FastForward on PPN with Ron Miller | Playaz Productions Network

Episode Summary: Sixteen years after pitching early cloud solutions to deeply skeptical enterprise IT buyers, Box CEO Aaron Levie is leading his organization through another massive transition: Artificial Intelligence. In this episode of FastForward on PPN, host Ron Miller sits down with Aaron to cut through the industry hype and discuss what it actually takes to scale AI across the enterprise.
They explore why scaling AI across standard knowledge workflows requires organizational patience, how human-in-the-loop oversight shifts layers of abstraction rather than eliminating jobs, and why the AI transformation is fundamentally larger in scope than the digital or cloud revolutions that came before it. Aaron also addresses the polarizing industry rhetoric regarding white-collar labor displacement, shares why he is bullish on the rise of the "forward-deployed engineering" model, and opens up about what keeps him deeply passionate after more than two decades at the helm of Box.
Episode Timestamps & Chapters
  • [00:30] Introduction & The History of Box Ron and Aaron take a trip down memory lane to 2010, discussing the early days of pitching cloud storage when enterprise buyers were still religiously tied to on-premise architectures.
  • [02:01] Early Days of Evangelizing the Cloud How sticking to a single cloud platform architecture paid off dividends when mobile arrived, how it enabled seamless SaaS integrations, and why it laid the groundwork for today's AI agents.
  • [05:58] Scaling AI & The Reality of Business Adoption Why technology transitions inherently take time. Aaron discusses the dual reality of massive tech breakthroughs versus the slow, localized diffusion of AI within traditional corporate workflows.
  • [10:13] Why Humans Will Always Be in the Loop A look at the iterative, multi-prompt realities of working with AI. Aaron explains why automating a workflow doesn't eliminate humans, but instead raises the bar for the final output.
  • [12:30] AI's Impact on the Future of Jobs Countering the "doomer" mindset. Why the tech industry is shooting itself in the foot with negative rhetoric, and how AI tool capabilities will expand the demand for specialized talent in pharma, healthcare, and manufacturing.
  • [17:39] The Rise of Forward-Deployed Engineering Ron and Aaron debate the emergence of specialized vendor deployment teams. Why complex enterprise systems and change management make professional consulting a bullish sign of maturity for the AI sector.
  • [21:42] AI vs. Traditional Digital Transformation Why AI transformation is vastly larger than previous cloud or digital transformations because it impacts every single internal crevice and workflow, not just the customer experience.
  • [23:21] Aaron Levie's Personal Drive after 21 Years How a lifelong obsession with technology keeps Aaron energized, and why the enterprise need for secure, unstructured data puts Box directly in the center of the AI revolution.
Key Takeaways from the Interview
On the Pace of Enterprise AI Adoption:"Even though it was obvious to us in 2006 and seven that [the cloud] had to be the architecture of the future... it still took 20 years for that to really roll out across every organization. So I think we're in for some amount of diffusion where it will take some time for AI to hit most organizations." — Aaron LevieOn the Doomer Mindset vs. Labor Demand:"If we end up scaring people about the power and the value of AI, absolutely people aren't going to go for it... What I tend to see is that the work just expands based on the kind of tool capabilities that we have... every pharma company on the planet would love to have access to amazing computer scientists that can go and automate drug discovery." — Aaron Levie
Links & Resources Mentioned
FastForward on PPN is a proud presentation of the Playaz Productions Network. If you enjoyed this episode, please leave us a 5-star review on Apple Podcasts and Spotify!

