Software finally eats services - Aaron Levie

23:05You're right. Which is like... I mean, PLG and a lot of these kind of high-velocity startups actually started pre-AI. Yeah, but they're... It's a pretty like phenomenal... Think about like GitHub, think about like Slack, think about like Figma. Yes. Like, you know, you have some pretty remarkable companies that came pre-AI that they're drafting on. And a lot of that was like basically cloud and then the classification and then unlocking kind of different routes to go to market. Zoom was a great early example of this. The thing that AI is, is this an accelerant in building the product, whereas what...

23:31What SaaS and the cloud did was accelerate getting paid, which was a thing that used to take two or three years. I mean, arguably, the cloud also made it much quicker to build a product, because you would have, like, these big... Well, it made it much quicker for customers to all have the same one. I don't think it made it, like, five times faster. Fair enough. So, like, I think it was like, yeah, you can see your website a lot quicker, but like I could see a website in 98 pretty quickly. Yeah, but well, maybe this is an infrastructure thing, building a big distributed service. No, no, sure, sure, sure.

23:59I was not doing distributed infrastructure in 98. Yeah, so I think AI productivity is hard to measure for two reasons. The first one I just mentioned is it's just really dazzling, so I think like people kind of like, oh, it's amazing. The second one is I think a lot of the productivity is actually hidden and people measure the wrong thing, right? Shocking that people measure the wrong thing in productivity. That's right. Literally the history of productivity measurement. But also what happens here is you have the board, and the board is like, we need more AI. And then so what happens? And they go to some CTO or some innovations lab, and then the innovations lab do AI.

24:31And so whatever, they bring in, build some internal tool, and it'll fail. Of course that'll fail, right? But the reality is this AI wave is so personal. Probably most people in the company are using ChatGPT. Probably there is some personal assistant. Probably they're using cursor or some coding thing. And that's much, much harder to measure just because it's not advertised. And so if you actually look at the reports on enterprise things fail, you guys look at what they're measuring. It's like, yeah, clearly some internal project pushed down by the board where they hired some consultant to do it is going to fail.

25:05Those always fail. But that's actually not what's going on. The movement that's happening is a very secular movement. This is the next time that bottom-up adoption is really changing the productivity equation. And that's a thing that it defies. Big companies do not know how to deal with that because they need to control it. They worry about safety and security and privacy and all of their corporate rules. And then also, the other thing I think to overlay on that is AI is a very unique, if that's not a bad way to say things, innovation in that it's like non-deterministic. And so all of a sudden, you have this very personal and non-deterministic thing, which the real problem in a large organization, to all these pilots and AI projects, is that you can't, you can't...

25:55Not just measure, but you don't even want to put out there a non-deterministic solution. Because your whole thing is like, well, we operate at scale, and we have 60 countries. Yeah, the white blood cells. We can't put like a customer support solution out there if five agents in five languages all have different answers based on the way that the customer or the CS agent asked the question. And I really feel like that is going to be the hugest challenge in large organizations figuring out how to adopt things. They're going to just get all bunched up over non-determinism. Yes. Yeah, I think that we will have to have some new form of measurement probably in general on this because so much of the improvement in productivity will sort of be this subtle change of like, I used to go to Google for that.

26:48No, my EA is writing emails using chat GPT. Right, and so where is that showing up? And other than just like, again, when we talked about this in the last podcast, I'm just like, we're going to just start to do higher levels of work and that will just end up looking different. But you won't be able to be like, okay, how do I measure what the productivity was when it's just like, I'm working just totally differently. Yeah. Yeah, it did. But, I mean, Coda, I think, is such a great example. So, like, what do people, in my experience, the more senior folks actually use AI for code? It's like documentation, writing, testing.

