AI Enterprise - Databricks & Glean | BG2 Guest Interview

I think we have AGI. I think we have artificial general intelligence. We really have.

You you hear these 95% of projects fail, but, like, you know, like, that's that's that's actually what you want.

I I think the LLM is a commodity. People are not saying that, but it is a commodity. Like, you can get gas from this gas station. You can get gas from that gas station. It doesn't matter.

Just compare price. Is AI in a bubble? There is an AI bubble. Okay. So then Gleen is also in the bubble.

Everybody's in the bubble. No. I would I would say there is a bubble. I I would say those three camps. Yeah.

There is a super intelligence quest camp. Mhmm. I would be very worried there. There's a second, the researchers doing the you know, that's definitely not in a bubble. They're like the

They're sober.

Yeah. They're they're super sober and nobody cares about them. Then there's right? And they're probably the ones that are right, 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 on what we are doing. We're just trying to get actual economic value inside of these organizations.

Two legendary builders, Ali, Ervind. 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, AI, not just through the super cycles, but also through the hype, the trough of disillusionment, and this time it's different. Today, we're gonna chop it up on the state of AI. You know, let's let's start with a 20,000 feet view. Take stock of where we are.

AI, we've seen consumer AI, billions of users, chat GPT, said the guns went off three years ago, cloud, perplexity, chat GPT, people use it in the room. On the SMB and developer side, we've got hundreds of millions of users with Cursor and Codex and Cloud Code and and 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 we should know that people use AI in their personal and work lives both. So there's not so much of a divide. Like, you know, everybody in your company is probably using ChatGPT and Claude and other tools on a daily basis. Thing that I feel is happening in enterprises, you hear these 95% of projects fail, but, like, you know, like, that's that's that's actually what you want. Like, you you like, when you are actually experimenting with new technology, if if if all of all of your projects are failing, that means you didn't just not trying enough, you know, at the moment.

So so I think when when I did the study, like, it was not a surprise for me. You know, we can actually see, hopefully, like, you know, similar stats next year too because we want everybody in the industry to be to be really eager and experiment and actually figure out, like, you know, how to mix how how how to actually make, you know, get better you know, get benefits from this technology.

This would make you guys by default the 5% of AI that is working, which is Yeah. One in 20. Maybe maybe you go to the 5%. What what is the 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 to the CFO while while the CFO will notice it, the legal won't shut it down.

Yeah. Right. Yeah. Well, I mean, look, we're seeing a lot of use cases that are working. It's just that you know 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 gonna have a company that's gonna be really differentiated, my company or your company or anyone's company and you wanna beat the competition, you can't just like quickly put something together and think that your competition is not gonna do the same thing. So that's gonna be something that needs evaluations, it needs something that you're gonna productionize, it's gonna 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 their job is to put together these reports that say like this is a buy, this is a 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 fifteen minutes from the earnings call. Industry standard is two hours.

Of course it's gonna get commoditized and others are gonna do that as well. But that's actually really important use case that we're seeing in finance. So that's like finance, example in finance, And there's lots of examples like this, sifting through hundreds of thousands of documents, SEC reports, so on. That's finance. Let's switch gears, let's go to healthcare.

Healthcare is completely different. In healthcare we have a customer Merck that in the life science space created a model called TEDDI. TEDDI stands for Transformer Enabled Drug Discovery. And this is a transformer model, kinda just like large language models that can predict But the next 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 this is really important for drug discovery. It's the beginnings, but this is gonna actually help us do things that we couldn't do before. Let's pick retail also. So I'm picking different. Healthcare is one.

I gave you finance, right, the RBC one. Let's go to retail, seven Eleven. Agents that completely automate the marketing stack. Actually the marketing stack is gonna get disrupted pretty heavily. So these agents can basically prepare, they can segment audience like this segment wants to hear this, and they can prepare all the marketing material that's like directly targeting you guys.

And it can put the campaigns together and do that. Seven Eleven was doing this before as well. But 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. Cause before you had to create the content for the groups that was a heavy content creation with 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 a target group. So these are examples where it is working. There are also lots of examples where it's not Even with Databricks, we're not just the 5%. Have some of that 95% too.

But some examples where we're seeing success.

Ali, follow-up on that. These are great examples. Thank you. Maybe if you if you were to book take it a layer up, what is common across these use cases or these organizations or these CIOs that's making these use cases work? Is there something that we can pattern match?

