Re-Air: Ringing Out the Old: AI's Role in Redefining Data Teams, Tools, and Business Models

Hey everyone, before we dive in, we wanted to take a moment to thank you for listening and being part of our community. Today we're revisiting one of our most popular episodes in the archives, a conversation full of insights worth hearing again. We hope you enjoy it and remember you can stay up to date with the latest content and subscribe to the show at DatastackShow.com. Hi, I'm Eric Dots and I'm John Wessel. Welcome to the Datastack Show. The Datastack Show is a podcast where we talk about the technical, business, and human challenges involved in data work. Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Before we dig into today's episode, we want to give a huge thanks to our presenting sponsor, Rudder Sack. They give us the equipment and time to do the show, week in, week out, and provide you the valuable content. Rudder Sack provides customer data infrastructure and is used by the world's most innovative companies to collect, transform, and deliver their event data wherever it's needed all in real time. You can learn more at RudderSack.com. Welcome back to the Datastack Show. Today you get me and John talking about data topics. We thought it would be fun for us to just shoot the breeze on a bunch of data stuff. We're going to talk about a number of different things on the show today. We don't have a guest, so I'm just going to say welcome to you. Welcome to you as well, Eric. Thank you. I feel so welcome. Okay. I feel like we're ringing the rag of AI on the show. I actually didn't plan on that joke. I don't know. That seems planned. I was thinking ringing a jive, but ringing, but it worked really well. Yes, thank you. Thank you. Talking about AI, but really we have to because we're living through such a fundamental shift in so many things and it's happening in real time. I feel like when this stuff first started coming out with the first couple iterations of GBT, it was really over rotating on so many podcasts and news articles about like, okay, this is crazy. But it's come so far that it really is, I think, the big topic, right? You know what I've wondered recently? What other thing if we had as much hype about it as AI would progress really fast if billions and billions of dollars got put into it, right? Because part of the success of AI is not like, yeah, there's a lot of advancement there that's cool. Yep. Yep. But it's also the crazy amount of investment from like every major technology company to make it progress. Yep. Yes. I don't know. I don't know the answer to that. Yeah. Yeah, that's an interesting question. It's also interesting to think back on the history of technology, even the conversations around data specifically. And they're just art. It seems like there aren't that many fundamental changes as significant as this. I would agree. And I don't think we've talked about this before. But one of the my original attractions to data was that it wasn't going to change so much, right? So like it was on the list because like you get into tech and you're like, oh, front end, like web frameworks is a joke. It's always like, oh, that changes every like five minutes. There's always a new one. And then it's like, well, databases and SQL, those have been around a long time. Like that's not going to fundamentally change. And there's practical reasons as far as on the front end stuff, like you can change things with like a lot less consequences, typically them back in. And then you get into like the world today or like that might not be true anymore. I love, I mean, I love that generally that you were like, this data is not going to change that much, right? Right. Right. And the pace of change, even outside of AI, is that it's not going to change. Outside of AI is accelerated. Even tooling, right? Like the tooling was like a couple major vendors all using these exact same language. Yep. Yep. One comparison that comes to mind is e-commerce. So the reason that came to mind as an example of the fundamental shift is that it was an entirely new way to do something, right? It fundamentally changed the way that people shopped. Right. That you're talking like we went from in person or a catalog to being able to shop online. To being able to shop online. Right. And I mean, there, I would say there are generally some interesting parallels for any sort of major change like that, right? The pre-existing, like the environment needs to be there, right? And so internet and browsers and other things like that, there was a lot of infrastructure that that proceeded being able to shop online, right? Yep. So there are a number of interesting parallels there, right? Where it's like, okay, you have compute power and you have the advances in the actual large language model technology themselves. You have the transformers, like there are a number of things that were pre-cursors to create the environment in which this could happen. You know what's really interesting around the compute? I had been thinking about this too. So crypto first, big boom lots around that and then the AI stuff. Like I don't actually know enough about the like background here. How much of stuff that like maybe was provisioned or thought of like, yeah, we're going to use this for crypto. I was like, oh, you're not an AI problem instead. Yeah. Yeah, that is interesting because there's this huge boom and we know that it's true from a like power consumption and like, based on stuff of like things. Yeah, that is interesting. Yeah, sure. If some of that is and like we'll continue to ship. Yeah. I mean, crypto still thing obviously, but we'll continue to shift to AI. Yep. But it's, I was thinking about the, my grandparents, my grandmother still alive, she's 97. My grandfather passed away a couple of years ago and they didn't use a computer. Really? They had a computer, but they just, they didn't use it, right? They just, it wasn't part of their day-to-day life. Yeah. And my grandmother shopped a lot on QVC, right? Like call this 800 number. Oh, yeah, okay. Yeah. Well, what's so interesting is that when e-commerce comes about, you have, it's, there are a, it was a very large group of people. I mean, it's kind of crazy to think about this, who didn't really have a personal computer, right? They went to work, they used a computer at work, but like they don't really have a personal computer. And so the very concept of shopping online and not having to use it is a wild concept, right? Yeah. And of course that changed very rapidly, but it feels the same because it's hard to even imagine that you would have an AI agent doing like