give agents the context that they need and to actually figure out what kind of prompting works. And to figure out how do you ensure that the agent has the right access to information to be able to execute on its work? I, you know, this is not the panacea that people were hoping for of the agent drops in, just automates your life. Like, you have to basically re-engineer your workflow to get the most out of agents and that's just going to take, you know, multiple years across the economy. Right now it's a huge asset and an advantage for the teams that do it early and then are kind of wired into doing this because you'll see compounding returns. But that's just going to take a while for most companies to actually go and get this deployed. I love pushing back. I think that that is what a lot of technology consultants love to hear this sort of thing. First to embrace the AI, to get to the promised land, you must pay me so much money to adopt the prescribed way of conforming to the agents. And I worry that you will be eclipsed by someone else who says, no, come as you are. And we'll meet you where you are. And what was the thing that went viral a week ago, open AI probably is hiring FDEs to go into the enterprise and then inthropic is embedded at Goldman Sachs. So if the labs are having to do this, if the labs have decided that they need to hire FDE and professional services, then I think that's a pretty clear indication that there's no easy mode of workflow transformation. So to your point, I think actually this is a market opportunity for new professional services and consulting firms that are like agent-pilled and they go into organizations and they figure out how to re-engineer your workflows to make them more agent-ready and get your data into the right format and reconstruct your business process. So you're not doing most of the work. You're telling agents how to do the work and then you're reviewing it. But I haven't seen the thing that can just drop in and let you not go through those changes. I don't know how that's kind of still spits goes over. You're saying things like, well, it might sort of nice, beautiful wall garden. And here's this beautiful box account that has everything. And I'm like, well, most real life is extremely messy and poorly named and there's no book in it. But the out data shit. 100%. 100%. And so this is actually, no, so this is, I mean, we agree that getting to the beautiful garden is going to be tough. There's also the other end of spectrum where I just, it's a technical impossibility to solve. The agent is truly cannot get enough context to make the right decision in the incredibly messy land. Like, there's no AGI that will solve that. So we're going to have to kind of land it somewhere in between, which is like we all collectively get better at documentation practices and having authoritative, relatively up to date information and putting it in the right place. Like agents will certainly cause us to be much better organized around how we work with our information simply because the severity of the agent pulling the wrong data will be too high. And the productivity gain of that you'll miss out on by not doing this will be too high as well that you that your competition will just do it and that they'll just have higher velocity. So and we see this a lot firsthand. So we build a series of agents internally that they can kind of have access to your full box account and go off and you give it a task and it can go find whatever information you're looking for and work with. Thank God for the model progress. But like if you gave that task to an agent nine months ago, you're just going to get lots of bogus answers because it's going to say, hey, here's here are our fight for the five documents that all kind of smell like the right thing. And I'm in it, but you're putting me on the clock 'cause my system prompt says, like, be pretty smart, but also try and respond to the user. And it's gonna respond and it's like, at, it got the wrong document. And then you do that once or twice as a knowledge worker. And you just-- - Never again. - You're just like done with the system. - Yeah, it doesn't work. - It doesn't work. And so, you know, Opus 4.6 and Gemini 3.1 Pro and, you know, whatever the latest 5.3 GBT will be. Like, those things are getting better and better and it's using better judgment. And they sort of like, that all of these updates to the agentic tool and search systems are we're seeing, we're seeing very real progress where the agent kind of can almost smell some things a little bit fishy when it's getting, you know, we have this process where we have it go fan out to a bunch of searches, pull up a bunch of data. And then it has to sort of do its own ranking of, you know, what are the right documents that it should be working with. And again, like, you know, the intelligence level of a model six months ago would be just throwing it dark. Like, I'm just, I'm gonna grab these seven files and I, I hope that that's the right answer. And something like an Opus, first 4.5 and now 4.6 is like, oh, it's like, no, that one doesn't seem right relative to this question because I'm seeing some signal that is making that, you know, that's contradicting the document where it would normally be in the tree and who should have access. Like, it's doing all of that kind of work for you. But like, it still doesn't work if you just have a total wasteland of data. Like, it's just not possible. Partly because a human wouldn't even be able to do it. So basically, if a really, really smart human could not do that task in five or 10 minutes for a search retrieval type task, you know, your agent and second building do it any better. You see this all day long. So. - This touches on the thing that Jeff's passionate about which is context engineering. I mean, you're just gonna let you ramble or riff on context engineering if there's anything. Like, you did really good work on context rot which has really taken over as like the term that people will use and the reference. 100%. We all we think about is the context rot problem. - Yeah, there's certainly a lot of like ranking considerations, gender searcher things incredibly promising. Yeah, I was trying to generate a question though. I don't have a question right now, Swix. - Yeah, no, but like, I think that there was this moment, you know, like I don't know, two years ago before, before we knew like where the gotchas were gonna be in AI. And I think someone was like, well, infinite context windows will just solve all of these problems. And. - 'Cause you'll just give the context window, like all the data. And it's just like, okay, I mean, maybe in 2035, like this is a viable solution. First of all, it would just, it would just simply cost too much. Like we just can't give the model like the 5,000 documents that might be relevant and it's gonna read them all. And I've seen enough to start believing in crazy stuff. So like I'm willing to just say sure, like in 10 years from now. - No, just they never. - In 10 years from now, we'll have infinite context windows at 1,000th of the price of today. Like let's just like believe that that's possible, but right we're in reality today. So today we have a context engineering problem, which is I got, you know, 200,000 tokens that I can work with. Or probably, I don't even know what the latest graph is before like massive degradation. Okay, I have 60,000 tokens that I get to work with where I'm gonna get accurate information. That's not a lot of tokens for a corpus of 10 million documents that a knowledge worker might have across all of the teams and all of the projects and all the people they work with. I have 10 million documents, which, you know, maybe as times five pages per document or something like that, I'm at 50 million pages of information and I have 60,000 tokens, like holy shit. This is like, how do I bridge the 50 million pages of information with, you know, the couple hundred that I get to work with in that token window? This is like such an engineering problem. And that's why actually so much work is actually like, just like search systems and the databases and that layer has to just get so locked in. But models getting better and importantly knowing when they've done a search, they found the wrong thing, they go back, they check their work, they