In-Ear Insights from Trust Insights

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In-Ear Insights from Trust Insights is a business podcast hosted by Trust Insights, with 150 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Trust Insights hosts In-Ear Insights from Trust Insights, a business show with 150 episodes published.

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  In-Ear Insights from Trust Insights

  In-Ear Insights: The Non-Techie’s Journey to Local AI

  Sep 9, 2026—

  In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how you will cut energy waste by shifting to local AI. You’ll discover how to structure your planning so you track tools after defining your goals. You’ll learn how to build a self-updating memory system that keeps every project organized without extra effort. You’ll walk away with a clear checklist to audit your routine and select the exact technology you need. 00:00 – Introduction 03:15 – The trap of picking platforms first 08:42 – Building a self-updating AI memory system 14:30 – Replacing heavy tools with simple scripts 21:05 – Balancing cost and environmental impact 27:50 – Call to action Press play now to uncover the practical strategies that will transform how you handle technology. Watch the video here: Can’t see anything? Watch it on YouTube here . Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-the-journey-to-local-ai.mp3 Download the MP3 audio here . Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about local AI and the reasons and, well, the implementation of it. We’ve talked in the past, and you’ve seen in the Trust Insights newsletter and on LinkedIn and all the places we post, about how local AI is one of the ways that you can reduce the environmental impact of generative AI by doing things on computers under your control, either on your literal laptop or maybe specialized desktop devices like the Nvidia DGX Spark. But either way, you don’t need a massive hyperscaler data center that vacuums up entire rivers and electrical power grids just to write email summaries. Katie, you’ve been on this journey for a bit now. What have you found and where are things going for you? Katie Robbert: It’s been interesting so far. One of the goals that I had when I started this journey was to document everything so I could share it in the Inbox Insights newsletter, which you can subscribe to at TrustInsights.ai/newsletter. Each week I plan on going through basically the big things that I’ve learned, which to be honest, is a lot. One of the first things that became very clear to me is that I was breaking my own rules when it came to the 5P framework by Trust Insights. And so if you’re not aware, you can go to trustinsights.ai/5p-framework. The 5Ps are purpose, people, process, platform, and performance. And so in this journey to understand what it looks like to migrate to local AI, I was leading with the platform. I was doing what we highly recommend everybody not do. And I was doing it. And I want to be very clear, and I want to call myself out because it’s such an easy trap to fall into. We’re like, oh, I should move to local AI because it’s going to be greener, it’s going to save the environment, it’s going to do this or that. I already chose the platform. I already chose the solution before really exploring the other P’s. And so, acknowledging that—I didn’t realize it until I was about partway through creating the first set of requirements. When I looked at it and said, I started with the platform. What am I doing? This is ridiculous. So all to say, this is why we talk about the 5P framework the way that we do. So where I started was with a user story. As a persona, I want to say that a user story is a simple three-part sentence: As the CEO and an environmentally conscious citizen, I want to figure out where I am negatively impacting the environment with my use of AI without losing productivity, so that I can feel good about the work that I’m doing. Kind of convoluted in my writing, it’s a little bit more clean, but that’s the gist of what I was trying to get. I don’t want to lose productivity, and I want to do better for bigger than my own little ecosystem. What I found out is it’s not an all-or-nothing. It’s not just migrate everything to local AI and you’ll be fine. I had to really dig in. So the first thing I did, aside from setting up the projects and the folders and all that good organizational stuff, was Chris. I borrowed your scaffolding from the Data Diaries, which is in the Inbox Insights newsletter, and you talked about the product requirements document. You talked about the technical specifications and you talked about the work plan, and you gave prompts for those. So as a user, I borrowed those and adapted what I needed to because I wasn’t coding; I was creating something different. And one of the things that you did in that prompt that was really helpful in the product requirements document was you gave the instruction to the AI that says, interview me until you have enough information. That’s where I’ve been spending most of my time. Because there are about twenty questions. The questions that came out were ones that I overconfidently thought I knew the answers to. And then when I really stepped back to think about it, they weren’t the case at all. For example, moving to local AI is going to be more environmentally sustainable. That might be the case. However, based on the nature of the work that I do in my process, that’s not the best move for me. And it’s something that I need to really consider. And one of the things that came up, and this is where I want to get your thoughts, Chris, is I rely really heavily on the memory that you can create within a project, specifically in something like Claude Desktop Co:Work. And then you have the projects, and you can actually commit information to memory to reference later. This is not true of every functionality of every iteration of every large language model. But this is something that I specifically use because one of the things that I’m trying to do is remember back in January, we had this great idea that we said we were going to do and then it died. And now it’s August, almost September. What are we going to do about it? I’m able now to go through lots of different files from different systems. Basically one of the good, solid use cases of generative AI is summarization and categorization of the data. So it’s not making decisions for me, but it’s taking everything from Slack, from transcriptions, from emails, from wherever—all these different places—and saying, I’ve summarized everything; here’s all the stuff. And I’m like, that’s amazing. Let’s commit that to memory so that tomorrow when I’m like, hey, let’s do this thing over here, it’s like, remember yesterday? Because I don’t remember yesterday, but the machine does. So that for me is such a big use case. And where I got blocked on my journey to local AI is how often the memory on the local version gets updated with things that are happening near real time. I want to kind of get your thoughts on that, so I’ll just stop there. That’s one of the learnings that I’ve had so far. Christopher S. Penn: Memory is writing things down, and that’s essentially one of the core functions that a lot of people don’t do with AI. Then they figure out, oh, these tools have no memory, and you constantly reinvent the wheel. And so there are a bunch of technical solutions for that when you’re using local AI that you can create. In fact, you can create a better memory with local AI than you can with the cloud-based ones because the cloud-based ones have pretty strict limitations in terms of how many resources you’re allowed to consume when you’re on your laptop. You can do whatever you want. There are some things you should not do, but the system will let you do them. So that’s one of those sort of bookmarks that you’d want to mentally say, okay, well what are the different options? In fact, Google just had a paper on this from DeepMind three days ago. Essentially instead of having things loaded in a project, you have your local tool create—in whatever folder you’re working—have it write a wiki with links to markdown files within itself. As you make changes to the project, it updates the wiki because it can traverse it and say, okay, I’m on the homepage of the wiki. And now I’m in Katie’s projects. And now I’m in Katie’s dog projects. And now I’m in Katie’s fence, the backyard project. It can keep just drilling down as though it were a person browsing a wiki until it gets to, oh, on August 26th, Katie decided we’re not going to fence the backyard with an electric fence. We’re going to use an old-fashioned vinyl fence for that. And the advantage of that kind of system is that it’s very human-readable so that you can browse your own wiki and go, I said that. Katie Robbert: And that’s what I’m doing with the cloud-based version right now. So the question for me as I’m making these decisions is what does my process of maintenance look like to make sure that the local version is staying as up to date as possible, since things are happening every single day that I’m trying to remember? I’m one person. And one of the things that I’ve been sort of thinking about is, I’m one person, one laptop, one project. What does this look like at an enterprise level when they’re trying to do something similar without getting too far into the weeds of that? Right now, a big consideration I have is, yes, I can create that on a local environment. How often am I keeping it