a16z Podcast - Software finally eats services - Aaron Levie

Episode Date: September 24, 2025

Should the US put a price on H-1B visas, or would that block the flow of new talent? Are AI coding agents actually making teams way more productive, or is it just hype? And in the AI platform shift, will the big winners be incumbents or new AI-native startups?Erik Torenberg is joined by Box co-founder and CEO Aaron Levie, a16z board partner Steven Sinofsky, and a16z general partner Martin Casado to debate the biggest questions in tech. They unpack pricing vs lottery for H-1Bs and what we’re actually optimizing for, why Box now ships a third of its code from AI, the shift from writing to reviewing code, and why bottom-up personal AI tools succeed where top-down “AI pilots” struggle. Timecodes: 0:00 Introduction1:07  Latest immigration policy and who benefits1:39  Debating the Price on H-1B Visas2:11  Startups vs. Big Tech: Who Benefits from Policy?2:31  Market Dynamics and Wage Impacts3:44  The Lottery System and Startup Challenges12:25  Labor Markets to Labor Productivity with AIs14:47  Startups Achieving 10x Productivity with AI16:43  Early Adopters, Hype, and Measuring Productivity33:50  AI’s Impact on Professional and Creative Work37:56  The Rise of AI-Native Startups40:58  Platform Shifts: Startups vs. Incumbents42:12  Disruption, Incumbents, and New Opportunities53:00  The Future of Work and AI Adoption54:38  Brand Effects and Early Leaders in AI55:22  Will Incumbents or Newcomers Win the AI Race?Resources:Find Aaron on X: https://x.com/levieFind Steven on X: https://x.com/stevesiFind Martin on X: https://x.com/martin_casadoFind Erik on X: https://x.com/eriktorenberg Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The universal adoption of this as a consumer technology and then bleeding into pro-summer is, it exceeds anything I've ever experienced. And I think it is, it will just fundamentally change people's sort of daily pattern. This is all early adopters and early adopters are very forgiving of mistakes on purpose. When something is brand new, a culture around it develops, the early internet people didn't complain that the internet was slow. Right. The more senior small teams that use AI are super human. Yeah, yeah, yeah. It's like they woke up and they were all fucking Tony Stark.

It's unbelievable. And like their productivity is insane. Should the U.S. put a price on H-1B visas or would that shut out new talent? Are AI coding agents truly boosting productivity or just heightened? And in this AI platform shift, who wins? Incumbents or new AI-NATA startups? Today, I sit down with Bok CEO Aaron Levy, alongside A16Z's Steven Zinovsky and Martine Casado to debate H-B reform

Why Box now ships a third of its code from AI, the move from writing to reviewing code, and why bottom-up AI tools beat top-down pilots. Let's get into it. First, I just want to comment, you posted in the group chat, that the news around autism updates your P-Doom. Yes, it only works if you show the image, though, so you'll have to do the overlay to make that make sense.

But there's so many memes you can do with that Fox News headline. Exactly. First, I want to get into the immigration news. Oh, you really want to kick off just, like, really with the fun stuff. Exactly.

Martine, you have some interesting reactions. Oh, Martin, yeah, exactly. Please. What were your reactions to what you think of the policy? Well, it's interesting because it seems like

any time the administration touches immigration, there's a huge outcry, Newtrick outcry. And we saw a lot of that from VCs even. But there's also very interesting that Reed Hastings,

who is a classic lefty. And has long been was like, I've been doing policy for immigration for 30 years, and this is the right approach. And this is very much my thought, which is this system has been game for a very long time. It's very hard for startups to hire because of the lottery system. It's locked up by the large

companies, the consultants, Amazon, and Google. And that has to change. And I think a very reasonable way to do it is to set price because you've got a market and you need to allocate supply. Price is a great way to do it. So I'm very, very positive on it. I comment about that. A lot of people seem to disagree. So I think it's an active discussion. Well, I think there's a couple elements to this. So one is, first of all, Reed was ultimately responding to a thing that was no longer the actual policy. Yeah. So he said, 100K a year was a great policy, and obviously the Internet had moved on. It's not, to me, obvious.

