AI Enterprise - Databricks & Glean | BG2 Guest Interview

I think we have a GI. I think we have artificial general intelligence. We really have. You hear these 95% of projects feel, but like, you know, that's actually what you want. I think the LLM is a commodity. People are not saying that, but it is a commodity. Like, you can get gas from, just gas station, you can get gas from, that gas station doesn't matter. Just compare price. Is AI in a bubble? There is an AI bubble. Okay, so then Gleene is also in the bubble. Everybody's in the bubble. No, I would say there is a bubble. I would say those three camps. There is a super intelligence quest camp. I would be very worried there. There's a second, the researchers doing the, you know, that's definitely not an bubble. They're like the sober. Yeah, they're super sober. Nobody cares about them. And there's the right. And there are probably the ones that arrived, unfortunately. And then there's the third camp, which is us trying to make this valuable. We're not in a bubble in a sense that we're not spending huge amounts of capital in what we are doing. We're just trying to get actual economic value inside of these organizations. Two legendary builders, Ali Irventh. I'm so thrilled to get into this with you because both of you have seen every super cycle I've lived through, internet, mobile, cloud, data and AI. Not just through the super cycles, but also through the hype, the trough of delusional disillusionment and this time it's different. Today, we're going to chop it up on the state of AI. You know, let's start with a 20,000 feet view. Take stock of where we are. AI, we've seen consumer AI, billions of users. Chad GPT said the guns went out three years ago. Cloud perplexity, Chad GPT. People use it in the room. On the SMB and developer side, you've got hundreds of millions of users with cursor and codex and cloud code and so on. Enterprise on the other hand. There's a lot of divide. It's hard to see a lot of fog of war. On one side, you've got models that are earning math benchmarks and science benchmarks and engineering benchmarks. But on the other side, you've got the MIT report that's saying 95% of AI deployments don't work. What's the reality? Bridge that gap for us, lay it out as you see it, view from the top. So I think first of all, I think we should know that people use AI in their personal and work lives both. So there's not so much of it to write. Everybody in your company is probably using Chad GPT and cloud and other tools on a daily basis. The thing that I feel is happening in enterprises. You hear these 95% of projects fail. But that's actually what you want. When you are actually experimenting with new technology, if all of your projects are failing, that means you didn't just not try enough at the moment. So I think when I read the study, it was not a surprise for me. We're going to actually see hopefully similar stats next year too, because we want everybody in the industry to be really eager and experiment and actually figure out how to actually get benefits from this technology. This would make you guys by default the 5% of AI that is working, which is 1 in 20. Maybe you go to the 5%. What is a use case that is working? And not just working like it's like saving me time, but like it's working and it's transforming my company. Something that you can take to the bank, to the CFO. While the CFO will notice it, but the legal won't shut it down. I mean, look, we're seeing a lot of use cases that are working. It's just that you just have to -- it's not just you can just unleash the agents and it just works. It's an engineering art. Like if you're going to have a company that's going to be really differentiated, like my company or your company or anyone's company and you want to beat the competition, you can't just quickly put something together and think that your competition is not going to do the same thing. So that's going to be something that needs evaluations. It needs something that you're going to productionize. It's going to take effort. You need a great team around it. But we're seeing a lot of them. Like I'll give you some examples. Royal Bank of Canada. Built agents with us that basically take -- as soon as an earnings report comes out. So equity research analyst or job is to put together these reports. That say, like, you know, this is a buy, this is a, you know, hold, and so on. The agent goes gets the earnings report, gets all the previous earnings reports, gets all the competitors earnings reports, gets everything that's going on in the market, does the full analysis, the news, everything, puts it all together and it can get the equity report out in 15 minutes from the earnings call. Industry standard is two hours. Of course, it's going to get commoditized. And others are going to do that as well. But that's actually really important use case that we're seeing in finance. So that's like finance, example, and finance, right? And there's lots of examples like this, sifting through hundreds of thousands of documents, SEC report, so on. That's finance. That's switch gears. Let's go to healthcare. Healthcare is completely different. In healthcare, we have a, you know, customer Merck. That in their life science space created a model called Teddy. Teddy stands for transformer enabled drug discovery. And this is a transformer model, kind of just like large language models that can predict the next word, but it instead can figure out which genome is missing if you remove a genome. So it really understands the gene regulatory network and can really start telling you what's happening with gene expression and so on. So it's really important for drug discovery. It's the beginnings, but this is going to actually help us do things that we couldn't do before. So we're going to do a quick retail also. So I'm picking different healthcare is one I give you finance