$4B Box CEO Aaron Levie: The Next 3 Years Are the Best Time to Build — Here's How to Use This Window

Aaron Levie built Box into a $4 billion company used by 64% of the Fortune 500. Now he says we're at the best moment to build an AI company in 15 years — and the window is 3 years. In this episode, Marina sits down with Aaron to talk about what's actually happening inside enterprise AI, why younger founders have a surprising advantage right now, and which jobs are genuinely at risk vs. which ones AI will make more valuable. Aaron shares why AI agents still need humans at the beginning and end of every process, why the real new constraint isn't code — it's sales and marketing, and what he'd do if he were 19 and starting over in 2026. If you're building, thinking about building, or trying to figure out your next career move in an AI world — this one is for you. Topics: AI startups, entrepreneurship, future of work, enterprise AI, career advice, business building, AI agents, Aaron Levie, Box, Silicon Valley Links: 📩 Follow my Newsletter: https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=AaronLevie https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=AaronLevie 🔗 My Instagram: ⁠⁠https://www.instagram.com/siliconvalleygirl/ ⁠⁠ https://www.instagram.com/siliconvalleygirl/ 📌 My Companies & Products: ⁠⁠https://Marinamogilko.co⁠ https://Marinamogilko.co

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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• 0:31Speaker 01

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• 0:59Marina Mogilko?

  The more I play with AI agents, I do realize that I need a person at the beginning of the process and the end of the process. So I still end up having more people.

• 1:07Speaker 03

  Some of it will be different roles, but I'm very optimistic that we're going to use this technology to grow more and do more as opposed to just replace.

• 1:14Marina Mogilko?

  This is Aaron Levie, founder of Box. Welcome! Four billion dollar company. 64% of the Fortune 500 uses his platform. He says we have three years to build the next generation of AI companies.

• 1:28Speaker 03

  These market windows happen every 10, 20, 30 years in technology. The mainframe, the personal computer, the internet, the cloud, slash mobile.

• 1:36Marina Mogilko?

  If you were starting today, what would you do to find the right idea to test it and to make first money?

• 1:41Speaker 03

  My first thing would be...

• 1:44Speaker 04

  Five days ago, you also said this. We're at a unique moment in history where anyone with high level of ambition and core skills in any area can overcome a lot of historical experience requirements for a role. Can you talk more about that?

• 1:57Speaker 05

  So it's this interesting dynamic where a younger group, not necessarily in age, but maybe in skill or time in that domain. So an earlier group in that domain can have as much leverage and in many cases even... More because of their mindset differences than somebody that is like super experienced in a field now. Now, interestingly, the the advice can go in all directions because you can, you know, you could have somebody maybe too early in that field and then um and then use AI in the wrong way and get the wrong outcomes. Uh, equally, you've got somebody extremely experienced that decides to adopt the technology and then they have a total superpower. Because they understand all of the contours of of whatever they're working on, you know, whether it's writing code or doing healthcare or doing

• 2:38Speaker 05

  biotech and they will be actually uh even more capable of of letters in these tools if they have the kind of right mindset wiring. Uh, to be able to lever them, uh, leverage them. So I think that the core idea is that we're just in this amazing moment where if you're super ambitious. You want to go deep into technology. Ideally, you're technical or or becoming technicals. You can kind of really know your way around these tools. You can make up for again lots and lots of years of of skills that that you would have otherwise had to go and develop. And I think that's an incredible thing for democratizing. Um, you know, knowledge and skill sets and expertise. Uh, I often am and building things or designing things or coming up with things that I have, you know, in any other, in any other version of the world, I would never have been able to go do.

• 3:19Speaker 05

  But now I I I know just enough to be dangerous in in in those areas and and it helps me prototype. It helps me generate new ideas. It helps me kind of work with call-ins faster because I can kind of like highlight the way I'm thinking about something where normally I wouldn't be like drawing paper when I'm coming up with, but that I just say, okay, this is the rendering that we're looking to do. Uh, and so again, I think that's an incredible technology that's available to everybody for those that want to to adopt it and and lean in right now.

• 3:44Speaker 04

  What would you say to someone uh who's watching this, but they've also heard a lot of news about layoffs, about college credit graduates not getting enough jobs because they're being replaced by AI. Uh, what would you say to those people?

• 3:58Speaker 05

  Yeah, I think there is. I think we're at a moment right now. Now where and these these happen in history, you know, every couple decades or every, you know, fifty or hundred years where there's a major technology disruption or transformation. And there's a lot of questions around, okay, where does that show up? Who are the the people that get enabled by that and they can do even more? Who are the people that may get displaced by that and what do they do next? So we're in one of those periods where it's a it's a serious topic and a real conversation. Uh, I do think that some of the negative kind of commentary and messaging out of, you know, the industry or even, you know, kind of political. Uh, you know, institutions uh probably is overweighting the negative side and underweighting the positive side.

• 4:40Speaker 05

  Uh, for instance, I'll give you one example. So there's the sort of death of the software engineer topic that that comes up. And that comes up because these AI models are really, really good at code.

• 4:50Speaker 03

  Generation. So they're really good at writing code and like you, you look at them and you're like, oh, my God, that's incredible how much code it just wrote and it. It wrote that code as as well as another engineer would have. And that's all totally true. But to get that code into production, to make sure that it's secure, to have it maintain an application on an ongoing basis that doesn't get hacked. To make sure it's integrated across all your other data systems and database and infrastructure. That still requires a tremendous amount of knowledge and expertise in the field broadly of coding and in software development. And so the people that are going to be able to best leverage the technology are actually going to be software engineers. Using code agents to be able to generate vastly more, you know, code output than they would have been able to before.

• 5:29Marina Mogilko?

  But that's that's today. Do you ever think about like in five years, AI is going to be able to do that? Uh, I don't know. Look at the market, strategize around some problem that the market is not solving yet.

• 5:40Speaker 03

  Yeah.

• 5:41Marina Mogilko?

  Build a company, develop software and that's it.

• 5:43Speaker 03

  There's a lot of data signal that isn't, you know, digitized in a in a format that the agent can go with. And there's a lot of ways the agent can get confused by accessing the wrong information or doing the wrong thing that you didn't intend. And so for all of these reasons, it leaves humans in in some kind of supervisory capacity. For what these agents need to go do. And so does it need the same number of humans as we have today for the exact same workflow? No, not not usually, but are there a lot? Lot new workflows that businesses will now do because they have access to those agents. That's the sort of bet that I have. And so the way I kind of think about it is, is if you think about that five year out scenario, let me, let me paint a slightly different one. I'm a small business, you know, pre-AI, I was three people, we were selling something online.

