David Epstein Says Early Specialization and 10,000-Hour Rule Impair Long-Term Success

The Diary Of A CEO

Broad sampling and delayed specialization maximize match quality, adaptability, and performance in complex, unpredictable environments.

AI-assisted summary of The No.1 Productivity Expert: 10,000 Hours Is A Lie! This Morning Habit Is Ruining Your Day! — original source. Check the original for full context.

Source published: 2024-09-02

Detailed notes

## Background This episode of 'The Diary Of A CEO' was published on YouTube on September 2, 2024, featuring journalist and New York Times best-selling author David Epstein. The video description highlights Epstein's work on human performance, focusing on his books 'Range' and 'The Sports Gene'. The episode explores why early specialization is often a myth, how focus and constraints drive success, and ways to optimize productivity and learning.

## TL;DR David Epstein debunks popular productivity myths, including the strict 10,000-hour rule, arguing that early broad sampling and optimal match quality ultimately lead to greater long-term success and fulfillment. He explains how early specialization can create a short-term lead that fades, whereas cross-disciplinary exploration and 'desirable difficulties' enhance cognitive flexibility. The discussion covers practical productivity strategies—such as avoiding early-morning reactive tasks—and examines how generalist 'foxes' outperform hyper-specialized 'hedgehogs' in complex, unpredictable environments. Ultimately, Epstein shows how strategic constraints and diverse cognitive experiences foster true innovation and personal growth.

## Atmosphere & style The conversation carries an intellectually curious and candid tone, blending rigorous scientific insights with relatable personal anecdotes. Host Steven Bartlett and guest David Epstein engage in an analytical yet approachable dialogue, smoothly transitioning from lighthearted debunks of cultural tropes to deep reflections on career transitions and personal vulnerabilities. The atmosphere feels thoughtfully grounded, encouraging listeners to re-evaluate their assumptions about success, talent, and life trajectories.

## Key takeaways - [00:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=0s) The 10,000-Hour Rule is a myth; deliberate practice does not guarantee mastery, and early specialization often leads to a short-term advantage that fades over time compared to broad sampling. - [05:33](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=333s) Personal fulfillment and performance are driven by match quality and personal growth trajectories rather than absolute performance or linear career paths. - [24:46](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1486s) Operating in the zone of optimal push requires maintaining a 15% to 20% error rate and creating a culture that embraces constructive failure. - [28:27](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1707s) Career hot streaks across various fields are typically preceded by periods of exploration and smaller team sizes rather than continuous exploitation. - [33:05](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1985s) Checking messages or emails first thing in the morning triggers the Zeigarnik effect and creates cognitive residue that harms daily productivity. - [42:07](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2527s) Desirable difficulties—such as active recall, spaced repetition, and interleaved practice—foster deep, flexible long-term learning despite short-term performance dips. - [47:05](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2825s) Kind environments (like chess or golf) reward early specialization, but wicked environments with changing rules demand broad experience and generalist approaches. - [54:24](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3264s) Multitasking is a myth; frequent context-switching and background distractions create cognitive friction, increase stress markers, and lower productivity. - [01:08:52](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4132s) Broad sampling across sports and domains increases match quality, reduces physical and mental burnout, and fosters flexible problem-solving abilities. - [01:17:29](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4649s) Retention issues and career dissatisfaction often stem from poor match quality rather than a lack of grit or effort. - [02:05:02](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7502s) Solving novel problems relies on analogical thinking and generating broad, diverse reference classes from different domains. - [01:30:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5418s) Strategic constraints prevent feature creep and focus creative energy toward viable, real-world execution. - [01:37:02](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5822s) In the era of AI, human value shifts from tactical execution toward broad strategic integration, machine coaching, and algorithmic guidance. - [01:43:08](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6188s) Baseline talent does not reliably predict trainability or long-term growth rate under structured training. - [01:44:58](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6298s) Serial innovators and top-performing forecasters behave like integrative generalist 'foxes' who synthesize diverse perspectives rather than hyper-specialized 'hedgehogs.' - [01:54:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6858s) Cross-functional experience across multiple departments within an industry is the single strongest predictor of executive advancement. - [01:56:23](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6983s) Over-specialization risks the Einstellung effect, where experts apply familiar, outdated procedures to novel situations despite contradictory evidence.

## Detailed walkthrough ### [00:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=0s) The 10,000-Hour Rule Myth and Long-Term Development

The conversation opens with a direct challenge to the popular culture premise that 10,000 hours of practice guarantees mastery in any field. David Epstein argues that this narrow focus misses crucial opportunities and undermines the broader mental and physical toolkit required for sustained, long-term human development. When optimization is geared entirely toward short-term gains—such as forcing children into early specialization in sports, music, or academics—it creates an initial lead that eventually leads to "fade-out." Over time, individuals with a broader base catch up and surpass those who specialized too early.

This dynamic comes down to a core psychological principle: *breadth of training predicts breadth of transfer*. Transfer is the ability to apply existing skills and knowledge to solve novel problems in unfamiliar contexts. Exposing individuals to a wide variety of challenges during early practice forces them to construct flexible, generalizable mental models rather than rigid, hyper-specific habits.

- The popular idea that 10,000 hours of practice creates automatic mastery is flawed because it neglects broad tool development. - Early specialization often creates short-term leads that vanish over time as generalists catch up. - *Breadth of training predicts breadth of transfer*—exposure to diverse problems enables better problem-solving in novel situations.

[05:33](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=333s) Fulfillment, Growth, and the Darkhorse Project

At the core of human motivation is the desire to improve. Epstein notes that people are often reacting to their personal trajectory rather than their absolute level of performance; the feeling of moving forward brings genuine fulfillment. Drawing from Harvard's *Darkhorse Project*, he explains how individuals who find high career fulfillment rarely follow a linear path. Instead, they navigate a "zigzagging" journey of trial, error, and mid-course correction.

These "dark horses" do not obsess over rigid 20-year plans. Instead, they continually assess their current skills, interests, and immediate opportunities, pivoting whenever they gain new self-knowledge. This process optimizes for **match quality**—the degree of fit between an individual's unique abilities/interests and the work they actually perform. Superior match quality dramatically improves long-term performance, personal satisfaction, and apparent persistence (grit).

- Human fulfillment relies heavily on perceived personal growth and upward trajectory rather than static status. - Harvard's *Darkhorse Project* demonstrated that fulfilled individuals typically follow non-linear, adaptive career paths. - Optimizing for **match quality** (the alignment between personal traits and daily work) drives high performance and sustained motivation.

> "The way to make the best 20-year-old, 30-year-old, 40-year-old is not the same as the way to make the best 10-year-old."

[08:06](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=486s) Becoming a Scientist of Your Own Development

To navigate personal and professional growth effectively, Epstein advocates adopting a **self-regulatory practice**. Originating from educational psychology and research on elite youth soccer players in the Netherlands, self-regulation involves taking explicit responsibility for one's own learning. Dutch researcher Marije Elferink-Gemser tracked young athletes up to world-class levels and discovered that those who broke through performance plateaus were the ones proactively questioning their training and directing their own focus.

The self-regulatory cycle consists of four continuous steps: 1. **Reflect:** Assess current strengths, weaknesses, and areas needing improvement. 2. **Plan:** Design a specific, actionable experiment to address those areas. 3. **Monitor:** Track progress using objective or subjective metrics during the experiment. 4. **Evaluate:** Analyze the outcome to determine if the experiment worked and what to adjust next.

Epstein highlights the *end-of-history illusion*—a psychological tendency for people at every stage of life to underestimate how much their personality, values, and preferences will change in the future. Because the fastest period of personality change occurs between ages 18 and 28, young adults should view themselves as works in progress and regularly run self-directed experiments rather than locking into fixed, rigid career goals.

- Dutch soccer research showed that elite athletes actively questioned their training and directed their own practice cycles. - The 4-step self-regulatory cycle: **Reflect $\rightarrow$ Plan $\rightarrow$ Monitor $\rightarrow$ Evaluate**. - The *end-of-history illusion* causes individuals to consistently underestimate their future personal evolution, especially between ages 18 and 28.

[12:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=738s) David Epstein’s Personal Journey and Purpose

Epstein shares the tragic personal story that shifted his career path from environmental science to investigative journalism. While pursuing a master's degree in geology and studying the carbon cycle in the Arctic, his 800-meter track training partner—a top-ranked runner and the son of Jamaican immigrants—suddenly dropped dead shortly after completing a race. Local media attributed the death to a simple heart attack, which made little sense for an elite young athlete.

Driven by curiosity and grief, Epstein obtained medical waivers from the family to examine his teammate's medical files. He discovered a textbook case of hypertrophic cardiomyopathy, a genetic condition that is the leading cause of sudden cardiac death in young athletes. Realizing that greater public awareness could save lives, Epstein left his academic science career and took an entry-level temp job at *Sports Illustrated*.

When an athlete dropped dead in Central Park during the 2008 U.S. Olympic Marathon Trials near the magazine's headquarters, Epstein's unique background enabled him to report and write a major cover story in just one week. Combining scientific literacy with journalism allowed him to uncover insights that traditional journalists missed, establishing his unique professional niche.

- Epstein shifted from studying the carbon cycle in the Arctic to journalism after his college track partner died suddenly after a race. - Medical records revealed the death was caused by hypertrophic cardiomyopathy, a genetic heart condition. - Leveraged a temp role at *Sports Illustrated* into a cover story during the 2008 Olympic Marathon Trials, pivoting his career toward science writing.

[14:13](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=853s) The 10,000-Hour Rule Myth and Skill Acquisition

David Epstein details his trajectory from a science writer and investigative reporter at *Sports Illustrated* into a best-selling author focused on human development. He explains that his motivation stems from uncovering popular misconceptions regarding human growth and skill acquisition. He targeted the widely accepted "10,000-Hour Rule"— popularized as the idea that 10,000 hours of deliberate practice is the sole route to world-class mastery, independent of innate talent. Epstein points out that this rule relies on a flawed concept called the *monotonic benefits assumption*, which incorrectly asserts that two individuals will progress at the exact same rate given equal units of practice.

Epstein shares data from chess research showing that while reaching International Master status takes an average of $11,053$ hours of deliberate practice, individual rates of learning vary drastically. Certain individuals reached that level in just $3,000$ hours, whereas others were tracked past $20,000$ hours without achieving it. He notes that understanding this individual variation liberates people from pursuing rigid paths and emphasizes the value of "skill stacking"—combining multiple proficient skills rather than focusing blindly on a single domain.

- **10,000-Hour Rule Origin**: Based on Anders Ericsson's 1993 study on 30 violinists at a world-class music academy. - **Monotonic Benefits Assumption**: The false premise that equal practice yields equal progress across different individuals. - **Chess Mastery Study**: Average time to International Master is $11,053$ hours, but individual extremes range from $3,000$ hours to over $20,000$ hours.

[18:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1098s) Flaws in the Original 10,000-Hour Study

Epstein dissects the methodology behind the original 1993 study on violinists, highlighting two primary statistical flaws: *restriction of range* and the omission of variance measures. By studying only students already accepted into an elite music academy, the subjects were pre-selected for high ability, artificially suppressing the true variance of skill acquisition across a general population.

To illustrate this mistake, Epstein provides a sports analytics analogy: evaluating basketball skill using only NBA centers would lead to the absurd conclusion that height does not correlate with success. In fact, within the restricted range of the NBA, height negatively correlates with scoring because shorter guards average more points. Furthermore, when Ericsson's data was later re-examined, it showed massive individual variations—some lower-practice violinists outperformed higher-practice ones. Additionally, the elite performers slept significantly more than their peers, averaging roughly 60 hours per week.

> "The original conclusion was wrong... There were actually people who were practicing less and doing better than people who practiced more."

- **Restriction of Range**: Analyzing pre-screened elite cohorts obscures true baseline predictors of success. - **The Sleep Factor**: Top-tier violinists in the study slept around 60 hours per week, suggesting rest and recovery were critical factors overlooked by the focus on practice hours alone.

[22:16](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1336s) Age, Career Trajectories, and the Founder Norm

Addressing Bartlett’s question about career progression at age 30, Epstein refutes the cultural obsession with early precocity. He notes that society tends to celebrate rare exceptions like Tiger Woods or a young Mark Zuckerberg, while ignoring standard career development norms. Epstein highlights a landmark study by researchers from MIT, Northwestern, and the U.S. Census Bureau that analyzed high-growth tech startups.

Contrary to the tech-bro stereotype of the 20-something founder in a college dorm, the study revealed that the average age of a founder of a top $0.01\%$ fastest-growing tech startup on the day of founding is 45. Moreover, a 50-year-old founder is statistically more likely to build a wildly successful high-growth company than a 30-year-old founder.

- **Average Age of High-Growth Tech Founders**: 45 years old at the time of founding (for the top 1 in 10,000 fastest-growing startups). - **Age Success Ratio**: A 50-year-old founder has a higher probability of founding a top-performing startup than a 30-year-old.

[24:46](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1486s) Overcoming the "Hammock of Competence" and Taking Risks

When discussing individual productivity and innovation, Epstein warns against falling into a "hammock of competence"—a phrase coined by economist Russ Roberts. When an individual or business reaches high levels of success, comfort creates a strong disincentive to take risks or alter established routines. To continuously improve, one must operate in a "zone of optimal push," where the risk level leads to an error or failure rate of roughly $15\%$ to $20\%$.

Epstein advocates for running low-stakes experiments, citing London Business School professor Herminia Ibarra's core finding: *"We learn who we are in practice, not in theory."* Rather than relying solely on introspection to plot career moves, individuals must test ideas empirically through action.

In response, Bartlett shares that after *The Diary Of A CEO* became the number one podcast in Europe, he deliberately hired a "Head of Failure" within his 40-person company. Her specific job role is to force teams—across production, social media, commercial, and guest booking—to increase their rate of experimentation and failure, preventing the organization from getting disrupted by younger, nimbler competitors.

- **Zone of Optimal Push**: Maintaining a $15\%$–$20\%$ failure rate during practice or innovation to maximize growth. - **Herminia Ibarra's Insight**: Identity and professional direction are discovered through external experimentation, not internal contemplation. - **Head of Failure**: Bartlett's internal role created explicitly to drive continuous experimentation and break organizational complacency.

[28:27](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1707s) The "Failure Assistant" Experiment and the Explore/Exploit Tradeoff

Bartlett outlines a new trial running across his company's departments: appointing a dedicated "failure assistant" in every team. Because employees typically prioritize daily duties over experimentation, these designated individuals drive and measure experiments, reporting back failures to the wider company so other departments can gain transferable insights. Bartlett illustrates this synergy with a recent example: his social media team discovered a TikTok tool that reveals any guest's most popular videos with a single click. When shared, the guest research team immediately adopted this button to save hours of manual scrolling.

``` ┌────────────────────────┐ │ Explore/Exploit Cycle │ └───────────┬────────────┘ │ ┌────────────────┴────────────────┐ ▼ ▼ ┌────────────────────────┐ ┌────────────────────────┐ │ EXPLORATION │ │ EXPLOITATION │ │ • Trying new styles │ ──────►│ • Drilling down on │ │ • Broad curiosity │ │ proven strategies │ │ • Small, nimble teams │ │ • Maximizing yield │ └────────────────────────┘ └────────────────────────┘ ▲ │ └─────────────────────────────────┘ (Toggles for Hot Streaks) ```

Epstein connects Bartlett’s system to the fundamental **explore/exploit tradeoff**. While "exploiting" means capitalizing on existing strengths, "exploring" requires searching for new knowledge. Most successful organizations default entirely to exploit mode once they find a winning formula—a trap epitomized by Kodak inventing and subsequently shelving the digital camera to avoid disrupting its film business. Epstein references the research of Dashun Wang at Northwestern University, who studied 26,000 career trajectories across film directors, artists, and scientists. Wang discovered that career "hot streaks" (clusters of high-impact work) are reliably preceded by periods of broad exploration, nimble experimentation, and smaller team sizes. Top performers continually toggle between exploring and exploiting rather than remaining static.

* **The HIPPO Effect:** Epstein references organizational psychologist Adam Grant's concept of the "HIPPO" (Highest Paid Person's Opinion), noting that formalizing titles like "failure assistant" signals top-level psychological safety for taking risks. * **Import/Export Business of Ideas:** Groundbreaking organizations operate an internal exchange of concepts across department silos. * **Gore-Tex Origin Story:** Founder Bill Gore observed that companies perform their most innovative work during crises when rigid disciplinary boundaries disappear and informal communication flourishes (e.g., in carpools). Epstein warns that remote and hybrid work models threaten this natural serendipity, requiring leaders to intentionally build structural channels for cross-pollination.

---

[33:05](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1985s) Curing Productivity Bottlenecks and the Zeigarnik Effect

Bartlett pivots to personal productivity, admitting that despite managing broad podcast and writing operations, he frequently succumbs to distractions—like watching YouTube videos on AI or rockets—and gets buried under multiple Monday to-do lists from his team. Epstein shares a personal lesson from managing a virtual assistant who categorized his public inbox into A/B/C/D priority tiers: realizing the low-tier lists existed made Epstein feel obligated to process them, ultimately becoming "efficient at doing things that I shouldn't be doing." He stresses that only the A-list matters for finite human beings.

``` ┌─────────────────────────────────────────────────┐ │ THE ZEIGARNIK EFFECT │ └────────────────────────┬────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────┐ │ Opening inboxes or uncompleted tasks early │ │ leaves an active "mental residue" in brain. │ └────────────────────────┬────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────┐ │ Result: Increased task switching, higher │ │ stress markers, and impaired cognitive focus. │ └─────────────────────────────────────────────────┘ ```

To optimize individual output, Epstein strongly advises against starting the morning by reading emails or messages. Opening uncompleted task channels triggers the **Zeigarnik effect**—the psychological phenomenon where unfinished tasks leave a cognitive residue in the brain, impairing focus on subsequent work.

* **Task-Switching Research:** Epstein cites studies by researchers like Gloria Mark, who track eye movements, computer activity, and heart-rate variability. Unfinished tasks lead to elevated stress responses, rapid task switching, and measurable performance drops in subsequent cognitive challenges. * **Optimal Morning Routine:** Choose the single most critical task the day before, and execute it first thing in the morning before exposing your mind to open loops or messaging channels.

---

[36:20](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2180s) Designing Real Experiments and Learning from NASA

Discussing how leaders can encourage real experimentation, Epstein clarifies that he does not "lionize failure for its own sake," but considers it an inevitable byproduct of genuine testing. He highlights Ed Hoffman, former Chief Knowledge Officer at NASA, who was brought in to establish a learning system after high-profile space disasters revealed structural communication breakdowns. Hoffman's primary diagnostic question when entering an organization is: *"What are you good at that we're not using?"*

> "You actually have to fail... like this can't be you go out for a jog and you trip on the curb or something like you have to fail something of consequence, and then your reaction to that can set a tone." — **David Epstein**

* **Leading by Example:** True culture shifts occur when leaders themselves run ambitious experiments that fail conspicuously and handle those failures constructively.

---

[38:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2280s) Cognitive Science of Retaining and Connecting Knowledge

Addressing Bartlett's frustration with forgetting actionable insights from interview subjects and books, Epstein outlines the mechanics of long-term memory retention. Memory construction relies on integrating fresh information into one's existing **semantic network**—the web of interconnected concepts inside the brain. Epstein illustrates this with a cognitive study on reading comprehension:

Epstein notes that context and existing framework dictate how effectively new information is processed and retained. When learning a new concept, halting to deliberately link it to an existing memory "vibrates the threads" of the semantic web, making the idea retrievable later.

``` ┌──────────────────────────────────────────────┐ │ EFFICIENT RETENTION TOOLKIT │ └──────────────────────┬───────────────────────┘ │ ┌──────────────────────────────┼──────────────────────────────┐ ▼ ▼ ▼ ┌───────────┐ ┌───────────┐ ┌───────────┐ │ SPACED │ │ GENERATION│ │ HYPERCOR- │ │REPETITION │ │ EFFECT │ │ RECTION │ └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ │ │ │ ▼ ▼ ▼ Re-encountering Actively forcing Confidently making information right the brain to recall a mistake primes before forgetting an answer beats the brain to retain strengthens long- passive reading or the correct fact term storage. highlighting. permanently. ```

To further solidify learning and bring knowledge into practice, Epstein recommends specific evidence-based tools:

* **Spaced Repetition:** Rather than repeating facts incessantly in a single sitting, re-exposing oneself to material at delayed intervals—just as it slips from short-term memory—dramatically deepens long-term retention. Epstein utilizes tools like Readwise to re-surface Kindle highlights at structured intervals. * **The Generation Effect:** Active recall, such as testing oneself with flashcards, forces the brain to produce an answer, proving far superior to passive highlighting or re-reading. * **The Hypercorrection Effect:** Confidently giving an incorrect answer on a quiz primes the brain's attention systems, making it far more likely to permanently retain the correct factual information once revealed.

[42:07](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2527s) Desirable Difficulties and the Power of Interleaved Practice

When trying to learn new information, David Epstein suggests making a guess before receiving the correct answer. Even if the guess is wrong and feels uncomfortable or shocking, that momentary friction creates salience and significantly improves long-term retention. This concept stems from psychological research on "desirable difficulties"—learning obstacles that temporarily slow down progress and make acquisition feel less fluent, but ultimately build far deeper understanding.

One of the most famous examples of a desirable difficulty is interleaved or mixed practice. In contrast to traditional "blocked practice"—where a student or athlete repeats the exact same skill or problem type over and over (e.g., problem type A, then B, then C)—interleaving mixes up the problem types at random. Epstein cites a study involving dozens of sixth-grade math classrooms. Students assigned to blocked practice made fast, visible progress, felt happy, and gave their teachers high ratings. However, when tested on new problems, the interleaved practice group completely outclassed them. The effect size was equivalent to taking a student from the 50th percentile and elevating them to the 80th percentile simply by shuffling the order of practice problems.

This distinction highlights the difference between "procedural knowledge"—executing a repetitive script—and "connections knowledge," which forces the learner to match an appropriate strategy to a given problem. Epstein notes that this same mechanism explains why roughly 90% of elite elite football (soccer) players grew up playing futsal on small courts rather than full-sized pitches. Futsal constantly forces players to recognize dynamic structural patterns and adapt flexible mental templates rather than relying on fixed procedural movements.

- **Desirable Difficulties**: Learning techniques that feel harder and slow down initial performance, but yield dramatically better long-term retention and adaptability. - **Interleaved Practice Effect**: Sixth-grade math students moved from the 50th to the 80th percentile merely by randomizing practice problem order instead of grouping them by type. - **Procedural vs. Connections Knowledge**: Blocked practice teaches repetitive execution; interleaved practice builds flexible mental models that identify underlying problem structures.

[45:12](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2712s) The Air Force Academy Math Study and Short-Term vs. Long-Term Learning

Epstein shares what he considers his personal favorite study in *Range*, conducted at the United States Air Force Academy. Because the academy enrolls roughly 1,000 new cadets each year who are randomly assigned to standardized math classes with identical exams, and then re-randomized in subsequent years, it serves as an ideal natural experiment. Tracking over 10,000 students over time revealed a striking and counterintuitive pattern: the professors whose students scored highest on the immediate end-of-course exam turned out to be the *worst* at preparing those students for follow-up, advanced math courses.

Conversely, professors whose students rated them lower because the coursework felt confusing or difficult produced students who dramatically overperformed in subsequent years. The teachers who produced high short-term test scores accomplished this by drilling a narrow body of procedural knowledge tailored specifically to the upcoming exam. The professors who fostered deep, lasting mathematical ability focused on a broad connection of concepts, equipping students for higher-level problem solving down the road. This findings underscores a central theme of Epstein's work: "sometimes what seems the best in the short term will undermine long-term development."

- **Sample Size**: Over 10,000 Air Force Academy cadets evaluated across randomized sequential mathematics classes. - **Short-term vs. Long-term Disconnect**: High immediate test performance often correlates with poor performance in advanced subsequent classes. - **Teaching Style**: Narrow, exam-focused instruction yields high immediate grades, whereas broad conceptual instruction yields lasting capability.

[47:05](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2825s) Kind vs. Wicked Learning Environments

Addressing the debate between specialization and breadth, Epstein reflects on his own past confusion while reading conflicting expertise research. One camp of prominent researchers consistently found that 10,000 hours of narrow practice yielded mastery, while another camp found that narrow focus led to overconfidence without real improvement—or even worsening performance. The mystery was solved after psychologist Daniel Kahneman invited Epstein to lunch following a lecture Epstein gave critiquing the 10,000-hour rule. Kahneman directed him to Robin Hogarth's research on "kind" versus "wicked" learning environments.

In "kind" learning environments—such as chess or golf—rules are clear and static, patterns repeat predictably, and feedback is immediate and accurate. In chess, a grandmaster's advantage relies heavily on recognizing recurring visual patterns; starting after age 12 drops a player's odds of reaching grandmaster status from 1 in 4 down to 1 in 1,100. By contrast, in "wicked" learning environments, patterns may be misleading or absent, rules change unpredictably, and feedback is delayed, absent, or inaccurate.

Epstein illustrates a wicked environment with a historical medical case: a 19th-century New York physician gained renown for predicting typhoid fever weeks in advance by manually feeling patients' tongues. In reality, he was spreading typhoid from patient to patient with his unwashed hands. His accurate predictions reinforced his flawed methodology, demonstrating how wicked environments can reward the wrong lessons. Anders Ericsson himself eventually acknowledged that the 10,000-hour framework only applies to domain-specific, kind environments with explicit instructions and immediate coaching feedback, missing complex domains like entrepreneurship, management, and programming.

- **Kind Environments**: Fixed rules, repeating patterns, immediate accurate feedback (e.g., chess, golf). - **Wicked Environments**: Unclear or changing rules, delayed or distorted feedback, dynamic human behavior. - **Chess Grandmaster Odds**: Starting study by age 12 yields a ~1 in 4 chance of reaching grandmaster rank; starting after age 12 drops the odds to ~1 in 1,100. - **Typhoid Doctor Case**: Illustrates how positive feedback in a wicked environment can falsely validate harmful practices.

[51:45](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3105s) Nintendo and Lateral Thinking with Withered Technology

A standout historical example of broad, creative problem-solving is Nintendo's rise into global gaming, driven by developer Gunpei Yokoi. Performing poorly on university electronics exams, Yokoi took a low-tier job maintaining playing-card machinery at a modest Kyoto shop while his peers joined top-tier tech firms in Tokyo. When Nintendo needed to innovate to survive, Yokoi recognized he could not compete on the bleeding edge of technology. Instead, he pioneered the philosophy of "lateral thinking with withered technology"—taking mature, cheap, and well-understood technologies and combining them in novel ways.

Yokoi combined existing calculator displays and credit card technology to create handheld electronic games, transitioning Nintendo from a 19th-century card shop into a toy powerhouse. His masterpiece was the original Game Boy. Released alongside color-screen competitors, the Game Boy was technically vastly inferior, featuring a decade-old processor and a screen Epstein describes as looking like "rotting alfalfa." Yet it annihilated the competition because Yokoi correctly identified what users actually valued: battery life, durability, affordability, and a vast game library made easy to develop due to mature technology.

Patents research reveals that while 20th-century breakthroughs were often driven by hyper-specialized individuals digging deep into single domains, modern Information Age innovation heavily favors generalists and multidisciplinary teams who connect disparate ideas across different technical fields.

- **Gunpei Yokoi's Philosophy**: "Lateral thinking with withered technology"—repurposing cheap, mature tech into creative consumer products. - **Game Boy Success**: Outsold superior color competitors by prioritizing affordability, battery efficiency, durability, and software variety. - **Patent Trends**: Shifted from deep domain-specific patents in the mid-20th century to cross-disciplinary, integrated patents in recent decades.

[54:24](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3264s) The High Cost of Multitasking and Attention Switching

When asked about the role of focus, Epstein clarifies that while broad career experience is beneficial, attempting to execute multiple tasks simultaneously is cognitive illusion. Humans cannot multitask; we merely toggle between tasks at high speed. Citing attention researcher Gloria Mark, Epstein explains that the human brain operates like a whiteboard: switching tasks requires erasing the whiteboard, but cognitive "residue" remains behind, hampering clarity and focus on the new task.

This constant context-switching inflicts a heavy psychological and physiological toll. Research tracking corporate employees equipped with heart rate variability monitors and activity trackers shows a direct correlation between high switching frequency, elevated stress markers, and reduced performance. In office settings, workers check email an average of 77 times a day, initiating a two-phase cognitive process each time: first deactivating the rules of the primary task, and then activating the rules of the secondary task. This relentless toggling keeps the nervous system in a state of chronic friction and fatigue.

- **Cognitive Residue**: Switching tasks leaves lingering mental residue on the brain's "whiteboard," lowering performance on the subsequent task. - **Email Frequency**: Office workers check email an average of 77 times per day, creating perpetual task-switching friction. - **Physiological Impact**: High switching frequency correlates directly with lower heart rate variability, higher immune strain, and increased felt stress.

[56:49](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3409s) Managing Distractions, Task-Switching, and Morning Habits

The transition between different tasks carries a significant cognitive switching cost, even when those tasks seem superficially similar (such as switching between different styles of writing). To protect productivity, fulfillment, and mental health, David Epstein explicitly advises against starting the workday with inherent multitasking activities—most notably checking email. Email sets a reactive, multitasking tone and leaves a persistent feeling of being unfinished.

Instead, Epstein advocates for blocking dedicated calendar time for single-task focus while factoring in a realistic buffer. Human beings routinely fall victim to the "planning fallacy"—the tendency to dramatically overestimate how much can be accomplished within a specific timeframe. Trimming the daily to-do list down to a select few priorities, or even identifying a single critical outcome that defines a successful day, allows individuals to "pay themselves first" by tackling high-value work before distractions creep in.

- **The Morning Email Trap:** Email forces cognitive switching from the moment you wake up, creating an open-loop mindset that feels perpetually incomplete. - **The Planning Fallacy:** People systematically underestimate task completion times; building deliberate buffers and shortening to-do lists prevents constant goal shortfalls. - **Environment and Solitude:** While exchanging ideas with colleagues is vital for creativity, deep concentration demands an environment stripped of extraneous stimuli.

---

[57:56](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3476s) The Hidden Cognitive Cost of Background Noise and Music

While music can boost mood, elevate affect, and provide an energizing or calming boost, it simultaneously taxes working memory and cognitive capacity. Because the human brain naturally processes external auditory inputs, listening to music while performing complex tasks creates an underlying distraction. Studies evaluating performance on cognitive tasks show a clear measurable drop when music is playing versus when it is off.

``` +------------------------------------+---------------------------------------------------+ | Audio Condition | Cognitive & Operational Impact | +------------------------------------+---------------------------------------------------+ | Novel / Unfamiliar Music | High distraction; brain actively tracks lyrics/melodic changes | | Highly Familiar / Repetitive Music | Lower cognitive load; acts as passive background noise | | Decibel Increase (70 dB to 80 dB) | Up to a 15% reduction in overall cognitive productivity | +------------------------------------+---------------------------------------------------+ ```

The impact of noise is further amplified by volume. Because the decibel scale is logarithmic, minor numerical increases represent exponential jumps in sound intensity. Moving from approximately 70 decibels (equivalent to a running washing machine) to 80 decibels (a vacuum cleaner) can induce a **15% drop in cognitive productivity**. Focus remains an evolutionary challenge: human brains evolved to stay alert to novel environmental stimuli, a survival mechanism that directly conflicts with modern demands for sustained, quiet concentration.

- **Deliberate Audio Use:** Music should be used intentionally—such as playing energizing tracks to restore waning motivation—rather than left running continuously in the background. - **Instrumental vs. Lyrical:** Tracks with spoken or sung lyrics actively compete with linguistic processing (reading, writing, researching), causing the brain to rapidly oscillate between the text and the song. Focus-oriented instrumental audio with minimal tonal changes minimizes this interference. - **The Limits of Working Memory:** When juggling dense networks of complex ideas, eliminating all extraneous audio provides maximum mental bandwidth.

---

[01:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=78s) Work Intervals and the "Distractometer" Mechanism

Sustained concentration is best managed in structured intervals. Alternating between intense periods of deep focus and brief recovery phases allows the mind to rest, incubate ideas, and refresh. Activities such as taking a short walk or taking a shower frequently spark breakthroughs by giving the subconscious room to process problems.

When attempting to eliminate digital distractions, individuals often encounter an internal hurdle described by researcher Dr. Gloria Mark: the internal "distractometer." When someone accustomed to frequent digital interruptions suddenly blocks all external notifications, their brain compensates by generating internal distractions.

> "You will start self-interrupting to maintain the Cadence of interruptions to which you have become accustomed... as if we have some internal like distractometer."

- **Self-Interruption Lag:** Turning off notifications does not instantly yield perfect focus; the urge to check email or address random thoughts will temporarily persist until new habits solidify. - **Cognitive Outsourcing:** Keeping a notepad nearby allows individuals to quickly record intrusive thoughts or forgotten tasks, offloading them from working memory without derailing the task at hand. - **Phone Proximity Deficit:** Research demonstrates that simply having a smartphone visible on a desk—even if switched off—degrades performance on cognitive tests, particularly for individuals who are highly phone-dependent.

---

[01:04:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3840s) Phone Proximity, Sleep Hygiene, and Lifestyle Efficiency

The pervasive presence of smartphones severely compromises sleep hygiene. Cuddling or checking phones immediately before sleeping leaves psychological "residue" that impairs the transition into restful sleep. Epstein notes that his own habits shifted dramatically after becoming a parent. Prior to having his son, Epstein was a night owl who wrote late into the early morning hours. Managing a young child's early morning routine forced him to overhaul his schedule and sleep environment.

- **Epstein’s Sleep Protocol:** During intense writing periods, Epstein places his phone on an entirely different floor of his house set to airplane mode. - **Optimizing Sleep Conditions:** Ensuring the bedroom is dark, quiet, cool, and free of electronics prevents lingering work thoughts from disrupting sleep onset. - **Non-Work Reading:** Transitioning away from work-related reading material before bed prevents active problem-solving during rest periods.

---

[01:05:26](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3926s) The Early Specialization Myth: Tiger Woods vs. Roger Federer

Parents and culture at large frequently revere early specialization, viewing immediate, hyper-focused training in early childhood as the primary pathway to world-class mastery. Host Steven Bartlett reflects on his own impulse as a Manchester United fan to have a future child kicking a football at two months old. However, Epstein’s research highlights that early specialization is statistically the exception rather than the rule across elite performance.

Tiger Woods was introduced to golf in infancy, appeared on national television showcasing his swing at age two, and declared his intention to be the greatest golfer in the world by age three. Conversely, Roger Federer engaged in a wide array of sports throughout his youth—including basketball, badminton, soccer, handball, skateboarding, rugby, swimming, and wrestling. Federer’s mother, a tennis coach, declined to coach him directly because he wouldn't return balls normally, and when local coaches tried to move him up to play with older boys, he refused because he preferred hanging out with his peers after practice.

- **The "CDs" Anecdote:** When a young Roger Federer gave his first local newspaper interview, the reporter asked what he would buy with his first pro paycheck. The reporter mistakenly published that he wanted a "Mercedes," horrifying his mother—until she listened to the tape and realized he had said "M CDs" in Swiss German (he just wanted music CDs). - **The "Human Cat Video" Effect:** Author Malcolm Gladwell jokingly characterized early-prodigy footage as human cat videos—virtually impossible not to share, yet misleading when extrapolated as a universal model for human learning. - **Kind vs. Wicked Environments:** Golf represents an unusually "kind" learning environment with static conditions and clear, repetitive feedback, making it an inaccurate benchmark for dynamic human pursuits.

---

[01:08:52](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4132s) Broad Sampling: Lessons from Serena Williams and Vasiliy Lomachenko

Society frequently misinterprets the developmental arcs of world-class performers, assuming they all followed early single-track specialization. Epstein recalls presenting his research on sampling periods—where elites try diverse activities before narrowing their focus—to an audience that included tennis legend Serena Williams sitting in the second row.

Rather than disputing the findings, Serena raised her hand and validated Epstein's thesis. She revealed that her father, Richard Williams, had insisted on a broad physical foundation, enrolling her in ballet, track and field, gymnastics, and Taekwondo, and teaching her to throw a football to develop the overhand movement pattern for her serve. Furthermore, he periodically pulled her off the intensive youth tennis circuit to preserve her academic focus and prevent burnout.

Similarly, combat sports showcase the power of cross-disciplinary training. Ukrainian world-champion boxer Vasiliy Lomachenko—renowned for his footwork and spatial awareness—was famously pulled out of boxing lessons by his father at a young age and enrolled in traditional Ukrainian dance classes for years before returning to the ring.

- **Sampling Period Benefits:** Diverse physical and mental experiences build adaptable skill sets, increase self-knowledge regarding personal strengths, and guard against early physical and mental burnout. - **Narrative Bias:** Media coverage heavily favors dramatic prodigy narratives like Tiger Woods while overlooking the broader, generalist foundations that underpin the success of icons like Roger Federer, Serena Williams, and Vasiliy Lomachenko.

[01:10:28](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4228s) Skills, Futsal, and the Power of Early Diversity

The conversation opens with a look at world-class athletes, specifically Ukrainian boxer Vasiliy Lomachenko, whose father, Anatoly, famously pulled him out of boxing for several years during his youth so he could take Ukrainian dance classes. Epstein notes that this kind of radical cross-training gave Lomachenko an unusually broad skill stack, eventually resulting in an amateur record of 396 wins and only one loss (which he later avenged). This unconventional trajectory illustrates three core reasons why delayed specialization pays off in sports:

* **Match Quality:** Early forced selection—especially pre-puberty—often puts the wrong person in the wrong spot due to "premature optimization." Epstein highlights the "relative age effect," where kids born earlier in their birth cohort dominate youth national teams simply because coaches mistake early biological maturation for talent. * **Injury Prevention:** Adult-style overuse injuries in children are heavily linked to playing a single sport for nine or ten months out of the year; diversifying physical movement acts as a protective shield. * **Skill Learning Advantage:** Much like growing up bilingual creates a slight initial delay but leads to vastly superior language acquisition skills down the road, trying multiple sports builds a broader motor vocabulary.

Epstein explains that this isn't just about switching jerseys—it’s about variability in problem-solving. A prime example is **futsal** (*futebol de salão*), the small-sided, heavy-ball game played on hard surfaces that produced many of South America and Europe's finest football prodigies.

> "Kids will be playing on cobblestones one day and concrete the next... and the touches are about six times as frequent as in full-scale football, so it engenders a lot more of this variability."

---

[01:14:27](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4467s) Outliers, Media Myths, and the Quest for Passion

The host points out that society tends to amplify extreme, sensationalized narratives rather than the realistic norm. The public obsesses over either toddler prodigies like Tiger Woods or extreme late-bloomers like Anthony Joshua (who didn't pick up boxing until his late teens/early twenties). Middle-of-the-road success stories—where someone gets broad early exposure and steadily ramps up—get ignored because they lack a catchy, simple prescription.

This leads into a major challenge faced by young people today: the desperate hunt for their "one true passion." Epstein dismantles the myth that passion is a singular, hidden Easter egg waiting to be found. Just like believing there is only one soulmate on Earth for a person, assuming you have only one passion limits your growth. In reality, the more experiences you try, the more interests you unlock.

Epstein recounts visiting the Pentagon to look at a U.S. Army pilot initiative called **Talent-Based Branching**. Instead of forcing cadets down a rigid "up-or-out" ladder, the Army assigned coaches and had cadets briefly dabble in five different career tracks while recording explicit written reflections in an online portal. The results were striking:

* **90% of Army cadets** changed their initial career preferences after just a small amount of hands-on dabbling. * Retention of high-potential talent dramatically improved once participants found a career track that actually matched their abilities and interests.

---

[01:17:29](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4649s) Re-evaluating "Grit" and the Army's Retention Crisis

Epstein connects the Army's talent initiative back to famous psychological research conducted by Angela Duckworth at West Point. Her 12-question "Grit Scale"—which measures consistency of interests and persistence of effort—successfully predicted which cadets would survive "Beast Barracks" (the grueling orientation program) and make it to graduation.

However, since the mid-1990s, nearly half of West Point graduates have been opting to resign the moment their required five-year active duty commitment ends. While military leaders initially blamed a "millennial grit problem," military researchers quickly realized the core issue wasn't a lack of grit—it was a **match quality problem**:

> "When you move into this knowledge, creativity, information economy... young people are learning things about themselves in their early 20s, and they have no agency over career switching to match it, so they were just quitting."

The military initially threw over half a billion dollars in taxpayer money at retention bonuses, but it failed to stop the exit; people who wanted to leave left anyway. This highlights a critical truth: you learn who you are, what you like, and what you excel at through *practice*, not theoretical introspection. Young people cannot figure out their passion by sitting around pondering—they have to launch real-world experiments.

---

[01:21:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4860s) Engagement, Curiosity, and Experimentation in the AI Era

Reframing the goal from "finding happiness" or "finding passion," Epstein argues that individuals should instead search for activities that are deeply **engaging**. Engagement isn't always comfortable—writing books or running an 800-meter race can be excruciating in the moment—but it creates a compelling drive to learn and improve. It sparks an active, relentless curiosity that pushes someone out of passive "motion-going."

Epstein shares a personal anecdote about helping his wife build a lifelong fitness habit. Dropping her off at a gym didn't work because she lacked his decades of acquired gym literacy. Instead, they experimented together with various activities—running, lifting, different cardio formats—until she stumbled upon a specific group class that sparked genuine excitement. Suddenly, she couldn't stop talking about it or showing him the moves she learned; her curiosity had been ignited.

Ultimately, continuous trial and error is mandatory. With AI rapidly altering career landscapes, the ability to experiment, pivot, and transfer skills will be far more vital than picking a single static path for life.

---

[01:23:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4980s) Divergent Thinking and Neurodivergence

The host brings up a video titled *"Why Divergent Thinkers Beat Geniuses in the Real World,"* admitting he initially assumed it was about **neurodivergence** (such as ADHD or autism, the former of which the host was diagnosed with around age 30). Epstein clarifies that while his work primarily uses "divergent thinking" in the psychological sense—exploring many possible solutions rather than converging on a single standard one—there are valuable intersections with neurodivergence. Combining diverse cognitive styles and alternative ways of processing information creates a richer environment for complex problem-solving.

[01:23:46](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5026s) Evolution, Diversity, and Mindset in the Workplace

David Epstein explores how evolutionary traits that once benefited nomadic populations can present challenges in modern environments. In ancient nomadic tribes, genetic markers associated with novelty-seeking and high attentiveness to varied environmental stimuli were essential for survival. However, as populations settled into agrarian and industrialized lifestyles, these same traits—often diagnosed today as ADHD—began to be selected against or viewed as maladaptive. Epstein argues that forcing someone with these evolutionary traits to sit still at a desk for ten hours a day creates an artificial conflict, whereas forward-thinking organizations recognize these neurodivergent thinking styles as critical assets rather than deficits.

``` +-----------------------------------+ | EVOLUTIONARY TRAIT DIVERSITY | +-----------------------------------+ | +-----------------------+-----------------------+ | | v v [ Nomadic Environment ] [ Modern Workplace ] • High stimulus sensitivity • Requires varied perspectives • Constant movement & vigilance • Cross-domain problem solving • Rapid adaptation to change • Broad analogy mapping | | +-----------------------+-----------------------+ | v +-----------------------------------+ | STRATEGIC TEAM DIVERSITY | +-----------------------------------+ ```

This evolutionary perspective underpins a compelling case for cognitive diversity in the workplace. When companies deliberately integrate employees who perceive and process information differently, they build teams capable of tackling unpredictable challenges. Rather than demanding strict conformity, organizations thrive when they position varied cognitive styles where they are naturally adaptive.

* **Evolutionary Selection:** Genetic traits tied to novelty-seeking and ADHD were advantageous for ancestral nomads but are often pathologized in modern, static classroom or office settings. * **Corporate Adaptation:** Innovative companies actively hire neurodivergent individuals (such as adults on the autism spectrum) to leverage specialized pattern recognition and unconventional problem-solving styles.

---

[01:25:02](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5102s) The Duncker Radiation Problem and Analogical Thinking

To demonstrate the power of cognitive diversity and broad mental models, Epstein presents a classic cognitive test known as the Duncker Radiation Problem. He asks the host to act as a doctor faced with a patient who has an inoperable stomach tumor. A high-intensity ray can destroy the tumor, but it will also destroy the surrounding healthy tissue. A low-intensity ray will not harm healthy tissue, but it is too weak to destroy the tumor. The host is asked how to save the patient.

