The AI Coding Companion: Vibe Coding Your Bespoke Productivity Universe

This week, we continue swimming in the river of AI within our series, The AI-Powered Professional. Today, we are exploring Vibe Coding. We discuss the next major leap in personal productivity: using AI to help you build the bespoke tools, workflows, and interfaces your work actually requires. This opportunity is now available not only to professional developers, but also to non-coders and power users who can think clearly, describe what they want, and iteratively direct AI toward a useful result. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/155 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing The AI Coding Companion: Vibe Coding Your Bespoke Productivity Universe from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | The AI Coding Companion: Vibe Coding Your Bespoke Productivity Universe Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | The AI Coding Companion: Vibe Coding Your Bespoke Productivity Universe Resources we mention, including links to them, will be provided here. Please listen to the episode for context. Lemmings Lemming Suicide Myth Disney Film Faked Bogus Behavior Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More Voiceover | 00:00Are you ready to manage your work and personal world better to live a more fulfilling, productivelife? Then you've come to the right place. Welcome to ProductivityCast, the weekly show aboutall things personal productivity. Here are your hosts, Ray Sidney Smith and Augusto Pinaud withFrancis Wade and Art Gelwicks.Ray Sidney-Smith | 00:17Welcome back, everybody, to ProductivityCast, the weekly show about all things personalproductivity. I'm Ray Sidney Smith.Augusto Pinaud | 00:24I'm Augusto Pinaud.Francis Wade | 00:26I'm Francis Wade.Art Gelwicks | 00:28And I'm Art Gelwicks.Ray Sidney-Smith | 00:29Welcome, gentlemen, and welcome to our listeners to this episode. Today, we are going tocontinue swimming in the river of AI with our series, The AI-Powered Professional. And today,specifically, we're exploring vibe coding. We're going to discuss really the next major leap inpersonal productivity, I think, which is using AI to help you build bespoke tools, workflows, andinterfaces your work actually requires. This is an opportunity now for us to available not only toprofessional developers, but to the rest of us, to non-coders and power users who can thinkclearly, describe what they want, and iteratively direct AI toward a useful result. Let's first talkabout what vibe coding is, and then we can get into really just three paradigms, three personasthat I've developed for us to bulk together our discussion for today. And so who wants todescribe what they think vibe coding is and or ask any questions about what vibe coding is?And then we can move on from there.Francis Wade | 01:35Nominated.Art Gelwicks | 01:37Yeah, you go ahead. You set up.Ray Sidney-Smith | 01:40Let's start with vibe coding having no official definition. There are going to be people who definevibe coding very differently. Vibe coding, from my perspective, is a way for folks to use somekind of artificial intelligence to support their software development practice. Now again, this isone of those cases where software development is a very broad term for what we're doing here.Just so that everyone is aware, this term was coined in 2023 by Andrei Karpathy. AndreiKarpathy is one of the co-founders of OpenAI—he worked on their deep learning and computervision—and then later joined Anthropic here in 2026. Be that as it may, he coined the term "vibecoding", and the way in which he describes it is as a form of coding where you can "fully give into the vibes, embrace exponentials, and forget that the code even exists."This is basically a way of not writing code manually. That means it's a very broad definition andgives way to a whole bunch of potentiality because now you're using your cognitive effort andnatural language in order to come up with code. Generative AI models today speak code andhave been trained on so much code that it's very easy for them to build software in certain ways.We can use those capabilities to our own best effort.Art Gelwicks | 03:33This whole episode, I'm going to be up on my soapbox about certain things because comingfrom old-school code writing decades ago, this to me screams of—for lack of a betterterm—executive coding. Being able to say: "Just do the thing, and I'll look at the thing that youdid and tell you how it's not right, and then you can do it again."There's no structure, no thought process involved, no ramifications or planning. It removes allthe logical friction necessary to provide something substantive and supportive. That's exactlywhat's desired right now: to eliminate those things and not have to go through that routine andrigmarole. You want to be able to just say, "Make it." That's great if you trust the thing that ismaking it to do so in a way that is sustainable, supportive, and reliable.Ray Sidney-Smith | 04:32I argue for the exact opposite, which is that this is about applying cognitive effort to solve smallproductivity problems in your ecosystem, not attempting to replace professional softwaredevelopers. I think it would be very foolish to think this is going to replace a software developer.This is about enabling software developers, and for non-coders, enabling us to infuse code intoour world where limitations previously existed.Let me define three personas for today's discussion: The Designer: These are non-coders. Your main skill is describing what you want withprecision to define the shape and function of lightweight tools or scripts (e.g., GoogleApps Script or Excel macros). The Integrator: Power users who understand configuration files (CSVs, JSON, XML) andcan connect tools together to accelerate custom workflows. The Developer: Professional software engineers using AI companions to refactor, review,and generate code while focusing on architecture and quality.With that, let's talk about the non-coder level and what they can achieve with AI.Francis Wade | 08:35I think the world of the non-coder is the one that's changed the most. All of a sudden anyonecan code, which means anyone can imagine solutions. Even if they aren't fully successfulinitially, getting people to think in terms of software, automation, and coded solutions at a smallpersonal level is huge.You don't need to take a college course or pass an exam. The learning curve isn't as steepanymore, so you can start playing around right away to solve daily problems or present smallincremental answers to clients or managers. That capability feels like having a virtual softwarefirm at your fingertips.Ray Sidney-Smith | 10:41Francis Wade | 10:45It's incredible to have that kind of power at your fingertips. We are in a shift that will change howpeople approach their work.Ray Sidney-Smith | 11:13I would say we are in a technological evolution. Over the past 20 years, we've reached aninflection point where natural language interfaces allow us to communicate intent directly tocomputing systems.However, non-coders must be cautious. Large language models are probabilistic,non-deterministic software. Math and precision require deterministic software (like accountingtools). The goal for the non-coder is to learn basic programming principles—inputs, outputs,logical branching, and structured formats like Markdown—so you can craft precise instructionsand think through your workflow logic clearly.Art Gelwicks | 15:10Here's the insidious part: this group stands to reap the greatest benefit, but also faces thegreatest risk. AI tools are marketed with the promise that "you don't have to know anything, justtell it what to do."Take Excel formulas as an example. An AI can generate complex formulas, but if you don'tknow enough to verify the output, you risk relying on incorrect calculations. You have to treat AIlike delegating to a human assistant—you need checks and balances to verify that what wasbuilt actually matches your requirements.Augusto Pinaud | 17:31It reminds me of when teachers told us in school that we wouldn't always have a calculator inour pocket. It wasn't about the tool itself, but understanding how to verify the result. We need topay close attention to output quality, whether it's checking drafted communications or verifyingreferences, to ensure AI hasn't hallucinated plausible-sounding facts.Francis Wade | 19:27That highlights the need for user judgment. AI gives us incredible technical capability, but lacksdomain judgment. When non-coders jump straight into building complex systems withouttechnical guidance or experienced oversight, they risk creating unmaintainable setups becausethe tool won't stop them from making poor architectural choices.Art Gelwicks | 22:17Exactly. Many vibe coders haven't experienced major system failures or database corruption, sothey don't know what a mess looks like. When something breaks, asking the AI to fix it mightintroduce new bugs if there isn't a structured review and rollback plan.Francis Wade | 23:22Some people prompt AI to act as its own reviewer by asking it to double-check work againstguidelines or software standards. Having multi-agent setups or explicit review steps helps addthat necessary layer of verification....

