in a world of probabilistic software you know the measure of quality is about that kind of Last Mile how do you get to 99.99 X you know sort of quality and so will the domain of sort of quality engineering that we typically associate with manufacturing kind of apply to software and and that really got me thinking in terms of you're not going to be able to necessarily get a deterministic result like you would with a traditional application talking to a traditional database so therefore the quality of your embedding models how you construct your rag architectures
mer it with the real time view of what's happening in the transactions in your business that's what's going to get you that high quality retrieval and result and unless it's high quality in a world where it's probabilistic I don't see it going after mission critical use cases in a conservative Enterprise and that that is a problem space we're very focused on right now [Music] today we're excited to welcome sahiram who leads product and growth at mongod DB sahir was one of the Architects behind Mongo's successful transformation from on Prem to the cloud and he's now
helping to steer Mongo's evolution in AI first world Mongo's journey into Vector databases began with semantic search for e-commerce but it's evolved into something a lot more fundamental becoming the memory and the state layer for AI applications we're excited to get sahar's take on the past and the future of vector databases and what shape infrastructure itself will take in A Brave New World of AI agents and applications and unlimited software creation so here welcome to the show we're so excited to have you here thanks Sonia I'm super excited to be here uh we're going to
dig into everything from Vector databases to embeddings to knowledge grafts and much much more on this episode I'd love to just start with the big picture question uh and maybe your hot take is AI going to change database Market uh that's an interesting question I think the the related and probably more interesting question is whether it's going to change software development in applications and I think that it really is you know I think we're seeing AI power generative AI powered applications address a set of use cases that traditional kind of deterministic software hasn't been able
to go after and I know I've read some stuff from seoa around the idea of like you know Services as software Etc in that whole space and we firmly see that in terms of our early adoption of what we're seeing in the market and so that in turn changes a fundamental way we will interact with software it changes the way business logic of applications will evolve over time with things like agents and that all has underlying implications on how the database layer will need to transform as well can we can we poke on that for
just a minute so I'm curious since you guys are operating at a layer will you see a lot of what's being developed um what what are people developing today that they could could not have developed a few years ago before these capabilities emerged yeah I think on one hand one Trend we're seeing is certainly that it's much more easy and efficient to create software than there ever was before so you know the fact that there will be more software in the world means that there will that will have implications in terms of data persistent storage
and processing so that's kind of one you know sort of related piece but in terms of use cases I think the fact that we can now interact with computers in completely different ways beond on just the Classic web and mobile applications that we're all used to you know the more interactive experiences I think the blending of the physical and virtual worlds in ways that I don't think we can you know we've really seen yet obviously there's a big Trend around how AI impacts robotics you know there's a great blog I read from I think it
was from Lang chain the other day around sort of ambient agents and sort of you know reacting to signals without necessarily intentional Human Action I think we're at the verly early stages of that top layer of sort of human computer interaction fundamentally changing and I think that can now tackle a whole bunch of use cases in terms of improving the productivity of our personal lives our professional lives and go after you know fundamental productivity that I don't think traditional software has gone after I think that's the biggest kind of meta change that I think this
all has the potential to go do you have a favorite example either one that either a customer obviously you love all of your customers but do you have a favorite use case either that you've seen a uh one of your customers build or a favorite use case that you yourself use yeah I would say generally we're seeing more and like most things we see more sophisticated Advanced use cases tend to come up first in you know more risk tolerant sort of faster moving startups but for that reason I'll pick a couple Enterprise use cases that
have kind of captured our imagination one is we worked with a large automaker in Europe and you know they have huge fleets of cars globally they have a bunch of first and third party you know mechanics and maintenance sites people whether they're dealers or other sites where people go to get TR you know help when their cars are having issues and you know the common problem if I hear something funny with my car like how do I go diagnosis means that you typically go in mechanic who has expertise has to go kind of Tinker around
figure out what it is and then go through a manual to figure out what the remediation steps is or what parts they have to order to fix it y we work with them to actually identify an audio embedding model that can allow them to record with a phone the corpus of and semantically match up with a corpus of sounds that are typical problems that are known problems with you know their their their cars or any cars which shrinks down the actual diagnosis time you know by typically could take hours if it was a tricky diagnosis