Yeah. Look. I I think the LLM is a commodity. People are not saying that, but it is a commodity. Like, and you know, when I took econ classes, commodity was when it's interchangeable.

Like you can get gas from this gas station or you can get gas from that gas station. It doesn't matter, just compare price. LLMs have 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 they're a commodity. So it's not about that. It really comes down to your company. What data does your company have that's 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 an AI out there that understands all your business processes in your company, your 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, 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.

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 to your that's that's I think the problem in a lot of the industry. Another problem in the industry is that a lot of demo ware. It's really easy to make cool demos with GenAI. 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 at Altimeter quite a bit is your AI strategy start as your data strategy. Yeah. And so you gotta get the data house in order first. And, you know, there's a lot of reasons for 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 of you had at Databricks, at Glean that did not work out and why it didn't work out.

It's actually an interesting thing, you know, with engineering today is you build systems and never before have you been in this mode where you start with a great idea and it doesn't seem like a good idea anymore, like within two weeks, you know, because we see a new development that happens. So there are we have, like, numerous failures in engineering on that front. Like, you know, for example, some of our fine tuning work, building models for specific use case within our product, like, know, didn't really pan out for us. And ultimately, the choice was that, you know, we can go with already built models, whether they are small open source models hosted on Databricks 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 are also, like, in in many ways in this mode where a lot of our work actually like, I would not say, like, fail, but it actually takes much longer than, you know, to actually generate success.

You know, there are you know, we're actually trying to automate a lot of our business processes internally. And, like, example, like, you know, one thing that I want is, in our company, I want everybody to actually know exactly what their top priority for the week is, what they want to work on. And and maybe, you know, we want an AI agent to actually first tell them what their priority should be, and we want it all to be documented, and we want a system which actually then, you know, rolls it 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 are they aligned is that aligned with, you know, what I want them to work on? Mhmm. And and this this is a simple thing, like, you know, companies are always tried to actually have this, you know, as CEOs, 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, the billing has all the context inside the company to make it happen, but I still don't have it.

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 in one day. Doesn't it? Yeah.

You know, the last time enterprises got this excited about a tool was called RPA.

Mhmm.

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 what's different this time?

How how is the nature or the architecture of the the the the technology different from the previous automation cycle? Either of you.

Yeah. I mean, I I first of all, RPA, like, it didn't take you know, it didn't capture my attention at all, so have no so I actually can't, Right. You know What is RPA? Yeah. So so so I think I I I think, like, I I would not compare these two technologies at all.

Like, you know, you know, what we're what we're seeing now with AI is so fundamental, you know, it's it's, you know, it's 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 just simply cannot do these kind of things, you know, that we saw them do. Like riding on their own, having emotion, understanding emotion. So it's a it's, you know, it's it's fundamental.

It's different. And and the yeah. And and that's why, like, you know, I don't think, you know, we 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, a bit, you know, like sort of a deep thinker on business. This is obvious stuff, like, you know, of us know, all of us feel it, all can of see the capability of this technology, and we know it's special and it's going to be around.

Yeah. Do you wanna hear my RPA? Please. You know, mean, was rule based and the problem with it, especially if you're, you know, you want something that automates 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 are the rules. And if it got something wrong you need to go and go back and expand the rules. Here you have something that's learning, So 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 Now there has been many startups that have failed in the generative AI.

We're gonna replace RPA with generative AI models. There's many startups that failed actually that I know of, 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 needs 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 therein lies the problem that we need an AI that really can sort of continue learning while it's using desktop and clicking around. So I do think this problem is hard to nail, but I think Arvind is right that it's like there's no comparison at It's like brittle, rule based stuff versus learning a genetic 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. Yeah. 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 of, like, hey, based on everything I know from my customer base, here's one thing or two things that you've gotta figure out and and align incentives on, or it could be a reliability problem or org design. What advice would you have for CIOs who are thinking about their AI budgets right now?

Well, spend more. Put it on Glean. Put it on Glean. Spend more, yeah, put it on Glean. But the I I think the like, 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 software company is also an AI company now. You can go and check their websites. 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 yet to be identified and so experiment with more vendors, do shorter term 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 had to also pick products that are are easy to test. I mean, those are the the ones that don't require you to, you know, spend the next six months trying to implement something and you have no idea what's gonna come out after that. Like, you know, the products of today, the the products that are built with the right AI, they should work, you know, very, very quickly for you.

