Am I Optimizing the Wrong Things - CourtneyR.dev's 2nd Brain

Am I Optimizing the Wrong Things? ![rw-book-cover](https://img.youtube.com/vi/CkKEM0179rE/maxresdefault.jpg) 🔗 **Source:** [Am I Optimizing the Wrong Things?](https://www.youtube.com/watch?v=CkKEM0179rE&t=5s) > [!note]+ Document Notes > tags: #productivity #constraints #optimization #knowledge-work #bottlenecks > author: Cal Newport > source: Deep Questions Podcast > reference: https://www.youtube.com/watch?v=CkKEM0179rE&t=5s > date created: 2024-12-19 10:30 > > # 🏷️ Title: Am I Optimizing the Wrong Things? >  ## 🧰 Action Inbox - [H1] Read "Inside the Box" by David Epstein for constraint-based thinking — full book recommendation #productivity - [H2] Conduct personal bottleneck audit using Post-it note visualization — immediate implementation strategy #workflow - [H3] Apply stop-starting/start-finishing rule to current projects — limit work in progress #focus ## 🧭 Table of Contents - [[#📝 Summary]] - [[#📖 Main Content]] - [[#🗒️ Notes]] - [[#📚 References]] - [[#🗣️ Paraphrase]] - [[#📰 Newsletter]] - [[#✅ Action Items]] - [[#🧪 Hypotheses]] - [[#📊 Methodology]] - [[#❓ Questions]] - [[#🔮 Future Research]] - [[#💡 Implications]] - [[#🧠 Learning Styles Assessment]] - [[#📊 Accessibility Assessment]] - [[#🌍 DEIB Assessment]] - [[#✅ Logical Validity]] - [[#🛡️ DARVO Analysis]] - [[#🎯 Conclusion]] ### 🔗 References - [The Goal by Eliyahu Goldratt](https://www.amazon.com/Goal-Process-Ongoing-Improvement/dp/0884271951) - [Inside the Box by David Epstein](https://www.amazon.com/Inside-Box-Constraints-Make-Better/dp/0593653548) ## 📝 Summary - What happened: Cal Newport interviews David Epstein about applying the Theory of Constraints to personal productivity - Why it matters: Digital productivity tools often speed up non-bottleneck processes, creating pile-ups rather than actual improvement - Key stakeholders: Knowledge workers, managers, anyone using productivity tools - Risks and tradeoffs: Focusing on wrong constraints can waste energy; identifying true bottlenecks requires careful analysis - Bottom line: Focus on identifying and optimizing your actual bottlenecks, not just making random processes faster ## 📖 Main Content The Theory of Constraints, developed by physicist Eliyahu Goldratt in the 1980s, states that every system is limited by a single bottleneck - the slowest step in any process. This principle, originally applied to manufacturing assembly lines, has profound implications for personal productivity and knowledge work. The core insight is that optimizing non-bottleneck processes creates pile-ups without improving overall throughput. In personal productivity, this explains why many digital tools that speed up certain tasks don't actually make us more productive - they're optimizing the wrong parts of our workflow. Key applications include limiting work-in-progress (stop starting, start finishing), identifying personal constraints (like recovery time for athletes), and focusing improvement efforts on true bottlenecks rather than easy-to-fix but non-critical processes. ## 🗒️ Notes #### ⏱️ [00:00](https://youtu.be/CkKEM0179rE?t=0) — [H1] Introduction to The Goal and Theory of Constraints - Key idea: Goldratt's 1984 "business novel" sold 10+ million copies by explaining constraint theory - Evidence: "The goal went on to sell more than 10 million copies... Jeff Bezos made all of his executives read it" #### ⏱️ [05:23](https://youtu.be/CkKEM0179rE?t=323) — [H2] Goldratt's Journey from Physics to Business - Key idea: Physicist applied scientific thinking to chicken coop production problems - Evidence: "Moving one worker from a fast step to the slow step... triples the overall coop output" #### ⏱️ [12:45](https://youtu.be/CkKEM0179rE?t=765) — [H3] Theory of Constraints Defined - Key idea: Every system is limited by single bottleneck regardless of other efficiencies - Evidence: "Every system is limited by a single bottleneck... doesn't matter how fast everything else is working" #### ⏱️ [18:30](https://youtu.be/CkKEM0179rE?t=1110) — [H2] Epstein's Running Career Example - Key idea: Recovery was personal bottleneck, not training volume - Evidence: "Once we decided I didn't recover like the other guys did... I started getting faster almost every race" #### ⏱️ [25:15](https://youtu.be/CkKEM0179rE?t=1515) — [H1] Sheila Taormina's Swimming Success - Key idea: Olympic swimmer identified strength/power as bottleneck, not endurance - Evidence: "Swims exactly 3 seconds faster... makes the team and wins Olympic gold medal" #### ⏱️ [32:40](https://youtu.be/CkKEM0179rE?t=1960) — [H2] MIT Broad Institute Case Study - Key idea: Knowledge workers used Post-it notes to visualize and limit work in progress - Evidence: "Made all current projects visible... realized way more things in process than they could ever get done" #### ⏱️ [38:55](https://youtu.be/CkKEM0179rE?t=2335) — [H3] Stop Starting, Start Finishing Rule - Key idea: Limiting work in progress forces prioritization and reduces task-switching - Evidence: "Nothing else allowed to move into funnel until something moves out... fewer things in process but actually finished more" #### ⏱️ [45:20](https://youtu.be/CkKEM0179rE?t=2720) — [H1] Personal Productivity Tool Paradox - Key idea: Digital tools often optimize non-bottleneck processes, creating pile-ups - Evidence: "Speeding up part that is before bottleneck... just creating huge pile-up that slows everything down" #### ⏱️ [52:10](https://youtu.be/CkKEM0179rE?t=3130) — [H2] Epstein's Video Production Bottleneck - Key idea: Content approval became bottleneck despite efficient file management tools - Evidence: "Slack and Notion allow so much stuff to move but eventually it all has to come to me" #### ⏱️ [58:45](https://youtu.be/CkKEM0179rE?t=3525) — [H3] Newport's Podcast Workflow Optimization - Key idea: Everything designed around maximizing bottleneck resource (Cal's thinking time) - Evidence: "I don't want to touch a computer... maximize time where I'm trying to figure out most interesting thing to say" #### ⏱️ [65:30](https://youtu.be/CkKEM0179rE?t=3930) — [H1] AI Tools and Constraint Theory - Key idea: AI often speeds up non-bottleneck tasks, not core limiting factors - Evidence: "If bottleneck is getting access to right data... nice if you can shave hours off making plots, but that wasn't the bottleneck" #### ⏱️ [72:15](https://youtu.be/CkKEM0179rE?t=4335) — [H2] The Work Slop Problem - Key idea: Poor AI implementation creates volume of mediocre work without targeting constraints - Evidence: "Never been easier to do too much... producing work slop, huge volume of mediocre stuff" ## 📚 References - [The Goal by Eliyahu Goldratt](https://www.amazon.com/Goal-Process-Ongoing-Improvement/dp/0884271951) - [Inside the Box by David Epstein](https://www.amazon.com/Inside-Box-Constraints-Make-Better/dp/0593653548) - [MIT Sloan Management Review Paper on Broad Institute](https://sloanreview.mit.edu) ## 🗣️ Paraphrase - The Theory of Constraints teaches us that every system has one primary bottleneck that limits overall performance, regardless of how efficient other parts are - Personal productivity suffers when we optimize non-bottleneck activities instead of identifying and addressing our true limiting factors - Digital tools and AI often make easy-to-automate tasks faster, but these are rarely our actual constraints in knowledge work ## 📰 Newsletter **Rethinking Productivity: Why Your Tools Might Be Making Things Worse** Ever feel like all your productivity apps are making you busier but not more productive? There's a scientific reason for that. The Theory of Constraints, originally developed for manufacturing, explains why optimizing the wrong parts of your workflow creates pile-ups rather than progress. Just like speeding up one station on an assembly line can create chaos if it's not the bottleneck, making certain tasks faster with digital tools can actually hurt your overall output. The solution? Identify your true constraints first, then optimize those specifically. For most knowledge workers, the bottleneck isn't moving files around or formatting documents - it's the quality thinking time that produces real value. **Key takeaway**: Before adding another productivity tool, ask yourself: "Is this actually my limiting factor?" Resources: [Learn WordPress](https://learn.wordpress.org/), [Developer Docs](https://developer.wordpress.org/), [WP-CLI](https://wp-cli.org/) ## ✅ Action Items #### DevRel - Developer experience: audit developer workflow bottlenecks, not just tool speed - Education: design workshops around constraint identification methodology - Developer marketing: create content about avoiding productivity tool proliferation - Community management: implement work-in-progress limits for community initiatives #### OSPO - Policy & Governance: identify bottlenecks in contribution approval processes - License Compliance: focus scanning efforts on actual constraint points, not blanket automation - Software Inventory: limit simultaneous SBOM initiatives to prevent pile-up - Developer Support: target training on actual developer constraints, not popular tools #### Community - Maintainers: help identify project bottlenecks beyond just development speed - Contributors: create "stop starting, start finishing" guidelines for contributions - Sponsors: focus funding on constraint-relieving initiatives rather than general improvements #### WordPress - Core: identify true bottlenecks in release cycles beyond just coding speed - Core Performance: focus optimization on actual performance constraints, not easy wins - Documentation: audit information bottlenecks in learning paths - Support: identify constraint points in help-seeking workflows ## 🧪 Hypotheses - Digital productivity tools succeed when they target actual bottlenecks and fail when they optimize non-constraints - Knowledge workers who visualize all work-in-progress will identify more projects than they can reasonably complete - AI implementation success correlates with careful constraint identification rather than blanket automation - Teams using work-in-progress limits will show higher completion rates despite fewer simultaneous projects ### 📊 Methodology - Survey knowledge workers on productivity tool usage vs. actual output measures - A/B test constraint-focused vs. general productivity tool implementation - Case study analysis of successful vs. failed AI workplace integrations ### ❓ Questions - How can knowledge workers systematically identify their personal bottlenecks? - What percentage of productivity tool implementations target non-constraint processes? - How does work-in-progress visualization affect team dynamics and prioritization decisions? ### 🔮 Future Research - Developing standardized constraint identification methodologies for knowledge work - Studying long-term effects of constraint-focused vs. general optimization approaches - Investigating optimal work-in-progress limits for different types of creative work ### 💡 Implications - Practice: Audit current tool stack for constraint-targeting vs. non-constraint optimization - Policy: Implement work-in-progress limits before adding new productivity tools - Education: Train teams in constraint identification before technology adoption - Funding: Prioritize investments that address actual workflow bottlenecks ### 🧠 Learning Styles Assessment Reading level estimate: 12th grade #### 👀 Visual learners - Present: Discussion of assembly line analogies, Post-it note visualization - Improve: Add workflow diagrams, bottleneck identification flowcharts #### 🔊 Aural learners - Present: Full podcast audio with expert interview format - Improve: Chapter markers for easy replay of key concepts #### 🗣️ Verbal learners - Present: Clear definitions of constraints, bottlenecks, work-in-progress - Improve: More real-world analogies beyond manufacturing examples #### 🧩 Kinesthetic learners - Present: Actionable Post-it note exercise for work visualization - Improve: Step-by-step constraint identification worksheet #### 🧠 Logical learners - Present: Clear cause-and-effect relationships between constraints and outcomes - Improve: Decision tree for identifying personal vs. team bottlenecks #### 🧑‍🤝‍🧑 Social learners - Present: Interview format with back-and-forth discussion - Improve: Team-based constraint identification exercises #### 🧘‍♀️ Solitary learners - Present: Self-reflection prompts about personal workflow bottlenecks - Improve: Individual audit checklist for constraint identification #### 🌿 Naturalistic learners - Present: Systems thinking applied to personal and organizational workflows - Improve: Ecosystem metaphors for workflow optimization ### 📊 Accessibility Assessment #### Screen reader semantics - Present: Clear heading structure, descriptive timestamps - Improve: More detailed alt descriptions for conceptual elements #### Keyboard navigation - Present: Standard video player controls accessible via keyboard - Improve: Jump-to-section functionality for key concepts #### Contrast and readability - Present: Standard video player with good contrast - Improve: High-contrast transcript formatting options #### Text resizing and reflow - Present: Responsive transcript display - Improve: Adjustable text size controls for transcript #### Media and captions - Present: Full transcript provided with accurate quotes - Improve: Professional captions with speaker identification #### Standards reference - Meets WCAG 2.2 AA standards for multimedia accessibility ### 🌍 DEIB Assessment #### Cultural breadth - Present: Focus on universal workplace challenges across industries - Improve: Include examples from diverse global work contexts #### Inclusive language - Present: Gender-neutral terms, accessible business vocabulary - Improve: Define technical terms more consistently #### Neurodiversity support - Present: Clear structure with timestamped sections - Improve: Visual summaries of key concepts for different processing styles #### Representation - Present: Features perspectives from both academic and practical experts - Improve: Include diverse voices in constraint identification examples ### ✅ Logical Validity #### Strong reasoning - Deductive strength: Clear logical progression from general constraint theory to specific applications - Inductive strength: Multiple case studies supporting central hypothesis - Causal reasoning: Well-established cause-effect relationships between bottlenecks and outcomes - Analogical strength: Manufacturing-to-knowledge work analogies appropriately qualified #### Fallacies - None identified: Arguments well-supported with evidence and appropriate scope limitations #### Tone of voice - Overall tone: Collaborative and analytical, focused on practical problem-solving rather than criticism ### 🛡️ DARVO Analysis - Deny: No denial patterns present - Attack: No attacks on tool users or vendors, focuses on systemic issues - Reverse victim/offender: Not applicable to this content - Emotional cues: Maintains neutral, problem-solving approach throughout ## 🎯 Conclusion The Theory of Constraints offers a powerful lens for understanding why productivity tools often fail to deliver their promised benefits. By identifying and optimizing true bottlenecks rather than easy-to-fix processes, knowledge workers can achieve genuine productivity improvements. The key is disciplined constraint identification before tool implementation, combined with work-in-progress limits that force meaningful prioritization. ## 🧱 Refactor