to something that could take now seconds it's almost like Shazam for car diagnostic and then on the other side of it instead of looking through PDFs or you know physical manuals on what the approved remediation steps are to fix it now it's sort of a natural language interface to say okay this is is the the issue that we match to what should I do next in terms of fixing the problem and you know that's all about unstructured data semantic meaning of the information both and the problem with that car and if you extrapolate the business
case of that though across thousands of dealerships or hundreds of you know different models and iterations of cars like that's millions of dollars of potential savings for them and a better better customer experience and you know consumer sentiment around their brand and so that was kind of definitely one cool one another one uh you know in a more very heavily regulated industry worked with Novo Nordisk uh you know one of the largest pH Pharmaceuticals you know obviously getting a drug approved is a highly um scrutinized process and so there's this idea of a clinical study
report that pharmaceutical companies have to fill out which typically takes a lot of manual effort to write and structure and review and you know kind of get approved they were basically able to use a large language model again train that against all their approved drugs all the process they do manually and now they can get that initial draft of a CSR as they call it within a few minutes and so it shrinks a lot of just the initial drafting Cycles the quality of that initial draft is higher than what they typically see if it was
you know manually done and so again like you can draw a pretty quick line towards true dollar Roi savings on use cases that are you know not necessarily even bleeding edge in some aspects of what we're seeing in the in the sort of early stage ecosystem but are being applied in a context and scale in industries that obviously have big implications for you know for them and for their customers y so now that the shape of these applications is changing um and you know they're multimodal as as you said they're they're agentic they're ambient agentic
what does that mean for the database layer and if you wouldn't mind just giving us the the 101 today of like the the role that databases play for software as we know it today deterministic software and what role do you see database is playing in this kind of new evolving market for AI applications is it good news or bad news uh we're excited so two reasons one you know the point I lightly brought up earlier which is if there's more software in the world which I think generative AI will just make it easier to create
more types of software experiences I think that in general is a Tailwind for any data persistence infrastructure you know technology it doesn't necessarily mean that mongodb or any other particular vendor is automatically going to be the beneficiary there's a lot of execution that goes into making sure we're technologically and you know for our partnership and ecosystems well set up for that which is where I spend a lot of my time but in general like more software means more data and needs for persistence and of that information so that's a very macro sort of I think
Tailwind that we're definitely definitely excited about I think the shift from relatively simplistic gen use cases often times where you're just interacting via chat with an llm doesn't necessarily need very Advanced kind of data persistence but as Enterprises need to ground the results of their AI applications to proprietary information or to control the result set so the retrieval is of high quality now there needs to be a lot of interaction with these foundational models and their underlying information about how they run their business and a lot of that is not necessarily publicly trainable information on
the internet and so whether that's you know Advanced or simplistic rag workflows whether that's fine-tuning different approaches around post training there this I think there will be more need to interact with an Enterprise's data and foundational models over time especially as these models become lower latency and so they interact more with the real-time business data that's being generated in an organization and that's really what we're seeing in the most advanced companies right now is they're they're building really sophisticated ways to control the output of these llms based on the use case that they're trying to
drive towards and merging it with the operational data that drives their application or their business and so I think we're still early days in that in terms of where I think that can go but I really do fundamentally believe the databases will get to get much better at high quality retrieval in particular of on structured data because you know when I look at all these embedding models and just what we can do with probabilistic software it takes the value out of 70% of the world's data unstructured data and makes it applicable to applications in a
way that just really wasn't possible before and I think that's the real opportunity what's the Devil's Advocate answer to that so for example I'm thinking of Jensen at our first AI S I think you were at that a he said something like every pixel is going to be generated not rendered and I think of rendered as you know retrieved from database somewhere uh like what what is the devil's advocate point of view to the you know is it good or bad for databases as generative AI takes off yeah I think the Devil's Advocate view to