In this week's episode, the ProductivityCast team continues their conversations about how AI is embedded across many of the tools that we currently use and how to integrate them into the places we already work. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/154 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing AI in Everything: Maximizing AI Within Your Existing Platforms and Tools Part Two from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | AI in Everything: Maximizing AI Within Your Existing Platforms and Tools Part Two Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | AI in Everything: Maximizing AI Within Your Existing Platforms and Tools Part Two Resources we mention, including links to them, will be provided here. Please listen to the episode for context. AI Models, Assistants & Specialized Tools Gemini (Google) CoPilot (Microsoft) ChatGPT Claude NotebookLM (now Gemini Notebook) Evernote AI Assistant Productivity, Office & Note-Taking Software Google Workspace Google Drive Google Sheets Google Docs Microsoft 365 / Microsoft Office suite Microsoft Excel Microsoft Word Microsoft Outlook Clipchamp Capacities Evernote Search Engines & Historical Web Tools Google AskJeeves Wayback Machine Publications, Media & Platforms CliffsNotes Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More Ray Sidney Smith | 00:00 Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you've come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney-Smith and Augusto Pinaud with Francis Wade and Art Gelwicks. Ray Sidney Smith | 00:17 Welcome back, everybody, to ProductivityCast, the weekly show about all things personal productivity. I'm Ray Sidney-Smith. Francis Wade | 00:24 I'm Francis Wade. Art Gelwix | 00:26 And I'm Art Gelwicks. Ray Sidney Smith | 00:27 Welcome, gentlemen. Welcome to our listeners and onward with this episode from our last episode where we were talking about basically how AI is being embedded in all of the tools that we currently use. It is becoming... Somewhat of a commodity feature in many of the tools. And I think that there's a value to that. And so we're talking through what that really means and what So at this point in our discussion, I want us to turn our sights on the more practical ways in which we can use these embedded tools. So that we can actually figure out where in our productivity stacks the tools can be most helpful. And I always think about it from really the simplest of things, right? And journey of a thousand miles begins with the first step, right? And I think that most people want to change the world with AI. And I just want to send the next email. And so I just really want it to do very simple things to augment my life. As I frequently say now to audiences, technology is there to empower human endeavors. It should always and forever be a tool to empower human endeavors. And so I don't need AI to live my life. I need it to augment my life in these little small ways that help my life be better and the lives of people around me. So let's talk through this. What are those ways in which you've implemented AI inside of your embedded work? AI tools within maybe Gemini and Google Workspace or within another tool or Copilot within Microsoft 365 or even ChatGPT or Cloud, which connects to many of the Microsoft products? Art Gelwix | 02:16 I'll start with a couple of examples. The application of AI within our, what I want to say, our daily driver apps is I think has caused some consternation for a lot of people because It's like this is a new feature, but I don't know what to do with it. I don't know how to derive a value. I've approached it from the standpoint of start with what you're doing already, which is looking for information and synthesizing information. You need to use it to leverage the information that you already have that you know is sound, sane, and... Validated So for example, within Copilot, within Microsoft space, if you're licensed for that, you can use it looking at your document assets. You can use it within things like Excel to generate new formula structures, things that you're already familiar with. Within capacity, the tool I use all the time. Being able to generate a chat around a topic that creates the note base that I validate that extends on the thinking around a topic that I've been working on, which integrates then into scheduled planned activities that I need to pursue. Why is that so critical? One, because you're doing something that you are comfortable to do. But verify what the AI system in that tool is giving you back. You're just not taking it at its face value. You look at it and you go, yeah, that's not right. Or yeah, that's really good. The second part is to look at it as search 2.0, to be able to go past that initial search prompt to say, hey, find me this stuff. But then get into it further. What else about this thing? Tell me more about this thing. Let's expand on that a little bit further. Getting comfortable with that process in the tools that we already use is a safe space to do it. Because again, we're familiar with what the end result should be. If we start to get familiar with that in just generic open spaces... We're not entirely sure what the result is, so we either have to take it at face value, Or we have to kind of doubt how things are coming back, have that constant question. I think back to the early days of, say, Google or Ask Jeeves, if you really want to go back into the Wayback Machine, is a great example of that. When they first started entering into the world of natural language questions, that was just earth-shaking. Everybody thought this was the coolest thing ever. I didn't have to put in keywords and connector parameters and ands or ors. I could just We just have the ability to ask more questions. We have the ability to have personas and structures that look at things a specific way. But starting from an application that you already know, When you look at the result that comes back, you can go yeah, it did look at it the right way, or I need to tune it a little bit better, or I need to adjust accordingly. Ray Sidney Smith | 05:20 I found myself going into Google Drive and now having these conversations with the files in Google Drive. And it's actually quite helpful to have that embedded conversational component that is very different than doing an advanced operation search, right? And I know all of the advanced operators in Google Drive, at least the ones that are publicly known to folks. And I use them very effectively to find things. But it's been really helpful to have a fuzzy search approach when I know that file is in there, I just don't remember X or Y about it and be able to just natural language ask for it is helpful. And then the other side is when you want to be able to say, "Okay, everything in this folder I want to talk about everything in this folder in kind of the same way you would with Notebook LM, which is a little bit more grounded in the data. Now you are similarly grounded in the data that is already in your system. So you didn't have to go to Notebook LM and add each individual file. They're just there and you're able to go ahead and engage with it. So it's really powerful. Francis Wade | 06:27 There's a mindset shift there. I believe is coming, which is away from changes being driven by what's available as opposed to what's needed. I'm riffing off of what Art said. But he said, you got to start with what we are doing already. And I think we should almost be skeptical or stingy about a new possible change, let's put it that way. Simply because partly because there's more changes that are being suggested to us that are being advertised to us than we can possibly implement in a lifetime. So this is not going to get better, it's going to get worse. Decide to play the odds and say, you know what, I can't implement all of the embedded AI options that are available in my I don't have enough lifetime to do it. And the question is, do you experiment until you find something or do you take? שעוד פרק, שזה I'm not taking any suggestions whatsoever, which is a bit. Draconian, but... I'm going to look for the one or two changes that are going to move the needle the most in my productivity. Which would mean that I need to have an understanding of the bottlenecks to my productivity and focus my efforts on them. Believing that if I "Focus on the few." they'll move the needle the most, that there's some Pareto effect in here somewhere. And I somehow believe that our future is going to be Less raw experimentation and more diagnosis, analysis and targeted searching. And again, partly it's because We're going to have to say way more no's than yes's to suggestions that we include an embedded AI in our productivity stack. We're going to... 99% of the time, we're going to, we got to say no. We got to, but the question is. Where do you get the Pareto effect from the 1% and how do you find the 1%? How do you find a few things that are going to make you 10 I believe that there'

This week, we continue our dive into the world of Artificial Intelligence with another episode in our series, The AI-Powered Professional. Today, we are exploring AI in Everything, focusing on the shift of AI from being a standalone tool to becoming a core, embedded capability within the software we use every day, making AI truly everywhere. The biggest productivity gains are coming from smarter versions of the platforms we already rely on, rather than from brand-new tools. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/153 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing AI in Everything (The Integration of AI: Maximizing AI Within Your Existing Platforms and Tools) from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | AI in Everything (The Integration of AI: Maximizing AI Within Your Existing Platforms and Tools) Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | AI in Everything (The Integration of AI: Maximizing AI Within Your Existing Platforms and Tools) Resources we mention, including links to them, will be provided here. Please listen to the episode for context. Microsoft 365 Google WorkspaceTodoist  Extended mind,  Opinionated  Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More Voiceover | 00:00 Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you've come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney Smith and Augusto Pinaud with Francis Wade and Art Gelwix. Ray Sidney Smith | 00:18 Welcome back, everybody, to ProductivityCast, the weekly show about all things personal productivity. I'm Ray Sidney Smith. Augusto Pinaud | 00:25 And I'm Augusto Pinaud. Francis Wade | 00:26 I'm Francis Wade. Art Gelwix | 00:28 And I'm Art Gelwix. Ray Sidney Smith | 00:29 Welcome, gentlemen, and welcome to our listeners to today's episode, where we are going to be continuing on in our AI-powered professional series. Today we're going to be exploring AI in everything, basically focusing in on the shift of AI from being a standalone tool—which we mostly have today—to becoming more of a core or embedded capability within software we use every day, making AI everywhere. We're seeing that happening in the market as well. Art Gelwix | 01:49 I would say the biggest impact that I see right away is within my note-taking applications. I've seen—especially in the web-based ones—a direct integration of AI at the base level within the application, primarily because it has your information there to leverage and to be able to run through its models. Augusto Pinaud | 02:31 I would say the same: the notes and the knowledge base. All those things that I have collected and refer to regularly, having the AI there has been really fantastic. Francis Wade | 03:57 The biggest impact for me has been on my writing. Not so much in the tools I used to use, because I switched—I'm no longer using the tools I used to use; I'm using new tools. The improvements for me have not been within the existing apps; they've been in entirely new capabilities. Ray Sidney Smith | 04:51 I'll say that the first place I saw AI really put into my own productivity stack was in email, specifically within the Gmail interface in Google Workspace. That's my primary email, although I have accounts across almost every major email system because I test them for work. The change in my workflow was remarkable. Francis Wade | 08:36 I'm seeing a new way of managing working memory. Supposedly, we can only keep seven or eight items in memory at a particular time. If I'm doing research for an article, for example, I can scan sources, but once I get above eight sources, I'm not very good at keeping them all in play. I end up using the ones I remember most recently, rather than remembering 20, 30, or 40 sources. Ray Sidney Smith | 12:46 It's akin to asking, "Did you use spell check or grammar check in your Word document?" That's the equivalent—it's silly. Francis Wade | 12:54 It's the exact equivalent. Years ago, people asked if you used Grammarly or said you weren't supposed to use it for class. Now we don't even think about it. I believe this is where AI is going. It's a question of skillfully manipulating your tools to expand your capabilities. Ray Sidney Smith | 13:18 Augusto, then Art—go for it. Augusto Pinaud | 13:22 That is exactly what makes it powerful. I've been a heavy journaler all my life, but now some of that has