Aaron Levie: The diffusion of AI capability is going to take longer than people in Silicon Valley realize.

Host: It's just absurd to think you're going to vibe code your way to like SAP. All of that domain knowledge, it's not just represented in some well orchestrated data layer.

Aaron Levie: The engineering compute budget conversation is going to be the most wild one in the next couple years.

Host: The biggest problem right now is everybody is trying to figure out the economics of all of this when they're off by at least an order of magnitude on how big the opportunity is. If you have a hundred or a thousand times more agents than people, then your software has to be built for agents.

Aaron Levie: People in the abstract say things like, "Now you're marketing to agents that you're like an API. You've got a good IDL." I actually think that's almost exactly wrong, which is—

Host: Wow, this is breaking podcast news.

Aaron Levie: If you start to imagine that we all have to build software for agents, I think we're like all clear on that, right? So like that trend is happening which is like we spend as much time now thinking about the agent interface to our tool as we do the human interface. Sure. And the reason we're doing that is because our hypothesis would be that if you have a hundred or a thousand times more agents than people—

Host: —then your software has to be built for agents. And then what is the way that those agents are going to interact with your system? It's going to be through an API or a CLI or MCP or whatever. And the paradigm that appears to be taking off and is quite successful so far in terms of efficacy is what if you give a coding agent access to your SAS tools and a coding agent access to you know your knowledge work sort of workflows and context and that kind of becomes this superpower which is it's not just like the agent is not only capable of reading some data understanding some information it can actually code its way or uses APIs through whatever task it's trying to achieve. That appears to be like a paradigm that is starting to compound. And you know that was the that's the Claude Code phenomenon. That's the whatever OpenAI is kind of cooking up, with the super app, Perplexity, computer control etc. And I actually think it kind of makes sense as the ultimate manifestation of this stuff.

Aaron Levie: I think I mean I think you're right. It makes sense in a theoretical way. Yeah.

Host: But in a practical way, we have to be really careful in that the way to say it is algorithmic thinking is really really really hard for the vast majority of people who have jobs.

Aaron Levie: Yeah.

Host: And so the easiest way to think about it is if you were to go into any person and ask them to create a flowchart for a particular thing that they have to go do, they would probably fail at producing that flowchart.

Aaron Levie: Yep.

Host: There. So within any organization, you know, say doing a marketing plan and there's 50 marketing people working on a giant product line, one person probably understands and could document the flowchart.

Aaron Levie: 100%.

Host: So if you put one of these agents or you put this coworking tool in front of people to create these things, their ability to explain to it what to do is really really limited.

Aaron Levie: 100%. But what if that becomes the new way you have to interface with computers and you just have to cycle that through.

Host: Well then you're basically just developing the next abstraction layer for how people interact. And the developing an abstraction layer has historically at each level of the abstraction layer been a highly skilled very specific individual within an organization developing that. And then the little parts that they build just become little toollets in the world of people doing particular tasks. And some people are able to stitch them together and some can't. But that happened with paper clips and thumbtacks before and it's going to happen with whatever we do next.

Aaron Levie: I think basically the timeless part is the job just moves up a rung and you learn a new set of skills and that's why I actually don't think anything about this is any different. It's just now the leverage you get is obviously fantastic. There was this viral kind of tweet that went around which was the Anthropic growth marketer. Did you guys see this? It's basically one person and he was using Claude Code at the time to basically automate what maybe five or 10 people would have done in various kind of silo jobs. And I think the reason why it's interesting is yeah you had to have been a systems thinker to be able to accomplish that. So like clearly he already was technical enough to be able to pull that off. But it did kind of represent what would each of these jobs look like if you had imagine you had X job in the economy and right next to that person was an infinite pool of engineers that could automate whatever that person wanted and what would that job look like in the future as a result of that automation that now is possible. Yes, I agree that you'd have to find a way to think through your job as a system to be able to pull that off. Maybe the agent gets better and better over time at being able to like nudge you in that direction. But it does sort of stand a reason that like you will start to try and automate a lot of that kind of work of like, well, why don't I take like the keywords that are working in Google Adwords and then port them over to Facebook and make sure that those are replicated and then take in the new signal from what's happening in the market.

