This week on The Data Stack Show, Eric and John are joined by the Cynical Data Guy (Matt Kelliher-Gibson) as the group discusses the challenges and misconceptions in the data field. They explore the unrealistic expectations often held by data professionals, including data analysts, engineers, scientists, and leaders. Through personal anecdotes, they highlight the importance of domain knowledge and the evolving nature of data roles. The conversation also touches on the implications of using enterprise-grade BI tools in small companies, the necessity for data professionals to balance technical skills with business acumen, and so much more along the way.
Highlights from this week’s conversation include:
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John Wessel 00:03
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.
Eric Dodds 00:13
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 Stack. Today we are welcoming back one of our favorite recurring guests, who is now referred to as the Stone Cold Steve Austin of the data, the cynical data guy, Matt, welcome back to the show. Or Steve, I guess I should say, thanks for having me back. Eckhart. Ricker, thank you for that little yes tidbit right there. Yes, we do have to thank Eckhart for actually showing us what we knew all along is that, you know, that the world of data is actually a wrestling ring where people act out elaborate, you know, elaborate, violent scenes. No one actually gets
Matthew Kelliher-Gibson 01:13
It’s okay, fab. That’s all. It really is. Okay.
Eric Dodds 01:17
Well, actually, stone cold. Steve Austin of data, we have a really good one to start out with. One of our good friends, Rogan, has been on the show multiple times, just a fun guy and a long time friend of the show man. He had such a great post about false hope. So I’m going to read this, and then I have a question for each of you, so cynical data guy and agreeable data guy, and this is just so good. No one is more of false hope than a data analyst building a dashboard they believe the entire company will look at this of that last year, a data engineer who just had someone tell them that the schema will never change, a data scientist analyzing a data source they thought was 100% accurate, a data leader planning out how to build the single source for their company. Okay, so here’s the question, I hope the listeners are laughing, because all of those are just unbelievable. Which one of these is the most? Is the is the highest level of false hope or the highest level of delusion. I’ll let
John Wessel 02:25
you go first, Matt, but I definitely have one, maybe two. You can’t have two,
Matthew Kelliher-Gibson 02:30
It’s what means the most. So I mean, this is probably also biased, just because of my background, I would say the data scientist who thought that was 100% accurate, because that’s just your sweet summer child that does not exist.
Eric Dodds 02:51
I have, can I ask one, one like question on that? Because I don’t. I’m not super familiar with the you know, I have, I don’t have a lot of direct data science experience, but the little that I do have, like, a really good data scientist sort of assumes that’s, they actually, like, assume that going into the pod, right? Like, that’s, it’s,
Matthew Kelliher-Gibson 03:11
it’s always wrong. The first step is usually to figure out where it’s wrong, yeah, so that you can correct for it, or remove things or whatever there, right?
Eric Dodds 03:21
Okay, I have more questions. But re able dating guys
John Wessel 03:24
well, I mean, as jumping off from that? I mean, that’s essentially the whole field of, like, Applied Statistics, right? It’s like, we know this was wrong. We’re gonna do things to make it more representative, yeah, but I’m gonna go to the dashboard. I think that, to me, is the largest amount of false hope for two reasons. One, because data analysts tend to be like that tends to be like a career starter, like, there’s a lot of people that start their career as a data analyst, some as engineers or scientists, but, you know, data analysts, most of those jobs, they’re like, a lot of companies have entry level data analyst jobs, and maybe don’t have entry level, you know, data science jobs, so there tends to be at least for juniors, like a, you know, higher level of naivete for that particular role, if you’re a junior data analyst. And yeah, and it just makes me laugh, because I’ve been that person. I’ve believed, like Yaw, like this dash, was gonna be great. This, even sometimes this executive, like, wants me to build it. So even have an executive that’s a little delusional about it and like, Yeah, we’re gonna have this, like, wonderful thing. It’ll be our North Star for the company. And everybody will look at it
Matthew Kelliher-Gibson 04:35
Well, I mean, so yes, I think there’s naivety there, because a lot of times they don’t know any better if they’re earlier in their career, but I kind of view that as not the most delusional because of that now sure if you’re and also because if you’re like a it’s more, excuse me, yeah. And if you’re like a 10 year data analyst, like your soul has been getting ground down for 10 years. So the cynic illness of it is going to. Pretty high, sure, right? There you have a pretty
