Box's Aaron Levie: On Reinventing Yourself in the AI Age and Enterprise Diffusion

0:00I want to send an emergency alert to like everybody who's like a sophomore or junior in college and just be like Follow these 20 accounts on Twitter and and also join Twitter because like this will just help your career You're either like a year ahead or a year behind Simply based on your feed and my feed is like so so wired in I'll still talk to to 20 year olds that are like Yeah, like you know, I I see some articles and I'm like what do you see articles like I don't even know what that means like Do you just like get lucky that somebody emailed you an article? You just have to be wired in I I enjoy it. It's a lot of fun. I play with everything I Today I'm excited to welcome Aaron Lovy founder and CEO of box box is building a collaboration platform and content management platform for the enterprise That is now taken on really new life with AI and I'm excited to chat with you today about box and AI

1:04About your general thoughts on AI because you are such a thought leader and then about how founders can reinvent themselves for this AI Wave so thanks for joining us. Thanks for having me a big fan of the podcast. I religiously Watch every episode and and so great work. I am wondering though I know this is a different podcast, but when do you talk about root canals? Oh my gosh is that I call in Doug Leoni. We figured out we've had a yet or not I mean, I'm happy to talk about different kind of pain and suffering that we should just do this pod with a with a live root canal Let's do it. See how it goes actually that would be like hot ones, but you get a root canal And you have to talk about your strategy while you're in a dentist chair And dog is just on the other end of it amazing amazing dog is so pleased with himself by the way right now Okay, I was seen every tweet. Oh, yeah, okay. Oh, oh, yeah He's very pleased with himself. Okay, okay, let's start with I'm curious you're taking on this application companies are the hottest neo lab Yes, agree or disagree? You know two years ago, I think it would have not made that much sense as like what does that mean?

2:06But but very clearly I think this is what what's playing out in the market and It's all working out mostly because of open source, but what's what's pretty amazing is right now You know the whole concept of Being a sort of LLM wrapper model wrappers actually working out because What what what I think people underappreciated was that in the real world in the enterprise What you need is some bridge from the models capability to the actual workflow that the enterprise has and that bridge You basically like probably a trillion dollars has been bet on On basically one of two outcomes either that bridge is is very kind of limited or that bridge is actually very vast and And needs to be able to to you know take on you know a lot of depth within organizations And that bet basically looks like are you only long, you know kind of the model itself in super intelligence or are you long this sort of application tier or You know, maybe previously would have been just pure neo lab

3:07But but I think it's very clearly playing out that that actually there's a lot of gap between the model and the workflow And as you bridge that gap over time you get to a point where you realize oh I should also do the model and and then you have enough data You have enough sort of domain expertise where that becomes its own, you know sort of flywheel So I'm very bullish on this on this idea and what's cool is it's opening up like multiple layers of of you know Sort of opportunity for startups because you could either be the actual applied company itself I even the lab or you could be the infrastructure provider to the neo lab and you have like multiple layers of you know going and attacking that space But I think a huge update for for the market and everybody's kind of use of it box labs. Let's go We already have it. It's a little bit. It's a little bit secret But we we we pay very close attention right now the main focus is let's make the agent really really good on any Model but over time obviously you would peel off certain use cases Either on a per customer basis or or kind of across the the whole data set. Yeah, I mean it seems like there's two forces that are happening

4:08One is people don't want the fox to be guarding the hen house They don't want the seller of the token to be the one that is also metering and gating, you know What is the best token for each use case? Yeah, and then the second is you know There's actually a lot of work to do on that bridge to cross and there's real research involved in it and It's pretty bespoke to the exact the exact workflow and exact and customer that you have yeah I think I think what would I tend to see happen in the valley is and for very good reason and it's actually why these companies have been so successful Is like everybody is so kind of research-pilled Which is again totally awesome big fan. It's it's let leads to all these breakthroughs But the the sort of sense that okay the model and the intelligence and the model is kind of the only form factor that matters And then you go to the real world and you sort of see how intelligence actually gets rolled out in people's workflows and The model could be the most intelligent, you know super intelligence in the world But that workflow still requires you to connect up to other data systems Still requires these moments where there's a human and a loop interaction There's delays in the workflow and so the the sort of agent has to sit idle

5:13There's change management of the actual business process. There's legacy systems that haven't been modernized So you kind of go through these five or ten things that are way sort of you know much more operational much more blocking and tackling than just the pure super intelligence of the model And the last thing that I think a classic sort of research organization wants to go do is go attack every single one of those things And this is sort of not a there's like this is not a sort of a one-off in history Like we've always had this relationship between infrastructure and application like you know Obviously AWS or GCP or Azure have created you know trillions of dollars in value of kind of market cap of infrastructure But guess what there's also trillions of dollars of value in software That only exists because of that infrastructure and if you were to go back You know 10 years ago and you were to look at what was happening in the data space and you looked at what what you know GCP your early kind of versions of GCP was building or 80-bussed building I guarantee you would not have predicted snowflake or data bricks existing You would have been like the infrastructure just already does that Why would you pay another 10 billion dollars of revenue to all these other products that are just making it so you can work with your data

6:16The same thing is going to be true for intelligence Which is the models will be insanely valuable But the application of bringing those models into real workflows and banking and life sciences and healthcare and government That's just going to be a lot of software now the sort of challenge for the next let's say two to five years Is how much do the model providers need to move up that stack and also try and you know go and compete at that layer Or do they either leave open intentionally or accidentally that entire space for the application sort of ecosystem and actually to some extent This is like a big question strategically for them because on one hand You want to be closer to the customer on the other hand You also want to be able to have an ecosystem so people trust you so yeah So that's going to be like a really interesting tension over the coming years. How do you think they'll play out? I feel like that's the question we spend every day Yes, you can yeah You know, I don't want to speak for for Sequoia But you can sense some of the existential dread of a VC right now is like should I just put another billion dollars into inthropic or You know, should I attempt to sort of see what what the applied layer is gonna is gonna play out? So nobody's you know that envious of of your guys position having to figure that out

7:20But but obviously it's even harder for the entrepreneur Um, but you know, I am you know, I'm pretty long obviously the application layer like I'm also equally very biased like I'm I'm very concentrated that With very limited diversification on on it working out that you still want to buy technology that that sort of understands your workflow And can get to the the core enterprise data But I don't know that that there's I just don't see a different event happening than the then all of history I mean, you know, we only have like 50 or 60 years of of computer software history But basically when you go to that law firm and you go to that You know, farm company and you go to that bank. They need something that bridges the core technology to their workflow and their business process And AI has not meaningfully sort of changed the need or the shape of what that looks like and the and the best sort of manifestation of this is Sort of this chatbot versus kind of a gentick workflow, you know, demarcation So the chatbot can be totally universal and can be totally horizontal

