Box CEO Aaron Levie breaks down Box AI and generative AI’s impact on business | E1738

0:00yeah ai is getting pretty pretty crazy just reminds me of like seeing the internet for the first time and you're like, how does HTML work? And they're like, look, view source. And I'm like, okay. And they're like, okay, now put it over here and use this hot dog editor. I'm like, what? Hot dog editor. You can do Dreamweaver. And I'm like, okay, wait a second. And now wait, you reload. Okay, reload the page. Remember those conversations? Reload the page. That's really good. Yeah, it's definitely feels like mid-90s, discovering the web, and then mid-2000s of like we had Ajax, like a bunch of just came together.

0:33Yes. and it all just worked. This Week in Startups is brought to you by Squarespace. Turn your idea into a new website. Go to squarespace.com slash twist for a free trial. When you're ready to launch, use offer code twist to save 10 % off your first purchase of a website or domain. Masterclass, learn from the world's best minds anytime, anywhere, and at your own pace. Get 15 % off an annual membership to Masterclass at masterclass.com slash startups. And Hampton, are you a startup founder or CEO seeking expert advice? Join Hampton, the private, highly vetted community for high growth founders.

1:18Get invaluable insights, connections, and support to accelerate your business at joinhampton.com slash quiz today. All right, everybody. Really thrilled to have a long-term friend of mine, incredible entrepreneur, and part-time internet comedian. Aaron Levy is back on the program for his fourth appearance. Just to tell you how long he's been around. Episode 224 in 2022. Episode 389, 2013. Episode 1173, February 21. and again today this uh puts you getting close to the five timers club what's the what's the record well there are some journalists who have done like news roundtables on a regular basis so taking them out we do have a number of five timers uh andy rackliff um has been on i think five times um glenn the ceo of redfin's been on maybe four or five times but it's it's it's rarefied air That's a good club.

2:23All right. Sounds good. It's a pretty good club. And it's, but Brian Chesky joined the First Timers Club just yesterday. Oh, good for him. He did a great episode. And Convicted Felon from Fyre Festival was on. Yeah. And that was our first convicted felon. If my PR team had known about that, I don't think I'd be on right now. So this is remarkable that we snuck this one in. We did snuck it in. We're doing the felons now. This is okay. All right. You know, Billy McFarland was like, I'm a super fan of yours. I've been tracking you since high school and I am turning it around. I mean, I learned a lot of lessons and I'm like.

3:02What's he doing now? Fire Festival too. Oh, okay. He learned a lot of lessons. He learned a lot of lessons. Would you like to sponsor it? Okay. But he has$28 million in restitution. He's paid off 40K. He's charging$1 ,800 an hour to do marketing for people. Yeah. uh so he's just basically like a marketing guy yeah who's two out of every i think like two out of every three dollars he makes goes to the government and restitution he makes like 30 cents on whatever dollar he makes for the rest of his life wow okay so 20 30 years from now it's paid off and and then he'll be he'll be making profit exactly so you talk i mean it's kind of like a series d company in silicon valley today uh hey oh um but i wanted to have you on uh because because you and i were just talking about my lord ai is moving fast yeah and box i was at my poker game last night and somebody said well box is the obvious best company to incorporate ai because i mentioned you were going to be on the pod uh so you got a nice shout out there at the poker game and i noticed you started sharing some demos so i think just your general perception at the start here to level set of the impact of this technology and how quickly it's moving because it does seem like this was a very slow kind of grind and then very quickly became wildly impressive so your thoughts on that as a technologist yeah i mean i i would characterize it almost exactly the same way which is and i think most people really deep in ai would probably say the same thing.

