Village_Global_Podcast_LIVE_The_Bull_Case_for_SaaS_in_the_Age_of

0:00SAP is a $300 billion company today. So how is it that, you know, SaaS, which was, and cloud was going to be incredibly disruptive to the incumbents. And yet those companies are all bigger today. It's because it actually was ultimately a sustaining innovation for the incumbent. The incumbent was fine to move to the cloud once they were able to. I think there's other AI strategies that are not, we have to build a foundation model. And so you have to, you have to nuance to that. And then the other AI strategies, How are you doing that smarter or less smart? And if you're like, oh, this won't really work. So for example, a lot of startups I think go, oh, I'm going to be building better coding agents. And you're like, well, OK, there's a lot of work in building good coding agents. What's your particular differentiation? That might be harder. I assume the startups in the remission presume stupidity.

0:52on the part of Box. Yeah, for all other comments, that's great advice. You should assume we're going to do the absolute best thing. Yes. Well, good afternoon, everybody. I'm Ben Keznoka from Village Global. Welcome to another Village Global luminary event featuring our chairman, Reed Hoffman, Aaron Levy from Box. We're going to have an hour conversation. I'll ask a bunch of questions. I'll also call on you if you have questions and some of you have submitted questions in advance. So we'll try to keep this interactive. Let's get right into it.

1:28You often advise founders to think about what's true and contrarian. The first question, though, is going to be for Aaron along those lines. He did too soon. You're like, this is my chance. Aaron, speaking of contrarian and maybe true, you have a thesis that agents will actually make SAS more valuable, not less.

1:47Wall Street right now has a certain narrative. It's every day I'm being tested on that theory right now. I started that theory when the market was like 40% higher than where it is right now. So I'm like, we'll see if this one's true. Give us the bull case on SaaS companies in the world and AI and agents. Yeah. No, my confidence is being tested very, very aggressively. So the point when I when I put that out there was as more of a maybe a philosophical architectural point as opposed to like all of sash should be worth more than it is now. And so I think I do think that the market is the market does need to decipher which software reduces in value and which one grows in value. And so I think the market has decided not to do that. And it sort of lumped everything together.

2:41So forgetting what's happened in the past maybe three months in software, I think the general point I was making was there's a lot of software where if you had a hundred times more users on the software than people previously were using it, or a thousand times.

2:58that the sort of value of the guardrails, the workflows, the business logic, the security of how that workflow worked and the data that was involved in it would go up because you actually have even more risk of something going wrong because now agents are roaming through these systems and pounding them at, again, 100x more than people ever did.

3:19And so in some areas, the value of that business logic now is actually multiplied because we've actually had a lot of categories of software that were artificially constrained by just the fact that the total number of users you sold into a company was constrained by the headcount of that company or that particular department for that use case. And so there's a lot of categories of software where like, you know, everybody knows Harvey at this point, but like the market size of the contract management market previously was like, the total spend on all software ever in time in the contract space is a couple billion dollars a year. That's like how big the TAM is of contract software. Well, now in a world of agents where those agents are doing a substantial portion of the work in the software, the value of that category can be tens of billions of dollars. There's sort of not a particular limit that is out there because now you just deploy these agents 24-7 and they're operating within the system.

4:14So I think the challenge is that we haven't as an industry, and Wall Street hasn't been able to figure out who looks like that. where the agents doing work within the system actually grows the value of the platform, versus there's going to be definitely software where the agent is now doing so much more of the value proposition of what the software used to do that it compresses the value. And there will be cases where that happens. And those will be the areas where the platforms are less valuable. And I think right now, everybody's just trying to figure out which side they end up in. So which are the categories that are growing and which are the categories or use cases that are shrinking? Yeah.

4:50What are that for you? Oh, OK. OK. I was like, yeah, that's a good summary of what I said. I probably should have said it that fast, actually. I mean, she's like, have you seen these market maps of software categories? Like the logos become this small. There's like 9,000 SaaS products in the world, not even the world, probably per category. So I think it's hard to say. I think my general rule of thumb would be which categories of software, again, would have had much more demand for that software if the population of users in the company could have just, there was a larger population. Which things structurally add a lot of value, but they get underutilized?

5:32Okay, those things should go up over time and which things have already reached kind of saturation and they probably have overbuilt their functionality and the functionality kind of gets underutilized because it's just not that you know, there's too many features. That stuff probably goes down in value and so to take an example like so HR attack.

5:47I think you can't even do it at a category level like that. I think honestly, it's like subcategories beneath that. Because CRM, as an example, there's lots of cases where I would spend more on our CRM system if it generated the leads, if it followed up with the leads, if it could connect a LinkedIn and roam around and get all of the updated contact information. We would spend more on CRM systems if it did that.

6:14whether that was Salesforce or a startup. So in that case, I would make the argument that actually a CRM system and a go-to-market world, you'll spend more money on because in a world of agents, they can be doing so much more work for you to go and actually grow your business. So that's actually the area where I'd say more dollars will go into the category over time. Again, does the incumbent get it? Does the starter get it? That's up to the execution of the teams. But I don't think that less value goes into the CRM or go-to-market technology stack because of agents. I think more value goes in there.

6:43I agree with everything Aaron said, but I think there is one additional question that is partially top of mind for people, which is, how much of the additional agentic value goes to the SaaS company, startup or existing, and how much it goes to the AI model agent companies? Yep. So that's another part of the variable. And so part of I think I completely agree with you that some SaaS will actually become more valuable and some SaaS will become less valuable.

