Becoming an AI-First Enterprise with Box

What does it truly mean to become an AI-first enterprise? 🤔

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Discover the answer with our Day 3 opening keynote at Six Five Summit: AI Unleashed! We are privileged to feature Aaron Levie, CEO and Co-Founder at Box, as he joins hosts Patrick Moorhead and Daniel Newman for a discussion on leading the charge in generative AI adoption, and how Box is integrating intelligent agents directly into content workflows to revolutionize team collaboration and decision-making processes!

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Key takeaways include:

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🔹Defining the AI-First Enterprise: Explore the foundational principles and critical importance of truly building an AI-first organization, going beyond mere adoption to deep integration.

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🔹Generative AI Revolutionizing Content Workflows: Hear how generative AI is transforming content creation, management, and collaboration directly within Box, and their strategies for streamlining processes and boosting efficiency.

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🔹The Power of AI & Unstructured Data Converge: Understand the crucial intersection of artificial intelligence and unstructured data, and how this synergy is dramatically enhancing enterprise productivity and delivering unparalleled insights.

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🔹Leading the Charge in AI Adoption: Gain exclusive insights into how Box is spearheading the adoption of generative AI, integrating intelligent agents to redefine the future of work.

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Learn more at Box.

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Watch the full video at Six Five Media, and be sure to subscribe to our YouTube channel, so you never miss an episode.

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Or listen to the audio here:

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Patrick Moorhead: The Six Five Summit is back. We are in our sixth year and, unsurprisingly, we're talking about AI. And this year it's really about making AI real for enterprises. Real ROI and real payback. Daniel, how you doing my friend?

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Daniel Newman: It is good to be here. Six years ago when we started this thing, we did talk about other stuff. It changed a lot in the last couple of years. I think it was maybe even the first year, there wasn't even a track for AI. By the second year there was a track. By the third year it was a little bit of a theme. And about, I don't know, the chat GPT moment came and it just became the summit. It's like you go to companies, they're like, "Hey, we want you to talk about applications." They're like, "Can we talk about AI?" You're like, "Well, you don't even have AI." It's just become one of those things, but the washing is kind of over now. And to your point, it's all about getting real and people actually getting value from these investments and are we pulling the hype out?

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Patrick Moorhead: Yeah. And one company, who not only is using AI internally, but also making AI real for enterprises is Box. And I'd like to introduce Aaron Levie, co-founder and CEO of Box.

Welcome to the show, Aaron. First timer, we really appreciate you coming on.

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Aaron Levie: Thanks for having me. Good to be here.

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Daniel Newman: Thanks, Aaron-

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Patrick Moorhead: Yeah. Seeing you on analyst calls and watching you on other podcasts, it's been... You like to mix it up. I like that. It's good.

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Aaron Levie: Tech is moving quite quickly. Lots going on. So there's always some drama to discuss.

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Patrick Moorhead: Exactly.

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Daniel Newman: It's a funny little walk down memory lane, Aaron, because I listened to you recently on with the other besties. Pat and I have each other as besties. We're hundreds of episodes further along than All-In.

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Aaron Levie: Oh, wow. Oh, wow.

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Daniel Newman: So yeah, I won't say they copied us, but maybe they copied. But we're manifesting maybe being top 10 since Jason always tries to manifest being number one, or whatever it is he does. But Pat and I actually did make the maiden pilgrimage to Miami and we went to the first All-In Summit.

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Aaron Levie: Oh, wow.

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Daniel Newman: So before it was cool, we actually went down there and…

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Aaron Levie: You were the hardcore OGs.

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Daniel Newman: We are. But I haven't been since, to be fair. We didn't go back, but in the beginning it was very cool. It's been very cool and we've met these guys over the time, but you were great. Really enjoyed hearing you on that show.

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Aaron Levie: Thanks.

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Daniel Newman: Glad you were able to come over here. We get a little bit more practical on the tech stuff here. We want to talk about your company a bit more. So let's just start off with this whole AI-first enterprise. We're hearing a lot about this. We're hearing companies coming out and saying, "We're going all in on AI." We hear other companies coming in, saying, "We're getting rid of all our people. We're just going to be AI." You seem to have a vision for what an AI-first enterprise is. Talk a little bit about what that is and the tipping point that took you down this path.