these operational things. Like it just like, it's, whoa, that's kind of, it's just fundamental, right? There's sort of this fundamental shift and it kind of seems like e-commerce. I don't know. That was the main thing that came to mind. The interesting thing about the AI operational agents was just say like in a personal, like for personal things like, hey, go book a reservation or book traveler, whatever. The interesting thing about that to me is that absolutely exists as a pattern today. It's like a personal assistant or whatever. But from an accessibility standpoint, you just opened up kind of a luxury service, right? Like not like, like not everybody's going to like be able to afford a one to use a travel agent or not everybody's going to have like a personal shopper or whatever. But you've got this like what's historically been this like kind of luxury thing that if the AI gets there to be able to do those things, like you open it up for a ton of people. So that's an interesting like space thing versus like opening up something that's more like mundane that like, yeah, we used to like do this main line out. It's more automated. Like that's one thing, but taking something that like people valued highly enough to pay a lot of money for it in commoditizing. Yeah. I think one of the really fascinating things about this technological shift is how many things it's impacting. Simultaneously. Simultaneously, which is in, I mean, completely unrelated to probably an overstatement, but let's just say completely unrelated spaces. So for example, with e-commerce, again, it changes the way that you shop and I mean, there are a lot of things, right? The way that payments and I mean, there is so much that grew sort of out of that, right? But if you think about the example that you just gave of, okay, I can use AI to book a reservation at a restaurant, it's also fundamentally changing the way that we think about developing software, right? Yeah. It's fundamentally changing the way that people are even thinking about finding information generally, right? Which is, which has an impact on the way that you even think about searching the internet at all, right? And I mean, it's changing the form factor there to some extent. Right. And so you have all these different areas where it's having sort of disruptive impact. Yeah. We have that conversation the other day of like like Google it, right? It's like we say that all the time, but you were like her Black City, yeah? Like Black City? AI, their Black City. Just like fun of little things like that. I don't know what the verbal is there, but there will be one. Yeah. Specifically with data, I had this horrifying, honestly horrifying thought the other day. We were talking about this before the show. And it's like, all right, you take all this stuff out five years or 10 years ago. or however many years, like what do companies look like? One of my horrifying thoughts was that it might really drastically change a bunch of roles, which, okay, a lot of people think that, but it might turn into essentially most companies have sales people that sell things to other people, and then like operations people that operate. And like, and beyond that, like, of course, there's still gonna be some levels of specialization and you probably still have some kind of finance accounting things, some kind of things. But I think a lot of companies will consolidate down into less divisions, and we were talking about SaaS specifically. I mean, the dream, like me being from like a technical background, the dream is like, yeah, like we're gonna put that landing page out there, drive some end down traffic to it, people put the credit card in to stripe, but I've got a SaaS product and we're gonna scale it and grow it, and I'll have to interact with people. Like because of the fundamental change of like this barrier to entry, I think probably continuing to lower into being able to create a SaaS product, I just think it's gonna fundamentally change the growth trajectory of most of them, and it's gonna be sure you'll still have the viral stuff and you'll have like influencer driven stuff. I think the other part is like, well, you're gonna have to sell a lot. Like probably have a lot more people selling, which if you're a founder, somebody that's sat in the course, like, yeah, this is terrible. Like I don't know, I don't like this future. So I think it's a real like disability, I think it's already happening. - Okay, let's, can we explore this topic by digging into this dystopian future? And by the way, I want royalties if this turns out if we went to a book that you read. - Perfect. - A book that you use AI to write. - Right. - But, okay, let's dig into this dystopian future. So I'm thinking about our listeners who are very similar to you, they are managing data at company XYZ, right? Which is roles that you've had, okay. In this dystopian future, let's actually make it specific. Okay, so when you were running data at the large e-commerce company, rough team structure, like rough team structure, how did your team look like? - Okay, yeah. So, analysts, couple of analyst type roles. - Yep. - Several engineer type roles and some specialization in that, like one that's more focused on like pipelines and integrations, one that's more focused on like front end stuff. - Yep. - Some, a team we had like an offshore group that we use for like certain parts of the tech stack, what else. Yeah, so that was the basic like tech side of the house. What am I trying to get missing something? - No. - But yeah, I think that was the basic tech side of the house. And then like on the digital side, marketing, paid advertising, agency. - Right, because you managed all that side. - Yeah, you managed that side of it as well. - Yeah, it's a part of it. - So yeah. - Okay, you manage both of those teams. Okay, so, which is not very common, right? - That's not very common, but it's actually interesting because it'll make the dystopian future spicier. - Right. - Okay. - In this dystopian future, what is, well, and you have the sales team that's, - Yeah. - That's taking calls from customers who wanna order 10,000 of this particular part that you were selling through the, that you were selling. - Yeah, right, yeah. - Okay, in the dystopian future, let's start with the data team. What does that actually look like, right? And let's just say, let's just say, for example, because we need a protagonist that you're still a human in this equation, right? As the leader of data. What other humans are there and what do they do and what's been replaced by AI? - So I, one, I think there will be more merging of like, like