Uncapped #3 | Aaron Levie from Box

[00:00] All right, Aaron, thanks for doing this. Really appreciate you making time. I know you're really busy, and it's really good to get to sit down and talk to you. Good to be here. I'm actually always here, and so thank you for coming. My pleasure. So I want to start by talking about something that you've been super vocal about online lately. AI agents, AI software in the enterprise. Obviously, everybody's talking about it, but you've been talking about it in great detail and how it relates to Box. I thought you were going to say that we were talking about it first. Oh, first. Okay. That's right, too. [00:30] you sort of, where are you thinking about, you know, AI as it relates to Box? How do you think agents are playing out right now? Obviously, that's what you're talking a ton about. But like, what's sort of the kind of high level about what's in your mind right now? So for us, the reason I'm just so, so pumped about AI right now is we've been building this platform for nearly two decades to help enterprises store, manage, share, collaborate on their most important data. And, you know, that data is their financial documents, their contracts, [01:00] And inside of that information contains an incredible amount of value, but most of it is underutilized. And so, you know, you just think about what you mostly do with your files. You, you know, you create one, you share it, you collaborate, and then it goes somewhere and you never see it again. And that, that we, we, we just see that with the life cycle of data. Data, you know, starts out hot for the first couple of hours, couple of days, week or two. And then it sort of, you go, it goes into a place where you just manage it. It's actually really important to keep around because you might need to pull it up like five years later, or there might be some legal reason why you need it.

[01:30] meantime. You don't ask it questions. You can't pull out value from it. And yet it actually contains an incredible amount of wealth of value for an organization because it might have an insight that would lead to your next product discovery. It might have information that would make a sales rep better at selling their product. It might have information that a new employee onboarding into a company will ramp up faster, but you have not been able to tap into it. So for us, AI is just this massive breakthrough, which is we can finally actually open up the value [02:00] sitting on. We kind of are in the right place, right time in terms of having built out a platform that 115,000 customers trust. We're in about 67% of the Fortune 500. And companies have been using Box to be able to manage that data, automate workflows around it, secure that data. And now AI kind of plugs in right at the core of everything we're doing. So we're sort of running the company and imagining the company as if, you know, if we had started the company in 2025, we're [02:26] What would we be building? How would we be running it? What would our business model be? And we're asking ourselves, are we doing everything as if we were starting from scratch in this new era of AI, as opposed to the traditional challenge with being either an incumbent or some company that's been around for a while is you take too long to address a new technology. You don't pivot hard enough. You don't reimagine your business model for that new era that's emerging. And so we're trying to learn all those lessons, maybe to some extent to prevent anything bad from happening. [02:56] just generally, I'm just way more excited about the upside of what's going on. You've been talking a bunch about agents. And so it's like, you've got all this data, you know, obviously with Box, you know, you're storing all of the documents and the files, and you know a ton about the company. So like, what in your mind are the agents then like doing? Like, let's say these agents get to a place that we all think they're going to get to in one, two, three years, like, what does that world look like now, where I've got like Box with all this data, and then I've got agents that can do whatever I want them to do, whatever they kind of

[03:26] within the box environment that you'll create agents. And we already have actually some of the core primitives available to customers in our AI studio. But we imagine a world where you'll have AI agents that you create or that are automatically created that are really, really good at... [03:42] content-oriented workflows. So, you know, what are those in an organization? Well, that's a legal assistant reviewing a contract for clauses that, you know, you don't want to agree to. That's a procurement assistant that is reviewing invoices or payment terms and then automating, you know, some part of that process. That's a marketing assistant that can look at digital assets, you know, pull out the data from those digital assets and then automate a, you know, marketing campaign sort of workflow process. So, you know, we'll have, you know, millions and [04:12] that get created. They'll help you automate the work that you do with content in every field, every industry, in every segment of enterprise and public sector, private sector, and so on. That's what we're going to build out. And we think that creates this [04:27] you know, huge productivity boost for organizations, because now as an employee, I can have agents sort of running in the background, executing tasks for me around my data. I can say, hey, you know, I'm a financial analyst. I want an AI agent to go do a deep research on 20 financial documents of the latest, you know, notes I took or other people took on a new earnings cycle. And I wanted to look at all that data and run a full report on what are the trends happening in the semiconductor industry, you know, where their GPU shortage is, you know,