Karl, walk around. We're gonna 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 on the semi side of things. Assuming that is just 50% of the CapEx, you're spending about half 1,000,000,000,000 on CapEx, and then you've gotta 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 $400,000,000,000 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, Arvind.

Wrong person to start with, but, you know, I'm an engineer, and I actually don't really, you know, think too much about who's spending what money, like, you know, we're able to build our product and add value. So that's so in some sense, you know, I'm I've not really thought too much about this problem. But 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 gonna move.

I mean, the 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 I think the, but with that said, know, maybe you have a more informed view on this.

By the way, do think that's just to build on, you said, you know, I'm an engineering, I wanna 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 will collapse. No, there's gonna 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 Arvind 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, I think all the frontier labs are doing this.

Like all three, four, or five of them, however you wanna 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 gonna win the quest for super intelligence, which is kind of intelligence that's like an almost 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 10x GDP over a few years period of time. So what the hell are you talking about that there's a physics problem? Like 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 and more energy and that's how they're going about it. And that's where most of the capital is going, right? 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, let's trust us.

They're very smart people working on this. So the way they're approaching it, they're saying, we'll throw the hardest questions we have at whatever AI we have now. And if it nails them, and we're making really rapid progress, so what's your problem? Like look at Math Olympiad. We're like nailing these Math Olympiad problems, Physics Olympiad.

And programming contest is like better than any human being. So like that's what they're throwing all the most intellectually challenging. There's a second camp which are the people that created the original technology, scientists who created the technology, got them the computer science Nobel Prize for it, it's called Turing Award. And that's Rich Sutton who created reinforcement learning, which a lot of this stuff is built on. You have Jan Lakun who was one of the three founding fathers, and many others.

They have for many years, actually I've been, I asked them for years, they've been saying that that first camp is not gonna, that's like not even the right approach, is their view. They're like no, that's just like autoregressive 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.

We operate in a different way. Your brain is not that way. And one example is that even a child learns very quickly to walk and talk and do things with very little data compared to, certainly no child is reading all of the internet's data four times over before they learn to speak. So that's like camp number two. Those guys by the way, they say it's twenty years out.

So they're saying, hey, it's a physics problem and it's gonna take twenty years to get there. Which to me it's like, 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 I don't think we need that super intelligence right now.

We already have AGI. I came to United States in 2009 at UC Berkeley, not far away from here. And I was in an AI lab, was called AMP Lab. The A was for algorithms and AI, machines and people. And these are all AI people.

And back then the definition of AGI we had, we already have satisfied that. 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 AGI, but we've changed the definition now. We have those definitions, you know ads.

So for thirty, forty years we had a definition of AGI, we've already hit that. Now we're changing it and moving the goalpost. But very obviously we already have AGI. Just use any of these LLMs and have it do some reasoning, and certainly it's smarter than a lot of friends that you have, right? Let's not them, or coworkers or whatever, right?

So you already have AGI. Now we're not haggling over exactly how smart is it. Do you have a friend that's smarter or not? So if we already have AGI 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 Armin's answer is actually a good answer. We have the AGI 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 we've cured cancer.

If they don't hopefully the second camp comes up with a new thing in the next twenty years, that's also awesome. We already have whatever we need. Yeah, let us just do our engineering. Right.

Yeah. 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. Mhmm.

There's the intelligence layer, which is where camp one is presumably 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 a 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?

All right. This is a this is a tough question. I mean, I think the all all those three layers actually are very fundamental. Yeah. I thought you're gonna add a few more which are not.

Yeah, I think, I feel like the, like as Ali 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 be hard to sort of see that it it the more spend goes to them versus, you know, these layers on top. The but how do you how do you like, you know, I it's hard to sort of come up with, you know, where the most value will be. And and I also don't know if if actually it changes from today's technology architecture where, again, like, you know, when you think about in a pre AI world, any any sort of, like, you know, enterprise, you know, application and data systems. You know, you have you have data systems. You you do have I guess, you you don't have enough of that intelligent layer today, and then you you have the application layer.

So so so I so I I guess, you know, some some dollars will shift into it. I know we do think that the intelligence layer is actually gonna be a pretty thick one. Maybe, you know, it'll capture half of the enterprise value.

Anything to add, Ali?