find a way to balance sort of appeasing the user versus double checking. We have this one, we have this one test case where we ask the agent to go find 10 pieces of information. This is the complex work you've got. This is actually not in the e-vow. This is sort of just like, we have a bunch of different-- We have a bunch of internal benchmark scenarios every time we update our agent. We have one which is, I ask it to find all of our office addresses and I give it the list of 10 offices that we have. And there's not one document that has this. Maybe there should be. That would be a great example of the kind of thing that like maybe over time companies start to have these sort of like, what are the canonical, you know, kind of key areas of knowledge that we need to have? We don't seem to have this one document that says, here are all of offices. We have a bunch of documents that are like, here's the New York office and whatever. So you task this agent and you get, you say, I need the addresses for these 10 offices. OK? And by the way, if you do this on any public chat model, the same outcome is going to happen. But for a different kind of query, you give it, you say, I need these 10 addresses. How many times should the agent go and do its search before it decides whether or not there's just no answer to this question? Often, and especially the, let's say, lower tier models, it'll come back and it'll give you six of the 10 addresses. And it'll just say I couldn't find the other four. It doesn't know what it doesn't know. It doesn't know what it does know. So the model is just like, when should it stop? When should it stop doing? Should it do that task for literally an hour and just keep cranking through? Maybe I actually made up an office location. And it doesn't know that I made it up. And I didn't even know that I made it up. Should just keep, should it read every single file in your entire box account until it exhausts every single piece of information? - Expensive. - These are the new problems that we have. So, you know, something like, let's say a new opus model, is sort of like, okay, I'm gonna try these types of queries. I didn't get exactly what I wanted. I'm gonna try again. I'm gonna, at some point, I'm gonna stop searching 'cause I've determined that no amount of searching is gonna solve this problem. I'm just not able to do it. And that judgment is like a really new thing that the model needs to be able to have. It's like, when should it give up on a task? 'Cause you just don't, it's a can't find the thing. That's the real world of knowledge work problems. And this is the stuff that the coding agents don't have to deal with. Because they just doesn't. Like, you're not usually asking it about, you're always creating net new information, coming right out of the model for the most part. Obviously, it has to know about your code base and your specs and your documentation. But when you deploy an agent on all of your data, now you have all of these new problems that you're dealing with. - Our follow-up research to context-ride, it's actually on a genetic search. And we've like, sort of stress tested like frontier models and their ability to search. And they are not actually that good at searching. So you're sort of highlighting this like, Explore's point. - You're just a Debbie Donner. Everything doesn't work. Like, well somebody has to. - Okay, so throw one more thing that is different from coding and the rest of the knowledge work that I've failed to mention. And so one other kind of key point is that, you know, at the end of the day, whether you believe we're in a slot apocalypse or whatever, at the end of the day, if you build a working product at the end of, if you built a working solution, that is ultimately what the customer is paying for. Like, whether I have a lot of slop, a little slop, or whatever, I'm sure there's lots of code bases we can go into an enterprise software company's where it's like just crazy slop that humans did over a 20 year period. But the end customer just gets his little interface. They can type into it. It does its thing. Knowledge work doesn't have that property. If I have an AI model go generate a contract and I generate a contract 20 times. And all 20 times, it's just 3% different. And like that kind of slop introduces all new kinds of risk for my organization that the code version of that slop didn't introduce. These are, so how do you constrain these models to just the part that you want them to work on and just do the thing that you want them to do? In engineering, you can't be disbarred as an engineer. You could be disbarred as a lawyer. You can do the wrong medical thing in health care. There's no equivalent to that of engineering. Do you want there to be? Because I've considered-- We stopped right now. Oh, is that? Civil engineering, there is, right? Civil engineering. Oh, yeah, for sure. But in any of our companies, you'll be forgiven if you took down the site. And we'll do a rollback. And you'll be in a meeting. But you have not been disbarred as an engineer. We don't change your computer science. I'm not quite good. Exactly. So now maybe we collectively as an industry need to figure out what are you liable for, not legally, but in a management sense of these agents, all sorts of interesting problems that have to come out. But in knowledge work, that's the real hostile environments that we're operating in. I do think a lot of the last years, 23-5 story was the rise of coding agents. And I think 23-6 stories definitely knowledge work. Yes, 100%. Right. And I think open-clunked lower-guard just the beginnings. Yes. The next thing that's going to just going to be absolute craziness. It is. And it's going to be-- I mean, again, this is going to be this wave where we are going to try and bring as many of the practices from coding. Because that will clearly be the forefront, which is telling agent to go do something and as an access to a set of resources, you need to be responsible for reviewing it at the end of the process. That to me is the kind of template that I just think goes across knowledge work. And on co-workers, a great example, open-close-a-grade example, you can kind of sort of see what codex could become over time. These are some really interesting platforms that are emerging. OK. I wanted to-- we touched on e-vals a little bit. You had the report that you were going to go bring up. And then I was going to go into boxes e-vals. But go ahead. Talk about your agent excursion. Yeah, mostly-- I think a few of the insights is like everyone. Frontier model is not going to search. Humans have this natural. Explore Exploite trade off where we can understand like when to stop doing something also humans are pretty good at like forgetting actually like pruning their own context Where's agents are not and actually an agent in their kind of context history if they New thing was bad and they even see in the trace the reason trace head that probably wasn't a good idea If it's still in the trace still in the context, they'll still do it again Uh-huh, and so like I think pruning is also gonna be like really it's already coming a thing right but like letting me also be self-prune the context windows Yeah, so don't leave the mistake Don't leave the mistake in there cut off the mistake, but tell it that you made a mistake in the past and so it doesn't repeat it Yeah, but I cut it out so it doesn't get like distracted by it again because you know what is so it will repeat its mistake just because it's been in the context It's in the context so much That's a few short examples You didn't know that It's like oh, this is a great thing to go try even if it doesn't get to work Yeah, exactly So there's like a bunch of stuff going around hogs day inside these models I'm gonna keep doing the same wrong thing I mean, that's right, if you know it's some career now You're kind of like fit a manifold in late in space which is doing great programs synthesis and one way thing about like well under doing right like Yeah, certain facts might be like sort of overly pinning it There's certain you know sex actors of late in space and so like plug clean space Yeah, and so our editor as a bell every time you say that You have to like remove those like You said a gon like the CBP and something Okay, you have to remove those