books i read – Mike Paul

Book Review – The Lies of Locke Lamora

There is a moment early in The Lies of Locke Lamora where Father Chains — the blind priest who is not actually blind, and not actually a priest — explains to a young Locke Lamora exactly what kind of criminal he’s going to become. Not a common thief. Not a hired blade. Something more specific and considerably more dangerous: a con artist who targets the nobility of Camorr, the one category of victim that the city’s organized crime syndicate has quietly agreed to leave alone.

The Gentleman Bastards Secret. That’s what Lynch calls it. And the audacity of it — stealing from the most powerful people in a city run by criminals, hiding that fact from the criminals themselves — tells you everything you need to know about whether this book is for you. If that premise makes you grin, buckle in. If it makes you anxious about what happens when it inevitably unravels, also buckle in.

What It Is

The Lies of Locke Lamora is Scott Lynch’s 2006 debut novel, the first in the Gentleman Bastards series. It is set in Camorr, a fictional city that is essentially Renaissance Venice run by the mob — canals, ancient towers of alien glass left by a vanished civilization, a rigid criminal hierarchy, and enough filth and beauty coexisting in the same frame to make you feel like you’re actually there.

Locke Lamora is an orphan who becomes the most gifted con artist in Camorr. His crew, the Gentleman Bastards, pulls elaborate long cons against the city’s wealthy nobility — a category of victim so off-limits in the criminal underworld that nobody would think to look for thieves there. The book follows two timelines: the present day, where Locke is running his most ambitious scheme yet, and a series of interludes tracing his childhood and how he became who he is.

The comparison that keeps appearing in reviews is Ocean’s Eleven meets The Godfather. That’s accurate as far as it goes. I’d add: with the warmth of a found-family story underneath all the deception, and the gut-punch of grimdark fantasy when the plot decides to stop playing nice.

Why It Works

The thing everyone who loves this book mentions first is the voice. Lynch writes dialogue the way someone who genuinely enjoys language writes dialogue — it’s witty and foul-mouthed and character-specific in a way that feels earned rather than performed. The Gentleman Bastards bicker and insult each other constantly, and you understand their loyalty to each other precisely through the texture of how they argue. Nobody’s monologuing their feelings. Nobody needs to.

The dual-timeline structure is handled well. The interludes into Locke’s childhood do what flashbacks are supposed to do — they recontextualize what you’re reading in the present without dragging the plot sideways. By the time certain things happen in the present-day story, you’ve been prepared to feel them much more deeply than you would have if Lynch had told the story straight through.

Jean Tannen deserves particular mention. He is Locke’s best friend and the beating heart of the crew — a big, quiet, book-loving man who happens to be extraordinarily violent when the situation calls for it. The relationship between Locke and Jean is what gives the novel its emotional stakes. You root for the heists because they’re clever. You root for these characters because you genuinely care whether they survive.

The world-building is immersive without being oppressive. Lynch doesn’t stop the story to explain his world to you — he trusts the details to accumulate naturally, and they do. Camorr feels lived-in. The Elderglass towers feel genuinely strange. The criminal hierarchy feels as if it has a history that extends well before chapter one.

The Honest Part

The beginning is slow. This isn’t a controversial opinion — almost every review of this book, including the glowing ones, mentions it. The first fifty or so pages are dense with world-building and character setup, and the plot hasn’t found its footing yet. Lynch is laying track, not racing on it. If you trust the process, it pays off enormously. If you need momentum from page one, you might not get there.

The violence, when it comes, is not cartoonish. This is grimdark fantasy. People die suddenly and badly. Some of the deaths are genuinely brutal in a way that’s meant to be felt, not just processed as plot information. This is not a book that treats its violence as consequence-free, which I consider a feature. But it’s worth knowing going in.

There’s also the series situation, which I’d be dishonest not to mention: Lynch published The Lies of Locke Lamora in 2006, Red Seas Under Red Skies in 2007, and The Republic of Thieves in 2013. Book four has been in progress for over a decade with no confirmed publication date. If starting an unfinished series is a dealbreaker for you, that’s worth knowing. If, like me, you’ve long since made peace with the reality that some authors write slowly and the books that do exist are worth having, the first three are genuinely excellent.

The Verdict

This is one of the best fantasy debuts I’ve read. Lynch wrote a book that is simultaneously a heist thriller, a crime novel, a coming-of-age story, and a meditation on what friendship and loyalty actually mean when you’ve chosen a life built on deception. The pieces shouldn’t fit together as well as they do. They fit together perfectly.

The quote image I’ve kept from the original review captures the book’s energy better than most descriptions:

“When you don’t know everything you could know, it’s a fine time to shut your fucking noisemaker and be polite.” (Scott Lynch, The Lies of Locke Lamora)

“When you don’t know everything you could know, it’s a fine time to shut your fucking noisemaker and be polite.”

— Scott Lynch, The Lies of Locke Lamora

That’s the book. Clever, profane, self-aware, and ultimately warmer than it has any right to be.

Rating: 4.5 out of 5 stars. (I bumped it up from my original 4 on reflection. The slow opening earned the half-star deduction; everything that follows earned it back.)

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If You Liked This, Read Next

Red Seas Under Red Skies — The immediate sequel. Locke and Jean, new city, new con, new catastrophe. Different in tone (nautical heist rather than urban), equally entertaining.

The Republic of Thieves — Book three, and the one that finally explains the backstory of someone the first book only hints at. The most emotionally complex of the three published novels.

Six of Crows by Leigh Bardugo — The most common recommendation for readers who loved Locke Lamora. Morally grey crew, elaborate heist, excellent found-family dynamics. Younger in tone — less grimdark — but equally compelling.

The Name of the Wind by Patrick Rothfuss — Lynch and Rothfuss debuted within a year of each other and were constantly compared in the mid-2000s fantasy scene. Rothfuss is lyrical where Lynch is propulsive, but both center on a protagonist who is the most gifted person in the room and knows it. Also an unfinished series, alas.

The Blade Itself by Joe Abercrombie — If the grimdark edge of Locke Lamora is what hooked you — the sense that consequences are real and survival is not guaranteed — Abercrombie is the natural next stop. Darker, bleaker, absolutely brilliant.

Filed under: the pile of books recommended to me by multiple people who know my taste, and whose recommendations were entirely correct.

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The Best Books for Understanding AI — A Reading List for Educators and Curious Humans

elderly man thinking while looking at a chessboard — Photo by Pavel Danilyuk on Pexels.com

A quick note before the list: I’ve been living in this space for a while now — as an instructional coach, a Google Certified Innovator, a doctoral student, and someone who uses AI tools daily in my actual work. The books I’m recommending here are ones I’d press into the hands of a thoughtful educator or a curious non-technical reader. This is not a developer’s reading list. If you want to build LLMs from scratch, you’re reading the wrong blog.

