 Welcome back everyone, live here at Palo Alto, SuperCloud 6, I'm John Furrier with Dave Vellante. The title of this episode of SuperCloud 6 is AI Innovators and we're excited to have our next guest, Shiraz Patel, head of MongoDB Ventures. Of course, MongoDB very famous with developers on the database side and they're writing checks to fund developers. Also the developer, Frenzy Dave. Shiraz, thanks for coming on theCUBE remotely from San Francisco. Next time you got to drive down in the studio. Come on down. Great to see you. Yeah, great to be here. Thank you for having me. So we're excited to talk to you guys. So we'll be at Mongo local in New York, May 2nd coming up. You got a big event, kicking off again the ground swell of developer actions all-time high, certainly on generative AI has just become really super exciting environment. People are being more confident every day that language model is getting better. You guys got vector search inside MongoDB now, full stop shop of developers. A lot of big customers that grow up from developers but you're writing checks to startups and sometimes they're small, sometimes they're big. I know you do a lot of hackathons but you're on the venture side. Can you give us a taste of a feeling of what's going on in that community of these new ventures? There's a big trend towards funding, founding teams out of the gate, just organic developer growth you're seeing in open source, just a ton of activity. Give us a feel for what you're writing checks for. Yeah, absolutely. MongoDB Ventures we launched it about two years ago and MongoDB as a company at the time we had just passed billion dollars in revenue, Atlas was growing nicely, 100,000 plus developers signing up for our product every month. And we were looked across our customer base and thought about what's a great way to introduce new and exciting technologies to the customer base. And that was really kind of the impetus for launching MongoDB Ventures. And so the mission of MongoDB Ventures is very aligned with MongoDB's mission which is to help innovators unleash the power of data and software. So for us, we're looking for new and exciting technologies that really help developers be more productive at their jobs, help them work with data better and can help others in our customer base. In terms of what that means generally we're looking at a lot of infrastructure software, a lot of developer tools. And to your point, the last year and a half has been very exciting, been focused a lot on looking at companies building in the AI space and in particular companies that are enabling developers which is our key audience to build with AI. You know, one of the things we're seeing out there and that came up earlier this morning on SuperCloud Six was, and of course we saw that at least a year ago but it starts to come into visibility now when you start to see the new formula. And the new formula that enterprises are driving towards is end to end new kinds of systems to power the AI but it has to be developer led, right? So that means a lot. Let's unpack that Dave with Sharad. You got end to end, developer centric and first, open source. Okay, so how do you become an enterprise motion end to end system with a developer led? So it's a combination of bottoms up growth but dealing with the data for generative AI is dealing with crown jewels of the company. You don't, haven't seen this movie before just this way where you have developer bottoms up growth and then you got to deal with core data, okay? Which is a very enterprise motion. Sharad, unpack that for us. You guys are seeing that at Mongo and what does that mean for the new solutions that are emerging? Yeah, actually I think this is the exact trend as to why we are positioned well as a venture organization. What we're seeing is that really the products that a developer needs to get dangerous and build AI into their applications even if they're at the enterprise those products are often startups. We see a lot of our developers even within the MongoDB ecosystem using products like lane chain, like law index a couple from our portfolio a company called NOMIC unstructured and these are integral products that people need to build AI infused applications. Now the data in an enterprise though lives in proven database technologies like MongoDB and that's where the magic of the partnership with some of these younger companies really comes into play. And so we work hand in hand with them we figure out what are the points of connectivity that we can enable and how do we make it easier for developers to use these new innovative companies with their existing enterprise data and really bring the right context to these AI applications. So Siraj, ecosystems have a voice and it's not always a singular voice but you're serving developers, developers are serving ecosystem you're the steward of that ecosystem. So while it may not be direct to say a financial services ecosystem or some other industry you are the beacon for that ecosystem. What is the ecosystem telling you that they need now through that developer voice and those multiple developer voices? What are the patterns that you're discerning over the last year or two? I think anybody that's spending a lot of time in the AI space and talking to enterprise customers in the space we all kind of have the same narrative where it felt like last year was a lot of prototypes. People were trying applications for the first time a lot of things like chatbots, a lot of retriever augmented generation and different applications of vector search. And what we're hearing now is that as people are maturing they're starting to think about, okay well if I'm using this model as part of my application what is the long-term cost gonna be of this model? And how am I gonna deal with that from a budget perspective? If I'm using this framework am I confident that this framework is now gonna scale? Or if I'm introducing AI powers into my application which are by nature probabilistic how can I design unit tests or different testing to make sure that the actual output of the application is as predictable as possible as well as safe? So if there's follow-up on that sort of the ROI question benefit over cost it sounds like there's a real focus on the nominator right now. Is that because they've figured out the numerator that the sort of value piece is there still a lot of experimentation or is it because there's pressure for them to actually show a return on their AI experiments? How do you think about the ROI sentiment? I think for some companies with some use cases the ROI is no. You take developer productivity we know how the massive shortage of technical resources there are globally we can't get enough developers in any enterprise and so you injected tool like what we're seeing with GitHub co-pilot or a company called Codium that we partnered with that really increases developer productivity and the ROI is in tune and people understand it. Other use cases it's a little bit more unknown people are still trying to figure out what is the exact ROI going to be on increasing the number of tickets per customer service agent that can be served at once. Some of these calculations are a little bit less well-known and others are well-known but I think there's generally a lot of momentum there's a lot of top-down desire to make sure that teams are using AI and figuring out where they can drive ROI. Siraj Prasul by the way some of the names you mentioned Lama Index, Langchain, Jerry and Harrison are going to be in the cube on the 19th this is our addendum addition to AI innovators so if you're watching we'll have another supplement coming in next week how we choose got a bunch of pounds we're putting on together here so you'll see them here on the cube. I got to ask you as you look at the landscape you're looking at deals you're looking at the trends and who to invest in who are the AI innovators if you can encapsulate kind of the pattern that's emerging obviously reg is hot the