Eric Dodds 05:02
clear view from the mountain of unused dashboards that used to be in the pond, right? I
Matthew Kelliher-Gibson 05:09
I think it was my first dashboard I built . I had just moved into a role from another part of the company, and my boss was like, we’re gonna build this thing for operations. I was like, Yeah, sure, and no one ever used it. And I was like, why? And then I looked at it, and I was like, I realized that a meeting we had with operations, no one had asked for it. And like, right there. I was like, Well, I’m never doing this again. Like, yeah,
John Wessel 05:33
I was so excited. One of my first staff members was so excited because it went on the TVs at the big operations center. I was like, man, you know, like, I don’t know 100, how many 100 people are in the operation center at this company. And then I found and then, and it was a remote site, so I’m, like, physically located somewhere else. And I occasionally visit this, like, Operation Center. And I remember visiting and the TVs were off. And then I inquired about it, and it was like, Oh, yeah. Like, TV has been broken for like, a month, and like, they may fix it at one point. Like, and then I just like, it occurred to me, like, oh, they don’t care that the TV doesn’t work. And like, oh, they don’t look at my dashboard that’s on the TV.
Matthew Kelliher-Gibson 06:14
Well, now one other thing I’ll just say is, I think on that kind of sliding scale, the data leader one would generally be, would be the highest, I feel like, because you should know better, except for the fact that I think a lot of them don’t believe it. That’s just what they’re telling management. Like, yeah, we’re gonna, we’re gonna work towards that. But I don’t think they really believe it. And if you do believe it at that point, you’re the biggest sucker in
Eric Dodds 06:39
the room, then that’s a great point in that the single source of truth, the single source of truth conversation, actually, a lot of times, comes from management or the business right, where there are all these problems from all these functional areas of the business, where they say, Oh, well, I need this data, or I don’t have this information, or there’s some sort of problem in me hitting my number because of this data, right? And so then someone from management says, Okay, we need to, this is a technical problem, a data problem. We need to solve this at the root. And so the data leader, sort of their big project is, okay, you need to go figure out how to build a single source
Matthew Kelliher-Gibson 07:16
of truth, right? Yeah, I feel like that comes up partially because you get the situation where you have an executive who, every month they’re doing reviews, and it’s always, well, our numbers say this, but finances numbers say that, but marketing’s numbers say that. And so they’re like, you know, what? If I could just have one place where, like, all the numbers were the same, and everyone had to use them. But the problem with that is they don’t realize that none of the business units want that because they’ve all skewed it towards what’s best for them, and they are going to fight you on that, so you’re just adding another number to the fight. And also, I think also, there’s this thing where they’re like, You data person, go define this. And a lot of this stuff is not, like, definable by the data. It’s like, okay, well, we need to know what a sale is. Well, finance has one definition. Marketing has another definition. So no one wants to be the person who says, Everyone shut up. This is what the definition is. Yeah,
John Wessel 08:12
I think I’m least cynical about this one, because I have had some success, like with that, but only, but like at a large company, I forget it like that’s never gonna happen, yeah? Smaller companies, you know, you never get there 100% but I think you can get closer and you just yeah, there’s a lot of things I have to go right to get close to that. I
Matthew Kelliher-Gibson 08:35
I think it’s one of those. If you can fit everyone around a table with a pizza, yeah, you have a chance of that Exactly, yeah, once you get beyond that, yeah, and especially once, and especially once budget and comp is tied to any of this stuff screwed. Okay,
Eric Dodds 08:50
quick question, because we have many more juicy morsels to move on to one question on the data analyst in the dashboard. So I was meeting with a customer in person, actually, which was great. Seems to be more and more rare. And we were talking about data, and they were, you know, discussing sort of some of the things they wanted to do in terms of capturing product telemetry to understand onboarding better, and, you know, just some basic things that they wanted to do. And so I was asking about what they currently do for analytics. And this is a startup, okay? So it’s a, you know, a venture backed startup company, not very big, but, you know, you know, sort of early stage, right? So it’s looking for product market fit, but, and they said, and this is a product manager, and they do some data stuff in their platform, actually, and this is the product manager for their data, you know, features and functionality, super smart. And he kind of laughed, and he said, Well, you know, we have Looker and Tableau. And I’m thinking, wow, you know, this is not a very big company. How does that happen? Because that, to me, gets at this false hope of some. Is onboarding, like, a, like, non trivial, enterprise grade BI tools at, you know, a small company, and they’re not dumb people, right? These are people,