Yeah. No. I I I think that, you know, yeah, there are more layers in the stack depending on how you wanna do it. But I think as I said, LLMs is a commodity as Arvind You can get them, but that doesn't mean those companies are not gonna be valuable. They can be very, I mean TSMC is very valuable.

They're gonna be kind of like these fab like companies. But they're interchangeable, and we've never seen something like that ever. I have not during all these, people just switch LLMs like in one day. That's not the case with your iPhone versus Android, or your Windows versus your Mac, or your anything versus anything. Like know, Google Sheets versus Excel is like huge religious battle inside our company.

LLMs it's like, it's a commodity as I said. It just speaks English or any language you like, and it gives you different answers every time, might as well just try the cheaper one, the cheaper commodity or the slightly 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 gonna have agents running around accessing this data, like oh that's his HR data. Oh here is the Purva's salary information.

Oops I blurped it out to all of you. So how do you lock it down? How do you make sure that there's governance? 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 a competitor, what if it's interacting. So the governance security layer is gonna be super super important.

But I do think most of the value will accrue to the apps. So it's kinda, and I think that's common sense, I just don't know which apps. I do think Glean is amazing. Do you think of it as an app or I don't know but

Now we see us as both app and a platform.

Yeah. So I think it's a, let's call it an app platform, I do think it's amazing 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, some organizations, or 50,000, 20,000. A lot of it is the coordination overhead of 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, or let's invite these other guys also and then write it down.

Then just the coordination overhead of organizations is massive, right? It's like this N squared problem that everybody needs to communicate with everybody and they're communicating inside their siloed org chart, but how do we get it across? So this, 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 Glean. That's why I think Glean is amazing.

But this is kinda like 2,000. And you ask what are the killer apps on the internet. By the way, back then we thought it's like Cisco routers, portals maybe with thousands of links on them. Actually I was like in, I just started college. And we knew that the future of internet would be portals.

Are Which these web pages with a 100 links on it, and you just click on the right link. This was before Google search. But the future of internet actually didn't look that way. It ended up being things like Facebook for friends, and things like Airbnb for rentals, and Uber for your cab industry, and Twitter and so on. Those became great companies.

So I don't know what those are for the future. They will pop up. Yeah. And they will be extremely valuable. But okay, 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 'ninety eight. There was already a Google actually existed already in 'ninety eight and so on. Cisco by the way is still around and it's only a $300,000,000,000 company or something like that, right? So it's not binary. We'll see what happens.

But I do think there's gonna be really a lot of value will go to the future apps that will emerge.

Let's double click into that. The $300,000,000,000 companies of today at that layer, software, apps, Salesforce, ServiceNow, lot of talk about softwares being dead. Satya calls them 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 the the value accrue to to to those to these this part of the layer?

Maybe start with you, Aravind.

Yeah. I I I think that's the oversimplification. Like, for example, Aravind to say that Salesforce is, you know, is just a database, you know, it's a full sort of ecosystem of workflows and other applications, you know, that are 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 it, like, you know, every business can, for example, just create all the UI by themselves on this. I don't think, you know, it's gonna be happening like that, because, yes, you know, AI makes it easy for you to build, can have a database and you can build, you can just talk to AI and create a UI and experience 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, you know, 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 you know, make people interact with it or modify it in a way which sort of is natural and which, you know, drives, you know, like more productivity from a human. So so I think ultimately, like, a software is an end to end stack in my opinion, and all of these companies, you know, I don't think they're going away. I don't think, you know, they're gonna relegate it to becoming a database.

Humans, you know, over the last twenty years, we got addicted to these screens. We scrunch over the screens, and we would input this information with our with our keys with the drop down and, hey. I met Arvin today, and this is what I learned. It should really be, hey, chat. Met with Arvin.

This is what I learned. Remind me in two days to catch up with

them. Right?

That will happen. I understand. Think it's gonna happen. Yeah. That will happen in the next couple years.

And even, Glyn, you'll you won't be wanting to type. You wanna 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, you know, just just to like I I think a company that would be well positioned to do that would actually kinda be Zoom. And a lot of people don't think about it that way. But Zoom is really, should be the perfect data entry application, right? Because that's where you're having all the conversations and that's where all the information's coming out. And if it could work with Wein and extract the most important information, store it all, not in like a structured date table, but like store that information in system of record.