Creators and Guests

Host
Ron Miller
'm a technology reporter with almost 30 years of experience, much of it covering the enterprise. These days, I run the FastForward blog and newsletter, a publication I conceived and built from the ground up. The publications cover analysis on emerging technologies with a large focus on AI, digital transformation strategies and industry challenges through commentary, features, executive profiles and in-depth coverage of news that impacts enterprise tech decision makers. I also do professional moderation — on-stage interviews, fireside chats and panels at executive and customer events, bringing the same editorial standards I apply to my reporting. Recent work includes interviewing Pax8 CEO Scott Chasin at Pax8 Beyond and Zscaler's Swamy Kocherlakota at Zscaler Zenith Live, on top of past interviews with Aaron Levie of Box, Dylan Field of Figma, and Cloudflare co-founders Michelle Zatlyn and Matthew Prince. Before taking FastForward independent, I was editorial director at boldstart, a first check, enterprise-focused venture capital firm. Prior to that, I spent a decade at TechCrunch covering the intersection of enterprise startups and larger more established public companies through news, features, profiles and in-depth analysis. In addition to reporting and writing, I moderated panels at dozens of events including TechCrunch Disrupt, Web Summit, Collision, and many others.
Guest
Aaron Levie
Aaron Levie is the co-founder and CEO of Box, an enterprise cloud content management platform that he launched from a university dorm room in 2005. Under his leadership, Box evolved from a consumer-focused online storage startup into a multi-million dollar public company utilized by thousands of organizations globally. A prominent technology evangelist and industry voice, Levie is widely recognized for guiding enterprise architectures through consecutive digital transformations—from the early days of shifting traditional on-premise IT infrastructure to the cloud, to today’s deployment of secure, agentic artificial intelligence platforms for unstructured data.

What is FastForward on PPN with Ron Miller | Playaz Productions Network?

FastForward is a featured digital broadcast series hosted by tech journalist and editor Ron Miller. Airing on the Playaz Productions Network (PPN), the show explores macroeconomic technology trends, enterprise architecture, and emerging digital innovations.

The show is structured around high-level executive interviews, bringing on prominent industry leaders—such as Box CEO and founder Aaron Levie, Pegasystems CTO Don Schuerman and Cisco CPO Jeetu Patel—to unpack the practical realities behind tech market evolution, software development strategy, and the shift from tech hype to operational business value.

Key Elements of the Show
Host: Ron Miller (Founder and Editor at FastForward).

Core Focus: Enterprise technology architecture, generative AI scaling strategies, software engineering evolution, and changing corporate technology models.

Network Ecosystem: The show integrates tightly with a companion newsletter/blog (FastForward.blog) and benefits from PPN's broader digital syndication and video infrastructure.

Whether you're an entrepreneur, tech professional, investor, or simply curious about where technology is heading next, *FastForward on PPN* provides the thoughtful analysis and expert viewpoints you need to understand the big picture and stay ahead.

Subscribe now and join us on this journey to explore the innovations that are defining tomorrow. *FastForward on PPN* is a proud production of Playaz Productions Network.

Here is the complete, verbatim transcription of the *FastForward on PPN* episode featuring guest Box CEO Aaron Levie, as hosted by Ron Miller:

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**Ron Miller:** I first met Aaron Levie in 2010 at AIIM, a content management industry conference, when I was running the *Fierce Content Management* newsletter. We met in a random side room off the floor because Box didn't even have a booth at that point. And on that same exhibitor floor, companies were literally selling cardboard boxes to store paper files. But there were also software companies there—amongst them giants like Microsoft, IBM, and EMC. And here was this guy trying to convince enterprise buyers to take a chance on his startup, convincing them to move their valuable files to this thing called the cloud.

16 years later, Box is a multi-million dollar public company, and Aaron is leading another major transformation inside his organization—this one involving AI, what else? And investors look skeptically on at SaaS companies like his.

I'm Ron Miller, and this is *FastForward on PPN*.

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**Ron Miller:** I like that intro, gets the energy going! Welcome, Aaron. Aaron is CEO of Box, as though he needs any introduction. But in case you don't know who he is, um, I'm going to start off, Aaron, with a little trip down memory lane, if that's okay. Um, you know, we all know by now that you started Box in a dorm room in 2005 and that you started as a consumer online storage before shifting to enterprise a few years in. And you know, I can tell you that having been covering all of this way back then, there were a lot of people in IT who were just pretty hostile towards the whole cloud idea in those early days. How challenging were those early conversations with enterprise buyers, and how prepared were you as an executive to answer their concerns about SaaS and cloud computing?