27:19I mean, it's a lot of the other stuff that may not actually, like, increase, like, the shipping schedule. But, like, you get a lot more robust code, a lot more maintainable code, a much better architecture, a much better future-forward architecture. So we could be building tremendously better software, but still be shipping features at the same velocity, right? We might need to just measure, like, quality of life as another metric of, like, literally... I'm just happier. I don't have to do stupid shit. Developers don't want to write documents. Exactly. But this is, you know, it's so easy to get accused of hyperbole and overstating it and things, but what I think is just so key to what's going on is this, it is what you were talking about, which is like the whole notion of what you're going to do with the job is going to be really different.

28:05You know, this is my obligatory super old person thing, but the pre-spreadsheet, post-spreadsheet is a perfect example of this. Classic example. I didn't say classic, I said perfect. But before spreadsheets, you would be a banker, and you would be like, I'm supposed to help this company get acquired. And you would come up with a financial model, And you'd have 50, you know, recent MBAs all churning away with their HP calculators, figuring out the financial model. And then you'd go like, okay, let's do this again, but if the interest rate changes. Or if their source of funds change. And you're like, okay, well, that's like a week.

28:44And everybody has to do it all over again. So you actually, your quality of decision was really bad. And so what happened was that just in 1985, like, that job just completely changed. And by 1990, instead of asking the recent grads to do it, you were doing it yourself. I actually remember this absolutely crystal clear. My two cousins were University of Chicago MBA grads in 1985. They did not use a computer when they got their MBAs. And when I was talking about going to Microsoft two years later, they were like, well, you know, we have these kids who use the computer for us. and they ended up using computers and stuff.

29:25But like their whole notion of banking was defined by this multi-week turnaround and then all of a sudden it's just more interns doing their Lotus 1, 2, 3 thing. And this is, it gets to the, you know, what's going on with code and startups is, it's just, there's just like a whole different mindset over how much you can do and how soon and iterate. Figma and Dylan, they're always talking about this. Like Figma is going to now change the trajectory of a design idea to go from like, oh, let's iterate over here to like, let's just do it. Yes, I mean, the, this is, and this is where it's like, again, a weird thing to like, think about the percentage of my productivity as an example.

30:04And like, my day is like not representative, obviously, because I'm bouncing between too many different things. But like, there'll be so many times where it's 10 p.m., in a prior world, I would have sent off a task to somebody, you know, some analyst or chief of staff, type role, go research this thing. It comes back three days later, and then you find the answer, and then now it's obviously just like, kick off a deep research, go to cursor to generate a prototype, do some kind of analysis, and then you have it back in 10 minutes or 20 minutes, and I've just compressed whatever the, whatever that was kind of then going to be as a, you know, serially connected to that task is now just fully compressed.

30:44And so then by the morning, you're now kicking off whatever that project was. Again, like nearly impossible for me to peg like what is that as a number? It's just like a fundamentally different thing on just what work looks like. And because you just compress so many different steps of a workflow into a single action. And so it's just like a completely different way of thinking about work. Where does that fit in for you in what we were talking about, what I was asking about earlier, which is how does the expertise, your expertise really contribute to that? And in particular, I think it'd be interesting for people to understand, like, when you talk to customers, how do you help them to avoid trying to get people to make AI, make them do jobs they couldn't do in the first place?

31:25Because that's an easy point of failure. um yeah i mean it actually is this is this is this really um counterintuitive thing where and you've talked about specialization on the last one is like it the the the biggest gains of ai go to people who have some degree of expertise in an area to know what is actually true what is not going to work what should i what should i integrate from the output of this ai you know like what are that 2 % of things that maybe are hallucinations or, you know, took the data in the wrong direction, if you don't have a deep understanding of your particular space or field or domain, you aren't able to then have the right judgment to make all those decisions.