John Wessel 10:10
and you’re talking head count of like, 50, yeah, yeah. Like, like, very small, yeah,
Eric Dodds 10:17
yeah, somewhere, yeah. So, how does that, like, that dynamic happen, right? Because that’s fascinating. And I guess, like, this stark contrast of, like, we have two enterprise grade BI tools, and we’re talking about how to get basic product telemetry so we can optimize onboarding flow. If that’s just fascinating. It’s a
John Wessel 10:39
speed optimization thing, usually where, like, some person comes in, they know Tableau, they like, it would take them X amount of time to ramp up on said other tool, the person, or they just don’t want to, or they just don’t want to, sure, yeah, yeah, the person perform used Looker and like, and then they make the argument, hey, we’re a startup, like, it would take me X amount of exaggerated time to ramp up on this other tool. Let’s just use this tool I already know, and then, like tout some benefits of whatever tool they already know that may or may not be true compared to the other tool, because they don’t really know the other tool.
Matthew Kelliher-Gibson 11:12
I think also one of the ways you can have that is the person comes in, they want to use Tableau, they don’t even have that discussion first. They just download it, build stuff, sure, and then they’re like, ah, would take me so long to do this in Looker, but look I already got it in Tableau, so now you have to buy tableau for me.
Eric Dodds 11:32
Yep, probably also BigQuery or the Google team. And what they did with Data Studio and Looker, I believe this company is running on BigQuery. We didn’t talk about this specifically, yeah, yeah. But then there’s an easy on ramp to go from, oh yeah, BigQuery, you can get it on in Looker studio. And then, you know, Looker like, that pathway is, is super easy if you buy BigQuery, yeah.
Matthew Kelliher-Gibson 11:56
And it, and if you started with there was like, one or two people with, like, a tableau license, then someone else needs to do something in another department, right? It was like, Look, we can just turn it on and we’ve already got it up there. And then once it’s up, nobody wants to change it, because nobody wants to actually consolidate it into one place.
Eric Dodds 12:15
Okay, moving on to round number two. This will be a double header here. So two posts. So one is from Tris, J burns, and this is, this is great. This is gonna be a great topic. Short zoom in here before starting a career in data, I highly recommend gaining experience in another field. And then I’ll follow that up from another long time friend of the show, multi time guest, Ben Stancil, who continually just produced his unbelievable thoughts generally. But a quote from a recent post, a couple quotes here, we can just be analysts or analytics engineers. We have to decide that we want to be true experts in understanding how to build consumer software first and product analysts second or define ourselves as working in finance, then become an analytics engineer at a fin tech company, because there’s a corollary to Dan Lou theory of expertise. While it implies that we can become pretty good at stuff pretty quickly, it also implies that other people can become pretty good analysts, and in almost every field that combination of a domain expert and 95th percentile analyst is almost always better than the inverse. And then I’ll close it out with another, this is a paragraph down. We probably can’t, we probably can’t get away with being good at asking questions. We need to know some specific things too. Cynical data guy
Matthew Kelliher-Gibson 13:41
that one cuts that last line a little bit right there, because I’ve used that before. I think, I mean, I think this is one of those that I don’t know you kind of like, if you’ve been in the field, you kind of go back and forth on it sometimes where it’s like, yeah, we really need people who know the business and stuff like that. But if you don’t have enough of the data working with it, then you run into a lot of problems with it, and then, like, you flip over to now, I just need someone who’s really good at data right? Like, I just need a data engineer who can just step in here and doesn’t care, and can just put stuff in place. So, you know, I think there’s, I think especially, like 10 years ago, you could kind of be more of, like, I’m a data person. I think that’s shrinking over time. I don’t think it’ll get rid of it completely in the near future, but the idea of we’re gonna have like, a really, you know, like a data team that does, like, okay, it’s all the data analysts, and they just are, like, data experts who help the other the other parts of the business. I don’t think that’s going to last. I think that’s already kind of gone in a lot of places.