4:40I mean, it's kind of like, you know, 10 years we've been, you know, incrementally making this progress. And, you know, you had the, the, the transformer paper five years ago and it's like, well, you know, that's obviously a big unlock. And then you had GPT two and three and, and, you know, I think we probably both played around with the early versions of GPT that, you know, the GPT versions. And, and it was like, you know, super interesting in terms of, oh, that's kind of cool. A computer can do that. But there was like, I don't, I don't, I certainly didn't have an aha moment of like, all of a sudden this is going to be incorporated it into all software i thought it was like a really you know compelling um you know demonstration of of what we can now do with text prediction and and whatnot and then obviously chat gbt kind of both the combination of of its improved you know 3.5 model and the just the right interface um you know to capture the zeitgeist of of what we can now do with with ai and interact with ai and so um so that combination you know obviously um you know put us in a completely different conversation about where AI would be incorporated.

5:37And so we had been doing seven or eight years of work in AI. And the challenge was every single use case a customer had, I want to understand my contracts. I want to understand my film scripts. I want to understand my blueprint files. Every conversation we had was a different AI model that you had to narrowly train and implement for that one use case. And so it never really scaled because it would be like if we were building on the web and every feature you wanted to build you needed a different you know you know tech stack or cpu for that feature just would never scale out to all the different you know kind of scenarios we had and you know with three with gpd 3.5 and now four it's like it can just solve all the things so it can do the contracts and the film scripts and the um and the press releases and the marketing documents and the blog posts and so it just understands everything and and then you can implement it in these much more generalizable ways which is which is a real breakthrough in terms of just you know how many use cases we can now solve with with ai so when you saw uh you know gmail 2018 2019 you know predicting your next word or two you know okay pretty clever yeah um that's helpful uh but it wasn't like an aha moment what was your aha moment in the last couple of months where you said, okay, I need to, and I'm assuming this is the case, correct me if I'm wrong, I need to get the entire company dialed in 100 % on this opportunity.

7:06Yeah. I mean, the specific revelation was probably no different than anybody else on the internet. You can give it literally any prompt and at 99 % likelihood, it's going to come back with a response. Now, accuracy aside for a second, and we can get into how we solve that problem um uh just the fact that it will attempt to do everything was was sort of the mind-blowing moment yes i remember uh like probably my first like like big aha was was you know you you initially start with use cases which are like these like very straightforward kind of facts um because like we're so used to like we go to google and we type in a thing and then we get an answer and so your first things are like wow like it's really cool that i got that answer um so it was like really basic questions of like you know what's what's the better way to to travel uh you know between you know x and y place and and you're like oh wow that's actually pretty cool like i thought through you know you know you might have to wait at the airport for this long and so maybe it's actually more efficient and some in this case to drive and so that was pretty cool but very fact-based um and then and then like when you when you start to give it things that are not fact-based but actually things that it has to take two pieces of distinct information and and kind of combine them and reason through it then i was like that was really where i was starting to be you know my my head started to explode where um i you know we were giving it like um uh so you know peter um michael porter uh uh sort of five forces framework which is this sort of competitive framework of how you think about suppliers and competitors etc and so so take that um uh take that concept and apply it to apply to things that that no one has ever done a michael porter five forces forever, like, like in the history of, of the world.

8:48And, and it just did a really, really good job. Like we were like, do Michael Porter's five forces on an airport? And, and it, and it, it was able to take, you know, one set of information it has, and then combine it with a completely unrelated topic and, and basically completely exceed what even a human would probably have answered, you know, for that. And that was the big breakthrough of like, okay, so this is not just going to be this sort of question answer engine of of raw facts it's actually going to produce new information for people and then become really an assistant where you can just go to it um and ask it anything and so that's that was you know some more of the aha moments when you start to take and combine those those you know two unrelated things and you're actually leveraging the fact that it has a wealth of knowledge within it um and um and you know way more than any what any person would would be able to keep track of um that's where you really get the power of it and And so then we said, well, what if we combine our data set with that?

9:43And then you start to, you know, kind of create new use cases that just never before would have been possible. And that's where we, you know, kind of, you know, sort of didn't like do a full company pivot, but about as close as you do when you're, you know, 2 ,500 people. And we just said, okay, this is a big moment for us. We're going to go in and incorporate this technology. If you can tell from the podcast lately, we've been doubling and tripling down on Founder University at launch. In fact, it's basically the future of our venture capital firm. And that's awesome because I'm working with a couple of hundred early stage founders really early and getting to see what tools they use.