• 6:28Speaker 03

  It was a good business. It sort of paid the salaries of these three people. But let's pretend I, I had even more ambition and I wanted to go after a bigger market. What do I do if I'm, if I'm those three people? Well, it's like, I have to hire a sales team. I have to hire a marketing team and a lot of people are just like, that's a really high barrier to entry to grow my business, you know, meaningfully. Now enter agents and you're like, oh, I want an agent to go and generate this marketing campaign. Or I want this agent to go and build a better website that delivers a better experience for my customers. Well, what happens next? If it works, now you have more customers. Now you have more supply chain issues. Now you have more customer kind of interaction challenges. You have new features they want you to build. Then all of a sudden, because you had agents go and get you some of the way to getting some of the work automated.

• 7:11Speaker 03

  My hunch is that same three person business becomes five people or becomes 10 people. Because they now have automation that's augmenting the prior constraints and limitations that they had. I think that's going to happen as much, if not more than the scenarios where you have a company that is sort of saying, okay, I have 2,000 engineers today. I'm going to have 1,500 in the future. I think it'll be a much more diffuse set of growth that happens through the economy. Some of it will be different roles, but I'm very optimistic that we're going to use this technology to grow more and do more as opposed to just replace.

• 7:42Marina Mogilko?

  And because we're doing more, we're basically consuming more, right? And solving more problems. So we're becoming a more abundant world.

• 7:47Speaker 03

  More abundant and you, you, you can't escape some ultimate constraint. There's always some constraint in the system. There's a new bottleneck that emerges. Um, I have lots of things that I've tried to automate where at the end of the automation, the very next thing you have to do is a human has to do some work. It has to follow up with the customer because I just can't fully automate that entire process. It has to, you know, update data in some system. It has to go into three meetings and, and, and kind of coordinate with some other kind of, you know, set of, set of people. It has to go to the customer site and do some implementation. So there's always constraints in the system. We just haven't identified all of the new ones that happen when agents kind of arise. There was a funny article about a week ago in the Financial Times.

• 8:32Speaker 03

  Where lawyers are now being inundated with questions from their clients. Because their clients are going to AI agents and asking questions about legal issues and they're drafting documents or whatever. But guess what, like if you were to go draft a contract right now. The very next thing I predict you would do is you'd go and send it to a lawyer and say, can you just make sure this is like gonna like, you know, hold up in court? Because in the 3% chance it's not, which is basically maybe the hit rate of like, like what an agent will get right or wrong. That's not worth the risk of of saving $500 of talking to that lawyer.

• 9:04Marina Mogilko?

  Yeah, same with like financial advisors, right? You still want to run something through a.

• 9:08Speaker 03

  I am like not that interested in automating my tax, my personal tax process. Like I am totally fine with with, you know, the the one time fee to just make sure that that is just like a clean process from from somebody that has like done this for 10 years or 20 years or 30 years. And and there are just some parts of the economy, which is naturally already where, you know, dollars tend to flow. Where you're like, I just want this done well. I want my doctor to be really good. I want my lawyer to be really good. I want my tax advisor to be really good. I want them using AI because if they could somehow like review more of my data or look at more of my patient history or look at more of my my legal history.

• 9:49Speaker 03

  That would only be a net positive, but I want that person ultimately to have some degree of accountability that's on the line. These agents have no accountability. They're not on the line. They're not on the line for anything. They're gonna disappear in in two seconds later.

• 10:01Marina Mogilko?

  And you're not gonna blame Claude or AI.

• 10:03Speaker 03

  I can't blame Claude. I can't blame Claude's weights. I can't sue Anthropic. Like, like all of those things, we have a, we have a, you know, we have, we have rules. We have laws, we have accountability for the rest of the economy. You don't in agents. And so somebody eventually needs to take on that that accountability. And this is more of like the the more like legal related issues. But there's still lots of things where you're like, you want to look at your contractor in the eye and say, you can you deliver this thing for me? Not in a, I'm gonna sue you, but. Just like I want to make sure that you can deliver on that brand campaign.

• 10:33Marina Mogilko?

  Yeah, it's one of the human brain.

• 10:35Speaker 03

  Yes.

• 10:35Marina Mogilko?

  Human brain behind. I even feel it with social media, right? I could totally generate a lot of posts with AI. But I just don't want to post AI-generated posts. I want a person who knows my taste and my tone of voice to look at them. Yes, maybe generate ideas with AI.

• 10:49Speaker 03

  There's another funny thing, this is like totally random and not, and this is probably more tractable in software over time, but there's another funny thing, which is I do think people will kind of get like, they'll probably get prompt fatigued at some point. Which is like, man, I have to always prompt this agent the same way every single time, just to make sure that it like works or whatever, like humans don't require that.

• 11:11Marina Mogilko?

  But then you can do Claude project with instructions.

• 11:13Speaker 03

  Yeah, sure, sure, sure. And some people will get really, really optimized on that. But the nuance is there are some parts of your business where you just want the person to be able to have that context. And you just want, like, there's a lot of things I could probably automate if I like put my mind to it really, really hard. But like now I am basically doing the work of like five people. And it's just now I have to hold all of that context in my head. As opposed to previously, that context was in the head of those teammates. And I, at some point, like my brain's going to explode. I'd rather those people hold on to that context. And it's sort of worth it, the value of the thing being done well is worth it and worth paying for. And so, again, I want that person to use agents. But I don't want to have to keep track of all their contacts either, because I run into a limit.

• 11:57Marina Mogilko?

  It's in your responsible for the process and it's in your brain.

• 11:59Speaker 03

  I don't want to be responsible for our company's legal review process. I don't want to be responsible for the invoice process. I don't want to be responsible for the brand creation process. But actually, that's maybe a kind of a really key point, though, that you just said, which is, which is, the more agents you deploy for yourself, you take on the role of the equivalent manager in another, you know, kind of organization, the human manager. You basically have to be responsible for whatever the output is. And so the more horizontal you go in what you're giving agents, the more functions you now have to.

• 12:31Marina Mogilko?

  The more your brain explodes.

• 12:32Speaker 03

  Yeah, exactly, really.

• 12:33Marina Mogilko?

  Yeah.

• 12:33Speaker 03

  And you see this in the valley, like people are like totally tired. Like I have never met a founder right now, or somebody working on a startup that's like, I'm getting great sleep.

• 12:42Marina Mogilko?

  Oh yeah, my 50 agents are running my startup and I'm just sleeping.

• 12:46Speaker 03

  Nobody's doing that. It's the exact opposite. They are managing the 50 agents and they are stressed out of their minds.

• 12:51Marina Mogilko?