shifted to AI. This weekend, I was working on a new book coming out and working through some stuff with AI. It gave me something where I was unsure, so I asked another chat to give me a summary of all the discussions we'd had on this topic so I could feed it to an AI agent. That process took 30 seconds. In the past, that would have taken a significant amount of time to review and rethink everything. Art Gelwix | 16:21 I love this idea of the AI intern because it provides a clear perspective on its role within your tools. In note-taking, if you expect AI to be an expert generating perfect solutions on everything, you'll be disappointed. But if you treat it like an intern—asking it to pull action items from meeting notes with reasonable expectations—it will be 80-90% accurate based on the context and training you've provided. Ray Sidney Smith | 18:10 Augusto, go ahead. Augusto Pinaud | 18:11 Exactly. We are all leveraging years of accumulated context rather than just installing a raw tool and expecting magic. For instance, I was writing the other day, and the AI pointed out where a statement contradicted something I wrote years ago. When I checked, I realized the old context assumed cell phones without data plans, whereas today everyone is connected. That prompt forced me to update my thinking and improve the article. Ray Sidney Smith | 20:58 When we talk about working inside an ecosystem, there are key advantages for corporate and enterprise environments—namely security, permission controls, and privacy boundaries. These environments keep data much safer than external, unmanaged tools. Art Gelwix | 23:08 I agree completely. AI works best for everyday users when it's invisible—like the underlying search and feature enhancements Google brings to Gemini. It should be a powerful feature within a tool rather than the main attraction. Ray Sidney Smith | 24:52 Augusto? Augusto Pinaud | 24:53 Apple's strategy lets others take early risks while they refine how AI integrates for non-technical users. Eventually, native integrations will leverage on-device context to automate everyday tasks smoothly behind the scenes. Ray Sidney Smith | 26:04 Apple is primarily a hardware and services company, so their timeline and incentives differ from software-first platforms like Microsoft or Google. They can afford to take a measured approach to embedding AI across their hardware ecosystems. Francis, go ahead. Francis Wade | 27:03 For the majority of users, background integration will be fine. But for those looking to optimize, we currently lack a structured framework for identifying exactly where AI can improve a workflow. Right now, it's mostly trial and error. Art Gelwix | 29:46 Building on that, working with AI requires learning its strengths and communication style, similar to onboarding a new team member. Problems arise when people expect AI to conform rigidly to flawed organizational processes without adjusting how they prompt or guide it. Ray Sidney Smith | 31:37 To wrap up this segment, embedded AI in major ecosystems like Google Workspace or Microsoft 365 offers two main benefits: reducing context switching to prevent decision fatigue, and enabling automated cross-tool workflows through standards like the Model Context Protocol (MCP). Ray Sidney Smith | 31:43 MCP acts as a universal open standard connecting different AI models securely to external data sources. It ensures controlled, deterministic outputs while allowing applications to communicate safely across systems. By connecting models and automation tools like Zapier, teams maintain quality control over outputs while linking spreadsheets, email, and notes fluidly. We'll pause here and continue discussing practical applications of embedded AI in our next episode. Ray Sidney Smith | 36:36 While we are at the end of our discussion, the conversation doesn't stop here. If you have a question or comment about what we've discussed, please visit our episode page at productivitycast.net. Feel free to leave a comment or question at the bottom of the page; we read and respond to them there. Voiceover | 38:54 That's it for this episode of ProductivityCast, the weekly show about all things personal productivity, with your hosts Ray Sidney Smith and Augusto Pinaud, with Francis Wade and Art Gelwix. Download a PDF of raw, text transcript of the interview here.

This week, we advance our series, The AI-Powered Professional, by exploring the "AI Coach." We'll dive into how AI is moving beyond simple task automation to becoming a force multiplier for personal transformation, focusing on "Accountability and Behavioral Design." We'll explore how these tools leverage data, psychological principles, and customized nudges to help us close the intention-action gap, consistently execute goals, and build lasting, productive habits. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/152 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing The AI Coach: Accountability and Behavioral Design from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | The AI Coach: Accountability and Behavioral Design Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | The AI Coach: Accountability and Behavioral Design Resources we mention, including links to them, will be provided here. Please listen to the episode for context. Loss aversion Stickk Positive reinforcement ELIZA Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More Voiceover Artist | 00:00 Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you've come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney Smith and Augusto Pinaud with Francis Wade and Art Gelwicks. Ray Sidney Smith | 00:17 Welcome back, everybody, to Productivity Cast, the weekly show about all things personal productivity. I'm Ray Sidney Smith. Augusto Pinaud | 00:24 And I'm Augusto Pinaud. Francis Wade | 00:25 I'm Francis Wade. Art Gelwicks | 00:26 And I'm Art Gelwicks. Ray Sidney Smith | 00:27 And welcome to our listeners to this episode of ProductivityCast. This week, we basically advance ourselves in our series, our ongoing series, The AI-Powered Professional. And we're going to be exploring today basically everything that we want to dive into how AI can move beyond simple task automation, simple task completion, to more of helping you with personal development, but just accountability and behavioral design. So let's talk about some of the tools we can use, some of the principles behind it, And we're going to start off with really discussing why we need... Technology to coach us in the first place. So Let's go ahead and dive into what the problem is, and then we can opine on whether or not AI can help us. So why would you need an AI coach? Who cares about having an AI coach? Francis Wade | 01:22 I had a friend who was a master's in exercise physiology. Shed clients. She was training them and she was meeting them at the gym every morning and She eventually gave it up because she said that with her Exercise Physiology Masters She was doing nothing more than bringing them all to bed. And delivering them to the gym. And standing by while they exercise. In other words, she wasn't actually applying anything she learned. All she was doing was being an accomplished nanny. It illustrates the reason why we do need this kind of assistance is because we're not, left to our own devices, we're not reliable. And there's a whole part of the coaching experience or delivering coaching experience That really is just about reminding on the one side and then holding accountable on the other. All the words. A lot of it has to do with just creating structure. And if that could be outsourced to AI, then, hey, Hold to us. Art Gelwicks | 02:22 AI coaching to me is... Better defined as coaching light. It doesn't carry any of the gravitas of an actual human coach. You can ignore an AI if it's telling you something that you don't really want to do? Yeah, it'll prompt you. It'll bug you. It can do all the things, but it's really... It's a way to get those reminders without the confrontation possibility. As part of the exercise. And I've seen people start to use AI in that kind of coaching approach and seen applications where they start to integrate this AI reminder into a process to encourage you to go through. And that's nice, and that's convenient. But it is still a device. So... I would definitely qualify AI as... Coaching light. A true coach is going to look at you and go, Yeah, but you can ignore that. You can't ignore me. Ray Sidney Smith | 03:17 Yeah. So stepping back to the core problem, which is that humans are really good at... Accomplishing things, utilizing accountability as one of the motivational paradigms. So we don't want to feel like we have reputational harm by not completing something. And so we can use accountability for that purpose. As I've talked about before, we have four layers of accountability that we can really think about. And the framework is that you have kind of self-accountability where you can keep yourself accountable to do something. You can have one-to-one accountability, whether that's a friend, a family member, or a professional. A professional coach, professional trainer, service provider and you are having them hold you accountable to do that thing, then you could step up to a group of then you can step up to what I call public accountability, where you claim publicly you're going to do something so that a vast majority of people beyond your close network know about you doing that thing. And so with these four layers of accountability, we know that we can get things done. And with AI coaching, we're really in that one-to-one accountability space. And I, like Art, really fundamentally believe that right now, at least as the technology is evolving, and how it's designed. Can't really replace a human coach. It can fill in gaps really well. I think that humans have the challenge of self-reporting We tend to be poor judges of our own consistency. We are not objective. And the AI can provide that objective, nonjudgmental, data to you on your actual behavior versus your stated goals. And since we have a human coach. Has a limited ability. To be available? The AI coach can basically provide nudges To be at a precise moment and place so you can have it monitor where you are, when you are and say, here goes this thing that you said you wanted to do and help you define whether or not you want to deviate from that plan. So say that I am in a particular application at work and I all of a sudden go to Instagram. The AI coach can now say, hey, Ray, I noticed that you went to Instagram instead of your work email. Is this a distraction? And now it can help just by that little nudge, helped me move myself toward the future. Planned activity as opposed to the unplanned activity that I was planning. We've had this indeterministic software for some time, but now we have AI versions of this, which is that it can block. Tools completely so that you can't go to a particular And that's a form of kind of coaching light, as Art was saying as well. So there's this notion that we're going to be able to do this. Providing a boundary to our access to things and that helps us stay on the pathway. So there's a lot of different reasons I think that can help us here in the coaching space but it's not a human. And even when we get to the concept of embodied AI, where we have robotics that are humanoid or humanoid-like robots, that then have AI embedded in them and they can be in your physical presence, I still don't see them as being the same thing as a full-blooded, bag-of-meat-and-bones person in front of you with the potential reputational harm of not showing up to do the thing. There is just something... Human to human about that I don't think a robot's really going to replace. Francis Wade | 06:54 Interesting you say that because my experience is that an AI coach, and I've not used the Actually, I have used an AI coach for my writing. But an AI coach... Is somewhere between just a Blind reminder, an alarm clock. On steroids. And a real person. It's moved up into that realm. But it's not doing the moving. I am. So what I'm doing is essentially I'm empowering people A voice. An automatic voice. To be more human than an alarm clock. And I think I could do some of the things you're talking about if I were to do it. I'm sure you've heard about the ideas around setting up a countermeasure mechanisms that involve creating a punishment for yourself if you don't. Accomplished the task. And I think there was a website. Allowed you to set up these punishments. And it was extremely effective. So there's nothing to stop us from having an AI persona. That For example, we say, if I don't stick to my diet this week, AI, you have the full permission to go into my... For the record. And tweet out to the whole world that you're punishing me in public for not... And you do something that's that I don't want you to do. And like, you praise up. Political party that I want to have no part of or something like that. But I sort of a punishment that creates a bit of a game that may not be necessarily dangerous or damaging in the long term, but it's at least somewhat annoying. I think I would respond to a game like that because I don't want my account tweeting out support for a party that I don't support. If that were the punishment, That would be enough motivation for me. ...