links like gonna give the freedom kind of do what you need to do so Yeah, we'll release more soon. That's awesome. Yeah, that'll be cool We're a cerebral podcast that people listen to us and sort of think really deep so yeah, we try to keep this subtle Okay, okay, thank you You guys do have you guys you talked about your your office thing But you've been also promoting apex agents and complex work. Yeah, whatever you want wherever you want to take this just yeah Yeah, apex is obviously recourse kind of Agent Eval we we supported that by sort of opening up some data for them around how we kind of see these Data workspaces in in the you know kind of regular economy So how do lawyers have a workspace how to invest in bankers have a workspace? What kind of data goes into those and so we we partner with them on their their apex Eval our own Eval is It's actually relatively straightforward. We have a set of of documents in a range of industries We give the agent previously did this as a one shot test of just purely the model and then we just realized we need to Based on where everything's going. It's just got to be more agentics So now it's a bit more of a test of both our harness and the model and we have a rubric of a set of things that has to get right and we score it And you're just seeing you know these incredible jumps in almost every single model in its own family of you know Opus for Yeah, sonnet 4 6 versus sonnet 4 5. Yeah, we have this up on screen. Okay cool This has some but you're seeing it somewhere like I forget the to it was like 15 point jump I think on the main on the overall yes, and it's just like you know these incredible leaps that that are starting to happen and I thought it doesn't know it like any it's completely held out from it. This is not in any there's no public data Which has you know benefit and this is just a private Eval that we do and then we just happened to show it to the world So you can't you can't train against it and I think it's just as representative of you know It's obviously reasoning capabilities. What it's doing at you know kind of test time compute capabilities thinking levels All like the context rot issues so many interesting, you know kind of capabilities that are that are now improving one sector that you have this interesting people are roughly familiar with healthcare and legal But you have public sector in there. Yeah. What's that like what what what is that yeah, and we actually test against I don't know maybe 10 industries We ended up usually just cutting a few that we think have interesting gain so all exact with one lot of like government type documents What is that was a government type document like government file and like a probably not tax returns What would the government be using as data so so think about research that that type of Data sets and then we have financial services for things like data rooms and what would be an investment perspective That one you can dog food. Yeah, exactly So so we run the models in now, you know more of an agent mode But but still with kind of limited capacity and just try and see like on a like for like basis what are the improvements and again We just continue to be blown away by How how do these models are getting? Yeah, I mean, I think every serious AI company needs something like that where like well This is the work we do here's our company evil. Yeah, and if you don't have it well, you're not a serious AI company There's two dimensions, right? So there's there's like how are the models improving and so which model should you either recommend a customer use Which one should you adopt but then every single day we're making changes to our agents and you need to know if you request You know, yeah, you know, I've been fully convinced that the whole agent observability and e-vow space is gonna be a massive space Super excited for what brain trust is doing excited for you know, Langsmith all the things and I think what you're gonna I mean this is like every enter like literally every enterprise. Well right now It's like the AI companies are the customers of these tools every enterprise will have this You'll just have to have an e-vow of all of your work and like well You'll have an e-vow of your RFP generation. You'll have an e-vow of your sales material creation. You'll have an e-vow of your invoice processing and and as you you know buy or use new agentic systems You were gonna need to know like what's the quality of your of your pipeline? Yeah, so huge huge market with agent e-vows Yeah, and you know, I'm gonna shout out your your team a bit of your CTO Ben did a great talk with us Nice here. He's gonna come back to give him for a wheelchair. Yep. Just talk about your team Yeah, you know, brag a little bit. Yeah, I think I think people take these e-vow numbers and pretty charts as for granted But no there I mean there's there's lots of really smart people at work doing all this big a shout out is we have a couple folks at Ditya Siddharth That kind of run this they're like a you know kind of tag tag team duo on our e-vows Ben our CTO heavily involved Yasha head of AI You know bunch of folks and e-vows is one part of the story and then just like the full you know kind of AI an agent team is Is a is a pretty you know is core to this whole effort So there's probably I don't know like maybe a few dozen people that are like the epicenter And then you just have like layers and layers of Of kind of concentric circles of okay, then there's a search team that supports them and an infrastructure team that supports them and It's starting to ripple through the entire company, but there's that kind of core agent team That's a pretty pretty close close net group this search team is separate from the infroteam I mean we have like every every layer of the stack We have to kind of do except for just pure public cloud, but you know we store I don't even know what our public numbers are in you know But like you can just think about it as like a lot of data is stored in box and so we have and you have every layer of the of the stack of You know At he managed the data the file system the metadata system the search system just all of those components and then they all are having to Understand that now you've got this new customer which is the agent and they've been building for two types of customers in the past They've been building for users and they've been building for like applications and now you've got this new agent user And it comes in with a different set of property sometimes like hey, maybe sometimes we should do Embeddings an embedding based you know kind of search versus you know your typical semantic sort like it's just like you have to build the The capabilities to support all of this and we're testing stuff throwing things away something doesn't work and not relevant It's like just you know total chaos, but but all of those teams are supporting the agent team that is kind of Coming up with its requirements of what what do we need? Yeah, we just came from Fireside chat where you did and you talk about how you're doing this It's kind of like an internal strata within the broader company the broader company is like 3000 people Yeah, but you know, there's there's this is a core team of like well Here's the innovation center. Yeah, and like that every company kind of is run this way. Yeah, I want to be sensitive I don't call it the innovation center only because I think everybody has to do innovation There's a part of the the company that is sort of do or die or The agent wave. Yeah, and it only happens to be more of my focus simply because it's existential that we get it right Yeah, all of the supporting systems are necessary all of the surrounding adjacent capabilities are necessary like we only reason we get to be A platform where you'd run an agent is because we have a security feature or a compliance feature a governance feature that that some team is working on But that's not going to be the maker break of of whether we get agents right like that already exists and we need to keep Innovating there. I don't know what the right exact precise number is, but it's not a thousand people and it's not ten people There's a number of people that are like the the kind of like you know start up within the company that are the maker break on Everything related