Eric Dodds 14:47
Agreeable data,
John Wessel 14:48
I think, and this is, I think this is in the same article, he kind of Ben, does this like, really good comparison with data and science, and says, like, hey, science is. A subject you can take in school, but enhance is, like, great. Like, science is what you take in fifth grade, not what you win a Nobel Prize for. So, like, there’s that, like, yeah, yeah, that’s so good, which is, and that’s especially what’s becoming data. Like, like, what data is becoming for businesses? It’s like, cool. You do data that’s like, great. You have a business degree. Like, like that tells me almost nothing, because it’s so broad. And I think part of what the domain knowledge brings to it is it brings specificity and like, more clear application, like, oh, like, you know about marketing data, or marketing data for law firms. Or like, you know, you can continue to drill into specificity, which makes it more valuable.
Matthew Kelliher-Gibson 15:45
And I think if you look at the original kind of data people, if you go back 10 plus years ago, there was no institutional or educational infrastructure for it, so you had to start at something else. And then you saw this thing as data, and you got into it. And then there was kind of the move towards semi professionalizing it. I would say it wasn’t completely because the entry barriers still weren’t super high, right? But there was this move towards, like, Oh no, this is going to be a professional thing that you do. But, I mean, I think where that’s going to go away partially. I think that was partially a generational thing. You know, the idea of like, hey, we need a data person to do this is a little bit like going back to the 90s and having someone be like, I need an assistant to do my email, because I don’t do email like that while there’s a generational aspect of it too. If you didn’t grow up with it, you don’t really want to do it, but there’s gonna be, you know, you’ve got people who are now in their 40s and 30s that like they’ve grown up with this basically, in their corporate life, they’re gonna be more likely to be a data person, you know, be a finance end data person, or a marketing end Data person. Yeah, sure.
Eric Dodds 17:00
I was working with someone actually. This person exemplifies someone who had domain expertise in multiple areas and wasn’t technically an analyst, and had never worked as an analyst, but was probably one of the best like analytical people that I’ve ever worked with. They worked in supply chain, they worked in marketing disciplines, and were unbelievable at, you know, at like, general math, right? And so you combine all those things and it’s like, wow, they’re unbelievable at, like, solving problems with data, or like, uncovering things. Anyways, coincidental that this person fits that exact profile. But returning to your point, about, like, someone answering my email. They worked for someone, I want to say they were like, I don’t remember the details. It was some sort of Chief of Staff type position where they were, you know, sort of assigned to an executive to solve problems, right? Go in and solve this problem, right? But one of the things they did was they had to print the executives emails out for them. Like, figure out what the important ones are and print them out and put them and so anyways, Matt, that’s awesome. I was like, Okay, what’s the data corollary to that? Like printing the email, you know?
