The Claude Workflow Nobody at the VP Level Is Showing You

1. 0:00 – 1:57

   Intro

   1. MWMatt Wensing0:00

      AI for leaders is ultimately a test: how good are you at decomposing problems? AI is very good at solving a problem, but it will simplify the problem space if you don't properly decompose it.

   2. AGAakash Gupta0:12

      Everyone talks about shipping with Claude as a product manager, but how do you do it as a product leader? [keyboard clicking] Or if you are a product manager, how do you handle those leadership tasks? Meet Matthew Wensing. He is the VP of Product and Design at Customer.io, which passed $100M ARR, just shipped an AI agent, and is dominating the market.

   3. MWMatt Wensing0:37

      Forcing yourself to dwell on the problem for long enough to really decompose it and see all the pieces separately is where you're gonna create the most value.

   4. AGAakash Gupta0:43

      Today, he is going to break down how he built an all-hands presentation in just two hours, and he's gonna break down the 80% that you should let Claude do and the 20% that you should focus on yourself.

   5. MWMatt Wensing0:55

      A lot of junior employees, which I would consider Claude one of, very talented but very junior, is very eager to please, and because of that eagerness, it will go too far too fast. You use it to generate alignment. I think that's bound to fail. Executives are the best at filtering out noise.

   6. AGAakash Gupta1:10

      If you stay till the end, you'll know exactly how to use Claude to build company-facing, board-facing level and quality documents. Before we go any further, do me a favor and check that you are subscribed on YouTube and following on Apple and Spotify podcasts. And if you want to get access to amazing AI tools, check out my bundle, where if you become an annual subscriber to my newsletter, you get a full year free of the paid plans of Mobbin, Arise, Relay app, Dovetail, Linear, Magic Patterns, DeepSky, Reforge Build, Descript, and Speechify. So be sure to check that out at bundle.aakashg.com, and now into today's episode. [fire crackling]

2. 1:57 – 3:15

   Why most AI content misses the leadership tier

   1. AGAakash Gupta1:57

      I've been looking online at all of the content on AI for PMs, and I noticed two really big gaps. The first is that most of the content is written for ICPM tasks, how to write a PRD, how to conduct analysis on a feature. What if you elevate that from PRD to all-hands presentation? What if you elevate that from analysis of a feature to metrics retrospective that you want to give to other C-suite leaders? That's something all PMs have to deal with, and especially product leaders have to deal with. So I brought in somebody who's willing to share the real stuff, not canned hypothetical conversations, but the actual documents that he presented to his peers. Matthew Wensing has been gracious enough to create unparalleled insight into how a real VP of Product at a real hypergrowth AI company uses AI. So if you stay till the end of this episode, you'll get to learn three things. Number one, how he builds all hands in just a single morning. Number two, how he runs metrics retrospectives with his peers. And number three, his entire weekly AI stack. So without any further ado, Matthew, welcome to the podcast.

   2. MWMatt Wensing3:11

      Thanks so much for having me.

   3. AGAakash Gupta3:12

      It's my pleasure. What are people gonna learn

3. 3:15 – 4:06

   Matt introduces what viewers will learn today

   1. AGAakash Gupta3:15

      today?

   2. MWMatt Wensing3:15

      So today, I wanna draw back the curtain a little bit here and show you what it's like for me to be adopting AI at the, uh, leadership level, uh, here at Customer.io. My job is, is no longer, uh, although it was for many years, to, to be an engineer, so as a full stack developer, uh, but always product-minded. It is now to really help entire teams understand what it is we're working on and why, and to, uh, be that leader. And I think that, uh, Claude h- can also help us, but it can also be very finicky, tricky, and hard to manage at times, and I'd love to share, uh, how I'm learning to manage it well.

   3. AGAakash Gupta3:50

      Amazing. One of the stories you told me [chuckles] is that you had an all-hands presentation to give to the company at 11:00 AM. Like most leaders, your week was booked, so you decided to wake up early at 5:00 AM the day of, and you built the entire presentation. Can you walk us through that

4. 4:06 – 6:19

   The all-hands presentation story begins

   1. AGAakash Gupta4:06

      and how you did it?

   2. MWMatt Wensing4:06

      Yeah, happy to do that, and, and as I do, I should say, you know, the all-hands presentation was broader than just me, but I had a significant portion of it. Let's say I was, uh, roughly a third of the presentation, and the idea was to present our Q2 roadmap, um, which is a common, common thing that you need to do as a product leader, is explain to the company what we're working on next. And I had a, a vision for what I [chuckles] wanted to do, um, but I came into it, uh, early in the morning. And I am a morning person, so I think maybe real quick aside, know yourself, right? So in this case, I am a morning person. 5:00 AM for me is, like, prime time. Let's, let's rock. Not for everyone, but, uh, that's what I did. I sat down, and I had these Google Slides and this template. I knew what I wanted to do and what I wanted to accomplish, but I really needed Claude to help me.

   3. AGAakash Gupta4:48

      So how did you work through it? What were the parts that Claude was able to do well? I know I was actually just recently giving a presentation, and I had been editing with Claude till the last minute, and then while I was giving the presentation, I found mistakes, which is, like, the worst-case scenario. [chuckles] So how do you actually wrangle Claude so that you have the confidence that you can use it as a slide editing partner?

   4. MWMatt Wensing5:10

      The first joke [chuckles] is that, uh, whatever you do, don't click enhance this slide. [laughs] We, we don't, we don't know what that does, but we don't click that, uh, around here. It's a, it's a good running joke that I have, that I terrorize people by saying, "I clicked enhance this slide [chuckles] for you." Don't do that. And so what I did instead is I... You know, I think, one, you know, you need to understand who are you communicating with. So go back to basics r- first. You know, who am I communicating with, and what do I want them to take away from this? I think reminding yourself of that is your anchor. And so in this case, it was, you know, the entire company, which is a blend of go-to-market, sales, marketing folks, and engineering. And so that got my gears turning around, "Okay, I'm trying to provide a, a window into our product roadmap and our plans for Q2 to folks in the company that are not, uh, involved in actually the making of this material." So, so that started my gears turning around what's the raw material that I want to put into Claude, and I think if you think about AI, and I'm gonna mention Claude a lot synonymously here, 'cause that's my tool of choice, but, but if you think about AI as a, you know, excellent at taking raw materials and turning them into something else, I think the first thing you wanna do is just have that inventory of raw materials, and go through that in your own mind. And don't jump into,

5. 6:19 – 7:27

   Take inventory before you open Claude

   1. MWMatt Wensing6:19

      "Hey, let me start building the presentation." It's like, "Let's take an inventory of what we have on hand first."