7:10But part of it also is how intrinsic is the value of that SaaS ecosystem, not as it is today, because the rebuild of the tech stack, all the deep features, lots of too many features or too few features or whatever, how that plays into essential value that is not just commodified by the agency. Yes. And I think this is why this is like, we could do a four hour, and we need a whiteboard because you got.

7:35three to four moving pieces in this and and we brought up the probably the most important one which is which is does the does the value of what used to happen in the software go to some horizontal system of intelligence of which there's only three or four major players that could probably capture that versus how much really is actually business logic you know kind of required where the agent equally needs so much that's in that business logic layer and either the agent is native to that platform or you know, the agent at the horizontal layer is paying some kind of tax to use, you know, be used in that system, then that will be like depending on where you are in that continuum will mean more or less value to again that SAS layer. And how do you read think about the startup versus incumbent framework in general? It's obviously top of mind for all the founders here. What's the general framework or specific agents and AI, but where startups should focus that incumbents might be laggers? Well, the general thing is, you know, since I'm mostly an entrepreneur and investor is

8:33you know, presume incompetence and stupidity from the incumbents. So go for it, right? Because presuming intelligence is not as good an idea. So for example, if you say, oh, what I'm going to do is I'm going to go after Google for desktop or mobile search, you're like, OK, well, what's your particular plan, right, in terms of how to do that. But generally speaking, incumbents will be slow at getting out of their own way. They have innovators, dilemma problems, They have, none of them are currently built in AI native ways. A question about how the marketplace is changing, because it's generally speaking, the default is go for it. Now that being said, what is not a particularly good thing, and this refers a little bit to one of the things that Aaron was referring to earlier, is that if you said, well, I have to build a beyond the foundation model,

9:31multi-billion dollar hamster wheel, then you would better be like intelligently on that wheel. I think there's other AI strategies that are not we have to build a foundation model. And so you have to nuance to that and then the other AI strategies, how are you doing that smarter or less smart? And if you're like, oh, this won't really work. So for example, a lot of startups I think go, oh, I'm going to be building better coding agents, and you're like, well, okay, there's a lot of work in building good coding agents. What's your particular differentiation that might be harder? Aaron, I assume the startups in the room presumes stupidity on the part of box. Yeah, for all other comments, that's great advice. You should assume we're going to do the absolute best thing. Yes, but where do you, because you recently said you're trying to, it's a startup reinvention moment for box or it's kind of a cultural reset for the company.

10:29In what ways do you think incumbents are vulnerable to startup disruption, either in general or in this moment of AI agents? Yeah, I think the only, you gave me a butt, and so I'll give you a butt. So I actually, at least in the way we internally think, we actually force ourselves to think, imagine if the incumbents are actually really good.

10:54How would you win and because if you can develop that strategy then you'll win if they're bad also, but if they're If they're if they're really good you're gonna need to know like what is your what is your angle of attack now the part where I where maybe it meets the middle is You know, you don't want to get yourself into this loop and people do which is you over assume what the incumbent will do, and then that kind of creates paralysis. And then you freeze and you don't go after the market. So I would still go after the market. But you should actually assume the incumbents are eventually going to figure this out. And again, if they don't, then you're going to be bigger than you thought. And if they are, then you at least have had an opening. And I think the only tricky thing is

11:39I think innovators dilemma is very instructive for this whole situation, which is you have to identify where is there something that. the incumbent doesn't want to have happened from a business model standpoint versus where they actually totally find for it to happen. And they actually, it's more attractive on the other end of that journey. And that's the classic innovator dilemma that tells us exactly what will play out in every given market because incumbents don't want to do things that will make them less money. But they are totally happy to do things that make them more money. And so you have to figure out which parts of AI

12:12are actually going to make the incumbent less money or change their business model so dramatically that it's going to be hard for them to to execute that or AI has this in some areas where it's so technically different that they just don't have the data structure. They don't have the ability to kind of bring agents into their workflows in a way that is actually going to work. And I would sort of study that for each market. I think the easiest example of all time that's already playing out in the market, and so we already have the perfect control for this, is what has happened in things like customer support software.

12:47You look at the incumbent models, you have to sell to the seats of the number of agents, the customer support agents, and then, you know, conversely, you have obviously startups, Sierra, Decagon, et cetera, that actually just say, okay, we're gonna do agents. We don't have a seat model. We have a different way of pricing this. It's obviously very disruptive to the traditional seat model. That is a...

13:07innovators dilemma 101. The incumbent has a hard time getting their business model to transition. Effectively, the disruptor has a very kind of clear path to going and disrupting that. So that one's really easy. And we're going to see that in a number of categories. There are going to be spaces, though, that are a little bit tricky, which is like it still requires the same seat to go do the work with the agent and the existing incumbent.

13:31the seat already contains like the data or the workflow that the agent already is going to need to kind of tap into. And so thus the incumbent actually has this sort of natural data advantage in some cases. And then it's really up to the incumbent to be able to execute sort of, you know, flawlessly from a technology standpoint, a business model standpoint, et cetera. The thing I go back to that, again, people need to be prepared for, and then I'll give really good news for startups probably, is if you, you know, like let's snap the line in 2003, let's say, right before the SaaS.

14:00kind of was like fully like we figured out the template for SAS. And then now you look, you know, 20 years later at the market, the vast majority of on-prem software companies are bigger today than they were back then. Of at scale, you know, like Intuit didn't get disrupted, Autodesk didn't get disrupted, Synopsys didn't get disrupted, Siebel sort of got disrupted, but they got acquired by Oracle. So we don't really know how that would have played out. So like Oracle's is 2x, 3x the size of Salesforce, like all of our predictions were wrong. SAP is a $300 billion company today. So how is it that, you know, SaaS, which was, and cloud was going to be incredibly disruptive to the incumbents. And yet those companies are all bigger today is because it actually was ultimately a sustaining innovation for the incumbent. The incumbent was fine to move to the cloud once they were able to.