32:07So I think the experts just get more powerful in this world. And so I would, that's why I'm not even like convinced that you can tell a college student to learn anything different than ever, any other, you know, period in history. Like, be really good at a particular field. And then AI is merely a turbocharger of your capability in that particular field. But like, if I didn't know, if I didn't just like generally know the things I know about, you know, SaaS, which is like obviously like a really like, like weird expertise, but like I'm like okay at understanding SaaS, then the things I give to, you know, a deep research agent that I then go incorporate back into work wouldn't make sense.

32:44Like to me, I wouldn't have the, I wouldn't have all the context for like that one thing that it mentioned. How do I like form that into the overall strategy? but because I have some understanding of this particular industry, that just makes me way more productive. So I don't think expertise goes away at all and I think any of the experts in their particular area just become more powerful. So we actually have a fair bit of anecdotal market data on this. So it's very, very interesting. So if you take a lot of these, let's just take kind of a non-text example like image or video and if you look at the customer base for any of the popular platforms, very interesting.

33:17So if you draw a dollar at random that's monetized, it's from a professional. And for obvious reasons, because they can produce... If you draw a user at random, it's casual and it's in the tail. And so it's fairly clear that this is a prosumer movement from monetization. And I'm associated with a number of companies that work with, say, professional designers or professional creatives. They spend just as much time on the AI tools as they would on traditional tools. It just turns out the output is far more rich and like, you know, it tends to be – They have taste that is – Yeah, but it's still human taste.

33:56There's still like very specific requirements. And so I think, you know, if there ever is an ads model that ever shows up for AI, I think that there's going to be a long tail of people that want to use AI, but they actually don't have the financial incentive to do it or it isn't tied to like, you know, their actual job. And we're already starting to see that bifurcated out and there's going to be another subsection that do. And my sense is, let's say I'm writing, whatever, I'm a casual developer and I'm writing a 3D game, and I want to have a 3D asset. I've got one of two choices. I can have AI create it for me, or I can contract a professional to do it.

34:31Me as a developer, I'm not going to create, even if I use AI, it's not going to be a great 3D asset. And so I think that you're going to have the same line that you have today. It's either you can hack something up yourself, or you can go with a professional, and the professional will be using AI. And I think what's super exciting is that AI has created a third category, which is I am, I'm not trying to do this as a job. I'm never going to monetize this, but there's some product utility gain to me that is worth 20 bucks a month. So my ability to now generate prototypes when I'm by myself just brainstorming, like, again, I'm not going to do anything in the ultimate delivery of that, you know, in any functional code, but like the, like it's worth$20 for me to be able to go and like realize the thing that I'm thinking about.

35:14And so there's just like all new ways of capturing TAM because there's all this utility that gets unlocked. And there's one more thing that's worth noting, which is people that are interested in an area get through AI right now. It's like this is the number one opportunity for somebody, you know, that wants to enter an area to do it as an AI native, right? Because A, like the tool actually can teach you, you know, and then there's just such a paucity of talent out there that you can fill. This is also, I mean, this is the history of productivity in general, which is the more tools that you have available, first the experts use them, and then more people are able to become experts.

35:54And as long as, but that's part of managing your own career. I love that point you made about, like, you still have to be really good at something. and I think that you know if you want to be good at finance or at sales you should become really good at it and assume you're going to use AI for that and you're going to be better than the people pretending and I think that that's very much like if you just take you know we were going back and forth about you know oh it makes a PowerPoint slide deck well it turns out to make a good PowerPoint deck is still a skill and people still pay McKenzie huge amounts of money for better PowerPoint decks with better pictures.

36:26So listen I So I contract a lot of work for videos and art, and I have for a very long time as part of different companies here at A16Z. I have seen the shift for contracting some of these traditional techniques to AI. Dollar amounts are the same. And so again, this is Jevon's paradox all over again. You spend just as much time. It's just the output tends to be more dazzling or whatever it is. And you should see more versions of it. You should run more simulations and other ideas. You've got more iteration with them. You've got more control. Listen, I can have a video where I want a dragon to fly out of the sky, you know?

