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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, underscore the point that even after it does this research,...

You’re in a strategy retreat. You see an opening to shift the conversation—a strategic insight you know could change the trajectory. You speak up with confidence. And then… blank looks. Awkward silence. The room moves on as if you hadn’t spoken.

It doesn’t matter if you’re the CEO, the board chair, or an ambitious director. The frustration is identical: you have strategic clarity, you know the frameworks, yet your interventions land with a thud while others command the room effortlessly. Most executives diagnose this as needing sharper frameworks or better presentation skills. Wrong problem.

This episode exposes what elite strategists do differently: they’ve built pattern libraries from accumulated case exposure that allow them to deploy diagnostic stories, pattern stories, and origin stories in the moment—not in PowerPoint decks afterward. You’ll discover why Julius Yego’s YouTube-driven Olympic medal validates cognitive science research on tacit knowledge, how Samuel Berger’s “intellectual dark matter” explains the gap between knowing frameworks and commanding strategic conversations, and why the three-season development model transforms in-the-room impact when executive programs don’t.

For global executives who’ve exhausted conventional development paths, this reveals the hidden capability that separates persuasive pattern recognition from forgettable framework recitation—and the deliberate practice method that builds it.

Enjoy the full video of this episode below for all subscribers.

This is a public episode. If you’d like to discuss this with other subscribers or get access to bonus episodes, visit longtermstrategy.substack.com/subscribe

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, that they, you know,...

There’s a number most government leaders would rather not think about. For Jamaica, it’s nine dollars.

That’s the country’s productivity measured as output per hour worked — US$9. Barbados, a neighboring island economy, produces more than twice that. Panama produces five times as much. Most strikingly, Jamaica’s hourly output is only marginally ahead of Haiti’s — a country that has experienced decades of political collapse and natural disaster.

The numbers are sobering. But they are not unique to Jamaica. Across the developing world, governments face a version of the same arithmetic: their economies are generating far less per hour of human effort than they should, the gap is wide, and it has been wide for decades.

What is less often discussed is the role that government itself plays in perpetuating that gap.

The Largest Economic Actor in the Room

In many developing economies, government directly produces somewhere between 15 and 20 percent of GDP through goods and services. That makes it the single largest economic actor in the country — larger than any company, sector or industry.

But its true influence extends much further. Government shapes the entire environment in which the other 80-plus percent of economic activity takes place, through four distinct levers: macroeconomic stability, institutional quality, infrastructure and public goods, and the signals and expectations it sends to investors, businesses and citizens about the future.

That fourth lever is the most underestimated. When a government is unpredictable, inconsistent or widely distrusted, it suppresses private investment and enterprise far beyond anything that shows up in its own budget. Conversely, a government that signals credible, long-term commitment to stability and growth creates a multiplier effect on every dollar the private sector deploys.

Jamaica has made genuine, internationally recognized progress on the first lever. Its fiscal turnaround since 2013 has been studied by other nations as a model of discipline. Debt ratios have fallen. Inflation has been tamed. And yet GDP growth has not followed at the pace the data might suggest it should. The reason is that macroeconomic stability, while necessary, is not sufficient. The other three levers matter just as much — and progress on those fronts is slower and harder to sustain across electoral cycles.

Why Elections Are the Wrong Unit of Time

The deeper problem is structural. Governments, by design, operate on four- or five-year cycles. The problems that most constrain developing economies — workforce quality, institutional trust, infrastructure, behavioral norms — compound over decades. They cannot be fixed within a single term. Often they cannot be fixed within a generation.

Take literacy. Jamaica’s literacy rate trails comparable peer countries by five or more percentage points — a gap that has been building since the 1960s. That gap is a direct and stubborn drag on workforce productivity. Closing it requires sustained investment and policy consistency across twenty or thirty years, not one budget cycle.

The same logic applies to institutional quality, infrastructure and public trust. These are slow variables. They respond to patient, consistent effort — not to whoever won the last election.

This is not pessimism. It is arithmetic.

The Countries That Played the Long Game

Two examples are instructive, and they have been cited often precisely because they are so striking.

Singapore in the early 1960s had a GDP per capita comparable to Jamaica’s. It was a small, resource-poor island with a mixed population, uncertain regional relationships and no obvious competitive advantages. Today it is among the wealthiest and most productive nations on earth.

Norway had oil. So did Nigeria, Angola, and Venezuela. The difference was that Norway resisted the temptation to spend its resource windfall immediately and instead built institutional structures — including a sovereign wealth fund now worth over a trillion dollars — designed to distribute wealth across generations rather than electoral cycles.