``` Ray 1 (Low Intensity) \ \ Ray 2 (Low Intensity) ----> [ TUMOR ] <---- Ray 3 (Low Intensity) / (FOCAL / POINT) Ray 4 (Low Intensity) ```

While the host ponders the solution, Epstein shares two seemingly unrelated stories:

1. **The General and the Fortress:** A general must capture a fortress located at the center of several converging roads. The roads are mined, preventing a large force from marching down a single path without heavy casualties. The general divides his army into smaller groups, sends them down different roads simultaneously, and has them converge on the fortress at once. 2. **The Village Fire:** A small-town fire chief arrives at a blaze near a lake where residents are inefficiently dumping individual pails of water. She orders everyone to surround the fire in a circle with filled buckets and throw their water simultaneously on the count of three, successfully extinguishing the flames.

Epstein explains that the solution to the medical dilemma is to direct multiple low-intensity rays from different angles so they converge at the tumor site, creating high intensity at the target without destroying surrounding healthy tissue.

> "When you're facing a novel problem, the number of solutions and the chance of coming up with a good solution are predicted by the number and breadth of analogies that your group can come up with."

* **The Reference Class Concept:** When confronting an unfamiliar problem, successful groups construct a "reference class"—a collection of structurally similar problems drawn from completely different domains—to discover non-obvious solutions. * **Homogeneity vs. Diversity:** A team composed entirely of experts with identical backgrounds performs little better than a single individual because they draw from the same limited pool of analogies. * **The Creative Cliff Illusion:** People often believe their best ideas occur immediately in a flash of insight. Psychological research proves that highly creative and effective solutions emerge later in the brainstorming process as a wider array of analogies is explored.

---

[01:27:45](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5265s) Practical Problem-Solving: The Studio Microphone Dilemma

The host connects Epstein’s research on analogical thinking to a real-world dilemma faced by his production crew just the day before. For the first time in the podcast's history, the desk lacked visible table-mounted microphones. The host explains that listeners frequently complained about guests tapping or thumping the table, which resonated through the standard microphone arms.

To solve this, the team—comprising an audio engineer, a video director, and the host—sat down to brainstorm using their respective backgrounds:

* **Audio Engineer (Will):** Focused on technical acoustic isolation and overhead suspension systems. * **Video Director (Jack):** Analyzed visual aesthetics and sightlines to ensure overhead equipment stayed out of frame. * **Host (Steven Bartlett):** Drew on his experience as a dragon on *Dragons' Den*, where performers wear chest-mounted lavalier microphones concealed under clothing.

``` +-----------------------------------+ | THE MICROPHONE PROBLEM | | (Table thumping creates noise) | +-----------------------------------+ | +-------------------------+-------------------------+ | | | v v v [ Audio Perspective ] [ TV Show Perspective ] [ Studio Perspective ] Overhead boom setups Chest-bound lavaliers Suspended spatial mics | | | +-------------------------+-------------------------+ | v +-----------------------------------+ | OPTIMAL SOLUTION ADOPTION | | (Overhead spatial arrangement) | +-----------------------------------+ ```

While the host initially proposed taping microphones directly to guests' chests, the team evaluated the trade-offs: guests might brush against their clothing, disrupting the signal, and affixing equipment directly to guests could feel intrusive. Ultimately, combining these distinct perspectives allowed the team to land on an overhead boom configuration that isolated physical table vibration while preserving audio clarity and video presentation.

---

[01:30:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5418s) General Magic and the Power of Strategic Constraints

Transitioning to his ongoing book research on focus and constraints, Epstein shares the history of General Magic—a legendary Silicon Valley startup spun out of Apple in the early 1990s. The team included core designers of the original Macintosh, and their vision was to build a handheld personal communicator that closely resembled the modern smartphone.

> "They had no boundaries. They had as much money as they wanted. They didn't have any customer in mind... and so the project just grew and grew and grew and grew and never found the focus to turn into anything usable."

Despite holding a concept IPO and assembling top-tier engineering talent, General Magic collapsed under its own lack of boundaries:

``` +-----------------------------------+ | GENERAL MAGIC (1990s) | +-----------------------------------+ | +-----------------------+-----------------------+ | | v v [ Unlimited Resources ] [ Unconstrained Vision ] • Endless capital & talent • Feature creep & scope bloat • No defined target user • No launch deadlines | | +-----------------------+-----------------------+ | v +-----------------------------------+ | COMMERCIAL FAILURE & LESSON | | (Focus requires constraints) | +-----------------------------------+ ```

From this failure, General Magic alumni learned that constraints are essential for execution. Envisioning a specific customer profile—even an incorrect one—creates a testable hypothesis and an active feedback loop. Without strict boundaries, teams fall victim to feature creep and produce bloated products that fail in the market.

Epstein notes how this insight shaped his own writing process. Historically, he wrote 15% more material than required, shoving in every interesting tangent before cutting it down. For his current project, he established an architectural outline upfront and forced himself to write chapters in chronological order for the first time in his twenty-year career.

---

[01:34:05](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5645s) Navigating AI, Knowledge Retrieval, and Technological Disruption

When asked about the threat artificial intelligence poses to authors and knowledge workers, Epstein expresses enthusiasm rather than anxiety. As an active subscriber to multiple AI systems, he uses tools like **Scite.ai** to streamline literature reviews.

``` +-----------------------------------+ | SCITE.AI RESEARCH WORKFLOW | +-----------------------------------+ | +-----------------------+-----------------------+ | | v v [ Target Paper Input ] [ Citation Indexing ] • Maps related literature • Classifies supporting citations • Extracts exact text snippets • Flags contradicting studies | | +-----------------------+-----------------------+ | v +-----------------------------------+ | RESEARCH TIME REDUCTION | | (8 Hours -> 1 Hour) | +-----------------------------------+ ```

Instead of manually spending hours in academic libraries scanning bibliographies, he uses AI to extract exact text snippets, map citation networks, and categorize studies by whether they support or refute a given hypothesis. This reduces tasks that once took an entire day down to less than an hour.

To place AI-driven disruption in historical context, Epstein discusses the introduction of the Automated Teller Machine (ATM) in the United States around 1970:

``` [ ATM Deployment (1970s+) ] ──> Reduced cost per bank branch │ ▼ [ Rapid Branch Expansion ] ──> More total branches operated │ ▼ [ Shift in Teller Role ] ──> Shifted from cash counting to: • Financial advisory services • Customer relationship management • Strategic product marketing ```

* **The ATM Paradox:** Media reports in the early 1970s predicted that 300,000 bank tellers would lose their jobs overnight. Over the next 40 years, the total number of bank tellers actually *increased*. * **Operational Shift:** By lowering the operating cost of individual bank branches, ATMs allowed financial institutions to open significantly more branches. * **Role Transformation:** The nature of the teller job shifted away from routine cash transactions toward higher-level, strategic functions such as customer service, relationship management, and financial consulting.

Epstein concludes that AI will similarly automate routine, "kind" learning environment tasks, freeing human professionals—including radiologists and data analysts—to focus on broad strategic integration, complex decision-making, and nuanced human interaction.

[01:37:02](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5822s) AI, Human Strategy, and the Freestyle Chess Paradigm

David Epstein addresses the evolving role of AI, noting that while artificial intelligence excels at handling high-volume tactical tasks, humans must shift toward strategic direction. Drawing an analogy to healthcare, Epstein points out that most medical interactions are not high-drama diagnostic puzzles like *House*, but rather standard cases. Delegating routine, tactical labor—like basic diagnostic image reviews—to AI allows human professionals to focus on the strategic, human-centered elements of care: asking what a diagnosis means for a patient's life and determining what lifestyle adjustments are feasible.

To illustrate how automation reshapes expertise, Epstein details the aftermath of IBM’s Deep Blue defeating Garry Kasparov in 1997. Recognizing that the computer possessed superior tactical memory, Kasparov pioneered "freestyle chess" tournaments where humans and computers could team up. Surprisingly, the winners were neither supercomputers nor grandmasters, but two amateur chess players operating three standard laptops. They won because they understood algorithmic search and knew how to effectively "coach" the machines where to look. When tactical execution is outsourced, human value elevates to strategic guidance—defining goals and steering the machine's focus.

- **Deep Blue vs. Kasparov (1997):** Kasparov realized the supercomputer outperformed him on tactics (memorized move patterns) but lagged in high-level strategy (arranging battles to win the war). - **Freestyle Chess Outcome:** Two amateur players with three laptops defeated grandmasters and supercomputers by excelling at machine coaching and algorithmic search guidance. - **The Core AI Lesson:** Outsourcing tactical work alters who excels at a task and forces human focus upward into strategy.

---

[01:38:58](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5938s) The Campfire Debate, Comparative Advantage, and Historical Disruption

When asked how his outlook might be wrong, Epstein acknowledges the possibility that AI could eventually surpass humans at strategy as well. However, humans will still retain the ultimate strategic role of determining what world they want to build. To highlight the uncertainty surrounding AI's trajectory, Epstein recalls sitting around a campfire with two tech experts—a founder of a generative AI company and his early investor. Despite their aligned financial incentives and shared technical expertise, one argued that Artificial General Intelligence (AGI) was guaranteed within three years, while the other dismissed current AI models as a "glorified toy" and admitted he still preferred Google.

Even in a hypothetical scenario where an AI outperforms humans across every metric, Epstein argues that the economic principle of **comparative advantage** will ensure human relevance. AI models are exceptionally energy-intensive; because energy resources are finite, societies will reserve energy-dense computing for high-value problems while delegating other tasks to humans. Epstein emphasizes that while human labor will persist, technological revolutions historically bring severe social disruption. Drawing on historical data, he notes that during the Industrial Revolution, a roughly 40-year gap occurred between massive productivity gains and actual wage increases for workers. Closing such gaps today is challenging because modern knowledge workers operate in a dispersed "invisible factory," making collective action difficult.

- **Campfire AI Debate:** Two industry insiders held wildly opposing views—one expecting AGI within three years, the other viewing generative AI as a toy compared to Google. - **Economic Comparative Advantage:** Due to the extreme energy requirements of advanced AI models, humans will remain essential for tasks where computational energy expenditure isn't cost-effective. - **Historical Disruption Data:** The Industrial Revolution saw a **40-year lag** between productivity spikes and wage growth for everyday workers. - **The "Invisible Factory" Challenge:** Unlike urbanized 19th-century factory workers who could easily organize, modern remote knowledge workers are geographically fragmented, complicating collective problem-solving.

---

[01:43:08](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6188s) Baseline Talent vs. Trainability

Turning to core concepts from his book *The Sports Gene*, Epstein presents what he considers its most critical yet overlooked insight: a person's baseline talent (their un-trained starting point) does not reliably predict their **trainability** (how rapidly and extensively they improve with practice). Just as individuals respond differently to the same medical prescription, two people subjected to the exact same training regimen will yield vastly different rates of growth. Epstein asserts that trainability is a distinct form of talent that is often far more consequential than initial performance.

Steven Bartlett immediately connects this insight to recruitment, observing that employers often make the mistake of evaluating candidates solely on their present skill level rather than their long-term growth trajectory. Epstein agrees, explaining that while baseline skill matters if a task must be executed immediately, long-term success hinges on trainability.

- **Definition of Trainability:** The biological and cognitive capacity to respond to and improve from structured practice over time. - **The Medical Analogy:** Training functions like medicine—individual physiological and cognitive differences mean identical inputs yield vastly different outputs. - **Predictive Fallacy:** Judging a person's ultimate potential based strictly on baseline, untrained performance is scientifically flawed.

---

[01:44:58](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6298s) Measuring Trainability and the Profile of "Serial Innovators"

Bartlett asks whether trainability can be measured during hiring, sharing his own practice of examining LinkedIn resumes to see how quickly candidates advance through different departments. Epstein notes that while physical trainability (such as $\text{VO}_2$ max or aerobic capacity) is easy to measure in lab settings, measuring cognitive trainability in business requires assessing a candidate's broader trajectory. Epstein links this to research by Professor Abby Griffin on "serial innovators"—individuals who consistently drive creative breakthroughs across various roles and organizations.

Epstein outlines the traits Griffin identified in serial innovators, warning that rigid job descriptions often inadvertently weed these high-potential individuals out. Rather than filling immediate "square holes with square pegs," forward-thinking organizations reserve hiring capacity for people who possess broad systems-thinking skills and self-directed curiosity, knowing that specific functional skills can be taught internally. To illustrate this, Epstein cites the Scottish investment firm Baillie Gifford, which historically avoided hiring MBAs in favor of individuals with unique domain expertise or diverse cognitive backgrounds, choosing to teach them finance in-house.

- **Traits of Serial Innovators (Abby Griffin's Research):** * Systems thinkers who read broadly outside their core discipline. * A persistent need to learn and communicate outside their home domain. * A tendency to "flit" between ideas and repurpose existing solutions in novel ways. - **Corporate Risk:** Narrowly defined job specifications select against serial innovators or force them out of organizations. - **Baillie Gifford Case Study:** The investment firm prioritized hiring candidates with un-teachable broader perspectives and analytical styles, opting to coach them on financial mechanics after hiring.

Here is a detailed, faithful walkthrough based on the transcript provided:

[01:50:13](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6613s) The Forecasting Tournament: Hedgehogs vs. Foxes

David Epstein describes a massive forecasting study involving 83,000 specific probability predictions on complex geopolitical, social, and technological events. To separate skill from raw luck, participants had to make precise, time-bound predictions—for example, evaluating whether there would be a 20% chance of a military confrontation causing at least five casualties in the South China Sea within 12 months. The results revealed a stark divide: the worst forecasters were the narrowly specialized "hedgehogs" who knew "one big thing" and viewed the entire world through a single, rigid mental model. By contrast, the best forecasters were the "foxes" who knew many little things, collected diverse perspectives, actively used social media, and relied on others to attempt to falsify their own hypotheses.

When these "foxes" were grouped together, their performance improved even further as they borrowed heavily from the scientific method to test their assumptions. Remarkably, these everyday individuals entered a tournament against the U.S. intelligence community—who possessed access to classified information—and beat them handily. The researcher leading the project described these elite forecasters as having "dragonfly eyes." Much like a dragonfly's eye, which synthesizes thousands of individual lenses into a single image, these forecasters gathered disparate viewpoints. Because they were circumspect and open to nuance, they made for terrible TV soundbites compared to loud, dogmatic pundits, but their predictive accuracy was unmatched.

- **Total predictions evaluated:** 83,000 specific probability forecasts with strict deadlines. - **Hedgehogs vs. Foxes concept:** Adapted from Philip Tetlock's superforecasting research, contrasting single-lens specialists with integrative generalists. - **Key finding:** Groups of generalist "foxes" without classified clearance outperformed U.S. intelligence analysts who had access to classified data.

[01:52:49](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6769s) Combating Cognitive Decline with "Openness to Experience"

Reflecting on these findings, the host observes that it is easy to get complacent in one's identity and knowledge, even though pushing into unknown territory goes against natural human inclinations. Epstein agrees, comparing human aging to the old adage about dogs: it isn't that an old dog *can't* learn new tricks, but rather that it no longer sees the point. In psychology, "openness to experience"—the Big Five personality trait most predictive of creativity—reliably declines as people reach middle age.

However, Epstein highlights a study showing that this decline is not inevitable. When older adults were forced to engage in novel tasks (such as learning Sudoku or trying an unfamiliar skill), their openness to experience improved, even if they never became highly competent at the new task. Engaging in novel, challenging activities acts as great exercise for brain health. Because human memory organizes time into narrative "chapters," trying new things creates fresh mental chapters, preventing life from feeling as though it is passing too quickly.

- **Personality trait:** Openness to experience is the Big Five factor most strongly tied to creative output. - **Cognitive benefit:** Forcing oneself to attempt unfamiliar skills stems the middle-age decline in openness and enhances brain health regardless of mastery. - **Perception of time:** New experiences create distinct memory "chapters," making life feel richer and less accelerated.

[01:54:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6858s) Career Mobility and the LinkedIn Study

The host references a LinkedIn research study covering roughly half a million members to explore what actually predicts corporate advancement. Epstein confirms that the single strongest predictor of who would eventually become an executive within a specific industry was the number of different job functions they had worked across. Epstein shares that when he discussed these findings directly with LinkedIn, he pointed out a subtle irony: the platform's standard linear profile format might actually discourage users from making lateral moves, because it lacks a space to narrate *why* a career switch occurred and what was learned from it.

Epstein clarifies that while changing industries offered certain lower-level benefits, the path to executive leadership primarily rewarded lateral breadth—moving across different functional departments within the *same* industry. Examples of such lateral moves include transitioning from an individual performer to a team manager, or in journalism, shifting from a pure writer to an editor.

- **Data pool:** A study analyzing approximately 500,000 LinkedIn user profiles. - **Top executive predictor:** Cross-functional work experience across multiple departments within a single industry. - **Product suggestion:** Epstein suggested LinkedIn allow users to add narrative context to explain the career growth behind non-linear job transitions.

[01:56:23](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6983s) The Hidden Dangers of Specialization: The Einstellung Effect

Moving into the risks of over-specialization, Epstein highlights two alarming Harvard-led medical studies. While specialization in medicine is undeniably vital, it can become a double-edged sword. The studies revealed that high-risk cardiac patients admitted to teaching hospitals on the exact dates of a national cardiology convention—when the most senior, esteemed specialists were away—actually had a *lower* mortality rate than those admitted when the top specialists were present.

This counterintuitive outcome is driven by what psychologists call the *Einstellung effect*: a cognitive bias where experts rely so heavily on a familiar, repeatedly practiced procedure that they continue applying it even when it is no longer optimal or when new data advises against it. To further illustrate the subtle factors influencing performance, Epstein cites a study analyzing 980,000 surgical procedures, which found that patients operated on during a surgeon's birthday suffered a statistically significant higher 30-day mortality rate, likely due to subtle internal or external distractions.

- **Cardiology convention study:** High-risk cardiac patients had lower mortality rates when elite specialists were away at conventions. - **Einstellung effect:** The tendency to solve problems using a familiar, habitual method, ignoring better or updated alternatives. - **Surgeon birthday study:** Sampled 980,000 procedures, finding elevated 30-day post-op patient mortality on surgeons' birthdays due to distraction. - **Personal mindset shift:** Epstein shares how his own beliefs evolved; as an environmental science grad student, he believed technological progress and nature preservation were strictly opposed, whereas today he views technological innovation as essential to saving the environment.

[02:00:01](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7201s) The Final Podcast Question: Facing Personal Fears

Following the show's closing tradition, the host opens a notebook containing a question left by the previous guest. Epstein laughs at how nervous guests become when the book is produced. The host reads the question: "What is your biggest fear, and how do you plan to face it?"

Epstein pauses to reflect deeply. He confesses that he has a persistent impulse to reset things—to burn down existing setups and start completely over from scratch, even when things are going well. While this trait serves him well in his writing career—such as abandoning his identity as "the Sports Gene guy" immediately after publishing to pursue entirely new topics—it has historically plagued his personal relationships. He candidly shares his regret over burning down important friendships because he felt an unhealthy urge that if a relationship wasn't perfect, it had to be destroyed.

To face this fear and curb the destructive impulse, Epstein is actively working on cultivating "narrative values"—objective, universally recognized virtues like heroism or loyalty that an individual deliberately chooses to adopt. Specifically, he is focusing on *forgiveness*. Acknowledging that forgiveness does not come naturally to him, he frames it as his ongoing personal project: learning to accept good things without destroying them and becoming a more forgiving friend and partner.

- **Personal vulnerability:** Epstein admits to a destructive habit of burning down healthy personal relationships out of an "intoxicating" desire to constantly restart and self-improve. - **Narrative values:** Core philosophical virtues across cultures that individuals actively choose to weave into their personal identity. - **Personal focus:** Epstein identifies *forgiveness* as the specific narrative value he is striving to master to face his fear of relationship self-sabotage.

[02:03:52](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7432s) Challenging Mainstream Narratives for Optimal Living

Steven Bartlett expresses profound appreciation for David Epstein's rigorous work and willingness to go the extra mile as an author. He notes that Epstein consistently presents original, challenging, and unconventional approaches to problem-solving. By dismantling false narratives that many people blindly accept as truth, Epstein prevents those misconceptions from causing short- or long-term disservice. Bartlett emphasizes that while debunking popular myths surely gives Epstein a headache, it ultimately serves a higher purpose: enabling people to live more optimized, fulfilled, happy, and productive lives, regardless of how they define success.

The conversation concludes on a warm and admiring note as Bartlett highlights Epstein's upcoming project on constraints, predicting that it will be just as groundbreaking as *Range* and *The Sports Gene*. Touched by the heartfelt gratitude, Epstein receives the compliment with simple warmth, remarking that it was a "wonderful compliment" to which he didn't even want to add anything.

* Bartlett praises Epstein’s ability to counter false, widely accepted life narratives to help people lead better lives. * Epstein is currently writing a new book focused on the topic of constraints. * "I'm so excited to read whatever you make next... if it's anything like these two books *Range* and *The Sports Gene*, it's going to be one of the most important books I've ever read."

---

[02:04:54](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7494s) Sponsor Message: PerfectTed

In the closing segment of the episode, host Steven Bartlett shares a promotional endorsement for PerfectTed, a green energy drink brand in which he is an investor. He describes how the company has taken the nation by storm, exploding with an impressive growth rate of nearly 10,000% in a short period and securing retail distribution in major supermarkets like Tesco and Waitrose. Bartlett attributes this rapid rise to consumers growing tired of traditional, unhealthy energy drinks and seeking a cleaner alternative.

Bartlett notes that PerfectTed is his drink of choice while recording long podcast sessions because it offers sustained mental focus without the dreaded energy crashes during four- to six-hour conversations. To wrap up the episode, he shares an exclusive, limited-time online offer for listeners of *The Diary Of A CEO*.

* **Growth Rate:** PerfectTed has grown by almost 10,000% in a short timeframe. * **Retail Availability:** Found in major outlets like Tesco and Waitrose. * **Special Discount:** Listeners can get 40% off online for a limited time using the checkout code `DIARY40`.

## Notable quotes > [05:33](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=333s) The way to make the best 20-year-old, 30-year-old, 40-year-old is not the same as the way to make the best 10-year-old. — David Epstein

> [18:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1098s) The original conclusion was wrong... There were actually people who were practicing less and doing better than people who practiced more. — David Epstein

> [24:46](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1486s) We learn who we are in practice, not in theory. — Herminia Ibarra

> [36:20](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2180s) You actually have to fail... like this can't be you go out for a jog and you trip on the curb or something like you have to fail something of consequence, and then your reaction to that can set a tone. — David Epstein

> [01:18:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4680s) You will start self-interrupting to maintain the Cadence of interruptions to which you have become accustomed... as if we have some internal like distractometer. — Dr. Gloria Mark

> [01:10:28](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4228s) Kids will be playing on cobblestones one day and concrete the next... and the touches are about six times as frequent as in full-scale football, so it engenders a lot more of this variability. — David Epstein

> [01:17:29](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4649s) When you move into this knowledge, creativity, information economy... young people are learning things about themselves in their early 20s, and they have no agency over career switching to match it, so they were just quitting. — David Epstein

> [01:25:02](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5102s) When you're facing a novel problem, the number of solutions and the chance of coming up with a good solution are predicted by the number and breadth of analogies that your group can come up with. — David Epstein

> [01:30:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5418s) They had no boundaries. They had as much money as they wanted. They didn't have any customer in mind... and so the project just grew and grew and grew and grew and never found the focus to turn into anything usable. — David Epstein

> [02:00:01](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7201s) What is your biggest fear, and how do you plan to face it? — Steven Bartlett

> [02:03:52](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7432s) I'm so excited to read whatever you make next... if it's anything like these two books Range and The Sports Gene, it's going to be one of the most important books I've ever read. — Steven Bartlett

## Mentioned David Epstein, Steven Bartlett, K. Anders Ericsson, Marije Elferink-Gemser, Darkhorse Project, Sports Illustrated, MIT, Northwestern University, U.S. Census Bureau, Russ Roberts, Herminia Ibarra, The Diary Of A CEO, Adam Grant, Kodak, Bill Gore, Gore-Tex, Dashun Wang, Gloria Mark, Ed Hoffman, NASA, Daniel Kahneman, Robin Hogarth, Gunpei Yokoi, Nintendo, Game Boy, Malcolm Gladwell, Tiger Woods, Roger Federer, Serena Williams, Richard Williams, Vasiliy Lomachenko, Anatoly Lomachenko, Anthony Joshua, Angela Duckworth, General Magic, Apple, Scite.ai, West Point, U.S. Army, Dragons' Den, IBM, Deep Blue, Garry Kasparov, Google, The Sports Gene, Abby Griffin, Baillie Gifford, Philip Tetlock, U.S. intelligence community, LinkedIn, Harvard, Range, PerfectTed, Tesco, Waitrose

[Source](https://www.youtube.com/watch?v=MsERVcx0Qg8)

中文简报

David Epstein：过早专业化与一万小时定律有损长期成功广泛涉猎与推迟专业化能在复杂且不可预测的环境中最大化匹配度、适应力与表现。 David Epstein 破除了包括严格的“一万小时定律”在内的流行生产力神话，指出早期的广泛涉猎与最优匹配度最终会带来更大的长期成功与成就感。他解释了过早专业化为何只会带来逐渐消退的短期领先优势，而跨学科探索与“必要难度”则能提升认知灵活性。讨论还涵盖了实用的生产力策略——例如避免清晨处理被动应对型任务——并探讨了在复杂且不可预测的环境中，通才型“狐狸”如何胜过高度专业化的“刺猬”。最终，Epstein 阐明了战略性约束与多样化的认知体验如何促进真正的创新与个人成长。 “一万小时定律”被证实存在误区，刻意练习并不能保证成为大师；早期过度专业化带来的微弱短期优势往往会随时间消退，而广泛涉猎与多领域探索则能培养更强的知识迁移与解决未知问题的能力。 (0:00) 个人成就感与长期表现主要取决于个人特质与工作的“匹配质量”（match quality）以及个人的成长轨迹，而非绝对绩效或僵化的线性职业规划。 (5:33) 要想处于“最佳推进区间”实现高效成长，需在实践中维持 15% 到 20% 的错误率，并在组织与个人层面建立拥抱建设性失败的试错文化。 (24:46) 早晨醒来第一件事就查看邮件或消息会触发蔡加尼克效应（Zeigarnik effect），在大脑中留下未完成任务的认知残留，从而增加任务切换损耗并破坏全天的工作效率。 (33:05) 国际象棋或高尔夫等规则固定的“良性环境”适合早期专业化训练，但在规则易变且反馈滞后的“恶劣环境”中，跨学科通才与交替练习等“合意困难”更能构建持久的竞争力。 (47:05)

Reading summary

Author David Epstein challenges the cultural dogma of early specialization and the 10,000-hour rule, arguing that broad sampling, delayed focus, and adaptive experimentation produce superior long-term performance and fulfillment across domains. The Flaws of the 10,000-Hour Mastery Paradigm (14:13) Epstein disputes the popular belief that 10,000 hours of deliberate practice automatically secures mastery, noting that the underlying research relies on a flawed monotonic benefits assumption—the incorrect claim that all individuals advance at an identical pace per unit of practice. Citing chess research, Epstein observes that while players take an average of 11,053 hours of practice to reach International Master status, actual times range dramatically from 3,000 hours to over 20,000 hours without attaining it. Furthermore, baseline talent fails to predict trainability, which represents an independent biological and cognitive capacity to benefit from instruction. The validity of narrow practice depends heavily on whether an environment is "kind" or "wicked," drawing on research by Robin Hogarth. Kind environments, such as golf or chess, feature repeating patterns, stable rules, and rapid, accurate feedback. In wicked environments, rules shift, patterns mislead, and feedback is delayed or counterproductive. While hyper-specialization provides immediate advantages in kind settings, Epstein warns it can induce the "Einstellung effect"—a cognitive trap where experts default to habitual solutions despite new contradictory evidence, as seen in studies where high-risk cardiac patients experienced lower mortality when top specialists were away at conferences. Match Quality and Non-Linear Career Trajectories (5:33) Rather than following rigid multi-decade plans, Epstein reports that fulfilled, high-performing individuals often navigate adaptive, "zigzagging" trajectories that optimize for match quality—the alignment between personal traits and daily work. Data from Harvard's Darkhorse Project indicates that people sustain effort and grit primarily when they perceive an upward personal trajectory. Similarly, a study by MIT, Northwestern, and the U.S. Census Bureau revealed that the average age of a founder behind the top 0.01% fastest-growing tech startups is 45, with 50-year-olds statistically outperforming 30-year-olds. Sampling periods across multiple disciplines also protect against burnout and premature optimization. In an initiative at West Point and the Pentagon called Talent-Based Branching, 90% of Army cadets shifted their career preferences after brief rotational dabbling in five different areas. Epstein highlights that West Point's retention struggles stemmed from poor match quality rather than a lack of grit: cadets learned about themselves in their early 20s but lacked the agency to switch roles. In corporate settings, an analysis of 500,000 LinkedIn profiles confirmed that cross-functional work experience across different departments within an industry was the strongest single predictor of reaching executive rank. Desirable Difficulties and Deep Knowledge Retention (42:07) Lasting competence frequently conflicts with fast, visible performance gains. Epstein highlights "desirable difficulties," such as interleaved practice, where problem types are mixed rather than drilled repeatedly in blocks. In a study of sixth-grade math classes, students using interleaved practice initially felt less confident but subsequently jumped from the 50th to the 80th percentile on novel tests. Similarly, a natural experiment involving over 10,000 randomized cadets at the U.S. Air Force Academy found that professors whose students scored highest on immediate course exams were the worst at preparing those cadets for advanced follow-on courses because they drilled procedural knowledge instead of deep conceptual connections. Analogical thinking and cognitive breadth also dictate problem-solving flexibility. Geopolitical forecasting tournaments evaluating 83,000 predictions showed that broadly informed "foxes"—who aggregated disparate information and actively tested assumptions—consistently outperformed narrow "hedgehogs" as well as intelligence analysts with classified access. Generating conceptual breadth acts like a flexible semantic network, allowing individuals to transfer established lessons to novel challenges. Distraction, Cognitive Load, and Strategic Constraints (33:05) Protecting daily output requires mitigating cognitive switching costs. Epstein strongly warns against opening emails or messaging channels first thing in the morning, which triggers the Zeigarnik effect—the psychological residue left by uncompleted tasks that fragments attention. Concentration is also physical: ambient noise between 70 and 80 decibels can cause up to a 15% drop in cognitive productivity, while the mere presence of a visible smartphone on a desk measurably impairs working memory. When external interruptions cease, individuals often battle an internal "distractometer," as conceptualized by Dr. Gloria Mark, leading them to self-interrupt until new habits form. To balance productivity, Epstein recommends operating within an optimal push zone that maintains a 15% to 20% failure rate, alongside explicit project constraints. Highlighting the 1990s startup General Magic, Epstein explains that unlimited funding, talent, and unconstrained scope caused the personal communicator project to collapse under feature bloat. Imposing strict boundaries and prioritizing single-task focus forces clarity and prevents organizations and individuals from settling into comfortable yet stagnant routines. While Epstein advocates for breadth, cross-training, and continuous experimentation, questions remain regarding how modern corporate systems can structurally accommodate non-linear career paths without penalizing workers during early, exploratory phases. Furthermore, as artificial intelligence automates procedural tasks, the precise point where tactical automation ends and high-level human strategy begins continues to be tested across industries.

精炼阅读摘要

作家兼调查记者 David Epstein 提出，“一万小时定律”与过早专业化严重误解了人类发展与技能习得的本质；长期的成就与幸福感并非来自机械式的重复训练，而是取决于广泛涉猎带来的可迁移能力与个人与工作的“匹配质量”（match quality）。一万小时定律的误区与早期涉猎的价值 (0:00) Epstein 指出，由 Anders Ericsson 1993 年针对小提琴手的研究所衍生出的一万小时定律，建立在“单调效益假设”这一错误前提上，即假设所有人以相同训练时长获得相同进展。然而国际象棋研究显示，晋升国际大师的平均时间为 11,053 小时，但个体差异极大，有人仅需 3,000 小时，有人超过 20,000 小时仍未达到。早期的狭隘专业化固然能带来短期领先，但往往会导致随后的“消退效应”，而更具广泛基础的学习者最终会迎头赶上并超越专门化者。这是因为“训练的广度决定了迁移的广度”，涉猎多种问题能够帮助人们建立更具弹性的思维模型。在体育与学习领域，这种优势同样显著。与 Tiger Woods 极早专精高尔夫这种规则固定、反馈即时的“友善”环境不同，大多数现实世界属于规则多变、反馈延迟的“恶劣”环境。Roger Federer 早期参与多种球类运动，乌克兰拳击世界冠军 Vasiliy Lomachenko 在童年被父亲要求暂停拳击去学传统舞蹈，均证明了跨领域运动技能积累的保护作用与发展优势。早期涉猎不仅降低了儿童因单一运动造成的劳损性伤害，还规避了因过早生物学发育成熟而误判潜力的“相对年龄效应”，为长期发展奠定了更宽阔的动作与认知词汇。匹配质量与动态试错驱动的职业发展 (5:33) 人类的满足感主要来源于感知到的个人成长轨迹，而非绝对的静态成就。Epstein 引用哈佛大学“黑马项目”（Darkhorse Project）的研究指出，高职业满足感的人群通常不遵循僵化的 20 年规划，而是经历曲折的试错过程，以寻找自身独特能力与具体工作之间的“匹配质量”。更优的匹配质量能带来更强的高绩效、持久毅力与工作满意度。美国陆军在西点军校的一项人才分支试点项目显示，当学员被允许在 5 条职业路径中进行短期涉猎并记录反思后，90% 的学员改变了最初的职业偏好，从而显著改善了留存问题；这表明军校毕业生服役期满后的流失并非出于缺乏毅力，而是严重的匹配失衡。文化常过度宣扬大学刚毕业就成功的极少数年轻创业者，但来自麻省理工学院、西北大学和美国人口普查局的数据显示，排名前 0.01% 的高成长科技初创企业创始人，在创立公司当天的平均年龄为 45 岁，且 50 岁创始人的成功概率在统计上高于 30 岁创始人。在企业内部，一项针对 50 万 LinkedIn 用户的研究同样表明，预测某人最终能否晋升为行业高管的最强指标，是其在同一行业内部跨越不同职能部门的工作经验数量。正如伦敦商学院教授 Herminia Ibarra 所指出的，“我们在实践中发现自我，而不是在理论中”，个人的职业方向必须通过真实的低成本行动实验来探索。适度困难与促进深度掌握的认知机制 (42:07) 在认知吸收与技能学习方面，直观感受上的顺畅往往会破坏长期记忆。Epstein 指出，在学习新内容时引入“适度困难”（desirable difficulties）能显著提升留存率。例如，六五年级数学课堂研究表明，打乱问题顺序的“交错练习”在测试中使学生的表现从相当于第 50 百分位数提升到第 80 百分位数，尽管他们在日常练习中感觉比进行传统重复性“成组练习”的学生更为吃力。美国空军学院对随机分配的 10,000 多名学员的长期数学成绩追踪同样揭示了这一现象：在期末即时考试中带出最高分的教授，其学生在后续进阶课程中的表现反而是最差的，因为这些教师仅训练了应对特定考试的狭隘程序化知识；而那些初期学生评分较低、注重深层概念联系的教授，则培养出了长期解题能力更优的学生。在记忆建构中，利用主动生成效应、间隔重复以及在获得正确答案前进行主动猜测（超纠正效应），能让新信息有效接入大脑原有的语义网络中。管理认知干扰与战胜能力吊床的实验机制 (24:46) 个人与组织的持续提升必须警惕经济学家 Russ Roberts 所称的“能力吊床”——在取得高水平成功后，舒适感会阻碍创新与风险承担。Epstein 提出应将练习或创新保持在 15% 到 20% 失败率的“最佳推进区”。在个人日常效率方面，早晨第一件事就查看邮件会触发蔡格尼克效应（Zeigarnik effect），未完成任务留下的认知残留会导致任务频繁切换、压力指标上升并损害专注力。此外，Gloria Mark 博士提出的“分心仪”（distractometer）机制显示，突然屏蔽外界通知时，大脑会产生自发性打断，手机即使处于关机状态，只要留在视线内也会损害认知表现。环境中的噪音也会对认知造成隐性消耗。由于分贝呈对数增长，环境音量从 70 分贝增加到 80 分贝会导致认知生产力下降高达 15%。针对这一系列挑战，Epstein 建议设立无电子设备的睡眠环境、推迟清晨查看通讯渠道、采用纸笔记录外包侵入性想法，并在组织中建立有意的容错机制。如 Steven Bartlett 在其 40 人的播客团队中设立“失败负责人”与“失败助理”，以确保团队在探索（寻找新路径）与利用（最大化现有收益）之间维持健康的平衡。跨领域类比思维、专业化陷阱与人机协作前景当过度局限在单一视角时，专业化会演变为思维定势。哈佛大学相关的医学研究发现，在国家心脏病学年会期间（顶级专家离开教学医院时），入住的高风险心脏病患者死亡率反而低于顶级专家在场时，这被归因于“定势效应”（Einstellung effect），即资深专家极易固守以往熟悉的程序化疗法，即使该方案已非最优。在预测领域的一项涉及 83,000 个具体概率预测的研究中，深谙许多小事并吸收多元视角的“狐狸型”预测者，其准确度大幅击败了固守单一僵化模型的“刺猬型”专家，甚至超越了拥有涉密情报的美国情报界人员。面对人工智能的普及，Epstein 认为人类的价值在于战略方向与宏观架构，而非低层次的技术操作。正如 1997 年 Deep Blue 击败 Garry Kasparov 后出现的“自由式国际象棋”，夺冠者并非超级电脑或特级大师，而是善于指导机器搜索范围的业余棋手。横向思维与战略限制同样是突破的关键，任天堂开发者横井军平正是通过“枯竭技术的横向思考”，结合成熟廉价的技术开发出 Game Boy 并击败性能更强的竞争对手，证明了无限资源与缺乏约束往往会导致项目失控。该讨论反思了将短期指标、单一通道和即时顺畅等同于长期卓越的文化惯性，但如何在缺乏明确制度支持的现实环境中承担 15%–20% 的试错风险，以及各类组织在多大程度上愿意打破线性的职能架构以容纳“跨界通才”，仍是实践中面临的结构性挑战。