in this particular thing, there's like maybe a digital ops type thing and then maybe the, because we did like, warehousing and actual physical, because maybe there's like physical ops. So there could be divisions, but I think it's like this digital ops thing. And you, so you, do you, someone reports to you, their title is digital ops something. - Yeah, digital ops, let's say director of digital ops. - Yeah, yeah. - It's a very flat company. - That's just point. - Yeah, right, but director of digital ops, and you essentially like, have what people that used to be in like marketing type roles, like rolling up to that, you essentially have people that used to be in like more specialized technical roles. And in that director of digital ops, like maybe should be manager of digital ops, because like it might only be a couple people, like one person that's like a little bit more on the like marketing creative side, that's like managing an army of like AI, more creative type things. And maybe some like, some outside help as well on areas that still require like, specialization or deeper knowledge. - Yep. - And then somebody that's a little bit more technical leaning that's doing like data movement and like, transformations of data and things like that. But I think the spread of like, hey, like right now, like the graphic designer could never do the database administrator's job. I think your spread is gonna be a lot tighter. You're gonna have, you're gonna have the ability to have people that are way more down the middle, that like, yeah, like this person is better, they're more creative, they're more the marketing side, this person is a little better on the like technical side, but it's gonna be way less extreme than it is. - Yep. - This is what I suspect. - So, I wanna know a couple of things, I wanna take a practical look at a couple of things in the Sassupian future, just as far as like, managing data and the data stack itself. - Okay. - And let's just say, okay, also do you have an analyst? - And that's what I'm saying. - So you have the director of digital ops, you have a creative person. - Right. - A creative person. - And then just a generic like, technical person. And then a generic technical person. So there's essentially a team of three, the digital ops team. - Right. - Potentially, yeah. - There's a lot of things that we have to go right or wrong. I'm not sure what for this to happen, but yeah, potentially. - Okay. And what was the team size previously just across? - Yeah, order of magnitude is more like, so we're at three, I don't know, call it like 18. - Wow. - Okay. - Yep. - So that's significant. - Right. - Okay, and each level of, and it will drastically depend on I think on what you're doing. And it will depend on how you want to architect the business. I think there'll be a lot of businesses that you can architect like, hey, we want to optimize for at least people possible. - Yeah. - For people opt for that model. Others will opt for like, hey, human touch, like this is a really big part for us. - Yep. - We're gonna optimize for another model. - Yeah, yeah. I agree with that. I actually think it, I mean, the new, there are already entirely new business models and like ways to think about operating a company. - Yeah, we talked to a founder recently, like single, one person founding a SaaS company, doing extremely well, doing all of it. - Yeah, Mike Drogallis, just a traffic. - Yeah, shout out to him. - Yeah, yeah. - And then can probably scale quite a while. - Yeah, yeah, just him. - Okay. - On the practical things, so you need to set up a new data pipeline. So you get into work on Monday. Okay, there's a meeting with, I guess a small number of people. Everyone fits in a company. - Yeah. - Whatever, okay. You need to create a data pipeline to do something. - Okay. - A feed of inventory to some system to do something, right? Let's just say update inventory in real time or something of that nature, right? So a new pipeline needs to be deployed. What does that process look like? So you leave this executive meeting because it's an AI world, like the notes and action items are already materialized for all these people. And so you set up a quick meeting with your digital ops team who does what and like what does that process look like and kind of how are they in this dystopian future creating a pipeline, all that stuff? - Yeah, so I think you're going to buy tools of like, hey, this is, we have an integration to only buy like a tool. So I think that's like number one. They'll have a lot of abilities for inputs and outputs of various formats. - Yep. - And probably storage component or work with this storage component you want. And then on this like this technical person, I think that would be the person is essentially going to go, okay, vendor A, like show me your docs, what are your specs? Okay, it's the origin and then destination like vendor B or think like show me your docs, show me your specs. And then like, all right, we use X, like middle layer tool and essentially like feeds all three of those with a little bit of guidance to some kind of like AI type tool or maybe that will get built into the integration layer eventually and says, okay, like go build this thing. - Yep. - And I do not think it will be perfect the first time for a long time. But I do think somebody slightly technical will be able to coach it through a few quick iterations and get to something that like is pretty good. And then it's going to depend on like, like how important is this? Like should we have some kind of code review step? Can AI do the code review? - I think that is, right now that's a really tricky step because you can get pretty far with this like vibe coding concept but it doesn't make sense. And I wouldn't become, well, I think most people are not comfortable for like true production use of a lot of this stuff. But I mean, actually there's a business model out there already. I think it's pullrequest.com where you just like have a third party review all of your pull request. So you can imagine like something like that integrated into your system and you've got one person kind of coaching AI like docs, docs, integration and then you send it off to pullrequest.com. They review it, they happen to have a specialist as an expert in whatever tool that you're using to integrate. And they're like, all right, it looks good. And they use a human maybe or AI in the loop with a human. So I don't think that's now, but I don't think that's forever from now. - I agree. - Yeah. - I totally agree. - So you need to generate analytics based on this new pipeline that you set up. Same basic