Stop treating AI like a simple search engine and start using it as your own personalized, tireless mentor. This episode explores how to leverage AI-powered tools to bridge knowledge gaps, master complex skills, and accelerate your professional growth through the science of adult learning. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/151 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing The AI Learning Partner: Socratic Skill Acquisition and Rapid Mastery from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | The AI Learning Partner: Socratic Skill Acquisition and Rapid Mastery Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | The AI Learning Partner: Socratic Skill Acquisition and Rapid Mastery Resources we mention, including links to them, will be provided here. Please listen to the episode for context. Andragogy vs Pedagogy Socratic Method Comprehensible Input / Comprehensible Output (in the context of language learning but generalizable) "The illiterate of the 21st century will not be those who cannot read or write, but those who cannot learn, unlearn, and relearn." ~ Alvin Toffler Future Shock by Alvin Toffler The Ultimate Deliberate Practice Guide: How to Be the Best  Strat Cinema (by Francis Wade) Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More The AI Learning Partner: Socratic Skill Acquisition and Rapid Mastery Raymond Sidney-Smith | 00:00 Welcome back, everybody, to Productivity Cast, the weekly show about all things personal productivity. I'm Ray Sidney-Smith. Augusto Pinaud | 00:06 And I'm Augusto Pinar. Francis Wade | 00:07 And I'm Francis. Raymond | 00:08 Raymond | 00:08 Wade. Welcome, gentlemen, and welcome to our listeners to today's episode. Today, we're going to be continuing our AI-powered professional series, And in this particular episode, we're going to focus in on how AI is... Helping people become better learners. Basically, how these tools are transforming personal and professional development. And so we're going to be talking about passive consumption, how we use maybe some Socratic method, and how we can do all of this through the creation of AI acting really as a personalized mentor tool. To basically challenge our understanding, accelerate our learning curve, and help us achieve proficiency, hopefully faster than before. And so let's talk about what traditional learning really looks like, and then we'll Talk about what the... Problems are in that space that we're trying to solve for when we're utilizing AI. Francis | 01:09 So the traditional model is that you sit down and receive knowledge from someone else. That there's an expert. Behind the scenes. But what's happening now is way more fluid than that. It's that. I'm somehow reaching out to an entity and it's reaching out back to me in real time. And it's an AI entity.  So no experience is give and take between you Something that's really smart. And where I am. And this give and take is not Obviously, it's not one directional, but It changes over time because I've noticed that cloud for example, is becoming better at finding stuff for me or teaching me stuff.  So it's like having this flexible space in which I'm being exposed to more things, I'm learning more things, I'm remembering more things. Because it's reminding me of more things. I'm accessing more options. And in so doing, I'm becoming a better professional. In my case, I'm thinking in particular of writing. I'm becoming a better writer. It couldn't have happened with a human being. The way it's happening with AI. And for the last year and a half, I'm really grateful for it because this kind of flexible space is Absolutely making me a better professional.  So it's again, It doesn't compare to anything that I've experienced before. So it's something that's brand new and it's evolving. I. Raymond | 02:38 Do, too. Augusto | 02:39 Hello. It is interesting. I'm going to talk about two cases that I recommend. For social media for artificial intelligence. One is I sometimes work with clients who have beaten up. Okay, beaten up as a child, beaten up as an early adult, beaten up. And one of the things that AI produce a different experience. Delusional, but a different experience is that AI will try to make you feel good. And I have recommended to a couple of my clients, go and have a glass of wine and a chat with your chat GPT. Okay, feel good. Because Feeling good, it's very important for productivity. It's very important for learning. It's very important. If you go, okay, I'm going to study this book. And you get into that, I'm going to study the book. But then you go like, okay, well... I don't know how to learn or I can't get into this material. The learning, the growing is very hard. Feel nice.  So that's one thing. But the second is how much Really? You can adventure. Into new topics, even if you don't want to master them. Okay. I remember Good day. Before Google, yes, because I'm that old. Okay. Where if you wanted to repair your car, okay, your best hope was to go to AutoZone or the equivalent and find a Hanes manual. Okay. And that thing was like the golden source. You could have every screw of that car had was on that manual. It was incredible. Then came YouTube and we asked how we did it before YouTube where you can see the video. Now it's even better. I have a chat for every vehicle we have in our house. Okay? And I can go and ask, not for, okay, how do you fix, no, for this specific model of this specific year of this. And it saves even more time. I enjoy a glass of wine once in a while. I'm very bad at it. Okay? According to a friend of mine, the reason is I don't drink enough wine. Okay? And if you get very good at it, you need to drink enough wine. I fail. Okay. But this is what is interesting. I've been putting into ChatGPT, okay, the wines that I enjoy. Okay. When I, and, What happened the other night, I was having dinner with some friends. We were in their house, and a friend of mine pulled three bottles of wine. I said, pick one. In the past, I will have picked based on the label. Guilty of charge. I know nothing about it. This time, I pull my phone, take a screenshot or a picture of the three labels and say, what matched my profile? And it picked something completely different to what I was going to pick. But It was actually fitting to the profile. It was a glass of wine that I enjoy because in the past what happened is I get the glass of wine and No, this one was. You can go from the mundane And you can really go with this. To the expert level. I was having a work issue with a VDI. For the people who don't know what a VDI is, it's a virtual desktop interface. And I basically have a client who gives me this VDI to do work with them. Fantastic. Except that I could not make it work for what I wanted to make it work. Chat GPT to the rescue? No. Okay, I went put all the details blah and it says the problem is you are trying it was an audio issue you are trying to make This on your iPad. The solution is try to remove I remove one of the layers it worked better and then I went and bought a And they're flying. Problem solved. The problem is the machine and the VDI cannot go and convert the audio to the VDI audio, to the iPad audio, to Bluetooth. Great. And remove two of those layers and problem solved. But these were things that in order to get to those conclusions, the number of trial and errors And the time that you need was... Among us. And now you can get those answers with two or three good prompts very quick. That bring me to the last point I'm going to make. I remember the first time I read Alan Toffer sentence saying, The analfabets of the future will be those who are unable to learn and unlearn. If he will be alive today, he will say that alphabets of the future will be those who can make prompts. And this is something when people say, then AI is going to take our work and the world and all this. Yes, it's going to change it. No question about it. And Terminator will be in the right of the corner. That said. The right prompt will really make this tool A tool? And if you don't learn how to do prompt and just think you can do what you did with Google, You are. Much likely dead in the water. Raymond | 07:48 There are a couple of things that I think AI can help with here in terms of learning. When you have full agency over your life, the way in which you learn is going to be different. When you have an employer who may be asking you to learn as you onboard into a new company, you may have to watch a whole bunch of learning videos to onboard into the company. That is a fundamentally different kind of learning than a teacher teaching you, a professor teaching you, a topic so that you can get a degree and move on in the education system.  So we need to think about the way in which we are thinking about learning as adults and that I think that perspective alone changes when you start approaching AI as what I talk about as basically being a learning partner. Because this becomes not just, I mean, there's so many different categories. I can go in a lot of different directions, but I will, I'll say that one, just learning on the fly, it becomes this wonderful learning experience....

