Aaron Levie on AI's Enterprise Adoption

23:17Can you like code up some inventory random plug -in for this product? It's like that now becomes 10 times easier. So the new prototyping scripting, the long tail of stuff that we've done. And that long tail is so long and people never get to any of those things in that long tail. And so I could underwrite a 10x growth of the amount of custom software that gets written and the fact that these core systems don't go away because there's just actually going to be way more software in the world that gets created. Let me press your test so like, okay, so I can imagine why it would be hard to rebuild box because what you do is actually hard.

23:48This is corner infrastructure, your store data, like that's really important. And so I don't think you just vibe code that away. But from my perspective, a lot of SaaS apps just look like crud. I mean, crud is, I don't know what the acronym stands for, but it's basically you're reading and writing data from like a back end. And so do you think that there is a world where the consumption layer evolves to just using AI and this class of companies go away? Or do you actually think, if I heard what you just said, that the durability of these companies is it it basically teaches you what the workflow is.

24:16Well, I'm still going to say the latter. Now, I don't know if you need to bleep it out, but if you want to share a couple examples of who you put in the not -hard crud layer, then we can parse that. But up to you. The not -hard crud layer? Yeah. I mean, I would say most vertical SaaS companies I see, the technology's trivial. Yeah, but the understanding of the domain is not. No, no, this is what you said before. Yeah, this is what I want to pass it to. This is like, I think. I think it's like, I think it's like, I think it's like, I've always underestimated vertical SaaS. relative to the outcome.

24:44And 20 years end of doing enterprise offer, I'm just like no longer gonna underestimate verticals assets. It's not about the technologies, the fact that somebody else has figured out the business all of that works. Like they have 10 people. What has the work from the farm industry that is like sitting next to the engineer be like this is how you should do the clinical trial workflow. And that becomes so much of the IP. Now that translates fine to agents, but I still would then bet on that vertical player doing that as opposed to somebody prompts their way into Chatchabee T to build a FDA compliance agent.

25:13I would so largely bet on complianceagent .ai to do that over the pure horizontal system that has no particular domain kind of expertise for that. And then I think the other thing, I still think that there's a relationship between some amount of GUI and the agent and the APIs because again, like you don't want it every day of your life go to a blank empty screen and say what's our revenue today. You just want to dashboard at some point and just show us the revenue. That's right, of course. It's almost like cash queries in a way. Like, somebody has made the decision of like a platform. Yeah, this is like a known way to solve this problem in the enterprise.

25:44And so I think that's why the theory of the full abstraction away from the interface and it's all an API call. I don't think that happens. And so ironically, probably what will happen is in a couple of years from now, we will see agents like rebuild entire web pages and dashboards. And then we're going to find ourselves like, why are we having an age? Why do I have to spend tokens to create a thing that is a config on a dashboard? And we'll just be back to where we started. for some amount of software, which will mean that basically these things are gonna live together. Cool. Let's move from software to decision process.

26:17So I won't say the name of the company, but I just spoke with a very, very legit company, household name. It's a private company though. It's not a public company. We're at the board level for every decision they ask the AI for like, basically, more information for the decision. And this has actually been great from like, discussion fodder to be provocative. And it also shows how fundamentally unoriginal the board members are. Like this founder was telling me it's literally better than half of my board members, right? And so how much have you thought about bringing AIs into help with decision process?

26:52Yeah. I think the board is low -hanging fruit because boards tend to not have a lot of context to the business. The incense are probably less anyways. But is this something that you've thought about? The board one is an instrument. So maybe we can unravel that one. but I already use it for let's say our earnings calls, where we'll do a draft of the initial earnings script. And then again, because BoxAID deals with unstructured data, I just load up the earnings script and I'll use a better model and say give me 10 points that analysts are gonna ask about this and like how would I improve the script?

