Transcription
Hello and welcome to A16Z speedrun office hours where we connect founders with some of the brightest minds in tech. Our guest today is Aaron Levy, the co-founder and CEO of Box, which if you don't already know is a content and collaboration platform that powers over 120,000 organizations around the world. Aaron, welcome. Thanks for joining us.
>> Thanks for having me. It's kind of interesting that, you know, you're actually still a young guy, only a few years older than me, and probably similar to Andrew, but you've been running Box since 2005, which is almost exactly half your life. Um, if if you could compare the world of startups now to the world as it was back then. Um, what do you what do you think about the startup experience has changed?
>> My my biggest takeaway actually is I feel really old. So, I I appreciate um I I appreciate that you're using your age as the anchor for what young is, but um uh but but I think unfortunately uh I I I tend to feel like very old now. Um and uh uh I mean I I am like so impressed and excited by um so many of these young founders right now. And you know what what's interesting is that in 2005 uh so we dropped out of college um our um middle of our junior year and in 2005 you know it was it was fairly rare uh to to drop out and kind of be you know 20 doing a startup. There were there were a few dozen of us in the in in Silicon Valley at at that time. Um, you know I think prompted probably mostly by like Facebook had made dropping out like a thing. Um, but it was still a relatively rare event. And what is so fascinating now is the amount of now 20-year-olds that are dropping out. It's it's like it is just like now a standard playbook uh for for these kids. And um and so I I think it's you know it's I think it's super exciting how uh just how many young people are are getting started and and and jumping right into uh into building startups. Um, so I I do tend to feel pretty old but um but yeah, the the playbook seems incredibly different. I mean, the way that startups are being run right now is is the most different than any other period of technological change that I've ever seen. Um, where where if you looked at like if you looked at how we ran Box in 2005 and you compared us to a startup 10 years prior, so let's say you took the 1995 company and the 2005 version of a startup, it actually wasn't that different. Um, you know, we we uh at least in 2005, it was a little bit right before the cloud wave, we still went to data centers, we racked servers. Um, you know, we we we coded morning and night. We we you know, we we we were, you know, a startup. But if you if you kind of like looked at how we ran the company in 2005 compared to 95, it would almost look exactly the same. Like the way that every 95 startup got started looked exactly like how we got started and and ran the business. If I compare the 2025 startup to us in 2005 or let's say the 2015 startup. So you if you just use 10-year increments, it is the most different uh that I've ever seen in the history of of you know my adult life and doing technology now for 27 years let's say. Um, and it's simply because of of of this AI first orientation where you can be a three, five, 10 person team and have the output of a 50 or 100 or 200 person team. And that just that just changes everything about how you run these businesses. So, I'm I'm wildly impressed by these startups. I also think some of them are just totally crazy and unhinged. Um, but it's it's fun to watch and fun to learn from, but it is the most different that I've ever seen uh you know, how to how to build a company today.
>> I mean, on that point, it's kind of interesting, right? like like if in in in the case of where you and your your co-founder you guys dropped out, right? If you had the ability to force multiply with like AI agents, for example, I'm I'm curious what you're seeing, you know, um I think I think you wrote about this recently, how some of these startups are in particular, I think like doing things in the background and they're using AI agents in sort of like interesting ways.
>> Um what's what's been the most craziest thing you [laughter] you've ever seen so far?
>> Well, well, like like if you just take the the basic premise um and then and then draw it out like
>> Yeah. Yeah.
>> When we started Box, our our rate of of progress as a startup was 100% correlated to how many hours we could be on a keyboard and type things.
>> That that was that was the that was the the the the only the only point of leverage we had was how fast we could type text into a keyboard. And we could be typing code, we could be typing marketing assets, we could be typing blog posts,
>> right?