me is less about whether there is a database somewhere behind the scenes more about where is that abstraction and is that something that's a choice of the application developer building that application is or is it abstracted behind some level API or is that a choice that an llm makes in terms of as it autog generates software or Auto renders that environment where does it choose to persist that data yeah but at the end of the day you know we like to joke internally an AI application is still an application you still need to persist transaction
safely to make sure People's Bank balances are accurate you still need you know the ability to search information based on text keywords not only on the semantic meaning and so I view all these generative AI needs from the data layer as ADD not necessarily substitutive to the needs of a traditional application and you know one of the reasons people love today is the developer experience right if you fast forward the clock and you know maybe there's x00 million human human software developers but there's trillions of call it agentic developers what makes a good Agent developer
experience like why would an agent choose to use as its database if that even makes does that make sense as the question yeah I think it does and it's something you know we think a lot about sort of how the nature of software development will change and I think one of the things as we move from more simplistic generative AI kind of powered applications to more advanced ones with more you know agent-- driven business logic state will be more necessary because now you're coordinating you know a more complicated workflow where you need to be able
to track the results of a particular piece of a transaction and coordinate that and all of that requires storing that somewhere and you know manipulating and updating it over time so I think in general things are becoming more state full in generative AI applications over time which is a drag of of data and database you know consumption uh overall you know in terms of where things are going now I think in terms of the abstraction you know I think the question is if you know developer experience is the thing that you know makes any technology
really accessible today for human developers does that same value proposition hold for AI and I think what we're seeing even if you look Beyond just the database space think of you know the adoption we're seeing of some of these uh call it AI platform as a service type companies you know look at the the adoption of things like versel vzero or you see things like repet or whatnot I think we're seeing that at least with early AI generated software there's a preference for great developer experience Allah higher levels of abstraction so um I think it's
too early to be definitive on that but you know I think we're seeing some promising signs speaking of higher levels of abstraction um I forget who had this oneliner somebody had a good oneliner which was you know English is the ultimate layer of abstraction right and at the limit you will just be able to describe and plain English you know what product requirements you have and a foundation model will spit out the code required to you know build whatever application you want to build first off do you believe in that as a future State and
then secondly is that great news for because there's just going to be so much more software and most of it's going to need a database sitting beneath it or is that bad news for because it neuters some of that development experience that is a good Advantage for you do you see that playing out and what does that mean for yeah I think I think for databases in general I feel pretty confident it's a absolutely a Tailwind I think mongodb specifically one of the advantages we have is that our data model is really well tuned to
managing structured data semi-structured data and now with embeddings unstructured data so I think we have some fundamental architectural advantages We Believe even more of an uh you know prevalent and important in AI as you're representing all these forms of data regardless of whether the software above it that's interacting with it is human generated or machine generated so to speak now that being said we're certainly not resting on our Laurels that that's going to happen without us being really intentional about it so we are working with you know the whole ecosystem of AI Frameworks and model
providers to make sure that we are well integrated whether it's inference players or you know Dev Frameworks Etc to make sure that just like JavaScript and Web 2.0 and Cloud were big Tailwinds and are big drivers of our business that the modern Stacks that are being used to generate these applications to be is well integrated as a default in so I think there's a lot of work happening there we're also focused on this idea of you know what is the equivalent of you know quality training or even SEO for llms meaning you know if you
go scrape the internet to train a code assistant on any technology is is that necessarily what the best practices are probably not but there's no standard way for you know an a vendor or a technology expert behind a particular area to submit the canonical training data for Quality mongodb code for example and so we're working with some of the labs on methodologies around that we're doing things just even without involvement to test sort of you know what we can be doing to create you know data sets that allow for the quality of the outputs of
these systems to be reliable you know last thing we want is some going and saying I want to use mongodb help me generate some code for some functionality and it's you know not high quality performs poorly and so there's various facets of this that I think are very intentional efforts to make sure that our technology fits well as things evolve over the next few years yeah so actually to that point I think there's been a lot of chatter and increasing chatter that you know we're we're hitting a wall uh in terms of just public data