27:23And it just spits out a bunch of things and it's, it all good is it, it's particularly nice. Oh, 100%. But the thing is, that's not surprising. Like it has access to every public earnings call in history. And like at the end of the day, analysts can only ask you like, tailwinds, headwinds, who's buying what? It's not because analysts are smart or not smart. It's just like those are the things that like you would try and deduce from an earnings call, buying a stock. And you wouldn't have thought of these questions beforehand or is it just like, I think you're doing the margin, on the margin itself.

27:50No, no, sorry. So what I'm using is then the specific parts of the document that is missing the answers to those questions. So I can actually inject the answers into that. Because like you're typing on a thing and they, I forgot to give two case studies in this section or whatever. It's a quick way to just do some analysis on something. But yeah, I mean, so Bezos famously had this memo oriented, essay oriented kind of meeting structure and we never did that, but I was always fascinated by the companies that could do it. And actually, we're entering a world where probably you could just pull it off, right?

28:19So imagine if whether it's a board meeting or a product meeting, you just do a quick deep research essay on the topic. Like obviously every meeting, every strategy meeting in history would be better off if you probably had that as a starting asset. Yeah. To get everybody informed. I think the argument against that would be the reason Besso said it is because it forced people to think clearly about what they're doing and writing it down. So the exercise meant that the people walking in the meeting had more context. Yeah. This would almost argue that they would have less context because something else did the thinking.

28:46Well, two things. It was to make sure that the person doing the thing had the clarity to write it for sure. But it was also still to inform everybody else that didn't do that work. And so it certainly would have helped everybody else in the room. And I'm not 100%. I mean, we should do a full longitudinal analysis of like the people that wrote the essay, did they actually have the better products or like, I mean, there's something Amazon products I don't like. And so they obviously wrote an essay also for those. So I don't know the hit rate, ultimately on the essay specifically, as much as the idea of like write down a strategy, think it through, and so why not have an agent do 90 % of the heavy lifting?

29:18So a lot of my workflows are like, if I have a topic where, maybe the direct change of my workflow in this front is the kind of thing that three years ago, I might sort of lob over to the chief of staff and say, hey, can you go research like the pricing strategy of this ecosystem or something? That's just a deep research query now. And then I'll wake up and it'll look at this thing. But what that does is because now I'm not having to calculate that person's time, their tasks, their trade -offs. I just do it for the most random things, which means like I'm expanding and exploring way more spaces mentally than I would have before.

29:54And these are the kind of parts, and this is equally why I'm actually more optimistic on the jobs front because what we do too many times with an AI is we look at today's way of working and we're just like, AI will come in and take 30 % of that. And it's like, no, no, no. We'll just do totally different things with AI. I wouldn't have researched that thing before when it was people required to research it because that would have been an inane task to send to somebody. One thing, so when we run the numbers, and by run the numbers I mean, look through how AI companies are doing, where does the value accrue?

30:24There's basically one takeaway, and that is like these markets are very large and growing very fast. And value is kind of accruing at every layer, everything from like literally chips up to apps. And so like the only real sin is zero sum thinking to be like, oh, like the models are not going to be defensible or whatever your zero sum thinking is, that just hasn't proven out. Now this is still large. You've been a consumer phenomenon. So what I've been thinking about and I don't have an answer, I'd love to hear your thought is when it comes to enterprise budgets, you can't just create budget out of thin air.

30:55So you actually do have a limited resource. And so as budgets get reallocated, what extent do you think this is zero sum, like the old budgets gets robbed versus budget accretive? Or how do you think about that? Because again, where we've come from, that is not an issue. I think in the enterprise, it probably will be. So it does have to come from somewhere, fully logical, a couple of things. Yeah. A large number for startups can also be a very small number for a large corporation. Yeah. So you have that dynamic playing out. I'll make up random stats, but you could probably take a meaningful engineering team and probably for the price of five of those engineers or 10 of those engineers, you could probably pay for cursor licenses for the entire engineering team.