  I was talking to a lot of scientists and they're the ones who tend to be most worried. I talked to godfather of AI, somebody who's been studying AI for 15 years, and they're the ones painting the picture.

• 13:02Speaker 03

  Was it Hinton or who?

• 13:03Marina Mogilko?

  Yoshua Benjou.

• 13:04Speaker 03

  Oh Yoshua, yeah, yeah, yeah.

• 13:05Marina Mogilko?

  Yoshua, he's like, we have two years. Like, what are they not getting?

• 13:09Speaker 03

  He's been saying we've had two years though for probably, probably 10 years.

• 13:11Marina Mogilko?

  That's true. Like, it's, but what are they not getting?

• 13:14Speaker 03

  Uh, listen, I have deep respect, obviously. Like, these are, these are the, the best minds in, in AI and we have, we are riding on their, their work. So, so, uh, so obviously a tremendous amount of re- respect for, uh, for what, you know, what all of this kind of category people have contributed and their ideas. I don't know if you've interviewed like Yann LeCun.

• 13:35Marina Mogilko?

  No, yeah.

• 13:36Speaker 03

  Okay, well, well, it'll, it'll come. And I, I kind of, you know, more am in Yann's camp, which is there's still just a fundamental limit to these systems. Um, they, uh, they, they have to be, the, the work has to be reviewed. Um, any, any error rate above like 2%, you know, you still then need some accountability in the process. And everybody kind of says, well, humans are already doing that. It's like, yes, but back to the point, I can fire the human. And so, and so there's some accountability at scale in the structure that, that, that, that exists. Where the agent just doesn't have any of that. And so somebody has to take on accountability for the output of that agent in your workflow at some point.

• 14:13Marina Mogilko?

  Mm-hmm.

• 14:13Speaker 03

  Because what you're not going to do is be fine when Bank of America says, ah, we lost your money because the agent, you know, kind of like made the wrong investment, you know, decision. And you're like, uh, okay, but that's not why I hired you.

• 14:25Marina Mogilko?

  Exactly.

• 14:26Speaker 03

  And so that part exists very broadly throughout our organizations and throughout the economy. And so I think, I think what some people in the AI ecosystem that lean more to the sort of rapid takeoff, you know, kind of quick takeoff scenario, is that they're, they're, they're thinking that because the agent can do lots of stuff really well, that that sort of diffuses across the economy in a way that is sort of this destructive scenario and, And I have, I don't know if it's a benefit, but it's certainly a reality. Like, I have the fun, pragmatic reality of, like, I work with enterprises, day in and day out. And these are enterprises outside of Silicon Valley, they're in the real world, they're the manufacturers of our products, they're the banks that we, you know, kind of bank

• 15:10Speaker 03

  with, they're the life sciences companies that develop drugs. And, and what these really amazing researchers and thinkers don't do is they don't talk to those people who are actually implementing these systems. And so they see this incredible capability take off, but they don't realize the diffusion of that AI across our organizations is ultimately constrained by and bound by 30 other things that doesn't really relate to the superintelligence that's in that model. It relates to, how do I implement this thing in a safe way with the right safeguard so it doesn't blow up my, my, my data structure? I, I just think that the timescales are wrong. The way that people imagine the AI being implemented in society is, is generally wrong. Now, there's a one real risk that I agree with, which is there is cybersecurity risks,

• 15:54Speaker 03

  there are risks of miss or disinformation challenges. Those are very real, we need to work through those, but I'm, I'm much less inclined to believe the, this thing takes off, it replaces all white collar work, and then we're in some really bad scenario on that.

• 16:10Speaker 00

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 16:40Speaker 06

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• 17:09Marina Mogilko?

  But we still hear all the news about layoffs happening due to AI. Do you think it's due to AI?

• 17:14Speaker 03

  Some of it is definitely not due to AI. It's over hiring during the kind of zero interest rate era, the COVID era. So there's some phenomenon that that kind of relates to that. I would say that some of it, it definitely could be related to AI. Like, there are some organizations that they're like, listen, I had 3,000 people working in engineering before. My product roadmap is sort of not, doesn't need to triple in sort of scale. It needs to grow by 50%. And so I think that if each engineer can be, you know, 2X more productive and my roadmap only scales, you know, 50%, then, then I think that there's some sort of savings there as a result of that. And then they might do a layoff in that scenario. So I think that is real.

• 17:57Speaker 03

  It's not something that I can, I can sort of gloss over. But what I see from customers, and you can go online right now, and I guarantee if you took five random companies in the Fortune 500, just as an example, take five random companies. I guarantee that every one of those five companies is hiring software engineers right now.

• 18:15Marina Mogilko?

  Yeah.

• 18:16Speaker 03

  And so where are they hiring their software engineers? They're hiring them, there's a, there's a, an interesting posting right now on Eli Lilly's career website, which is a lab software automation engineer. This is a role to use AI to help sort of automate and increase review of lab results and automate the lab process in life sciences discovery. The kind of general idea of like, AI is going to destroy software jobs? That is not playing out empirically, and I, I predict it will not play out ultimately.

• 18:46Marina Mogilko?

  Interesting. So you think we're going to get more jobs in the next few years? When you're hiring now, how is it different from hiring five years ago? What are you looking for in a candidate?

• 18:54Speaker 03

  So I think right now is a great time to be going deeper technically. You don't have to like, doesn't mean like you have to like be vibe coding all the time and building entire products. But you should try and really understand what is the agent doing? How does it work? How does MCP work? How do CLIs work? How do skills work? And getting really well versed in that. The people that are doing that will have a huge leg up in the next kind of three to five years. Because all of these companies will be hiring for people that can do that within their workflows. So we're definitely looking for people that whose technical acumen is growing. Whose AI sort of savviness and fluency is growing. You want to be using these tools, you know, in your free time as much as possible. So you again, understand kind of how they're working and what's going on.

• 19:37Speaker 03

  At the same time, I don't think a lot of the, I think it actually still matters that you have like some degree of domain expertise. Like you're really good at marketing. You understand what customers want. You're really good at selling. You're good at product management and interviewing customers and assessing markets. Like those are the timeless sort of, you know, skills that transcend any kind of technology revolution. And so AI is just a way of augmenting those domain skills. So in some respects, any role that we're hiring for, marketing, sales, finance, engineering, et cetera. We need all of those domain skills. But also we need you to now be increasingly kind of AI fluent or a little bit more technical.

• 20:18Marina Mogilko?

  Can you recommend top three apps that people should be using?

• 20:23Speaker 03

  I mean, it probably won't be much of a surprise. I would download Codex, I would download Claude.

• 20:27Marina Mogilko?

  Even for non-technical people?

• 20:28Speaker 03

  Yeah, 100%. Well, especially partly is because Codex is becoming more inclined toward knowledge work use cases.