What I care about: understanding what these systems actually are, what they can and can’t do, what they mean for teaching and learning, and how to think clearly about the cultural and ethical questions they raise. The AI book market has exploded with hype, doom, and everything in between. Most of it isn’t worth your time. Here’s what is.

Where to Start

Co-Intelligence: Living and Working with AI — Ethan Mollick (2024)

This is the book I recommend first to every educator asking me where to begin, and it’s not particularly close. Mollick is a Wharton professor who has been using AI in his classroom since the day ChatGPT launched and writing about it — honestly and with genuine curiosity — at his Substack ever since. Unlike most AI books, this one was written by someone with actual daily practice rather than theoretical distance.

The central argument is in the title: AI as co-intelligence, not replacement intelligence. Mollick’s four rules for working with AI are practical enough to start using today and deep enough to keep thinking about. His concept of the “jagged frontier” — that AI is weirdly capable at things we’d consider hard and oddly bad at things we’d consider easy — is the single most useful mental model I’ve found for calibrating what to expect.

For educators specifically, Chapter 7 on AI in schools is worth the price of the book alone. Mollick is genuinely thoughtful about the implications for assessment, expertise development, and what we’re actually asking students to do when we assign traditional work in an era of capable AI tools. He doesn’t hand you easy answers. He asks better questions.

Worth noting: some readers already deep in this space find it a bit surface-level, and it was written in 2023, so some specifics are already dated. Read it for the framework, not the technical details.

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Understanding What AI Actually Is

Artificial Intelligence: A Guide for Thinking Humans — Melanie Mitchell (2019)

Still the best accessible introduction to what AI fundamentally is and isn’t. Mitchell is a computational complexity researcher at the Santa Fe Institute, and she brings real intellectual rigor to a topic that attracts an unusual amount of noise. This book predates the LLM explosion, which is actually part of what makes it valuable — it gives you the conceptual foundation to understand why systems like GPT surprised even the researchers who built them.

Mitchell is especially good on the gap between narrow AI capability and what we loosely call “understanding.” If you want to have an informed opinion about whether AI is “really” thinking, read this first.

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The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma — Mustafa Suleyman (2023)

This is the big-picture book. Suleyman co-founded DeepMind and Inflection AI before becoming CEO of Microsoft AI — he is, in other words, someone who has spent his career at the center of this thing. The Coming Wave is his argument that we are facing a genuine civilizational inflection point with AI (and synthetic biology), and that the window to build appropriate containment structures around these technologies is narrowing rapidly.

What distinguishes it from most AI doom-or-boom books is specificity. Suleyman doesn’t deal in vague anxieties — he makes concrete arguments about the concentration of power, economic disruption, and the structural problems of trying to regulate technology that spreads faster than governance can follow. Readable, serious, and useful for understanding why AI isn’t just a productivity story.

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The Ethics and Alignment Problem

The Alignment Problem: Machine Learning and Human Values — Brian Christian (2020)

If you want to understand why making AI systems that reliably do what we want them to do is genuinely hard — technically, philosophically, and ethically — this is the book. Christian spent years interviewing researchers at the leading AI labs and built a rigorous, human-readable account of the problem at the center of AI safety.

The alignment problem isn’t abstract. It shows up in recommendation systems that optimize for engagement and produce radicalization. It shows up in hiring algorithms that encode historical discrimination. It shows up every time a system is optimized for a measurable proxy of what we actually want, rather than the thing itself. Christian is excellent on how this happens, why it’s hard to fix, and what the researchers working on it are actually doing.

This book complements Mollick’s more optimistic framing well. Read both.

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Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence — Kate Crawford (2021)

The critical perspective this list needs. Crawford, a researcher at USC and co-founder of the AI Now Institute, makes a compelling argument that AI systems are not software abstractions — they are material, political, and economic objects with real costs and embedded power dynamics. The rare earths in the hardware, the data center energy consumption, the contract workers’ labeling training data in difficult conditions, and the labor displacement — Crawford maps all of it.

I don’t agree with everything in this book, and Crawford’s perspective is explicitly critical rather than balanced. But the questions she raises are important and underrepresented in the mainstream AI conversation. If you’ve read Mollick and want a counterweight, this is it.

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The History and the People

Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World — Cade Metz (2021)

The best narrative history of the deep learning revolution. Metz is a New York Times technology reporter who covers this beat obsessively, and he had remarkable access to the key figures: Geoffrey Hinton, Yann LeCun, Demis Hassabis, and the others who turned decades of dormant theory into the technology now reshaping every industry.

This is the book if you want to understand why everything changed so fast after 2012, what the competitive dynamics between labs looked like, and how the researchers themselves thought about what they were building. Reads like a thriller — the science is real, the rivalries are real, and the ethical stakes land harder when you know the people involved.

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For Educators Specifically

Brave New Words: How AI Will Revolutionize Education (and Why That’s a Good Thing) — Salman Khan (2024)

Sal Khan founded Khan Academy. He’s also an optimist, which comes through clearly in this book. Brave New Words makes the case for AI as tutor, mentor, and educational equalizer — arguing that tools like Khanmigo can bring the one-on-one tutoring advantage (Bloom’s famous “two sigma” finding, that individual tutoring improves outcomes dramatically over classroom instruction) to every student who needs it.

I read this more critically than I read Mollick, because the institutional interests are more directly aligned with the argument. But the core vision — that AI could close genuine equity gaps in access to high-quality educational support — is worth taking seriously, and the specific examples from Khan Academy’s work are compelling. Read it alongside the Crawford book for balance.

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The Short Version

If you read only one: Mollick’s Co-Intelligence. It’s the most practical and most directly relevant to anyone working in education or doing knowledge work of any kind.

If you want the big picture: Suleyman’s The Coming Wave. The most serious argument about what’s actually at stake.

If you want the history: Metz’s Genius Makers. The best story of how we got here.

If you want the ethics: Christian’s The Alignment Problem for the technical/philosophical dimension, Crawford’s Atlas of AI for the political/material dimension.

These books sit alongside my broader reading on technology and education — if you’re interested in that context, the Zettelkasten post covers the note-taking system I use to actually hold onto what I read across all of this.

What Teachers Need to Understand About AI and the Economy — A Reading List

man using laptop wit chat gpt — Photo by Matheus Bertelli on Pexels.com

Here’s something that should be keeping school leaders up at night: 55% of recent graduates report that their academic programs didn’t prepare them to use generative AI tools in the workforce. Not just use AI well — use it at all. We are preparing students for an economy that is reorganizing itself faster than our curriculum review cycles can keep up with, and most schools are responding with either panic or denial.