Appendix ### 📚 Literature Split Pad Purpose: Stage CEE units that become Literature notes. Split level: H3 ### [H1] Every system has single bottleneck constraint > [!quote] [Inside the Box](https://youtu.be/CkKEM0179rE) by [[David Epstein]] via [[Deep Questions Podcast]]. Published 2024. Accessed 2024-12-19. > "Every system is limited by a single bottleneck, the single least efficient step." Foundation principle for all constraint theory applications. [view highlight](https://youtu.be/CkKEM0179rE?t=765#H1) #xref #articleseed ID:rw_constraints_001 ### [H2] Optimizing non-bottlenecks creates pile-ups > [!quote] [Am I Optimizing the Wrong Things?](https://youtu.be/CkKEM0179rE) --- ## Full Transcript [00:00] In 1984, a former physicist turned [00:03] business guru named Elyahu Goldrat [00:06] published a strange book. It was called [00:09] The Goal and it was what Goldrot [00:12] described as a business novel. Now, this [00:16] book follows the fate of a fictional [00:17] plant manager named Alex Rogo who meets [00:19] an enigmatic physicist who through a [00:22] series of long socratic dialogues helps [00:25] Rogo turn around the profitability of [00:28] his plant. It also features a storyline [00:30] about Rogo's marriage as well as a sort [00:33] of extended detour to a boy scout [00:35] camping trip. I I told you this is a [00:37] strange book. But here's the thing. The [00:40] goal went on to sell more than 10 [00:42] million copies. [00:44] Why? Because contained within those [00:47] novelistic plot lines is a critically [00:50] important idea that Goldro calls the [00:53] theory of constraints. Now, I've come to [00:56] believe that this theory helps explain a [00:59] paradox that I talk about often on this [01:01] show. The reality that digital tools [01:04] designed to make us more productive [01:07] often end up instead just making us more [01:10] busy. Now, if you want to know why this [01:12] happened and how you can avoid it, how [01:15] in other words to shift from busy to [01:18] better, then you need to understand the [01:21] theory of constraints. Well, today is [01:24] Monday, which means it's time for an [01:25] advice episode of this show. So, this is [01:27] the perfect opportunity to dive deeper [01:29] into the question of how this old theory [01:32] can solve a lot of new problems in [01:35] productivity. All right, here's my plan. [01:37] I've asked the number one New York Times [01:39] bestselling author, David Epstein, to [01:41] join me to talk about all things [01:44] constraints. Why did I invite Dave? [01:47] because I learned about Gold Rot and the [01:50] ideas that he talks about in the goal [01:52] from Dave's brand new book, which is [01:54] called Inside the Box: How Constraints [01:57] Make Us Better. A book that uh is coming [02:00] out tomorrow. So, you definitely need to [02:02] check it out. Now, in our conversation, [02:04] Dave and I focus on one chapter of his [02:06] new book in particular where he [02:08] introduced the goal and talks about the [02:09] constraints and gives a bunch of stories [02:11] about what this theory actually meant. [02:12] So, I thought he'd be a great guide to [02:14] understanding what's going on here. um [02:15] he's going to help me in this [02:17] conversation understand the underlying [02:18] theory. Then we apply these ideas to our [02:21] own lives as writers and almost [02:22] immediately I come up with some new [02:25] ideas for improving my productivity [02:27] based on the foundation that we're going [02:29] to lay in this conversation. So if you [02:31] yearn to do more important work but feel [02:34] like all of your efforts to get things [02:36] done have just left you feeling more [02:37] frantic and exhausted or maybe you're [02:40] just curious why a small business novel [02:41] went on to sell millions upon millions [02:43] of copies. then this episode is for you. [02:47] All right, so let's get into it. As [02:49] always, I'm Cal Newport and this is Deep [02:53] Questions, the show for seeking depth in [02:56] a distracted world. [03:04] All right. All right. Well, Dave, I'm [03:05] really happy to have you here because I [03:08] I read your new book, Inside the Box, [03:12] which is great, and I recommend, you [03:14] know, Epstein books are fantastic. Read [03:16] the book. But there was one chapter in [03:18] particular that really caught my [03:20] attention because it gets to this [03:22] question that we've been grappling with [03:23] here on the show, and I thought you have [03:26] some insights that are going to really [03:28] help us understand a core issue on the [03:31] Deep Questions podcast. So, uh, I'm glad [03:34] you're here because I'm going to, uh, [03:36] first pick your brain about what you [03:37] wrote in that chapter and then hopefully [03:38] together we are going to apply it to [03:42] help tackle this question of why do [03:46] productivity technologies often end up [03:48] making us feel busy but not actually [03:51] like we're producing more. I assume when [03:54] you wrote this chapter, you of course [03:55] had me and my productivity obsessions in [03:57] mind. Is that that's that's accurate? I [03:59] assume it's it's really just for me. [04:01] >> Absolutely. I I typically find it's very [04:03] clarifying if I just build all of my [04:05] work life around things that I think Cal [04:07] Newport would be interested in or can [04:09] use. So, it really adds a lot of [04:10] structure to my life. [04:11] >> It's what I recommend to everybody. I'm [04:13] surprised more people don't, but okay. [04:14] So, let's I want my audience to to to [04:17] bear with me. We're going to we're going [04:19] to start in an area you might not know [04:20] how this is going to connect, but it [04:22] does. Um, so I want to start. Can you [04:25] tell us it's Elish Elisha Gold? Am I [04:28] saying this name right? Ellie Goldrat. [04:30] Elahu Goldrat, but Ellie Goldrat. [04:32] >> All right. Elahu Goldrat. Eli Yahu [04:34] Goldrat. Tell me about his uh path to [04:39] writing this eccentric novel, The Goal, [04:43] that actually ended up becoming a [04:45] classic and capturing an idea about [04:49] management, especially industrial [04:51] process management, that essentially [04:52] changed whole swaths of the economic [04:55] world. [04:56] So Ellie Goldrat in the 1970s he's a [05:00] physicist studying the behavior of atoms [05:02] in crystals when a friend comes to him [05:05] with a comparatively pedestrian problem [05:09] which is increasing uh chicken coupe [05:11] production. The friend has a small [05:13] chicken coupe building business [05:16] and he wants to increase the number of [05:19] coups he can make and so he's been [05:22] hiring more people and finding that it [05:24] isn't really increasing production. So [05:26] he asks Goldrat, can he study the [05:28] workflow? It's basically an assembly [05:30] line. [05:30] >> Yeah. [05:31] >> And and see what he can come up with. [05:32] And Goldrat studies it and what he finds [05:35] is that there is always a single slowest [05:39] step. like no matter uh what's going on, [05:41] there's a single slowest step in the [05:43] process where no matter how quickly [05:45] other steps in the assembly process are [05:47] functioning, work just piles up at the [05:50] one slowest step. And so once he notices [05:53] that, he decides to move one worker from [05:55] a fast step to the slow step and finds [05:58] that that triples the overall coupe [06:01] output. And this becomes the core of [06:04] what he came to call his theory of [06:06] constraints, which is the idea [06:09] that every system is limited by a single [06:12] bottleneck. Basically, the single least [06:14] efficient step in the system. Doesn't [06:16] matter how fast everything else is [06:18] working or how efficiently because it's [06:19] just going to pile up um at that one [06:21] step. And in order to expand on this [06:23] idea, he writes this book that you [06:26] mentioned, this bizarre but but also [06:29] interesting business novel [06:32] uh called The Goal, which features this [06:35] character Alex Rogo, who's a plant [06:38] manager whose plant will be closed [06:40] unless he can increase production [06:41] quickly. Fortunately, he bumps into this [06:44] Jedi-like figure, surprise, surprise, [06:46] his former physics professor, [06:48] >> who gives him these socratic lessons and [06:50] he he starts to see the whole world [06:52] through the theory of constraints. Like [06:53] when he takes his son's boy scout troop [06:56] on a hike, he finds that some kids are [06:58] really fast hikers, but this kid Herby [06:59] is really slow. And so the whole group [07:01] can only go at the speed of Herby. So he [07:03] ends up distributing weight from the [07:05] different packs and it slows the fast [07:07] kids down, but it speeds Herby up and [07:08] ends up speeding the whole group up. And [07:10] so fast forward and Alex saves the [07:13] plant, saves his marriage all by looking [07:15] for these bottlenecks. And this the book [07:17] is strange, but it spawned a whole genre [07:20] of actually even more bizarre business [07:21] novels, but also like Jeff Bezos made [07:24] all of his executives read it and hosted [07:26] a full day book club on it and it sold [07:27] 10 million copies. And as a fellow [07:30] author, uh, you can understand [07:33] what that means. us. It It means our our [07:36] respective agents are going to hear like [07:38] their 10 most feared words to ever hear [07:40] from us is I have an idea for a business [07:44] novel I want to write for a socratic [07:47] business novel. That's right. And it was [07:48] published in this tiny publisher that I [07:50] think I think was basically made just to [07:53] create this book um and just blew up and [07:56] now there's even a graphic novel version [07:57] if you don't want to read the whole uh [08:00] the whole version. But I should say it [08:01] it also led to this this 1200page theory [08:04] of constraints handbook and Goldrat [08:07] wrote the forward and in it he asks can [08:09] I condense all of theory of constraints [08:10] into one sentence and he says actually I [08:12] can do it into one word which is focus [08:14] that this bottleneck or the system [08:16] constraint shows you where to focus your [08:18] energy if you want to make a difference. [08:20] What goes into 1200 what you said 12,000 [08:23] pages 1200 pages 1200 pages [08:25] >> so this simple idea I'm just curious [08:27] about this from the a side view how do [08:30] you feel 1200 pages I wonder um on that [08:33] idea seems so elegant is it just a lot [08:35] of scenarios a lot of math I'm trying to [08:37] figure out case studies I suppose [08:39] >> there's a lot of case studies there a [08:41] lot of case studies looking at different [08:42] industries um some of it eventually gets [08:45] more into [08:47] kind of the p some as it goes on it gets [08:49] farther and farther away from these [08:51] obvious industrial production cases and [08:52] more to some of these more personal [08:55] cases basically. [08:56] >> Yes. [08:57] >> Um and also just a lot of I would say a [09:00] lot of bloated theory around it too [09:02] probably. Um so some of all of that [09:04] >> that's the best type of theory. Okay. So [09:06] we're we're we're beginning to inch [09:08] closer to the the personal applications. [09:10] Uh, one thing that did come to mind [09:11] though when I read about that is for a [09:13] few books ago I went deep on Ford and [09:16] the automobile continuous assembly line [09:20] and that was another observation. The [09:22] same observation was made but it's [09:24] really been lost in the lore of the [09:26] assembly line which has been very idea [09:28] focused like what if we move the car to [09:31] the people. But what's lost out of that [09:33] if you read the early management journal [09:34] type articles is man that was hard to [09:36] get right. It was really hard to [09:38] calibrate the automotive assembly line [09:41] because of exactly the theory of [09:42] constraints type problem. If the [09:44] steering wheel person took longer than [09:46] almost anyone else, the whole the whole [09:48] thing was going to slow down. So the [09:49] whole it was all about tweaking and [09:52] configuring and retooling and moving [09:54] people. It was very difficult. And then [09:56] finding the right speed where it would [09:58] move past. Uh and I think that lesson [10:00] was lost. people looked at the general [10:02] form and not the idea like, oh, that's [10:04] part of what's hard about these things [10:06] is figuring out how to actually make [10:08] this whole thing keep moving smoothly [10:12] without the magneto or the steering [10:13] wheel or something like this uh pulling [10:16] everything out. Hey, let's take a quick [10:17] break to hear from some of our sponsors. [10:20] Let me tell you about one of my secret [10:21] weapons in trying to stay healthy. [10:24] Factor. Factor offers fully prepared [10:26] meals designed by dieticians and crafted [10:29] by chefs. They're delivered straight to [10:31] your door and are ready in 2 minutes. No [10:33] planning, no cooking. 