• 20:37Marina Mogilko?

  And what should they be doing with it? Like automate a process within their?

• 20:40Speaker 03

  Automate a process? Give it just a crazy problems and see what happens. Like go do this research in this market. You know, wire up multiple MCP servers to data sources you have. So you understand how does it work? Like how is it querying that other system? How does it accessing my email? Like, oh, scary, oh.

• 20:58Speaker 03

  No, actually, I understand it now. Like, like, get, get a sense of how that all kind of is working together. So I think just any one of the top AI tools for productivity, maybe for coding, uh, is a good way to get started and it'll already get you like 90% of the way there.

• 21:11Marina Mogilko?

  So Codex?

• 21:13Speaker 03

  Uh, probably Claude Co. Work, Perplexity, like, these are some just easy ones to just get started with.

• 21:17Marina Mogilko?

  Yeah.

• 21:17Speaker 03

  And you'll have a good sense of kind of what the market looks like.

• 21:20Marina Mogilko?

  Do you have any examples of workflows that you've automated for yourself and you'll never go back to manual?

• 21:25Speaker 03

  The kind of things that I'll never do again is like, I'll never do like market research in a traditional way.

• 21:31Marina Mogilko?

  Yeah.

• 21:31Speaker 03

  So I'm often asking an agent to like go and analyze, you know, a hundred different companies worth of trends or information. I'll just never do that again. Like, I'll never go to Google and type each company in and, and do the research. Like, I'm gonna have an agent go and, and fan out, do all of that, and then maybe I'll click like all the underlying sources and verify something or double check something. So lots of market analysis. I'll never, I'll never open up Code Editor and like, you know, type code again. And I wasn't for the past, you know, many years anyway, but like, the reverse is true, which is now I can actually like get prototypes built when I couldn't have before. Um, so anything coding related, even design is like, you just go to chat to between, you're like,

• 22:12Speaker 03

  "Hey, I need this idea done. Could you just like make it like this?" And then it gets you like image, you know, the new image rendering model gets you like 75% of the way there. You hand that off to a real designer and then they kind of do the, the full thing. Um, so there's a lot in the ideation, the creative process, the market analysis, customer research, all of those domains that, um, that I, uh, am heavily using AI for.

• 22:36Marina Mogilko?

  Is there a certain way you structured memory? Uh, like, did you, uh, cause I hear some people like upload personal constitution, like their principles of work. Is there anything like that that you've done?

• 22:46Speaker 03

  Um, I'm less fancy on that front, um, and partly because I don't even know what I would write down, um, because I'm all over the place. So, uh, so I am, I don't have a lot of things yet that I would, I would know how to really document. It's more process specific, in which case, in which case, back to this sort of reprompting issue, I'm more just on the fly, just giving it pretty clear instructions of exactly what To go do. So like, I feel like I'm a pretty good prompter, like, like.

• 23:11Marina Mogilko?

  So every time it's a long, long prompt, right?

• 23:13Speaker 03

  Every time it's a long, long prompt, and I'll store those off in, in various places. You know, by virtue of Box, like, we're like, we store lots of data. So I have lots of documents that have information in them that I'm using, you know, constantly. But it's, it's not as, as awesome as like a sole file or a, or a personal constitution.

• 23:31Marina Mogilko?

  So it's not like we're talking right now and 50 agents are replying to emails. It's not.

• 23:35Speaker 03

  At the moment, if you were, if you are, if you get an email from me, that's a, that's a very, that's a huge mistake in our system. So I am not emailing you right now via an agent.

• 23:46Marina Mogilko?

  Got it, got it. Okay. So another thing.

• 23:48Speaker 03

  Now, five years from now, could that be a process that gets automated somewhere? For sure. Like, just as we've always had, you know, automated email systems for sales reps or whatever. But it probably won't be that, that it would be like, oh, hey, Aaron, like, I, I have a question about this thing. And then I'm gonna have like an agent go do that. Partly because like, that's actually just like the kind of context that informs me of what's going well, what's not working well. If I, if I automated all of that, we wouldn't know the next thing in the business to go fix.

• 24:16Marina Mogilko?

  So you don't have an agent that's running your business, basically. Is there like, because I talked to some entrepreneurs and they're like, I share all my business decisions with my AI.

• 24:25Speaker 03

  Yeah.

• 24:26Marina Mogilko?

  And then it looks at all the conversations I've had with my team and it gives me strategies.

• 24:31Speaker 03

  I, I think, first of all, I think that's really cool, that use case. I think more startups are doing that. I think if we were at a brand new company and it was like five employees, there's a very real chance I'd be doing more of those types of things. Because I would be like, okay, I probably need to like, build out our first marketing engine, and I need to build out our sales engine. And so I would be kind of, I would be documenting way more of that. At our scale, you know, the really interesting, important work is being done across the organization. So, so that type of, of work is more knowledge that like our head of brand design, or our head of product design, or our best brand designer, you know, designer needs to know, or our product managers in each of the individual domains.

• 25:12Speaker 03

  The stuff that, that I do is, is sort of look across those areas and try and add, you know, extra nudges in the right direction and kind of course correct and. And you know, an agent could certainly help, you know, give me advice for how to do that. But but I I'm still at the point in my life where I'm like, I'm gonna, I'm gonna see if my brain can do it.

• 25:31Marina Mogilko?

  It's also the founder energy, right? When you're talking to your team, you don't want your agent to be talking to your team.

• 25:36Speaker 03

  I think there's some, I think there's some, um, there'll be some like spectacularly hilarious examples like that, that probably over time sort of.

• 25:45Marina Mogilko?

  Oh, we're already seeing them, like company data being deleted.

• 25:48Speaker 03

  Yeah, yeah, you're gonna do that kind of stuff and and and I I like,

• 25:55Speaker 03

  you know, the, these agents are like, I I I could be proven wrong about this and maybe five years I'll be like, yeah, I was totally wrong. And this is where Jan, Jan LaKoon, I think, would agree and some other, some, you have a, you know, really interesting divide in the industry, which is are these like probabilistic pattern recognition machines? Or are they truly able to to kind of go off on their own and think for themselves? And depending on kind of where you land on that continuum, then you have some big judgments that get made. So I kind of think about it as, there's lots of business decisions I have to make or that that lots of people have to make, where just it's a brand new, net new event that happens. And and I couldn't have documented what to do in that situation.

• 26:39Speaker 03

  And I, maybe I could have if I I spent like a year writing down every single thing. But it's it's just a new thing that happened. And so if I, if I try and imagine an agent running around, everybody's asking the agent questions, it's only going to be able to answer the thing that that previously I have in my, in my sort of repertoire of answers. Many of the things I'm working on are the brand new, net new things in the, in the organization. So me being an agent across the company would kind of be useless because because it would only have, you know, help with the things we already know.