[04:57] in. And that agent is going to go out, look at your data, pull back, run a report, maybe connect to outside systems. You know, recently, OpenAI has a sort of agent SDK where you can have tool use so you could pull from the web. So that might combine into that agent, you know, generating a full report for financial analysis. And again, you could imagine the same thing in legal or life sciences or healthcare and so on. So that's what the box agents will do. Then I think you kind of [05:27] certainly beyond any given SaaS product or existing platform. And you say, okay, but Box is not going to be the only place where work happens, very clearly. Work happens in Salesforce, and work happens in ServiceNow, and work happens in Slack, and work happens in Workday, and 500 other technologies. And so we're going to eventually need agents between these platforms to talk to each other. So how do you have an agent that can go off and pull data from five [05:57] construct a full picture of the report you're trying to run or the workflow you're trying to automate or the decision you're trying to make. And that's where the industry is so unbelievably early. But we're starting to see this emergence of either protocols or at least concepts of how agents will talk to each other, where I'll go to something like a Chachabut and I'll say, run a report on this customer. And it's going into Box and pulling data. It's going into Salesforce

[06:27] you know, proprietary, but semi-public data. And then it runs a full report on that. And we imagine a world where you might go to, you know, three or five or 10 different sort of horizontal systems for that query. Or you might go to Salesforce and via something like AgentForce, you might run that and then that's going to go call out to Box. Or you might be running an ITSM workflow in ServiceNow and that's going to go call out to Box. But no matter what, our agents are going to eventually have to talk to each other. It seems like anybody who's in any like enterprise software company would want these agents because they're going to be so valuable. [06:57] would be whoever owns the data and whoever has all the integrations is going to be in the best position to sort of make the agents that do all this stuff inside the company. Do you think that, um, [07:08] new startups that want to get into building agents and stuff like that. As I'm listening to you talk, it seems to me that in most cases, incumbents ought to have the structural advantage. Unless they're asleep at the wheel, basically, incumbents have the data and the integration of customer relationships already. So how are you thinking about where... [07:26] a startup ought to win? - Yeah. - Like, are there places where a startup ought to win, other than the incumbent being asleep at the wheel? Or for the most part, is it really just, the incumbent should always win, but big companies are tired and therefore startups? - So I think you're gonna see, first of all, there's plenty of opportunity just in that latter category. Like, like most large companies today, [07:42] exist because of just incumbents who are asleep at the wheel. There did not have to be white space in that Netflix found. Like that could have just been Blockbuster one day saying, I'm going to go digital and we're going to do the digital version of our business model. You know, the classic of Barnes and Noble didn't have to let Amazon sort of, you know, get e-commerce and then they built out from there. So first of all, you could underwrite probably a trillion dollars of startup

[08:12] It's just going to exist in a whole bunch of categories. It won't be boxed because we're religiously betting on this. But we're very worried about competition from lots of angles. But it won't be because we're asleep at the wheel. So we're going to be 100% focused on making sure we're building out a platform that works in an agentic way. But lots of companies maybe won't be the case. And it's very early to kind of figure out who's in which bucket. But yeah, you can tell Salesforce is not asleep at the wheel. ServiceNow is not asleep at the wheel. And the list goes on. [08:42] sort of the asleep at the wheel case. Then there's the innovator's dilemma problem, which is maybe it's like the sister to asleep at the wheel. Innovator's dilemma problem is, okay, I'm an incumbent company. I really like this sort of recurring revenue thing I'm getting on a seat basis. You know, it's a very clean business model. I can price in a certain way. And this is an opportunity for a startup to come in and say, you know what, actually AI has sort of flipped the model. You know, it's a consumption oriented model as opposed to your pre-buying a number of seats [09:12] where that becomes pretty obvious is things like, let's say, customer support software, right? Where the innovator's dilemma dynamic is they used to sell 500 seats of software to the 500 customer support agents. And now we have a world where AI is going to start to automate more of that. Can my business model evolve to be able to go and support that? Because that really, like innovator's dilemma, it's funny that we think about that as a tech-oriented issue.