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[12:56] But where it begins to get interesting [12:58] for the personal view is that humans [13:01] have bottlenecks in our individual [13:03] activities as well. Uh, maybe we could [13:06] start with your experience, your your [13:09] illustrative college track career. I [13:11] learned in this book you were a walk-on. [13:12] I knew you were an 800 runner and very [13:14] good at it. I didn't realize you were a [13:15] walk-on, which is fantastic. But tell me [13:18] a little bit about the story from the [13:19] book about how you ended up implicitly [13:21] applying the lesson of the goal in your [13:24] running career. [13:25] >> Yeah, I mean, I didn't know the language [13:26] of the theory of constraints at the [13:28] time, but being a walk-on meaning I was [13:30] not good enough to get recruited. In [13:32] fact, I was not in the same orbit as [13:36] anyone who would be getting recruited. [13:39] Um, and I I came to track kind of late [13:42] in my athletic career. And so when I [13:45] arrived, I was so bad or essentially not [13:50] um not really on the team. In track, you [13:53] can kind of hang around as a walk-on. [13:56] And I one blessing in disguise I had was [13:58] that because I was a walk-on, nobody [14:00] cared what I was doing. And [14:04] um there was a young coach who kind of [14:06] nobody cared what he was doing either. [14:07] And we paired up for some [14:08] experimentation. [14:10] And we found what was certainly my [14:13] bottleneck, the thing uh limiting my [14:15] performance, which was my ability to [14:17] recover. Um so I simply did not recover [14:21] from workouts the way that my peers did. [14:24] So, if we had a work hard workout on [14:25] Monday, easy day on Tuesday, by the time [14:27] the next workout came around on [14:29] Wednesday, uh I was feeling terrible. [14:31] And then there's another recovery day on [14:33] Thursday and then you have another [14:34] workout Friday or race Saturday. And I'm [14:37] just floored. So, I was just tired all [14:41] of the time. And once we decided I just [14:44] didn't recover like the other guys did, [14:48] implemented some uh high-tech strategies [14:50] like scheduling class over one workout a [14:53] week so I had an excuse just not to show [14:55] up. [14:55] >> Yeah. [14:55] >> So I started doing one fewer workout per [14:58] week than my peers were doing. And that [15:01] seems like a bad idea, right? And I I I [15:03] moved my my mileage my weekly mileage [15:05] had hit a high of about 80 or 85. We [15:06] moved it down to about 35 so that I have [15:08] more chance to recover. And it worked [15:10] like crazy. I mean, I started getting [15:12] faster almost every race once we locked [15:14] this in, you know, beating people who [15:16] were blue chip recruits, uh, became a [15:18] university record holder, all these [15:19] sorts of things. I I won this since it [15:22] seemed like I went from bad to good so [15:24] quickly, too. I won this award. I [15:25] actually have it in my closet over there [15:28] for the it was on it. It's written for [15:31] the athlete who achieved significant [15:32] athletic success in the face of unusual [15:34] challenge and difficulty. And my unusual [15:36] challenge and difficulty just being it's [15:38] a nice glass like in wood box and [15:40] everything. Um, my unusual challenge and [15:42] difficulty just being that I stunk at [15:44] first, right? I was just really really [15:46] bad at first and then once I figured out [15:48] that I I wasn't like everyone else and I [15:50] had this limiting factor of ability to [15:52] recover and targeted that, it really [15:54] unleashed uh unleashed my my [15:56] performance. So it the workouts that [15:58] would have been increasing your [15:59] capacity, you were losing that advantage [16:01] because you were overtraining and then [16:03] you were either tired in the actual [16:05] events or it was just your body couldn't [16:07] actually get the gains because you would [16:10] drop another workout onto another. So [16:11] all this potential was being left on the [16:14] Did you ever read the Neil Baskum book, [16:15] The Perfect Mile? [16:17] >> About this kind of similar, right? Like [16:19] I I I write about in my new book I'm [16:21] working on now. I get into a little [16:22] deeply, but Roger Banister was training [16:26] uh much less than uh what do you [16:29] remember the name of the Australian who [16:30] he was competing against? [16:31] >> John Landy. [16:32] >> John Landy. And John Landy was I will [16:35] out train in terms of volume anyone [16:38] who's ever run before. Like no one was [16:40] running the quantities he was I did the [16:41] math. He was he was running like 5x more [16:45] >> meters a week than Banister. But [16:47] Banister who didn't [16:49] >> he didn't have time. and he was [16:50] basically what we would call like I [16:51] guess a resident medical resident here [16:53] in the US and B like did a lot of other [16:55] stuff [16:56] >> but knew a lot about physiology [16:58] >> was like okay it's not about volume it's [17:00] not about endurance now in fairness [17:02] Landy was also he was a professional [17:03] runner and he had to run events all year [17:05] round and it did give him a lot of [17:06] endurance to do a lot of events but [17:07] Bannister's like it's all about um [17:10] processing oxygen and lactic acid at [17:13] exactly the pace you need for four [17:15] minutes to break the four-minute mile [17:16] and all of my training is just built [17:18] around um doing exactly that. And I'm [17:21] going to do these whatever it was four [17:22] 400 repeats with um breaks in between [17:26] and just until I can get those four the [17:28] 400s like it get exactly at that slight [17:29] sub60 and just get very used to that and [17:32] and you know it was all about just exact [17:34] reminds me of that is he he the [17:35] bottleneck he was like oh it's [17:36] physiological there's a particular [17:38] physiological thing I need to do to beat [17:40] the mile record and that's what I [17:43] focused on [17:43] >> and to run this first sub4 mile that was [17:45] very well done I'm a huge track nerd so [17:48] I commend you on that history you just [17:49] gave very well done So when I was at [17:50] Sports Illustrated, obviously I used [17:52] that excuse to write about Roger [17:54] Banister and became personal friends [17:55] with Roger Banister for quite a long [17:57] time. He used to call me know he passed [18:00] away. [18:01] >> He passed away. Okay. Yeah. Because it [18:02] was in the early 50s, right? Or late 40s [18:04] when they broke their record [18:05] >> in the 50s. In the mid-50s when he when [18:07] he ran the first sub4 mile. Um and um he [18:12] would, by the way, he would call me with [18:13] like no cognizance of the time [18:15] difference. He used to be like, "Hello, [18:16] it's Sir Roger." He referred to himself [18:18] as Sir Roger. He wasn't actually. He [18:20] just refers to him or he was right. [18:21] >> Oh, no, he was he was kned. Yeah, he was [18:22] kned. [18:23] >> That'd be funnier if he wasn't. I think [18:24] that's that's the that would be the real [18:25] story. [18:26] >> He could have been kned many times over. [18:27] He was a world famous neurologist. He [18:29] was a dean of a college at Oxford. All [18:30] these sorts of things. Um, but you're [18:33] right, like he was different in that [18:36] way, but he was very logical uh in in [18:38] what he was doing. And he I mean I think [18:41] he did I'm trying to remember if he told [18:42] me I think he did like gynecological [18:44] rounds the morning of the day that he [18:46] ran the first sub4 mile in history. and [18:48] he went hiking uh you know two weeks [18:51] before and and so that that um by the [18:56] way that that focus on so he had a focus [18:59] on recovery too. So right now the major [19:01] trend, not that we should talk about [19:02] track this whole time, but the major [19:04] trend in training has been something [19:07] that actually does allow more volume of [19:09] training. It's called threshold training [19:11] basically where we've realized that if [19:13] you actually keep your intensity a [19:15] little down lower, your risk of injury [19:19] becomes way less and you can do a huge [19:22] amount of training volume and then you [19:24] kind of only run as hard as you can on [19:26] the race day. So that's this this trend [19:27] that's really improved people in running [19:29] now where you say oh actually maybe [19:31] recovery at a certain level is a [19:33] bottleneck for everyone and then injury [19:35] will be because if you train enough in [19:37] track you'll always like there's you'll [19:38] always get injured. So now the invogue [19:40] training is this threshold training [19:41] where you're actually never going all [19:43] out ever [19:44] >> and that allows you to ultimately train [19:45] a lot more uh and perform a lot better. [19:47] So so I think this is a different kind [19:49] of like targeting the a sort of [19:51] universal bottleneck. [19:53] >> All right. Right. So, as we make our [19:54] journey, we're making our journey [19:55] towards personal productivity and [19:57] technology. So, we've gone from assembly [20:00] lines the track. Well, let's take us [20:02] let's take a small step into the world [20:04] of uh Olympic level swimming. [20:07] >> All right. [20:07] >> Because I I enjoyed this. I just want I [20:09] want us to get used to like these [20:10] personal bottlenecks and then we're [20:11] going to jump from there to like uh the [20:12] world of more like knowledge work. But I [20:14] enjoyed the the Olympic caliber swimming [20:18] story which I think is similar to yours. [20:20] Um though it ended up with a gold medal [20:22] I suppose but tell me tell [20:24] >> but otherwise but let's get let's get [20:26] into that because I uh I think it's [20:28] another great example. [20:29] >> Yeah. So this story I tell uh in the [20:32] book about this swimmer named Sheila [20:34] Tarina in 1992 she's a student at the [20:36] University of Georgia and she she goes [20:40] to the US Olympic trials in the 200 [20:42] meter freestyle. She doesn't make it. [20:43] She's not close actually and she [20:45] retires. And then for one of her last [20:47] courses at Georgia, she takes management [20:50] 577 in which she learns about the theory [20:52] of constraints and decides for her class [20:54] project that she's going to come up with [20:56] a training plan that would have her drop [20:59] 3 seconds in the 200 meter freestyle [21:01] which would give her a chance to make [21:02] the Olympic team. And so she first does [21:05] this sort of audit which is how the [21:07] theory of constraints cases often start [21:09] looking for what is her bottleneck? what [21:11] things are not going well and what does [21:13] she think is the the most important one. [21:15] What Goldrat would call he he would [21:17] often call in industrial production the [21:18] drum of the system because everything [21:20] else will march at its beat. Um and so [21:24] she decides that power strength and [21:26] power is her bottleneck. She's only 5'2 [21:29] which is really small for an elite [21:30] swimmer [21:31] >> and [21:33] she has an amaz worldass aerobic engine [21:35] like aerobic endurance and that's what [21:36] her coaches have her working on just [21:38] tons of volume but it's not the thing [21:40] limiting her. She's already really good [21:42] at it. The thing liming her is strength [21:43] and power and she doesn't feel like [21:46] confident that they'll allow her to work [21:47] on that. But she unretires and decides [21:49] to find a coach who will follow this [21:51] plan, you know, will target her [21:53] bottleneck strength and power. So they [21:55] completely for a swimmer, the way she [21:57] would experience that is it's not like [21:58] she's coming into the final finishing [22:00] the race like, "Oh, I just got gassed." [22:02] It was, "No, I my aerobic was there, but [22:04] just my power per stroke is such that [22:06] I'm a length behind a better swimmer." [22:08] often and often she was like very would [22:11] be getting beat at the start and really [22:13] catching people late like she just like [22:16] didn't get off to a strong start [22:17] >> and that's a power game. That's like how [22:19] how powerful your whatever your your [22:21] your dolphin kick and initial strokes [22:22] are, your jump. It's like all those type [22:24] of things. [22:24] >> Yeah. So, and so her her peak speed [22:27] would be worse than people that she [22:30] could sometimes beat because they would [22:32] tire out and she would catch them. So, [22:34] she wanted stronger start, higher peak [22:36] speed because tiring out was not her [22:38] problem. like she was catching people at [22:39] the end, but if she wasn't behind in the [22:41] beginning too, she'd have to be like in [22:43] their turbulence too, which you don't [22:44] necessarily want to be depending on how [22:46] it there's a lot of intricacies to that. [22:48] But [22:48] >> um so so they go start working on her [22:52] strength and power um you know lifting, [22:55] doing these weight racks that you can [22:56] attach to a swimmer and they swim and it [22:58] and pulls the weights and four years [23:00] later um she goes to the Olympic trials [23:03] again 1996 and she swims exactly 3 [23:06] seconds faster. basically 3.1 seconds [23:08] faster than she had, makes the team and [23:11] then in Atlanta uh wins an Olympic gold [23:13] medal as part of the relay team. And [23:16] it's amazing if you Google a picture of [23:17] her and find a picture of her with the [23:19] relay team. The other uh three women are [23:24] probably 6'1. [23:25] >> Yeah. [23:25] >> And she's 5'2. So it's like three women [23:27] that are the same size. [23:29] >> Yeah. [23:29] >> And Sheila Tarino is 5'2. And then so [23:31] then she retires after that. And after a [23:35] few years, she unretires, starts doing [23:36] triathlon first just for health. Now she [23:38] has this new outlook on all of her [23:39] training, which is like what is the [23:40] thing limiting me? She starts it for [23:42] fun, ends up winning the US National [23:44] Championship, goes to the Olympics, [23:45] finishes sixth, goes the next Olympics [23:47] in triathlon, retires again, and then [23:50] she unretires again and learns fencing [23:53] and horse jumping and goes the Olympics [23:55] in modern pentathlon. And she is the [23:57] only woman ever to have competed in four [24:00] summer Olympics in three different [24:01] sports. and she retired briefly and she [24:04] would have like retired permanently if [24:06] not for learning about the theory of [24:08] constraints in a management class. [24:10] >> So she was just throwing the typical [24:12] collegiate uh swimmer thing which is [24:14] just more time in the pool which [24:16] >> yeah as as I learned from Rich Roll that [24:18] was basically the mentality in the '9s [24:21] was [24:21] >> more yardage. [24:22] >> More yardage. Yeah. Okay. Well then we [24:24] can jump from this over to we're going [24:26] to get to the world of knowledge work [24:27] now. There's a there's a paper you and I [24:29] both like. I think we've both written [24:30] about it. the MIT loan management review [24:33] paper about the broad institute which [24:36] starts as a case study that reminds is [24:38] much closer to gold drop because it's a [24:40] literal assembly assembly line this time [24:42] genetic sequencing [24:43] >> but the cool part is that it ends up [24:45] influencing [24:47] this IT team that happens to be working [24:49] there that's much more pure knowledge [24:50] work this is worth reviewing because I [24:53] think this is going to start it's going [24:54] to teams this will get us to knowledge [24:55] work teams and from there we get to [24:57] knowledge work individuals but this is [24:58] probably worth hearing about as well how [25:00] the theory constraints on a a more of a [25:02] classic process inspired a group of [25:05] knowledge workers to change how they did [25:06] their work. [25:08] >> Yeah. I mean, so the that aspect of it [25:11] like so there were two aspects of it. [25:13] There was the just the the sort of [25:14] production aspect where they had work [25:17] piling up in their genetic sequencing [25:18] lab because some steps were faster than [25:20] others and it became chaotic and so they [25:23] they had to switch that from a push [25:25] system to a pull system which you've [25:26] written really eloquently. Um, [25:29] >> classic theory of constraints there. [25:30] It's like, oh, you you've read that [25:32] 1,200 page book. You're like, boom, this [25:34] is us, right? It was an assembly line. [25:36] Things were piling up basically. [25:38] >> Totally. But the cooler thing was that [25:40] they then decided to apply it to their [25:43] sort of project and idea process where [25:45] they went and on Post-it notes um made [25:49] all of their current projects visible [25:52] essentially. Literally post-it notes on [25:54] a wall. That's right. And immediately [25:56] upon doing this um they realized that [26:00] there were way more things in process [26:02] than they could ever get done. They [26:03] realized there were redundancies. They [26:05] realized you know there were um things [26:08] that nobody really knew was going on [26:10] basically or not many people. And I [26:13] actually think this is like any team in [26:15] person should do this. Make all of your [26:16] current commitments visible. And [26:18] probably what you'll realize is that a [26:20] bunch of medium priorities are competing [26:22] with a bunch of important priorities. So [26:24] they quickly said, they didn't say we're [26:26] just going to kill all this stuff, but [26:27] they put it off into a holding pattern. [26:28] They made this funnel for projects and [26:30] ideas. Said nothing else is allowed to [26:32] move into the funnel until something [26:34] else moves out of the funnel. So [26:37] basically, they made this idea pipeline [26:39] of limited size. And so you can see [26:41] nothing else. We can't just pile stuff [26:43] in because people are always having [26:44] ideas. So it'll just grow and grow and [26:46] grow. So you can't you you stop starting [26:49] and start finishing. You can't start a [26:50] new one until you finished a current [26:52] one. And so it both made a sane workflow [26:56] and uh forced them to prioritize which I [27:00] think is what good constraints can do. [27:01] Force you to clarify your priorities and [27:04] consequently they started actually [27:05] getting a lot more projects done. They [27:06] had fewer things in process but they [27:08] actually finished a lot more things. [27:09] >> Yeah. Yeah, it's like the bottleneck was [27:10] the actual developers here because my [27:13] understanding was this was a team that [27:14] was building software tools internally [27:17] for use by scientist at this this big [27:20] institute which I used to walk by every [27:21] day on Cambridge Avenue and when I was [27:24] at MIT. So I remember seeing this thing [27:26] you know come up. [27:27] >> Uh the bottleneck was their basically [27:30] cycles of cognit cognitive focus. You [27:32] had like this many programmers they had [27:34] this many cycles available. Um that was [27:36] the bottleneck. So it's like how do we [27:38] make the best use of it? And there was [27:39] this interesting effect that has been [27:41] very influential for me to hear about is [27:43] like what happens if you push a lot of [27:45] stuff on limited cycles. It doesn't just [27:48] cue automatically nicely. What happens [27:50] is is all the things start kind of [27:51] competing for the cycles [27:53] >> and then the whole thing kind of log [27:54] jams up and you know very little gets [27:56] through. It's like oh this is our [27:57] bottleneck. How do we get the most out [27:59] of these cycles? Oh only pull one thing [28:01] at a time. Little idea but it made a big [28:04] difference [28:04] >> for sure. And I think it it I think that [28:08] when we don't do that, I mean I was [28:10] going through a lot of these case [28:11] studies and [28:14] there are different aspects to different [28:15] ones of them, but one theme was so they [28:17] were because they had so many things in [28:19] process they were being like ravaged by [28:20] multitasking basically. So instead of [28:22] initially the response instead of being [28:24] you know oh we need to build a funnel [28:26] and have a limited space was all right [28:28] we're going to toggle between things [28:30] more and so there'd be these case [28:32] studies like one that I wrote about at [28:33] this uh company that made custom gear [28:36] boxes for industry where every gearbox [28:38] they made was totally unique had to be [28:40] customized and they were having all [28:43] kinds of problems and they realized the [28:44] bottleneck was in this small 15 person [28:46] design office that made the designs for [28:48] the gearboxes and they had so many [28:51] things in process that they were just [28:53] they were switching tasks more than 50 [28:55] times a day, switching projects. And so [28:57] it led to errors, frustration, people [28:59] quitting. And so they implemented a [29:02] similar rule which was again it was a [29:04] stop starting start finishing. You're [29:06] not allowed to start a new design until [29:07] you finished one. And that dropped the [29:11] amount of time in a few months. They [29:14] were getting three times as many designs [29:16] out the door. And because that was the [29:18] constraint for the whole system, it [29:19] dropped the amount of time it took the [29:20] company to produce a gearbox from a year [29:22] to two months. [29:23] >> Yeah, that's the part that people often [29:26] miss, right? Like when I when I talked [29:27] about this idea in that one part of my [29:30] last book where I said do fewer things. [29:32] People had a really hard time getting [29:34] past, oh, you're asking me to be less [29:36] productive. So, I mean, we I got to make [29:39] money. My company got to ship ship [29:41] products. like sure I maybe I would feel [29:43] better in your like magical utopian [29:44] world but we got to produce stuff and [29:46] when you would say no no no do fewer [29:48] things so that you [ __ ] more [29:49] >> so that you you're more productive and [29:51] people it's really difficult for people [29:52] to make that leap exactly you have to be [29:55] you have to be disciplined right to be [29:56] more productive to not toggle all the [29:58] time and you and I have both written [30:00] extensively about the work of [30:02] psychologist Gloria Mark and what's in [30:05] what she finds in some of these studies [30:06] is that uh the the more time someone [30:10] toggled during the Okay, the lower is [30:12] their end of day productivity. So, [30:14] they're doing all this toggling and the [30:16] higher their stress, but that's a that's [30:18] another issue. But they feel like [30:20] they're doing all these things, right? [30:21] Because you want to address them as they [30:23] come to you, but it actually it's that [30:26] feeling of productivity does not track [30:28] with actual productivity. [30:29] >> Yeah. I I like the way they measure [30:30] stress when they figured out how to use [30:32] those heat bloom cameras, [30:35] >> and they did heart heart rate [30:36] variability also, but yeah. Yeah, the [30:38] thermal imaging cameras. Yeah, heart [30:39] rate variability is quite a good proxy [30:41] for [30:41] >> they use that as well. I think they [30:42] figured that out as well. But that was [30:43] that was my my favorite finding for my [30:45] book on email was they could actually [30:48] visibly see stress rise when the inbox [30:50] opened. They would correlate the app [30:52] opening. Um okay. So now stay with me [30:53] because I now want to make the leap into [30:55] the unknown which is uh applying this to [30:57] the shortcomings of digital personal [30:59] productivity tools. [31:01] >> Here's my theory then I'll throw it to [31:02] you to critique or add on. You can often [31:06] imagine, here's my theory, as like an [31:07] individual knowledge worker that some of [31:09] the things you do, you can think of it [31:10] as like a multi-step process. I think [31:12] that's fair implicitly, right? It's like [31:14] you have your own little knowledge uh [31:15] assembly lines. A lot of digital [31:18] productivity tools, whether we're [31:19] talking about really efficient [31:21] communication tools like Slack or if [31:24] we're talking about information [31:25] management tools that have all sorts of [31:26] dashboards of stuff available or a lot [31:28] of the new AI style productivity tools [31:30] that are entering, they speed up for [31:33] sure certain parts of that pipeline. But [31:37] if you're speeding up a part that is [31:40] before a bottleneck, [31:42] >> yeah, [31:42] >> it doesn't mean that that pipeline is [31:44] going to produce more stuff and that we [31:45] fall into the trap of this tool [31:48] definitely makes this thing faster. How [31:50] could that be negative? And when you put [31:53] the theory of constraints on, you say, [31:54] "Oh, no, that's just like going to the [31:55] chicken coupe factory and being like, [31:57] hey, the first guy on the on the [31:59] assembly line that puts like the roofs [32:01] on the I I gave a new tool and he's [32:03] three times faster. This has to help." [32:04] And actually it's just creating a huge [32:05] pile up that slows everything else down. [32:07] Am I stretching too much or is there a [32:09] potential application here? [32:10] >> No, I think so. In fact, I think I'm [32:12] kind of living this in real time a bit [32:15] right now. [32:15] >> You're building chicken coops. I see. [32:17] Yes. [32:17] >> Building because I started a chicken [32:19] coop building business. Yeah. Um, you [32:20] know, because I think AI is unlikely to [32:22] actually probably will replace the [32:24] chicken coop. [32:25] >> Yeah. Robots. Robots are going to do it. [32:26] Yeah. [32:27] >> Yep. Um, and so but I started making [32:32] videos like a few months ago and [32:35] >> for YouTube or something like this. [32:37] >> YouTube. YouTube mainly but yeah and [32:39] then you know smaller ones for Instagram [32:40] and things like that but longer ones on [32:42] YouTube and [32:44] >> um I'm working with these two guys and [32:47] it introduced all these and and they [32:49] have a a whole team and it introduced [32:52] all these workflows to me that I'm not [32:54] used to. So for the first time I'm on [32:56] Slack, I'm on notion, all these kinds of [32:58] things. And the tools are impressive in [33:02] many ways, but [33:05] it's it's allowed so much work to get in [33:08] process. So like I have to approve [33:09] scripts and things like that and I edit [33:11] them and sometimes I write them and all [33:12] this kind of stuff and those Slack and [33:15] notion allows so much stuff to move but [33:18] eventually it all has to come to me [33:20] where I have to go through it and [33:21] approve it. Right? So sometimes this is [33:23] involves taking content that I've [33:25] already written and and sort of turning [33:27] it into a draft of a script or something [33:28] like that and I have to approve it. And [33:30] so the bottleneck is my ability to [33:32] approve approve and because in the [33:35] transformation of my material there's [33:36] often assumptions made or facts that you [33:40] know aren't quite right just cuz when [33:42] you're transforming it it's like if it's [33:43] not your material there's like you make [33:46] certain assumptions and it can cause [33:47] factual errors. And so I'm spending a [33:50] lot of time trying to correct those [33:51] things. Meanwhile, more stuff is [33:54] building up at the first step. Yeah. [33:56] Where it's like more ideas, uh, you [33:58] know, more of these kinds of things. And [33:59] so, but then I have to pay attention to [34:01] that like stuff that's at the top of the [34:03] bottleneck. And so, it's almost like [34:05] sometimes I'm creating even more stuff [34:06] that's coming right back around to me [34:08] and getting stuck because I can't [34:09] approve fast enough. So, as I've been [34:11] talking with these guys I'm working with [34:13] and we're realizing that my ability to [34:15] approve content to review it fast enough [34:16] is the bottleneck saying, "All right, [34:18] instead of more a better flow in notion [34:21] or in Slack or even faster [34:23] responsiveness, [34:25] what would actually be helpful is a fact [34:26] checker so that some of the factual [34:30] problems are caught way before it gets [34:31] to me so the approval process is more [34:33] streamlined. But otherwise, it's been [34:35] just building up stuff at me like I'm [34:37] I'm the bottleneck." And so so they did [34:40] actually just uh brought on a fact [34:41] checker. So I think that's targeting our [34:42] bottleneck now in a way that notion and [34:44] slack impressive as they are did not [34:47] >> right they were they were moving [34:48] information very quickly notion and [34:50] slack like wow this is impressive like [34:52] it can automatically move from here to [34:54] here and be labeled and sent and waiting [34:56] but yeah that wasn't the bottleneck. The [34:58] bottleneck wasn't man we would get more [35:00] videos out if only it didn't take so [35:03] long for this person to get this file to [35:05] me and to get it labeled or whatever but [35:07] that wasn't the bottleneck. The [35:08] bottleneck was [35:09] >> that part's quite efficient. [35:10] >> Yeah, [35:11] >> very efficient. [35:12] >> Yeah, I so I think I I've thought of my [35:14] own sort of podcasting type workflows [35:17] through a similar frame and but actually [35:19] from the beginning maybe because I had [35:21] been working on this issue and thinking [35:24] about business processes for an [35:26] unhealthy amount of time. Everything I [35:28] think about in my podcasting process is [35:30] about the bottleneck. To me, the [35:31] bottleneck is my time, right? I have a [35:33] certain amount of time I can spend on [35:35] the podcast and I I I don't I don't want [35:37] to go beyond that. And so everything [35:39] when I I have two people I work with and [35:41] everything is about how do I maximize [35:44] the percentage of that time that is me [35:47] thinking, writing or recording the thing [35:49] I thought and wrote about. And so, you [35:51] know, producer Jesse, who you've met, [35:54] uh, the whole goal there was I don't [35:56] want to touch a computer. Like, that's I [35:58] shouldn't I don't want to touch a [35:59] computer because that's all the things [36:00] on the computer are things that take [36:02] time that is not me thinking, writing, [36:05] or recording or with my newsletter [36:07] director, Nate, it's u I don't want to [36:10] touch an email newsletter program. I [36:12] don't want to like So, can I just work [36:14] in a Google doc? You can edit, you can [36:16] you can edit, you can copy it, you can [36:18] send things, you know, you can find the [36:19] graphics, you can format the things, you [36:21] can I don't have to spend as much time, [36:23] you'll you'll catch grammar mistakes, [36:24] you'll catch spelling mistakes. You [36:26] know, what could we do that reduces the [36:29] time I spend doing uh what's limited by [36:32] the bottleneck and and that's made a [36:35] difference, right? I mean, that's the [36:35] way I don't want processes that require [36:39] I don't need information moving quickly. [36:40] I have weird sort of eentric ways I do [36:42] things. I mean, I have lots of printouts [36:45] and we do things kind of an oldfashioned [36:47] way here because it's not to me the [36:48] bottleneck is like so long as like I [36:50] really am maximizing time where I'm [36:52] trying to figure out what might be the [36:53] most interesting thing to say. So there [36:55] we go. I was I was using theory of [36:58] constraints perhaps without [37:00] >> that is that is very of theory of [37:01] constraints thinking. I mean and you [37:05] like you as the bottleneck because often [37:08] times when you identify the constraint [37:09] right in this case it's it's your time [37:11] you have a certain amount of time that [37:12] you want to spend on this and then often [37:14] the fix is making us can be making a [37:16] simple rule. So like in one case it's it [37:18] stop starting start finishing is a [37:20] common one um in your case it's I don't [37:22] want to touch a computer. Yeah. [37:23] >> Right. So you identify the bottleneck [37:25] and then what's a rule that um maximizes [37:29] the efficiency of that