If you had that, that would be the full disruption of the SaaS

We have that, Raj. That's actually, is 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 talked to the customer, what were the action items, and then the agent goes updates the notes in Salesforce with that. Like, these these kind of things are happening already. Meeting meetings is, yeah, is you know, like, we in in clean, we have this policy where we record every single meeting, internal meeting, external meeting if our customers allow, because there's there's so much so much information, you know, in there.

I I joined a meeting last week. It was four humans and six AI notetakers.

Yeah. I heard about I think yesterday, we were talking about 17 notetakers in one of the, you know, discussions. This it

felt like the first, you know, it's like the first scene of a movie where where the AI takes over. Clearly, there's a lot of sprawl. There's there's, like, only almost too many tools and and consolidation coming Yeah. At some point. But but maybe your guys' personal workflow, you know, you guys are CEOs in the age of AI.

A lot of CIOs in the room, they've got more jobs and 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 to adopt AI and and benefit from it? Maybe give us a glimpse of your leadership in 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. Everything from, we have agents that are really good at understanding our customers. We have an agent, Rafi, Rafi's is the name which Rafi.

Yeah.

If I wanna understand anything about any, tell me best customer story on this. I told you about RBC, Royal Bank, Canada, but I can just ask it. I need a use case, I'm gonna 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 when I get on stage like this.

But also customer, if you go into a customer meeting, I wanna tell customer X about their biggest competitor Y, how they're using Databricks. Now maybe Y 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 prepared 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's 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 kinda failed. Even there's nothing wrong with 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 6,000 person go to market org and three, four thousand person R and D org. 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 there's 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 based. But it took them a long time because they had their Excel models and they're very proud of them and they didn't wanna, 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 now finance have kind of moved from Excel to Python largely at Databricks. But it was a journey because most of us speak with Excel. Similar thing is now happening to HR and other departments as well. But I think they're like, I think in general HR departments are like, they're not the closest to doing this kind of analytical work with Excel and so on. So maybe that's not quite as far along, but yes, we're seeing it everywhere.

Yeah, anything to add Arvind?

Same for us. And I think I can share some of my own personal views with with it, so I I so one of our agents is daily prep agent, which I really love Yeah. Because, you know, every morning it tells me, like, you know, my day is gonna 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 that's one of my favorite agents, you know, that helps me feel more confident, like, you know, for, like, how I'm gonna do my meetings in the day. The other one, which I which I shared yesterday also, the like, you know, I've I've changed my instinct, and I think, you know, changing changing instincts, you know, take take take take long time.

Yeah.

And, you know, when when you're the CEO, like, you're the boss and everybody listens to you and you can just, like, you know, whenever you have, you know, a small question, curiously, just go and ask somebody. And they're gonna, you know, put 30 people on the task to actually get that get that answer from me. This is They're gonna

have a prep meeting before the prep meeting. Yeah.

So it's all of that. So so that so that's sort of like, you know, and and so but it's sort of like, for me, it was easy. I just get to ask somebody and and that, you know, I changed that because I knew I was actually causing, like, you know, a lot of those that was very expensive. So the so today, like, you know, my my instinct is to whenever 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, know, Glean in this case, but to actually help me do my tasks. Yeah.

More and more, I think the, you have to, you have to sort of have that belief. A lot of people don't do it. You you have to have that belief that AI is a good collaborator. It's not gonna do the work for you, but if you use it, you're gonna 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 gonna 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 twelve months from now, are the big AI companies that we know of today up or down?

We'll start with OpenAI twelve months from now, stocks up or down. Ali and then Arvind. Up.

And I'll say revenue will be up. I don't really understand how stocks work.

Entropic. Ali, Arvind. Up. Same. Yeah.

Okay.

We me let

me pull

can I get you a Of little bit course? Because ChatGPT is gonna continue growing and it's on fire and it's what everybody uses. Mhmm. So is Gemini, by the way. And then Anthropic because more and more, you know, coding, we've only, like, eaten into a small portion of that market.

They just started. So

Yeah. Is AI in a bubble? Yes or no?

There is an AI bubble. Like saying like okay. So then Lean is also in the bubble. Everybody's in the bubble. No.

I I would say there is a bubble. I I would say those three camps. Yeah. There is a super intelligence quest camp.

Mhmm.

I would be very worried there. There's a second, the researcher's doing the you know, that's definitely not in a bubble. They're like the

They're sober.