31:34But this would argue that it's actually coming out of headcount. So here's where the asterisk is. There's an infinite set of ways. This is why you can never take a point in time snapshot on these kind of things. There's an infinite set of ways that this actually plays out. Next year's planning process, maybe in a perfectly, like parallel universe, the salary increase that year would have been 3 .5 % for employees. And this coming year, it's 3 % because we're gonna take 0 .5 % and we're gonna deploy AI for the company. Or maybe next year, we would have added 50 engineers, but we're gonna add 25 and then pay for AI.

32:10But guess what? But the year after, we're gonna have engineering productivity gains. So the increases, because it's still a competitive environment. We then now add engineers the year later, because we're getting higher productivity gains. I think that most companies of any regional scale, post a hundred employees, let's say, have enough sort of dynamism in the financial model within a one to two year period, where it doesn't look like what the economists would think it looks like. So I just spit this back. I think this is actually a very good point that's buried in there. So I want to make sure I'm following along, which is the software license cost to a startup Relative to like a large people organization is relatively small It's just a couple of headcount which if you just look like normal performance management normal attrition normal like variability and even like hiring timelines Yeah, it's kind of in the noise Yeah, and so you already have an annual budgeting cycle to fix that up Yeah, so like basically within the noise even of just like headcount planning.

33:03Yeah, all of this could work out without some massive disruption Totally, and I think that's such a cool point. And there could be an upper limit of this point, but let's say the going rate in Silicon Valley of a new engineer coming out of college, let's just say it's somewhere between 125 and 200, okay, I'm just making up, okay? Let's say your most aggressive cursor usage or something is like a thousand bucks a year, two thousand bucks a year. So you're at like one percent salary, maybe. Here's the question, again, do this like crazy apples, so apples thing, if you went and recruited from Stanford right now and he said, okay, you Stanford grad have a choice.

33:34You can work at this company and get paid 125K with no AI, or you can get paid 123K with full access to AI, which one are you gonna do? They would do the 123 all day long. But even that, yeah, I mean, you're on a human, which makes a lot of sense to me. It's kind of on the margin when it comes to like 12. But just as a way of exploring why these things are not the high order bit of the cost increase on budgets. Oh, I love it, that's great. And I did one kind of late night sort of like modeling once, but I'm afraid to say all the numbers here because I think they're just gonna be so wrong. But I think it's something on the order of like five or six trillion in knowledge worker headcount spend in the US.

34:09Everybody says for developers, say 40 million, let's just say it's 30 million. Let's say that the average is 100K, you're at three trillion. And these are just massive, massive numbers. So it's many trillion. Yeah, so you have many trillions of dollars. If you take a couple percent of that, or five percent of that, you're already doubling the entire sort of US enterprise software spend. So you can just make it work within, and this is why I don't think that people will not make cuts because they have to pay for AI, they may make cuts for other reasons. But even in those cases, I think you'll often have it be for myopic reasons temporarily.

34:38Yeah. And there's enough flexibility to basically consume this. And then actually like recap on the productivity. Yeah. I think that's great. I try and parse everything you say through the lens of like, where are you landing on AI coding? And you seem to have a very pragmatic view of where things actually are at. Yeah. Where are you landing right now? Well, it's been an evolve. So I would say in the entire AI, seeing the biggest surprise to me is how effective it is at code. And so my sense is, so I'm just going to say a couple of, I think, facts. And then we can kind of back out what this means in aggregate.

35:10Because I think one fact is, the reality is, I do think that AI helps better developers more than not better developers. And the reason is you just have to deal with, and be able to like, know what to ask for, know how to deal with the outcome. So I think that's one. Someone said it, I thought beautifully, which was, this is a very good developer, and this is on X. I forgot who it was. I thought it capsized. He's like, you know, 90 % of what I know, the value of it has gone to zero, but 10 % has tripled. It's more than 10X or whatever it is, like, got 100X. I think that's exactly right. I do think that for a lot of wrote use cases, the AI can do it and it doesn't need to be double checked.