• 27:09Marina Mogilko?

  Yeah, previous strategies. That that makes sense.

• 27:12Speaker 03

  Now, now, now, just to, just to share the opposite for one second. There's a lot of stuff. I'd say 80% of our corporate information is to be reused purposely. Like, you don't need people, like, making up a new answer to an HR policy. You don't need people making up a new answer to what is Box's security functionality? And how should I position it to a customer? So in those cases, actually, all of your enterprise information, which is what we do, you know, as a business, is like, that enterprise information becomes valuable for agents, because they can look at the documentation, they can look at the sales pitch, they can look at the meeting, you know, that was recorded. That actually becomes very useful information for that kind of run rate, 80% of your company's work.

• 27:51Marina Mogilko?

  Yeah, but it's for specific work, so like a founder's strategy. Yeah. And you said some next, uh, some next great companies are gonna be founded in the next three years. And you gave a very specific timeline. Why three years?

• 28:04Speaker 03

  Well, uh, I mean, it could be three and a half years.

• 28:09Marina Mogilko?

  It's like, not 10. It looks like we have a very limited gap in the market, where you can build something great, because then it's gonna be another, like, boring 10 years.

• 28:18Speaker 03

  Basic theory is like, you know, these market windows happen every, every 10, 20, 30 years in technology. Uh, the mainframe, the personal computer, the internet, the cloud slash mobile. So there's already been kind of four of these eras. And if you look at the biggest companies, you know, in, in, in tech, they generally correspond with when these windows open. There's a couple ones, a couple sort of examples that don't. Facebook sort of didn't correspond with any particular window. It was more of a, a social change that occurred as opposed to a technological change. But most other, other things, Google, Amazon, Microsoft, Apple, uh, you know, sort of the, the real turbocharging of IBM and, and in that era, and Intel and so on,

• 29:01Speaker 03

  they kind of correspond to a new technology, kind of found, at the foundation level emerges. And then, and then you have this opening where a bunch of new companies kind of respond to that. In our era, it was Salesforce and Workday and enterprise software companies like Box that were able to capture that moment. And then in mobile, it was like Uber and DoorDash and another set of companies. So we're in a window right now that has all of the makings of that, which is AI is now emerging. Companies are going to want to apply this intelligence in various areas. And so there are going to be a lot of applied AI companies that bring that intelligence

• 29:43Speaker 03

  to businesses, to society, to consumers in these applied use cases. And the only reason it's not like 10 years is because there's a lot of network effects remotes that get built. So if you build one of these companies and you're capturing data from the customer and you're improving the feedback loop of the agent, that'll just make your technology better and better over time, whereby, at least on paper, that product should become more strengthened in its competitive advantage over time. So that's why it's like, yeah, it's not like an infinitely long window. Because it's very hard to disrupt Walmart today because customers have been using it for decades. And so you kind of want to be in one of those spots as these markets are emerging.

• 30:26Marina Mogilko?

  Are you seeing any gaps in the market where a startup should be working on right now?

• 30:33Speaker 03

  Still, I mean, tons. But I think there's still like, I think everyone sort of knows the example of like Harvey right now for legal. I think there's still lots of job functions, industries that will have their Harvey. Like, I don't think we've heard the end of the Harvey for X. I think there's going to be new infrastructure that gets built out because these agents are going to need new kinds of tools beneath them. There's all this new interesting stuff around when agents are doing work within software, they need more headless technology that they have access to. They might need payments. They might need, and so Stripe and this new company Tempo is providing payments for agents. Well, now if an agent can pay money...

• 31:17Speaker 03

  Then you can start to think through like, well, what would the agent pay money for? And there might be new businesses that emerge that the agent is now going to transact with. Like, they're going to need data, probably.

• 31:27Marina Mogilko?

  Mm-hmm.

• 31:28Speaker 03

  They're going to need infrastructure. Uh, they're going to need to do tasks for you in the economy. Like, there's lots of things that you can start to imagine that will become these new business models because of, of what happens with, with agents doing this work.

• 31:41Marina Mogilko?

  With a whole new layer of, uh, active creatures in the market.

• 31:44Speaker 03

  Yes.

• 31:44Marina Mogilko?

  Which are agents.

• 31:45Speaker 03

  Yeah, 100%.

• 31:46Marina Mogilko?

  Uh, if you were starting today, can you walk me through a plan? Like, what would you do to find the right idea to test it and to make first money?

• 31:53Speaker 03

  My first thing would be some mix of like, you know, assume that we've got the most intelligent sort of system on the planet. So that we have this incredible AI intelligence and just imagine that emerges. Then the question is, where in the economy would that add the most amount of value? And then try and think through like, like, are there spots where like an incumbent isn't effectively responding to that? Um, uh, so that'd be like one framework.

• 32:18Marina Mogilko?

  Mm-hmm.

• 32:18Speaker 03

  Another framework would be like, where in the economy is it hard to deploy agents? Because there's a lot of other kind of systems that, that those agents need access to. And that's usually where like, there's lots of work to be done to get the agent to work within the environment. I'm pretty excited by a lot of these new, um, kind of professional services, IT integration consulting firms that are emerging. Because, uh, when you go to the real world and you're like, oh, would you like to automate your work with, you know, coworker or, or Codex or any of these systems? They're like, yeah, that'd be awesome. And then they show you their environment and it's like, ooh, like, it's gonna be a lot harder than you think.

• 32:52Marina Mogilko?

  What markets?

• 32:52Speaker 03

  Anything.

• 32:53Marina Mogilko?

  Anything?

• 32:53Speaker 03

  Everybody.

• 32:54Marina Mogilko?

  Mm.

• 32:54Speaker 03

  Uh, healthcare, law, life sciences, um, bank, I mean, just every industry. Uh, because if your company is more than five years old, pre-AI, your data is all over the place.

• 33:05Marina Mogilko?

  Yeah.

• 33:05Speaker 03

  You've got 30 different systems you're working with. Your workflows aren't documented to the prior point. So that's a lot of change management you need to go do to implement agents. So what does that spell? That spells opportunity for new services startups. That spells opportunity for the existing Accentures and Deloitte's of the world. It's kind of like a, a little bit of an up for grab market at the moment because of how much work there's gonna be.

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• 34:27Marina Mogilko?

  How do you decide between like building versus?

• 34:29Speaker 03

  Maybe the only thing like Mark Cuban has had this riff, and I fully agree with it. There's going to be like a lot of opportunity, both for companies, but even just these will be roles that if you're like graduating right now, you might want to think about is like, who's the person that shows up at the 10 person consulting firm in Minneapolis? Just to like pick a non-valley location. Who's the person that shows up that helps them take advantage of AI?