Detailed notes — zh

## Background 该内容由 YouTube 知名播客频道 The Diary Of A CEO（主持人为 Steven Bartlett）于 2024 年 9 月 2 日发布。视频对话了《成长的边界》（Range）与《运动基因》（The Sports Gene）的作者、纽约时报畅销书作家大卫·爱泼斯坦（David Epstein），深入剖析了关于成功、生产力、一万小时定律以及人类发展的科学真相与认知误区。 ## TL;DR 爱泼斯坦通过严谨的实证科学系统性打破了“一万小时定律”和早期专精化的神话，指出为了短期表现过度优化往往会摧毁长期潜力。研究表明，探索广度决定了迁移能力，顶尖创业者与创新者多为曲折前行的“大器晚成者”，而“匹配度”（Match Quality）才是激发持久坚毅与成就感的基石。在个人生产力层面，晨间查阅邮件造成的蔡加尼克效应与数字通知的自我打扰机制会严重侵蚀专注力，推行“先付给自己”的整块时间规划与低成本试错实验更为关键。面对现代复杂多变的“恶劣学习环境”与 AI 的冲击，具备跨界重组能力的连环创新者与具备蜻蜓眼视角的“狐狸型”通才，将远比狭隘的单领域专才拥有更大的战略优势与抗脆弱性。 ## Atmosphere & style 对谈散发着深邃而充满智性张力的对撞氛围，Steven Bartlett 保持着极度敏锐且毫无防备的探求欲，频繁抛出自己在管理企业与个人专注力上的真实挣扎；而 David Epstein 则如同一位手握海量论文的严谨拆局者，用温和却颠覆性的科研数据逐一瓦解大众习以为常的心智模型。对话节奏舒展而富有层次，既有横跨体育科学、航空航天到认知心理学的宏大视野，又在结尾关于“自我毁灭冲动”与“宽恕”的日记追问中骤然收拢为极具脆弱感的情感自省。整场录制给人一种在深夜剥离迷思、直面真实自我与长期主义力量的沉浸感与解脱感。 ## Bulletin - [18:27](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1107s) 大众熟知的“一万小时定律”源于统计学上的样本受限偏差，最新大数据证实全美前0.01%高增长初创企业创始人的平均年龄为45岁，而非年少成名。 - [33:57](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2037s) 早晨首选查阅邮件会触发蔡加尼克效应与认知残留，研究表明环境噪音从70分贝升至80分贝即可导致认知生产力断崖式下滑15%。 - [46:31](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2791s) 刻意练习仅适用于规则固定的“善意环境”，面对现代复杂未知的“恶劣环境”，基于多元经验的类比思维与技能叠加才是突破瓶颈的核心竞争力。 - [01:55:34](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6934s) 顶级心脏病专家外出开会期间留守患者死亡率反而下降，印证了过度专业化所诱发的“定势效应”会机械套用惯性疗法，进而带来系统性风险。 ## Key takeaways - [02:30](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=150s) 训练广度决定迁移广度：早期过度专精虽然在短期内建立微弱优势，但长期来看容易触发“淡化效应”，而早期涉猎广泛的通才在后期具备更强的综合爆发力与解决陌生问题的迁移能力。 - [14:34](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=874s) 拆解一万小时定律：奠基性研究仅选取了顶尖音乐学院中被高度预选的30名小提琴手，忽略了极大的个体差异，国际象棋实证显示拿到国际大师所需练习从3,000小时到20,000小时不等。 - [22:12](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1332s) 年少成名是认知偏差：麻省理工学院等机构联合研究显示，高增长科技初创公司创始人的平均年龄为45岁，50岁创业成功率高于30岁，绝大多数成功者都是非线性的“大器晚成者”。 - [25:41](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1541s) 最佳挑战区间与试错容忍：个体与组织要在巅峰期持续成长，必须承担明智风险并设计低成本实验；若尝试中的失败率低于15%到20%，说明挑战不足、未能最大化突破上限。 - [29:34](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1774s) 探索与利用权衡（Explore/Exploit）：顶尖人才的高光产出期前往往经历过小团队灵活探索的跨界阶段；设立“首席失败官”等机制有助于打破HIPPO效应，促进组织内的思想进出口贸易。 - [33:57](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2037s) 晨间禁开收件箱：未完成的任务会在大脑中产生蔡加尼克效应与“认知残留”，显著增加全天任务切换频率与生理压力，正确做法是在晨间专注攻克最重要的单一目标。 - [42:07](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2527s) 必要难度与交错练习：比起短期机械重复的阻塞式刷题，被打乱顺序的交错练习虽然短期进度慢且痛苦，却能强迫大脑识别底层问题结构，形成高留存、强迁移的心理模板。 - [46:31](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2791s) 善意环境与恶劣环境的光谱：高尔夫和国际象棋等规则固定、反馈即时的温和领域适合狭窄的刻意练习；而规则易变、反馈滞后的现代工作与决策环境则要求宽广的多元工具箱。 - [51:48](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3108s) 枯竭技术的水平思考：任天堂前传奇设计师横井军平利用计算器与信用卡等过时成熟零件打造 Game & Watch 与 Game Boy，证明将成熟技术跨界重组往往优于死磕前沿硬核技术。 - [01:01:33](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3693s) 多任务处理是伪命题：大脑无法真正并行工作，所谓的多任务实质是高昂的认知切换；若平时习惯高频打扰，大脑在关闭通知后甚至会触发“自我打扰”来维持原有的受扰节奏。 - [01:10:03](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4203s) 延迟专业化的三重红利：儿童推迟过早专项训练不仅不会落后，反而能提高兴趣与天赋的匹配质量、避免因单一运动引发慢性劳损，并通过复合动作库培养出更灵活的应对变通性。 - [01:15:53](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4553s) 破除唯一真爱神话：盲目追求抽象的“热情”不如在具体行动中寻找“极度投入感”；五角大楼的试验表明，90%的学员在涉猎不同职业路径后主动重置了职业偏好，大幅提升了人才留存率。 - [01:25:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5100s) 类比思维与参考类构建：解决陌生难题（如邓克尔辐射问题）的能力直接取决于团队能调用类比的广度，异质背景的团队能有效打破思维定势并跨越“创意悬崖错觉”。 - [01:30:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5418s) 约束是创新的边界锚点：硅谷 General Magic 汇聚顶尖天才且资金充沛，却因彻底缺乏边界和明确客户导致无序膨胀覆灭；设定哪怕不完美的约束条件，才能建立有效的反馈回路。 - [01:37:47](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5867s) AI时代的人机协同与战略分工：战术模式识别与海量运算被AI接管后，人类的核心价值在于战略调度、人机协作，以及最顶层的战略目标定义——“我们究竟希望生活在怎样的世界”。 - [01:43:08](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6188s) 可训练性优于基线才能：未经训练的初始表现无法准确预测未来上限，具备系统性思考、跨界沟通驱动力及理念快速迁移特征的“连环创新者”，才是推动组织突破的稀缺资产。 - [01:49:45](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6585s) 狐狸型思维完胜刺猬型专家：20年预测学追踪研究证实，拥有跨领域碎屑知识、秉持科学证伪态度的“狐狸”型人才，其长期预测准确率远超死守单一理论框架的各领域“刺猬”权威。 - [01:55:34](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6934s) 过度专业化的致命盲区：哈佛医学研究显示顶级专家离开医院时部分患者死亡率反降、外科医生生日当天手术死亡率上升，暴露出长期深耕单一领域所引发的“定势效应”与认知分心隐患。 ## Detailed walkthrough ### [00:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=0s) 专才神话与破除认知误区节目开门见山地抛出了广为流传的“一万小时定律”：人们常被告知只要在任何领域投入一万小时，就能成为顶尖大师。然而，大卫·爱泼斯坦（David Epstein）直截了当地指出这一观点存在严重缺陷。过度执着于早期专门化训练，反而会剥夺个体构建长远发展所必需的“广泛工具箱”，导致错失更多机会。作为《纽约时报》畅销书作者，爱泼斯坦的研究长期挑战关于专业化、生产力以及成功法则的传统认知。在快问快答式的预告中，爱泼斯坦勾勒了多个颠覆常理的研究发现： - **自我调节实践（Self-regulated practice）**：成为自己成长的科学家，形成“反思—规划—监控—评估”的闭环。 - **最佳成长区间（Zone of optimal push）**：在任何练习中，若失败率未达到 15% 到 20%，说明挑战不足，无法实现最大化提升。 - **注意力与邮件陷阱**：绝不要以查收邮件开启一天，研究表明这对全天专注力有巨大损害。 - **通知诱发的自我打扰**：习惯了频繁被打扰的大脑，即使关闭所有手机通知，也会主动产生“自我打扰（self-interrupting）”来维持原有的刺激频率。 - **专精化的隐秘代价**：哈佛大学的一项研究发现，当某些顶尖心脏病专家离岗去开学术年会时，留在医院里的特定心脏病患者死亡率反而降低了。 ```mermaid flowchart LR accTitle: 传统专精化与通用发展模式对比 accDescr: 对比早期过度专门化带来的衰减效应与广泛探索带来的灵活迁移能力 subgraph EarlySpec["早期过度专门化（传统路径）"] direction TB A1["过早专一锁定单一领域"] --> B1["短期成绩领先"] B1 --> C1["能力淡化效应（Fade-Out） 缺乏灵活迁移能力"] end subgraph BroadBase["广泛探索模式（爱泼斯坦模型）"] direction TB A2["多样化探索与宽广基础"] --> B2["短期进展可能较慢"] B2 --> C2["高匹配度（Match Quality） 强迁移能力与长期超越"] end ``` ### [02:30](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=150s) 纠正科学研究的误读与短期优化的陷阱谈及自己工作的根本初衷，爱泼斯坦表示，他最痴迷的事情就是去纠偏大众对人类发展科学研究的“误译”。这项工作是为所有对自我提升抱有好奇心，却没有时间或科研背景去翻阅海量原始学术证据的普通人而做的。他的研究早期聚焦于体育竞技领域的动作技能习得，后来逐步扩展到更广泛的职业生涯和个人成长领域。爱泼斯坦指出，贯穿他多年研究的核心洞见之一是：**为了短期发展进行极度优化，往往会摧毁长期发展的根基**。无论是在体育、音乐还是学术启蒙中，直觉上最吸引人的策略似乎是“赢在起跑线”——尽早选定目标、毫不动摇、排除其他一切干扰并进行大量刻意练习。这种模式确实能让孩子或初学者在早期建立起显著优势，但心理学与教育学中记录了一种被称为“淡化效应（Fade-Out）”的现象：随着时间推移，那些早期建立起广泛知识基础、涉猎多门类的人群会逐步赶上并实现超越，使得早期专精者的优势显得“淡出消失”了。 - 心理学核心定律：**训练的广度决定了迁移的广度（Breadth of training predicts breadth of transfer）**。 - 迁移能力（Transfer）指的是将掌握的知识和技能灵活运用到此前从未见过的全新复杂问题中的能力。 - 只有在成长初期接触过更丰富多样的问题类型，大脑才能被迫构建出可泛化、具弹性的底层认知模型。 ### [05:33](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=333s) 成长轨迹、掌控感与“黑马项目” 当被问及人们在生活中追求的核心到底是什么时，爱泼斯坦认为不仅仅是外在的成功，更是“变得更好（getting better）”的过程本身。研究证实，人们的成就感与幸福感，很大程度上取决于对自己当下成长轨迹（Trajectory）的感知，而不仅仅取决于绝对的表现水平。感受自己在精进、打破瓶颈，能够赋予人持续的满足感。试图在 10 岁或 12 岁就达到巅峰的培养方式，绝不适用于培养出最优秀的 20 岁、30 岁或 40 岁的成年人。在探讨职业满足感时，爱泼斯坦重点提到了哈佛大学开展的“黑马项目（The Dark Horse Project）”。该研究追踪了一批在各自领域获得高度成就与内在充实感的人士： - **曲折前行的常态**：多数受访者在初期都认为自己是个“异类”，常对研究员说：“我起初学医或做别的事，中途发现不适合，转向新方向后又发掘出意料之外的潜质，千万别让别人学我这种野路子。”然而数据表明，这种非线性的“Z字形”转折才是现代社会获得长久成就与幸福的普遍常态。 - **匹配度（Match Quality）**：经济学家用该词形容个人的兴趣、天赋与所从事工作之间的契合程度。高匹配度不仅直接决定了一个人的表现上限和职业满足感，也是个体在面对困难时表现出真正“坚毅（Grit）”的前提。 - **告别过度长期规划**：社会文化往往过度推崇五年、十年的刻板长期目标，但成熟的践行者更倾向于基于当下的自我认知（当下的技能、兴趣和眼前涌现的机会）进行阶段性尝试，并在反馈中动态调整航向。 ### [08:32](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=512s) 自我调节学习：打破瓶颈的四步循环设定宏大的长期目标往往无法直接指导当下的行动。David Epstein 以自己曾作为 800 米竞速跑者的亲身经历举例：如果在比赛开始前只设定一个最终完赛时间目标，这对跑步过程中的动作没有任何帮助，跑完看到记分牌时要么开心要么沮丧；但若设定更具体的战术实验——比如“在剩下 300 米时开始发力加速”，就能为自己提供一个切实可行的即时实验。因此，短期规划与建立“自我调节学习”（Self-regulatory learning）机制是持续进步的关键。自我调节学习本质上是“对自身思维的再思考”（元认知），并对自己的学习进程全权负责。荷兰学者 Riet Elferink-Gemser 在青少年足球领域开展过一项长期追踪研究，从 12 岁起跟踪记录年轻球员，其中部分人后来一路踢进了世界杯亚军球队。研究发现，虽然登上顶峰有严格的生理硬性门槛（例如冲刺速度若达不到每秒 7 米就绝无可能跻身职业顶尖），但真正能打破成绩瓶颈期、不断突破上限的球员具有鲜明的自主学习特征。调取他们年轻时的录像会发现，这些孩子会主动找教练发问：“为什么我们要练这个项目？我觉得这项技术我已经掌握了，我真正需要提升的是另一项能力。”即便有时教练会不耐烦地命令他们“回队伍里排队去”，但这些孩子始终在主动思考自己的优劣势与改进方向。 ```mermaid flowchart LR accTitle: 自我调节学习循环 accDescr: 包含反思、规划、监测与评估四个阶段的闭环学习流程。 subgraph Cycle["自我调节学习模型 (Self-Regulatory Cycle)"] direction LR A["1. 反思 (Reflect) 审视优劣势与需求"] --> B["2. 规划 (Plan) 设计具体测试实验"] B --> C["3. 监测 (Monitor) 客观或主观追踪指标"] C --> D["4. 评估 (Evaluate) 总结效果并指导下一步"] D --> A end ``` 这种自我调节机制构成了持续进步的闭环： - **反思（Reflect）**：审视自己擅长与不擅长什么、到底需要提升什么、应该通过何种方式达成。 - **规划（Plan）**：构思一套切实可行的实验方案，用来专项攻克薄弱环节。 - **监测（Monitor）**：建立客观数据或主观体会的度量指标，全程监控执行状况。 - **评估（Evaluate）**：检验刚刚运行的实验是否奏效，并根据结果规划下一步动作。职场中的“黑马”同样践行着这套逻辑：他们不盲从既定轨道，而是不断反思现状、设计适合自身特质的实验、追踪反馈，并快速迭代下一阶段的职业选择。 --- ### [10:47](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=647s) 历史终结错觉与做自己成长的“科学家” 当年轻人探讨如何在 20 岁出头的职业起步期落实这套方法论时，必须正视心理学上著名的“历史终结错觉”（End of History Illusion）。这一心理学现象表明：人类在人生的每一个阶段，都会严重低估自己未来可能发生的改变——无论是擅长与不擅长的领域、时间分配的方式，还是挑选朋友的偏好与价值观。人们终其一生都在经历心智与个性的蜕变（尽管增速会随年龄增长逐渐放缓），但却总觉得自己“已经定型了”。在 18 岁至 28 岁之间，一个人的性格转变速度达到一生中的峰值。荒谬的是，社会偏偏在这个时期不断对年轻人施压，要求他们“必须想清楚这辈子要干什么”。正是在这种剧烈变化的时期，将自我调节学习融入日常日记尤为关键。成为“自己发展的科学家”，需要反复在日记中向自己抛出几个基础却尖锐的问题： - 我目前试图达成的目标是什么？为什么要达成它？ - 要实现该目标，我需要掌握哪些新知识或新技能？ - 谁能在这个过程中指导我？我怎样才能确保得到对方的帮助？ - 我可以设计怎样的实验来小试牛刀？ - 实验结束后，我该如何评估结果并挑选下一个实验？科学研究表明，人类仅靠被动经历日常事务，并不能自然而然地将经验内化为深层能力。唯有通过显性、主动的实验与复盘，才能从经历中汲取最大的学习价值。 --- ### [12:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=738s) 跨界缘起：从北极科考到《体育画报》的非典型调查记者 David Epstein 谈及自己职业定位的转变，起点是一场意外的个人悲剧。他早年原本在接受环境科学家的学术训练，曾长期待在北极的帐篷里研究地球的碳循环。与此同时，他也是一名高水平的 800 米田径运动员。当时他有一位实力顶尖的训练搭档，在全美同年龄段 800 米选手中名列前茅，是牙买加移民家庭中首位即将大学毕业的骄子，却在一次比赛刚冲过终点几步后突然猝死。当地媒体草草报道其死于“心脏病发作”，这让 Epstein 感到不可思议——对于一个年轻且处于身体巅峰状态的精英运动员，这种解释毫无科学说服力。Epstein 顶住内心的不安与顾虑，恳请好友的家属签署授权豁免书，调取了全部病历记录。结果证实，那是一例教科书式的单基因突变所致的心脏疾病，而这种隐性遗传病正是绝大多数年轻运动员运动猝死的元凶。Epstein 意识到提高公众认知可以挽救更多生命，于是萌生了将体育与科学研究相融合的念头，立志要在陪伴自己长大的《体育画报》（Sports Illustrated）上发表关于运动员心源性猝死的重磅调查。拿到硕士学位后，Epstein 毅然退出了纯科学的学术晋升轨道。起初他只能以临时工（Temp）的身份进入《体育画报》，做一些打杂琐事。当他向编辑提案写运动员猝死调查时，对方甚至轻蔑地打发他“临时工回座位坐好”。转机出现在 2008 年美国奥运马拉松选拔赛进驻纽约中央公园期间，全美排名第五的选手在距离杂志社仅仅 10 个街区的位置突然倒地猝死。震惊的编辑部这才想起问他：“你之前不是懂这个东西吗？”Epstein 凭借长期的扎实积累，仅用一周时间就写出了一篇如同经过两年严谨调研的封面重磅长文，自此正式成为《体育画报》的资深科学作者。这次经历让他深刻认识到跨界组合的威力：与比自己年轻 6 到 7 岁、专业出身的传媒同事相比，他起步落后且只能做基础工作；但他那套在科研圈内或许只能算普通的科学素养，一旦移植到体育杂志领域，就堪比“诺贝尔奖级别的稀缺利器”。他无需在拥挤的单一赛道上与别人卷同质化竞争，而是自己开辟了全新的生态位。这也构成了他如今对自我的定位——游走于科学作者与调查记者之间的交叉者，专门针对大众在人类发展与认知领域的流行谬误展开深度剖析。 --- ### [14:34](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=874s) 拆解“一万小时定律”的神话与认知偏差 Epstein 投身撰写畅销书《范围》（Range）的核心催化剂，正是大众文化中影响深远的“一万小时定律”（The 10,000-Hour Rule）。在学术界，这一理论对应的是 Anders Ericsson 提出的“刻意练习框架”（Deliberate practice framework）。该学说主张：通向任何领域卓越专家的唯一路径，就是积累 10,000 小时的刻意练习——即必须进行高度专注、需要持续耗费认知努力、时刻以纠错为导向的练习，而非在练习场漫无目的地随手挥杆。该框架的核心推论是：人与人之间不存在真正的先天禀赋差异，能力差异无非是刻意练习时长差距的体现；因此，人们应当尽早开始针对单一领域的专精训练。支撑一万小时定律的深层理论假设在学术上被称为**“单调收益假设”（Monotonic benefits assumption）**。该假设认为：两位处于相同技术水平的选手，只要投入相同单位的刻意练习，其技能进步幅度就应当是一致的。然而，现代实证科学研究早已证明这一假设根本不成立。在日常语境中，“你还没投入一万小时”常被滥用为“你练得不够才没成大师”的说辞。但国际象棋的追踪研究彻底打破了这种单一线性叙事： - 棋手晋升为“国际大师”（International Master，仅次于特级大师的头衔）所需的平均刻意练习时间约为 **11,053 小时**（因此“10,000 小时”作为平均数本身甚至略显不足）。 - 然而这一平均数掩盖了巨大的个体差异分布：部分极具学习敏感度的棋手仅用 **3,000 小时** 便拿下了国际大师头衔；而另一部分被追踪的研究对象在训练时长突破 **20,000 小时** 后，依然无法迈过这道门槛。统计平均值完全无法解释人类技能习得过程中极为宽广的差异性与非线性特征。盲目坚信投入固定小时数就能换取大师地位，不仅忽视了不同个体在特定领域的适应度，也让大量学习者陷入了低效消耗的认知误区。 ### [17:13](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1033s) 匹配度、技能叠加与一万小时定律的研究起源个人发展的核心在于“匹配度”（Fit）。每个人的学习速度在不同领域差异极大，找到自己学得更快、更顺畅的领域，是最大化自身优势的前提。但人们对自身的认知往往受限于过往经历的广度，如果不去尝试各种各样的事物，就很难发现自己的比较优势。对于许多人而言，成功的路径并不是孤立地在一件事上死磕一万小时，而是所谓的“技能叠加”（Skill Stacking）——在多个不同领域达到熟练水平，再将它们巧妙重叠，从而塑造出不可替代的独特性。大卫·爱普斯坦（David Epstein）回顾了自己最初为何会去深究“一万小时定律”。在大学时期，他曾是一名自费田径队员（Walk-on，未获得体育奖学金、水平原本不够被招募的选手），主攻800米跑。然而，凭借刻苦训练，他最终打破了校接力纪录，从一名无名小卒跃升为校队主力。这段经历一度让他极度迷信一万小时定律：“没错，这一切全靠我的苦练！”但当他作为科学记者真正翻开那篇奠定该理论的学术文献时，却发现了令人震惊的逻辑漏洞。 ```mermaid flowchart TD accTitle: 传统专精与技能叠加路径对比 accDescr: 对比传统一万小时单一专精与多技能叠加创造独特优势的两种路径 subgraph SingleDomain["单一领域死磕路线"] A["单一领域深入投入"] --> B["耗费10,000小时"] B --> C["边际效益递减 / 极度内卷"] end subgraph SkillStacking["技能叠加探索路线"] D["多样化探索尝试"] --> E["发现高匹配度领域"] E --> F["掌握数项熟练技能"] F --> G["跨界重叠创造独特竞争优势"] end ``` ### [18:27](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1107s) 样本受限与平均数的骗局：拆解安德斯·埃里克森的小提琴家研究这篇发表于1993年的奠基性研究由心理学家安德斯·埃里克森（Anders Ericsson）主导，其研究对象仅为一所世界顶尖音乐学院里的30名小提琴手。爱普斯坦指出，这项研究从设计上就犯了统计学上的致命错误——“样本受限”（Restriction of Range）。这些选手本身就已经是被层层筛选过的顶尖苗子，他们的初始能力本身就与因变量（演奏技能）高度相关。为了形象地说明这种统计偏差，爱普斯坦举了NBA球员身高的例子：如果研究“什么决定了篮球运动员的得分技能”，却把研究样本严格限定在NBA的中锋身上，研究人员可能会得出“身高对篮球得分毫无影响”的荒谬结论，因为这些中锋全都在7英尺（约2.13米）左右，变量的多样性完全被抹杀了。在全美成年男性总体中，身高与进入NBA并得分的概率呈极高的正相关；但如果只在现役NBA球员中做统计，这种相关性反而会变成负相关——因为得分最多的往往是矮个子的后卫。如果无视总体样本、直接套用这种受限样本的研究，研究者甚至会得出“父母应该把孩子培养得更矮，以便在NBA拿更多分”这种荒唐建议。研究的另一个巨大疑点在于“平均数”对真相的掩盖。论文声称，到20岁时，最优秀的10名小提琴手平均进行了10,000小时的“刻意练习”，并且练习时间与水平存在完全对应（即练得少的人绝对不可能超越练得多的人）。但爱普斯坦注意到，论文通篇只报告了平均值，完全没有提供方差数据。当他联系埃里克森希望查阅原始数据时，埃里克森却以“受访者对自己过去练习时间的多次回忆存在前后不一致，因此方差并不重要”为由予以推脱。 - **样本量极小且高度预选**：仅选取顶尖音乐学院的30名小提琴学生，无法推广为人类技能发展的普遍规律。 - **20年后的数据反转**：在受到爱普斯坦等人的长期质疑后，后续更新的数据披露了真相——“完全对应”的结论是错误的。有些人练习时间远低于10,000小时却更优秀，有些人练习时间远超10,000小时却表现平平，个体差异极其巨大。 - **被忽视的睡眠变量**：爱普斯坦戏称该研究其实更像“625,000小时睡眠研究”——表现最好的顶尖小提琴手平均每周睡眠时间高达约60小时，休息和恢复状态对表现的影响可能远大于单纯的时间累积。 ### [22:12](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1332s) 对“年少成名”的迷思：创业成功的平均年龄其实是45岁社会上充斥着将一万小时定律用于青少年体育选拔、商业投资和职业规划的说辞，宣扬“尽早定型、专注单点、拒绝探索”，这让爱普斯坦感到极度荒谬。史蒂芬·巴特利特（Steven Bartlett）对此感同身受，并提出了自己的困惑：自己今年30岁，涉足的投资与商业版图跨度极大（涵盖迷幻药研发到SpaceX等多个行业），但他经常焦虑自己是否已经走过了职业生涯的早期黄金期，变得僵化而难以开拓新局面。爱普斯坦用扎实的大数据研究打破了这种年龄焦虑。麻省理工学院（MIT）、西北大学与美国人口普查局联合开展的一项重磅研究，统计了全美增长最快的前万分之一（Top 0.01%）科技初创企业。数据显示，这些顶尖公司创始人在创立公司当天的平均年龄不是20多岁，而是整整**45岁**；事实上，一个50岁创始人的成功概率甚至高于30岁的年轻人。大众之所以产生“唯有年轻才能成功”的错觉，是因为文化上对“年少成名”（Precocity）有着病态的推崇。人们津津乐道于高尔夫神童泰格·伍兹（Tiger Woods）的直线式成长，或是马克·扎克伯格（Mark Zuckerberg）22岁时狂妄宣称的“年轻人就是更聪明”（如今年过四十的扎克伯格再也不提这种话了），却完全忽略了绝大多数成功者都是曲折前行的“大器晚成者”（Zig-zaggers）。此外，研究中定义的“科技”远不止宿舍里捣鼓出的社交软件，还包含了农业科技、工业基础设施软件等广阔领域，这些行业极其依赖对复杂现实体系的深刻理解，年轻的天才光环在这些领域并不具备统治力。 | 常见认知误区（特例宣扬） | 真实统计常态（大数据规律） | |:--- |:--- | | 成功必须在20岁出头尽早显现 | 顶级高增长创业公司创始人的平均年龄为 **45岁** | | 30岁已过职业黄金期，难以转型 | 50岁创业成功的几率在统计上高于30岁 | | 一万小时单一领域闭门苦练 | 跨界经历、采样探索与技能重叠带来更持久优势 | | 练习时长绝对决定技能水平 | 个体差异极大，睡眠、天赋匹配度与方法权重极高 | ### [24:46](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1486s) 生产力困境与“胜任力陷阱”：为何成功反而阻碍了进步面对巴特利特的提问——如果已经选定了一个目标（例如将这档播客做大做强、持续创新），应该如何从个体层面提升解决问题和推进目标的生产力？爱普斯坦指出了一个成熟成功者必然会面临的独特挑战：“胜任力陷阱”（Rut of Competence），或者如经济学家拉塞尔·罗伯茨（Russ Roberts）所形容的“胜任力吊床”（Hammock of Competence）。当一个人在某个领域已经做得极其庞大、极其熟练时，他就会陷入这种极其舒适且成功的“吊床”之中。此时，进一步的突破反而会变得更加艰难：因为现有的模式已经在高效运转，任何改变或尝试新事物都意味着打破现状并承担风险。对于巴特利特而言，这不仅关乎个人的试错成本，还关乎整个演播室里依靠这个平台谋生的团队团队成员，这种对风险的顾忌往往成为阻碍生产力创新和认知跃升的最大阻力。 ### [25:41](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1541s) 实践中的低成本试错与“最佳挑战区间” 大卫·爱泼斯坦（David Epstein）指出，无论是做播客还是进行职业探索，关键都在于如何承担“明智的风险”（smart risks），并设计低门槛、低成本的试错练习（low-stakes practice）。他提到自己畅销书《成长的边界》（*Range*）中最喜欢的一句话，出自伦敦商学院教授赫米尼娅·伊瓦拉（Herminia Ibarra）关于职业转型的研究：“我们在实践中了解自己，而不是在理论中”（We learn who we are in practice, not in theory）。很多人误以为自己可以像克拉克·肯特冲进电话亭撕开衬衫变身超人那样，仅凭深度内省就能顿悟出自己的天命所在。但工作是个人身份认同的一部分，内省无法直接带来改变，你必须躬身入局去尝试：看看结果如何、有什么意外收获、发现了什么此前未察觉的新兴趣，或是意识到自己具备某种优势却从未派上用场，然后再据此迈出下一步。然而，当一个人或一家机构极具能力、极度成功，周围充斥着赞美与正向反馈时，冒险就变得极其困难。爱泼斯坦对史蒂文·巴特利特（Steven Bartlett）直言，这也是后者面临的巨大挑战：要保持最大化的个人与组织成长，必须处于“最佳挑战区间”（zone of optimal push）——如果你的练习或尝试中没有至少 15% 到 20% 的失败率，说明你根本没有使出全力去挑战上限。当数据指标出现暂时下滑时，并不代表真正的失败，而是为了推向极致必须经历的试错。 ```mermaid flowchart LR accTitle: 职业与创新迭代循环 accDescr: 展示从低成本实践出发，经历意外发现，从而动态调整下一步的认知与行动闭环。 subgraph Action ["行动层"] A["低成本试错实践"] --> B["获取实际反馈"] end subgraph Reflection ["洞察层"] B --> C["发现隐藏兴趣 / 优势"] C --> D["打破纯理论内省假设"] end subgraph Pivot ["调整层"] D --> E["明确下一步行动路径"] E --> A end ``` --- ### [27:28](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1648s) “首席失败官”：在巅峰时期主动自我颠覆史蒂文分享了自己长期以来的一个执念，也是常常让他在深夜甚至洗澡时都在苦思冥想的难题：当一个团队已经非常成功时，如何促使他们继续做实验并主动承担失败？当《CEO日记》（The Diary Of A CEO）登顶欧洲第一播客时，为了防止团队陷入路径依赖与行业常规，史蒂文专门设立了一个“首席失败官”（Head of Failure and Experimentation）的职位。 ```mermaid flowchart TD accTitle: 跨部门失败经验与实验流动机制 accDescr: 展示首席失败官与各部门失败助手如何推动实验并实现跨团队知识共享。 HF["首席失败官 Head of Failure"] --> FA1["制作团队失败助手"] HF --> FA2["社交媒体团队失败助手"] HF --> FA3["商务团队失败助手"] HF --> FA4["嘉宾统筹团队失败助手"] FA2 -- "发现 TikTok 一键爆款工具" --> Hub["公司内部实验复盘库 （思想进出口枢纽）"] Hub -- "免去全量逐条刷视频成本" --> FA4 ``` 整个公司大约有 40 多人，划分为制作组、社交媒体组、商务组以及嘉宾邀约统筹组等。史蒂文意识到，团队总会被眼前的日常业务填满，实验与试错往往沦为次要事务。因此，他们在每个部门都派驻了“失败助手”： - 深入部门业务，专门负责提出实验假说并落实推进； - 记录并衡量每一次失败的指标和过程； - 定期把实验与失败经验向全员通报复盘，形成“1 + 1 = 3”的复利效应。史蒂文举了一个实际例子：社交媒体团队在 TikTok 上摸索出了一个功能按钮，只需轻轻一点，就能按播放量排序抓取特定嘉宾历史最火爆的全部视频。原本这项技能只在社媒运营端有用，但嘉宾统筹团队得知后，直接省去了人工从头滑动刷视频的庞大工作量，筛选潜力嘉宾代表作的效率大幅飙升。这种跨部门共享使得团队不必在同一个坑里跌倒两次。 --- ### [29:34](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1774s) 探索与利用权衡，以及“思想的进出口贸易” 听到史蒂文的做法，爱泼斯坦兴奋地记录下笔记，指出这直击了组织与个人发展的核心命题——**“探索与利用权衡”**（explore/exploit tradeoff）： - **探索（Explore）**：搜寻未知的新知识、新技能或新打法，挖掘潜在增量价值； - **利用（Exploit）**：针对现已掌握且擅长的知识技能不断深挖、放大变现。绝大多数组织在做大做强后，便会本能地放弃“探索”，死守“利用”。例如柯达发明了数码相机却将其束之高阁，唯恐颠覆现有的胶卷暴利。爱泼斯坦提到了美国西北大学王大顺（Dashun Wang）教授的一项大数据研究：该团队跨领域分析了 26,000 名电影导演、艺术家和科学家的大规模职业生涯数据，发现人才的顶峰高产期（hot streaks）往往以集群方式出现。数据表明，**每一次迎来井喷式爆发期之前，毫无例外都伴随着一段广泛涉猎、小团队灵活试水、跨界探索的“探索期”**；在探索中锚定方向后，他们才会切换至“利用期”深度深挖。如果不持续在两相之间切换（toggle），人就会陷入中规中矩的平庸。此外，爱泼斯坦极力赞赏给员工冠以“失败”职衔的做法。他搬出心理学学者亚当·格兰特（Adam Grant）曾向他提及的 **HIPPO 效应**（Highest Paid Person's Opinion，即屋子里薪酬最高者的一言堂）：高管如果仅在口头上表示支持失败，底层是感受不到安全的；给岗位直接贴上“失败与实验”的标签，相当于从制度和心理上为员工“承担风险兜底”（underwriting risk）。这种机制构建了网络科学中所说的“思想的进出口贸易”（import-export business of ideas）——它正是自适应学习型组织的灵魂。戈尔公司（W. L. Gore & Associates，GORE-TEX 制造商）创始人比尔·戈尔（Bill Gore）当年就发现，企业最有影响力的突破往往爆发在危机时刻，因为彼时学科界限荡然无存，人人都开始向身旁的同事请教。戈尔曾戏称“真正的沟通发生在拼车上下班的路上”。爱泼斯坦警示，在现代混合办公与远程办公盛行的当下，人们无法仅靠偶发碰撞（serendipity）来共享信息，必须刻意在内部搭设思想互通的机制。 --- ### [33:05](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1985s) 个体维度的生产力困局与待办事项沼泽讨论顺理成章切入到个人实操层面。史蒂文坦承，在管理组织之余，他个人也面临繁杂的任务超载困扰：自己当下不仅要主持播客，还要写书、推进多项商业事务，但注意力极易涣散——常常不知不觉就点进 YouTube 连续看好几个小时的人工智能或航天火箭视频。更令他焦虑的是，他手中永远被各种待办事项填满： - 幕僚长（Chief of Staff）、个人助理、经理等各个核心岗位，每周一都会向他同步汇总各自部门列出的清单； - 杂糅在一起的清单林林总总多达 10 张，堆满了不同层级的待办任务与决策项。爱泼斯坦回应称，看火箭或 AI 视频本身毫无问题，很多创新灵感恰恰源于这种偶发性的广泛浏览。但他对“多达 10 张待办清单”的状态提出了质疑，追问史蒂文在面对如此庞杂的任务输入时，到底能否真正搞定清单上的大部分事项。对话随即从组织层面的创新探索，深入转向了顶尖执行者如何梳理任务优先级、破解时间碎片化的生产力痛点。 ### [33:57](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2037s) 优先级的陷阱与“蔡加尼克效应” David Epstein 分享了自己在应对工作超载时的一段切身经历。他曾经雇佣过一名虚拟助手，尝试将收件箱里的邮件按照 A、B、C、D 等级严格分类。然而不久后他发现，这种精细的管理系统反而带来了一种虚假的成就感——它使他变得极其高效地去处理那些本不该由他做的低价值琐事。原本被他忽略的公开邮箱，一旦被整理成工整的待办列表呈现在眼前，他便忍不住觉得“每一封都必须回复”。直到后来他才顿悟：人的精力与生命是有限的，最终真正能被完成的只有“A 级清单”上的核心事务。拥有丰富支持资源的人很容易陷入丧失优先级的危险之中，误以为自己能够搞定一切。基于这一洞察，David Epstein 向主持人 Steven Bartlett 提出了一个明确的晨间习惯建议：**绝对不要以查阅邮件或即时通讯消息开启新的一天**。这背后的心理学机制正是著名的“蔡加尼克效应”（Zeigarnik effect）——未完成的任务会在大脑中留下“认知残留”（cognitive residue），极大阻碍个体随后全身心投入到其他事务中。收件箱是一个永远不可能彻底清空的无限黑洞，若早晨第一件事就是打开它，大脑便会被未结的任务所绑架。正确的策略应当是在前一天晚上就确定好：“明天只要搞定哪一件事，这一天就算成功？”，并在第二天醒来后，趁大脑处于专注状态时优先拿下这个核心目标，而不是过早陷入多任务切换与认知残留的泥潭。 ```mermaid flowchart TD accTitle: 晨间邮件引发认知残留的负面循环 accDescr: 展示以查阅收件箱开启一天如何通过蔡加尼克效应损害专注力并增加压力 subgraph MorningTrap ["错误模式：晨间查阅通讯"] A["开启新一天：检查邮件与消息"] --> B["陷入永远处理不完的待办项"] B --> C["触发蔡加尼克效应：产生认知残留"] end subgraph CognitiveImpairment ["执行力受损"] C --> D["任务切换频率剧增"] C --> E["心率变异性异常、压力激增"] C --> F["深度认知任务表现显著下滑"] end ``` - 认知科学家 Gloria Mark 的职场实验证实：研究人员通过追踪员工的视线移动、电脑操作轨迹以及心率变异性（HRV），发现任务未完成造成的心理残留会显著提高任务切换频率，加剧生理压力指标。 - 实验室对照测试表明：当中途强行打断受试者且不准其完成初始任务时，其在后续认知测试中的专注度与准确率均出现明显受损。 --- ### [36:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2178s) 打造创新型团队文化与承担真实失败 Steven 转向团队建设的话题，询问管理者该如何带领团队实现非凡的创新、更快速地试错，以及应该如何设计实验机制。David Epstein 明确指出，绝不应为了失败而盲目崇拜失败；失败不是终极目标，它仅仅是开展高频、充分探索时的必然副产物。真正关键的是如何在团队内部建立学习闭环与探索机制。为此，David 提到了对他影响深远的导师级人物 Ed Hoffman。Ed Hoffman 曾任美国国家航空航天局（NASA）的首席知识官（Chief Knowledge Officer）。在 NASA 遭遇几起震惊全球的重大航天灾难后，调查发现机构虽有一流的人才，却并非一个“善于学习的组织”，屡屡重蹈覆辙。Hoffman 被委以重任，为 NASA 搭建起从过往事故与经验中汲取教训的知识体系。如今作为组织顾问，Hoffman 走进任何一家企业时，都会向一线员工抛出一个极具启发性的基础问题：“**有什么技能是你非常擅长，但我们公司目前完全没用上的？**” 员工面对该问题几乎总能给出明确回答，管理者便可顺理成章地顺藤摸瓜：“那我们能设计一个怎样的实验，把你的这项隐藏优势释放出来？”此外，David 强调了领导者以身作则的残酷法则：管理者若想在团队内部营造容许创新的安全感，最有效但也最艰难的做法就是**领导者本人亲自去承担一次真实的、具有实质后果的业务实验失败**。这种失败不能是无足轻重的作秀（例如“跑步在路牙上绊了一跤”），而必须是有份量的尝试；领导者面对真实失败时的坦然与复盘反应，才能真正确立整个团队的创新基调。 --- ### [38:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2280s) 深度学习的基石：语义网络与生成效应讨论随后深入到个人学习力层面。Steven 坦言自己每周与顶尖学者、行业领袖对谈，虽然当下醍醐灌顶，但往往几分钟后就遗忘了绝大部分细节，因而迫切寻求如何更好留存知识并指导实践。David 从认知科学底层原理解释道，初次接触陌生领域的知识与术语时，我们的大脑工作记忆（working memory）几乎被全部占用，仅仅勉强维持理解，根本无暇固化。认知实验表明，当让阅读能力好但不懂棒球的学生，与阅读能力稍逊但深谙棒球规则的学生同读一篇棒球文章时，后者在单次阅读后的理解度与记忆留存上完胜前者。这是因为具备背景知识的人能够迅速将新信息挂载到大脑原有的**“语义网络”（semantic network）**之中。人类的认知体系如同一张蛛网，节点由不同的概念与事实交织而成；每当学到新知时，必须刻意暂停并思考：“它与我过往知道的哪件事相通？与哪位嘉宾的观点一致或对立？”拨动蛛网的一根蛛丝，便能引发整个关联记忆网络的共振。在具体记忆技巧上，单纯机械性地短时间内连续重复收益极低。高效记忆依赖于两项核心机制： 1. **间隔重复（Spaced Repetition）**：在知识濒临遗忘的临界点再次提取它，才能最有效地将其刻写入长期记忆。David 提及自己日常使用 Readwise 工具，将 Kindle 等电子书中的划线笔记按科学的间隔周期定期推送到眼前，实现抗遗忘复习。 2. **生成效应（Generation Effect）与过度校正效应（Hypercorrection Effect）**：被动划线或重读的效果远不如“抽取卡片自测（Flashcards）”。强迫大脑主动检索并“生成”答案的过程，能极大地激活神经回路。更为神奇的是，若在自测时给出了一个极度笃定却完全错误的答案，大脑随后在接收到正确答案时会产生强烈的认知震荡，这被称为“过度校正效应”——越是自信答错的地方，修正后的记忆反而会烙印得越深。 ### [42:07](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2527s) 检索练习与“必要难度”：为何学得痛苦反而记得更牢大卫·爱泼斯坦（David Epstein）在讨论认知学习策略时提出一个反直觉的建议：在获取正确答案之前，先主动猜测结果。即便猜错了、心理上感觉糟糕也无所谓，因为当最终看到正确答案时，大脑的记忆留存率反而会显著提高。主持人插话道，如果自己猜错了，心理上产生的冲击感（shock）会更强烈，从而使这个新信息变得异常显著（salient）。爱泼斯坦对此表示赞同，指出这正是他在《成长的边界》（*Range*）一书中探讨的核心概念之一——“必要难度”（desirable difficulties）。 ```mermaid flowchart TD accTitle: 必要难度的学习机制对比 accDescr: 对比机械刷题（阻塞式练习）与交错练习（必要难度）在短期表现与长期迁移上的巨大差异。 subgraph Blocked["阻塞式练习 (Blocked Practice)"] B1["重复同一题型: AAA -> BBB"] --> B2["执行机械步骤"] B2 --> B3["短期进展快 / 自我感觉良好"] B3 --> B4["缺乏迁移能力 / 考试表现较差"] end subgraph Interleaved["交错练习 (Interleaved Practice)"] I1["混合不同题型: 随机抽取 A/B/C"] --> I2["必须识别问题结构并匹配策略"] I2 --> I3["短期进展慢 / 感到挫败吃力"] I3 --> I4["建立心理模板 / 长期留存与迁移能力极强"] end ``` 所谓“必要难度”，是指那些在短期内让学习体验变得不顺畅、令人沮丧甚至拖慢进度的训练机制，但它们却能带来极高水准的长期记忆留存。其中最著名的策略之一就是“交错练习”（interleaving）或“混合练习”（mixed practice）。为了直观说明，爱泼斯坦引用了一项以初中六年级数学课堂为对象的对照研究： - **阻塞式练习组（Blocked practice）**：学生按固定顺序重复练习同类题型（例如先连续做 AAA，再连续做 BBB）。这组学生短期进步飞快，对学习感到满意，给教师的评分也极高。 - **交错练习组（Interleaved practice）**：题目顺序被打乱，如同将所有题型放进帽子里随机抽取。学生学习进度明显变慢，由于感到吃力，情绪上也不那么开心。然而在最终涵盖新题型的迁移能力测试中，交错练习组彻底击溃了阻塞练习组。其效应量相当于“把一个处于第 50 百分位的中等生，仅凭调整练习结构就直接提升到了第 80 百分位”。 --- ### [44:24](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2664s) 建立心理模板：从五人制足球到美国空军学院的教学悖论爱泼斯坦指出，交错练习之所以强大，是因为它彻底改变了大脑加工知识的方式。传统的机械练习培养的只是“程序性知识”（procedures knowledge）——一遍又一遍执行固定套路；而交错练习迫使学习者去提取“建立联系的知识”（making connections knowledge），学会辨识底层问题的结构，并将合适的策略与之相匹配。这种方法在学习者脑海中构建出灵活且通用的“心理模板”（mental template），而不仅仅是单线条的操作能力。他以体育领域为例：世界上近 90% 的顶级足球运动员童年时都是在五人制小场地室内足球（futsal）的环境下成长，而非标准的大球场。狭小局促、瞬息万变的空间逼迫他们从多角度理解比赛结构，形成了极深层次的通用决策模型。随后，爱泼斯坦分享了他在《成长的边界》中最中意、也是让他最震惊的一项实证研究：美国空军学院（US Air Force Academy）对 10,000 名学员展开的长达数年的随机分配实验。空军学院每年接收约 1,000 名新生，学生被随机分入数学初级班，所有班级参加相同评分标准的考试；次年进入后续高级课程时，再次全员随机分班。这一大规模自然实验揭示出了一个极其荒谬又残酷的悖论： - 那些在第一学年能让学生在期末考试中拿到极高分数的“明星教师”，其教出来的学生进入后续进阶课程后，成绩普遍显著下滑。 - 相反，那些第一年教学评价较差（学生抱怨学得太难）、期末考试成绩并不拔尖的教师，他们的学生在后续数年的高阶数学课程中却持续表现优异、全面领跑。爱泼斯坦感叹，这几乎成了《成长的边界》每一页都在传达的核心思想——**“短期内看似效果最好的方法，往往会彻底毁掉长期的发展潜力”**。要想在单次期末考中拿高分，老师只需填鸭式灌输狭隘的解题套路即可；但要让学生真正理解数学，就必须帮他们搭建宽广的思想连接网络。 --- ### [46:31](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2791s) 丹尼尔·卡尼曼的指点：善意环境与恶劣环境的对决主持人联系自身实际提出困惑：如果自己当前正在跨界探索（例如最近在学 DJ），光是搞清设备上眼花缭乱的按钮顺序就已经焦头烂额了，难道真有精力去从底层乐理、节拍编排甚至自己创作音乐这些宽泛的维度入手吗？面对这一困惑，爱泼斯坦分享了他研究过程中的重大转折。他曾长期困惑于专业技能研究领域的严重对立：一派以诺贝尔奖得主级别的顶级学者为首，认为靠“一万小时定律”进行狭窄而重复的刻意练习就能让人持续精进；而另一派同样严谨的学者却发现，人们在狭隘领域重复练习非但不会提高能力，反而常常只会盲目增加自信——即“水平没涨，自信心爆棚”，甚至越练表现越差。 ```mermaid flowchart LR accTitle: 学习环境的光谱划分 accDescr: 心理学家罗宾·霍加斯提出的善意环境与恶劣环境对比，决定了刻意练习是否有效。 subgraph Kind["善意环境 (Kind Learning Environments)"] K1["规则恒定 / 模式高度重复"] K2["反馈快速、精确、直接"] K3["典型代表: 国际象棋、高尔夫"] K4["结果: 狭隘刻意练习极度有效"] end subgraph Wicked["恶劣环境 (Wicked Learning Environments)"] W1["规则易变甚至无规则 / 模式具有欺骗性"] W2["反馈滞后、缺失或具有误导性"] W3["典型代表: 真实世界决策、司法、复杂医疗"] W4["结果: 经验可能强化完全错误的直觉"] end ``` 爱泼斯坦在一次演讲中公开质疑了“一万小时定律”背后的科学根基。巧合的是，《思考，快与慢》的作者、诺贝尔经济学奖得主丹尼尔·卡尼曼（Daniel Kahneman）当时就坐在台下。卡尼曼要了他的邮箱，数月后约他共进午餐。在餐桌上，卡尼曼向他引荐了心理学家罗宾·霍加斯（Robin Hogarth）关于**“善意学习环境”（kind learning environments）**与**“恶劣学习环境”（wicked learning environments）**的理论框架，瞬间解开了这一学术矛盾： - **善意学习环境（Kind）**：下一步与目标极其明确，规则恒定不变，完全由重复模式主导，且能获得迅速、精准的即时反馈。典型代表是**国际象棋**与**高尔夫**。在国际象棋中，特级大师的优势极大依赖于对重复棋谱模式的记忆；如果一个人没有在 12 岁之前开始背诵这些模式，成为特级大师的概率就会从约 1/4 暴跌至 1/155。这种环境也极易被自动化和 AI 取代，因为明年的工作形式与去年完全一致。 - **恶劣学习环境（Wicked）**：模式不会简单重复甚至会误导你，不存在固定规则或规则频繁变动，反馈往往被严重延迟、失真甚至完全缺失。明年的工作与去年完全不同，极其依赖复杂的真实人类行为。狭窄练习能否让人变强，完全取决于该技能究竟落在善意到恶劣光谱的哪一端。为了阐明“恶劣学习环境”的致命陷阱，爱泼斯坦举了一个医学史上的荒诞案例： - 纽约曾有一位名噪一时的医生，号称单凭用手触摸病人的舌头（palpating tongues），就能在伤寒症状显现前一两周精准预测谁会患上伤寒。 - 直到后来一位同行揭露，这位医生的双手根本没有经过消毒，他触碰病人舌头的举动本身就是在疯狂传播伤寒杆菌——其传播威力甚至超过了著名的“伤寒玛丽”（Typhoid Mary）。 - 这种医生摸舌头预测发病的“准确命中”，每次都在强化他以为自己医术高超的错觉。这就是最典型的“恶劣学习环境”：现实给予你的闭环正反馈，恰恰在强化一个彻底错误的假象。同样，许多刑事司法系统中的法官也很难提高判断力，因为他们判决后极少获得客观的效果追踪反馈，完全在黑盒中依据主观臆断行事。 ### [50:52](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3052s) 现代工作的滞后反馈与“一万小时定律”的局限大卫·爱泼斯坦（David Epstein）指出，绝大多数现代职场人所从事的工作，其反馈机制往往是极度迟缓且不稳定的。这与打高尔夫球有着天壤之别——在高尔夫球练习中，只要你击出一杆，立刻就能看到球是打出了向左的右曲球（hook）还是向右的右偏球（slice），随后就能马上调整杆面重新尝试。然而如今人们面对的工作，今年要做的事和去年截然不同，反馈往往模棱两可且严重滞后。就连“一万小时定律”背后的原始研究者安德斯·埃里克森（Anders Ericsson），后来在自己的专著中也专门加上了重要的免责声明：一万小时刻意练习框架仅适用于那些“规则极其明确、优秀标准固定，且有专业教练在旁随时观察并纠正每一个微小错误”的封闭领域。这一框架根本无法套用到现代人从事的大多数工作中，比如软件开发、企业管理以及创业。在这些复杂领域中，人们真正需要的是一个宽广得多的多元工具箱。 ### [51:48](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3108s) 任天堂的突围之道：“枯竭技术的水平思考” 主持人谈及爱泼斯坦曾分享过的任天堂（Nintendo）早期成功案例，其中最核心的理念便是“枯竭技术的水平思考”（Lateral thinking with withered technology）。这套哲学始于传奇设计师横井军平（Gunpei Yokoi）。横井军平在大学时电子学考试成绩很差，未能像更优秀的同龄人那样挤进东京的大型科技巨头，只能在京都一家濒临破产的传统花札纸牌小作坊里找了份低阶的机器维护维修工作。面对生死存亡的转型危机，横井军平清楚自己没有能力研发最前沿的硬核技术，但他敏锐地意识到市场上到处都是现成且成熟的技术信息。他选择寻找那些已被行业充分摸透的“过时成熟技术”，并以高度专业化工程师完全想不到的方式将其跨界重组。他把计算器行业的现成技术与信用卡行业的成熟零件结合，开发出了风靡全球的掌上游戏机 Game & Watch。这也促成了任天堂从 19 世纪的木制门市工坊彻底转型为现代玩具与游戏巨头。横井军平的集大成之作是掌机 Game Boy。从技术角度看，Game Boy 简直就是个笑话：它的处理器早在十年前就已面世，屏幕更是泛着像“腐烂的紫花苜蓿”一样的暗绿色。然而在面对同时期其他竞争对手推出的全彩液晶竞品时，Game Boy 却以压倒性的优势将对手碾压出局。因为横井军平深刻理解用户真正在乎的核心价值根本不是彩屏，而是耐用性、高性价比、便携性、长续航以及丰富的游戏生态。由于使用的是高度成熟的现成技术，开发者能够极快地为该平台制作游戏。爱泼斯坦指出，这种将已知成熟技术迁移至全新场景从而化为重大创新的模式，在 20 世纪后期的技术创新中已从特例变成了常态。 ```mermaid flowchart LR accTitle: 横井军平的枯竭技术创新逻辑 accDescr: 展现横井军平如何利用计算器与信用卡等成熟技术跨界重组，最终打造出颠覆性的 Game Boy 掌机。 subgraph MatureTech["成熟枯竭技术"] A["计算器成熟元器件"] B["信用卡显示/工艺技术"] end subgraph Innovation["水平思考与跨界重组"] C["横井军平 cross-domain 整合"] end subgraph MarketHit["产品落地与核心优势"] D["Game & Watch 掌机爆款"] E["Game Boy 核心体验： 超长续航 / 耐摔 / 低成本 / 丰富游戏"] end A --> C B --> C C --> D D --> E ``` - 20 世纪大部分时间里，最具影响力的专利通常由深挖美国专利局单一分类细分领域的顶尖专家或小团队产生。 - 自 20 世纪 80 年代步入信息时代后，跨领域获取信息的门槛大幅降低，最具颠覆性影响的专利越来越多地由具有多元领域背景的复合型团队或能够跨分类整合技术的个人所创造。 ### [54:25](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3265s) 多任务处理的假象与大脑的“注意力残留” 针对“涉猎广泛”是否等于“缺乏专注”的疑问，爱泼斯坦明确界定了其区别：真正的通才是在整个人生或跨越整个职业生涯的尺度上去体验和探索多种事物，而不是在同一时刻试图一心多用。人类在大脑生理机制上根本无法真正进行“多任务处理”（Multitasking），所谓的并行工作，本质上只是注意力在不同任务之间极高频率地反复切换（Task-switching）。知名注意力研究专家格洛丽亚·马克（Gloria Mark）将人类大脑比作一块白板：当你在进行一项工作时，白板上写满了内容；若要切换到新任务，你必须先把白板擦干净。但擦拭过程必然会留下“注意力残留”（Attention residue），导致你在转向新任务的相当长一段时间内无法全神贯注。在微软等大型机构进行的生理学研究中，研究人员为员工佩戴心率变异性（HRV）监测设备并全程追踪其任务切换轨迹。数据证实，仅凭一个人全天在电子邮件、各类弹窗通知和核心工作之间切换的频次，就能极其精准地预测其压力水平与工作表现的下滑程度。办公室白领平均每天要查看电子邮件多达 77 次。爱泼斯坦解释，每一次任务切换在认知层面都包含两道严苛的步骤：首先要强行关闭当前正在执行的任务思维，随后必须激活下一项任务所需的全新规则库。即便你觉得自己做的都是“打字”，比如正在写核心报告的同时在 Slack 里回复同事，这两者的写作风格和心智规则也截然不同，频繁切换同样会产生高昂的“切换成本”（Switching costs）。 ### [56:58](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3418s) 掌控注意力的实操法则与环境噪音陷阱面对多任务干扰对健康与效率的双重侵蚀，爱泼斯坦给出了具体的执行建议： 1. **晨间绝对不碰碎片化任务**：早晨千万不要以检查电子邮件等具有强大多任务属性的工作开启一天，因为这不仅会瞬间将心智带入多任务模式，而且永远让人觉得“事情没做完”。 2. **践行“先付给自己”的原则**：在日历上划出整块的防打扰时间槽（Time-blocking），并预留冗余缓冲，以对抗人们总是高估自己完成速度的“规划谬误”（Planning fallacy）。精简待办清单，每天甚至只确立最关键的一件事，只要拿下它，今天就是高效胜利的一天。 3. **隔离干扰环境**：在需要极度专注时，务必保持环境纯净，甚至应当关掉背景音乐。虽然背景音乐可以改善情绪、调动积极性，但它终究会不可避免地侵占大脑有限的认知带宽。研究表明，如果背景音乐是极其熟悉、毫无新奇刺激的曲目，对心智的侵占相对较小；但凡有新鲜感，大脑就会不由自主地分流注意力。爱泼斯坦透露自己如今在处理极高强度专注的脑力工作时会果断关掉音乐，仅在精力衰退需要提振时才阶段性开启。此外，环境噪音对生产力的打击更为惊人。由于分贝属于对数刻度（Logarithmic scale），微小的分贝数值上涨在物理强度上都是巨大的跃升。背景环境噪音若从 70 分贝上升到 80 分贝——相当于从洗衣机运转声提升到吸尘器的轰鸣声——会直接导致个体的认知能力和生产力遭受高达 15% 的断崖式下跌。人类大脑在演化历程中天生被塑造成对周围的新奇声响与外部刺激高度警觉，这种为了在原始荒野中生存而保留的本能，恰恰与现代人所需的深度专注背道而驰。 ### [59:39](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3579s) 音乐对专注力的影响与脑力负荷的平衡史蒂芬·巴特利特（Steven Bartlett）提到自己在进行需要重度用脑的工作（例如阅读或写作）时，如果背景音乐带有歌词，就会极易分心。他分享了当天的亲身经历：在酒店房间研读本期播客嘉宾的背景资料时，音箱里放着一首说唱歌曲，他能极其清晰地感知到自己的大脑在电脑屏幕和歌词之间来回“摇摆跳跃”。他意识到自己根本无法有效阅读，于是立刻关掉了音乐，工作效率随即大幅提升。不过，他在写作时偶尔也会播放没有歌词的器乐背景音，并且注意到市面上已经出现了专门针对专注力的纯音乐应用程序（Focus Music App），这些音乐通常旋律变化少、结构简单且具有规律的循环。大卫·爱普斯坦（David Epstein）对此表示赞同。他解释说，环境中出现的新奇刺激越少，注意力被分散的概率就越低。不过，他建议甚至可以尝试在“完全无声”的环境下工作。背景音乐的选择往往取决于大脑当时的负荷程度：如果你并未将认知能力推向极限，播放一些愉悦的音乐提升心情和动力是完全划算的；但如果你正在全力冲刺，任何外界声音都会成为认知负担。爱普斯坦举了自己的跑步经历作为例证：当他在慢跑时收听外语课程，一旦加快配速全力冲刺，大脑就会完全切换到极端专注的运动状态，跑完后甚至根本记不起听力里讲了什么。在面对高强度的智力输出时，爱普斯坦常常需要同时在大脑中调度、平衡多个复杂的概念构思，甚至会产生一种“大脑即将过热”的感觉。在那种阶段，他会尽全力消除一切哪怕极其微小的干扰。他建议采用分段间隔工作法： - 规划高强度的冲刺时间段，效仿马娅·安杰卢（Maya Angelou）那样全神贯注地投入深层工作。 - 随后切换到轻松、不需要高度用脑的日常活动（“小脑袋模式”），通过洗澡或散步让灵感在大脑中进行潜意识孵化。 --- ### [01:01:33](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3693s) “分心仪”与大脑的自我打断机制史蒂芬接着将话题引向数字通知，询问虽然自己尽力关闭了各种消息提醒，但那些潜在的未读信息依然在潜意识深处制造焦虑。爱普斯坦引入了注意力研究学者格洛丽亚·马克博士（Dr. Gloria Mark）的经典发现：人类大脑存在一种内在的“分心测量仪”（Distractometer）。 ```mermaid flowchart TD accTitle: 习惯性分心与自我打断循环 accDescr: 展示从外部频繁打断到关闭通知后大脑产生自我打断的心理过程 subgraph S1["高频打断常态"] A["外部通知频繁轰炸 （如每分钟10次）"] --> B["大脑建立高频打断习惯 适应固定的受扰节奏"] end subgraph S2["突然切断外部刺激"] B -.-> C["人为关闭所有外部通知"] C --> D["内在分心仪被触发 感到不适应与焦虑"] end subgraph S3["自我打断机制接管"] D --> E["大脑启动自我打断： '我忘了什么事？' '谁的消息没回？'"] E --> F["维持原有的受扰节奏"] end ``` 这种机制表明，如果一个人平时习惯了每分钟被打断多次，即使突然关闭所有通知，大脑也会为了维持已经适应的被干扰节奏而开始“自我打断”。大脑会主动弹出各种杂念：“我是不是忘了什么待办事项？”、“刚才那条消息是不是没回复？”。史蒂芬对此总结道：这就好比一个人习惯了每分钟产生10次被打断的刺激，在屏蔽外界后，大脑自身也会每分钟凭空想出10个杂念来打扰自己。研究表明，即使手机处于关机状态，只要它出现在视线范围内，那些高度依赖手机的人在认知测试中的表现依然会显著下滑。爱普斯坦建议在手边常备一本实体便签纸，一旦脑中浮现出未完成的杂事或未回复的人名，立即将其写在纸上。这种方法将大脑工作记忆（Working Memory）中的认知负担“外包”出去，从而缓解大脑对遗漏事务的执念，帮助人逐步过渡到低干扰的专注习惯。 --- ### [01:03:55](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3835s) 睡眠卫生与手机物理隔离法史蒂芬将这种持续的神经警觉性延伸到了现代人的睡眠问题上。许多人在白天整天承受手机的高频打断，夜晚甚至抱着手机入睡，这严重摧毁了睡眠质量。爱普斯坦对此深有感触，他强调手机在某些工作场景下确实好用，但在睡眠层面极具破坏性，因为睡前使用手机会在大脑中留下挥之不去的“认知残留”（Cognitive Residue）。