We got Aaron in the chat here. Bring him in. There we go. How you doing, Aaron?
Boom. Hey, guys. How's it going?
We're doing great. Great to finally have you. You should have been on like, you know, we've had like 50 plus guests at this point. You should have been on the first one.
Why wasn't I a launch partner for this thing? Seriously.
Oh, well, we're making up for today. Okay, great. Another podcast. Awesome. Good work.
Yeah. Yeah. Yeah. I mean, that was the genesis, right? The world didn't need another.
You said technology needed a podcast. That was the idea for sure. I want to talk about the enterprise AI eval and how the different LLMs are interacting at Box. Is this a sustaining innovation? Is this a disruptive innovation? Is Google arrival? You're gassing them up. What's going on?
There were like nine questions in there. Where do you want me to start?
Yeah, I mean we're pretty pumped about AI as you can imagine. So we've been building this platform to help enterprises store their data for a couple decades now and we have hundreds of billions of files within Box. And the one challenge with content, you know, financial documents and resumes and marketing assets and contracts, is you create it, you share it, and then you kind of never look at it again. And so what happens is you see this sort of curve of content remains hot for a day or two and then it kind of goes into an archive state. And AI is the first thing that lets you actually tap into all of the value of all of your data, no matter how old it is and when it was created and who it was shared with and where it is. So for us it's this massive breakthrough which is you can turn all of this unstructured data into effectively usable knowledge for an organization. And so we're kind of betting the whole company on this idea of in the future when you work with your content, you're going to have AI agents that go out and read your documents for you, create analysis, go through all your contracts, find exactly the data you're looking for, automate workflows across any business process. So that's effectively why we're all in and so we're trying to think through what are the implications in the future of enterprise software. I'm very much on the side right now that it's a TAM expansion of software as opposed to kind of a TAM compression. I think this lets enterprise software, startups or incumbents, go after much bigger markets because you're now tapping into just a completely different budget pool in an organization. But yeah, lots happening at the moment.
What about training a foundation model? Did you consider it? It seems like can you talk a little bit about value accrual versus foundation model layer versus application layer?
We thought about it for like six and a half minutes.
That's a long time in 2025.
Exactly. So back in the moment we saw ChatGPT, there was like one brainstorm of like, oh, do we need to like fine-tune our own models or even train our own models on certain data sets. And we basically quickly concluded like, no, that doesn't make any sense. And the math is really clear. It's like if you have Google and xAI, we didn't have xAI at that point, but you had OpenAI, Anthropic, Google, Meta at a minimum, you just don't want to be fighting that war from a capital standpoint or even just a deep research talent standpoint. So you always want to be, as a startup or any size company, you kind of want to be on the side of the tailwinds of the market. And the tailwinds in this case are that there's like now five or six at-scale companies that are deploying hundreds of billions of dollars on computers and training data and talent. So you want to be riding that wave as much as possible. And so we've built an architecture that lets us tap into any of the AI models, whoever makes them.
Great. Jordy, you had quoted somebody, I think it was a couple days ago, that basically delivered a pretty good prompt, about a paragraph of text describing a website that they wanted and it just sort of pops out this beautiful landing page and the designer said, 'We are so cooked.' That was a reaction for a lot of people last week. Maybe that was a reaction if you were Studio Ghibli. I'm kidding. But you said, 'Here's how this plays out. AI makes producing amazing designs more affordable and faster. Designers get to offload the longtail work, iterate faster, and serve more clients or projects. Customers get better results faster or cheaper, inducing more demand for great design.' It's basically calling Jevons paradox on design. Is there any context that you feel like is worth adding there? Because I think generally you see these sort of big, exciting releases or updates to models and there's like a lot of sort of doom. But if I was in high school and I enjoyed design, I would be calling up every single SMB and I wouldn't even say, 'Hey, will you pay for this website?' I would say, 'I designed this website for you already and you can have it for whatever bucks and you could just be printing in like seconds,' right? So I had a positive takeaway. I think you did too, but I was curious if you had anything else.