**Aaron Levie:** Um, well, uh, yeah, I would say—I would say they were very challenging in the early days. Um, and we were—we were fortunately probably decently prepared. It doesn't mean it convinced that many people. So, so we had our—we had our answers. Um, uh, not—not everybody went with them, and, uh, and there was a tough period, um, between kind of like '07, '08 to maybe 2012, 13, where, where, you know, we—we absolutely would lose a number of deals because the customer wanted the system on-prem and we—we were so religiously committed to the cloud, um, that we—we were—we basically said we are never going to veer off this architecture.

And, uh, and the reason for it was we wanted all of the leverage gains of being in a—in a single cloud platform so we could just ship new features constantly. Customers wouldn't be on kind of forked or legacy versions of the codebase. And, um, and what's interesting is it—is it started to pay off in multiple ways. It paid off when mobile arrived because it meant that instantly you could just pull up your iPhone and you could access your files. And if your data was on-prem, you often couldn't do that except for some weird kind of VPN workflows.

Uh, it meant that we could integrate much more seamlessly with the SaaS ecosystem that was rapidly emerging, like products like Slack and Salesforce and whatnot. And then now is the kind of ultimate moment where it's paid off, which is if you want agents to be able to work with your data securely, they obviously need really easy APIs to access that data. They're not going to like go and, you know, traverse a VPN and network system to do that. And so, so kind of we're finally at the point where that architecture decision has paid off, uh, at its most—in its most significant and important way. And, uh, and that's for a world of AI. So we're incredibly excited for this moment, and it's—it's a great example of staying very committed and true to your vision, um, despite losing lots of deals in the early years and not—not necessarily convincing every customer to move to our—our approach.

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**Ron Miller:** But it seemed like the whole industry moved towards your approach. I mean, that was what—that was what happened ultimately, right? Even by the following year, everybody was talking cloud. You know this, by 2011, even though there were still IT people who were saying things like, "Over my dead body will I go."

**Aaron Levie:** Yeah, I—I mean, I—I don't—I don't know if it felt like within a year that everything had shifted. But, but I would say, you know, firmly by 2026, it's pretty clear that the cloud, you know, won. Um, I felt like it took another 10 years, but, uh... and, and you know, there's an interesting analogy here that actually will be—um, that—that will be related to AI, which is, which is really kind of how long these transitions take. Um, and even though it was obvious to us in 2006 and seven that this would—this had to be the architecture of the future—there was no other way that—that—that you would want to deploy it at scale—even though that was super apparent and obvious to us, it still took 20 years for that to really roll out across, uh, every—every organization. So, so I think we're in for, you know, some amount of—of, uh, diffusion and, and kind of rate of diffusion where it will take some time, uh, for this—for AI to hit most organizations, uh, and, uh, and we have to, you know, be prepared for what that looks like.

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**Ron Miller:** You know, and I think—I think you had a lot to do with the success because, um, you know, you weren't just selling Box, you were evangelizing SaaS. And I think that was an important distinction. And I see you doing the same thing today with AI. You're, you're, you know, all over social media, you come on, uh, you know, podcasts like this one, and you're talking about the—the value of—of AI, and you're trying to get customers to understand that. But there are a lot of companies who are struggling with that, and we keep seeing study after study saying that people are having trouble scaling it. And I'm wondering, like, how do you reconcile this honest idea that you believe in the future of AI, but it may not be where people want it to be right now?

**Aaron Levie:** Yeah, I—I would actually say that that is, um—uh, I don't really have to reconcile that. That actually is our business model. Uh, so our—our business model has always been, in some way, shape, or form, take incredible breakthroughs that we get to see in the tech industry and figure out how to translate them into real-world organizations within a particular domain of—of working with your unstructured data. We obviously don't do this for—for every kind of category of work, but when it comes to how do you work with your enterprise information, like your contracts and your marketing assets and your financial documents, uh, our job has always been to bridge breakthrough technology with the real world. And AI is no exception.