14:54They got the business model wired up for subscriptions. It actually was a better business model, because you had recurring revenue from your customers, as opposed to the perpetual license model was this kind of crazy treadmill you had to be on. So it was actually a better business model. So the question is, which parts of the software stack actually have a better business model because of agents? Versus, again, which ones are going to be under pressure because of what agents are not going to do? That's kind of the question that you have to do category by category. There's no way to do it in any kind of broad brushstrokes.

15:23SAP was a $300 billion company. That is a mind blow. Yeah. And Oracle's 450. So these are gigantic companies, and they're bigger than any SaaS company that's ever been produced. And so the agents will have some aspects of that with one big asterisk that there is a new kind of disruption, because as you move away from the UI layer into the agent layer using APIs, that will change the value proposition. So anyway, so here's what I'd say for startups. The cool thing is there's actually so much white space for startups.

15:52But it's not going to look like just do the old software product but with agents. It's going to go and do a completely different kind of. of, you know, software category that previously you couldn't have done in software. And that's what agents actually let you do is like go after markets where there was no software incumbent because, and this is the lesson of all the great disruptions in the past two decades is like go build a company in an area that there wasn't software for. There was no Uber before Uber. There was no Airbnb before Airbnb. There was no incumbent that had those business models. Those are where the biggest startups will emerge.

16:30So in agents, you would go after, instead of going after, like, let's CRM plus agents, or it's HR plus agents, you go after the parts of the market where there is no incumbent piece of software because you previously never had digitized that workflow. So this is where you're, I mean, we never had like that much software for doing like audits of companies. We didn't have that much software for being able to do, you know, for being able to do legal work. Like you could go after all the services areas that now agents for the first time ever can go and automate. So there's gonna be gigantic companies that get built.

17:04because there's so much new software opportunity, but I don't think it'll be that you go ahead on with workday and you're like, we're just agent plus workday. Yeah, fantastic. And so we'll do a few more topics and questions on AI then we're going to shift to broader entrepreneurship themes. I've heard both of you say, read a few months ago and Aaron just a few minutes ago in the green room about that we've been a bit surprised at how slow in some sense, AI deployments have been in enterprises. Like if we roll the clock back to the day chat, GPT was launched.

17:34and then said, how many companies two or three years later would have massive AI implementations, we would have thought the number is a lot higher than it is. It's an incredible number of companies we're talking to today that do very, very little internally. So I'd like to hear from both of you about where we are in the rollout of AI, especially in the enterprise. And especially as it relates to predictions like Aaron, you've said in multiple occasions, you think there'll be 1,000 X more Asians inside of a company than employees, human employees.

18:00when, when, when does this actually happen? So maybe read, we can start with you. How would you reflect and you were at open or helping the open AI team at the very beginning, uh, knew the launch was coming of chat GPT. How has the orange nobody? Yeah. Yeah. Well, you didn't tell anybody. That's my business. Okay. Okay. Okay. Like why, why has it been slower in some sense? I mean, so revolutionary on the one hand, but still so many companies haven't really adopted these tools at all.

18:28What does that diffusion look like? And then Aaron, I'd love to hear from you. So one of the things that I think is interesting is all startups are basically, well, if a startup is in this, they're just basically already dead, is essentially coding agent accelerated and is using it. When you go to the smart, competent, incumbent companies, it's very funny because you can look at like a map of what the token count is across the developers, and what you'll find is that it's lumpy. There's a few people, usually a much smaller than 50% that's using it a lot, and there's a bunch of people who are using it very little. The using a lot is not always indicative of being much more productive, because, for example, currently, in all the use of coding agents, because one of the challenges you have with incumbent companies is working on legacy code bases is not yet

19:26a superpower within these agents. So I've talked to multiple very smart teams, not just Microsoft Google and other, but many others, and they're working on it still. So because it's the question of the cold startup. I've got a new idea and I'm going to start writing something. I got some some spectrum. Great. The I have 300 million lines of code and I'm doing the maintenance and the upgrade of it.

19:54much, much more uneven. Now, I don't think that's gonna stay that way. I think everyone's working on that. But I think that's part of the reason why even when you look at the incumbent tech companies with coding development agents, which are the current leading edge of the competence of AI acceleration, that's still, and it's happening very fast, but that's still in process versus like started with kind of OPIS 4.6 as a driver. And so part of it then gets to, what are the things that enterprises care about? They care about reliability of work. They care about security. They care about provisioning. And for example, there really isn't an LLM right now that isn't, if it's generally speaking, open to the world, like just about any one of the brain can figure out how to do a prompt.

20:53injection attack, it's not perfectly defensive. So the enterprises kind of go, right, we kind of want to use them, but we want to make sure it doesn't open up attack services, it's not bleeding information, et cetera, et cetera. So you have all of that as well. And then you add in group coordination, a bunch of things. So I do think we will see the fastest revolution of work processes in human history, but we in the Valley tend to go, it's gonna happen next month. And it's like, that's not how human organizations, especially at scale work because of all this. And one of the things that I strongly, and this is a room I don't need to do that in, but like when I'm talking to a lot of people, I'm like, you should be using these coding agents yourself some to see what it is. Not just because of the magic part of it, which is amazing, but you start going, oh right, it's still got this jagged edges where sometimes

21:51The coding agent goes, I'm just going to brute force my way through this problem. And it's like, cycling and cycling and cycling. And it's on the wrong approach. It's like, no, no, no, you got to appreciate the magic and also still the stunning shortcomings by actually using the product. Yes. So anyway, so I think we will see a major acceleration. I do think there will be a bunch of changes. I think most of the things that are.