to AI agents, you know leveraging our platform and letting you work with your data And that's where I spend a lot of my time in ban and yosh and Diego and Terry You know these are just you know people that that you know kind of across the team are working. Yeah, amazing I think about you talk a lot about like kind of read workflows over your box data Yeah, you know, gender search questions queries, etc But like what about like right or like authoring workflows? Yes, I've already probably revealed too much actually now that I think about it So Okay, yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, of course, of course, so I guess I would just I'll make it a little bit conceptual Because again, I've already I've already said things that are not even GA, but we've kind of like dance around it publicly So yeah, okay, just like hopefully nobody watches us. It's it's for the high-engage to go figure out like what exactly You know, it's easier sort of line of thinking they can connect the thoughts. Yeah, so I would say that that We you know as a as a place where you have your enterprise content There's a use case where I want to you know have an agent read that data and answer questions for me And then there's a use case where I want the agent to create something and Use the file system to create something or store off data that it's working on or be able to have you know Various files that it's writing to about the work it's doing so we do see it as a total read right the harder problem As so far been the read only because because again, you have that kind of like 10 million into one ratio problem, whereas writes are, a lot of that's just gonna come from the model and we just like, we'll just put it in the file system and kind of use it. So it's a little bit of a technically easier problem, but the only part that's like, not necessarily technically hard, it's just like, it's not yet perfected in the state of the ecosystem is, you know, building a beautiful PowerPoint presentation is still a hard problem for these models. Like we still, you know, like these formats are just, we're not built for-- - They're working on it. - They're working on it. Everybody's working on it. - Everybody launches like, well, we do a PowerPoint now. - Yeah, getting a lot better each time, but then you'll do this thing where you'll ask the update one slide and all of a sudden, like the fonts will be just like a little bit different, you know, on two of the slides, or it moved, you know, some shape over to the left a little bit. And again, these are the kind of things that like in code, obviously you could really care about if you really care about, you know, how beautiful is the code, but the end user doesn't notice all those problems. In file creation, the end user instantly sees it. You're like, "I," like paragraph three, like you literally just changed the font on me. Like it's totally different font, and like midway through the document. Those are the kind of things that you run into a lot of in the content creation side. So we're gonna have native agents that do all of those things. They'll be powered by the leading kind of models and labs. But the thing that I think is probably gonna be a much bigger idea over time is any agent on any system, again, using box as a file system for its work. And in that kind of scenario, we don't necessarily care what it's putting in the file system. It could put its memory files, it could put its specification documents, it could put whatever its markdown files are, or it could generate PDFs. It's just like it's a workspace that is sort of sandboxed off for its work. People can collaborate into it. It can share with other people. And so we're thinking a lot about what's the right, you know, kind of way to deliver that at scale. I wanted to come into sort of the AI transformation or AI sort of operations things. One of the tweets that you wanna talk about, this is just me going through your tweets, by the way. - Oh, okay. - This is like your average-- - You're the easiest guest to perform because you already have like, this is what I'm interested in. Okay, well-- - Are we gonna get to like, just like, February or something? Where are we in the timeline? So how far back are we going? - Can you describe boxes to set a skills, right? That's like one of the extremes of like, well, if you just turn everything into a markdown file that your agent can run your company. You just have to find the right sequence of words to do it. - Oh, I'm sorry, is that the question? - So there's something in the question that's like, what if we documented everything the way that you exactly said, like, let's get all the 4,500s prepared for agents and like, you know, everything's in golden and nicely filed away and everything, what's missing? Like, what's left, right? Like, you've run your company for a decade. - Right, yeah. - I think the challenge is that that information changes a week later and because something happened in the market for that customer or us as a company that now has to go get updated. And so the systems are living and breathing and they have to experience reality and updates to reality, which right now is probably gonna be humans, you know, kind of giving them the updates. And, you know, there is this piece about context graphs as the kind of-- - Yes, very viral. - Yeah, and I was like, I thought it was super provocative. I agreed with many parts of it. I disagree with a few parts around, you know, it's not gonna be as easy as just if we just had the agent traces, then we can finally do that work because there's just like, there's so much more other stuff that's happening that we haven't been able to capture and digitize. And I think they actually represented that in the piece to be clear. But like, there's just a lot of work, you know, that has to, you just can't have only skills files, you know, for your company. Because it's just gonna be like, there's gonna be a lot of other stuff that happens. - That's changed over time. - Yeah. Most companies are practically apprenticeships. - Like every new employee who joins the team, like you spend one to three months, like wrapping them up. - Yes. - All that tacit knowledge is not written down. - Yes. - Like it would have to be if you wanted to like, give it to a nation, right? And so like that's gonna seem to be like to be-- - One is I think you're gonna see, again, a premium on companies that can document this. Much, there'll be a huge premium on that because, you know, can you shorten that three month ramp cycle to a two week ramp cycle? That's an instant productivity gain. Can you dramatically reduce rework in the organization? Because you've documented where all the stuff is and where the answers are. Can you make your average employee as good as your 90th percentile employee? Because you've captured the knowledge that sort of in the heads of those top employees and make that available. So like, you can see some very clear productivity benefits. If you had a company culture of making sure, you know, your information was captured, digitized, put in a format that was agent-ready and then made available to agents to work with. And then you just again have this reality of like at a 10,000 person company mapping that to the, you know, access structure of the company is just a hard problem. It's like, yeah, but you just, not every piece of information that's digitized can be shared to everybody. And so now you have to organize that in a way that actually works. And it's a pretty good piece. This piece called your company as a file system. Did you see that one? - Nope. - Yes. - Yeah. And I actually would be curious to your thoughts on it. Like an interesting kind of like, we agree with it because that's how we see the world. And we have it up with the screen. - Okay, yeah. But it's all about basically like, you know, we've already organized in this kind of like, you know, permission structure way. And these are the kind of, you know, natural ways that agents can now work with data. So it's kind of like this, you know, kind of interesting metaphor. But I do think companies will have to start to think about how they start to digitize more of that data. What was your take? - Yeah, I mean, like the company is probably like an acid compliant file system. (laughs) Which it can be a single box