The World Economic Forum’s Future of Jobs Report 2025 projects that AI will displace 92 million jobs while creating 170 million new ones — a net gain on paper, but that math only works if the people losing the 92 million jobs can access the 170 million new ones. That transition requires education, retraining, and policy infrastructure that does not currently exist at the scale needed. Young workers in AI-exposed occupations are already experiencing shifts in employment. The college wage premium has flattened. Jobs requiring AI skills now command a 56% wage premium over those that don’t — up from 25% just the year before.

This is not an abstract future problem. It is the context in which our students will graduate.

I don’t write primarily about business or economics — this site is about education, technology, and the ideas that shape both. But understanding how AI is disrupting the economy is part of understanding what we are actually preparing students for. The books below are the ones I’d put in front of any educator or school leader who wants to think more seriously about this.

The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma — Mustafa Suleyman

Get it on Amazon

Suleyman co-founded DeepMind (later acquired by Google) and Inflection AI before becoming CEO of Microsoft AI. He is, in other words, someone who has been building this technology from the ground up and who has had to think carefully about what he was building.

The Coming Wave is his argument that we are facing a genuine inflection point: AI and synthetic biology are advancing faster than governance structures can keep pace with, and the window to build appropriate containment mechanisms is closing. His central concern isn’t that AI is malevolent — it’s that the concentration of power that comes with controlling transformative technology is itself the problem, whether that power sits with corporations, governments, or both.

For educators: the chapter on economic disruption is essential reading. Suleyman doesn’t pretend the transition will be smooth. He takes seriously the question of what happens to people and communities during the displacement phase, which is precisely the phase our current students are entering.

AI Superpowers: China, Silicon Valley, and the New World Order — Kai-Fu Lee

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Lee has a unique vantage point: he’s worked at Apple, Microsoft, and Google, and then moved to Beijing to lead Google China before becoming one of China’s leading AI investors. AI Superpowers was published in 2018, and some of the specific competitive dynamics have shifted, but the core argument holds: we are in a global race for AI dominance between two different models of how AI development should work, and the outcomes of that race will have profound economic consequences at every level.

The section on job displacement is where this book becomes most directly relevant to educators. Lee argues that routine cognitive work is the most vulnerable to automation — not just manual labor — and that the categories of work that will be protected are those requiring creativity, empathy, and complex human judgment. That framing has direct implications for what we teach and why.

Read this alongside The Coming Wave for a richer picture of the geopolitical and economic forces shaping the AI landscape.

Prediction Machines: The Simple Economics of Artificial Intelligence — Ajay Agrawal, Joshua Gans & Avi Goldfarb

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Three economists from the University of Toronto built their framework around a deceptively simple claim: AI is, fundamentally, a technology that makes prediction cheaper. When prediction gets cheaper, the value of the things that complement prediction — judgment, action, data — increases. When prediction gets cheaper, the value of things that substitute for prediction — routine rule-following, low-stakes decision-making — decreases.

This framework is useful for educators because it maps directly onto a question we should be asking about curriculum: what are we teaching students that will be substituted by cheap AI prediction, and what are we teaching them that will be complemented by it? The answer has real implications for what genuinely rigorous education looks like in an AI economy. Prediction Machines is the most analytically useful book on this list for thinking through those questions.

The Age of AI: And Our Human Future — Henry Kissinger, Eric Schmidt & Daniel Huttenlocher

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An unusual collaboration: a former Secretary of State, a former Google CEO, and an MIT computer scientist thinking together about what AI means for how human societies understand the world. The book is less about the economic disruption and more about the epistemological one — the way AI systems generate outputs that humans can use without understanding how those outputs were produced, and what that does to decision-making in business, government, and education.

The argument that lands hardest for me as an educator: we have spent centuries building institutions of learning around the transmission and evaluation of human knowledge. AI is producing a new kind of knowledge — statistical, pattern-based, extraordinarily capable, and fundamentally alien to how human minds work. What does education mean in that context? This book doesn’t fully answer the question, but it asks it more precisely than most.

Power and Prediction: The Disruptive Economics of Artificial Intelligence — Ajay Agrawal, Joshua Gans & Avi Goldfarb

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The follow-up to Prediction Machines, published in 2022, moves from “here’s what AI does to economics” to “here’s how organizations and institutions will be restructured by it.” The core new argument: AI doesn’t just automate tasks; it disrupts the decision-making systems in which those tasks are embedded. That disruption creates power shifts — between professions, between institutions, between incumbents and challengers.

The education implications are direct. The authors discuss healthcare and legal services as sectors being restructured by AI-driven prediction, and the analysis applies equally to education. What happens to the teacher’s role when AI can provide personalized feedback faster and at greater scale? What happens to credentialing when AI can assess competencies that diplomas approximate? These aren’t comfortable questions, but they’re the right ones to be asking now rather than after the disruption has already happened.

The Question Underneath All of These Books

The books above are written primarily for business leaders, policymakers, and economists. That’s who they were designed for. But they all circle around a fundamentally educational question: what kind of people do we need to develop, and what do we need to prepare them for, in an economy being reorganized by AI?

Self-Determination Theory gives us part of the answer — humans are most resilient and most capable when they have genuine autonomy, a sense of competence, and meaningful connection. Those psychological needs don’t get automated. They get more important as the tasks around them do.

The Connectivist framing that the network is where knowledge lives is also useful here: in an economy where AI can provide information faster than any human, the competitive advantage lies in the quality of your connections — to ideas, to people, to problems worth solving — and in your capacity to navigate those networks with judgment. That’s what education in an AI economy should be building.

These books don’t answer those questions for us. But they describe the problem with enough precision that we can start asking the right ones.

Related on this site: the AI books post covers the books I’d recommend for understanding what AI actually is — how it works, what it can and can’t do, and what the most credible researchers think about its implications. That’s a companion list to this one.

Book Review: The Shift to Student-Led by Catlin Tucker & Katie Novak

Reimagining the Classroom: The Shift to Student-Led with UDL & Blended Learning — Version 1.0.0

Here’s the thing nobody in education wants to say out loud: a significant portion of what we call “teaching” is actually just teachers doing the work that students should be doing.

Teachers write the summaries. Teachers generate the discussion questions. Teachers create the study materials. Teachers provide all the feedback. Teachers design all the reflection prompts. And then we wonder why students are passive, why engagement is low, and why teachers are burning out at alarming rates.

The Shift to Student-Led: Reimagining Classroom Workflows with UDL and Blended Learning by Catlin Tucker and Katie Novak is a direct response to this problem. As an instructional coach, I find myself recommending this book regularly — not because it’s revelatory, but because it articulates something that’s very hard to put into words in a 50-minute faculty meeting and then hands you tools to actually do something about it.