Host: That's a big leap. Like so one thing first.

Aaron Levie: I almost had you. You were nodding a little bit and then I said something that went too far.

Host: The Anthropic growth person as an example like that's a job where demand is infinite and frankly supply is infinite. This is not a difficult job. So let's instead say the guy that runs the petrol pump in Australia right now is amazing.

Aaron Levie: Right. Right. So like instead be the $600 PC marketing person and see how you can do against the Neo. That's a real job.

Host: All right, fine. We need a better example. But there is really interesting. Let me do an old person example like my cousin, MBA elite school, joined her first job right on the cusp of computing. She actually didn't use a spreadsheet in grad school and then a spreadsheet showed up but she wasn't a spreadsheet person so instead they told her hire as many interns as you want and so her first year on the job she supervised essentially a whole room of agents.

Aaron Levie: Yeah.

Host: And the kids who was me not literally but they were in college came and just did all the spreadsheeting.

Aaron Levie: Yeah. But then what happened magically over the next couple years was she and her cohort all became the spreadsheet people.

Host: Yeah. And then this idea that you being a manager in a bank or just two years in meant you had a cadre of people doing spreadsheet. No, the whole abstraction layer moved up. And the old job before those interns was you sat there with basically calculators and an HP calculator figuring out the model for some M&A deal or whatever and you only got to do like two iterations before you had to put out the pitch deck or just go to the customer or the client or whatever and then all of a sudden they're doing 30 iterations themselves. But I think where we are with agents is just at this step where you think you need 50 and the abstraction layer is such that we're dividing up in these really small pieces with one super smart person coordinating them all and pretty soon that whole thing is just gonna collapse on each other and there is just going to be like a skill set amount of code call it an agent that is like marketingish. And you'll be able to ask it marketing stuff. And then the next step will be and have it go do things. I'm a little skeptical of the until the whole like non-reproducible, non-random element of this AI stuff goes away, the doing stuff is going to get very costly and so then you get into the human in a loop discussion and all of that. But I think we're just at that exact I feel like when I talk to people trying to do stuff that we're right at I feel like I'm at Thanksgiving dinner talking to my cousin six months in her job when I'm using a spreadsheet already and I'm like I don't know why this is so hard. You should just use one—

Aaron Levie: —and then two years later she's doing it.

Host: And I think this right now you have to be an absolute you have to be a rocket scientist and the growth marketing person to create 42 agents and spin them all up and do all of this stuff. But the rocket science part of it just is going to evaporate in very short order and then you're talking about wow there's a giant chunk of domain expertise that—

Aaron Levie: —it goes back to the domain expert.

Aaron Levie: So I actually think something that you said I'll take the other side of which is I think it's very tempting to be like these agents are going to code and do X. Yeah.

Host: But I think we're going the opposite way. So I think actually where we started was we'd like take like a piece of SAS software and we'd add AI. Yeah.

Aaron Levie: And then that's like the new kind of like AI enabled. So that's like the extreme version of using code for these types of things. But now what are we actually doing? We're like okay the SAS software is still SAS software and the agent uses it as a computer because like it's actually very good at that. So I'd say like we started with code—

Host: —then we went to the terminal which is actually less code. Yeah.

Aaron Levie: And now this year is going to be the year of computer use. Yes. So it's almost like they're much more like humans using computers than them generating code. And that feels like very much like this mezzanine step. Yeah. And I actually come from like the generating code type of the world. Like I would argue that that's happening less, not more.

Host: Yeah. I think to me whether it's computer use, API use, or writing code on the fly, I kind of maybe erroneously put that all in one blank category.

Aaron Levie: They're very different.