Yeah. They're they're super sober and nobody cares about them. Then there's right? And they're probably the ones that are right, 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 on what we are doing. Yeah. We're just trying to get actual economic value inside 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, 30,000,000,000.

That's a bubble.

Yeah. Same. I mean, I think the, there are quite a few companies where there's poor 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 multiples, and, but I think, you know, this 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 gonna grow more than non AI companies for sure.

Yeah. My favorite game at Ultimate 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 gonna be a bigger deal than it is today, what is that? And then short, which is, you you know, there's more sizzle than their steak, more more hype than reality. Pick along, something that you're very optimistic on. Same order, Ali and then Arvind.

I am very long on agents. Yeah. You know, I think I'm very long on speech Speech. As an interaction. Like I think keyboards are kind of basically gonna disappear completely.

We haven't actually nailed speech. I know it feels like we have, but we haven't because you're still using your keyboard. So as long as you're using your keyboard, we haven't nailed speech. But I think we're this close to completely eliminating keyboards. I think that's that's a big one.

What's what would I say? It's like, you know, I do think coding is a little bit overhyped. I don't know if I would short it. It's I mean, I think it's still the future. So I think that's that's one of them.

I think automating our customer service and support is a little bit overhyped. So, you know, I basically I think the things that the industry thinks are like amazing and we've made great progress. We probably haven't done as much progress on it. And then a lot of the other things that are being ignored, you know, we're gonna have breakthroughs in those. Fascinating.

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 very deeply and actually bring AI to them, that's the category that I'm excited about. I want to see more proactive proactive AI products coming coming into the market next year. Yeah. That that's when that's that is what is going to actually take it from a 5% of the users being power users to a 100%.

Yeah. Yeah. Yeah. Your favorite AI tool that you use in your lives?

I think Dune is awesome. I mean, if that was not fair

Let's Let's go.

So I use it all the time. I actually a lot of the questions I would ask from the team, the the thing you said you changed, I I I first ask Glyn and then see, you know, if it nails it or not. Then if it doesn't, then I'll spin up a 30 person team to go spend a week and have pre meetings and all that to get, you know, the explanation of some simple concept for me. But usually, nails it.

Yeah. Yeah. For for me, I'm excited about note takers. I've I've used Granolah myself and Fathom and a few others. Yeah.

But note taking is actually fascinating. I mean, I think the I I feel like, you know, if you if you take those notes and then if you utilize it the right way, like for example, what Ali was saying, like, you know, that becomes a source of what then actually creates knowledge, saves data in your systems, that's gonna change, you know, how companies work.

Yeah. You know, in closing, I'd love to get your vision for your companies. We'll start with Ali's favorite tool, Glean. Congrats. You just announced crossing a big milestone, $200,000,000 in revenue run rate.

You've you're signing big deals, $10,000,000 deals. You've got super users. I'm seeing you're seeing casual users. Paint us the vision for for Glean from here to a billion in revenue.

I think they're still doing annual planning, which also some, you know, AI companies are telling me that's that's so old school. But we're doing it regardless.

Doing it. That's just because there's early startups. Like, did you do annual planning when you started clean?

No. No. So the, but I think for us, 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 Glean is a heavily used product, but still there's a big variance between the top users and the ones, you know, at the bottom.

And that's what we want to change. So for the future for us is we want to be, we want Glean to be this very personal companion for every person in every company in the world. This this companion with which, you know, is is is, you know, you have a very confidential relationship with this companion in the sense that whatever you ask this companion, you know, whatever communication you have with them, you know, it's it's fully privileged. Nobody else gets 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 are you gonna meet, you know, in the day to day. It knows your weekly goals. It knows what, you know, what things you're not good at or what your career ambitions are.

And with all of that, you know, this this personal companion is is sort of helping you now with your work. It, you know, hopefully takes majority of your tasks automatically, you know, works on them before you ask it to work on them. And that's sort of the vision that we are, you know, taking our part to. We we have most of the found you know, foundation for this in place already. Today, you have to come to Glean to get most of that work done.

In the future, we want Glean to actually come to you and do that work.

Fascinating. Well, we can keep going for a bit, but Andi called on time. Thank you so much for chopping it up with us. You got a lot of alpha, a lot of insights here. Really appreciate it.

Thank you. Thank you.

Thank you.

Thank

Thank you. Gentlemen. Thank you so much.