• 34:52Marina Mogilko?

  Yeah.

• 34:52Speaker 03

  Because they don't have like a big IT department. They don't have a way to wire up their agentic workflows very easily. That's going to be like, there's going to be tens of billions of dollars, hundreds of billions of dollars that get made between jobs and services firms in just that over the next decade.

• 35:08Marina Mogilko?

  Also, as an entrepreneur, when I'm thinking about that, but what if Claude just makes the process really easy? I don't know, you just deploy an agent. They build a specific agent who goes into your email, whatever you have, your box.

• 35:19Speaker 03

  Yeah.

• 35:20Marina Mogilko?

  And creates the whole ecosystem for you. How do you think about that? Because these companies are getting more and more powerful, right?

• 35:26Speaker 03

  If I took your exact scenario and I'm like, okay, um, an agent's going to read through my entire email inbox in that scenario, and then it's going to access Salesforce. And then it's going to have some kind of like workflow that participates in. Like even me as a, I've been building software for 25 years. And I use every single tool that has ever been produced in AI. Obviously not literally, but like pretty much. I don't feel comfortable implementing that workflow right now. So the idea that that 10 person company is gonna go and set that up. And just because Claude becomes super powerful. I am skeptical that we ever get to that point. Interesting. Because, but the reason why I'm not comfortable with that is like.

• 36:11Speaker 03

  I don't, I have to think through the guardrails of like. What happens if somebody emails me? And then says, hey, Aaron, I, um, you know, you know, you, I need you to pull up this Salesforce record for me that you told me you would, you would, you know, look at. And, um, and you can send me that information. Well, if my agent has access to my email in my Salesforce. Then the agent should by design. Answer that email question and go pull in the Salesforce record and then send it out. That's like a non-starter. Like you can't just like take any untrusted email coming in. And then have the agent. Distribute information. And distribute information that you, that your tool has access to. So, so even me trying to think through how to implement whatever your scenario was just now. I would have a hard time. Thinking through, how do I set the right guardrails?

• 36:56Speaker 03

  How do I have the right like alert mechanisms to me? How do I have the right sort of human in the loop of like? Should I review all of the emails before they go out and have an interface to do that? Should I have some escalation mechanism that like pings me on my cell if like? I need to look at something. How does the, how does the, the, the person on the other end of that email inbox? Not, how do they, how do they get to me as the real person and like? Get, you know, how do they escape the agentic loop that they're in? There's like 30 questions that I even have thinking through whatever that workflow is. So it's not a matter of Claude is so powerful. It's a matter of like. How the systems talk to each other, the safety mechanisms of those systems. How do you define the, how do you define the actual workflow? So it, it gets done in a kind of safe and reliable way.

• 37:41Speaker 03

  That's the work that a technical person generally needs to go do.

• 37:44Marina Mogilko?

  Okay, so that's like a big shift from, you know, being manual to getting automated as a company.

• 37:51Speaker 03

  Yeah.

• 37:51Marina Mogilko?

  What about niches where, like, we see Figma stock go down when Claude releases?

• 37:56Speaker 03

  Yeah.

• 37:56Marina Mogilko?

  The design feature, right? And if somebody is working on that type of feature and they're afraid, you know, with the next Claude upgrade, it's gonna be gone.

• 38:03Speaker 03

  Well, that's a different issue. So, so, so that's a different category altogether, which is, which is, you know, how much will Claude or these AI models eat into the business models of different industries or different, you know, providers? I think that's more of something where you just have to be very thoughtful right now to not just build anything. You have to build things that like, what, what are you building where even as AI agent progress continues, no matter what, no matter how much it continues, it could be infinitely powerful, there's still some other thing that that agent is gonna need to do.

• 38:36Marina Mogilko?

  Mm-hmm.

• 38:37Speaker 03

  It's gonna need to put its data somewhere, it's gonna need to incorporate into a workflow, it's gonna need a, a human to take the information and put it into the real world. Over time, more and more value will start to look like things where, where again, like, it's a well-governed process that has lots of security or compliance needs. You have to trust the underlying system. Um, you know, probably just like quick personal productivity tools maybe will be less relevant. At the same time, like in, in the Figma example, I think Claude design is, is actually very, very cool, very powerful. I've played with it a bunch and it like, you know, generates, you know, amazing designs.

• 39:11Marina Mogilko?

  Yeah.

• 39:12Speaker 03

  But at the same time, I still want, I still want our design team kind of going and doing the last mile of work. And right now, they're doing that last mile still in Figma. Even these things are not as binary as, as, as I think maybe the, like Wall Street, for instance, would suggest. Um, uh, so, so that's, that's why it's still kind of a, you know, we're, we're in a pretty dynamic period right now.

• 39:30Marina Mogilko?

  Yeah, and it's also because I feel like the stock reflects what we're thinking about the next two years. And because this is evolving so, so fast, sometimes as an entrepreneur, I'm like asking myself, okay, I'm building these apps.

• 39:41Speaker 03

  Yeah.

• 39:41Marina Mogilko?

  Why don't a language learner just go into chat GPT and like build the app, the vibe code and app for themselves?

• 39:47Speaker 03

  I mean, I, I think it's, it's a question that every, every entrepreneur should, should have a very big whiteboard that like thinks through. Various game theory events that could happen and where will your value get compressed and where will it where will it not? And and it's it's hard to, you know, in any kind of generic way, have a perfect answer, uh, because it is a very busy, complicated time. Um, I but but but again, kind of ironically, in like the more macro sense. I think the more that AI is sort of doing in these kind of automated things. You're just going to see new constraints begin to emerge like. Like, you know, a lot of people like have like healthcare classically is this example, and I think Jeff Hinton. You know, had, uh, I don't have the perfect quote, but I think, you know, he felt like radiology would reduce as an example.

• 40:32Marina Mogilko?

  Yeah.

• 40:32Speaker 03

  Because, you know, AI will get really good at at looking through radiology images and.

• 40:35Marina Mogilko?

  And and the self-driving cars.

• 40:38Speaker 03

  Yes.

• 40:38Marina Mogilko?

  And now we still have radiologists driving to work every day.

• 40:41Speaker 03

  Oh, sure. Yeah.

• 40:42Marina Mogilko?

  I think it was that.

• 40:43Speaker 03

  Oh, yeah. So so, um. What also happens is these other things that that occur, right? So like. We might have like AI that gets the radiologist ninety percent of the way there to like look at the right thing or get some suggestions. At the exact same time, what what that's meaning is we're doing vastly more imaging. We're we're doing vastly more scanning, way more people now can go do it.