   2. AGAakash Gupta6:23

      How did you go from there?

   3. MWMatt Wensing6:25

      Yeah, so, so in this case, the raw materials I had were, fortunately-You know, our company, um, and I think you see this in a lot of cases, our company had prior materials. One of those that was really valuable was our demo day, uh, presentations. And so I'm, I'm showing you a, a, a screenshot now of this is the Zoom recording of the demo day, and this is me going into Slack at, you know, 5:10 AM and going, "Okay, there's a link to the Zoom recording of the demo day." That's excellent raw material for this presentation, but, you know, this presentation, this demo day was for the engineering team to share amongst itself. And so I was like, "Okay, what I'm really doing here is this is great raw material. This is one of them, but this isn't shaped correctly. This isn't actually pointed at the right audience. This is sort of engineering for engineering's sake." And so I, I had this and I downloaded, you know, the eight files from this. I said, "Give me the video and give me the transcript with all the timestamps." Uh, and that was the first bit of input that I put into, uh, put into Claude.

   4. AGAakash Gupta7:20

      Is there anything around using Zoom transcripts people should be aware of? Can you just copy-paste a whole transcript

6. 7:27 – 9:44

   How to use Zoom transcripts as raw material

   1. AGAakash Gupta7:27

      in?

   2. MWMatt Wensing7:27

      Yeah, so, well, I, I, I did try that at first. I copy-pasted the entire transcript in, and I gave it very clear, um, I gave it very clear directions in terms of what I wanted to do, um, which was I gave it, I gave it framing, I gave it context. I treated it like a junior employee, which is something you'll hear me say, you know, elsewhere as I walk through these examples today. Here's the thing. All I want you to do is extract the timestamps from this, because what I really need is I need this for my screenshots. I want to put screenshots into this presentation, um, and so you'll see, you know, I have screenshots here. I need these screenshots, and the only place I'm gonna find them is in this Zoom call, but I don't have 50 minutes to go through the Zoom recording, so here's all the timestamps. I need you to help me find the right places in this video to grab these screenshots for me.

   3. AGAakash Gupta8:11

      So if we walk through this deck, which parts of this deck are you and which parts are Claude?

   4. MWMatt Wensing8:15

      You know, the shapes and the, and the art is obviously, is from our, you know, marketing team or our in- you know, brand design, uh, team. So that was handed to me as templates, but the parts that were me were I knew I created this basic shape of, okay, for the Q2 roadmap, we had just had a huge launch. So in terms of the parts of this that are me and Claude, um, again, going back to that inventory, one of those is also just the shape of the narrative. Um, I'm a huge believer in storytelling and upping your craft in terms of storytelling. For me, I said, "Okay, we just had a huge launch." This was the middle of April. It's important for me to acknowledge, you know, people when it comes to stories, they wanna know, like, where do we leave our hero? Where do we leave off last? And so in that sense, it was we need to acknowledge the launch. We need to acknowledge that we're, you know, what are we doing immediately post-launch, and then use that as a bridge into the rest of the material. So I decided ahead of time that, you know, that's just my own storytelling craft of we're gonna do a, a, a few slides on, you know, adoption metrics, where we're at and what are the fast follows that we have.

   5. AGAakash Gupta9:13

      Yeah.

   6. MWMatt Wensing9:14

      And then we're gonna go into the strategic, right? So I wouldn't outsource that to Claude. I mean, perhaps you can, and I could turn that into a skill, but, you know, when you're doing these things maybe three times a year [chuckles] , you know, it's not worth the tooling investment just yet. Um, but in this case, I decided that from there, that helped me think through, okay, what are the raw ingredients again for each of those? So the raw ingredients for the, the, the strategic look-ahead part, which is what I'm showing here, where I'm gonna need all these screenshots, that's the Zoom transcript. Um, or that's the Zoom call recording

7. 9:44 – 12:14

   Ads

   1. MWMatt Wensing9:44

      transcript.

   2. AGAakash Gupta9:44

      I ran an eval suite on my own site this week. Same 10 questions, same 30 session traces. Two replay tools, LogRocket's Galileo AI and PostHog's Max. The score was 47 to 28. I wanna tell you why. Here's the thing. Every analytics tool now ships an AI layer. You ask it why users churn, why they rage click, what broke. It's a cool pit. But you and I both know confident is not the same as correct. If your replay AI hallucinates one bug out of 10, you're shipping the wrong fix on Monday. So I treated it like an eval problem. Ground truth, rubric, score. Here is the setup. My site is landpmjob.com. Bolt hosted, real traffic, real bugs. I instrumented LogRocket and PostHog both running on the same session side by side. I wrote 10 questions a PM would ask. Top user paths. Where do users hesitate? What bug is hitting the most users? What's the mobile versus desktop conversion gap? I asked each tool's AI the exact same question, captured the trace, then scored each response on four axes: correct, complete, hallucinated, and a one to five overall. No vibes, just the rubric. Here's the headline. Galileo got 47 out of 50. Max got 28. The gap came from one place. I asked both tools, "What is the highest impact bug on the site right now?" LogRocket Galileo came back with this. Minified React errors 418 and 423, hitting roughly 47% of users. Breaking my Apply Now buttons across the header, the pricing cards, the FAQ accordions. This is what PostHog Max returned. PostHog Max returned zero exceptions. Why? PostHog's exception capture is opt-in. You have to flip a flag. LogRocket auto-captured the most expensive bug on my site without me asking. The other one that surprised me, Galileo found a single session where someone stared at my $5,000 pricing page for four minutes and 15 seconds before bouncing. Four minutes. That's the most useful piece of qualitative evidence on the site. PostHog's AI didn't service it. I'm not telling you to rip out your stack. Amplitude, PostHog, they're great. Pendo, all of the options are great. PostHog actually built sharper funnel on question nine, and that's worth saying out loud. But if you live in front-end bugs, console errors, network waterfalls, what the user actually saw on their screen, Galileo's eval performance lined up with what LogRocket is built for. Full write-up, all of the details is on the link in the description. Thanks to LogRocket for sponsoring. Do check them out using my link, and now back into today's

8. 12:14 – 13:50

   Matrix multiplication, pivoting content into strategic shape

   1. AGAakash Gupta12:14

      episode.