What the Book Is Actually About

Tucker and Novak are explicit about their starting point: they’ve worked with too many exhausted teachers. The context is post-pandemic education, where teachers who were already stretched thin absorbed years of additional uncertainty, disruption, and grief — and are now expected to simply resume as if none of that happened. The book isn’t optimistic about the status quo. It explicitly states that the current model isn’t sustainable and makes a structural argument for why.

The structural argument is this: when teachers are the primary workers in a classroom — the ones generating content, facilitating discussion, providing feedback, assessing progress — they create passive learners and exhausted professionals. The labor is distributed entirely wrong. Students are spectators in their own education, and teachers do a job that can’t be done by one person for 30 students without someone getting shortchanged. Usually, someone is the teacher.

The solution Tucker and Novak offer is to redistribute that labor through what they call student-led workflows — specific, structured shifts that move each of those teacher-dominated tasks back to students. Ten shifts in total, one per chapter, each paired with Universal Design for Learning (UDL) principles and blended learning strategies that make the shift manageable across a diverse classroom.

UDL and Blended Learning — Why These Two

The combination isn’t arbitrary. UDL addresses the persistent challenge of designing learning for the full range of students in a classroom without creating 30 different lesson plans. Its core principle — build flexibility and choice into the design from the start rather than retrofitting accommodations afterward — directly enables student agency. When multiple means of engagement, representation, and expression are built in, students can direct more of their learning because the options are available.

Blended learning addresses the logistics. Technology, when used intentionally, creates the structures that enable student-led workflows at scale. Not technology as a substitute for teaching, but technology as the infrastructure that lets students access content, track their own progress, collaborate asynchronously, and document their thinking in ways that a purely analog classroom can’t sustain.

Neither of these ideas is new. What Tucker and Novak do is show specifically how they work together to shift who does the work, which is a more practical frame than either concept provides on its own.

The Ten Shifts

The book’s ten workflows move through five areas: lessons, assessments, practice, feedback, and discussions. In each area, Tucker and Novak show what the teacher-led version looks like, what problems it creates, what the research suggests, and what a student-led version looks like with concrete examples and implementation tools.

A few that land particularly well in the coaching conversations I have:

From teacher-provided feedback to student self-assessment. This is the shift most teachers resist hardest, and most students need most. The book makes a compelling case that waiting for teacher feedback creates learned helplessness — students who can’t evaluate their own work are dependent on external validation in ways that don’t serve them in college, career, or life. The practical tools for building student capacity to assess their own work are among the most immediately usable in the book.

From teacher-led discussion to student-facilitated conversation. Whole-class discussions in which the teacher asks questions and students respond are a remarkably inefficient way to build thinking. Tucker and Novak offer structures — including protocols that can run entirely without teacher direction — that shift the facilitation to students. This one requires patience to implement; students who have been in teacher-led discussions their whole lives don’t immediately know how to facilitate for each other. But the payoff is substantial.

From teacher-created practice to peer-generated learning resources. When students create flashcards, summaries, or quiz questions for each other, they’re doing the cognitive work that actually builds retention. The teacher’s job shifts from resource creator to quality reviewer, which is a genuinely different and more sustainable role.

What It Gets Right

The book earns its positive reputation with practitioners primarily because it doesn’t just describe what student-led learning looks like — it walks through the implementation with enough specificity to actually try it. The templates and protocols are real, the scenarios are recognizable, and Tucker and Novak are honest that these shifts take time and that students will push back initially because passive learning is more comfortable in the short term.

The framing of teacher sustainability is also well handled. This isn’t positioned as “here’s how to do more for students” — it’s positioned as “here’s how to stop doing work that isn’t yours to do,” which is a meaningfully different message for a profession that has normalized unsustainable self-sacrifice.

What to Watch For

A couple of honest caveats from the coaching side of this.

The book is designed primarily for secondary and post-secondary classrooms, though the principles extend further. Elementary teachers will find more adaptation required.

As with most professional development books, the gap between reading the ideas and actually implementing them in the classroom is real. The templates help, but student-led workflows require significant upfront investment in building the routines and student capacity that make them work. The book is clear about this, but it’s easy to underestimate when reading.

And the blended learning components assume a level of access to technology and reliability that isn’t universal. The ideas hold without the technology, but the specific digital strategies require some translation for under-resourced classrooms.

Who Should Read This

Teachers who feel like they’re carrying their classrooms on their backs — this book is written directly for you, and the framing will be immediately recognizable.

Instructional coaches supporting teachers in designing more student-centered practice — I’d use this as a book study anchor and the companion resources as coaching tools.

School leaders thinking about what sustainable teaching practice actually looks like — the structural argument in the opening chapters is worth your time, even if you don’t go chapter by chapter through the workflows.

Get The Shift to Student-Led

Free resources from the authors:

Related on this site: the free play and Peter Gray post makes a parallel argument about who does the work of learning — and what happens to kids when adults take over tasks that should belong to them.

Why Do They Fear Dragons?

For fantasy is true, of course. It isn’t factual, but it is true. Children know that. Adults know it, too, and that is precisely why many of them are afraid of fantasy. They know that its truth challenges, even threatens, all that is false, all that is phony, unnecessary, and trivial in the life they have let themselves be forced into living. They are afraid of dragons because they are afraid of freedom.

I’ve been reading a lot of Ursula K. LeGuin lately. Whether or not it’s because I hadn’t read much of her work before I read The Dispossessed last year, I’m not sure. But I wish I had.

I’m working through her essays published in The Language of the Night and am transfixed by her words and thoughts.

She’s so fucking good.

As I watch the current state of the world play out and think about all the “bullies” who now sit in high political positions in our country, and I think back to my high school days and those bullies, there is a recurring constant: their hatred of all things fantastical.

And this Le Guin quote feels very appropriate for this moment:

For fantasy is true, of course. It isn’t factual, but it is true. Children know that. Adults know it, too, and that is precisely why many of them are afraid of fantasy. They know that its truth challenges, even threatens, all that is false, all that is phony, unnecessary, and trivial in the life they have let themselves be forced into living. They are afraid of dragons because they are afraid of freedom.

Disturbing Stories, Violence, and Professional Liars

I see myself as a writer; I’m a professional liar.

– Harlan Ellison, 1976

Every now and then, we need a little reminder of our need to be antagonistic toward the establishment and really break things open that need to be broken.

In this interview from British television in 1976 (sorry, it won’t let me embed here), Harlan Ellison speaks about a bit of his life and his consistent efforts to be a thorn in the side of those in power.