In this episode, we continue our discussion of the AI-Powered Professional by returning to the AI Researcher persona. Picking up from the prior conversation (episode 149) on information overload and information toxicity, Ray, Augusto, and Francis explore how AI can help professionals move from traditional search toward more collaborative research, synthesis, comparison, and knowledge discovery. They discuss deep research tools, source verification, using multiple AI systems to challenge each other, Google NotebookLM as a grounded research workspace, AI-assisted book reading and writing, proactive information discovery, and the importance of treating AI research outputs as drafts or hypotheses that still require human judgment. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/150 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing The AI Researcher: From Information Overload to Active Knowledge Synthesis (Part 2) from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | The AI Researcher: From Information Overload to Active Knowledge Synthesis (Part 2) Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | The AI Researcher: From Information Overload to Active Knowledge Synthesis (Part 2) Resources we mention, including links to them, will be provided here. Please listen to the episode for context. ResearchGate Google Search Google Scholar Academia.edu ChatGPT Claude Google Gemini DeepSeek Google NotebookLM Google Alerts Feedly Feedly Pro Zapier Evernote Evernote AI Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More Voiceover Artist | 00:00 Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you've come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney Smith and Augusto Pinault with Frances Wade and Art Gelwix. Ray Sidney Smith | 00:19 Welcome back, everybody, to ProductivityCast, the weekly show about all things personal productivity. I'm Ray Sidney Smith. Augusto Pinaud | 00:25 I am Augusto Pinaud. Francis Wade | 00:26 And I'm Francis Wade. Ray Sidney Smith | 00:28 Welcome, gentlemen, and welcome to our listeners to this continuation of our discussion on the AI-powered professional. In our last conversation, we were really defining the problem around information overload and many of the issues that the modern professional or knowledge worker really deals with as it relates to all of the information. In our lives today. And what we wanted to do in this episode is continue that conversation. And talk through really how to take the sometimes overwhelming amount of information, but the treasure trove of information that we have every day coming into our world and really utilizing it in productive ways. I think that today, Thanks to AI, we no longer need to think about the concept of a search engine. We need to really think about this from the perspective of it being a collaborative engine and there is this kind of reality that it could be considered an answer engine, a research engine, all of these kinds of ways in which we can coin it. There are lots of different use cases today. We're particularly focusing in on the research And these more sophisticated AI tools can now perform tasks previously reserved for a research assistant or for you to take intensive manual effort to produce. And so let's talk through some of the ways in which you're utilizing AI for research purposes. And let's think through perhaps some of the pitfalls that people fall into as they're trying to use AI for research. Francis Wade | 02:12 I've been in a whole different world as a result of deep research in the last year. I remember before It was available. I used to do... Research via looking for documents like ResearchGate, I can search for a PDF using Google. I could search Google Scholar. You could go to academia.edu and What it would give back to me, these different sources, is Stuff that was close to what I was looking for, but not exactly what I was looking for. Matter of fact, it was often not close at all because I would have a specific question. And I'm trying to get a specific question answered. But I have to find somebody who actually answered that question in a document. Or maybe a book or in something. And usually I'd be looking for an academic source. And usually I wouldn't find anything.  So that's just, The game I would play was would be hunt and never find and that was 50%, 75% because I'd be looking for Esoteric stuff. Today, however, I have at my fingertips multiple A few different subscriptions to deep research and chat GPT does it for free up to a particular limit. And I can ask a very specific question. And to my shock, I can receive a plausible reply to my question Right. Pulls from credible sources for the most part. In the beginning, it When it first came out, they would pull from hallucinated sources, which was pain in the neck. But today... They've gotten to the point where They give credible... Specific answers to my very specific questions.  So my research has just multiplied by, it's hard to even compare what it was like No, Versal, what it was like before. Because I do so much of it now. It's really been a game changer.  So that's at the high level. The game is completely different for me right now. See you next year. Ray Sidney Smith | 04:15 And it will be different in a year from now even. More so. As the technology gets better. Francis Wade | 04:21 - I've told people that different parts of my work. Have undergone more change in the last year than in the last decade. 30 years before that, 20 years? And this is certainly one era that is completely different. Augusto Pinaud | 04:37 Sometimes digging and research in a topic and sometimes more than the papers, find the books. What is the book that, okay, I read this book. Now, What other... Go. Into this line and with books go on the opposite line.  Sometimes it's not only The papers, it's the one to give a more... Book rented? What books? Hey, I'm dealing into... And sometimes once I want to deal or work or research into this particular idea, Bye. Where can I find those books? Because you think, okay, I want to get, how do you get granular and now fast? But then now how do you find those book, those authors, who are the authors who I'm researching this, the same areas that I'm research, it doesn't matter if they're agreeing or disagreeing with you, but how you find them, that was a labor Of love. A lot of times, to find those books and to find those authors.  And then after that, then you needed to start Figure out which one was good, which one was bad. That job? One from weeks to hours. And you in hours can get a list that is better than what I was able to produce in months. This gets very interesting, the issue. Who's this? The expectations that now the people have. Because for what you're describing, similar to mine, it's not only get the information, now that just you were able to get to the sources pass through. But the other part of the process is still, you need to still read it, still download them, still digest them, still trying to connect those dots. That is still takes the same amount of time, but then First part, it's fantastic. The issue I see with this is I find a lot of people who think that find the sources is enough. And find the sources is just a step one of X number of steps to be able to get to the next conclusion. Ray Sidney Smith | 06:47 So I think about AI in a research context, when I say this is an AI researcher, Bye. That AI can still hallucinate. I know Francis is a little more, maybe more trusting than I am when it comes to these tools. But I've found ways to revalidate information even after it has pulled research And again, I Preface this always with everything I do with AI, I presume to be a first draft when it puts it out. And so I'm reviewing everything as though an intern handed it to me and it's an intern's work product.  So I need to make sure that it is correct. So we were all on the same page there. I think there are certain areas where AI is really good right now and where it will get better. I think that the deep research functions within all of the major tools that AI chat bots are pretty good right now.  So you have this deep research function in Claude Gemini, and ChatGPT. Personally, I've found that Gemini's does the best. I'm not sure why, but I just feel like it gets the most right when you prompt it correctly. And I don't like the verbosity around the deep research that Google puts out, but it's fine. It gets the data right, which is what I care about most. And that's one piece, which is you have this complex question and you need it to go out there and scour lots of sources and come back to you with an answer. And you don't know what the sources are. And I think in that sense, it can go ahead and find sources and then go ahead and do that analysis and synthesis that is really complex and therefore laborious and make it simpler.  Though Concern I always have with folks is that We're a little too trusting. So I'm going to, again,...