is, right? So, yeah. - Yes. - Yeah. - Which you have a good piece on. But yeah. Well, my direction is a little bit like, I want to rewind a little bit to the graph word. You said that's the magic trick award for us. I always ask, what's your take on knowledge graphs? - Yeah. Especially every data database person, I just want to see what they think. There's been knowledge graphs, high cycles, and you've seen it all. So. - I actually am not the expert in knowledge graphs. So that you might need to be an expert. - You don't need to be an expert. I think it's just like, well, how serious need do people take it? Like is there a lot of potential in the HWI? - Well, can I understand first of it? Is this a loaded question in the sense of, are you super pro, super anti-medium? - I see pros and cons. But I think your opinion should be independent of mine. - Yeah, no, no, truly. I just want to see what I'm stepping into. - No, I know it's a huge trigger word for a lot of people in our audience. And they're trying to figure out. Because it really is such a hot item for them. - Because a lot of people get graph religion. And they're like, everything's a graph. Of course you have to represent as a graph. Well, how do you solve your knowledge, changing over time while it's a graph? - Yeah. - And I think there's that line of work. And there's a lot of people who are like, well, you don't need it. And both are right. - Yeah. And what do the people who say you don't need it? What are they arguing for? - Markdown files. - Oh, sure. - A specificity. - Yeah. - It's structure versus less structure, right? - I do. I think the tricky thing is, again, when this gets met with real humans, they're just going to their computer. They're just working with some people on slacker teams. They're just sharing some data through a collaborative file system and Google docs or box or whatever. I certainly like the vision of most knowledge graph, kind of futuristic ways of thinking about it. It's just like, you know, it's 2026. We haven't seen yet kind of play out as, I mean, I remember the, in like, actually, I don't even know how old you guys are, but for the show, my age, I remember 17 years ago, everybody thought enterprises would just run on wikis. And, and influence. - And not even, I mean, confidence actually took off for engineering, for sure. Like, unquestionably. Like, this was like, everything would be in the wiki. And I think based on our general style of, of what we were building, like, we were just like, I don't know, people just like want to workspace, they're going to collaborate with other people. - Exactly. So you were anti-knowledge graph. - Not anti, not anti-knowledge. - You were not, not anti-know. - I'm not, I'm not anti-know, because I think your search system, I just think these are two systems that probably, but like, I'm not in any religious war. I don't want to be in anybody's YouTube comments on this. - There's not a fight for me. - We love your two comments here. - We're in the comments. - Okay. - But like, it's mostly just a virtue of what we built. And we just continue down that path. - Yeah, yeah. - And, and that was what we pursued. But I'm not, this is not a, you know, kind of, this is not a, it's not existential for you. - Great. - We're happy to plug into somebody else's graph. We're happy to feed data into it. We're happy for agents to talk to multiple systems. Not, not our fight. - Yeah, but I need your answer. - Graphs are nerd types. - It's very effective nerd type. - See? - See? - This is one, one opinion and I've, - I think that the actual graph structure is emergent in the mind of the agent. And the same way it is in the mind of the human. - That's a more powerful graph, 'cause it actually evolved over time. - I'll figure it out myself. - Exactly. - Okay. - And what's yours? - I like the wiki approach. I'm actually like, you know, obviously I spend some of my time in cognition, which you know very well. And they've had a lot of success with deep wiki. - Yeah. - There's a lot of deep and brain. Super powerful. And it's useful for humans, but it's, oh my god, it's useful for agents. - Yeah. Tell me if you think I'm wrong on this, but not much of an access control structure issue. - No. - It's like the whole, you get the whole code base and everybody gets it. - Before I speak to them, I love them. - There may be some enterprise controls on the enterprise deep wiki offering that I'm not familiar with. - Yeah. - But yeah, I don't have anything on the public side. But yeah, I think like almost like every agent should have it's only. wiki that it's updating and that's persistent memory. Yeah. That is a very weak knowledge graph. Yeah. And you could strengthen it if you want more structure, but you may not need it. Yeah. Markdown files having links and wiki style, right? Yep. Very effective. Right. Lindy. Yep. And like that, as a general pattern. OK. So last couple questions. Sure. But you feel free to jump on in or if you want any rants. I see you as a very interesting and unusual founder where you've been in a business and you're both of two worlds, like you're of Silicon Valley, but you're also of the Fortune 500s. And like, I feel like your kind of founder mode is very different from the branchesky founder mode. And I'm just kind of curious if you have like reflections on like how you operate as a founder. What would his founder mode be? Don't delegate. Ah, right. Anyway, how would you put me? You do delegate. Ah. OK. I see. The-- I think that-- I don't know that Brian and I would be that far removed from each other when you get to the specifics. So there's a whole bunch that I delegate. 90% of the work that happens at box is fully-- fully delegated. We've got great leaders running all that stuff. It's just too much for my brain to handle. And probably 70% of the work-- I'm going to make up all the numbers here-- probably 70% of the work at box, or 70%, 80% of the work at box. I only need to really look at about 5% of that for some high leverage decisions to be involved in. What's the marketing message that we think is going to resonate with customers? So that's a little bit of high leverage thing that we do in marketing. But most of marketing activities I don't get involved in. What's our sales pitch? Maybe I'll be involved in that a little bit, or what's roughly the investments or push we're going to do in certain verticals. That's about 5% of the total bandwidth of the key areas of sales are going to market. So 78% of the company, I can just do about 5% and then just operationally we've got great leaders, and they're going to execute on that. And we collaborate on the 5% anyway. It's not like I'm just making up a decision and saying to go and do it. Then there's this part that is the existential part of the business, which is if we don't do this right, we're out of business. And by virtue of just being a founder, you get sucked into that part of the work because you can feel it. This is like you can just see how the AI tsunami could wipe you out if you make just two, three, four, or five wrong decisions in this space. Couple wrong architecture decisions, couple wrong AI feature decisions, couple wrong API platform decisions, and you might be out of the game in a year from now. And you just feel it in your bones. We feel this all day long in this space, given what's happening. And so that in that area, it's, you can't kind of delegate in a classic sense. You still need to make sure you've got great leaders and strong hires and people that have high agency because they want to be able to own part of the strategy in the roadmap, or else you can't hire good people. But there's going to be a lot of little micro forks in the road that they will compound to determine whether you've succeed or fail. And so you're kind of founder energy just like automatically draws you into those because they are the determining decisions of your company's future. And that's kind of where I spend my time. And you have to kind of do it in a collaborative way again because if you are only dictatorial and just, you just won't eventually be able to hire the best people because they won't want to work on that environment. But you also just can't like abdicate all the responsibility because the risks are just simply too high. Like, and so you have to somehow, obviously