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Aaron Levie: Yeah. So I think you laid out a pretty good continuum there. There are some companies that are probably fully resisting heads in the sand from what the impact of AI is going to be. And then on the other end there are companies saying, "Okay, we're going to only use AI and we're going to do our best to just basically automate out everything." We're probably squarely in the middle, obviously leaning more toward the acceleration toward the future side, but with a deep belief that AI is really a technology that augments people and will help us just have way more capability both as individuals and as organizations. So we laid out our AI-first principles and it's been kind of the past year that we've framed them and then recently we've been more public about it. The core premise is, we want to use AI to do more as an organization. That means more innovation, better features for customers, being more responsive to customers, solving their problems faster, hopefully even proactively being able to better sell to our customers so we know exactly the next set of capabilities that they likely would want, be able to deliver better marketing campaigns, and those that are global by nature. 

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So when you think about that list of attributes, that really is about doing more with AI, not doing the same amount that we already do, at a lower cost. So I'd say as a very clear line in the sand that I think most customers or companies will have to deal with is, "Is your metric and KPI for its success using AI to do your current operations at a lower cost, or just vastly more as an organization possibly at the same cost or even more because of the flywheel of you get AI productivity gains that get reinvested back into the business?"So that's the core premise of our principles. And then there's a bunch of sub elements of that. So what types of platforms do we standardize on as an organization? In areas where we do automate some degree of work, let's say frontline customer support tickets, where we can more efficiently solve those with AI, how do we reinvest those savings back into the business to be able to drive better customer success outcomes? 

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So our approach is really about how do we exploit AI to just do more as an organization and be even more competitive? That's our ultimate takeaway and what we're driving with. Again, we're seeing a variety of different options that enterprises around the world are deploying. But our firm stance is, use AI, move as quickly as possible. We want every single person in the organization to get trained up on AI. We want to have AI-first and AI-native ways of working across every job function. So this is even doing things like changing how we hire and our interview process and the kind of skill set and capabilities that we look for employees, but that's a little bit of a vignette on what we're up to.

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Patrick Moorhead: No. I think it's important. I mean, listen, customer zero has been a real phenomena forever, especially when you're trying to sell other customers and convince them that a technology is the right one to adopt. And as I've seen from mini computer to client-server, client-server, social, local, mobile cloud, a lot of enterprises, they don't want to be first. So at a minimum, the tech company, who's trying to propose this stuff, has to be using it themselves. And I'm still dumbfounded. And as industry analysts, we're like professional event attenders. We're like circus clowns. We go from event to event and it still astonishes me when senior leadership gets up and they don't talk about the ROI that they're experiencing from their own AI. So, it's super important.

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Also, competitively, we forget internet 1.0 wiped out a cadre of multiple companies and whether that's travel agencies, classic stockbrokers, big-box retailers, and then we went to music and movie downloads and that churn, I think this generative AI and agentic is going to wipe out different companies as well. I get a lot of questions and it's going to be the sea of cubes that are still there that wasn't transformed. Accounts of payable, accounts receivable, and a lot of the, quite frankly, the backend work that really hadn't been touched because if you look at traditional backend SaaS, it's really been... I'm going to... sure, a little bit of BPO. I get it. Okay. But let's take this process that was paper and facts and we would shuttle it around on a cart. And then we digitized it, digital transformation. But this one's very different.

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And the data has changed as well. I mean, for 50 years it's always been from a relational database, what can I stick into DB2, or Oracle, or an SAP ERP system, a giant intelligence spreadsheet. But really the bulk of the information that we haven't lit up yet are the documents, are the emails, videos, and even customer service call recordings and even videos.

Talk to me about why this is such an asset and what can people do in this new AI era?

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Aaron Levie: Yeah. So you just hit the nail in the head. So if you think about, there's... Obviously, this is quite simplistic, but let's just say at a minimum there's two major data types in the enterprise. There's structured data that goes into a database. You just enumerated a bunch of those data types. So if I want to look up HR information, it's in a database. If I want to look up the amount of pipeline we have for a customer, that's in a CRM system. If I want to look up certain ID records of our customer base, that's in some database. So all structured data, queryable, synthesizable, calculable, that's structured data we've had since the relational database world years.