So, so I—I think you can have this duality, which is, you know, in some areas of work—agentic coding is a great example, uh, or in some types of organizations like AI-native startups—you can see these amazing breakthroughs happen with AI. Conversely, when you go to the, let's say real world, and you go and you say, "Hey, let's just look at a marketing team at a large CPG company," you might not be seeing those same gains today because their systems look very different from that small startup, their practices look very different from what a coder, you know, ends up doing, and these are just totally different ways of—of working.

And so it's going to take time for AI to actually diffuse into those—those, you know, parts of organizations and, and those ecosystems. Um, and this is why I'm, you know, I—I really suggest that people have some degree of patience where this is going to take many, many years, and it's going to take a lot of change management to actually go and—and make this happen.

So for me, the reconciliation is just, yes, it's—we—we live in this kind of crazy dual world, which is, you know, you can right now go and talk to, you know, a super intelligence on your phone and get, you know, legal advice and medical advice, and—and you could generate code, and that's—that's this amazing thing that—that makes us instantly more productive and effective as individuals. And then when you go to an enterprise and you say, "How do I get that same level of intelligence attached to my IT systems, and that accesses my data, and can be incorporated into my workflows?" That's a massive project and undertaking that companies are really only now just starting. This is not the same as when you're just like asking a question and chatting with an AI system. This is: How do you have an agent that—that has some—some kind of goal, and it can generate a plan, and it can work inside of your environment, and it can execute work and incorporate into a broader workflow? That is a much more both meaningful type of problem to solve as an enterprise, but it's a lot harder. And, uh, and that's why we're still in these very early stages. So that—that's kind of how I look at the data right now, and, and, uh, and how I make sense of it because we're seeing it every day with our customers. Like, they are super advanced in some areas, like let's say AI coding, and super early in other areas, like workflow automation across the—the general knowledge worker base.

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**Ron Miller:** Yeah, I mean, I think that's—that's really tough. I can tell you from my experience just trying to build, um, you know, agents on my own, that, you know, this—there seems to be like this continual tweaking cycle that goes on endlessly where you try and communicate to the—to the, uh, AI what you want, yes, it delivers you something, and you're like, "Okay, it's not quite what I wanted."

**Aaron Levie:** Well, but this is—this is actually why I'm very optimistic about people, by the way, in the—in the workflow. Um, AI is like... I have almost never generated, uh, something of important, you know, of—of any kind of important value where, in a—in a single shot, it generated it properly. Um, almost every single time, I still had to do two, three, five extra prompts or go in and modify things manually myself for—for producing real value. So if—if that sort of sustains as a—as a, you know, kind of a component of—of how work is going to look, then what's going to happen is in, you know, maybe—maybe two years ago, AI could do 20% of our workflow, and—and, you know, we got very minimal kind of productivity gain as a result of that 10 or 20%. Maybe now it can do 40 or 50 or 60% of our workflow. Maybe in the future, it'll do 70 or 80 or 90% of that workflow.

But here's the thing: What ends up happening is as that occurs—so first of all, you still need a human in the loop that entire time, because it's just... the human in the loop sort of gets to move up a layer of abstraction because now you're only doing the final 10%. So, so in—in none of that kind of process could you fully eliminate the human oversight.

But here's what's happen—here—here's what ends up happening that—that's super interesting to think about: is—is what happens next is we actually then raise the bar of—of what the work output we want to—what we want to have in that—in that particular work. And once we raise that bar, that 90% now drops back down to about 40 or 50%, and we've just jumped another sort of step function because what we do is we expand the—we—we sort of move the goalposts of what we want AI to automate. It's no longer sufficient to—to just have read the contract and provided me a summary accurately. It's no longer sufficient to generate a, uh, you know, a file or two of code. We end up raising the bar of what—what kinds of projects and tasks we want to take on, and then, again, human in the loop then is necessary for that next sort of remainder part to go and review the work. So, so I think we have to actually get used to this idea that we will always be in the loop. Um, AI will almost never generate something perfectly because what's—what will happen is whatever we used to think was perfect, we—we will expand the definition of in the future to the point where it's imperfect again, and we are back to having, you know, additional work that we end up having to have oversight on.