>> We would we were managing servers, but but the four of us what we we all kind of dropped out. We lived and worked in this in this renovated uh garage kind of live live work kind of thing. And and that was the only speed at which we could execute was how fast we could work on computers. the startups that I'm talking to now, it's this is not, you know, exclusively the case, but but let's say it's it's it's increasingly the case. It it seems like there's now a new sense where the startup founders and the early employees and and I think this basically, you know, sort of scales to to every industry over time. They're more manage they're more managers of work >> and the work is being done by agents and so they're no longer rate limited by how quickly they can type and how quickly they can write code and how quickly they can come up with marketing assets. They are deploying agents to go and do a lot of that work. And I was talking to a startup founder recently that you know claims at least and I I mean I basically believe it. he's just reviewing thousands of lines of code a day that the agent did. And the ability to be an editor or reviewer is like 10x the leverage of of the person who actually has to create the thing. And so if you if you kind of have that as your mental model where this person now is the editor, orchestrator, reviewer of of code and they can go on and deploy multiple agents. So they're no longer rate limited by again like one terminal, one user interface that they can interact in. You can only type in one IDE at a time as a as a, you know, individual kind of programmer, but I can review I can, you know, have lots of agents now running in the background. I can go review their work, you know, when they're all done. That's just a totally new form of leverage. And so now the bottleneck is just reviewing code, orchestrating it, integrating it, kicking off agents. The bottleneck is not just, you know, how much code have you written. So I'm mostly seeing it on the engineering side. So the big benefit is it's giving startups um a new form of leverage that never existed in the startup world. Usually you're you you have this deficiency as a startup which is you have two or three people and you're attacking some big incumbent that is 100 times more people and you can basically move faster than that incumbent but that incumbent can always deploy more resources.
>> Well we are increasingly neutralizing that delta because of agents. We're not fully there because agents make mistakes. There's no it's not a panacea. You know, a lot of people describe it like a slot machine. You have to keep, you know, trying over and over again and seeing what the agent comes back with. But you can kind of squint and sort of see what this could turn into, which is imagine a world where the agent could do a few thousand lines of code that was really high quality and it adhered to your codebase standards and it would and it and it really worked and you reviewed the code and you only changed a few things. Well, that would mean that you do have now 10x the engineering capacity as a startup, which mean you could actually go and compete in almost any category uh on the dimension of scale and speed, which would which would change everything about competitive dynamics. And then as an interesting just asterisk, the big companies uh find it very very hard to actually go and equivalently do that because they have a lot of workflows, a lot of processes uh that that are so wired into how they already operate that they won't be able to make the the change fast enough. So you have a window where startups can actually outrun these big companies um in a way that was just never economically feasible before. Uh and it's it's effectively all due to AI agency. So again, starting in code, but I think I think the most kind of wired in companies are figuring out how to do this in marketing. They're going to figure out how to do in legal work. They're going to figure out how to do in in outbound sales. Um, so that that will be a pretty interesting evolution of the of the start formation.
>> I >> I guess the thing I'm wondering about, Erin, is then like, you know, we've always talked about before that like sometimes everyone wants to work at at the work at the speed of strategy, right? And and they want to be able to work at the speed of, you know, creativity and thought, right? like nobody wants to spend all their time writing marketing copy unless you're you're the you know start you're Silicon Valley's favorite internet comedian like Aaron Aaron Levy. But I I mean I I do think that um there is this question though that if you are now can be more strategic though the decisions you make actually matter quite a bit right like you could really like you might really like mess up and then luckily it's it doesn't cost that much but you're still going to mess up and in fact like really the speed of creativity and and sort of the the chess board actually matters more.
>> Yeah. I think um I you know one thing that that is interesting is is so uh that the tendency appears to be um that the way you're building software now at least in these startups is you get really really clear prompts or specs right >> you get very very clear ways of working with agents to understand your codebase to um you know build sub agents at the right you know parts of your codebase um and there's an increasing you know view of the sort of spec driven development which is you write you write very very indepth specifications about what you
>> the idea of the product engineer, right? Like the product person actually, you know, this the pointy-haired Dilbert uh product uh product manager now truly actually has the power to to do their own stuff.
>> You you wait long enough and they become yeah very very powerful. But um [laughter] but so yeah, so it turns out if you're really good at writing a spec and you're really good at knowing and having clarity about what you want to build, that's actually extreme leverage now. So, so there's a huge premium on knowing on on actually being very good at at strategy, being very good at at like what is the market opportunity for what you're building. Um, and um, and not just, you know, previously there used to be probably a premium on just like how quickly can I hack something together. Well, now actually, because the hacking something together is is fairly commoditized, the premium is is have you thought through what what you're actually trying to build. Is there a real market opportunity for it? Is it a good idea? Does it look good? Does it have taste? like those are now the new the new, you know, forms of value creation. Um, and so I I I do think that the companies that are really religious about getting that right will be the ones that that get ahead in the future.