35:47Yeah, so there's a lot to your point. Things like prototyping, things like scripting. And so I do think if you look at usage at like open AI, if you actually look at code, usage. The primary use is actually professional developers, which means it's part of a developer workflow. And then probably the most controversial stance I have is, and this is probably sunk -cost fallacy because I've been a programmer for, I mean, my PhD is in computer science, so maybe this is a cost fallacy, but I just don't see a world where you get rid of formal programming languages, just because they arose out of natural languages for a reason.

36:20We started with English, and then we made programming languages so that we could formally describe stuff and so it'd be kind of a regression to go back. So I still think we'll use languages, and maybe they'll change, maybe more like a scripting language. But I think like the existing toolset will evolve, but it'll still be a professional developer. I think we'll still have developers, so have developer tools. So that's where I am. I'd love to hear where you're at. If I fully am in the exact same page, the fun thing to me is how coding is just at the tip of the kind of iceberg. It's the best thing to first sort of experience agentic automation, but I think you'll see this in basically every other space.

36:52But what's so fun is just in a one -year shift, let's say, of the nature of the relationship with the AI. So if you think about the GitHub co -pilot moment, it was like, oh, this thing is incredible. It's gonna type ahead and predict what I'm typing. And then you're basically using it to work 20 or 30 % faster, and which parts of it do you take on or not. And now the relationship is totally different within, again, a year or two period, where you're using cursor, winsor, or whatever, and the agent is generating this chunk of an output, and then you're just reviewing it. But what's incredible is none of your expertise is any less valuable in that review.

37:30In fact, it's probably even more important than ever before, because in some cases it's just gonna be like wrong 3 % of the time, and then you review it, but then you're literally doing three XC amount of output. And the nature of how that changes both programming, but just why not have that for basically everything is sort of this new way that both software should work and then actually we will work is like, you know, the big joke a year after Cheshire PT is like, okay, this thing generates a legal case and it's like wrong 10 % of the time. And it's like, well, actually, if you think about what the new paradigm of work looks like and it's like such a weird inversion of, it used to be that AI was fixing your errors.

38:07That's what we thought the AI was gonna be. And it's just like a total flip. It's like the human's job is to fix the AI errors and that's the new way that we were going to work. Right, so this begs a very obvious question, but I'm gonna work up to the question. So there is a great paper and NSDI from an MIT team, which basically says, you can optimize a running system with agents. And the way they did it is they basically have a teacher agent and then more junior agents and then the more junior agents would go try a bunch of stuff. And of course they had much more knowledge of the literature than any single human being.

38:37So they try all of different things to try it. And then the one at the senior agent would say, oh, this is good, this isn't good. And then once it optimizes the system, they would do it. and the human being is then helping the teacher agent decide what are the parameters, what is good, what is not good, and provide high level direction. So you're already starting to see cases where human beings are running multiple agents, and even that already is starting to have some kind of bifurcation, which one way to think about it is in any R &D organization. Of course, people start as like ICs, but then they very quickly get interns and go into management, and so maybe we're just skipping that step.

39:12So the obvious question is, what happens to entry level engineers? Like does this change how people get introduced to computer science, for example? The cool thing is probably more people will even now get introduced to computer science, because you'll be able to... Anybody can learn it. Anybody can learn it. And it's been 25 years for me, but in the early days of programming basic applications or putting other websites, it was just extremely frustrating that you would spend days and days is being like, why is that thing not work? Yeah, yeah, yeah, yeah. And like, I've very few resources of figuring out why the thing didn't work.