为了彻底改善睡眠，爱普斯坦严格执行睡眠卫生原则： - **物理隔绝**：睡觉时将手机调至飞行模式，并放置在不同的楼层，绝不在临睡前看手机。 - **环境控制**：保持卧室内绝对黑暗、安静且凉爽。 - **阅读脱敏**：睡前绝不阅读任何与工作相关的材料，否则大脑会不由自主地持续高速思考，导致难以入眠。爱普斯坦坦言，在有了五岁大的儿子之后，这一改变变得尤为迫切。他曾经是一个典型的夜猫子，习惯在凌晨夜深人静时写作。但由于孩子早晨无论如何都会准时醒来，他被迫彻底调整作息，放弃熬夜，转而通过严苛的就寝环境管理来换取更高效的深度睡眠。 --- ### [01:05:26](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3926s) 早期专业化 vs 广度探索：伍兹与费德勒的截然路径史蒂芬提到爱普斯坦的研究颠覆了自己未来对育儿的设想。作为曼联的铁杆球迷，史蒂芬原本幻想在自己的孩子出生两个月后就让他练习踢足球，一路进行早期极限专精，以便将来能入选曼联一线队。但爱普斯坦的研究揭示了早期过早专精（Specialization）与广泛探索（Generalization）之间完全不同的成长结果。爱普斯坦对比了体育界的两个极端样本： - **泰格·伍兹（Tiger Woods）——极致的早期专业化路径**：伍兹在10个月大时，父亲就给了他一支推杆当作玩具（虽然伍兹曾公开表示父亲从未强迫他，每次都是自己渴望去打）。2岁时伍兹就登上全美电视节目展示挥杆技巧，3岁时就公开宣称自己要成为世界上最伟大的高尔夫球手，21岁时登顶世界第一。这种超早期的专注常被大众奉为主流成功的模板，但爱普斯坦指出这在顶尖运动员中实属特例。 - **罗杰·费德勒（Roger Federer）——广泛采样与延迟专精路径**：与伍兹完全相反，费德勒在童年时期涉猎了极其广泛的运动项目，包括篮球、手球、滑板、足球、滑雪、游泳和摔跤。费德勒的母亲虽然本身就是网球教练，却一度拒绝指导他，因为他总是不按常理回球，极度反感刻板的“刻意练习”。甚至当教练提议让他越级和年龄更大的男孩打比赛时，费德勒也拒绝了，原因仅仅是他训练结束后只想和同龄伙伴一起聊职业摔角。费德勒在很长一段时间内并未立志成为网球传奇，而是通过广泛的运动体验培育了更全面的运动协调性与心理适应力。 ### [01:07:36](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4056s) 费德勒的“买车”乌龙与小威廉姆斯的惊人自白大卫·爱普斯坦（David Epstein）分享了罗杰·费德勒（Roger Federer）年少时的一个趣事：当费德勒的球技初露锋芒、引起当地报纸关注并接受采访时，记者问他，如果未来成为职业选手，拿到第一笔假想的薪水最想买什么。费德勒回答说是“Mercedes”（梅赛德斯奔驰）。他的母亲得知后感到惊恐万分，心想孩子怎么变得如此拜金虚荣，于是特地找记者调取了采访录音。听了录音才发现，原来费德勒用瑞士德语说的是“mehr CDs”（想要更多音乐光盘），一场虚惊这才平息。费德勒日后成为了与泰戈·伍兹（Tiger Woods）齐名的体坛传奇，但即便许多网球迷，也对他的成长轨迹一无所知。科学研究表明，费德勒式的多样化发展道路其实才是培养顶尖选手的普遍常态，而社会却一味传颂泰戈·伍兹式的“神童神话”。爱普斯坦透露，他曾与《异类》作者马尔科姆·格拉德威尔（Malcolm Gladwell）一起跑步时讨论过这个现象，格拉德威尔打趣说伍兹两岁打高尔夫的视频就像“人类版猫咪短视频”，谁看了都忍不住转发。但爱普斯坦强调，大众迷恋伍兹故事的深层原因在于它提供了一个极其工整、看似能简单推演到其他领域的叙事模板；然而高尔夫是典型的“温和学习环境”（Kind Learning Environment），规则固定、环境不变、不需要对对手做出动态反应，几乎是人类最糟糕的通用学习模型。爱普斯坦还讲述了一次惊心动魄的演讲经历：当时他在一个小型场合分享体育科学研究，阐述顶尖运动员通常需要经历一段“广泛尝试期”（Sampling Period）——广泛尝试各种项目、了解自身兴趣与潜能、推迟早期专业化。正当他讲得起劲时，网球巨星塞雷娜·威廉姆斯（Serena Williams）恰好坐在第二排。爱普斯坦心里直犯嘀咕：“无论你摆出多少数据，要是历史最佳选手站起来当面说你是个傻子，那今天就彻底搞砸了，千万别让她提问。”不料小威廉姆斯第一个举手发问，她说道：“我认为我的父亲走在了时代前沿。他当年让我练芭蕾舞、田径、体操、跆拳道，还让我像扔美式橄榄球一样练习过头扣杀动作来辅助发球；当青少年巡回赛差旅过频时，他立刻让我退赛，专心回学校念书。”连曾任《体育画报》（Sports Illustrated）资深记者的大卫也从未听过这段往事，大家向来误以为威廉姆斯姐妹也是纯粹的“伍兹模式”。 ### [01:10:03](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4203s) 洛马琴科的芭蕾舞步与早期过早专业化的三大危害主持人史蒂文·巴特利特（Steven Bartlett）对此深有共鸣。他回忆自己在纽约看拳击比赛时，被乌克兰拳王瓦西里·洛马琴科（Vasiliy Lomachenko）不可思议的步法深深震撼。洛马琴科业余战绩近400场仅输过1场（并在复赛中成功复仇），其独特的步法正是源于童年时期被父亲阿纳托利（Anatoly Lomachenko）勒令暂停拳击训练好几年、专心去学传统舞蹈的经历。爱普斯坦指出，推迟专业化之所以能在体育运动中发挥巨大作用，主要在于三大核心机制： ```mermaid flowchart TD accTitle: 延迟早期专业化的三大核心优势 accDescr: 延迟早期专业化通过匹配质量、防伤保护和多元认知技能带来长期优势。 subgraph Advantages["延迟早期专业化的三大机制"] MQ["1. 匹配质量 (Match Quality) • 兴趣与天赋的真实契合 • 避免青春期前的错误筛选 • 消除相对年龄效应 (RAE)"] IP["2. 伤病预防 (Injury Protection) • 避免单一运动每年超9个月 • 降低成人式劳损在儿童期发生 • 多运动交叉训练保护身体平衡"] SL["3. 技能学习红利 (Skill Learning Advantage) • 类似双语学习的后期爆发 • 提升复杂场景的应对变通性 • 培养对身体动作的预判直觉"] end MQ --> Outcome["成就长期顶尖高水平发展"] IP --> Outcome SL --> Outcome ``` - **匹配质量（Match Quality）**：过早强推专项选择往往是“过早优化”（Premature Optimization）。孩子若过早被固定在某项运动上，很可能错失真正契合自己身体条件和热情的领域。此外，过早筛选还会引发“相对年龄效应”（Relative Age Effect）——同一年龄段中年初出生的孩子由于生理发育更成熟，常被教练误判为更有天赋而占据青年队名额；类似现象也发生在学校里，年级里较小的男孩常因行为相对幼稚而被过度误诊为多动症（ADHD），但在成年顶尖赛场上这种年龄优势会彻底消失。 - **劳损与伤病预防（Injury Prevention）**：如今大量儿童身上出现了成年人特有的慢性劳损。数据显示，诱发这类损伤的主要风险因子是一年中连续 9 个月以上仅从事单一运动。多项运动交叉训练不仅不是单纯减少运动量，反而为身体骨骼和肌肉发育提供了至关重要的多元保护平衡。 - **复合技能学习红利（Skill Learning Advantage）**：这种机制类似双语儿童的学习模式。同时学习多门语言的孩子早期或许开口稍晚，但这一延迟很快会被追平，并且他们未来学习其他语言的敏锐度远超常人。运动训练同样如此，多元化的动作库能显著增强应对变化环境的问题解决能力。爱普斯坦以巴西室内足球（Futsal，葡萄牙语原意为“室内足球”）为例：孩子们用较小的重球在狭窄场地、鹅卵石或水泥地面上对抗，触球频率是正规十一人制足球的约六倍。在极度紧凑的空间里，每个人都必须时刻通过观察对手躯体动作来预判下一步，极大锻炼了超常的决策速度与动态变通能力，这正是孕育世界级足球巨星的温床。 ### [01:14:17](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4457s) 为什么极端特例总能霸占公众注意力？史蒂文提出，大众媒体往往极度热衷于传播极端特例，因为常态缺乏戏剧性。人们乐于讨论两岁握杆的泰戈·伍兹，也乐于宣扬安东尼·约书亚（Anthony Joshua）24岁左右才起步却拿到重量级世界拳王的逆袭神话，唯独没有人去报道那些在十五岁按部就班接触专业训练并逐步成长的选手——因为“常态不具备传播爆点”。爱普斯坦表示赞同，他指出：大器晚成的故事让大众觉得“奇迹战胜了概率”，令人津津乐道；而两岁神童的故事则给家长和社会提供了一个看似容易效仿、清晰简单的线性药方。但这种处方化的思维方式脱离了事物发展的真实规律。谈话由此引申到了年轻人最普遍的焦虑上：如果“匹配质量”如此重要，青年一代究竟该如何摆脱空想，真正找准属于自己的“热爱”（Passion）与人生方向。 ### [01:15:53](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4553s) 破除“唯一真爱”神话与五角大楼的“基于才能分流”实验大卫·爱普斯坦（David Epstein）首先反驳了人们对“热情（Passion）”的误解。很多人把热情当成一个单数词，认为命中注定存在一个像复活节彩蛋一样的“唯一热爱”，必须耗尽心思去把它找出来。爱普斯坦幽默地调侃道，这就像坚信世界上只有唯一一个灵魂伴侣一样——“虽然我显然找到了我的唯一真爱，但对大多数人来说并不是这样”。事实上，一个人可能对许多不同的事物感兴趣，而且尝试得越多，就会发现自己感兴趣的领域越多。为了说明接触多元领域的重要性，爱普斯坦分享了自己前一周在五角大楼的经历。陆军曾面临严重的人才流失问题：军方认定的最具潜力的人才纷纷选择退役离开。为了解决这个问题，军方启动了一项名为“基于才能分流（Talent-Based Branching）”的试点项目。过去，陆军的晋升体系非常死板，基本遵循“要么晋升要么出局（Up or out）”的单一路径；而在新项目中，年轻学员会被配备类似教练的导师，并被要求在五个截然不同的职业路径中进行浅尝辄止的涉猎（dabble）。在教练的指导下，学员们要进行反思、参加匹配度测试，并必须把这些反思明确记录在线上门户中——这是典型的显性自我调节学习（Self-Regulatory Learning）。结果令人震撼： ```mermaid flowchart TD accTitle: 基于才能分流项目流程 accDescr: 展示五角大楼如何通过涉猎多种职业路径和教练反思，使90%的学员调整职业选择并大幅提高人才留存率。 subgraph EXPLORATION["探索与体验"] A["涉猎5条不同职业路径"] --> B["与专属教练共同反思"] B --> C["完成适配度测试并记录在门户"] end subgraph OUTCOME["成效与转变"] C --> D["90%的军校学员自愿变更职业偏好"] D --> E["实现更佳的岗位匹配"] E --> F["显著提升高潜力人才留存率"] end ``` 仅仅因为稍微尝试了一下以前没接触过的领域，高达90%的陆军学员就主动改变了自己最初的职业偏好。因为在此之前，他们根本不知道外面的世界存在什么机会。这项举措大幅改善了陆军的人才留存率，因为当人们找到更适合自己的匹配时，留在系统内的意愿会大大增加。 ### [01:17:28](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4648s) 坚毅（Grit）迷思与西点军校的“匹配质量”危机五角大楼的案例自然引出了关于“坚毅（Grit）”的经典讨论。爱普斯坦指出，心理学界最著名的坚毅研究正是安吉拉·达克沃思（Angela Duckworth）等人在美国西点军校（West Point）进行的。达克沃思设计的12道题“坚毅量表”中，一半的分数取决于“兴趣的一致性”（不随意改变兴趣），另一半取决于“努力的持久性”（面对挫折坚持不懈）。这项研究证明，坚毅得分在预测谁能熬过西点军校残酷的新生入伍集训（绰号为“野兽集训，Beast”）以及谁能最终毕业方面，比传统测试成绩具有更强的预测力。这套理论随后风靡全球，很多管理者深信“坚毅”就是决定员工成功的最关键品质。然而，鲜为人知的后续事实是：自1990年代中期以来，那些在西点军校表现出极高坚毅度的新生，在毕业后完成5年现役服役期的当天，竟然有近半数的人选择立即退伍。军方高层起初将其归咎于所谓“千禧一代缺乏毅力”的刻板印象（“吃了太多牛油果吐司，背了太少房贷”）。但几位同时拥有军官身份的科学家深入研究后给出了截然不同的诊断：这根本不是毅力问题，而是“匹配质量（Match Quality）”问题。 ```mermaid flowchart LR accTitle: 匹配质量与人才流失的演变对比 accDescr: 对比传统工业经济与现代知识经济中横向流动能力对离职率的影响。 subgraph TRADITIONAL["传统工业经济形态"] T1["晋升或淘汰模式"] --> T2["年复一年面对同质问题"] T2 --> T3["横向流动极低/环境适应稳定"] end subgraph MODERN["现代知识经济形态"] M1["创造性解决问题与知识创造"] --> M2["具备巨大的横向流动机会"] M2 --> M3["20岁出头快速自我认知"] M3 --> M4["体制内缺乏转轨自主权 -> 坚决辞职"] end ``` 在过去的工业时代，军队和外界经济环境一样，流动性差、分工固定，通过培训后长年做相似的工作是常态。但在现代知识与创意经济中，具备创造性解决问题能力的人拥有极高的横向流动能力（Lateral Mobility）。西点军校培养出的优秀年轻人在二十出头时飞速认清了自己的特长与真正追求，但死板的晋升体系没有赋予他们自由转岗或改变职业路线的自主权，导致他们唯一的选择就是彻底辞职。军方一开始并未意识到本质，曾斥资5亿美元纳税人的资金用于发放高额留任奖金，结果原本打算留下的拿了钱，原本打算走的依然毅然离职。这笔巨款没有解决任何问题，因为单纯靠金钱或强调坚毅，无法弥补匹配质量的严重错位。 ### [01:20:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4800s) 在实践中认清自我：用“极度投入”取代“寻找激情” 爱普斯坦重申了学者赫米尼娅·伊瓦拉（Herminia Ibarra）的名言：“我们是在行动中、而非在理论中认清自己的（We learn who we are in practice, not in theory）。”年轻人最大的误区就是整天坐着向内自省，试图凭空想出自己的激情所在。唯一的正解是走出去尝试：第一份尝试的工作几乎百分之百不会是你的终极挚爱，但只有通过建立实验过程，才能建立对外部世界的认知模型，看清自己真正有哪些选择。谈到激情与幸福感时，爱普斯坦坦承，现实往往不是童话般的快乐： - “就像我以前跑800米，或者现在伏案写书，如果你在特定时刻问我‘你现在享受吗？你感到快乐或充满激情吗？’我会说：‘才怪，我简直想把电脑扔出窗外！你疯了吗？’” - 然而，这项工作极具“吸引力与投入感（Engaging）”，它迫使人以一种平时休闲无法达到的强度去挑战并学习。因此，寻找职业不应只执着于虚幻的“激情”，而是要找到某种让你感到“极度投入”的事物。当主持人询问这种状态是否就是“心流（Flow）”时，爱普斯坦指出，心流在冲浪、绘画等即时具身动作中更常见，在知识工作中往往没那么容易持续出现。判断某件事是否真正吸引你的核心标志，在于它是否激发了强烈的求知欲与好奇心： - 你会迫切想要了解如何才能做得更好、接下来还能学到什么，这种内在自驱力在那些例行公事、走过场的人身上是绝看不到的。 - 爱普斯坦分享了自己与妻子的生活故事：刚同居时，他希望两人都能保持终身运动的健康习惯，便直接把妻子带进健身房。但他很快意识到，自己习以为常的几十年健身房经验并不适用于她。随后他们开始一起尝试各种项目——先是跑步，妻子并不喜欢；继续尝试其他课程，直到有一天妻子参加完某类特定的健身课，兴奋地冲回家滔滔不绝地讲：“我们今天做了这个又做了那个，难度特别大，让我来演示给你看我们还练了什么！”爱普斯坦立刻意识到：“你找到属于你的东西了。”这种投入甚至直接影响了后来的买房决策：他们在跨州搬家选房时，必须确保新家步行15分钟内能到达那种健身房。爱普斯坦强调，面对未来的职业变迁，这种试验机制不可替代。尽管未来 AI 可能会改变竞技场，但在充满未知变化的时代，人们几乎不可能在整个职业生涯中做一模一样的事情。随后主持人谈到爱普斯坦的一期视频《为什么发散型思维者在现实世界击败天才》，并分享了自己在30岁时被确诊 ADHD（注意力缺陷多动障碍）的经历，询问爱普斯坦的研究是否涉及神经多样性（Neurodivergence，如 ADHD、自闭症等）。爱普斯坦表示，虽然自己的研究并非直接聚焦于此，但在一个团队或体系中，汇聚的思考方式越多元、将不同类型的思维“炖成一锅汤”，群体的创造力和应对能力就越强，而 ADHD 等特质在特定环境下确实能发挥出传统思维难以企及的独特优势。 ### [01:23:44](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5024s) 神经多样性与进化的适应性：寻找适合差异化思维的环境大卫·爱普斯坦指出，研究游牧部落定居后的基因变化能带来极具价值的启示：某些与寻求新奇感以及多动症（ADHD）相关的微效基因，在人群从游牧转为定居后会逐渐被自然选择所淘汰，而在游牧生活中这些基因的频率则要高得多。这表明，对周围各种不同刺激保持高度敏锐的注意力，在人类的远古祖先时代具有极为重要的生存价值，即便在今天也依然不可或缺。如果把一个具有这种特质的人硬塞进教室，强迫其一天静坐十个小时，这种特质就会表现为“适应不良”（maladaptive）——然而在爱普斯坦看来，那种环境对任何人而言都是极端难以适应的。同样的逻辑也适用于工作场所。大卫提到，如今一些开明的企业开始主动为自闭症谱系群体寻找适合他们的岗位。与其将这种思维定势判定为无用或低效，不如去探寻“这种独特的思维方式在何种情境下是具有适应性（adaptive）与高价值的”。如果我们不主动发掘并创造这种土壤，就会彻底错失那些与众不同的思考者所能带来的突破性价值。斯蒂芬·巴特利特对此深表认同，并指出大卫的研究无论初衷如何，都为职场的多样性（Diversity）提供了无懈可击的有力论据。 --- ### [01:25:00](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5100s) 邓克尔辐射问题与类比思维的力量为了直观展示多元化思维的作用，大卫在现场给斯蒂芬做了一个心理学测试。他让斯蒂芬假想自己是一位身穿白大褂、脖子上挂着听诊器的医生，而大卫是患有恶性胃部肿瘤的病人： - **医疗难题**：有一种全新聚焦的高强度射线能够彻底摧毁肿瘤，但其致命缺陷在于，达到足以杀死肿瘤的强度时，也会同时穿透并摧毁胃部周围健康的正常组织。医生该如何拯救病人？在斯蒂芬陷入沉思时，大卫讲了两个看似毫无关联的故事： 1. **将军攻打城堡**：一位将军率军推翻暴君，目标是占领一座处于中心辐射状道路枢纽的堡垒。道路上布满了地雷，全军如果集中走任何一条路都会遭遇惨重伤亡。于是将军将部队分散成单列纵队，分派到不同的辐射状道路上，大家对齐手表时间，在同一时刻从四面八方同时向城堡汇聚发起突击，最终成功破城。 2. **小镇灭火**：一个小镇发生火灾，濒临蔓延至周边建筑，附近的居民纷纷从湖里打水一桶桶泼过去，但杯水车薪。消防队长赶到后立即喊停，命令所有人打满水桶在火源周围围成一个大圆圈，听口令数到“一、二、三”同时将水泼向火心，大火瞬间被扑灭。大卫随即揭晓了胃癌难题的答案：医生可以在身体四周布置多束低强度的射线，让它们从不同角度射入体内，由于单束射线强度极低，穿透胸腔组织时完全无害，但当它们在肿瘤所在的核心靶点精确汇聚重叠时，就会叠加成为高强度的毁灭性能量，从而在不伤及健康组织的前提下杀死肿瘤。 ```mermaid flowchart TD accTitle: Dunker Radiation Analogy Mapping accDescr: 结构相似性类比映射图，展示不同场景下的分散与汇聚思维 subgraph Problem["目标问题：邓克尔辐射问题"] A["高强度射线会破坏健康组织"] --> B["解法：多束低强度射线环形分布，在肿瘤焦点处汇聚重合"] end subgraph Analogy1["类比故事一：将军攻打地雷城堡"] C["单路大军推进会踩雷溃败"] --> D["解法：分散各路纵队，同步向中心堡垒汇聚突袭"] end subgraph Analogy2["类比故事二：小镇合力灭火"] E["个人单桶泼水无法压制火势"] --> F["解法：众人围成圆圈，口令同步向中心火源齐泼"] end D -. "结构相似性 (Structural Similarity)".-> B F -. "结构相似性 (Structural Similarity)".-> B ``` 大卫解释道，这就是著名的**邓克尔辐射问题**（Duncker Radiation Problem）。大量实证研究证明： - 当团队面对全新的陌生问题时，能否找到优质解决方案，完全取决于团队能够联想出的**类比数量与广度**。 - 而类比的丰富度，直接取决于团队成员**过往背景与经验的广度**。 - 如果一个团队全都是背景完全相同的专业人士，那“多个人其实并不比一个大脑聪明多少”。 - 正确的解决策略是构建“参考类”（Reference Class）：召集背景迥异的人坐在一起，从各个八竿子打不着的领域挖掘在底层结构上相似（structurally similar）的案例。即便受试者最初并不知道这些故事有关联，随着听到的类似故事越多，独立解出辐射难题的人数比例就会出现戏剧性的攀升。 --- ### [01:27:45](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5265s) 麦克风的日常难题与“创意悬崖错觉” 斯蒂芬结合录音室现场的真实经历印证了大卫的观点。他指着两人头顶上方透露，本期播客是他们有史以来第一次撤掉了桌面麦克风。之前每期节目都会收到大量观众在 LinkedIn 上的吐槽，抱怨嘉宾激动敲桌子时发出的剧烈杂音。昨天团队开会解决这个问题时，就上演了一场小型的类比风暴： - 音频工程师 Will 拿出了专业音频技术的方案； - 联合制作人 Jack 提出了自己的录音设想； - 斯蒂芬自己没有任何专业音频经验，但因为主持商业真人秀《龙穴》（*Dragons' Den*），他提出：“我们在节目里都是直接把麦克风用胶带粘在胸口的，为什么不直接粘在嘉宾胸前？” 大家迅速在白板上做成本效益与可行性推演。斯蒂芬的方案很快被否决了——因为嘉宾录制时常常会无意识地抓挠胸口弄出杂音，而且“总不能每位嘉宾一到场，我们就得上去摸人家的胸部贴麦克风吧”。最终他们采纳了 Jack 提出的从顶部向下悬吊的麦克风方案。斯蒂芬感叹，正是这种来自完全不同领域、看似荒谬的跨界方案碰撞，才逼近了最佳决策；然而大部分商业团队在招聘和组建项目组时，却总是机械性地去重复搜罗“干过这行的人”。大卫借此提醒大家警惕两个常见的认知陷阱： 1. **不要直接抓取跳入脑海的第一个类比**：必须建立备选方案清单（Menu of solutions）。 2. **警惕“创意悬崖错觉”**（Creative Cliff Illusion）：人们往往误以为最好的点子要么如闪电般迅速降临，要么就再也不会来了。而心理学研究表明事实恰恰相反——最卓越、最具创新性的想法，往往出现在持续深入推演的**中后期**。如果人们把创意当成“灵光一闪”，一旦第一下没有灵感就彻底放弃思考，那就会永远错失真正伟大的突破。 --- ### [01:30:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5418s) 缺乏约束的灾难：General Magic 的神话幻灭大卫正在撰写一本关于“约束”（Constraints）的新书，斯蒂芬顺势引出书中一个极具警示意义的硅谷商业案例——传奇初创企业 **General Magic**： - **梦幻开局**：该公司由当年设计第一代苹果电脑 Macintosh 的核心功勋团队创立，汇聚了全硅谷最顶尖的天才工程师，是当时科技界万众瞩目的焦点。 - **超前构想**：早在 20 世纪 90 年代初，他们就画出了与今天的 iPhone 如出一辙的手持智能设备概念草图，理念超前于整个时代。 - **概念上市**：由于团队光环过于耀眼、愿景极具震撼力，他们甚至在连一件可用实体产品都没做出来的情况下，就凭借所谓概念 IPO（Concept IPO）成功上市套现，并获得了近乎无限制的研发资金。 - **失控与覆灭**：然而，这出科技神话最终演变成一场灾难性的溃败。根本原因在于**彻底缺乏约束与边界**（No boundaries）：他们手握永远花不完的热钱，脑子里却没有任何明确的目标客户群体。工程师们只要觉得某个功能“很酷炫”就肆意往上堆砌代码和硬件，导致项目范围恶性膨胀、无休止地蔓延失控，最终从未能在清晰的焦点中落地。 ### [01:31:31](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5491s) 约束的力量：从通用魔术的教训到写作流程的重构大卫·爱普斯坦（David Epstein）提到，从苹果早期衍生公司通用魔术（General Magic）走出来的许多校友身上，沉淀出了一个极其深刻的教训：在做产品时，即便设想了一个错误的客户画像，也远比完全没有客户、仅仅凭空做自认为“很酷”的东西强得多。因为哪怕目标设想错了，你在试错的过程中至少能获得反馈机制，从而明白自己错在哪里；若完全没有目标受众，你连学习和迭代的抓手都没有。与这些前员工的交流激发了大卫对“约束”（constraints）这一课题的极大兴趣。这种在广度（breadth）与约束、专注之间的张力看似充满矛盾，但这恰恰是他在出版畅销书《范围》（*Range*）后最常被读者追问的核心问题：“一个人究竟什么时候该停下探索、转向专注？”大卫指出，没有人能够永远像弹珠一样漫无目的地四处乱撞，广泛积累工具箱的最终目的，是在某个节点将其收敛聚焦，打出一段爆发性的“连续高光期”（hot streak）。对于大卫自己而言，这种研究也是一种审视内心的“自我探究”（me-search）。在以往的著作中，他总要写出超过成书体量 150% 的文字，把所有感兴趣的事物都塞进去，然后再痛苦地大刀阔斧删减；而且他习惯跳着写（比如调研完第五章就直接动笔写第五章）。为了真正学会运用约束，大卫在创作新书时首次逼迫自己设定先验框架：提前制定好章节架构，严格按顺序写作，并把篇幅控制在目标字数内。这种从业近二十年来的工作方式剧变让他觉得既新鲜又有些惶恐——当他试图按老习惯先写第五章时，立刻发现跳跃式写作会破坏整体结构，且由于不知道前面章节的具体交代而留下无数空白。约束不仅逼迫他重新审视结构，也让他首次有望交出一本从一开始就不严重超篇幅的初稿。 ```mermaid flowchart TD accTitle: 探索与约束的循环机制 accDescr: 展示从广泛探索到约束聚焦、建立反馈并产生高光表现的闭环结构。 subgraph Explore["探索阶段（广度）"] A["多领域尝试与工具箱积累 （Broad Toolbox）"] end subgraph Focus["收敛阶段（约束）"] B["设定明确边界与假设目标 （即使可能设想错误）"] C["获得现实反馈机制 （Feedback Mechanism）"] D["聚焦形成高光期 （Hot Streak）"] end A --> B B --> C C --> D ``` - **反馈机制的价值**：凭空做自认为酷炫的东西缺乏现实锚点；设立具体的约束与服务对象（哪怕是错的）能形成最起码的反馈回路。 - **写作习惯的颠覆**：此前习惯撰写 150% 篇幅再删减、章节任意跳序；新书强制遵守预设架构并首次按章节顺序顺推。 --- ### [01:34:05](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5645s) AI 冲击下的工作范式：从自动取款机到放射科医生的战略转型当被主持人问及面对日新月异的人工智能模型、作家与普通脑力劳动者是否应感到焦虑时，大卫展现出一种务实且充满好奇的态度。他透露自己每天至少会玩转四种不同的 AI 工具，其中对他帮助最大的是文献分析平台 `scite.ai`。以往为了理清某篇科学论文的学术脉络以及后人的赞成与反驳，他必须泡在大学研究图书馆里一整天手动扫读论文；现在只需一键输入，AI 就会生成文献引证图谱，并标明后续研究究竟是支持还是反驳原观点，甚至直接摘录出引用的上下文切片。“以往耗费一整天的工作，现在缩短至一小时。”大卫坦言，哪怕 AI 的普及导致自己的图书销量下滑，只要能换来以十倍速汲取科学新知的效率，对他而言也是一笔无比划算的交易。对于 AI 对更广泛就业市场的冲击，大卫引用了 1970 年代前后美国引入自动取款机（ATM）的经典经济学案例。当时主流媒体普遍预测全国三十万名银行柜员将一夜失业，但事实却是随后的四十年里，银行柜员的总数不降反升。原因在于 ATM 大幅降低了单家银行网点的运营成本，催生了大量新分行的设立；尽管单店配置的柜员变少了，但全社会网点总量激增。更关键的是，柜员的工作性质发生了本质跃迁——从机械重复的数钞票、办存取，转变成了客户关系维护、营销咨询与理财顾问。这种技术变革把人类从“温和学习环境”（kind learning environment）下的机械重复中解放出来，推向了需要综合战略思维的角色。同样的逻辑也适用于当下的医疗领域。许多投行报告预测放射科医生首当其冲会被 AI 图像识别彻底淘汰，但现实中公众面临的痛点从来不是“放射诊断服务过剩”，而是优质医疗资源的稀缺。大多数临床医生日常处理的并不是《豪斯医生》（*House, M.D.*）里的罕见怪病，而是成千上万次常见的常规病灶；医生真正无可替代的价值在于制定后续的战略干预——如何向病人解释病情？怎样将治疗方案落地到病人的真实生活中？哪些改变对病人的生活方式是现实可行的？AI 承担的是战术层面的读片识别，而医生则应顺势升维至整体健康的战略管理者。 - **`scite.ai` 的研究效能**：自动分析学术文献引用网络，智能归类赞同或反对观点，并精准提取关键引文片段。 - **ATM 悖论**：自动化降低网点边际成本导致分行数量激增，柜员职责由单纯柜面交易转为复杂的营销与顾问职能。 - **放射科与医疗升维**：AI 释放机械识图精力，医生回归战略性医患沟通与个性化治疗落地。 --- ### [01:37:47](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5867s) 自由式国际象棋与战略高地：人类的最终阵地大卫进一步用国际象棋的历史转折点来阐释人机协作的本质。1997 年 IBM 的“深蓝”（Deep Blue）击败了当时世界棋王加里·卡斯帕罗夫（Garry Kasparov）。卡斯帕罗夫敏锐地意识到，计算机在“战术”（tactics，即精细计算几步之内的具体杀法模式）上展现出压倒性优势，但彼时在统筹全局战局规划的“战略”（strategy）上仍逊色于顶尖人类。为此，卡斯帕罗夫开创了“自由式国际象棋”（Freestyle Chess），允许人类棋手与任何计算机软件自由组合参赛。出人意料的是，自由式锦标赛的最终冠军既不是算力顶尖的超级计算机，也不是单打独斗的国际特级大师，甚至不是带着超级电脑的特级大师，而是两名配备了三部普通笔记本电脑的业余棋手。这两位业余玩家既懂得基础国际象棋知识，又懂得算法搜索的逻辑边界，他们扮演的角色是指导计算机“往哪个方向算”。在夺冠后的新闻发布会上，他们甚至连自己的棋局都无法像大师那样进行极具深度的战术复盘，因为他们缺乏大师级的记忆积累；但正是由于战术环节被外包给算法，胜负的关键彻底转移到了战略调度与人机协同的水平上。面对“万一未来 AI 在战略层面上也远超人类”的质疑，大卫思考后指出，即便到了那一阶段，人类依然拥有不可剥夺的最高层级权力——决定 AI 的战略目标是什么。“我们究竟希望生活在一个怎样的世界里？”这本身就是最顶层的战略决策。大卫回忆起前一年在一次围炉夜话中的场景，围坐在一起的有一家生成式 AI 公司的创始人及其早期投资人，两人同属行业顶尖技术专家且利益深度一致，但一人断言通用人工智能（AGI）三年内必将实现，而另一人则坚定地认为这纯属无稽之谈。这种行业内部的巨大分歧表明，在底层目标与未来走向的确立上，人类依然需要承担最核心的探索与定义工作。 - **1997 年深蓝破局**：深蓝在依靠海量计算与模式匹配的战术微操上胜出，暴露了纯人类记忆型战术的局限。 - **自由式国际象棋启示**：两名普通业余棋手操控三台电脑击败超级算力与顶尖棋手，证明“算法指导力 + 战略把控”超越了单纯的专业知识垄断。 - **终极分工界限**：战术外包将人类推向更高维度的战略定位；决定技术目标与社会愿景（"What kind of world do we want to live in"）是人类保留的终极阵地。 ### [01:39:37](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=5977s) 比较优势法则与 AI 时代的经济冲击围绕人工智能的认知分歧十分显著，即便是同样处于技术前沿、利益诉求完全一致的两位专家，对于 AI 的定位也大相径庭：一人可能将其视为变革未来的力量，另一人却将其贬为“被美化的玩具”，日常依然更依赖传统搜索引擎。这种内部分歧表明，即使是亲身开发 AI 的业内人士，也远未完全摸透其真实能力与底层逻辑。针对主持人 Steve Bartlett 提出的假设——如果隔壁坐着一个智商高达 1000 的“超级 Steve”，而自己智商只有 100，是否所有任务（从送孩子上学到回答日常问题）都该交给超级个体，甚至让人类沦为无所作为的废人——David Epstein 借用经济学中的“比较优势法则”给出了反驳。即使未来 AI 在几乎所有细分领域的能力都超越人类，现实中也绝不存在取之不尽的算力与能源。大型模型是极其耗能的系统，由于资源有限，人类必然需要战略性地将能源集中于最具战略价值的刀刃上。正如企业创始人即使在某些执行层面上胜过基层员工，也不会事必躬亲，而是依靠相对优势进行分工协作。因此，人类依然拥有不可替代的角色与定位。然而，更紧迫的危机在于技术落地与社会财富分配之间的剧烈脱节。回顾人类文明史，前 30 万年人类如松鼠般采集渔猎，随后的 1 万年过着农耕生活，而近 250 年的工业与技术革新则以每代人一颠覆的狂暴节奏推进。尽管工业革命最终帮助数十亿人摆脱贫困，但历史证据表明，生产力的飞跃与实际工资的上涨之间存在长达约 40 年的时间鸿沟（即“恩格斯停顿”）。如果现代 AI 引发的新一轮生产力爆炸再度伴随 40 年的共享繁荣真空期，社会结构将难以承受这种动荡。 ```mermaid flowchart LR accTitle: 技术红利转化为共享繁荣的历史与现代对比 accDescr: 对比工业革命时期工人面对面联合行动与现代数字化无形工厂下分散个体的集体行动困境。 subgraph IR["工业革命模式"] A1["工厂高度物理集聚"] --> B1["直面共同剥削与困境"] B1 --> C1["集体行动与劳工权益博弈"] C1 --> D1["跨越40年鸿沟实现共享繁荣"] end subgraph AI["现代 AI 时代"] A2["数字化与远程协作（无形工厂）"] --> B2["个体高度分散且孤立"] B2 --> C2["难以形成集体行动与诉求"] C2 --> D2["亟需前瞻性干预以避免社会撕裂"] end ``` ### [01:42:15](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6135s) 无形工厂困境与被动发展的技术反思工业革命之所以最终走出了 40 年的收入滞后阵痛，关键在于工人们被集中到实体城市与厂区，能够直面彼此相同的困境并迅速形成工会等集体行动。相比之下，当代劳动者分布于分散、孤立的“无形工厂”（Invisible Factory）中，在缺乏物理连接的环境下，发起协同集体行动的难度显著上升。面对这种现状，人类不能再仅仅因为一项技术“很酷”就盲目投入开发，而必须共同发问：我们究竟想要生活在一个怎样的世界里？需要先明确希望技术解决的现实难题，再去针对性构建系统。尽管主持人对人类能否保持这种理性与主动性持悲观态度，但 David Epstein 观察到了一个相对积极的微小转变：即便平时最热衷于夸大其词、动辄鼓吹技术奇迹的硅谷技术狂人，如今在 AI 发展的早期阶段也罕见地发出了审慎与警惕的声音。这种担忧不仅不应被忽视，反而是必要的清醒剂——面对如此巨大的变量，心存戒惧远比盲目乐观更为安全。 ### [01:43:08](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6188s) 终极潜能指标：基线才能与“可训练性” 谈及著作《运动基因》（*The Sports Gene*）中常被大众忽视的核心洞见时，David Epstein 指出：人们在未经专业训练时的初始水平（即“基线才能”，Talent at Baseline），与经过训练后所展现出的提升幅度之间，往往并不存在必然关联。训练机制本质上类似于医学用药——由于个体生理与神经特质的差异，完全相同的训练方案作用于两个人身上，产生的效果天差地别。现实中，人们习惯仅凭候选人眼下的初始表现去仓促评判其未来潜能。然而科学研究证明，“可训练性”（Trainability）本身就是一种独立于初始天赋的高阶才能，其长远价值甚至远超基线水平。如果企业或教练仅在个体尚未摸索出适配训练法前就草率筛选，很容易错失真正的顶尖人才。可训练性虽然在摄氧量等生理维度上极易量化测量，但在复杂职场技能中则更具主观性。主持人 Steve Bartlett 以实际招聘为例予以印证：在考察播客制作人等长期核心岗位时，比起对方当下的技能熟练度，他更看重成长轨迹。通过翻阅求职者的 LinkedIn 履历，他会重点拆解候选人是否在短时间内跨部门快速晋升（如一年内从实习生升至经理、总监乃至全球负责人）： - **跨部门快速跃迁的能力**：能够在人力、企业文化乃至业务部门之间自由切换并迅速晋升，是极强适应力、认知敏捷度与“快速学习素养”的铁证。 - **群众基础与协同认可**：每一次跨级提拔都意味着既有领导层的主动背书，以及团队成员对其领导能力的认同投票。 ### [01:46:26](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6386s) 连环创新者的特质与狭隘招聘的系统盲区这种对跨界成长速度的推崇，与 David Epstein 在《成长的边界》（*Range*）中探讨的“连环创新者”（Serial Innovators）概念高度契合。伊利诺伊大学的阿比·格里芬（Abby Griffin）教授及其团队通过系统性研究发现，无论被安置在哪个部门或领域，连环创新者都能持续为组织带来突破性的创造贡献。通过对这群创新者的行为画像进行追踪，研究提炼出了一系列极其独特的共性： - **系统思考者（Systems Thinkers）**：具备穿透表象的宏观系统视角，能够洞悉不同业务模块间的深层逻辑关联。 - **跨领域深度阅读**：其阅读广度与涉猎范畴显著超越同侪，有着强烈的内在冲动去涉足自身专业领域之外的未知世界。 - **跨界沟通驱动力**：本能地渴望与不同背景、拥有异质专业知识的人士进行深度交流。 - **思维在点子间“频繁跳跃”**：在日常工作中常常展现出“在不同想法之间轻盈穿梭”（Flit among ideas）的特征，这种表现常被传统眼光误判为缺乏专注，实则是高频进行概念重组的表征。 - **存量资源的重组与活化**：极度擅长将市场上或企业内部已有的现成工具、材料与技术，通过全新构架转化为创新的解决方案。格里芬在学术论述中向招聘部门提出了严肃警示：现代企业若继续秉持过度狭窄、刻板的职位描述（Job Description）进行筛选，本质上是在用系统化手段将最具颠覆性的连环创新者排除在外，甚至逼迫他们离开组织另谋出路。 ### [01:47:19](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6439s) 招聘难以传授的特质与自我颠覆型组织大卫·爱普斯坦（David Epstein）指出，企业无法凭空凭造出具备跨界思维的通才，但如果不允许员工在内部自由轮岗或横向移动，绝对会扼杀这类人才的发展。在企业招聘策略上，那些能够持续实现自我颠覆、而不是坐等被外界淘汰的优秀组织，往往会特意保留一部分招聘配额用于逆向选拔。普通公司习惯于“照着方孔找方榫”，寻找明天就能直接上岗的螺丝钉；而具有前瞻性的公司会反思：“有哪些关键素质是我们想要却极难在内部教会的？先去把具备这种特质的人招进来，至于我们擅长的本行业知识，后续完全可以培训培养。” 爱普斯坦举了苏格兰著名投资机构 Baillie Gifford 的极端案例。他在走访这家极其成功的投资公司时获悉，他们甚至一度有一条规则：不招收任何拥有 MBA 学位的人。爱普斯坦虽然并不赞同完全将某些背景的人一棍子打死，但极其认同其背后的底层逻辑——Baillie Gifford 倾向于寻找具备独特经历或某种稀缺思维方式的人才，因为这类思维习惯是无法短期教会的；至于金融和投资技能，多花几个月时间培训即可。组织若想自我颠覆，就不该将招聘重心全放在最容易传授的技能上，而是要主动寻找那些不可替代的底层思维。 ```mermaid flowchart TD accTitle: 传统招聘与自我颠覆型招聘策略对比 accDescr: 展示常规招聘只看现成技能与前瞻企业看重不可教学特质的流程差异 subgraph Conventional["传统匹配模式（容易被颠覆）"] A1["岗位需要特定技能（方孔）"] --> B1["寻找具备现成技能的人（方榫）"] B1 --> C1["立即上岗，但思维趋同、缺乏突破"] end subgraph Disruptive["自我颠覆模式（Baillie Gifford 案例）"] A2["确定极难内部培训的稀缺思维 / 视角"] --> B2["招募具备跨领域经验与开放思维的人"] B2 --> C2["花数月系统培训行业基本功与财务技能"] C2 --> D2["融合外部视角，带来持续颠覆与创新"] end ``` ### [01:48:44](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6524s) 赞助商插播：ZOE Daily30+ 植物营养补充剂主持人史蒂芬·巴特利特（Steven Bartlett）穿插了一段播客赞助内容，推荐了 ZOE 推出的 Daily 30+ 每日植物营养补充剂。该产品由 30 种天然植物配制而成，主打非超加工的药丸或冲剂奶昔，而是真实的食物粉末，可以直接舀起洒在早餐（如牛油果和鸡蛋）上。主持人强调该产品有扎实的科学研究支持，自 7 月推出以来已两次售罄，并为播客听众提供了专属 10% 折扣代码。 ### [01:49:45](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6585s) 刺猬与狐狸：颠覆常识的预测学研究话题回到爱普斯坦的研究。主持人问他在所有涉猎的研究中，哪一项最令他感到震撼并彻底改变了他的思维范式。爱普斯坦表示，关于“未来预测（Forecasting）”的科学研究彻底颠覆了他的认知。这项历时 20 年的经典预测学项目收集了超过 83,000 个具体的概率预测，涉及地缘政治、社会和技术事件。为了区分真实技能与单纯的运气好坏，研究要求参与者给出极其明确的概率和截止日期（例如：“在未来 12 个月内，南海是否会发生导致至少 5 人伤亡的军事对抗，概率是 20%”）。研究结果令人大跌眼镜：表现最差的预测者往往是那些在极其狭窄领域钻研了一辈子的顶尖专家（在研究中被称为“刺猬”）。他们习惯于通过单一的思维模型或理论框架去解释世界上的一切事物，并将所有新信息生搬硬套进自己的叙事中。相反，预测准确率最高的群体是“狐狸”型人才。他们知道许多不同领域的琐碎知识，更重要的是，他们拥有科学的思考方式： - 极其善于吸收多元视角，甚至利用社交网络等一切工具搜集不同意见； - 主动拿出自己的假设，邀请持不同意见的人来进行“证伪”； - 借鉴科学方法的精髓，把观点当作随时可修正的假说。最让爱普斯坦震惊的是，在后续的预测锦标赛中，这群各行各业的普通预测者组成小组协作，其预测准确率居然轻松击败了能够接触顶级机密情报的美国情报界专业分析团队。 - **刺猬（Hedgehogs）**：精通一件大事，用单一理论框架解释世界；因态度决绝而常受电视访谈节目青睐，但实际预测准确率垫底。 - **狐狸（Foxes）**：懂得许多小事，思维具备“蜻蜓眼”（Dragonfly eyes）特征——如同蜻蜓复眼由成千上万个独立晶状体构成并在大脑合成画面一样，他们能整合海量碎片视角；虽然在电视上显得含蓄犹豫，却是顶尖的预测高手。 ### [01:52:49](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6769s) 保持“经验开放性”与 LinkedIn 晋升大数据主持人感慨，爱普斯坦的书让他意识到人很容易安于现状和已有身份，而这种自满恰恰是未来成功与幸福感最大的敌人。爱普斯坦回应道，俗话常说“老狗学不会新把戏”，其实真相是“老狗根本不想学新把戏”。在心理学大五人格特质中，“经验开放性”（Openness to experience）是预测创造力最关键的指标，而这项特质在人步入中年后通常会不可避免地显著下滑。然而，爱普斯坦在书中引用的一项研究显示，这种衰退并非不可逆。哪怕只是强迫年长者尝试完全不熟悉的新鲜事物（例如从没玩过的数独或一项全新爱好），即便他们根本没有把这门技能练精，也能有效遏制经验开放性的滑坡。做不擅长的新鲜事不仅能促进大脑健康，还能让人生感觉更漫长——因为人类记忆是以“生活篇章”来存储的，不断尝试新事物等于为人生翻开新篇章，从而防止时间在日复一日的惯性中飞速虚度。两人随后谈及领英（LinkedIn）基于约 50 万名会员数据展开的大型追踪研究。该研究得出了一个极具说服力的结论： - **高管晋升的最佳预测指标**：在同一个行业内部，个人跨越不同工作职能（Job Functions）的数量，是预测其未来能否晋升为企业高管的最强预测因素。 - **职业叙事的缺失**：爱普斯坦在与领英团队沟通时直言，领英现有的产品展示形态往往鼓励用户展示一条笔直、线性的升职路径，这在一定程度上阻碍了人们进行跨部门尝试；他建议领英应开发允许用户构建“职业叙事”的功能，让求职者能够向外界清晰阐释：“我当初为何主动切换职能，以及跨职能经历让我学到了什么。” ### [01:55:34](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=6934s) 跨职能轮岗与过度专业化的致命盲区在讨论高管与通才的培养路径时，大卫·爱泼斯坦（David Epstein）强调，除了跨行业跳槽外，在同一行业内部经历不同的“职能”（job functions）同样至关重要。这里的职能转变并不是简单的部门轮转，而是必须从事“本质上完全不同的工作内容”。最直观的例子就是从一名个人绩效出色的执行者（performer）转变为管理他人的领导者，或者在媒体出版行业中，从一名撰写稿件的作者转向负责统筹与管理的编辑。这种横向侧移（side step）虽然在某些组织中被视为平级调动，但它能逼迫大脑打破单一的执行逻辑。随后，主持人斯蒂芬·巴特利特（Steven Bartlett）提到了爱泼斯坦在书中记录的一项令人震惊的医学研究：心脏病患者在顶级专家离开医院去开学术会议期间被送入教学医院，死亡率反而更低。爱泼斯坦解释道，医学的专业化分工虽然带来了巨大的技术飞跃，其发展不可避免且大有裨益，但过度专业化往往是一把未被充分认识的“双刃剑”。哈佛大学主导的两项研究显示，当全美顶级心脏病专家集体前往参加全国心脏病学会年会时，留守医院的患者死亡率显著下降。这种反直觉的现象源于心理学中的“定势效应”（Einstellung effect）——顶尖专科医生极度精通某一种复杂的手术或治疗手段，以至于他们会惯性地继续使用该手段，哪怕客观数据已证明它不再适用，或者该患者的病症根本不需要如此激进的介入手段。他们陷入了“手里拿着锤子，看什么都像钉子”的认知盲区。 ```mermaid flowchart TD accTitle: 专业化双刃剑与定势效应 accDescr: 展现过度专业化如何导致心理学上的定势效应并引发次优后果 subgraph Specialization["过度专业化的陷阱"] A["长期深耕单一术式/技能"] --> B["思维固化：产生定势效应 (Einstellung Effect)"] B --> C["面对问题机械套用既有惯性手段"] end subgraph Paradox["反直觉的临床结果"] D["顶级心脏病专家外出开会"] --> E["高风险/不必要介入手术减少"] E --> F["重症心脏病患者死亡率反而降低"] end C -.-> E ``` ### [01:57:53](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7073s) 外科医生的生日效应与深层认知的自我颠覆爱泼斯坦进一步将手术室的现象与前文探讨的“专注与分心”联系起来。同一批研究人员对高达 98 万次外科手术进行了海量数据分析，结果发现了一个离奇的事实：如果病人的手术恰好安排在主刀医生的“生日当天”，病人在术后 30 天内的死亡率会出现可测量的统计学上升。研究人员认为，这归因于主刀医生在生日当天面临更多来自外界或内心的分心干扰（无论是手机收到的祝福信息，还是下班后庆祝活动的心理预期）。虽然单次手术的相对风险增幅看似微小，但在庞大的人口基数上，这直接关系到成百上千条人命。巴特利特深受触动，坦言深度的专业知识虽然能成就一个人，但在长期来看也可能成为毁掉一个人的诅咒。面对飞速变化的世界，保持认知谦逊、时刻准备“颠覆自我”至关重要，不能盲目坚信昨天的正确在今天依然成立。爱泼斯坦对此深表赞同，并拿自己的人生经历现身说法：他在读研究生攻读环境科学时，曾坚信环境保护与技术进步是势不两立的；而如今他的认知发生了 180 度大反转，他清醒地意识到，虽然某些技术的滥用会破坏生态，但全人类真正拯救地球环境的唯一希望恰恰必须依赖于新技术的突破。 - **980,000 例手术数据**：大样本研究证实，在主刀医生生日当天接受手术的患者，术后 30 天内死亡风险显著增加。 - **走出“终身一次性培训”的幻想**：爱泼斯坦指出，除非未来的人工通用智能（AGI）和无限能源彻底接管了所有人类劳动，否则绝大多数人必须抛弃“年轻时受过一次专业教育就能吃一辈子红利”的过时思想，必须终身处于持续学习与自我更新的状态。 ### [02:00:01](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7201s) 最后的日记提问：推倒重来的冲动与学会宽恕按照《CEO日记》的经典传统，上一位嘉宾会在黑皮笔记本中为下一位未知的嘉宾留下一道亲笔问题。巴特利特笑着调侃，许多见多识广的嘉宾在看到他翻开这本笔记本时都会肉眼可见地紧张起来，甚至比回答前一百个硬核问题还要局促不安。在开玩笑猜是不是问“最喜欢什么三明治”之后，巴特利特郑重念出了留给爱泼斯坦的问题：“你最大的恐惧是什么？你打算如何去面对它？” ``` 上一位嘉宾的提问： "What is your biggest fear and how do you plan to face it?" （你最大的恐惧是什么？你打算如何去面对它？） ``` 面对这个问题，爱泼斯坦罕见地展露了极为脆弱和私密的一面。他坦言自己内心有一种根深蒂固的病态冲动——极度渴望“把事情彻底推翻、从零开始”（burn them down and start over）。这种追求新体验、拒绝固守成规的特质在职业探索上是他的超能力：例如第一本畅销书《运动基因》（The Sports Gene）大获成功后，经纪团队极力劝他把自己打造成“运动基因专家”，但他内心却觉得“这本书在出版前对我来说就已经死了”，从而决绝地投身到完全不同的领域写出了《范围》（Range）。然而，当这种冲动蔓延到人际关系中时，却造成了破坏性的后果。过去当一段亲密关系或友情发展得很顺利、原本已经很好时，他却无法安于平淡，总是执拗地追求完美，导致他亲手推倒并毁掉了一些本可避免破裂的宝贵关系。爱泼斯坦将这种心结归结为自己对“永远处于演变与成为之中”（always want to be in becoming）的近乎中毒般的迷恋。为了面对并克服这种潜在的恐惧，他近期在一本哲学书籍中接触到了“叙事价值观”（narrative values）——即跨越不同文化被普遍认可、且个体内心也强烈渴望融入自身生命故事的美德。爱泼斯坦意识到，在忠诚、英勇等众多价值观中，自己最欠缺却最需要修炼的品质是“宽恕”（forgiveness）。他直言不讳地承认自己目前极其不擅长原谅别人，但他正在将“成为一个懂得宽恕与接纳的人”作为当下人生最重要的课题，去努力打破非黑即白、动辄将关系推倒重来的破坏性循环。巴特利特深有共鸣地总结道，这恰恰呼应了大卫一生研究的核心主题：学习的本质就是敢于识别自身的严重缺陷，并在未知中不断重塑自我。 ### [02:03:48](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7428s) 反思与致敬：打破常规叙事的深层价值主持人斯蒂芬·巴特利特（Steven Bartlett）向大卫·爱泼斯坦（David Epstein）表达了由衷的敬意与赞美。他指出，大卫的作品之所以如此震撼人心，是因为它们展现了一种完全原创、充满挑战性且反传统的解决问题视角。在现实生活中，许多人习惯性地把一些未经审视的说法当成不言自明的真理，但如果这些被普遍接纳的叙事本质上是错误的，无论在短期还是长期来看，它们都会对我们的发展造成损害。大卫的研究之所以弥足珍贵，正是因为它为大众深信不疑的教条提供了扎实有力的“反叙事”（counternarrative）。巴特利特感慨道，尽管核实真相的过程无比繁琐艰辛，甚至常常让人头痛欲裂，但大卫始终愿意比绝大多数作家多走一步，去深挖流传的说辞背后是否真有实据支撑。这种刨根问底的努力绝非为了争辩而争辩，其终极目标是为了让每个人都能在属于自己的领域里，过上更具优化性、更充实、更快乐也更高效的生活。面对大卫正在撰写的关于“限制/约束条件”（constraints）的新书，巴特利特寄予厚望，坚信只要它延续了《成长的通才》（*Range*）和《运动基因》（*The Sports Gene*）的水准，必将再度成为一部不可多得的重要力作。大卫对此深受触动，微笑着回应道：“这是一份极高、极美好的赞美，我甚至不想再多说什么去破坏这一刻了。” - **对抗错误叙事**：许多被社会广泛采纳的常识如果脱离实际，往往会误导大众的选择与人生轨迹； - **探寻真相的成本**：鲜有作者愿意付出大卫这样繁重的心力与时间，只为检验主流理论是否真正站得住脚； - **后续创作期待**：大卫正在构思并撰写一本关于“约束与限制（constraints）”的新作，备受瞩目。 --- ### [02:04:54](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7494s) 赞助支持：PerfectTed 带来的持续专注力对谈结束后，节目进入了赞助商推荐环节。巴特利特介绍了席卷英国市场的绿色天然能量饮品 PerfectTed。随着消费者对传统高糖、不健康能量饮料的厌倦，该品牌迅速走红，并全面入驻了 Tesco 和 Waitrose 等主流连锁超市，在极短时间内取得了接近 10,000% 的惊人业务增长。巴特利特坦言，自己不仅是 PerfectTed 的品牌投资人，更是其忠实用户。在录制播客时，面对动辄三到六个小时的高强度深度对谈，这款能量饮料能帮助他维持长时间的敏锐专注，且不会产生传统含糖饮料所导致的突发性精力崩溃（crashes）。为此，节目特别为《CEO 日记》的听众争取到了线上专属限时优惠，使用促销代码 `DIARY40` 结账即可享受 40% 的折扣。 ## Notable quotes > [02:30](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=150s) Breadth of training predicts breadth of transfer. — David Epstein > [25:41](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1541s) We learn who we are in practice, not in theory. — Herminia Ibarra > [25:41](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=1541s) If you don't have a 15% to 20% failure rate in your practice, you are not in the zone of optimal push. — David Epstein > [36:18](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2178s) What is a skill that you're really good at that we don't use here? — Ed Hoffman > [44:24](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=2664s) Short-term optimization can lead to long-term impairment. — David Epstein > [51:48](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=3108s) Lateral thinking with withered technology. — Gunpei Yokoi > [01:07:36](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=4056s) I think my dad was ahead of his time. — Serena Williams > [02:00:01](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7201s) What is your biggest fear and how do you plan to face it? — Steven Bartlett > [02:00:01](https://www.youtube.com/watch?v=MsERVcx0Qg8&t=7201s) I always want to burn things down and start over. — David Epstein ## Mentioned David Epstein, Steven Bartlett, Range: How Generalists Triumph in a Specialized World, The Sports Gene, The Dark Horse Project, Riet Elferink-Gemser, Sports Illustrated, Anders Ericsson, Tiger Woods, Roger Federer, Herminia Ibarra, Dashun Wang, Adam Grant, Bill Gore, W. L. Gore & Associates, Gloria Mark, Ed Hoffman, NASA, Readwise, US Air Force Academy, Daniel Kahneman, Robin Hogarth, Gunpei Yokoi, Nintendo, Game & Watch, Game Boy, Maya Angelou, Malcolm Gladwell, Serena Williams, Vasiliy Lomachenko, Angela Duckworth, West Point, General Magic, Apple, scite.ai, Garry Kasparov, Deep Blue, Abby Griffin, Baillie Gifford, ZOE, LinkedIn, Harvard University, PerfectTed ## Video description David Epstein is a journalist, speaker, and New York Times best-selling author of books such as, ‘Range: How Generalists Triumph in a Specialized World’ and ‘The Sports Gene: Talent, Practice and the Truth About Success’. 00:00 Intro 02:31 Why Do You Do What You Do? 03:04 What Areas Of Self-Improvement Do You Focus On? 05:33 How Can People Get Better 06:16 The Connection Between Fulfillment And Growth 08:06 How To Be Successful And Fulfilled 12:18 How David Found His Purpose 14:34 What Is The 10,000-Hour Rule? 22:20 Why People Focus On Exceptions Rather Than The Norm 24:46 How To Boost Productivity 27:34 The Explore/Exploit Tradeoff 33:11 How To Increase Productivity At An Individual Level 36:27 Experiments You Should Be Running For Success 38:00 How To Become A Better Learner 42:23 The Hypercorrection Effect 47:35 Building Connections Through Knowledge 54:06 What Is A Wicked Learning Environment? 56:11 The Secret Behind Nintendo’s Success 58:51 How Important Is Focus For Achieving Success? 01:01:23 Is Music Hurting Your Concentration? 01:05:58 The Impact Of Notifications On Your Brain 01:09:52 Why General Learning Beats Specialization 01:14:27 The Risks Of Specializing Too Early 01:20:00 How To Discover And Pursue Your Passion 01:22:28 Why Grit Is The Key To Success 01:25:31 How To Achieve Flow In Your Passion 01:27:29 Are Neurodivergent People Geniuses? 01:34:44 Apple & General Magic: How Focus And Constraints Lead To Success 01:38:32 Should We Be Concerned About AI? 01:47:24 The Most Important Idea We Haven't Discussed Yet 01:49:23 Can Trainability Be Measured? 01:50:46 What Are Serial Innovators? 01:54:11 The Most Important Idea In David's Work 02:00:49 The Dangers Of Specialism 02:04:33 What Is Your Biggest Fear & How Do You Plan To Face It You can purchase David’s book, ‘Range: How Generalists Triumph in a Specialized World’, here: https://amzn.to/3XrnUYb My new book! 'The 33 Laws Of Business & Life' is out now - https://g2ul0.app.link/DOACBook You can purchase the The Diary Of A CEO Conversation Cards: Second Edition, here: https://g2ul0.app.link/f31dsUttKKb Follow me: https://g2ul0.app.link/gnGqL4IsKKb Sponsors: PerfectTed: https://bit.ly/PerfectTed-DIARY40 with an exclusive code DIARY40 for 40% off ZOE: https://www.zoe.com/daily30 with an exclusive 10% discount on their first order of Daily30+ with the code PODCAST10 [Source](https://www.youtube.com/watch?v=MsERVcx0Qg8)