8:22But you know, all of a sudden you kind of look at that and you say well My workflow kind of needs something to kind of ping me at the right time in the process or it needs access to a certain kind of data That that just you know the chatbot can't need to be good access to so somebody has to go into that organization Like get it set up and somebody has to go and provide domain expertise to this model So it really understands our particular business process Um, and so then unless you really just underwrite, you know, the big labs that at honestly like I'm not exaggerating like 100,000 employees If you don't underwrite that then then the diffusion kind of economy is going to be massive Because every single one of those companies whether that's a 50% firm or certainly a multi-hundred thousand person firm is going to need an army of people To go in and help them with that transformation that change management And so this is you know, like whether it's the FDE phenomenon or just again understanding that domain expertise That that's going to be a very good deal And then you you kind of alluded to to this point uh fox kind of garden headhouse and And that that might actually be singularly the biggest sort of reason this has to happen

9:26Which is even even under all sort of like complete benevolence and like nobody's actually doing something Uh in any kind of like you know sneaky way It just stands the reason that if I'm going to sort of give a task to an to an agentic system I just want that task to be cost-optimized like with accuracy as as kind of holding constant And so who can do that? It's the company that doesn't care among you know, 10 different models which model is performing that task Like definitionally you would want that to be the the the company that does not have a sort of a preference And then the force you have going against that is that the model companies can subsidize their models or offer them at different different different rates I don't know how long that lasts though because when when these companies become public I think they will be held to basically the same laws of capitalism that everybody else is So so the subsidization is working very well up to a certain threshold of spend and we might have we may have exceeded that spend When you're at like tens of billions of dollars of of kind of capital And in fact if anything it might even be worse because

10:27Eventually you have to go in and sort of pay for your training runs as well So like they like the subsidization of tokens is a I think it just has to be a temporary phenomenon You mean from a gross margin perspective or from like an antitrust perspective and entirely gross margin Like if I but they have such high gross margins and their friends right now But that's what are they subsidizing the like then then it's actually then they're then they're charging actually like you know decent rates Yeah, but they can afford to price the API higher than their own first-party products right so that part Totally fair, you know, there's an interesting kind of you know dimension Which is well like if API is the high margin thing that is sort of paying for the subsidization But you've moved all your customers over to the applied product and there's no API revenue So like there's like an equilibrium you have to strengthen this um and and and so then all the while if you have some sort of either Non-economic actors or people just with a totally different game theory in this you know Meta-bing one maybe space x-beam one China certainly being a giant one even Nvidia being one like that changes the calculus as well

11:28Which is those four kind of cohorts don't necessarily need to make money on On inference in that and in sort of at least that the same margin structure at like what an anthropic or nobody I needs So they would might be fine to bring down inference to 10% margin because they just want to basically You know pay for massive compute clusters And then as long as that happens and as long as like there's not insane proprietary sort of you know You know sort of closely held secrets then no matter what you're gonna have kind of cost per token go down on a on a like for like basis All of which means more value accrues to to the application layer Which is I think like a very long-winded way of saying like I think there's just value in kind of everybody in this sack I just don't know that that the I the only thing I probably wouldn't bet on is just okay one or two labs get 95% of the value creation I think there's just gonna be a much more dynamic environment and honestly if I were one of the you know two or three biggest labs I think I'd prefer this outcome to because back to your antitrust point like at some point you'll just be Nationalized if you're the only thing that that is is sort of exists as intelligence

12:31So you kind of want a little bit of healthy competition in this ecosystem anyway. Yep totally Let's let's transition talking about box. I want to come back to talking about tokens and jevans fair docs and China and all these all the stuff But let's talk about box for a second. I love that topic Give folks I mean I'm guessing a lot of people that listens to this podcast use box But give give people like a brief kind of explanation of the history of box then how you how you're re-immenting yourself Yeah, we started the company as a way to be able to kind of securely store and share data in the cloud and And it was a very simple idea, but but we we kind of just kind of cracked a Another or struck a nerve And um, you know for for Doug out there We we were able to sort of scale up quickly we pivoted rapidly in the enterprise and the idea was Enterprises would be moving from on-premises systems to the cloud And they would need a better way to be able to sort of store share or collaborate manage their all this unstructured data their corporate documents their financial documents their marketing assets their research materials in the cloud securely

13:32So that was the the kind of company and we we had been sort of flirting with AI kind of products and and sort of experiences really since like 2015 If you remember like the first kind of rise of the you know, maybe the the at least in modern times the the AI winter that happened in like the 2015 to 2018 period Which is like we think it's gonna happen now and it didn't um and uh That was a period where we were like okay These very you know sort of early AI models were showing us signs that okay If you if you you know looked at an image and you could classify the image. Oh, that's pretty useful if you're in an enterprise Because now maybe you take all of your image data and sort of label it or maybe you'd OCR something and you'd be able to kind of pull out You know kind of you know sort of the the text in there that's enormously helpful The problem was is insanely expensive and you had to have a model for every single use case required a You know kind of a hyper-trained model for each workflow that you wanted to do so we kind of shelved it Um, you know few years later You know sort of paying attention to the GPTs. We had some hackathons where people were like oh we could do like you know

14:34Type ahead and in one of our kind of you know note-taking products and and that was that was sort of early kind of versions We did some some early work in in sort of text detection and classification which helped with security use cases Then obviously chat to PT moment sort of hits and um and that was the the big sort of you know head exploiting moment If for no other reason why you know Then then they've kind of figured out a form factor that opened up everybody's mind to oh these could be these interactive systems That you just like ask a question get an answer back of of sort of you know kind of increasing complexity and length Yep, so we looked at that very quickly we jumped all in we sort of did the whole company pivot like like everything was was kind of exactly Like academically what you should do we had a team carved out we put you know the best people on the team We you know met every day looked at the updates and then slowly but surely sort of built out what what today is is kind of our You know our AI stack and then basically the box agent and um and for us So you can imagine the use cases is you know very straightforward We sit on hundreds of billions of files