Both Norway and Singapore have populations of around five to six million — comparable in scale to many Caribbean and Central American nations. Scale was not destiny. What separated them was institutional patience: the willingness to make commitments that no single government could unilaterally reverse.

The Design Flaw in Most Development Plans

Many developing nations have tried long-term national development planning. The typical failure mode is nearly always the same: the plan belongs to one party. When the government changes, the plan either changes with it or quietly fades from view.

Jamaica’s Vision 2030 — an ambitious plan built around the aspiration to become “the place of choice to live, work, raise families and do business” — started with a bipartisan commitment, but has largely followed this pattern in recent years. It is rarely invoked by either major political party today. Its successes have not been studied. Its failures have not been honestly diagnosed.

Trinidad and Tobago offers a cautionary parallel. Multiple governments there have attempted national development plans under single-party mandates. Without cross-party commitment, each plan has been vulnerable to revision or abandonment when power changed hands. The structural challenges — growth, productivity, crime — remain largely unresolved.

The evidence from countries that have succeeded suggests a different architecture is required. Long-term national commitments need to be insulated from ordinary political interference — protected by cross-party agreement, legal frameworks and institutional norms in the same way that independent central banks or electoral commissions are protected. The goal is not to remove politics from policy. It is to place the most consequential long-horizon commitments beyond the reach of short-term political calculation.

This is not a utopian idea. It has been done — in countries that once looked very much like Jamaica does today.

The Immediate Return on Patient Thinking

There is a paradox worth naming. Patient, long-horizon thinking doesn’t only produce results over decades. It produces its first results immediately — in the minds of the leaders who adopt it.

The moment a government leader genuinely shifts from “what can I deliver before the next election?” to “what structural commitment can I make that a successor will be bound to honor?” — that shift is itself a form of progress. It changes which conversations happen, which trade-offs get made, which investments get prioritized. Institutional culture changes before the metrics do.

For any leader in the public sector who recognizes the structural arithmetic above, the question is not whether to think long. It is whether to do so quietly or loudly.

Either way, the calculus is the same. The countries that changed their trajectories did not do it in four years. They did it by making four-year decisions that pointed consistently in the same direction for forty.

Only the nations — and the institutions — willing to make and protect patient commitments have a realistic chance of closing the gaps that actually matter.

5 Prompts to Put These Ideas to Work

The arguments in this article become more useful when applied to your own institution. These prompts are designed for use with any AI assistant (Claude, ChatGPT, Gemini, etc.). Work through them in order — each builds on the last.

Prompt 1 — Reflect “I lead [describe your ministry, agency or department] in Jamaica. The article I just read argues that government influences GDP through four levers: macroeconomic stability, institutional quality, infrastructure and public goods, and public signals and expectations. Ask me a series of questions to help me identify which of these levers my organization influences most directly — and where the biggest performance gaps are.”

Prompt 2 — Reflect “Here is my organization’s current strategic plan: [paste it]. Review it against this standard: which commitments are genuinely structural — meaning they require ten or more years to fully realize — and which are short-term fixes unlikely to outlast the current administration? Then identify what is missing from the long-term column.”

Prompt 3 — Apply “Jamaica’s literacy gap has been building since the 1960s and is described as a ‘stubborn contributor’ to low productivity. Help me identify the equivalent stubborn contributors in my sector — the slow-moving structural gaps that no single government can fix alone. What data would I need to make this diagnosis rigorously, and what would a credible 20-year improvement trajectory look like?”

Prompt 4 — Create “Using Singapore and Norway as reference points — both small nations that made long-horizon institutional commitments that outlasted individual governments — help me draft a one-page strategic hypothesis for my organization. It should answer: what is the single most important structural commitment my institution could make today that would still be bearing fruit in 2040? Start by asking me three questions about my organization’s current situation.”

Prompt 5 — Master “The article argues that Jamaica’s Vision 2030 failed partly because it lacked cross-party commitment — and that durable long-term plans must be insulated from political interference the way electoral commissions are. Help me design a cross-party commitment framework for one specific policy priority in my sector. What institutional mechanisms would make it durable enough to survive a change of government? What would have to be true politically, legally and culturally for this to hold?”

P.S. The impact of AI on strategy creation is forcing its way into our thinking every day. You wish you could keep up, but so much is changing so quickly that it’s hard. The good news is that this is the theme of our September 15-17, 2026 strategy conference. Save the date in your calendar!

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, stylus type input or touch inputs into the device....

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, it starts to bleed over one another. I.