add some value. And so the value I add is, I've seen 20 years of this business. So I think I can kind of piece together what I expect the value propositions are going to be and how customers will react to certain things. So that's what I can bring to the table. And then you have this kind of existential fear of if I get it wrong, it's all on me anyway. I don't get to blame the engineer that was working on that project. Like it's all, it's my fault, right? Like at the end of the day, it'll be my fault if it doesn't work. So by virtue of that liability, responsibility, you just get pulled into needing to make sure like it's all going according to kind of how you think it needs to end up. I don't know if I don't know how Brian would answer that, I guess. But like, I, yeah. - It's a long I say, it's an interesting I say, if people should go and compare and contrast your answer versus his, I do think that systems have a way of letting entropy get to them. - Yep. - And if you step away for too long, you need to have a way to like check in and go like, well, do I need to come back in or are we good? And people are going to tell you things are good, but they're not good. - Yes. - I'm just, yeah. - And that's actually, I'm a fan of actually process for that 70 to 80%. So that's 70 to 80%. The process is you're gonna do a quarterly business review and you're gonna have a brand check in and you're gonna do those, like you're gonna make sure that you're seeing all the right episodes of what's changing and how it's kind of evolving and make sure it's kind of going the right direction. And then there's some areas which is like, you know, it's 24/7. Like I guarantee after this podcast at 11 PM, I'll be doing a Zoom with Ben and probably some other people 'cause we're gonna be talking about agents and new platform features. And like that's your just in the cauldron, you know, kind of grinding on that side. - Yeah, that's extremely realistic. - Yeah, exactly. - Like what is like, and I just wanna have people hear your perspective on what it would be like. - And this is this, like you read the post about, you know, everybody having agents running in the weekend and it's like, you know, you just, I mean, first of all, anybody crazy enough to come to Silicon Valley, like we don't bring good news about the sort of like, healthiness of our environment right now. (laughing) Like you have to know what you're signing up for. But like, you know, there's a real issue which is like, shoot, do I have enough agents running? And, - I made a meme that was like, it's the semi viral for me. - Yes. - That's exactly it. (laughing) - You can't even enjoy a party these days. - No. - 'Cause you're working with your tokens. - You just compute out there that you're not utilizing. - I know what the hell, like, I pay for the $200, I'm gonna spend the $200. - Yeah. - I'm gonna spend $6,000 out of the $200. (laughing) - We need to make a philanthropic, very untrothed world. So. - Yeah, yeah, we're not doing a good enough joke. Cool, I have a closing question if you, unless you're gonna-- - I have a question. I've got this question in private before, I'm gonna ask it again, which is, it's a question that Tyler Cowan asks his guests on his podcast, which is, what is the Aaron Levy production function? And I love that. - I love this question because there are so few people that I think are good at both executing, but also, like, distilling and, like, just putting good ideas into the ether. You put a lot of good ideas into the ether. And so, like, what is the Aaron Levy production function that allows you to do that versus others? - How do I get that information or-- - I can give you an variant, which is what goes into Aaron Levy and what goes out, and how does it turn inside? - I'm just trying to think of, 'cause, I mean, there's some very, I guess, read a lot of Twitter as well. And so, like, I just-- - And if you spend a lot of effort to your tracks, you don't see, like, great mini essays from Brian Chesky every day. But you do for me. - Oh, yeah. - You're kind of weird in that way. - Maybe he's healthier than me, actually. We should just, like, we should just text him to see if he's got a more-- - I think he does work out. - Yeah, he has bigger muscles. - I know that I said it. I work out less than him. (laughing) And I tweet more than him. So that's how we're balancing things out. I'm mostly the way I just think about it is just, there's lots of work that's happening in the business. I'm getting to see all the problems that we are running into constantly. And I'm trying to be a little bit of a creative flywheel between what we're doing internally, what then we talk about, getting a feedback loop on that, and seeing other people's experiences of what they're doing, bring that back into the business. And so I just see that my job is, as, hopefully, being able to kind of connect the dots of what's going on in the world, with what's going on in box. And then I just happened to tweet about that along the way. Because-- - It's all you. There's no editor. - That's all you know. - Yeah, wow. I got-- there was a funny-- I tried to get an internship in between freshman and sophomore here of this company and it was a film's kind of production company in New York. And I got the internship. And then I emailed my liaison kind of guy who sponsored me for the internship. And I said, hey, I'd like to do a blog of my summer internship, where I blog about, you know, the being an intern at a production company in New York. And about like a half a day later, they emailed me back saying they've rescinded the internship. (laughs) - No. - Yeah, because I showed a lack of judgment on professionalism or whatever. Like just even the idea that I would ask that question, Red Flag's went up of like, who the fuck is this guy? So anyway, I only say that to say that like, to me, just like, you know, building in public is just like a natural thing. And so I just, you know, go through the day, we deal with interesting problems. I tweet about them, I get information back in the process. I see your work, I see your work, you know, I see a bunch of folks and try and, you know, kind of incorporate that back into box. My job is to try and connect all these things together and make it useful. - And you're, I mean, you're the number one spokesperson, right? So you do have to be out there. - Yeah, but I kind of would be doing it whether or not, I guess I don't really think it was a job requirement, as much as like, I just like social media. - So good at it. - Yeah, it's so hard to believe. So like, okay, sorry. - You get up at 5 AM. - I'm like, coffee, is that your secret? - How do you work? - You actually just sit in the back of Waymos, like, is that you do that way? Like, how do you do this? - It's mostly that though. - It's mostly, there's a, you know, I have a commute home each night. I try and see, you know, my kids most, most week days before I have to hop back online. So there's like a 20 minute window there, where I can kind of like distill the information that's happened and I'd be like, "Ah, is there anything I learned today "that would be interesting to throw out there "or anything that I saw?" And then probably somewhere between like, 7.30 and 9.00 PM, I finally get a chance, like, look through the feed and see, like, did anything crazy happen in AI? And then that will also kind of catalyze, you know, something, as like, that's the best I can kind of, you know, respect. - Yeah, okay, thanks. (laughing) - And now I know your cut off is 8.00 PM. I will try to get AI news out before 8.00 PM, so I can help him do his thing. - Basically, if I don't see it before 8.30, I'm not gonna, I'm not gonna go like, court-tweeted or something. - Yeah, yeah, yeah, yeah, yeah, yeah. - 'Cause then I'm back on Zoom after that. - Yeah, yeah, yeah. - I wasn't gonna, I'm not gonna ask you this, but you've mentioned it, you mentioned it, film stuff. - Yeah. - And I know from one of my favorite parts of doing a research on you was that, you got the idea for box from the Paramount blog, pushing paper. You're a film guy? You're a big-- - I would say it used to be more of a film guy. - Yeah, what's your favorite, if you wanted to, this stuff any? - Kind of the classic of wannabe film student classics. - Are we talking Scorsese? - Yeah, 