The Data Stack Show - 234: The Cynical Data Guy on AI, Data Tools, and the Future of Coding

Episode Date: March 26, 2025

Highlights from this week’s conversation include:AI in Transcription Services (1:11)The Future of AI Companies (5:09)Potential Risks of AI Tools (8:57)Learning vs. Dependency in Programming (10:17)The Journey of a Data Analyst (12:07)AI and Coding Skills (14:06)Abstraction in Data Tools (16:59)Data Design and AI (19:07)User Experience vs. AI Automation (22:10)AGI and Data Mesh (24:36)Blank Screen Interaction Challenges (27:10)Understanding User Value in Data Platforms (32:22)AI's Role in Simplifying Data Interaction (34:04)Final Thought and Takeaways (35:05)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Hi, I'm Eric Dotz. And I'm Jon Wessel. Welcome to the Data Stack Show. The Data Stack 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. Welcome back to the Data Sack Show. We have our favorite monthly installment

where we go deep into the bowels of corporate data America and get some hot takes from your favorite cynical data guy. Matt, welcome back. Yeah, I'm back. Okay, this is going to be, we're just gonna talk about AI the entire episode. I was told there would be no AI in the episode. Is that different than other episodes?

Yes. I was told we would have AI enabled features by the end of the quarter. So for the podcast. For the podcast. We the end of the quarter. Yeah. For the podcasts. For the podcasts. We don't have to do all this work. AI just does the talking. I mean, interestingly enough, actually, all the transcription stuff and whatever, it's

actually been AI for a long time. Well, there's a startup. Did you see there's a startup competing with 11 labs that allegedly can take like a 15-second voice sample now and we can generate essentially. I tried this recently. Yeah? I tried this recently, actually. I generated a bunch of research using deep research, which was outstanding, by the way.

It was really... Open-ended. Yeah, it was really good. The ergonomics are a little bit weird because it's so much text, which is like the point in the chat just, it gets really unruly around that. But the content was really amazing. And I, it was to the point where I, well, first of all, in the mobile app, you can have it read you a response.

Yeah. But I tried that and it's really janky because it's such long text. So it would have buffering issues, all that sort of thing. So it's like, okay, I'm just going to go turn this into a recording so that I can listen to it. There's tons of AI tools out there, which we're going to talk about the types of tools. There's a service out there where you can just upload a train.

You can actually generate an MP3 like with GBT or whatever. I need to try a couple other models, but the voice and audio is just, it's a robot. Like again, I'm not going to listen to 30 minutes of deep research with this. Unless you have trouble falling asleep at night. Unless I'm having trouble falling asleep at night. Exactly. But the, I think it's 11 labs, the student like audio books and Okay.

I maybe I need to check that out. Five translations out. One of the major podcasts did like four or five translations. Yeah. Like they did in English, but then they also like, I had four or five other languages that apparently didn't pretty well. So is this going to be where we're just going to have all of our audiobooks are going to be in like the same four people's tongues? Because someone's going to sell the rights to their voice. That was interesting.

I just signed up for some tools and I mean, I actually hit the limit on the free tool and I was just trying to figure out if there's a quick way to do it. And anyways, there's these services out there and I literally recorded like 10 seconds of my voice and had it like read it and it was astounding. Really?

It was astounding. Yeah, it was pretty wild. The next thing you should try is where you purposely like pitch your voice really weird. Yes. And see what that turns into. Yeah. Yeah, although the interesting thing was, I was like, wow,

that did a really good job. And I showed my wife, and she's like, that doesn't really sound like you. No, no, no. Interesting. Anyways, the first topic I wanted to hit, actually, is not a spicy LinkedIn post, but we were chatting before the show and those transcription services are, there's so many of those that are just

going to get completely wiped out by the foundational models themselves. And we're starting to see that. I mean, doing a bunch of tests even internally with some AI tools that we're using, the foundational models are just now beating them with a generic, just completely vanilla. Right? Even for things that have been purpose trained on documentation for technical questions, it's just really way better, which is wild.