up to date, and is that taking more time and more resources to do that than it is to do it in the cloud? And again, these are questions that you probably know the answers to, but I, as the person who’s trying to learn, don’t yet know what that looks like. And so for me, that’s been an incredibly helpful exercise because here’s the thing, I have a Chris Penn. I can ask Chris Penn questions all day long, but not everyone has access to a technical expert on their team to just ask questions. So they’re just kind of winging it and hoping for the best. And so I’m trying to really remove you, Chris, as a crutch of like, well, Chris will. Chris can tell me the answer. Chris can tell me the best way to do this. Yes, I have that safety net, but at the same time, I need to be able to figure it out on my own without you saying, do it this way. Like what you just shared with me about the wiki that hasn’t come up at all during my planning. Christopher S. Penn: And this is where, and we talked about this in previous episodes of the podcast, being well-read and looking at those edge cases and digging around in places you don’t normally read is so important because you will get vocabulary that you didn’t have previously. For example, when I’m working in Claude Code, there’s this concept in all the AI coding tools called hooks. And a hook is an automatic function that fires at a certain point in the system. So there are hooks for when you start a session, there are hooks when you run a compaction, and there are hooks when you complete an action. And one of the things that is in my Claude Code is I use not a wiki, but I use a knowledge graph. In mine, I have a hook that says post-task hook, update the graph. So I don’t have to remember to update my knowledge graph. I don’t have to remember to update the wiki. The system is built into the system to say, hey, I just finished a task; I better update the knowledge graph so that the next time I look at this, it’s fresh. So part of those requirements would be to say, yeah, the system has to maintain itself. I don’t want to be doing that. The system had better do it for me. Katie Robbert: But in your example, so you’ve completed a task, what about those external documents, that external context, that external data? Is that part of what gets updated into it? Christopher S. Penn: Yeah, you have to take notes to say, hey, you’re going to take notes and put the notes in the notes folder wherever your notes folder is in your project, and it will update its notes and say, okay, hey, today it looks like Katie had to nag Chris again about updating his expense reports. And then in the next day’s notes, Katie has to nag Chris again. Day 71. Chris still hasn’t done his expense reports, but it’s in the journal. If we take away all the technical language, it really is just journaling. It’s like every project has a journal, and the AI is responsible for doing the journaling. Katie Robbert: Okay, all right, offline I’m going to ask you more questions about that. But one of the other big learnings, and this I think was really eye-opening to me but also kind of like a duh moment—which I’m a little embarrassed about, but I’m sharing it because I’m trying to be transparent and accountable—is that a lot of what I’m trying to do and where I think I’m saving resources and environmental impact has nothing to do with AI. I’m using AI to do things that can be done with a script. So for example, I’ve built an App Script into a Google Sheet that pulled from a different API. Like, I’ve built it, I now know how they work. Maybe it wasn’t the most efficient thing, but it worked because I was brand new to it. And we were on a call with a client a couple of weeks ago where the person was sharing this entire process and it was very copy-paste heavy. There was a lot of moving pieces in spreadsheets. And you, Chris, sat back and said, have you looked at the VBA scripts? Do you have access to that? And they were like, well, I think we do. And you’re like, yeah, you just need a script for that. And for me, like the light bulb—if you could see it go off above my head—I was like, oh, damn. Yep, there’s a lot there that I’m doing that doesn’t need AI to be doing it. And that has nothing to do with cloud-based or local. It has everything to do with being aware of the kind of work that you are doing repeatedly and what is the best solution for it. That for me was one of the biggest learnings in going through this requirements-gathering process, which is why we always stress doing the requirements before you start building anything. So Chris, I’m aware of the App Script and the VBA scripts in Excel, but what are some of the other examples of scripts that people who aren’t overly technical can build and aren’t using? Christopher S. Penn: If you’re doing this on your computer, which presumably a lot of people are, your computer—whether it’s Windows or Mac—has a command line, a terminal, and in that terminal you can install Assoft Corporate. It allows you lots of little extra utilities. And these utilities are free and open source. Many of them are proven; many have been around forever. And one of the things that I do, which is actually part of my project setup, whenever I set up a project on my computer, this text file goes in it and lists all those utilities that I have installed on my specific computer. So this is not universal to everybody. A lot of these things you would have to install. But this is, hey, AI, don’t invent. If you’re working on a task and I tell you, hey, we need a database, don’t write one from scratch. Here’s a long list of everything that’s already installed on this computer so you don’t have to reinvent the wheel. And these are really like one level up from scripts. These are apps, right? These are text-based apps like FFmpeg, which is an incredible video editing tool. Another one is yt-dlp for downloading YouTube videos and captions. Another one on here is SQLite Local Database, right? Which is, by the way, once you get started. Doing the wiki thing is a really important tool because it’s an actual database. Pandoc is for converting data from one form to another. There are all these different tools that I have installed on my machine. They’re all free and they’re not right for everybody because you’d want to ask maybe your existing system, hey, for this project, what free open-source command-line applications are the best fit so you don’t have to reinvent the wheel. There are thousands of these things. There’s another one here: ImageMagick. I use this one all the time. Whenever you see my You Ask, You Answer videos on YouTube, all those title cards are programmatically generated by ImageMagick and a Python script. I’m not going to sit there manually typing out every show title. That’s ridiculous. I give the show titles to the script. The script just has a template, fills in the template with text, and spits out 10 or 20 image files at a time. This is where you will find a lot of extra savings. Another one that’s super important for Trust Insights is the Google Workspace command-line tool. Right. So Claude or ChatGPT whoever can use a terminal script to talk to Google Sheets, Google Docs, and Google Slides without having to reinvent the wheel or use an MCP, which is very resource-intensive and very token-intensive. When there’s a command-line tool that says, hey, here’s how to update a Google Doc, that one is one of my workhorses. I can say, when John sends over a proposal or scope of work, okay Claude, you’re going to use the GWS CLI to read this doc and here’s my criteria for how I want you to review it. It will now add comments to this doc, redline this, or whatever, and it’s all built into that tool. Zero tokens, zero AI because it’s just a command-line tool. Katie Robbert: This breaks my heart because I fought learning command lines for so long, and yet if I’m really committed to reducing my use of AI where it’s not appropriate, then I really do have to learn them. And so I will go on record and say I’m going to start exploring the command-line apps that make the most sense for me based on the work that I’m doing, which is what is coming out of my requirements gathering. And so I have a very strong use case where I had the methodology clear and I was generating report after report about more than a dozen of them. They were all identical, just with different data inputs. And then I had to change something in every single one of those reports, and I regenerated them again. And when I was doing the requirements, it very clearly flagged that should have been a script. Why are you using AI for that? And it’s just… For me, it was just my lack of education around what was possible. But now I’m looking at everything that I do and I’m like, could that be a script? Could that be a script? Could that be a script? Could that be a script? And that is the third category of how to save on AI use that I wasn’t expecting. I was looking at it very binary: local or cloud. And that was it. And now there’s this whole other category that I’m like, well, that just changes the game altogether. Christopher S. Penn: This is something I wrote about on LinkedIn not too long ago. One of the best uses of AI is to make tools, scripts, or applications so that you don’t have to use AI at all, because these tools are the best coders in the world. And as long as you’re good at following the SDLC, you can say, we’re going to make this piece of software, and then I’m going to install it on my machine and then I’m going to use it.