[09:42] And it just exists because the incumbent, they don't want to do something that basically erodes their core profit. Like nobody in the management team wants to have less revenue the next year. Of course. And then have, you know, Wall Street hate them and so on. And so the CEO just keeps kind of grinding it out until basically it's too late. So it's a business model dilemma, ultimately, an innovator's dilemma. And there will be companies that sort of face that. And it's going to be in a variety of ways. There'll be consumer versions of this. There'll be enterprise versions. And then there's a third category. [10:12] a lot on Sleep at the Wheel. I think you could underwrite quite a bit at Innovator's Dilemma. And then I think probably the biggest category will be kind of net new use cases for AI. [10:21] where there's not an obvious incumbent who sort of is in that market in traditional software, where an agent is sort of now better off serving the problem. Replacing human work, basically. Replacing human work or just doing the human work that we never got around to. And I think this is probably one of the things that we haven't been able to quantify, which is like, how much of AI are we going to use in the future? Like, you know, Goldman Sachs or, you know, an economist's view of these kind of analyses [10:51] - Sure. [10:52] And then we're going to just like figure out ingredients, like how much can AI replace? It's a pretty myopic approach because it just implies that like the world is like perfectly right now. You know, we have this perfect equilibrium of supply, demand for talent, and we can all pay for the talent, you know, and it all kind of works. But actually, it just turns out that like we have a lot of things within Box that I'd like to do.

[11:13] that we don't have people doing right now. And we might be willing to spend X amount, millions of more dollars of just net new spend. In other words, it's not just a cost saving endeavor. Like we should be generating new work. You'll be generating new work with AI. And so there's going to be a lot of startups that will emerge in categories where there's just not an incumbent software player or even incumbent services player that will begin to automate things that are just net new spend categories. We're already seeing this in a bunch of examples, [11:43] is kind of an interesting one. You know, obviously, two different takes on what the future of now coding as a profession will look like. I'm more in the optimistic take, but let's just say if you froze right now and you just said, okay, at today's moment when you add up Cursor and Replit and Codium and everybody's revenue, I would argue that all of that spend on AI coding is sort of net new spend in the software stack. [12:08] Like it's all just like all we, every company and every individual just has now decided to swipe their credit card and now buy AI to augment how they work. Right. People haven't been replaced, you're saying. No, nobody's been replaced in those categories. Again, there's going to be asterisks like there could be, you know, long term kind of consequences of this. And we can talk through what will that look like. But it's net new spend. [12:28] There's not an incumbent that's sort of inherently being disrupted, because there was no incumbent called like the code writing incumbent. And we very clearly have new startups. [12:36] that are actually out-executing the incumbents that were in adjacent categories, all at GitHub, and delivering better products. And so that actually, I think you can also underwrite to hundreds of companies that are going to be worth billions or tens of billions of dollars. I mean, my mental model there is there are some activities where it's just the more the better. So writing code, selling products, those things are the more the better. There are some that are not like that, though. You know, like customer support is an example that's not really like that. Once all the tickets have been answered, the tickets have been answered.