I wouldn't conclude the same outcome that you just concluded in that I think that you'd have a situation where the Amazon's and Google's would probably actually capture the vast portion of the talent in this situation. So it's not clear to me that, like, startups sort of come out ahead or better off as it was nice, from this particular implementation. Maybe Amazon and Google who are probably more easy to regulate, but there are a number of organizations that are consultancies that actually are price sensitive that would be squeezed by this. I would think that given that they're in the top 15, they make up like four or five of them.

That would be a significant freeing up for a higher level. I think my thing would be like if you could just get all the people in the room that have an opinion on this topic and you, but you actually have the practitioners in tech in the room as well and the, let's say, the most kind of, you know, you can't even say, like, right-wing, because actually, I don't think this is even classic Republican. So it was just like, the polls, if you got everybody in a room,

and he says, he's sort of say, what are we optimizing for? Are we optimizing for we don't want to have wages go down? That's an interesting thing. Are we optimizing for a particular kind of job not going to, let's say, certain populations of Americans? Are we optimizing for just ensuring that we only have the highest merit people on the planet coming here? Like, those are all totally different kind of goals to optimize for.

And I think that the framework you end up with and the system that you end up with should probably hopefully have like a cohesive sort of strategy behind it. My strategy would be we want the absolute best in the world here. There's not exactly clear that there's a fixed number on that. Some years there might be 5,000, some years there might be 50,000. Some years there might be 80,000. We probably want them to be net positive to wages. So let's agree that, you know, in any given industry or locale, wages should go up with

this talent pool as opposed to down. So I think that's actually totally reasonable. And so you should have the market kind of sort of some market dynamic to that. And you shouldn't be able to kind of game and exploit the talent pools for saying, now in Detroit, we can go wipe out IT jobs because we can go and offshore those. Like, I think you could build a system that basically meets all of those goals while still ensuring that you can get somebody that goes to their master's program and name your state school. They come out of it. They're an AI engineer. They're not yet at the sort of meta is going to pay them $100 million, but they are going to be totally valuable contributors to our economy. It's all sort of positive sum. It's not taking a job from anybody else. It makes us more

competitive. And I think there's a way to do that without sort of overly, let's say, putting constraints in the system that make it maybe, so a startup wouldn't be able to kind of economically viably participate in this. And I think 100K per year would be at a point where the startups would be directly impacted. Worked with a lot of startups. I'm not sure that's still. Respectfully, the kind of startups in Drescentralour, are not all of the base of startups in the world. So you can quibble about the number. Is it 20K, which Keith Robles said when I tell it was very sensible,

or is it 100K, I don't know. But like the idea that you get... Wait, Keith throughout 20? Well, then let's just go with Keith's number. If Keith were on 20, I think we can be good with a Keith number on this one. But I think the number, it's easy to fixate on the number. Yeah. But you have to always look at what is the number replacing. And I don't think the average person having this debate,

other than the people that really work at this, have any idea the amount of productivity that has lost working this system. I mean, the incredible amount of resources. And, of course, the bigger companies, the ones that you mentioned, have enormous teams that spend all of their energy, like literally, essentially as lobbyists

working this system. And then the back end of that are all the justifications and all of the management, and all of the handling. And now they've just deployed all their resources to manage, like, in-house call centers

to deal with getting their employees back to the United States to just deal with it. You hit on one point that I think is really important to this debate that is sort of getting lost, which is there's no doubt that within the big tech world, that they, for the past 25 years or so,

they really went on this sort of bifurcated curve, which is hiring for the people in the office focusing on, say, 25 or 30 university departments. And then basically everybody else was like, Well, it's so much easier if we just hire huge numbers of people from these eight international locations and schools. And I think a lot of this is missing that a big part of this is, well, the tech industry, like if you look at Intel

and where they all went to college, and if you look at the history of Silicon Valley, it's all these people from all the schools in the middle of the country, none of which are like the target recruiting schools by the main tech companies these days. And that's been a place where I think that the big companies have been somewhat lazy. And as a person who spent decades flying to all of these schools and recruiting,

there's work that the universities have not done to be better programs and that there's work that the big companies have not done to be clear what it is that why they've stopped recruiting or haven't seen the numbers. And that change would be better for everybody to really make. So my expectation of what gets impacted is kind of an even different job set than that, which is if you go to Florida today and you try and get like an IT, job for 100K, you just can't, right?

