The a16z Show - The New Startup Playbook | Box CEO Aaron Levie

Episode Date: October 25, 2026

Can a 10-person startup outperform a company with hundreds of employees? Box CEO Aaron Levie argues AI agents are giving small teams a level of leverage that simply wasn't economically possible before, creating a window for startups to outrun incumbents.In this episode of Speedrun Office Hours, Aaron explains why founders are shifting from doing the work themselves to orchestrating agents, and why that puts a new premium on strategy, taste, and knowing exactly what to build. He makes the case that nearly every industry and job function could support specialized AI agents, potentially creating hundreds of new billion-dollar companies.They also discuss why enterprise AI may capture much of the next wave of model improvements, how dramatically cheaper AI labor could unlock demand that doesn't exist today, and one thing Aaron thinks AI won't change: distribution. If you build it, they still won't necessarily come. Resources:Watch the original Speedrun Office Hours episode: https://www.youtube.com/watch?v=wZ1joGIUyK8Learn more about Speedrun: https://speedrun.a16z.com/Follow Speedrun on X: https://x.com/speedrunFollow Aaron Levie on X: https://x.com/levie Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Startups can actually outrun these big companies in a way that was just never economically feasible before. You could actually go and compete in almost any category on the dimension of people are missing this whole thing. I really think that there will be hundreds of companies that are all $1 billion market cap, $5 billion markup, $10 billion markup, and beyond, that basically are just, there's near infinite opportunity for new startups to emerge on the basis of... AI is giving small startups something they've never had before. the ability to deploy resources at a scale that once belonged only to big companies. In this episode of Speed Run Office Hours, Box CEO Aaron Levy explains why a three, five, or ten-person team can increasingly produce the output of a company many times its size.

Aaron breaks down how founders are becoming managers of AI agents, why strategy and taste matter more as execution gets cheaper, and why he expects specialized the agents to emerge across nearly every industry and job function. But one part of the startup playbook hasn't changed. Distribution. As Aaron puts it, if you build it, they will not necessarily come. Aaron, welcome. Thanks for joining us.

Thanks for having me. It's kind of interesting that you're actually still a young guy, only a few years older than me, and probably similar to Andrew, but you've been running boxes 2005, which is almost exactly half your life. If you could compare the world of startups now to the world as it was back then,

What do you think about the startup experience has changed? My biggest takeaway actually is I feel really old. So I appreciate that you're using your age as the anchor for what young is. But I think, unfortunately, I tend to feel like very old now. And I mean, I am like so impressed and excited by so many of these young founders right now. And, you know, what's interesting is that in 2005, so we dropped out of college, our middle of our junior year. And in 2005, you know, it was fairly rare to drop out and kind of be, you know, 20 doing a startup.

There were a few dozen of us in the Silicon Valley at that time, you know, I think prompted probably mostly by like Facebook had made dropping out like a thing, 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 like, it is just like now a standard playbook for these kids. And so I think it's, you know, I think it's super exciting how, just how many young people are getting started and jumping right into building startups. So I do tend to feel pretty old. But, but yeah, the playbook seems incredibly different. I mean, the way that startups are are being run right now is the most different than any other period of technological change

that I've ever seen. Where 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. You know, we, at least in 2005, it was a little bit right before the cloud wave. we still went to data centers, we racked servers, you know, we, we, we coded morning and night. 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 ran the business. If I compare the 2025 startup to us in 2005 or let's say the 2015 startup, so if you just use 10-year increments, it is the most different that I've ever seen in the history of my adult life and doing technology now for 27 years, let's say. And it's simply because of this AI-first orientation where you can be a 3, 5, 10-person team and have the output of a 50 or 100 or a 200-person team.

And that just changes everything about how you run these businesses. So I'm wildly impressed by these startups. I also think some of them are just totally crazy and unhinged. But it's fun to watch and fun to learn from. But it is the most different that I've ever seen, you know, how to build a company today. I mean, on that point, it's kind of interesting, right? Like if in the case where you and your co-friety, you guys dropped out, right,

if you had the ability to force multiply with like AI agents, for example. I'm curious what you're seeing, you know, I think you wrote about this recently, how some of these startups are in particular, I think, like doing things in the back, or they're using AI agents in sort of like interesting ways. What's been the most traziesest thing that you've heard seen so far? Well, like if you just take the basic premise and then draw it out, like when we started Box, our rate of progress as a startup was 100% correlated to how many hours we could be on a keyboard and type things.