Host: They're very different. But what we have an agent that we're working on where it just makes a determination whether it should use an existing skill, it should using an existing tool from Box, or it should write code to solve that problem. And its ability to do any one of those three at any moment ends up being incredibly useful because sometimes there's just some specific operation you want to be able to do where writing code to be able to do that operation is just faster and we don't have to we can't possibly pre-plan for every thing that anybody would ever want to do on their documents. And so the fact that the the model is good enough to also write code on the fly for that use case ends up just being like an amazing property even though maybe 90% of the things that it's going to do should just be using an existing tool.

Aaron Levie: Over time there's like literally like seven apps on her iPhone there's seven SAS apps we end up like over time these things tend to consolidate.

Host: But the seven apps on the iPhone is a issue of humans don't want to learn these things over and over again and so I as a human I don't have the mental bandwidth to learn that many apps but an agent that is going to use tools and APIs and be able to code things doesn't have any of the same constraints that we have.

Aaron Levie: Well you could argue that there's just so many things to do and you can make interfaces sufficiently general.

Host: Let me I think I like what you said then because I'm back. We're back. Okay, we're aligned. No, but I think there's something super interesting here which I do really really like which is that where software has evolved, you know, like I use SAP all day. I work in finance. I have to go and generate all these reports and then somebody shows up and says I want a report that does this view slice this way and I'm like oh god I don't know how to make that right and like now let me go wade through the SAP help system and try to find it. One thing that let's just say AI could be very good at is it actually can navigate that surface area much much better. the help is all there and so it's a matter of finding it mapping language and humans have been a bottleneck in tapping the past 25 years of software capabilities. I mean like I spent my life sitting next to people on airplanes saying how can I make PowerPoint do X?

Aaron Levie: "Just go to the ribbon!"

Host: Well and you know it was because it hurt physically hurt to watch somebody suffering with bullets and numbering in Word or trying to figure out you know like oh let me just make a two-sided a two axis graph in Excel which like is rocket science like almost no one can do that but yet it's super common and so that impedance mismatch was a human user interface.

Aaron Levie: I totally buy it on the consumption layer I totally buy it which is like the the perfectly fluid like UI or consumption layer I just feel the backend like the systems of record it'll probably converge into like some database like some generic set of APIs like that they'll connect to and like that seems to be the direction it's going.

Aaron Levie: Well like so I spent all weekend like implementing my nano cloud bot and when you first start out it's like you're building an integration for everything.

Host: Okay, fine. Work productivity and then an SAP system and so there's an infinite amount of complexity when you get to okay some company that has a global supply chain and they're dealing with 75 pieces of information across you know 30 different systems that does require a certain amount of horsepower from the agent that is just we've I mean we just haven't been able to get from any architecture up until now. But that what you just described is literally what it has been doing for 50 years and will continue to do which is you know I have a friend who was the CIO of the VA and he spent his time on was gluing those 75 VA systems together and it's all just integration redundancy.

Aaron Levie: Perfect for integration. Yeah. This I totally agree. Okay, great.

Host: For integration, these things are the best, but it's integration, right? It's literally how do I stitch these two systems.

Aaron Levie: But now the thing that I think is happening is it's kind of like integration on demand.

Host: It's my new query in the system that the IT team didn't pre-wire. Now I need it to happen at runtime.

Aaron Levie: So, okay. So, the reason I just was in a room filled with a bunch of CFOs and CIOS and they all looked at me when I said something along these lines, although not as optimistic as you can imagine, but they just realism. No, it caused like six of them to come running up afterwards and say, "You're insane. You've lost all credibility with me." Because it's—

Host: Wait, wait, what? What specifically that the the agents are going to do integration?

Aaron Levie: That the integration is a problem that will get a lot easier. Yes.

Host: They were against that.

Aaron Levie: No, one's against practical, but their fear is like unleashing not just the agents themselves but humans to do integration because you put people creating new integrations and you just say please break my system of record. Oh yeah. And so this idea that you just create like a new API between you know system 27 and system 38 and then that might be fine for a report because if that person wants to be wrong that's their business but you're not—

Host: I think I think we have a readonly version of this for a number of years before—

Aaron Levie: We actually have so we just rolled out the official box CLI. Thank you for the liking the tweet on that.

