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).
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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).
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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).
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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).
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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.
...
You’ve started leaning on AI for your strategy work. ChatGPT, Claude, Gemini — pick your favorite. You’ve fed it your hardest question, the one that’s been on your desk for weeks. And the answer came back in seconds. Polished. Balanced. Articulate. It mentioned differentiation. It mentioned long-term thinking. It suggested you augment your team rather than automate them. It read like advice from a Harvard Business School case writer.
And it sounded like good advice.
But something is off. Because last week, a colleague at a different firm — different industry, different size, different problem — mentioned over coffee that she’d been doing the same thing. And when she described what her AI told her, it sounded suspiciously like what yours told you.
So now you’re sitting with a question you didn’t expect to be asking. If everyone is getting roughly the same advice, in roughly the same shape, dressed in roughly the same words — is anyone actually getting strategy at all?
Tune into this episode to join me in tackling this wicked problem which I first heard of in the famous Harvard Trendslop article from April 2026. It’s why AI keeps handing strategists the same answers — and what’s really going on underneath that has very little to do with AI.
AI in Strategy Creation | Beyond “Trendslop”
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