[13:06] agents are good at answering tickets. So I do think we will probably have some areas quickly where like not as many jobs are available on a per million of error basis on a company. Yeah. And I guess the, you know, if you sort of looked at like global customer support and you broke it out by different categories, there's definitely some categories that AI, you know, is going to be very, very good at. And it's going to, you know, the next task or, you know, [13:37] But even for us, let's say, and we're going to be deploying more AI around these types of use cases, if I can save money, let's say, in customer support, [13:45] or customer success, a lot of times the dollars that we save there [13:50] are going back into that exact same function, [13:53] It's just, it's moving upstream to now I want to do proactive customer success. We have always been constrained on the number of customer success managers that we can afford. Like I would, you know, anybody in SaaS knows this. You have all these like sort of ratioed roles. Like how many SDRs do you have per sales rep? How many CSMs do you have per customer? And we've always, I've never been happy about the ratio. I've never been like, oh, like we have too many customer success managers. And it's always been because of the cost of it in the business model. [14:23] in one area of that function, I can just move now to human labor in other parts. Now, you have to get trained up. But even today, most of our customer success managers were probably customer support reps

[14:37] five years ago or 10 years ago, either at Box or a different company. And so I think there's actually a journey here in a lot of jobs. I don't want to be like overly optimistic and totally say nothing's going to happen and there's no transition because there will be areas that are more disrupted. I think we're all kind of familiar with those. But I tend to think we have more of a myopic view about how talent sort of mobilizes over time and then the new skills or the new demands that the company has as this technology rolls out. Connecting this back to the innovators [15:07] business model thing. One of the things that I find at least interesting at the moment is like, you know, software can charge, you know, 10 bucks, 20 bucks, 30 bucks a month, but agents now you're comping to human labor. And so as a result, some of these agent companies can charge like surprisingly large amounts because even being a quarter of the cost of a person is like a lot more than you would have expected it to be. But you would think that that's going to sort of play out quickly over time and that, you know, we won't stay with agents staying priced relative [15:37] back to cost. How are you thinking about this from a business model and disrupting yourself in that picture? I mean, if I had to bet on, let's say, let's just make it really binary. Like, does AI remain comped at labor or does it remain comped at sort of like infrastructure cost plus software and some margin? I'm going to bet on the latter just because competition almost sort of is going to cause that to happen. Let's say there's this human type of task that costs $100 an hour. And you're just like, I'm going to build AI to do that at $50 an hour. But like the

[16:07] is $1 an hour of compute. [16:09] somebody is just going to say, okay, I'll just do it at 40. And then somebody is going to say, I'll do it at 30. And then somebody is going to do it at 20. And eventually it's going to converge to, okay, we're like, just going to do it like a normal software gross margin type model. So unless you have incredible proprietary access to something like, you know, for anybody listening, like read seven powers it's, it's, it's one of the best books on this, but there's this concept of cornered resources. And, and if you have like a cornered resource that just like literally [16:39] data set that's just like everybody needs and you're the only one that get it? Sure. I think you could like always have AI agents that then get comped as people. But if you're automating, you know, work and it's code work or it's, you know, outbound sales rep work, or it's, I'm going to generate, you know, marketing asset work. I think eventually it's going to converge on more software type margin. But the benefit is, is that you're no longer capped by the number of people that can use the software. So the thing I'm most excited about is not necessarily that AI, [17:07] will get comped at the price of labor, but it's that AI doesn't have the same cap as labor. Like right now, in the SaaS world, when you're selling seats of software, you can only sell to the number of people in the company. That's the TAM. So if you have a 20-person company, you're selling 20 seats. But now with AI, that 20-person company could have 10 AI lawyers and 10 AI SDRs and 10 AI marketers. And all of a sudden, now you're going to be spending way more on software than you were in a prior generation of that company. And so that's the big TAM