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  In-Ear Insights from Trust Insights

  In-Ear Insights: How to Create New Content Ideas in a Sea of AI Sameness

  Sep 2, 2026—

  In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how you will overcome creative blocks while navigating information overload and create new content ideas. You’ll discover why AI keeps offering generic advice and how you can bypass those predictable suggestions. You’ll learn to pull unconventional strategies from unrelated fields and apply them to your marketing plans. You’ll identify two distinct thinking patterns that unlock fresh content ideas when your usual topics feel stale. 00:00 – Introduction 03:15 – The challenge of information overload 07:40 – Why AI defaults to common advice 12:20 – Deductive versus inductive thinking 16:50 – Finding psychological safety for exploration 21:10 – Cross-pollinating ideas from other industries 26:35 – A practical research workflow 31:00 – Call to action Hit play to uncover how shifting your perspective transforms stale ideas into bold marketing breakthroughs. Watch the video here: Can’t see anything? Watch it on YouTube here . Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-content-marketing-inspirations.mp3 Download the MP3 audio here . Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights. This is the age of AI, where there is more information than ever, more data than ever, more everything than ever before. And yet behind the scenes, when we were prepping for this episode today, Katie, I said, hey, what would you like to talk about today? And your response was, I have no idea. Let me ask you a couple of different questions. One, is it that the material recovering feels like it’s been done before, or is it also that there’s just so much to choose from that you end up with what Barry Schwartz calls the paralysis of choice? Katie Robbert: Yes. So let me pull those apart. So much is changing in the field of AI and where we focus down in on with analytics, marketing, operations, and helping businesses. We do such a good job of thoroughly exploring topics, and the process that we have for creating content is so efficient that we can churn out a lot of it very quickly. So I feel like personally, the things that we would cover that are relevant to our business and relevant to our audience now there’s always another stone to turn over. But then you start getting really deep into the weeds. And where I wrestle is do you start alienating parts of your audience to really focus on those very specific niche pieces? To the other part of the question? Yes, I do feel like there’s so much information. Again, this is sort of the side effect of generative AI that you sort of become numb and blind to all of the information because there’s so much. I remember when we worked at the agency and you used to produce this report, I think it was yearly, on the number of pieces of content created. And you know, this was prior to generative AI, this was just the number would go up exponentially year over year. And you know, I can’t even imagine what that number would be now. And just sort of seeing the uptick. And so I don’t, I feel like I’m deep in the weeds of stuff that I’m working on day to day in the business. Part of it is I’m like, I don’t know if anybody would even care to hear about this, if it even resonates or if it makes sense or if we’re supposed to be doing that big thinking and like introducing new concepts and you know, doing the thought leadership of what’s happening with AI and analytics. Here’s the thing about analytics. There’s only so many ways. There’s a lot of ways to analyze data, but there’s only so many ways to analyze data. And so I feel like at this point, unless a brand new analysis methodology is invented, which I could happen, but I feel like we’re pretty good, we’re solid, you know, it’s not worth talking about again. But I could be wrong. I could just be having a very blah Monday morning where I’m like very feeling very existential of like, what’s the point of any of it? Christopher S. Penn: Which, given the macro picture, which is a totally separate podcast, is true. In the catalog of analytics that we use, that I use, there’s about 1400 techniques that span 22 disciplines, everything from weather forecasting to agronomy and agriculture and stuff like that. And one of the things that I was talking about this weekend in my own personal newsletter was how limited marketers are in their choice of analytics when you use AI, because AI naturally gravitates to what we’ve already written about. So we’ll talk about attribution analysis, we’ll talk about social media marketing, we’ll talk about impressions. Our advisor, Ginny Dietrich’s most hated measurement of all ad value equivalents. And these are all associated statistically, probabilistically in these AI tools with marketing analytics. So if you say, hey ChatGPT, I need some marketing analytics help with this, you know, Google Analytics data source, it will give you the most probable things which will be old hat. Like, it will be like, oh, have you tried doing, you know, basic trend analysis? Like, yes. Have you tried doing time series analysis? Yes. Right. And that is a blind spot that AI can never overcome because of its nature as a probability based tool. You, the human, have to have the largeness of vision and the vocabulary to say, hey, you know, chat GPT or co pilot or whatever. I’ve got this issue here and I’m having trouble understanding what’s driving the set of changes. What analytics techniques from agriculture and agronomy might be a good fit for this data and suddenly be like, I know all about agronomy, which it does. And have you tried pairwise combination holdout? And you’re like, what is even? I don’t even know what those words mean. Katie Robbert: Nope. And I hear what you’re saying and I feel like we’ve talked about versions of this where in order to get the most out of the tools you have, you really have to be curious and thinking outside of the box and that’s something, Chris, that you do very well. That’s one of your strongest professional attributes is you say, where can I look outside of this lens and see like and pull out like agriculture. So I don’t think that’s the default thinking for most. Take marketing out of it. For most people, it’s like I’m looking at a problem. I’m trying to figure out why my bread dough isn’t rising. Let me look at the fashion industry and see what I can learn from that. About, like, it doesn’t make sense because it feels like putting square pegs in round holes and I don’t know, maybe that’s sort of where I’m stuck, is maybe I’m not someone who’s good at looking outside of. Like, I’m a very rules and process oriented person. I like things to be predictable. So perhaps for me, that’s where I struggle with this concept of I’m trying to analyze my marketing data. How does agriculture do it? Like that to me is just like, why would I ever do that? That seems ridiculous to me. That’s not how this rule works. And so that’s definitely not one of my strengths. Which again is why you and I are very complimentary in our strengths and weaknesses. And you know, I just went. When we come back to the purpose of today’s podcast, I feel like, you know, maybe that’s where thinking about topics to talk about is where I’m struggling because I feel like I’m a fairly creative person. But I also, I remember I started taking art classes in first grade and went all the way through 10th grade of high school. And I, my skill set really didn’t progress. It really wasn’t something that, like, it was something I wanted to be good at. But I just, I’m not an overly creative person. I’m creative when it comes to problem solving. But I feel like I also have a really Good toolbox. But if you start to say, okay, your toolbox is now going to contain these things that belong in, you know, biochemistry and welding, those are not industries I know a lot about. I’m like, nope, I don’t want that in my toolbox because I can’t, I don’t understand it. I don’t get it. I don’t know. There’s a lot of like therapy for me this morning. Christopher S. Penn: Well, what you’re describing is the difference between inductive and deductive reasoning. So as a rules based person, you would be a deductive reasoning person. You have a set of rules and principles and concepts in a domain that you break down to specific conclusions. If we think about the 5P framework by Trust Insights is exactly that. Five different areas, purpose, people, process, platform, performance that have governing principles for each of these. And you could take that general thing and distill it down for any problem that you run into, right? And say, okay, how can I make this. We’ve you’ve talked about even making it a prompt framework for AI because it’s so good at it. The opposite is called inductive reasoning where you say, I got a bunch of stuff, it’s all very confusing, it’s all not related. What is in common? And so for example, if you look at agronomy and weather forecasting and econometrics and marketing, what is the through line, the weird thread that goes through all of them for marketing analytics, and that is math, right? The mathematical techniques that each discipline has to use that are different from discipline to get to the answers. And so inductive reasoning says, can we find those threads from these crazily not related fields, hold on those threads and turn them into something like, oh, we can use this in marketing. We have data that resembles this. How do we adapt it? And so that’s the difference. And so when we look at like content creation, saying what are we going to talk about today? If we start from that deductive reasoning perspective of, well, we’ve done AI 18 ways to Sunday. We’ve done prompting, you know, 22 ways to Sunday. Well, they’re left to do that is a deductive perspective. And there’s, that is a great way to do it because you cover everything. The inductive way is okay, say okay, this is what you flip to when you’re like, we’ve done it all, what’s left? Okay, now you flip to inductive reasoning. Say what about this weird edge case over here? Let’s pull that apart and see where it leads. Katie Robbert: Well, I mean, and this aligns with what we always joke about, that you know, what will be written on your tombstone is what does this button do? You know, and there’s a part of me that wishes I was a little bit more open, more curious, you know, I feel like I am, but at the same time, not nearly as much as I could be in terms of, you know, in this context of like breaking out of what I know. And so what kinds of advice would you give to someone like me who, to your point, like, if I do the deductive reasoning and I cover all the different known angles, you know, and I think about things from a risk averse lens, what advice would you give to someone like me to then find ways to turn on some of that inductive reasoning when I need it? Christopher S. Penn: You can generally teach inductive reasoning in a couple ways. The one of the things, and this is very important, what you just said, you said from that risk averse lens, that’s the first switch that has to flip is to say, okay, you can do exploration if you can cycle, give yourself a sense of psychological safety that you’re not taking a risk. Like going out and reading random stuff on Reddit and things or people you don’t Normally follow on LinkedIn is not a risk. It can feel like a risk because it can feel like distraction, it can feel like confusion, but it’s not a risk. And so, one of the things you have to find for yourself, I don’t know how to do it, is to say, okay, let’s create a sense of psychological safety that says it’s okay to go look under this, under the stone. You may not like what’s under there, but it’s not dangerous to you. And the second thing is those fringe edge cases where, and this is where, like, LinkedIn is a terrible place to spend time because everyone is stuck in the same bucket, right? We’re all talking about, oh, AI is going to do this. Sam Altman did this. And it’s the same theme over and over again. You have to leave that and go to the fringes, go other places. Reddit is a good place because there’s, it’s a different set of fringes. Talking to our friends and colleagues, right? Chatting with them in group texts and stuff is a great place to do that, where you start to hear very specific edge cases. You’re like, oh, we’ve never run into that before one of the things. So it was really interesting. I did a drawing for my newsletter for filling out a survey back in July. And the giveaway was a one hour. Ask me anything private. And the conversation I had with the person was an industry that we don’t work in at all, like heavy industrial marine stuff. And just. And I learned as much as I talked, like, oh, I didn’t know that was a thing. And those, all those weird little edge cases was like, that’s interesting. And so that might be a possible antidote too, is to say, like, you know what spend. Say, I’m going to spend an hour talking to this person over here. I. Who we’re probably not going to do business with them. We’re pro. I’m. I’m not there to drum up business. I’m there to say, what kind of weird stuff are you running into that maybe wouldn’t cross my desk. Katie Robbert: H. I hear you. I think, you know, I like the idea of, you know, doing more reading outside of what you’d normally read. I agree that LinkedIn is very homogenous, very bland. The challenge. And, you know, this could be again, my own personal bias and my, you know, I like the term that emotional safety. Because if I’m being honest, that’s a big part of it is it’s not that I don’t feel safe talking about things outside of what we’ve talked about. And perhaps this, you know, this is all just sort of like a, you know, a horse of a different color is that I get concerned that if we stray too far from what our audience wants to hear about, they won’t come back. And that’s, you know, that could be my own personal concern and anxiety about it. And maybe they’re like, no, give me something different. You know, there’s really only one way to find out and that’s to ask them. So if you want to pop over to our free Slack community analytics marketers and let us know. But to your point, you really. What I’m hearing you say is it’s breaking outside of those comfort zones, those routines, which admittedly is not something I am personally great at. I’m very introverted. But reading outside of what I normally read and interacting with people online is not so uncomfortable that I can’t do it. I just need to make more of an effort to do it because then it’s, you know, it’s making sure that we’re staying in touch with things outside of our own little bubble. It’s a big reason why we have the community that we have. So know a little bit of tongue in cheek like join our free Slack community but at the same time join our free Slack community because we need it just as much as the people who are joining need it. So that we’re not just locked into well this is what we’re doing. This is how we’ve always done it. So this is how it shall be. End of sentence. Like I think that by asking the question of the day it that’s partly to understand like what’s going on in your world, what are you dealing with, what are you, what’s your perspective? And I’m always not. A question goes by and we ask them every single day that I’m not like huh, that’s interesting or I never would have thought of it that way. And I think that’s such an important thing. But I never thought of that in this context. Chris of that inductive reasoning of trying to find that sort of like through line through different topics. Christopher S. Penn: I will show, I will share with you an interesting observation about our analytics from Market Slack group. There’s a, there’s a topical split by gender. When you look at the questions people ask, even that itself is interesting. You know the, the people who are male identifying tend to be asking very technical questions. Hey, I’m doing this with this agent framework and you know, what’s the best? And the people who identify as female typically asking about outcome based things like you know, what’s better for getting this done. I need to get, accomplish these things. And so one of the things because obviously, you know, equality in general is important to us, but gender equality is very important to us as a company. You might want us to do privately, quietly reach out to some of the individual women in our community to say like hey, I’d love to have a one hour private, ask many things, I just want to know what’s going on. Talk to the folks who are already reasonably talkative in the community and just see what’s happening at the edge cases of their Worlds. You have Dr. Leslie who spends a lot of time on psychology and research. You have Hannah, you have Minion who spends a lot of time on writing in AI. And that’s where you can be reasonably assured of a psychologically safe experience and still get some really cool edge cases. Katie Robbert: Yeah, I think if I’m being honest, I would probably start with Reddit forums or something like that just because that’s for me the path of least resistance and doesn’t take extra coordination and time to figure out how to, you know, book this conversation with someone. But again, that’s all, you know, minor details, but. In general, you know, so let’s say every week we sit down to record the podcast. And every week I know you’re going to ask me, hey, what do you want to talk about this week? I mean, that’s just how it goes, you know. And I appreciate that you give me the opportunity to propose a topic versus it just being, you know, led by whatever you’re reading that week, which often happens because I’m like, whatever, sometimes I have a topic. You know, tell me a little bit about your process for like you mentioned agriculture and some other industry that I’ve never heard of. And I’m guessing I haven’t had a chance to read your newsletter yet for full disclosure. No, I mean, and I do. It’s on my list for later today. Is it more that like you look at like a list of something, you go, I don’t know about that. That’s interesting. Let me learn more. Or is like does it sort of organically just come up because you’re like searching for something and it just sort of leads you down a rabbit hole? Like what does your process kind of look like? Christopher S. Penn: It’s both. It is both. This past week though was on idea transfer. So I ha. I shared some prompts, some basic prompts to look at four level naics codes which are like profession codes,