22:18like for example, oh, hiring college interns and so forth. It's an AI problem. This is actually a problem of instability of a business environment and correction from COVID, right? So of course companies, when they go, we don't know what the fuck we're doing in hiring. They don't hire interns, right? Because it's like, we don't know what to do. It needs a stable basis. And stable basis is not just a question around AI. There are some questions there. But it's like, oh, are we gonna have growing number of wars, what is that going to mean? Like tariffs, what is that going to mean? And I want to hear from you in a sec, but just we'll take some questions on AI and agents in a minute. So if you have a question, think about it and raise your hand. But Aaron, next three to five years, what are you seeing across all the companies you're working with? How fast do you see the development of these agents? Yeah, I would kind of just underscore what Reed said. I think this is going to take a lot longer than we in the valley have.

23:14maybe at least what the soundbites represent. And if I can just layer in two to three other things. So I don't know if everybody saw the Dworkash Dario podcast recently. Dworkash actually kind of nailed one key point on this that relates to this, but I'm gonna expand it a little bit. And he kind of hypothesized that coding agents are working really well because the code base, as a lot of the context that the agent needs to be effective, maybe 300 million line code base a lot harder, but let's just say starting in a smaller environment. But if you just think, so we all, our measure of AI progress mostly in our industry is related to AI coding progress. And we do this thing where we try and extrapolate that to other areas of knowledge work, but let's just do a ledger for a second. So in coding,

24:06First of all, these models have had a two to three year head start on being trained specifically for coding. So all the RL has been done for coding related tasks. It's a type of work that essentially is just text in, text out. So the thing that it needs to produce is just text. It doesn't need to be particularly pretty text. It just needs to be text.

24:30The outcome is effectively verifiable. Did the feature work? Did the product run? So instantly you know within about 10 seconds, did the thing actually do what it wanted that we wanted? The user base, prompting the agents, are the most technical uses on the planet. And they have no problem changing their tool chain literally in a week if there's new alpha that has happened.

24:58So you take kind of four or five aspects of that work and then at this other key point there's so much Greenfield stuff that you do where where you could just prompt something for the first time and it's you know Generate something very useful if you want it so you have four or five kind of huge benefits in the world of engineering Now go to the rest of the world. Let's just take like law or you're doing You're doing accounting or payrolls or you're something like that or you're an investment banker. You're a consultant your context 70% of it is probably completely undigitized. Like it's in meetings, it's in conversations, it's the lunch you just went to with the client. Your data is not in a singular code base. It's fragmented across three or five or 10 different places of where your existing context is.

25:44there's a lot of legacy infrastructure. I forgot that one in engineering. We've all standardized in GitHub over the past 10 years, 20 years. It's like 100% of all work is done in GitHub or GitLab or whatever. But in the real world of work, there's 40 different places where the knowledge is that any kind of agent would ever need to be able to automate the workflow. The users are not technical. They don't want to do any of the things that we're all doing here. They don't want to install Open Claw.

26:13Like, they literally, they were if they do, they don't know to do it on a fresh mat minute. Yeah, sure, yeah. I mean, if they did, then they've given up all of their information, you know, to the internet. And then you have this other problem, which is the work isn't verifiable until it hits real life in most of these jobs. So if you go and generate a contract, your ability to verify whether that contract was good doesn't really happen until either like somebody else redlined it or like there's litigation.

26:40So you could have weeks before you know that the thing that was output is actually a thing that is going to work and be sturdy enough for you to put in production in whatever that type of work is. So just compare that ledger. And we've got every perfect condition for AI coding to take off and rapidly self-improve and us be able to adopt these systems and the best practices.

27:02to the rest of knowledge work. Imagine going to a knowledge worker and tell them about agents.md. You just wouldn't be able to explain that. This is the delta that we're going to have. So it's going to take years and years for this to play out.

27:19There's one interesting X factor, which is you'll have AI native companies in a bunch of these categories that get built. I'm actually pretty excited by that. Like the AI native law firm will totally outrun the traditional law firm. They'll do 5x the amount of legal work. The AI native, you know, ad agency will produce campaigns for, you know, five times the number of clients. So that'll be this really interesting pressure. And that's the entrepreneurial opportunity. Yeah. So either you're building the tools for them or you're building the agency themselves. That'll put some really compelling pressure in the market. But even just

27:48Again, a little bit of pouring some water on reality here. Even when we've had that kind of disruption, we had this in the digital, non-digital world. in the early 2010s where, again, Uber, et cetera. Even if you look today, we've had Robinhood. We've had all these things. JP Morgan is a $700, $800 billion company. So even when you will have pressure coming from these new startups, the incumbents, again, eventually rewire their practices to support that. So I think it's going to take a while. This is actually why I think there's so much opportunity is because the opportunity is which companies can actually bridge

28:25these incredible breakthroughs that we see in Silicon Valley with real-life work environments. One thing that is not going to be surprising is the headcount on professional services, whether you call it FDE or something else. This is a multi-tens of thousands of person problem.