[17:37] And I haven't seen great math yet on what that will look like, but I'm betting that that would be a very large number. I would say generally like the consensus AI view right now is like it's slightly overvalued in the short term and things are, you know, things. People are too excited. Prices that people are paying for these companies are too high. But in the long term, it's all good. And maybe we're like. That's your fault though. Yeah, that's probably my fault. No, I'm very early. I mean, if you're naming your podcast uncapped. [18:01] Like you are the problem. Yeah, you gave me good feedback. We might need to adjust that. Maybe just like medium-sized cap. Medium cap. Medium cap. Exactly. Yeah, but like, do you think that, do you see us there or do you see it somehow different? Like are we, is this the internet in 1999 and it's a little bit of a bubble, but it's all going to be good in the long term? Or does it actually not even feel like it's over, like the excitement is too great right now? Because it's kind of all, you know, like these recent YC batches, everybody's working on it. It seems right, but. [18:31] in the near term that are undervalued over the long run, because by definition, the value should price in what the long run value is. So I have a hard time with that particular framing as much as just I'd say that there'll be a lot of companies that don't work. [18:43] And a lot of companies that do, and the companies that do today's prices will actually look small. [18:49] And those that don't, they'll look big. And in a market where you have a lot of energy and hype and excitement, you'll have probably more on average companies that don't work, that will get valuations that don't make sense. That's, that's kind of true of every environment, but it's like, it almost like there's nothing you can do about that. Like, because like the good assets are still going to be the good assets. And in a lot of cases, you don't know the good asset until, until you see three or five attempts at a particular kind of product area. Yep. So I, you know, it's like, it's like,

[19:16] Yes, but then what do you do about any of that? Yeah, sure. Yeah, who cares? That's fair. Okay, so on that point, so you're 20 years now in the box, which is amazing. And I think you launched 2005. Yeah. And that was like before AWS, I think slightly. We were like three months before AWS or five months before AWS. And it's sad because that actually meant that we had to get good at infrastructure. And then you had this sort of path dependency where like because we were good at infrastructure, we kept building out infrastructure. [19:46] to actually be like, no, we should probably be a cloud company. And then another five years to actually make the transition. But yes. But you were at the beginning of cloud and you saw cloud. 100%. And then GCP comes out and Azure. I mean, the timing, looking back on some level, it's like shockingly, like you were kind of first... [20:05] at this layer in cloud. So congratulations. That's really good. As you kind of look back, did it play out the way you expected? If you, aside from that sort of like, you know, you wish you could have launched a year later or something like that. Are there other things knowing what you know now, looking back at that last cloud cycle that you would do differently? Or are there things that you like got lucky with that were super right that you're like, thank God we did it that way? If I have any regrets or things that I would postmortem, it would usually just be situations

[20:35] thing that eventually did work. There were some key decisions that we made early on. This is more the positive, where even in the very early days, for obvious reasons, customers would say, hey, could we run you on-prem? [20:46] And we were just very stubborn and steadfast. And we said, no, we're a cloud company. It's all multi-tenant. It's all SaaS. You can't deploy this on-prem. And for a number of years, it was like, is this the right decision? We didn't really question it that much, but the market certainly did. And customers were, you know, we lost a lot of customers in the process. You know, I'm extremely thankful for some very religious architecture decisions that we made early on that we stuck with because now it's set us up where, you know, apropos [21:16] customer turns it on for the entirety of our customer base. You don't have to be on like version, you know, 19 of box. Like it's just like box is box. Everybody's on the same exact version. And when you have AI, it just like plugs into everything you're doing when you, when you turn on the capabilities. So, so we, we've been able to kind of keep this architecture kind of clarity, the whole, the whole journey. Like even when we make acquisitions, we're very, again, like rigorous on like, we don't keep other things running with different architectures. Like it's sort of, [21:46] plug into the platform. It's got to be on the common file system. It's got to be on our cloud. And it contributes to all of the other kind of multi-tenant capabilities we have. So I think we're benefiting from a lot of that and then drafting off of just, you know, some fortunate architecture decisions, you know, a number of years ago. Do you think like the sort of end state of the market where you had, you know, you had the hyperscalers and then you have you and maybe there's like one or two others that kind of play at this sort of slightly more neutral layer across