flow. - Like the. - Okay, yeah. So we got all the data flowing, and we wanted some insights on the orders we have flowing through the scene. - Yeah, yeah, yeah. - Or something. I think so. I think it would start with some kind of like, okay, what is X executive wanna see, or whoever wanna see, and then there's probably, again, somebody that's responsible for feeding that into the system, and then like coaching it through a couple iterations to get something like that they know is the right thing. - Yep. - And then saving it off for the executive to look at, and then theoretically, the executive has an easy way to tweak it a little bit more if they want to. - Yep, yep. - Okay, what we're kind of getting at here, one of the interesting threads, thank you for giving me a glimpse into your dystopian future. I think I do think there's another version of this that also is likely to happen, where this stuff is, where we have lots of horror stories that come out of where like AI really screw stuff up, we've got, and this will happen, it just depends on like at what rate, and who's to blame, of security breaches because people are just like slinging code, and of things going horribly wrong. And companies will probably react to that and like pull way back, at least first on the industries, maybe everyone, and that would definitely drastically decrease, I think adoption, and depending just how rocky that gets, I could definitely see another version of this, where like the future is actually five years when I was not that different, but the reason I think that is less likely to happen than I would have believed previously, is how much money is tied up and all the major tech companies for this to succeed. - Yeah. - And if Microsoft pushes it, it tends to happen, if film, the blank was mother, like, "Company, things just tend to happen." Because that's who the big companies trust, with like, what should we do with their tech, and that'll work. - Right, right, right. Fascinating point. So what area is, that's a great, that's actually where I wanted to go next, is which areas do you see in data AI having the most impact, and where is it gonna have the least impact, and I mean, like a couple specific examples of that, right? Like one of the things we talked about was producing the analytics around this, right? And so you don't need a team of analysts anymore. AI is, it's going to get to a point where it can generate, could sequel, whatever, right? That sort of concept, right? That it's gonna have a huge impact there, but there may be other areas. - Probably still have analysts depending on what it is, but they'll actually be analyzing. - Yeah. - So those analysts don't actually analyze anything. - Yeah, so I do think, yeah. - It's true, right? They clean data, they move data around, they copy data. - Yeah, so I actually think you probably still have analysts in some form or fashion. It's either combined with another job, 'cause it doesn't need to be a full time job, or to your point, or to what I was saying earlier, like they actually start analyzing data, versus just moving it around. - Yep. And what do you think, what are examples that come to mind of things that won't change? Things that won't change? I mean, what's gonna be largely the same in five years? I mean, I know the actual answer. I largely the same in five years from now. There's one glaring storage. Essentially, we will probably still use similar commodity storage for data. - Yep. - And there'll be a lot of noise happening above the storage, but I don't think the storage changes fundamentally. - Yeah, I agree. - This spreadsheet. - Oh, sure. - Well, he has to die. - This spreadsheet will probably never die. But I also think that-- - I think the one thing that AI can't kill. - Yeah, but I also think since essentially S3 or S3 equivalent is behind every one of these things, still, that probably doesn't mean that. - Yeah, I agree with that. - But on the user-facing side, yeah. Some version of a spreadsheet, highly, highly bathed out will still be around. And that's like a, is it Lindy principle? There's a principle around, especially how long something's been around, drastically impacts how long it will be around the picture. - Okay, we'll look that up and put it in the show notes. My laptop, the way we're sitting, it's too far away from you. - For you to reach, yeah. - It's too far away to Google that live, to perplex it live. - Yeah, perplexing. - So one of the big things that I think is gonna be really interesting as we think about where AI is gonna have impact and where it's not, is the dynamic of, how do I wanna frame this? Essentially being an interface to all sorts of other platforms and tools, right, which is really interesting. So the, here's an extreme example, right? How often, if you could essentially manage your infrastructure, let's just say a flake or whatever it is. If you could just manage all of that through AI in the same way that you talked about, right? Okay, here's some documentation, here's whatever. Like just go do this thing, right? How much are you logging in to snowflake? - Sure. - How much are you logging in, logging into these platforms? - Right. - It's just interesting to think about the interface for that changing, right? - Yeah, whereas essentially to all these platforms, we used to like directly interface with essentially like you'd never look at anymore, or you only look at if there's a problem. - Right, right. - Well, yeah, it essentially becomes the platform's utility becomes troubleshooting and other things like that. I mean, visibility, observability, those sorts of things. - Right, right. - But that's really interesting. I mean, one of the ways this is already having an impact is like general decreases in website traffic in certain areas. I was talking with a friend who works in that in and around that industry. And it's like, okay, it's like, oh wow. Like the actual inquiries that people are making, I think are dramatically increasing. It's just that some of that is shifting over to GBT, right? Instead of going into Google and then going to a website, right? It's just that it's delivering, it's delivering that end user visit directly to you. - Right. - Right. - Well, I mean, I think it's fascinating 'cause we've talked recently just in the customer data space and attribution specifically, like a tool like a Rutter stack, doing server-side attribution and looking through like kind of raw data around that, seeing open AI and seeing like these other tools pop up in the attribution is really interesting. - Yeah. - Like, and I'm actually seeing that happen, especially in like, in