>> I I I guess the question behind this is okay, so let's assume that if you're a startup, you're you you effectively have all these tools at your disposal. You could obviously adopt one of the bigger folks, right? And then the the issue with that is you you've argued around this idea of like sort of AI context rot which basically like the way to solve that is to actually have these sort of like point solutions potentially or like great sort of well well designated agents as opposed to having the the single sort of ultimate the ultimate fighter who actually have like a team of inventors to go and help you in the world of in the world of AI. I I guess like if you're that startup right and you're trying to figure out like what's the agent architecture that you kind of ship with first? What's the fast way to get off the rails? It's it's one of these things where you know it's at what point do you just give up everything and at what point do you start filling out you know do you just start with like a basic toolkit for example?
>> Well the the the point I was making in in that context is is not so much about the number of tools you use as much as as the need for specialization in some in some virtual or or very tangible way. And so like for instance, Cloud Code has created a way to have that virtual specialization by just letting you go and and create sub agents that that own a particular part of your codebase or a particular part of your workflow. And that's sort of how how you resolve the context rot issue within within the cloud code um uh kind of paradigm. So so I it's not so much that you want a tool for everything. It's it's that it's that we have a very real issue which is this you know on like super powerful super agent doesn't exist. It doesn't work. And so you do have to have some degree of specialization and that specialization can come, you know, in a variety of of packages. Uh I think the there's like there's a YouTube video on every every way to solve this. Um, and if you ask, you know, three startups or five startups, you're going to get three or five different answers. And it's totally fine because this is sort of this is the stage where everybody's kind of figuring out the best practices and it's all emerging right now. But I don't have a I don't I can't give any kind of general principle on that because it's just different by team, by company, by by what tool you choose.
>> But it would seem that at the very least your perspective is there's definitely not like this is not just going to be able to go and benefit incumbents. There's this unique opportunity if you could be one of those specialized tools as a startup.
>> Yes, that's you're in a really really good spot. Like you can basically remake the whole whole area of the stack like the number. Now, if I flip it, if I flip it a little bit, because I was mostly answering the question through the lens of generically speaking, the fact that AI startups can can move 10 times faster than an incumbent. That's fantastic. Now, if you think about it now, okay, what does that mean in terms of what you should build? How should you compete in this market? you know what uh like I think there's infinite you know that's obviously you know little hyperbolic but but there's near infinite opportunity for new startups to emerge on the basis of we are going to see a complete shift in the in in the software landscape because so many um so many industries and parts of business that uh previously didn't have software now will have software in the form of AI agents for the first time ever. uh and if I if I look at like most domains of knowledge work uh will have a some kind of agent that is associated with that type of knowledge work. Incumbents will take some categories, but there'll be startup opportunities in every single category for basically building agents that do knowledge work and the the first ones that that have reached some degree of critical mass are coding agents. And there's been, you know, very clear startup opportunities in the form of Replet, Cursor, Windsurf, Lovable, Cognition, like like holy crap. If if we had said 10 years ago that there'd be 20 30 40 billion dollars of market cap in the coding space and we would have been like well kind of GitHub is the only thing that's ever really exited in the coding space. How could there be 30 40 50 billion dollars within just a three-year period? That wouldn't sound plausible. And so and yet that that that's that's not only happening but it's actually just just scratching the surface. I am I am perfectly comfortable underwriting a couple hundred billion dollars of market cap in the AI coding space that don't go to incumbents purely because there's just so much surface area, so much TAM that needs to exist. Okay? Now, do that for every single field. Do that for for life sciences. Do that for healthcare. Do that for legal work. Do that for consulting. There is going to be new a AI agent companies in every single one of those spaces. And not only will they outrun the traditional software incumbent companies, they'll actually they're going to outrun the traditional services companies in those spaces as well. Um, and and it's just a completely new environment for startups to emerge.
>> You said something uh at the beginning the beginning of this conversation that I thought was interesting, which was when you talk to founders, you feel like you're you're doing the learning about 70% of the time. I was curious if there's anything that comes to mind like what are you learning from kind of the new generation of founders when you do speak with them?