28:42as we enter the real world. You have to rewire your workflows in an enterprise if you're really going to get the full power of agents. This is why Deloitte is still going to exist. This is why Accenture is still going to exist. But they'll be smaller, right? In terms of head counter. I don't know. I mean, who knows? So you think in five years Deloitte will play the same number of humans? I can argue both sides. I think... I can argue more, same, and less. Yeah, exactly. Exactly. Well, I think we can all argue for less. What's the argument for more? It's Chevon's paradox.

29:11It's actually, in fact, at a certain price point, given the work is so much more amplified, there's a huge amount more demand for it. Almost all of the mistakes presume that there's fixed demand.

29:21Yeah, and there's many things grow the pie. Yeah, there's not fixed man And if you're imagine, you know, let's say you're you know, every company in the economy we agree needs to will eventually need agents running around doing all this work So who's in a really good position to help companies do that? Is it is it each company one by one themselves learning how to do that?

29:42Or is it five or 10 companies that have built best practices that can go and diffuse them across the economy? This is what systems integrators do. They're doing their 10,000th Salesforce implementation. So when you are in line, you get the benefit of being 10,001. So when they go and re-engineer your banking workflow with agents, you're going to want to hire the one that just did it across the street as opposed to you have to go learn all the sort of mistakes from scratch by yourself.

30:11So there's going to be so much business for all the layers of professional services in this environment. Questions on AI or agents. Is Lavanya here, by the way? Okay, let's go down here. My question is about how do we size this market opportunity? Because you said something, Erin, at the very beginning, which is it's about we can think about how much our company is spending on contractors for a given work and I don't think that's right because imagine that I'm a farm owner back in the day and I'm spending a lot of money on farmers and now I get to buy a tractor that spend is going to go way down and I'm not going to go look at that money and say okay how else do I spend it that's it's more about I think the type of work

30:55that is gonna become more or less valuable. And I agree with you that there's gonna be new types of work that no one was spending money on at all because it was just too out there, too expensive. And now all of a sudden they're gonna spend money on. And so my question is, could you shed light on...

31:11How do we think about these different types of work because I also agree with you that it's not pervertical It's not going to be so this is going to happen marketing that's going to happen It's going to be different types of work And I don't think we have a good thesis or philosophy yet on how do we categorize the different kinds of work that get commoditized that open air and probably completely take it over versus other kinds that don't then companies actually spend a lot of money on yeah, I mean this is the tens of trillions of dollar question because it represents all of all of work I mean

31:40You know, back to introduce Jevons Paradox into this, I think a lot of it will come down to which work actually did we have a artificial sort of demand constraint on, or supply constraint on, where there was actually way more demand. And I think it's why you kind of look category by category. And you have to kind of look at what parts of the economy are you know, either don't change or gonna change in different ways. So as an example, we are hiring more sales reps than ever before because there's nothing you can really automate other than like we can automate the.

32:15pitch that they generate, we can automate the email scripts. But if we can be in more conversations with customers, then we will hire more sales reps because that's just even more deals we want to go close. So there's almost nothing we've been able to, we haven't automated away the job, we've just automated various tasks that make them more productive. So there's lots of parts of the economy that will actually have that aspect. And Aaron, do you think in enterprise sales, it will still be as human to human? 100%.

32:41OK, so my agent's not going to talk to your agent to do a deal. It'll talk to them as much as it can to then eventually just get us back into a meeting to discuss the same set of things we were discussing before. Because at the end of the day, you still want to look at somebody and you want to say, you're going to be responsible for making this project successful. I can't hold an agent accountable to anything.

33:06Me turning off an agent and pulling the plug, it doesn't care. That is not accountability in any real sense. I have to look at somebody and say, your job is on the line for this thing to be delivered. That's going to be humans. And read you by that. It's certainly true for longer than most technologists think. Precisely because business is built on trust. It's built on a trust of, you're actually going to deliver on the product. I'm going to look.

33:36good for it, I have a person to call in order to make it happen. That is years. It may eventually as, hey, we have this trust protocol that's actually really working for agents and that works and that can build up, but that won't be like this year. Right. And it will take some time. Now, the other thing I would add. But read, sorry, before you transition, but like it's interesting to think about people.

33:56In high school today say anything about the jobs market 20 years from now like in 20 or 30 years Are we hiring human sales reps in the way we are today or and to do that face? I actually would guess we're not hiring the way that we are today, but I would also I Could look this is all looking through a glass darkly. You might be hiring sales reps. They look somewhat different they'll be differently empowered. Like the agent's done 99% of it. Two humans show up to shake hands and say, well, but also maybe like, Hey, this is the things that I want to make sure that in the agent dialogue, you're really backing up, et cetera. Okay. I got off a customer call, you know, two hours ago and they, they can't decide if they should go with anthropic or open AI for something. Okay. So what do you think they're going to do next? They're going to have a ton of meetings with both of those vendors because there's no like the agent.

34:46could have made any recommendation on how to determine that, they're still gonna then go and be like, well, shoot, I did hear this one thing from this one peer, and I think they deployed it this one way, but this other direction, that's all can only be adjudicated by people talking and working through that. So I think the human in the loop component will be much longer, maybe transformed than people think. Now, the other thing about market sizing as a general principle, is, it is exactly right, markets change. Like for example, part of a bunch of people were like, silly about Uber, because they were like, oh, it's just limous, right? And market changes. Similarly, when I invested in Airbnb, I probably could have spent the week calling everyone who was actually doing a stay that week, right? If I'd spent my whole week doing it, because it was so few. But what you're trying to do is you try to go, OK, where do I think

35:45there will be natural, strong economic trends, and that there's something that you can build a useful asset in there. And that's actually part of the entrepreneurial bet. Now, sometimes it's a pure substitution effect. I have a much cheaper product that's much better, and I'm going to replace a lot of it. And as per Aaron's earlier examples, frequently what happens is, like, for example, people have been going, PCs, totally dead, it's all going to be mobile.