15:37Every single one of those files contains critical information for an enterprise that could be their contracts The research files their marketing assets their loan documents like all of this critical information The problem is they rarely know what's actually inside of it So they they they unless you literally look at the document and and kind of search and find it You just don't know what's inside of it So now agents can go and basically be farmed out to go and answer questions about that data They can pre-process it and extract metadata from those documents and turn it into structured data You can use agents to automate sort of steps and workflows So we're we built a platform that basically lets you deploy agents against all that unstructured data And that's been the the kind of core focus are you using agents to create new content? We are There's a couple modalities where that shows up one is we have again an online sort of collaborative product that that an agent can just like generate any amount of content in it And then and then we've done most of the I think probably more exciting work with with OpenAI and Anthropic on just how do you do like advanced document creation powerpoint creation

16:39We've we've decided that their tech is you know At this point can always be frontier So so we have an agent that goes and interacts with those systems To produce you know a high quality powerpoint etc Awesome your tagline your business lives in content unleash it with AI So what are the hero home run use cases for how people are unleashing it today? Yeah, and then if you had to fast forward a few years What do you think people will be doing with AI and your products and fears? Yeah, so they probably the easiest hero for again more of a traditional enterprise to think about is is just as simple as You have a million contracts. Why don't you find out what's inside them or you have a million research documents Be able to go and pull out all the critical structure data put that into a database and then be able to query analyze Automate workflows around that So that that's kind of the thing that that just knocks it out of the park every single time because it's been a longstanding problem That people have never been able to go in and and sort of apply human You know sort of labor to because it's just too expensive to read every every contract every research document You know, maybe you could do it if you had like a loan document process

17:41But most other data just never gets read at that at that scale And then I think the the stuff that that were probably you know As much if not more excited by is is really the equivalent of what we see with let's say coding agents or other other other other complex agents But just you have a long running agents that are just executing your entire kind of workflow process and And this would be in the form of you go to a bank and you're on boarding at a bank And they've basically like automated every step that is possible to automate And then sort of jumps out to a person in the in the steps in the process For extra review or extra verification But now instead of that sort of one or two week back and forth It just happens in like an hour Like that's the dream state of most of these enterprise workflows is what if we can onboard a client faster What if we could discover kind of critical data inside of our research much more quickly What if we can alert to a security event much more much more quickly So to do that you need these sort of background agents or workflows that are that are sort of pre-established For those processes. Totally. It's the year of the long running agent. It is. Yeah. Yeah

18:43Um, I'm curious like you made the analogy to cloud code. It seems to me that in the coding domain Using AI is like not only accepted. It's embraced. Yes In the content domain, which is I think I'll wear a lot of the content and box it's yep Using AI to produce content at least. It's just like there's this almost this allergic reaction to it I got a pan gram stuff on Twitter. There's the um, you know, it's like this concept of workslop I'm curious what you think about workslop and like will this still be a thing in a few years I'm gonna sort of separate the box corporate hat and just now kind of maybe riff as a as a as a as a consumer of Cash I wish there was better term but workslop as you know inside of an enterprise context um I get board decks there and entirely rely on these days and it kills me So here's the difference I think on the acceptability Um, so there's probably like more um symbolism to this actually topic then then then just like the the the slop element But like actually like diffusion of AI in general is sort of almost ties to this

19:47Code like other than you know the the top engineers that we hang out with that like are like they have you know Deep taste in the code like like you know and in the judgment is incredible and like it is them and as much an art at as a science So take that group aside For most of the world code is a utility. Yeah, it is just trying to accomplish something You're you're just trying to automate something You're just trying to put a sort of interface up there That somebody presses a button and moves to the next step So for most of the world the value creation of code has been to automate things and to be able to have it as a utility So so at the end of the day Like Like we're and we'll probably still use the term slot for a while because because there's Tasting kind of front and design and there's tasting in sort of systems And you don't want to have vulnerabilities in your code So that's going to exist for for a while But at the end of the day if you can tell an agent like please go and generate my entire backend system or my frontend system Like it's just like it's not only acceptable It's it's preferable because it's just like that was the thing that was blocking us from moving forward

20:50So we need to go do that At least the way society functions and the way the world works and our brains work at the moment Maybe this changes You know when you when you get a presentation from somebody There's still this association which is like I'm trying to decide if I can trust that person To go and execute on that thing or deliver that result or understand that topic And so if when you see workslot You're like I'm I'm losing my ability to sort of know for a fact That like like how much of the of the thought process was them versus how much was the AI How much I'd even care about that because I and myself I'm doing the same thing So like we have this weird like is it is this very weird sort of like collective issue that we have Which is like which is like I'm doing workslop for some of my you know brain storms and decisions But when I get it from somebody else I'm like hmm should I trust you And and I don't know I you know I mean it just might be a thing that as a society We have to kind of keep cranking through over the next kind of three to five years and end up at the other at the other side Like like you know, I hate to use like these like totally busted analogies

21:53But you know obviously you don't care when you see somebody's financial model You're like yeah, that was generated clearly by like a macro Or you know, I was like not you did not personally go and compute all that But but you're showing it to me and we're talking about it So why can't the same exist for a strategy deck or what not But I think right now we're going through this evolution of like what is the person's role What is the content a proxy for is it supposed to be a proxy for how much that person knows Is it a proxy for what we think they can go and execute on I think I think we're just in this very messy period where we have to kind of figure that out Totally Do you read the stand-druck and Miller well-street or I read the I read the discussion about it yeah the the reaction to it But actually I didn't I didn't read it But was it like very sloppy? I don't think it was love. I loved it And so to me it was just a nice counter example of I have this like a logic reaction How many it's not exits wise? I don't think they were okay, okay, okay, but it does show up as a hundred percent AI in pangram. Okay, and that's just

22:55I think they're okay. Yeah But it's like it was a nice counter example to me because normally I read something that's clearly written by AI and I just have this allergic reaction Whereas with the sand piece I didn't yes I'm not sure how much of that was just you know, it's Dan therefore I trust in Stan versus yeah I know but it's it is psychologically kind of like weird because I'll read these like you know X articles and I'm like now like doing two X the amount of work to read these yeah I'm reading it one for the substance I'm also reading it two for the Calculation of like did the person write it or am I just literally reading like a Claude prompt and then like I'm like I'm literally like my mental processing is now like should I now Does that upweight or look or like lower my my sort of judgment of the person or the post and I think we're Yeah, we're in for some weird times because of this. I would hate to be a college professor I would just I would totally quit because you're just like I don't I don't know anymore what you did I like what what's cool it does seem like the calibrator is the closest analogy though Yeah, except it's just like that was like more like finite in terms and you still had to piece together so many more things