In this episode, we continue our series on the AI-Powered Professional by introducing the AI Researcher persona. Ray, Augusto, and Francis discuss how AI is reshaping research, learning, and knowledge work by moving us beyond simple retrieval toward active knowledge synthesis. Along the way, they explore the problems of information overload, low-quality information, over-trusting AI-generated answers, news and social media overwhelm, and what Ray calls “information toxicity.” The ProductivityCast team also discusses practical ways to curate inbound information, reduce cognitive friction, use AI-generated briefs and drafts responsibly, and stay in control of your attention while working with smarter tools. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/149 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing The AI Researcher: From Information Overload to Active Knowledge Synthesis (Part 1) from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | The AI Researcher: From Information Overload to Active Knowledge Synthesis (Part 1) Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | The AI Researcher: From Information Overload to Active Knowledge Synthesis (Part 1) Resources we mention, including links to them, will be provided here. Please listen to the episode for context. ResearchGate Academia.edu ChatGPT Google Gemini Google Workspace Microsoft Copilot Feedly Evernote Social Fixer The New York Times The Onion Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More Voiceover Artist | 00:00 Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you've come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney Smith and Augusto Pinault with Francis Wade and Art Gelwick. Ray Sidney Smith | 00:18 Welcome back, everybody, to Productivity Cast, the weekly show about all things personal productivity. I'm Ray Sidney Smith. Francis Wade | 00:24 And I'm Francis Wade. Ray Sidney Smith | 00:25 Welcome, gentlemen, and welcome to our listeners to this episode of ProductivityCast. This week, we are going to be continuing our dive into the world of artificial intelligence, which I like to call smart software, with another episode in our series of the AI-powered professionals.  So today we're going to be focusing on research and what I'm coining here is the AI researcher persona and how these new tools are really transforming the process of learning and researching and knowledge work for us. We're moving to a place where we can understand retrieval as basically active knowledge synthesis. And we're going to be talking through some of the challenges that folks face with regard to information overload and otherwise.  So let's first talk through the problems with research today. What do you find are the good or the positives around research today? And what are some of the problems that we experience? One of them we're going to talk about, which is information overload. But there are others that are out there.  And then we can give that context. Color with regard to how we can use AI as a researcher to help us with that process or those problems.  So what do you feel like are the primary problems today with research. Francis Wade | 01:47 I think in the past, very much a hit or miss kind of proposition. Where if you could find someone who had done the research... Answer the research questions that you have. You were extremely lucky. And the game was, how can I increase odds of success how can I be luckier So that meant that dwelling in places like Research Gate. Maybe at academia.edu.  Yeah. But ResearchGate was my goal, though. And For certain topics, especially the two that I specialize in, which are task management and strategic. Planning. I've pretty much got to the bottom of everything that I could find easily. It took a few years for each one, but I've sort of gotten to what I think is like the bottom. Where I read what they have to say. And I've noticed sort of where all the faults are why in neither field the research academics do is very useful in the real world?  You know, it's very esoteric and it's meaningful. Academics tend to write for each other. And for journals. And for advancement in their field. They don't like to go into areas that are cross bouldery that I like to mix and match different fields. They don't go interdisciplinary. It makes a real mess of the nice, clean, lines that they like to follow. And I don't like to go into areas that, you know, If you become an expert in an area where there's no conferences and no journals, no chairs and no departments anywhere in the world. If you go into an area like that, you know, you're sort of dooming yourself to obsolescence.  So with those problems, It means that for the two areas that I'm interested in, there's a, Not a lot of useful research. There is to find.  So finding something useful used to be a lucky proposition. And I would have to basically find someone who has enough experience in both areas to be able to do research in both areas so that they would have the questions. And finding that was like a needle in a haystack.  So it's always been difficult in the two areas that I Try to find research written on. It's always been an uphill struggle. Augusto Pinaud | 04:02 I think it's important to make an distinction between professional researching practices and the non-professional one. I agree in the professional researching the impact of AI has been incredible because now these people who Say. Knows better when they're trying to search and look into information. Cinta was not available. When you go to the noun informal research. It's interesting because I feel that we used to have Three levels of research, bad research, middle ground research, and good research. And now with the AI, we have gone and disappeared that middle because people think that they can find the answer that they believe is legit. Doesn't matter if it's true or it's fake information or what it is. They can go bump into any of these agents. Get an answer. And because of that, people stopped digging. Into is this really legit? But when you think in the world of productivity, When the first book of David Allen came out, we were talking about 2001, It was hard to find the information. It was hard to find the principles behind unless you have access to them. 25 years later, you can find A ton of information. The question now is, How did you know that information is legit or not? And that's why I think that middle ground has disappeared. You have the people who goes and do a prompt, and get an answer and assume Dad. The answer they're getting is the truth. And because of that, that's the stop of the research.  So what was part of the issues 20 years ago is, okay, I want to research this topic and now I have 20 books. No, they just go, ask two questions, get what they think is a truth answer, and take that That's a fact. Then you have the other level that is the people who are going to get that and try to figure it out. Is this a fact? They're going to try to dig out or it's not a fact. And what is the fact? What is interesting for me with AI is That middle ground, that guy who will have get that fact and tried to see why. I don't look legit or not legit. That disappeared. What I have seen is people getting the output that AI is giving them I'm taking them. It's a truth. It's an absolute truth that is even more scarier. And I have seen this In academic settings, I have seen this in professional settings, okay, where people go What is the obsolescence of this? Okay. Can you repeat that? I didn't get an answer.  So when that is, they never really dig. Hold on, did you want to do the vendor? Did you, did the chat GPT was floating you know, That, I mean, how been... Wonderfully. Last week. My son is a baseball fan, so he was watching the baseball and he wanted to see the score, so he asked, Madame Eyre. And But I may say, the game has not started. It was time for the game to start. That's true. The radio. Fuck. And you know, like, You've got me in the life. Damn, man. Give us whatever is for them. I've nothing to do. With the reality. And it was a great moment of, teach an opportunity because of that. If we will have the initial answer, what most people do, This other game has no authority. Okay, and you move on. But the reality is minimal. The game had started. We were in the middle of the game and there was a different score than what she was giving us on the third answer. And that is what Most people don't notice when they go into this research. AI will give you an answer. The question is if that answer is actually the answer or. Ray Sidney Smith | 08:11 Not. When it really matters, right? Learning that the game is not trivial, maybe not to your son, but to the rest of the world, you know, when it's... I will. Augusto Pinaud | 08:19 Make sure to tell him that right thing, that when the game is on, it's not trivial. You are going down in that scale of people he likes. You're going down, my friend. Ray Sidney Smith | 08:27 The unfortunate part is if you say, hey,...

In this episode, we continue our conversation on The AI Assistant as part of The AI-Powered Professional series. Picking up from Episode 147, the ProductivityCast team shifts from using AI merely to offload administrative friction and shadow work to thinking about AI as a true collaborative assistant. Ray, Augusto, and Francis discuss how to define roles for AI assistants, train them with useful context, manage multiple AI tools and personas, review AI-generated work as drafts, and build prompt workflows that help professionals get better results while staying firmly in control. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/148 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing The AI Assistant: Automating Administrative Friction and “Shadow Work”, Part 2 from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | The AI Assistant: Automating Administrative Friction and “Shadow Work”, Part 2 Ray Sidney-Smith Augusto Pinaud Francis Wade Show Notes | The AI Assistant: Automating Administrative Friction and “Shadow Work”, Part 2 Resources we mention, including links to them, will be provided here. Please listen to the episode for context. Microsoft Copilot Google Gemini Google NotebookLM ChatGPT Claude Evernote Zapier Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More [00:00:00] Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you've come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney-Smith and Augusto Pinaud, with Francis Wade and Art Gelwicks. [00:00:18] Welcome back, everybody, to ProductivityCast, the weekly show about all things personal productivity. I'm Ray Sidney-Smith. Marco is jumping out. And I'm Francis Wade. Welcome, gentlemen, and welcome to our listeners to today's episode, where we're gonna continue our discussion on AI, and this is our series on the AI-powered professional. [00:00:44] in our first episode, we started the discussion about the concept of utilizing generative AI. in this episode, we also started the process of talking about what an AI assistant is really [00:01:00] like, talking about some of those administrative frictions, being able to get rid of, and automate that out of, your world to some extent, and dealing with shadow work as well, defining shadow work and so on and so forth. [00:01:13] We're gonna continue this topic into discussing today about really how to partner with your AI in a lot of ways, what the collaboration process really looks like. And so I'd like for us to discuss shifting using AI tools as a mechanism of just kind of offloading something, which it can do, but then becoming a more collaborative partner with that particular AI tool in order for it to become a true AI assistant. [00:01:45] And so I'm thinking of things like how do we ensure that AI is taking over the right kind of work and that it's not taking over the work that we should be doing, and how do we maintain control and accuracy? And of [00:02:00] course, there are a bunch of boundaries and ethical considerations that we should be thinking about and some thoughts about the future. [00:02:05] So let's start with what are some of those first principles, for us to be able to create a true collaboration partnership with our AI assistant? [00:02:19] Sure. I'm thinking about this from the perspective that If I want to work with my AI assistant, I need to choose particular categories of work in which it can actually collaborate. So for example, I want it to be able to help me take a rough sketch that I've made on either my iPad or on paper, and then to have the AI turn that into a full-fledged drawing, a full-fledged cartoon perhaps. [00:02:49] So the AI assistant is acting as my cartoonist, and so that's a role that I want the AI assistant to do. And while I can draw my [00:03:00] own cartoons, 'cause I've taken this drawing class, I feel competent to draw, you know, one part of a cartoon, but then it can fill in the rest by creating the other panels of the cartoon. [00:03:13] And this is really helpful to me because now I can make the first drawing. It can be roughish, you know, to give it the idea of what I want, and now I can help it help me, quickly generate more panels and get the cartoon done by virtue of that. But the idea is that it's now a role that I want it to continually be helping me with, and so that is the cartoonist role. [00:03:38] That's just one. I mean, like that, it doesn't, it doesn't have to be just role. It could be any number of things. But it's just like, that's the kind of thing that I'm thinking about. well, in the last episode, we sort of established the notion that, an AI assistant is like an intern who remembers everything, but doesn't have a whole lot of judgment. [00:03:56] isn't, a really good judge of, you know, the [00:04:00] things, whatever it is that we happen to be expert at. It, it's too much to ask the AI to rise to our level of, insight and understanding. Having said that, there's a whole bunch of stuff that now looks to me that, it looks different to me because I can now see it as automatable. [00:04:23] Like the example that you gave of, doing repetitive drawings or repetitive, animation. There's a bunch of things that I, and the list keeps growing, which is why I don't have a fixed answer. but it does start with this notion that I have an untrained intern that has infinite memory and infinite patience and doesn't have an attitude and works at all hours. [00:04:48] And if I train that intern, then there's more and more things that the intern can do, and there's gonna be a new app tomorrow that- allows the intern to [00:05:00] do even more. So it's hard to say what specific role because the roles keep changing, and they keep being added to. if anything, I would say there's maybe a rule, which is that, try to give the intern as much as possible, but always be the person of last kind of decision. [00:05:20] Be the one who's at the end checking to make sure the intern didn't make some, you know, gross error. So if there's any rule, that's the rule that I'm applying right now. Try to find more and more to give and then be the person at the end to do the checking. and then don't try to stress the intern out with judgment calls. [00:05:42] and even the limit-- even the line on what I call a judgment call is changing with AI because it's getting better, You know, the AIs that I use, I use memory, so it understands me and what my judgment calls are, better and better each day. So it's a tough question to answer.