So this is me reading from my own, this is my internal LinkedIn feed. If you post on LinkedIn, this is what you get. If I was going to post on LinkedIn, this is my internal LinkedIn feed. If you post it on LinkedIn, this is what you get. If I was going to post on LinkedIn, this is what I would put. Behind the scenes, kind of. Yes.

Is that I think just on a weekly basis, we're going to see failures of these companies that were doing something that was truly value-add because of the limitation of the model, and now it's not anymore. And so I think we're at the tip of the first wave of failures. But say you, cynical data guy. Well, I think the thing that's probably interesting about that was if you go to the beginning when all this happened, when we started seeing all these AI companies pop up, the assumption was the people who were just simple rappers

around open AI, like, oh, they're going to be gone in four years. And now, and the ones that we thought were actually adding, value were the ones that were going to hang around. And now, with all these foundational models out, it's like, oh, well, all I really want is a platform that's a wrapper that I can just choose whatever I want.

And they're getting better. So I don't really need your specially built one for this purpose. Yeah. Yeah. It's heard it itself. That is such a good observation. Yeah. Yeah.

Well, and the interesting thing about that too, and I've seen this in a lot of platforms, is if you build your rapper toward whatever industry or whatever like use case, and then you have that ability to hot swap in models, there's this perception I think of like, well, I don't know which one to take, I don't know which one's best.

It's like, oh, well, these guys solved my problem. And they've got five options. And like, as the weeks go by and one model's bad as another, I can just flip it. I think there's kind of a like comfort in that. Like, oh, I didn't actually like pick the wrong one that might not be the best. Does it also give the illusion that you're not in vendor lock-in? Oh yeah, a little bit.

Oh, I'm not going to be locked into a vendor. Yeah, well you are. You're just the sub-vendor is changing. Yeah, yeah. Yeah. That's a really interesting point. I think one of the most amazing, actually, I would say, just in terms of the interface, but also the company that I think has done maybe one of the best jobs of all of them of incorporating AI into their product is Raycast,

which if you use a Mac, you know, their spotlight. So you do command space and it pulls up like the global circuit. This would be long term Mac users. This is the new Alfred. This is the new Alfred. Exactly. And A, it's just an outstanding tool standalone. I mean, it makes Alfred look like it makes Alfred feel so primitive, but it is actually an interface with all of the AI models, one. So all of them. You can do all this custom configuration for various commands

to use different models and all that sort of stuff. But they're now using extensions to integrate it at the operating system level so that you can do all sorts of stuff to run it against basically in your day-to-day workflow. It's pretty wild to see. Which is essentially connecting an LLM and they already have the OS level 100. Do an actioner. Exactly.

Yeah. Interesting. That's really wild. So many people are going to wipe out their computer. Yeah. R is RLM. Yeah. That's really wild. So many people are going to wipe out their computers. Yeah. Oh yeah. Oh yeah. It's going to be the thing. What did you do? I didn't do anything. Yeah.

I was talking with the LLM and then they closed me out. Yeah. I still remember there was a junior developer, this was probably 10 years ago, that started in like two weeks and he had switched. He started out and he was getting windows to start. Anyway, he had switched to the Mac like somewhat recently was I asked for the command line and he didn't delete all this files, but he somehow managed to take every single file from like all of the like separate directors in the computer and put them all in directory,

which is also just about as kind of strong, including system files, not just like more documents. Please tell me they were all on his desktop. Please. I don't know. That would be amazing. I can't remember. But it was one of those things like, you know, still learning, like, yeah, great, great person,

like good developer, but just like still learning like terminal and like, and then they just like all end up in one directory. That's also where you look at them and you go, I don't even know how you can do that. Yeah. There's not really an undo button from that. Okay. So look out for companies to short because it's getting spicy. It's getting spicy out there as the models become better and better. Anything else? I do have a couple of