23:57Like we like the and some of these analogies are breaking down of like it's just a task because it's like Well at some point like this thing is doing like at least 10 tasks at once. Yeah But yeah, let's talk about harnesses. How does the how does the box Speaking of calculators is not harnesses This is back to the kind of new lab kind of applied applied layer There's there's a bunch of things that that we know about our file system permission structures our search our search engine That that you know certainly and by all means we actually would love the The we love all the labs to train on our understanding of this because it would only make external agents You know better use box. Yeah, like like we we always talk to labs like hey We'll give you as much data as you want about kind of how the system works But but let's just say like that aside We we have a lot of depth of understanding of what do people do in box? How do they search box? How do they decide when they look through 10 files? Which is the one to go pick how to what what is the in their their internal kind of calculus or heuristic

24:59On sort of figuring out the most relevant document to look at So we know all that and we basically you know have built an agentech harness that that you know attempt to Understand that set of domain understanding about our system It it obviously has access to our search system our file system It has a bunch of kind of mechanisms for just pulling out just the text of a document Just pulling out chunks from the document at doing embeddings on the document on a fly So there's a set of kind of tools that it can use And effectively it's a harness for asking questions of a large data set So in my box account, I have I don't even know the latest number But on the order of tens of millions of files Just because it's like it's every everything that has ever kind of accumulated over over 20 years But I can now ask any question of of all that data set using the box agent And it it goes around it does it does a multiple searches in one It re-ranks it it then very quickly sort of pulls out the most relevant information Then in to in some cases read the full document you know does all the steps

26:00And then we compare that against like well, what if we just gave You know clawed our API or gave opening I or API and we see like meaningfully better results on accuracy and latency Because again, we kind of know exactly how to how to tune it for our workflows So that that's effectively the harness that we built out and then what evals matter the most to you Um, so I have a couple I have a couple like you know Just funny personal ones that I just keep track of of like my own use cases But we do we have I don't hundreds of different tests that we do on every single model We actually have two evals at the moment one is we put out a thing called the complex work eval Which is a which is a set of domain specific work in life sciences financial services public sector Tech etc And it's it's kind of exactly what you think of as a as a document centric eval So given these five documents and this set of problems like what would your answers be and we test every single model against Those with our agent and then we have a holdback eval which is um actually the first one is holdback also But but the second one is just like then our box instance and how box employees use use their data

27:01And then we evaluate every model again on that um, so we're able to kind of roughly keep track of of all of the incremental progress I will we see when things move by half a point uh in terms of model improvement And then we roll out sort of default models um based on different kind of cost and accuracy Accuracy thresholds and then we let customers also choose any model they want from effectively our model garden What's your current view of the race and like where all the horses are in terms of model performance on your use case They more or less closely correlate code with one exception, which is actually in some of our use cases Gemini But is disproportionately better than then what you would see from coding and uh and it might be you know Sort of just better tool use um, you know given given kind of you know the the the Gemini ecosystem and what they need to build for Um, it it solves you know kind of a strong set of sort of general knowledge work use cases as well But I think mostly correlating to to kind of code so fable 5.1 was clearly kind of you know state of the art um and the best model that that we've seen

28:04There's obviously rumors about other models and so we'll see how the kind of race. Yeah, you know kind of you know continues on this On this front but but basically by and large like when you look at gdp vow um mrcore has their apex eval These things will all generally follow the the coding models and and so I think it's we're just neck and neck on like Grok muse the fable class and uh, you know gpt 5.6 little slash whatever the building next like these are just like It's a total race right now and your customers typically express a preference on which model they want to use or do they just use your So they they You know kind of by volume they use our default because it's just easy and and it works extremely well and it's tuned for There's a few ways our our kind of agent manifest So like the way that you'd most commonly experiencing as an end user is you would just be searching and and kind of you know asking questions of your data The but by volume the the volume of tokens tends to go through more of our workflow agents or data extraction

29:05That's where actually you have customers actually doing evales and they're basically saying okay I want to really make sure that at this cost profile I can get 98% accuracy on data extraction And that's a place where like we'll have an FDE that goes in and and helps you know It helps understand your data environment tests against five different models. Yeah, and then you're just basically at the mercy of of the eval Yeah, whether you sing in terms of the adoption of open late models in your customer base um, so probably higher than people think lower than what enterprises actually want and Much much much much much lower than what it'll be in five years So so like some mix of that would be the kind of message like it's primarily cost that's driving that decision I you know, I have to probably attribute 30 plus percent to just kind of the the sexiness of Yeah, I want to try to do them. Yeah, I think there's that like I've heard I've heard you know CIOs of 4500 companies say we're playing with open source here and I look at that and be like well I know for a fact like like Geminiermuse would have been just fine at that particular cost profile that you're trying to do or probably even like you know

30:10You know five six lunar terror or whatever whichever you know one had the had the crazy discounting that just did like it probably would have been totally fine But you want to be able to be like I came a little hedged like it's cool like like where that phase still um over time I think it stands a reason that that you'll see meaningful different costs because you'll be able to peel off workloads that That just only makes sense at at sort of you know grinding down to the cost of inference in which case open open weights will have this sort of economic advantage Right now there's you know this challenge of like sometimes it's more token inefficient You know sometimes like randomly like I've heard stories like random leaders like speak Chinese like like mid-chains So you're like okay, well that'll be weird for a bank um, so So like we need to like probably work on some of those things but long term I think it has to be you know the case that that you're gonna You're gonna peel off those workloads You know one of the more interesting posts I think on this that I totally subscribed to is Jesse at Deckergon Do you probably read that post of like of like this paradox of like we're gonna have like you're gonna see close just you know Continue you know go exponential

31:12But what's gonna happen is each use case that kind of matures you can peel off to open source and once you have kind of stability in that Use case it starts to make sense to veer it toward an open weights model Assuming one of two things is true one that that it's actually literally cheaper or two Having some post-training gets you x x percent more performance and and so I think you will just back Being a reality where we will we will and this is gonna be very confusing probably for like the press more than People in the valley because you'll be like wait a second like the revenue of and thrott pick opening I etc are like off the charts But somehow open weights is like also growing exponentially, you know like well how's this like how is open? Is growing so fast? Yeah, and it's like the pie is growing so fast. Yeah, but what's happening is is actually there's there's an interesting duality It's it's not even just like like rising tide lifts all boats. It's like no no like we either use Fable or or five six for orchestration and then we farm out all these long tail tasks to a cheaper model or the opposite is true Like you use you have some kind of orchestration agent that like by default does the cheaper stuff