That was the, that was the, 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, right. We were managing servers. But the four of us,

we all kind of dropped out. We lived and worked in this renovated garage, kind of live work kind of thing. 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 exclusively the case, but

let's say it's increasingly the case. It seems like there's now a new sense where the startup founders and the early employees, and I think this basically, you know, sort of scales to every industry over time. 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 claims at least,

and 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 a reviewer is like 10x the leverage of the person who actually has to create the thing. And so if you kind of have that as your mental model where this person now is the editor, orchestrator, reviewer 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 individual kind of programmer, but I can review, I can have lots of agents now running in the background, and I can go review their work 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 a new form of leverage that never existed in the startup world. Usually 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, because of agents. We're not fully there because agents make mistakes. 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 code-based standards. 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 on the dimension of scale and speed, which would change everything about competitive dynamics. And then as an interesting just asterisks, the big companies find it very, very hard to actually go and equivalently do that because they have a lot of workflows, a lot of processes that are so wired into how they already operate that they won't be able to make the change fast enough. So you have a window where startups can actually outrun these big companies in a way that was just never economically feasible before. And it's effectively all due to AI agents.

So again, starting in code, but I think the most kind of wired in companies are figuring 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 outbound sales. So that will be a pretty interesting application of the start. I guess I think I'm wondering about Aaron is then, like, you know, we've always talked about before that like sometimes everyone wants to work at the work at the speed of strategy, right? And they want to be able to work at the speed of, you know, creativity and thought, right? And like,

nobody wants to spend all their time writing, mark and copy unless you're the, you know, start your Silicon Valley's favorite internet comedian like Aaron, Aaron Levy. But I mean, I, I do think that there is this question, though, that if you are now can be more strategic, though, the decisions you make actually matter quite a bit. Yeah. 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 to mess up. And in fact, like, really the speed of creativity and sort of the chess board actually matters more. Yeah, I think, you know, one thing that is interesting is, so the tendency appears to be 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 code base, to, you know, build sub-agents at the right, you know, parts of your code base. and there's an increasing view of this sort of spec-driven development, which is you write very, very in-depth specifications about what you know. Sure, the idea of the product engineer, right? Like the product person, actually, you know, the pointy-haired Dilbert product manager now truly actually has the power to do their own stuff. You wait long enough and they become, yeah, very powerful.

But 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 there's a huge premium on knowing on actually being very good at strategy, being very good at like what is the market opportunity for what you're building. 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 fairly commoditized, the premium is, have you thought through what you're actually trying to build is 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 forms of value creation and so I do think that the companies that are really religious about getting that right will be the ones that get ahead in the future. I guess the question behind this is okay

so let's assume that if you're a startup, you effectively have all these tools at your disposal. You could obviously adopt one of the bigger folks, right? And then the issue is that is 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, designated agents as opposed to having the single sort of ultimate the ultimate fighter you actually have like a team of the ventures to go and help you in the world of AI. I guess like if you're that startup, right, and you're trying to figure out like what's the Asian architecture

that you kind of ship with first, what's the fast way to get off the rails? 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 feeling, you know, do you just start with like a basic toolkit, for example? Well, the point I was making in that context is not so much about the number of tools you use as much as the need for specialization in some virtual or very tangible way.

And so, like for instance, Claude has created a way to have that virtual specialization by just letting you go and create subagents that own a particular part of your code-based or a particular part of your workflow. And that's sort of how you resolve the context rot issue, within the cloud code kind of paradigm. So it's not so much that you want a tool for everything. It's that we have a very real issue, which is this 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 packages. I think there's like, there's a YouTube video on every way to solve this. 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 can't give any kind of general principle on that because it's just different by team, by company, by what tool you choose. But it would seem that at the very least, your perspective is there is definitely not like, this is not just going to be able to go in benefit incumbents. There's this unique opportunity. If you could be one of those specialized tools as a startup,