Matthew Kelliher-Gibson 18:16
Well, I think, I think some of that is, like, self service, bi, right? Where you’re like, hey, look, you can go do it. And they’re like, can you put that in a PDF and send it to me, right? Like, answer my question for me, copy it over into an email and send it to me? Yeah, that’s what they want. I don’t want to go look through
John Wessel 18:36
Yeah. Can you make my Google Sheet update instead? Like, I don’t want to open the dashboard. Yeah,
Matthew Kelliher-Gibson 18:41
can you, I don’t want a Google Sheet? Can you just, like, screenshot it and send it to me, for me, like, that’s what I want,
John Wessel 18:48
or put it back in Salesforce or my marketing tool I don’t want to, like, go anywhere. Just let me, yeah, let me use my tools and shove the data in
Matthew Kelliher-Gibson 18:55
there. So I think it also depends on the job, because there are those infrastructure jobs that, like, you know, realistically, you’re not going to go from kind of, necessarily, I was in the business to now, I’m doing what’s becoming more and more, a very like software engineering type role, right? But, but I think for those analyst ones, and I will say, I think also, if you come from like, there’s two ways you can kind of go about this. You can either be a person who really cares about data for some reason, and then you have to learn the business aspects. But I found a lot of people who come in through that data, one, especially in the last, I don’t know, five years or so, they don’t really care about the business enough to, like, want to learn those things. So it might be easier to go the other way around. I do think though, we also need to start defining that a data person is not just someone who knows SQL and a little bit of Python like that tends to be if you look at like, Hey, we’re going to turn our data, our business people, into data people. We’re going to teach you SQL. And it’s like, okay, but to do this, well, you have to learn how to think, Well, yeah, and that’s the part that’s missing in a lot of this. And if you don’t do that, you’re given, it’s gonna sound bad, but like, you’re given a monkey, you know, like a chainsaw, and you’re like, look, he can do this. Well, no, he can’t do this. It’s gonna cause damage a lot like,
Eric Dodds 20:20
you know, WWE in the, you know, monkey, the monkey, the chain in the wrestling ring.
Matthew Kelliher-Gibson 20:30
What could go wrong? I’m sure there’s some ring in Japan where they’ve done that. Yeah, yeah, no, I
Eric Dodds 20:36
think of me as moderator, I don’t even think it’s a hot take on that. I agree with Matt, and I believe that this has always been true, actually. And what I mean by that is, when I think about people who are the people that I’ve worked with, who are incredibly good analysts, they fall on an extremely broad spectrum of technical skill with data, to your point, you know, SQL, what they’re really good at is understanding and breaking apart a problem into its component pieces, so they know what type of analysis even needs to be done, right? And even those people, like we kind of made, joked about, you know, printing the emails, or, like, putting the thing in a PDF. But the thing is, some of those people may not have the technical skill to do it, but they know how. They know the best way to solve the problem, and they may need to bring someone with the technical skills in to help solve a legitimate, really tricky statistical analysis problem because of some very, you know, outliers underlying data issues or whatever, right? But to your point, Matt, they understand the best way to approach solving the problem because of the larger context. And that’s actually the core of, like, good analysis,
Matthew Kelliher-Gibson 21:56
yeah, and I think that’s the part that’s missing when we talk when, like, if you make the comparison to, you know, data is like science, well, science has a method to it that is then applied in all these other ways. We don’t really have a codified method. Like, I think there’s people who are good at it, and if you talk to them, they all kind of fall within this narrow range of how they do it. But there is no, one’s teaching you to do that. You have to kind of figure it out yourself. Yep.
Eric Dodds 22:23
Okay, round three, and then if we have some time, we’ll get to a bonus round. This is a really, really great post. So this is Kurt Muelle, I think I’m pronouncing that correctly. So sorry, Kurt, please come on the show and correct me. We’d love to have you as a guest. Is AI democratization a myth? No, but it’s a 20 year project. This is a deeply insightful discussion with data IQ, my saying that, right? Data Q, data IQ, data IQ, I like to read that in my mind all the time, but I never, I don’t think, right, yeah, it’s like Haiku. It’s like the Haiku, yeah? Data IQ, yeah, yeah, okay, discussion with the data IQ, co founder and CEO of foreign do to two main takeaways. One, best AI, ml data applications will be built by multi disciplinary teams that blend data and domain expertise. Two, the capabilities of llms are not what’s holding back enterprise deployment of generative AI, it’s all of the boring stuff, security, data, quality monitoring, all right, cynical data guy.