SAP. - Totally. - And of like, wow, like there's a decent amount of traffic being driven from these A-actuals. - Yeah, yeah, that's super interesting. - I do wonder though what the impact of the trust factor is gonna be, right? Especially when you think about things like production pipelines. - Yeah. - Where, I mean, it's just hard to trust, right? Like you just, it's really hard to trust. As opposed to writing SQL, right? Which is sort of exploratory by nature in many cases and iterative and-- - And I think that's the thing, right? If it's, if you can get to a result that's like abundantly clear if it works to a human, AI is actually really great for that. And I found like, we talked about this too, like visual like front end stuff. Oh, like it's pretty cool for that. Like when you're working on a website, tweaking visual front end stuff because it's more evident that like, oh cool, it's in the right place, it's the right size, it looks good. Now like you can still have some bad stuff going on in the count console, you can still have a security problem. Like there's things that can happen. But I do think it can be less evident like further down the stack of like, oh, like we made a major problem with that. And it's an edge case, yes. We'll find eventually, but no idea when. - Yep. - And then the question becomes like, well, that's true of humans too, right? And we make mistakes that show up later to buy this. And the question becomes, is it more frequent with AI? Is it, could we use the AI to try to catch those? - Yeah. - And a separate tool that doesn't have the knowledge of the original tool, just like you wrote with humans, like a double-blind like audit. Yeah, super interesting. We're gonna take a quick break from the episode to talk about our sponsor, RutterSack. Now I could say a bunch of nice things as if I found a fancy new tool, but John has been implementing RutterSack for over half a decade. John, you work with customer event data every day and you know how hard it can be to make sure that data is clean and then to stream it everywhere it needs to go. - Yeah, Eric, as you know, customer data can get messy. And if you've ever seen a tag manager, you know how messy it can get. So RutterSack has really been one of my team's secret weapons. We can collect and standardize data from anywhere, web, bobble, even server side, and then send it to our downstream tools. - Now rumor has it that you have implemented the longest running production instance of RutterSack at six years in going. - Yes, I can confirm that. And one of the reasons we picked RutterSack was that it does not store the data and we can live stream data to our downstream tools. - One of the things about the implementation that has been so common over all the years and with so many RutterSack customers is that it wasn't a wholesale replacement of your stack. It fit right into your existing tool set. - Yeah, and even with technical tools, Eric, things like Kafka or Pub/Sub, but you don't have to have all that complicated customer data infrastructure. - Well, if you need to stream clean customer data to your entire stack, including your data infrastructure tools, Head over to Rutter Sack. to learn more. Okay, well, continuing on with the discussion about AI, Snowflake made a big acquisition, dataVolo. Is that how you pronounce it? - DataVolo. - They announced it late last year and it seems like they're starting to kind of roll out and have customers using it more in this first quarter, going into second. - Yep. - What, okay, industry-wise, we'll just do industry-pundent, we'll do an industry-pundent segment. What's the move by Snowflake there? What's the, how are you reading it? - Yeah, I think. - What is also, I guess, - Yeah, we've been established, like, what does DataVolo actually do? - I'm not deep in the tool, but just from like reading about it some, it is one of these data pipeline tools that is kind of marketed toward, and it may be specifically like adapted toward people that want to pull data and then do AI things with it. - Yeah, at a really high level. So the specialization is unstructured data. - Right. - So you can, yeah, you can move data that is in various different formats that you would want to run, AI workloads over, which is different than a typical, - Yeah, then a typical like ETL tool, for example. - Right, exactly, exactly. - It is interesting, 'cause I've seen, there's a number of tools in the space, and then there's some interesting ones like, Altrix is one, there's several others that have been around a while. This Altrix is like more of a graphical, like you can, and they've got all these little modules. - Yep. - I'm not. I haven't super-capped up with what they're doing, but I believe they're getting into the AI piece as well, and it's interesting with that space where there's gonna be these all like, all in one tools of like data, AI use case, great, ETL, great, like whatever you wanna do. - Yep. - Like a graphical, more of a graphical interface. Then I think there's these specialists, more specialized tools, like, hey, I wanna move customer data around. I wanna move, like I said, AI data around, I want to move structure data around. And so it's like, there's two axes, there's like the no-code, low-code versus like, as-code, like movement, like, hey, I want drag and drop, I don't wanna do any code or hey. I don't wanna interface at all, I wanna all to be like, yeah, I'm all, or something. - Yep. - So those two axes, and then there's a specialization in it. Access as well as far as like, really attuned to like the customer data movement problem, we really know that. Like, use case versus like, we're a generalist tool, like, we'll move data wherever you want it. - Yep. - To be interesting to see what happens there. - Yeah, yeah, yeah, it really will be interesting. I mean, it is going to be fascinating to see how, because the cost is decreasing as well. And if we go back to our previous example, like an interface on top of this tool, and you think about Snowflake, and actually, interesting, we were talking, who is that talking about someone? They were running Neo4j inside of Snowflake, which was really cool. A graph database. We were talking about some identity resolution, identity resolution type use case. Anyways, they were talking about, run, it's super cool, right? You just, you can run Neo4j inside of Snowflake, and actually you can sort of like, push stuff out as tables reviews, a couple other things. I don't know all the specifics, but just like, whoa, that's cool. But you think about, I heard that, and then you think about like, data volo doing unstructured data for AI