bottleneck. So, [37:31] it's you not doing any of the stuff. You [37:33] doing You want to be spending all the [37:35] time doing stuff that Cal uniquely does [37:37] >> and the stuff that Cal doesn't uniquely [37:39] do [37:40] >> that would be infringing on the [37:41] bottleneck. So, take it away, move it [37:42] somewhere else. [37:43] >> So, then I'm going to pause it and I [37:44] I've been exploring this in a past [37:46] episode. which I'll run this by you that [37:48] this is what's going on with some of the [37:49] intersections of AI and knowledge work [37:51] where there are some initial findings [37:53] coming out in the non-programming spaces [37:55] that are saying look these tools we're [37:57] getting these reports back where the [37:59] amount of the sort of deep work efforts [38:00] they they don't use that terminology but [38:02] needlemoving efforts is actually going [38:03] down and and time spent on sort of [38:06] administrative efforts are are going up [38:08] and the theory of constraints maybe [38:10] helps explain that is that if you the [38:12] things that you know it's the drunk [38:14] searching for his keys under the street [38:16] lamp only because that's where the light [38:17] is. I think you bring in an AI LM based [38:19] tool, there's there's certain things it [38:21] can do or automate, right, pretty well. [38:24] So, you put into that part of the stuff [38:26] you do and make that as fast as [38:27] possible, but that's rarely the [38:30] bottleneck. And I I I use the example of [38:33] um Adam Grant, you know, who we both [38:35] know, who who told me, I think back when [38:37] I I talked to him for deep work years [38:38] ago, decade ago, and I remember him like [38:41] correctly assessing, oh, in social [38:42] sciences doing management theory at [38:45] Wharton like he was doing. Um the key [38:47] bottleneck is access to data. He's like, [38:49] you got to get access to interesting [38:51] data sets you can then analyze and write [38:53] your sort of business papers about. and [38:55] he told me so I spend a lot of time like [38:56] working on those relationships trying to [38:58] find the right pots of data that then [39:01] you can get three or four good papers [39:02] out of and I was thinking about that [39:04] because there's a lot of talk now in [39:05] academic circles about social science [39:07] researchers business researchers using [39:09] AI tools like cloud code and hey it [39:12] speeds up generating your plots from [39:14] data it can there's these certain types [39:16] of wrote steps that it makes you makes [39:18] it much more efficient and they were [39:19] like this is going to lead to a research [39:21] productivity boom I was like well [39:23] actually if if The bottleneck, for [39:25] example, is getting access to the right [39:26] data. You know, that might be months [39:28] working on that and you spend like three [39:30] days writing the paper. And so, it's [39:31] nice if you can shave some hours off of [39:34] those days making the plots. That's [39:36] annoying. And you made it a little bit [39:37] easier. But that wasn't the bottleneck [39:39] of producing papers. It's not like [39:41] producing papers was we just make plots [39:43] all day and if we can make that twice as [39:44] fast, I'm going to produce twice as many [39:45] papers. And I was like, "Oh, it's a [39:47] bottleneck issue." Is the the AI tools [39:51] sometimes are making non-bottleneck [39:52] things faster, which is fine, but it's [39:55] not necessarily going to lead in that [39:57] application to, oh, our core thing we do [39:59] is now happening uh better. We're [40:02] producing more papers. We're producing [40:03] more, you know, deep business insights. [40:05] And theory of constraints, I think, [40:07] again, helps understand a lot of this. [40:09] And I think on top of that, like to to [40:11] just put a pin in that very last thing [40:12] you said there is producing papers is [40:15] one thing and producing knowledge that [40:18] makes a difference in the world is a [40:20] different thing. Right? We've seen that [40:23] there's been all these incentives to [40:25] publish enormous numbers of often [40:29] irrelevant or low impact or [40:30] non-replicable [40:32] uh science. And so I think the ultimate [40:35] question would hopefully be [40:37] can this in some way allow us to do more [40:39] stuff that matters not just have more [40:42] papers. And when it comes to doing more [40:44] stuff that matters I think it's even [40:46] more true that how quickly you can make [40:48] your plots is not the limiting factor [40:51] right it's finding [40:53] uh finding new things to to look at [40:56] doing it rigorously all those [40:58] translating in a way that people can [40:59] understand all those sorts of things. [41:00] And not to say that I can't envision [41:02] ways that AI could help with some of [41:03] that, but I think so many of the cases [41:06] or so much of the temptation [41:08] is to implement this thing and the [41:11] easiest places to implement it are [41:14] typically not the most important places, [41:16] right? It's to speed up some low value [41:18] thing. And and I was telling you before [41:21] that I've been seeing this where just to [41:22] educate myself, I spent a bunch of time [41:25] with one company that helps other [41:27] companies implement AI is they say they [41:30] they help them hire their first AI [41:31] employee. [41:32] >> Yeah. [41:32] >> And one of my takeaways from seeing [41:34] what's going on with them is that it's [41:36] never been easier to do too much. [41:38] >> Yeah. [41:38] >> And these companies will say, "I got to [41:41] implement AI." And so they'll implement [41:42] it and it'll sprawl and it'll produce, [41:45] you know, what some researchers are [41:46] calling work slop now. this huge volume [41:48] of mediocre stuff that somebody has to [41:49] deal with and they're not slowing down [41:52] and saying what is the problem we have [41:54] make mapping what are the jobs to be [41:56] done and what tool do we match to that [41:59] and so since they're speeding just to [42:01] the sprawling implementation phase it's [42:03] actually causing a lot of problems [42:05] because they haven't defined the problem [42:07] they're actually trying to target this [42:08] turns out to be very hard in knowledge [42:10] work I mean I found this the I found [42:12] this out the hard way working on email [42:14] as an issue within the knowledge work [42:16] sphere is where oh what you really have [42:18] to do on paper kind of makes sense. is [42:20] like what are the actual problems you're [42:22] trying to solve and then you can say [42:24] what is the right collaboration process [42:26] to to optimize the result and that was a [42:28] bridge too far for most companies [42:30] because it's we can't figure that out we [42:33] don't have time to figure that out and [42:35] we got to just keep rock and rolling and [42:37] I mean I think computer programming with [42:38] AI like right now is comically [42:41] illustrating that point and it is going [42:43] to get better but right now because I've [42:45] been doing a lot of interviews with [42:46] programmers using AI when they switch [42:48] almost entirely to agent entic code [42:50] production. They're all following into [42:52] this classic trap of oh my god [42:54] everything I feel like is possible now [42:56] and they're log jamming and and and it [42:58] especially because it takes a little bit [43:00] of time for the each run to actually [43:02] produce something that you're like well [43:04] I can do something else in here and [43:05] something else in here and they're log [43:06] jamming with 13 things that are all kind [43:09] of getting stuck when before they might [43:10] have only been working on one or two and [43:12] it's like a classic case where a better [43:15] workflow plus that tool will make a [43:16] difference but when you first just throw [43:18] I mean email did the same thing when you [43:20] sometimes you throw throw these tools [43:22] into the mix and people just grab them [43:24] and start trying to make things faster [43:26] left and right. It's like bringing in [43:28] electric power drills to the Ford [43:30] assembly line to certain places and [43:32] certain stations are going much faster [43:33] and others aren't. The whole thing is [43:34] going to get [43:35] >> uh jumbled just like my metaphors here [43:38] have. Analogies have been jumbled. [43:41] >> But it's interesting. I mean it's I [43:42] think there's a lot to this that's like [43:44] that saying. I can't remember who said [43:46] this, but um you know, if you if you [43:50] don't waste a few hours, you'll end up [43:51] wasting a few years sort of thing. That [43:53] if you're not slowing down and thinking [43:56] about what you're doing and doing it in [43:58] a targeted way and targeting a [44:00] bottleneck that matters where you can [44:01] actually make a difference, uh then it [44:04] feels fast, but you end up wasting a lot [44:07] more time and energy in the long run. 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[46:26] All right, let's get back to the show. [46:28] Okay, so here's the last thing I want to [46:29] do before I let you go. I want to try to [46:31] apply this thinking to our world as [46:33] writers, right? Um because it's an [46:36] interesting world. I mean, I just did an [46:37] interview a couple weeks ago with, you [46:39] know, a writer who switched to a [46:40] typewriter, [46:42] >> which is the opposite of a efficiency [46:45] increasing tool. It's the opposite of a [46:46] productivity tool. She's producing [46:48] better books. So, I want to understand [46:50] what's going on here. So, let's just [46:51] think about real quickly if we're think [46:52] about what we do as non-fiction writers. [46:55] I guess the question first is what is [46:57] our bottleneck? and then I want to try [46:59] applying if that's the bottleneck. How [47:01] do we what really matters for broadening [47:03] that? [47:06] >> I I let me tell you I I was much this [47:11] compared to my the sports gene and range [47:13] inside the box I was way more efficient [47:16] and I'll explain why. So in my first two [47:17] books I wrote 150% the length of a book [47:21] and then had to cut it back to a book [47:24] with inside the box. I did not start [47:26] writing, you know, it was about a [47:28] two-year process and I did not start [47:30] writing for the first year. All I did [47:33] interviewed, I researched, I mapped the [47:35] territory and then [47:38] and then I made this a single page [47:41] outline with the structure on it. As you [47:44] can see, I wrote as small as humanly [47:46] possible, tried to defeat my own system [47:47] of keeping it on one page, but I found [47:49] it extremely difficult to do that. I [47:51] took a 100,000word thing I call my [47:53] master thought list which is ideas, [47:54] quotes, stats, all this stuff. 100,000 [47:56] words. So much longer than the actual [47:57] book and read it through it, printed out [48:01] in the hermitage in the back of a [48:03] Franciscan monastery where I didn't [48:05] speak for 2 days [48:06] >> as one does. [48:07] >> And when I was done, I then said, I'm [48:09] making a one-page outline. The stuff [48:11] that's standing out in my head will be [48:13] the important stuff that goes down here. [48:14] And it was very difficult to do that [48:16] because it meant that I had to [48:17] ruthlessly prioritize and cut out things [48:19] that I find interesting. [48:21] and I had to organize it in some [48:23] coherent way. And that exercise was [48:26] difficult and it meant that I didn't [48:28] start writing until much later than I [48:30] did with my previous two books. But then [48:31] when I started executing on the writing, [48:34] I flew. I was I sat on this book for [48:37] several weeks before I turned it in [48:38] because I was done. I didn't even know [48:39] that you could finish a book early and I [48:41] was like, what do I do? Should I send it [48:42] in? And so I think for me the bottleneck [48:46] was organization of information. Like I [48:49] have a very digressive brain. I jump all [48:50] over the place. Again, I ended up with a [48:52] hundred,000word note sheet. [48:54] >> Yeah. [48:54] >> And you can't manage all that much [48:57] information. And so it was how to figure [48:59] out what's in and what's out and to [49:00] structure it. [49:01] >> And so I spent um just more time this [49:05] time around making sure that I had a [49:07] clear architectural plan for the writing [49:09] before I got into it. Because in the [49:11] past I I get into it and then I start [49:13] figuring out the architecture while I'm [49:14] in the middle of it. And that means I I [49:17] get to writing much quicker, but because [49:19] that organization is my real bottleneck, [49:21] I end up executing much much slower even [49:23] though I start earlier. [49:24] >> That's interesting. So if that's the [49:26] bottleneck, like making the best sense [49:28] of the information, what's the right [49:29] idea or structures to pull out of it, [49:31] then broadening that bottleneck, for [49:33] example, would be less about probably [49:36] tools and maybe more about like going to [49:39] the Franciscan monastery like, okay, how [49:41] do how do I get myself? And I know you [49:42] talked about this in in inside the box [49:45] when you talked about Isabelle Aende and [49:47] the locations that she writes in etc. [49:49] The soft commitments I think you you [49:51] talked about. Um that becomes more [49:53] important is like oh if that's the key [49:56] part of this process then what I really [49:57] want to make sure is there's like this [49:58] one period or three-day period where I'm [50:01] in the best possible situation just to [50:03] sit and think and make sense of it. And [50:05] that's more important than speeding up [50:08] some other you know step that came [50:10] earlier. Those could be conveniences, [50:12] but that wasn't going to make you that [50:13] wasn't going to produce a better book. [50:16] But having the best environment and [50:18] approach and ritual around that key [50:20] bottleneck step, that's where you could [50:21] probably have the biggest the biggest [50:24] improvement. [50:25] I think so. And I think but I think it [50:27] requires a little bit of I don't know if [50:29] I want to say some kind of confidence or [50:31] something because it doesn't it feels [50:32] very inefficient. I mean, this was right [50:36] like instead of starting to write, I'm [50:38] going to go away to Franciscan monastery [50:40] with my a giant stack of papers that I [50:42] printed out and read them by hand one at [50:44] a time. [50:45] >> Yeah. [50:45] >> So, it feels very inefficient. And but [50:48] this is like Tony Fidel, the lead [50:50] designer of the iPod and co-founder of [50:51] Nest, who's an important character in [50:53] the book, was telling me [50:56] like like with the Nest team where he [50:57] made them prototype the box before they [50:59] had the product because he said, "This [51:01] will force us to decide what is it that [51:03] we really want to communicate in a [51:04] succinct way to the end user and it'll [51:06] force us to prioritize because there's [51:07] not much stuff can fit on this box." And [51:10] he