And so that, I think, is actually the area that's the most directly impacted by the large consultants. Meaning there aren't jobs that pay 100K or that you just can't find a job? They're all taken. They just don't exist. Like any sort of IT administrator, like the services, like the basic consulting gigs, like all of that has been saturated.

It's very, very tough to get a job between like 80 and 120K in much of the United States because of this, right? And so this isn't about a new grad being a software engineer, because the reality is the expected value of the software engineer over their lifetime is high enough that I think that, like, the market kind of navigates that, but it's almost these kind of lower level more like IT admin jobs that have been squeezed out. And listen, if we want to bring them back, then I do think, and we don't want to do this kind of arbitrage that a lot of these companies are doing,

that we're going to have to change the pricing. So, but don't, but the, wouldn't a minimum salary band effectively solve that problem for you? Sure, yeah, yeah, for sure. Yeah, I think there's a lot of mechanisms to do it. Okay, okay. I actually agree with you. We should actually talk about the problem that we're trying to solve. I think we would all agree, if you come to the United States, you get a college degree, you should have a visa. Yeah, yeah, yeah, okay, so that's for sure. Do we all agree on that? Okay, yes, I agree.

Okay, so the bill. I don't think that the people proposing this strategy actually agree on that. Well, so Trump famously said this. Oh, he famously said a lot of things, for sure. And then he unsaid it. Yeah, this wouldn't be an interesting debate. If we all agree and we have a policy that says, if you go from IIT and then you come to Kansas, state, then you get a job.

But that became part of the gamification of the system because you would do the IIT thing, then you would get a company sponsorship for a master's degree at some school. So it didn't really accomplish the goal of investing in coming to the U.S. the same way that going to a four-year school would have done. Wait, wait, why is that? What was the problem about? Because you ended up getting sponsored by a company and it changed the whole dynamic of, were you seeking out the U.S. or did a company pull you to the U.S.?

It's a little bit different. Okay. But I also think so often this discussion goes the direction. This one is going on is where we focus on, like, new grad software engineers. And I actually don't think that is getting impacted. Right. I really don't. I really think it is.

Like, what do the consultant shop do? Yeah. Admin work, IT work. And it's just a different salary band. Yeah. I think it's, I think these are body shops. And like an approach like this directly targets them, I think, in a way that's actually quite positive. Yeah, I just think there's, I'm then, I mean, I would just then favor

Keith's approach, because I think there is a number in which it becomes, you're then making other trade-offs in your business just to be able to- Yeah, yeah, yeah, the 100-K number is. Well, I do think that the dollars, but I do think that just to reinforce this, that the whole system can do without this immense cost and uncertainty. Yep. And any solution should really, if you address that, then the whole, the rest of the dynamics will follow. But as long as it's a huge, complicated, expensive system, then the big companies are going to continue to to benefit from it disproportionately. Yeah.

And I'll tell you right now, like, it is much harder for a startup to deal with the lottery system. Yes, it's impossible. Then it would be for them to pay 100K. Like, at least for the start-ups. But I don't think it changed the lottery system. It's a hundred-k to participate in the lottery system. Right. Oh, I understand.

I mean, hopefully, like, it'll change the calculus of a lot of people that are in the lottery system. I just think there's- But I would prefer to remove the lottery system. Yeah, I think you can absolutely pull off a system that for probably in a shared definition for us and anybody else would say this is clearly a high merit job, it's going to increase the wages in this particular sector on average,

and we want to make sure that we've got the best talent in the world that comes in to do that. It's not going to drive down wages. We probably want as many of those individuals here as possible. And so I think some elements of this are intriguing in that they push the conversation forward on the dimension of, you know, like the 100K is like a hammer to do that, and maybe there's a more nuanced approach that I would certainly prefer.

But again, it's important, like the 100K will scale with the skill level, right? So the higher the skill, the more like that is amortized. And so you could argue that this is just kind of a dial to get higher skills. Well, you, once you put a dollar amount on it, you have to keep in mind that people are going to pay it. That's right. You're going to make up their own mind. And the skill might not be relevant to some people.