   2. MWMatt Wensing12:14

      It was also for this part, "Hey, here's our strategy doc." It needs to know the three themes of our strategy, and then what I did with those two, and this is where Claude created a ton of value, so the, the Claude part is we had all of these, uh, presentations, these lightning round presentations, what we're working on next. I also had the strategy doc, and now in order to take that Zoom call and turn it into something more strategic, what do I need to do? I basically need to pivot that Zoom call into... the same shape as our strategy docs. And so I said, "Okay, I've got this doc which outlines our three themes for the year in terms of, uh, product and engineering. Can you please go through that Zoom call recording now for me and instantly organize all of those presentations by the category, like investment theme that we have for the year?"

   3. AGAakash Gupta13:00

      Mm.

   4. MWMatt Wensing13:01

      And so that was instantaneous as well. So it was that like, I have this raw ingredient over here, I have this one over here, and I essentially want you to do what I call like matrix, matrix multiplication. Um, it's a transformation. It's like a pivot, right? I want you to pivot this content to match this content, and that becomes something I can actually use and go, "Okay, this content is the shape I need. This is the strategic shape, but this is the raw material over here. Can you please adapt this to this?" And once I did that, it was like y- you could almost feel that audible click or hear the audible click of, oh, okay, now that Zoom call recording is strategically shaped and I can start to flow it into these slides.

   5. AGAakash Gupta13:40

      So that happens pretty quickly. I imagine you could've done most of what we-- you, you just described in 30, 45 minutes. What went on from there? How did you edit it and polish it from there?

9. 13:50 – 15:08

   Build slides first, then talk track

   1. MWMatt Wensing13:50

      Yeah. So the edit and polish from there was the talk track, and the talk track was, um, just as much work. You can see I kinda X'd out a bunch of stuff here at the bottom, but this talk track was really important to nail the, the beats of the story. And so I did-- I had it do that last. I believe that some people write the talk tracks and then they do the slides. In this case, I chose to do the slides 'cause I knew... I knew what I wanted people to see, and I'm a big believer in show, not tell, so I wanna get the show part right first. But then the tell part becomes [chuckles] and this is what I did. I actually took screenshots of the finished slides, and then I fed those back to the same Claude session and I said, "Hey, write the talk track for this, and what I don't want you to do is I don't want you just to regurgitate. Like, you have all this context now. You've, you've ingested the entire call recording, you've ingested the entire strategy docs. You know what I'm showing, so I'm showing you what I'm showing. Now I want you to write the talk track last that doesn't just repeat what's on the slide using all of that context that you have." And it came up with a much more interesting talk track then, as opposed to write my talk track and then I'm gonna put together some, some slides. So I think the order in which you do things really matters.

   2. AGAakash Gupta14:57

      You said something that stuck with me on my c- on our call. You said that Claude sometimes has a leash, and sometimes it goes too far and you have to reel it back in. Can you explain that and a time you had to reel it back

10. 15:08 – 19:02

    The eager junior problem and how Claude races ahead

    1. AGAakash Gupta15:08

       in?

    2. MWMatt Wensing15:08

       Yeah. I, I think reeling Claude in is a constant challenge. Um, I'll, I'll give you an example. I actually have two examples of that, and I'll, I'll start with the first one, which is, um, I have a rule when it comes to, and I even published this in my how to work with me doc that I share with, with new hires, that a lot of junior employees, which I would consider Claude one of [chuckles] uh, very talented but very junior, is very eager to please, and because of that eagerness, it will go too far too fast. And so this one was an example where I was doing, uh, a write-up, like a root cause analysis, uh, on some metrics of ours. And so I had, I had extracted some data, I gave it to Claude, and I asked it to look at that data, and I was working through it and, you know, the first thing it did was, uh, want to go through and just generate, and I'll s- I'll find the example here. It wanted to generate a whole, um, pricing strategy doc for me in Word, you know, and, uh, 'cause it's super eager to please. And, and back to the rule of thumb I have, you know, I think a junior employee will hear a prompt or a first instruction and go, "Ooh, I know what to do. I wanna please my manager, I wanna please the boss, and I'm gonna go do this thing and just knock it out of the park," right? And they won't ask any follow-up questions. They'll just race to the finish line and go do that for you. And then when they come back, inevitably you need to change it substantially because they didn't ask enough clarifying questions first. And I don't know what it is in the training, and I think likely a way that you can improve sort of your harness and, and get it to behave differently, um, I've been iterating on this with, uh, my own, but helping it not rush ahead and say, "We don't use Word docs around here, and if I share, if I share a strategy doc as a Word doc, people are gonna think I'm crazy. Like, that's not how we work." But it's so proud of itself, right? It just go-- it just launches into that. And so I have a habit of doing what I call, um, very iteratively rolling context into it, into the session so that it doesn't... Couple things: One, it, it's not e- it doesn't have that eager to please, and if I see that eager to please, like, "Hey, do you want me to do this next?" I very quickly say, "Stop. Stop recommending the next step. I will tell you [chuckles] when I want you to do the next thing." Just to kill that thinking out of the session, and instead-- 'cause it also is very, um, you know, it's, it was like that drip torture. Like, at some point it keeps nudging you like, "Hey, do you want me to write the thing? Do you want me to write the thing?" And it almost feels like you're being aggravated into, you're being coerced into saying yes, where you're like, "It's not time yet," right? [chuckles] It's like, you don't have enough context yet and you don't know that, and, and you really need to stop asking me to run ahead and generate the deliverable. So I think these sessions, you know, they can take... You know, it's better to have that 50, 100, even 200 iteration session with a great deliverable at the end than to say yes to that first ask to generate that deliverable and then try to revise it. Because inevitably, that first draft of the deliverable is just full of so many, so much slop. And, and I actually have a way of thinking about slop. I think of them as like micro hallucinations. It, it's not that it's totally wrong. It's like, okay, you need a doc, but here's a Word doc. It's like, well, wait a minute. You're not hallucinating in the fabricating data sense, but you are hallucinating about the way work gets done here, and we don't use Word docs, right? And so it's just, it's trying so hard to please that you end up having to just tear down and redo so much work, it's not worth it. And so I, I've developed a few tricks on how to force it to kind of slowly crawl along with you through the mud [chuckles] and really earn its right to generate that final product instead of rushing ahead and developing it right away. Which, you know, we, we think of that as like the magic of AI, right? As like it, oh, it can just do this thing so quickly. But as a leader, you don't wanna microwave your output, right? If that's all you're doing, that's low value. You wanna really slow cook these things more often and produce something that is really, you know, to produce something that's really compelling, impactful, resonates with your audience, you've gotta slow it down.Enough to build that story that you're trying to tell

    3. AGAakash Gupta18:58

       So how do you slow it down correctly? How do you avoid kind of becoming the slop cannon?