[22:16] Do you think the market was always destined to be oligopolis at the hyperscalers with their version? And then there's going to be a player-like box that is going to sort of sit neutrally across? Or do you think that the market could have been shaped other ways and it was just like happenstance that it could have been? [22:34] played out the way that it did. I could totally imagine a world where, yeah, it would not have played out this way. I mean, I think it was largely through brute force that we were able to build fast enough, better functionality, more enterprise-grade capabilities, integrate with basically everybody's software to sort of show that, hey, maybe you don't want your data just stuck in one of the clouds because if it goes there, you're stuck in this sort of vertical stack. And it's [23:04] So one of our advantages in AI is when your content is in box, and this is more relevant for a midsize to larger enterprise, but when your documents and financial documents and marketing assets or HR records are in box, and one day there's a new model from Gemini that is really breakthrough, it just works. [23:34] choose to kind of deploy. And so that sort of flexibility and maybe future proof of what capabilities you have access to became very, very important. But I don't think it was obvious on day one to most customers that that would matter. And now it's actually paying off for customers massively because you're not stuck now and not getting the access to whatever the latest innovation breakthrough is. Yeah, I mean, it seems like the neutrality is very valuable in cloud.

[24:04] the argument in, you know, equivalently in AI now. Yeah. You're seeing different versions and flavors of this pop up. So, you know, I would imagine like Databricks feels the exact same way. Like we're going to run on anything. We'll work with whatever the models. And so, you know, you kind of have to know your, one thing about startups that I find is like, you need to fully exploit your advantage and your position to its maximum degree. If you like only half exploit it, then like, like, then just all bets are off, like good luck. So what we've decided is like, okay, [24:34] like take that to the max of all the software that we're going to work with, the models that we're going to support. We want to be just a neutral open platform, first mover, first, you know, the first company to support any new kind of technology for our customers. If we're not training a model, we better be really, really good at enabling all the models for our customers. And like that's a flip that you just make as a strategic decision. One thing that I find sort of like impressive, particularly impressive is that you've been [25:01] in the public markets for a long time, and you've been doing the company for 20 years, and still you are extremely energetic, very positive. You still want to make huge bets with the company. I did Lattice for nine years, which felt like a long time. I was really tired, but by the end, in a lot of ways, I think that happens to more people than not. One of the types of founders I really love investing in is young founders who, by the time they're- They don't know how painful it's going to be. [25:31] it's like by the time somebody's 25 or 27, they've already got a real company and they have so long ahead of them. You know, you've been doing this for 20 years and you're still young. And so, you know, I imagine when you started, you were like, you know, obviously you were really young. Like how old were you? We launched a company, I figured it was like, basically I got the idea like,

[25:48] late in my 19s and then we launched it when I was 20. And so many of the like best companies of all time were started by people really young who did these really long runs with it. And so I guess like my distilled question on all of this is like, what goes into you staying so... [26:03] excited for so long? Like, how do you bring that? Like, is that just who you are? Is that something that you learned? Is that just, you know, you just enjoy it so much? I mean, it has to be the latter of all those. Like, so, I don't, like, [26:17] Like it only works because I enjoy it. And then maybe the reasons why I enjoy it are... [26:22] are these somewhat timeless things, which is what we all did [26:27] growing up and then eventually getting into tech and into startups. You like to build things. You like to create things. You like to solve problems. You get excited about new technologies. Probably most people in our orbit all have that [26:40] set of conditions. And then the question is, do you have a platform in which [26:45] you can do interesting stuff that keeps that cycle going and keeps you energized. And I think it's probably maybe more lucky than not that this became a platform where I could just keep doing that. We never got stuck in one vertical or one type of use case where you're just grinding out just incrementally, sort of improving that one use case. We've always had some range of motion. We help NASA go to space, major movie studios make a film. We help the research process, [27:15] of a new breakthrough drug. So you can kind of be, you can be a little bit ADD of like all the things that you're doing, the use cases on the platform. And so that's what keeps it exciting. And then my answer...