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  In-Ear Insights from Trust Insights

  In-Ear Insights: Why Does AI Write Slop?

  Aug 26, 2026—

  In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to stop AI from turning your writing into repetitive slop and replace it with authentic human voice. You will discover why AI drifts into repetitive phrasing and how to stop it. You will learn to measure your unique writing style with simple numbers that lock in your voice. You will apply a structured editing process that transforms machine drafts into polished content. You will gain confidence to command AI tools without wasting hours on endless revisions. 00:00 – Introduction 02:15 – The AI writing frustration 06:30 – Measuring your voice 12:45 – The five performance steps 18:20 – Building your writing blueprint 24:10 – Call to action Take the new course at: https://academy.trustinsights.ai/courses/ai-for-writers Watch this episode to finally break free from generic AI output and start writing with total control. Can’t see anything? Watch it on YouTube here . Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-how-ai-writes.mp3 Download the MP3 audio here . Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, let’s talk about AI for writing and for writers. We have seen no shortage of people talking about AI watermarking and all this stuff and how you can tell whether somebody’s using AI for writing or not. And we at Trust Insights have put together a new course, Trust Insights AI for Writers, for how to get AI to write better and not coincidentally, help you as a human also become a better writer. So, Katie, to start off, what are the things that when you are writing with the assistance of AI, what are the things that sort of you wish AI would do better? Katie Robbert: You know, I wish it would listen better. And by that I mean I feel like you can craft a really strong prompt. You can say, here are my writing samples, here are things that I don’t want you to do. And it kind of just like freewheels and does its own thing anyway. And I feel like that is frustrating for a lot of people. So, you know, I really try to write the first draft of things myself as the human and then know, and I’ve talked about this in the newsletter and on pod on our podcast where I bring in AI is to double check with like our ICPs or to use it as an editing tool. But then the editing gets carried away. You know, I’m not an editor and grammar is. I would like to say that the public school system failed me. You know. So I’m like a half decent writer. I have good ideas and I write in a stream of consciousness. And so I need tools or human editors to help me clean things up. And this is where I look to generative AI because the team doesn’t always have time to like fully edit my stuff. But then when I read back what the edits are or what the suggested edits are, I’m like, where did this come from? Or why did you make up a whole anecdote that never happened, but you’re saying it authoritatively? So I feel like the hallucination is the big thing. And the, you know, depending on the large language model, each model has its quirks in terms of the way that it writes or the way that it, you know, quote unquote, articulates thoughts. And so I think one of the things that you’ve shared about Claude, for example, is it likes its, you know, things in threes, like three punchy points. And it’s very much a tell that, like, oh, that’s a Claude thing. And I found that when I have, you know, an AI assistant help edit my stuff, it turns it into a lot of that, like the individual sentences, the three punchy points, like the this and this, then this or this or, you know, those kinds of ways that it writes. And that’s not what I, the human had put in. And I’m just sort of sitting there exhausted, like. But I just, I need this edited and I need it coherent and is it good enough? But did it change too much of my own human writing? So I think that, you know, when I think about using AI for writing, that’s what I’m personally struggling with is, you know, I still want to be the one writing it, but, like, I need someone to help me polish it. And the polish is like subpar. Christopher S. Penn: Why do you think AI does that? Why do you think AI behaves the way it does and turns original writing into something that sounds like slop? Katie Robbert: Oh, gosh, if only there was a course that was going to tell me the answer to this question. Christopher S. Penn: There’s something else that will also tell you the answer to that question. And that happens to be the fifth P. Katie Robbert: I should have guessed that one. Sneaky, sneaky. The framework at Trust Insights is purpose, people, process, platform, performance. Chris, you’re specifically talking about performance. And I think that where a lot of us get caught up in prompting these large language models is we think we’re being clear on the performance, but we’re not as clear as we could be. And that’s where the frustration sets in and that’s where we want to throw up our hands. And so the perfect purpose could be, I need you to edit this, you know, five thousand word essay. I need you to look for spelling and grammar and, you know, a cohesive thread like all those things. People, here’s my audience, here’s my authoritative voice, here’s my samples process. I want you to go through this and just list out the changes. Don’t change it for me, platform. This is going to be published on my blog, which is hosted here, and I’m going to add images in these places. And then performance. We typically think of performance as did we get the polished thing from our purpose? But it sounds like we are missing a lot of opportunity in the performance part of the 5Ps to really spell out what we need. Christopher S. Penn: And that is the premise of the new course. The biggest chunks of the new course that we have are twofold. One, we spend a lot of time on research because good research leads to better outcomes typically. And two, we spend a lot of time on math, which is your average writer is like, But I started with the premise for this course, that writing is code. If I put nonsense words together, you’re like, did you just get hit in the head? Like, what happened? Did you actually put decaf in the coffee maker this morning? If I don’t say words in the right order in a statistically predictable pattern, you have no idea what’s going on. You might say, these tests, coverage, adding branches, empty. Like, what? What does that mean? That’s word salad. Language follows patterns, and those patterns are predictable. And the reason why AI writes the way it does is because it’s choosing the most probable patterns, even when it doesn’t sound like you. So the first thing that we have to do is give AI performance, right? To say, this is what success looks like. And it has to be in a tangible form. The percentage of passive voice that you use in a text when you write as a human, how much passive voice do you use? The number of sentences that begin with a noun or a pronoun. What percentage of your copy is that? Is that the number of EM dashes that you use naturally in your text as a human? What is that? Our friend Anne Hanley says, I use the EM dash because I’m an actual writer, but I don’t use it in every sentence. And where AI typically goes off the rails is when it knows that a construction is probable, like using EM dashes, like using triadic rhythm, like using bicolon or isocolon. And it says, hey, I’m going to use the most probable things. But it has no concept of frequency, so it overuses it. And you get, it’s not this, it’s that in every single sentence or in, you know, Claude in particular loves bicolon. It’s. It sounds like a drum beat. One and two and one and two. And you’re like, could you please vary the beat? Katie Robbert: Right? Christopher S. Penn: When you look at a, like a slide deck Claude generates, everything is bicolon, all the headings, you know, this and this, sharp insight and this. And you’re like, my God, this is so mind numbing to read. If we give AI analysis of how our writing to begin with and say success looks like this set of numbers, now go right, then check your work and compare what you wrote versus what the blueprint is and it will go, oh, I didn’t do this at all. Like, I use 82% passive voice. Yeah, go back and fix it. But the fifth P in the 5P framework by Trust Insights is so important. We have to establish what success looks like for it so that it mathematically can go back and fix its code. Katie Robbert: So let me ask you this question though, because I will give Claude like samples of my writing and say this is what it’s supposed to sound like. Am I doing it wrong? Because I have all of these samples from like literally years and I feel like Claude or a large language model, you know, is trying to evolve my writing so that my writing fits its format, not its editing, to my writing. Like, I feel like that’s where I’m struggling. Christopher S. Penn: You are not doing anything wrong except you are asking it to count and it can’t count. And so even though it will analyze your writing as a language model, it has no clue of how to count. One of the things that’s in this course is probably worth the price of admission alone is a Python script that it has to run on a writing sample you provide that will do that fingerprint mathematically not letting the language model