yes. You're in a really, really good spot. Like you can basically remake the whole. whole area of the stack. Like the numbers are still remigate out of the part of it. Now, if I flip it a little bit, 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 move 10 times faster than an incumbent. That's fantastic. Now, if you think about it, oh, okay, what does that mean in terms of what you should build? How should you compete in this market? You know, what, like, I think there's infinite, you know, that's obviously, you know, little hyperbolic, 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 software landscape because so many industries and parts of business that previously didn't have software, now we'll have software in the form of AI agents for the first time ever. If I look at most domains of knowledge work, we'll have 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 first ones that have reached some degree of critical mass are coding agents. And there's been very clear startup opportunities in the form of replet,

cursor, windsurf, lovable, cognition. Like, holy crap, 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 even 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's, that's not only happening, but it's actually just scratching the service. 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 service area, so much TAM that needs to exist. Okay, now do that for every single field. Do that for life sciences. Do that for healthcare.

Do that for legal work. Do that for consulting. There is going to be new 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. and it's just a completely new environment for startups to emerge. You said something at the beginning of this conversation that I thought was interesting, which was when you talk to founders, you feel like 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 think it's mostly, I mostly just have to rethink the leverage that you, get from agents in every in every conversation. So if I took an example conversation I would have had with a founder a year ago, and you say, hey, how are you using AI? And again, the standard to that point would have been get up co-pilot, maybe a few users on cursor. They would say, yeah, I'm getting 10 or 20 percent productivity gains. It's really helping me with, you know, type ahead, auto-complete functionality. It's like replaced me needing to Google, you know, when I have a, when I have a search when I have 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 not a, it's not a, it's not a,

it's not a 20, 30 percent, you know, form of leverage. It's a possibly a three to five to 10x form of leverage because of this ability to have background agents. But the problem is, again, 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 code base isn't designed in that way. You don't have the documentation in the code base in a lot of times to support agents

to go and operate on the code base. You don't probably have the tool stack to be able to do that. You haven't done the engineering methodology change to enable dozens or hundreds of people to go do all this at once. It actually is something that benefits a small team where they're not going to have

all these conflicts happening at all time across all these agents. And so this is something that naturally is advantaged for the smaller to start up 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? 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 take a lot of the benefit of these learnings and port them into how we run box. That makes so much sense. All right.

So let's, you know, circling back to the first of our very first question. I'm curious how you're thinking about, if any, like, important things you think folks in 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. Effectively, that's equal to, like, 2,000 people. Yeah, exactly. Yes. But, but, 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 tell Aaron now?

We've already talked a lot about it, but what are the sort of like more basic building block things? Maybe does anything change there? I think a lot of the core principles of company building are relatively, you know, timeless and they transcend any particular technology wave. Yeah, yeah. To your exact point, you know, team probably matters more than ever before

because of the leverage on your co-founders or co-founding team. So that, I mean, that's a great insight. So that was yours. So, like, I just would understand. underscore that point. What a 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, then. No, no, no. So, okay. So 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 I'll be able to start there.

I think, I really think that there will be hundreds of companies that are all, all $1 billion market cap, $5 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 I think you could literally do, maybe Indriason has one of these, you could literally do a matrix that is every industry and every line of every job function in the business. And I think, 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, we want to get the whole vertical. 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, you know, if I had to literally start from scratch, I'd probably be looking at that matrix and saying, okay, is there an industry I know extremely well and that I can go and kind of really reverse engineer, like, what does the work look like in that industry and build an agent for that? Jared Friedman had this great tweet. He's at Ycombinator, and he basically said,

go do the job of what you want the agent to go and automate and be 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 is 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 the opportunity is, can you make an agent that is like the life sciences FDA regulatory review agent? Can you go do the, I am the legal review case agent?

Right. And there's going to be, again, there's going to be a lot of flameouts, 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, the T.M wasn't big enough. There'll be lots of that. But then there's going to be a lot of surprises where 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, 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 it previously, you know, was priced at, that there would be actually, you know, 50x of 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 for $5 or $10 an hour. and it just turns out that 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 this new category of...

Everybody's filing patents now. Everybody is going to be an inventor of some sort, yeah. Like, I can name a bunch of use cases even internally. I'm not going to give too many startup ideas out there, but I can name a bunch of ideas internally where if the cost of the particular function was a tenth of the price, 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 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 amazing, 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 $100,000, $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 that general space. I look forward to after this, there's a gl, all of a sudden, there's this giant surplus of young 20-somethings who end up as like admins in FDA-regulated back offices. Where do these people come from? They're like, oh, Aaron sent me.