Host: I've used it. I have some feedback. We'll talk about—

Aaron Levie: I'll take all the feedback. But it's a really interesting thing. So we had these all these debates internally of like okay you give Claude code the box CLI and you can now interact with your entire box system via natural language and you get the horsepower of Opus or 4o being the orchestrator of doing a bunch of operations and it's like it's like you know blows your mind. I guess I'll get some feedback, but it blows your mind in some ways because you could just be like, upload this entire folder from my desktop into box and it'll work or process all these documents in this folder and it'll work. And it's amazing. And then we started thinking through like well let's say you were a company with with you know 5,000 employees and everybody had access to some shared repository like you know engineering documentation and you know marketing assets or whatever and everybody had running with the CLI. Wow. We now have some really interesting new challenges which is like how do you coordinate you know possibly the fact that you might be hitting the system like you know 10,000 times an hour or something. Not from a like a performance standpoint but just like how do you make sure that people didn't move like a file from one thing accidentally from one folder to another folder while the other person was trying to do a write operation and somebody else was trying to delete something because you have these agents running wild. This is going to be like the new big question that every CFO, CIO, etc. is running around trying to with their hair on fire.

Host: There's just that's exactly what I ran into which is I played around with your example which is create a marketing plan directory or something and like all of a sudden I'm like in some loop creating directories—

Aaron Levie: It's going to go on as long as it can.

Host: Right? And I was like I wonder what the limit is on box for nested directories because I'm about to hit it.

Aaron Levie: Actually we're gonna find out too.

Host: Yeah. Yeah. Yeah, but it it does feel to me that that a lot of the intuition is to like build a new layer of controls and whatever. But what's actually happening on the ground is the opposite. So I'll give you an example. Like when we all picked up a lot of these personal agents, we would like give them our API keys. Yeah. We would give them our email addresses and then they would kind of access those things and they're like, "Oh, but how can I stop it from like whatever?" And so what everybody's doing now is you give it its own phone number. Yep. I actually gave my Nano Claw its own credit card came in.

Aaron Levie: Hopefully just a Visa debit card that you bought at CVS.

Host: All the money, but I gave it its own Gmail account which you can log into and then Gmail actually has all of these arback permissions. So you could make an argument that you know we've actually built in a lot of these permission systems. You have to treat it like a human as a separate human and then instead of like building another off layer—

Aaron Levie: Okay now can I instantly take do a take down of this element that we're going to run into? Okay so that is fantastic for personal productivity and the question that we're going to run into is in an enterprise let's say I have a 50 person team of something—should everybody basically will we have a hundred people now collaborate basically 50 humans and then 50 agents in that same shared space and do I have I obviously have complete oversight over my agent but what if my agent collaborates with somebody else and then accidentally gets access to some resource because they were sharing with that other person and I'm not supposed to have access to that resource and now this autonomous sort of stateful agent is running around working on somebody else's information.

Host: The default end to end argument is you treat them like human beings and—

Aaron Levie: —it doesn't work. So you can't fully treat them like humans because here's the thing and with regular humans you don't get to look at the Slack channel of the person that that is working with you or working for you. You don't get to log in as them. You don't get to oversee them. They are accountable for their own set of execution in the real world. You don't get penalized for what how they screw up the agent. You have all the liability of whatever they're doing. you do have complete oversight and you're probably going to need to have that complete oversight. They have no right to privacy. So, so there's going to be these some of these breakdowns that aren't as clean as just treat them like a person because I need to be able to kind of I need to be able to give access to something to them, but I also need to be able to like log in as them at some point and be like, "No, no, you [ __ ] up the whole thing and I need to undo it all." But if I can log in as them, how could they have operated in the real world working with other people and keeping anything, you know, confidential or secure or whatever? So, it really is still an extension of you. It's like almost impossible to get around them being an extension of you.