try to count, because language models can’t count. The Python script goes through and it counts in your original sample, this is the percentage of passive voice that you use and it writes it down as a fingerprint, as a file in your language model of choice. It works in ChatGPT, it works in Copilot. It tested in all the systems. It can then rerun that script on its output and say, initial sample, 4% passive voice, my work, 18% passive voice revision loop. I need to go back and keep revising until I hit this number. But it can’t count that by itself. It needs the support of actual code to do it. And that’s what’s in the course is pre baked. Nobody has to be coding, no coding involved. It’s bundled in. But you would drop that into your Copilot or your ChatGPT or your Claude and say, this is how you’re going to measure yourself. Yourself, you’re going to do the fingerprint and you’ll reuse that fingerprint and then you will go back and you will count using this script. And that’s, you know, again, you’re not doing anything wrong. It’s just the average non technical user doesn’t think, hey machine, I remembered you can’t count well. Katie Robbert: And I say, okay, so that’s an interesting distinction because so up until now, you know, I’ve been saying like, hey, this is the sample of my writing. And you’re right, it absolutely, it takes what I give it and it like, is like, oh, you said to do this, let me make sure that I do this. And by making sure that I do this, I’m going to do it 16 times out of the 18 sentences, but I’m also going to do this in 15 of the 18 sentences. And so it’s taking everything it knows about my style of writing and trying to jam it all into one sentence. And I’m like, whoa. Like, yes, grammatically it’s correct, but now it’s garbage because that is not at all what I wrote. And I sort of. I feel, it’s not that I feel like my hands are tied because I can’t fix it myself, but like, the reason I turned to a system for help is because I’m not an expert editor. And so I miss things that an editor would find. And I need that, like, for me, I need that kind of support of like, hey, you started a point over here and then you dropped it halfway through and you made a different point at the end. Like, you gotta pick a story and stick with it. Christopher S. Penn: And this is where these tools, these AI tools simply by themselves cannot do that. Like, they just do not understand. How do I, how do I even count? So I’ll show you an example of one of the things that is bundled in the course. And again, the average user does not need to look at this. The average user is not going to. You will just drop the file and say, machine, off you go. But it will look at thing. There’s functions in this. It says like, look at the rate per word of the this kind of word. For example, it’s often said that good writing relies on relatively few adverbs. Adverbs are words that end in ly, you know, actually, etc. AI loves adverbs, obviously, actually, and stuff like that. Which also sounds condescending. Katie Robbert: Yeah. Christopher S. Penn: And so if your writing style uses almost no adverbs, when you do a fingerprint, it will say, hey, your adverb rate per 1000 words is like 1%. And so when it goes back and counts, it’s revision, it’s edits that you had it make. And it goes, oh, I used 9% adverbs in my revision. But the target, the performance says 1%. I need to go back and fix my work. It’s a diagnostic. And that’s what’s missing from all of our prompts, because it can’t do that. That’s what’s missing from every single AI for Writers course I’ve ever taken or every session I’ve ever sat in. Nobody thinks of writing as a system of measurement, of analytics. And therefore, when AI just follows its own internal process programming the probabilities that it generates, we’re all like, why is this keeps coming out like slop? Why can I not prompt this thing to sound like me? It’s because it can’t count. And so the cornerstone really, of this entire course that we’ve created is let’s give AI the tools it needs to count, let’s give it the measures that it needs to count, and then let’s give it clear guidelines. You know, one of the things that is in the toolkit is a. Again, this is another piece of code the user, you. The user will not use. This is built into a skill that you just install, and there’s instructions on how to install it. But it will say, if you have this cadence, don’t do that. Here’s how you do it instead, right? If you do short, long, short, long cadence over six consecutive sentences, you’re writing us. You’re writing a dead drum beat. Dead. Don’t do that, do this instead. Things like that. So, for example, if you look at AI writing ChatGPT, Claude, Gemini, and you just take a step back and you look at the page, all the paragraphs are about the same length. They’re all. They all kind of look like the same gray rectangles on a page. If you were to step back and stop looking at the letters and just look at the shape. If you look at your human writing, there’s a good chance that it’s. There’s some paragraphs are real short. Maybe it’s even one word, like an emphasis one like, no, don’t do this. Right? And that’s just its own paragraph. That frequency of change in paragraph length is something that you can measure. Machines don’t know to look for that. Machines don’t even think about that. And so if we give them the tools, the counting tools to go. Katie tends to alternate her paragraph length. Sometimes her paragraph length is 11 sentences, other times it’s one. I should replicate that general pattern. Because once you give AI a pattern, it’s like, oh, I know how to do patterns. I can do this. And it goes off and it creates it. Katie Robbert: I mean, I have a lot of thoughts and comments, you know, and so like, the big elephant in the room is, you know, why are we teaching people how to train the models to write when there’s real writers out there? So, I mean, that’s a big question. So I’m just going to like, put that, like, stick it up here for a second. When I think back to like high school and middle school, for example, we generation were taught the five paragraph writing. But for a lot of us, this is like, this is how were taught to write. So the first paragraph is your opening argument. The second, third and fourth paragraph are your supporting reasons for your argument. And the fifth paragraph is your conclusion. And so a lot of us who were, you know, that was like drilled into our heads on our like yellow piece of paper with the green lines, like that’s how you’re supposed to write. And so I think what’s often frustrating for someone who writes their own stuff is because were taught to structure our writing in a certain way. It can come across as well. AI must have written that because of the structure. It’s like, no. My sixth grade middle school English teacher, when slapping rulers on tables was legal, scared the bejesus out of me and told me, this is how you have to write. And so I write the way that AI writes because it was drilled into my brain. This is how you write. And I guess I’m wondering, so when you’re saying like pattern recognition, like it learned these patterns from us. We taught it the patterns it did. Christopher S. Penn: But it is averaged together everything. And that’s why it often comes out so different than the way an individual writes. Because everyone has their own pattern distortions. Everyone has words they like, everyone has words they don’t like. Everyone has words, life experiences that will show up in your writing. AI is averaged all of that together into. And then what it does is it spits out the highest likely probability except for when it’s using watermarking. And so the five paragraph essay and that kind of blocky set. Yeah, it’s going to do that because the majority of writing it has seen on a bell curve is exactly that. If you look at the, you know, an earnings report or a press release, it is exactly that dead metronome of boring writing. The thing about writing to that. And we say this in the beginning of the course and we’ve said this in many different places. Good creative work that’s interesting is low probability. Right. You, the way you write should be surprising and different than what the way that middle school teacher taught you to write. Right? I will. There’s all sorts of expressions I’ve used for this. But if you say, you know, this is a, this works like a Prius and other people the average is this works okay. Right. This works okay. It’s boring. This works like a Prius, has a very specific connotation and A mindset behind it. And so what we want AI to recognize with the tooling in our course is recognize how the. In our individual style looks and replicate that specific pattern, not the general patterns. You’ve been trained on the general patterns that you will generate without these very rigid mathematical guardrails. Katie Robbert: Okay. One of the things that I’ve noticed and I’ve seen, and actually, this is a phrase that you use a lot. And so I guess I’m sort of asking, like, have you adopted this from AI or is it a phrase that people use and AI has adopted it is something that I see a lot in my conversations with a large language model is. And here’s the shape of the thing, and here’s the shape of it, and here’s the shape of the problem, and here’s the shape of the challenge. Well, that’s not the same shape as it