37:01So you have a lot more control as the customer. But for sure, this is not somehow dropping the cost of output. I want to... Were you about to jump in? I want to circle back to a point you made earlier about that there are 20-year-olds who are building companies in new ways. Because remember a few years ago, I think Patrick Coulson and a few others were asking, hey, where are all the Gen Z super successful founders? Remember that? And of course, there was Dylan Field and Alexander Wang. but their companies took a few years to really work. But now, you know, we're seeing the cursor founders, the more core founders sort of, you know, get to massive scale in a very short period of time.

37:36And maybe it was the foundation model companies required, you know, a certain level of, you know, experienced founder because of the fundraising amounts and maybe the applications are, you know, more conducive to younger founders. But what's your sort of reflection on this? Well, I don't remember exactly the date at which he mentioned that, but I do think there was a period between, in the sort of mid-2010s to early 2020s, where we were actually in kind of a bit of a lull as an industry. And the reason for that was like, we kind of did check off a lot of boxes of the core things that people needed in the world.

38:16And so we checked off a lot of the... Like once you have Slack, you don't need five other chat tools. Once you have Zoom, you don't need five other video conferencing tools. And so it gets kind of derivative, you know, past these kind of core platforms. And so once you have like SaaS, you know, kind of check off all the major like things you do at work. And then in the consumer world, we like, we had ways of delivering food and listening to music and watching videos. So like there's like not an infinite set of things that we do as consumers. Then what is the 20-year-old founder supposed to work on?

38:46Like, they're gonna, it's like, you have pretty finite opportunities as compared to in the mid-2000s, let's say, the whole world was open. You could start anything. And because every single category had to be reinvented, kind of post-mobile, post, you know, kind of cloud maturity. So we now have that era in AI. And that is why I'm like so unbelievably pumped up. And it's because you have a complete reset of the landscape where there's like, there is incumbent advantage in distribution but that is it. There's no other real advantage. Well, there's a bunch of disadvantages. Yes, and then there's a bunch of disadvantages.

39:22But I just, yeah, go ahead. Sorry. No, no, no. But like, I mean, you know where I'm going. So like, so you have this, you have the exact makings of a landscape where new startups can come in and do things that incumbents either can't or there's no obvious incumbent to even do that thing. Because again, you're taking maybe like services and turning them into AI laborer and there was no software incumbent previously to even attempt to do that. And then you have incumbents that have a whole lot of complexity in terms of their ability to go and execute in some of these spaces, and they're not going to retool their entire internal engineering workflows to move at 10x the pace, and so a brand new startup can go and do that and then instantly get the scale of a larger company.

39:59So it's the first time in history where you have none of the disadvantages of a big company, and the traditional advantage you have as a big company is you have scaling of distribution. Scale because you can look at a feature and you say, we're going to go build that next month, and obviously it's harder because there's just lots of complexity to that. But at least you have the human power to go do that. Now, as a startup, you instantly have scale because background agents, et cetera. And so then it's a distribution game. And a lot of these pieces of software can go viral now in a way that wasn't possible 10 or 15 years ago.

40:32So we've kind of neutralized a lot of the incumbent advantages. And so thus, it's a ripe opportunity for brand new startups. Often will be people just coming right out of college saying, hey, it's my first time building a company. Like, they're crazy enough to not know how hard it is. So they'll jump right into markets that otherwise we would assume are like, that market's already solved for. There's no way that you're going to build a company. And you'll just have new startups that actually go and do it and actually produce real companies in these spaces. Yeah, because, I mean, this is just so critical because what's really happening is this is why you know it's an actual platform ship.

41:08So Silicon Valley has seen this movie many times before. And that's why, often, there's a lot of this, you know, is this crying wolf or not? Because everybody knows that when there's a platform shift, that's the moment in time that startups are at an advantage. And so, each time there's a platform shift, like, everybody's like, oh, this is it, this is gonna reinvent everything. And then it doesn't, and people get really like, oh, it's always incumbents. But historically, like, the advantages to incumbents are wildly overestimated. And really, I mean, this is this one where, you know, like, you know, was, did the internet undo Microsoft or not undo Microsoft?