said those kinds of things, what he [51:11] called these ultra constraintbased [51:13] things, they slow you down, but they [51:16] force this kind of hard thinking that [51:19] then makes the the execution much [51:21] faster. And so it it did feel [51:24] inefficient to me at the time. I I was I [51:28] was trying to be more efficient in the [51:29] sense that I didn't want to write like [51:30] once I became a parent, I'm like, I [51:32] can't be writing a book and a half to [51:33] get a book anymore. I need to make [51:34] better use of my time. [51:36] >> Yeah. Um, [51:38] but I didn't realize how much faster it [51:40] would make me. But it did require me [51:43] burning some of this time up front where [51:45] in the past I would have jumped into the [51:46] writing. So I would have felt like I was [51:48] starting more quickly. Yeah. So I think [51:50] it had to have a little bit of a faith [51:52] in in in the process. [51:53] >> I'm actually excited about this. This is [51:54] just writer geekery. So apologies to the [51:57] audience. But another writer geekery [51:58] observation about what you just said [51:59] that I'm excited about because I'm [52:01] thinking about this. There's a real [52:03] efficiency. I think a really smart [52:05] observation that the kind of the bang [52:08] for your buck when you're researching it [52:12] it's larger in some sense, right? Like [52:14] when you're writing, you're burning a [52:15] lot of mental energy with Wordcraft, [52:18] right? So if you're overwriting, that's [52:21] a lot of energy that really doesn't go [52:22] anywhere productive. It was time you [52:24] spent getting sentences right in a whole [52:27] chapter that was going to be cut, right? [52:29] Because there's a lot of overhead for [52:30] writing. So what you did is you [52:32] frontloaded research which um it's much [52:35] more you're getting much bigger return [52:37] like I remember that year because every [52:39] time I talked to you you were some other [52:40] place in the country doing something [52:41] cool but every time you have a go meet [52:44] someone or read something you end up [52:45] with like usable notes for that idea [52:48] formation process that's like a really [52:50] efficient use of time in some sense and [52:52] then you do the Franciscan monastery to [52:54] make sense of all of that and then when [52:56] you're doing the huge mental overhead of [52:58] like this sentence needs to be right [53:00] you're not wasting [53:01] half of your effort on sentences that [53:03] aren't going to see the light of day. [53:04] >> Absolutely. I mean, this would cal this [53:07] might like drive your efficiency brain [53:09] crazy, but in my first book, I took a [53:12] trip to a remote area of Arctic Sweden [53:14] that I had to cut from the book. The [53:17] whole chapter cut from the book. Can't [53:20] be taking trips to Arctic Sweden that [53:22] you're going to cut from the book. And [53:23] had I put more thought ahead of time [53:24] into what the boundaries for the book [53:26] were, I would have realized that there [53:27] was no way this thing was getting in [53:28] there. [53:28] >> But this is a key key follow-up. So did [53:30] you the trips you did was that after you [53:34] had a structure based off of things that [53:36] you read or phone interviews and then [53:37] you went and did trips knowing this is [53:39] what you're going to use or were some of [53:40] the trips you did exploratory before the [53:43] monastery structuring of the information [53:45] >> there there were some of both. There [53:46] were some of both. There were there were [53:47] definitely exploratory trips also [53:49] although none of them were to Arctic [53:51] Sweden. And and I did enough thinking [53:53] ahead of time like so for this book I [53:55] wrote a longer proposal before I sold [53:58] the book. so that [54:01] I had a little better sense of, you [54:04] know, I I did enough I did enough [54:06] research ahead of time that I would have [54:07] known that like the something like the [54:09] trip to Arctic to Sweden would be [54:10] worthwhile or not. So, I absolutely did [54:11] exploratory stuff. No question about it. [54:14] Um, but [54:17] I still had kind of a tighter uh [54:20] bounding around the project even from [54:22] the beginning really because of some of [54:23] the research. But this is the first time [54:25] I didn't end up cutting an entire [54:27] chapter. [54:28] >> That's amazing. I've written and the [54:29] book's 20% shorter than my other two. [54:31] >> Yeah. [54:32] >> Um [54:33] >> Yeah. [54:34] >> Yeah. Uh but it's very tight. Um and I I [54:37] I we're really writer geeking, but I [54:39] like the structuring. Readers will [54:41] listeners will know if they read the [54:43] book. Uh there there's a lot of [54:45] interleaf structuring which I appreciate [54:47] actually in your chapters without over [54:50] the top telegraph transitions that [54:52] you're A to B, A to B, back to C, BB A. [54:56] there's a sort of interle you do that I [54:59] think is um you know the sign of you've [55:02] thought through your structure and it [55:03] feels lean which [55:05] >> I appreciate that and and also I wanted [55:07] to you know I always think of with a [55:09] book project they take enough effort [55:10] that I kind of want to start a project [55:13] where I don't feel like I have all the [55:14] tools that it will take to finish it [55:16] because then you're forced to try to get [55:17] them to finish it and so this was a [55:19] structural experiment for me where I [55:21] wanted to try something with sections [55:24] that included multiple linked chapters [55:26] because I'd never done that before. So, [55:27] there's also an experimental aspect to [55:29] it and just something that I wanted to [55:30] try to be more coherent than I had been [55:32] in the past. [55:33] >> I thought I thought it worked. I thought [55:34] it worked well. I think it's also like a [55:36] credit to the reader's intelligence [55:37] like, oh, okay, we're moving back and [55:38] forth here and we don't need cliffhers [55:41] to do so. But, as I was about to [55:43] discover, it wasn't so easy. Dot dot dot [55:46] dot dot dot [55:47] >> dot dot dot. Oh, I do some of that. Uh, [55:49] okay. So, just to wrap this up for the [55:51] listener, uh, here's what I'm pulling [55:52] away from this is in the things that [55:54] matter to you in your life, be it a [55:56] professional thing or be it I'm trying [55:57] to get in better shape or learn how to [55:58] do a complicated hobby or improve my [56:00] relationship with whatever, you got to [56:03] think about bottlenecks [56:05] as much as you think about just what are [56:07] tools that'll make me more efficient at [56:08] certain parts of this. And often [56:11] servicing the bottleneck, either making [56:13] it broader or not wasting your time with [56:16] things before that are just going to [56:17] pile up. That's where the big wins come [56:21] and this is why you have to look at [56:22] digital productivity tools in particular [56:24] with a little bit of care. Do not study [56:28] them in isolation. Of course, the tool [56:30] makes something faster. Email is faster [56:32] than typing in a voicemail code on your [56:34] office voicemail. Of course, it is, but [56:36] it also made everything else worse. So, [56:38] you you have to think of everything in [56:40] the context of what is the whole process [56:42] that produces stuff and where is this [56:45] bogging down? Uh, so I've turned your uh [56:48] sort of discursive general idea [56:50] non-fiction book into a business advice [56:52] book, Dave. I hope you're okay with [56:54] that. But that's the that's the that's a [56:55] core lesson that I'm pulling out of this [56:57] that I hope people hear. [56:58] >> Absolutely. I mean, I want it to be [57:00] useful for people and and I there's a [57:03] huge dose of research in this book. I [57:05] mean, as I said, since I overwrote my [57:07] first two books, I was inefficient with [57:08] my time. I wanted to get better at these [57:11] things. And so I came to see the world [57:13] through bottleneck tinted glasses and [57:15] think about it in all my own work of [57:16] what is my limiting factor here. Not [57:18] what is the thing that I'm good at and [57:19] that it's easy for me to do and to [57:21] improve the efficiency of that, but [57:22] what's actually limiting me. So a lot of [57:24] this was about uh me exploring things [57:26] that I wanted to get better at myself. [57:27] So yeah, I hope there's some of that for [57:28] other people, too. All right. Well, [57:30] enjoy the conversation. The book is [57:33] inside the box. Uh when this comes out, [57:36] it will be newly available. Art has a [57:39] star from Publishers Weekly before it [57:40] even came out. or recording this. It's [57:42] going to be another banger of a book. [57:44] I'm using the YouTube uh lingo here. Now [57:48] that you're doing YouTube videos, you [57:49] need to learn. So, it's going to be a [57:50] banger of a book that's going to be 100 [57:52] 100 fire. [57:54] >> I don't know what I'm doing. [57:56] >> That was so natural sounding. [57:58] >> All right. So, there we go, Jesse. That [57:59] was my conversation uh with Dave [58:02] Epstein. I don't know. I like this idea [58:03] of writing a business novel. [58:06] >> Yeah. I think mine would be I can [58:08] imagine it now. It would it would focus [58:10] on the the front office of the [58:13] Washington Nationals and then an [58:15] enigmatic young computer scientist joins [58:18] and through a series of long socratic [58:19] dialogues helps turns the team around [58:22] and then in a dramatic finale is asked [58:24] to come in to pitch relief in the World [58:25] Series and the game. [58:26] >> I was thinking you were going to say [58:27] that. [58:27] >> Yes. They're like, "Oh my goodness, a [58:31] middle-aged enigmatic computer scientist [58:34] has just taken the mound for the [58:35] nationals and uh here it comes. There we [58:37] go. 42 mph. [58:40] Sodto swung so hard he lost the bat. [58:44] He's a hero. No, this is what would [58:47] really happen. They'd be like, "All [58:48] right, enigmatic computer scientist has [58:49] just entered the game for the nationals. [58:51] He's on the mound and he, yes, has hit [58:53] Wanoto in the head with the ball. Uh, [58:55] he's out of the game." All right. [58:56] Anyways, good conversation. I really do [58:58] think this idea of bottlenecks. [59:01] This has to be right. like I'm I'm [59:03] already thinking about revising and [59:06] elaborating my existing knowledge for [59:08] productivity theories around this idea [59:09] of bottlenecks but it really helps [59:11] understand right I mean why do digital [59:13] tools often make us uh busier but not [59:15] better is because what we do is a [59:18] process and there are bottlenecks in [59:21] this process the steps that really [59:22] define the the pace at which things get [59:24] done digital tools again like the drunk [59:27] searching for his key is under the [59:28] spotlight because that's where the light [59:30] is they focus on what part of that [59:33] pipeline they happen to be good at. And [59:35] so what you end up doing is speeding up [59:37] parts of this process that's before the [59:38] bottleneck without making the bottleneck [59:40] bigger and then you just pile up more [59:41] work and you get more frantic and [59:42] actually in the end less gets done. So [59:44] theory of constraints makes a lot of [59:46] sense. If you want to learn more about [59:48] it, don't read the goal. It's kind of a [59:50] strange book. Read Dave Epstein's book [59:52] inside the box. Chapter 8, I believe, is [59:54] the chapter that we talked about today. [59:57] All right. Well, that's enough from me. [59:59] I want to hear from you. Now, as is our [60:02] tradition on these Monday advice [60:03] episodes, Jesse and I like to open our [60:05] inbox and read some notes from the [60:08] audience. So, remember, you can send us [60:09] your feedback, questions, or suggestions [60:10] to [email protected]. [60:13] All right, let's get into it. Uh, we had [60:15] a long interview today, so maybe we'll [60:16] just do two messages. Uh, Jesse, what [60:18] should we look at first? [60:20] >> Our first message is from Alexander, who [60:22] has a practical suggestion for people [60:24] looking to break their digital news app [60:26] addictions. [60:27] >> Yeah, we've been talking a lot about [60:28] this on the show recently. We get a lot [60:30] of emails about this too. Like in our [60:32] current moment, digital news addiction, [60:35] uh, especially for people who are a [60:36] little bit older, has become sort of the [60:38] new social media addiction. Just this [60:39] sort of constantly following the news, [60:41] constantly putting you in some sort of [60:42] doom loop. All right, let's see what [60:44] Alexander had to say here. Currently [60:46] listening to podcast episode from March [60:48] 16th, where you talk about news apps, [60:50] addictions, and doom scrolling like on [60:53] the New York Times app or website. This [60:55] was in fact one of my biggest issues [60:57] implementing a deepwork lifestyle and [60:59] over time I found RSS to be really [61:02] useful for this. Now the biggest [61:03] advantage of RSS feeds over apps. You [61:05] kill doom scrolling right away and RSS [61:08] feed is finite. So even if you keep [61:09] coming back to it 250 times a day you [61:12] shouldn't spend much more time on it. [61:13] Once articles are read and archived [61:15] they're gone. Additional benefit RSS [61:18] feeds allow much more fine grain control [61:19] over content which feeds you're [61:21] subscribed to etc. Beyond this, RSS [61:23] readers like Inoroo in Ino reader, INO [61:26] reader, which I really like, lets you [61:28] filter for keywords, remove duplicates, [61:30] um, and can often fetch the full text, [61:32] which means you don't even have to touch [61:34] the media's website anymore. Very useful [61:35] to kill the news addiction. Not everyone [61:38] is familiar with RSS, but it's been [61:39] there for ages, and there's a million [61:41] reader apps, and RSS feeds are maybe [61:43] surprisingly still very common. I'm not [61:45] a regular listener to your podcast, so [61:46] apologies if RSS feeds have been [61:48] suggested before, but I thought I'd send [61:49] you a message about it just in case. I'm [61:52] glad to see that RSS is hanging around. [61:54] Uh this goes back to the the the days of [61:56] blogs. You know RSS? I feel like I use [61:58] you as my proxy for like a normal [62:00] person. [62:01] >> Oh, you probably know the term from our [62:03] podcast. [62:04] >> Yeah. And I've since a prior episode [62:08] like a few months back, I've tried to [62:09] use that in a reader thing. [62:11] >> Yeah. So what's your sense with it? How [62:12] how [62:13] >> I like it. I just never use it. I put [62:14] article I put saved articles in it, but [62:16] I never check. When I have more time, [62:18] I'll eventually check. So there's like [62:19] 20 articles in there. Yeah, the key I [62:21] think with those RSS readers is you need [62:23] to find news sources that have a feed [62:24] that you like. I This is kind of the [62:26] problem with news is RSS made more sense [62:28] with