• 41:02Marina Mogilko?

  More accessible.

• 41:03Speaker 03

  And it's more accessible. And so so actually now the demands on that role. End up increasing as a result of that. So there's a lot of parts of the market where where actually AI. Facilitates lowering the barrier to doing that work. And by lowering the barrier to doing that work, more people participate in it. And as more people participate in it, a new constraint gets kind of backed up that now. Real people need to go and kind of, you know, get involved in or or go and work on.

• 41:28Marina Mogilko?

  Hmm. Yeah, I feel like the more I play with AI agents, I do realize that I need a person at the beginning of the process and the end of the process. So I still end up having more people.

• 41:38Speaker 03

  Yeah. And and and again, it's just like a question of like, how many roles do you want to play within your company or your team? Like, do you want to play designer, developer, marketer, strategist?

• 41:49Marina Mogilko?

  Not really.

• 41:50Speaker 03

  Sales rep? No, you're just like, ah, at some point.

• 41:54Marina Mogilko?

  Yeah.

• 41:54Speaker 03

  So. Yeah, the solo entrepreneur is already used to that because was, you know, we or they have been doing that forever. Like when I did, uh, startups before Box and I was a solo founder. Like, man, you had to do 10 things and you're like, you're so tired. And if I could have ever hired somebody to go do half of those things, I would have. So does, could AI allow us to get these companies to a little bit more scale to the point where then you can hire that next person? That's, that's more of where, where I think this would go.

• 42:22Marina Mogilko?

  And do you think it's the best time to start a company now?

• 42:25Speaker 03

  I'm kind of stickler for this one key point, which is it's only the best time to start a company if you've got a, a great idea. I think that great ideas can exist in, in any kind of period of time. But I'm not in the camp of just like, everybody should start companies.

• 42:40Speaker 03

  Because, because it's a, I mean, you know, it's like, it's like really hard work. It's, it's extremely stressful. You're working like mad. I don't think that people should feel pressured into, into starting something because, because it's, it's one of these windows. When I say it's one of these windows, it's, it's just to reinforce the point that like, this is the moment where the best ideas probably will get built. That doesn't mean that you should start one.

• 43:01Marina Mogilko?

  It doesn't mean you have to rush yourself to starting that.

• 43:04Speaker 03

  Yeah, because if you, if you rush yourself to starting a bad idea with one of these moments, you're no better off. So I would say it's a, it's a, it's a good moment. You have an incredible amount of leverage. That also comes with more competition.

• 43:15Marina Mogilko?

  Yeah.

• 43:16Speaker 03

  Because, because basically by lowering the barrier of, of getting ideas out in the market. What do you get? You get more competing ideas. If you get more competing ideas, that's more noise that customers have to deal with. So interestingly, and back to the job thing again, interestingly, it's not so much like now the idea, getting the idea out there that's going to matter. It's going to be like, man, do you have like, do you have somebody talking to customers? Do you, are you doing sales? Are you doing marketing? And so there's a new constraint, which is like, the constraint isn't code generation. The code, this constraint is, is are you in front of customers enough and, and are you marketing enough?

• 43:51Marina Mogilko?

  Marketing.

• 43:51Speaker 03

  Which is a new set of dollars.

• 43:53Marina Mogilko?

  Yeah. If you could become 19 again today and start over, would you exchange that?

• 44:00Marina Mogilko?

  To what you've currently built, like to start over again, would you do that?

• 44:03Speaker 03

  Am I starting in 2026 or back in 2005?

• 44:05Marina Mogilko?

  Yeah, 2026 as a 19-year-old, would you do that? Or would you just stay put with what you've done?

• 44:11Speaker 03

  Oh, I see, I see. Well, I'm the most excited we've ever been on what we're doing now. So I would certainly pursue what we're currently doing because part of it is historical, which is, you know, we've earned the trust of 120,000 customers, which is a good launchpad for the next set of things we wanted to go do. So, and then I just love the kind of things that we get to do with customers. We get to work with every industry and every size company. And we get to help, you know, space launches and medical discoveries and blockbuster films get produced. So like, I'm very excited by what our platform does with agents. At the same time, I have lots of friends that are doing, you know, companies. And I'm like, oh, that's a really cool idea right now and it looks very exciting.

• 44:56Speaker 03

  And, and so I'm just in a period of like, I'm impressed and excited by lots of stuff. While also being, again, incredibly stressed constantly.

• 45:05Marina Mogilko?

  Any jobs that are going to disappear in the next five years?

• 45:08Speaker 03

  I think there's going to be work that gets compressed. And, and then, and I think you're going to take those people and often repurpose for, for again, more of the agent manager escalation path or proactive versions of that work. A very kind of clearly obvious one is, is, and this is something that companies always, you know, try to sort of automate to some degree. Like, like if you're emailing a company and you're saying like, I need you to reset my password, that is probably not going to be a person.

• 45:38Marina Mogilko?

  Like customer support, right?

• 45:39Speaker 03

  Customer support, but even that, customer support is this funny one, which is, which is like, we think about it as a monolithic thing because we call it customer support. There's tiers of customer support. There's like the first line of customer support that we will most certainly automate.

• 45:54Marina Mogilko?

  Change password?

• 45:55Speaker 03

  Change password. And I don't mean to like, you know, over minimize that thing, but like, there's a lot of tasks like that, which is like, I need to download this thing, I can't log in, I have this issue, whatever. That, we're going to fully automate. But there's a lot of customer support, which is like, I need you to get on this call with me and look at my, my specific problem in my computer and why this thing isn't working. And we just have no way to automate that.

• 46:16Marina Mogilko?

  Yeah.

• 46:17Speaker 03

  Like, maybe we'll automate like the next line of the, of the set of questions, but you can't, you can't get to the final thing. I had, I had a friend have a problem with Box two weeks ago. He just sent me some screenshots and there's a 0.0 chance that he would be able to have asked the question with an agent.

• 46:36Marina Mogilko?

  So it had to be you.

• 46:38Speaker 03

  Well, in this case, it actually, it had to be a senior product manager. I had to get the senior product manager to the person. But he couldn't have talked to a chatbot and answered the question.

• 46:48Marina Mogilko?

  But what about like bookkeepers?

• 46:49Speaker 03

  I had, I had an issue with, with my Mac, uh, last week. I spent 10, 20, 30 minutes on AI trying to diagnose it. Never, never worked. Had to call IT. They had to come and diagnose it. Couldn't replace that.

• 47:02Marina Mogilko?

  Oh, okay. What about jobs like bookkeepers or?