great LinkedIn posts that I do want to get to. Let's do it. Let's go on. Okay, moving on. Okay, the first one is from Kevin who actually has been on the show. Great guy. He's a CEO of Metaplane. And so Kevin, if you're listening, we'd love to have you come back on. We could talk about AI even. Okay, I'm just gonna read this post. How much should we rely on AI to generate production code? This forum post about Cursor's LLM suggesting the user to learn code has me thinking about our field, okay? And so just to, we can put the post in the show notes,

but there's a screenshot in a forum, someone had posted in a forum, it says, AI told me I should learn coding instead of asking it to generate it. And the response from the LLM is, I can't generate code for you as that would be completing your work. The code appears to be handling skidmark fade effects in a racing game. You should develop the logic yourself. This ensures you understand the system and can't maintain it properly.

Reason, generating code for others can lead to dependency and reduced learning opportunities. So Kevin, that's the forum post this finch had to do. That may be faked, but it's really funny. Yes, it almost certainly could be faked. So he said the vibe coding trend trend using LLMs to generate entire applications without understanding the underlying code raises interesting questions for data engineering. Why this matters for data teams?

One, SQL queries generated by LLMs often look correct, but can silently introduce errors, especially with complex transformations or edge cases. Two, when data engineers don't fully understand their pipelines, debugging becomes challenging when something breaks. Three, the path of least resistance is tempting and there are genuine efficiency gains to be had. He, to summarize, says, the most effective data engineers I know are finding the sweet spot using AI

to accelerate routine tasks while deepening their understanding of core systems. So first I'm gonna say, whether that post was faked or not there, it does make me think of my kids were watching the Willy Wonka and the Chocolate Factory movie, and there is a scene. Original or Johnny Depp?

No, the original. Okay. Which is not my favorite, but that's another story. Um. It's a classic. The book's better, is that where this is going? It deviates too far. All right.

There's this one scene that they have where this guy says he's programmed his computer and has a bunch of tapes and mainframes and tell him where the golden ticket is. Yeah. Oh yes, this is a great scene. And it says, I can't do that for you, that would be cheating. So he tries to tell it, I'll share the prize with you. And the computer replies back, what would a computer do with a lifetime supply of chocolate?

I completely forgot about this. That is such a good scene. Yeah. So, yeah, that is maybe that. But yes, see any part there, I think coming from, I started as a data analyst and then having to manage and train data analysts. This is the thing that you can kind of see that you don't want to see from a data engineer, which is kind of that like, why is that number that way?

What's the data said. That's not an answer. Like, you need to have an answer. You need to understand it a little bit more. So that would just be one where you're like, why did the data get transformed this way and put it in here? I don't know. That's what the process does.

Oh, no, that's not going to work. Yeah. I think it's going to be so interesting, like how this actually plays out. I can think of like two or three scenarios. One where it can be really dangerous. So say you've got like a junior engineer right out of school and like just vibe codes through like full pipelines, full apps, they get released into production. Like that could be a problem, especially like in a small org where there's just not a lot

of people and they hired that person as like their data person or tech first year or whatever. Like, I think that's going to like result in some pretty bad disasters. On the flip side, I think it's very interesting for people that are in architect roles or even like product roles, they can do where essentially like they know how it's supposed to work roughly. They like understand risks, they understand how things typically break. They understand ops decently. Like that person, I think it's, will be really interesting how it develops. Because then, cause they can kind of see around like, okay, cool.

Like you just introduced a major like security problem, like, cause that was, you know, what happened. And then the third use case will be people that are in that more junior role that lean really heavily on like educate me, help me learn about this code and like are primarily like pushing those types of prompts through. I think that'll be great for those people. Yeah. I think that also gets to something that is there that right now,

one of the things you can see is that the people who can use AI to code need to know how to code first. But I do wonder if we're going to get to a point where there's like, there's people who've learned it, some people who learned to code and then went to theirs, and others that use the AI tools to learn to code. Yeah. And what is that going to look like? What are the differences and how those people are going to look at it?