11. 19:02 – 23:21

    The biology metaphor session begins

    1. MWMatt Wensing19:02

       So I, I have a, another example here, and this is the most extreme version o- of that. And I was actually, um, so I do a lot of my interactions with, uh, AI, um, by voice, and especially when I'm on walks. So I tend to use Chat, the mobile app, more often for this 'cause I just really love the, you know, the voice mode on that. Uh, but in this case, I was... I, I am getting won over more and more by Claude, and so I was using Opus 4.7 and I was, um, which I know there's some, like, debate on 4.7, 4.6. But I just kind of launched into 4.7, and I started to use the, um, the microphone button, and I did something very deliberately for the first time, having learned this enough. I think like a lot of leaders, I do tend to use analogies a lot, probably you can tell even, even on this call. And, uh, I think analogies are super helpful to kind of clear away the noise and to focus on... The analogy's purpose is to kind of say, "This is the, the main idea," right, is embedded in this analogy. We're gonna transfer that over later to the domain that we're talking about. But let's, let's actually just work through the analogy. And so in this case, I was trying to build, uh, an entire, like, life cycle model. So I was thinking through the life cycle of our customers and how they kind of enter the business and they, you know, sometimes they churn, they expand, you know, all these things they do. But I didn't want AI to know that yet. I-- So what I did is I insisted on using, in this case, like, a biology metaphor, and I just started talking in terms of, okay, let's imagine we have this, um, this ecosystem, right? And it's, i- in this ecosystem, it's sort of like the, it's like the water cycle or a life cycle. You know, everybody knows that, like, caterpillar drawing of, like, the, the caterpillar and it, you know, or whatever, and it grows up and it hatches and it... Or the water cycle. Like, let's think in metaphors for a minute. And I think what's interesting is that Claude at this point, it has all this context of me. It knows I'm a VP of Product. It knows that I work at Customer.io. It knows, you know, it knows things about my personal life, frankly, if I share, you know, the things that I'm doing, um, outside of work. And so it's putting together this picture of me, and it's always trying to add value by anticipating or reading me or, like, understanding, okay, what-- I know where he's going with this, and so then it jumps to that next step. I think what was fun about this exercise is, and you can, you know, start here, is assume you have a two-by-two matrix, and you have each of those representing a stage of life, right? Where things begin at the bottom left and proceed to the top left or the, uh, or the bottom right, and then they finish at the top right. I'm talking in very abstract terms, right? At this point, I think it has no idea-

    2. AGAakash Gupta21:26

       [chuckles]

    3. MWMatt Wensing21:26

       ... where I'm going with this. And that is, to me, a, a, it's a virtue, right? It's, it's a benefit because if you look down at how it responds, it goes, "Okay, before I give you a number, I wanna check a couple things here because the count swings a lot depending on what you mean." I asked it a question about, like, "Hey, can you summarize the number of permutations of these pathways or these, like, life cycle things that are going on in this, like, fictitious system?" It is, like, so far out, out there now in terms of its brain and what it knows. It's like, okay, this is, this is weird. You know, I, I don't know where he's going with this, but, like, I'm gonna, I'm gonna go on this journey with him. And, and you can notice at the end of this prompt or this response, Claude, like, doesn't know where, where I'm going yet. It's like, before it-- So what is it doing? It's asking a clarifying question. And I thought that was such good immediate validation that what I was doing was working, is that rather than jump into, "Do you want me to generate a presentation on, like, customer life cycle and, you know, bring in all of it?" It didn't do that, right? What it did instead was it goes, "Hey, before I answer this question with this very, like, esoteric thing we've never talked about before, and I don't really understand where you're going with this, let me ask a clarifying question." And a senior person asks clarifying questions of a leader before they go do a thing. And so I thought, to me, this was a s- a, a sign that it was thinking more like a senior person. And you could probably codify this, right, into a, into a skill or a prompt, right, where you say, "Please ask clarifying questions before you act." But this is a example of me going, okay, even if I don't do that, can I lead it down a path? And so, you know, here we're over and over and over again, I'm going through these, like, layers. And what, what I also did was I started to add complexity. So at the very beginning of this, this is a very MBA, like, SWOT analysis or two-by-two or something extremely simple. By the middle of this, I'm starting to add in, okay, um, there's actually a-- The, the rules of the game say that actually, you know, the thing can enter from the bottom right or the top left or the bottom left. Like, and it

12. 23:21 – 26:08

    The game night rule for layering complexity

    1. MWMatt Wensing23:21

       goes, "Oh, I get it." And so have you ever been to, like, one of those game nights with friends and they basically-- somebody explains the, the rules of the game? And there's two kinds of people. Like, one person explains the rules, and they feel like they have to explain every single rule, but also exception to the rule as you go through. And, like, when those people explain, in my experience, people's eyes tend to glaze over 'cause they're just like, "Okay, so there's these rules, but then there's all these exceptions to those rules, so there's, like, rules on rules." And they're, like, so lost, right, in terms of understanding, like, the, the overall game that we're playing. Like, what are the goals? What are the objectives? What are we really trying to achieve? How do you win? I think with Claude, it's very similar. Like, I think it benefits when you tr- when you start very simple with, "Here are the rules of the game. Here's how we're gonna win. But I'm not gonna give you all of the exceptions to the rules and the nuances at first. I'm gonna treat you like-- I'm gonna, I'm gonna give you just the very crude, you know, heuristics or very crude outline of what we're trying to-- of what I'm thinking." And then, you know, like I said, layering in, iteratively layering in the complexity. So, you know, now I can see it's going through, and I'm actually going, "Okay, there's, there are these nuances, there's these exceptions, there's different ways these things can do." You know, oh, actually, you know, the speed matters. And each time I added in that complexity, stabilize that foundation of we're not relitigating or revising what we've already established. You know, the rules of the game, these are the basics, but we can add in this complexity. I think if you dump all of this complexity in at once, or you try to, it, it's just like a person. It gets indigestion, it gets, you know, mental fatigue, and it goes through and it's like, oh, and it ju- it just overdoes it, right? And you just end up with this, like, super complex, lost in the woods... experience where it, it's really proud of itself and, and maybe you should be too, but you're not 'cause you're like, "Where did I, where did I go wrong here?" [chuckles] Right? I think you've actually-- You've not only done the AI a disservice, I think as a leader you've also... It, it can be a little counterproductive to you as well, where I think you benefit from, am I really developing clear thinking each time I go through these iterations or these loops? Or am I, like, rushing to conclusions, right? Because another thing that can happen is if you add in too much complexity too fast, you yourself aren't challenging enough of your... So, so a lot of this was me going, "Oh, that's, that's interesting." Like yeah, I, I guess this is, like, another aspect of this that I hadn't really thought about. I was, like, giving myself-

