We finally did it: devoted an entire episode to AI. And, of course, by devoting an episode entirely to AI, we mean we just had GPT-4o generate a script for the entire show, and we just each read our parts. It’s pretty impressive how the result still sounds so natural and human and spontaneous. It picked up on Tim’s tendency to get hot and bothered, on Moe’s proclivity for dancing right up to the edge of oversharing specific work scenarios, on Michael’s knack for bringing in personality tests, on Val’s patience in getting the whole discussion to get back on track, and on Julie being a real (or artificial, as the case may be?) Gem. Even though it includes the word “proclivity,” this show overview was entirely generated without the assistance of AI. And yet, it’s got a whopper of a hallucination: the episode wasn’t scripted at all!

Links to Items Mentioned in the Show

• Zenlytic


• (Article) Speaking Things into Existence by Ethan Mollick


• (Article) Why AI Won’t Take Your Job by Eric Sandosham


• (Book) The *NEW* Big Book of KPIs: (Key Performance Indicators) by John Lovett


• (Article) What is Vibe Coding? by Cassie Kozyrkov

Photo by ChatGPT-4o (obviously, right?) as prompted by the Analytics Power Hour’s Senior AI Specialist, Michael Helbling.

Episode Transcript

[music]

0:00:06 .9 Announcer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

0:00:18 .2 Michael Helbling: Hey, everybody, welcome. It’s the analytics power hour, episode 270. Back in 2023, I asked AI to write an intro to the podcast, and in the words of Moe, AI did a pretty shit job. But AI hasn’t gone away, and the possibilities, capabilities and potential of these LLMs are expanding, seemingly by the minute. So today we’re strapping on our robot helmets and plunging head first into the wild, worrying world of artificial intelligence. So what’s AI really for? Is it just a fancy predictive model? Maybe just a massive checkbox on your boss’s latest buzzword bingo card? I don’t know, hype versus help? Automation versus annihilation? And whether or not your next co worker might be, I don’t know, a chatbot with boundary issues. So grab a drink, mute your Slack notifications, and prepare to find out if your career path is evolving or being quietly replaced by a GPT powered spreadsheet whisperer. Speaking of spreadsheet whisperers, let me introduce my co-hosts, Julie Hoyer.

0:01:23 .2 Julie Hoyer: Hi there.

0:01:28 .1 Michael Helbling: I’m very excited that you’re on the show, Julie, because I feel like you’re probably one of the most knowledgeable people about AI in our group, so I’m going to be leaning on you quite a bit.

0:01:36 .6 Julie Hoyer: I don’t.

0:01:37 .9 Michael Helbling: No, I’ve been observing all of us and pretty sure, yeah, you’re going to be.

0:01:40 .6 Julie Hoyer: I’m the sleeper.

0:01:45 .9 Michael Helbling: I mean, we’ll see. We’ll see. All right, next up, Val Kroll.

0:01:49 .5 Val Kroll: Hello.

0:01:50 .5 Michael Helbling: Val. I did love the April fool stuff that you and Tim put together for facts and feelings.

0:02:00 .6 Tim Wilson: A month ago.

0:02:01 .6 Michael Helbling: So I guess that’s a good use of AI. Yeah.

0:02:02 .6 Val Kroll: Yeah. Facts and furious.

0:02:04 .3 Michael Helbling: Yeah. April Fools a month ago. Everyone knows when April Fools is, Tim. Moe Kiss…

0:02:10 .2 Val Kroll: He’s just remembering back.

0:02:12 .2 Michael Helbling: Yeah, welcome, welcome.

0:02:17 .5 Moe Kiss: Thanks. Excited to be here.

0:02:18 .3 Michael Helbling: Would it surprise you to learn that good chunks of that intro were written by AI?

0:02:25 .0 Moe Kiss: Yes. Yeah, yeah.

0:02:25 .8 Michael Helbling: They were.

0:02:26 .3 Moe Kiss: Yeah. Sounds legit.

0:02:28 .9 Michael Helbling: The models have progressed quite a bit. And speaking of people who haven’t progressed quite a bit, Tim Wilson. Count…

0:02:37 .8 Julie Hoyer: Insert cheering sound.