Because, you know, and so that's the tricky part. I want to segue from labor markets to good opener. What's the next really interesting political topic that we can engage in? Yeah, exactly. From labor markets to labor productivity with AI. Offline, Aaron, we were talking about the meter paper, and the papers suggested that their developers were actually less productive with AI, but that doesn't square with your experience talking to a lot of different startups,

seeing a lot of different startups and how there's so much more productive. So why do you talk about where you're seeing startups say they're, more productive and why is it happening? Yeah. So I'll first just represent our own case study. And then there's the really extreme version. So our own case study is we've adopted a few different kind of AI coding tools. You know, Cursor being a super popular one internally.

And as I talk to people, let's say, in the hallway who have, you know, maybe they're trying to get me excited by AI, but like I think they know I'm bought in. So the kind of qualitative answers I get from people, and then I'll give you our internal metric. You know, some people say I'm getting, you know, a 20 or 30% productivity gain. Other people will say 75%. Interestingly, I have not been able to pinpoint

the demographic difference on the answers. Oh, but this is self-reporting. This is self-reporting. How happy are you? Yeah, so, no, but we have internal metrics as well. So about 30% of our code right now is coming from AI. So we've got the...

30%, 30%. So we have some of the pure internal metrics that show this. But what's interesting is that I have a two-by-two of, I have senior people that are saying that they're getting 75% productivity. I have junior people that are saying they're getting 75% and then vice versa

on the 25%, let's say. And I haven't been able to quite figure out a pattern maybe except for... And we've talked about this a little bit, but you kind of see this online, except for maybe the biggest criteria is just the people that actually push the AI to do more, which is sort of this other new kind of psychographic,

which is just like, who is willing to just be like, you know what, I'm going to yolo this task and just see what the AI comes up with. And your sort of willingness to just do that, I think probably somewhat then shows up in the ultimate productivity gain. So that's us as a relatively larger company on the startup side. Oh, it's crazy. And this is the thing that just blows my mind.

mind, I will regularly talk to three, five, 10-person startup founders that self-report they might be getting somewhere on the order of like three to five to 10x productivity improvements. And the big difference is that a year ago, if we were to have this conversation, the conversation would be about AI sort of doing type ahead and it can add maybe like a few lines of code to your productivity per, you know, incremental sort of unit of work that you give it. And then now, obviously, the big phenomenon is background agents where I give it a very, you know, detailed prompt. I send it off. It comes back. You know, people talk about it as like a slot machine of like some percent of the time it's not going to come back with the right thing.

You have to decide what you actually, you know, kind of pull in from it. But the kind of startups that are getting like real multiples of productivity gain are just, they're fundamentally engineering in a different way. They're sending off a task. The task goes off, comes back in 20 minutes, and then they're really in the business of doing code review, not code writing. And it's going to obviously change, you know, quite a bit of what computer science looks like in the future. And then the only question is, like, you know, what are all the things that that's good for? Where does that break down? What kind of teams can actually evolve to that state?

But that one has been blowing my mind the most recently. And I think that kind of fundamentally changes what the future of, you know, kind of engineering looks like. I think what you said is super interesting. Let me ask you, I think that there's an overlay that goes beyond junior, senior, and there, which is, is we're all talking characteristics that have two a solution that has two really important characteristics right now. One is that it's engineers

doing stuff for engineers and they understand the domain super, super well. And I think that that's in a really, really important part and a really big thing that people aren't talking enough about, which is maybe what's going on is that you have AI accelerating

for people that work in the domain and are very smart. And then the other is we shouldn't forget, and this is to your your self-reporting a little bit, but also, which is that this is all early adopters, and early adopters are very forgiving of mistakes on purpose. And it's a super interesting dynamic where when something is brand new,

a culture around it develops, which just lets anything happen. I mean, you know, like, the early internet people didn't complain that the internet was slow. Right. The only people complain the internet or slow were the late adopters who were like, wow, this is so much slower. Or like take online video, which was like, I'm watching this tiny postage stamp video.

And the early adopters, like, this is the coolest thing I've ever seen. Everybody else is like, why would I want to watch anything like that? And the same with downloading music. And so I really feel like what's going on is just this incredible... Do you remember that you guys... Do you remember this watch that you guys made?

The Spot Watch. Yeah, Spot Watch. So in like 2004 or 3 or something. Something. So I bought one. And it used like... FM... Unused FM radio, white noise.

So you can get like stock codes, 45 minutes delayed if you're outdoors in a field. Yes, and so I had one, and I was in this camp, like, this is the coolest thing in the entire world. And obviously, this is gonna be the most mass market product of all time. And, you know, I was like 20 years too early with the Apple Watch. No, turn-by-turn GPS.