[00:06:00]  [00:06:00] So just stepping up a level, I would say that just the concept of establishing roles for the AI is the first principle. It's not necessarily that you're going to ever be exhaustive in terms of creating the roles, because sometimes the role you need for a specific chat is defined in only that chat, and then there will be ones where you're gonna need that as an ongoing kind of recurring thing. [00:06:29] It depends. You know, last episode I was talking about that wine help. You know, help me identify wine that I may enjoy based on my profile and educated that profile. But same thing on, on the professional side. I have a client who we, because of what they do, they, it's a report that is run every morning, and that report gets to them. [00:06:53] And the problem is it's impossible to, to analyze it long enough. You know, you can see the report daily. You can maybe go a [00:07:00] couple days back. But human, it's hard to really create trends and things from that specific report. Where it's been very cool is a play on the role we create a chat for that neural network, okay? [00:07:15] And now that report is dumped, for lack of a better word, into this chat. But this has now allowed us to identify trends not in three months, not in 90 days. Hey, this server last time this failed, okay, it was seven months ago. And it failed for three days. That information no human can provide for me. Okay? [00:07:38] But allows you to start seeing that, and that make it very, very specific. Okay? Same thing, when you write. After you train, yeah, it required to train the intern, but after you train, say, "Okay, this sound like me. This doesn't sound like me." You know, one of the things that I love to do is when I get an [00:08:00] idea, okay, let me discuss this idea with the content of, okay, or the ideas or the understanding that AI has of X person. [00:08:08] And you can say, "Hey, I want to look what will be the perspective of this text if Einstein read it." assuming you, you know what, physics and stuff. But that give you... Is the perspective you're going to get accurate? Well, it may be, it may not. But it will give you a counter that is very interesting. [00:08:30] One thing that I do very often is find the arguments in favor and against this i- this concept, this idea that I'm working on. And it now get... You know, I think the definition, part of the definition or the issue is this, for a lot of people, is the first time they get access to an assistant, to an administrative assistant,  [00:08:53] For most people, that is a concept that they heard,...

In this episode, we’re discussing how to use AI to automate "shadow work", the boring, repetitive tasks like data entry and invoicing that drain our energy. By viewing AI as a "million interns" that need clear instructions and human supervision, the hosts share how to streamline everything from professional billing to personal life choices like cooking and movies. While AI can sometimes make mistakes or "hallucinate," the episode explains that investing time in training your AI assistant can remove administrative friction and help you focus on the work that actually matters. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/147 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing The AI Assistant: Automating Administrative Friction and "Shadow Work" from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | The AI Assistant: Automating Administrative Friction and "Shadow Work" Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | The AI Assistant: Automating Administrative Friction and "Shadow Work" Resources we mention, including links to them, will be provided here. Please listen to the episode for context. "Deep Work" and "Shallow Work" by Cal Newport Flow Theory by Dr. Mihaly Csikszentmihalyi Personal Productivity Club Raw Text Transcript Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More Voiceover Artist | 00:00 Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you've come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney Smith and Augusto Pinault with Frances Wade and Art Gelwix. Raymond Sidney-Smith | 00:18 Welcome everybody to productivity cast the weekly show about all things personal productivity. I'm Ray Sidney Smith. Francis Wade | 00:25 And I'm Francis Wade. Raymond Sidney-Smith | 00:28 Welcome, gentlemen, and welcome, everyone. To ProductivityCast this week. We're diving into the world of artificial intelligence and its growing role in our personal productivity. This is going to be a part of an ongoing series we're calling the AI-powered professional. And in today's episode, we'll be exploring how AI tools are moving beyond simple task management to tackle tedious shadow work, things like administrative friction, small repetitive tasks, and context switching overhead that drains our time and energy. And so we're going to talk about how to approach shadow work with AI, how to overcome the problem, and we can talk about some of the solutions that we have utilized throughout the So let's get into this. Raymond Sidney-Smith | 01:13 Episode. Raymond Sidney-Smith | 01:16 And first, let's spend a little bit of time talking about what exactly is administrative friction and shadow work. Does anybody want to kind how they... Raymond Sidney-Smith | 01:26 Of tackle. Raymond Sidney-Smith | 01:28 Perceive what shadow work or administrative friction is. Francis Wade | 01:32 Sure, it's the stuff that I have to do to Execute the day. So it tends to be repetitive stuff. I don't have a choice. I must do it. No one else can do it. Typically, or I could train someone to do some of it, but most of it It's not worth training someone else to do it because there's probably some nuance that only I know. I see these things as a bit of a tax in the sense that In order to achieve your overall objectives, you have to do them. And yes, if you can get them automated, "Polite you, but point is that they're mandatory, they're required, and you don't have a choice.  So I know how I feel when I'm doing them because my heart sinks and my energy drops and I go through a whole metamorphosis into someone who wishes he were doing something else. So that's an emotional Sugar for me. Or a Marco. Augusto Pinaud | 02:34 You know, for me, the shadow work is all that work that is required to do to really be able to focus on being productive. And I'm dead. And the reason is, as Francis was saying, it's normally it's not the fun work. Okay. But it's the fun, it's the work that you need to do. To be able to get to that fun work to get to that. Work in which you can really focus and you can shine. And Us bored. As tedious That's it is. It's critical to be able to get a good session of productivity. Raymond Sidney-Smith | 03:13 Yeah. Cal Newport defines his concept of Shallow work. As I think what we think of as what we call shadow work. And so there's this concept of often performed while you're doing other things and doesn't create a lot of value. And I've always had a problem with the concept of shallow work and deep work. I think that those definitions, deep work being high value, items that you're highly focused on just really sounds like flow work to me. And so, but I define flow work, of course, the way that Dr. Mihaly Csikszentmihalyi talks about it. And The goal here is to really look at this from a lens of if we're doing all of this administrative work, I think that there is some great value to some of it. As I typically use the example that you could have a five minute phone call and have a client say, let's sign the million dollar contract. Right. Was the five minute phone call worth it? Absolutely. And some people would consider that an administrative issue. Burden, right? That would be administrative friction having to take and have that five minute phone call.  So we have to kind of parse apart those things that are high value and don't take a lot of time. Those things that are low value and take little bits of time or lots of time, each of which are kind of problematic in their own way.  And then being able to overcome those through various mechanisms. Artificial intelligence is just one of them.  So we have to be mindful that what we're talking about here today is just one of the ways in which you can overcome this. But I don't think that AI ultimately becomes the panacea. It is just one option that you have among many for being able to overcome these kinds of issues. Couple things that I wanted to kind of talk about here is that I think that Data entry generally is administrative friction and is a classic type of shadow work that you can think of. And Honestly, data entry is what computers were built for.  You know, the whole concept of being able to take data and run calculations or structure it in a way I think is the archetypal form of shallow, shadow work. Francis Wade | 05:50 The data entry I have an intern that works with me or an associate who works with me and She was doing an entry job And I thought about it for a minute, because she was not enjoying it. And I said, It's one of those, again, it's one of those taxes that at some point you've got to pay because We all have to enter data at some point. And we'll try all our best to not have to do it. But again, it's one of those sinking feelings that after you thought of everything and you realize that at the end of all your thinking, you still have to enter the data. There's no shortcut available. You got to do it. And I entered I remember. , entering data early in There's a way to it. Into data and I was trying to share this with her That's more skillful. Because you're paying attention to what you're doing. You're not checking out to the point where you're making mistakes. And at the same time, you're taking care of your well-being.  So you're not engaging in conversations that are dragging you downhill and getting you depressed. That there's a way you have to manage your mind. And manage your attention. To do good data entry so that you don't do nonsense.  So it's actually, I tried to paint this picture for her. It's actually a skill. That Once you have it, you can always Do it. Use it. But it's not a skill to... Resist in the sense that you wish it weren't there. It actually has so much, sometimes the data has so much value, that you need to bring your best skills to your data entry.  So that you don't end up resisting it. You don't end up resenting it. And you don't end up making mistakes so it's a skill in and of itself. And funnily enough, after I gave her this, very inspirational explanation. I ended up spending It must have been at least six hours entering data. There was no way. I was yeah. And I actually did some automation. I did a little lab coding to kind of speed it up. But that could only get me so far. But not go any further. And I thought, I'm eating my own dog food, taking my own medicine. But I agree. Raymond Sidney-Smith | 08:19 Yeah, I'll say this. There are so many ways to ease the burden of data entry. Again, it's about the ways in which input and output in computer communication in human-computer interaction that we just don't think about.  So, you know, Our input options for a device are typically in an input perspective. There's audio, right? You can put audio through a microphone into it, which is what we're doing right now, recording the podcast.  And then there's video, having the device take in video. Or images and then of course mouse and keyboard and if you have a touch screen or a touchpad like a Wacom board, you're capable of putting in some kind of handwriting or hand drawing some kind of, you know,...

