Aaron Levie and Steven Sinofsky on the AI-Worker Future

23:24And so, like, if you go back, this is every single evolution of computing. I mean, how long did it take for Steve Jobs to get rid of the number buttons on a smartphone? They still had number buttons, or you look at cars and until Elon got rid of all the controls, everybody kept all of the controls. I don't want to get in that fight. But what happened with every technology shift is, if you were to look at what accounting software looked like in the 60s before IBM said, stop, we all use double entry, but we need to have people skilled and how computers can do the accounting, not how people can, because we're never going to figure out how to close the books.

24:03If we have to automate this whole room of people's green ice shades that have a manual process based on how far apart the desks were. And everything that happened with the rise of PCs and personal productivity started off. And I always use this example because I've watched it happen like five times now, which which is the first PCs that did word processing, the biggest request was how do I fill in like expense reports? And so this whole world grew up of tractor fed paper that was pre -printed with the expense report. And so then software, we wrote all of this code like are you using an Avery 2942 expense report?

24:42Or is it a New England business systems A397? and then you had these adjustments in the print dialogue, like 0 .208 inches, and you moved little things, and then you would print out eight dinner, $22, and that was all you printed. And then someone said, you know, we could use the computer to actually print the whole thing. And then fast forward and finally concur, said, you know, why just take a picture? Why not just take a picture of the receipt, and then we could do all of it? And so then the whole thing gets inverted and every single business process ended up being like that. And then there are things that really, really do change the tools.

25:23When email came along, you know, it used to be to prepare an agenda for a meeting. Somebody would open up word and type in all the things and then print it out and everybody was shocked the meeting with this very well format. And now, and then email came out. And that whole use case for word just evaporated. And then an email agenda became no formatting, nothing, just like, here are the eight things we're going to talk about. And you show up and everybody's like, did you get the agenda? It was interesting about the AI one. It's kind of, it's like, we're seeing the same thing, but vis -a -vis AI.

25:55So nobody really predicted the generative stuff. And we've had AI for a very long time. So we had chat bots, we've had, you know, and so you have these kind of like AI shape holes in the enterprise for a long time. And a lot of the mistakes that we see today as people are taking the generative stuff of trying to kind of cram it into the old models. We're like really a new behavior that's emerging. That's very much more. And like it used to be you'd centrally sell, you know, AI to some platform team. And then they would kind of try to get the NLP thing to work or the voice to work for like talking to people on the phone for support.

26:25And it was this kind of very central. A lot of the adoptive we see is like my two are individual for example. And so I just think that there is a bit of a mixed message we're seeing now that it's getting ironed out too. Well, and so I think the question is, Yeah, are we in the phase where we're trying to graph the agents and work in basically the what we've been doing for 30, 40 years of software? And is this going to be actually like the first real step function shift we've seen in what the workflow itself should look like? Oh, we are. I mean, if you remember, people like I tried to jam the internet into office.

26:58Right. And it was fun to watch. But you were not watching it. But everybody around was trying to jam the internet into their product because that's the only way you could envision it. And it didn't really, like, you were like, well, where else would the internet go? Like there's no word processor on the internet. Like there's no spreadsheet on the internet. And then other people would be like, well, let me just try to implement Excel using these seven HTML tags with no script. That turned out to not be a really good idea. Yeah, either the best was like, let's do PowerPoint. Well, how do you do it?

27:33You give them five edit controls, tell them they're bullet points, and then we'll generate a gift on the back end and send it back to you as the slide. Yeah. Okay, that, that, that wasn't, and so there was that whole, like that. I think actually maybe the main point is just the durability of office and transcends all. It does, all this roughness. I like to think it pretty much rises above everything. Yeah, exactly. But the thing is, is that that's where we are now. Yeah. Is everybody, and you know, like do you, but do you think, no, I mean, just to take a little bit. So do you think this is similar to the internet?

28:01And that is a consumption layer change. So that was viewed the internet very much a consumption layer change. Like I go to, you know, instead of going to my computer, I go to the internet. But otherwise things kind of are the same where, hey, I just got this weird quirk, which for the first time I can recall, programs are abdicating logic to a third party. Like we've always abdicated resources. Yeah. Like so we'd be like, okay, I'll use your disks or whatever, but like I'm writing the logic. But this time it feels like we're changing the consumption layer. So like, you know, when my son, you know, talks to an AI character and, you know, he's not going to Wells Fargo.