right the RBC one. Let's go to retail seven 11 agents that completely automate the marketing stack actually the marketing stack is going to get disrupted pretty heavily. So these agents can basically prepare the consignment audience like this segment wants to hear this. And it can prepare all the marketing material that's like directly targeting you guys. Seven 11 was doing this before as well, but you know this and we're seeing this at Databricks as well more and more is being done by agents and being automated so you can just do it faster and you can segment more fine grain. Because before you had to create the content for the groups that was a heavy content creation was something that was human manual labor. Now you can actually do that much much more you can have all your web materials completely customized for target groups. These are examples where it is working there are also lots of examples where it's not working even with Databricks for not just the 5% you know we have that some of that 95% to but some examples of where it's where it's where it's in success. Ali follow up on that off. These are great examples. Thank you. Maybe if you if you're to take it a layer up what is common across these use cases or these organizations or the CIOs that's making these use cases work is there something that we can pattern match. Yeah look I think the LLM is a commodity. People are not saying that but it is a commodity like and you know when I talk to key con classes commodity was when it's interchangeable like you can get gas from this gas station or can get gas from that gas station doesn't matter just compare price. LLM has become that way like it doesn't really matter this one is better right now next week that one is better you can't even keep up anymore right what's happening so there are commodity. So it's not about that it's really comes down to your company what data does your company have that special that your competitors don't have can you leverage that and can you build AI that really understands that data because that's not a commodity there's not a AI out there that understands all your business processes in your company or secret sauce and your data that's not a commodity in fact that's closer to the 95%. It really comes down to that or if you have a complicated process that just your company has this is how you deliver your product and services in your company and it's you know it's that portion can be disrupted with AI somehow if you can do that now you can get ahead of your competition but it comes back to what makes your company special. Unfortunately a lot of companies are just building commodity stuff like you should not be building that because it's a thing that every company can do it's not special to your company or to your that's that's I think the problem in a lot of the industry. Another problem in the industry is a lot of demo where it's really easy to make cool demos with gen AI and you know therefore we're seeing a lot of cool demos but that's all they are. Yeah well you know something we say around it ultimately quite a bit is your AI strategy starts as your data strategy yeah so you got to get the data house in order first and you know there's a lot of reasons for for use cases that are you know we were trying we're not working maybe give us an example of the 95% of an AI bet that either if you had add data bricks add lean that did not work out and why it didn't work out. Actually interesting thing you know with engineering today is you build systems and and never never before have you been in this mode where you start with a great idea and doesn't seem like good idea anymore like within two weeks you know because we see a new door open that happened so there are we have like new tools and engineering on that front like you know for example some of our fine tuning work building building models for specific use case within our product like you know didn't really pan out for us and ultimately the choice was that you know we can go with already build models whether they are small open source models host non data breaks or or one of the large you know foundation models the but internally like you know from a corporate you know use cases perspective actually like we know we are also like in many ways in this mode where lot of our work actually like I would not say like failed but it actually takes much longer than you know actually generate success you know there are you know we are trying to automate a lot of our business processes internally. And like for example like one thing that I want is in our company I want everybody to actually know exactly what the top priority for the weakest what they want to work on and maybe you know we want an AI actually first tell them what the priority should be and we want it all to be documented and we want a system which actually then rolls it all up and I get a view every week where I can actually quickly see you know what are all the different people working on in the company and is that aligned with you know what I want them to work on and this is a simple thing like you know companies always try to actually have this you know I see you always want it and it's always hard to make happen and we thought that AI would simply just like you know magically do all of this work because you know like it has all the context it has all the context inside the company and make it happen but I still don't have it so so things do take time to actually you know Dolly's point like you know there is AI is just one more tool that you have in the toolkit it does not suddenly make building complex enterprise systems you know you know like it doesn't make it like that you can you know build it up like in one day yeah you know the last time enterprises got this excited about a tool was called RPA and we know how that ended it unfortunately fizzled out and you know somebody in the