39:43It would have been a hundred times easier if I could have had an agent write the thing. I would have learned 10 times faster. Yeah, yeah, yeah. Obviously what you did is you were like, well, not 25 years ago, but 10 years ago, you'd go to Stack Overflow. And so it was like the slow version. Yeah, but so think about how many people missed the window pre -stack overflow that got sort of pushed out of the ecosystem because they're just like, this is you frustrating. And so you're gonna have a way bigger funnel at the top of people now learning programming computer science. I think a similar percentage of people will fall out so it's not like again you're going to get a 10x increase in programmers because you still have to enjoy it and you have to like solving problems and whatnot.

40:17It's going to change the nature of the incoming class of engineers that you hire. They will literally not be able to code without AI assisting them and it's not 100 % obviously that's a bad thing because assuming you have internet and the site stays up like we should have access to the agents. So I think it's mostly just like we have to adapt how we think about the role of an engineer and what these tools are giving us in terms of the productivity gains. Actually, I meet with a lot of larger, not tech -oriented companies as customers. And generally, the thing I'm recommending is hire a bunch of these people because they are going to flip your company on a TED of how much faster the organization can run.

40:55So I do understand I wanna be sympathetic to the job market for anybody coming out of college because I don't think it's easy right now and it probably hasn't been easy in a number of years. But if you are graduating, the thing I would be selling any corporation some way or another is that if you are AI native right now coming out of college, the amount you can teach a company is unbelievable. And then conversely, if you're a company, you should be actually like prioritizing this talent that is just like, why does it take you guys two weeks to research a market to enter? I can do that in deep research and get an answer to you in 30 minutes.

41:29They will be able to show companies way faster ways of working. Do you think there's any stumbling into problems this way, which is like you kind of adopt too quickly? And like you get into a morass you can't get out of, or you think at this point is pretty clear. This stuff can be practically consumed. What would the morass be that you'd get into? You hire a bunch of vibe cutters, and then they create something that nobody can maintain in a truly small way. Oh yeah, totally. Which by the way, I will say I have seen this. Yeah, yeah, yeah. Okay, you could easily overdo this whole thing. Okay. So I think as with anything, deploying these strategies and moderation while we're all collectively still getting the technology to work better and better is super important and understanding the consequences of these systems.

42:09So yes, this is not like a moment to just have your whole company vibe good. I will say one of my favorite things that I'm witnessing in the whole coding thing. I don't know, the point of this talk is kind of AI and the enterprise generally, but like the coding thing is just so salient. Is that a lot of these OG programmers that have known for a long time that are off creating companies or CEOs of public companies and he's like yourself, we're all back to programming. Then you talk to them, many of them. Like I could, most nights with cursor just because it's really enjoyable. And the reason I didn't code before is because I just couldn't keep up with the fucking framework.

42:37I'm like, dude, I don't know how to install the fucking thing and what is this Python environment stuff. And like, you're literally learning bad design choices that somebody else just made up. Like they're not fundamental to the laws of the universe. They don't make you any smarter. It's just like waste of brain space. And so in some way, the AI just gets rid of this kind of crafty stuff that you probably shouldn't be wasting brain space on anyway. The amount of frustration I have when I look through, let's say, our product roadmap, let's say, pre -AI, although this still obviously happens to be a fully transformed everything about how we work, but pre -AI, when you would see things like, we have to upgrade the Python library in this particular product, and it's like three -engineered two quarters.

43:15Exactly. And like, at the end of that project, zero customers will notice that we did something We resolve some fringe vulnerability that is not going to even happen. But you have to do it because there's some compliance thing where you have to make sure you're on the latest version, which is super important. But like the thing is never going to happen. And all of a sudden, you are wasting hundreds of thousands of dollars of engineering time. And the fact that that's now like a Codex task is just unbelievable. And the amount of just now things that you can actually relieve your team to go and work on is incredible.