28:34He's going to an AI character. And so like that's changing kind of how we're interacting to computer. But also these programs are no longer kind of written by a human in the same way. So I feel like the change is maybe a bit more sophisticated. Oh, I think, but this is the, this is why it's a platform shift, right? Not just an application shift. like where each platform shift changes the abstraction layer with which you interact with computing. But what that also does is it changes what you write the programs to. Do you remember ever abdicating logic? Oh, here's an example of how disruptive this can be.

29:11The first word processors in the DOS era, the character mode era, they all implemented their own print drivers and clipboard. So if you were Lotus and you wanted to put a chart into a memo, you couldn't, because you didn't have a word, you didn't sell a word process. So you actually made a separate program to make something that the leading word process are consumed. And if you were perfect, your ads said we support 1700 printers. And you won reviews because you had 1700 and Microsoft had 1200. And so then along come, that's a great one. And this Windows comes along. And if you were trying to enter the word processing business, step one, I need to hire a team of 17 people to build device drivers for Epson and Okidata and Canon printers because you can't get them anywhere.

29:58Microsoft came along and for Windows built print drivers and a clipboard and all of a sudden and also Macintosh did it. All of a sudden, there was a way that two applications that had no a priori knowledge of each other. But of course, if you were perfect or lotus, that's a disc you got creamed by that because your ability to control your array. And so, and what happened was a bunch of developers were like, wow, this is cool. Because now I'm just by my, when we did C++ for Windows, like we were like, where the demo, in fact, at that Coverly Community Center, I would go and I would show brand new Windows programmers in 1990, like, hey, you don't have to write print drivers and use the clipboard.

30:39And like literally standing ovation of, you know, 10 people at the And but but they were like more than happy. Yeah. To let data interchange between products. Because they were like, that's nothing but opportunity. Can we get you like they probably from an emotional standpoint felt exactly the same way as like a vibe code or does today, which is like, you've just given me the platform that it was just a print driver. The writing code for Windows book was like this big, but the writing a device driver for an Epson printer was this big writing it for a Canon printer was this big. And so I'm just actually trying to think of like the paradigm shift is the same, which is there's been many times where we've reduced the amount of work a developer takes.

31:20But I just don't remember ever where the program is at its logic. Like so for example, SDN didn't not logic. Like I would always say what is correct and what's not correct. Right. I think you're undersold it though. No, this is the thing. By the way, everybody, Martin invented and worked on. That's the, but it's a big deal. Maybe I should post more of your pitch at the time if you want to. No, I'm putting it this way. Well, no, I, let me, like logic specifically, which is I am writing an app. My app is whatever, some vertical SaaS app for a certain customer base. The answer the app gives is based on logic that I've written historically, right?

31:59Like if I run it on the cloud, the cloud is not producing an answer. is providing resources. If I'm using your device driver, it's providing access to a device resources. But if I'm like, hey, large model, tell me the answer here. You're actually abdicating application. I think maybe you're right. Maybe this. No, I just think I think what you're you're you're almost playing like incumbent in in the sense of trying to no, trying to like decide this is abdicating the logic and this isn't. When in fact, like it really was like a huge competitive advantage for word perfect. And they didn't want to give it up.

32:35And they fought. Yes. And the number of people who didn't want to do like great clip. And the next example, of course, is the browser where people literally gave up like you in a in Windows or in Mac, you could rasterize anything you wanted. You wanted a button that you pushed and it spun and animated like a rainbow. You could do that in your product. But then the web came along. and you're like, wow, I have to use a gray button that says submit. And that was like, I guess I used a bunch of third party things. Well, it took a long time for those to show up. And so early in the internet, the magazines in particular, and the printed media were the ones who absolutely wouldn't go to the internet because they would not give up their ability to format.

33:17And this is another part about the tooling and what's going to happen with AI is that that like a huge amount of the productivity software space today is like the preparation of output. Like office is basically a format debugger. Like all it is is like 7 ,000 commands for how to do kerning and bold and italic. And like it turns out AI not only doesn't care. You could ask it to make whatever you want. Like you could just say I'd like this to be a double index part chart thing. That's not the thing I just, but you can do that and it will just figure out something that looks like that and you'll go, ooh, cool.