41:46It's a super interesting thing, because of course there's a$3 trillion company now, but not on the internet in a way that you think about the internet. Like, none of the consumers, none of the platforms, none of the assets that we had in the 90s became internet assets. I mean, even if you look at Azure today, it's an amazing accomplishment. It's not running Windows anywhere. And I think that that's why, you know, it's not crazy to go, wow, is this your internet? to be good or bad for Google, because there's a bunch of stuff that becomes really, really difficult if you don't make a transition. And then it turns out, historically, even if you do make the transition, you really didn't, and you just have to wait for time to pass.

42:25Well, and this is like Intel with the GPU. Like, they missed the GPU in 2005. And they missed the opportunity to buy the company, to do the work, whatever. And they kind of missed the data center too. It just took a longer time to figure out that they missed that as well. Well, and we have a pretty narrow definition of, like, disruption in the sense of, like, we expect that the incumbent has to lose for this new startup. And that never happens. And never happens. And so it's the whole radio, TV, you know, theater, you know, movie analogy. It's like, no, like, it turns out that Microsoft can be a$4 trillion company.

43:00And you can have all these new categories emerge that maybe Microsoft should have owned if everything was, like, perfectly analogous to the desktop days. but they just don't and it all works together as one sort of ecosystem because it just turns out like software did eat the world and these markets are actually just like 100 times larger than what we realized and so incumbents can grow and then you have new disruptors that sort of emerge along the way. Yeah, anytime you bring in a new technology that brings the marginal cost down then like the market's going to expand and like the incumbents can do it.

43:28I will say incumbents are very bad when new user behaviors and bi-behavior show up, like in particular that they don't know how to cater to it. AI is definitely a new user behavior and a new buying behavior. And so this is very much an advantage of startups just because, you know, to change a large company around a new user behavior, it cuts to the entire company, everything from basically marketing all the way to support in the back end. That's just too much of a lift. I mean, if you look at the best practices of like even how you would create an agent in the last 18 months, I think we've gone through two to three architecture pattern changes.

44:02And so like, it's just like, you know, I can barely keep up as a mid-sized company. I can't imagine if you just had so many more people you had to organize around that. Disruptive new technologies require people to understand how to use them in consumption in different ways. That evolves over time as you get best practices. That sort of flexibility can only come. It's actually a very interesting question of how Microsoft actually did co-pilot to begin with because it was actually one of the first ones that these products was very successful. I learned recently it was created by OpenAI, so that kind of explains it.

44:33But you had a startup person. And conveniently, he was a startup guy. Yeah, yeah, yeah. 100%. And conveniently... Even then, by the way, it's remarkable that it came out of Microsoft. What's always remarkable is when something new and defining comes from these big companies, 100 % of the time, you look into it, and you're like, it was basically Skunk Works, basically they had nothing to lose, and it didn't interfere. Like, the iPod is this classic one. People, or the iPhone even. People always talk about, like, the brave, it's like, their computer business was dead, dead, dead. Like, there was, it was like 3 % share and going nowhere.

45:12So, like, it was like a Hail Mary. The iPod was a Hail Mary. And then the phone, they weren't in the phone business. It didn't matter. And the fact that they made a phone a little computer is what Nokia was like, whoa, what is that? And I think people really need to wrap their heads around the fact that, to your point, that the big companies stay around a very, very long time. And this is something I've seen a bunch of people in the past couple of weeks. You know, oh, it's basically Clay Christensen, Innovators Limeo. Of course, nobody's ever read the book. And Clay is a great guy. You know, he was down the hall when I was teaching there.

