 Let's bring in Jerry Chen from Greylock. Is he here? Let's bring him in. There he is. Hey, John. Good to see you. Hey, congratulations on an amazing talk and thesis on the castles on the cloud. Thanks for coming on. Thanks. Well, thanks for reading. It's always, we're going to put a piece of work out on the ether, not sure what the response is, but it seemed to resonate with a bunch of developers, founders and investors and folks like yourself. So the smart people seem to gravitate to us. So thank you very much. Well, one of the benefits of doing the cube for 11 years, Jerry, is we have videotape of many, many people talking about what the future will hold. You kind of were on this early. It wasn't called castles on the cloud, but you were all, I was, we had many conversations. We were kind of connecting the dots in real time, but you've been on this for a while and it's great to see the work. I really think you nailed this. I think you're absolutely on point here. So let's get into it. What is castles on the cloud? New research has come out from Greylock that you spearheaded. It's a collaborative effort, but you've got data behind it. Give a quick overview of what is castles on the cloud, the new modes of competitive advantage for companies. Yeah, it's a group project that our team put together. But basically, John, the question is, how do you win in the cloud? Right? Remember the conversations we had eight years ago at Amazon re-event was, holy cow, like, can you compete with them? Like, is it winner take all, winner take most? And if it is winner take most, where are the white spaces for some startups to emerge? And clearly the past eight years in the cloud, this journey, we've seen big companies, Databricks, Snowflakes, Elastic, Mongo, DataRobot. And so the spawn of the question is, why are the castles in the cloud, the big three cloud providers, Amazon, Google, and Azure winning? What advantages do they have? And then given their modes of scale, network effects, how can you as a startup win? And so look, there are 500 plus services between all three cloud vendors, but there are like 500 plus startups competing against the cloud vendors. And there's like almost a hundred unicorns of private companies competing successfully against the cloud vendors, including public companies. So like, Elastic, Mongo, Snowflake, Databricks, not public yet, HashiCorp, not public yet. These are some examples of the names I think are winning and, you know, watch this space because you just see more of these guys storm the castle, if you will. Yeah, and you know, one of the things that's a funny metaphor because it has many different implications. One is we talk about security, the perimeter, the gates, the modes being on land. But now you're in the cloud, you have also different security paradigm. You have a different new kinds of services that are coming on board faster than ever before, not just from the cloud players, but from companies contributing into the ecosystem. So you have a combination of the big three, making the market, the main markets. You got, I think you call it 31 markets that we know of, there probably may be more. And then you have this notion of a sub market, which means that there's like, we used to call it white space back in the day. Remember how many whites, where's the white space? I mean, and if you're in the cloud, there's like a zillion white spaces. So talk about this sub market dynamic between markets and that are being enabled by the cloud players and how the sub markets play into it. Sure, so first, the first problem was, what we did, we downloaded all the services from the big three clouds, right? And you know, what Azure calls a database or a database service, like a document DB and Amazon is like CosmoDB and Azure. So first thing first is we had to like, look at all three cloud providers and, you know, re-caggerize all those services, almost 500 apples to apples to apples, number one. Number two is you looked at all these markets or sub markets and said, okay, how can we cluster these services into things that, you know, you and I can grok, right? Because what Amazon, Azure and Google think about it is very different. And the beauty of the cloud is this kind of fat, long tail of services for developers. So instead of like Oracle as a single database for all your needs, there's like 20 or 30 different databases from time series, analytic databases. We're talking to Roxette later today, right? Document databases like Mongo, search database like Elastic. And so what happens is there's not one giant market like databases, there's a database market and 30, 40 sub markets that serve the needs of developers. So the great news is cloud has reduced the cost and creates something that new for developers. Also the good news is for a startup you can find plenty of white speeds with solving a pain point very specific to a different type of problem. Yeah, and then you can sequence up to power law too. I love the power law metaphor. You know, there used to be a very thin neck, no torso and then a long tail. But now as you're pointing out this expansion of the fat tail of services, but also there's big tabs and markets available at the top of the power law where you see companies like Snowflake essentially take on the data warehousing market by basically sitting on Amazon and refactoring with new services and then getting a flywheel, completely changing the economics, completely changing the consumption model, completely changing the value proposition. Literally overnight. So if Snowflake has created like a storm, create a hole in that mode or that castle wall against Redshift, then companies like Rockset doing real-time analytics is rushing right behind Snowflake saying, hey, Snowflake's great for data warehouse but it's not fast enough for real-time analytics. Let me give you something new. So to your power law argument, even the big optics, Snowflake, have