Host: I I I just doesn't logically follow. Yeah, maybe. But, for example, um for my employees, I can log in as them. I can get access to their email.

Aaron Levie: You don't though. You don't you don't. Yeah. No, in like in a if you get like sued. You're not logging in. You're not logging in as them on a regular basis because they sent one email.

Host: Isn't the right operating model with an agent the same thing?

Aaron Levie: The risk is like a thousand times greater. Like these people like they will just leak your information whenever they want. Like they will happily just go and send some email to somebody because they got prompted. You think the terminal state is that these things are still these sloppy computers and therefore they will always—

Host: I don't like the word sloppy. Um unless we're saying it very in a colloquial sense. Um but like—

Aaron Levie: —they'll never be able to contain information. They'll never—

Host: Um so we're like I think the ability for you to keep the something in the context window a secret like as in like you tell it do not reveal X thing in the context window. I think that's a very hard problem to solve.

Aaron Levie: Let's so then so then thus if anything can ever enter that context window because they have access to a resource then in theory you should assume it can be you know prompt ejected out of the context window. And I don't know that we know of a way to solve that at the moment. Like that's like and so so if I know your new agent's, you know, email address and I email it like it's an assistant, but like I can social engineer it 10 times easier than a human, like it'll be hard for you to pull off that that agent is now also has access to your like M&A documents and stuff.

Host: But isn't this like literally all of AI right now?

Aaron Levie: Which part?

Host: I mean, the fact that we've got these shared systems that we use the intelligence for that have shared context.

Aaron Levie: But this is why they're working as you effectively right now and we don't yet know how to make them not work as you. And then solving this problem though like the issue will be like you will just be able to trick the agent to reveal information. So then that's why like having them have access to their own resources where they can fully make their own decisions is not yet something that we've been able to pull off.

Host: But there's a perfect example for solving your problem which is we already lived through this with open source. The model for open source was it's all there and you just use it and you pick and choose. And then like nobody debated it because the world was much smaller then and we weren't all on X doing podcasts when this was all happening. But then quickly everybody realized all the problems you were just talking about. Like if you're running a big company, you can't have some person just go copy in a bunch of source code from open source into your commercial product like that. There was a whole licensing problem, a whole quality a whole bunch of stuff. And so all these norms got developed. The debate that we're happening right now is just is this really interesting modern artifact of how new technologies develop, which is this is all happening in real time. during open source like we met in a conference room this big and debated how much open source we could use in Windows or office right and nobody in the internet knew we were having this debate it was a very and it I think it's just so interesting that not just this the debate about specifics but this whole notion of where is this heading is happening in writ large and everybody is just trying to get to the end state. Like way way way more like in a sense more quickly than we can actually reach the end state. And so what really needs to happen is people just need to go build. We need standards.

Aaron Levie: I think I think we've got different intuitions on the end state.

Host: One could make an end to end argument that these things actually converge on the same type of reliability as a human being, which is exactly how we view like self-driving. And in that case, you use the exact same mechanisms that we use to protect with human beings. like you consider insider threat, you consider the fact that people can be bought off, you consider the fact that people make mistakes and you build and that's operational processes. So, so one intuition is like that will be the end state.

Aaron Levie: Yeah. Well, the point is like I'm JUST SAYING I'M TALKING ABOUT WHERE we're at now. I actually I don't know that we disagree on the end state and by the way like strategically we're hedging because we're going to build Asian users and we're like I love the idea of open claw having a box account and it operates and it's like twice as many accounts. Exactly. THIS IS GREAT. I'm just saying on the ground right now, we don't yet know how to give it an M&A data room to fully securely be able to. But part of what's going to happen is you're we're going to go through this phase where like the enterprise customers are just going to like close everything off until there's some sense of sanity in all of this and then but in the meantime the individual and specifically the developers have such a big—the startups will start to move much much faster than enterprises because they just don't have any of these problems.