• 47:06Speaker 03

  You know, some of these jobs, like again, they've, they've been on the path already to, to how do you already automate as much of that away as possible? And so agents kind of are just another sort of layer in that. But again, you know, sort of the same answer. There's still always an escalation path. Because there's always the exception. There's always the, the weird anomaly that occurred. Um, and you can't have, like the thing that you can't do is I can't moonlight as a bookkeeper. I can't moonlight as a lawyer. So, so at some point, there still is this final path in the escalation, which is I may have been able to automate 90%, but you just, you still have that one part.

• 47:45Speaker 03

  I had this, uh, I had this legal question two months ago and I asked every AI agent the, the, the same legal question. And, and every single one basically gave me the same answer. And then I called a lawyer and they basically gave me a more, a much more, kind of contextualized answer because they could decide how much risk I needed, I wanted to take on or not take on.

• 48:08Marina Mogilko?

  Knowing your personal situation, right?

• 48:09Speaker 03

  No, they know my personal situation. They also know the fact pattern of like, like how does the industry tend to think about this one thing? And all the AI agents were, were giving the, the, the, the sort of mean answer of that, of that particular topic, which is in this case, it's, it's like, it's the more conservative answer. It's the thing that it should be trained against because it, it can't give you the, it's not going to give you the more liberal risky answer. But, but when you talk to a lawyer, they're like, well, actually, yeah, this situation won't actually occur.

• 48:36Marina Mogilko?

  Yeah.

• 48:37Speaker 03

  Because, because of XYZ fact patterns. And so those are the kind of things where like you then are like, I want somebody that has seen 20 years of this stuff.

• 48:44Marina Mogilko?

  Yeah.

• 48:45Speaker 03

  I don't want a model that was just like looking at Reddit and and deciding, you know, how to use it, how to use that information.

• 48:51Marina Mogilko?

  Okay, my two last questions. You have a six-year-old, right?

• 48:56Speaker 03

  Uh, almost seven, but yes.

• 48:57Marina Mogilko?

  Okay.

• 48:58Speaker 03

  And four and and seven and seven and a half months.

• 49:00Marina Mogilko?

  Oh, congratulations. Okay, are they gonna go to college?

• 49:03Speaker 03

  Shoot, maybe I shouldn't have leaned in so much to to the question. Um, uh, I, okay, I'm, here's the one problem with me. Um, as a B to B enterprise software person, I am like boringly pragmatic. And so I just think change happens more slowly. And and it's funny because I have this like weird duality, which is like, I adopt every tool. Like I was, I was wearing Google Glass, like, like in week two, I buy every VR headset. Like I I lean into every one of these tools because I'm just like, I'm excited as a personal user. I love technology breakthroughs. It's amazing. And then I'm like, uh, and then I go in the real world and I'm just like, man, that whole system over

• 49:48Speaker 03

  300, 500 years that we've we've built up is like, is that really gonna change because of this one variable? So on the college thing, I I I struggle because I'm like, on one hand, from first principles, it doesn't have to exist. My seven-year-old, almost seven-year-old, is already way smarter than I was. Because he can, every question that comes to his mind, we're like looking up the answer right away. Whereas, like, I don't have like a perfect memory of being seven, but like, like, I didn't have like an instant resource for like every question. But like, he wants to know like how fast a paragon falcon can fly, we get the answer. He wants to know like how big the the Atlantic Ocean is, we get the answer. Like, like, and so he's just like a sponge for like unlimited information.

• 50:32Speaker 03

  On one hand, you're like, wow, that could probably replace like a lot of the traditional sort of, you know, ways that we think about these institutions. But then you're on the, on their hand, you're like, well. You know, what is college other than another four years of high school? But with a little bit more vocational orientation, a network of people that you want to be with and learn with and make connections with, a kind of transition period into the real world. Because you're still kind of young at 18. So then I'm like a pragmatist, I'm like, does that really change in this superintelligence world? Or is the curriculum just changing and the format maybe changes? But like, does everybody want to just be at home with their parents, talking to an AI bot?

• 51:17Marina Mogilko?

  No.

• 51:17Speaker 03

  Like, no. So it's like I have these other like, you know, kind of sort of counter pressures. Now, things that should and must change like at a societal level is like, man, can we have college cost a fifth of what it does? Because it's insane. Like, should you really go into debt for 20 years? Because you went to medical school or you went to, you know, get X degree? Like, that's incredibly insane. So like, should we use abundance to bring the cost down? And try and do that as much as possible? Absolutely. So there's some things that need to change about college. But does the very concept change? I always struggle with that one.

• 51:52Marina Mogilko?

  Yeah, same here. I feel like as a society, we're really slow to just change dramatically when it comes to foundations, when college is one of them. Okay, last question. Advice for entrepreneurs who's starting today?

• 52:03Speaker 03

  I would say just like, back to the earlier point, lean into the tools. Like, learn the technology, see what's possible with it. Make sure you're riding the tailwind of what's happening in technology. You don't want to be, you know, kind of hitting a headwind where you're kind of going against the grain of the AI. You want to be like riding the AI wave out, which can mean a number of things. It might mean do things that actually, in a world of AI, become more important because people don't want AI to do that thing. So it's like, it's like this counterintuitive, like riding the AI wave might mean do a live events business.

• 52:38Marina Mogilko?

  That's what all the people are doing.

• 52:40Speaker 03

  Yeah, like do something where we will appreciate this other thing in the economy because AI is sort of so abundant. Or AI makes getting healthcare questions answered so quickly.

• 52:52Speaker 03

  So you should probably be doing hospitals like because now more people are going to be, you know, go like, like actually needing.

• 52:59Marina Mogilko?

  Wellness clinics.

• 53:00Speaker 03

  Wellness clinics like, like, so there's just like, so sometimes it's a technology thing that you do. Sometimes it's a thing that the technology sort of is, is related to an underlying broader societal trend that will become more important as well. Build one of these, you know, consulting businesses that helps deploy the AI. I, I just think there's going to be like, build a childcare service because we're all sort of our brains are exploding and we need help with kids. There's all this kind of stuff that, that, that is going to need to exist.

• 53:27Marina Mogilko?

  Yeah. Thank you so much. I, I like your positivity, especially after talking to some scientists.

• 53:33Marina Mogilko?

  Thank you. Thanks a lot.

• 53:35Speaker 07

  Now more than ever, we need someone on our side, but Rob Whitman? Whitman's votes drove up costs, cut Medicaid and risked 350,000 Virginians healthcare to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the HouseMajorityPac.com, not authorized by any candidate or candidate's committee. Your website can do more with Base44. Describe what you want in plain language and build a site where people can book with you or buy from you. Payments, data, security and hosting are all built in. Go to base44.com.