    2. AGAakash Gupta25:44

       Yeah

    3. MWMatt Wensing25:44

       ... time to think instead of, again, rushing to that conclusion, which is like, at the end of this, what did I need? At the end of this, I'm actually trying to build a pretty holistic view of our customer base. And it, um... And it wasn't until here, so we're now, you know, many iterations through. It wasn't until here where I finally told it, um, what this is for, like, what's the purpose

13. 26:08 – 36:26

    Revealing the domain only when the model is clean

    1. MWMatt Wensing26:08

       of this. And then it, it was really funny. [chuckles] As soon as I told it the purpose, it got really excited and it started running down the path and I think there's a, there's a, a response here that I shared where I go, "Do you want me to draft this as a Notion page in a pricing philosophy section, or as a one-pager that Colin could have?" Like, Colin's our CEO, who I report to. "No. Let's stay abstract for now." And it says, "I was sort of afraid of telling you what this was for because you would get a little excited like a junior intern. We need to stay academic for just a little bit longer." And so this is me literally coaching it through like, I was afraid this would happen, but like, again, I kinda held, I held things at bay for as long as I could. I, I wrung all the value that I could out of the abstraction and out of the analogy. Only once I was sure that, you know, okay, gosh, now I have to tell him what this is for, [chuckles] like, then I did. But I think once I did, it like... It was like, "Oh, I get it." Now we're gonna take the output of this exercise and again we're gonna pivot it or, or we're going to apply it to this domain, right? I could've gotten to the end of this exercise and honestly applied it to any number of businesses. I could apply this to a, to anything from a, you know, e-commerce business to a bakery to a dry cleaning business to a Customer.io. And the fact that I didn't let it assume that it was relevant to Customer.io at the beginning let me then build what I think is a much more, like, clean mental model to work with, and then apply that to a domain, right? Almost as like a stress test. Like, hey, if this... Go ahead and apply this to Customer.io now and if it generates, like, nonsense, then I know something's wrong with the model. But if it generates things that, like, "Oh yeah, we see that," or like, "That lines up with reality," it actually gives me more confidence that the model itself is correct.

    2. AGAakash Gupta27:49

       I think there's a really important point embedded within here, which is that as a leader, we don't exist just to, like, generate Claude outputs, right? We generate, we exist to create mental models, to reframe, to drive alignment, in this case, with a bunch of other leaders on the pricing philosophy. So what we need to do is actually present-

    3. MWMatt Wensing28:09

       Yeah

    4. AGAakash Gupta28:09

       ... a way for them to think about these are our pricing options. And so you use Claude-

    5. MWMatt Wensing28:14

       Yes

    6. AGAakash Gupta28:14

       ... as a thinking partner to drive what you're gonna show in this metrics retrospective. You didn't just jump to, "Claude, let's build out this metrics retrospective." You actually focused on the thinking.

    7. MWMatt Wensing28:26

       Yeah. Yeah, focused on the thinking and then, and then used what I think it's really good at. And I think when-- I remember when Chat... I think it was GPT-4 came out, and we all kind of remember. It was like one of the, "Where were you when GPT-4 came out?" kinda feelings. I remember telling my father-in-law, who is a, um, you know, he's a, he's a doctor, and so he, he does a lot of intellectual... And I remember talking with him and saying, "You know, it's, it's getting scary good." And, and what I realized was it was suddenly able to take what I think is the bulk of a lot of... And you just said it, to generate Claude output. I think a lot of, a lot of the work that we do as leaders and as, um, sort of that knowledge worker, I call it, uh, blue... I, I intentionally call it blue collar knowledge work. And people are like, "What do you mean blue collar?" Because, like, those are different. And I'm like, blue collar knowledge work is taking information and just, again, pivoting it or translating it from one place to another and going, "This is a slide deck. It needs to be a Google Doc. This is a Google Doc. It needs to be a slide deck." And for a long time before LLMs, we-- people were paid to do that translation work or that transformation work. That is the sort of lowest tier of, uh, work that's being j- It's been wiped out, right? It's that if you need to transform the form factor of this material, that's not enough anymore, right? It's not enough for me just to take, like you said, a snapshot of the pricing page, ask one question, then say, "What should we consider?" Right? [chuckles] And then have it generate an output. I have to bring some kind of novel way of thinking to the table, and it, to me, it's not the thing on the right or the thing on the left. Those are the... This is the source, if you will. This is the target. The source and the target are not new, and I think even the work to translate source to target is not, i- is what we used to do. That's where we used to create a lot of value. That arrow of, like, "Hey, translate this source to this target," and people would just work away. A lot of middle management would just work away at that kind of stuff, consultants, et cetera. The real work that's left for us is how do I choose the right source [chuckles] and how do I choose the best target? And so if you think about the targets as the stories you tell, the shapes of those stories, the form factors, the deliverables. Should this be slides? Should this be this? Should this be that? That's a choice, right? You're still making that choice. And in terms of the sources, is the source a clean mental model about pricing life, you know, philosophy? Is the source, you know, these seven conversations in Slack? I think we need to take a step up and go, "Oh, my value is actually choosing the sources or set of sources to use, and then being very deliberate about or strategic about what I translate those into," right? And so you've gotta say, "I'm not, I'm not just running the function. I am being very deliberate about the inputs and the shape of the output." And that is still extremely valuable. And now what's awesome is you have AI to do the, the transformation work in between. So even if you're like, "Oh, that didn't really... I don't like how that's like this. Okay, run it again, but, you know, change this or change that." But I think before we would almost take the, the source and the target for granted, and then work really hard to do that translation work. I think now we can go, "Oh-"Translation work is easy. That's almost free. Spend the time thinking more seriously about the source, developing a really clean source information or mental model or, or, or conceptualization of the problem, a framework, right? And then be very deliberate about the target. I want to tell a story. I want to show, not tell. I want to hit on these points. I'll give you another example. In this exercise, I had developed some kind of, like, funny terms or, or, or cute terms almost for some of the things I was working with in terms of like, you know, these, these boxes, these four box... I had, like, a name for each box. They were kind of cute names like unicorn or, or horse. [chuckles] And, uh, Claude just kind of ran with that right away, and it's like, "Oh, I'm gonna, I'm just gonna, like, I'm gonna run with that. I'm gonna put that into the target," if you will, into the story. I had to come back and I, I have, um, I have the example for you in, in a separate, uh, in a separate slide here, but I had to come back and say, um, "Hey, here's..." At the very end of this, I said, "Here's what I ended up creating." It's this Google, it's this, it ends up in Notion. And I said, "What's, what's different about this compared to what you had generated?" And it confessed to me, and I'll, I'll, I'll bring up this example because I think it's really cool. It confessed to me that it goes, "You know, I, um, I myself used, I used those terms immediately. Like, I didn't stop and think, wait a minute, you know, those terms, those are new terms. If I put those into the story, if I, if I let those get into the sort of the target output, there are gonna be people who read this who are like, 'What? W- what the heck is a, a unic- like, what are these new terms you're, you're mentioning?'" Like, uh, and, and they're gonna, they're gonna focus way too much on those, and they're gonna resi- there's always a chance that somebody in their mind goes, 'Those are silly,' or, 'I don't understand those,' or, 'That's jargon,' right? It didn't know that, right? I had to, as a leader, tell it, like, "Don't... [chuckles] Those terms are between me and you. Like, don't use those terms in this output because the audience isn't going to react positively to those terms yet. We need to introduce those later, right? Maybe, maybe a month from now, right? As like, you know, 'Hey, by the way, you know, if you ever need, like, a, a silly way to refer to these kinds of customers or whatever, we call those this.'" And it's like, oh, that makes a lot of sense. But it doesn't have that social IQ, right? It's, that's not built in, and I think as leaders, we need to bring that to the table as well and go, "How," again, "how is this story gonna resonate, and are there gonna be things that people focus on?" And, and Claude just doesn't know that yet.