audience yesterday is like hey how is this time different from RPA it seems like the same movie bigger budgets better actors what's different this time how how is the nature of the architecture of the the technology different from the previous automation cycle either a few you know I mean I first of all RPA like it didn't take you know it didn't capture my attention at all so I know so I actually can't you know what is so so I think I think like I would not compare these two technologies at all like you know what we're seeing now with AI is so fundamental you know it's you know it's the you know when when we saw it first it was basically magic and and we couldn't believe that this is a machine that is doing this work machines are simply cannot do these kind of things in that we saw them do like riding on their own having emotion understanding emotion so it's a you know it's is fundamental is different and and the and that's why like you know I don't think you know this this this technology is going to fizzle out and it's not like you know you don't have to be like a financial expert or you know like sort of a deep thinker on this is this is obvious stuff like you know all of us know all of us feel it all of us can see the capability of this technology and we know it's special and it's going to be it's going to be around yeah you want to hear my please you know I mean it was rule based and the problem with it especially if you're you know you're you want something to automate what's going on on your desktop and automate the work that's happening it's just that there's too much unexpected things that happen and it's just hard and brittle to set it up it wasn't learning ever so there was like zero learning it was like you tell it exactly here the rules and if it got something wrong you you need to go and go back and expand the rules here you have something that's learning right so it can it can improve and it can generalize and it can understand the patterns and do pattern recognition so that's the fundamental difference between these two now there has been many startups that have failed in the generative AI we're gonna replace our PA with generative AI models there's many startups that failed actually that I know of like pretty some high profile ones it's because the paradigm we live in today with AI is there's still problems the biggest problem is that you bake a model and that's where it's learned everything it is to learn and then you freeze it and then you launch it and then maybe you give it some context but that's it it's frozen so there's a nice the problem that you know we need we need an AI that really can sort of continue learning while it's using the desktop and clicking around so I do think this problem is hard to nail but I think Arvin is right that it's like there's no comparison at all it's like rural brittle rule-based stuff versus learning a gentec system I think it's gonna nail it perfectly but we haven't really nailed computer use yet yeah working on it the number one shift is this move from if then else statements to a more generative solution that figures out the solution and so you're trading breath for for maybe determinism that seems to be the difference and you know there's a lot of CIOs in the room we've got here and they've got budgets coming up to plan if you were giving advice to them off like hey based on everything I know from my customer base here's one thing or two things that you've got to figure out and and align incentive zone or it could be reliability problem or org design what advice would you have for CIOs who were thinking about their AI budgets right now well spend more yeah put it on clean but the I think the one thing you know which which is important in AI market today is that it's very new and there are many players in fact every every software company is also an AI company now you can go and check their websites so so the I think it's just hard to actually figure out where to allocate those budgets and what we tell people is that I think the winners are get to be identified and and so experiment with more vendors do shorten to shorter time contracts and and you know while that's easy to say it's hard to actually implement because every product that you try has you know it's a cost that you have to pay to make it make it sort of even tested so so you have to also pick products that are are easy to test I mean those are the ones that don't require you to you know spend the next six months trying to implement something and you know idea what's going to come out after that like you know the products of today the products that are built with the right AI they should work you know very quickly for you. Crawl walk around we're going to take a peek into the future shifting gears you know one of the things that keeps investors like me up in the nights is a quarter trillion being spent on Nvidia on the semi side of things assuming that is just 50% of the CapEx you're spending about half a trillion on CapEx and then you've got to earn about a trillion dollars of AI revenue for all of this CapEx to be worth it. This and just to put this in context the entirety of the software industry earns about four hundred billion dollars of revenue. This seems like a physics problem at this point. How do you how do you think this this plays out? You know you've got you've got to make about a trillion dollars of revenue to justify this present spend that's already happening. How do you think this shakes out? Maybe we start with you Arvan. A wrong person to start with but you know I'm an engineer and I don't really think too much about who's spending what money like you know we have to build our product and add value. So in some sense I'm not really thought too much about this problem but if you think about AI the you know AI is not actually you know extending software in a marginal way. It's a different