The show about all things personal productivity
ProductivityCast
Claim This Podcastby Ray Sidney-Smith
4.9(15 reviews)
150 episodes
Updated Daily
Accepts GuestsHas Sponsors
Podcast Overview
The show about all things personal productivity
Language
🇺🇲
Publishing Since
7/8/2017
1 verified contact email on file for ProductivityCast
Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.
Recent Episodes
May 25, 2026
The AI Researcher: From Information Overload to Active Knowledge Synthesis (Part 2)
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,...
May 19, 2026
The AI Researcher: From Information Overload to Active Knowledge Synthesis (Part 1)
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, I just swallowed this thing mineral....
May 11, 2026
The AI Assistant: Automating Administrative Friction and “Shadow Work”, Part 2
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,...
150 total episodes available
Similar Podcasts
Discover related shows you might enjoy
Deep-dive analytics for ProductivityCast
Frequently asked questions
Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.
- What is ProductivityCast?
- How often does this podcast release new episodes?
This podcast updates daily.
- Where can I listen to this podcast?
This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
No, this podcast does not typically feature guests.
Legal Disclaimer
Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.
All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.
We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.
While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.
By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.