Matthew Kelliher-Gibson 23:28
I mean, yeah, I think there’s the truth in that is probably that we overestimate all of this stuff in the short term, and then we run the risk of underestimating it in the long term. So, I mean, I think that part is true. I think the idea of you needing multi disciplinary teams, I think for a lot of this, for a lot of data and technology stuff, that’s always been true, it’s not a one team thing. It’s not a one person thing. And I think like, Yeah, the thing that’s holding a lot back is the boring stuff. But I also feel like that feels slightly hand wavy to me. And it’s not hand wavy. It’s hard. It’s hard. There’s a lot of details, you know, this is where it’s this is that like, you know, it’s almost like saying, Oh, it’s like, the last 10% well, that last 10% is going to take one to 2x as much energy as the previous 90%
John Wessel 24:24
Yeah. Tim, the best analogy I can think of would be when video, like, when video first worked over the internet, like, you could stream a video like, you’re like, Wow. This is really neat. This is great. This can revolutionize everything. Like Netflix with, like, this massive streaming platform with, like, you know, hundreds of 1000s of videos that can be accessed instantly from anywhere in the world, like that was not like a short, trivial, you know, journey between those two things. And I feel like that’s a good thing maybe. Even should be like. That might even be under representing the effort, but that feels like a like, okay, just because, you know, we can stream one person can stream a video at a college on a t1 connection, or whatever, like, there’s all these other things that need to happen, you know, to make this a reality. But the interesting part is that the boring stuff is going to be like, lagging. And he says, enterprise like, I think that’s almost any company. Yeah. So I don’t think it’s just, if you’re thinking my mom, my mind went to big enterprises. Like, no. I mean, I just replaced the word with like, any company deploying some kind of generative AI. But the interesting part is that the LLM like curve, I think, is just going to keep going. And then what are we going to just have a growing and growing span of like LLM capability from actual implementation capability, if that makes sense. So that’ll be interesting, because essentially, like, I don’t know, I don’t know what that gap is going to be. And if the llms keep developing, like, X rate. Like, are there things we can do to speed up the y, which is the boring stuff, like, yes, but I don’t, but it’ll be interesting. Like, how does that gap progress over time? Does it decrease or increase?
Matthew Kelliher-Gibson 26:12
That’s almost kind of like the, you know, there’s how quickly technology can develop and there’s how fast people can actually absorb it and integrate it. It sounds similar to that. I mean, I think also just learning, because we’re I think we’re slowly starting to come out of it. But for the first couple of years, especially, there was this idea of llms, we’re going to do everything, and we were just going to shove everything into the LLM right, instead of realizing it’s gotta, it’s gotta have a it has a part, and it’s an important part, but it may not even be the central part of whatever system or agent or whatever it is you’re using, like there, you still need the deterministic elements in there, and that probably has to be the driver, not the passenger.
Eric Dodds 26:59
It’s gonna be a wild 20 years. Okay, lightning. Do we have time for a lightning round? Yeah, I think so. Okay, bonus round, sorry, bonus round. So Matt Turk has created so much great content over the years, and he had an amazing post about his VC resolutions for 2025, okay, here’s what we’re gonna do with this one, Matt and John, can you pull this up and just pick your favorite resolution? Pick your favorite VC resolution, yeah.
John Wessel 27:33
And most of these, I think, are, like, written kind of humorously. Oh, it’s so tongue in cheek, but is also, like, you know, like, fun, like, tongue in cheek, but has some, like, nice nuggets in them as well. Oh, man, go first. I’ll go. I am honestly the first. Yeah, there’s a couple I can pick on here. The first one just really got me laughing. Made
Eric Dodds 27:56
you pick one earlier in the show. And this is a bonus round. So there, yeah, for one or two of them, yeah, back and forth for one or two they’re, oh, great,
John Wessel 28:04
great, yeah, the first one really got me laughing. And so its VC resolutions for 2025 shed the past. There’s number one on here. Delete all my posts from last January about why the apple vision Pro will be the big story of 2024 which is so great, like, the, you know, like, it’s, I think one of the things in, like, the age of social media, the way that, like media cycles work now, is no one looks back. Like, occasionally, like, posts will recirculate, right? Like, you know, 510, years later, or whatever. But like, pretty much no one looks back. So I think it’s actually really cool. Actually, it’s really cool to pull something up and be like, hey, like, this was a projection or whatever I had from January that, like, you know, didn’t work out as I expected. I
Eric Dodds 28:52
I will say, though, just one quick comment on that, and this is one anecdotal data point, but one of the best engineers that I know, a very successful entrepreneur, moved to an Apple Vision Pro for their workstation, and they said, I’m never going back.