product and in fact you know it's actually going to grab a lot of revenue that actually today is in services industry which is 25 times larger than software industry so there's a lot of spend that is going to move I mean that they spend that you see happen on AI is actually sort of you know those service dollars that are converting into AI or software dollars and and I think the but with that said you know maybe maybe you have a more informed view on this. But I do think that's that's just to build on you said you know I'm an engineering I want to just build something that's cool I do think it's not binary right. It's not like okay so the physics doesn't work out so the whole thing but collapse. No there's going to be things that work and so it is a good idea to continue focusing on the stuff that is obviously already working continue expanding on that but I think if you zoom out I think there's like three paradigms or three kind of camps and I put Arvin in the third camp I actually put myself also in the third camp but let's start with the first camp I think the first camp is this quest for super intelligence camp and it's you know I think all the frontier lab labs are doing this like in an all three four or five of them however you want to count them and I think it's really still being a lot of it comes from the scaling laws mentality which is whoever has the most GPUs and the most data is going to win the quest for super intelligence which is kind of intelligence that's like on almost like Godlike it leads to recursive self-improvement of the AI which then once you have that it can cure cancer and solve all economical problems and we can probably 10 x GDP over a few years period of time so what the hell are you talking about that there's a physics problem like it'll anything any of your cost equations are gonna pale in comparison to the economic value that this thing is gonna provide so that's like one camp and the way they're developing it is bigger and bigger clusters more more energy and that's how they're going about it and that's where most of the capital is going right right that's not you that's not the kind of capital you're spending or I'm spending but that's that camp and then how do they know that they're succeeding they're not just like oh it's trust us they're you know very smart people working on this so the way they're approaching if they're saying you know we'll throw the hardest questions we have at the whatever AI we have now and if it nails them and we're making really rapid progress so what's your problem like we're like look at math Olympiad we're like nailing these math Olympiad problems and physics Olympiad and programming contests like better than any human being so like that's what they're destroying all the most intellectually challenging there's a second camp which are the people that created the original technology the the scientists who created the technology got then the computer science Nobel Prize for it's called Touring Award and that's you know Rich Sutton who created reinforcement learning which you know a lot of this stuff is built on you have Jan LaCoon who's one of the three founding fathers and many others they have for many years actually I've been you know I asked them for years they've been saying that that first camp is not gonna that's not even the right approach is their view. They're like, no, that's just like auto-regressive next token prediction. It's just probabilistically predicting the next token. That's not how, and usually they will say, that's not how humans learn. That's not how animals learn. You know, that we operate in a different way. Your brain is not that way. And one example is that, you know, even a child learns very quickly to walk and talk and do things with very little data compared to, you know, like certainly no child is reading all of the internet's data four times over before they learn to speak. So I don't, that's like camp number two. Those guys, by the way, they say it's 20 years out. So they're saying, hey, it's a physics problem and it's going to take 20 years to get there. Which to me, it's like, I don't know, like leave me alone. Let me do research. Third camp, which is I think what we are in, is I don't think we need super intelligence. Like, you know, I don't think we need that super intelligence, right? Now, maybe they'll get there. That's awesome if they do. But I think we have a GI. We really have it. We absolutely have it. It's like anyone who says we need to get to a GI. That's like, it's a, it's a false premise to start with. We already have a GI. I came to United States in 2009 at UC Berkeley, not far away from here. And I was in a AI lab. It was called AMP lab, the A-WATS for algorithms and AI machines that people. And these are all AI people. And back then, the definition of a GI we had, we already have satisfied that. Like, I know the discussions we had. And I actually went back to some of those folks to see like, is it just me or what was the sentiment back in 2009? And everybody that I talked to said, yeah, that's by those standards, we had a GI. But we've changed the definition now. We have those definitions, you know, ads. So for 30, 40 years, we had a definition of a GI. We've already hit that. Now we're changing it and moving the goalpost. But very obviously, we already have a GI. Just use any of these LLMs and have it do some reasoning. And certainly it's smarter than many, a lot of friends that you have that, right? Like, you know, that's not the more coworkers or whatever, right? So you already have a GI. Now we're like, haggling over exactly how smart is it? You know, do you have a friend that's smarter or not? So if we already have a GI, we just need to make it useful inside the enterprise. We need to just expand that 5% to be 10%, 20%, 30%. So that's why I think Arvind's answer is actually a good answer. Like, we have