[27:26] Maybe it would be like, [27:28] 20% less excited three years ago. Yeah, but it's gotten much more fun with AI now. But AI is just like, holy shit. Like, wow. Like, the demos that I see now on almost a daily basis from the team are just like, this is just absolutely crazy. It's shocking. It's shocking. And so now it's like, wow, you're totally like dopamine and you're just totally amped up. And you've got this platform with all the data, all these customer relationships. Yeah, so you're not starting from scratch. I would be very stressed out if it was like, [27:58] you know, you got to totally go from zero. And I'm, and, and I love the energy that new founders have because they don't know what they're in for and they're like so pumped. And what's great is like some of those will be like freaking fantastic and there'll be $50 billion companies. Some won't work, but like, you kind of need that, you need that energy, you know, no matter what, because you are going to, you know, I forget if it's Elon or like Levchin, like somebody like the chewing glass thing, like you're going to do it. Like everybody, you're going to, you're going to chew the glass and you're going to, you know, bust through every wall possible. And, and you really [28:28] it, which is great. All right. We only have a few minutes left. I'd love to ask you about politics for a little bit. Oh, it'd be great to not do that. Yeah. Okay. We can just wrap up. Let me ask you a couple of things. Surprisingly, you are kind of now one of the few vocal voices on the left in like this way that I think is very authentic. And five years ago, I would have been shocked to see that. And you've been willing to sort of through, you know, the text shift to

[28:58] of watched a shift around you, I think. And you've done it sort of, you know, very vocally on Twitter where, you know, you're a public CEO and all this other stuff. Has it been a weird experience to, like, watch it shift around you? Or has it been predictable to you in any ways? Elon and others have had this, like, chart that sort of says, like, I've always been here and the Democrats have kind of, you know, moved here. And I actually think it's, like, pretty accurate. You know, some people get pretty mad when they see that. But I think it's actually a very accurate [29:28] The left has moved left on a number of variables. I think depending on who you are and what you're impacted by, those variables have become just like they raise an importance or they're not as sort of so fundamental. And so I can actually appreciate, let's say, Elon at some point was just like, we can't go to space. I can't build things. We're regulating everything. There's a culture problem that I have a problem with. And so obviously, then you have to eventually switch [29:58] And I think everybody has their own version of that that I can't speak for. People can do that on their own. And my set of variables have just been, you know, I'm completely convinced that all those variables are probably true. And then I just have a different set of variables that are just like, okay, well, I also think that like high skilled immigration is really important. And like, you know, I don't think that like we need to have, you know, so many culture wars on certain topics. And I think we probably, you know, should fund different, different maybe kinds of research things.

[30:28] whatnot. So then I kind of stay still on, you know, on a different side. I'm actually very compelled by many of the arguments of the people that, you know, can maybe change direction or move more to the right. And so, and if anything, I'm just actually kind of upset that the Democrats couldn't kind of get their act together. That's what I was gonna say. I mean, one of the things that's been sad watching that this whole shift over the last, you know, four to six years thing play out is that I feel like the left moved really far, the right made clear arguments, [30:58] to make, but like hasn't made them in a lot of ways. And just like, I think they have clear arguments, but they have some also bad policy, right? So, so we, we live in California. It should be like the greatest place on earth on every dimension. Like, like, holy, like, how do you beat this weather? How do you beat the, just you've, you've completely created the atmosphere of every major tech company. You know, you have Stanford, you have Berkeley, you have Caltech, you have all the surrounding institutions, you have all the venture capital, like you're sitting on this incredible asset. And then like, literally you can't make it affordable to live here. Like, [31:28] that is 100% due to the bureaucracy of our state. So, and that's basically a Democrat problem. So, and unfortunately, like Democrats can't out message that with their policy views because their policy views are in many cases, just the wrong policy views. Like you actually just have to build and you have to create an environment where like you can build things during the election cycle. When, when, you know, one of my, one of my whole kind of pushes was like, Hey, maybe like we could like get some tech policy and sort of pro progress policy, you know,