10, 10, 10, 10. - Yeah, yeah. - And Magnolia, Requiem for a Dream. Basically, if there was an art house film in the '90s, to early 2000s, that was my genre. That got me into, like, wow, wouldn't it be cool to do, you know, film, and then I thought maybe I could connect digital into it, like, could you do film online? That just seemed too hard from a licensing standpoint, and then obviously Netflix, you know, kind of existed. So I never quite was able to fully connect the dots on these things, but the internship at Paramount was one kind of catalyst for starting box because we were using just traditional enterprise software, and I was like, wow, it's like really hard to share data, you know, just like files going back and forth. But the same thing was happening in school as well, and so that all led to led to box basically. - Well, A24 is kind of giving back this sort of researches of the independent film, I guess. - 100%. - In a phase of all the Marvel Slop. - You know, let's think about this the other day, and A24 is certainly the best example, I'm sure of this today, but you know, they just don't, you know, it's hard to make a film like, you know, no country-ferral men, or there will be blood, like what is that movie today? - Yeah. - Like what is a brand new movie that is just like, originally? - You're just watching, you're like, what did I just watch? - So my, you know, Sixth's movie bench is Forrest Gump, which iconic in its time. - Yeah, 100%. - Never again. - Yeah, yeah. We did not make, we don't know how to make Forrest Gump anymore. Maybe we'll try it with the sequel though, that's the point for sure. Forrest Gump too, and 30 years. - I love you to find with it. - No, that Forrest Gump has a kit. - Yeah, he's still right. - I think Forrest Gump has a grand kid, would be like a good movie. Like what is the grand kid of Forrest Gump doing in Forrest Gump? - In '26. - Goes tropical. - Yeah, but yeah, I definitely, let's, I wanna see more movies out there. You know, I'm a little bit conflicted on AI and film because-- - Oh, that, let's see that. - Well, because I, the world does not need more slop on AI entertainment, but I'm kind of like, in a mode where I think that AI is, is gonna be, you know, generally a pure positive because if I'm a, if I was me 25 years ago in high school, for sure, I would be making a full production film that had explosions and car chases and, but then there'd be like, people that would show up there. So like, I think that ability to, to just, you get to be Spielberg, you know, is, you know, completely amazing and democratizing that as incredible. And I, you know, I'm concerned about like, how do you make sure that we still get PT Anderson along the way? And can we make sure that those guys exist? And then interestingly, I never, and I never saw it, but Darren Aronovsky, I believe has either put out, or you're gonna put out an AI film, you know, even some of the best artists are, you know, starting to adopt this. But, but yeah, I definitely don't want to, but I don't want to do is just be like in this like, TikTok feed of just films. And it's just like, oh, this is a film about the car chase that does this thing. And it says like, we don't need that. Like, like, like, this should be a form of entertainment and art. And let's use AI to accelerate the production process, do the really hard CG work that, that you just, you had to spend way too much money on previously to do the, you know, kind of like, let's use it to test out all new kind of plot ideas. - Yeah, previous. - Yeah, exactly. - And it's incredible. - And all those things are super incredible. I still like the, it's very nostalgic, but I still like the idea of like, this is the camera and a person, and a person that says, you know, action. And then, and let's hopefully like surround AI around that. But we'll, we'll see how that plays out. - Yeah, I think, you know, so one of the things that stability AI made impression on me was like, well, you know, at least now we can remix Game of Thrones season eight. And like, you know, like, like it was meant to be not, not rushed. - Yeah. - And then you watch, I was six and a half year old, and I, you know, you see a lot of these kid movies, and you're like, yeah, that probably will be AI. I don't totally know the job math, 'cause I don't know how many animators are today, but I actually think weirdly, I think we could be producing more high quality, maybe even slightly educational kids entertainment. And so, it's maybe that's a positive, is like, we could just have like more, we could just have a Pixar for like, you know, things where kids learn stuff. And it used to be these like very, you know, lo-fi, you know, kind of less than things. - I mean, we had Teletubbies, you know, that it was so slow. - So we could have way more of that, and maybe every animator that today is making a Pixar film is now, you know, like we fragment that out, and, but now they're responsible for more content, and they've got AI agents running. So like, so, I think there's some optimistic scenarios on the entertainment side is like, there's a lot of great use cases for being able to do, you know, generative media. - Yeah, yeah, I do. - Any entertainment as well. I guess one question, it's kind of like a self-serving one, and almost like an advice side of the question. One of the things I really enjoyed researching you was that Michael Arrington had some influence in the box journey because he went to his house party. - Yes. - And that's how you got funding. - Yes. - One of latent spaces, that's a deep cut, right? - Yeah, very deep cut. - That's a '06 deep cut. - Yeah, I mean, do you wanna tell that story? I don't know if you've told it very much. - It's a matter of the story. - Yeah, probably. - It's like a random intro, right? - Well, he used to have house parties. TechCrunch had these house parties, and it was probably no different that somebody's doing a house party in SF. - Just go? - Yeah. - And you just go and you meet the VCs and founders, and like, I'm gonna make up examples, so I don't want to, like, you know, there'd be like chat early over there, pitching his, you know, YouTube to people, and like, that's just like how it worked, and it was just like, wow, like, that was this era where all these new companies were emerging, and I met our first investor in Silicon Valley at one of these house parties, Emily Melton, who then brought us into the FJ, that became our series A, so that was all because of Aarrington's backyard party. - One of my inspirations for the inspace is to be as helpful, influential, whatever, as TechCrunch was. - That's awesome. - Yeah. - What would a new TechCrunch today look like? You know, what, what should I do? (laughing) - There used to be TechCrunch disrupts. I could do that with my conference, but I haven't done it yet. - Well, I mean, I think-- - That is full. - I don't know. - Well, you know, actually, interestingly, I would argue, disrupt came after the period that was that deep cut period. So, I think disrupts, you know, ended up being, you know, catalyzing, I don't even, I think out of flare launch, disrupting the story, right? - Yes. - The little runner's up. - Okay, okay, so like, so like, I think anytime, anytime you can be in a launch pad is just great, because it draws in people that are trying to do-- - So, in that creative moment, and whether it needs to be a contest or just like, everybody gets like five minutes and you're fundraising, I mean, I who knows, but, I mean, for what it's worth, like, I don't know, I have that much advice 'cause I think you're already doing it effectively. Like, I just like watched the YouTube videos the late at night from the events. I haven't been to one of your events, but like from the camera angles, it looks like everybody's there. So, like, what's great is that people are gonna be in the audience as like two random people, and they'll be like, you know, the next big AI company will come from, you know, people coming to a meetup 'cause they were like, "Ah, I came in from Chicago, "and I'm from, you know, Poland, "and let's go do a startup." Like, that's the magic of the Valley. - Thanks for the phone, it's GoFundered. E-I-E. - Oh, and I know if at least one marriage, this-- - Wow, you have marriages already? (laughing) I never heard that about that. That's