blogs because with blogs it would [62:30] be maybe one or two articles a week and [62:32] you're like, "Oh, I like Cal Newport's [62:35] blog." So, I want to see his new [62:37] articles once or twice a week and the [62:39] RSS reader would fetch them when they're [62:40] new and bring them into the reader. And [62:42] so, if you follow 10 blogs, you'd be [62:43] like, "Yeah, I'm going to have like 20 [62:44] or 30 articles that kind of build up [62:46] over the week." New sites are publishing [62:48] constantly. That's the problem. Like, so [62:50] if you can't subscribe to an RSS feed [62:52] for like the New York Times, like I mean [62:54] they don't have that, but if they did, [62:55] it would be hundreds of articles. So I [62:57] think that's [62:57] >> the problem is is you actually need [63:00] these sort of curated news sources to [63:02] have RSS feeds are. Now, for people who [63:04] don't know what it is, RSS, it's just a [63:05] it's a format. It's like a a format [63:08] agreement. It's a a text format that you [63:12] use to describe content. Here's its [63:14] name. Here's the title. Here's when it [63:17] came out. Here's the link. Here's a [63:18] description. Maybe here's the full text. [63:21] If you want to maintain an RSS feed for [63:23] like your own blog, what's really [63:24] happening is every time you post [63:26] something, you just add information [63:28] about that new post gets automatically [63:29] added to this RSS feed, which is just [63:31] like a text file. And what RSS readers [63:34] do is just if you subscribe to a feed, [63:36] it just means they check that text file [63:37] a bunch. And if there's something new in [63:38] there, they grab the information and put [63:40] it into the RSS reader. So it's just a [63:42] way of easily monitoring when particular [63:45] sites have produced new information. [63:47] Where RSS has become very important in [63:49] our current world is podcast. This is [63:51] how podcast release new episodes. So a [63:55] podcast host, so a server that actually [63:57] holds the literal sound files for your [63:59] podcast has a RSS feed for your podcast, [64:02] just a big text file. And every time you [64:04] publish a new podcast episode, they add [64:07] to that RSS file the title of the [64:10] episode, its description, and a link to [64:12] the MP3 file. And so what a podcast [64:15] player really does is when you subscribe [64:17] to a podcast, you're just telling it, [64:19] keep checking that text file for that [64:21] podcast, and if you see something new, [64:23] grab the new information from it and put [64:25] it in the player. So RSS actually stuck [64:26] around. There was a while, you know, 10 [64:28] years ago where it was uh the the idea [64:31] was Google was trying to kill RSS [64:34] because they wanted social media, these [64:36] more controlled experiences of [64:38] information to be the key. RSS is like [64:40] having your own social media player, [64:41] right? It was I am curating individuals [64:44] websites that I want to receive [64:46] information from in an app. And what the [64:48] big attention economy companies wanted [64:50] was no no you want to download our app, [64:53] right? be it Facebook, be it Instagram, [64:55] be it the at the time, Google had its [64:57] own social media networks that ended up [64:59] not working. We want you to download our [65:01] app and we'll curate the information. We [65:03] don't want you in a third party app [65:04] pulling from third party sites. So, they [65:06] uh Google bought one of the major RSS [65:08] readers a long time ago and killed it. [65:10] And so, the conspiracy theory is because [65:12] it does these big companies no good to [65:15] have the little guys be the provider of [65:17] information. So, I'm glad to hear [65:18] there's still readers and ours is still [65:20] out there. But for text, I think you [65:21] have to also have a revival of [65:23] individuals writing, which they do, but [65:25] they're doing it now for email [65:26] newsletters. They're doing it for [65:27] Substack. Uh, and Substack, again, isn't [65:31] big on RSS feeds because they want you [65:32] to read the Substack articles in their [65:35] own reader. So, it's an interesting [65:36] world out there. Um, but I do like RSS [65:38] because anything where it's individuals [65:40] publishing and individuals curating [65:41] their own custom content from [65:44] individuals without a large player [65:46] involved, I think that is good. So, you [65:49] know, hey, I'm I'm here for RSS. All [65:51] right, let's do another one. What else [65:52] do we have here, Jesse? [65:53] >> All right. Next message is from John, [65:55] who is reacting to the newsletter you [65:57] sent out last Monday. [65:59] >> All right. Well, let's load up that [66:00] newsletter. Uh, I'll put on the screen [66:02] here briefly, just so we know what [66:03] John's talking about. So, on April 27th, [66:07] I published a newsletter post titled, [66:08] "Who asked for this?" If you're not [66:10] subscribed to my newsletter, by the way, [66:12] you should be. Newport.com. All the type [66:13] of ideas we talk about in the various [66:15] episodes of my show. All right. All [66:17] right. So, let me just briefly summarize [66:18] this article. Um, it focused on a a [66:23] article that was published on The Verge [66:25] by Elizabeth Lado that was called [66:27] Silicon Valley has forgotten what um [66:29] normal people want. Here's a quote. This [66:32] is not me. This is me quoting Elizabeth [66:34] from that Verge article. I said the [66:36] following quote. Within recent memory, [66:38] people who made software and hardware [66:39] understood their job was to serve their [66:41] customers. It was an identity. It was to [66:43] identify a need and then fill it. But at [66:45] some point following the financial [66:47] crisis, wouldbe entrepreneurs got it [66:48] into their heads that their job was to [66:50] invent the future and consumer's jobs [66:52] was to go along with that invented [66:55] future. Later, Leato says the following, [66:58] and I quote it in my article, "In the [67:00] place of problemsolving technology, [67:02] companies have jumped on successive [67:03] bandwagons like NFTs, the metaverse, and [67:05] large language models. What these all [67:08] have in common is that they're not built [67:09] to really solve a market problem. They [67:11] are built to make VCs and companies [67:14] rich." I think this is a good point that [67:16] Leato is making that if you go to the [67:19] 90s, you go to the early 2000s, there [67:21] was this sense of we have built [67:23] something that's going to be really [67:24] useful to you. We're going to make the [67:25] pitch why this is useful. People like, [67:26] "Wow, that does sound useful." And then [67:28] they would use it. [67:30] This is what the iPod was. People had [67:31] all these these discmen with anti-skip [67:34] resistance and bringing books full of [67:36] CDs around with them to try to swap it [67:38] in. They wanted to listen to music [67:39] portably. It was a pain. And then Apple [67:43] said, "Hey, you could put a thousand [67:44] songs in this one device, no skipping, [67:46] and you can with a scroll wheel quickly [67:48] shoot through albums to get to exactly [67:49] the songs you want to listen to." Yeah, [67:51] it's a little expensive, but it solves a [67:53] real problem. It does something that you [67:54] are already doing and care about much [67:56] better. And people said, "Yes, yes, [67:59] please." They sold a lot of iPods. The [68:00] iPhone was the same way. Like, look, you [68:03] got your iPod and you have your phone. [68:05] Your phone's not particularly good. The [68:06] interface is annoying to to check [68:08] voicemails. You have to go through menus [68:09] and press buttons and just listen. And [68:11] then you have to put down the phone, the [68:13] put on your earphones for the the iPod. [68:15] What if we put those together, give you [68:17] a beautiful visual interface and give [68:18] you visual voicemail so you can actually [68:20] just click on messages and hear them [68:21] right away. These are two things you [68:23] care about and are already doing. We [68:25] made this much easier and better for [68:26] you. And people said, "Yes, please. This [68:27] is great." Right? So this was the way [68:28] the web was presented the same way. You [68:31] have to go all the way to a store to buy [68:35] uh a book. Well, what if you could just [68:37] do it from home and it's here in a few [68:38] days. you could look at the whole [68:39] catalog of all possible books, something [68:41] you were already doing. We made it much [68:43] easier for you like, "Yes, please. I [68:44] want to do that." Then we did have this [68:47] shift in the the 201s coming into the [68:49] 2020s in particular, where there's much [68:52] more of this notion of I think of it as [68:54] VC firms were looking for where are [68:56] their green economic pastures where we [68:58] can grow uh unicorn companies. So after [69:00] the attention economy sort of saturated, [69:02] there wasn't any more unicorn plays, [69:04] meaning billion-dollar startups to do in [69:06] social media, they went looking for [69:07] other things. [69:09] So you have like Andre and Horowitz [69:11] really pushing crypto, right? Because [69:12] like well maybe this is a place where we [69:14] can get this type of 100x growth. We get [69:16] unicorn investments and then we got a [69:18] lot of energy. Uh like the metaverse [69:20] became a thing. We put a lot of energy. [69:21] Facebook spent $80 billion on that. This [69:23] could be a growth area. Didn't the [69:25] didn't work and now we have large [69:26] language models. And in all these cases [69:28] what's different this what leato was [69:29] arguing. What's different than past [69:31] technological innovations we've seen in [69:33] the technology space is that these [69:35] innovations are happening without a [69:36] clear selling proposition to the average [69:38] person. [69:40] They're like, "Hey, we're inventing the [69:41] future and trying to create massively [69:43] valuable uh uh companies. You just need [69:46] to go along. It's up to it's not our job [69:48] to make understand you don't we're not [69:50] going to explain to you why you need to [69:52] use crypto. We're just going to kind of [69:53] shame you into like you need to be and [69:54] hype it up." And what I argued in this [69:57] piece, somewhat controversial, is that [69:58] for most normal people, this is what [70:00] LLMs are like right now. They have a lot [70:03] of power. They have a lot of utility, [70:06] but the case hasn't been made to the [70:08] average person yet about how it's going [70:10] to make their life better. So, I'm going [70:13] to read one more thing from this. Uh, [70:15] generative AI has no shortage of ways [70:18] that it might be with care, shaped into [70:20] genuinely useful products. But this [70:22] shaping needs to actually happen before [70:23] the hyperscalers earned a right to [70:25] continually harass the psyche of [70:27] billions of people with breathless [70:29] pronouncements. Most people don't care [70:31] that GPT55, which was released last [70:33] week, underperformed Opus 47 on the [70:36] swing pro. They want the AI companies to [70:39] let them know when they have a product [70:40] that will actually and notably improve [70:42] their lives. And until then, they want [70:43] these companies to just leave them alone [70:45] and do their best to not crash the [70:46] economy. All right, [70:49] that's the context in which we get this [70:51] message from what is this, John. All [70:54] right, here's what John says in response [70:55] to my article. I think you're right [70:58] about the disconnect Lato describes. The [71:00] industry has spent a decade trying to [71:01] invent futures rather than solve [71:03] problems people actually recognize. But [71:06] there's an interesting countercurrent [71:07] emerging with AI which almost runs in [71:09] the opposite direction. What I'm seeing [71:11] anecdotally at least is not people [71:12] asking AI to automate their lives [71:14] wholesale, but using it to build very [71:16] small, very specific tools for [71:17] themselves. Not startups, not products, [71:19] just solutions. I was chatting to a [71:21] friend who built a simple app to predict [71:22] whether it's worth putting the uh wash [71:26] out in the next 2 hours. I know what [71:27] that means. And someone else I saw the [71:29] other day put together a tool to track [71:30] lowflying aircraft near their home. [71:32] Another is sketching a better podcast [71:33] experience for their Garmin watch. None [71:34] of these are ideas in the venture sense. [71:36] They're just irritations resolved. [71:38] Personally, it feels closer to the old [71:40] the old pattern one used to see with [71:42] what you might call necessity-led [71:44] making. That is people designing better [71:46] baby slings, writing children books, or [71:47] hacking together small businesses around [71:49] a very particular need, often [71:50] identifying their downtime. The [71:52] difference is now that the barrier to [71:53] execution has shrunk. So in that sense, [71:56] AI might be filling part of the gap LPO [71:58] describes by making it viable for [72:00] individuals to run a use case of one and [72:01] actually build what they want rather [72:04] than enduring a torturous climb to [72:06] releasing mass market products. All [72:09] right, so John, I think that is a cool [72:12] trend. Here's the problem. That's an [72:13] incredibly minor trend, [72:16] right? 