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  In-Ear Insights from Trust Insights

  In-Ear Insights: AI Enablement and Jobs AI Can Do

  Aug 5, 2026—

  In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why a popular claim about artificial intelligence taking over jobs misses the mark. You will discover what AI enablement is, how to break your daily tasks into clear steps that reveal what computers handle. You will learn a testing method that separates work worth automating from tasks requiring your human touch. You will uncover ways to upgrade your routine without fearing career changes. You will gain the confidence to restructure your workflow for lasting efficiency. 00:00 – Introduction 04:15 – Debunking the takeover statistic 08:40 – Breaking work into clear steps 13:25 – Separating automation from augmentation 18:50 – Finding your hidden opportunities 23:10 – Managing AI like a direct report 27:45 – Call to action Watch the full episode to see how you can put these insights to work immediately. Can’t see anything? Watch it on YouTube here . Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-ai-enablement-jobs-ai-can-do.mp3 Download the MP3 audio here . Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about AI enablement and specifically what AI can and can’t do. Early this year, Anthropic, the makers of Claude, released a paper about the labor effects of AI. And they cited and used a paper way back from 2023 from Ilondo et al that said in some professions like management, computer science, etc., up to 94% of tasks could be consumed by AI. And when this paper came out, everybody and their cousin copied and pasted the radar chart. We all accepted it at face value. However, we did some digging, we did some reading into this and we used our job-to-AI plugin, which is located in the Trust Insights Academy, along with a hefty amount of AI to try to replicate the results of that original paper. And it turns out the original paper that Anthropic cited used about 10,000 or 18,000 tasks that human judges and GPT-4, which was OpenAI’s model at the time—a model that now feels like a crusty old dinosaur—to guess whether or not AI could do a task in half the time. So what we found in our version of this, by decomposing job descriptions—I want to say we did what, 90,000 something odd—into individual tasks with tangible deliverables, and then used the Trust Insights TRIPS framework to assess how good a fit each task was for AI. Plus, we used the latest benchmarks from Artificial Analysis to judge what AI’s capabilities were and determine whether AI could do this. So Katie, that was a lot of preamble in your first reads of our version of this paper. What were the big things that stuck out to you? Katie Robbert: Well, first I want to react to your comment about Anthropic using what GPT-4, which you said is like what? Christopher S. Penn: A crusty, old GPT-4 for the original paper from 2023. Katie Robbert: Oh, the original paper, yeah. Here’s the thing. If you’re doing your work correctly and you have your foundation, methodology, and requirements, the model change. This is something it’s not the purpose of this particular podcast, but it’s worth mentioning. People tend to panic every time a model changes, thinking, well, this one’s modern. Now I have to change things. Now I have to start over. If you are structuring your work correctly, like an academic paper should, the methodology and research should all be fairly repeatable. It shouldn’t matter that the model changed. So I just want to acknowledge that. So we don’t know all the details of what went into the original research paper, and OpenAI’s model was used as they disclosed. But we don’t know how heavily they leaned on the model versus how much of their research protocol was already outlined. So I just want to sort of acknowledge that first. Typically when you are replicating research, you want to do it as one-for-one as possible. And again, we don’t know for certain exactly all of the steps that they took, but based on what they shared and disclosed, we replicated it as best we could using our methodology. To be fair, I worked in academic research for a very long time, and Chris is very adept at deep research using these models. So we’re not just kind of winging it, hoping that we’re getting close. I feel confident that our methodology is sound. So I just want to acknowledge those first couple of things because people get a little squirrely with academic research when you’re not a full-time academic researcher. So there’s that piece, the thing that I found. My initial reaction was 90. Was it 94%? Christopher S. Penn: The original paper was 94%. Ours had a maximum of only 78%. Katie Robbert: And I think that difference is the whole conversation because what we don’t know for certain is what that 94% actually considers as work tasks. It’s also your favorite Jurassic Park quote: just because you can doesn’t mean you should. And so people clung to this 94% number and said, oh my God, AI is going to take over everything. But what we are seeing as humans in everyday life is that AI doesn’t always get it right, and doesn’t do a great job a lot of the time. And so even our finding of 77% still feels really high. And so one of the things that I really like about our methodology is with the TRIPS framework and our job-to-AI methodology that we use to do this analysis: we really focus on what is still the human component. Where should you never give this piece of a task? Because it decomposes tasks, not a job as a whole. I feel like there’s a difference. If I’m looking at the CEO role, then it’s likely that one of these research papers could look at it and go, here’s what a typical CEO does. Can AI take over the CEO role? Yes or no? That’s like a whole big cluster of tasks. Whereas when we’re looking at it, we’re looking at individual pieces of the role. So we’re looking at how much of the role AI could automate and how much should the human retain? I feel like that’s another distinction. So these were sort of my initial reactions. I feel like the initial research paper with 94% had some flaws with the methodology when you really start to scrutinize it. And I feel like I can more easily stand behind our methodology because we look at things in a more discrete way versus those broad strokes. Christopher S. Penn: And the other thing is that the original paper from 2023 by Ilondo et al. At the time, generative AI models like GPT-4 were text-only models. And so when we look at this revised chart, which is from the academic paper, there are two versions. We published two versions of the paper. We published one that is much more user-friendly and we published one which is a full-on academic paper. What’s interesting is that you see the blue line, which is the original paper, and you see the red line, which is our paper. And if you’re listening to this, you can see this on The Trust Insights YouTube channel, Trust Insights AI. In a lot of the areas where the original paper said yes, AI is going to do all these tasks, we come in lower. And that was actually opposite what my original hypothesis was. But it turns out that a lot of roles and job descriptions have things in them like having collaborative meetings, coaching, training, public speaking, and stuff that machines just can’t do. So those big roles in things like computers, business, and management. Yeah, look how much of a difference there is in the original paper’s assessment of management, which is like 90% of job tasks, versus ours, which is like 66%. Because so much of management deals with humans. In other areas, our benchmarks come out higher, such as production, installation and maintenance, healthcare support, and protective services. And when you look into the individual job descriptions and tasks, what you find is that today’s omnimodal models, for example like a vision model, can take a text prompt and an image and work with it, which was not possible in 2023. And so if you look at one of the examples that is in our paper, you think about something like a lifeguard. What use does a lifeguard have for AI? Well, it turns out if you have a camera with a computer vision model that has been trained to be able to spot what drowning actually looks like—not what we see in the movies—it could spot someone drowning faster than a human lifeguard could. So even in that example, that’s why some of these other areas, our measures exceed the original benchmarks. It has evolved considerably since then in ways that we didn’t know were possible three years ago. Katie Robbert: The lifeguarding example is an interesting one. You said that drowning doesn’t look the way it does in movies. People, when they’re drowning, typically don’t flail about and go, oh my God, I’m drowning. It’s a very quiet, subtle, almost immediate thing. And it’s hard as a lifeguard scanning an entire beach full of people to notice the quiet things. And so that’s an interesting example. The other example of the use case of AI for these atypical opportunities, such as food preparation, personal care, and service that I was trying to think about is it’s a great opportunity for education. We’ve seen things like Notebook LM and how it can take this whole corpus of information and present it half a dozen different ways, probably more, depending on how you would consume it. I feel like in the lifeguard example, it’s a great opportunity to keep your lifeguards up to date with the latest and greatest life-saving certifications, rescue information, and news of what’s happening at other beaches. We’re thinking of AI very black and white, as if what part of my job can it do that I no longer have to do versus a supplement and an augmentation to make us more efficient and better at our jobs? And I feel like that’s just another distinction. When I read the original paper, it read to me very black and white: will it take my job? Christopher S. Penn: No. Katie Robbert: Period, end of sentence. And that is not a useful conversation to me. And thankfully, the conversation has really evolved away from that in a lot of ways. Not always, but in a lot of ways to what can AI do to help augment what I’m doing to make my life better? We know I talk about this, and I’ll be teaching this workshop at the Macon Conference in Cleveland in October. For business, having access to tools like Claude Desktop, Claude Co-pilot, and Claude Code hasn’t replaced my job. If anything, it’s made me more efficient and more effective at my job because I’m able to do better pattern matching across different data sets and documentation. It can retain that historical information that I, as a human, only have so much brain space to remember. What did we say we were going to do in January that we haven’t done? Claude can do that for me. So I’m looking at these tools like a really great assistant. But I still have to do all the same stuff I’ve always had to do. It hasn’t actually taken anything away. It’s given me the ability to do more. And I feel like that is also an important distinction. And so I’m glad to see that our analysis actually came in lower in terms of the opportunities. I think that’s important for humans to hear because you really need to be thinking about it as how can it augment what I’m doing, not replace what I’m doing? Christopher S. Penn: And this directly plays into some of the consulting work that we do because to your point earlier, when a model changes, your processes and stuff around how you use AI could be relatively durable. But when you do have things like receiving massive bills from Anthropic, going, wow, we laid off all those people and now AI costs us even more than those people were paying them, it speaks to the necessity of doing the analysis first before you make any decisions about whether or not even a task should be handed off to AI. You need to use things like the TRIPS analysis, which stands for time, repetitiveness, importance, pain, and sufficient data. If you do the analysis or you hire Trust Insights to do the analysis for you… Of all the different tasks, if you want to enable AI at your company, one of the easiest wins is to focus on that fourth factor: pain. Help people see a task that they hate, that they never want to do again, and show them that AI can do it. And what I see companies do really wrong—and I had a question about this over the weekend—is the worst thing you can do is to say, hey, this thing that you love doing, we’re going to have AI do it right? That just pisses people off. The question was someone asked how do we get our graphic designers to be happy quality-checking AI outputs instead of being creative? Like they got into graphic design, creatives to be creative. You were taking the one thing they love to do away from them. You can’t do this. I mean, you can, but you were going to lose all of them. And then you were just going to be a company that generates AI slop. Katie Robbert: Yeah, and I wholeheartedly agree with that. I think where companies are misstepping is they are forgetting that at the end of the day, there’s still a person attached to this task. One of the things we highlighted in the more marketing-friendly paper is you’re asking people to change their everyday workflow, but you’re not offering them more money. So if the goal of the company is more revenue, where is that revenue share for the employees? You haven’t given that to them. You’re asking them to do more and not giving them that incentive. So don’t take away the things that they enjoy doing. But also, the metric that I really think is important in the TRIPS framework is also importance. And so this helps you with your risk assessment. Let’s say something is highly repetitive. You do it all the time. People don’t enjoy doing it. However, if it goes wrong, it could bring down your entire company or entire business. Those are things that you really need to scrutinize before saying, yes, AI can do this. Because you know what? AI hallucinates. AI makes mistakes. AI is software. It can be programmed incorrectly. AI is not a set-it-and-forget-it system. And yet somehow people treat it that way. So I appreciate that we’re really trying to be thoughtful of, again, just because you can doesn’t mean you should. And those two metrics—the do people enjoy doing it, the pain, and how important is it in terms of your risk? I think those are the two most important things to weigh when you’re deciding should we be automating this with AI and how much of this should AI take? Christopher S. Penn: Yep. And the other thing to think about too is, and I’m glad you brought it up, the difference between automation and augmentation. Automation means the human stops doing it. Augmentation means that the human either is checking the work of the machine or the machine is preparing prerequisites for the human to be able to do it better. Your example of training helps a person become better trained. Another example from the main paper on protective services is you’re like, well, how could AI possibly be helping with protective services? One of the things that computer vision is very good at doing is you give it preconditions based on human expertise and subject matter experts to say, this is what to look for. So let’s take a picture of a neighborhood. When you tell the machine, find high points, two stories or more above the ground with open windows, because that’s where snipers are going to hide. They’re going to fire through an open window. They’re not going to be leaning out the window. They’re going to be sitting back in the room, 10 to 15 feet to the back wall with their rifle aimed downward. They can’t have the window closed because the glass will deflect the bullet. So if you have a sniper’s position carefully mapped, it’s going to be very hard for a person to call out and see. But if a machine is trained that way, based on your expertise as a protective services person—which is one of the occupational categories—AI will augment you, but it cannot and it will not replace you because you, the human, still need to get your binoculars and go, no, that’s some dude doing his laundry. Katie Robbert: Someone’s seen a few too many movies. But it’s a good point because these machines are pattern matching. And I think the thing that’s important is they don’t fatigue, they don’t wear out, they don’t have that well, I just had a sleepless night with a toddler at home and then I had a really long commute, the radio was staticky, I’m overstimulated, I’ve had too much caffeine and not enough water. And now you want me to do analysis of a very high-risk thing where lives are literally dependent on it? Yeah. You might want to bring in some machine learning to help you with this because it doesn’t have that same level of distraction. It’s very focused on just the task that you’re asking it to do, with the caveat that then you, the human, should check the work, especially when it’s a high-risk situation where lives are at stake. Christopher S. Penn: Yeah, exactly. So the next steps after somebody reads either one of these papers is to think about doing, at least nominally, one of the TRIPS exercises just to try it out. Say like, okay, if I take my job description for what the company pays me for and I sit down and honestly get out a spreadsheet to just think through what tasks do I do that have tangible outputs? Is this a time-intensive task? Is this a repetitive task? Is this an important task? Is this a painful task? Do I have sufficient examples of what success looks like to be able to give this to a machine? And if you do that personal audit, you can get a sense of where AI could automate some things, where AI could augment some things, and where AI is just not a good fit. And one of the things I think a lot of people would be surprised about… Katie Robbert: Whoa, I’m trying to share my screen. I’m trying. I got to remove yours to share mine. You’re always sharing your screen. Christopher S. Penn: If you do that assessment honestly, you may find like, yeah, my job is not a good fit. Oh, Katie’s giving me my review. Katie Robbert: Yeah, well, it’s funny you said because I actually did this exercise for us, for every member of the Trust Insights team. Surprise. My turn to surprise people. And so Chris, this is yours. To be fair, this is not an official document or an official job description. That’s something that we’re working on in the background. But that being said, a 49-task analysis is a 6.1 out of 10 across all 49 tasks. Your average TRIPS score out of 10 is a 5.7, and your TRIPS opportunity is 8. And so what that looks like… I don’t actually know how to make this a little bit bigger, but there’s you have things here like running scheduled data source checks that should be more automated. You know, data analysis. This is actually something we surfaced in both the academic and the marketing versions of the papers: that data analysis is one of the highest li