the GI we need. Let us just focus on solving the actual problems inside the organizations. And I think we can already, that's enough to automate a lot of the tasks and get huge economic value out of it. We don't actually need super intelligence for that. That's a good idea. If the super intelligence guys nail it, amazing. Then with Cured Cancer, if they don't, hopefully the second camp comes up with a new thing in the next 20 years, that's also awesome. We already have whatever we need. So yeah, let us just do our engineering. Right. Yeah. That's really good framing. And the way this manifests in the, in the, you know, in the world is there's a data layer. There's the intelligence layer, which is where camp one is presumed to be producing a lot of great models. And then there's the software layer where the users engage with. Where do you think value will accrue if you were to design 100 units of value across these three layers, the data layer, the intelligence layer, and the software or the application layer. Where do you think value accrues in the next five years? This is a tough question. I mean, I think the, all those three layers actually are very fundamental. Yeah. I thought you're going to add a few more, which are not. You didn't. Yeah. Semis. Yeah. Because I think I feel like the, like as Alia was saying, that the models are going to be available to all of us. You know, they are going to be commodity and it's going to hard to sort of see that it more spend goes to them versus, you know, these layers and top. The, but how do you, like, you know, it's hard to sort of come up with, you know, where the most value will be. And I also don't know if it actually changes from today's technology architecture. Where again, like, you know, we think about in a pre-AI world, any sort of, like, you know, enterprise, you know, application and data systems, you know, you have data systems. You do have, I guess you don't have enough of that intelligent layer today. And then you have the application layer. So, so, so I guess, you know, some dollars will shift into it. I know we do think that in the intelligence layer is actually going to be pretty thick one. Maybe, you know, it'll capture half of the enterprise value. Anything to add, Ali? Yeah, no, I think that, you know, yeah, there are more layers in the stack, depending on how you want to do it. But I think as I said, the, the, the, the, Alan Lambs is a commodity, as I said, you can get them, like, you know, but that's not, that doesn't mean those companies are not going to be valuable. They can be very, I mean, TSMC is very valuable. But I'm saying, they're going to be kind of like these fab-like companies, but they're interchangeable. And we've never seen something like that ever. I have not doing all these. People just switch Alan Lambs like in one day. That's not the case with your, you know, your iPhone versus Android or your Windows versus your Mac or your anything versus anything like, you know, ex, you know, Google's sheets versus Excel is like huge, religious battle inside our company. But, but Alan Lambs is like, you know, because it's a commodity, as I said, it just speaks English or any language you like. And you can, and it gives you different answers every time. It might as well just try the cheaper one, the cheaper commodity or the flight is smarter commodity. You can't even really tell the difference. Can you? So then what is special is the data that you have. Again, if your company has data that it has actually collected, that your competitors do not have. Like, Glean is amazing. But if you remove all the data from Glean, there's no use to it, right? So it's all about the data that you have. And can you secure the data also? So if we're going to have agents running around accessing this data, like, oh, that's a HR data. Here's the Prova's salary information. Oops, I blurped it out to all of you. Like, you know, like that's, you know, so how do you lock it down? How do you make sure that there's governance? There's, you know, there's also a lot of worry around what if it's using a Chinese model, what if it's accessing this information, what if it's sharing this information with the competitor, what if it's interacting? So the governance security layer is going to be super, super important. But I do think most of the value will accrue to the apps. Yeah. So it's kind of, and I think that's common sense. It's just, I just don't know which apps. I do think Glean is amazing. I don't think if it is an app or, I don't know, but now we see a says both app and platform. Yeah. So I think it's a, that's called an app platform. I do think it's amazing. Yeah. Because it has the potential to automate so much of the overhead inside of an organization. Like if you think about why do organizations have hundreds of thousands of employees, you know, some organizations or 50,000 or 20,000? A lot of it is the coordination overhead of like, you know, so many people have to communicate with each other. Hey, what happened? What did you exactly mean by this? Let's do a meeting where you explain to me. I asked some questions. Let's invite these other guys also and then write it down. And then just just the coordination overhead of organizations is massive. Right? It's like this N squared problem that, you know, everybody needs to communicate with everybody and they're communicating inside their siloed org chart. But how do we get it across? So this like, you know, through docs and Excel sheets and PowerPoints and meetings is how we like move companies and organizations forward. So much of that can be augmented and be made more efficient with lean. So that's why the Glean is amazing. But this is kind of like 2000. Yeah. And you asked what are the killer apps on the internet? By the way, back then we thought it's like Cisco, routers, you know, portals maybe with thousands of links on