We've actually already been through the iteration of this. We've been through the iteration of like people that learned Java in school 20 years ago and like came out and like did a traditional route. Or the people who like just like did more like a boot and like did a traditional route or the people who like just like did more like a boot camp route did and then were 11 numbers and essentially learned from Sack Overflow, right? Yes. So like that's already kind of a thing.

Yeah. And but it's different because in Sack Overflow like it's like here's a rough example, you still have to do a fair amount of work to like understand what's going on. Well could then it also could also then make worse the problem that we have with some where it's like, I know how to execute a thing, but I don't understand some of the theory or what behind it. One of the things that I found was I had to take a warehousing class when I was in school. This is just the concentration I had. You had to do entity mapping and understanding,

go through this. You learn one second, first, second, third normalization, stuff like that. And I don't think much about that until it came up recently with something where it's like, oh, that's actually really saved me because I have to go clean up a bunch of people who've never even been introduced to that concept. And they just do really stupid things with databases. Yeah, yeah. Okay, the thought around what is it going to be like for the people who actually learn their skillset with AI there is gonna be really interesting.

And I also think that what's pretty likely is that the entire methodology changes. Yeah. Right. I mean, of course there's a question. I mean, you go talk to anyone who's like reasonable out of basic. I was talking with one of our principal engineers recently about this, right? And he was like, yeah, I mean, I use AI to like do a bunch of stuff, but like it can't like architect a complex system well, like I wouldn't put the code in production like blah, blah, blah, right?

Which yeah, I mean, of course you have, your business depends on everything working well in production and so you're gonna do what you need to do there. However, the improvements are going to continue to be dramatic in the way that we think about developing applications is going to change dramatically along with that. So it won't be like,

I have AI do some stuff and then it'll be like, okay, the way that we conceive of doing this, I think is going to change. I think there's this level of abstraction thing. There's a really interesting post I've linked in the other day. I think it was somebody talking about abstraction thing. There was a really interesting post on LinkedIn the other day. I think it was somebody talking about data and they said something like, I never write recursive queries,

I never use recursion and data. And then in the comments, somebody was like, yeah, you do, it's just abstracted away from you. You just don't know that you're using it. And I think that is like what abstraction level is it necessary where like, do I like understand compilers deeply? No, but I use them all the time.

Do I understand like, do we have to mess with like memory management much anymore? Like in data, not really. So there's all these things that are already abstracted that like fewer and fewer people need to understand the details though. And it's just like, where is that level gonna be? My personal. That tends to be more a way I think of it almost like it's just another form of a framework

or something like that. Right. So yeah, exactly. You can even think of it in a little bit of when does it become like a new version of WordPress? Yeah, it's going to be kind of heavy. It's going to have this extra stuff with it. But it's this, there's this core part of it that it can do for you. Yeah, 100%. And that's probably going to be something we see, which is where AI, well, can't do everything for you, but here's this set of core things that like, nobody does that anymore. Totally. Totally. I mean, but all of the tools out there, Replet, V0, there are a bunch of those,

right? Which it'll be interesting to see where that whole thing goes anyways, relative to the conversation we were having those, right? the architecture, right? Like those types of things are going to get better and better. And then to your point, Matt, if you start with an underlying framework as the starting point, you can essentially cover a number of use cases and probably get close to something

that is production ready. Now, if it allows front-end engineers to have some better understanding about the backend that's going to look like from a data standpoint, I'd be very happy to see that. And vice versa too. Yeah, yeah, totally, it's great.

Okay, okay, next. Yeah, the data design thing actually, that's a really good point. Actually, even to actually, I have another LinkedIn post, but that's a really interesting point in that even if you think about capturing data, I think there's going to be a lot that happens relative to AI being able to infer

what data needs to be captured, what the shapes of schema is going to be, like all that sort of stuff. Right? Right. Possibly even printing is a two-proper normalized format when needed. Not that I've dealt with that before. It's probably more likely to do it correctly. Yes. As far as when high level design, the implementation do tell you the tricky part. Yeah, totally.