workloads, et cetera. And it's, which this is clearly like Snowflake's long term intent is, okay, what do you want to do? - Right, right, yeah. - What do you want to do? - You want to, do you need to, do you mention customer data, right? Do you want to build an identity graph? Do you want to do something, do something with generative AI over this or whatever, right? And it's got the pipelines, it's got the query engines, like all that sort of stuff. It's pretty fascinating. - Yeah, yeah, for sure. And this actually reminds me of, you're familiar with thinking fast and slow, the book. I haven't read it. - You haven't read it? - It's a good read. - And I am probably gonna butcher this, but it got me thinking about, there's this concept that he talks about the book, not specific to this book, but regression to the meme, right? So it's essentially you've got outliers and there's this principle of things regressed for the middle. The example he gives in the book, which I think is fascinating, is it talks about coaching. So you're coaching you up, so you're a baseball player and you strike out and you've got a guy on third base and like, "Oh, I was like, come on Eric, "and the coach yelled at you, right?" - Yeah. - And you go up and next time you play better, it's like, "Oh, that must have worked." Opps, it's pretty good. Like, you go up, you hit a home run, it's like, "Oh, good job, Eric." And the next time you do worse. So you're the coach, you're like, "Oh, I need to be hard on Eric." He does better every time. But the actual principle here is you were regressed to the meme. So you hit a home run and then odds are you're gonna do worse next time. You struck out odds are, or whatever, struck out two times. - Right, right, right, right, right. - Yeah, yeah. - This is an interesting thing. - You're going to, you're going to, you're going to eventually reach your batting average. - Yes, exactly. - Yeah, exactly, exactly. So, like, the way it relates to like this and AI that I've been thinking about is, I think there's gonna be a stronger pull to the meme if people are using AI tools. Because if you're thinking about this, like, AGI, concept and stuff of like, and this is kind of a pushback on like, the generalist concept that we like launched off with, there is, I think, going to be this, like, these unique scenarios where like, there's such a pull to the meme of like, "Oh, we should solve this in this one common way." Where people are going to be like, "Yeah." Like, no, actually, there's this like, novel way that like is much better. It's not pulled to the meme. And that's where I think a lot of the engineers, like really good engineers are going to gravitate toward those problems where it's like, like, ETL. Of like, "Oh, okay, cool. 98% of the time, 90% of the time." Like, "Yeah, use this generic ETL tool, it's the right tool." And there's gonna be a stronger pull there where that could have been 60% of the time before maybe it becomes 80% or 90% of the time. - Yep. - But the last 15 or 20% I think will exist for a long time. And then engineers will work on those like, really interesting problems. Because they, I don't think that goes away completely. And I don't think that strong pull to the meme like why you might get to 80% or some high percentage. It's gonna go to 100%. - Yeah. I need to borrow that book from you. - Yeah. - It's a good read. It's just got a lot of, I should read it again. It's got a lot packed into it. - Okay, let's dig into that, I wanna dig into that topic a little bit more in terms of specialization and generalization. Right, so if you, okay, there's kind of a, there's kind of an accepted narrative, right? And we'll take snowflake for example. We'll praise them and then we'll pick on them a little bit here, right? Okay, they, and they've acquired a number of companies, right? I mean, they've been very acquisitive which makes sense. Streamlit, data below, a number of other companies, right? And so, and which makes total sense, right? Because they're clearly building towards the scenario that we talked about, right? Which is like, what do you wanna build? I mean, you can do whatever you want. - Right, right. - Like stream, right? Do you set up a real-time with streamlit? Do, you know, whatever, AI stuff. - And it just becomes this like really big platform to like accept to build something. - Right, and so the narrative that sort of generally accepted is as that happens though, that it becomes a big platform, right? - Right, right. - That you can do anything in and that's actually part of the problem and what creates the opportunity for a smaller specialized company to disrupt. And so in the world of data, like, I mean, it clearly, the storage aspect and the things that we've talked about with Snowflake and with Databricks and there's consolidation there because they wanna be like large cloud platforms, right? - Right. - What are the other tools that you think are gonna get generalized like that? - They end the data space. - Yeah, yeah. - I don't know, I mean, I mean, Microsoft is already from a marketing perspective approaches it like, hey, Microsoft Fabric and then like, that's the marketing thing of this one thing of all these components to it. In reality, like, it's essentially just a bunch of different components that they brand it as fabric. But I think that happens for Databricks for Snowflake for others, where it's like cool, like, data stuff, like do it in our platform, you can do it all. You can have the Viz layer, you can have the pipelines, you can have the storage, you can have the AI, LLM, built into it, you can do all the things. And the question in my mind is, does those companies being able to use AI internally change the equation where it used to be like, oh, well, yeah, that happens. You become a generalist as a company, you grow big, great. But then opens up a bunch of doors for specialization to do X, piece better. - Yep. - Is that still true when these companies have these sophisticated AI models where maybe they can juggle more? - I think probably, yeah, it is. Because guess who else has the AI models and so more technology, the innovator has the same thing, right? - Right. - I don't know that's a better advantage. - Right. - But that's a great point, right? That's not actually a competitive advantage. - Right. Which that comes up all the time with people in security, like, oh, like all these thieves are gonna have all this AI and it's gonna be terrible, it's like, yeah, sure. But so will all the security companies. - Right, right. - So it's kind of like, there's equal force both directions. - Yep. - But other than those