33:53And this was where to this disempowering and experts and who's not an expert, when productivity software rose, the big thing about it was that there were people who figured out how to like make like killer charts. Like Ben and the Evans, like killer chart guy. And there were people who were like, every meeting started with, how did you make that chart? Like I could be at an airplane and somebody would be like, making a shit chart. I was so interesting. So like, in this case, the abdication is like, actually what's the way to to visually represent the data? Which is absolutely nothing. Right. And it turns out well, because like 90 % of the people never really got to be expert at doing that task even though 90 % of the tool is about like to even so what happens each But the programmer didn't have to get the logic in this case.

34:33This is the user But you know, what's the user? What's the programmer in that and and in fact what the programmer was doing was like Like we would invent the thing called wizards or whatever You know, and that would make a whole bunch of choices for you style sheets or whatever And so in a sense we were making a bunch of choices for the user which to the experts look like disempowering the experts who were tweaking all and so This is all like this There's some Steve Jobs quote that he loves about show but how or how if you've seen the the conjurer And continue to have seen the conjurer. It's not a trick anymore And I really feel like this is like the third or fourth time that this has happened just in my lifetime watching this Well, so something that's really caught my attention because it's the most senior people I know Is that a lot of very senior developers are spending up a lot of background agents like code agents and they're interfacing it like to get hub PR level, right?

35:25and so It's not obvious to me why you do a bunch as opposed to one and it's not obvious to me Well, you wouldn't interact directly so it feels like something's going on here But I'm not quite sure what and I would love your thoughts. Well the my read on it And then the quite I guess I would kind of sort of throw out like what then happens next as a result of this. Because to me, it's actually a little bit of an epiphany on what the future work design could look like in this world. Because engineers back to the prior conversation are just the first to experience this. But I think what my read from talking to kind of similar folks that are like all in on this is, is this mix of basically the effectively the context rot problem, which is, you know, the more that we put in the context window, the more it gets confused, the lossier the answers get.

36:13And so you have to have some kind of a way to partition what an agent should work on. And we see this in building agents internally, which is, you know, the panacea that I think we maybe would have hoped for is like, well, you just put a million tokens into the context window and then obviously, you know, oh, so you're seeing this is almost like a counter trend to the AGI is almost like the opposite. It's the opposite, but it's, it's, It only works because the models are so good. Yeah, but you're giving more things, more specific tasks, rather than one thing less specific task. Right, and so that's like, so, but like, I think this is why it's happening.

36:43So basically, the craziest version of this is I was talking to somebody who is in startup land and they have, they have to your point, they have all these sub agents. But what's amazing is that it maps one to one to each microservice in their code base. And so they have an agent per microservice. They've effectively a read me for the agent And that agent owns the microservice. And they, I don't know the specific number, but let's just say you could have dozens or hundreds of these things going on. And you're effectively mitigating this issue, which is if you just said, here's my entire code base, go run wild to one agent, it will just produce worse and worse code over time because it's gonna have context rot.

37:23It's not gonna know exactly what you're trying to do in that one area of the microservice, but the sub agent model seems to be working for that paradigm. I love this calendar pattern. Yeah. Everybody's like, they're gonna, like, you know, models will get, you know, smarter and you'll give them higher level tasks and they'll do things longer. Yes. This is a counter one. I want to tweet that, but you have more Twitter followers. Oh, we can, we can collectively do it. We'll do it. But then, so then the question is, okay, so let's just assume this works in engineering. You have this interesting dynamic, which is, well, then that means that like, some of the coding practices will be pretty different in the future.

37:54We've talked about this idea of, you know, the individual engineer becomes the manager of agent, So that was already kind of, I think, a well -undercised path. This is like a supercharger of that concept. And then the question is, how does that translate to almost every form of work? Because if I am now the lawyer and working on cases, and I can have 20 sub -agents that all do a different case, and then basically come back in some kind of task queue that I'm going through, like obviously, one, just the sheer leverage now you get is going to be insane. but I do think the way that you might even organize the work and what the workflows within an organization are inevitably going to change as a result of that.

38:37Oh, but I think this just gets to essentially that the flow in the workflow has been serialized or linearized based sometimes on knowledge, but other times on tooling. And so what happens when the tooling changes is you just get this realignment of what's truly serial and what's not. Like if you're, if you're planning an event for a company, which is still going to keep happening, you know, like, oh, I have to book the venue. I have to invite all these people. We have to create all these materials. Well, they're actually not particularly gated on each other. Right. But if you have an events person, they're gated.