45:46And the thing is, is that this is a book, like about two and a half inch disk drives and a bunch of really crazy industries. I really don't think it applies to modern software. But the thing that he missed, aside from he missed the cost and low, high end and things, but was also that the companies don't evaporate. And your point about that is really important. And so what it does is there's this shadow that everybody is worried about. And so you just see it constantly. Well, like, I assume, like, when a company is like, we're not worried about if Google does this. That's what you want to hear.

46:17Right. Because, like, it's just, I mean, and you live this. Because you were like the classic, you know, Steve Jobs said you're a feature. And here, not just that you do, there's like two whole companies that do this stuff. Fortunately, he only said that to Drew. So I got the Gates version of that one. But the one thing that is very timeless about Clay would be the thing that does transcend floppies or whatever thing it is. The incumbent doesn't want to do something that's against their business. Oh, of course. And that part is fully timeless. And to your point, it's actually very rare whenever we say these companies disrupted themselves, it's almost never the case that they disrupted themselves.

46:56It's the case that they went after a market that they had no actual market share in, and it just worked. Which was the development tools for Microsoft. Because there was no business in Microsoft development tools anymore, because nobody was writing Windows programs. So really, it was like, what could we do for the cloud? Honestly, it's even a little bit of a silly discussion to do this with AI. Like, AI is very disruptive. So we're like, okay, well, then it allows startups to work against incumbents. But even in non-disruptive technologies, startups often have a play against so for example every year, I'm an infrastructure investor every year for the last 10 years after AWS re-invent I move into therapist mode and all of my founders call me they're launching my open source it happens every single time and I'm always like you know what, you'll be fine I can't think of one company AWS has ever put out a business by launching a service and you know what, they've all been fine in some ways even in the normal state of business without massive disruption, startups still have...

47:58Don't build a SQL server and compete with Oracle directly. Don't build a word processor. Maybe they're doing that. We're pretty evergreen for a long time. Never do a spreadsheet. There are some categories where people should call us first. They won't change. There's a far side. We should definitely show people that. Can I have 10 cents? Why do you want 10 cents for your startup so I can buy a loaf of bread and beat you over the head with it? It's such a dumb idea. It's a good one. We should have a call-in on this thing, right? Oh, yeah, yeah. Here's my startup. The thing, though, that... The other thing that I just don't think we've had, at least I don't know of a modern kind of case study for, is, again, this opening up of non-software TAM for software.

48:42So there's not even incumbents in the classic sense. The incumbents are really just professional services categories of work. And so it's really, for the first time ever, you're packaging up intelligence for a particular domain and workflow. And so there's no software company you're competing against for the dollars. But it could be the vertical company, right? Like, you have to become an ag company. But the vertical company will probably also be your customer. You have to become a construction company. Yeah, yeah. They're also your customer. So it's actually this amazing thing where the people you're probably disrupting on paper are actually the primary users of your technology.

49:15That are going to actually take advantage. Yes. And so then it's like, there's really no inherent competition until, you know, eventually, like, more companies flood that space to do that idea. So this plays out in practice. If you have a company, an AI company, that goes after, say, like, agriculture or construction, they end up, like, realizing the competitors that are agriculture and construction, the buyer knows how to price things, like agriculture and construction, they end up becoming basically agriculture and construction companies, and then they end up doing exactly what you're saying, is selling to the agriculture.

49:43Yeah, yeah. Because they're not good at that, right? There's a whole world. In fact, the earliest PC software was extremely vertical. Like, if you actually look at the TRS-80 catalog from the early 1980s or late 1970s, it would be like, this is crop rotation software. Literally, like, okay, this is what you should do. And the salesperson for Tandy would show up in Nebraska and sell crop rotation software. And then there was like, I run a dentist's office, and this is scheduling for a dentist's office. And what happened was, And this is what I think is going to happen, is these professional services organizations are, there are going to be some that are like computer savvy.