Source quotes

[2:30] David Epstein: Breadth of training predicts breadth of transfer. [25:41] Herminia Ibarra: We learn who we are in practice, not in theory. [25:41] David Epstein: If you don't have a 15% to 20% failure rate in your practice, you are not in the zone of optimal push. [36:18] Ed Hoffman: What is a skill that you're really good at that we don't use here? [44:24] David Epstein: Short-term optimization can lead to long-term impairment. [51:48] Gunpei Yokoi: Lateral thinking with withered technology. [67:36] Serena Williams: I think my dad was ahead of his time. [120:01] Steven Bartlett: What is your biggest fear and how do you plan to face it? [120:01] David Epstein: I always want to burn things down and start over.

Source quotes

[5:33] David Epstein: The way to make the best 20-year-old, 30-year-old, 40-year-old is not the same as the way to make the best 10-year-old. [18:18] David Epstein: The original conclusion was wrong... There were actually people who were practicing less and doing better than people who practiced more. [24:46] Herminia Ibarra: We learn who we are in practice, not in theory. [36:20] David Epstein: You actually have to fail... like this can't be you go out for a jog and you trip on the curb or something like you have to fail something of consequence, and then your reaction to that can set a tone. [78:00] Dr. Gloria Mark: You will start self-interrupting to maintain the Cadence of interruptions to which you have become accustomed... as if we have some internal like distractometer. [70:28] David Epstein: Kids will be playing on cobblestones one day and concrete the next... and the touches are about six times as frequent as in full-scale football, so it engenders a lot more of this variability. [77:29] David Epstein: When you move into this knowledge, creativity, information economy... young people are learning things about themselves in their early 20s, and they have no agency over career switching to match it, so they were just quitting. [85:02] David Epstein: When you're facing a novel problem, the number of solutions and the chance of coming up with a good solution are predicted by the number and breadth of analogies that your group can come up with. [90:18] David Epstein: They had no boundaries. They had as much money as they wanted. They didn't have any customer in mind... and so the project just grew and grew and grew and grew and never found the focus to turn into anything usable. [120:01] Steven Bartlett: What is your biggest fear, and how do you plan to face it? [123:52] Steven Bartlett: I'm so excited to read whatever you make next... if it's anything like these two books Range and The Sports Gene, it's going to be one of the most important books I've ever read.