    8. AGAakash Gupta33:35

       Would love to see it. Let's see how that exchange went.

    9. MWMatt Wensing33:38

       So this was me coming back to the LLM afterwards and saying, "Hey, so I did just share, uh, the final document with Jason, who's our CMO, and a few direct reports." And I said, "I'm curious why an LLM like you might struggle to write this for me automatically. In other words, I, I, I wrote this instead." And, you know, we're getting to this point where it's like, is this, is this AGI? Like, you know, the holding up the butterfly and... 'Cause it's self-aware a little bit. It goes, the voice problem, right? It, it doesn't know how to speak like I speak just yet, and I think we can work on that. Like, I would be an optimist in terms of that. This one I'm less optimistic, at least I think this is farther along, the political calibration. So you knew to drop the animal names. I had these clever animal names in the source material and use simple small and large sophisticated instead. The choices of those were deliberate. You know how to read the room. Like, you know, and, and I don't mean political in the, um, oh, business is politics, like negative way. I mean more the, uh, who's the philosopher that said, you know, people are political animals, right? And, and we can't escape the fact that we're all thinking in terms of, you know, we're all judging and evaluating what we read as we read it, and it's, it's, it's second nature, and we do that for good reason. It's to help filter out the noise. And it's just not good yet at guessing what's going to translate well for your audience. And so as a leader, I think being on guard or vigilant about those. Th- that's not even a micro hallucination. That is a misreading the room and being too eager, and that's the same thing a junior employee would do, right? Is they wouldn't realize, oh, you know, [chuckles] there's a lot of baggage around that term, to give you a completely different example. It reads some source material from three years ago, right? And it, it adopts a term, and you know, you should know as a leader, there's a lot of baggage and maybe even some careers that are attached to that term, good or bad. I need to think about u- how I use that and when I use it, and Claude just doesn't have that historical context yet, right? So I think that's gonna take longer to figure out. And then I think the persuasiveness, I, I do think that there's a reason that the best authors and writers and, and storytellers of our generation are still not just using AI to generate those stories. It's bringing the reader along with you, knowing, knowing that they just came out of an all hands, knowing that they just had the biggest launch day in their history, knowing where they come from and leading them through. It's another thing that you can't delegate yet. That means that you need to be really good at going, yes, what's the, what's the mental head space or what's the sort of emotional space of my reader as they pick up this document on a Tuesday and they're like, are they, like, really excited? Are they exhausted? Are they, did they just get into a board meeting room, uh, after traveling all night? You know, you need to think about those things, and Claude just is never going to yet. Um, but that's where I think you can be exceptional as opposed to just, you know, accepting what the AI generates for you.

    10. AGAakash Gupta36:10

        So we just walked through two of the most important examples that a product leader needs to be able to use AI for, all hands style presentations, metrics retrospectives. If you were to synthesize against those, what are the key lessons for how to use AI and how not to use it for these

14. 36:26 – 38:17

    How to decompose problems before building anything

    1. AGAakash Gupta36:26

       types of work?

    2. MWMatt Wensing36:26

       Yeah. I, I'll give you the, the shortest version, which I think cuts across all these, is that AI for leaders is ultimately a test. How good are you at decomposing, uh, problems? Um, AI is very good at solving a problem, but it will simplify the problem space if you don't properly decompose it. And so I would think about AI as, you know, we all know one shot is not the answer in most cases. I think as a leader, not one-shotting something means not just, you know, iterating with it, but being very deliberate about decomposing a kind of a nasty problem into its pieces and then saying, "Ah, okay. The right series of transformations to do, you know, starting with this. If we want to get here, how do we decompose this problem right to left?" That's, you know, my brain works left to right, but, or the other way. How do we decompose this into a series of transformations that I'm confident you're gonna be good at, you know, performing... each of those transformations, and then we're gonna get to where we need to get to. I think where we fail is when we flatten problem spaces and we oversimplify, and then it's just, "Well, clearly the solution is this," right? So I would say challenge yourself to really take a, a nasty problem or a deep problem in your business and really explode it, right? Or decompose it into all of the pieces you can, and then put those pure observations, those pieces into the context window before you start to then assemble a solution. I think when you oversimplify and you just have this sort of flat projection of the problem, y- that's where you get slop, and the people reading it go, "This thing doesn't really understand the multidimensional nature of this problem, the complexity of this problem, why we haven't been able to solve this problem yet," right? Forcing yourself to dwell on the problem for long enough to really decompose it and see all the pieces separately is where you're gonna create

15. 38:17 – 40:56

    Why AI alignment decks backfire on executives

    1. MWMatt Wensing38:17

       the most-

    2. AGAakash Gupta38:17

       So I think this relates to kind of a central thesis we have, and you have a take on this that I don't think other people have said anywhere else. What happens when leaders try to use AI to drive executive alignment?

