Why generalists triumph in a specialized world

By: David Epstein

Published: 2019

Read: 2019


In an increasingly complex and competitive world with a ballooning knowledge base, people are pushed to focus and specialize early (in education, music, sports, jobs, etc.). Pressured by the need to specialize, people see ever smaller pieces of the puzzle, making it more difficult to find and recognize solutions outside of their areas of expertise. While both specialists and generalist are needed in the world and there are many paths to peak performance, this book is a plea for more generalists, the kind of people that start broad, experiment and embrace different experiences as they progress; a plea for range, the long-term strategy of making deep and distant connections across multiple domains using abstract reasoning skills, for acquiring knowledge slowly and for avoiding specialization too early in life.

The book leans heavily on Kahneman (mentioned 31 times), essentially starting from his idea that relying on experience and intuition is efficient most of the time and works well in stable, predictable environments. This book does a good job at answering the question: in situations where specific experience and intuition work less well (maybe increasingly the case in today’s knowledge economy), then what?

Worth Reading:

Well written, entertaining and interesting observations. The format is the typical popular science format of abstracting lessons from a mixture of anecdotes, research and personal experience. Using anecdotes is especially tricky – this book unearths some very interesting stories that are worth being told and that are thoroughly entertaining. But stories are often more complex than can be captured in a couple of pages or paragraphs and the process of editing a story can sometimes lead to strange results. I’m not sure how many people would consider Van Gogh a role model in life and career planning, but this book does, conveniently skipping over his severe mental illness and the impact this illness had on both his life and work. While I understand that these stories provide entertainment value and are perhaps needed to fill up the pages, they often detract and distract from the main arguments and are probably not needed to make the thought provoking points.

Practical Takeaways:

The biggest practical takeaway should be try and be more of a generalist. But I’m not sure you can take that step, yet. While the book mentions that one requirement for being a successful generalist is curiosity and while the book also highlights that personalities change over time, not much time is spent on the fit between personality and pursuing a specialist vs. generalist path. Questions such as “are certain people better at being or are they more inclined to be a specialist versus a generalist” are important and not addressed here. I think that is an important omission.

Because if there are limited degrees of freedom in terms of what fits you best or which path you are inclined to pursue, then the practical implications of what is being preached in this book may be limited as well: perhaps you simply are or are not curious enough to be the effective generalist this book says there should be more of.

Not everyone may be interested, willing or able to pursue a less efficient, more uncertain path where pay-offs are not necessarily around the corner (nor may it be necessary – the world still needs a lot of specialists, as acknowledged by the author).

Having said that, some practical takeaways are:

  • Slow and sticky learning techniques (make learning hard, not easy; generating your own answers, testing, spacing, interleaving).
  • The out-sized importance of early work experience as demonstrated by the stories people tell themselves about it and the meaning they derive from it.
  • Overcoming the limits of knowledge complexity and specialization by repurposing existing knowledge and reapplying it to other domains.
  • The limits of efficiency and the benefits of meandering (if you are so inclined).
  • Similarly, in organizations, balancing the risk of conformity (efficiency) and the cost of reckless deviation (meandering).


Key Concepts:

  • Specialists do well in stable environments.
    • Specialists create and advance knowledge …
      • Narrow, deep, domain specific.
      • Through repetition, practice, experience.
    • … developing shortcuts …
      • Limited tool-set.
      • Re-create prior performance with minimal errors.
      • Chunking:
        • Taking individual pieces of information (chunks) and grouping them into larger units.
        • Grouping each piece into a large whole improves the amount of information you can retain (e.g., grouping words in a sentence; numbers in a phone number).
    • … becoming experts …
      • Developing intuitions for immediate recognition of patterns.
    • …  of familiar patterns ….
      • Works well in “kind” environments.
        • Stable, predictable, recurring patterns, feedback is rapid.
          • Chess, surgery, etc.
        • Associated with tactics: recognizing patterns to gain an immediate advantage.
    • Focus on efficiency.
      • Developing a head-start, the hunt for the most efficient way to develop a skill.
  • Specialists don’t do well in less stable environments.
    • If environments are less predictable ….
      • “wicked” environments…
        • Unpredictable, rules of the game are vague, patterns may not repeat often, feedback is delayed.
          • Most areas of daily life… (“Martian tennis”).
        • Associated with strategy: recognizing the “bigger or deeper picture” to gain an advantage long term.
    • … instinct and experience are less valuable…
      • One big thing: instinct may lead to applying (only) the first solution that comes to mind (hedgehogs).
      • Inflexible: experts may be unwilling or unable to discover or apply any other solutions.
      • Unpredictable: experience may backfire under higher levels of uncertainty.
      • Reductionist: seeing only one piece of the puzzle may lead to pursuing sub-optimal partial solutions (surrogate markers).
      • Simplicity: overlooking complexity by reducing all problems to simple cause-and-effect relationships.
    • … requires other “thinking” skills …
      • Find ways to learn beyond practice and experience (requires being better at understanding and learning abstract concepts).
      • Find ways to take knowledge from one domain and apply it to another.
    • Need to sacrifice efficiency.
      • Tolerance of failure.
      • Mental meandering, personal experimentation and probing for new solutions are by definition more inefficient.
  • Generalists do better in these environments.
    • Abstract thinkers.
      • Relying less on specific experiences and develop a broader tool-set of abstract concepts.
      • Flexible thinkers that are able to transfer knowledge across domains.
    • Samplers that learn by doing.
      • Sampling allows you to develop broad, creative, abstract thinkers.
      • Test and learn to find best fit between work and your (developing) abilities (avoid committing too early).
    • Slow learners.
      • Make knowledge stick (spacing, testing).
      • Make knowledge flexible (interleaving, making connections).
    • Adopt the outside view.
      • Insiders (experts) make judgments based on surface similarities among problems.
      • Outsiders reframe problems (analogies) and look for deeper structures.
    • Capitalize on specialist knowledge.
      • Gather and re-apply specialist knowledge and facts (know many little things – foxes).
      • Made possible by communication technology (specialist knowledge more widely available).
    • Use habits versus experience.
      • Tools for sense-making in dynamic, unfamiliar environments: curiosity, probabilistic thinking, appreciation for chance, unknowns.
      • Analogical thinking, updating beliefs.
  • The case for abstract thinking.
    • Measured through the Flynn Effect:
      • Increase in correct IQ test answers with each new generation.
      • Specifically, parts of the test that measure more abstract capabilities (classifications, similarities among shapes).
    • Kids are getting better at solving (certain) problems on the spot.
      • Ability to solve problems without previously having learned how to.
      • Extract rules and patterns from new material where no previous instructions have been given (“eduction”).
    • Brain is getting better at abstract thinking …
      • Relying less on concrete experiences.
      • Relying more on abstract thinking: classification schemes, hierarchies and layers of concepts, networks and linked information.
    • … which provides more cognitive flexibility …
      • Abstract concepts, schemes, classifications are flexible.
      • Allows you to arrange information and ideas across a wide variety of usages.
    • … which allows for knowledge transfer…
      • Ability to transfer and apply knowledge to new situations and domains.
    • … and computational thinking …
      • Using abstraction and decomposition to attack a large complex task.
      • Choosing the appropriate representation for a problem.
    • … which is better suited to modern working environment.
      • Knowledge economy increases the demand for conceptualization and knowledge creation.
      • Demand for brains that have conceptual reasoning skills that can connect new ideas and work across contexts.
  • The case for sampling.
    • Early narrow focus and practice.
      • Mostly focused on repetition and conscious error correction.
      • Not always the best path to excellence.
    • Sampling and breadth of training.
      • Requires improvising: the opposite of conscious error correction.
      • The brain turns off focused attention, inhibition, self-censoring.
    • This allows for breadth of transfer.
      • More contexts in which something is learned, develop more abstract models.
      • Rely less on particular examples.
    • And creativity.
      • Essence of creativity: applying knowledge to a situation you have not seen before.
      • Best course of action: sampling followed by narrow focus, increased structure and practice.
    • Find the goal with highest match quality.
      • Fit between your work and your interests and abilities.
    • Early specialization and persistence may not work.
      • Specialization may increases skills for work, but you learn less about your own interests and abilities.
      • Passion is important, but avoid persistence for the sake of persistence.
    • Sampling may make  more sense:
      • Capitalize on varied experiences to identify better matches.
    • We change more than we think.
      • Work and life preferences, personality traits all change significantly over time.
    • Early specialization and long-term planning make little sense:
      • Finding match quality for a future person that doesn’t exist yet…
      • Based on limited time, context and experience.
    • Learn who we are by doing:
      • Maximize match quality by sampling.
      • Test and learn: find experiences that provide quick and direct feedback.
  • The case for learning slow.
    • Make learning more challenging in the short run to increase knowledge “stickiness” and flexibility.
    • Create “desirable difficulties”.
      • To make knowledge stick.
        • Generate your own answers, spacing and testing:
          • Struggling to generate an answer on your own enhances subsequent learning.
          • The more confident you are of a wrong answer, the better the information sticks when given the right answer.
          • Space time between practice sessions.
          • Test before you’re ready.
      • To make knowledge flexible.
        • Making connections and interleaving:
          • Making connections allows for deeper understanding of the material.
          • Interleaving: mixed practice (mixing up different examples or ways of doing things).
          • Better prepared for unfamiliar scenarios.
    • Closed versus open skills:
      • Closed: procedures that can be acquired quickly through repetition.
      • Open: general knowledge, slow contextual learning.
    • Knowledge with enduring utility must be flexible, composed of mental schemes that can be easily mapped onto new problems.
  • The case for the outside view.
    • Helps to solve problems you have not encountered before.
      • Can’t fall back on previous experience.
    • Need to be reminded of things that are only abstractly or relationally similar.
      • Analogical thinking.
      • Allows people to think through problems they have never seen in unfamiliar contexts.
    • Danger of taking the “inside view”.
      • People often use only one or the same analogy all the time.
      • Make judgments narrowly based on the surface details of a problem.
    • Pursue the “outside view”.
      • Multiple analogies.
      • Ignore surface features (on which you may be the expert) and look for deeper structural similarities and analogies.
    • Instead of trying to understand and explain complex features, try to capture them by analogy.
    • Determine the deep structure of a problem before matching a solving strategy to it.
      • Reframing the problem to unlock the solution.
  • Generalists in organizations.
    • Individuals better than teams.
      • When the path is unclear, individuals are more capable of integrating diverse experiences.
    • But if you have teams, make them porous:
      • Individuals moving in and out of networks of teams.
      • Build bridges, increase cross-fertilization of ideas.
    • Benefits innovation, leadership and organizational culture.
      • Ability to use more than one tool, flexibility, learn rapidly.
      • Balancing the risk of conformity and the cost of reckless deviation.


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