50:23Yeah. And today they're really good at using existing. They're just going to go, you know what? We should just build like a company. And huge numbers of these verticals are just going to be existing pro-serve that turn into software providers. I think it's an incredible time where, let's just pretend that you're just, you just are committed to not building a software company. Like you don't want to do that, or you don't maybe have the team to do that. So you're going to build like a real world company. it's an incredible time if you started from scratch with now AI as your foundation. If you wanted to be a new systems integrator, and your whole point is that we are a systems integrator, but we use Cloud Code or we use Cognition or we use, you know, Cursor to get the output, you will have such an advantage over any incumbent because the incumbent is not going to be able to rebuild that.

51:06So I've seen examples of people building new ad agencies. Because obviously, like, if you can do literally a million-dollar ad, you know, campaign for, you know,$5 ,000, somewhere between those two numbers, you can charge the customer. So there's like this incredible time where you can just be building all new kinds of companies from the ground up, leveraging the breakthroughs that we've now seen in AI. That actually did, that was an early internet thing that did really happen, which is particularly in the advertising space. And I think it's going to happen in everything that's text, which was there were these digital native ad agencies that just knew how to use Flash.

51:39And they got, like, they would get bought for a billion dollars. The same thing happened with social, by the way. Yeah, exactly. What did you think of this survey that was how many people use AI every week? I thought this was pretty interesting. Yes, what was your Pew response? Well, it turns out, like, the number of people, it's like up to 75 % of adults are using it many times per week. And, of course, you just see it when you use Google search. It's all self-reported, so you don't really know. But it was pretty interesting because I pulled a 1999 Pew study on internet usage. As you would. As I would.

52:11Yes. like basically in 1999, like half the country owned computers. Yeah. And they were all online. Yeah. Even four years post Netscape, it was like still half the country. Yeah. And so you look at that as being slow or being fast. It was fast back then. I mean, that felt fast actually. Well, you had to spend$3 ,000. Activation energy was buying a computer. But still, you know, if you discount the search AI, there's still, you know, activation energy to figure out what this new thing is. nobody knows how to ask questions to a blank edit control, like make me smart about something. I think the universal adoption of this as a consumer technology and then bleeding into prosumer, it exceeds anything I've ever experienced.

52:53And I think it will just fundamentally change people's sort of daily patterns. Like my sister, not in tech at all, teacher, like she was in town and she was like, yeah, I was asking chat this question. And like I had to like do a double take. I was like, chat. And I was like, oh, chat should be tea. That's what normal people call this. And it's just like, it's completely pervasive as just a standard technology. And so that, to me, is just like, okay, we now have the conditions laid for the next phase, which is, and we've now seen this for a couple decades, which is consumer adoption now goes first, and then it gets basically pulled into the enterprise because you go to work and you're like, why can't I ask questions of my enterprise systems the way that I can everything else in the world?

53:36And why am I not getting that same level of productivity gain? And then you're going to have the kids coming out of college that, like, they only know how to do homework with Chachaputee. They come into the workforce, and they're like, why would I spend two weeks writing this report when I just came out writing essays in an hour? Like, obviously, something has to kind of give on this. So this is why, this just lays the foundation for why we're going to see just a massive upgrade cycle in the enterprise. Also, to build on your point earlier, like, about distribution, and the reason that distribution is not the advantage that it used to be is because it already exists on seven billion phones.

54:11And so at every other platform shift, there was an upside to getting new distribution that didn't exist before, but you had to overcome that. Like, you had to get, like, the internet to people who didn't have the internet before. You had to get SaaS to people who didn't. Now, everybody has all of the ingredients right now. Anytime your business strategy relies on Comcast showing up in a neighborhood for me to get distribution, you're going to have some problems. So this is a very different trend. Honestly, last quick point on this is like, for the first time in a very long time, we're seeing brand effects with an early technology.