36:14the growth is in mobile, PCs continue. And I don't just mean Microsoft PCs. I mean, you know, desktops and so on. So what it is, is you've got this big area where all the growth's going, but a bunch of things also still continue. So that's part of what you're doing when you're trying to figure out like what, what happens here? One more question on AI or agents from anyone. Let's go over here. We'll do, if you can make it quick, we'll also get us to meet them, but let's do sure here. Yeah. I'm sure you're a couple of minutes of view with built agents for recruiting. Is it?

36:43unreasonable or a waste of time to think that there will come a point where the intelligence of models of GPT-10 or OPUS-9 is so high that it makes no sense for Anthropic Open AI to make that publicly available because they can capture all the value theoretically or practically a loss of it and not give it away to us.

37:12Is that unreasonable or like who cares or yeah. Well you definitely care as we should. I think part of the thing that I as I look at this is I think it's best for the startup ecosystem in society when there's multiple models that are competing because as long as there are at least call it two or three that are really competing then they have a reason to compete with each other and they say well you know I have one that I'm not going to share with you but Aaron does.

37:40And so that creates the incentive. And so we want to make sure that it gets that way. And it isn't like one model to rule them all to be the lord of the rings. And I think that division is very important. And it's also one of the reasons I help them start open AI. I think you'd have to really do a big, trying to jump in your mental model of the AI landscape to have that outcome.

38:09you're always going to have a counter pressure from China with open source. The way that markets always eventually play out is there'll be an Android to the iPhone. So somebody with enough compute capacity will say, well, our only way into this market now is to do the open model because all the closed models are the incumbents. You have enough players like Nvidia.

38:37just said something about $25 billion or something in open models. You have nation states. Enough players want to make sure that this doesn't get locked down. So sure, there could be a six-month advantage from the kind of closed models. Closed, in this case, being not even an API I think was the kind of intent of the question. But I think unless there's some secret.

39:00to some algorithm that gets invented somewhere. And as long as these ideas continue to be shared, as long as everybody can keep buying the same training data and have the same RL setups, it would be hard to imagine that these APIs won't be available. Let's do Sumit. Thank you. Hi, I'm Sumit, founder of a de-stream firm called WorldBuild, which village has backed since day zero. My question is regarding agents.

39:28unbounded complex tasks, do agents just become coding agents? Don't all the agents become coding agents for these complex tasks or do you think there's room for specialty types of agents? I think there will be room for specialty agents. I do think that part of the reason why the coding thing is so important is because I think it will underlie a number of the different kinds of tasks that involve planning, logic, et cetera, and they're the natural kind of baseline for that. But I think that we haven't really, we're so early in this that I think there's actually different compute fabrics that we'll be deploying that aren't just kind of loops with LLMs, right, in terms of this. And I think there will be ways that that will play into interesting things. Like I think there are ways that diffusion models reason that aren't just purely

40:24like graphical and so forth and I think some of that like that's just one clearly present example but I think there'll be others. Which ones there are is a very interesting bet from an entrepreneurship perspective because there will also be a whole bunch of compute fabrics that we tried that won't get adoption in the field. Aaron any specialty agents that you're bullish on? Well I would hope I maybe I would just kind of make it a hybrid thing. Right now it seems like the winning paradigm is agents that can code but are given a certain kind of domain specific task and context and because it's kind of like this amazing combination is like what if you took a life sciences researcher like level of knowledge but they were also an expert engineer.

41:13So they could do unlimited amount of, they could write any amount of code to do their task, but they know that the task is within the domain of life sciences and research. And so now there's like not all areas of knowledge work are benefited from that, but I think there's actually a reasonable number, which is like, if I could do my job, but also have a staff of 10 engineers that could write any script to do anything I wanted at any time, what would I be able to automate?

41:38But that is what will come for all knowledge work, which is like, what would a lawyer write code for? What would a marketer write code for if they had an expert engineer working on their behalf? There's this thing that went viral like last week, who's anthropics growth marketer. There's kind of one employee of anthropic growth marketing. I kind of skimmed it, so I'm probably not even, you know.

42:00Restating the facts but it seemed like it was one person that like did the SEM that the growth hacking the email marketing like the whole stack of marketing And it was because you have all of the expertise of a coding agent that can wire up your systems They can write scripts when when they don't exist you can connect your data flows between things But it would have only worked because you had a marketer telling it what to do for that workflow So I think you package that up you bring that to a lot of areas of knowledge work and I think there's a lot of a lot of interest And yet, constructing agents can still be a pain in the ass, Aaron. You were saying that you recently are close to throwing in the towel on a LinkedIn-related agent. Do you want to tell us about? I thought we weren't allowed to mention that part. I was giving some product feedback, but this is more like in a time machine. This is how founders hard to improve this. Oh, no. This is back to even just Jeff's paradox, which is like, I have a particular thing that I wanted to automate for my own workflow. And I'm like,

42:59Like, to me it's even stressful to think about the steps of going and automating it, because I just know all the, once you know how automation works, I just know all the things I'm gonna have to go wire up, all the data systems something's gonna need access to, and so I'm probably gonna just hire somebody to do it. And yet, I could do it, but I will probably just hire somebody. You could build the automation, but it's simpler and easier and cheaper even to hire. No, but I'm just tired, like there's so many things going on.