>> Yeah, I I think it's mostly um I I mostly just have to rethink um the leverage that you get from from agents in every in every conversation. So, so if I if I took an example conversation I would have had with a founder a year ago um and you say, "Hey, how are you using AI?" And again the the the standard at that point would have been GitHub copilot maybe a few users on cursor. They would say yeah I'm getting 10 or 20% productivity gains. It's really helping me with you know type ahead autocomplete functionality. It's it's like replaced me needing to Google um you know when I have a when I have a search when I have a a coding question. Now if you ask the most advanced startup founders you know how are they using AI? It's back to this point. It's it's not a it's not a it's not a 20 30% you know form of leverage. It's a possibly a 3 to 5 to 10x form of leverage because of this ability to have background agents. But the problem is again I in your existing teams and your existing organizations your existing way of doing engineering is not wired up for kicking off lots of background agents to go and execute on your codebase. Your codebase isn't designed in that way. You don't have the documentation in the codebase. in a lot of times to support agents to go and operate on the codebase. Um, you don't probably have the tool stack to to be able to do that. You haven't done the engineering methodology change to enable, you know, dozens or hundreds of people to go do all this at once. It actually is something that that benefits a small team where where they're not going to have all these conflicts happening at all time across all these agents. And so and and so this is something that naturally is advantaged for the smaller startup to be able to move quickly on and so when I'm talking to startups I'm mostly just learning how are you doing that like and then trying to figure out like what's my way of bridging our company to do that as quickly as possible so we can we can take you know a lot of the benefit of these learnings and port them into how we run box.
That makes so much sense. All right. So, um, let's let's, uh, you know, circling back to the to the first sort of our very first question, I'm I'm curious how you're thinking about, um, if any like important things you think folks need to know. Obviously, we've talked about leverage, agents, the ability to basically be you're a two person start. So, actually choosing your founder really matters a lot more because effectively that's equal to like 2,000 people.
>> Yeah. Exactly. Yes.
>> But but I I mean like thinking through that that paradigm now like if you were you know picture Aaron from dropping out now, right? What would you what would you you you tell Aaron now?
>> Um we've already talked a lot about it, but what are the sort of like more basic building block things? Maybe has anything changed there?
>> I think a lot of the core principles of company building are are relatively, you know, timeless and they transcend any particular technology wave. um team to your exact point, you know, team probably matters more than ever before because of the leverage on your on your co-founders or or co-founding team. So, so that I think that's a great insight. So, that was yours. Um so, so like I I just would underscore that that point.
>> Um what about market for example? Like some folks have said, you know, try to find the most highly specialized really boring market on the planet to find regulatory capture maybe or you like forget about that. That's not going to work for you, man.
>> No, no, no. So, so okay. So, so there's well there's some things that are downstream from you have the idea, but if you want to go all the way up to you don't have the idea yet, then then I'll I'll maybe I'll start there. I I think I I really think that there will be hundreds of companies that are all 1 billion market cap, five billion mark cap, 10 billion mark cap and beyond that basically are just they're taking a job function in the economy and they're building an agent for that job function and they're selling that agent to corporations to services firms to system integrators to everybody. And [clears throat] uh and I think you could literally do maybe Andre has one of these. You could literally do a a a matrix that is every industry and every line of every job function in in you know per industry and it would be like it would be literally 5,000 cells and it would be like there's sales reps in life sciences there's lawyers in financial services and some of the markets you know probably will be TAM constrained but some will be much bigger some need to be kind of like okay we have to get the whole horizontal some will have to be to get the whole vertical article, but I think you could very easily go through the entire economy and underwrite AI agents for any one of those slots in the economy. And um, you know, if I had to literally start from scratch, I'd probably be looking at that matrix and saying, "Okay, is this is there an industry I know extremely well and that I can go and and and kind of really reverse engineer like what does the work look like in that industry and build an agent for that." Jared Freriedman had this great tweet. um he's at Y Combinator and he basically said like go do the job of what you want the agent to go and automate and be like the like the expert in how to do that job and then you're going to be very potent at being able to go and create an agent to do that job, right? And so, you know, probably like the best case scenario is you've already done that job in the past and you just go and build an agent for the work you already know how to do. Second best scenario, have a co-founder that has done that. Third best scenario, go literally find a way to like do that, you know, shadow somebody, do it all day long. And then and then the opportunity is can you can you make an agent that is like the the life sciences FDA regulatory review agent? Can you go do the I am the, you know, legal review case agent, right?
>> Um, and and there's going to be again there's going to be a lot of flame outs, you know, in this space that are again like they just didn't nail the scale. It wasn't the right market fit. you know that the TAM wasn't big enough. There'll be lots of that. But then there's going to be a lot of surprises where it it'll turn out that there's more categories than we think where right now the supply demand equilibrium in the economy is actually not sort of as as sort of kind of perfect as we would have imagined where if somebody could have introduced labor at a tenth of the cost that that it previously, you know, was was priced at that there would be actually, you know, 50x the demand for that particular form of labor.