39:16Right. And so now an events person can start spinning up all of these, these different elements. And then they're going to come back like, I've gotten as far as I can on collateral until I get a logo for this event. Like, I've gotten as far as I can on invites until I get the date and the time and the venue. And I think there's no reason why you can't spin of all those in parallel because, of course, how does that happen today? Well, if you're accompanying a new box and you've done, this is your 58th event, you know, you have a folder called event. And people take the folder and go event 59. And they make a copy of it and all the stuff in it.

39:52And well, if you think about that workflow, that's exactly what a series of different backgrounds has where agents could go do. And so I think the reason that you could be doing all that in coding is, well, there was a natural way to break that up because there's a bunch of programs. But there's the other side, but there's also a bit of an indictment on the ability of you to give it a high level. You know, it kind of suggests that the human being needs to be, you know, giving the more granular orders. Otherwise, to start a company, you'd issue one prompt, you'd go to the beach for six months and you'd be right back and you'd have a full company.

40:27Which is this almost re -anthropomorphizing effect, which is it turns out we did figure out division of labor. We figured it out in the context of a lot of physical analog limits that we clearly had that agents won't have. But we now, there's no total free lunch, so you have of this context -wrought issue, which is that you do actually have to subdivide the tasks at some point. Then the question is, what are the right... I mean, it may not be a context -wrought issue. The Occamal's razor here is you need to give them specific instructions for specific tasks. And if you give them higher level instructions, it's going to end up in context.

41:07They just don't know what you want. And this gets to the formal language part. Like at some point, if you try to use the Uber frontier to get the whole thing done, you have to tell it the whole thing. Yeah, exactly. And that just seems like a lot of work, whereas if you have to tell it less because the part of the model you're using knows more, it's basically a different way of thinking about templates or a different way of thinking about starting artifacts or scoping the context in a generic world. Well, but then there's this, I mean, it might though be the right architecture in general. If you assume that there's never going to get to a point where the model is just 100 % and perfect, right?

41:47And so it might also be the right kind of architecture design because at some point you're gonna have, you don't want an agent or a set of agents to go so far down a path when there was a step that it needed to check in with you. Right. Because there's just a compounding effect of that. So you do need to kind of subdivide the work also because if you do have gating, you know, moments that are gonna have a bunch of dependencies, the agent does need to know like at what point should I roll that back up to the user? Yeah, against the common narrative, now that I think about it, it seems that the trend is, is prompts are getting more complex, not less, and we're seeing more agents not less doing more narrow tasks, which is almost as kind of counter -age -y narrative.

42:29It's almost like these are much more specialized and much more deep, with much more specific instructions. And there's like a sort of a history of this, this, wow, maybe we can actually solve it if we're specialized a little more. Or if you take expert systems, at first they thought expert systems would just be experts and they would just, no, and then like, by the time you got to the actual published research, like it's tampered, it was like, this is an expert system in deciding on what type of infectious disease you had. You're like, I'm just gonna have one of these seven. No, no, no, no, there was a paper, it was like there's just one digestive disorder that actually is a medical type.

43:02I do want to, though, because you wouldn't want, like there is one big difference, which is somehow the model itself is packing in the inherent intelligence or capability to solve all of these. Like, like, like, we are benefiting from the fact that at least you can build these all on cloud four and GPG five. That all on a computer. Show, like, demonstrate this one with an old person example on this one, which was, like, early in the PC era, there were worse processors and spreadsheets and graphics and databases. And a lot of people were like, why are there these four programs? There should only be one program.

43:41And my answer to that, like, which often involved screaming, was, have you been to an office supply store? Because if you go to an office supply store, there's like paper with numbers, and then there's blank rectangles of paper, and then there's transparency paper for people. And like, this has been around a really long time. There's some reason that these are difficult. There's a human context for two minutes. How many minutes did it take you for you to know Google Wave wasn't going to work. Zero. Okay. Okay. It was the answer. No, it was the answer. Okay. Okay. No, I mean, and, and, but this was the thing.

44:12There was a product, ancient Mac product that was lauded by the industry called Claris Works, which was like, oh, it does, you could have a spreadsheet inside a war processor. And my first reaction is, have you seen a person use a spreadsheet? Because their monitor can't be big enough. So they just want as many cells as you could possibly have. And you're sitting there saying it has to fit on an 8 .5x11 sheet of paper on a Mac. And I think that one of the things that happens is that these lenses that humans bring to specialization like really, really matter. And if you think about the medical profession and you think about going from a GP to the radiologist to a specialist to a nurse practitioner through the whole series, they're each gonna look at and use AI in a different way.