43:26And so and this is but like I am a microcosm of the world like like a lot of people are just gonna be like Yeah, I could totally set up my open claw to do this thing But I just like there's too many things to type and click and connect and make sure our sustain and something's gonna break So I'm gonna hire somebody to go do that and that's actually this is where everyone gets it wrong with like the number of engineers that are going to be in the world. And they get it wrong of like, oh, you don't need to be an engineer anymore. It's like, no, no. We're going to build the quick, levelable prototype and be like, this thing is awesome. But then to move that thing into production and have it maintained and have it running ongoing, you're going to hire an engineer.

44:05And so think about how many companies now will do that in their workflows, that this is why you're just gonna have this incredible boom of anything where, again, like anything where you can lower the cost of executing that task. As long as there's more demand for that task in the economy, you will see people get hired to do that work. Let's talk about how to improve as entrepreneurs. So Reed, you were the first or early investor and helped mentor Mark Zuckerberg.

44:34Brian Chesky, Sam Altman at Facebook, OpenAI, and Airbnb respectively. And Aaron, you've been on a now 20-year journey with Box, which is incredibly impressive. Stay tuned. I thought you were going to list my version of that. And Aaron Levy, number four. And you've been mentoring this guy, Aaron Levy. I've been working on it. It's so hard. But you know, I'm listening. Yes, he doesn't listen. I'd love to hear and obviously read you through your own journey at LinkedIn.

44:59Over, how old is LinkedIn now? 21 years old? No, 23. 23 years old. So if you reflected on... Graduated from college, looking at the job market. Yeah, exactly. If you think about the evolution and maturation of Sam and Brian and Mark and even yourself, how have they leveled up as entrepreneurs to go from pre-seed to seed to A to B to now global Titan? And then, Erin, I want to ask you to reflect on 20 years of being an entrepreneur.

45:28How have you leveled up to be all you can be today? So I think the primary thing is to be constantly learning. Part of that constantly learning is that the game changes. And you have to recognize which parts of the game change. And so, you know, part of the reason, you know, you and I wrote Startup View, part of the reason, you know, Blitzscaling is because you're looking at, I played this game really well, got here, have won the game changes. It might be, what is the way the company is operating?

45:58What is the key way that, you know, kind of information flows and decisioning flows work within the company and how do executives and mid-level managers and information systems work? What is the strategy relative to competition? What are the different kinds of risks you take? So example, like one of the things from a very early Master of the Scale podcast with Zuckerberg was I basically said, so you changed move fast and break things to move fast with stable infrastructure. Yeah, it's the same thing.

46:26And the reason it's the same thing is, how do you be net moving faster? When you was small, moving fast, the best way was being able to break things. When you get large, if you break the infrastructure, it takes a long time to fix. So you now have to say, OK, keep the infrastructure stable. That's still net velocity as a goal. But that's still a change of the game, because in terms of what you're doing.

46:54a microcosm example through the entire thing like, who are your execs? What's the competition like? Sometimes even, what are you evolving in business model? And all of these folks are examples of extreme learners who are always learning the new game. Just to try this on for size, I hadn't thought about this till now, but it almost seems like the key meta-skill is unlearning as much as it is learning. Remarkable learners over 20-year periods.

47:22unlearn the habits and the frameworks that actually got them to that point, but won't serve them for the next chapter. It's what got you here, won't get you there idea. Selective unlearning. Yes, selective unlearning. But it's such a rare combination, or it's such a rare trait to have been so successful at something with a set of habits and perspectives and market understandings, and then to systematically like remove those from your brain so as to insert a new framework. It's so hard to do. So few people can pull that off. I mean, Aaron, what have you learned and unlearned over your 20-year journey? Yeah, I think my version of how I kind of think about this and scaling and it's interesting. You've caught me at an interesting time in life where I probably operate more like we did when we were 10 people than at any other point. How many people now at Box? About 3,000. And so, but like I'm sort of

48:17behaving for better or worse. More like, again, we're like back in the grind of the 10 person company. So you've locked yourself in a closet with cloud code. Almost just as bad. Ramen noodle supply is piling up. I'm eating a lot of top ramen actually. That to some extent is a moment in time we're in where the kind of Brian Chesky founder mode is actually very real because of how much change is happening in our industry. And it kind of requires both you understanding the company's history and and so like I like my context is pretty critical to make sure that we can make the right moves next and it's all on my shoulders if we screw it up and so that kind of forces a level of just intensity of of kind of focus but I think as we've scaled over the years like my best lessons are usually some form of delegate the heck out of everything you're bad at and that you don't want to be involved in and

49:13Apply just incredible focus and intensity on the things that you're uniquely either good at or like the things that are like the highest leverage parts of the business What's the thing you're uniquely good at the thing that that I'm uniquely at least like to be good at is is I mean we have to nail every every kind of product kind of juncture as a company. And so there's just fundamental architecture decisions that we have to land on. There's fundamental product decisions that we have to land on. And so that tends to be where I lean in in my time. And then the things that I feel comfortable delegating is I have a fantastic team that one person runs all of our go to market.

49:50and I don't have to really meddle too much other than some marketing messaging strategy things or like rough funding allocation decisions of what markets we're going to enter and so you know the more that you can get leverage by not being involved in everything But being involved in the applied areas that are going to have the highest impact that are just the do or die things of the company, of which, again, there's a longer list today than there have been in the past many years. That tends to be how I think about being a founder. And I think there's a lot of founders that I think when you're three people, this advice doesn't work. If you're 20, it starts to work, which is if you find yourself just grinding on something that you hate and you're just not good at.