Like the first time you could put turn-by-turn GPS in your car, it was super cool, except for the fact that most cars moved faster than the ability for the computer in the car to calculate when you were going to turn. And so you just drove around and it was like a U-turned machine. But I think that it's just so interesting because I think that people use the AI tools today and they assume like, well, I'm not a doctor. I don't know anything about being a doctor.

Let me ask you to cure me and diagnose me. And it's like, whoa, that is the worst. And it just like all the people who see failure, like they're just, they weren't great programmers to begin with. And they didn't know how to ask. They didn't want to review it, and whereas great programmers or just professional programmers know that code review is really important.

And that's just how you do it. I think there's an aspect of this that makes it very difficult to measure. One of them is, and I don't think it's just an early adopter thing, like these models are so magic that you get dazzled. Oh, yeah. Even if it's not what you want, you're like, it was great, you know. And I think it's very easy to complate that with being productive.

Like, it's not what I wanted, but it was amazing. So therefore it must be great. So like maybe over time we just abdicate having an opinion and like the model does everything. But right now, and I see this a lot, people are like, they're so enthusiastic about using AI, but it really hasn't impacted, you know, their output. They're just enthusiastic.

The second one is I feel like there's almost shadow productivity. Oh, sorry, how would you, how would you verify that with a five or 10 person company who, who kind of empirically is operating at, like, a 50 to 100-person company. Like, like, you just, you can see the sheer scale of their code, and you're like, okay, you could not have done that 10 years ago. Oh, I actually agree with everything that Steve was saying. So, listen, this is anecdotally.

Anecdotally, I work with a lot of companies. Anecdotally, the more senior small teams that use AI are super human. Yeah, yeah. It's like they woke up and they were all fucking Tony Stark. It's unbelievable. And, like, their productivity is insane, but they're all super senior. And look, they were, they don't, I don't want to take anything at all away, But those companies were also incredibly productive relative to a 10-person team in a big company.

Because there's no code. And they're starting from a clean slate. No, they really wake. Yeah, for sure. And, like, you know, they're very senior. But they're also almost to a person where AI skeptics to begin with and are incredibly sober about the value. And so they just use it in these very pragmatic ways. There's one other category that I'm seeing because I just want to be intellectually honest on the full spectrum.

I'm seeing these 19- and 20-year-olds that are like, first of all, I don't know what is in the water at, like, Stanford, MIT, et cetera right now, but like, everybody's dropping out. So, like, just, like, literally, like, people are going there for, like, a week just to drop out. But there is a tendency of that cohort. It's the 996 people, and there is this tendency, which is, you know, they would have been maybe 10x engineers in a prior world, but now they're, like, 100x engineers. And so, you know, senior in terms of, in their own kind of relative cohort. but like the way that they are building their startups are just like completely different than it's probably the if I look at at you know so we dropped out of college god that's carried 19 years ago

if I look at how these companies run versus today it's the biggest change in in how you start and run a company that I've ever seen and like and I think if you looked at like in 1995 if you were to drop out of college versus 2005 when we dropped out of college I don't think you like I don't think, like, the company building process was all that different. You get into a garage. The internet fundamentally changed.

No, no, post internet. Post internet. Okay, yeah. So you're building. You have to rewind the internet because that was a... Yeah, yeah, 100%. We're not in 85. So at 95, you're dropping out to do an internet startup.

Okay, you drop it under an internet startup. By 2005, other than the fact that our resources were in the cloud versus, you know, you'd have to go to a data center. In our case, we actually still went to the data center. Like, not that much about the company building process was different. Today, in 2025, everything about how you're starting. your company is completely different because of AI. I think that the key, the through line in all that is velocity.

Yes, exactly. And I think that that cutoff, the internet increased velocity. Yes. And AI increases velocity the same way. And I think that that's just super, super important to what's going on. So we're basically, if you go back, even with the, in the early internet, like when you started a company, there was still a lot of old school, like what's your business plan, what's your plan, we're going to be stealth for two years. There was all of the stuff. And it was really Mark and Ben at Netscape

that changed the velocity of how companies work. And the cloud was an accelerant to that acceleration of velocity. And AI is a refactoring of how velocity works. Yeah, but what was interesting is even in the cloud, like, that was this great virtualizer of the physical stuff you would have to deal with. But that was a two-year build-out.