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So that data, as you just noted, while it's absolutely some of the most important data in the enterprise, it's only about 10% of our corporate data. When we analyze how much information is in an organization, it's about 10% of the data. 90% of the data is all this unstructured stuff. And what's incredible is, if you look at any graph of the growth of unstructured versus structured, the unstructured data is just truly exploding exponentially. And the reasons are, everything we're doing in a digital world is producing some degree of unstructured data as an output of that thing. Every video call we're on that you record, massive amounts of unstructured data. Every video that we take with our phones on a construction site or doing any kind of maintenance call, that's unstructured data. Every thread in a Slack channel is unstructured. Every document that we collaborate on, every invoice, every CAD file, every design asset, all unstructured.

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So 90% of our data is unstructured. It's growing exponentially, but it's had one sort of less desirable property, which is that computers, when left to themselves, can't really do that much with this information because until a person cracks open the file and a person looks at the document and a person watches the video and a person looks at the design image, we don't get that much value out of that information. So for Box, we've been in the business of helping customers govern that data, manage it, ensure they can collaborate securely around it, enable the permissions and access controls around that data. But the life of that content is actually fairly brief from a value creation standpoint once it's in our platform. It's when you're looking at it, when you're sharing it, when you're collaborating around it. And then it gets stored and maybe you look at that file again every couple years if you can go and find it. So that means that the vast majority of our data, the vast majority of the time is not working for us. We can't ask it questions and we can't glean insights from that data that we can repurpose in the future in a very natural and easy way. So for us, this is where the GenAI breakthrough kind of happened. And we got instant religion on this from a ChatGPT standpoint.

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Actually, maybe similar to as you introed the conference lineage on this, we had a big AI effort about seven or eight years ago. And unfortunately, it sort of petered out because what we saw was that every single AI use case we had, we had to have a different model. We had to do a different training run for a different data type and it was just never going to scale. So the large language model phenomenon actually ended up being this massive breakthrough because these LLMs with hundreds of billions of parameters can basically handle now any data type. An invoice, a contract, a document, a screenplay, a memo, every type of data that an enterprise uses, we've basically trained these models to be able to work well with.

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So it's a massive breakthrough because now for the first time ever, when you connect this enterprise content to AI, you can start to use the AI as effectively a reasoning engine for having the computer interact with all of this information. So you can ask any type of question of this data. You can use AI to read the documents, or listen to the audio, or watch the video to extract the structured metadata from that content. And then as a result of understanding what's inside the content, we can now automate any workflow. So these are the three big focus areas we have, which is talk to your data, extract the most important intelligence from it, and then automate any workflow around it. And for us, that's quite literally a 10 to a 100X increase in the number of use cases that we can go solve for because now when you put your content into Box, it's actually the moment where the value creation starts as opposed to the tail end of the value creation on that data. And it's just a complete inversion of the value proposition that we can offer our customers.

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Daniel Newman: Yeah. It's really interesting because we hear Jensen from NVIDIA talk a lot about this next era of compute architecture, that basically the GPU era is going to completely reinvent the way infrastructure looks. And what you're saying is the interesting point is, the old compute infrastructure really only worked well with structured data. The apps only looked at data rows and tables. And now, it can look at everything. And it's being kind of reset, redesigned where all this data can be instantaneously accessed. That's the problem we're trying to solve for because, to your point... I mean, I've heard the data point, 99% of enterprise data has not yet touched data. So while we've already scraped the entire world internet for consumer use cases in enterprise, we're actually still super early. But inside solutions like yours, inside tools and technology like what Box does, a lot of that sits there, where if you could actually query it, you could ask it a question. And of course, in the future it might be the way we ask an LLM something. It might be the way we walk around with our RayBan glasses and just talk to the environment and be able to tap all that data and start to interact. That's really exciting.