retrieval side of it with vector embeds only one that's context but you got other enrichment of other data sources enriching vector embeds and other things got transaction data Dave has been all over that in his research what are the patterns what is an AI innovator that you guys are looking at and seeing what are some of the early signs of what that entrepreneur looks like what that success case looks like what's the team formation what's just a high level observation. Yeah I will say that from a venture standpoint we focus again on infrastructure as well as products aimed at developers not necessarily application layer I think at the application layer our insight is again limited because we don't focus from a venture standpoint but essentially that the cycle between when an application becomes dominant and when it potentially becomes disrupted is decreasing and your AI innovators or your innovators in general are now using AI as a way to increase the velocity and the amount of disruption that they have within their application so I'll give you an example again from our portfolio we have a company called payload it's content management system it's headless developer first has seen tremendous open source adoption and it really grew initially off the developer first nature of the product now beginning last year all of a sudden they started infusing a lot of AI into the product and they've seen an accelerant of that adoption and disruption so for example now when I'm writing content for my website they have an AI co-pilot that's going to help auto-complete whatever I'm writing or if I need an image for my website you can actually generate that image using AI instead of bringing that image into your content management system so we're seeing savvy entrepreneurs that are able to input AI into their products no matter where they started a couple of years ago on the innovation side of the infrastructure AI ops is a top conversation we're seeing a lot about that DevSecOps teams are looking at specifically on the platform engineering side AI automating a lot of the data wrangling data management piece of it data hygiene which is once you know data science exercise the data engineers set the table so to speak and let the AI run so that platform engineering is getting a lot of AI assistance where does that go how do you guys see that happen because this is going to help a lot in fact we showed numbers early on before we came our opening segment around some of the the market data market research data that gen AI is pulling up cloud orchestration kubernetes container management those are like the hot areas around where gen AI is pulling up that tells us that there's a lot of DevOps work going on in the in the stack there what are you seeing what's the innovation there because it seems to be a lot of action yeah I think in general what we're seeing is that projects that require significant developer effort whether it's DevOps or otherwise major infrastructure changes where in the past you've been bottlenecked by the cost of rewriting application code and the cost of modernizing an infrastructure application you now see that people are starting to realize that those costs are going down with the productivity gains that developers get from AI we think it's going to massively accelerate how quickly enterprises are able to modernize their applications and move off of legacy infrastructure into more modern infrastructure I think that's one side of it the other side of it is the velocity of new applications is going to increase tremendously not only again because developers are becoming more productive but also as our definition of who is a developer expands right now I can go to AI ask them to create a base application for me and they're going to produce something that's pretty good and so we think who is considered a developer will continually expand as the AI gets better at developing software so Mongo's obviously done very well in the developer world being a system an operational database there's certainly a lot of discussion and we've seen this the rise of a lot of these analytical systems and there's a lot of discussion we've been talking about today here at SuperCloud 6 of bringing analytics and transactions together I mean that's not anything new but from an investor standpoint what are you seeing there is it something that people are driving you towards I mean obviously Mongo has announced some extensions into analytics but as you see LLMs and AI bring in less structured data what are you seeing there in terms of is it going to be in your view the database big database vendors that extend that or are there investment opportunities that can complement what you guys are doing today as part of that ecosystem well I am certainly biased but I have to say that what we've seen is that customers don't want to manage an additional database if they don't have to there's certain requirements for these workloads that are coming in based on applications that are being built and as we've added things like our vector search technology we've been able to meet the requirements for those applications so I think it's it's one of the things where incumbents have the right to win and certainly our customers look to us to solve an increasing amount of their data problems before they're going to look to add another data system to their stack just in terms of total cost of ownership, simplicity and a good high quality developer experience as well as reducing the complexity of their architectures but to your point we are also seeing the trend that developers are asked to do more so developers are asked to embed analytical charts into their applications increasingly developers are now asked to add search add similarity search add recommendation engines to applications so as developers are asked to do more the data platforms that they rely on need to keep up with that increased responsibility and that's very aligned with our data developer platform yeah I mean it's almost like data management because not quite like security yet with all these different platforms and tools but it's getting there there's a lot of fragmentation and that causes problems for customers Sir I agree we really appreciate you coming on the cube and spending time with us for the final minute we have left give a plug for the venture group there what are you looking for investments do you invest for profit or is it more for ecosystem development or both or and how does someone know whether they should knock on your door to get it checked do you follow do you lead do you throw seed out there give us a quick overview of how you guys engage now I appreciate it we invest from formation stage through growth stage we are investing in our ecosystem more broadly so it's a combination of strategic and financial reasons that we invested and the best way to think about whether we're a good investor for you is if you're going after the developer ecosystem and other data practitioners that's the ecosystem that we care tremendously about and any company solving a real problem for the constituents of that ecosystem we're interested in talking with and working with all right well thanks so much and congratulations on your mission good to see more strategic and targeted checks for the developer community obviously we as we believe developers will set the agenda they are the canary in the coal mines so to speak that combined with the gender of AI wave makes for the perfect storm so we'll see you and the team in New York City on May 2nd for Mongo Local put a little plug in for your team over there MongoDB is evolved from the developer open source product to mega enterprise hit with Atlas congratulations thank you so much thanks for having me John and Dave okay SuperCloud AI innovators continues we'll be right back with another startup founder coming back on here talk about the future of AI infrastructure we'll be right back live in Palo Alto I'm John Furrier with Dave Vellante we'll be right back