John Wessel 29:09
How long have they been? Are they still using it? Do you know, have you Yeah, they
Eric Dodds 29:13
use it exclusively. Like, anyway, there’s have a laptop and stuff, but like, a year, like, they’re
John Wessel 29:18
a year into it. Then, yeah, wow, yeah. And
Eric Dodds 29:21
then actually, someone who works here at RudderStack is gone, I need to just call them and say, Hey, can I come try this out? But they went, they were at their house, and they tried it out, and they said, it is, like, absolutely unbelievable. The cost is super high, and it’s a very dramatic change. But anyways, just one anecdotal data point, but like, someone who I generally respect, and then we’ve talked a lot about workflow and touring and all that sort of stuff. And so it was really shocking for me to hear them say, like, this is it? I’m not going back.
John Wessel 29:54
That’s super interesting. And I’ve heard people say that, and they do it for like, a couple weeks. Vermont.
Eric Dodds 30:00
I’ll, you know what, I’m gonna text him today. You should, and on the next Stone Cold Steve Austin, but
John Wessel 30:07
if they’re a year into it, and they really have stuck with that, like, that’s a huge win. And, like, that’s a really interesting like, we’ll
Matthew Kelliher-Gibson 30:14
have him on the show, you should, you should ask if he’s not going to the chiropractor, because native it was one of the biggest complaints,
Eric Dodds 30:24
Okay, cynical, the lady guy.
Matthew Kelliher-Gibson 30:26
All right, we’re gonna go with six focuses. Add a filter to my inbox to automatically discard startup pitches that do not start, that do not use the word agentic and say that I’m using AI in my deal flow process. Just leaning into that very obsession. Man
Eric Dodds 30:46
Kostas, the former co-host of the show when he was, has a startup, so at some point we need to ping him and have him come on the show. I think they were getting close to being ready, yeah, publicly. But God, this was maybe a year ago, maybe a year ago. We were messaging back and forth and just catching up on life. And I was asking about, you know, are you talking with investors and all this sort of stuff? And I need to try and see if I can pull it up. But he had the funny statement about, you know, sort of just going up and down Sand Hill Road saying, you know, a foundational model. And he’s like, I mean, I don’t know how much money you would walk, sort of the digital version. Okay, one more in the bonus round. Okay, I’ve got it. I’ve this
John Wessel 31:33
one. I’ll choose between. Okay, I’m gonna pick, I’m gonna pick number nine here. And it really got me laughing. So each of these like thoughts are pre there’s like, a little like, summary, like, one of them says Inspire. One of them says, anticipate. So this is the one under add value, which makes me laugh. And it says, like, I’ll skip down. Like, tell my founders to be more like Sam Altman, which I know they really appreciate, even though they often don’t say anything in response. Man, good. One like, and he’s like, and that used to be like Elon Musk, like, I feel like, you know, like four or five years ago, Steve Jobs, there’s always been a guy that’s been that guy. But that one made me laugh too. That one
Matthew Kelliher-Gibson 32:23
that it’s under add value. Yeah, that one’s gonna be related to I chose number eight. Inspiring my CEOs should not forget about founder mode. Text them helpful reminders from the pool during my upcoming mid winter break in Cabo,
Eric Dodds 32:38
he really just goes for the jugular on the stereo.
John Wessel 32:43
And Matt, if you’re listening, love to have you on the show.
Eric Dodds 32:45
Yeah, we’d love to have you on the show. All right. Well, that concludes our bonus round stone cold. Steve Austin, thank you, as always, for joining us and sharing your war stories from deep in the bowels of corporate data America. And thank you to the listeners. We’ll catch you on the next
Matthew Kelliher-Gibson 33:03
one. Stay cynical,
John Wessel 33:04
See you guys later.
Eric Dodds 33:06
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