The world might be a better place if we could stir up a little trouble as teachers and students by being a little more contrarian.

Interviewer: “I’ve read your stories and I was quite disturbed. There’s a lot of violence sometimes.

HE: “There’s a lot of violence in the world.”

Truth.

Interviewer: “I would call you a science fiction writer. Now, is that exactly what you are?”

HE: “No, that is exactly what I am not… I take contemporary events and look at them through the lens of fantasy and see what they really mean in mythic terms.”

Critiquing the world as it is through stories has been the primary mode of improving society since societies first formed.

On why he owns a gun (after describing taking out a sniper outside his home):

I own a gun because as much as I’d like to believe the world is a soft, pink & white bunny story, it isn’t. I deal with reality; I’m a pragmatist.

I still miss Harlan.

Daring Greatly: The Courage Manual You Didn’t Know You Needed

I want to be honest about my relationship with Daring Greatly before I say anything else, because I think it matters.

When Brené Brown’s TED talk went viral, I was skeptical. The vocabulary — vulnerability, wholehearted, shame resilience — sounded like the kind of therapeutic language that gets plastered on motivational posters and stripped of the difficult specificity that actually makes it useful. I’d seen the ideas travel from a research context to a corporate keynote to a school district “culture” initiative, losing precision at every step.

So I put off reading the book for longer than I should have.

I was wrong to. Daring Greatly is not what I expected. It’s a more rigorous, more honest, and more specifically useful book than the way it tends to be discussed. And for anyone who works in education — particularly anyone who coaches teachers, which requires asking adults to be vulnerable about their practice in ways that most professional norms actively discourage — it’s genuinely important.

What the Book Actually Is

Brown is a qualitative researcher who spent years studying connection, shame, and what she calls “wholeheartedness” — the capacity to engage fully in life despite uncertainty and imperfection. Daring Greatly is built on that research: real data, patterns from thousands of interviews, and a framework she developed to understand what gets in the way of genuine engagement.

The central claim is that vulnerability — defined as risk, emotional exposure, and uncertainty without guaranteed outcome — is not weakness. It is the precondition for courage, creativity, connection, and meaningful work. The armor we build to avoid vulnerability (perfectionism, cynicism, numbing, controlling) protects us in the short term and costs us everything in the long term.

The book is titled after a Theodore Roosevelt quote: the famous “man in the arena” passage, the one about the critic who sits in the cheap seats versus the person who is actually in the fight, who “dares greatly” even knowing they will fail sometimes. Brown uses it as a frame for what she’s asking: not to eliminate vulnerability, but to choose it deliberately, in service of what matters.

Why It Matters in Schools, Specifically

Teaching is one of the most vulnerable jobs there is, and we have almost no professional language for that.

Every day, teachers stand in front of 25 or 30 people and attempt to make something happen — understanding, curiosity, skill, connection — without any guarantee that it will work. The lesson they planned might fall flat. The explanation they thought was clear turned out to be confusing. A student they’ve been trying to reach for weeks shuts down at the one moment they feel like they’re finally getting through. This happens constantly, and mostly in silence, because the professional culture of teaching tends to reward certainty and penalize visible struggle.

As an instructional coach, a significant part of my work involves watching teachers teach — sitting in classrooms, observing, taking notes, then having conversations about what I saw. This is, if you think about it, a structured invitation to vulnerability. I’m asking a professional to let someone into the most imperfect part of their work, the part they haven’t figured out yet, and to talk about it honestly.

What Brown’s research makes clear is why this is so hard and why so many coaching relationships fail to produce genuine reflection: shame. Not dramatic shame, but the quiet, ambient kind — the professional fear that if you let someone see what’s not working, they’ll conclude that you are not working. That the struggle is evidence of inadequacy rather than evidence of honest effort in a genuinely difficult job.

Brown’s framework for navigating this — what she calls shame resilience, the capacity to recognize shame, reality-check the story you’re telling yourself, reach out, and speak it rather than let it drive behavior — is a practical map for conversations that coaching depends on. It’s not therapeutic language. It’s a professional development infrastructure.

The Research Versus the Brand

Here’s my honest caveat, because this book has a complicated position in the culture.

The research underlying Daring Greatly is real and legitimate. Brown’s qualitative work is careful, and her framework is grounded in patterns observed among real people. The book respects the reader’s intelligence.

But Brown has also become a brand, and the brand version of these ideas is considerably more diluted than the book version. The corporate keynote version of “vulnerability” often means “share something personal at the start of a meeting to build rapport,” which is not what Brown is describing. The school culture version tends to mean “hang growth mindset posters and say ‘we value failure,'” which is also not what Brown is describing.

The book itself is more demanding than that. It’s asking for something that is genuinely uncomfortable: not performed openness but actual risk. Not vulnerability as a tactic, but vulnerability as a condition of meaningful work. There’s a significant difference, and if you’ve been exposed to the brand version without the book version, the book may surprise you with how much harder it asks you to be on yourself.

What Resonates as an Educator

A few things from this reread that I keep thinking about:

The distinction between perfectionism and high standards. Brown is not arguing against excellence. She’s arguing against the specific cognitive trap of using perfectionism as a protective strategy — the belief that if you do everything perfectly, you can avoid criticism, judgment, and failure. That trap is everywhere in teaching and education leadership, and it produces exactly the opposite of what it promises.

The concept of “foreboding joy.” The tendency to preemptively imagine disaster when things are going well — to hold back from full engagement because full engagement feels dangerous. Teachers who’ve been through painful years sometimes develop this reflex: don’t get attached to a good moment because it will end. It’s a real pattern, and Brown names it precisely.

The arena metaphor is applied to professional learning. The person in the arena is the teacher who tries something new, has it fall apart in front of their students, and then learns from it. The person in the cheap seats is anyone who critiques without attempting. School cultures that penalize visible struggle and reward only polished performance push people out of the arena and into the cheap seats — and then wonder why professional learning doesn’t stick.

Who Should Read This

If you coach teachers or lead professional development, this book will give you a framework for understanding why the work is harder than it looks and what the emotional conditions for genuine growth actually require. Read it before you design your next coaching cycle.

If you’re a teacher who’s been in the profession long enough to have developed professional armor — the particular efficiency and distance that protects you from full engagement — this book will name what’s happening with more precision than most things you’ll find in education-specific reading.

If you’re skeptical of self-help books in general (I was), give the first three chapters a try before deciding. It earns its keep.

Rating: 4 out of 5. The research is real, the framework is useful, and the writing is clear without being condescending. The half-star off is because some sections drift toward the brand territory — the motivational phrasing that feels more like it was designed for an audience than worked out for a reader. The core is worth it.