my favorite KPI. - Wow, with AI marriages at the AI engineer conferences. - These are real humans, you'd be clear. - Okay, that's very exciting. - I think there's a very good collaboration. - I like that you have to check. - Yes, that's a very good clarification. - No, but I think you're insightful, the business leader with like a lot of thoughts on media, so I just figured I would-- - I mean, media is such an interesting space right now because, you know, with the GoDirect model, every company is gonna have to be a media company. - You are going to, you are the OG GoDirect. - Yeah, but, you know, We're. We're still like, I think what you guys are doing, and I don't even know all the overlapping relationships, but I watch your guys' videos of your events, watch your event videos, but it's clearly like, this is the new format, right? Companies have to become channels to communicate with audiences. I think the resurgence, resurgence maybe is a bad word 'cause it implies decline, but like, death rel is hot, like the hottest thing of all time right now. Like, if you could produce a fricking factory of Deverelle people, like, there's just like unlimited jobs right now on the other end of that, 'cause everybody needs their services and APIs to be used by agents, and so we have to all find a way to like, like, hey, look at me, like, like, agent over, oh, please come over here, agent, and that's a content game. Like, how do you get the agents to see your stuff? - Yeah. - And know your APIs, and like, this is like a new world that we are in, and it's gonna be, it's gonna completely be a digital marketing, you know, kind of world that we're in. - Yeah, for what it's worth I'm trying to help by doing little writing boot camps and basically turn into a Deverelle boot camp, where, you know, well, it's a demand supply problem, there's huge demand, there's no supply, - Wow. - While there's increases supply. - The really good ones were for themselves. Creative economy, screwed you over. - So I see. (laughing) - Substack and YouTube payouts, and that's, is that really-- - They're making Patreon. - Yeah, the most talented guys are making, you know, millions and just working for themselves, whether they're for you. We don't want them to make that much money. (laughing) We need to be able to hire people. - I mean, I think, like, you know, do what some communities are doing, you know, not saying it's my situation exactly, but like, give them equity, and like, you know, it should probably would be worth more just, like, sort of helping them out. - Well, they are getting, oh, sorry, as full-time employees or not. - Oh, part time. - In full-time. - I'm part time. But you're, you're, you're end of one. Like, we also people that are full-time. - Yeah, my classic joke, or like, observation was, like, this was when HubSpot bought, like, they're, they bought it like a newsletter business. And then they bought the, my first million, like, the podcast, that, "DarMesh, you must know the Meshaw." - Yeah. - So he's like obsessed with this guy. So my conclusion was, like, every company must either build or buy a media company. - Yes. - Right? And until you, unless you realize that, you have to take it that seriously, that you are running a media business in your company, you will never be good at it. - Yes, 100%. - Yeah. No, we're very much taking that seriously, but still, and yet, DevRel, I mean, I gotta do one plug. I don't, - Go ahead and listen to that. - Go ahead and listen to that. - We're hiring in DevRel. - No, we, all engineers here, like, yeah. - Yeah, like, you've made it, like, and I just say every agent needs a box. Like, let's go. - Thank you. - No, that's the headline. - Thank you. - And you're hiring in DevRel, so make that happen. But yeah, I think DevRel is like the future job. So we're all just gonna be doing DevRel in some form. - Okay. - So what is FD? - Developers are ruling the earth. What is FD? I don't know. - No, it's DevRel. - Yeah. - Okay. - Yeah, you're going to come. - Isn't it just like glorified consulting? That's the doubt. - Sure, I mean, I guess nobody can actually, you know, fully define this, but I think it's micro DevRel. Like, you're in the company, you're helping them with the services. You're doing a little bit of extra implementation. - Yeah, yeah. - But yeah. So it's, I think we're all, you know, the thing that's gonna happen on the ledger of software is we're gonna produce far more output of code and thus features per dollar. But on the other end of this, we're gonna actually end up spending probably just as much on how do you get all of that stuff to the customer. And it's gonna create a new set of roles that we are all doing. Partly because either because there's so much choice, now you have to kind of fight for attention there. Or because the stuff is just changing so quickly that you have to technically help your customers along the journey. So I just think like, this is why I always laugh when people say, you don't need to be an engineer, don't do computer science. I actually think like, that is like, still one of the most protected job categories. Because things are only getting more technical, things are only gonna get harder. And anybody in a technical position is in the best position, get agents deployed, get them built, get them adopted, build the custom code software for the IT system, all of that. - So yeah, my classic founding story of why it picked AI engineer as a title and as a theme for this podcast, as a theme for my conference was back into, like early 2023, someone non-tenant came to me and said, I'm all in on AI, what should I do? And I was like, I just looked at her, I was like, God damn it, there's nothing you can do. Like, engineers are about to get so much more powerful than you, you don't even understand. - Tell me this, should she go and learn? - No, I didn't say any of that to her. - Oh, oh, okay, okay, okay. - Yeah, I'm not that honest. - I hope somewhere out there, she went to some online academy and we can't make it. - Exactly, learn to code. - But there's a lot of people. - There's a lot of people who believe AI too much and then they are like, well, you don't need to learn to code so I won't learn to code. - Yeah. - And then there's a bunch of us who are just in that sweet spot or we can code and we can wield AI a thousand times more effectively than you can. - Yeah. - And like, well, who's gonna win here? - I think this is another tweet, but it was like the observation that like, really software engineering for the past 30 years was the primary career track for like, technical, high agency people that wanted to have a large outsize impact on the world. - Yeah. - And like software means to do that right, effectively. And so with AI, is it like that? - And for AI to eat software engineering or say it's software engineering gonna eat all their kind of domains at discipline. - Those same principles then get applied to every other - And then the same people ran. - Yeah, exactly. - Yeah, exactly. - And the GTI engineering is that. - 100% else. - Well, this is the, you know, anybody who believes that an enterprise, and I'm mixed on this, is, but if you believe that an enterprise is going to build its own software for all of its problems, then you must be the most long on computer science, you know, as a discipline of all time. Because guess what? The most of the economy does not have enough engineers to then maintain all those systems, to update to all the systems, to figure out the relationship between the business problem and what the code needs to do, to go and actually manage that. And so, like, that's a very pro engineering job argument of what the future is gonna look like. I'm still, like, I'm gonna go back and forth, I'm like, are you gonna really build all these things versus no prepackage software? But no matter what, there's gonna be 10 to 100 times more code. So, I think you can be very long engineering right now, as just, you know, purely on the dimension of software is gonna become increasingly more important once agents are, you know, turning everything into software. - Yeah. All right, three software guys, say software again. (laughing) - Not by us all. - Okay. - But Aaron, your inspiration. - All right, thank you. - It's such a pleasure. - All right, good to be here. (upbeat music)