Are we ready? What is this, round two or round three? This is round three. Rocking along here. Here we go. The future is no UI and we'll design agent first. AI is eating the interface. The other day I was going into my reporting software,

which requires me to input data from a contract. I need to go find the contract, which has been sent by a signing service to my email. I find the relevant data and input my relevant data and input it in my software. Then I extract the data from the software to do analysis on this data, where I have to set up the data properly, then figure out how to write a formula to run an analysis.

It's a very usual workflow. You get data from someplace and put it somewhere else and do something with it. What I really need to do is store the new data about X in my email and run analysis Y. AI can do that now. So really, I only need to tell my AI to do that and it will execute faster and better than I can myself.

The new mobile interface will be empty just to chat. You can talk, but you will not need to go into apps and press buttons. The future iPhone and software interface will be just a blank screen that brings up what you want. In the background, we will have agents running on top of software talking to other agents, such as a docuSign talking to my data and document storage

and putting the data and other information in the right places. But I will not need each interface for this anymore. I'll probably have a dashboard and a chat with access to everything I want to do. For software and AI companies, this will mean designing agent first as we used to have mobile first. The best software won't be the one with the best interface.

It will be the one you never have to see. PS, it might seem like the human role is vanishing, but I don't think so. AI will take over execution, but humans will still do what AI isn't good at, communicating with people, making decisions, and thinking about what to do next. Work will be a lot more enjoyable

when we don't have to fight with software. Yeah, good luck with that. You're gonna still fight with software. I think on this one, I think they're pretty under indexed on how much people like you, like UIs. Yeah, definitely. I think if you look at, let's think about YouTube shorts, TikTok, at let's think about YouTube shorts, TikTok, Instagram, although the most engaging platform it's full UI. There's just that like brain visual connection or if I have to like type and stuff, it's just extra cognitive load. So surely AI is going to be burnt for sure. Yep. But I think there's going to be a ton of apps that like still have you. I still have the visual part and then they have this nice seamless like handoff with an Asian to do a thing. Yeah, let's also what do we see for things? Oh, do I want to say something and then stare

at a blank screen? No. Yeah, I have a progress bar. I have something that shows me what I'm doing. Right. You can you know, when, even if you install something on a computer, there's always a thing where you can click and you can see the files that are being installed in real time. We like feedback and progress of things that happen. Visual feedback system. Yes.

Visual. So this idea that it's gonna be like, oh, all I've gotta do is this little chat and then I just wait for it as I see that. Like, that's gonna drive people crazy. I mean, think about computers 40 years ago. That's essentially the interface. It's like a terminal interface.

And clearly at that point, you could type in the computer, you could just do something. It's not quite human-like in the chat, but that didn't work out, right? Yeah. Was it Misha from Reflection AI who was involved in DeepMind and his co-founder. Yeah, from Google, yeah.

I want to say it was him we got to show recently. Yeah, they worked on Gemini stuff. I think it was his co-founder or someone he knows from the space who said, AGI is going to happen, but no one's going to notice it. Yeah. Which is fascinating. And that's kind of, I don't agree with everything that this post is saying, and totally agree with what you're saying,

but I think what is interesting is that reinforces that point. Yeah. Right. Around this sort of fading into the background. Well, one thing I would say there is people may not know AGI shows up because no one actually knows what AGI is anymore. It's just a term. I mean, if you look at all the marketing literature, they've already moved on past AGI, right?