54:44And what I mean by that is, if you look at like, whatever, the major model providers, you know, how much better are they from each other? Like, you know, maybe, you know, a little bit, maybe not. Like, you know, it changes all the time, but you actually see clear leaders if they break out early just because people learn like the house only, and people learn mid-jury, people know OpenAI, et cetera. So these markets are so big, they're growing so fast, that, like, you know, if you become a leader in your segment, people will just adopt you. But don't discount, you know, like, the early leaders of search were, like, Excite and Yahoo, and so there's, like, this is just to not discourage people from thinking.

55:16Right, like, it's so early that names that you never heard of existed before Google, and that's going to be really important. It's never the first people. When we look at mobile, there were big companies built, you know, like Uber and WhatsApp and Instagram and TikTok, but the biggest beneficiaries were Facebook and Google. In AI, do we think it will be different that sort of the biggest companies in the world in 10 to 20 years from now will be created, you know, after ChatGPT? Or will it be similar to that? What's your timeframe? You know, post-2019, I don't know. No, no, no. How many? 10 to 20 years, you said?

55:50Oh, yeah, sure. Okay, so we can't know if we're wrong until we do this podcast in 10 years. Okay. I think, this is so boring, but I think it's gonna look like what we saw in something like SaaS or cloud. which is the incumbents get bigger but then there's all of these new categories that we would not have been able to predict and then there's lots of 10 and 20 and 50 and 100 billion dollar companies that also emerge and then over time those will just continue to scale. Similar to mobile? And some don't make the transition. Yeah, some will go down on a relative basis or like their market wasn't as ripe for agentic kind of workflows but I think that you can kind of say you know, maybe this is then for another conversation is just like, if you have a current system of record that has a set of workflows on it, where agents make sense to make that workflow much more powerful, that's a good position to be in.

56:41But I bet you that if we look back in 10 or 20 years from now, the vast majority of things agents do don't relate to just those things that we are currently looking at because there's just so many more fields that are now open. And so all of those use cases, I would favor the disruptor or insurgent. And then in today's spaces, I would kind of favor the incumbent on the margin, but the markets are so large that you're going to see kind of growth in all of them. There's a key attribute across all of those, which, you know, is sort of like thought leadership or like who is really setting the agenda for what people are talking about.

57:18And I think that's the thing that really changes. The incumbents become bigger, but nobody wakes up in the morning wondering what they're up to. Nobody starts to wonder, well, if they're going to do it, we need to understand it. And that's the shift. And you can think of it in the enterprise space or the business space, like, what do the CIOs, who do they wake up thinking about? Yeah. And that was a huge shift that sort of goes under the radar. And in the consumer space, it just, like, it becomes the, I understand, I use ChatGPT at school, I need ChatGPT. Yeah. And there's nothing you could do about it as a company.

57:48I think the more provocative question is, is are there any laggards that will use this to get ahead? And we've seen this in the past, right? Like, will Cisco do something interesting? Oracle is making some kind of crazy moves. Like, are we going to see those that, like, missed, like, social? No, I think everybody missed Oracle as an example, right? Yeah, yeah. No, totally. From three years to today, like, you would not have been like, oh, definitely the next point in our company. Microsoft had its moment of, like, you know, you didn't know the future, and then it used cloud and Azure to kind of come back.

58:14And so, you know, this actually is an opportunity for laggers that are behind the curve to come back. Yeah. To the Cisco point, like, data centers are sexy. Like, it turns out that we're just going to be building out lots of AI factories everywhere, So you're going to get more scale from, from parts of the stack that we stopped paying. Like Broadcom, like again, people were not. Hawk, Hawk. Yeah. Everybody look to Jensen and maybe somebody else. So we'll table the rest for the next conversation. Thank you so much. Thanks for coming on. Thanks for listening to the a 16 Z podcast. If you enjoyed the episode, let us know by leaving a review at rate, this podcast.com slash a 16 Z.

58:49We've got more great conversations coming your way. See you next time. 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. Oh