Okay, they're just here. They're really excited to go and work here. We really love our industry. I mean, what we probably need is like, we need this like pairing mechanism between, maybe you guys start it, it's like 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 Pfizer, and you're like, you know how to do regulatory review processes. Right, right, right, right. 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 100 of these. So I'm curious to hear about this, right? Because Bach started out in the consumer world, then you guys went to Enterprise. And in the past, even Mark himself over, you know, Ed Greenerowitz once said, he's like, you know, really great founders

that 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 a tack where we kind of push toward,

toward basically enterprise. How do you think about consumer? Do you still think that that's a proven ground or is it one of those things where it's like, hey, that's just a complete different world that we know who knows how that's going to change as well they are? I think there's plenty of consumer opportunities.

I don't think I would sort of pre-plan a pivot. So like, I was not planned, Aaron? It kind of breaks the definition of a pivot. Yeah, yeah, that's right. Okay, in six months from now, we are going to pivot the company, but we're going to first start in a different state.

So I would say that 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 partial to the enterprise because of the space that I spent a lot of time in. I think that by TAM, I would largely bet on AI being a breakthrough for the enterprise market by just sheer size of the TAM. simply because, 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 we think about as much. So I just think like most of the use cases tend to lean enterprise. You know, and it's interesting. Dario from Anthropic had a point on a recent podcast that I just, I think is the most salient way to think about it, which is, you know, If they improve, if Anthropic improves the AI model from basically being like an undergrad in chemistry to a grad student in chemistry, the consumer won't notice in clod. Like there's no question you're going to ask about chemistry in your personal life that sort of is going to require that Ph.D. level. I mean, you know, barring, barring, you have some deep health care related issues.

Yeah, yeah. But the PhD jump is going to have massive gains for Pfizer and the life sciences industry. And so just if you think about where we are in the kind of progress of AI, like I think like most of my, I'm going to just pick on her just because 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 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 even no more chemistry questions. She's done with her chemistry questions. She's not trying to create a new life sciences drug. 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 net positive for me and like, we'll make songs for the kids.

We draw things. We get stories made. I don't need stories to be any better for my six-year-old. Like we have reached the full saturation of capability that is needed in most of my personal life. But again, you go to a law firm 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 into enterprise use cases. So from here on out, it's an enterprise, you know, kind of industry, you know, 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 sort of brought this experience together in a better or a new way. I mean, this is the sort of test, the iOS check. We have Terry Tao, the Australian mathematician, right, like says something. Then I'm just like, oh, things are going really well. He's going to get us warp engines very soon. So 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's 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. 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 that regard? 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 going to sound really lame and possibly, you know, possibly very wrong in five years from now.

I don't think distribution changes that much. Oh, interesting. I think that the, you know, there'll be new channels. Like you'll, you'll be like, I'd like to show up in the Open AI App Store, you know, Asian store. Like there'll be new ways that you think about getting distributed. But I don't think this is a fundamental like, you know, transformation or disruption of the distribution model of tech. If you're a consumer, you better figure out how to go viral. If you're enterprise, you better figure out how to do a, you know, build a go-to-market

machine. If you're fortunate and you're kind of like, and you're like enterprisey, but your audience is somewhat technically savvy or able to do self-serve, then you'll do PLG plus a go-to-market engine. But I don't think we're going to see a new invention of go-to-market because of I don't think AI is a go-to-market related disruption model. Maybe there's an important corollary, actually. And this might be appreciated by AI startups.

If you build it, they will not come. So if you think that because you built this amazing agent that now you've solved your problem, that is like totally you're going to fail. Like 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 it just won't work.

And I think we might have a little bit of an over-orientation toward the, hey, I've 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. Distribution is just the grinding of like, you know, the way I kind of think about it, and I think AI affords this 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 you're building, you need to figure out how to just continue to keep access to that relationship. So you better own your community via LinkedIn and X and Reddit and wherever those people are. And you need to find a way to just bear hug them as an industry or as a cohort. And then that's going to be marketing. That's going to be salespeople.

That's going to be PR, all the things. and the companies that discount that, 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 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. Aaron, thanks so much for doing this. That's a great one to end on. So we really appreciate you for joining us. Thanks, guys. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family.

For more episodes, go to YouTube, Apple Podcast, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com forward slash disclosures.