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**Ron Miller:** Right. That's a really interesting point, that you can, uh, you know, you can increase the amount of work that you can do because this—you know, every business has this list of things that they want to do, but budgetary constraints prevent them from doing it and, and you know, personal constraints prevent them from doing it, and they kind of fall off the list. If you can start to push those things into the "done," you know, column, suddenly, you know, you're going to be a lot more productive.

But, you know, one—one thing I want to talk about a little bit because you have this very optimistic view of—of AI, but what we're hearing from, you know, some of the—the heads of the AI labs and, and you know, uh, people who are in charge of AI at companies like Microsoft is that it's going to be the end of white-collar jobs. Um, what you're saying is that, you know, there's always going to be a human in the loop. You know, that kind of negativity is leading to, you know, students booing the mention of AI at a, you know, commencement this week. Um, so—so how do you kind of think about what those people are saying and like this negative, um, you know, energy that's being put out there about AI when there's also this positive side, and nobody's talking about that?

**Aaron Levie:** Yeah, I mean, um, I think we're shooting ourselves in the foot in the tech industry. Um, you know, if you—if you actually do believe that—that we're in a race with China, um, the tech industry is—is almost single-handedly doing everything possible to lose that race. Um, because if we end up scaring people about the power and the value of AI, absolutely, people aren't going to go for it. Uh, they—they will totally, you know, vote this thing out of existence if—if it's not something that feels like it's helping us and making us more productive and making work actually more interesting as opposed to less interesting.

So, so I—I, um, uh, I—I don't subscribe to the rhetoric that—that you just referred to, um, uh, from—from some of the—the companies out there, partly because I'm not even seeing it. Um, uh, I—you know, the—the work that we have put most AI on, I'm more excited about hiring more people for those types of jobs because AI is making them even more productive, which makes it even more interesting to go and be able to get higher velocity and output of that work. So, so I'm—I'm just—I'm—I'm seeing the other side of this, which is, if I can automate something in the organization and make an—an actual human more productive—assuming it's an area where there's still more demand to do that type of work, and, and in most of our organizations, we—we still have more demand than we can kind of meet—um, then actually, I want to hire more people to—to do that.

And I think that's the part that—that a lot of the—the labs and some of the maybe more doomer mindset kind of misses is—is this idea that—that we're just going to take, uh, the current fixed amount of work that we do and AI is just going to eat into that fixed amount of work, and then obviously labor is that remainder that goes away. But what I tend to see is that the work just expands, you know, based on the kind of tool capabilities that we have. And I've seen no evidence at—at any kind of macro scale where that—that doesn't happen.

Um, I have—I have friends that are hiring engineers for the first time ever because—because now AI is letting them go and develop software in a way that they wouldn't have before. So I think what you're going to see is just a shift of—of talent, maybe from some organizations that aren't, you know, kind of approaching AI in that way to the kind of organizations where—where there is growth and there is demand.

One more kind of small note. You know, we—we, uh, tech—tech is sort of obviously the epicenter of this conversation because AI coding is sort of the most powerful, you know, kind of form of AI at the moment. And, and what's interesting is we kind of myopically assume in—in the tech industry that tech jobs equal tech industry, and—and like, and, and that we are the—we—we are the—the house, you know, we—we are the—the home of all the tech jobs.