them. Actually, I was like in I just started college and that we knew that the future of internet would be portals, which are these web pages with a hundred links on it. And you just click on the right link. This is before Google search. But the future of internet actually didn't look that way. Ended up being, you know, things like Facebook for friends and things like Airbnb for rentals and Uber for your cab industry and, you know, Twitter and so on. Those became great companies. So I don't know what those are for the future. Yeah. They will pop up. Yeah. And they will be extremely viable. But okay, so does that mean that Databricks and Glean then basically will die. And there'll be a new set of companies. No, back then there was actually an Amazon.com already in 98. There was already a Google actually existed already in 98 and so on. Cisco by the way, is still around and it's only a 300 billion dollar company or something like that. Right. So it's not binary. We'll see what happens. But I do think there's going to be really a lot of value will go to the future. Yeah. You know, apps that will emerge. Speak it. Let's double click into that. The $300 billion companies of today add that layer. Software apps. Salesforce service now. A lot of talk about software is being dead. Satellite calls and the crud apps. What is the future of this layer that today is called software that seems to be heading towards becoming a database. And what do you see the value of cruel to those to these this part of the layer? Maybe start with you, Irvin. Yeah, I think that's a more simplification. Like for example, I'm going to say that Salesforce is, you know, it's just a database. You know, it's a full sort of ecosystem of workflows and other applications that have sort of built on top of that infrastructure. So I sort of like, you know, haven't really understood this concept of that, you know, you have this like a, you know, database, you know, where all your enterprise data is and then and then people can just go and create dynamic UI experiences on their own on top of that data on like every business can for example just create all the UI by themselves on this. I don't think it's going to be happening like that because yes, you know, AI makes it easy for you to build, you can have a database and you can build, you can just talk to AI and create a UI and experience that that is exactly what you want it to be. But most times you actually won't know what you want. Like, you know, I think a lot of like good thing about software companies is that they actually think about how to actually take that data, but then present it in a way, let make people interact with it or modify it in a way which sort of is natural and which drives more productivity from a human. So I think ultimately, software is an end-to-end stack in my opinion and all of these companies, I don't think they're going away, I don't think they're going to relegate it to becoming a database. Humans, the last 20 years, we got addicted to these screens, we strung over the screens and we would input this information with their keys, with the dropdown and hey, I met Irvin today and this is what I learned. It should really be HAT, Met with Irvin, this is what I learned, remind me in two days to catch up them. Right, that will happen in the next couple of years and even Glean, you won't be wanting to type, you want to talk to it. But I think the big thing is data entry. How does the data appear in that database? And that's today not completely automated. So just like, I think a company that would be well positioned to do that, would actually kind of be Zoom. You know, a lot of people don't think about it that way, but Zoom really should be the perfect data entry application, right? Because that's where you're having all the conversations and that's all the information's coming out. And if it could work with we and get to extract the most important information, store it all, not in like a structured table, but store that information in system of record. If you had that, that would be the full disruption of the SaaS. We have that. That's actually one of the most common agents these days, you know, with Glean, which is you take these meeting recordings, you figure out like, you know, what you talk to the customer, what were the action items. And then the agent goes updates the notes, you know, and says, "Force with that." Like these kind of things are happening already. Yeah, meeting meetings is yeah, is, you know, like we in Glean, we have this policy where we record every single meeting, internal meeting, external meeting, if our customers allow, because there's so much, so much information, you know, in there. I joined a meeting last week. It was four humans and six AI note takers. Yeah. I heard about, I think yesterday we were talking about 17 note takers in one of the discussions. It felt like the first, you know, the first scene of a movie where the AI takes over. Clearly, there's a lot of sprawl. There's like almost too many tools and consolidation coming at some point. But maybe your guys is personal workflow. You guys are CEOs in the age of AI, a lot of CIOs in the room. They've got more jobs than time on their hands. How are you using AI for both your personal self? And how are you driving your organizations, or both large organizations, to adopt AI and benefit from it? Maybe give us a glimpse of your leadership in the age of AI. Maybe I'll leave you start with you this time. Yeah, I mean, we have agents for all kinds of stuff that we use, you know, everything from, you know, agents that are really good at understanding our customers. We have an agent that, that, that is the name, which, right? Yeah. If I want to understand anything about any, like, you know, tell me best customer story on this. Like, you know, I told you about RBC, World Bank Canada, but I can just ask it. I need to use