[31:58] like studying these issues, caring about these issues. And so it was like, okay, let's like nudge them on all these topics. And as I would talk to friends and, you know, folks around tech, and, you know, they would, I would say like, what's the problem with the Democrat party as an example. And there's actually, it was a very long list. Like it was like, hey, I'm in like climate tech. [32:13] And I can't build in California because of the regulations in California. And so like, how, how inane is that where a climate tech entrepreneur doesn't like Democrats? Like that is a big cell phone. And, and so there's a lot of these types of things where, where just like, what if we could just like stop doing cell phones? Like, what if you could just like build houses and do manufacturing and, and we could like lower the cost of things because we actually can like, like have more competition as opposed to more regulation. [32:43] do a bit of a reset. The next four years will be super interesting to watch which side comes out ahead because you do have this warring dynamic of the more progressive, more left versus the more centrist. And even watching the—it's funny because I didn't want to talk about politics. Now I'm just going to unravel. The post-election, you could just watch that there were two completely different understandings of the election. Yeah. [33:10] right? Like one side on the left, uh, one side was like, we weren't centrist enough. And, and, and then, and then the other side was like, see what happened when Harris was, would try to be centrist. Like it didn't even work. And it was like, it was like, no, no, like you don't understand. Like, like nobody believed that, that it was actually centrist. The left feels less unified than the right. And so on the left, you got two sides, one that is actively, you know, feels kind of anti-capitalism, even if they want, you know,

[33:40] possibly win. No, that will never win a national election. It's not possible. So basically, the powers that be have to get in a room and figure out that probably the way to win future elections is there's like five or 10% of people that shifted to the right. And you have to figure out why that happened. And you have to be like, well, maybe it's because our policies lost them. And we have to like be be able to kind of, you know, [34:07] bring back that cohort, however we lost them. Because literally, I don't think, yeah, like the country's not going to lean even more to the left over time. [34:15] It does seem, it seemed like a big cell phone that the left didn't get tech, you know, as like a close ally, which, you know, there's, to my eye, at least, there's no particular reason that this closeness that tech and the government have now couldn't have happened under like a Democrat. A hundred percent. You know, at least now it's there. Are you like, when you think about now sort of tech's sort of like government. [34:34] kind of connection? Do you see that as a strong positive at least? Yeah. So I made a decision, um, [34:41] like actually like three months before the election, [34:46] uh, was just like, okay, post-election, no matter what happens, like I'm just all in on the country. So like, whatever happens, like, cool. Like just like lean in whatever direction. And, you know, in 2016, it was a little bit different. 2017 was a little bit different. Cause it was like, it was such a shock to my world understanding that I was like, you know, like, oh, everything's bad and whatever. And, and it just wasted a lot of time. Like, like there's no positive energy that was, was generated in that. Um, and so, so this time around, I was like, okay, like, like, let's find the positives. Obviously there'll be negatives of things I don't agree with at times.

[35:16] But then there's probably a lot of things I do agree with. And so one of the things I do largely agree with is there is a very pro-tech, very pro-innovation, pro kind of like let's advance in a number of categories, set of individuals around the Trump administration in key cabinet positions that are going to make really positive, I believe, positive decisions for the future of technology. [35:46] build companies, to build technology, to drive innovation. And so I think there's a number of people that are good and strong allies in the administration to go drive that in a way that actually didn't exist in the first presidency. And so basically, I'd say I'm optimistic on tech policy during this administration, for sure. I'm anti-tariffs, just because I think that's I believe in just a global trade system that we actually get a lot of benefit from that. [36:16] I think there's actually a number of potential for pro-tech. You know, the AI, you know, sort of messaging coming out of the government aligns more to my view of where we're at in AI right now, where we just need more progress. We need more attempts at innovation. I think that's going to be strong. And then I think we're going to need to deregulate in certain categories where we need to be doing more building. And I think that's going to be positive. Cool. All right. Well, Aaron, thanks so much for doing this. Thanks for having me on. Really great to have it.