Host: Which is why the diffusion of AI capability is going to take longer than people in Silicon Valley realize because what's happening is like we see startups that can start from the ground up without any of the risks that we're talking about because they have nothing to blow up and so we look at that as the trajectory that we're on and then you go to like JP Morgan and you're like how are you going to set up NanoClaw to actually automate your business anytime soon and it's like oh okay there's going to be like a little bit of a gap there. The current SAS vendors who are all struggling in this SAS apocalypse weirdness, they don't really sell the line of business data they actually sell this intelligence and domain expertise in this whole system and the agent side of things wants to only buy the data now and they only want to license the data and they want to have unlimited access to the data but they've actually never really enabled that.

Aaron Levie: Wall Street's all wrong about the economics and the problem and all that stuff. They're viewing the world of revenue as sort of this linear step literally linear growth curve and trying to justify all the expense when people are going to create like this was the problem with PCs. People viewed PCs as a finite market because they just viewed the consumption of MIPS as some finite thing and they didn't think what would happen if we put all those MIPs on every desktop. And the same thing happened with the cloud which was people looked at the cloud and they said, "Oh, we're going to take all of the the server business," which was like 60,000 units a year, and we're just going to move it to someone else's data center, right? And nobody went, "Oh, people are going to use a thousand times as much resource." And that's exactly I mean that's the thing that it just drives me absolutely bonkers that the Wall Street models have this fixed revenue pie—zero sum thinking.

Host: Let me give you an example of this. I've been in for investing for 10 years now I probably have a portfolio of 240 companies. Every single one of them has gone asymptotic in the six months and you're like, "Okay, why is this?" It just turns out there's so much more software being written now than ever has been before. And so with more software, with more agents, there's going to be a lot more consumption of computer resources. So once everybody's phone and on device is consuming AI, the amount of it is going to go up by a billion.

Aaron Levie: The engineering compute budget conversation to me is going to be just like the most wild one in the next couple years. It's just like how much should you allocate of your engineering expense to tokens. And what CFOs have to literally they actually have to know the answer to. I understand they have to know but CFOs always want to know the answers to things that don't have answers. R&D is somewhere between 14 to 30% of revenue of any public technology company. The difference between compute being 2x the cost of your engineering team or 3% more is all your EPS.

Host: I'm perfectly willing to sacrifice a few CFOs at the altit of this.

Aaron Levie: I want that. That's a good clip by the way. But the reason is because again this is trying to know what we just don't know right now. And this has happened with internet bandwidth. It happened with vacuum tubes. It happened with transistors. It has happened with every technology. There was a time when programmers were going to swallow every company. But I don't think we've ever had a point where every end user in an organization has sort of a completely elastic ability to spin up a resource on their behalf.

Host: This is all going to go away. There's absolutely no doubt that this just goes away. And the biggest reason it does is because you you have to do the Benioff kind of math which is if you're paying an enterprise salesperson a million dollars a year you have to ask how much is their tool worth. And if you're paying an engineer X dollars a year well at some point their tooling is worth—it's absolutely worth it. I think law of large numbers solves this because eventually you have enough engineers, they're using this much compute, but like we're in a transition phase where like most people thought, the two-year ago level of spend on AI, which is a chatbot. But they were wrong.

Aaron Levie: Like the vacuum tube thing you made fun of. But there was a time when they thought that all of the Dakotas would be covered in vacuum tube warehouses and people on roller skates would be running up and down the aisles replacing vacuum tubes just so we could fight World War II. And then someone said hey how about a transistor. And we are going to have a transistor moment with all of this. It might just be more supply, but it also might be an actual algorithmic fundamental change. It's just this I think it's particularly weird that everybody has just gotten to token. which is the same thing that happened with IBM and mainframes. People were on MIPS and then one day the reality was IBM was selling more MIPS for fewer dollars every year. And they were still pricing their mainframes by MIPS until it got pointed out to them that they were on a decreasing curve because they were making MIPS faster than they can charge for. And that's what's going to happen guaranteed.

Host: Love it. That was great. sound like it sounds really great to sound like I know what I'm making.

Aaron Levie: Guaranteed. I actually probably believe it.