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The other thing that's super exciting is what we're hearing about agents. I liked your question about, are you going to 10X or 1/10th, meaning do you want to do the same amount of work with 1/10th the amount of expense, or do you want a 10X and maybe the same amount of expense? Agents seem to be the way. They work 24/7. They don't have weekends. They don't ask for vacations. But at the same time, they work alongside humans. At least in the beginning, I think there's a very interdependent relationship. Over time, I think it will be more autonomous. I think we all see that happening. But you have to be thinking about how it's transforming productivity models, Aaron. How is it actually changing the way a business can derive value, derive productivity? Talk a little bit about that because you started alluding to a little bit GenAI, but now when agents come into play, how do the use cases evolve? And what are you seeing, your customer base, in terms of their adoption and utilization of AI?

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Aaron Levie: Yeah. So we almost have to rewire our understanding of AI, which is amazing because we're only two and a half years into literally this modern movement in the first place. But it was a little bit of a mistake at first with AI to think about it as this sort of chatbot interaction. That was just the form factor that I think illuminated the potential of what AI could finally do. And the right way to think about AI is now, to your point, on agents. Really any ability to have an AI system that can go off and do work for you. And that work could be as simple as just answer a question for me. So today's kind of ChatGPT experience, but it could be as advanced and as comprehensive as an agent that goes off and does multiple months worth of work in a matter of minutes or hours across any number of systems and any amount of data.

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So if you just think about that, any data in your organization, any type of work using any system in the organization for any amount of time, all of those variables now become possible based on the type of agentic experiences and architectures that we're delivering and that we're starting to see in the market. So what does that mean? I mean, that's a fundamental transformation of enterprise productivity because now as an employer, as a worker, I can start to think about what type of work do I want to farm out to an agent that is discreet enough, where I think I can review its output and I have some ability to comprehend what it came up with, but something that would otherwise have taken me hours, or days, or weeks to go and execute. 

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So I'm getting that much compression of efficiency and time when that agent goes and does that work for me. It's going to take some time for every organization, or every team, or every job function to start to understand what those use cases are, but they're becoming more and more obvious as time goes on. So if you're in an organization and somebody says, "Let's go research that market opportunity", and if the answer is not to come back in an hour with that research as opposed to a week or a month, which is what the answer would've been two years ago, then that probably means you're not being AI-first.

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 If somebody says, "Let's go write a strategy document for a new product", and that takes more than a day, then probably you're not fully exploiting the power of agents. If somebody says, "Hey, I want to update our SDK from one version to another version because there's a library change in React or Python", and the answer is not that that'll be done by the end of the day, and instead it's two or three weeks, then you're probably not using AI agents for coding. So this is where, over the coming one, two, three, five years, this is the ripple that will happen throughout organizations. And the impact is that we will collectively be able to just get way more done. And the very optimistic thing is that we will get to work on the things that are absolutely much more value creating and much more interesting to do because it's just not that interesting at the end of the day to go update a Python version in some SDK. That is not the work that anybody decided to become a pure programmer to go and work on, and agents will be the ones executing those types of tasks in the future.

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Daniel Newman: Rapid question, because I know Pat's got a better one. But I do want to ask you, you just said something about nobody became a programmer, what is the Aaron Levie belief on all the juniors at Stanford right now that are in computer science and engineering that are hearing the rumblings that there will be no, or very little, or a lot less need for programmers in the future? I'm just curious what your quick take is on that.

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Aaron Levie: I'm still very bullish on programming. I think, first of all, the leverage you now get with AI is just enormous. So, if anything, it's probably the best time in history to be an engineer because of the impact that you can have, where one person can now do the coding of two, or five, or 10 people. And you can work on the much more interesting parts of problems. I think it really pays to have a deep understanding of the core principles of engineering. I don't think any of that goes away or becomes less important in an era of AI. Martin Casado had a great tweet about a month ago, which is, as an engineer, you always want to understand the layer of abstraction below you. So if you're building software in the cloud, you really do want to understand the cloud infrastructure and what it's capable of, or you're less effective as an engineer. 