    3. MWMatt Wensing38:28

       I think we know what happens. You end up in this... I, I think that's bound to fail. I think people can feign alignment really easily. I also think it's important to define alignment. Like, what do you actually want about that? Some cultures have a, you know, disagree but commit attitude about them. Um, other ones have a, "We really need to agree on all the details," uh, like shared consciousness version of alignment. I think if you use it to generate alignment, I think executives are the best at filtering out noise and detecting BS and detecting slop. [chuckles] And so depending on where you are in that sort of hierarchy, you are going to get a variety of responses to what you've created. I think if you're more senior and you j- and you do that, I think you're gonna have a lot of people who feel obligated to smile and nod or go along with the flow or accept, like, what you've created and be-- and say like, "Okay, you know, I, I guess we can work with this, but have you considered this?" So if you start to hear those things, you, you might have a problem. That's if they feel psychologically safe to even, even say that. Um, if they don't feel safe, they're just going to roll their eyes or, or ignore you, right? Which is the worst kind of, uh, misalignment. But if you're lower, meaning, you know, okay, director level, senior director, uh, you know, kind of higher up VP, senior VP, so not, not C-level, but, um, elsewhere, i- if you will, in that leadership hierarchy, I think you're gonna find out that people, um, ignore your work or ignore your output and they, and they don't even really feel, uh, a, an onus or a responsibility to take it into account because they've basically filtered it out as noise. That's really disheartening. You work, you know, you think you worked hard on something and then you, you-- it doesn't get airtime or it doesn't get incorporated into the corporate, you know, lexicon or, or conversation. You kinda know when you're being ignored, 'cause you can see that you're not getting attention. [chuckles] But the diagnosis might be, yeah, you are generating a lot of your points of view in that very flat way, and the best leaders, the ones that are gonna help you, you know, grow in your careers, are going to be the best ones at filtering out that stuff immediately. And so I think the, I think the symptom of that AI slop generation or alignment goal is gonna be different depending on where you sit in that hierarchy. The ultimate result, the outcome, is gonna be that, that, that alignment doesn't end up happening.

    4. AGAakash Gupta40:34

       So that s- really puts a nice summary over what we've just described across these two really important leadership tasks. We zoomed in on what we think are two of the most important for y'all. Now let's zoom out and I wanna understand what is your overall weekly stack with AI? What's always on? What are you using it for? What's the whole list that people should be and

16. 40:56 – 45:06

    Matt's full weekly AI stack

    1. AGAakash Gupta40:56

       shouldn't be using?

    2. MWMatt Wensing40:56

       Great question. I think, uh, the reality is, as a leader, I live in, in a few places. One is, yes, I live in Claude, uh, Desktop in this case, and, you know, things with Cowork and, um, Cowork and Claude, uh, in general, as I just walked through as examples. I think the other place I live all the time, um, is Slack, obviously. Uh, and I think Customer.io's done a really great job of bringing more and more AI and automation into, into Slack, and so we have just a growing number of agents and, uh, that are internal, and I'm using those on the regular to essentially, uh, do a few things. So one of them is ad hoc analysis. Uh, in this case, I'm sharing on the screen, um, we have a bot and y- you can see I have a 215 reply-

    3. AGAakash Gupta41:34

       Wow

    4. MWMatt Wensing41:34

       ... thread going with it. And so t- uh, 215, I guess back and forth would be like over 400 essentially, 'cause I was using another bot as well, going back and forth between the two, and we can talk about that. But the idea here was I had 2,000 customer records. I wanted to do some analysis. I fed it to this. It has access to Snowflake, and it's doing some querying for me where I can just use natural language to, to do this analysis, uh, with it. And so this is my, uh, go-to for, "I have a question and I need to verify some data that's going into, uh, some research or report or a summary for other executives." And I will say it on two fronts. One is, yes, have a bot like this, but the other one is having a data team [chuckles] that can chime in and either help unblock it when it's not performing the way it should. As I like to say, kinda kick the vending machine or slap it, uh, or verify the data. It's always important, uh, to say, you know, "I'm not just going to take a, a non-deterministic answer for this." Um, so this is one way I'm using it. Another way is Josh Childs, uh, is a member of our team. Um, he and many others have built their own, uh, tools to, to do really cool things, and one of them is we're a fully remote async company. We have over 350 employees, I think over 400 now maybe, uh, might be new news. And that means there's just a ton of conversations happening. You can see I've got 99 read laters. I don't really use that feature very much on the left. 32 activity things that I need to clear out, apparently, and then two DMs, which I do actually clear out constantly. But there's just a lot of conversation happening within the company. And so, uh, Josh built, for the sake of the product team, a scanner. It uses AI and it goes through I think a few dozen channels that we have and just tries to find any conversations happening anywhere where a product manager should probably be involved. I think we got to the point with our company where it was just too difficult to, to expect anyone to read through all the threads and conversations all the time 'cause there's just so many things happening. So this has been a huge help. It, it's, it's a scanner. You know, we don't think of this as like a, a, it's not a police car or something going around, like patrolling-It's more of a, this is our radar, uh, or our sonar. It's on all the time, and it's really helpful to just, like, deep link through a thread where you're like, "Hey, in this channel or on this support ticket with this customer over here, I see signs that a product person probably needs to weigh in, but I don't see any product person yet." And then tuning that to, you know, report at certain times of day and over- be overly zealous. And then I, as a leader, am able to take those and I said, I know I blocked out a lot here because this is, like, a, a specific issue, but, uh, this is an excellent example of dot, dot, dot. And I was able to say, "Hey, I, I see this conversation happening. I see where a product person could create some value for the company." But also, you know, let's not just stop at solving that question or answering that question. Let's also think of this as a process improvement opportunity or, you know, something we wanna work on later and then kind of follow through and tag folks that can improve the way we work, right? Um, so it's-- Again, it's awesome that we can upgrade the way we work with AI, but then it creates more opportunities to improve the way we work as well, so. So this is another example of I'm using it all the time to be more how does this help me. It helps me stay close to the ground, if you will, in terms of being really in tune with the-- all those moments that otherwise there's just no way to, to be everywhere all at once. And I think it's helped to help me focus a little bit more when I, when I am, you know, 200 replies deep in some analysis or, or with Claude going through some kind of, uh, abstract exercise. The fact that I have this scanner running all the time that still helps me pay attention to the details is important. And I think this, this dovetails nicely to a lot of what we're hearing these days of, hey, leaders need to be players and, you know, players as well, like I see involved as well. How can I do that? This is a, this is a great way to do that. And then one more, and I think it ties into what we were talking about, uh, earlier in this conversation, is one,