globally available mhm there's a lot of data still left in private Enterprise data you guys sit in the middle of of a lot of it um I'm curious how you think about uh your role in kind of that you know as as the market of all sources next leg of finding that next uh trillion tokens worth of training data um do you see yourselves you know being a training data provider for your customers do you see yourselves partnering with the labs um are your customers mostly you know looking to use their data in for uh
for for rag or are they looking at also training models on on the data they have in your systems yeah definitely you know I think just to be clear any of the data that we manage on behalf of our customers is owned by our customers so you know we're certainly not taking that data and you know training any models that are outside of what that customer wants us to train or use for rag so I think that definitely is where more of our focus is and you know we see a variety of different things very
simplistic kind of use cases where people are just using you know core operational data stored in mongodb or metadata as part of their kind of rag workflows we're seeing obviously a lot of vector adoptions are fastest screwing new product areas they try to merge you know metadata transactional data and semantic search sort of together into a single sort of system for more quality uh retrieval kind of use cases which is sort of I think where the Market's going and then we see instances where people want to use the data they have in mongodb and other
systems to either fine-tune or straight up train smaller models that are specific to a particular use case and I don't believe that there'll be kind of one particular modality that suits every single you know use case I think there's going to be a plethora of different things that customers will begin to optimize for their latency requirements or performance requirements so I think you have the most fascinating seat to what's happening in the vector database Market we constantly pull our portfolio on what their AI stack is and consistently has been the number one vendor that everyone
uses for Vector databases so I think you have the deepest and most interesting perspective on this um maybe like from from the 20,000 foot view it seems like people View are using llms as you know they have World Knowledge up to some pre-training cut off date but beyond that you need Rag and you need Vector databases in order to supplement knowledge uh to provide you know specific domain knowledge uh you know almost as in like information retrieval like knowledge Source but if I look at Vector databases they kind of came from the semantic search world
and you know e-commerce and things like that and so that's that's that's a very different world so how how do you think about you know what are people using Vector databases for today is it a technology of the past that's being improperly shoehorned into this information retrieval use case or is it the ideal data structure um to kind of be the the knowledge uh infrastructure for llms like how do you think this all plays out yeah kind a quick question on that of course did I'm aware of Mongo's Vector database because of generative Ai and
seeing people use it for generative AI sure did you guys have a vector database pre generative AI we started because of of a more classic classic now semantic seuse case so a few years ago one of the things we noticed were that you know many of our customers would use Monga Tob as an operational data or any operational data and side by side with it have an adverted index kind of search engine for full Tex kind of lexical search and our customers were basically like why do I have to copy data between these systems to
run two different databases just to get the search results I want to empower my application with and so being focused on developer experience and simplicity where like this seems like an obvious problem for us to go after and so we started there with our um search product to really just simplify it so a developer interfaces with one database but really it has different modalities of indexing and storage that can serve you know allp type queries as well as full Tech search queries um some of our e-commerce Advanced e-commerce customers were the ones then saying okay
that's great but I want to start to do semantic similarity search and blend full teex lexical search alongside it search because that's what's going to give me higher quality search results and that's where we started getting pulled into building the vector capabilities into our engine and for us it's you know one of the things we we're always trying to do is remove the need for customers to have multiple systems so when we say we add this capability it's a lot of it goes to how do we integrate it in an elegant way to our data
model how do we extend our query language so it's very easy for a developer to just feel like it's not a separate system they're just interacting with it as part of their application development and so we were down that line and then obviously you know the world explodes post chat GPT and you know we were like all right you know this is going to be even more relevant than we thought and so we poured the gas on things accelerated things expanded the strategy to be well integrated into a whole bunch of new Frameworks you know
working a lot more closely with the AI Labs because it is to Sonia your point it's it's certainly a different use case to leverage you know vector embeddings or even just metadata or transactional data integrate to rag than just a pure semantic search use case but as we look at our most advanced customers now in 2025 they're actually seeing that the integration of all those modalities is really important because you need to filter based on metadata you know about your your unstructured data your what whatever it is you're you're building an application around there are