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  In-Ear Insights from Trust Insights

  In-Ear Insights: The Problems With AI Detectors

  Jul 29, 2026—

  In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the flaws behind AI detection tools and how creators can protect their reputation while using generative writing assistants. You’ll discover why these detection tools misread human writing and how to stop false accusations from damaging your reputation. You’ll learn simple steps to preserve original drafts and voice recordings as undeniable proof of your authorship. You’ll explore ethical disclosure practices that build trust with your audience while keeping your creative process transparent. You’ll gain confidence in navigating AI ethics so you can create content without fear of unfair judgment. 00:00 – Introduction 02:15 – The AI detector dilemma 06:40 – Katie shares her newsletter workflow 11:20 – Why detection tools consistently fail 16:50 – Protecting your authorship with proof 21:30 – Navigating ethical AI disclosure 26:45 – Call to action Watch the video here: Can’t see anything? Watch it on YouTube here . Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-the-problems-with-ai-detectors.mp3 Download the MP3 audio here . Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, let’s talk about AI detectors, one of my personal favorite subjects to rant about. But Katie, before I foam at the mouth for 30 minutes, let’s have you foam at the mouth about it. Katie Robbert: It’s such an interesting topic and obviously very polarizing right now. AI detectors in a nutshell are meant to help someone determine whether or not AI was used in any kind of writing. Now our good friend Anne Hanley pointed out, oh sure. So these AI detectors that were trained on human authors’ writings without their consent are now meant to tell these people that they didn’t write the things that the model was trained on. I’m paraphrasing, but it was basically that was the gist. And last week when we published the weekly Inbox Insights newsletter, we had a reader provide some very unpleasant feedback. This reader felt, in their opinion, that they had determined that the post I had written about “if you don’t know what AI can do, just ask it” was completely written by AI and that I should be fired. That I was lying about the use of AI in terms of it wrote it for me, that it was AI slop, that this person was going to write their own blog post about how I, the CEO of a company called Trust Insights, can’t be trusted. So that was the feedback this person had for my contribution to the newsletter last week. This week, if you subscribe to Inbox Insights, I fully disclose my use of AI in writing the newsletter and writing in general. I’m going to give you a spoiler because there’s no secret. I write the newsletter myself. I then use various AI tools to hopefully clean it up. Because the feedback I got when I was in college in my creative writing class is that I write the way that I talk. And it’s kind of a stream of consciousness. Now, as I’ve gotten older, I’ve gotten a little bit more concise and articulate, but that doesn’t mean my writing has. And so it still kind of comes out as a stream of consciousness, which I think for any writer that’s doing draft one is you just get it out. It’s why it’s called the ugly first draft, and then some people… I used to have John, our head of business development and our partner, read through and edit my posts for me. This was prior to having tools like Hemingway or AI editors. I had a human editing it. Now John’s busy making sales. He’s still happy to edit my posts, but it’s not the best use of his time. And so now I use a tool called Hemingway, which a lot of people use. Hemingway has a lot of really great features for grammar and sentence structure. I’m not trained, and I don’t have a degree in writing or English. My grammar is really bad sometimes. Sometimes I overuse passive voice. Sometimes my sentences aren’t structured well. It’s helpful to have a tool that can clean up the thing without losing the intent and sentiment of the writing. I also use our Ask the ICP skills in our cloud environment to make sure that the post that I’m writing resonates with our audience. Because if it doesn’t, why am I writing it? So I use those various tools. So the point of the newsletter this week that I dive into is, yes, I use AI to supplement and clean up my writing. No, AI does not write for me. I’ve got 10 fingers, one with a bandage, so it goes a little slower. And I type with my thumbs, typing very slowly. So sometimes I use an audio recording of me speaking something. Chris, this is something you do. But, yes, I painfully type all of my newsletters very slowly. And then AI helps me clean them up to be more concise. I don’t think that’s an uncommon practice, especially among people. This is true of, I think, Ann even posted in her newsletter this past week. Christopher S. Penn: Week. Katie Robbert: Total anarchy if you’re not subscribed. How she uses AI with her writing as well. And she said she gives it explicit instructions: read through it, review it, don’t edit anything, tell me what the edits are supposed to be. So she’s also someone who we know and love, who is a very fantastic writer, finding ways to use these tools to help enhance the writing. It can be cost prohibitive to have a human editor on your team. You may not have access to a copywriter, or you may not have a team of people who are really great at editing. There’s a lot of… So AI can fill that role for you. I’ll say it like this: I wrote the newsletter. AI helped me edit it, so it was coherent. So unfortunately for this reader, I will not be firing myself. I would appreciate you not trying to destroy my credibility, but should you choose to do so, we will deal with it at that time. Christopher S. Penn: I’m surprised you didn’t bring this up because this is the heart of the matter to me. If we think about these AI detectors, why are you using them? Why do you care? What is the purpose of an AI detector by the 5P Framework by Trust Insights? Of course. Katie Robbert: Well, the five P’s are in this week’s newsletter, so you can certainly get your healthy dose of the 5P Framework by Trust Insights. But you’re absolutely right, Chris, and that’s a miss on my part because I am human and not a sentient machine. I missed the mark on calling out that the 5P Framework by Trust Insights is a great place to start. Why are you using these AI tools? So, for me, my purpose is to edit the grammar and spelling of my content so that it’s coherent. I’m also checking with our ICP to make sure it resonates with the people I’m writing it for. But our ICP is the people part of it that really matters, because I’m not writing it for myself. I’m writing from my experience and my expertise, but I’m writing it for… For our ICP so that they get something educational out of it. I outlined my process in this week’s newsletter of how and when I use the tools and platforms. It depends. I might write it in a document, I might create an audio file, and then I’ll bring it into the large language model. I might use Hemingway. I definitely use the skills that we’ve created. And then the performance is, do I have a piece of content that I wrote and AI helped me edit that gets people to respond to the newsletter? Christopher S. Penn: It is the 5P Framework. From the perspective of the people who are using or advocating for AI detectors, what is their purpose? Because this is where I have the biggest problem I see. Yeah, no, no. From the AI detector perspective, what is your purpose in the case of this particular reader? Is your purpose just that you have a burr up your ass and you need to yell at somebody? Like, okay, you don’t need an AI detector for that. You can be a jackass. Regardless, in the case of its use in academia, the purpose is very often for academic integrity, which makes these tools very dangerous because of their false positive rate. Pangram, which is the tool that Substack most famously just implemented, has a false positive rate of 0.02 percent. If you fed every college student’s papers in America to it and said, run disciplinary proceedings, you would flag 200,000 students a year with false accusations. In the corporate world, if you’re using these tools to enforce contracts, again, that false positive rate—particularly for business-related content, which is what a lot of these tools have been trained on—is going to have a fairly high false positive rate. So the first thing people need to be very clear about is why are you using an AI detector? And is your purpose a good use of the technology? Spoiler, there really isn’t a great use of the technology for AI detection. And we’ll talk about why the technology itself is so flawed on this week’s live stream, which you can tune into Thursdays at 1 PM Eastern Time at TrustInsights.ai YouTube. But going back to the 5P Framework by Trust Insights, my biggest issue with these tools is that very often the purpose people are using them for is deeply flawed. Katie Robbert: And that, you can sort of generalize and say that, well, people don’t want AI-written content. They want content written by a human. So you could say that’s the purpose. So if this particular reader decided, I don’t want AI-written content, but this content is written by AI, this