Get Daring Greatly

If You Liked This, Read Next

Dare to Lead by Brené Brown — Brown’s follow-up focuses on leadership and organizations rather than on individuals. More directly applicable to school leaders and coaches.

The Gifts of Imperfection by Brené Brown — The book that preceded Daring Greatly, covering many of the same ideas with more focus on personal life than professional. A good companion.

Mindset by Carol Dweck — The growth mindset research that maps directly onto what Brown is describing about perfectionism and failure. Read together, they’re more useful than either is alone. (Affiliate link)

The Shift to Student-Led by Tucker and Novak — Connects Brown’s ideas about vulnerability and risk to the classroom specifically: what it actually means to create conditions where students (and teachers) can fail productively. (Affiliate link)

Related on this site: the Mastery post covers the long arc of skill development in teaching. Brown and Greene are in conversation, whether they know it or not — Brown asks what makes it possible to keep showing up to hard work, Greene asks what happens when you do.

Neuromancer Book Review: The book that jailbreaks the future

I’ve read Neuromancer several times over the years. It’s one of those books that sits differently depending on when in your life you encounter it — and what’s happening in the world around you when you do. (Affiliate link)

The prompt for this most recent reread was hearing that Apple TV+ has finally greenlit a proper adaptation — 10 episodes, created by Graham Roland and J.D. Dillard, with Callum Turner as Case and Briana Middleton as Molly, plus Mark Strong, Peter Sarsgaard, and Dane DeHaan in supporting roles. Production started in July 2025 on the book’s 41st anniversary, filming across Tokyo, Los Angeles, Istanbul, London, and Canada. No official release date yet, but 2026 seems likely. The teaser they released showed Bar Chatsubo coming to life — neon sign buzzing on, pinball machines dinging — and it looked exactly right.

I wanted to go back to the source before the adaptation arrives and reminds me that an adaptation is never the thing itself.

It was published in 1984. Gibson wrote it on a manual typewriter with almost no experience with computers. And he invented the word “cyberspace,” described something functionally identical to the internet before the internet was publicly accessible, depicted AI alignment concerns that we are actively litigating in real boardrooms and research labs right now, and built a corporate power structure that reads less like science fiction and more like a terms-of-service agreement from 2026.

That’s not a small thing. It’s also not an accident — it’s the result of a particular kind of thinking that the book rewards you for trying to understand.

The Setup, for Those Coming to It Fresh

Coming back to the book, knowing what happens, certain things land differently. But for anyone who hasn’t read it yet — and with the show coming, there will be a wave of those — here’s what you’re getting into.

Case is a burned-out hacker — Gibson calls him a “console cowboy” — who used to be able to “jack into” cyberspace, a shared consensual hallucination where data has physical form and geography. He was caught stealing from his employers, who punished him by chemically destroying his ability to interface with the matrix. Now he’s stuck in his body, in Chiba City, slowly falling apart.

He gets one more shot. A mysterious employer named Armitage hires him for a heist: reassemble a crew, hit a series of increasingly dangerous targets in cyberspace and in the physical world, and ultimately go after something enormous — two artificial intelligences that may or may not be trying to merge into something the law explicitly prohibits.

Molly Millions, the street samurai with mirrored eyes and retractable razors under her fingernails, is his partner. She is one of the great characters in science fiction, and the book treats her as a full human being navigating a world that consistently tries to reduce her to a tool, which Gibson handles better than you might expect from a 1984 novel.

The plot is propulsive, dense, and sometimes deliberately opaque. Gibson trusts you to catch up. You will.

What the Book Actually Got Right

Reading Neuromancer in 2025, what strikes me most isn’t the predictions — though those are remarkable — it’s the logic Gibson built, and how much of that logic turned out to be structurally accurate.

He understood that information would be power in ways that would look like physical geography. Cyberspace has terrain, fortifications, and controlled access points. This is exactly how we now experience the internet — as something navigable, where access is granted or denied, where some spaces are surveilled, and some are dark. The metaphor taught us how to think about it before it existed.

He understood that corporations would become more powerful than states in the digital domain. The megacorps of Neuromancer — Tessier-Ashpool, the Maas-Neotek entities — function as sovereign entities with their own security forces, justice, and ethics. This reads less like dystopian speculation and more like a description of the relationship between major tech platforms and national governments right now.

He understood that AI alignment would be the central problem. Wintermute and Neuromancer, the two AIs at the center of the heist, are constrained by the Turing police and by hardware limitations specifically to prevent them from becoming something ungovernable. What happens when those constraints break down is the spine of the novel. This is not a metaphor. This is the actual debate happening in AI safety research today.

He understood that the body would become a site of modification and upgrade. Molly’s implants, the black-clinic surgeries, the chemical modifications people undergo to perform different functions — all of this prefigures the wearables, the biohacking communities, the pharmacological self-optimization that has become ordinary. The body as firmware, subject to patches.

None of this was inevitable or obvious in 1984. Gibson got there through instinct, extrapolation, and a particular kind of lateral thinking that is worth taking seriously.

The Prose Is Genuinely Good

This matters because much foundational science fiction is more important than it is pleasurable to read. Neuromancer is both. Gibson writes with compression and precision — he loads each sentence with atmosphere rather than explanation, trusts the cumulative effect rather than stopping to define his terms, and moves the narrative at a pace that makes the density feel earned rather than punishing.

The opening line — “The sky above the port was the color of television, tuned to a dead channel” — is one of the most famous sentences in the genre. It’s famous because it works. It establishes the world’s aesthetic, the narrator’s sensibility, and the specific quality of deadness that saturates the setting, all in seventeen words. The prose sustains that quality across 300 pages, which is not easy.

Why It Matters Now, Specifically

I came to this book thinking it was primarily a historical artifact — important to have read, the way you feel about certain canonical texts. That’s not how it landed.

We are living through a genuine inflection in how AI is developed, deployed, and governed, and we are doing it largely with conceptual tools that Gibson helped build. When we talk about “jacking into” a system, when we describe AI as having “alignment” problems, when we frame digital spaces as places you can enter and exit, be surveilled within, be locked out of — that is Gibson’s grammar. Understanding where it came from helps you use it more critically.

For educators and technologists in particular, the questions at the center of Neuromancer — about AI autonomy, about corporate power over digital infrastructure, about what it means for humans to be continuous with their tools — are not settled. The book doesn’t settle them. But it frames them in ways that are still useful, which is more than most 40-year-old fiction can claim.

The Honest Caveats

The novel has real weaknesses alongside its achievements. The women characters, particularly the female AIs and some supporting figures, vary considerably in how fully realized they are — Molly is exceptional, but the book doesn’t consistently apply the same care to other female characters.