companies, like, consolidation, for good or for bad, and actually it would probably a little bit more for bad, I do think it ends up, you end up picking more mainstream winners where you have a digger gap between like, the mainstream winner for the most use cases. - Yep. - It's like down the middle and that's like a really big, like chunk of the market. And then you still have like the, like I was saying, like the people that really nail like a specific. painful problem on the sides, but I do think it probably makes that gap bigger where it's like down the middle like CRM, for example, so where you're in CRM. Salesforce does not have 90% of the CRM market. I don't remember the number of it, it's low. Interesting. I think it's below half. It's really low. I might be wrong about that. It might be like a little bit, but it's not like 90%. Interesting. One of the books, I'm going to reach over to my computer and Google this. One of the most important books is, you've got somebody like HubSpot. Second is industry-specific CRMs that pop up. Third is a number of companies that don't really use CRMs, though. I am. Proplexifying it? I'm not. I actually should use Proplexity. Sorry, I just use Raycast and it defaulted to the D40. But look at Raycast is doing a really nice. Very rare. Very cool. 21.7 to 21.8. 21.8. 21.8. No. Yeah. It's tiny. Wow. Less than a fourth. Wow. I am processing this. Yeah. Less than a fourth of the market. That seems so crazy. Yeah. Now, the real question will be, though, I say there's going to be more like regression to the mean and more like that middle lane, maybe it gets less congested, but it could just get more competitive and not necessarily combined in like one product. Because you could still have a three or four major people that are in that middle lane that are essentially the same, but they're still competitors and they still have fine. And they're selling different ways and people just prefer one over the other. Yeah. I mean, think about clothing brands. That's right. We'll just close and we've got a ton of those or car like same with cars, right? It could become more like a car shopping experience of like does it have four wheels? Yes. Does it have four doors? Can it track? Yeah. Like they're all. And car people are going to be super like, no, they're not. But like from a transportation standpoint, like they're all pretty much the same. But technology could become more of a car brand thing of like, yeah. Well, like you got four options like down the middle in this like 89% lane. Yeah. Then you just go with your preference. Yeah. Essentially. Yeah. It is, you know, what's interesting to think about? This was very early on in the show, but Seattle Data Guide Ben. Yeah. He's been on the show multiple times. I don't even remember the. He's been on the show a couple times. I don't remember this specific episode. But we're going to ask him, I mean, he was at meta. Yeah. Right. Right. Doing data stuff. And then he does consulting in different projects or whatever. Right. And so we kind of ask him, okay, what is like, what's your go-to tool set? This is several years ago. I think this is like early in the life of the show. Like what's your go-to tools? Like what do you use? If you're building a data stack like blah, blah, blah. And he mentioned that he mentioned Snowflake. He's like, I. He's like, there's obviously if you're building out a data stack, you have to have a data store or blah, blah. Okay. He's like, so you get a data warehouse for data warehouse stuff. And he's like, I like Snowflake. And he kind of paused. He said, yeah, he's like, I like Snowflake just because I like it. And he's like, there's just something about it. Like I just. I like it. I think it's already starting. We're like a lot of these. There's definitely not future parity. So I'm not saying that between all of them. Right. Right. But I think there will continue to be closer to future parity. Yeah. And I really think it's going to be more of a car thing. Yeah. Which they'll be definitely differences. Like as there are with cars about like, I want to optimize for this use case. I like off-roading or I don't know. I want good highway miles. Like there's obviously going to be that. But it's going to be like a really strong like I'm on this team. I think it'd be more of that. Yeah. Yeah. For sure. For sure. I mean, I think the other thing that's going to be really interesting circling back to progression to the mean, the. If we think about different data tools, right. And you mentioned like Altrix. There are a couple of up-of-date. Yeah. There's a couple in that. Tools that have been around for like a long time. Yeah. Howland is a good length but I don't know how long time. Anyways, one of the things that modern companies, a common tool in their toolkit in terms of creating competitive advantage from themselves from giant incumbents, is a dramatically better user experience. Yeah. Okay. And I mean, one. I actually think 5Train is a great example of this. I mean, they just have a phenomenal. It's just so easy to use, so easy to set up. It's great. Right. Right. It's just as great. Like compared to a lot of other tools. And we sort of end up paying a money because you're just like, this is just a great tool. Yeah. And the data space, another classic example is linear project management space. It's just like, okay, wow. I mean, that's not the only thing that's not the thing that made 5Train successful or that they made linear successful. But it was a big part of it and sort of reflects like the DNA of the company. But what's so interesting is it's getting so much easier if you think about these different data tools to deliver an absolutely phenomenal user experience. Right. Which is super interesting. Right. And I'm really fascinated because you've got, I think you're going to get a stronger and harder split between audiences here of like, because for data tools for me, like I'm gravitating really heavily toward filling the blank as code, BIS code, data pipeline. Well, that's because the productivity increases drastically. And it will continue to increase. I think with AI tools, because AI tools are good at text. Yeah. If you've, there's some neat stuff out there with like, like, GB, chat, GBT's operator thing where it can browse the web and stuff. But that is nowhere near where it is with text. Yeah. It knows that. But from a human perspective, humans are like, no, I've yet, and maybe this day is coming. I've yet to say like, man, this product is just like a killer user experience. It's just so ergonomic. And it's like, all command line. Like, like that, I've never had that feeling. Maybe