50:33And then you probably have just not delegated effectively. Help me understand how do you juxtapose founder mode, which is not a well-defined concept. It's a concept people like to invoke to justify whatever their pet theory of entrepreneurship is, with how I use it. It's a very handy phrase. I was actually going to get away with it up until this. But at least as I think I understand Brian Chesky's original use of it, It was about CEOs and founders getting into the weeds. It's one of Brian's favorite phrases. That's kind of the opposite of delegation. If your point is, well, delegate unimportant stuff, but being the weeds unimportant stuff. It's not important. It's just the stuff that you're not going to add the.

51:16the unique level of leverage onto. And I guess it's a difficulty then that a lot of founders and their egos think that they're better at more things than they are. Like is one of the first starting points to get honest with yourself about what you actually are good at? No, well yes, but also I think it's a time allocation thing. You should get really, you should really understand what are the actual real things that are the deeply high leverage.

51:37things that you have to do from everything else. Maybe you are the world's best salesperson, but if you are the constraint in your sales led, in your founder led sales motion, then I can just guarantee your business will not be as big as it likely could be if you hired a really good head of sales and you freaking turbocharged that as a machine. And so I'll talk to the founders all the time where you talk to them that they have got an incredible product, and you're like, how's your sales pipeline? And they tell you all the customers that they are personally handling in their pipeline. And it's like, why have you not hired a head of sales yet? Why are you the bottleneck in every single sales company? What's a common mistake that founders make when they hire their first head of sales? You're going to make that. Are two senior, do they get the fancy resume? Honestly, these days, we're still replaying this set of issues from 15 years ago.

52:32I think the number one mistake founders have right now is they don't hire the head of sales. They don't hire soon enough. They do not hire soon enough. I think that is more frequently happening than people are hiring the wrong head of sales. And it's just because back in 2011, somebody wrote a tweet that said, here are the three types of heads of sales you're going to hire. And it's going to be wrong. And so you need to do it. And that tweet is wrong. And you are slowing down your company.

52:59I just would more look at your business through that lens, which is what things are you slowing down because you don't have leverage in that particular function. And so we've always tried to just hire for and delegate the kind of things that require that. And then the founder mode is just what decisions get made in all of those areas of work that are the single most impactful decisions. So in our marketing strategy, it's going to be like, what's the message that we're going to go and deploy to all of our customers?

53:29I'm in founder mode in that particular thing, but there's 40 other things that have to happen that you don't have to be in founder mode on on that topic. Conversely, in product, it's probably more like 70% of the decisions are a bit more kind of highly important and going to be long-term impactful. And so I'm in those set of conversations at the moment because, again, of how the impact downstream that those will have. Let me add in two things. One.

53:56you should be hiring, the bar you should have for hiring people or people you're learning from. People you're learning from. That doesn't mean that they are perfectly better for you than you at all things, but if you're not learning from them, you're not hiring well enough. And are you evaluating that in the interview process you mean? References, learn, interview process, the whole thing, but that's the target, right? So, because the theory that if anyone whose theory is founder mode is I am better skilled at this entire set of things, That's an idiot. But the second thing is, what I think where is useful in term of founder mode is some kinds of decisions have some high risk coefficient. They might be the sacrifice of like, oh, it's supposed to invest thing in the thing that's obviously going to increase revenue. This will be more strategic position over time or something that's relative to a foundational asset. It's unclear if we can accomplish that or not.

54:54et cetera, so set a risk. And I think the utility of thinking about founder mode is only really owners who feel like I can take that big risk, can do that. And that's the kind of thing to think about is doing founder mode. Not, I'm better at marketing than anyone I could possibly hire. You're like, that's silly. Just to enclose, I want to ask each of you to reflect on the other. Aaron, what do you admire about Reed?

55:22I'll pay you afterwards. I don't know if we use this term that much in our industry, but he's an entrepreneur's entrepreneur. The academics of entrepreneurship, I think Reed will have been probably the biggest generator in my generation of entrepreneurs and get us to think about it as a practice.

55:51And as a art form, I think is really important. And in high school, I did magic. And you'd go to these magic events. That's cool. Yeah, yeah, yeah. And you'd go to magic conferences. And they had magicians, magicians. And it's basically like their art form of card handling was regular people wouldn't understand it or care. But you could watch them. And it was just like, like, the deep intensity of their practice and their craft was in that. And I think you've been able to bring out that in entrepreneurship in a way that is, I think, unparalleled. And then read better. I don't know how your card handling skills are, but not very good. OK, OK. No, terrible. But by the way, because this is one of the things that I will now track as a thread, I have on occasion gone and hired some of the most amazing

56:44magicians that are fine to do things that next time I'm doing that, I'm inviting you. Because I've seen some things that are like, how the fuck do you do that? One of the things about, there's relatively few entrepreneurs that are actually both systematic thinkers and generous. And I think Aaron embodies that because a lot of time, When I hear entrepreneurs say things, it's like just selling your own book. It's not particularly useful to other entrepreneurs in navigating. And not just here on stage, but also what you do in your Twitter presence and everything else, I think has been an impressive leadership. Thank you. We will have some mingling and appetizers and drinks if you'd like to stay with us. Reid Hoffman, Aaron Levy. Thank you very much. Appreciate it.

57:45Hey, this is Ben Keznoka, co-founder of Village Global. Thanks so much for tuning in to the Village Global podcast, where we go deep on all of the biggest topics in tech. If you enjoyed this conversation, please subscribe to our YouTube channel. You can check us out on Spotify, Apple, wherever you get your podcasts. We'd love to see you for the next one.