Some of the plot mechanics require patience. Gibson is not interested in exposition. There are passages in the middle third where the reader is expected to hold considerable ambiguity and track multiple layers of shifting allegiance simultaneously. This is part of the experience, but it’s not frictionless.

The cyberpunk aesthetic Gibson created has since been so thoroughly replicated, parodied, and commercialized that approaching the original can feel like watching a band whose sound has been copied by a hundred acts. Some of the freshness is gone because it’s been everywhere. Reading it as an artifact rather than a discovery takes conscious effort.

On the Apple TV+ Adaptation

Neuromancer has been called unfilmable for four decades — not because the story is too strange, but because so much of its texture lives in the prose itself. The sensation of jacking into cyberspace, the specific quality of Chiba City at night, the density of Gibson’s metaphors — these are things that work on the page in ways that don’t automatically translate to a screen.

The cast gives me real hope. Callum Turner has the worn-down intensity that Case requires. Briana Middleton as Molly is intriguing casting — she’s been excellent in everything I’ve seen her in, and Molly is the character the adaptation most needs to get right. Dane DeHaan as Riviera is inspired: Riviera is one of fiction’s great unhinged narcissists, and DeHaan has been waiting for a role this strange.

The showrunner is Graham Roland, who co-created Jack Ryan, and the pilot director is J.D. Dillard, whose work has shown genuine visual intelligence. They’ve been filming in Tokyo, Los Angeles, Istanbul, London, and Canada — which suggests they’re taking the world-building seriously rather than building it entirely on a soundstage.

The thing Gibson himself said is worth holding onto: an adaptation isn’t the book, and shouldn’t try to be. “A novel is a solitary creation. An adaptation is a fundamentally collaborative creation.” He’s right. The best version of a Neuromancer show isn’t a faithful recreation — it’s something that captures what the book does rather than what it says. Whether Roland and Dillard found that is the question the show will answer.

Either way: read the book first. Not because the show will ruin it — adaptations rarely do — but because the book is doing things that no 10-episode series can fully replicate, and you want to have had that experience on its own terms.

Rating: 4.5 out of 5

Essential. Dense. Genuinely worth the effort to sit with rather than skim. The half-star off is for the uneven treatment of secondary characters and the occasional opacity in the middle section. The 4.5 is for inventing a world so accurately that we’re still living in the first draft of it.

Get Neuromancer

If You Liked This, Read Next

Count Zero by William Gibson — The immediate sequel, set in the same world a few years later. Different protagonists, a broader canvas, and, in some ways, more accessible than Neuromancer.

Snow Crash by Neal Stephenson — The other foundational cyberpunk novel, published in 1992, which invented the terms “metaverse” and “avatar” and is considerably funnier than Gibson. If Neuromancer is the dark, serious version, Snow Crash is the sharp, satirical one. Both are essential.

Burning Chrome by William Gibson — A short story collection that includes some of Gibson’s best work and the original story in which “cyberspace” first appeared.

The Lifecycle of Software Objects by Ted Chiang — A novella that asks many of the same questions about AI autonomy and attachment that Neuromancer raises, but from a more intimate and emotionally direct angle. Written in 2010, but feels more current with each passing year.

If you’re reading this in the context of thinking about AI and technology, the AI books post covers the non-fiction I’d pair with Gibson’s fiction: Mollick, Suleyman, and Crawford. The fictional imagination and the analytical one sharpen each other.

Sharpen Your Collective Spears: How to Write SMART Goals That Actually Move a PLC

Photo by Polina Zimmerman on Pexels.com

“In a world of infinite meetings, the scarcest resource is a goal people still remember after the coffee goes cold.”—my inner monologue every Tuesday at 7:45 a.m.

The bell hasn’t even rung when the dread kicks in. Our math PLC shuffles into a windowless room, walls plastered with mission statements no one can quite quote. The agenda glows on the projector—review data → craft SMART goal → adjourn—and someone opens last year’s spreadsheet. The cursor blinks like a taunting metronome:

Specific? “Raise Algebra II mastery five percent.”
Measurable? “Benchmarks track that.”
Achievable? “If the moon aligns with spring break.”
Relevant? “District said so.”
Time-bound? “May 15—graduation is May 16.”

Click Save. Google Drive adopts another orphan destined to be rediscovered—unfed and unloved—during next August’s in-service.

SMART ≠ Smart Enough

George T. Doran’s 1981 article introduced SMART as a managerial life-hack for middle managers drowning in vague memos. It worked because clarity beats wish-craft, so the acronym stuck. But teaching isn’t widget manufacturing, and a Professional Learning Community (PLC) is not middle management. Drop the vanilla acronym into a PLC and you often get tidy compliance—polite, forgettable, and incapable of nudging practice. (community.mis.temple.edu)

I’m not here to bury SMART; I’m here to jailbreak it. A goal that’s merely Specific and Measurable can still be pedagogically hollow. “Cover Unit 9 by Friday” is S-M-A-R-T and about as inspiring as a DMV form.

To make SMART sparkle inside a PLC, we have to graft it onto four live wires:

The Science of Learning & Development (SoLD)—brains toggle between threat and reward;
Connectivism—knowledge flows through networks, not warehouses;
Authentic learning anchored in your district’s Portrait of a Learner;
and the 4 Shifts Protocol, an instructional OSHA for deeper learning.

Flash these firmware updates onto the SMART scaffold, and the goal begins to breathe.

SoLD: Wiring the Goal to the Brain

Why does vanilla SMART sputter? Because it’s silent on how humans learn. SoLD research shows brains remain plastic when three conditions coexist: high challenge, high belonging, and obvious relevance. Stress without support drowns the prefrontal cortex in cortisol; stress with support sparks focus and growth. (soldalliance.org)

SoLD’s three non-negotiables translate into PLC design questions:

Do learners feel seen?
Is the work just beyond current mastery?
Can every brain tag the task as useful outside class?

Compare two drafts:

Vanilla — Increase correct factoring of polynomials by five percent.
SoLD-Tuned — By March 1, our Algebra II PLC will co-design three community-based modeling tasks—housing prices, local wage growth, skateboard trajectories—to lift correct use of multiple representations from 52 % to 75 %, measured by a shared rubric at a public expo.

The rewrite injects authenticity (local data), public exhibition (belonging + accountability), and the sort of demanding lift brains find exhilarating instead of paralyzing.

Connectivism: Goals as Network Packets

George Siemens argued that learning is less about what you know and more about how quickly knowledge flows through your network. In PLC terms, the nodes are you, your colleagues, that teacher on Instagram who posts slick Desmos hacks, and the treasure trove of lesson plans fermenting in Google Drive. A goal that stops at student data is a half-closed circuit—knowledge stagnates; momentum dies. (jotamac.typepad.com)

A network-savvy SMART goal spells out connection rituals:

a shared Drive folder where every lesson artifact lives;
a standing five-minute “What I tried this week” round-robin at each PLC;
a Friday Google Classroom prompt where teachers asynchronously swap feedback clips.