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10:56And they also have appointment scheduling. So, you know, if you're doing a business where you're a consultant, you want to charge for your time. Well, you have scheduling built into it as well. And this is the brilliance of Squarespace. It's going to look beautiful, as you know. So here's what I want you to do. Just head to squarespace.com slash twist for a free trial. When you're ready to launch, use the offer code twist. You save an extra 10 % off your first purchase of a website or a domain. We love you, Squarespace. You know how it is. When you're a technologist, everybody in your family, your friends, your circle, your network come to you and say, hey, I got to get a website up.

11:24Can you find me a developer, a designer, a product manager? And you just say, you know what? Yes, I can find you all that and more at squarespace.com slash twist. how many customers broadly speaking do you have how many documents are in the repository and then how do you start this process of uh the very scary um prospect i think for some customers of oh my god am i feeding my data to this and is it going to wind up jumping the fence am i training the box model or the open ai model and and are they going to use some corporate secrets and a strategy document and then somebody else says hey how does michael border you know reinvent an airport and a new airport in new york and that was their proprietary stuff so that's like got to be objection or concern number one yeah yeah so let's let's start first with that and then and then we can kind of talk about actually what we what we're doing so so first and foremost just emphatically first of all every interaction you have with Box.ai is explicitly you have to decide to use it as either a user or the enterprise so we're never having AI kind of do anything in the background without your explicit kind of consent on using the service, that's the first thing.

12:41The second thing is that at least for all of the use cases that we're talking about right now, there's no training whatsoever. You're just using the AI model in its sort of stateless form as really a reasoning engine to be able to help us with natural language type tasks. And so there's no training of the AI model. There's no logging of the information that you gave the model so it can train later. All of that is done in this very kind of stateless ephemeral way where we're just kind of asking the AI model to help us with the user's query. And then probably the third big element is just we have a very, very high standard for security, compliance, data privacy, just by virtue of our customer base, our, you know, very, you know, large enterprises across every industry, you know, hospitals, federal government, et cetera.

13:32And so we really, really take, you know, security and privacy very seriously. So, so we're not, you know, trying to lean into any of the privacy elements from a risk standpoint, this is completely, you know, meant to be a very, very, you know, conservative approach to how we're going to treat you know data privacy and compliance now certainly there must be some early adopting folks and some ceo calling you saying hey i do run this organization i would like you to create a language model for me across these 4 000 employees and i as the ceo god king or queen uh would like to be able to do queries or my management team across our entire corpus of documents so is that on the roadmap and are you starting to get those you know sort of um i'm all in requests from your customers we we are actually and and uh and what's really interesting about this is um is it's not even um it's not the obvious sort of cut lines of you know kind of conservative businesses versus more aggressive businesses and and you know sort of unregulated um uh we're seeing that most cios most CEOs are recognizing that, you know, AI is a real platform shift.

14:45This is much like, you know, mobile or the web in terms of we think about it as a platform shift. It's a scenario where, you know, this is now me talking, but our customers are kind of reinforcing this, which is it's a general productivity uplift where we can use this technology to, you know, make decisions more effectively, find information faster, spend more time on the right areas of our business that only humans can do as opposed to computers could do better. And so we're seeing almost universal optimism about how to leverage this technology. Of course, there's a long list of items that you have to just go through from a compliance security privacy standpoint.

15:23But in general, across our 110 ,000 plus customers, obviously, we spend more time talking to some of the larger ones, but there's a lot of excitement around it. And, um, and there's a lot of nuances though. So for instance, this idea of training, you know, um, uh, a model with your enterprise data, it's actually not, not clear that that is a, a, a problem that, that, you know, we can really easily solve at the moment. Um, because, because when you train a model, um, you know, with, with proprietary data, it's very hard to sort of figure out, well, which things should we leave out of the dataset?