times when you need to sort by keywords and relevance ranking like a more traditional search engine and then you need to understand and extract semantic meaning from vector ined and there's a whole bunch of things around how to improve the quality of that and only then can their overall application get the percentage quality predictability for especially for large Enterprise to trust putting something in front of their customers especially in a regulated industry and so that's turned out to be a real advantage to have all of those in a single system because otherwise it requires a
whole bunch of what you know I call kind of rag gymnastics to try to tie all these things together which is possible but it puts a whole huge burden around the development cycle what happens in app code and frankly you need to be a pretty sophisticated team to figure that out on your own and so we're trying to democratize that all by making it just much simpler for the average application developer H how do you think about Vector versus graph are they substitutes are they complement what are the trade-offs because we see Vector uh vector-
based rag we also see graph rag yeah yeah I I every week goes by and there's some new sort of approach to higher quality retrieval is kind of what I think everyone's sort of trying to chase I think they're complimentary you know like there are reasons why you want uh graph relationships because that's an augmentation of understanding that you may not be able to just infer by the vector embeddings themselves so we view that as additive just like pre-filtering based on some sort of metadata you know about your your unstructured data and embeddings is additive
and improves the quality of results and so I do view these modalities as very complimentary you know our goal is to just make it simple to combine all of those for a developer so they don't need to have their graph representations of their objects in a one style of database their metadata and another database their transactional data and a relational database and then have to have a separate Vector search database and try to you know rationalize all that which is kind of what happens we're trying to just make that dead simple is it fair to
simplistically think about you know in an agentic system the llm as the brain and the database whether it's a vector database or uh or suet of those as the uh the memory is that is it brain and memory is that the right abstraction the right mental model I think that's definitely one way to think about it because absolutely you need to persist memory and state especially when you have agents that are having more complex workflows and need to drive interaction across multiple endpoints not necessarily a single foundational llm with a One-Shot call so you need
to persist more of that state I view them as sort of two pieces of an emerging architecture you know you've got obviously compute storage networking is sort of the under Primitives but now there's this whole set of use cases that foundational llms can go after that are more probabilistic in nature that can automate tasks that knowledge workers would typically have to do manually and which is super powerful but then that needs to store its state and be grounded and interact with the transaction that the application is driving and the other information that's either semi-structured or
structured and those things together come to create a great application experience and end user experience it's not in either or I think it's it's complimentary in a really powerful way which will only become more important as llms become lower latency and faster where now you can really use what's happening in a real world setting to augment the results of an llm in much closer to real time than today where it's you know it's just a very different interaction speed so you're saying the database is not only the memory for the llm but it's a reflection
of world State yeah like you llm needs to interact with World State exactly well I think that rough framing is consistent with what we've talked about internally which you know if you think about the bottom as raw infrastructure compute Network and storage you think about the top as the application you've got all this stuff in the middle and for anything that is deterministic you're going to be better off with Vector database graph database relational database nosql database kind of the traditional database world for anything that's more probabilistic you want something that looks like an llm
the functionality that gives you is a little bit of human computer interaction and a little bit of reasoning right which is complimentary to what you get from this part of the world but I want to take it one step further because it sounds like we pretty similar view on this default architecture of the future or kind of this emerging pattern if you take it one step further does that imply that the mental model investors should have for the API portion of anthropic or open AI or the other Foundation model companies is meaning they're occupying a
similar lay in the stack they both reside on top of the public clouds they both reside beneath the application layer is is a good frame of reference for what the API businesses of open Ai and anthropic would or should or could become over time yeah I think it's an interesting proxy because it you know you sometimes read like okay the llm is the new operating system I that never felt logical to me in terms of how application capability and functionality should look maybe I'm wrong things you know are changing so fast these days but um
but what we see is really these are side by-side complimentary components that drive and serve the business logic and interaction layer of the application above yeah and there's a whole bunch of use cases obviously that large language models can now reason about and provide human Interac around that weren't possible before that's the amazing powerful aspect of them but it doesn't in you know in any architecture we've seen supplant the need to have deterministic outputs from structure data to manage transactions and search and all the other data components it's it's really complimentary and I think it's