Original transcript

[0:00] I always told that if you do 10,000 [0:01] hours in anything you become a master in [0:02] it well that's wrong this idea [0:05] undermines this broader toolbox that you [0:07] need for long-term development if you're [0:09] doing that then you're missing [0:10] opportunities David Epstein is a New [0:12] York Times best-selling author whose [0:14] Infamous work challenges the [0:16] conventional wisdom about specialization [0:18] productivity and what it takes to become [0:20] successful what advice would you give to [0:22] a person that's thinking about how to [0:24] navigate their way to being really good [0:25] at something first of all being a [0:27] scientist of your own development and [0:28] creating what's called a self-reg [0:30] practice what is that so the cycle is [0:32] flect what do you need to work on plan [0:34] come up with an experiment for how you [0:35] can work on that is that getting a job [0:37] is it taking a class Monitor and then [0:39] evaluate and people who do that [0:40] repeatedly they just keep improving two [0:43] so for anything you're doing if you're [0:44] not 15 20% of the time failing then [0:47] you're not in your zone of optimal push [0:48] where you're getting as much better as [0:49] you possibly can what about Focus I get [0:52] distracted easily and I want to be more [0:53] productive in the time that I spend [0:54] working don't start your day with email [0:56] it's been shocking to look at the [0:57] research how big of an impairment that [0:59] is what about notifications so if you're [1:01] getting distracted all the time if you [1:02] say well now I really have to hunker [1:03] down I'm going to get rid of the [1:04] notifications you will start self [1:06] interrupting to maintain the [1:07] interruptions to which you have become [1:09] accustomed really yeah that will go away [1:11] but not immediately but there's a lot of [1:13] things that you can do for a productive [1:14] day for example if you that has enormous [1:18] influence in your productivity [1:20] interesting the other thing I found [1:21] which was pretty shocking was they start [1:22] talking about some of the dangers of [1:24] specialism yes Harvard Leed studies [1:26] found if you're in hospital with certain [1:27] cardiac conditions when the most [1:28] esteemed Specialists are aware way [1:30] you're less likely to die gosh that's [1:32] terrifying the conclusion was that's [1:35] because this is a sentence I never [1:37] thought I'd say in my life um we've just [1:39] hit 7 million subscribers on YouTube and [1:41] I want to say a huge thank you to all of [1:43] you that show up here every Monday and [1:45] Thursday to watch our conversations um [1:48] from the bottom of my heart but also on [1:50] behalf of my team who you don't always [1:51] get to meet there's almost 50 people now [1:54] behind the D of a CEO that work to put [1:56] this together so from all of us thank [1:57] you so much um we did a raffle last [2:00] month and we gave away prizes for people [2:02] that subscribed to the show up until 7 [2:03] million subscribers and you guys love [2:05] that raffle so much that we're going to [2:07] continue it so every single month we're [2:09] giving away money can't buy prizes [2:11] including meetings with me invites to [2:13] our events and ,000 gift vouchers to [2:15] anyone that subscribes to the DI Co [2:17] there's now more than 7 million of you [2:19] so if you make the decision to subscribe [2:21] today you can be one of those lucky [2:22] people thank you from the bottom my [2:24] heart let's get to the [2:25] [Music] [2:28] conversation David [2:30] yes how do you summarize the work that [2:33] you do and why you do it and who are you [2:35] really doing it for I am obsessed with [2:40] correcting what I view as [2:41] mistranslations of scientific research [2:43] about human development and so that is [2:46] the core of my work and I think I'm [2:48] doing it for everyone who is curious but [2:51] either doesn't have a scientific [2:53] background or doesn't have that [2:54] particular scientific background curious [2:56] but interested in self-improvement but [2:58] doesn't either have the time or or the [3:00] means to to go sifting through this [3:02] evidence themselves and what is the sort [3:04] of Realms of self-improvement that you [3:06] have focused on thus far in your career [3:08] well earlier on I was focused in [3:10] physical skill acquisition like in in [3:12] athletics but increasingly uh I've moved [3:14] into career and personal development [3:16] generally and looking at that with a [3:19] very very kind of long lens right so one [3:21] of the most important things to me one [3:24] of the most important messages that I've [3:25] been working on the last few years is [3:27] the fact that sometimes optimizing for [3:29] short-term development will undermine [3:31] your long-term development so let's say [3:33] if we're thinking about sports or music [3:35] or something like that the obvious thing [3:36] to do is to get a head start and [3:38] whatever you're doing pick something [3:40] stick with it don't don't switch things [3:42] because then you've lost time Focus ver [3:45] very narrowly and do as much of it as [3:47] you possibly can to the exclusion of [3:49] other things that's such an obvious way [3:51] right and you will jump out to to a lead [3:54] right we see that in sports and music we [3:56] see that in school with certain Head [3:58] Start programs that give people an [3:59] advantage in some academic [4:01] skills the problem is that kind of [4:04] narrow Focus creates short-term results [4:06] but undermines this broader toolbox that [4:09] you need for long-term development and [4:11] so you'll see what scientists call Fade [4:12] Out in these advantages which isn't [4:14] isn't necessarily actually anything [4:16] going away it's the fact that people [4:18] with this broader base will catch up and [4:20] surpass so it appears to be a fade out [4:23] okay so if you take more time to get a [4:24] broader understanding of something [4:26] whether it's in sports If you're sort of [4:27] a child prodigy um over the long term [4:30] that's going to benefit you better and [4:32] help sustain your development but in the [4:33] short term you might lose out because [4:35] there's some kid who is doing you know [4:38] really deliberate practice obsessively [4:40] and he's going to have a it's kind of [4:41] like the tortoise in the hair yeah [4:43] analogy where you know the tortoise uh [4:46] eventually wins the race yeah I mean [4:49] there's a there's a a big body of [4:51] research in Psychology that can be [4:52] summarized with the phrase breadth of [4:54] training predicts breadth of transfer [4:57] okay transfer is the [5:00] ability of someone to take skills and [5:03] knowledge and use it to solve a problem [5:05] they haven't seen before right you [5:07] transfer to a new situation and what [5:08] predicts your ability to do that is the [5:10] breadth of problems you've been exposed [5:12] to in practice if you're exposed to like [5:14] a broader set of problems you're forced [5:16] to build these [5:18] generalizable flexible models that [5:20] you'll be able to apply to new things [5:22] going forward across all of your work at [5:24] the very heart of what people are trying [5:26] to achieve in their lives what is that [5:28] at the very very heart of what they're [5:29] trying to achieve that you're speaking [5:31] to getting better getting better at [5:33] things right obviously people want [5:34] success but I think there's pretty [5:35] significant research showing that people [5:37] are often actually reacting to their [5:39] trajectory as much as their act actual [5:42] absolute performance level that the [5:43] feeling of improvement the feeling of [5:45] moving on it gives them some sense of [5:47] fulfillment right and eventually [5:49] obviously will get them to to a higher [5:51] level and so I think really this is for [5:54] people who are interested in how do I [5:56] get off sort of my plateaus going [5:57] forward and viewing it as as a lifelong [6:00] journey as opposed to trying to Peak [6:02] when they're 12 right it turns out that [6:03] the way to make the best 20-year-old [6:05] 30-year old 40-year-old is not the same [6:07] as the way to make the best 10-year-old [6:08] is is there sort of a tie here with the [6:10] subject of just happiness and how to [6:12] live a happy life fulfillment for sure [6:15] yeah those aren't exactly the same but [6:18] they're important so so to think about [6:19] this in a career development perspective [6:22] right I think probably the most [6:24] interesting research on fulfillment in [6:26] careers was this project at Harvard [6:28] called the Darkhorse project [6:30] and this was looking at how do people [6:32] find a lot of these people were very [6:35] financially successful and all that [6:36] stuff but the dependent variable was [6:38] fulfillment okay sense fulfillment and [6:42] when people would come in for sort of an [6:43] orientation in this study they would say [6:46] things to the researchers like uh you [6:49] know I started off doing this one thing [6:51] I was Medical School whatever didn't [6:53] really fit me so I went over to this [6:54] other thing and I I learned I was good [6:56] at something I didn't expect so then I [6:57] went this other direction and you know I [7:00] came don't tell people to do what I did [7:01] because like I came out of nowhere and [7:03] the large majority of people that was [7:06] their story that's why became named the [7:08] Darkhorse project Darkhorse is this [7:09] expression that means coming out of [7:10] nowhere and that the norm in this day [7:13] and age was that people who found [7:15] fulfillment would travel this kind of [7:17] zigzagging path where they would learn [7:19] maybe I'm good at something or bad at [7:20] something that I didn't expect maybe I'm [7:22] interested in something I didn't expect [7:24] and they would keep pivoting and they [7:26] would say instead of saying you know [7:28] here's this person younger than me who [7:29] has more than me they'd say here's who I [7:31] am right now here are my skills and [7:34] interests here are the opportunities in [7:35] front of me uh I'm going to try this one [7:37] and maybe I'll change a year from now [7:39] because I will have learned something [7:39] about myself and they keep doing those [7:41] pivots throughout their career [7:43] throughout their career until they [7:44] achieve what Economist call better match [7:46] quality that's the degree of fit between [7:48] someone's interests and abilities and [7:49] the work that they do turns out to be [7:51] extremely important for both your [7:53] performance and and sense of fulfillment [7:55] uh and your apparent grit if you want to [7:57] talk about that so so just on that [7:59] before we move on to grip the does what [8:01] advice does that then mean you would [8:04] give to a young person at the start of [8:05] their career that's thinking about how [8:07] to navigate their way to being both [8:09] really competent really good at [8:10] something and successful in any sort of [8:12] uh monetary way but also maintaining [8:15] fulfillment um throughout their life I [8:18] think there are two two main things to [8:20] take away from that one is to not over [8:22] focus on long-term planning like I think [8:24] we we lionize having long-term goals and [8:27] that's okay there's nothing wrong with [8:28] having long-term goals [8:30] but those aren't necessarily always so [8:32] useful for you in the moment right when [8:33] I think about myself when I was a [8:35] competitive 800 meter Runner I could [8:36] have a time goal for the end of the race [8:38] but that didn't help me actually do [8:39] anything that just you see the clock [8:41] when you're done and you're either happy [8:42] or sad having goals that are let me try [8:45] let me try moving with 300 meters to go [8:47] that gives you an actionable experiment [8:49] so short-term planning I think is is one [8:52] of the takeaways uh and and creating [8:54] what's called a self-regulatory practice [8:57] so self-regulatory learning [9:00] is means basically thinking about your [9:03] own thinking taking accountability for [9:05] your for your own learning and some of [9:06] some of the coolest studies in [9:08] self-regulatory learning actually came [9:09] out of soccer football done in the [9:12] Netherlands where this woman named Ry [9:14] elfring gemer was following kids from [9:17] the age of 12 right up through some of [9:20] them went on to teams that um you know [9:22] were Runners up in the world [9:24] cup and what she'd see in the kids who [9:26] got off performance plateaus there were [9:28] certain physi measur someone had to have [9:30] like if a kid couldn't hit at least 7 me [9:32] a second sprinting which isn't that fast [9:33] but if they couldn't hit it they weren't [9:34] making it to the top that's so there [9:36] were physiological parameters but also [9:38] the kids who would get off performance [9:40] plateaus were the ones where if you look [9:41] at them in video when they're they're [9:42] younger they're saying going to the [9:44] trainer like why are we doing this drill [9:46] I think I can do this already like I [9:48] think I need to work on this other thing [9:49] and and you know sometimes a trainer [9:50] might be like oh man just get back in [9:53] line you know but these are the kids [9:54] that are thinking about what they need [9:57] to work on what they're good at they're [9:59] making this cycle the the [10:00] self-regulatory cycle is reflect what [10:02] are you good or bad at what do you need [10:03] to work on how do you need to do that [10:05] plan come up with an experiment for how [10:07] you can work on that monitor a way to [10:09] try to measure whether objectively or [10:11] subjectively and then evaluate did that [10:14] experiment that I ran work and making me [10:15] better at this thing or not and people [10:17] who do that repeatedly they just keep [10:20] improving and I think that's what the [10:22] dark horses are doing in their careers [10:24] they're saying I'm reflecting on what [10:26] I've got I'm planning a way to test [10:27] something that'll fit me I monitor it [10:30] maybe subjectively maybe objectively and [10:32] then I evaluate what that tells me to do [10:33] for the next step and you just get [10:35] better and better and better over time [10:36] so if I'm say I'm in my early 20s in my [10:39] career how do I take that and then [10:40] Implement Implement that in a within my [10:43] life to make sure that I'm going to get [10:45] to the World Cup metaphorically speaking [10:47] Yeah so and there's something [10:49] interesting about the 20s that I think [10:51] is worth saying which is there's this [10:53] finding in Psychology called the end of [10:54] History illusion and this is the finding [10:58] that we always underestimate how much we [11:00] will change what we think we're good at [11:02] what we think we're bad at how we want [11:03] to spend our time what we prioritize in [11:05] friends [11:06] Etc and EV at every step in life people [11:11] underestimate how much they'll change in [11:13] the future change continues for your [11:14] whole life it does slow down so we're [11:15] constantly Works in progress claiming to [11:17] be finished constantly through life the [11:19] fastest time of Personality change is [11:21] about 18 to about [11:22] 28 when you're telling but it never [11:24] stops but that's about the fastest time [11:26] when we're telling people hey now you [11:27] have to have it figured out right [11:30] and that's when they're changing like [11:31] crazy and so I think it's even more [11:32] important to have this self-regulatory [11:34] practice in a journal I would say I mean [11:36] I do it these questions can be basic [11:39] what am I trying to do why what do I [11:41] need to learn to do it who do I need to [11:42] help me learn that how am I going to [11:44] make sure that person is there to help [11:45] me what experiment can I set up to try [11:47] it and then come back and evaluate the [11:49] experiment and pick a next one be being [11:51] a scientist of your own development I [11:53] think is Inc it it's it's [11:55] counterintuitive because you would think [11:57] that we would just internalize this [11:58] stuff just from doing things M but the [12:00] science is pretty clear that we we don't [12:02] get everything we can out of our [12:04] experiences from a learning perspective [12:05] unless we're doing it more explicitly so [12:07] I would recommend for someone in their [12:08] 20s to start this self-regulatory [12:10] practice what got you into the work that [12:12] you do and how did you define your [12:14] profession okay so in my past life I was [12:16] training to be a scientist environmental [12:18] scientist I was like living up in the [12:19] Arctic studying the carbon cycle like in [12:21] a tent um and I had been a competitive [12:24] Runner I had a training partner who was [12:26] one of the top ranked guys in the 800 [12:28] meters in his age group in the country [12:31] uh first family of Jamaican immigrants [12:34] was going to be the first one to [12:35] graduate college Dro dead a few steps [12:37] after a [12:38] race uh and our sort of Hometown paper [12:42] said well he had a heart attack I don't [12:44] even know what that means for someone of [12:46] that age and health [12:48] right and I got curious and eventually I [12:52] kind [12:53] of worked up the courage or whatever [12:55] that sounds silly to say it that way but [12:57] um was nervous about it to ask his [12:59] family to sign a waiver allowing me to [13:00] gather up his medical records did that [13:03] turned out he had like a textbook case [13:04] of this disease caused by a single [13:06] genetic mutation that's almost always [13:07] the cause of young athletes dropping [13:10] dead and I said we can save some people [13:12] from this with more awareness and I [13:14] wanted I decided to merge my interests [13:15] in sports and science said I want to [13:17] write about sudden cardiac death and [13:18] athletes for Sports Illustrated which I [13:20] grew up with so I got off the science [13:23] track I left after my [13:24] masters uh kind of weaved my way to [13:27] sports illustra I got in there as a temp [13:29] pitch this story about sudden cardiac [13:30] death and athletes they're like temp sit [13:32] down right and then the Olympic Marathon [13:34] trials for 2008 US team uh came to [13:37] Central Park and the guy ranked fifth in [13:38] the country dropped dead like 10 blocks [13:40] from our office and then they said don't [13:42] you know something about this and so you [13:45] know in a week I was able to write a [13:47] cover story making it look like we had [13:49] done like two years of research in a [13:50] week and I became the science writer at [13:53] Sports Illustrated it was an [13:55] interesting you know I came in there as [13:57] a temp six seven years behind people who [14:00] were younger than me MH doing sort of [14:02] more remedial work for them but I [14:05] realized pretty soon that my Oddball [14:08] background right I I think I was shaping [14:09] up to be like a typical average [14:11] scientist but you take those average [14:12] science skills and you bring them to [14:13] sports magazines like you're like a [14:15] Nobel laurate you know um and so I [14:18] realized I I could just make my own [14:19] ground instead of having to compete with [14:21] anybody but the initial impetus for [14:22] getting into this merger of sports and [14:24] science was was a personal tragedy and [14:27] how did you define yourself from a crib [14:28] perspective of a writer are you a [14:30] scientist how' you I view myself as this [14:33] merger between a science writer and [14:34] investigative reporter because what [14:35] really fires me up is when I view that [14:39] there's a really popular misconception [14:41] about something really important to [14:42] human development and that that's that's [14:45] what led to range I mean I was at Sports [14:48] Illustrated the 10,000 hours rule work [14:53] was the most [14:54] famous science in human development [14:57] perhaps ever in terms of popular [14:59] consumption and I said well I want to [15:01] write about it and then I started [15:03] reading the research and saying this is [15:06] wrong it's the most popular finding in [15:08] our field it's maybe the most popular [15:10] skill acquisition human development [15:11] research ever done and it is not right [15:14] and so those you know these things kind [15:16] of stick in my brain and I I have to do [15:19] something about it 10,000 hours what is [15:21] that for someone that's never heard [15:23] about it before yeah and what people [15:24] think about it probably depends where [15:26] they have heard of it if they've heard [15:27] of it but it's the idea and scientists [15:29] call it the deliberate practice [15:30] framework but it's this idea that the [15:33] only route to True expertise is through [15:36] 10,000 hours of so-called deliberate [15:38] practice which is this effortful [15:40] cognitively engaged like not just [15:42] swatting balls at the driving range [15:44] you're focusing on correcting errors [15:46] kind of practice and that there is no [15:49] such thing as Talent differences it's [15:50] really just the [15:51] manifestation uh of 10,000 hours of you [15:54] know of differences in your amount of [15:56] hours of delivered practice so you [15:57] should start as early as possible and [15:59] there's something underlying it this is [16:00] a little nerdy but called the monotonic [16:02] benefits assumption I know scientists [16:05] not going to win any marketing [16:07] competitions but that basically means [16:09] that the idea that two people at the [16:10] same level of performance will progress [16:12] the same amount for the same unit of [16:14] deliberate practice also false and it's [16:17] one of the underlying premises of a [16:18] 10,000 hour rule yeah because I've [16:20] always I've always heard that I mean [16:21] it's become a bit of a colloquial phrase [16:23] to say you've not put your 10,000 hours [16:24] in which means you've not put enough [16:26] practice to become a master I I I mean I [16:28] mean I was told that if you do 10,000 [16:30] hours in anything you become a master in [16:32] it that's the kind of narrative right [16:34] well to take some chess research for [16:36] example there's uh people have been [16:38] tracked and it takes about 11,053 hours [16:41] on average to reach International Master [16:43] status in chess so that's one level down [16:46] from Grandmas so first of all 10,000 [16:48] hours in that case would be a little low [16:50] but some people made it in 3,000 hours [16:52] because they learn a little bit more [16:53] quickly other people were continuing to [16:55] be tracked past 20,000 hours and they [16:57] still hadn't made it so you can have [16:59] 11,053 hours rule on the average doesn't [17:03] actually tell you anything about the [17:04] breadth of human skill development so [17:06] why why is that so important for me to [17:08] understand how does that liberate me [17:10] from from wasting my time or aiming at [17:13] the wrong thing well fit turns out to be [17:15] really important so people learn at [17:16] different rates and different things so [17:17] finding where you learn better is really [17:19] important if you want to maximize your [17:20] advantages and I think that goes back to [17:24] one of the reasons why people need to [17:25] try a bunch of different things CU [17:28] you're insight into yourself is really [17:30] like limited by your roster of [17:31] experiences right um and so you kind of [17:35] need to figure out where you have [17:36] comparative advantages but for a lot of [17:37] people that's so-called skill stacking [17:40] where instead of doing the one thing for [17:41] 10,000 hours you get proficient at a [17:43] number of things and overlap them in a [17:45] way that makes you very unique and so I [17:47] think this idea of just head down doing [17:48] the same thing I mean we can should we [17:51] go back all the way and talk about the [17:53] the research underlying the 10,000 hour [17:54] Ru because that's where I first got onto [17:56] this I wanted to so I was a walk-on [17:58] meaning I wasn't good enough to get [17:59] recruited as an 800 meter runner in [18:00] college and I ended up being part of a [18:02] university record holding relay so I [18:04] went from being uh you know a nobody to [18:08] being quite good and so I was inclined [18:10] to believe this 10,000 hours like yeah [18:12] just you know just my hard work and then [18:14] when I started reading the research and [18:16] I'm looking through the original paper [18:18] written in 1993 and the original paper [18:20] was done on 30 violinists 30 violinists [18:24] at a world-class Music Academy okay so [18:27] let's let's start dissecting the the [18:29] problems here the first problem was [18:32] what's called a restriction of range [18:34] these people were already in a [18:35] world-class Music Academy already highly [18:38] pre-selected pre-selected for something [18:41] again for the stat heads here that is [18:43] correlated with your dependent variable [18:44] which is skill that's a problem if [18:46] you're trying to develop a general skill [18:48] development framework that would be like [18:50] to give an analogy if I did a study of [18:52] what causes basketball skill and I used [18:54] it as my subject's only centers in the [18:56] NBA and I said well height has no effect [19:00] on skill in the NBA because they're all [19:02] 7 feet tall so I've squashed the [19:03] variation in that variable so in in my [19:06] first book actually did an analytics [19:07] project where I took height among [19:09] American male adults and height in the [19:11] NBA as you might imagine there's a very [19:14] high positive correlation between a [19:16] height of an American male and their [19:18] chance of scoring points in the NBA but [19:20] if you restrict the range to only [19:22] players already in the [19:23] NBA the correlation turns negative [19:26] because guards score more points than [19:28] other positions mhm so if you didn't [19:30] know that if you just did that study [19:32] with only NBA players you would tell [19:34] parents to have shorter children to have [19:35] them score more points in the NBA so [19:37] when you don't bring some sense of [19:40] what's going on to your research and you [19:42] rest strict range that way you can end [19:44] up with the wrong message aside from [19:46] that Gods score more points or less [19:47] points they score more points and [19:48] they're shorter ah okay right so if you [19:51] if you don't look at the whole [19:52] population and you just look at people [19:54] who are so highly pre-screened they're [19:55] already at the top you can end up with [19:57] these sort of backward advice [19:59] the other issue that caught my eye when [20:00] I first read the study was that there [20:03] was they only reported the average [20:04] 10,000 hours was the average number of [20:06] hours of deliberate practice by the the [20:09] 10 best violinists by the age of 20 and [20:13] then there was a second group and a [20:14] lower group and they said there was [20:16] complete correspondence meaning nobody [20:18] who had practiced fewer hours was better [20:19] than anyone who had practiced more hours [20:22] but they only included the average so I [20:23] couldn't tell that so I said oh I would [20:25] like to know if that's true can I see [20:27] the data to see if that's true and I so [20:31] I contacted the you know Andre Ericson a [20:33] wonderful guy who was the so father of [20:36] the 10,000 hour rule although he hated [20:37] that uh moniker actually um and I said [20:42] you know can I see the data or the [20:43] measures of variance to know how much [20:45] variation there was between individuals [20:47] and he said well you know people were [20:50] inconsistent on their repeated accounts [20:52] of their practice so we don't think [20:53] that's important I said well everyone [20:55] has trouble with getting good data that [20:56] doesn't mean they don't report the [20:58] measure variance so after I started [21:00] criticizing This [21:01] research 20 years after the study came [21:04] out they did a paper updating it with [21:06] some of the actual data and you could [21:08] see the original conclusion was wrong [21:10] there was not complete correspondence [21:11] some people who had practiced less were [21:13] better than some people who had practice [21:14] more some people had gone way over [21:16] 10,000 hours some people were way under [21:18] and had done better there were all sorts [21:19] of other factors that mattered right [21:20] like I like to call it the 625,000 hours [21:23] of sleep study because the top tier [21:25] group got a lot more sleep they were [21:27] sleeping like 60 hours a week on average [21:28] compared to lower groups and that was a [21:31] a huge difference in the study how much [21:32] they were sleeping so it could have just [21:34] been sleep sleep or but there was just [21:36] tremendous individual variation yeah so [21:38] this idea of an average completely [21:41] obscured the real story which was that [21:43] there were actually people who were [21:44] practicing less and doing better than [21:46] people who practiced more so there were [21:47] all one problem after another I just [21:49] said you know I'm getting Youth Sports [21:52] pitches I'm getting investment pitches [21:53] like citing the 10,000 hours rule it's [21:56] not right and it's giving the wrong [21:58] impression [21:59] of how hum devel and this idea that you [22:02] need to just Pi something and with it [22:04] and that's sampling to try to figure out [22:05] where you have your best shot is [22:06] worthless and that's wrong and so I [22:09] became kind of obsessed with getting [22:11] after [22:12] that I really want to become successful [22:16] in the things that I'm applying myself [22:17] to in the season of my life so whether [22:19] that's podcasting or starting businesses [22:21] my business portfolio is quite varied of [22:23] sort of different industries from [22:25] everything from sort of psychedelics to [22:27] um SpaceX to whatever it might be and so [22:30] when I was you know thinking about [22:32] sitting down with you today I thought [22:34] maybe I'll just tell him where I'm [22:35] trying to get to in my life I'm a I'm a [22:36] 30-year old man so you know I'm I'm not [22:39] in the early phases of my career does [22:41] that mean for example that I can't make [22:43] ground now what phase of your career are [22:45] you in I don't know because I had this [22:46] 18 to 28 thing so I thought maybe I'm a [22:48] little bit more rigid and you know there [22:50] was research a few years ago from MIT [22:52] and Northwestern and the US Census [22:54] Bureau that found the average age of a [22:55] founder of a fast growing Tech startup [22:57] top one in 10,000 [22:59] guess what the average age was on the [23:00] day of founding guess 25 45 and a [23:04] 50-year-old had a better chance than a [23:05] 30-year-old but we never hear just like [23:07] we never hear the story of these like [23:09] Zig zaggers we only hear the Tiger Wood [23:10] story we only hear like Mark Zuckerberg [23:13] famously said young people are just [23:15] smarter when he was 22 do you hear him [23:17] saying that anymore no surprise surprise [23:20] but we just we never we we like valorize [23:24] precocity so I would not say that you're [23:27] not in the early stages of your career [23:28] you're certainly not by by that metric [23:30] and that's not to say that there aren't [23:32] tremendous companies or if you know [23:34] measured by market cap that some of that [23:36] there are these amazing young [23:37] Founders but they get outsized attention [23:39] compared to what's the norm that that's [23:41] another thing that's really important to [23:43] me is not to say there aren't acceptions [23:44] because there as many different ways to [23:46] the top as there are human beings but I [23:48] think we're constantly focusing on the [23:50] exception when people should at least be [23:52] aware of the norm so the average so the [23:54] fastest growing did you say Tech [23:56] Founders Tech startups but Tech in Tech [23:58] in this context also included things in [24:00] agriculture right it's not just photos [24:02] sharing apps like Tech broadly speaking [24:04] which I think is important because it I [24:05] think it's fair to say that it's less [24:07] likely a 55-year-old would understand [24:12] some of the more emerging platforms that [24:13] are native to say you know like a Mark [24:15] Zuckerberg at 22 who's messing around in [24:17] his dorm room with with computers and [24:19] the internet yeah I think that's fair [24:21] but technology touches a lot of other [24:22] areas of the you know it's like [24:25] yesterday on the way here there was a I [24:26] was learning about a software that I had [24:28] never heard of because all of the [24:29] computers were down in the airport right [24:31] Technologies in all these places that we [24:33] that are not as kind of uh don't have [24:35] the sort of figure head that's publicly [24:37] profiled the same way so if I if I do [24:39] want to become okay so I understand that [24:41] this season of my life I can do whatever [24:42] I want in terms of I can aim at whatever [24:45] I want doesn't mean I'm going to be good [24:46] at it but if I just want to be more [24:47] productive in the goals that I am aiming [24:49] at so say you know this podcast means a [24:51] lot to me so I want to be more [24:52] productive when it comes to figuring out [24:55] how to move this podcast forward how to [24:57] innovate um how to solve some of the [24:59] problems and challenges that we [25:01] face what are the first things that [25:03] spring to mind when I start speaking [25:04] about productivity with a very focused [25:06] task I mean I think your a challenge for [25:08] you is going to be that this podcast has [25:09] gotten so big and you've gotten so [25:11] competent at it that you're going to be [25:13] in what uh a rut of competence or what [25:17] Economist Russ Roberts told me a hammock [25:18] of competence you're in an area where [25:19] you're so comfortable and so successful [25:21] that getting better is going to be [25:23] harder because there's disincentive from [25:25] changing anything that you're doing [25:26] right and you have to take some risks I [25:28] mean you know that you're an [25:29] entrepreneur if you're going to want to [25:30] get better you're going to have to take [25:30] some risk I think that's going to be a [25:32] difficult thing to do because you know [25:34] there are people in this room that [25:35] depend on you uh risk for you is risk [25:38] for them too and so I think you have to [25:40] start thinking about what would be some [25:41] smart risks if you want to innovate with [25:42] the podcast what might that look like [25:44] and finding ways to run small [25:47] experiments I'm a huge fan of low stakes [25:49] practice right how can you set up some [25:51] low stakes practice for what might be a [25:52] worthwhile larger experiment and I think [25:54] that's the same [25:56] for individuals dring in their career [25:58] like I love this phras my my favorite my [26:00] absolute favorite phrase in range was is [26:03] a paraphrase from this woman named [26:05] herminia Ibara who's a professor at the [26:07] London business school and she studies [26:08] how people make work transitions so her [26:11] phrase was we learn who we are in [26:13] practice not in theory so the thesis of [26:15] her work is that there's this idea that [26:16] you can just introspect and go forth and [26:19] know what you should be doing you know [26:20] like like Clark Kent running into a [26:22] phone booth and ripping off his and [26:23] becoming comes out as Superman but work [26:26] is part of identity and it doesn't [26:27] change like that from introspect you [26:29] actually have to go try something see [26:31] how it went what was unexpected what did [26:34] you learn that you might be interested [26:35] in or or that you're better at that you [26:37] didn't what's something that you're good [26:38] at that you realize you're not [26:40] using and then you make your next step [26:43] based on that right and I think when [26:45] you're so competent and successful and [26:46] getting only you know tons of positive [26:48] feedback for something uh it becomes [26:51] hard to take risk and so I think that'll [26:52] be a challenge for you because if you [26:55] take a sufficient amount of risk right [26:57] you want to be in your zone of of [26:58] optimal push so for anything you're [27:00] doing if you're doing practicing [27:02] whatever physical skill anything if [27:04] you're not at least like 15 20% of the [27:07] time failing then you're not in your [27:09] zone of optimal push where you're [27:10] getting as much better as you possibly [27:12] can and I [27:13] think when you have something that's [27:15] very successful that's hard and so I [27:18] would start thinking about what risks [27:20] you're willing to take and it doesn't [27:21] mean it's a failure if something goes [27:23] backward right if the views go down or [27:24] whatever metric you're you're measuring [27:26] on it's interesting it's supp to see [27:27] something it's one of my great [27:28] obsessions in life and it's also one of [27:30] the things that keeps me up at night [27:32] bugs me in the shower is um how to keep [27:35] a team conducting experiments and [27:37] failing more when they are successful so [27:40] when this podcast went to number one in [27:42] Europe I hir ahead of failure and her [27:44] sole responsibility is to increase the [27:46] rate of failure and experimentation in [27:47] our team which means just get all of our [27:49] different departments we've got [27:50] different departments in this particular [27:51] business so there's 40 40 odd people in [27:53] this company called the D CEO and there [27:56] is a production team there is the social [27:58] media team there's the commercial team [28:00] for example and there's the guest [28:01] booking and Logistics team and I I felt [28:05] we're actually in La driving down the [28:06] road and I was speaking to jamaa who's [28:07] the head of the guest booking and [28:09] research team and I was saying like one [28:11] of the the most important thing now now [28:12] that we're number one is that we keep [28:14] like disrupting ourselves because [28:15] there's going to be some kid like we [28:16] were three three years ago that because [28:19] of their naivity that they're not [28:21] encumbered by all of this sort of like [28:23] convention and all this success so hide [28:26] a head of failure and experimentation [28:27] who's in our team has been working and [28:29] now in in the last couple of days we're [28:32] running an experiment where every single [28:34] one of those departments has essentially [28:36] like a failure assistant in it who is [28:39] who's because you know what happens with [28:40] people they they get busy doing their [28:42] job and experimentation and failure is [28:44] always secondary to their job so if we [28:46] put failure people into the each team [28:49] and they drive the experiments they [28:51] understand the team they drive the [28:52] experiments they measure them and most [28:53] importantly they report their failures [28:55] and experiments back to the whole team [28:57] because there's really transferable [28:58] learnings for example there was one the [29:00] other day where the social media team [29:02] discovered this thing on Tik Tok which [29:03] allows us to look at a guest like you [29:06] and find your most popular videos ever [29:08] on Tik Tok with a click and the social [29:10] media team had figured that out which [29:11] was really useful for them but then the [29:13] research team over here that are booking [29:14] guests who are trying to find the best [29:16] videos that a David has ever made they [29:19] also benefited from just the discovery [29:21] of that button because instead of having [29:22] to scroll through the entire Tik Tok [29:23] they can press one button and see your [29:24] most popular videos so it's all there's [29:26] this real 1 plus 1 equal 3 getting the [29:28] teams to share their fails failures and [29:30] experiments so they don't have to fail [29:32] in the same ways so what did you just [29:34] write down this this brings up so much [29:35] stuff because the fundamental problem [29:37] you're getting at here is the one called [29:38] the explore exploit tradeoff right um [29:41] and so explore is what it sounds like [29:43] looking for new knowledge or new things [29:44] that you can do that'll add value [29:46] exploit is taking stuff you're already [29:47] good at that you already know and [29:48] drilling down on it and this is like the [29:51] fundamental challenge for people in [29:53] organizations that did good is once [29:55] they've find something they're really [29:56] good at and they drill down in it [29:58] they tend to ditch explore modee yeah [30:00] right and balancing that explore exploit [30:03] and there's all these of course you know [30:04] these like famous business cases like [30:06] Kodak invents the digital camera and [30:07] scuttles it because they're like why [30:08] would we disrupt our own business but [30:11] there was this fascinating work led by a [30:13] guy named dashen Wong in Northwestern uh [30:15] who does like people will do Career [30:17] Development studies looking at 20 people [30:19] and he'll look at 20,000 people you know [30:20] so his work's just fascinating and he [30:22] what he saw in this work with his [30:25] colleagues was that people tend to have [30:28] hot streaks in their careers their best [30:30] work tends to come in [30:31] clusters most people will only have one [30:34] some people will have more than one if [30:36] they're lucky and reliably what precedes [30:39] a hot Ste he was looking at I think it [30:40] was like 26,000 like film directors [30:43] artists [30:44] scientists reliably what precedes a hot [30:46] streak is a period of exploration where [30:48] they're trying these different styles [30:50] they're going broad they're they're [30:51] keeping a smaller team so they can be [30:53] nimble they're moving between teams and [30:55] then they find something and they they [30:56] drill into it and if going to have [30:58] another hot streak they do it again they [30:59] Zoom back out and they go to this [31:01] explore explore explore and then exploit [31:03] so they toggle between these modes [31:05] instead of staying just in one but the [31:08] clear message of his work is that [31:11] exploration precedes a hot streak and if [31:13] you don't do the exploration you just [31:15] settle into exploit at sort of a [31:16] middling level then you're you're kind [31:18] of sacrificing your your hot streak so [31:20] that that was one of the things that [31:21] came up for me the other thing was this [31:24] you got to something this idea of people [31:26] not only doing things that might fit [31:27] fail and I think that's great that they [31:28] have the title failure right cuz you'll [31:31] have [31:32] the uh you know Adam Grant who I think [31:34] we both know is he he he mentioned me [31:36] some once something called the hippo [31:38] effect where it's like the opinion of [31:39] the highest paid person in the room I [31:41] think the is the acronym where their [31:44] their signaling is really important for [31:46] everyone else so if you're not just [31:47] giving lip service like yeah failure is [31:49] good but actually giving people that [31:50] title I think that's a great signal for [31:52] you're underwriting risk you're [31:54] underwriting risk for people [31:56] psychologically and you're creating what [31:59] what scientists who study sort of [32:00] networks like groups of teams call an [32:02] import export business of ideas and this [32:04] is one of the [32:05] Hallmarks of organizations and and [32:08] ecosystems that learn and adapt to to a [32:11] changing world and the import export [32:13] business of ideas means you need to have [32:15] information flowing through an [32:17] organization you have people doing [32:18] different things maybe people even [32:19] moving teams here and there so I always [32:21] think of the engineer uh Bill Gore who [32:25] created the company or founded the [32:26] company that created gortex and he [32:28] fashioned the company based in his [32:29] observation that organizations often do [32:32] their most impactful work in times of [32:33] Crisis because the disciplinary [32:35] boundaries go out the window and people [32:37] start what can I learn from my neighbor [32:39] you know and working together or do he [32:41] like to say real communication happens [32:42] in the carpool which I think is a funny [32:44] saying but I worry about that with more [32:46] hybrid and remote work where you can't [32:49] necessarily just rely on Serendipity for [32:53] people to be sharing these ideas in this [32:55] informal way and so I actually think we [32:57] have to be a lot more thoughtful about [32:59] setting up our own import export [33:01] business of ideas internally and it [33:02] sounds to me like that's what you're [33:03] doing okay so what about then on in an [33:05] individual level how do I as an [33:07] individual I've got you know lots of [33:09] things I'm doing I'm writing some books [33:10] at the moment I do the podcast lots of [33:12] other things how do I become more [33:14] productive within an organization [33:16] because there's my to-do lists I've got [33:18] 10 to-do lists from all of my different [33:19] team members who can put things on there [33:21] um I get distracted easily I think [33:25] because I end up watching a video about [33:26] AI on YouTube or about Rockets or [33:27] something and I want to I want to get [33:29] more done really I want to be more [33:31] productive in the time that I spend [33:32] working so this is when you know what [33:34] you should be doing when I know and you [33:37] know there's nothing wrong with [33:37] sometimes like watching YouTube and [33:39] Rocket like that's you get ideas from [33:40] doing kind of stuff myself but T Todo [33:43] lists is a lot of to-do lists yeah do [33:45] you get most of the stuff done on those [33:46] to-do lists it's more so each team from [33:49] my chief of staff to my assistant to my [33:51] manager has a to-do list on Monday that [33:53] they send things to me on and then I go [33:55] through there and it's either a task or [33:57] it's an approve or it's just letting me [33:58] know something and that's kind of how it [34:00] works so I at one point had when I was [34:02] getting overwhelmed with some stuff I [34:04] had a virtual assistant for a little [34:06] while and we would categorize like [34:07] emails into list a priority b c d all [34:09] this stuff and eventually I realized [34:12] that was empowering me to do a lot of [34:13] low value things I became efficient at [34:15] doing things that I shouldn't be doing [34:16] so I was seeing this public email [34:17] address of mine that when I was [34:19] oblivious to it I wasn't answering and [34:21] that was fine and but once I knew it was [34:24] there I'm like oh I have to answer this [34:25] I have to answer this I have to answer [34:26] this and so one important step for me [34:28] was realizing that only the A-list is [34:31] the stuff that's going to get done [34:32] because I'm a limited person with a [34:34] limited life so one I think it's maybe [34:36] you do need to do all that stuff or you [34:37] just need to be aware of it but some of [34:38] it is just I think there can be a danger [34:41] in someone who has a lot of support [34:44] resources uh where they can lose some of [34:46] the aspect of prioritization where you [34:48] just need to say this is the list that's [34:49] important other things I might not get [34:51] to but for someone like you I would I [34:52] would suggest something like not [34:54] starting your day with email or [34:56] messaging [34:58] because you know we were talking a [35:00] little bit before about this thing [35:01] called the zarnik effect which is this [35:03] idea that an unfinished task leaves like [35:04] a residue in your brain basically and [35:07] makes you it makes it harder for you to [35:09] fully transition to doing something [35:11] else and because I expect your various [35:15] inboxes will always be an unfinished [35:17] task right yeah if you start the day [35:19] with that no matter what you do the [35:22] residue is going to be there for what [35:23] you try to switch to next so I'm not [35:25] saying don't address your email but I [35:27] wouldn't start with I would the day [35:28] before what is the thing that if I get [35:30] done tomorrow it's going to be a good [35:31] day and start with that before you do [35:33] the things that might leave residue on [35:34] your brain and start multitasking how do [35:36] they know that's true have they done [35:37] studies on this aonic effect yeah yeah [35:39] absolutely I mean you can see you can [35:41] give people one you can do it in a [35:42] workplace environment where researchers [35:45] like Gloria Mark for example will be [35:47] tracking everything from someone's [35:48] Vision to what they're doing on their [35:49] computer to their heart rate variability [35:52] and seeing how long it takes them to get [35:53] back to a task um increased switching [35:57] when there's like like a residue in [35:58] their brain so their rate of switching [36:00] will go up uh you know some of their [36:02] indicators of stress response will go up [36:04] or in a cognitive task they'll perform [36:06] more poorly if there's something still [36:07] stuck in their brain so there's also [36:08] sort of laboratory experiments where you [36:10] give somebody something don't let them [36:12] finish it give them a cognitive task and [36:13] you see does it impair their performance [36:15] if they weren't allowed to finish the [36:16] thing that started [36:18] before I want to close off on that point [36:20] of just team culture then how to get a [36:21] team of people to do really exceptional [36:23] Innovative work and to fail faster is [36:25] there anything else that's sort of [36:26] pertinent to you and I'm saying this [36:28] purely selfishly because it's one of the [36:29] things I think a lot about even with [36:30] this podcast is how to get our teams [36:33] failing more often if that's even the [36:34] right thing to be aiming at the type of [36:36] experiments we should be running how we [36:37] should be running them anything else at [36:39] all yeah I mean I don't think I don't I [36:41] don't lionize failure for its own sake [36:43] right it's just I think it's inevitable [36:45] if you're experimenting enough that [36:46] you'll have some failure but I think one [36:48] useful thing to do like a guy I love [36:50] who's made a big impression on me named [36:51] ed Hoffman was used to be the chief [36:53] knowledge officer at Nasa that's like [36:54] after NASA had some disasters most every [36:57] they did was very successful but [36:59] obviously they had some high-profile [37:00] disasters he was brought in because they [37:02] were deemed not a learning organization [37:04] they weren't learning from lessons of [37:05] the past and he was brought in to help [37:06] create a knowledge system so that people [37:09] would learn from the lessons of the past [37:11] and one of the things he does in [37:12] organizations when he goes in because [37:14] now he consults is he goes around he [37:16] asks people what are you good at that [37:18] we're not [37:19] using right and people always have an [37:21] answer for that and that leads to well [37:23] what's some what's an experiment that we [37:24] can run to try to use that thing that [37:27] you're good at that we're not using so I [37:28] think that can be kind of a foundational [37:30] question to help people set up some of [37:31] those experiments but also a big impact [37:34] would be and this is a tough one you [37:37] going ahead and failing in an experiment [37:39] because that's going to set the agenda [37:41] right but you would actually have to [37:42] fail like this can't be you go out for a [37:44] jog and you trip on the curve or [37:45] something like you have to fail [37:47] something of [37:48] consequence uh and and then your [37:50] reaction to that can set a [37:52] tone um so that's on the the team level [37:55] so I just I want to really think about [37:57] how on an individual level I can become [38:00] a better [38:01] learner because one of the things I do [38:03] obviously for a living is I do this [38:04] podcast and I meet all these incredible [38:06] people and they say things to me that in [38:07] the moment change my life but I feel [38:09] like I forget them five minutes later [38:10] often some of them stick some of them [38:11] don't so I've always wondered how can I [38:13] become a better learner people come up [38:15] to me in the street and say you might [38:16] you must know so many things about so [38:18] many things and also my audience they [38:20] they tune in every week they listen to [38:22] these incredible people how can we [38:24] become better Learners what is it we can [38:26] do to ret retain information better and [38:28] then also bring it into practice in our [38:30] lives oh to retain inform okay for [38:32] retaining [38:33] information one repetition and [38:35] familiarity is important right so if [38:37] there's something that's really [38:37] important to you you should reread it [38:39] because the first time you go through if [38:41] you're hearing new things new terms [38:44] you're using your working memory just to [38:47] keep up basically so so to put this in [38:50] kind of a simple way like there's [38:51] research where you look at school kids [38:53] and if they're given um like an essay [38:56] about baseball say the kids that are [38:59] deemed really good readers and there are [39:01] kids who are deemed poor readers and the [39:02] the kids who will do the worst on [39:03] comprehension are the poor readers who [39:05] don't know anything about baseball but [39:06] the kids who know about baseball but are [39:07] not as good readers will still have [39:08] better comprehension than the kids who [39:10] are good readers but don't know anything [39:11] about baseball if they only get to go [39:13] through once because having some [39:14] knowledge helps you fit it into what's [39:16] called your semantic Network the [39:18] spiderweb of all the ideas in your brain [39:20] so one going back over things that can [39:22] be taking notes whatever it is but when [39:25] you learn something new try to fit it [39:27] into your semantic Network when you [39:28] learn something connect it back to [39:31] something you already know so like when [39:32] you have these conversations you [39:34] probably have a better tendency to [39:35] remember things where you say you know [39:37] that reminds me of some other guest that [39:39] either agrees or disagrees with [39:40] something that some other guest said and [39:42] you've attached it if you think of your [39:44] brain as like the spiderweb things are [39:46] attached by threads and if you vibrate [39:48] one thread it's more likely to shake [39:50] these other ideas into your brain so [39:52] when you're learning something new stop [39:54] and try to fit it into your existing [39:56] base of knowledge if you want to return [39:57] better can can I use that to sort of fit [40:00] it into an example so I'm thinking of [40:02] you you said something about um what is [40:05] something I don't use but I'm good at [40:08] would the listener that's listening to [40:09] this now in order to embed that think of [40:11] something that they are not using that [40:13] they're good at because then it kind of [40:15] brings it into their absolutely okay [40:17] absolutely use it as quickly as you can [40:19] again repetition but fit it into your [40:21] network of ideas like stop if you have [40:23] to because you know you can read a ton [40:25] but if you're not kind of and and I [40:27] think I think reading even things that [40:29] you don't retain still change your [40:31] sensibility at some level even if you [40:32] can't consciously pull up all of the [40:34] ideas and statistics and so on but for [40:36] things that you really want to be able [40:37] to [40:39] access connect it to other things that [40:41] you already know and someone's called [40:44] space repetition like if you can have a [40:46] way where you come back to it at [40:49] intervals that'll be much better so I [40:53] use [40:54] this um like readwise as a programming [40:57] I'm not like affiliated with them in any [40:58] way it's just a thing that I use where [41:00] if I have highlights in Kindle books or [41:03] ebooks it will feed me back my [41:05] highlights at intervals things that I [41:07] thought were important [41:08] regularly and that's taking advantage of [41:11] what's called spaced spaced repetition [41:13] where if you you want to actually leave [41:15] a space almost to the point of [41:16] forgetting something and then if it's [41:18] brought up again you're embedding it [41:20] better in long-term memory so this is [41:21] for for learning anything spaced [41:23] repetition language learning all this [41:25] kinds of stuff so you would think that [41:27] you just repeat a thing a million times [41:30] as soon as you have it and that's the [41:31] best way to Grapple on to it that's not [41:32] the most efficient use of time it's [41:34] actually to to space it out and quizzing [41:36] yourself is a great way to retain so [41:38] there's something called The Generation [41:39] effect which is if like if