case. I'm going to get on stage. I need to talk about finance sector. Give me a use case that has these, it'll just find you all the information collected. So it's really, really helpful for me for these kind of things, like, when I get on stage like this, but also customer, if you go into a customer meeting, you know, I want to tell customer X about their biggest competitor, why, how they're using Databricks. Now, maybe why is not a, is not using Databricks. So then I shouldn't use, I should use Z, which actually is using Databricks. Maybe that's like the number two competitor. How do I get this information super quickly? All of those are paired at Databricks. So on the go to market side, a lot of this is being completely automated. And we're using this. The marketing stack I already mentioned, is heavily automated already, like a lot of the tasks that happening in marketing. So we're seeing that stack that happening. Then there's engineering. That's like a whole big thing. That's how we sort of, and I think there's a whole change management and how to do it right. Initial attempts at automate a lot of the software engineering at Databricks kind of failed. Even there's nothing wrong with the AI. The problem is the humans and how we were organized. But that's, so those are like the two big orgs. Databricks is a big, you know, 6000 person, go to market org and 3000 person, R&D org. And then there's some back office stuff. Those two already, we're seeing heavy automation using agents for all kinds of the tasks. Then there's back office. So that finance and these functions, finance is all on Databricks. And it's all the forecasting, all the sort of, it's all moved to machine learning base. But it took them a long time because they had their Excel models and they were very proud of them and didn't want to, you know, but again, there's a change management there. We actually had an external data science team build the AI models. And then eventually they became good enough. And now, finance has taken those over. And like, you know, finance has kind of moved from Excel to Python, largely at Databricks. But it was a journey, because, you know, most of us speak to speak Excel. Similar thing is now happening to HR and other departments as well. But I think they're like, you know, I think in general HR departments are like, you know, even like they're not the closest to doing this kind of analytical work with, you know, Excel and so on. So maybe that's not quite as far along. But yes, it's, we're seeing it everywhere. Yeah. Any to add, Irvin? Same for us. And I think I can share some of my own personal use with it. Like so, one of our agents is Data Prep Agent, which I really love because, you know, every morning, it tells me like, no, it's going to be what I need to read, what I need to prepare. Like most of the meetings, you know, I will not have context, it actually brings, you know, like the plan for those meetings for me. So that's one of my favorite agents, you know, that helps me feel more confident, like, you know, like how I want to do my meetings in the day. The other one, which I shared yesterday also, the like, you know, I've changed my instinct. And I think you're changing instincts, you know, take take take take take long time. And you know, when when you're the CEO, like you're the boss and it really listens to you. And you can just like, you know, whenever you have, you know, a small question, you know, so you just go and ask somebody and they're going to like, put 30 people on the task. I actually get that answer for me. And this is going to have a prep meeting. They forward the prep meeting. Exactly. Yeah. So it's all of that. So that's sort of like, you know, and so but it's sort of like for me, it was easy. I just get to ask somebody. And that, you know, I changed that because I knew I was actually causing like, you know, a lot of the, the, the expensive. So the, so today, like, you know, my instinct is to whenever I have curiosity, whenever I have questions, when I need to do data analysis, when I need to write something, you know, my, my letter to the company every month, all of those things, you know, like fundamentally, I use, you know, AI, of course, you know, clean in this case, but to actually help me, do my tasks. Yeah. More, more, I think the, you have to, you have to sort of have that belief. A lot of people won't do it. You have to have that belief that AI is a good collaborator. It's not going to do the work for you, but if you use it, you're going to actually produce better output eventually. Even if you don't save time, you know, for the first, you know, first few months, but you're actually going to improve the quality of your output. Fascinating. Well, this brings me to my favorite part of this conversation, which is rapid fire. Short answers are fine. Long answers are welcome. Start with 12 months from now are the big AI companies that we know of today, upper down. We'll start with OpenAI, 12 months from now, stocks up or down. Ali and then Arvind. And I'll say, Revenue will be up. I don't really understand how stocks work. And Tropic. Ali, Arvind. Same. Okay. Let me let me let me kind of get a little bit more. Of course, because chat GPD is going to continue growing and it's on fire and it's what everybody uses. So is Gemini, by the way, and then Tropic, because more and more, you know, coding be only like eaten into a small portion of that market is just started. So yeah. Is AI in a bubble? Yes or no? Like saying, like, okay, so then green is also in the bubble. Yeah. There's a second. The researchers doing the, you know, that's definitely not in a bubble. And there's, right? And they're probably the ones that are right, unfortunately. And then there's a third camp, which is us trying to make this valuable. We're not in a bubble in a sense that we're not spending huge amounts of capital on what