15:58Because they might, they might accidentally inform the model and then, and then give up, you know, kind of sensitive information to you know the wrong employee as an example so so we have permissions are paramount here and permissions are in the previous file sub file organization are just not the paradigm that we have just shifted to the paradigm is here's everything and well well so that that's actually so this is i mean humbly this is where we think we play a role which is which is actually you do want permissions on the data that the user is going to want to be able to query against. And the AI model is really used as basically this brain to understand the information that you're giving it that the user already is allowed to access.

16:48And you're using that basically AI model to reason through what that user can already access. So at least in today's architecture landscape and there'll be different approaches that different companies take. The point is not to train a model on your enterprise data, because again, then all your permissions are going to be leaked into that model. It's to separate the underlying access controls that people have to information from the AI model, which you use as a reasoning engine for that data. And that's at least for our kind of use case, which is a lot of sensitive data, but you do want to be able to work through it with natural language um that might not be as relevant for maybe if you wanted to have it train against your code base where all of the engineers are allowed to see all of the code base that's something where you might want to have then a kind of a specific enterprise example would be legal and hr the legal department everybody being able to query the legal departments could be any kind of settlement agreements or confidential agreements exactly come up and then somebody typing in who are the most underpaid people at this company and they're like oh it's me that there's the most overpaid people at this company oh yeah so you can instantly imagine like past like 10 employees you can't train you know your entire enterprise on on you know uh your entire enterprise data set on a single model so this is the this is basically where our work has has gone it's only you know we're only four months into it um but it's it's gone to this abstraction layer where we can um keep the access controls in place but use an ai model to reason through information that the user is allowed to access and that's we think that's going to be the breakthrough for how we implement this makes total sense the head of human capital has access to all of the human resources information but a recruiter might have only access to 10 of the information and they might do different queries about different on different data sets and summarize or whatever so how about some demos here of uh you know the i would suspect there are things that are really easy to implement uh and uh provide massive value you know like we like to do as product people we put those on an x y chart uh those would be like the quick wins yep big impact everybody's going to use it and it's uh easy to implement what did you get to first and then we'll get to the harder ones uh sure so we um uh i feel uh this is this is fun going back to like the startup roots of uh a forced live demo so um here's basically how it works so i'm in um i'm in my box account right now just looking at at one file and uh this is actually it's a little meta because it's the press release for the announcement but um it's just an easy kind of public document so um uh so this is the press release and you'll be here you know you just click this box ai uh icon we're still going to continue to play with with the ways that this gets incorporated um in the product but but for now it's this sort of chat interface on top of your content and so now you know this would work whether it's a three page document or a 500 page document but again for demo purposes want to keep this simple and you just say you know please summarize this announcement and if it if it works pretty quickly because we're in a dev environment yep it'll just go in and summarize the announcement and then you say what are three use cases for box.ai

20:10and and so the key is it's saying according to this document here are three cases so it's not it's not attempting to kind of go into the large language model and say what do you know about box ai and can you respond to it it's just saying from this document uh what are you finding inside of this piece of information that could answer this query so that that's sort of the the way that we set the prompting up and that also then dramatically reduces kind of hallucinations that you might see because it's not attempting to make anything up. We're really kind of forcing it to stay within the document.

20:44But there's one kind of cool thing, which is while we're telling it to only answer questions based on the document, we're giving it a little bit of free reign when you want to do more transformative type use cases. So let's say you want to take this document and turn it into something. We're instructing the model to go along with that. So as an example, we'll just see if this works out to prove my point or not. Please write an email to an automotive company pitching Fox AI, and please include three auto-specific use cases. So in this query, this information is not in the document, but we're sort of having it go along with that instruction to say, use this source information to answer this question still.