still early days I think you know SEO has done a great job sort of writing about as well like we don't know what the real Next Generation business models and applications are yet today I think we're still seeing the the early years of it and that's what's fun to be able to see all these different early stage companies or these Enterprise use cases that I highlighted earlier even then you know I think there's a lot more to come yeah all hypotheses at the moment yes I mean speaking of hypotheses there's all these hypotheses about you
know what model architectures are going to Leap Frog and you know what the next model architectures are going to be I'm curious your hypotheses on the database side so we went you know we went from nothing to Vector databases pretty quickly it seems like do you think we're going to Leap Frog to a new type of data structure for for AI for these AI systems or do you think this is kind of the the the ideal architecture yeah I think the the fundamental data architecture at least as far as you know vectors are concerned seem
to be strong Primitives that seem to hold and where I think we're still trying to figure out how we extract all the possibility there now if something else comes along you know certainly open-minded to it but I think it is a primitive in my mind you know I think there was a question in the Market at some point of like all right is it only is a vector database a whole new segment in the market or new is that going to replace core databases we view it as a primitive like you know if you want
to manage unstructured data the combination of the ability to index and you know Vector embeddings combined with high quality embedding models that can represent the meaning of that unstructured data is sort of a new primitive just like you know text indexes or B indexes and databases Etc so we view it as a foundational element I I don't see that going away I think the how you create high quality results from that data and how you have high quality vector embeddings or how you augment that with other information there's a whole lot of of evolution happening
there right now and I don't think that's by any means settled I see so the data structure the data storage that's you know vectors and and the way you store them seems pretty sound and the thing that's you know yet to be optimized is how do you go from all these vectors to like ultimately meaning and um yeah and I'm not saying there aren aren't going to be optimizations or room for Innovation and how that can be more efficient more performant more cost- effective there's plenty uh you know always in the database space happening there
so I'm not trying to make a statement that that there's certainly Innovation going on there but I think the more interesting thing is when you're in a world of probabilistic software and um I Heard a really interesting take on this from uh Ben Thompson uh for who writes try where he kind of said in a world of probabilistic software you know the measure of quality is about that kind of Last Mile how do you get to 99.99 X you know sort of quality and so will the domain of sort of quality engineering that we typically
associate with manufacturing kind of apply to software and and that really got me thinking in terms of you're not going to be able to necessarily get a DET terministic result like you would with a traditional application talking to a traditional database so therefore the quality of your embedding models how you construct your rag architectures merge it with the realtime view of what's happening in the transactions in your business that's what's going to get you that high quality retrieval and result and unless it's high quality in a world where it's probabilistic I don't see it going
after mission critical use cases in a conservative Enterprise and that that is a problem space we're very focused on right now how do you think all the innovation in the reasoning model side interplays with what's happening uh in your in your corner of the world yeah I think um in terms of you know whenever there's reasoning memory comes into place long running logic you know I think then when how reasoning plays into more advanced agentic workflows all of that needs State as I kind of mentioned earlier so at a very loose level I think database
are going to be more important to that than just a one shot simple C you know answer engine from an answer from an llm so I think that's the kind of meta Trend as an end user I'm fascinated by these types of reasoning models I mean I am definitely a uh I know this is very not exactly novel in the last couple weeks but Google's uh Gemini deep research and the product experience around that I think is amazing so like I think like there's a lot that can be done there in terms of the user
experiences and the types of use cases that applications can build off of that at least the first wave of of LMS that we saw haven't been able to really Drive in terms of adoption very different direction so one of the things about your background that people uh who are listening might not be aware of is is that you sort of like architected and led the transformation of mongod DB from being a traditional on- Prim enterprise software business to being a cloud native consumption based business um with which is now most of mongodb and um I
think any transformation of that magnitude is really hard to pull off and you guys did it at reasonable scale and of course now the company has you know billions of Revenue scale the reason I'm the reason I'm harping on this a little bit is I think there are probably a lot of Enterprises or even a lot of start startups who are currently faced with a similar challenge where they need to undergo a transformation of their business and yours was an on-prem to CL transformation which not a lot of companies got right the one we're looking