Nobody's even marketing, like OpenAIM, the big ones are moving past AGI. That's agentic. And if there was a clear definition, you would know about it because their marketing department would never shut up about it. Yes. Yeah. Yes. Is AGI like the data mesh of the world? I don't know. I don't know if I go that far, but maybe. Well, I mean, it's actually the one parallel that's

interesting is that's an academic concept at the root, which makes it really hard to sort of. That's really interesting. One of the things, when I read this post, one of the first things that came to mind was Alexa. Okay. And if you remember, at one point there was an article that came out

that said how many people were working on Alexa and the number was mind boggling. I wanna say at the peak it was 10,000 people or something. Okay, so just absolutely unreal. And then we can have Rick's fact check me and put it in the share notes but it was a very large number, right. Can we also talk about why none of the voice assistants have any AI,

like anything yet from what I've seen? Well, okay. So yes, we can. But to finish out the first point, what was fascinating is you could do all sorts of crazy things with Alexa end to end, like almost agentically if you will, right? So like I could speak and then I could get groceries, I could whatever, right? And people used it for like the top five use cases that comprised overwhelming majority were like checking the weather, checking the sports scores,

making a grocery list. It was just the most basic stuff. I would say time, weather, lights on and off. Yeah. Music. Maybe music, maybe a couple of other things. Yeah. Then there's a very like.

Super long tail. But what's interesting, the reason I bring that up is what is really interesting about it is the, and you made this is just another way to some stealing your points in the whole data guy is really what's happening here. It doesn't force you. But a blank screen or maybe I'll be a spin on it. A blank screen is really hard to

interact with because it requires an immense amount of creativity. Right? I mean, an example I think about with, even within Rutter Stack is our most loved feature. I mean, we're talking 80, 90% adoption. Every customer call I'm on, people are like, this is so great, right?

It's actually just a code editor, right? And so you can run real-time transfer makings on payloads. It is so useful. The number of use cases that people implement is, it is mind-boggling what people have done with it. But what's so interesting is the first time you show someone, is that so unimpressive? Even like when a new customer comes on,

it's like, okay, you have this super powerful tool and they're like, okay. But then they run into a situation where they're like, I need to do this really critical thing. And over time, it becomes the most loved thing because it's just so sinking useful. But as a blank slate, it's really hard. When you guys have that template library now,

which I think helped. Yeah, for sure. Yeah, yeah, yeah. I'm painting it's really hard. When you guys have that template library now, which I think helped. Yeah, for sure. Yeah, I'm painting it in really extreme terms because we've done a lot of things to overcome that. But the vision this person has is almost a continual blank page problem. I don't even necessarily know what I can do. I don't know what I want to do or should be doing.

That becomes the thing. I mean, because as you said, there's a lot of stuff for likes that can do. I don't know what I want to do or should be doing. That becomes the thing. Because as you said, there's a lot of stuff Alexa can do. Most people have no idea they can do that. You use Alexa a little bit like an Excel worksheet. What do you do with it? I make lists. You realize you do all this other stuff in the game. Yeah, but I just need to make a grocery list in Excel.

Yeah. Yeah. That's fascinating. Okay. Voice assistant. Yeah. Like I'm just confused. So like Siri and GCD have this like little integration thing where it does a handoff and like that's fine. But like Google Alexa, like I don't, I haven't noticed any sort of like they're just how they always have been. It seems like there's been no effort to implement like cloud into the Alexa or Gemini into Google. I haven't really kept track of it, but I just don't.

That was just a compute power problem or like, I don't know why it's not being done. Is it too unpredictable? Maybe. I don't know. Yeah, I don't know. Maybe somebody knows. Maybe they're just stubborn. Is it like, it's Amazon.

Like that's not, we don't have the world. Or it can, or just a silly thing of like, that's not, we don't have the world. Or just a silly thing of that's a separate team. Yeah. And they got their funding side and all the money went to the OOM team and their different team. It just could be something very simple like that. Yeah. Yeah. Yeah, it's also fascinating to think about if you imagine this future world, right?

So the blank screen as the interface or whatever. Right. And you think about what this means for people who are trying to build stuff around AI, et cetera. You sort of go back to the one who wins distribution wins, right? So the iPhone, it actually doesn't really matter what happens in the background, which models like all that sort of stuff. But like the this interface will Apple will distribute it with the iPhone. Right. Or Amazon with Alexa or like whatever. Right. The distribution thing is. Right.