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So even as a vibe coder, if you don't understand what's happening in that system, then you're going to be less effective. You're going to use the tool in a less impactful way. So I do think that what's going to happen is, you're going to see that the barrier to entry as an engineer goes down so more people can participate. That's what's so powerful about vibe coding. The impact you can have as an expert engineer goes up, and so the ceiling rises in the process. Dylan Field came up with this analogy from Figma, so that the floor lowers, the ceiling raises, more people can participate. I think we're only scratching the surface of the amount of software that the world actually needs. If you think about the amount of software that we could have for life sciences, and healthcare, and biotech, and industrials, and education, we are actually, from a societal standpoint, relatively low on the amount of software that we have, given the amount of impact software could have on our daily lives and our societal and business experiences. So I'm still very bullish on engineering. And maybe my answer will change in 10 years from now, but I'm extremely optimistic on the function.

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Patrick Moorhead: Yeah. Historically, I mean, I've heard a lot of tropes. I mean, virtualization was going to kill data center growth. And what happened is, it increased by 5X in 10 years.

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Aaron Levie: Yes. Yes.

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Patrick Moorhead: I was around for desktop publishing and everybody was saying that all the creatives are going to die. My gosh, Harvard Graphics and PowerPoint was going to kill everybody, and it didn't. Final example was iPhone was going to put all photographers and creative processors out of business, and it created more new jobs.

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Aaron Levie: Yes.

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Patrick Moorhead: There were jobs that were eliminated, but then we had social media. And-

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Aaron Levie: Yeah. Do you remember in mid-2000s, the case that IT was going away because of cloud? Do you remember this one?

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Patrick Moorhead: Yeah.

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Aaron Levie: So-

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Patrick Moorhead: Oh, and outsourcing to India too.

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Daniel Newman: Yes. I heard that the mainframe was supposed to die too [inaudible 00:22:46.

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Aaron Levie: Right. Yeah. So the thing that went viral, as viral as anything could go in 2008, or whatever the year was, was this idea that the cloud is going to render IT basically obsolete because in a world of cloud, everything comes to you for free, and you no longer have to manage anything, and you're just consuming these services. So the role of the CIO is sort of obsolete. The role of IT is obsolete. And what actually happened was the exact reverse because in a world of infinite abundance, curation matters, integration matters, making decisions on what systems do you go with and how do you architect those systems, the bar on that raises tremendously.

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 In a world where I only have five or 10 vendors to think about or choose from, it's actually not that strategic, my integration work, because there's only one way to do it. In a world where I have a million options, now all of a sudden my talent level for how do I deploy it and get use of it goes up, and necessitates actually having a strong function in that area. So I could see the exact same thing happening, where in a world of AI agents being abundant and work is done for us at a much lower cost per unit of work, the bar for curating that work goes up, the bar for making really intelligent high judgment decisions about which work to go with... If I can have an AI agent go and deploy research across 20 fields at once, now, all of a sudden, it's actually really important that I figure out which decisions to go with that those agents came up with. So creativity, judgment, human agency, these things actually start to matter a ton, which is why I am very bullish on humans as a result of AI.

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Patrick Moorhead: Yeah. Just to close the loop on this, kind of where Dan started on the question about coding and historically what we've seen, I'm going to give you an anecdotal. My son, who's 22, is an entry-level AI software engineer. And he's doing work not entry-level. He's doing it for fifth year. And he's using the heck out of these coding tools to help him do that. So it goes both ways. And Aaron, to your point on how you're hiring people on, "Hey, tell me about what AI tools..." It even goes back to the fire days, where you would even put a mastery of Word, or PowerPoint, or Excel, right?

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Aaron Levie: Yeah.

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Patrick Moorhead: But now it's going to be all these great tools out there. So Aaron, this has been a great conversation. I want to round this out and talk a little bit holistically about enterprise productivity. Today, we have roles. If you have a role, you're expected to do this set of work. You have certain KPIs and this is the work you do. How does this change in this new AI era?