44:55So then the only thing would be, okay, So that was that level of specialization and division of labor emerged over a hundred -year period with you know alongside tools and but but also with driven by a lot of the physical constraints and realities of how organizations emerged. So the only question would be in a post -agent world in 10 years from now through those divisions of labor look exactly the same or do those shift also because the agents collapse you know some of the functions and is there some blurring and then And then there's just a new set of roles. Clearly there's a role in a bunch of organizations emerging, which is like, no, I'm just like, my role is like, I'm the AI productivity person.

45:35And I just have a way of creating all new forms of productivity in the organization with AI. So clearly, we have a bunch of new roles, but is our current division of labor going to also collapse in some interesting ways because of AI? Well, I think that if you actually stick with the medical example, we're just going to wake up and there's going to be way more people with way more specialties. And AI will have created more jobs. And in the interim, the AI causes more specialization over time. Absolutely. Because everyone's going to be way better and more knowledge will amount. And I think this is a thing that has really happened with computing that people forget.

46:12Like, there used to just be this morass of marketing and R &D. And all of a sudden, there used to just be coding. and then there was coding and testing and design and product management and program management and usability and research and all of these specialties and all of those had their own tools. Go to a construction site. I remember growing up, our neighbors built a house. We lived in a department and they built a house and there was Klem, the carpenter. And you built a house with a guy named Klem who used all the tools and everything. And now, like you build a house and it's like this 20 person list of subcontractors, all who have whole companies that do nothing but like put in pavers, you know, and that's what it's gonna be.

46:52I mean, there's been a long disaggregation in the history of IT, right? Like everything in the same sheet, metal, then you know, disaggregate the OS in the hardware, then you disaggregate the apps. Right. And then it was kind of interesting. Like in the last 15 years, we saw the app and like independent functions got disaggregated, right? It's like almost everything became like, like an API would become a company, right? You know, with the Twelios, like, auth became a company, like PubSub became a company, et cetera. And so it may very well be the case that every agent becomes like a whole new vertical and a whole new specialization.

47:26And then you can actually build a company around it. Like it may be the case that today, just like with APIs, one company will have a whole bunch of agents. And maybe the case in the future that a third party will provide that agent as an independent company. Well, it's so it's the opportunity to your point is really there for that. Yeah. Because like there used to be like the the impedance to creating a company and distributing. No, exactly. It was infinite. And so, you know, if you used to be ridiculous to think that a single API like Auth could become a company, but then, you know, of course, it would be.

47:56Or it used to be ridiculous to think you could build a whole company out of signing documents. Right. And like that, not just a whole company, but then all of a sudden you realize, wow, the addressable market for that is huge. And it's way bigger than signing because of all the stuff that got done that was just right surrounding baked into a company causing headcount and waste and a bun and fraud and abuse. Well, I think you can kind of underrate thousands of of these companies emerging. So Jared Freeman had a tweet about basically like go deep on a workflow. You know, basically do the job of some part of the economy, payroll specialist, and then build an agent for that.

48:36And it's not obvious that there's not literally like a thousand of those. So by every vertical and every line of department. I just love this because this is like literally the anti -AGH. Basically following like the long arc of computer science, where as the market grows the level the granularity can create a company. Well, it's also economic growth. Like take that example. Exactly. Like today, just like Salesforce, which is always my favorite example. Like the idea of having a productive Salesforce used to just be a consultancy. And the only way you could ever fix it was hiring a consultancy to show up and analyze it every day and then do a report that says, this is how you need to re -organ it usually meant go the opposite of whatever you have.

49:11And then they would leave. And then, you know, people tried, but there was no cloud. So to build like CRM, you had to do all that consulting work and then roll it out. And then it was static and you couldn't maintain it. And then all of a sudden there's like, oh, here's Mark Benioff and here's a whole way to do all this. And not only did the people actually like it. And they think they're better at selling, selling because they're using their phone and they're putting in a few notes about this client which helps everybody. and I think that's what's really going to happen with all this. And so suddenly, something that looks really, really small becomes like a whole thing because there's no problem with distribution, there's no problem with customization, you know, we'll actually have ways to solve security and privacy and just like we solve reliability and things like that.

49:58And I think it's just, I mean, look, you know, the stuff that your world expert in in the stack of internet technology, of networking technologies. I mean, you would have asked me 15 years ago was CDNB companies. I never would have, I'm like, I don't even make any sense. Like, how could you have a company that's a cash? Yeah. I think people are probably way too afraid of the model providers kind of eating them. And I think it was, I think it was basically a phenomenon in the first wave, which was if you were just doing like basic, like, like, if you had figured out that you could do something on GPT, you know, two and three, where It was a text interface that produced more text.