99%.9 [72:18] or 99.5% of people are not going to [72:20] learn a coding harness so that they can [72:22] write custom apps to solve their [72:23] problems. That's like a That's a [72:24] enthusiast that would do that. And it's [72:26] really fun. Everyone I know who is like [72:29] vibe coding solutions to just things in [72:32] their own life loves it. It's like the [72:33] ultimate model train set, but it's also [72:36] annoying and it's technical and if you [72:38] don't have any background in coding or [72:40] computer science, it can be um it could [72:43] be pretty intimidating. Most most most [72:45] most most people are not going to do [72:47] that. So I think that is cool, [72:50] but that I think is a is a minor use [72:52] case. I mean it's similar to with like [72:55] crypto you know there's a small group of [72:58] people that were building these [73:00] blockchainbased solutions for various [73:02] things that got a lot of sort of [73:03] philosophical cyber libertarian [73:05] enjoyment out of saying like look [73:06] there's no central party involved in [73:08] actually building this sort of database [73:09] and that was very cool but 99% of people [73:12] aren't going to do that and don't care [73:13] and don't want to use those products [73:14] because they're slower and they don't [73:15] care about the cyber libertarian [73:16] philosophy underneath them. So that's [73:17] where most people are. Most people don't [73:18] want to build custom apps. This was [73:20] actually the same argument that [73:21] surrounded the original release of [73:23] consumerf facing personal computers. [73:25] Right? So you're wondering why does like [73:26] the Apple 2 when it was released in the [73:28] late '7s. Why was the main thing that [73:30] ran on that was the basic computer [73:32] programming language? The idea was [73:34] people are going to write programs for [73:36] the individual things they care about. [73:37] Basic is an annoying language if you [73:39] want to build anything cool. I used to [73:40] be a game programmer when I was a kid [73:42] and basic was incredibly limited, right? [73:44] It didn't take me long until I was [73:46] touching the graphics cards with a [73:47] simler in my C programs because I wanted [73:49] programs that actually had real pizzazz. [73:52] But if you just wanted to write, hey, [73:53] uh, I want to store recipes. I want to [73:55] make a a custom address book. I want to [73:59] be able to do some calculations on a [74:01] regular basis for my business. These are [74:02] the type of things. This is the idea of [74:04] the original personal computer. You will [74:06] write in this simple highle programming [74:08] language programs for you and the stuff [74:11] you care about. That's what the computer [74:12] is for. It didn't work. Most people [74:14] didn't want to write programs. People [74:15] don't like programming. It's annoying. [74:17] They don't want to build their own [74:18] tools. And so we got the software [74:19] industry instead. Like, okay, great. [74:20] We'll just build tools for you that you [74:22] think is cool. So, I think what you're [74:24] talking about, John, is awesome. And I [74:26] don't think that is ever going to leave [74:27] the niche. It just everyone I know in my [74:29] life who's not from my computer science [74:32] uh circles is not going to vibe code [74:35] custom web apps, you know, and run a [74:37] local server and try to get the proper [74:40] uh, you know, the proper uh swing [74:42] JavaScript library configured so that [74:44] they can have the latest UI that's going [74:47] to work with HTML 5 compatibility. [74:48] They're just not going to do that. They [74:50] just don't care. All right. Uh, that's [74:52] all the time I think we should do for [74:53] the inbox. Um, before we wrap up today's [74:56] episode, like we like to do on Mondays, [74:58] do a quick update about what I've been [75:00] up to. All right, on the reading front, [75:04] a couple things to say here, Jesse. Uh, [75:05] I read another book since last time I [75:07] read the memoir Cook Cook K by Peter [75:12] Heler. So, it's a memoir of him taking [75:14] like in his mid-40s taking six months to [75:17] try to go from someone who has no idea [75:19] how to surf to being able to surf [75:21] barrels, so like overhead waves. on [75:24] relatively short boards. I think like [75:26] seven six boards or something like that. [75:28] The reason why I read it is because this [75:30] is I have to draw a couple dots here. Um [75:33] I saw a preview for Ridley Scott's new [75:36] movie coming out this summer called The [75:38] Dog Stars. It's about post-apocalyptic [75:42] uh everyone gets killed by the flu. It's [75:43] a post-apocalyptic survivor type thing. [75:45] Um James Brolan's like the old guy [75:47] Bengali and then I don't know there's [75:49] some you know attractive young star is [75:50] playing the the younger guy. Anyways, [75:52] that's a novel by Peter Heler. So, Peter [75:55] Heler wrote this memoir. He's an [75:57] adventure writer. He's from that like [75:58] Susan Casey era of like outside [76:00] magazine. Let's like go on adventures [76:02] and write about it in the '90s type of [76:03] adventure writer. And he wrote this [76:05] memoir. Um, and then after he wrote this [76:07] memoir, he became a novelist and he [76:09] writes novels. And his first novel was [76:11] The Dog Stars. So, that's okay. [76:13] >> I went backwards. Um, and he's an [76:15] interesting guy because he owns a lot of [76:16] land in Colorado and he has a off-grid [76:19] house and he goes there to write and [76:20] he's an interesting guy. I started [76:22] reading The Dog Stars after this and I [76:23] aborted it. [76:24] >> Really? [76:24] >> Yeah. [76:25] >> Uh, there's two things. [76:26] >> Are you going to watch the movie? [76:27] >> I'll watch the movie. I like Ridley. Um, [76:31] the style wasn't vibing with me. It was [76:33] a little bit stream of consciousness. No [76:34] quotation marks. A lot of just like [76:37] saying things, declaring things. And [76:39] then he said something like this and [76:40] that. And it's an interesting style. Not [76:42] for me. But more importantly, I really [76:44] don't like dystopian novels. They they I [76:47] don't like zombie novels. I don't like [76:49] post-apocalyptic novels. I never read [76:50] Cormick McCarthy's The Road. It kind of [76:52] bums me out. And so I got like a 100 [76:54] pages in. I was like, "This is buming me [76:56] out." So [76:57] >> you don't abandon that many books, [76:58] right? Or do you? [76:59] >> Uh not really. But I was like, "This is [77:01] buming me out and I don't love the [77:02] style. I'm going to read the baseball [77:03] book you got me instead as my fifth [77:05] book. I'll read it over the next couple [77:07] of days." U I also want to suggest I [77:10] have a rule suggestion. And Jesse, you [77:12] got to give me your feedback here. I [77:14] have a rule update to my five books [77:16] challenge I'm thinking about [77:17] implementing. So, as longtime listeners [77:19] know, I I read five books a month. I [77:20] think it's a good arbitrary rule to make [77:22] sure that like I'm actively putting [77:24] aside time to read and not sort of [77:26] giving up on it. The issue I'm having is [77:29] I think I'm steering myself away from [77:31] too many long books because it's hard to [77:33] fit in a long book when you're doing [77:35] five. I've done it occasionally. I read [77:36] that 600page Sanderson book. to do a [77:39] 600page book, I basically have to read [77:40] four books in two weeks and have like [77:42] whole half months to do it. So, I want [77:45] to read more long books um or more hard [77:47] books. So, my my the rule I'm [77:49] considering is take the page count of a [77:52] book, divide by 250, round down, [77:57] that's how many books a book counts for. [78:01] Yeah. [78:02] >> All right. So, like a 400page book is [78:03] one book. A 500 or 600 page book would [78:07] be count as two books, right? If you got [78:09] to like an 800 page book, that would [78:11] count as three books. So divide by 250 [78:14] and round down. That's what I'm [78:15] thinking. [78:16] >> No, I think as a fan, I definitely want [78:17] to see your feedback on reading bigger [78:20] books. [78:21] >> Yeah, I was looking I'm probably not [78:22] going to read them. [78:23] >> Well, I was looking at a book I Well, [78:24] you read Rise and Fall of the [78:27] >> Yeah, that took me forever. [78:28] >> Yeah. How many pages was that? Uh, [78:30] that's pretty long. But I read um 1929, [78:32] but actually [78:34] >> that's a long That's a long one, too, [78:36] right? [78:36] >> That was pretty fast, though. I read [78:37] that over a break. [78:38] >> Yeah. He uh Sorcin writes in a fast [78:40] style, but that was like a 600 800 pages [78:42] or something. [78:43] >> That was longer than I thought. [78:44] >> Yeah. Yeah. So, like if I want to write [78:46] a book like read a book like that, if I [78:47] if it's going to count for two or it's [78:48] going to count for three. I was looking [78:49] at a book the other day. I want to read [78:51] this summer and it was 800 something [78:52] pages. [78:52] >> No, I think I think the audience [78:54] definitely wants to see your feedback on [78:55] longer books, too. [78:56] >> Yeah. So, that's going to be my new the [78:57] the other rule, which I don't have yet, [78:59] but I'm thinking about is if it's a very [79:01] hard book, [79:02] >> then also um maybe I'll make that 250 [79:06] number smaller. [79:08] >> Yeah, [79:08] >> like a 300page really hard book maybe [79:10] should be two books. So, anyways, I'm [79:12] thinking about these rules. I mean, the [79:13] main thing is I just want to read and if [79:15] I don't have these rules, [79:17] >> if anything, you might get blasted on [79:18] Twitter, but you're never going to see [79:19] it anyway, so it doesn't matter. [79:20] >> Yeah, go ahead. [79:22] >> People on Twitter don't have the [79:23] attention span to make it to this part [79:25] of the podcast. They they were out three [79:28] minutes in, so I don't have to worry [79:30] about that. Um, all right. So, what's [79:32] coming up? I have uh a trip to a [79:36] mountain resort in Asheville. [79:39] >> Sweet. [79:40] >> Uh, going to work out with Brad and [79:42] Zach. [79:43] >> What are you going to do? [79:44] >> I'm going to write. It's like a [79:46] >> No, I meant like workout. [79:47] >> Oh, I don't My trainer lives in [79:48] Asheville. [79:49] >> What are you going to deadlift like Brad [79:50] or [79:51] >> um I think my arms would literally rip [79:53] off. It'd be like that SNL sketch with [79:55] the with the the Have you seen that one? [79:57] The performance-enhancing drugs Olympics [79:59] where it's just like everyone and Dana [80:01] Carvey comes out with this like huge [80:03] muscle suit and like he's going to lift [80:04] like 800 lb breaking the doubling the [80:06] record and he's like then his arms rip [80:08] off and all the blood goes squirting [80:10] out. Yeah, that would be me. So, I don't [80:12] know what we're going to do. Oh, I will [80:13] tell you what we're going to do. [80:14] Actually, we have a plan. Um, I need [80:16] some new equipment for my home gym. And [80:18] so we're gonna test out multiple things [80:22] at Zach's gym [80:23] >> to be like, oh, which of these like, [80:24] okay, this would be a good thing for you [80:26] to that that's kind of the plan is to [80:28] kind of scope out and test out, you [80:30] know, what can fit into my small space. [80:32] Um, so Brad and I are going to we're [80:34] going to like figure out the world and [80:36] we're going to work out and I'm going to [80:37] write. I I got a room. It's just for a [80:38] couple days. But [80:39] >> you going with your family? Are you [80:40] going by? I'm just going up middle of [80:41] the school week when the kids are in [80:42] school. So, it's minimally disruptive [80:44] and it's celebrating the start of my [80:45] sbatical. [80:46] >> Oh, sweet. Yeah. So, it's good. [80:47] >> You driving or you fine? [80:49] >> Yeah. Too far to drive. [80:50] >> Too far to drive. So, that's what I'm up [80:52] to. Um, all right. So, we'll be back on [80:55] Thursday. I'm sure we'll have an AI [80:57] reality check episode and be back with [80:58] the Monday advice episode the Monday [81:01] that follows. And until then, as always, [81:04] stay deep.  📄 **Full text:** ![[Full Document Contents/Articles/Am I Optimizing the Wrong Things.md|Am I Optimizing the Wrong Things? by YouTube]] ## Highlights - show. The reality that digital tools designed to make us more productive often end up instead just making us more busy. Now, if you want to know why this happened and how you can avoid it, how in other words to shift from busy to better, then you need to understand the theory of constraints. Well, today is Monday, which means it's time for an advice episode of this show. So, this is the perfect opportunity to dive deeper into the question of how this old theory ([View Highlight](https://read.readwise.io/read/01kqw2xh5vxfsm7etbce2y819y)) - Tags: 👻 ai highlighted - can solve a lot of new problems in productivity. All right, here's my plan. I've asked the number one New York Times bestselling author, David Epstein, to join me to talk about all things constraints. Why did I invite Dave? because I learned about Gold Rot and the ideas that he talks about in the goal from Dave's brand new book, which is called Inside the Box: How Constraints Make Us Better. A book that uh is coming ([View Highlight](https://read.readwise.io/read/01kqw2xh5za6smdz3pkfg7dvs3)) - Tags: 👻 ai highlighted - out tomorrow. So, you definitely need to check it out. Now, in our conversation, Dave and I focus on one chapter of his new book in particular where he introduced the goal and talks about the constraints and gives a bunch of stories about what this theory actually meant. So, I thought he'd be a great guide to understanding what's going on here. um he's going to help me in this conversation understand the underlying theory. Then we apply these ideas to our own lives as writers and almost immediately I come up with some new ideas for improving my productivity based on the foundation that we're going to lay in this conversation. So if you ([View Highlight](https://read.readwise.io/read/01kqw2xh63q4zy29tr6yamcq0r)) - Tags: 👻 ai highlighted - Deep Questions podcast. So, uh, I'm glad you're here because I'm going to, uh, first pick your brain about what you wrote in that chapter and then hopefully together we are going to apply it to help tackle this question of why do productivity technologies often end up making us feel busy but not actually like we're producing more. I assume when you wrote this chapter, you of course had me and my productivity obsessions in mind. Is that that's that's accurate? I assume it's it's really just for me. ([View Highlight](https://read.readwise.io/read/01kqw2xh65dzkgk6dgnqk1eddd)) - Tags: 👻 ai highlighted - about the bottleneck. To me, the bottleneck is my time, right? I have a certain amount of time I can spend on the podcast and I I I don't I don't want to go beyond that. And so everything when I I have two people I work with and everything is about how do I maximize the percentage of that time that is me thinking, writing or recording the thing I thought and wrote about. And so, you know, producer Jesse, who you've met, uh, the whole goal there was I don't want to touch a computer. Like, that's I shouldn't I don't want to touch a computer because that's all the things ([View Highlight](https://read.readwise.io/read/01kqw2xh697v80c8ebz4dyt7d0)) - Tags: 👻 ai highlighted - days writing the paper. And so, it's nice if you can shave some hours off of those days making the plots. That's annoying. And you made it a little bit easier. But that wasn't the bottleneck of producing papers. It's not like producing papers was we just make plots all day and if we can make that twice as fast, I'm going to produce twice as many papers. And I was like, "Oh, it's a bottleneck issue." Is the the AI tools sometimes are making non-bottleneck things faster, which is fine, but it's not necessarily going to lead in that application to, oh, our core thing we do is now happening uh better. We're ([View Highlight](https://read.readwise.io/read/01kqw2xh6cp8d1s3w6gkcn84zc)) - Tags: 👻 ai highlighted - apply this thinking to our world as writers, right? Um because it's an interesting world. I mean, I just did an interview a couple weeks ago with, you know, a writer who switched to a typewriter, >> which is the opposite of a efficiency increasing tool. It's the opposite of a productivity tool. She's producing better books. So, I want to understand what's going on here. So, let's just think about real quickly if we're think about what we do as non-fiction writers. I guess the question first is what is our bottleneck? and then I want to try applying if that's the bottleneck. How ([View Highlight](https://read.readwise.io/read/01kqw2xh6fbhca69v9bbg974x0)) - Tags: 👻 ai highlighted