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Aaron Levie: Yeah. So I do agree that the roles will evolve. So we have sort of overtime established a high degree of specialization by necessity. So very kind of Adam Smith. We want to compartmentalize the work, so we get efficiency gains by that work being done over and over again. Very clear division of labor theory there. And AI is letting us expand to a few of our adjacent functions. So the product manager can prototype their idea effectively as a front-end engineer. The designer can quickly write the spec of their idea as a product manager would have. The marketer can take on two to three or five different types of marketing roles because AI can augment their skillset. So I do think roles will begin to evolve. I think we'll see some degree of collapsing of maybe some of the hyper specialization that we've evolved to. But I see that as mostly an optimistic thing because at some point you do want to do more inside of an organization. You do want to have a greater impact. You infrequently want to be just the person that you go to when you want that little button rounded. You want to be able to have more service area that you go and cover. So I think this is actually going to be better for employees because they can actually get more done. They can touch more surface area.

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 And then what it'll mean also is this really incredible thing happens, where it's almost a leveling of access to resources. So if you look at the kind of job functions and level of specialization a 10,000 person company will have versus a 10 person company, obviously the 10,000 person company is going to have a role for every permutation of every specialization that exists. And the 10 person company just can't do that. They can't afford it, which means on some dimensions they can move faster. But on other dimensions, they're missing out on the person that can go and translate their marketing to a new region. So that means they can't enter more markets. The access to this one sort of specialized engineering skill that makes it so they can't build certain functionality, AI is the great neutralizer of those gaps because now all of a sudden that 10% company can instantly have a set of SDRs that can help them grow faster. They can instantly have a set of marketers that have a specialization in some marketing program that they want to get into. So what's going to happen is, is actually it's a fantastic time to be a 10, or 50, or 100, or 500 person company because you now have access to resources that were never affordable to you and that were never available to you previously. 

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So these are the kind of things that economists can never really quite capture because they're thinking about, okay, AI takes... Let's say AI offers a 20% efficiency gain. So that means 20% of the labor force is impacted, and now we're going to see the jobs impact of this. But guess what the economist never does? The economist doesn't say, "Wait a second. All of a sudden the millions of firms below 50 employees have resource access in a way that is unprecedented, where they have a marketing team, a sales team and engineering team at a scale that was never possible before. Those firms will now grow at an accelerated rate, which, guess what, it means they're going to hire more people. What will happen is, you're actually going to see firms expand with humans because AI makes parts of their functions more productive that allows them to go and reinvest as a result of that. So you're just going to ultimately see this just shift the labor market as a result. And our roles in the process will begin to evolve and, in many ways, just expand the kind of things that we can go into. So-

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Patrick Moorhead: Yeah. Economists don't get it because they don't actually work at companies and they work in universities.

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Aaron Levie: Right.

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Patrick Moorhead: That's my snark. I was in product management in 1995. And as a product manager in 1995, you did product management, product marketing, program management, and sometimes regional marketing, like you are responsible for everything. And now, there's a freaking group for everything. Oh, product managers, they're ex-engineers that don't want to do this anymore. Core value... It's like it just got enormous.

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Daniel Newman: It did.

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Patrick Moorhead: And I think what it gets down to is being able to... By the way, everybody's going to think they're an expert in everybody else's-

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Aaron Levie: Yeah. Oh, yeah. And it's going to cause lots of problems. I already do this today with vibe coding is, I'll say, "Hey, what do you think about this new product idea? I just prototyped it last night." So it's the CEO is sort of now playing the PM probably. And I think it's going to cause a lot of... There'll be some interesting waves that happen in organizations.

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Daniel Newman: It's actually interesting, Aaron, because the way you tell the story, it really gracefully splits the middle, meaning that there's this philosophy... You hear Dario from Anthropic, you've heard Sam Altman, all talk about the one-person billion dollar unicorns in the future. You didn't exactly say this, but then you also see these mega companies almost creating efficiencies. I call it... They're kind of pruning to grow, meaning you see the biggest companies in the world are actually cutting headcount because of AI, at least they're saying they are. But on one side you're saying there's going to be an economic boom that's going to come out of these smallest companies. Maybe it's not one person, but your point is like a company that used to be 20 people that was really packed, and even to…