32:16But occasionally sort of see some something that is just way too hard And then it pops it out to to one of these heavier models And so you might have blended 50% spend on each but 10 times the amount of tokens You know on the open weights model and so like everybody's kind of winning But there's an it there's an interplay between why they're winning I'm curious about how you think about memory and customization or personalization and where that's gonna go It seems like today the dominant architecture is Kind of like rag-based system still yeah, you get fancy on the rag But it's still context look up where the weights themselves aren't fundamental changing. Yeah, it seems like I mean If I listen to my friends at the labs like continual learning this idea of like the models weights should adapt as it gets to know you Yeah, um we had n-gram on the podcast You know Dan like I just got introduced him. I listen to podcasts. Oh amazing. I would have loved to be in For some in the room or amazing. Yeah, they they I think they're working with customers that to help Basically bake in some of the context into the weights themselves Yeah, what direction do you think is gonna go? You know, you're catching me to time right before I'm actually doing my call with Dan

33:21So so I wish I could have talked to him first and then I'll have like a way more eloquent answer Um, I'm like extremely fascinated by by the approach like I have no reason for not wanting to it to work and exist We live in a world at box where we see these sort of that the the high degree of complexity On permissions and access controls and data That that tends to be sort of the rub on a lot of these types of of approaches And I'm gonna put n-gram aside because like I'm sure they've already thought this through so I'm gonna take more generic full Philosophically I think sometimes you will talk to a researcher that You know sort of imagines the world working the way they work Which is like I'm a researcher. I have access to everything and so if I had a model that was trained just on my world This would be amazing and then you're like let me introduce you to a lawyer The lawyer like has this tiny little You know access point of just like the five projects they're working on because somebody right You know one door over is working on the competitive project to

34:24To another company in the space that they can't have any sort of overlap with with what they see or what they know And there can't be a single document that passes between those two walls and they have to be these kind of hard barriers so You know so like sure like you could still you could still now train a model just for that one user But like what happens if every single day they get added or removed from something that that adds important context To sort of what they need to understand and and and you know again Like I think there's gonna be probably breakthroughs and continual learning that that sort of all resolve this But like this is why previously it was just like you know There was no way you could pull this off five years ago because it would be insanely expensive impossible to kind of wrap your head around Around how those access controls are supposed to work But you know obviously with like as the cost curve goes down as open weights You know get you know cheaper smaller faster better I think this becomes super interesting one thing on the podcast that I found very fascinating And I just need like a t-shirt honestly is just like you know Like what is the decision point of what goes in context and what goes in the weights? Like you you have to be a little bit thoughtful about like where is the the massive performance gain that you get by by

35:30Baking in the weights but and there's probably some like incredible like like calculation of like like when the rate of change of the data is not you know so far But the upside of the of the weights, you know, you know dramatically change the accuracy of the model like you know You got to kind of land on some sort of you know rubric like that I mean if you could wave a magic wand it almost seems like I think Carpathy has said this in some prior interviews like if you could almost remove all the memorized information from the models And just have have it encapsulate the specific Reasoning capabilities like the ethos of how we do things for example at Sequoia And then you have all the you know actual content Yes, and it look up system that almost feels like the if you could wave a magic wand That's what the system was like yeah, and so so that one's super inching and the I think the the question will be like How much our enterprises different at that level versus it's actually their their literal IP that is what makes them different It like how many different types of styles of execution are there in the world? Yeah versus no It's just like the depth of knowledge about that particular legal case and and how do I apply it to this other project

36:35I'm working on that's where so much of the value sits so so but again If you can just like wait till my zoom call with Dan and then I'll really know the answer But like I'm a fan because no matter what there's gonna be like like I've jumped right into like the individual You know, but like no matter what like at a firm level There's probably ways to take this approach like I'm a big sort of fan of what trajectory or applied computer doing or prime intellect because Because I think there's like there's there's no question that if you're Eli lily You want a model for how you do drug discovery and that probably does need to go like farther or deeper or more sort of specific Then what you're getting it off the shelf and there's not a lot of sort of you know kind of church and state problems for For drug discovery, you know workflows. They probably want as much of that information available to as many people as possible So I think it's gonna be like domain specific and you know, you're just gonna have different outcomes based on which vertical or or you know type of use case and where the Firewalls need to be in that process make sense. Okay, so you hint at the beginning that there's a box labs what what type of work is box labs doing? So it's the equivalent of box labs. I don't know if we've used capital L yet um but but basically you know, it's our it's our applied sort of AI team and

37:43What research areas are most interesting to your your team right now? Yeah, so so the the biggest areas that uh that the most sort of research kind of or you know of the continuum of engineers There's you know some cluster that is sort of more on the research bent and of that cluster the things that we've spent time on Still again is at the kind of applied layer but but it's a lot around how do you how do you take agents and make them you know Another 10 points of accuracy improvement given x problem So what what is the well, you know, how do you build a map of the of the problem set you know with a given set of data To sort of best execute on that task So we spent a lot of time on on that style of work We have a team for instance working on how do you do effectively at the agent level at the harness level You know some form of kind of auto research on on sort of hill climbing on accuracy of answering questions or sets of problems on on given a kind of a set of client data So if you're a bank You have a bunch of loan documents coming in and these like a hundred-page documents

38:43Like whether you're getting 70% accuracy with an off-the-shelf model or like 97% is like basically Obviously a world of difference in can you actually go and automate that process? So somehow you have to hill climb from the base model to the 97% and there's a lot of work going into the systems to basically pull that off Maybe zooming out What do you think of as and this can be a box question or a non-box specific question the role of systems of record in a world with agents And I'm sure you saw some of the Twitter discourse on like you know every software company is trying to sell me their own agent right now I don't want another agent from them. I want I want their system records to work well with my agent And so like how do you think about that hashtag clawed forces? So I can't you name by the way very catch me. I mean literally it's like one of those things where Where like the first three minutes you're like man that seems funny and then Four minutes later, especially when you see the stock you're like ah really move like like this is great We're doing this and then when you I think somebody somebody actually Said this the best there like when they heard Matthew McConaughey say that loud that was like really the that sealed

39:47That was the aha moment and it's like man he can sell software like it's actually incredible like His voice is so good for selling systems of record in agents if you're in like like our Sort of you know contemporary group Like you built the SaaS platform and you have a subset of data and workflow that that you know your customers kind of operate in There's there's effectively two things you just have to do and I think anybody attempting to do one over the other is just gonna lose You have to build an agent that is insanely great at your product like that agent has to be you have to Proveably be like 10 or 20 points better that an office shelf agent using your system Not because it's hobbled the other side. It's just like you are so e-vow maxed and you're like so tuned to your particular workflow That that you can improve your system you have to have that and You probably because you understand your domain unless you're like totally asleep with the wheel You probably have use cases that no one has has sort of thought to build products around because you you talk to customers every day