**Ron Miller:** Mhm.

**Aaron Levie:** But what we don't realize is that like every pharma company on the planet would love to—to have access to amazing computer scientists that can go and automate drug discovery and, and go work in life sciences for—for clinical, you know, trial automation. Or healthcare providers would be able to love to streamline their workflows to be able to get more throughput in their healthcare system. Or manufacturing companies and industrial equipment makers are leaning into AI because they know that, that, you know, industrial, um, you know, processes in the future are going to be AI-enabled. Well, guess what? That's going to produce a ton of jobs for engineers that, that, you know, are going to be working on incredibly important, very mission-critical work in—in the future.

And so, yes, you might not go and work on a mobile game app like you would have before—and there's nothing wrong with mobile games, it's just an example of the kind of tech job that people kind of think is like classically tech—and maybe that same engineer is now going to go work at John Deere or Caterpillar or Eli Lilly or, or Pfizer, and they're going to be working on incredibly important work. It's just a very different domain of—of the applied use of that technology. And so, so this is again where the doomers maybe don't understand that actually, no, there's actually a lot of demand for this work elsewhere. There's just going to be a shifting of how do you get access to that talent pool in those organizations, and that's—that's the kind of change that we're about to embark on.

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**Ron Miller:** So speaking of engineering jobs, you—you've been—you've been writing a lot about, um, the forward-deployed engineering, um, movement lately, and you see it as positive. I—I wrote about it, and I wrote about it as negative because my point of view was that it adds a lot of cost because these—these engineers are highly trained, they're basically like consultants. And if you look at like the—uh, AI or the OpenAI deployment company, it's basically OpenAI trying to create, you know, like a mini Accenture or, or Deloitte, go out into the—these large enterprises and help them, you know, implement AI at scale because they can't do it themselves. It's kind of a tacit admission, in my view, that whatever is being delivered isn't—isn't working. Um, but you—you see it as positive, and I'm wondering why.

**Aaron Levie:** Yeah, uh, I—I, yeah, I—I mean, I have—I have sort of the other—other kind of take on that. I—I think that now, maybe—maybe from where an AI sort of super-accelerationist would have thought five years ago, maybe it's a—maybe this is a—a disappointing outcome where you thought the agent would enter the enterprise and like, automate everything, and it was just going to all work amazingly well. But for anybody practical in the enterprise, you just instantly know, like, these are really, really difficult systems to—to implement securely, to get them the right data that they need to be able to—to work, to do the change management of the workflow that the per—that the people are involved in. All of those things require a significant amount of change both in technical implementation and kind of human and process change management.

So I don't see that as a failure by any means. I see that as a natural occurrence in organizations, which is, which is: Why do organizations ever hire a consultant or a professional services firm? It's actually a really straightforward concept, and, and, uh, every—um, every, uh, uh, you know, sort of core business, you know, management science sort of piece of literature that studies this, you know, can explain it very easily, which is: Firms get really, really good at—at some core competence.

And if you're... you don't sit around all day long as—as a mid-sized life sciences company thinking about the core competence of doing IT change management for AI. You just don't think about it. What you're thinking about all day long is: How do I go and develop and design drugs? And so all of a sudden, boom, AI shows up, and you're like, "Oh my gosh, how do I go implement this thing to actually go and turbocharge my workflows?"

You have a choice. Do you want to implement that yourself and learn all of the—the ways of doing that, including many of the mistakes that—that—that are out there? Or do you, theoretically—and I'm—I'm—I'm presupposing that you're going with a good vendor—do you want to work with somebody that has seen it 20 times or 50 times, and they can kind of cross-check the way that they've done it across 10 other customers, and they can bring you best practices to go and implement this?

So it's actually just like—it's just like capitalism is—is like doing exactly what it should do. That life sciences company is not in the business of doing IT automation. There are IT companies and services providers that are in that business, and they can bring those best practices to the life sciences company. Now, at a certain scale, some companies build this competence internally, but most organizations just don't have the capacity for doing all of this work themselves. So, so I would say, if anything, it's a—it's a bullish sign that our industry in tech and AI is actually growing up, realizing the—the real path forward for how these systems are going to be implemented.