you have to [41:43] do highlighting versus uh flash cards [41:46] flash card quizzing is much better the [41:48] generation effect is being forced to [41:50] come up with an [41:52] answer primes even if it's wrong in fact [41:54] sometimes especially if it's wrong [41:55] primes your brain to then retain the [41:57] right answer it's actually something [41:58] called the hyper correction effect where [42:00] if you're really wrong about an answer [42:01] you're much more likely to remember the [42:03] right answer once it's given to you so [42:05] if you're looking up a piece of [42:07] information I suggest you guess what [42:09] it's going to be before you get the [42:10] answer it doesn't matter if you're right [42:12] or wrong might feel bad to be wrong but [42:14] it doesn't matter you'll better retain [42:15] it when you see the right answer but if [42:16] I'm wrong then I'm I guess I'm more [42:19] shocked so there's even more retention [42:21] of that new answer it's Salient I mean [42:24] this is this is what this is so this is [42:26] one of this kind of quizzing where it [42:28] feels hard because you should do it [42:29] before you know the answer is something [42:31] I wrote about in range called desirable [42:32] difficulties these are things that make [42:35] learning feel less fluent they are [42:37] unpleasant they may slow you down much [42:40] better for long-term retention [42:42] interesting so the more difficult the [42:45] learning the more you learn often I mean [42:49] but I guess there can be a case where [42:50] something so over your head that you're [42:52] not learning anything right but these [42:55] desirable difficulties are like one of [42:56] the most famous ones is called [42:59] interleaving or mixed [43:01] practice and this is if you're training [43:03] at something you you want to vary the [43:07] types of pro so let's give an example [43:10] DJing I'm I'm DJing at the moment okay [43:12] so I don't know all the skills that go [43:14] into DJing but if there's a way to do it [43:16] you should try to instead of doing the [43:17] same skill over and over and over again [43:19] well let me give you let me give you a [43:20] research example and then you can Port [43:21] it into DJing so in a recent study there [43:24] were dozens of uh middle school math [43:27] classrooms Middle School of sixth grade [43:30] that were assigned to different types of [43:31] math learning some of them randomly [43:33] assigned some of them got what's called [43:34] blocked practice that's you give like [43:37] problem type a AAA bbbb [43:40] Etc kids make progress fast they're [43:43] happy rate their teachers highly Etc [43:45] other other classrooms got what's called [43:47] interleaved or mixed practice where [43:49] instead of doing a followed by B it's [43:51] like you took all the problem types [43:52] threw them in a hat and Drew them out at [43:54] random progress is slower they might be [43:57] less happy because they don't feel like [43:58] they're getting it but instead of having [44:00] to just execute a procedure they're [44:02] having to match a strategy to a type of [44:05] problem and when the test came along [44:08] where everyone has to transfer to new [44:09] problems the inter Le group blew the [44:12] block Practice Group away it was like [44:14] the effect size was like taking a kid [44:15] from the 50th percentile and moving them [44:17] to the 80th just by arranging the [44:19] practice in a way that made it more [44:21] difficult what's going on there I think [44:24] I mean it seems to be and this this work [44:27] for physical learning as well I think [44:29] this is one of the reasons why this if [44:31] you want why fotsa is like why like 90% [44:33] of the best footballers grow up on fotsa [44:34] instead of like playing on full-size [44:36] pitch um is that it forces you to [44:39] instead of doing using procedures [44:41] knowledge which is you learn how to [44:42] execute this procedure over and over [44:44] you're doing making connections [44:45] knowledge which is identifying the [44:48] structure of a problem and foring out [44:50] how to match a strategy to it and so [44:52] you're building this like mental [44:53] template instead of just an ability to [44:55] execute this like flexible template that [44:57] can be applied going forward so you're [44:59] getting like a broader context of the [45:01] challenge versus a very narrow solution [45:04] perspective to how the challenge is [45:05] solved you're kind of understanding it [45:07] from a deeper level right from different [45:09] sides and and you're building this [45:10] generalizable model in your head of how [45:12] to approach it I mean my my favorite and [45:15] I'd be the only person to say this but [45:16] my favorite study that went into range [45:18] was this one the one that surprised me [45:20] the most I guess was this one that was [45:23] done at the United States Air Force [45:24] Academy which is this amazing place for [45:26] experiments because they get a thousand [45:27] new students every year those students [45:30] are randomized to math classes that all [45:33] have the same test and same grading and [45:34] everything then they are randomized the [45:36] next year and randomized again so you [45:38] can get these huge experiments [45:39] randomizing people to math classes and [45:41] they looked at 10,000 students and found [45:44] that the teachers who are the best at [45:46] getting students to do well on the test [45:48] in their own class in their own intro [45:49] class right teacher year one has [45:52] students who score highly on their test [45:55] those students go on to underperform in [45:57] the subwing classes and teachers whose [45:59] students sometimes rated them lowly [46:01] poorly because they thought it was hard [46:03] don't do as well on the test the first [46:05] year overperform in subsequent classes [46:07] and the difference is the way to get [46:09] someone to do really well in the test is [46:11] to teach this very narrow body of [46:13] knowledge that they'll have to execute [46:14] at the test the best way to prepare them [46:16] for math learning is to give them this [46:17] much broader connection of ideas that [46:19] will serve them later on so again this [46:22] is like to me the theme on every page of [46:24] range that would have made a crappy [46:25] subtitle is is sometimes what seems the [46:28] best in the short term will undermine [46:29] long-term [46:31] development the tricky thing with that [46:33] as you say is I I think about all the [46:35] areas and industries that I'm playing in [46:36] now so I go do I have the time to go [46:41] broad like if I'm learning to DJ at the [46:43] moment and at at the moment I'm just [46:45] trying to figure out what these [ __ ] [46:46] buttons do you know what I mean like [46:47] there's all these buttons I'm trying to [46:49] press them in the right order but you're [46:51] telling me that the thing that's better [46:52] for my long-term development might be [46:53] just to spend some time understanding [46:55] music and how it's made and how and [46:57] understanding like the the Beats of [46:59] music and make maybe spend some time [47:01] making music myself cuz right now I'm [47:03] just trying to smash two songs together [47:05] at the right time I think this gets at a [47:07] fundamental issue that that that maybe I [47:09] should have brought up earlier actually [47:10] so and it has to do with how you [47:13] characterize the different tasks that [47:14] you're trying to learn so there was a [47:17] period where I was really confused about [47:20] the research I was reading in in [47:21] building expertise because there were [47:23] two camps of [47:25] researchers both led by [47:28] eminent scientists one that would study [47:30] people doing sort of more 10,000 houry [47:33] kind of approach same thing over and [47:35] over and they would get better and this [47:37] other camp that would find if people did [47:38] that approach Not only would they not [47:40] get better they would often get more [47:41] confident but not better which was a bad [47:43] combination and sometimes it would get [47:44] even worse with really narrow [47:47] focus and I could not figure out how to [47:49] reconcile these things why are they [47:51] finding such different results again I'm [47:53] looking through for all these signs of [47:55] you know bad data not not finding it [47:58] and fortunately um I I gave a talk uh [48:02] where I was doing some of the critiquing [48:03] of the science underlying the 10,000 [48:05] hours Rule and the Nobel La Daniel Conan [48:06] who wrote Thinking Fast and Slow was [48:08] there and someone asked he asked someone [48:10] for my email address and like months [48:12] later he followed up and invited me to [48:13] lunch and we go and have lunch and I'm [48:16] like I'm and he was he was interested in [48:19] my critique of some of the research and [48:20] I was saying I'm really confused you [48:22] know what are you working on now I'm [48:24] working through my confusion about this [48:27] why do people sometimes get better with [48:29] narrowly focused practice and why [48:32] sometimes don't they he said oh I've [48:33] done I've got the paper for you and [48:36] basically he referred me to this body of [48:37] research about kind versus wicked [48:40] learning environments these are terms [48:42] coined by a psychologist named Robin [48:44] Hogarth kind is like next steps and [48:47] goals are clear rules repeat uh it's [48:50] based on patterns repetitive patterns [48:52] rules never change give me an example [48:55] chess golf uh in chess the grandmaster's [48:58] advantage is largely based on knowledge [48:59] of recurring patterns so you better have [49:00] started studying those by age 12 or your [49:02] chance of reaching Grandmaster drops [49:04] from about one in4 to about 1 and [49:06] 155 also why it's relatively so easy to [49:08] automate uh feedback is quick and [49:10] accurate uh not a lot of human behavior [49:12] involved work next year will look like [49:14] work last year on the other end of the [49:16] spectrum are wicked learning [49:17] environments where patterns don't just [49:19] repeat they might fool you rules may [49:21] change if there are any feedback could [49:23] be delayed or inaccurate um work next [49:26] year may not look like work last year [49:29] and so whether or not people get better [49:31] with this very Nar in a predictable way [49:32] with this very narrow practice depends a [49:34] lot on where on that kind to Wicked [49:37] Spectrum the the task happens to be [49:40] what's an example of a wicked lining [49:41] environment so let's say one one of the [49:43] examples uh that I loved that he turned [49:45] me to was uh in medicine because there's [49:48] a lot of areas in medicine where [49:49] something is done and the person making [49:50] the decision actually never learns of [49:52] the consequence of the decision um or [49:55] I'd say I would say like judges some [49:56] cases in like the criminal justice [49:59] system are set up to have maybe the [50:02] worst judgment they could have in some [50:03] ways because they almost never get [50:04] feedback they have like very little they [50:06] can do whatever they want and they [50:07] almost never get any any feedback but so [50:10] in in medicine there was this one [50:12] example in one of the studies that I [50:13] thought was just interesting and [50:14] illustrative where um this this [50:18] physician became famous for being able [50:19] to diagnose typhoid it's a New York [50:21] physician by feeling around palpating [50:23] people's tongues feeling around their [50:24] tongues with his hand and he could tell [50:26] you know we or two before they would [50:27] even get it this person's going to get [50:28] typhoid and as one of his colleagues [50:31] later observed he was a more prolific [50:34] spreader of typhoid than even Typhoid [50:35] Mary he was spreading it with his hands [50:38] by touching their tongue making the [50:40] prediction they would get typhoid which [50:41] would turn out to be correct so would [50:42] reinforce the lesson that he was really [50:44] good at prediction that's a really [50:46] wicked learning environment where the [50:47] feedback he's getting is reinforcing the [50:49] exact wrong lesson right but I would say [50:52] most of the things that most of us are [50:54] doing have feedback that tends to be [50:56] delayed [50:58] sometimes it's accurate and sometimes [50:59] it's not it's never as accurate as like [51:02] I hit that golf shot and I see if it [51:03] hooks or slices and then I changed the [51:06] the club face and and try it again and [51:08] so most of what most of us are involved [51:10] in increasingly right like work doesn't [51:15] next year doesn't look like work last [51:16] year for most of us anymore and in fact [51:18] Andre Ericson again the guy who did the [51:20] research underlying the 10,000 hour rule [51:22] when he eventually wrote a book he he he [51:25] made this caveat the book that said the [51:28] 10,000 hours framework uh it applies to [51:31] things where we know exactly how to be [51:32] good and a coach can watch you do it and [51:34] correct everything that you do wrong so [51:35] it doesn't apply to most these other [51:36] things that most of us do like computer [51:38] programming and managing and [51:39] Entrepreneurship and all these other [51:41] pretty big loophole right in those areas [51:43] you want this much broader [51:45] toolbox I am I was really compelled by [51:48] something I saw you talking about which [51:50] was the story of Nintendo and why they [51:52] were so successful in the early days [51:54] because they have a a very Broad they [51:56] take wrote down the quote um a lateral [52:00] thinking with withered technology yeah [52:03] that started with a guy named gune yokoi [52:06] who [52:07] was scored poorly on electronics exams [52:10] in University and so he had to settle [52:12] for a low tier job as a machine [52:13] maintenance worker uh at a at a company [52:16] in Kyoto that made playing cards with [52:17] flowers on them whereas like his more [52:19] prestigious peers went to big companies [52:22] in Tokyo and the company was in huge [52:24] trouble uh it had to diversify if it was [52:26] going to survive survive and he knew [52:29] that he wasn't equipped to work on The [52:30] Cutting Edge but that there was all this [52:33] information available that maybe he [52:34] could just look for technology that's [52:36] already well understood and combine it [52:37] in ways that his more specialized peers [52:40] couldn't see and so he went and he took [52:42] some well-known technology from the [52:44] calculator industry some well-known [52:45] technology from the credit card industry [52:47] and combined them and made handheld [52:48] games and those were those were a hit [52:51] right that's what made Nintendo which [52:54] was a found in a wooden storefront in [52:56] 19th century that's what turned it into [52:58] a to a toy and game Operation so he [53:00] moved from machine maintenance to [53:02] developing toys and games and his [53:04] magnopus was the Game Boy right where um [53:09] it was a technological joke in every way [53:11] it's like the processor was a decade old [53:13] the screen looks like you know rotting [53:14] alala or something it's like and it came [53:17] out at the same time as color [53:18] competitors and it blew them out of the [53:19] water because he knew what customers [53:21] cared about wasn't color as much as it [53:24] was durability affordability portability [53:27] battery life game selection by using [53:28] well-known technology people could make [53:30] games quickly and so he kind of set this [53:33] philosophy this lateral thinking with [53:34] withered technology that was his phrase [53:36] which means taking things that are [53:37] already well understood and moving them [53:40] somewhere where they're seen as [53:41] invention and that actually turns out to [53:43] be more the norm than the exception in [53:45] terms of technological innovation [53:47] particularly sort of later in the 20th [53:48] century forward before that it wasn't [53:50] necessarily the case much of the 20th [53:53] century actually the most impactful [53:54] patents if you look at patent research [53:56] were authored by teams and individuals [53:58] that Dove deeper and deeper into one [54:00] area of Technology as classified by the [54:02] US patent office but starting in this [54:05] sort of Information Age period um you [54:07] know particularly 80s and accelerating [54:10] forward suddenly it becomes a lot easier [54:12] to access information more broadly and [54:15] the most impactful patents started to be [54:17] authored by teams that include [54:18] individuals who've worked in a whole [54:19] number of different classes and they're [54:21] often merging things from different [54:22] areas uh for invention so how important [54:25] is focus in this this equation focusing [54:27] on one thing because you're talking for [54:29] much of this conversation about being [54:30] Broad and people will associate that [54:32] with being [54:34] unfocused yeah it's it's a right I think [54:37] the differen is between doing a bunch of [54:39] things sort of over your career over [54:41] your life or a span and doing attempting [54:43] to do a bunch of things at once we can't [54:45] technically do a bunch of things at once [54:47] like we we don't really multitask we [54:49] don't have the capacity to do it we're [54:50] actually just toggling between things [54:52] really quickly um and it's it's been [54:56] shocking to me to look at the research [54:59] how how big of an impairment that is for [55:01] people's performance particularly [55:03] because it takes time to switch and so [55:06] you're not again it it's it the the [55:09] scientist Gloria Mark who who I think [55:10] has been at the Forefront of study of [55:12] attention describes your brain as like a [55:14] whiteboard where you're doing something [55:17] and to do something else you have to [55:18] erase and that residue is left and it's [55:20] still going to be there when you move to [55:22] the new thing for a while and so you you [55:23] can't totally get into the next thing if [55:25] you're [55:26] interrupted um and and it impairs your [55:29] performance and it's stressful that's [55:32] been the most surprising part to me is [55:33] that when people are heart rate [55:35] variability is measured and some immune [55:37] parameters um that when people switch a [55:39] lot like if you just saw how many times [55:42] people switched their task you know [55:43] email to this other thing to some [55:44] notification over a day you'd have a [55:46] pretty good bet at predicting their [55:48] stress level and their performance level [55:49] over the day really yeah they've done [55:51] studies on this she has done that she's [55:53] hooked people up you know at Big [55:55] organizations too like inside Microsoft [55:56] and and places like that um where people [55:58] are wearing heart rate variability [56:00] monitors everything they're doing is [56:01] being tracked in the old days she was [56:03] like sitting behind people with a [56:04] stopwatch but technology obviously [56:06] progressed from then um and I think [56:08] that's a surprising aspect of it one of [56:10] the reasons that email makes people so [56:12] stressed is because it leads them to do [56:15] this like con I think in one of her [56:17] studies people were checking email [56:19] office workers were checking email and [56:20] average of 77 times a day that's a lot [56:22] of switching when you're switching in [56:23] and out of email and that just turns out [56:26] to a stressful thing because there [56:27] there's switching actually takes place [56:30] in two uh kind of phases where you the [56:34] first phase is is shutting down what you [56:36] are doing and the next phase is [56:38] activating the rules for the next task [56:41] so even if you kind of think you're [56:42] doing the same thing like you're working [56:43] on focused writing but you're also in a [56:46] slack Channel or something with a friend [56:47] or colleague those are both writing but [56:49] they're not the same style of writing [56:51] and so you're still having to activate [56:53] different cognitive rules and that that [56:55] comes with a switching cost so if I can [56:58] do something about it what should I do [57:01] to to make sure that I'm both happy um [57:03] more productive and healthier I would [57:05] again not start your day with something [57:07] that is inherently a multitask so if you [57:09] cannot start with email I would not [57:11] start with it because I view that at [57:13] least for me as something that will [57:16] start the day with multitasking and will [57:18] always feel unfinished like never feel [57:20] like it's finished um blockout times [57:23] where you designate on your calendar [57:25] that this is the only thing that you're [57:26] doing and leave some buffer for it [57:28] because there's something called the [57:29] planning fallacy we always overestimate [57:31] how much we can get done in a given [57:33] amount of time so I'd say fewer things [57:34] on your to-do list fewer things and on [57:36] the top maybe even just one thing that's [57:38] if I get this done this this was a good [57:40] productive day focus on that thing you [57:42] know pay yourself first do the important [57:44] thing first and really try to have some [57:46] when you're trying to be focused it's [57:47] important to mingle with people and [57:48] exchange ideas when that's what you want [57:50] to do but when you really have to be [57:52] focused to try to be in a place that's [57:54] as distraction free as possible and un [57:56] that includes even you know turning down [57:58] or off music even though it's Pleasant [58:00] and can help your affect and can [58:02] motivate but it also does have an [58:04] impairment on cognitive function because [58:05] you are paying attention to it to some [58:07] degree so don't listen to music while [58:09] I'm doing my work I mean that's hard to [58:11] say because I do it sometimes too [58:12] because it can have an energizing effect [58:13] or it can have a calming effect and [58:15] those are good but it does take up brain [58:17] space so you have to balance those how [58:20] do they know it takes up brain space you [58:21] can see how people perform on tasks when [58:23] the music is on and when the music is [58:25] off and it's it's a it's it's not as big [58:27] a deal if the music is very familiar [58:28] where you're kind of like it's not novel [58:31] so you're not attending to it the same [58:33] way but you know when I'm trying to be [58:35] super focused now I'll I'll turn the [58:37] music off but if I feel my sort of [58:38] motivation waning then maybe I'll tune [58:40] it back on but I want to use it [58:41] deliberately instead of just having it [58:42] in the background all the time because [58:43] it takes up a little space if it's and [58:45] if it's real noise like decb is a [58:49] logarithmic scale so small differences [58:50] are actually a big deal but if you go [58:52] from I think it's like maybe 70 to 80 DB [58:54] that's like the difference of going from [58:56] a like a washing machine to a vacuum [58:59] cleaner thereabouts in your background [59:02] noise that has a [59:04] enormous influence on your cognitive [59:07] ability and your productivity like like [59:09] a 15% decrement in your because of sound [59:13] yeah volume s because because you attend [59:14] to it you attend I mean our that's how [59:16] our brain [59:17] like focus is a challenge because this [59:20] is not the situation that we evolved in [59:22] right we evolved in a situation we're [59:23] paying attention to novel stimuli is a [59:26] really good thing and sometimes it's [59:27] still a very good [59:29] thing but it's at odds with a lot of [59:32] these modern things that we're trying to [59:33] do that are pretty new tasks for for [59:35] people what about instrumental music [59:38] because I tend to find that if I'm [59:39] listening to music that has lyrics in it [59:42] then I find it quite distracting when [59:43] I'm trying to do some work specifically [59:44] writing work or reading work so when I'm [59:46] researching guests for the podcast like [59:48] I was today in my hotel room I had a [59:50] song playing it was a rap song and um it [59:52] was I could I could feel my brain subtly [59:55] jumping from the screen that I was [59:56] reading to the rap lyrics to the screen [59:59] to the rap lyrics almost like just [60:00] oscillating between the two yeah and I [60:02] thought you got to turn that off cuz you [60:03] you're not reading I turned it off and I [60:05] really made progress but I but I [60:07] sometimes when I write like books and [60:08] stuff like that I put instrumentals on [60:11] and there's actually some apps in the [60:12] App Store now that are called like focus [60:13] music and they're lyric free music and [60:16] maybe like lot not lots of tonal changes [60:18] or not very complex maybe repeating I [60:21] mean I think that's going to be better [60:22] right the less novelty there is for you [60:24] to attend to that's better but think [60:27] it's also worth trying it with with [60:28] nothing and it depends how much you're [60:29] pushing yourself right like a tiny an [60:31] improvement of motivation or your affect [60:33] or feeling good might be worth it if [60:35] you're not all the way at the edge [60:38] pushing yourself right I don't know if [60:39] you've ever been on a there was there [60:41] was a time where I was trying to uh you [60:44] know do some foreign language lessons uh [60:47] that I was listening to while I would be [60:49] running and if I started hitting it hard [60:51] while I was running I couldn't even [60:53] remember what was said because it's you [60:55] switch into being really focused right [60:58] and so I think it it depends if you're [61:00] pushing yourself all the way you need [61:01] everything like there there are times [61:02] when I'm writing where I'm trying to [61:03] balance a lot of ideas in my head and I [61:05] almost feel like I'm overheating a [61:07] little bit yeah and if I'm in that phase [61:09] I I I want every Advantage I can have um [61:12] so push the distractions out but but [61:14] like there's also times to be to be [61:16] pleasant I think I think part of what's [61:18] sensible is working in intervals [61:20] planning to work in intervals Focus hard [61:22] for a little while do the myangelo then [61:24] switch to your your little mind where [61:26] you're we're doing something that's sort [61:27] of more fun and refreshing and maybe let [61:29] you incubate for a few minutes also take [61:30] a shower take a walk you know what about [61:33] notifications uh because you know I have [61:35] a lot of notifications I try to turn [61:37] them all off but they're still there in [61:39] the background and um you was talking [61:41] before we got going about this sort of [61:42] internal barometer of distraction that [61:44] we all have yeah yeah this [61:47] is so this is another aspect of of Dr [61:51] Mark's Work where she found that we have [61:54] this kind of internal mechanism if [61:56] you're getting distracted all the time [61:57] by notifications or whatever it is and [61:59] switching a lot if you say well now I [62:01] really have to hunker down I'm going to [62:02] get rid of the notifications or whatever [62:04] this stuff [62:05] is you will start self- interrupting to [62:08] maintain the Cadence of interruptions to [62:10] which you have become accustomed right [62:12] as if we have some internal like [62:13] distract ometer that is saying this is [62:16] your normal Cadence of interruption I'm [62:17] going to continue it by popping into [62:19] your brain oh here's this thing I need [62:20] to check oh here's this person I didn't [62:22] respond to you know you'll self- [62:23] interrupt that will go away but not [62:25] immediately so if you want to have a [62:27] lower Cadence of [62:29] interruption you need to like build by [62:31] getting rid of those external [62:32] interruptions know that you're going to [62:33] be self- interrupting for a while and [62:35] that'll go down more slowly so it has to [62:37] be more habit formation instead of just [62:39] today I shall be you know [62:41] uninterruptible okay so just want to [62:43] make sure I'm clear on this so say that [62:44] I get a notification every M every I get [62:47] 10 notifications a minute and that's [62:50] what I'm used to right and then I decide [62:53] to turn my notifications off because I'm [62:56] used to 10 notifications per minute [62:59] you're saying that I will basically [63:01] think of 10 things per minute to [63:02] interrupt myself with yeah for a while [63:05] because that's what I'm used to so we [63:07] get comfortable with a certain level of [63:09] interruption at a certain Cadence and [63:11] even if you we remove the thing that's [63:12] interrupting us we'll just replace it [63:14] with something else that interrupts us [63:16] that amount at that Cadence yes so you [63:18] can see in studies where people are [63:19] taking cognitive tests if they have [63:21] their phone invisible even if it's off [63:24] the people who are more phone dependent [63:25] or sort of more used to interruptions [63:28] they'll have a a bigger impairment on [63:30] the test if the phone is even like [63:31] visible or around them because they'll [63:34] yeah and so it's you know what thing did [63:35] I forget to do and I think something [63:37] that can help with this is keep a pad [63:39] nearby and when that thing pops into [63:40] your head of the of what you forgot to [63:43] do or who you forgot to respond to write [63:44] it down so at least it's maybe that [63:46] helps it not stick in your mind where [63:48] you're trying to hold it in working [63:50] memory like cognitively Outsource it so [63:52] at least it's not sitting in there and I [63:54] think that can help the adjustment it [63:55] makes me think a lot about people that [63:57] struggle with sleep and just sleep [63:58] hygiene generally because if we're you [64:01] know if our phone is this thing of [64:02] interruption throughout the day then we [64:04] go to bed cuddling our phone which a lot [64:06] of people do um it's probably going to [64:09] have quite a big impact on our ability [64:10] to sleep yeah I mean I wonder if you [64:13] know I think there's some I think our [64:15] phones are really useful for certain [64:16] things and I think they are disruptive [64:18] for other things and I wonder if sleep [64:19] is one of the most important [64:21] because you don't really want to be like [64:23] leaving residue on your brain when [64:25] you're trying to go to sleep so I would [64:27] put the phone as far away as possible [64:30] when you're really trying to sleep and [64:31] not at the last minute either personally [64:33] which you do oh I leave it in a [64:35] different uh floor and airplane [64:37] mode have you always done that no when [64:41] did you start doing that well I [64:44] definitely do it when I'm in the process [64:45] of writing a book because then all these [64:46] things that I take for granted I'm like [64:48] now I really got to lock in and and be [64:49] better um [64:52] and I have a I have a five-year-old son [64:56] and I was more of a night person who [64:58] would work at night like I would do a [64:59] lot of my writing in the wi hours and [65:01] he's getting up early no matter what and [65:04] so I realized that I had to start being [65:06] a lot more efficient about some of my [65:08] schedule and started thinking a lot more [65:11] about having it be dark having it be [65:13] quiet having it be cool not having the [65:14] phone around um the last thing I'm [65:17] reading not being work rated otherwise [65:19] I'll be thinking about that and it'll [65:20] take me longer to go to sleep so I think [65:22] I became a lot better about it when I [65:24] when I had my son when my son came [65:25] around [65:26] it's funny you mentioned that you've got [65:27] a son because much of your work made me [65:29] think about what I'll do when I'm a [65:31] parent someday because you talk about [65:33] how these early years where if a child [65:35] focuses on being a specialist in [65:37] something particular or a generalist [65:39] they have worldly different outcomes and [65:40] I think as a big football fan and a big [65:42] Manchester United fan I've always [65:44] thought when my kid comes out of my wife [65:46] someday the first thing I'm going to get [65:48] him doing from the age of two months old [65:50] is kicking a football around because [65:52] then he'll be a Manchester United player [65:53] I'll get to go to the games I'll be in [65:54] the players box everything will be great [65:56] but your work seems to suggest that that [65:58] if I want him to become a Manchester [66:00] star maybe I shouldn't do that you know [66:02] I'm I'm not convinced that you are going [66:03] to be like a vicarious living kind of [66:05] dad maybe you'll turn out to be but I'm [66:06] not convinced um but let me tell you [66:09] this you just reminded me of an [66:10] interesting story where I was once [66:11] giving a talk about some of this [66:12] research in sports that shows that the [66:14] people who go on to the highest levels [66:16] again there are a ton of different uh [66:18] paths but they tend to follow the Roger [66:20] path not the tiger path so Tiger Woods [66:22] we know are very early specialization [66:24] famously Roger feder played whole bunch [66:25] of different sports uh didn't specialize [66:28] until later than some of his peers so [66:29] tiger was playing golf since he was he [66:32] was um at his father gave him a putter [66:35] when he was 10 months old uh when he was [66:38] two just as a toy he wasn't trying to [66:40] teach him to be a golfer he gave him a [66:42] toy as tiger himself said my father [66:45] never once to ask me to Play It Was [66:46] Always me asking him to to let me play [66:49] but that's ignored but at at two he was [66:52] on National Television you know you can [66:53] go on YouTube see him on national TV [66:55] showing off his swing and then by three [66:58] he's saying I'm going to be the world's [66:59] next great [67:00] golfer uh he's world famous as a [67:02] teenager by the age of 21 he's the [67:04] greatest golfer in the world right on [67:06] the other hand Roger played a variety of [67:09] different Sports basketball uh rugby [67:12] skateboarding soccer mother was a tennis [67:15] coach but declined to coach him because [67:16] he wouldn't return balls normally I [67:17] guess didn't like deliberate practice [67:19] kept doing let's let's see he did [67:21] handball uh he did some [67:23] rugby uh SK swimming wrestling when his [67:28] coaches wanted to move him up to play [67:29] with older boys he declined because he [67:30] just wanted to talk about pro wrestling [67:31] with his friends after practice and he [67:33] was not focused on being the next great [67:35] from an early age like tiger was in fact [67:36] when he became good enough to Warrant an [67:39] interview with his local newspaper the [67:41] reporter asked him what he'd buy with [67:42] his first hypothetical paycheck if he [67:44] ever became a pro and he said a Mercedes [67:46] his mom was a gas right didn't she [67:48] thought this was like go sure and so she [67:51] asks the reporter to hear the interview [67:53] recording turns out he just said M CDs [67:56] in Swiss German he just wanted more CDs [67:58] not a Mercedes right she was fine with [68:00] that so he went on to be every bit as [68:04] famous as Tiger Woods but even tennis [68:07] enthusiasts don't usually know anything [68:08] about his developmental story even [68:09] though it's the norm according to the [68:11] science we only tell we only tell the [68:14] tiger stories even though that that [68:15] one's the exception right and this is [68:18] why do we only tell the tiger story this [68:19] is part of the debate I've had with with [68:21] Malcolm Gladwell when we're running [68:22] together and he said well he told me [68:23] it's a human cat video you know you go [68:25] YouTube and see them at two years old [68:27] and you can't you got to share it I [68:28] think that's true but I think it's also [68:30] because it feels like this tidy [68:33] narrative that we can extrapolate to [68:34] anything we want to be good at in our [68:36] own lives the problem is as we talked [68:39] about golf is almost a uniquely horrible [68:41] model of almost everything else that [68:42] humans want to learn it's like the [68:44] epitome of a Kind learning environment [68:46] where the situation isn't isn't changing [68:48] you're not having to react so I think [68:49] it's a bad model and we we underplay [68:52] even for famous people the normal [68:53] developmental trajectory like I once [68:55] gave a talk to a small group of people [68:57] about some of this research in sports [68:59] showing that the typical path to [69:00] becoming Elite is with a sampling period [69:03] you learn a broad range of skills learn [69:05] about your own interests and abilities [69:06] delay specializing till later than peers [69:09] and Serena Williams sat in the second [69:10] row and I'm freaking out because you can [69:14] present all the data you want but if the [69:16] goat stands up and says you're an idiot [69:18] it's going to be a bad day right and I'm [69:22] like please don't let her ask a question [69:26] of course she raises her hand for the [69:28] first thing and she [69:29] goes I think my father was ahead of his [69:32] time he had me do uh ballet track and [69:35] field gymnastics Taekwondo uh learn to [69:38] throw a football for the overhand [69:40] snapping motion of a serve when there [69:42] was too much travel on like the you know [69:44] a youth tour he took me off so I could [69:46] focus on [69:47] school I'd been a senior writer at [69:49] Sports Illustrated and I had never heard [69:50] that like I assumed that she was this [69:52] kind of quintessential tiger story so I [69:54] think even those stories [69:55] when you look more more deeply uh [69:58] they're not as clearcut as we tend to [69:59] think well I I learned this myself when [70:03] I I didn't know this as as the rule but [70:06] I I found the story of lomachenko [70:09] because I I my friend of mine brought me [70:10] ringside to a fight in New York City and [70:12] I sat at the side of the Ring watching [70:14] this guy called Vil lenko that I'd never [70:15] seen in my life and I just couldn't [70:18] believe his footwork I'd never seen [70:21] anything like it in my life and then I [70:23] after the fight he won the fight of [70:25] course after the fight I looked into his [70:26] win record and it was something like [70:28] he'd won 300 of his amateur fights and [70:30] only ever lost one and then he' gone [70:31] back and beat the guy that he had lost [70:33] against um and in my mind I'd never seen [70:35] a boxer like it ever and then when I [70:37] read into your work you've mentioned him [70:40] as well as being one of these examples [70:42] that had a really varied early [70:43] upbringing didn't just focus on boxing [70:46] and that's ultimately what made his [70:47] skill stack so unusual and therefore [70:49] probably what made him the best his [70:51] story surprised even me where he took [70:54] several years off [70:55] to learn dance like d i mean I wouldn't [70:59] usually expect someone to take years off [71:00] it's just sort of do things in those [71:02] same years so that was amazing but his [71:04] father's called Anatoli and I think it [71:06] was his father that took him off into [71:07] Yeah dance classes or something and then [71:09] let him go back to boxing so for your [71:11] perspective child I wouldn't say like [71:13] don't expose them to soccer I think [71:16] because I think a lot of this is I think [71:18] there's there's a few things going there [71:20] are three buckets of things going on [71:22] with why this delayed specialization [71:23] Works in sports one is match quality [71:26] again the degree of fit between who you [71:28] are and what you do is that about [71:29] passion like what you're passionate [71:30] about ability and interests both and the [71:33] earlier you force selection the more [71:35] likely you put the wrong person in the [71:36] wrong spot so especially when selection [71:38] is way pre puberty okay you're probably [71:40] putting people in the wrong my kid might [71:41] want to be a boxer but I'm forcing him [71:43] to be a soccer player and he might miss [71:45] his potential with boxing premature [71:47] optimization yeah okay and and that's [71:49] also why we often see on junior teams [71:51] the relative age effect you know where [71:53] kids born earlier in their Birth Cohort [71:55] are way over represented on Junior and [71:58] youth national teams because when [71:59] they're eight or whatever and selected [72:02] if they're eight and 10 months versus [72:04] just turned eight that's a huge [72:06] difference of development in that age [72:07] and coaches mistake that biological [72:09] maturation for talent and so youth teams [72:12] are overloaded with kids born early in [72:14] their youth cohort and also in school [72:17] especially boys if they're younger in [72:18] their age C are much more likely to get [72:20] diagnosed with ahd but they're just [72:21] acting like the younger boys that they [72:24] are um okay and so and then that [72:26] disappears at the top level so it's not [72:28] it's not a good thing so there's the [72:30] relative age effect that's one or [72:31] premature you know choosing there's [72:34] injury which is we now see a lot of [72:35] adult style overuse injuries in kids and [72:37] the main predictor of that is nine [72:39] months a year of one sport and one sport [72:41] only so this isn't about less Sports [72:44] there seems to be a protective effect of [72:46] diversifying that is separate from just [72:47] doing less but actually you know [72:50] balancing yourself out in some way but [72:52] then there's a skill learning Advantage [72:53] where it's similar to language where [72:55] you know kids who grow up in a like with [72:57] multiple languages they will often show [72:59] a little delay in some of their language [73:01] skills but that delay is totally wiped [73:02] out in the long run and they have an [73:04] advantage for subsequently learning [73:06] other languages looks very similar in a [73:08] lot of these skills where if you're [73:10] diversifying there may be some delay but [73:13] you have an advantage for picking up [73:14] other skills later on and I don't think [73:16] this is about whether you're putting on [73:17] a basketball jersey or a football jersey [73:21] I think it's about variability in your [73:23] problem solving which is why I think so [73:24] many of the great footballers grew up on [73:26] fotsa where what's foots it's foot's [73:29] with a small ball soccer like game with [73:31] a small ball um I think I think the the [73:35] Brazilian name is like football day Salo [73:37] which it means like football in a room [73:39] small ball stays on the ground played in [73:40] a small space and kids will be playing [73:43] on you know cobblestones one day and [73:45] concrete the next day and and it's like [73:47] in a phone booth you know at hypers [73:49] speed and so there's no no one's [73:51] drifting down the field and everyone's [73:54] having to judge even if you don't have [73:55] the [73:56] ball pick up on body movements to try to [73:58] anticipate what's coming next and the [74:00] touches are about six times as frequent [74:02] uh as in as in full scale football and [74:04] so I think it engenders a lot more of [74:06] this sort of [74:07] variability um than does just sort of [74:11] the full scale game it makes your [74:13] reactions a lot faster as well you have [74:14] to make decisions faster with the ball [74:15] but under yeah it's funny when you're [74:17] talking about the tiger example and why [74:20] people um broadcast that story more than [74:23] they broadcast what you consider to be [74:25] the the average which is just people [74:27] having this varied upbringing and then [74:28] eventually finding one thing and taking [74:30] it forward it made me think that from my [74:32] experience people broadcast that they [74:34] basically broadcast anything that's the [74:36] exception because it's the exception so [74:38] the story of you know tiger WS as one [74:41] example but the on the other side with [74:43] someone like Anthony Joshua who started [74:46] boxing at I'm going to Butch of this but [74:48] let's say [74:50] 24 I hear that all the time because it's [74:53] so unusual that he would become world [74:56] champion but start at 24 and the other [74:59] story that you hear all the time is like [75:00] the child prodigy story of like I don't [75:01] know Michael Jackson or Tiger Woods that [75:04] started when they were two you don't [75:05] hear about the person that starts at [75:06] like 15 right because it's not [75:09] interesting right because it's the norm [75:11] right or who ramps up in sort of a [75:12] normal way if they started because early [75:14] exposure is great yeah early exposure is [75:16] good but yeah and and it's a little it's [75:18] a little more equivocal right it's a [75:20] it's less of a prescription also like so [75:22] when when someone starts late we think [75:24] they defied the odds this is amazing and [75:26] when someone starts early that's a very [75:28] easy example to emulate and so I think a [75:31] lot of it is about that ease we [75:33] referenced the word match quality but [75:35] also we talked about passion a little [75:36] bit which kind of is one factor of match [75:39] quality a lot of people are trying to [75:41] figure out what they should be aiming at [75:42] in their life and they one of the most [75:44] popular questions I get from young [75:45] people is um how do I find my passion [75:48] how do I know what it is or at least [75:50] like what's the process def finding it [75:52] and it's they refer to it as if it's [75:53] this sort of Easter egg that one of them [75:55] and have to find it there's not one and [75:57] it's singular passion is a singular word [76:00] yeah no I don't I first of all I think [76:02] losing the idea that it is sing I mean [76:03] that's like the idea that there's like a [76:05] single soulmate out there for you you [76:07] know and I mean obviously I found my [76:10] single soulmate but for most of the rest [76:11] of [76:12] you uh there's a lot of things you might [76:15] be interested in in [76:18] fact the more things you try you'll [76:20] probably figure out the more things that [76:22] that you're interested in I was just I [76:24] was just like last week spending a [76:25] little time I was at the Pentagon [76:27] spending uh some time with a lieutenant [76:29] general who helped with a program they [76:31] call talent-based branching there where [76:33] they were losing a lot of their the [76:34] people they identified as The Highest [76:36] Potential were leaving the the Army and [76:38] they started this pilot program called [76:40] talent-based branching where instead of [76:41] saying here's your path you know here's [76:43] your your career path get uper out [76:47] they'd pair them with sort of a coach [76:48] like figure and they'd have them dabble [76:50] in like five different career paths a [76:51] little bit reflect on it with their [76:53] coach take some tests how this fits you [76:55] they have to keep track of the [76:56] Reflections in online portal again [76:57] self-regulatory learning got to do it [77:00] explicitly and in that process [77:04] 90% of the army Cadets who went through [77:06] that process changed their career [77:07] preference 90 and this is just from a [77:09] little bit of dabbling because you don't [77:11] know what's out there you don't know [77:13] what the opportunities are and that and [77:15] you know it helped retention so people [77:17] were more likely to stay if they find [77:18] better fit this is I think actually one [77:20] of the really important things [77:22] about um and I'll I'll Circle back back [77:25] to Passion a little bit there but when [77:28] we think about grit right which everyone [77:30] thinks of is I think about this and the [77:32] reason that the Army made me think about [77:33] it my semantic network is that the most [77:35] famous grit research was done at West [77:36] Point at the United States military [77:38] academy by Angela Duckworth and her [77:39] colleagues and it found that the grit [77:42] survey the grit survey is a 12 question [77:44] survey half the points are awarded for [77:46] consistency of interests not changing [77:49] what you're interested in and half the [77:50] points for Persistence of effort or [77:51] perseverance turns out to be a good [77:54] predictor of who would get through this [77:55] very rigorous orientation at West Point [77:57] called [77:58] Beast also has some predictive value for [78:00] who would graduate so just to give you [78:02] some context for the listeners that from [78:04] the way that I understood this is that [78:06] Angela Duckworth did this study to [78:07] basically figure out what it was that [78:09] made people more likely to get through [78:11] this very rigorous selection process at [78:12] an army barracks or something and she [78:15] determined that this this grit as she [78:17] she called it was the thing that allowed [78:19] people to be successful so from that [78:21] study I've heard this all over the place [78:23] that actually what makes people [78:24] successful even in my team is great yeah [78:27] yeah and that survey turned out to be a [78:29] better predictor than were some [78:30] traditional metrics of who would get [78:32] through Beast like test scores and stuff [78:33] like that it also had some value for who [78:36] would get through the military academy [78:39] as did some of those traditional [78:41] metrics but tons of those like since [78:43] about the mid1 1990s those very gritty [78:45] cadets at West Point have been almost [78:46] half of them have been quitting almost [78:48] on the day that they allow they have a [78:49] 5-year active duty service commitment [78:50] after they graduate and almost half of [78:52] them have been quitting and so the army [78:54] at a certain point said oh we've got a [78:57] millennial grit problem you know like [78:59] too much avocado toast not enough [79:00] mortgages or like [79:02] whatever and then some scientists who [79:04] also officers decided to study the [79:06] problem and they said we don't we [79:07] haven't gotten a grit problem overnight [79:09] we've got a match quality problem right [79:13] when the Army looked like the rest of [79:15] the economy where it was more upper out [79:17] and you fac the same kind of problems [79:18] year-over-year and you could have a [79:19] period of training followed by a period [79:21] of working doing similar things lateral [79:23] Mobility was limited [79:25] that was fine it just mimicked the rest [79:26] of the economy then you move into this [79:27] whatever you want to call it knowledge [79:29] creativity information economy and [79:31] people who can engage in Creative [79:32] problem solving and knowledge creation [79:34] have tremendous lad Mobility they have [79:35] lots of opportunities these young people [79:37] are learning things about themselves in [79:38] the early 20s and they have no agency [79:40] over career switching to match it so [79:42] they were just quitting right when the [79:44] Army first didn't realize this so they [79:45] threw retention bonuses at [79:47] people and the ones were going to stay [79:49] took it ones are goingon to leave left [79:50] anyway half billion dollars taxpayer [79:52] money didn't didn't uh fix the problem [79:55] but what I think it shows is that how [79:57] limited your insight into what you might [79:59] want to do is based on the things that [80:01] you've tried again Herman bar as we [80:02] learn who we are in practice not in [80:04] theory and so I think the biggest [80:06] problem for young people is if they're [80:07] sitting around introspecting to try to [80:09] figure out what their passion is go and [80:11] try something it's not it's almost [80:13] certainly not going to be the first [80:14] thing it may be you may get lucky but [80:18] it's probably not going to be the first [80:19] thing so you should get going on that [80:21] experiment process and start building a [80:22] model of the world so that you can [80:24] understand what your options are cuz I [80:26] also think the issue with passion and [80:28] happiness is again like I was talking [80:30] about I think it was can't remember [80:32] everything that was before the recording [80:33] started and after um but like when I [80:38] used to run the 800 meters or now when I [80:39] write books if you ask me to any given [80:41] moment am I enjoying this am I happy [80:44] about it you know am I passionate about [80:45] some's like no I want to throw my [80:46] computer out the window are you crazy [80:48] but it's so engaging it's so compelling [80:51] and it pushes me in a way to learn that [80:53] I can't do just on my free time and so I [80:55] don't think we have to think about just [80:57] passion find something that is so [80:58] incredibly engaging to you uh and then [81:02] go from [81:03] there and engaging really is how do you [81:06] know that it's engage it's when you sort [81:07] of FL drop into that flow state where [81:09] nothing else seems to matter or flow I [81:11] mean flow is a tricky one because it's a [81:13] lot easier it it shows up a lot more in [81:15] people that are like surfing or painting [81:17] than it does in some kinds of knowledge [81:18] work but I think it when you when you [81:23] get really engaged in something you a [81:24] curiosity about how you can get better [81:26] at it what else you can learn next so I [81:28] think it it stimulates this kind of [81:29] curiosity That You Don't See in people [81:31] when they're just in something where [81:32] they're kind of going through the [81:33] motions so you you start to understand [81:35] like when I I remember when my my then [81:38] girlfriend now wife um it was important [81:40] to me you know for health that like both [81:43] of us be lifelong exercisers for example [81:45] and the first time we we moved in [81:46] together and I'm saying like all right [81:47] we got to [81:48] identify uh something that works for you [81:50] and I take her to a gym and drop her off [81:52] and not realizing I have you know [81:55] Decades [81:56] of learning how to do stuff in a gym [81:59] that I take for [82:01] granted and then I realiz okay I need to [82:03] sort of walk this walk with her and so [82:05] we would try different things like [82:06] running she wasn't as into that so then [82:07] you know try some other thing etc etc [82:09] and finally she found one kind of class [82:11] and she comes home this day we did this [82:13] and then we did this it was so hard let [82:14] me show you this other thing we did I'm [82:15] like you found your thing and the one [82:18] problem was then when we were looking at [82:19] moving States we had to be within 15 [82:22] minutes of that kind of class walking [82:23] distance for any house that we were [82:25] going to buy but it's like you can see [82:26] this curiosity develop uh when someone [82:29] hits something it's so engaging that [82:30] they want to understand how to be better [82:31] they want to talk to other people doing [82:33] it they get so curious about it but you [82:35] have to experiment you have to [82:37] experiment I wish there were a way out [82:38] of that I wish you could say this is the [82:39] thing that's going to work for you may [82:41] maybe someday with AI I maybe but highly [82:45] unlikely highly unlikely um and and AI [82:48] just like changes the field that you're [82:50] playing in right and so I think [82:53] experimentation I think it's going to be [82:54] even more important as people can't [82:56] expect to be doing the same thing their [82:57] whole careers anymore I mean they're [82:58] threads that they can expect to to carry [83:01] through but not the same exact thing [83:02] when I saw your video called why [83:04] Divergent thinkers beat Geniuses in the [83:06] real world I thought you were going to [83:08] talk about neurod Divergence in the [83:10] video so someone that was you know [83:12] diagnosed with ADHD maybe when I was [83:13] about 30 years old I thought oh he's [83:15] going to explain why neurod Divergence [83:17] things like ADHD and autism result in um [83:21] better outcomes in the real world has [83:23] your work ever had any crossovers with [83:24] NE Divergence not a lot but I mean I [83:27] have read some of that work and I do [83:29] think something that's really important [83:31] is the more different types of thinking [83:33] that we can like get into a stew the [83:35] better off I think I think we all are I [83:37] mean there are reasons [83:38] why um ADHD like there's there's some [83:42] it's not it's not a big body of work but [83:44] I think it's relevant where you can look [83:46] at nomadic populations that then settled [83:49] and you can see certain genes that are [83:52] associated with and these are these are [83:53] small effects [83:56] um but you can see certain genes that [83:57] are associated with uh like novelty [84:00] seeking with with ADHD um will [84:03] apparently start to be like selected out [84:06] once they settle and it's more common [84:09] when they're nomadic and what that [84:10] suggest to me is that these are things [84:12] this attentiveness to lots of different [84:14] stimuli that are really important have [84:16] been important for us ancestrally and [84:18] are still important and so they're still [84:19] here they may be maladaptive if you're [84:22] telling