created kind of a wake behind them that created even more white space for guys at Rockset. So that's exciting for guys like me and you. And then also as we were talking about our last episode two or quarter two of our showcase from a VC came on, it's like the old shelf where you didn't know if a company was successful until they had to return the inventory. Now with cloud, if you're not successful, you know it right away. It's like, it's no debate. I mean, you're either winning or not. This is like, that's so instrumented. So a company can have a good, better mousetrap and win and fill the white space and then move up. It goes both ways. The cloud vendors, the big three, Amazon, Google and Azure for sure, they instrument their own class. They know, John, which ecosystem partners doing well and which ecosystems doing poorly and they hear from the customers exactly what they want. So it goes both ways. They can weaponize that info just as well as use a starter to weaponize that info. And that's the big argument of do the snowflakes still pays the Amazon bills. They're still there. So again, repeat creation comes back. That's a big conversation that's come up. What's your quick take on that? Because if you're going to have a castle in the cloud, then you're going to bring it back to land. I mean, what's that dynamic? Where do you see that competing? Because on one hand it's innovation. The other ones maybe cost efficiency. Is that a growth indicator? Slow down. What's your view on the movement from and to the cloud? I think there's probably three forces you're finding here. One is cost advantage and the scale advantage of cloud. So that I think has been going for the past eight years. There's a repatriation movement for a certain subset of customers I think for cost purposes makes sense. I think that's a tiny handful that believe they can actually run things better than the cloud. The third thing we're seeing around repatriation is not necessarily against cloud but you're going to see more decentralized clouds and things pushed to the edge, right? So you look at companies like Cloudflare, Fastly or a company that we're investing in, Kato Networks. All they do is focus on secure access at the edge. And so I think that's not necessarily a repatriation of my own data center but it's kind of a disaggregation of cloud from one giant monolithic cloud in like AWS East or like a Google region in Europe to multiple smaller clouds for governance purposes, security purposes or latency purposes. So I'm looking at my notes here. I have to look down on the screen here for this to read this because it's a cut and pace from your thesis on the cloud, that's on the cloud. The of the $38 billion invested this quarter, AI and ML number one, analytics number two, security number three, actually security is number one but you can see the bubbles here. So all those are data problems. So I need to ask you, I see data is hot. Data as intellectual property. How do you look at that? Because we've been reporting on this and we just started the CUBE conversation around workflows as intellectual property. If you have scale and your mode is in the cloud, you could argue that data and the workflows around those data streams is intellectual property. It's a protocol. Yeah, I believe both are. And they just kind of, they go hand in hand like peanut butter and jelly, right? So data for sure is IP. So if, you know, people would talk about data in the oil, the new research, that's largely true because it powers a bunch but the workflow to your point, John is sticky because every company is a unique snowflake, right? Like the process used to run the CUBE and your business is different how we run our business. So if you can build a workflow that leverages the data, that's super sticky. So in terms of switching costs, if my workflow is very bespoke to your business, then I think that's a competitive advantage. Well, certainly your workflow is a lot different than the CUBE. You guys are investing a lot of billions of dollars in capital. We're talking to all the people out here. Jack, great to have you on. Final thought on your thesis. Where does it go from here? What's been the reaction? I know you put it out there. Great. Love the research. I think you're on point on this one. Where did, where does it go from here? We have two follow-up pieces in the near term. One around, you know, a deep diver on open source. So look out for that pretty soon and how that's been a powerful strategy. A second is this kind of disaggregation of the cloud be a blockchain and, you know, decentralized apps be it edge applications. So that's in the near term, two more pieces of deep dive we're doing. And then the goal here is to update this on a quarterly annual basis. So we're getting submissions from founders that wanted to say, Hey, you missed us or you screwed up here. We got the big cloud vendor saying, Hey, Jerry, we just lost this new thing. So our goal here is to update this every single year and then probably do a look back saying, Okay, where were we wrong? Where are we right? And then let's say the cast in the clouds 2022 we'll see the difference. We're the more unicorns, we're the more services, we're the IPOs happening. So look for some short term work from us on analytics like around open source and clouds. And then next year we hope to roll this forward saying, Hey, year after year, what's happening? What's changing? Great stuff. And congratulations. I just saw the news, you guys put another half a billion dollars into early, early stage, which is your roots and you're still doing a lot of great investments and got a lot of unicorns. Congratulations, that great luck on the team. Thanks for coming on and congratulations. You nailed this one. I think we're going to look back and say that this is a pretty seminal piece of work here. Thanks for sharing. Thanks, John. Thanks for having me as always.