21:30We'll see if it, if it kind of comes up as a desired answer. Um, and, uh, and so, you know, it's, uh, it wrote this email to an auto company, um, about, uh, about use cases that they might have for box AI. And so, so now you can imagine, you know, you're a sales rep and you're looking at, you know, a product marketing, you know, PDF, and you really quickly want some ideas in front of a customer of like, so what are some use cases for this new technology? You know, box AI would be this assistant for you to go and quickly, you know, answer that question. um and so any basically any kind of um you know tasks we give it other than just using the large language model as the the sort of database of of answers um it will it will generally do on your content and then um yeah sorry well i was gonna say this you know when you when you have a good demo like this your mind starts to just come up with ideas yeah i was immediately like who in the organization has to approve this and uh i wonder if i can connect this to my email and i can source 100 leads from the database who haven't been contacted so you start to get into information you probably have which is the org chart and the permission so who you know you could be asking who handles the automotive category on our sales team in the united states can you get them to approve this and in our sales force or wherever that data is stored can you get me uh you know all the sales leads in automotive that haven't been contacted this year yeah yeah exactly and so your your mind can instantly kind of extrapolate now what's possible now what's interesting is this is where this is where you know we in the valley and everybody you know and i mean you know figuratively or literally you know have to respect that um the technology can do all that but we're probably like five years away from from you know society actually understanding how that would all come together and having the technology actually integrated in this kind of capacity and so this is where And I remember when we were first rolling out cloud to enterprises, it was 06, 07, 08.

23:30We were in front of enterprises and we had our early adopters. And my hunch was like, okay, in like three years from now, obviously every single enterprise workload will be in the cloud. It's so clearly obvious to the entire world how much more efficient this is, how much more scalable it is, how much more secure it is. And, you know, fast forward, it's now, you know, 15 years after that prediction and we're still in the phase of like mass adoption of cloud computing. And so, and the reason for that is just like, you know, the world takes a lot of time to change on these really big tectonic shifts.

24:02And so, you know, the use case you just mentioned, it's got to connect email and Salesforce and your data from Box. That's going to be a lot of wiring together that we have to, you know, come up with. And then what will be interesting is do the AIs talk to each other? So does the box AI talk to the Salesforce AI, which talks to your email AI, and they are coordinating that information exchange in a secure way? And we're at just the starting point of how does that actually get architected? One of the reasons why criticism can feel obnoxiously aggressive is that sometimes people use criticism to sort of dominate or assert superiority.

24:45And that is not helpful criticism. So state your intention to be helpful. That was Radical Candor author and friend of This Week in Startups, Kim Scott. And she just did a masterclass session called Tackle the Hard Conversations with radical candor. If you're a business leader, you can learn so much from Masterclass. There are amazing lessons from Bob Iger on leadership, Chris Voss on negotiations, and our friend Alexis Ohanian on startup investing and so much more. Legends of their craft are on Masterclass teaching you whatever you want to learn. Paying for an unlimited Masterclass subscription is a total no-brainer.

25:24We just had an awesome insight from Kim in just 15 seconds. Imagine how much you're going to learn in 10 minutes or an hour or maybe two. If you invest that in the next couple of weeks, I highly recommend you check it out. Get unlimited access to every class. And as a twist listener, you can get up to 35 % off for Mother's Day. What a gift. Go to masterclass.com slash startups. Now that's masterclass.com slash startups to get 35 % off for Mother's Day. Yeah, this guide on the side concept feels really powerful um in terms of just efficiency when you look internally at your own company what would you predict if i had to ask you in a percentage basis that you know the average team member or the entire organization how much more efficient will they be by the end of 2023 yeah if you get everybody to adopt using chat gpt and other ais how much more efficient will your company be yeah it's this is this is the you know the the big question i think for all of us and so if you if you kind of imagine let's say every every knowledge worker uh for for now just starting out with that demographic um you know has an ai or multiple ai assistants that do relevant tasks for them um you know workday will have one for hr salesforce has one for sales um you know yeah no everybody but everybody has kind of ai incorporated their workflows I think that the way that we should think about it for now is, is how much time do you spend in your job trying to, you know, triage information sources, find answers to questions, sort of do more information, you know, based tasks, you know, writing something, brainstorming something, you know, discovering something.