at now is sort of a non AI to AI transformation and so so the question is what made that work for maybe just say a little bit about sure the nature of the transformation what made that work for you guys and do you have any advice for people who are looking at an AI transformation of some sort now yeah I I appreciate you bringing that up and and certainly you know we're very lucky and fortunate that you know we were able to make this pretty Monumental shift in terms of the the business model the product
strategy of the company and and certainly by all means it required a lot of different people doing a lot of different things uh to make that happen but I think one important piece I want to key off is you you're using the word kind of business transformation yeah that is really important because I think for a lot of companies that have tried to drive this type of a transition they just view it as okay this is a new skew a new product that's all I have to worry about but I think you know certainly I
took it as a sort of business transformation is the goal here and therefore we made sure that every functional leader in the organization one understood that they had a really important part of that transformation and we also accountable for working you know to think about in a consumption based Cloud first model how customer success changes how our financial model changes how you can name any single function how did you guys get Buy in in the early days when the thing that generates all the revenue was not this right like how did you get people to
care yeah absolutely so one definitely having strong top down support you know like it was very clear to the company that launching Atlas making this transition was a super critical you know business priority you know there's nothing that that gets around the fact that you need that level of top- down consistency you know that included empowering me as sort of the person to help drive that and so when I went knocking on one of my peers you know doors in a particular function I said hey I really think we need to you know fund some
headcount here to think about the cloud side of the business that you know I had the sort of ability to kind of drive that level of influence but I think what's important about that is we didn't treat it as this sort of separate mini Buu that's isolated from The Core Business we wanted every functional leader to feel like they were part of that transition and it wasn't some competing thing for uh you know and they were going to lose some sort of you know part of the the function they rant so I think that was
a really important thing certainly it meant a lot more you know shuttle diplomacy for me versus direct Authority but that was critical to bring the whole company along for that transition as opposed to it just being a starved new business initiative in a corner which you see sometimes yep uh start to happen certainly you know in terms of the sales organization the revenue functions in particular it took a lot of one just really rolling up sleeves and being a seller meaning being in the early deals learning what's objections are coming up whether that's a product
objection we had to go build on the road map or whether it was just an enablement issue or a positioning or messaging exercise or pricing thing so really really taking a mindset of like all right I'm you know our team the product team launching this is going to be side by side with the sellers and the SAS in every single one of the first deals and I'm going to remember in our smaller New York office at the time I used to make the rounds every you know every evening and be like all right what's happening
with this deal what help do you need like where are we on this what are you hearing and that got a lot of sort of one all right the sales team isn't just being asked by some stranger to do something CU it's important like I was trying to show that I'm with in them in it with them and then certainly you have to drive incentives around it when something's working and people know how to drive Revenue a certain way in any function there's going to be so much inertia around that already because you know the
software business is still a growth business for us so we had to be very intentional about putting spiffs heavy emphasis on enablement inspection and accountability to make sure enough momentum got built in the new business until we could kind of neutralize it because ultimately we're we're about customer choice we don't want to artificially push a customer that's on Prem to the cloud if they're not ready that's largely out of our control but in the beginning we needed the sales team to get a lot of attention on something that they felt was not necessarily the needle
mover until we got a certain level of momentum yeah yeah interesting the lessons I heard for anybody going through an AI transformation is a lot of top- down support which I imagine requires a lot of conviction that this is where the future is going um fully integrated not some project sitting off in a corner getting stared for resources but actually part of the Core Business and holistic transformation it's not a skew it's a wholesale reinvention of the business in a lot of ways right and some of the most important things were not technology decisions it
was you know business model transition it's sales enablement to sell to a different segment of the buyer in the organization different buyer within the organization that we were traditionally so almost every function had to change in pretty fundamental ways and I think sometimes outsized amount of our time went to those things that you wouldn't think were were needed to change that much or that would be easier versus you know what you assume to be the hard part which is how do you deliver a highly reliable Cloud database that's by no means easy yeah but you