>> That's right. And so, like, you'll all of a sudden be like, "Okay, I'm a legal review agent." And it turns out that like traditionally legal review costs, you know, $2,000 per hour or $1,000 per hour for high-end work. Now, the agent can do that for5 or $10 an hour. And it just turns out that like we didn't actually have as many lawyers as the world needed. It was just that they were so expensive, so nobody could actually afford them. And then boom, there's an explosion of of this new category of of
>> everybody's filing patents now. Everybody is gonna be an inventor of some sort.
>> Yeah, like I I can name I [laughter] can name I can name a bunch of use cases even internally. I'm not going to, you know, kind of, you know, give too many startup ideas out there, but but um I can name a bunch of I can name a bunch of ideas internally where if if the cost of the particular function was a tenth of the price, we would we would spend we would we would have many more kind of full-time people equivalent of that function. It's just it's too inefficient right now. And so it is always at the bottom of our our stack ranking budget planning list. So we never get to it. But if it was an AI agent that could be compressed at a tenth of the price, then we actually would be spending on that agent. And and amazing
>> because there's no ceiling. There's no ceiling to the ROI potentially, right?
>> There's no ceiling to the ROI and there's no minimum floor price to get started. And that's the like people are missing this whole thing like like to hire a person costs a h 100red 200 $300,000 or whatever depending on the function. to bring in an agent might cost $1,000. And so you will start using them in use cases that you never hired the person for. And that will then all of a sudden it'll it'll kind of reduce the bottlenecks in your business in ways that you didn't anticipate, which then will cause you to actually go and fund those areas even more. So I think there's a lot of opportunity that will kind of look like that, but again for an industry, for a job function, and I think there's endless opportunity, you know, in in in that general space. I I look forward to um after this um there's a GL there all of a sudden there's this giant surplus of young 20somes who end up as like admins in FDA regulated back offices. Where did all these people come from? They're like, "Oh, Aaron sent me. Okay, I'm just they're just here. They're really excited to go and work here. I really love our industry."
>> I mean I mean what what we probably need is like we need this like pairing mechanism between maybe you guys start it. It's like you're you're a strong you're like very clearly central casting CTO, you know, founder. You dropped out of MIT and then you pair them with somebody who just like you pluck them out of Fizer and you're like you know how to do regulatory review processes and we're gonna we're just going to match and then there's just it's just a matchmaking service between the person that knows how to build agents and the experts in the workflow and we just go create a hundred of these.
>> Well, so I I'm curious to hear about this, right? Like cuz box started out as a in the consumer world then you guys went to enterprise and in the past even Mark Mark himself over at you know Eden Horowitz once said he's like you know really great founders actually start out in consumer just because it's so hard to do consumer you have to build products it's very difficult to do but in this wave obviously with AI agents it's way more focused on enterprise and we're sort of making attack where we kind of push toward um toward toward uh toward basically enterprise. How do you think about consumer? Do you still think that like that's a proving ground or is it one of those things where it's like hey that's you know that's just a complete different world that that no one knows how that's going to change as a result of AI.
>> Um I I think uh I think there's plenty of of consumer opportunities. Um I don't think I would sort of pre-plan a pivot. Uh so so like [laughter] like
>> that was not planned Aaron.
>> It kind of breaks the definition of of a pivot. Um
>> yeah yeah that's right that's right. So, okay, in six months from now, we are going to pivot the company, but we're going to first start in a different [laughter] space. Um, uh, so, so, so I I would say that it's it's probably still very good advice if people do need to pivot, but I think there's going to be plenty of consumer opportunities. And
>> I'm I'm partial to the enterprise because it's a space that I spend a lot of time in. I think that the I think, you know, by TAM, I would largely bet on AI being a breakthrough for the enterprise market by just sheer size of the TAM. um simply because uh you know AI is like fundamentally a form of productivity and productivity generally shows up in a meaningful way in the enterprise in the consumer space it's sort of like not a thing that that we think about as much um so I just think like most of the use cases tend to lean enterprise um you know and it's interesting um Daario uh from Enthropic had a had a had a point on a recent podcast that that I just I think is the most salient way to think about it which is you know if If they improve, if Enthropic improves uh uh the AI model from uh basically being like a an undergrad in in chemistry um uh to a grad student in chemistry, the consumer won't notice at, you know, in claw.ai. Like there's no there's no question you're going to ask about chemistry in your personal life that that sort of is going to require that PhD level. I mean, you know, barring barring you have some deep healthcare, you know, related issue.
>> Yeah. Yeah. Yeah.