27:14and depending on the job, if you're in, let's say, customer support, that could be like 50 % of your time because you're always going back to the knowledge base and trying to get an answer to something. If you're an engineer, you'll go talk to engineers and they're spending two or three hours to find who on the internet has optimized this particular SQL query before and that's theoretically instantly solved inside of GPT-4 or Copilot it and and that just becomes a sun lock and so i think depending on where what your job looks like um you know somewhere on that continuum uh is is you know you might get back 50 of your time you might get back you know 20 30 of your time and and my um and my kind of like like totally amateur hour macroeconomic you know kind of um uh you know kind of view of that is is that is that we won't really even notice what what you know how to measure that because because that that engineer that that customer support rep will just be doing they'll be doing more of they'll just get to the next task faster they'll get down the roadmap faster they'll get to the next sales call faster exactly so it's not eliminating their position it's augmenting them and when i talked to brian chesky about this you know what number he came up with what 30 and i had come up with 30 because i was like of efficiency improvement across all of airbnb employees and i just thought about that Well, hiring has been in that 10 or 20 % a year range.

28:40So on a meta prediction, I think you could see organizations, instead of trying to hire somebody, you know, to throw a body at it kind of management culture, I think the next two years of management culture, who knows how long will be, how do we get the AI to do this? Is there a prompt we're missing? Is there a different AI tool? Should I be using Poe from Quora? Is Bard going to do a better job? you'll be just ai shopping chatbot shopping and i think 30 is the right number i think it's like yeah so so so i think that this this is going to be this will be this will be um extremely interesting to watch um you know clearly but um i think there's a couple ways to cut it so so there are some things there are some tasks in a business that are are not kind of like infinite they're they're like they just have like this one discrete thing has to get done And so if you make that one discrete thing 30 % more efficient, then you scale that out and then almost by definition, you would have 30 % less labor across the economy or across a particular business for that thing.

29:42Then there's a lot of tasks that are not kind of finite. They are literally kind of bottomless and boundless in terms of - Ongoing. Ongoing. And so I think engineering is one of those things, which is we are always constrained. We are universally always constrained by the number of engineers we have. And so if I can make our product roadmap go 20 or 30 % faster, that does not, that we are going to hire the same number of engineers that we can afford. We're just going to accelerate the, we're just going to be on a relative basis to what we would have been doing, 30 % more productive. Yeah. No different from 10 years ago, we were probably 30 % less productive then because we had engineers working on different open source libraries or different systems that now the cloud just does for free for us.

30:27But we didn't hire fewer engineers as a result of that efficiency. We actually, if anything, we hired more engineers because they could actually go and do more productive tasks as opposed to these sort of lower level, maybe a lot less differentiating tasks. And so I think for areas of your business where there's no particular upper limit, there's no upper limit on how many sales reps you need, how many engineers you need, how many account customer success managers you need, because those things are only constrained by how many customers you have or how innovative is your company and your roadmap.

31:02And so I think these just become accelerants into the future. And then there are going to be areas where there wasn't a finite limit that you actually need. And we can make that area of the business more efficient but i'm i i think there's more areas of businesses that are boundless and and um uh and kind of limitless than those that are you know inherently finite yeah i i i agree strongly because if you just think i was trying to think of which category would be one that could be like telephone operators you know like okay that's just completely been replaced or you know travel uh agents who book your tickets for you right and they type in the search instead of you doing okay obviously those went away um and then i just thought well okay maybe customer service or success and then i thought about it and i was like you know what you're always trying to get a customer to be more successful with the product right so instead of dealing with login issues or navigation issues those will be done by the ai yes but then you'll be getting to like here are some scenarios for you to use our product here's some you know advanced scripting stuff you can do with this product you're going to just go to the higher level stuff that makes people churn less um and yeah it just feels like for there's going to be a group of people who resist this but i don't think they should be scared i think this is like an incredible opportunity it feels to me like we just went from dial-up to broadband right all that did was increase joy uh you know and usage when this is going to speed up developers i think the developers are going to be stoked to be more efficient and get more done yeah there's no developer uh that i've talked to that has been using any form of AI for productivity that has been like, I really wish I could go do that research again of how people optimize this query or how they interact with this external library or API.