0:02:38 .4 Tim Wilson: XLOOKUP.

0:02:41 .2 Michael Helbling: XLOOKUP.

0:02:42 .8 Tim Wilson: That’s right.

0:02:44 .4 Michael Helbling: I’m whispering to the spreadsheet. That is actually the first…

0:02:50 .0 Val Kroll: Eye roll.

0:02:51 .6 Michael Helbling: No. I literally read Tim’s blog way back in the day with Excel tips and tricks. Like, I learned things from Tim Wilson about Excel. So that is true…

[overlapping conversation]

0:03:02 .1 Tim Wilson: 2008.

0:03:04 .5 Michael Helbling: Hey, listen, it’s working for you, so don’t give up, all right? I’m Michael Helbling. So, yeah, let’s… What do the kids call it? Vibecast or Vibe podcast? I don’t know. Let’s do this thing. All right. So, Julie, is it going to take our jobs, this AI thing?

0:03:25 .6 Julie Hoyer: No, definitely not.

0:03:26 .0 Michael Helbling: All right. No, thank you.

0:03:26 .1 Julie Hoyer: My experience.

0:03:26 .4 Michael Helbling: Great show, everybody.

0:03:29 .6 Julie Hoyer: I’m not worried. See you next time. Rock flag.

0:03:41 .0 Michael Helbling: See you next time. Okay, but why isn’t it going to take our jobs? We should probably dig into that a little bit. And let’s also maybe dig into what our jobs are a little bit so that we can kind of see where AI helps, where it doesn’t. And I guess other people can also chime in too.

0:03:57 .5 Julie Hoyer: I guess. Okay. Most recently, something I’m running into a lot is… And I feel like this is an example we’ve talked about previously on the podcast, multiple times. A lot of people have written blog posts about it. And it’s just funny because now I’m fighting this battle in multiple fronts at work. The same discussion of. I think for analysts, AI is not ready to just replace us. Even for, like, writing queries. There is no, like, talk to your AI and ask it your business questions and have the data insights come from, your big data warehouse or anything. People are still so excited about that.

0:04:30 .7 Tim Wilson: Wait a minute.

0:04:32 .5 Julie Hoyer: From what I have seen, it’s not…

0:04:32 .4 Tim Wilson: Debugging versus giving it a… I mean, Julie, just said from business question to having it write it.

0:04:37 .9 Julie Hoyer: Yeah.

0:04:40 .3 Tim Wilson: Which…

0:04:40 .9 Val Kroll: Yeah. Fair.

0:04:43 .1 Michael Helbling: Yeah, Yeah. I think the distinction is important, but let’s let you keep going.

0:04:49 .6 Julie Hoyer: Yeah. I think it’s still that there’s a lot of this, like, fantasy of, like, it’s gonna be so much faster for an analyst. Like, go into your analytics tool and just like, type away your questions that you have to answer and get insights really quick. And I have just had some specific, like, experiences recently where I’m like, see, it’s still not. You guys are saying that that’s like the promise, that’s what they want, but it’s not true. So I’m still not seeing it even in that sense of for an analyst and reporting, we’re not close to that, which that takes a ton of time as an analyst to synthesize the data, put it into a coherent answer, and have it be insightful for your business stakeholder.

0:05:26 .4 Moe Kiss: Without giving away too much, this is a delicate tightrope to walk. Ah, so what we’ve been trialing, and there’s some super smart people at Canva. Adam Evans had a really brilliant idea, and then Sam Redfern, who I used to work really closely with, has been exploring kind of productionizing it. It’s been really cool. It’s like looking at like, what are the top queries that are getting asked, like SQL queries, versus like a table, right? Or like a report table or a model table, and then using AI to help like generate the best query possible to get back the data. And what we’ve noticed is if we do that, and then we return the data back, and then ask our business question, it’s doing a better job. And we’re starting to like test that out across multiple different business streams. And I’ve decently played with it and pretty comfortable. Like I think the thing is like you definitely, we’re not at a point where you don’t need a data person involved at all. Like