particular reader could have just moved along. But they decided to try and pick a fight. By the way, screenshots last forever. And it was a very unprofessional feedback session from this person, just as an FYI. And you know, if this person decided, okay, I feel like this is written by AI, let me put it through the detector and determine if this is written by AI. They could have just said, you know what? I don’t care for this. I don’t want this. Christopher S. Penn: Yeah, that’s what I always come back to is like, if you don’t want this, great, here’s the door. It’s like if people complain, oh, well, you didn’t write this fiction novel the way I wanted, well, then write your own damn novel. Right? No one’s stopping you from writing the novel you want to read. If you didn’t like the way I did it, go write your own and you’ll probably use AI to do it. This was the rather harsh commentary I had about Substack. Things like, we don’t really care if it’s human-written or AI-written. We care if it’s worth reading, right? If you’re publishing something that’s worth reading, there’s one Substack I subscribe to that is 100 percent AI-written. No editing passes. It is 100 percent Claude. You know it’s Claude because of Claude’s particular mechanisms. And I don’t care because the information is genuinely useful. I read it and go, I learned something. I don’t care who wrote it. I learned something. Katie Robbert: But that’s you and I, and I don’t disagree. People should be looking at it from that lens. But a lot of the general population is still stuck in the, AI is bad. It’s very black and white. Humans are good, AI is bad, don’t give me AI-written content. And so that’s still the challenge that we’re trying to overcome in the conversation that we’re trying to change. And so if their purpose is, was it written by AI, yes or no, then that’s what we have to work with, because that’s their purpose, not ours. Our opinion of their purpose is very similar to this reader’s opinion of my use of AI. As the old saying goes, opinions are like… well, you can fill in the blanks if, I won’t say it on the podcast. It’s very rude. But the point being is that you do need to figure out why you care if it was written by AI or not. And then you can go ahead and determine, was it high quality? Did I learn something? Was it useful? And then go back to the purpose, like, does it matter if it was written by AI? Now it brings up the bigger conversation, which Chris and I have talked about: AI disclosures and why those are important. And so in your newsletter, you do a very good job every week of disclosing. Here’s how much of this is AI. Here’s how I used AI. And I want to say thank you to the person who called me out because it reminded me this is a good opportunity to start doing my own AI disclosures so that hopefully we don’t continue to find ourselves in the situation of being called names. Christopher S. Penn: And so you cannot rely on them. I will give you a very solid example this week in my personal newsletter. I said, It’s 90 percent written by human. There’s 10 percent written by Claude. And I mark the section: This is what Claude said. And then just for giggles, I put it through the detector. And it said, Congratulations, it’s 100 percent human. I’m like, well, you clearly missed the part where I labeled it this is AI. So anyway, just more ranting about the tooling. The disclosures are important. And I do understand from some perspectives. There are some folks who correctly say they have problems with the ethics of AI companies or the environmental impact of AI. Totally get that, totally fair, completely reasonable. But again, it goes back to what you were saying, which is if we label it—which we all, everyone should be doing—and you are still mad, go read something that isn’t. There is an infinite amount of content out there that’s video or audio. Where I do have a problem and I think is very relevant to the conversation on the topic of AI detection is when it is not labeled or when it is intended to deceive. There is no shortage, for example right now on Instagram and TikTok, of various politicians making faked videos and photos. And thankfully they’re not doing it very well. But, okay, clearly that’s not a… that doesn’t work. But they are. The intent is to deceive. So if we go back to the 5P Framework by Trust Insights, their purpose is deception, right? And therein lies one of the valid reasons to want to use AI detectors to say, is this entity or person attempting to deceive me? Katie Robbert: I’m going to be, I’m going to challenge you on that for a second. Okay, so let’s say I’m a politician and I’m going to use AI. I can almost guarantee I’m not going to state that my purpose is deception. I’m going to state that my purpose is engagement, my purpose is attention. My purpose is oh gosh, anything probably except deception. So it’s interesting because like we can say as an outside observer, well, they’re trying to deceive us. They’re going to say with that lack of self-awareness, this is the way that I saw this thing happen. So, you know, I’m using the tools to reenact it or recreate the way that I see this. So it’s really an educational tool or whatever. So I do feel like it’s interesting that we’re saying their purpose is deception. They’re saying, no, that was never my purpose. Why would I ever want to deceive you? I’m totally honest. I’m showing you what’s possible. I’m showing you the way that I see things. Christopher S. Penn: And this gets us into the extremely deep and sticky morass known as AI ethics, which is again going back to the 5P Framework by Trust Insights. What is the purpose and is the purpose that you think you have aligned with the audience and the goals you’re trying to achieve? Because yes, attention can be a goal, but what’s the purpose behind that attention? Is it to garner more votes? Is it to beat the social media algorithms that are gatekeeping various viewpoints? What is the purpose of creating something that you know is not real? Katie Robbert: You are giving these fictional politicians a lot of credit for that deep thinking and self-awareness, but it does. You brought up AI ethics and Inbox Insights in the same issue this week coming up, where I talk about my process for using AI tools in my writing. You conclude a four-part series on responsible AI using our RAFT framework, and part four being transparency, which is really timely for what we’re talking about. One of the things that you bring up in that four-part series, and it’s brought up in a few of the different issues, is so companies whose mission statement is, and I’m paraphrasing—I apologize, Chris—something along the lines of companies who state out that they’re going to do bad things and they also are doing them, are technically following their own code of ethics. And so it’s the “do as I say, not as I do” or no, it’s the “here’s what: you do what you say and you say what you do, right?” So they do that. So therefore they are following a code of ethics. And that’s where, again, it gets really tricky. But I want to bring that up. Because responsible AI is not black and white. Ethics is not black and white. Christopher S. Penn: So no, and the reason for that is because ethics and morals are often conflated. They are different; they are completely different philosophical disciplines. But in the utilitarian ethics that a lot of the business world works on, “I do what I say and I say what I do” are essentially sort of the heart of that. So going back to the purpose of things like AI detectors, if you say this is real and it’s fake, that is unethical. If you say this is fake and it’s fake, that is ethical, right? It may or may not be moral. That is a different question because morals are based on the culture of the person and the culture that it occurs in. But from an ethics perspective, if I say this is fake and this is fake, I am behaving in an ethical manner. And so where this loops back around is to say, on the part of publishers and creators, we have an ethical obligation to be transparent and disclose. And on the part of AI detectors and the people using them, you have an obligation to be clear about what your purpose is. If your purpose is you just want to feel morally superior to someone else and you say that’s fine, you’re, I think you’re a jerk, but at least it’s clear. If you say that you’re trying to preserve the environment or what have you, but you really just want to feel morally superior, that is itself unethical because you’re not doing as you say and you’re not saying what you do. And so it is incumbent upon everybody using these tools in whatever capacity to disclose why you’re doing it and disclose how the results are going to be used. This is especially true for academia, for law, and for contracts. You have to be clear and say, we are using these tools for this purpose. And here is how we will measure the success of these tools. The performance, the fifth P in the 5P Framework by Trust Insights. You have to declare that, and if you don’t, yourself may have an ethics problem. Katie Robbert: I recently submitted an academic paper, and it was very clear in the instructions that I had to do a very large AI disclosure section on how AI was used to assemble the paper. And in that paper, if I recall corre

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