we are doing. We're just trying to get actual economic value instead of this organization. So I don't think it's binary, but there is a bubble. I mean, there are startups with zero revenue worth, you know, 10, 20, 30 billion. That's a bubble. Yeah. Same. I mean, I think the, there are quite a few companies where there's all core optimism and valuations, which are well ahead of the business that those companies have. And like, I guess you can say, like, you know, compared to non AI companies, like, of course, AI companies do have higher, higher, higher multiples. And, but I think, you know, that sort of comes from that, you know, that there's a good reason for it. You know, because, you know, these are, these AI companies are going to grow more than non AI companies, for sure. Yeah. My favorite game at ultimately we ask our CEOs is a long short game is if you were to pick a company, a product, an idea that you're long that you think is going to be a bigger deal than it is today. What is that? And then short, which is, you know, there's more sizzle than there's steak, more, more hype than reality. Pick a long something that you're very optimistic on. Say, I'm order Ali in the Northern. I am very long on agents. You know, I think I think I'm very long on speech as an interaction. I think keyboards are basically going to disappear completely. We haven't actually nailed speech. I know it feels like we haven't, because you're still using your keyboard. So as long as you're using a keyboard, we haven't nailed speech. But I think we're disclose to completely eliminating keyboards. So I think that's a big one. What would I say? It's like, I do think coding is a little bit overhyped. I don't know if I was short it. I think it's still the future. So I think that's one of them. I think automating a customer service and support is a little bit overhyped. So basically, I think the things that the industry things are like amazing and we've made great progress. We probably haven't done as much progress. And then a lot of the other things that are being ignored, we're going to have breakthroughs in those. Fascinating though. - Yeah, and for me, I think the products that are going to change the paradigm where instead of you building a product and expecting people to come to you, if you understand your user, your customer, are very deeply and actually bring AI to them, that's the category that I'm excited about. I want to see more proactive and proactive AI products coming into the market next year. - Yeah. - That is what is going to actually take it from a 5% of the users being power users, 100%. - Yeah, yeah, yeah. - Your favorite AI tool that you use in your lives? - I think Gleene is awesome. I mean, if that was not clear, let's go. So use it all the time. I actually, a lot of the questions I would ask from the team, the thing you said you changed, I first asked Gleene and then see, if it nails or not, then if it doesn't, then I'll spin up a 30% team to go but a week and have three meetings and all that to get the explanation of some simple concept for me. But usually Gleene nails it. - Yeah. - But for me, I'm excited about notetakers. I've used Gleene or La myself and Fatem and a few others. But notetaking is actually fascinating. I mean, I think the, I feel like if you take those notes and then if you utilize it the right way, like for example, Ali was saying, like that becomes a source of what then actually creates knowledge, saves data in your systems. That's going to change how companies work. - Yeah. In closing, I'd love to get your vision for your companies with a start with Ali's favorite tool, Gleene. Congrats, you just announced crossing a big milestone, $200 million in revenue run rate. You're signing big deals, $10 million deals. You've got super users. I'm seeing you're seeing casual users. Paint is the vision for Gleene from here to a billion in revenue. - I think we're still doing annual planning, which also some AI companies are telling me that's so old school. But what we're doing it regardless, we're doing it. - That's because there's early startups. Like did you do annual planning when you started the queen? - No, no. So, but I think for us, the thing that I'm most excited about again is, so we think a lot about AI literacy and how do you get everybody along on this journey? And we're not seeing it right now. Like Gleene is a heavily used product, but still there's a big variance between the top users and the ones at the bottom. And that's what we want to change. So for the future for us is, we want to be, we want Gleene to be this very personal companion for every person in every company in the world. This companion with which, you know, you have a very confidential relationship with this companion in the sense that whatever you ask, this companion, whatever communication you have with them, it's fully privileged. Nobody else has to see it. But this companion knows everything about you and your work life, it knows your day, it knows your week, it knows who I even meet. In the day to day, it knows your weekly goals, it knows what things you're not good at or what your career ambitions are. And with all of that, you know, this personal companion is is sort of helping you now with your work. Hopefully takes majority of your tasks automatically, works on them before you ask it to work on them. And that's sort of the vision that we're taking our product. We have most of the foundation for this in place already. Today you have to come to Gleene to get most of that work done in the future we want Gleene to actually come to you and do that work. - That's amazing. Well, we can keep going for a bit, but I'll be called on time. Thank you so much for chopping it up with us. You got a lot of insights here. - Really appreciate it. - Thank you. - Thank you. - Thank you. - Thank you. - Thank you. - All right, John, thank you so much.