Yeah, exactly. You know, like, you had no... You could have customers. Yeah, 100%. As soon as you had code, you could have customers. Which is, like, yes. I mean, PLG and a lot of these kind of high-velocity startups actually started pre-AI.

Yeah, but they're pretty like phenomenal kind. Like, 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 SaaSification and then unlocking kind of

different routes to go to market. Zoom was a great early example. This is, the thing that AI is, is an accelerant in building the product. Yes. Whereas what what Sass and 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, like you can see your website a lot quicker, but like I could see a website in 98 pretty quickly. Yeah, but well, yeah, maybe this is an infrastructure thing,

building a big distributed service. No, no, sure, sure, sure. I was not doing distributed infrastructure in 98, so. Yeah, so but so there's, sorry, I just want, so, so, so I think AI productivity is hard to measure for two reasons. The first one I just meant is it is just really dazzling so I think 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. Literally the history of productivity measurement. But also what happens here is like you have the board and the board is like, we need more AI. And then so what happens? And they go to like some CTR, some innovations lab, and then the

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

Those always fail, but that's actually not what's going on. Like, the movement that's happening is a very secular. Well, so it's, this is, this is the, the next time that bottom-up adoption is really changing the productivity equation. And that's a thing that, that it defies, big companies do not know how to deal with that. Because they, they want, 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 overland that is, AI is a very unique, if that's not a bad way to say things,

innovation in that it's like nondeterministic. And so all of a sudden, you have this very personal and nondeterminous thing, which the real problem in a large organization to all these pilots and AI projects is that you can't, not just measure, but you don't even want to put out there a nondeterministic solution. Because your whole thing is like, well, we operate at scale and we have 60 countries.

Yeah, it's a white blood time. Like, 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 is like they're going to just get all bunched up

over non-determinism. Yes. Yeah, I think that the, we will have to have some new form, of measurement probably in general in this because so much of the so much of the improvement and productivity will be will sort of be this like subtle change of like like I used to go to Google for that no my my EA is writing emails using chat TPT for me and so where is that showing up in 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 yeah 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. 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. I 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, these 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 that, exactly, yeah. But this is, I, you know, it's so, 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. You know, I'm, this is my obligatory super old person thing, but the pre-spreadsheet, post-spreadsheet is a perfect example of this. The pre-splastic example.

I didn't say classic, I said it. But before spreadsheets, you would be a banker, and you know, 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 turning 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. And everybody has to do it all over again. So you actually, your quality of decision was really bad. Yes. And so what happened was that just in 1985,

like that job just completely changed. And by 1990, instead of asking the recent grads 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. But 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 one, two, three 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 Dillon, they're always talking about this. Like, this is, 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 a, again, a weird thing to like think about the percentage of my productivity as an example. And, 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 that was kind of then going to be as a, you know, serially connected to that task

is now just fully compressed. And 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? Yeah. 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?

Yeah. Because that's an easy point of failure. Yeah, I mean, it actually is, this is this is this really counterintuitive thing where, and you've talked about specialization on the last one, is like, 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 integrate from the output of this AI? You know, like, what are the 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 of those decisions.

So 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. Yeah. 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 SaaS which is like obviously like a really 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 and incorporate back into work wouldn't make sense like 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. So if you draw a dollar at random that's monetized, it's from a professional. And for obvious reasons, because, you know, like, you know,

they can produce... If you draw a user at random, it's casual and it's in the tail. Right. And so it's fairly clear that, you know, this is a pro-sumer movement from monetization. And, you know, I'm associated with a number of companies that work with, like, say, professional designers

or professional creatives. They spend just as much time on the AI tools as they would on traditional tools. Yeah. It just turns out the output is far more rich. And, like, you know, it tends to be... They have a taste that is... Yeah, but it's still human taste.

There's still, like, very specific requirements. And so I think, 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 in my sense is, let's say if I'm, say I'm writing like, whatever, I'm a casual developer, and I'm writing a 3D game. And I want to have, like, 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. Like me as a developer, I'm not going to create, even if I use AI, it's not going to be a great 3D asset, right? And so I think that you're going to have the same line that you have today is 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 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, into any functional code. But like, it's worth $20 for me to be able to go and, like, realize the thing that I'm thinking about. Yeah.