know that's the part I think everyone gravitates to but it's all these other things around the different functions that drive the business and making sure all those line up in a in a coherent way that a lot of attention went to I also think one of the analogies to draw on tell me this is just you know I'm off in L La Land but you were you and in our conversations you were really focused on driving the developer experience uh through that period of transition and you know the developer was going to choose uh the
database um for this kind of new new mode of operating um it feels like to me for companies going through the AI transition right now right now it still is developer developer developer to your point developers are choosing AI tools eventually if we have trillions of Agents running around it might be the agent experience that's the thing to to Really prioritize yeah know especially if agents are the ones who are going to be driving a lot of the business logic without necessarily custom development Happening by the organization I could see that I think you know
often times from the outside I get the question of like how did go from Enterprise to plg and I always sort of like winse at that you know I think to me those things are absolutely complimentary and more have to do with where a customer is in their adoption Journey or what style of organization they are whether they're a you know technical founder-led fast-moving startup that doesn't want to necessarily engage with sales in the beginning of their Journey or whether it's a large Enterprise that's never going to show up via a self-service type Channel and
so you know we spent a lot of time thinking about the whole system holistically and trying to map that to how the users and the buyers actually want to engage with us as a company and so I think a lot of that is what has been behind the cloud transition sort of success is not trying to be too philosophical of saying you know credit card C business C you know customers are the right ones and Enterprise sales no way I mean there it's neither or you know both of them have to be cohesively integrated to
reach the global scale of customers that we have at at this stage should we wrap with some uh AI rapid fire questions all right sounds good good okay first one favorite new AI app ah all right I I mentioned that I'm definitely a Gemini deep research fan so that I got that I mentioned I think um that and also perplexity for me they're not new by any definition you know in my mind run counter to the you know okay thin thin AI rappers aren't really sustainable because I see a lot of product craft and I
know Gemini obviously has a deep model you know uh training behind it but just the product craft is what I think is really interesting like the way you know perplexity makes the user experience the design sense for example is really great as an end user so I don't think it's so simple that you know AI models are suddenly going to make software go away I think there's a lot around adoption and understanding your user having great design sense and there'll be a version of that as we go to other interactive modalities as well even if
it isn't visual so I think that's kind of one thing in terms of what's new to me I don't know how new this product is but somebody last week turned me on to uh snipp I'm a uh s snpd I'm a big uh podcast listener okay and it's a great example of an application that I think is woven AI really well through the user experience so um it like subcribed all your podcasts it like Auto summarizes it allows it surfaces up some of the key insights in readable form or in a shortened version allows you
to take kind of notes we need this we've been looking for this okay I just found out about it last week and um I am loving learning how to use it well love it who do you admire most in the world of AI that's a tough one I mean certainly I think some of the just researchers that see the future and are probably have a sense of where things are really going every time I listen to them on this podcast or read you know some of their writing you know I feel like really excited about
the future and you know the typical names there so I think that cohort of of people is always inspirational to me um you know I think it's fun to listen to the large company CEOs kind of uh mudsling a little bit about whether their applications are just systems of record or who's going to win the agent race and all of that so I think you know there's it's interesting to see the Battle of Titans happening in terms of who are going to really be the incumbents that can survive and thrive versus the ones you know
that uh that may not make the transition so without naming names I'd say those are the two most interesting cohorts of of leaders that I tend to to listen to fair enough okay agree or disagree every developer will become an AI uh agree you know I think that traditional machine learning is you know was typically specialized in a centralized ml or data science team and applied to probably a subset of the use cases that could it could potentially add value to what we're seeing though with generative AI being integrated into applications whether it's Greenfield or
to an existing application is it's the average full stack or application developers that are the ones that are responsible for that so you know really democratizing that capability across the organization is something we're trying to do and so if I had to give a simple answer I would agree with it wonderful soah here thank you so much for joining us today I think you have super fun you have really profound uh thesis on how AI is going to change not just databases but software and technology and the way we interact with technology as a whole
and how that ripples uh over to the Daily based market so thank you for taking the time to share your thoughts absolutely thank you and Havey happy to be here and you know we'll see if any of these thoughts actually hold water things are moving so fast awesome thank you [Music] [Music]