>> But but the but the PhD jump is going to have massive gains for Fizer and and the life sciences industry. And so just if you think about where we are in the uh kind of progress of AI, like I think like most most of my I'm going to just pick on her just because it's it's you know kind of makes the point like my mom's AI use cases are like they're like fully solved. Like we're done. like she doesn't she she would be fine. [laughter] She would be fine if GPD5 was just like the like the last thing that AI ever produced because it's already solving every problem
>> because she's not no more chemistry questions. She's she's done with her chemistry questions.
>> She's not trying to create a new life sciences drug. Um, even even in my personal life, like for parenting, let's say, I think AI could stop right now and it would already have been a huge a huge net positive for for me and and like like we'll make songs for the kids, we draw things, we get stories made. Yeah. Like I don't need stories to be any better for my six-year-old. Like we're we have reached we have reached the the full saturation of capability that is needed in in most of my personal life. But again, you go to a law firm and you're and you're like, "Hey, I ran this contract through an AI model and it's like, well, there's like 70 mistakes." So, by definition, and back to the original point, by definition, most AI progress is going to now amortize in into enterprise use cases. So, from here on out, it's an enterprise, you know, kind of industry, you know, with with again plenty of opportunity for starting consumer companies to be clear. But it's not because we needed the PhD thing. It's because we needed new packaging in a consumer app that that sort of brought this experience together in a better or a new way.
>> I mean, this is the ter this is the sort of test that I always check. If Terry Tao, the the Australian mathematician, right, like says something, then I'm just like, oh, things are going really well. He he's going to get us warp engines very soon. [laughter] Um, so so I I guess curious how you think about the we talk about this a little bit, which is that like you should have product insights, but in addition to product insights, you sort of need distribution insights. Yes. And it doesn't seem like this is like a big debate where some people say AI has provided some clear product insights, but it hasn't changed the world of distribution anyway, but we obviously see some natural adoption of AI tools. Um, but I'm curious how you think that might evolve. Is it because like, you know, we're just waiting for things to platform and then we'll have distribution insights in in that regard or or do you think that actually it's a whole new ball game. We're thinking about it like radio and television right now.
>> I am gonna take the I'm gonna sound really lame and possibly [laughter] you know possibly very wrong in five years from now. I don't think distribution changes that much.
>> Um interesting. Okay.
>> I think that the, you know, there'll be new channels like you'll, you'll be like, "All right, I'd like to show up in the OpenAI app store, uh, you know, agent store, like like there'll be new ways that you think about getting distributed, but but I don't think this is a fundamental in like, you know, transformation or disruption of the distribution model of of tech. Um, if you're consumer, you better figure out how to go viral. Uh, if you're if you're enterprise, you better figure out how to do a, you know, build a go to market machine. Um, if you're uh if you're fortunate and you're kind of like and you're like enterprisey but your audience is is somewhat technically savvy or or able to do self-s serve, then you'll do PLG plus a go to market engine. But I don't I don't think we're going to see a new invention of of go to market uh uh because of AI. I don't think AI is a is a go to market related disruption uh model. Maybe there's an important correlary actually and maybe and this might appreciated by AI startups. Uh if you build it they will not come. So so if you think that because you built this amazing agent that now you've solved your problem like that is like totally you're going to fail like like you you are going to be drowned out by 10,000 other startups and priorities that companies deal with all day long. So, if you do not think about distribution, then then you it just won't work. And and I think we might have a little bit of an overorientation toward the, hey, I made this really viral video and everybody saw it and I got all the likes and that's not distribution. That's like a cool one-time marketing stunt. Like distribution is is just the grinding of like like you know the way I kind of think about it and I think AI affords this quite quite effectively. You have to own an audience. There has to be a universe of people that are just so passionate about the thing that that you're building. You need to figure out how to just continue to keep access to that relationship. Um, so you better own your community via LinkedIn and X and Reddit and wherever those people are and and you need to find a way to like just bear hug them as an as an industry or as a cohort.
>> Um, and and you need and then that's going to be marketing, that's going to be saleseople, that's going to be PR, all the things. Um, and and the companies that discount that, uh, you're just waiting to be rolled over by somebody who has better distribution and just says, "Okay, I'm going to get this in the hands of all the all the customers."
>> I look forward to every founder basically requiring every employee to have worked in that industry as a blah blah blah person. And then being like, "Oh, I worked in that industry for five, three, four months." Just like when people say like, you have to have worked in customer service to know how to deal with customers.
>> Exactly.
>> All right. Uh, Erin, thanks so much for doing this. That's a great one to end on. Uh so we really appreciate you for joining us.
>> Thanks guys.