The Exchanges

Q you've just seen so many cycles now. You mentioned earlier, like, where the puck was, just that line that you said when we were talking about Nutanix. Like, where do you see things going? What's the end state with AI? Like, you know, what happens to work? What happens to SaaS? What is the future of software sort of thing? I'm curious if you have big picture ideas on that.

A Yeah, I mean, as I mentioned before, I think the biggest revolutions are integrative. You know, they integrate stuff. Like the iOS integrated stuff, the AWS integrated stuff. So AI's biggest value creation is integration. And in ease of use, which is, can you miniaturize the complex app, which was built for specialists and the partner ecosystem to go and customize the heck out of it over one, two years of implementation. I think we basically just made it so complex that No one could end up using it anymore, except for a few people who are stuck onto it for eight hours a day. I think if you have to democratize business software and think about the casual user, you know, the rubber necker, the window shopper, you say that interface has to be simpler and probably miniature, and that's what conversational brings to the table.

AI assessment note: “AI's biggest value creation is integration. And in ease of use”

Q Wow. What are you doing different, especially in this case, from, you know, 10 15% to 85%, like, that, that is, you know, leading to that kind of a delta in terms of quality?

A Yeah, so, um, let's start with the basics, like, Crawling and indexing. Recently we had a customer that had almost a hundred terabytes of SharePoint and, uh, Dropbox to actually get in. And that's enterprise grade stuff. They had documents that were worth gigs, like single documents. If you try to do this in the old service-oriented architecture way, you'll be dead. And then you have made assumptions about limits and file sizes and folder sizes and data sizes. The way we have done crawling and indexing is through serverless. We said that it's going to be completely through lambdas, you know. A lot of the work that we do on behalf of indexing and crawling is like swarms. We do swarms. When a new customer is getting onboarded, we can get done a hundred X faster than any other company out there, simply because the way we have done the cloud native architecture, you know, and I'm not even talking AI native yet, just cloud native thinking, you know. Uh, we probably one of the only AI companies that uses lambdas like the way we use it, you know.

AI assessment note: “The way we have done crawling and indexing is through serverless.”

Q Does the emergence of public clouds, of AWS, all that stuff, which then happens in the background through that, the time frame, is that competitive to you because it's taking loads away, or what's the dynamics?

A In a Nutanix, the problem statement was where the puck was. The puck was EMC, NetApp, VMware, HP, Dell, IBM, Cisco, all these companies, and said, hey, we're going to really go and modernize all this stuff, but the consumption model was still ownership. You were owning data centers, you're building data centers, and then by 2014, 15, we started to see how, you know, the idea of not owning, but streaming infrastructure, you know, started to take some, you know, gain some crown, especially amongst the digital natives, so all these Dot coms or e-commerce companies, you know, were beginning to say, look, we don't need to own data centers. We can start to rent it out. Startups started doing it as well.

AI assessment note: “the idea of not owning, but streaming infrastructure, you know, started to gain”

Q And by the way, are you doing this, you decide to leave and you figure it out or all this is happening kind of simultaneously as you're thinking about the next thing?

A I mean, it was at the back of my mind for like three, four years, because we had done a, I was always big into systems, including business software at Nutanix, like, you know, the best implementors of NetSuite and, uh, one of the best implementors of Workday, you know, the way we did a lot of these things. I, I didn't look at them as cost center. I'm like, look, these things have to be done really well. Uh, the best support portals, not using Salesforce like the way most other people use Salesforce, you know, to using Slack and all this stuff, you know. Uh, so I always was passionate about process and systems. So I'm like, you know, it was at the back of my mind, but I would say the last half of twenty-twenty, these things became more concrete. And yet it was nebulous because, you know, you just didn't know what converging business software would mean.

AI assessment note: “it was at the back of my mind for like three, four years”

Q This is the mix, the SQL, NoSQL mix database?

A Well, it was data. So we said, we're going to start with storage and data management for virtual machines. It wasn't SQL, NoSQL, but it was just data, you know, because, and data has multiple layers, you know, you start with data for virtual machines, data for databases. Data at large is a really hard problem, by the way, because it has gravity, it has scale, it has reliability issues, availability, security, all sorts of issues. If anything, the whole idea of DevRev, and we'll get to that too at some point, we said, look, AI has to be a data problem. So we made AI into a data information knowledge problem as well. But we started building this thing for the new use case, which is virtualization. And we also had to say, okay, so how do we deliver this thing? Because it's otherwise just an abstract platform. So we had to put it into a device. We call it appliances. We said, we're going to take these commodity servers that don't get sold in the enterprise market because enterprise people were buying enterprise servers from HP and Dell and IBM and companies like that. And we said, that's the antithesis of private cloud. The way Public clouds are being built, at least consumer clouds are being built, was using commodity servers, commodity hardware. That's the way Google and Facebook, Amazon, they're all using lots and lots and lots of just servers and software on top of it.

AI assessment note: “It wasn't SQL, NoSQL, but it was just data”

Q So right now you have this one use case. Do you stick with a subset of customers and build out use cases and then you go to market with all of them, or do you do everything at the same time? Like you go to market with this one use case as you develop the other ones, you know, on the product side.

A So we have been very clever architecturally to know what is platform and what is custom. Really, really. So like the idea of workflows, we're like, no, we're not going to just going to build it for support departments only. A workflow engine is across all departments. It's platform. I'm not going to hard code the fact that they're the ticket to be in the search itself, you know, as opposed to others will actually make it. So in fact, I have to make it work for custom objects, things that I don't know what they mean, like an HR record, which I'm not sure what it means for me because it's not a native object. But if somebody sucks in custom objects, I should be able to search it. I should be able to SQL it. I should be able to workflow on top of it.

AI assessment note: “we have been very clever architecturally to know what is platform and what is custom”