50:35Like, yes, Chatsbyt8U. Like, that clearly happened. Yeah. But basically since then, most enterprises want kind of applied use cases for AI and AI agents. And so it's not obvious that the current crop of companies, if you're doing AI for healthcare, if you're doing AI for life sciences, if you're doing AI for financial services, if you're doing AI for coding at the right parts of the stack, AI for coding maybe the one astric area, which will be hyper competitive, of Cyblic because the model companies don't want to use somebody else's product to build their own models. And so that kind of almost forces them to get really good at AI for coding.

51:09But with that as the one kind of exception, I think basically we're just in a five year period right now where you're gonna have to build agents for every vertical, every domain. And there's a playbook that's starting to merge of what that needs to look like. I mean, so I think there was kind of a technical headsafe that happened early on, which was pre -training. So the pre -training really was a 10 out of 10 technical innovation. I can't tell you like two years ago if somebody was like I had a friend that was building them like their own aging model, push training, aging model like we're going to make it so good at aging.

51:40You know this is a text to image model and they wanted to make it so like old people looked really good at it. And then of course the next version of like miniature, when it comes out and it does a better job of it. And the thing with pre -training was you're just kind of consuming all of the world's existing data, you're training all of that energy and it perfectly generalized, right? But it feels like technically that's passed. And now we're more in post -training in RL. This is a lot more domain -specific. And so, well, in the moment that you have access to some set of data that is only - Exactly.

52:09Just for that enterprise. And so who gets permission to access that data? Who gets permission to do the workflow on it? It's going to be applied companies. Yeah, so yeah, if we had an infinite number of tokens, then the models would just continue to generalize. But it's pretty clear that that's not happening. And so now we're going into, which we all understand very well, which is now companies have to choose which domains to go into. And they've got to solve the long tail problems there. I get access to the data etc. And I also think that there's that the shadow having been the shadow, the shadow cast by large companies over, we're going to put you out of business and stop you.

52:40It's ridiculous. And it has never in any technology way, have lived up to the fear that people had, look, if you built a new word processor in 1995, you were an idiot. Like that was not the thing to go build. Yeah. But there was a time just 10 years earlier where companies built standalone spell checkers. It was just the thing you went to the store and you bought a spell checker. It had more words than the other spell checker. The thing is that's not being said now, which we should do a whole one on is, what is the actual platform? This is because it's all well and good to say that the large models will go and subsume every application.

53:23The thing is the minute they start doing that, no one will be in their platform. Because like, no developer is gonna sit around and say, if you're gonna subsune me, then, and this is, there's a phrase that it's Sherlocking and the Mac and the Apple World. Yeah. To this thing. And so it does, it has a real chilling effect. And that's one of the things all the model people are gonna learn very, very quickly. There's a chilling effect, but there's also just, I think there really is just a problem of like, it's hard to go deep in 50 categories. Like you just can't, Montelo preacher, I think everybody's scared because pre -cending was actually the one thing that was good at that.

53:53And then now they have to actually, too. Yeah, I agree. You do have to, like, at some point it becomes purely just an execution issue, which is like, I don't know how anybody would set up a company to be able to beat 50 startups across 50 different domains. No, it's ridiculous. And in fact, like, it's only good. Because what happens is, is that the big company raises, the big company raises the awareness of all category. And then you just swoop in and you go, So to them, I'm just a feature. But to you, this is my whole life. And I always come back, there's a whole company that just signs things.

54:31I cannot believe there's a whole company that just signs things. I have so much to say about this topic. I mean, even minimally, if you graph the cost to produce, so willingness to pay for an inference versus the cost to serve it. Something like for most companies for most bases 20 % of the emphasis are 80 % of the cost like actually the problem of the application is just to choose those ones on which tend to be a more domain specific Yeah, this is a problem of inviting the three of us on Okay, which is like we just open up like the next Just getting us to shut up in the track Guys, thank you so much for coming on this fantastic Thanks for listening to the a16z podcast if you enjoyed the episode Let us know by leaving a review at ratethispodcast .com slash a16z.

55:20We've got more great conversations coming your way. See you next time. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a16z fund. Please note that a16z and its affiliates may also maintain investments in the company's discussed in this podcast. For more details, including a link to our investments, please see a16z .com forward slash disclosures.