43:46And the other big like boon for the economy, and this is again where the economists just totally missed this stuff is think about every small business on the planet of which there's millions, tens of millions, whatever. For the first time ever in history, they have access to resources that are somewhat approximate to the resources of a large company. Like they can do any marketing campaign. Did you see the NBA finals video from Colchey, the VO3 video? Oh yeah, yeah, yeah, yeah, yeah. You can now put together an otherwise million dollar marketing video for a a couple hundred bucks of tokens. And that being applied to every domain in every service area, I can run a campaign that translates in every language.

44:25I can have this long tail of bugs that I never got around to automatically get solved. I can have the analysis of a top -tier consulting firm done for my particular business. So for the people or companies that are resourceful and are creative and imaginative, the access to resources right now is just truly unprecedented. What do you think is the best metric for anybody interested in tracking this stuff as far as like how fast is going? Is it GDP? Is it margin? Is it top line? Is it headcount growth? Is it all the above? Like how do you measure it? Yeah, I mean, for us, so internally first, and then maybe we'll spit both some macro solutions to this internally, we've actually explicitly taken the stance that we want to use AI to increase the capacity and capability of the company.

45:09So just do more. For anything that you track, just make sure it happens. Just do more or do faster. You can read it in the make of a time period. And so that somewhat relieves the pressure from people that like this is about cost -cutting. It's just like, no, just like do more right now. Let's figure out what works. Somethings won't work. We want experimentation. So just use AI to do more. Okay, so that's us. The way you should measure that then in a couple of years from now is either the growth rate of the company should be faster or the amount of things that we're collectively doing should be more and the only reason that wouldn't show up in growth rate is that every other company also does more.

45:43and so that gets competed away, which is also a very viable outcome as this is just the new standard of running a business. There's no shift in the equilibrium. Right, there's no shift in the equilibrium. You just have to do it. And then the ultimate product of all of that is some other kind of metric of satisfaction of our products get better. Maybe we can be like consumer pricing that's just like that. But did the iPhone show up in GDP? I don't know, but my life is better with the iPhone than without the iPhone. Pretty sure it did. Okay, I find it. So, but it would ultimately then show up in like new curious to diseases, better healthcare.

46:15I don't know that the dollars would move around all that differently as much as just like life expectancy should go up. Like cost of housing should go down. Like weird metrics that productivity gains will then drive that the economists wouldn't naturally associate to like enterprise software, NAI. By the way, this is where I am, which is like clearly there's a disruption because marginal costs are going down in a bunch of stuff like riding code and language reasoning and whatever. And like some companies will take advantage of that. But I don't think like the fundamentally equilibrium changes.

46:43I think to your point, I think we just do more tech Products get better faster. Yeah, we saw problems that we haven't before But like it's not a symmetric that we've seen in other Yeah The way I kind of think about is you know if we go back to like 1985 and we just looked at how everybody works Yeah, I think we would just be totally stunned by how slow everything is and just like how long did it take you to research a thing or Analyze a market or create a campaign or whatever But like we just have been baked into our human productivity that we just do all those things really fast now And so in 10 years or now when we all have AI agents running around we will just look back to today and be like How did we function like you spent two weeks to Decide like the message for the marketing campaign like how is that possible like what we do now is we run 50 experiments with AI agents They all come back with versions we look at them all together and then we make a decision in an hour We move on that's obviously like how work works and like that's what we will be saying 10 years or now Do you think we'll ever saturate the consumer?

47:38I caveat this by saying This comes up every one of these infection points and so I just wanted to ask it again for the umpteenth time. I think, I'll say yes just because at some point maybe, but like my list of purely consumer demands has not gone down. Like healthcare is like a totally unmet, 100%. You know, need that I have. I do not like to go to doctors or dentists or anybody because of just how hard it is to get scheduled. I mean, buying a fucking car, man. Like, so many things that like just need to be sold. The cost of housing, we clearly don't have enough houses. Like now where will AI drive that?