It's pretty wild to think about that. Right. What other spicy? I don't even know. I haven't even been keeping track of time. Oh, yes. Okay. That was the bonus round. That was the bonus round. Wow. How did I forget the bonus round? I have it pulled up right here in front of me.

There you go. Yes. I have it pulled up right here. Okay. Bonus round is actually related to the interface question. So that LinkedIn post said the future is basically like a blank interface with agentics stuff happening in the background. Interestingly enough, DuckDB rolled out an interface. So here's just a hit a couple high list posts. DuckDB introduces a local UI for easier SQL exploration. DuckDB and MotherDuck release a local web-based UI for seamless interaction with DuckDB introduces a local UI for easier SQL exploration. DuckDB and MotherDuck release a local web-based UI for seamless interaction with DuckDB.

It's available out of the box, simplified SQL query management with a user-friendly interface. It's a local web interface launched directly from the CLI. And let's see, run full queries, just selections, table summaries, MotherDuck integration. It'll preview the first 100 rows. Uh, I mean, pretty cool.

It's a really interesting thing, right? Cause like they're starting up, like it's not like this is a tool that's been around forever, but it's a really interesting model because all of the other, all the like modern analytics competitors have not done this. I think the reason is like this kind of security angle for sure. And then there's another practical angle of like, they want you to use their compute and to charge you for development essentially. Yeah.

Yeah. I was telling you guys before the show, I've got a client that I think as far as the amount of their compute bill for their Cloud Data Warehouse, I think it's 20 to 30 percent ingestion. I think stacked to another 50% development. And that little remainder is the actual people accessing the data. Wow. And I don't think that's that uncommon.

Yeah. Yeah. I... People wonder why data teams fold. And transformations are part of that too. But yeah, but essentially like the user value are as a relative of like the entire like computer, which is directly corresponded to your bell and most of these platforms is small. And that's probably always been true, but it's just more apparent

when you're getting charged for. Yeah, I am. Yeah, for sure. Yeah. Yeah. I mean, I remember when I was a senior director, we worked with Google and they were trying to sell us on the new thing, which was a fully integrated developer environment in their platform. Meanwhile, then we'd also brought on a consultant who was a software

engineer and they were trying to turn Google's tools into stuff that you could then do completely locally. Yeah. Which they had to inform them, that's great except nobody but you will ever know how to use this. Right. Yeah. Yeah, I think it's interesting.

I mean, I think that, I think answer blurring number one in that, I think, especially in the world of data, where non-technical people are becoming more technical, like legitimately, AI is helping people interact with this stuff in a way that's like way easier. I mean, the number of people I talk to who are product managers who just use AI to write like basic SQL and it's the most helpful thing in the world to them because they can just query the data. It's astounding. I mean, almost everyone is doing that now because you're not like writing a huge model to do like BI,

right? But you're interacting with data, like with materialized views and you can run actually get, that's really helpful. And so it is really interesting that I think those lines are blurring and so this strictly dev tool versus an interface for a non-technical user is really blurring. I think there's also that tension. For a lot of people, they would like to use something that's more local, even because they like the feel of it

or it's more convenient or things like that. I mean, that's even why you have all of these integrations of like VS code and stuff like that. Look, I don't have to go into your web UI app. I can do it from my own place. But then there is also that whole of the people who have all this out, wanna try to draw you back into it that entire time.

But you're never gonna be able to make a one size fits all tool that everyone's going to like. That's why we keep coming back to local, I feel like. Yeah. Yeah. I mean, whenever you're going to hear about this tool, Rosa, a local UI, and it's just blank, actually. You can run this UI locally and it's just blank and you just talk to it. As long as there's a progress bar. just talk to it. Yes, that's exactly right.

Okay, well, thanks for joining us for a fun AI edition of The Cynical Data Guy. Matt, as always, great to have you on the show and we'll catch you on the next one. Stay cynical. The Data Stack Show is brought to you by Rutter Stack, the warehouse native customer data platform. Rutter Stack is purpose-built to help data teams turn customer data into competitive advantage. Learn more at ruddersack.com.