32:53No matter what everybody is like, if I could just see a quick example of how everybody on the entire internet solved this problem, I'm going to be able to leap forward faster. It doesn't mean you copy and paste the code and you just implement it without a any additional labor um but the speed of which now you can jump to the next uh task and the ideation you have uh is just so much faster and so i don't think there's any any returning you know from that um but i'm i'm way more in the optimistic uh camp of this just acts as a general productivity lever uh for the economy yeah it this feels to me like the way out of whatever economic situation we're in um just like mobile broadband it just kind of helped the economy uh and you know and founders get enthused i mean i can hear the enthusiasm in your voice you're in the office on a friday you know just getting it done and are you feeling that the enthusiasm enthusiasm level inside the org has gone up because listen it's been a tough 18 months stocks going down layoffs and big tech is this changing morale i mean yeah we we we we have um you know we've been in kind of hunker down mode for, for three plus years, we went through an activist battle.

34:10And, and so we've, we've kind of already had this grinded out, you know, kind of mindset. So fortunately, so fortunately that's been kind of baked in, but, but I think, you know, independent of our specific, uh, you know, um, uh, you know, morale on that front, I think this is, this is sort of what we all live for in tech is like, you want there to be a platform shift at some interval, you know, 10 years, 15 years, that just shakes things up. And you just, I mean, it's exciting that the companies who thought were monopolies now are, you know, they actually have, you know, real, you know, strategic crises that they have to go solve.

34:46There's an all new technology that is just really fun to play with. I mean, you know, most of the work that went into to box AI was, you know, these are like 1am sessions, you know, with, with the, the, the team or CTO on like, okay, can we, how do we solve this? And, and how do we figure this thing out? We hired a, we hired an intern at, at midnight on a Saturday. Um, uh, and just, just, you know, some, some kid at Stanford that was just like online and ready to rock right away. Um, so it's just like, those are like the fun, I mean, I'm probably being like overly nostalgic in, uh, in our strategy here, but that's what makes startups fun is something comes out of left field, you have to adapt to it, you have to figure out is this a tailwind or a headwind?

35:29How do you incorporate the technology? The other fun thing about AI specifically is that it has the service area of it is just so large where every day either somebody externally is doing a discovery that opens up. We saw the auto-GPT thing just a month ago and you're like, okay, well that's a whole new vector of innovation. plugins well the plug-in data analysis did you use the data processor yet um i have not used that one uh is that is that pretty nuts like you upload a csv file and you're like it's like okay here's the columns in it and you're like okay um tell me about the data and it's like tell me three trends and it's like okay it seems like uh and i just uploaded like an airbnb csv of like all the different la things and it was like yeah it seems like santa monica has more than this place and this you know and the average price is this and i'm like whoa i make some charts and it's like okay here are some charts and somebody did it internally we were doing diligence on a startup and we just took the diligence folder uh and uh took their revenue and projections and everything and said make some charts and tell us about this and he hallucinated there were some bum charts that didn't make any sense you're like this makes no sense um but then there were some that were like oh yeah that would be something that an associate in a venture firm would spend two hours on and you know the the researcher just did it and it's like okay great it's it's it's the crazy and the fun thing is actually like you know even the demo i just gave you are kind of like the obvious things that you would do with ai but um when you start to think about about like the non-obvious stuff and actually part of our problem is just gonna be how do we help encourage people to leverage this um in these non-obvious ways that's where it gets really fun so um uh so what one scenario we gave it like our earning script from from one quarter and we said um uh you know if you were warren buffett um how would you improve this earning script and um nice it's a random use case, nobody in the entire world would ever have asked that question of an earnings script.