And so there's just, like, all new ways of capturing TAM because there's all this utility that gets unlocked. Right. And there's one more thing that's worth noting, which is people that are interested in an area, get there through AI right now. I was 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 posity of talent out there that you can fill. This is also, I mean, this is the history of productivity in general, which is you, the more tools that you have available, first the experts use them, and then more people are able to become experts. and I and I and I and I is long but that's part of your managing your own career I love that point you made about like you still have to be really good at something and and and I think that that 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 yeah and I think that that's very much like if you just take you know we were going back for us about you know oh it makes a PowerPoint slide deck well it turns out to make a good PowerPoint deck is still a skill yeah and people still pay McKenzie, huge amounts of money for better PowerPoint decks with better pictures.

Yeah. So listen, so I, so I, I contract a lot of work for like videos and art, and I have for a long time, you know, as part of like different companies or even here at A16Z, you know, I have seen the shift for contracting some that use traditional techniques to AI, dollar amounts are the same. Yeah. And so again, like, you know, this is Jevon's paradox all over again. Like they spend just as much time.

Yes. You know, it's just the output tends to be more dazzling or whatever it is. Yes. And you should see more versions of it. It 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.

We're like, and I want a dragon to fly out of the sky. You know, so like you have like a lot more control as the customer. But for sure, you know, this is not somehow like dropping the cost of output. 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 Carlson and a few others were asking, hey, we're 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. And maybe it was that 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, the, the, 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, you know, mid-2010s to late, 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, like, we kind of did like check off a lot of boxes of like the core things that people needed in the world. And so we like checked off

like a lot of the, like once you have Slack, you don't need five other chats. tools. Once you have Zoom, you don't need five other video conferencing tools. And so it gets kind of derivative 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? Like they're going to, 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. 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 than incumbent has. Yes, and then there's a bunch of disadvantages.

But 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, 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 labor 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. So it's the first time in history where you have none of the disadvantages of a big company and the traditional advantage you have is 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 like, you know, 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, you know, 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. So 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 like 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 It's art, like, the 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, you know, real companies in these spaces. Yeah, because, I mean, this is just so critical because it, what's really happening is this is why you know it's an actual platform ship. So Silicon Valley has seen this movie many times before.

And it, 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. Yes. And so each time there's a platform shift, like everybody's like, oh, this is it, this can 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? There'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. Right. And I think that that's why, you know, it's not crazy to go, wow, is this going to be good or bad for Google? Because there's a bunch of stuff that becomes really, really difficult if you don't make 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.

Well, and this is like Intel with the GPU. Yes. Like they missed the GPU in 2005. Right. And they missed the opportunity to buy the company to do the work or 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. 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. And it's like, no, like, it turns out that Microsoft can be a $4 trillion company. And 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 a hundred 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. I will say incumbents are very bad when new user behaviors and buy behavior show up. In particular, 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, 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, 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. And so, like, it's just, like, you know, I can barely keep up as a, you know, mid-sized company. I can't imagine if you just had so many more people you had to, like, organize around that. Disruptive new technologies require, you know, people to understand how to use them in consumption in different ways, that evolves over time as you get best practices.

Like, 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 of these products was very successful. I learned recently it was created by OpenAI, so that kind of explains it. Well, but you had a startup person.

And conveniently, he was a startup guy. Yeah, yeah, yeah. So, and conveniently. Even then, by the way, it's remarkable that it came out of Microsoft. What's always remarkable is when 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 skunkworks, 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. So, 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, what is that? And I think people really need to wrap their heads around just 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, Lemon. 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. And the thing is, is that this is a book, like about two and a half-inch disc 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 thing, but was also that the companies don't evaporate. And your point about that is really important. And so what it does is that there's this shadow that everybody is worried about. And so you just see it constantly. Well, like, I assume when a company is like, we're not worried about if Google does this. that's what you want to hear. Right. Because, like, it's just, I mean, and you've lived 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. Yeah, yeah, yeah. But, no, but the one thing that is very timeless about, about Clay would be the thing that has, the thing that does transcend floppies or, you know, whatever the, whatever thing is, 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. It'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 like the window, 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, so 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, like, work against incumbents. But even in non-disruptive technologies, startups often have a play against income. So, for example, every year, I'm an infrastructure investor, every year for the last 10 years, after AWS reinvent, I move into therapist mode. All of my founders call me. They launched us on. And they're launching my open source, they're competing.