we'll get there. But see, yeah, that'll be a really interesting path of like, how do you handle that? Or is somebody going to be able to really nail like the as code piece and then also just build like a beautiful like interface and you can seamlessly switch back and forth. Seems like an impossible, but way more work than just the only one or the other. Yeah, for sure. Now, this is a really interesting point. It's, I kind of think about postman as an interesting example there. Look, because you can do a bunch of different stuff in a command line. There's so many niceties that they provide for doing all sorts of different. Yeah. Yeah. Like graphical organizations. Right. Right. Right. Yeah. And I think we'll be more of that. Yeah. And I think that's when you can switch into like, YAML mode and like, quick stuff and like, switch back to like, yeah, yeah. That's a good example. Totally. Yeah. Yeah. That is, yeah. It's super interesting. All right. Any last AI thoughts before we turn off the recording? I don't know. I think in conclusion, I really am torn between like, does the future look like that generalist future we talked about? Or does it look like that like regression to the mean where there's like the x percent that is like generalist, but there's like a ton of stuff on the edges where you actually get more hyper specialized because like the general problems are solved like technical really gravitating hard toward the edges. I think that's a real possibility too. Yeah. And they're not necessarily mutually exclusive. Sure. What's okay. Last question. We'll both answer this. I feel like I did kind of interview you this. Yeah. Yeah. Old habits die hard, I guess. It was supposed to be like whatever. Yeah. Back and forth. What's the craziest thing you've done with AI lately? Or like the thing that's sort of you're like, whoa, that was crazy. Yeah. I think front and stuff like messing with like, hey, here's a landing page like, really a new agreeable data website. Yeah. That's true. Yeah. Launch that. Definitely use that on some of the front end layouts. But just yeah, like this general like very vague because I'm not like I'm not a designer by any stretch of the imagination of like, hey, make this landing page look good. Like very vague language. Yeah. And it like and then like being pretty surprised. But look at outcome. Yep. Yep. Super interesting. Yeah. Yeah. My turn. The I think the coolest. Oh, this is basically building prototypes, which we do like an immense amount of different things at that. But today actually I did something new. So there's a tool out there that I was looking at like, oh, I wonder if I should use this tool. If we should get this tool to use like in the Rutter stack in Rutter Sacks platform. Yeah. Just sort of a priority feature. Right. Right. And so I mean, it's like a it's a component and it's a set of APIs, etc. Right. And so I thought, okay, like I go create a test account for this thing. That's great. I get the API key. And I was like, you know what I'm going to do actually as I'm just going to spin up like a dummy thing and I'm going to actually try to install this like and try to install this and actually kind of see see what it's like to use this thing and see how it actually works on the back end and see the whereas before you have to like get a call with engineering like like, hey, let's do it totally like to isolated environment. Right. And I'm not a software engineer. Right. I mean, I'm not a software engineer. I know enough to like make great problems for others. For others, right? Yeah. Okay. But this is what's astounding. And this to me was just we I think we're we were talking about this the other day. So I create an account with this thing. I get the API key. I just hop over into Versel, which we use Versel. We've used a number of different tools, but we've deployed a number of different things on the Versel platform. And so we have an account, and you can add VZR to the account. And it does a number of nice things. - Which VZR is like their AI agent, - Yes, yeah. - for generating apps, software, websites, all that sort of stuff. - Absolutely astounding, by the way, if you haven't, it is a really cool tool. Okay, but this is what was so wild. This to me, I was like, this is amazing. I go in there and I create new project in Versel. And I was like, okay, I'm just gonna grab any, they have templates, right? And so it's like, I'm gonna grab something, or I'll just create something it doesn't have. - Yeah, right. - So I go to create a new project. It's like, oh, there's a template thing. And I was like, I wonder what templates in there. So I go and look, it's like this product that I had signed up for has a starter kit. - Okay. - Right. - And it's just a fully functioning next JS app. - Okay, right. - And so I was like, oh, that's great. So I grab that, I create a Git repo from Versel, 'cause my account is connected. - Right. - It creates a Git repo for me. It does everything, right? And then the thing that I had to do to get it running locally was create a.env. - Right, yeah, just feeling the environment. - Literally the environment variable to get it running locally. I pull the repo and using cursor, I'm literally like, I'm using this tool, right? And I'm seeing how the API works. And I'm seeing like, I mean, it was just totally astounding to actually go through with that. Right? And then I can push it and Versel will deploy it. And I can share it with people on the team and like have a discussion about it. - Right. - And everything's fully transparent. And we can sort of see how this thing works. And just to me felt that is a product demo. I mean, holy cow. - 'Cause we talked about this too of like, there's my last take, or last hot take on this. There is this future where like, everybody in software now is selling generic things. - Yeah. - For people to use their imagination to implement in their company. - Yep. - I think there's a future where like, one of the major human value ads is like, hey, we looked up what your company does. We imagine for you what it can do. And here's a demo of it like exactly what it would do for your company. - Yep. - That's huge. - Total. - That is really big. - Totally. - Yeah. - And it's wild. - All right. We're at the buzzer. - Thanks for joining us. - It's a good one, John. - All right. We'll catch you next time. - You guys got it. - We haven't. - The data sac show is brought to you by Rutterstack. The We're House native customer data platform. Rutterstack has purpose built to help data teams turn customer data into competitive advantage. 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