17. 45:06 – 50:16

    Chiefys and how Customer.io audits strategy docs

    1. MWMatt Wensing45:06

       so we call this Chiefy. This is something that Colin made and, and he's mentioned this, uh, on stage before, so I, I feel comfortable sharing it. This is a bot, uh, that we've created that works, again, inside of Slack, and it, it has two primary use cases. One is anytime we create something new, like that, like that analysis, like that pricing documentation, et cetera, we can run that through Chiefy, and it has this corpus of here's the 20, 30, 50 relevant company docs that are kind of the, called the gold standard or the, the ratified, verified documents that we operate on, like the operating model, if you will. And it can help to find discrepancies, and that can work two ways. So the other use case is there's a discrepancy because we released something new and we really like it. It, it's aligned with our strategy, but hey, we have to go update all these other docs that have already been written, or we need to correct those. You know, as a leader, it's really painful to publish something or create something that gets alignment this month, and then we do something new in three months, and that document now is either stale or showing its age or needs to be updated. Great to use AI, like sort of another part of the stack, to automatically go through, you know, dozen or two dozen or however many it is documents that you have, and just help to audit those to know, "Hey, we need to bring those up to date," or, "Hey, you know, eight out of these other 12 disagree with this." You might not realize it because you're very recency biased, but is that intentional or not intentional? And kind of, you know, being that accountability check to say, "Oh, yeah, we, yeah, we didn't mean to say that we're gonna do this instead of that. We're actually doing both." Or, "Oh, yeah, we're actually changing our strategy a little bit. Let's go back and change those documents and update them." Super helpful. Obviously, you know, no one has time now to go back and look at all those, and that tends to be why Notion gets stale and, and these other places we publish to, because there's just not enough time to keep up with the, uh, auditing and reviewing of past artifacts.

    2. AGAakash Gupta46:52

       This is really cool. So what do people need to do to reverse engineer your Slack setup with AI here?

    3. MWMatt Wensing46:58

       Yeah. So, um, uh, it depends on, on how your company works. Um, we took a, uh, a strategy of, in a controlled way, letting there be experimentation with, you know, OpenClaw and other agents like it. We actually have a sort of our own version of OpenClaw that we're working on where it's, it's, it's an agentic loop. We have individual team members who have, uh, that predilection for being technical who are building their own instances, and then we are supporting them as a company and saying, "Here's how to host it. Here's how to make it secure. Here's where it can run and live." And so I think that enablement is really key, making sure that there's a budget and that, that space, that margin for people to experiment. So it starts from the top, heavy emphasis on experimentation and building with the latest and greatest. So this all kicked off, uh, in February, right, as OpenClaw was exploding. And so that, that's what we did, sort of reverse engineer it. Um, OpenClaw or your own version of that, having a place for people to host their own instances and then letting those have access to Slack. Um, I won't get into all the integration details because I'll probably misspeak. Um, but I think from a leadership team perspective, budget, support, and then lead by example. You know, use these tools, maintain these tools, give feedback on these tools, create some of your own. Um, that's all gonna really help to, to drive that forward.

    4. AGAakash Gupta48:10

       I think that's really eye-opening because I keep talking to people about OpenClaw, and they keep saying, "Oh, well, my company doesn't allow it." I like your guys' approach of we'll create our own version that is enterprise-

    5. MWMatt Wensing48:21

       Yeah

    6. AGAakash Gupta48:21

       ... data safe, so we can use it with our enterprise clients-

    7. MWMatt Wensing48:24

       Yes

    8. AGAakash Gupta48:24

       ... and then deploy it. Wow.

    9. MWMatt Wensing48:26

       Yeah.

    10. AGAakash Gupta48:26

        So most content you guys have seen online, it's teaching you some advanced Claude code setup that is loading in millions of context files and your entire Notion and your entire Slack and collecting all your MCP. We just showed you the realistic, simple version of how you use AI to do the most important tasks that a product leader has to do and how you even enable your teams with some more advanced use cases to use things like OpenClaw. If people want to get in touch with you to learn more, Matthew, where can they go?

    11. MWMatt Wensing48:59

        Uh, you can message me on LinkedIn. Uh, I do, I do see those, and I'm, I'm always recruiting, so you can find me there for sure. I check those. Um, you can also find me on X.

    12. AGAakash Gupta49:06

        All right. I think it would be an amazing PM job if I were a PM. So reach out to him if you are one of those AI native, AI forward PMs who have watched all the way to the end of this episode. That means you are embracing AI in a way that I think Customer.io would appreciate. Matthew, thank you so much for actually showing the real stuff. Nobody shows the real stuff. Really appreciate you.

    13. MWMatt Wensing49:27

        You're very welcome. Thanks for having me.

    14. AGAakash Gupta49:28

        All right, guys. See you in the next episode. I hope you enjoyed that episode. If you could take a moment to double-check that you have followed on Apple and Spotify podcasts, subscribed on YouTube, left a rating or review on Apple or Spotify, and commented on YouTube, all these things will help the algorithm distribute the show to more and more people. As we distribute the show to more people, we can grow the show, improve the quality of the content and the production to get you better insights to stay ahead in your career. Finally, do check out my bundle at bundle.aakashg.com to get access to nine AI products for an entire year for free. This includes Dovetail, Mobbin, Linear, Reforge Build, Descript, and many other amazing tools that will help you as an AI product manager or builder succeed. I'll see you in the next episode.

Episode duration: 50:16