40:50And you see what they run into and you're like oh we could just like have our agent go do that for you Like I've had at least a dozen I mean, so I probably talked to a couple hundred customers a year in in variety capacities I've had at least two dozen times where the customer has a use case that is like a breakthrough moment for me of like shit That would be actually totally insane like what example? Unfortunately since you put me on the spot. I don't know if my example will pay off the level of Exciting that I just had Because it'll be no, no, I it's just like I think like the thing I was thinking of is just like I think it's gonna be a want-wamp for the podcast but There was there was basically I customer had this idea of they wanted an agent in the background trying to sort of figure out when documents sort of met their governance Policies and like does something need to go into some kind of archive or something into some kind of legal hold or whatnot and Exactly, you see that exactly the voice that was exactly the voice I was worried about yes. Yeah, no you couldn't even you couldn't even pull it off

41:51Okay, so so but in our world this is awesome because you're like oh yeah like no because you think about it Every company has a head of governance. I might call sincerely. Okay. I know I believe you I mean listen you do enterprise so like I think that it was at least half serious Imagine you're an enterprise you have a head of compliance and I had a governance. Okay They can only be like overseeing the whole sort of enterprise They've never been able to be everywhere at once now imagine if they could sit next to the employee and and basically be able to be like Oh, you're about to go do something that breaks our governance policy So so like the idea was like oh, what if there was just an ongoing agent that just like automatically was just like now That's gonna break your governance policy instead of the user having to like try and predict or understand the stuff Those are the kind of things where if you have an agent within your product You're going to be able to identify sort of sooner and better than than the rest of the market And or just like do things that maybe would be impossible to do off platform On the other hand It's just like obviously you have to go headless

42:51You literally have to make sure that your APIs are exposed to cloud and chat to BT and In you know all the different platforms and you have to make sure that you have a either a direct way into deterministic APIs So that those agents can use your your APIs make calls via mcp or whatever Or at least make your agent be headless and be exposed in those systems And then the only reason maybe this is like a like I can even remotely a hard debate is you have to just make sure as a As a system of record that you can find a way where commercially It sort of makes sense on the other side And it's sort of valuable and interesting and the reason why I think a lot of people got that wrong That that weren't in these companies was Just under estimating the amount of sort of new use cases that are just total upside There are like complete white space opportunities for these systems of record So like in the sales force example I use sales force more today probably by an order of magnitude than I ever have Because I mcp into it via cloud or chat to BT and so And so I just am always asking questions about the data inside of our CRM system And do you think that means the system's a record become more tall booth businesses then to make sure that it capturing the opportunity

43:56I don't love that term because like no one's had a good experience at a tall booth I love tall booth Yeah, he's like you love it more than governance agents So So I would say that because they they sort of have a Have a depth of purpose of organizing the workflow managing the data securing the data providing guardrails Then it's really just yeah, you need to you have to have some kind of volume oriented business model on that other side And and I just think there's like if you're solving real problems for customers It'll just like make money. I've like like this is so cheesy, but like I've I've told like LinkedIn product managers I'd probably pay 10x more for LinkedIn if I just could mcp into it If I just had a way of just like always understanding like like okay This CIO is doing this thing and I need to reach out or whatever. I'll like take my money Yeah, so so there's these systems actually have a tremendous amount of value Based on the data that they that they have and customers will absolutely sort of find some way to reward you For that value creation if you're doing a job super interesting

44:56Maybe related Let's talk about product UI. Yep big, you know generic chatbot chat box agent Is that going to be the dominant UI for how people using AI and AI in the future especially when it comes to the application layer This is why I think the applied layer, you know how so much room to run is because Probably the the sort of you know universal chat system that you ask a question to you get an answer back or does Sort of some work in the background. I think it's gonna be obviously like that's gonna be a mainstay that that UI will always exist It'll be incredibly powerful the horizontal products will have it the vertical products will have it everybody will have it It's just like your product has a search box obviously it does so So that that's always going to be here for these sort of one-off asks of an agent or go find this thing or answer this question or produce something for me On demand but most of the enterprise Is sort of made up of these processes and workflows that are kind of just happening behind the scenes Sometimes they're happening with computers and computers are running these things or sometimes they're happening with other people

45:59That are doing these things or sometimes they should be happening with people But you could never afford to have them happen with people so they just didn't happen and so that's That sort of is a slightly different Kind of you know metaphor than then a chatbot where you ask a question and it comes back with an answer That's like okay, I kind of want agents in the background to do things for me read every contract look at every log triage every security incident and then instead of me chatting maybe I'll chat as a as a means of doing kind of a catchup I want a dashboard. I want a workflow. I want a queue I want a task list So then the the challenge becomes well Does the horizontal product kind of take on every one of those those components and and manifest every one of those Experiences in one in which case I think you'll start to be like man that thing is like really like That's pretty heavy and like and then we'll start to like be like oh This is no longer this simple easy delightful thing anymore So then the vertical players actually like like they actually sort of understand the process and can manifest all the right

47:03Buttons and tabs and the names of the things for that particular workflow So I think it puts I think as you have agents that are doing more work in the background Doing more you know, async work that is just like I've farmed out a bunch of agents to review things as they happen or whatnot That leans more toward the applied companies that can understand those workflows that can understand those processes I think you're gonna have these in every field you're gonna certainly we already know that you know What how they're gonna look in legal with Harvey Ligor etc We are seeing them start to emerge in areas like security We've seen them start to emerge in the long-running kind of coding agents with cognition and factory So I think that that will be you know one of the bigger kind of applied AI Sort of use cases and ultimately like in five years from now I would bet like 90% of all tokens in the enterprise are things that a user never kicked off And they just see a result. They just they see a task show up and they have to go review it And it's just like it's just happening. Yeah, yeah make sense. Okay. Let's talk about AI diffusion

48:04Coding agents it was like you know boom January 1st, 2026 happened and like the fastest diffusion of anything into the economy We've ever seen this happened the diffusion of You know the rest of the AI magic into the rest of our jobs seems like it's been a lot slower. Yeah, what are your thoughts on that and Where are the areas where you think we're gonna see faster diffusion and how is that gonna happen? Yeah, so you always have to kind of compare and contrast coding versus everything else to really understand the the dissimilarity So encoding Ascension and this is back to the kind of utility point on on you know slop Like the utility of code is almost 100% represented by the amount of text that you can generate like like all obviously an insane amount of value went into the text But but like and then like knowledge and expertise and meetings and everything but like ultimately like the text Is the thing that produces the the program that is actually the thing that you're trying to do You know if you could have the world's greatest programmer Like and never had to sleep never had to eat they they could intuit what to build and they could just sit on a computer all day long