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 of business by launching a service. And you know what? They've all been fine. And so in some ways, even in like the normal state of business without massive disruption, startups still have. Yeah, it was don't build a SQL server and compete with Oracle directly.

Don't know the word processor. Those are two. Maybe they're pretty evergreen for a long time. Never do a spreadsheet. Like there's a few things that are. No, there are some categories where people should call us first. Yeah. Yeah, 100%.

because they won't change. There's a far side that we should definitely show people that. It's a really, can I have 10 cents? Why do you want 10 cents for your startup so I could buy a loaf of bread and beat you over the head with it for 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-soft TAM for software. So 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 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.

They're also your customer. So it's actually this amazing thing where, where the people you're probably disrupting on paper are actually the primary users of your technology. I don't actually take advantage. Yes. And so then it's like there's really no inherent competition until eventually 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 realizing the competitive asset are agriculture and construction. The buyer knows how to price things like agriculture and construction. They end up becoming basically agriculture and construction. construction companies, and then they end up doing exactly what you're saying,

is selling to the agriculture, right? 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 you, and the salesperson for Tandy would show up in Nebraska and sell crop rotation

in the 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. 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 to 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 Claude 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. So I've seen examples of people building new ad agencies. Oh, yeah.

Because obviously, like, if you can do literally a million-dollar ad, you know, video 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 particularly in the advertising space. And I think it's going to happen in everything that's text and everything, which was

there were these digital native ad agencies that just knew. how to use Flash. And they got, like, they would get bought for a billion dollars. The same thing happened with social, by the way. Yeah, exactly, 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 the, what was your Pue 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.

Like, basically, in 1999, like, half the country owned computers. And they were all online. Even four years post-Netscape, it was, like, still half the country. And so you look at that as being slow or being fast. Well, fast back then.

I mean, that felt fast, actually. Well, you have 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, I mean, the, the universal adoption of this as a consumer technology and then bleeding into pro-summer is, it exceeds anything I've ever experienced. And I think it is, 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, like, chat and I was like, oh, chat should be tea. That's what they, 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? And why am I not getting that same level of productivity gain. And then you again have the kids coming out of college that, like, they only know how to do homework with Chachabit. 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 used to be is because it's already exists on 7 billion phones. And 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, like that, 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. And 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 housewilling, people learn mid-juring, people know open AI, et cetera. So these markets are so big, they're growing so fast that, like, you know, if you be able to be able to be able to, become a leader in your segment, people will just adopt.

But don't discount, you know, like the early leaders of search for like Excite and Yahoo. And so there's, like, this is just to not discourage people from thinking. Right. 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.

Yeah, yeah. 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 chat GPT or um or will it be similar that what's your time frame uh you know post 2019 i don't know um no no how many 10 to 20 years you said oh yeah sure okay so we can't know if we're wrong for and really do this podcast and 10 i think this is so boring but i think i think it's going to

look like what we saw in something like sass 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 dollars companies that also emerge and then over time those will just continue to scale similar to mobile and some and some don't make the transition yeah yeah some will go down on a relative basis because their or like their market wasn't as right for agentic kind of workflows but i think that you can kind of say um you know maybe this is then for another conversation is just like if you have a current system of record that that has a set of workflows on where agents make sense to make that workflow much more powerful,

that's a good position to be in. But 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 the 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

than all of them. There's a key attribute across all of those, which is sort of like thought leadership or like who is really setting the agenda for what people are talking about. And 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? 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 chat GPT

at school, I need chat GPT. And there's nothing you could do about it as a company. I 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? Yeah, yeah. Oracle is making some kind of crazy moves.

Like, are we going to see those that, like, missed, like, social? No, I think we, everybody missed Oracle as an example, right? 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 they used cloud and Azure to kind of come back. And so, you know, this actually is an opportunity for laggars 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 parts of the stack that we stop paying. like Broadcom. Like, again, people were not... Hot content. It's going to end up right of that. Everybody looked at Jensen. It may be somebody else, so... We'll table the rest for the next conversation.

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