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Aaron Levie: Yeah. If I can just highlight just the thought experiment about why this is so intuitive? So Facebook has been rumored that they're going to do AI for helping you optimize your ads in Facebook. So I'm a three-person startup. I have some novel widget that I sell. And two years ago, I want to go sell it online. I have to become an expert in online marketing. I have to figure out what is the right message at the right time to hit my audience. What regions, what parts of the market, what campaign assets should I go and create? So all of a sudden my innovation is capped out because I'm not good at marketing. So the growth of my organization is inherently slower until I can pick up that skill, until I can figure out who to market to. Let's say Facebook AI comes in and they basically just say, "Tell us your product. Tell us how much you want to sell it for. We will find you a customer base. We'll find you an audience that wants this thing." It doesn't take that much imagination to think about, well, when that AI agent is running these campaigns, it's going to test a thousand messages. It's going to test a thousand creatives. It's going to find the exact optimal message for the exact right part of the market for the exact regions that you're in. So if you're bullish on AI, then all of a sudden it's very easy to underwrite that that business will grow faster because of AI. And if that business grows faster, guess what? That three-person company is going to be hiring more people for sales, supply chain management, all of those other functions.

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Daniel Newman: But the revenue per headcount, which is historic... If you look across the timeline of history, you're still seeing companies exponentially grow the revenue per headcount. The companies are so much bigger. We didn't have trillion-dollar companies or multi-trillion dollar companies. You were a 100 billion dollar company, and not that many years ago you were massive.

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Aaron Levie: I know. Yeah.

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Daniel Newman: Now, it's like, oh, you might be in the top 200. So we've seen this happen. And what I'm saying is, so the productivity has created this massive... And of course, you've seen the money supply increase, the inflation, all these other things that has created such a bigger economy. So the point of a company now getting to 100 million of revenue or a couple hundred million, which used to be big, can happen a lot faster with fewer people. But it still creates more people because as the AI grows, you still will need more people. So maybe it's not about big companies adding tons and tons of jobs with lots of entrepreneurial, fast-moving startups that are going to create lots of opportunities, lots of jobs. Let's end this on an optimistic note, because AI does come with its bits of doom..

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Patrick Moorhead: Pretty optimistic to me.

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Daniel Newman: No. I'm saying that's very-

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Patrick Moorhead: Looks great.

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Daniel Newman: No. That's super optimistic. 

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Patrick Moorhead: I want Pat bot to come in. And I'm going to choose that I'm going to lay myself off. And-

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Daniel Newman: I have been waiting for the bot so that we can do this thing without actually having to do this thing. And then we're probably drinking beer somewhere together. All right. Aaron, on the way out, thanks so much for opening day one. Give us the quick sort of what's your big... How does Box evolve with AI and what are you most excited about in terms of the future of enterprising?

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Aaron Levie: Yeah. I mean, so for us, the reason we're so excited is just back to this point of think about how much data in your organization or information in your organization is unstructured. The question we ask our customers now is, what if you could ask any question of all of your information? What would change about your business? And if I could know every single aspect of every contract we've ever signed, would you know the next thing to sell to a customer? Would you know that there's a clause in a contract that you think is risky? If you could ask any question of your entire product feedback from every customer interaction, every log that you've ever had, would you build a better feature for them next? Would you be able to auto plan your product roadmap and strategy that then you could go review as people? 

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If you could onboard any person in your company and they instantly had access to all of the expertise of that organization so they don't have to go ask around over a six, or eight, or 12 week period to random interactions that they have, but instead, they can ask a question of the entire corpus of information in the organization to get smarter and be able to jump into their role even faster, what would that do to our productivity? Could we make better products? Could we launch better marketing campaigns? Could we discover new forms of life sciences? Could we make bigger blockbuster films? That's what the power of our data is and what we're building at Box. So we're incredibly excited when you bring AI to all of this unstructured data and information, what kinds of answers you can now get from that content?

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Daniel Newman: Yeah. I think the future looks really bright here. And I want to thank you so much. Very provocative, very thoughtful conversation. Congratulations on all the progress, success over at Box. Good to have you with us here at the Six Five Summit. Let's do it again sometime soon.

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Aaron Levie: Awesome. Appreciate it, guys. Take care.

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Daniel Newman: Thanks for joining us for this day three opener at the Six Five Summit. Stay connected with us on social and explore more conversations at sixfivemedia.com/summit. More coming up next.

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Disclaimer: The Six Five Summit is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we ask that you do not treat us as such.

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