48:10Okay, maybe robotics would be then the play there. But I don't think we're anywhere close to consumer satisfaction or satisfying all needs of consumers. Well, actually I meant more. Well, things change so fast that it saturates the ability to adopt new things. I do think that that is certainly possible. I think I track sort of, let's say my parents as a decent kind of proxy, or even just like college friends that aren't particularly in tech. and they're still in their chat chat. We T face of adoption and they haven't moved on from that. They haven't made a video yet. They're just using chat. We T to ask questions about life experiences they have.

48:46And so maybe ironically, one of the problems with chat. We T was so good. If you imagine what people thought AI should be able to do for them, it already met 80 % of where they would have projected it. But when we know actually, no, it can probably still do 10 to 20x more, but their needs are gonna be satisfied for some time on those core use cases. So I think for the most basic consumer query type things, but then this is the opportunity for startups, which is like now AI will show up in ways that maybe the person isn't even in the market for an AI thing. They just want a better, or sort of that kind of work.

49:21This could just simply be another market constraint. As soon as it saturates, you just make the product better, given the existing. If I could just get better healthcare, And I don't need to think about that as an AI problem or not an AI problem, but AI will be behind the scenes delivering that Yeah, then I don't think you're saturated anytime soon. Yeah, it's just a consumption capacity just becomes another market constraint But there's a ton of other ways that you can improve things 100 % this great good. I love that you're so optimistic about I am I think 98 % I'll optimistic say good all right So I think we've had a fairly pragmatic conversation about the current impacts and the return impacts if you do a longer view, can you dare to guess what things look like in five to 10 years?

50:01So, same old man and Jack, old man had a podcast recently. And I'm going to paraphrase probably in some wrong way, but they were going back and forth about how like we just got what we would have predicted as AGI five years ago. And it's just like we use it. And like it's now just built in the most anticlimactic. Yeah, it's climactic. And I think that's my instinct for a lot of this. is five years, ten years, whatever your number is. And this is why I'm so optimistic on just society and jobs and all this stuff is I don't think it's the terminator kind of crazy outcome scenario of we automate away everything.

50:34I think the human capacity for wanting to solve new problems, for creating new products, for serving customers in new ways, for delivering better healthcare, to trying to do scientific discovery, like all of this stuff is just built in us. And it will continue. and AI is this kind of up leveling of the tools that we use to do all those things. And so I think the way we work will be totally different in five years or 10 years. But you're already seeing enough of probably what it will look like that I think it's an extrapolation of that. It's when you want the marketing campaign done, you have a set of agents that go and create the assets and choose the markets and figure out the ad plan.

51:15And then you have a few people review it and debate it and say, okay, let's go in this direction instead. and then you deploy it and you're onto the next thing. And so each company, their units of output grow as a result of that growth, we're all still in competitive spaces, so some of it gets competed out and others will keep growing faster than they would before, so they'll hire more people. And you'll have new types of jobs, like we'll have jobs for people just to manage agents and like you'll have operations teams, you know, Adam D 'Angelo had this cool role that just kind of got announced.

51:44So that was really cool. Yeah, the role is to work with Adam at Quora and figure out which workflows can be automated with AI. I think you'll have a lot of those kind of functions. But I think one of the exciting things about these being in Silicon Valley or anybody kind of tuning in being in this ecosystem is like we're seeing the change happen faster here. And it's gonna be five or 10 years of this rolling out to the rest of the economy. And so I think we will spend the next five years making the technology actually deliver on the things that we're all collectively talking about to make it more and more robust and the accuracy goes up and the costs go down and the workflows that can tie into our better, and we will be working on that for quite some time.

52:21And you think ultimately, this leads to the biggest piece dividend of better products for users, better user experience. Yeah, I think the software gets better, our healthcare gets better, the life science and discoveries increase, I think it's all a society net positive. I love it. Thanks for listening to the A16Z podcast. If you enjoyed the episode, Let us know by leaving a review at ratethispodcast .com slash a16z. We've got more great conversations coming your way. See you next time.