In this episode, we're discussing the concept of perspective as a contrast to the GTD concept of control. We explore the "Horizons of Focus" and the different altitudes of self-management, ranging from the "runway" of daily actions to the "50,000-foot" level of life purpose. We debate whether perspective is a tactical relationship or a pseudonym for context while contrasting top-down visioning with bottom-up execution. From discussing the "elasticity" needed in planning to identifying incongruencies between our values and our careers, we explore how these various layers of focus can transform mundane tasks into meaningful progress toward a fulfilling life. (If you’re reading this in a podcast directory/app, please visit https://productivitycast.net/146 for clickable links and the full show notes and transcript of this cast.) Enjoy! Give us feedback! And, thanks for listening! If you'd like to continue discussing what perspective means in the context of GTD from this episode, please click here to leave a comment down below (this jumps you to the bottom of the post). In this Cast | What Does Perspective Mean in GTD? Ray Sidney-Smith Augusto Pinaud Art Gelwicks Francis Wade Show Notes | What Does Perspective Mean in GTD? Resources we mention, including links to them, will be provided here. Please listen to the episode for context. Getting Things Done by David Allen The 8th Habit by Dr. Stephen Covey Unlimited Power by Tony Robbins Tony Robbins Audio Programs GTD "Horizons of Focus" Model Personal Productivity Club Raw Text Transcript | What Does Perspective Mean in GTD? Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio). Read More Voiceover Artist | 00:00 Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you've come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney Smith and Augusto Pinot with Francis Wade and Art Gelwix. Raymond Sidney-Smith | 00:18 Welcome back, everybody, to Productivity Cast, the weekly show about all things personal productivity. I'm Ray Sidney Smith. Augusto Pinaud | 00:24 And I'm Augusto Pinaud. Francis Wade | 00:26 I'm Francis Wade. Art Gelwicks | 00:28 And I'm Art Gelwicks. Raymond Sidney-Smith | 00:29 Welcome, gentlemen, and welcome to our listeners to this week's episode. Today, we are going to be talking about perspectives as a contrast to last week's episode on the GTD concept of control.  So in order to start off the conversation, I want to lay the groundwork with perspectives. The Getting Things Done March 2015 edition appendixes. Glossary of terms definition given for perspective. And it says, one of the two key elements of self and organizational management, along with control, and it refers to point of view, focus, altitude of horizon. We have this concept of horizons of focus in getting things done. And what I'm hearing from the definition is that David Allen is saying that each one of those horizons of focus is a perspective. That's what the terminology is talking about. And so with that in mind, I wanted to ask, Is. Perspective the right term? And what does perspective mean to you in your own. Francis Wade | 01:33 My mind perspective is a pseudonym for context. And as someone smart said, context is decisive. The background story from which you take your actions is all important. And it's malleable. And most of us just wake up into the context that human beings wake up into every day, which is something close to survival. Survival, desperation, fear, you know, how can I make it through the day kind of, point of view. I think that's the default.  And then to Create a new context, tics. Or any perspective takes. Conscious act for the most part conscious action it takes deliberate effort. I think this is all important. I think the book GT is really about that. And perspectives is one way of seeing it. I think this idea that... The context of your life is decisive is an idea that's been around probably since the Greeks. And that's something worth paying attention to.  So yes, it's vitally important. Art Gelwicks | 02:41 What I think is interesting about this looking at perspective and how it breaks down is that often perspective is not related to ourselves. If you look at the GTD structure you've got, and I'll work from the bottom up, Actions, Projects, Goals, Vision, Life The first four of those categories are often... Stolen for lack of a better term by corporate and work environments. To set the messaging. For everything that you're supposed to be doing and that becomes the context, for lack of a better term, that those get applied in. It's difficult for us to translate goals, vision and life into actual real life things, because every time we think about it, we think about, you know, the next five year plan at work.  So... If we think about it from the perspective of how they relate to each other, perspectives in my book, are pretty much whatever you're dealing with and the thing above and the thing below it.  So projects is a good example. If you're working on projects, The projects need to align to the strategic goals that you have. If it's business, obviously you have business strategic goals. If it's personal goals, The things that you're doing should align to those. You have to determine what those are, but they should be tied to it. And below that, the actions necessary to execute those projects.  So the perspective is less... What the term would lend me to believe, which is a view on things and more of a relationship. Raymond Sidney-Smith | 04:27 I certainly see perspectives for me. As a vertical planning tool. And so each of the additional perspectives above the actions level or the ground level in at least GTD lingo, you are seeing a different and therefore you were planning at a different time. "layer of time" So it's for me, I always think of time horizons is that, you know, projects are within our time bound within about a year and then you keep stepping up to further and further distances of time. Accepting the horizon too, which is areas of focus and accountability, which ends up being something that is it's a maintenance level.  So, While each of the time horizons keep going up in that sense, Horizon 2 is the exception there because it's maintenance. It's ongoing maintenance. And if you're like me and you've created other horizons, other perspectives, then you may have other time horizons in that sense as well. They're not going away. And so that has an infinite horizon in essence.  So I'm looking at it on a regular basis purely because it's perennial. It's always going to be there. The interesting thing about perspectives for me is that I think that there is a clear... I don't know if I want to say contrast or argument between other systems, other methodologies, other thought processes on this and perspectives. I'll give you one example, which is Dr. Stephen Covey's The Eighth Habit, where he talks about the concept of the four intelligences. And if... Control and perspective are really the only two vectors of a productivity system, then Where does something like the four intelligences fit. And I would say that Dr. Covey would say that this is perhaps a completely different dimension.  You know, the four intelligences being mental intelligence or IQ, physical intelligence, PQ, emotional intelligence, EQ, and spiritual intelligence, SQ. And so he has this perspective on the four intelligences and how they map to all of the rest of how you would develop in his language, you know, finding your voice and helping others find theirs. And so I'm always... I don't know if I'm conflicted because I have myself learned to deal with where these things fit in my own system. But I can see that there can be a difficulty for folks to think about how Picking. IQ, PQ, EQ, SQ, and turning that into vision, discipline, passion, and conscience in the eighth habit world, and then seeing the somewhat limited I wouldn't say limited, but more simplistic view of the perspective concept. And I'm curious from all of you, Do you see a conflict or do you see... Perspective is just basically holding other I. Art Gelwicks | 07:38 Think perspectives are a more tactical term. Approach. They're more tangible. We associate them with things and activities that we have going on. Stephen Covey's concepts are more to understand people. I want to say spheres of influence. I don't know that they necessarily relate directly to each other. There is no dotted line. It is difficult at best. To translate. For example, projects into mental intelligence, you're not going to be able to make that connection easily without it stopping. Significant amount of work. Raymond Sidney-Smith | 08:18 Yeah, but to interject right there, like, so if we step up a few horizons and we get to vision and purpose and principles, then we're really starting to connect. The concepts of what he's talking about there at the very top levels of the system.  So it's not really closer to the tactical level, but more when he starts talking about, you know, creating vision, right, which he defines kind of as applied imagination. And then he we keep stepping down to these other, not stepping down, but going around the four quadrants, right? To figure out passion and discipline and conscious and otherwise.  So it's, At those upper horizons,...