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**Ron Miller:** I mean, I think anybody that uses this—and I—I—I use it, you know, these tools a lot because I want to figure out how they work. And if I'm going to report on them, I have to understand them. And I find them incredibly frustrating. Um, and—and I—I'm sure that, you know, people—and not that I'm super technical—but people who are less comfortable with technology than I am must, you know, really have a lot of—a lot of trouble with it. And, you know, I don't... I think it's like a super big organizational problem. If you go back to, uh, you know, uh, digital transformation 10 years ago when everybody was, you know, looking at companies like yours and saying, like, "How do we digitally transform our systems?"—it's an even bigger transformation than that, isn't it? And, and it's like companies have never been good at those big transformations.

**Aaron Levie:** Yeah, I think it's, um, it's much bigger than any digital transformation because when you think about digital transformation, you know, in general, the thing that went digital was often the customer experience. So the customer experience went from, you know, an analog, maybe in-person-based process to, you know, e-commerce or mobile apps, or, you know, kind of better real-time feedback and, and analytics. That was kind of digital transformation in the enterprise. That only really affects, you know, 10, 20% of the organization. Maybe it affected the product team, maybe it affected the go-to-market team.

AI affects every single, you know, crevice of the organization. It affects every workflow in the company. And so this is a—this is a transformation far larger than, uh, than the scale of, um, of digital transformation or cloud transformation or even kind of moving companies to the internet. Um, and it's going to... This is actually why it's actually very easy to be bullish on Accenture and McKinsey and the OpenAI deployment company, because actually, there's so much work ahead for companies on going through this—this transformation journey.

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**Ron Miller:** So, I mean, we're already at the bottom of the hour, so I had so much more I wanted to talk to you about, but I'm going to ask you a personal question to close things out.

**Aaron Levie:** Yeah.

**Ron Miller:** Um, you've been at this for 21 years. You were—you were a kid in a dorm room when you came up with the idea. Um, same company, same mission, with, you know, things changing over the years. Um, so—so how do you stay passionate and engaged, and you obviously are, and do you ever long for a challenge outside of Box? Because that's been your whole professional life, your whole adult life.

**Aaron Levie:** Um, so, uh, I think the reason why I get so excited is because I'm just a—I'm a—I'm a kind of technology fanatic. Uh, I've—I've loved technology since I was 13, maybe, is when I got, you know, my first computer. And, and, um, you know, we had a computer in the house, but 13, I had my own computer. And, and this was like a very big, you know, moment to—to be able to build websites and build internet products, and I had friends that were—were—were engineers. And so, so, um—uh, so I love technology. I'd buy every new, you know, tool that comes out and use every tool, and buy every VR headset and try and quickly see, do I like it or not, and, and, you know, what thing is exciting and not. So I just love technology, and AI is, um, is another amazing breakthrough that I'm always wowed by every day. Um, and, uh, and so that—that's what keeps me extremely excited and, and kind of, you know, really on top of this.

As it relates to Box, I think what's interesting is that everything happening in AI right now, I think actually reinforces and makes the value proposition of Box even more, um, maybe, you know, substantial and, and important because what agents really need is they need access to your most important data if they're going to be useful. So, so, you know, for right now, I mean, we—we see ourselves as becoming directly in the—in the very center of of what AI is going to need to be productive in the enterprise. So this is kind of this incredible moment where it combines 20 years of—of building some degree of expertise in the space with, uh, with now a—a complete technological revolution that needs exactly the thing that we've built. So, uh, so I—I have not found an opportunity to be bored, uh, in the past, uh, you know, number of years on this.

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**Ron Miller:** Well, I'm—I'm sorry we didn't get to geek out on headless content management or talk about token pricing models or any of that—that kind of stuff because things just went by too fast. But I want to thank you, Aaron Levie, for joining me. I want to thank everybody who tuned in for—for watching. If you like what you heard, um, you know, please subscribe to the *FastForward* newsletter. You'll see the QR code on the outro. Thanks again to everyone. You've been listening to *FastForward on PPN*.

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