37:20But all of a sudden, it starts to amp up the cash flow message and the share repurchase message and the capital allocation message. And so now, when you're preparing to go in front of a whole bunch of financial analysts and you want to figure out what's the right tone, what things should we lean into, you have this instant expert that's seen the entire world of finance, everything ever written online about you know every earnings call every you know ever and you have that now instantly at your disposal and so you know if you if you have a little bit of imagination on how you can start to incorporate ai you know per your due diligence use case or um or any way that you want to work with your data the you know literally there's just not not there's not a limit to what you can do with this stuff the most interesting thing i found is when you push it to become more creative and you say give me five more ideas give me five ideas that nobody's ever thought of before.

38:12Give me 10 ideas that are crazy. You literally use words like this and it's like, okay, permission granted. And then the genie starts doing your wishes and you're like, whoa. When you're the founder of a high growth startup, things can get chaotic. We all know that. You face a ton of questions, you got problems all day long, and you don't know the answer to everything, especially if it's your first time as a startup founder. But you don't have to face these issues alone, that's where Hampton comes in. Hampton is a highly vetted private community exclusively for founders and CEOs like us. Hampton's mission is to create the most valuable and engaged community for high growth founders.

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39:22And you know, community is super important. You can get very lonely out there as a startup founder. So here's a very simple call to action. If you're ready to scale your business, join the hampton community today at joinhampton.com slash twist that's join h-a-m-p-t-o-n dot com slash twist today joinhampton.com slash twist today i don't know if you've been following the writer's strike which started this week in hollywood and on page two of like the update to their members the last like item before like the coffee and donuts or something completely meaningless in their negotiation was uh we're asking for a ban of ai to write scripts to ingest previous scripts and to use it in the future to create derivative works and uh then there was like what the uh studios said back to them the studio was like we will agree their counter was we will agree to do a yearly meeting on new technologies and i was like yeah you know what you need to do writers and i just took i asked it to come up with just for giggles and to you know put it on twitter i said give me like five themes about biden and five themes about trump that a late night writer uh for a late night talk show could use uh to brainstorm jokes and it was like yeah biden you know sometimes makes gaffes and he loves ice cream and trump uses weird words and he's a narcissist and he's addicted to twitter and i was like okay well that nailed it then i said oh do a twitter exchange between the two of them uh that's funny and it started making some and they weren't zingers like they weren't ready for prime time but they were good brainstorms and so i guess the question is like did you get early access for your twitter because you're hilarious on there and what impact is this going to have on your joking on twitter um i think um uh i have not i have not yet vetted uh uh any any twitter stuff on and you know twitter is its own vector of uh of yes of uh you know kind of landscape right now but um yeah on the on the writer on the writer strike thing here's the thing i'm actually uh uh super sympathetic to uh to to any uh any demographic that that thinks that ai is going to have an impact on their job simply because it is this foreign technology coming out of nowhere nobody wants to feel um you know vulnerable um in in everything they've learned their their whole life so i actually i'm i'm like i totally am um um uh uh you know of the view that that these are super important conversations there's not like an obvious thing of like you know silica valley is really going to have to you know try to avoid the the kind of like like oh i you know what learn to code yeah exactly exactly it's just it's like it's like we have to be thoughtful about about these kinds of of trends the reason i'm optimistic is is just because i think there's still you know um uh there's to even your point like they weren't zingers like like you know and so um so i i think there's a lot of uh stuff that that you know is is still in this like it's novel because it's new but it it is not actually going to you know really be able to go and replace the level of creativity that a human has for the vast majority of ways that that you know we actually pay creative people for you know today and and i think there's a kind of a little bit of a divide i think there's there's the use case that you'll see from something like mid journey, which is, which is, you know, really, really compelling, you know, images.

42:42Um, and, and I, I think the scenario there is that we just end up having more ubiquitous creativity, uh, from so many more people, but the experts get even better. Um, and, and the idea generation increases, you know, tenfold across the world because now you can be, you know, some random kid in high school and you could, you know, be ideating on a new product design that would never have existed in the world, but now you have this AI engine that can help you with that. So I think creativity just explodes because of this. And on more of the professional trade side, I'm just bullish long-term on humans really, really like other humans to create things.