49:11Your value creation would be 100% correlated with how many hours they could sit at that computer and type code like lines of code Ideally ideally good code is the most thing that will be correlated to whether you produced software that that people wanted So it's all text the models are hyper trained on these everybody in a i labs treat coding As as a competitive benchmark to constantly try and exceed they they get to do their own e-vows on every single day Because they are the ones coding the models themselves And it's the most technical audience of all time where when they deploy it in a gentics system And they run into a either a bug or a problem or like some mcp server comes back with like connection invalid They know how to triage the problem. They don't call it t They just like oh yeah, now I didn't open up that port. Sorry. I'll go fix it So that's like five things Oh, and maybe like the six like it's just like a very very high-paying vertical that that like it's just like Automatically valuable if you could get 10% or 20% productivity gain let alone five x productivity gain

50:11So so take those five or six things That coding has as as sort of beneficial properties to sort of automation Then compare that to you know, you know Every other form of knowledge work and you'd probably have like a histogram And I don't know if anybody's publishes maybe you can like the similarity to coding and like like what what are the domains that like like start to sort of sort of You know look closer like like less and less like coding as you as you kind of scale out And it's like okay, well lone behold legal is kind of interesting because because like there's a lot of value creation to somebody sitting at a computer Reviewing legal documents writing legal documents like processing large amounts of information Okay, so that's kind of blowing up and then you kind of go down the list Now let's take something like like a sales rep Okay, so much farther down the list in terms of sort of likeness The sales reps value creation is basically convincing an external customer To buy software or technology or a caterpillar truck, you know From then that is the value creation to the to the economy of the sales rep

51:14And so let's say we brought the world's best automation to them Like first of all again, they'd have to like figure out how to technically wire it up They'd have to make sure they give it all their data all these kind of things But no matter what the they're still rate limited and constrained by like did the customer respond to them? Do they want to meet can they meet next Tuesday or can they meet today like does the customer have budget all of these other things So that that's maybe the entire continuum right there is like one is like I mean of knowledge work like obviously this is not even you know touching the sort of working with with Adam So that's a continuum which is on one end you have somebody rate limited by so many external factors On the other end you have somebody who could sit at a computer all day long and just type type text And that is your ability to basically automate things is is that continuum So for the real world We have to basically bring intelligence to these workflows in ways that are that sort of Somewhat feel like the shape of their workflow some what feel like the shape of their work and then find a way to Deliver the change management deliver the implementation get data into a format and into an environment that actually works with these systems like

52:18Astrix like one of the other big things is like if you go to most engineers in 2026 like maybe like minus two months ago given the given the latest phenomenon But like like the codes in GitHub you just like connected to GitHub like remember There's this period where like you're when you launched a coding agent like there was no like sign up or register It was just like give us your GitHub that doesn't exist in knowledge work There's no like give us your GitHub for knowledge work. There's like let's give us your box Well, box customers have a much easier time with all of this unfortunately like We're only 1.3 billion revenue run rate so like like that means there's a lot of people not using box And so what are they using their data is in on-premises systems legacy file shares legacy infrastructure You know enterprise Environments that don't talk to agents particularly well So just think about that sort of distinction between you know Implementing coding agents versus everything else Even even something again, you'll sort of fall asleep about is like access controls in the enterprise are totally different I actually totally forgot that that point about coding in coding you get access to basically most of the stuff ever relevant for your job in knowledge work

53:23You're like you're like hey Sally can you open up that that sort of file share for me? Can you open up that that you know sort of project because I didn't get access to it How do you make sure the agent has access to those set of things all of that work has to get done So the thing I think we have to prepare for is two things one Silicon Valley has to prepare for diffusion taking a lot longer than they think Or the then then I'll say we but I really think they because because I like I know how long it'll take And then the second thing is is the good news is this is sort of all correlated to the applied layer Value creation like the because the companies that will just have the patience the sort of that the full sort of domain expertise The the just the sheer work ethic because it's not like everybody's just from the floodgates like you have to go and and just You know pound pavement and get out there that will be the applied layer So I think this actually represents you know a trillion dollars of applied layer AI value is is actually How do you go get the technology to the lawyer or to the sales rep or to the life sciences researcher

54:26Or to the person that runs the customer support team that's all sort of opportunity right now that exists Awesome, I'm enclosed by asking some advice from the founders Um, maybe let's start with founder of ice and company building and vice on the founder side Seems like you are in every AI cap table, you know every cool new company like you know, you know, and Graham you know Um, how did you kind of get yourself in the middle of the AI conversation? There's probably two two parts one Was just very well primed for it like working with unstructured data for 20 years You just like can instantly see the the benefit of agents on that so like I honestly like took longer than I would have wanted That we we got to have this conversation because we tried to have this conversation, you know eight nine ten years ago And now it's finally happy so so first of all just super well primed Our product sort of you know shape and what people do with our product already Lends itself extremely well the agent so like obviously we had to bet the company on that and and then lo and behold We had the positive feedback loop of customers actually saying yeah that would actually be very powerful If I could go read every document and answer any question

55:28So that that's the first and certainly by you know biggest by kind of a factor of ten And then the other is just like I'm extremely fascinated by the technology And it's just fun. It's like We're learning about it Your podcast Your cash is podcast The I mean I'm probably unfortunately for brain cells like it's probably 95% Twitter I have a routine where like at the end of each night I just go through the feed and it's just like I do Like I look like some sad meme probably of just like I'm just scrolling and scrolling and scrolling and like Attempting to like triangulate all the information. That's amazing because it is it is the global 10 It is and I want to send an emergency alert to like everybody who's like a sophomore or junior in college And just be like follow these 20 accounts on Twitter Yeah, and and also join Twitter because like this will just help your career You're either like a year ahead or a year behind yeah simply based on your feed and my feed is like so so wired in This is a number one advice that give to people who are asking how to get current on AI

56:29It's like follow follow these hundred accounts. It's it like but I mean First that join Twitter like you'll still talk to to 20 year olds that are like yeah like um, you know I see some articles and I'm like what do you mean? How do you see articles? I can't even know what that means like Do you just like get lucky that somebody emailed you an article like just join Twitter like what are you talking about? So so I would you know you just have to be wired in I I enjoy it. It's a lot of fun. I play with everything What's what's your favorite new AI products please say instinct? Uh, so um, okay Full full disclaimer. I've not done the instinct invite code yet um Simply because I have a backlog of like three other personal assistant products You guys try and sync it's for the other I know I know everybody's rate. I'm very excited. I'm on And I'm not an investor. So oh you're not okay. So this is like totally genuine. Okay, so I I had a percent We'll have what I don't know when this is gonna run But I'm sure I will have played with it by the time that runs I'm in pre-release in a couple right now that I'm spending some time with Um, I think the personal assistance of is super exciting