someone they have to sit still [84:23] in a classroom for 10 hours a day which [84:25] I think is a difficult environment for [84:27] anyone to adjust to but I think to some [84:30] extent and I think this has happened [84:32] sometimes in in some companies um that [84:34] look for opportunities for people with [84:35] Autism where you say okay where is this [84:38] adaptive where is this type of thinking [84:40] adaptive instead of maladaptive so [84:42] useful instead of unproductive and I [84:44] think if we're not doing that then [84:45] you're missing opportunities to really [84:46] use people who think differently from [84:48] you yeah I mean it's interesting because [84:50] your work does whether it is endeavoring [84:53] to or not it really does make a great [84:55] case for diversity in the workplace yeah [84:58] yeah yeah uh you want to do a quiz sure [85:02] pretend you're a doctor okay and I'm [85:05] your put my white coat in my head with a [85:07] little stethoscope around my neck yeah [85:08] okay because that's that's what all [85:09] doctors look like um and and I'm your [85:12] patient okay and I've got a malignant [85:13] stomach tumor and there's a new type of [85:16] Ray or Focus radiation that can destroy [85:17] the tumor if it's at sufficient [85:19] intensity the problem is at that [85:21] intensity it will also destroy healthy [85:23] tissue in my stomach so how can you save [85:26] me okay while you're thinking of that [85:28] tell you a story there was once this [85:30] General had to capture a fortress to to [85:32] liberate a country from a brutal [85:33] dictator and he had plenty enough troops [85:35] to do it and there were roads radiating [85:37] out like wheel spokes from The Fortress [85:40] but they were strewn with landmines so [85:42] if he marched all his troops down any [85:43] one road he'd suffer a lot of casualty [85:45] so he had the idea let's split up into [85:46] single file lines go to the different [85:49] spok likee paths and we'll synchronize [85:52] our watches and they converge there at [85:53] the same time and they Liberate the [85:55] Fortress okay or they capture the [85:57] Fortress Liberate the country One More [85:58] Story Once a fire in a small town in [86:01] danger of spreading to neighboring [86:03] structures fortunately was near Lake so [86:05] neighbors are coming and they're filling [86:06] pales and bailing water on it not [86:08] working fire chief shows up she says [86:10] stop what you're doing everyone get in a [86:12] circle fill up your buckets get in a [86:13] circle around the fire on the count of [86:14] three one two three dampens the fire and [86:18] good they put it out fire chief gets a [86:20] raise okay have you can you save me now [86:22] doesn't matter you should this is I'm [86:23] giving you a very quick version but the [86:25] answer is you can arrange multiple low [86:27] intensity Rays around me so they [86:29] converge at the focal point so they go [86:31] through my torso without damaging me [86:32] because they're low intensity but they [86:34] converge at the right spot making high [86:37] intensity and this is a very truncated [86:40] version don't like if you were getting [86:42] the real test you would have had a lot [86:43] more time and still most people don't [86:44] solve it so don't worry it's called the [86:46] dunker radiation problem this is a very [86:48] truncated version of a large body of [86:50] research that shows that when you're [86:51] facing a novel problem the number of [86:53] solutions and the chance of coming up [86:54] with a good solution are predicted by [86:56] the number and breadth of [86:58] analogies that your group can come up [87:00] with and what predicts that is the [87:02] breadth of experience of the people in [87:04] the group so if you're facing a novel [87:06] problem and you have only people with [87:08] the same expertise it's not much better [87:09] than having one brain okay what you want [87:12] to do is come up with what's called a [87:13] reference class where you sit down you [87:14] come up with as many structurally [87:16] similar analogies from all sorts of [87:17] different areas like this sounds kind of [87:19] like this and this other thing and they [87:21] don't have to be as far flowing as what [87:22] I just did but in in the studies where [87:25] people have more time with each [87:26] successive story you tell them more [87:27] people will start solving the original [87:29] one even though they don't know that [87:31] they're related and so you want to get [87:33] people together who are really [87:35] different come up with a whole bunch of [87:38] uh of these like this kind of feels like [87:40] this look for which ones are [87:41] structurally similar and you'll start [87:43] thinking of possible solutions yeah I [87:45] mean that's just such a brilliant [87:46] brilliant case for diversity in thinking [87:48] and experience when you're building a [87:49] team when you're co-founding a team when [87:51] you're coming coming at a problem and I [87:53] was thinking actually yesterday about um [87:55] microphones so this is the first podcast [87:57] we've ever recorded if people watching [87:59] they might notice this all of a sudden [88:01] it' be interesting to know if you notice [88:02] this before I mentioned it but there's [88:03] no microphone here and this is the first [88:06] podcast we've ever recorded where there [88:07] isn't a microphone here and I was [88:08] thinking as you were speaking then about [88:10] how when we had the debate about how to [88:12] solve this problem with and the problem [88:14] that we were trying to solve for is that [88:16] there's guest bang on the table and it [88:18] comes through the microphone people send [88:20] me messages on LinkedIn saying hey it's [88:21] so annoying that people bang and then [88:23] whatever um so the microphones are now [88:25] above us and coming down and as we sat [88:28] around the three of us yesterday it's [88:30] kind of a little analogy for what you [88:32] what you're describing you've got will [88:34] who's got his experience in audio you've [88:36] got Jack who's got his experience and [88:37] then you've got me who's got basically [88:38] no experience but I do do a TV show [88:40] called Dragon's Den where we wear a [88:41] different type of microphone and we were [88:43] all chucking in our solutions to this [88:45] problem based on our own perspectives of [88:47] audio recording so um we Jack's solution [88:51] one but my solution before that was [88:53] while on drag and then we have one glue [88:55] to our chest so why can't we just glue [88:57] it to the guest's chest and it was [88:58] interesting watching us all it iterate [89:00] through these different solutions that [89:01] come from different places um doing this [89:03] kind of cost benefit analysis on each of [89:05] the solutions obviously one of the [89:07] problems with my solution is guests will [89:08] touch their chest right and then that'll [89:10] [ __ ] that up so yeah yeah yeah they [89:12] touch so and then also we have to grope [89:14] them when they arrive which we also we [89:16] didn't like but it's the same thing and [89:18] you need you need a really diverse set [89:20] of experiences to hone in on the winning [89:22] solution um but in most Pursuits what we [89:25] do is we collect people who have done it [89:26] before collect people who've done it [89:28] before and and that it's not that you [89:29] don't want those people you just don't [89:30] want only those people yeah and and the [89:32] tendency also is often to use the first [89:35] analogy that comes up you don't want to [89:36] do that either you want to like have a [89:38] menu of possible solutions to to look at [89:41] because like there's this thing called [89:42] the creative Cliff illusion people think [89:44] their most their best ideas and most [89:46] creative ideas will come either quickly [89:48] or not at all and in fact they tend to [89:50] come later as you're trying to come up [89:52] with ideas interesting yeah so but our [89:55] inclination is that it's like this flash [89:57] of lightning and either it comes or it [89:59] doesn't [ __ ] that's made me question a [90:01] lot of things I do because sometimes I [90:02] get an idea I write it down and I share [90:03] it straight away oh there's nothing [90:04] wrong with sharing the idea right but if [90:05] you're if you're like trying to solve a [90:07] real problem I wouldn't stop at your [90:08] first idea throw it out there for [90:09] discussion and then allow it to and then [90:11] keep yeah stay open to it and and don't [90:12] assume that you know if something didn't [90:14] come to you with like a flash of insight [90:16] that you should just stop thinking [90:18] you're writing a book about constraints [90:19] yeah and you you know I'm not going to [90:21] give away all all of the things in the [90:22] book because you know you to sell it [90:25] especially since like half of it isn't [90:26] written so it's I can't even give away [90:28] half but I found this story about Apple [90:30] really important because it's helped me [90:31] think about some of the things I'm doing [90:33] in business but also in my life are you [90:35] able to share that story of of Apple [90:36] what you've discovered in terms of focus [90:38] and constraints sure not not not Apple [90:40] so much as as um another company uh [90:45] called General magic that was uh a lot [90:48] of the team that designed the original [90:50] Mac came to this company and it was like [90:53] the the the hottest thing in Silicon [90:55] Valley and they were going to build um [90:58] uh the iPhone they had the idea they had [90:59] the vision like the drawings they have [91:01] look like the iPhone they had the team [91:03] from the Mac they had this incredible [91:04] Talent uh they went public in a [91:06] so-called concept IPO they didn't have a [91:08] product yet but the idea was so hot that [91:11] they were taken public uh and long story [91:13] short it turned into a disaster because [91:16] they had no boundaries they had as much [91:18] money as they wanted they didn't have [91:20] any customer in mind um they uh anything [91:24] they thought was cool they built it and [91:26] so the project just grew and grew and [91:28] grew and grew and never found the focus [91:31] to to kind of turn into anything usable [91:33] but a lot of the alumni that came out of [91:35] there realized that that was a problem [91:37] and so going forward they would have [91:38] lessons like you're better off [91:40] envisioning a customer even if it's the [91:41] wrong person than none at all and just [91:43] building something that's cool because [91:44] even if it's wrong you can learn that [91:46] you were wrong by trying something [91:47] whereas if you don't have one you don't [91:48] even have sort of a feedback mechanism [91:50] for Learning and so it was spending some [91:53] time with with some of those alumni um [91:55] got me really interested in in [91:56] constraints and uh I'm still putting [91:59] together some of that some of that [92:00] writing so I don't know that I can do it [92:02] justice at the depth um that I could if [92:05] I'd already written it but it's [92:06] interesting with the words you use [92:07] because there appears to be a bit of [92:09] kind of like a paradox or contradiction [92:10] in this idea of like breadth and then [92:14] constraints and focus and it's this [92:15] interest you know I mean that's part of [92:17] the reason I got interested in it [92:18] because a question after range that [92:20] people had for me was you know how do [92:22] you know when to focus right like so it [92:25] really very much came out of this [92:26] question because eventually you get this [92:27] broad toolbox you have to focus it into [92:29] achievement at some point right you [92:30] don't want to just pinball forever which [92:32] is what you said as your like hot [92:33] streaks hot streaks right you want to [92:34] folus into a hot streak eventually and [92:37] it also came out of this aspect of me [92:38] search you know research is research um [92:42] where my own biggest challenge the [92:44] bigger my projects are and the you know [92:46] books being big projects for me the [92:48] harder it is for me to draw the [92:50] boundaries of what is in Bound because [92:52] the the topics I take on in my my books [92:54] are by definition can't be perfectly [92:56] answered balance of Nature and nurture [92:57] and developing a skill how broad or [92:59] specialized to be and when and so I've [93:02] had so much trouble saying this is the [93:04] boundary for what fits in here and so I [93:06] myself wanted to get better at at [93:08] learning how to use constraints uh in in [93:11] my own work so for the first time with [93:13] this book for the first time I said [93:15] because I I for both of my previous [93:16] books I've written like 15% the length [93:19] of a book and then had to cut back [93:20] because I just shove in everything I [93:21] think is interesting this time I said [93:23] I'm going to have an architect ahead of [93:24] time forc myself to adhere to that and [93:27] one of the first things I noticed was [93:30] it's I I usually don't write the [93:32] chapters in order and I I started with [93:34] this book with my normal process of I'm [93:35] going to jump in with chapter five [93:37] because I just did the research and I [93:39] realized because I was starting to see [93:41] like this is going to break some of the [93:43] structure up and [93:45] downstream I'm leaving all these blanks [93:47] because I don't know what I will have [93:48] already said so I actually have to go [93:49] back and start an order so now you know [93:51] after being in writing for whatever like [93:53] almost 20 [93:55] years suddenly I have a totally new work [93:57] process and I'm writing an order for the [93:59] first time which is interesting and and [94:00] a bit scary but I'm writing at length [94:02] too I'm actually going to turn in a book [94:03] at the length of a book for the first [94:05] time are you not um at all concerned [94:07] about AI as a writer because you know [94:10] these models are getting smarter and [94:11] smarter every single week and just [94:13] generally how do you look at the the [94:14] sort of future of work in a world of AI [94:16] it feels like it's going to be such a [94:17] disruptive force in you know oh I think [94:20] it is career planning and like what do I [94:22] do with my future I mean it might touch [94:24] everything but one I love playing with [94:26] it so I'm I have these competing forces [94:28] of like maybe it'll um you know I'm a [94:32] very curious person and so if I'm I I I [94:36] play with probably about four different [94:37] AI programs a day but the the one that's [94:40] the most useful to me is called site. [94:42] again is it I don't have like any [94:45] affiliation with any of these things I'm [94:46] just a subscriber s. where I can put in [94:51] a scientific paper and it'll make like a [94:53] app showing all the other papers that [94:55] cite it and it'll try to automatically [94:56] sort them into those that agree and [94:58] disagree and it really helpfully will [95:00] show the snippet of how that the target [95:03] paper was cited in these other ones that [95:06] I used to have to spend like go sit in a [95:09] research library and be doing that by [95:11] like scanning down a paper to find that [95:13] so it's like a day now is an hour so and [95:18] I love that like if that means my books [95:20] don't sell as well but I get to learn 10 [95:23] times as science that's a trade-off I'm [95:25] definitely willing to make personally [95:27] I'm not saying everyone should be [95:28] willing to make that but I'm willing to [95:30] make that tradeoff but in terms of work [95:32] generally being [95:34] disrupted yeah I mean I think the the [95:37] model that I think of for sort of [95:39] there's no singular model but for how [95:41] technological [95:43] innovation has disrupted work in the [95:45] past a model that I like that I can tell [95:47] sort of [95:48] quickly is the introduction of the ATM [95:51] in in the United States happened around [95:52] 1970 uh so cash machine mhm um and I [95:57] went back and looked at news coverage [95:58] and it says like every uh you know [96:01] 300,000 bank tellers are going to go out [96:03] of business overnight and instead what [96:05] happened over the next 40 years as there [96:07] were more ATMs there were more bank [96:09] tellers not fewer because ATMs made [96:11] branches Bank branches cheaper to [96:13] operate so there are fewer uh branches [96:16] overall a fewer tellers per Branch but [96:18] more branches overall [96:19] sorry um but it fundamentally changed [96:22] the job from someone who's doing these [96:23] repetitive transactions repetitive cash [96:25] transaction to someone who's like a [96:27] marketing professional and a customer [96:29] service representative and uh you know [96:31] maybe a financial adviser it shifted [96:33] them to these [96:34] strategic goals where it's much broader [96:37] mix of strategic skills so if we can [96:39] Outsource some of that Kinder learning [96:41] environment repetitive stuff to shift [96:43] humans to being more strategic I think [96:45] that's like a good thing right we think [96:48] about I I know Radiologists have been [96:50] some of the people deal with medical [96:51] imaging have been some of the people who [96:52] have often in these reports by banks [96:54] that say who's going to be replaced [96:56] they're often high on the list because [96:58] they say the technology can you know [96:59] read these pictures very easily radio [97:01] just looks at a scan and tells you if [97:02] you've got a cancer or something yeah [97:04] but first of all I have yet to hear the [97:07] problem of like wow too many people are [97:09] having too easy access to Radiology [97:11] right like I think we want more of this [97:12] service but I think most doctors are not [97:15] doing doctor house you know most of the [97:18] stuff they're seeing is something [97:21] they've seen a million times and I think [97:23] a really important role for them is [97:25] strategic of well what should this mean [97:27] to the person how should I deal with [97:28] them and what what's reasonable to [97:30] implement in their life and what's [97:32] feasible for them to do to make a change [97:34] and so I think it'd be great if we could [97:36] Shi I don't think it will replace those [97:37] doctors I think it might shift them to a [97:38] more strategic role where they don't [97:40] have to spend time doing the sort of [97:43] more tactical stuff and can do the more [97:44] strategic stuff so that's that's been e [97:47] even in even in chess you know like [97:50] when well like when when IBM's deep blue [97:52] beat Gary casprov in chess in 1997 and [97:55] he noticed that it beat Gary casprov he [97:57] was so much better when he was he was [97:58] the best in the world at the time now [98:01] like a free app on your phone would be [98:02] Gary casprov but and he noticed the [98:05] computer was so much better at tactics [98:06] these are these like small patterns of [98:08] moves that he had spent his life [98:09] memorizing but he noticed it wasn't as [98:11] good at strategy which is how to arrange [98:13] the battles to wage the war so he [98:15] promoted what he called freestyle chess [98:17] tournaments where humans and computers [98:18] could play in any combination and the [98:20] winners were neither supercomputers nor [98:22] Grandmasters nor grandm with [98:24] supercomputers two amateur chess players [98:26] with three laptops they knew something [98:28] about Chess they knew something about [98:30] algorithmic search and they could coach [98:32] the computers where to look like they [98:33] couldn't even analyze their own games in [98:35] like the winners press conference at a [98:37] deep level because they didn't know [98:38] enough about chest but I think the [98:40] lesson there is it when the Tactical [98:41] part was outsourced it shifted it first [98:45] of all changed the people who were the [98:46] best at the task like this and it [98:48] shifted the humans to the more strategic [98:50] level and so I think that's what we need [98:51] to be ready to think about what can we [98:54] hand off so that we shift to a more [98:55] strategic level how might you be wrong [98:58] maybe the Strategic level maybe these [99:00] tools will be better at the Strategic [99:01] level than we would ever be I still [99:03] think there'll be a role for us in [99:04] determining what their goals should be [99:06] and that's a whole other level of [99:07] strategy is like what kind of world do [99:09] we want to live in I don't think in the [99:11] near term that we're going to be taking [99:12] our cues from them in that role but I [99:14] think even the people like last year I [99:17] was sitting around a campfire with one [99:19] guy who's running a generative AI [99:21] company and another guy who was like his [99:22] first investor [99:24] and who himself had worked in AI like [99:27] you know they were both uh [99:28] technologically Adept incentives [99:31] aligned and one guy was saying we'll [99:33] have artificial general intelligence [99:35] within three years for sure and the [99:36] other guy was saying I think this is a [99:37] glorified toy I still use Google more [99:39] and these were two people with similar [99:41] expertise with incentives aligned which [99:43] to me suggests the degree to which even [99:45] the people working on this stuff don't [99:46] totally understand what its capabilities [99:49] are or what it's doing um and so I think [99:51] there's a lot that's I think there's a [99:53] lot that's unknown someone made the case [99:55] to me that they said uh think about it [99:57] like this Steve you've got this Steve [99:59] here say my IQ is 100 and there's [100:00] another Steve through that war whose IQ [100:02] is a thousand what would you give me to [100:05] do as a task versus what would you give [100:07] him to do as a task who would you want [100:09] to drive your kids to school who would [100:12] you want to I don't know answer you're [100:13] saying we give everything to that person [100:15] well this is the analogy he gave me he [100:16] was like what are you left with okay [100:18] even even if it comes to that point even [100:20] if it comes to that point there'll still [100:22] be the issue of comparative Advantage [100:23] which is that these these models are [100:25] incredibly energy intensive right and so [100:27] you'd want to delegate energy to them [100:30] for the things that you really want them [100:31] to do so even if they are do end up [100:33] better than us at everything because [100:35] energy is not unlimited there will still [100:36] be things that are more valuable to have [100:38] us doing than to have them doing right [100:40] even like I mean that's the case all the [100:42] you may be better at certain things in [100:43] your business but you're not doing them [100:46] because it's a comparative advantage for [100:48] you to do this instead of those other [100:49] things so I think even if they do get to [100:52] the point where they're better than us [100:53] at everything there's still roles for [100:54] humans but incredible amount of [100:55] disruption right like what really [100:58] worries [100:58] me I mean I was reading [101:01] about last year about technological [101:03] innovation in history you know and we [101:06] have like to put it in a very coarse [101:08] nutshell it's like for 300,000 years we [101:10] lived like squirrels and then for 10,000 [101:12] years we lived like farmers and then 250 [101:14] years it's like everything changed every [101:15] generation like [101:17] crazy um and that's been hard to to [101:20] adapt [101:21] to um and I think [101:24] you know I thought that the Industrial [101:27] Revolution this you know which pulled [101:30] ultimately led to pulling billions of [101:32] people out of poverty you know changed [101:34] everything I thought that because [101:36] productivity increased so much that [101:37] wages and things would have increased [101:39] right along with them but it turns out [101:41] that there's pretty good evidence that [101:42] there was actually a gap of probably [101:43] about 40 years between the increase of [101:45] productivity and the increase of [101:47] Wages that's not good like a 40-year gap [101:50] between a huge technological disruption [101:52] and like Shar shared [101:54] Prosperity that's not something I think [101:55] we can really afford and and what sort [101:59] of helps solve the problem is that when [102:00] lots of people got urbanized for the [102:02] Industrial Revolution and looked around [102:03] and said hey you have the same problem [102:04] that I have we need to band together for [102:05] Collective action I think the challenge [102:08] now is we're like an invisible Factory [102:09] so it's it's harder to get people to [102:11] collectively act because we're not [102:12] sitting next to each other dealing with [102:14] this problem but I think we need to [102:15] start thinking as a group [102:18] of this technology is [102:21] cool but identifying problems that we [102:23] want it to work on not just building it [102:25] out be for the sake of Jus it's cool [102:27] what kind of world do we want to live in [102:28] I think we need to be asking those [102:29] questions I think it's quite unlikely [102:31] that we'll be intentional with it in the [102:32] way that you're hoping it'd be [102:34] unfortunate I mean I think a good sign [102:36] though I think is that even [102:39] the kind of technologists who I think [102:42] are usually prone to Hyperbole and [102:44] saying like this will be the greatest [102:45] thing even when it's obviously not going [102:46] to be are sounding some notes of caution [102:48] with this one in an early stage and so I [102:51] think that's a tuned other people to [102:53] some of those notes of caution I don't [102:54] think that gets us out of the woods by [102:56] any stretch the notes of caution worry [102:59] me oh well that's the point they should [103:01] worry I think if we if if we were where [103:03] we are and not worried right now I think [103:05] that would be a lot worse what is the [103:08] most important idea in your work that we [103:10] haven't discussed in your opinion in the [103:12] sports Gene I think the most important [103:13] idea um that we haven't discussed is [103:16] that uh Talent at [103:19] Baseline like the talent you if you take [103:22] a test in something your let's say you [103:25] haven't trained in that thing that we'll [103:27] call that your talent [103:28] Baseline is sometimes correlated with [103:32] your ability to improve from training so [103:34] people training looks just like medicine [103:37] because of differences between us some [103:40] medicine might work for you in a way [103:41] that it doesn't for me training is [103:43] similar two people will get different [103:45] results from the same exact training and [103:47] sometimes how good you are to start is [103:49] predictive of how rapidly you'll improve [103:52] but very often it is [103:54] not and that's a huge deal because we [103:57] usually judge people's potential based [103:58] on what we see right now or what we see [104:00] at Baseline before they've really had a [104:01] chance to train what I think the science [104:03] shows is that this Talent of [104:05] trainability is even more important than [104:07] Talent at at Baseline and so if you're [104:09] trying to evaluate people before they've [104:11] really had a chance to find a training [104:13] that fits for them again it's a messy [104:15] answer because it means people have to [104:16] experiment with the kind of training [104:17] that works for them and that [104:19] trainability is the most important kind [104:21] of talent and I think that's a different [104:22] picture of talent okay this is quite [104:24] this is very important because it [104:25] immediately as a employer I thought when [104:27] I'm hiring [104:28] people I you know if I'm hiring a [104:31] producer for one of our podcasts [104:32] whatever I shouldn't be focusing so much [104:34] on if I'm was planning for them to work [104:36] with for me and with me for 10 years I [104:38] should be thinking about their [104:40] trainability yeah I was going to say it [104:41] depends how quickly you need them to get [104:43] going right if you need them if you need [104:44] to know what they know today and they [104:45] need to be using that thing tomorrow [104:47] yeah that's one thing um but if if it's [104:50] about how good they're gonna get in the [104:52] long run you just shouldn't assume that [104:54] what you're seeing today predicts like [104:55] their ability to improve at a certain [104:57] can you measure someone's train ABY I [104:59] mean you can measure it very easily and [105:00] things like their aerobic capacity you [105:02] know the amount of oxygen that they can [105:04] uh move through their body I mean some [105:05] of the initial studies of this were done [105:06] in in scenarios like that where you had [105:08] everyone doing the exact same training [105:10] and you were literally measuring [105:11] physiological parameters you can do it [105:13] in other types of cognitive testing and [105:14] ability testing if you're looking for a [105:16] sort of specific task that's a little [105:19] harder if you're looking for a task [105:20] that's customized to something in your [105:21] business I think that's more difficult [105:23] it's going to be a little more [105:24] subjective I guess you could you can [105:26] kind of look at other areas of their [105:27] life I guess in the professional context [105:29] to see how quickly they developed one of [105:31] the things I look at when people apply [105:32] for jobs to work in one of my businesses [105:34] is I look at their LinkedIn resume but [105:36] specifically how quickly they got [105:37] promoted and moved through departments [105:39] because that's kind of an indicator it's [105:41] obviously not the most important thing [105:42] but you'll go you click on someone's [105:43] LinkedIn and you'll see they joined as [105:44] an intern and then a year later they [105:46] were a manager of the team then a year [105:47] later they were the like director of the [105:49] team then a year later they moved up to [105:50] a different department a year later they [105:51] became the global head and I'm like oh [105:53] my God that this person really moves [105:55] through the system well um and that is [105:57] an indicator of a few things they get on [105:58] with people because someone's pulling [106:00] them up and saying that person go up [106:02] their team are also um basically voting [106:05] that they should be the manager um they [106:07] have Proficiency in in learning rapidly [106:10] because especially if they jump between [106:11] sort of departments from HR to um [106:13] culture whatever um and I I always think [106:16] that makes them a bit more adaptable and [106:18] teachable if they've shown that track [106:20] record of [106:21] changing profession and moving up the [106:23] organization quickly interesting because [106:26] that feels a little related to I think [106:27] the an important idea that we didn't [106:29] talk about from range has to do with [106:31] so-called serial innovators these are [106:32] people who make repeated creative [106:33] contributions to their organizations no [106:35] matter where they are even when they're [106:36] changing like I said changing places and [106:39] these people like a woman named Abby [106:40] Griffin a professor and her colleagues [106:42] who studied these people some of the [106:44] descriptions of who they are uh these [106:46] are like literal phrases from her work [106:48] they are systems thinkers they read more [106:52] and more widely than their peers they [106:54] have a need to learn outside their [106:55] domain they have a need to communicate [106:57] with people with expertise outside of [106:58] their own area they appear to flit among [107:00] ideas which doesn't usually sound like a [107:02] compliment um they repurpose things are [107:04] already available in new ways all these [107:06] sorts of things and you can feel in her [107:08] writing almost she's almost like talking [107:09] to HR people saying just so you know [107:12] when you define a role too narrowly [107:14] you're making sure you select these [107:16] people out or force them to go somewhere [107:18] else to try to cultivate that kind of [107:19] breath and I don't think you can create [107:22] these people from Whole cloth but I [107:24] think you can absolutely stifle them by [107:27] not allowing them to do that kind of [107:29] moving around internally and so I think [107:31] when you're looking at [107:32] hiring I think the organizations that [107:35] I've been around at least that disrupt [107:37] themselves continually instead of [107:39] waiting to get disrupted Reserve at [107:41] least some of their hiring for instead [107:43] of saying here's a square peg for a [107:45] square hole that we need tomorrow they [107:48] say what is something we want that we [107:51] would have trouble teaching [107:53] let's go get someone with that and we [107:54] can coach them up on the stuff we're [107:56] good at so [107:57] like an extreme example of this was this [108:00] investment firm in Scotland I spent some [108:02] time with bayy gford this like [108:03] incredibly uh successful firm and I [108:08] think they this is Extreme but someone [108:09] there told me like they won't hire [108:10] anybody with an MBA I think that's I [108:12] don't think you should rule out things [108:14] like that but anyway but what they would [108:15] go is they'd say we want someone who has [108:16] experience in this or that or this kind [108:18] of thinking let's go get them because we [108:21] can't teach that thing and then we can [108:23] coach them up on finance it's going to [108:24] take them an extra few months to get [108:26] going because we're gonna have to teach [108:27] them but the stuff that why should we [108:30] hire for exactly the stuff that we can [108:31] most easily teach let's hire for the [108:33] stuff we want but that we would have [108:34] trouble teaching and then we can teach [108:36] them on it and I think the places that [108:37] are looking to disrupt themselves keep [108:39] sort of an eye open for that kind of [108:40] thing not for every hire but for some I [108:44] want to share something with you that I [108:45] found to be quite gamechanging if you're [108:46] like me and always looking for new ways [108:48] to add more plants to your meals then [108:50] you'll want to keep listening for the [108:51] next 30 seconds because I've got [108:52] breaking news specifically for you from [108:55] Zoe who sponsored this podcast [108:57] introducing Zoe's daily 30 which is [109:00] creating a supplement Revolution made [109:02] from 30 plants it's designed to be add a [109:05] your meal of choice every day no Ultra [109:08] process pills no shakes just real food I [109:12] just scoop and sprinkle it onto my [109:13] breakfast every day which is usually [109:15] eggs and avocado it's easy seamless and [109:18] it's a great way to fuel my day and the [109:20] best part is it tastes great and and [109:22] because it's Zoe you know there's [109:24] rigorous science backing it too which I [109:26] love for my listeners Zoe is offering an [109:29] exclusive 10% discount on your first [109:31] order of daily 30 plus with the code [109:34] podcast 10 just go to zoe.com [109:37] da30 to get started it's already sold [109:40] out twice since launching in July join [109:42] me and let me know what you think one of [109:45] the things I really love about your work [109:47] is you always cite different studies and [109:48] they're particularly fascinating I wrote [109:50] down tons of different studies from [109:51] different points that you've made made [109:52] today but are there is there a favorite [109:55] study that you that surprised you the [109:58] most or shifted your your sort of [109:59] Paradigm the most some of the the [110:01] research about forecasting totally [110:05] shocked me so the most famous work ever [110:06] done on on forecasting making [110:08] predictions was a [110:10] 20-year uh program of research that had [110:13] people making predictions about [110:15] geopolitical you know social [110:16] technological events and they had to get [110:20] they got 83,000 probability predictions [110:22] because people had to make specific [110:24] probability predictions of the [110:25] likelihood of an event by a specific [110:26] deadline so like 20 20% probability of [110:29] this happening by this time correct like [110:31] that there's going to be and it would be [110:33] very specific it would be a 20% chance [110:36] that within the next 12 months there [110:37] will be a military confrontation that [110:39] causes at least five casualties in the [110:41] South China Sea like it had to be very [110:42] specific so that they could say if [110:43] someone was right or wrong and they [110:45] needed so many because they had to [110:46] differentiate luck from uh good luck and [110:49] bad luck from skill and the worst [110:51] forecasters turn turned out to be like [110:53] the most narrowly specialized people who [110:55] were it do not that these people are not [110:57] important for generating knowledge but [110:59] who came to see the whole world through [111:00] sort of one lens or mental model and [111:03] would they had spent their whole careers [111:04] kind of studying one problem and and [111:07] would see the whole world around that [111:10] and they would wrap everything into that [111:12] story basically so in in this this [111:15] research they were called The Hedgehogs [111:16] who knew one big thing whereas the good [111:18] forecasters were the foxes who knew many [111:21] little things and sometimes [111:23] they had an area of expertise and [111:25] sometimes they didn't but more important [111:26] than what they thought was how they [111:27] thought they would collect different [111:30] perspectives they use social media [111:32] anything they had to take their own [111:33] hypothesis and tell get other people to [111:36] falsify it for them uh and those people [111:39] turned out to be the the best [111:42] forecasters and when they were put [111:43] together in groups with one another they [111:45] became even better because they had this [111:47] approach of of sort of borrowing from [111:50] the scientific method to test their own [111:51] ideas basically [111:53] and it just surprised me that these sort [111:55] of random people in many cases in a [111:58] tournament where they were pitted [112:00] against the intelligence community in [112:02] the United States that had access to [112:04] classified information that they did not [112:06] they beat them [112:08] handily and I just wouldn't have [112:10] believed that unless I unless I saw it [112:12] that that body of research about [112:13] forecasting the ability to see around [112:14] the corner a a big aspect of what made [112:17] people good at it was actually the [112:19] researcher who led this work described [112:21] those people as having dragonfly eyes [112:22] dragonfly's eyes are made of thousands [112:24] of different lenses each one of which [112:27] takes a separate picture and they are [112:28] synthesized in the dragonfly's brain and [112:30] so these people are gathering all these [112:32] different perspectives and they can seem [112:34] sort of confused and equivocal so they [112:36] might not make for good TV guests they [112:37] actually found in the research because [112:38] they don't go on and say this is how it [112:40] is like the housing crash is coming and [112:42] bl blah you know they're more [112:44] circumspect in some ways they might not [112:45] be as good TV guest but they're very [112:47] good forecasters it just made me think [112:49] that on a personal level I need to keep [112:51] pushing myself out outside of my zone of [112:53] comfort more that's one of the big [112:54] things I took away from the book range [112:56] but also just much of your work is it's [112:59] easy to get complacent in What I Know [113:01] Who I Am My identity what I do and in [113:03] fact that's probably the biggest risk to [113:05] my future success but also probably to [113:07] my fulfillment as well and it it goes [113:10] against our natural inclination to push [113:13] into uh unknown territory yeah because [113:17] the older we get the more like you know [113:18] they say you can't teach an old dog new [113:19] tricks I think it's more like the old [113:20] dog doesn't really want to know learn [113:22] new Tri tricks you know can't see the [113:23] point in learning a new trick and to [113:25] that point of the of the so-called Big [113:28] Five personality traits in Psychology [113:30] one of them is called openness to [113:32] experience which is the most predictive [113:33] of creativity and in in middle age it [113:35] reliably goes down goes down but [113:38] actually a study I loved in the book [113:40] found that if you force older people to [113:43] do something new can be some sodoku or [113:46] something even if they don't get good at [113:48] that thing if it's new to them it will [113:51] improve their openness to experience so [113:52] you can actually stem the decline of [113:54] openness to experience it's not [113:55] inevitable just by forcing yourself to [113:57] do new stuff that you're not competent [113:59] at is like great for brain health uh it [114:02] makes your life feel longer because our [114:04] memory Works in sort of chapters where [114:06] when you try new stuff it's like a new [114:07] chapter so it'll make your life feel [114:08] like it's not passing as quickly and it [114:11] keeps your openness to experience from [114:12] declining and so just like picking [114:14] something to do that's new even if [114:16] you're not planning on getting really [114:17] good at it I think is important it's [114:18] funny I said that thing a second ago [114:20] about um when I look at someone's [114:22] LinkedIn and then I looked down and I [114:23] found this little research um piece that [114:27] LinkedIn did that I'd pulled out that [114:29] said one of the best predictors of who [114:31] um would become an executive in a [114:33] company yeah was the number of different [114:35] job functions that individual had worked [114:38] across an industry so that's research [114:39] done by LinkedIn wasn't it yeah wasited [114:41] that was on about a half million members [114:43] yeah and and the interesting thing about [114:45] that was I when I was in contact with [114:47] LinkedIn uh talking about that and [114:49] trying to get some of that [114:51] data I said I kind of feel like your [114:54] guy's product might militate against [114:55] people doing this because you're saying [114:59] this is this is who's doing the best but [115:01] they might want a much cleaner kind of [115:02] linear trajectory yeah um so maybe you [115:05] should build another product where they [115:08] can build a narrative into it and say [115:10] Here's why I switched here's what I [115:11] learned and what so what's the actual [115:13] can you recap to me what the actual [115:15] finding was I mean that was that was [115:16] pretty much it that across a half [115:18] million members that the strongest [115:20] predictor of who was going to go on to [115:21] become a future executive um was uh the [115:25] number of different job functions that [115:26] they' worked across in industry in a [115:28] specific industry in Industry so not [115:30] changing Industries not changing [115:31] Industries although changing Industries [115:33] there was a bunch of lower level stuff [115:34] and changing Industries was useful at [115:36] times also but to be an executive in a [115:39] particular industry lots of job [115:41] functions across that across an industry [115:43] and does that mean different departments [115:44] within that industry they characterize [115:46] job functions you have to be doing [115:47] something fundamentally different okay [115:50] so give me an example I mean let's say I [115:53] think probably the easiest one is where [115:54] you go from being a a a a performer or a [115:57] good performer to being someone who's [115:58] managing other performers right classic [116:00] one doesn't have to be progression [116:02] though because but that's I think a very [116:05] a very simple one right or in my [116:06] industry it'd be like going from writing [116:08] to editing would for sure be one which [116:09] is kind of a mix of writing and and [116:11] managing but that's the side step in [116:13] your industry and side step yep for sure [116:15] I mean some people would well I guess it [116:18] depends some people would would view [116:20] that and in some places it's going up [116:21] but i' a side step the other thing I [116:23] found which was uh pretty shocking was [116:25] the in the part of your book where you [116:26] start talking about some of the dangers [116:28] of specialism and you referenced a study [116:30] that found cardiac patients were less [116:32] likely to die if they were they were [116:33] admitted to a hospital yeah when the [116:36] doctors were away we can tie in a few of [116:39] the things we've been talking about to [116:40] uh uh cardiac s to surgery here so so [116:43] this was this study so I think because [116:45] I'm I'm conscious when I write about [116:46] dangers specialization hugely important [116:49] obviously and in medicine it would be [116:52] crazy to say that specialization in [116:54] medicine increasing specialization [116:56] hasn't been both inevitable and [116:57] beneficial in many [116:58] ways but the point I was trying to make [117:01] is that it's also an underrecognized [117:02] double-edged sword to the point where [117:03] these two Harvard Le studies found that [117:05] if you're checked in to a teaching [117:07] hospital with certain cardiac conditions [117:09] on the dates of a national Cardiology [117:11] convention when the most esteemed [117:12] Specialists are away you're less likely [117:14] to die that that makes no sense right [117:17] that's suboptimal outcome and and the [117:19] conclusion was that's because these [117:20] researchers or these these [117:23] surgeons have done the same procedure so [117:27] many times that they will continue to do [117:30] it even if it's not the right solution [117:31] to the problem or if data shows that it [117:33] doesn't work anymore and so this called [117:35] the einstellung effect in Psychology [117:37] where you've done you've solved a [117:38] problem a certain way so many times that [117:40] you will continue solving problems that [117:41] way even if the problem has changed or [117:43] if new data emerges that shows it's not [117:44] the right solution so it's not to say [117:46] those people aren't important but they [117:48] are human and so they fall prey to the [117:50] Ein stong effect that's again why you [117:51] want some of this this mixture and to [117:53] tyion surgery you know we've also been [117:55] talking about distraction and focus one [117:57] of those same researchers did some work [117:59] that showed that if you have a surgical [118:02] procedure and this this research looked [118:03] at 980,000 [118:06] procedures that if you have a procedure [118:08] on the surgeon's [118:10] birthday you're more likely to die [118:12] within the 30 days after the surgical [118:14] procedure and they attribute it to the [118:15] increased distractions that the surgeon [118:17] is having on their birthday they don't [118:19] know whether it's external or internal [118:20] distraction um but you might not want to [118:22] have your again and you know and this [118:25] these are not huge [118:26] effects but over a large number of [118:28] people it makes a difference and if yeah [118:30] gosh that's [118:32] terrifying so you one of the things I've [118:34] come to learn today really is that [118:35] knowledge is a double-edged sword like [118:37] deep knowledge on one thing really is a [118:39] double-edged sword it will be your [118:40] making but in the long term it might [118:42] also be your breaking yeah and that [118:44] really resonates with me because as we [118:46] started the conversation with there's a [118:47] lot of things that I'm like really [118:48] knowledgeable about and know a lot about [118:50] and in fact that's my biggest curse and [118:52] I have to find a way to basically self-d [118:54] disrupt myself continually and always [118:56] assume that I am wrong and not not [118:59] always assume I'm wrong always assume [119:00] that there's a significant possibility [119:02] that I'm wrong today and maybe yesterday [119:05] I was correct but today I could be [119:06] entirely wrong um I mean I've changed my [119:08] mind about like fundamental beliefs I [119:10] had you know when I was younger and it's [119:12] weird to [119:13] think I mean like I was a grad student [119:16] environmental sciences and I was firmly [119:18] of the belief that uh environmental [119:21] preservation and technological progress [119:23] were at odds and I feel completely the [119:25] opposite now you know I think there are [119:27] technological things we can do that ruin [119:28] the environment but I actually think the [119:29] salvation of the environment requires [119:32] technological progress It's just like [119:33] fundamental beliefs about the world so I [119:35] think we should be open to that updating [119:36] and from a career perspective you know [119:38] if artificial super intelligence and [119:40] like some new form of free energy does [119:42] everything better than us then it does [119:43] and we'll have to reorient life in some [119:45] pretty dramatic ways uh but until then I [119:48] think we need to dispense with the idea [119:50] that you can live in a world where you [119:52] did a period of training for most of us [119:55] and then you're just going to benefit [119:56] off only that training for the rest of [119:57] your life you don't have to keep [119:59] relearning we have a closing tradition [120:01] on this podcast David where the last [120:03] yeah I know I love this tradition I want [120:04] to do it to like my friends when they [120:05] come [120:06] over interesting the last guest leaves a [120:09] question for the next guest interesting [120:11] oh boy it's so funny watching people's [120:14] body language when I open this book they [120:16] start to get quite nervous and it's so [120:17] funny I've asked I don't know [ __ ] [120:19] [ __ ] 100 questions today and it's when I [120:22] come to this question that people take [120:23] the longest time to answer so I'm like [120:25] these questions just better than my [120:26] questions um no some reason people get [120:29] nervous those other questions are things [120:30] that are so top of mind for me that [120:32] there's it's like a choice between which [120:34] of the three things that are in my mind [120:35] should I spit out this is like yeah this [120:38] is very different yeah uh what's your [120:40] favorite sandwich I'm joking imagine if [120:42] that was it after all I'm going to get [120:43] off easy no it's much more difficult [120:45] than that the question is what what is [120:48] your biggest fear and how [120:53] do you plan to face [120:55] it I have [120:58] a [121:01] tendency that I think in some ways is [121:04] good um and fits with some of the things [121:06] I've said but in some ways is bad to [121:08] want to start things over a lot and [121:11] sometimes that means burning them down [121:13] even if they're going well and in the [121:15] past I think I had that tendency with [121:17] some of my personal relationships to I [121:20] couldn't accept something going well [121:22] and it had to change or get better and [121:26] that led me to sometimes I think burn [121:29] down some personal relationships in ways [121:32] that I'm embarrassed of that I [121:34] regret um and I see this even in my own [121:38] work where I actually value it because I [121:39] end up doing all these new novel things [121:41] but it's almost like I can't and it's [121:43] good because like after my first book [121:44] they're like brand yourself as a sports [121:45] Gene guy I'm like no that's dead to me [121:46] now it's dead to me before it's even [121:48] published it's dead to me and that led [121:50] me to do these other interesting things [121:51] but I sometimes worry that I have this [121:53] like [121:54] pathologic why can't I just accept this [121:57] is this thing is good um and and let it [122:00] be good and it worries me much less in [122:02] my work life it worries me a little in [122:03] my work life that I'll always want to [122:04] burn something down and start over but [122:06] it does worry me there but I have a more [122:09] of a fear of it in the context of like [122:11] friendships because I know what I've [122:13] done in the past I think I'm better with [122:15] it now but thinking about the values I [122:18] have in my life going forward I don't [122:20] want [122:22] you know several relationships that were [122:24] hugely important to [122:26] me uh went away for things that were [122:29] preventable because I was like if it's [122:32] not perfect burn it down and I think [122:34] that was a really destructive impulse [122:38] um what is that in you what is that [122:40] where does that come from I don't know I [122:42] think it's like this feeling of always [122:44] want to be in becoming like this feeling [122:46] of starting over and improving that I [122:47] find [122:48] intoxicating um but I don't think that [122:51] has to apply to to personal [122:53] relationships uh and so a value that I [122:56] really want to work on I read this this [122:57] book that kind of influenced me about [122:58] philosophy and it's centered what's [123:00] called narrative values these values [123:02] that are objectively across cultures [123:06] things that people value so it's could [123:07] be like heroism right loyalty people [123:10] value that other country and that you [123:11] are subjectively attracted to and one of [123:13] the ones that I think is valued in a lot [123:15] of cultures that I'm attracted to but [123:17] that I've not been good at is [123:18] forgiveness and so my project is that's [123:22] a narrative value I want to start [123:23] building into my story to be a more [123:25] forgiving person because it's not it's [123:27] I'm not good at it uh and I need to get [123:30] good at it and I'm afraid that I won't [123:31] get good at it but I really want to well [123:34] we learn don't we and that's um that's [123:36] much of what is what is at the very [123:38] heart of your work how to become better [123:40] at learning and you've clearly [123:41] demonstrated that you're learning in [123:42] that regard I think much of the first [123:44] the first step in learning is figuring [123:46] out that we have a problem or some [123:47] something to solve as you said with your [123:48] experiments book and your books are so [123:50] unbelievably wonderful because they [123:52] present a completely original [123:53] challenging unconventional approach to [123:56] solving problems and you you do go at a [123:58] lot of the things that many of us have [124:00] accepted as narratives in our life and [124:01] if we've accepted them as narratives and [124:03] they're false then they're probably in [124:04] some way doing us a dis service in the [124:05] short or long term and that's why I find [124:07] your work so wonderfully important [124:09] because in many respects it is that [124:10] counternarrative to a lot of the things [124:12] that we've accepted and you do go the [124:14] extra mile even though it probably gives [124:16] you a headache I'm sure because a lot of [124:17] authors that I speak to don't go the [124:19] extra mile to figure out um if if what [124:21] we're being told is true and ultimately [124:24] that's a means to an end and the end is [124:25] to allow all of us to live more [124:27] optimized fulfilled and happy and [124:30] productive lives in whatever domain in [124:32] whatever definition we class those words [124:34] so thank you David for doing the work [124:36] you do I'm so excited to read whatever [124:37] you make next um and you're writing a [124:39] book on constraints and I just already [124:40] know that if it's anything like these [124:41] two books range and the sports Gene it's [124:43] going to be one of the most important [124:44] books I've ever read so thank you that [124:47] was a wonderful compliment I don't want [124:48] to add anything to that [124:51] [Music] [124:54] perfect Ted has quite frankly taken the [124:56] nation by storm a small green energy [124:59] drink that you've probably seen popping [125:01] up through a Tesco or through waitrose [125:03] they've grown by almost [125:06] 10,000% in a very short period of time [125:10] because people are sick and tired of the [125:12] typical unhealthy energy drinks and [125:14] they've been looking for an alternative [125:16] perfect Ted is the drink that I drink as [125:18] I'm sat here during the podcast because [125:20] it gives me increased f focus it doesn't [125:22] give me crashes which sometimes might [125:24] happen if I'm having a 3 4 5 6 hour [125:27] conversation with someone on the podcast [125:28] and it tastes amazing it's exactly what [125:31] I've been looking for in terms of energy [125:33] that's why I'm an investor and that's [125:34] why they sponsor this podcast and for a [125:36] limited time perfect Ted have given [125:38] Diary of CEO listeners only a huge 40% [125:43] off if you use the code diary 40 at [125:46] checkout don't tell anybody about this [125:49] and you can only get this online for a [125:50] limited time so make sure sure you don't [125:52] miss out [125:53] [Music] [126:13] [Music]