57:29At least in some of my use cases it's still showing some of the limits of like browser use as an example Like man, we still have some work to do there like probably the entire internet needs just like a CLI for their product So we still have like there's still some blocking and tackling at the infrastructure level for these things to be totally awesome um, and then and then I kind of look like your average probably you know AI pill knowledge worker on you know Every day I'm asking one of five different AI systems You know 20 to 30 questions on just like doing research looking for talent looking for what is a competitor doing? What's happening in this market? How do you expand there? So to kind of that without any company building side. Yeah, how do you you know 20 year old company at this point What's your best for other people that are trying to reinvent their companies to make sure they have you know max adoption of AI Not just at the individual level But also at the kind of company level. Yeah, how do you make your business legible for AI? Yeah Again some of this we have as a as the byproduct of how we've always thought about information systems in the company so

58:31like To know exaggeration if you have a question that you would like to ask about the the the business that has ever been documented in a form of unstructured data So a meeting note a project plan a road map a presentation a financial document a A financial planning session. It's a hundred percent in box So like we benefit from a data architecture that already is insanely sort of tuned for because this is how we've run the company We didn't let anybody use anything else and and so the data is very easy for us to be able to work with at scale And then of course we have sales force and and all the other kind of core canonical systems. We've been able to kind of Have I think pretty good data hygiene. So agents running on top of that makes it a little bit easier to be kind of AI AI first and how we operate Maybe a couple best practices or things that we've seen You know first of all trying to figure out where the highest leverage impact workflows are going to be and and you know Trying to kind of target those so we've you know our CIOs very I pilled We have a little bit of like a center of excellence on AI. We've hired some internal AI FDEs

59:33They kind of help with these processes. We don't token max Uh, we do actually like I mean like I guess like literally we have a list of People buy a number of tokens But but it's usually to like go and inspect like okay. Do we do we think that's useful or is there a learning there that we should take back to some other function So like probably our top you know in the top three all AI users that box like Probably one of them is wasting you know half the tokens and then two of them are like oh shit Whatever they're doing we need to go like do like an internal training session for everybody else So you know we have this thing like two weeks ago where I was like Can you just get everybody in a room? You know on this particular team and just like like show them how this one person is is using AI And then like you know six hours later They were like in a room and the guy was doing like a full demo of what he was doing shout out to Mick um and uh And so like that's the that's the kind of stuff that we're trying to do Which is just like how do we show everybody what what it looks like to work in this way? But you know For as fast as we're moving and and we are shipping at some parts of the stack two or three times more sort of you know

1:00:39Actually customer facing products. So like I don't care about how much code But like did we actually deliver more functionality that customers asked for some parts of the stack we're doing that Then you'll go talk to a friend and then drop it and they're just like and you're like oh my god We still have we still have ways to go like like I you know the particular meta kind of constantly changes Which is like you know two years ago you'd be like you're just using a plug-in in your IDE And you're like man, that's we're not gonna be that like we're not ready to fully you know be AI first And then you're like okay everybody roll out roll out cursor and then you're every rolls out cursor And then you're like and then that finally happens and then you're like You know you're walking around you're like you're not only working from slack Just at mentioning bots doing your code like what are you doing? Like it's like is we we're constantly just changing what are the what are the workflow paradigms on this? Do you guys have like a slack co-worker agent black movie term? We have a few that have that shape I'm pretty excited about like like cloud tag as like a form factor You know you still have to again kind of get the the team construct right and the data right

1:01:40But we have a variety of ways that people you know kind of work with agents and slack I don't know if it's as as sort of slack piled as bending off would like us to be Or as like a thrott picker opening eyes, but like we're heading in that direction Yeah, I think that history books will be written about you know the art of business in this time Like I would love to read the new VR the war yeah with like everything that is happening here because I think it's pretty extraordinary stuff We're seeing what do you think it takes to win an AI versus pre AI? And like what does it feel like to be a founder right now versus when you started books? I am both jealous of And also not jealous of like like the young founders you meet because you're just like like ought to be young again And and the whole world is your oyster and you can go and you know any direction and the leverage you have You'll meet with them and they're and you're like oh my god like I saw a product We can have to go and they they did a demo of the product and I was like I was like Absolutely, this would be enough 40 person project Five years ago like or especially when we were starting out easily 40 person project

1:02:40And it was it was two people and you're just like how do you have so many tabs that work And they all seem to have stuff behind the tabs that all seem very functional like this is not fake Um and and so I'm very jealous of that like it's like incredible because you could just like you can Start your company from scratch with that as the design principle now we will we will get there because we're just going to muscle through it There's a couple things that we just can't do which is like we're not like we're very uncomfortable at the idea of like You sort of you know removing the code review and like some of these things to get talked about because our customers can't possibly And trust us with their data security and compliance if we don't take that seriously So we're always going to have a little bit of a discount on the productivity because of of where we are in the stack what we what we do as a business But so jealous of of being able to kind of be fresh in that and then on the other hand like it's like man like at the same time For every great idea. It's like instantly five competitors We didn't have that you know problem We had a we had a good kind of couple of years where we just could like we could just grind on our product and our and our sort of experience And it wasn't like every three days you were like oh like so koya funded this thing and benchmark funded this thing

1:03:44And like we you know we weren't going kind of nuts like with that now we we had our own version of that So like at the time I probably was going going nuts, but it was like like in retrospect it was like not Worthy of going nuts. Yeah, now it's like oh man. This is like a real race in every one of these markets So I think I'm probably pretty consensus on this which is in a world where AI builds things so much faster Then probably the shift moves to whoever can actually get it to the customer is in the best position. So You know, it's it's fun talking to founders that are pretty kind of pilled on that You know Scott or Matan are like they get the mandate They're just like this thing is going to be an enterprise diffusion play and and so you have to get it to the enterprise Um and and anybody who kind of mistakes the the mandate right now you're just going to lose is just like it's game over Sorry like there is quite literally trillion to trillions up for grab at the applied layer and the companies that That know how to build the teams and get to the enterprise will be the ones that win

1:04:44It's just like obviously guaranteed well said Aaron. This is a very fun conversation. Thank you so much for joining thanks for having me You You