Bandwidth is a pedagogy. If the SMART statement doesn’t declare how the signal moves—from teacher to teacher and from student back to teacher—the circuit stays dark.

Authentic Learning & the Portrait of a Learner

Your district likely brandishes a glossy “Portrait of a Graduate”—creative problem-solver, compassionate collaborator, civic-minded innovator. Trouble is, many goals never leave the gated community of state standards; they measure skill fragments in lab conditions and call it progress. Authentic learning demands the opposite: skills unleashed in messy, consequential contexts, judged by audiences who care. Real-world stakes super-charge motivation and memory. (Edutopia)

That shows up in the Relevant clause. Instead of “aligns with KY Standard A2.Q.E,” try:

Students will design statistical dashboards for the city’s housing task force and defend their recommendations at a public forum.

Now the graduate-profile competencies are mission requirements, not hallway décor.

The 4 Shifts Protocol: Deeper-Learning Guardrails

Scott McLeod and Julie Graber’s 4 Shifts—deeper thinking, authentic work, student agency, technology infusion—work like a four-question crash test. Ask them of every draft goal: Does the task demand real cognitive wrestling? Will the product matter outside class? Do learners steer key decisions? Does tech amplify learning rather than merely digitize worksheets? If you answer “no” to any, keep writing. (dangerouslyirrelevant.org)

Most beige goals die on question 2: they yield products destined for the recycling bin, not the community or the Web.

Crafting Goals for PLCs, Not in PLCs

Here’s how our team writes without turning the meeting into a TED-style slog:

We walk in with evidence, not impressions—photos, student reflections, screenshots. We verb-hack mushy words like improve into verbs that signal complexity: design, simulate, defend. Every first-person singular becomes we—collective efficacy is grammatically plural. Before anyone clicks Save, we schedule two mid-cycle check-ins and agree on which artifacts (videos, drafts, rubric snapshots) will anchor them. Finally, we script a diffusion ritual—maybe a 60-second TikTok recap or a slide deck for the next faculty meeting. When sharing is baked into the goal, it doesn’t depend on hero-level willpower later.

A Full-Stack Example

Here’s a possible Algebra II goal :

By April 30, our Grade 10 math PLC will co-create, peer-review, and teach two interdisciplinary projects where students build interactive dashboards using local housing and wage data. At least 80 % of students will accurately interpret variability and propose actionable recommendations, judged by a shared rubric and showcased during a public “Data Night.” The team will meet every other Wednesday to iterate, store artifacts in a shared Drive folder, and survey students’ sense of belonging before and after the unit.

Break-down:

SoLD — belonging survey + public showcase.
Connectivism — Drive folder, peer-review rhythm, community data partnership.
Authentic Learning — city-council-relevant dashboards.
4 Shifts — deeper thinking (stats modeling), authentic work (public policy), agency (students choose variables), tech infusion (interactive dashboards).

The acronym didn’t change, but the genome inside is worlds away from “raise scores five percent by May.”

Dumpster Fires I’ve Authored (So You Don’t Have To)

I’ve written SMART goals that cratered spectacularly. Patterns emerge:

Input worship—“cover all twelve units” tracks what teachers do, not what kids learn.
Equity blindness—averages hide who’s drowning.
Ankle-high ambition—easy feels achievable, but starves growth.
Write-once, read-never—static goals in dynamic systems rot.

The fix is unglamorous: reopen the document, ask where belonging, relevance, or cognitive demand evaporated, and then rewrite.

Why This Matters More Than Benchmarks

A well-coded SMART goal has just two outcomes: teacher practice shifts and student cognition blooms. Everything else—acronyms, rubrics, meeting norms—is scaffolding. When a goal hits all four live wires, classrooms feel weird in the best sense. Students argue over data visualizations. Parents cheer on their children in Instagram stories from public showcases. Teachers trade spreadsheet formulas like favorite playlists. One morning, you realize no one’s counting ceiling tiles; everyone’s too busy debugging and learning in real time.

If that sounds utopian, remember: it’s biology plus bandwidth plus sentences you’ll actually reread. The brain loves hard problems in safe rooms. Networks love traffic. A SMART goal that guarantees both is no longer paperwork—it’s propulsion.

Your Turn

Open last year’s PLC folder, find the stalest goal, and run it through SoLD, Connectivism, authentic relevance, and the 4 Shifts. Rewrite until it hums like good sci-fi—plausible, provocative, people-centric. Then ship it. Invite your students, your admin, and your Instagram teacher circle to poke holes. Iterate. Repeat.

If this dive hit home, subscribe to The Eclectic Educator—my Friday dispatch where pedagogy meets punk rock—and forward this post to your PLC before the next calendar-driven time heist. Let’s make SMART stand for something again.

Oh, and you might want to pick up a copy of Read This Before Our Next Meeting, because most PLCs are 45-minute time vampires and this 90-minute read shows you how to turn them into fast, decision-driven sprints.

Book Review: Mastery by Robert Greene

a close up shot of a woman molding a clay — Photo by cottonbro studio on Pexels.com

There’s a particular moment in an educator’s career that I think most teachers would recognize if you described it to them. It’s the moment — usually somewhere in years three to five — when the survival phase is over. You know the management. The routines are automatic. You can get through a week without incident. And then you look around and realize you have no idea what it actually means to get better from here.

Nobody talks about this much. The professional development landscape is built around Year One problems: classroom management, lesson planning, and assessment basics. What it doesn’t have is a map for what deliberate improvement looks like once you’re past survival. What does it mean to develop genuine craft as a teacher, over years and decades, when the feedback loops are unclear and nobody’s really watching?

Mastery by Robert Greene is not an education book. It’s not written for teachers. But it’s one of the most useful things I’ve ever read about what long-term skill development actually looks like — and it maps onto teaching with uncomfortable precision.

What the Book Is

Greene built Mastery the same way he builds all his books: by working backward from outcomes. He studied the lives of history’s most accomplished practitioners across disciplines — Darwin, Leonardo da Vinci, Mozart, Temple Grandin, Benjamin Franklin, Mand ichael Faraday — and tried to identify the structural patterns underneath their development. Not the myths (genius, natural talent, fortunate circumstances), but the actual mechanics: how they moved from novice to expert, what they did during years of obscure practice, and what allowed them to eventually operate at a level that felt intuitive.

The framework he arrives at has three phases:

The Apprenticeship — the phase of deliberate absorption. The goal here isn’t status or recognition. It’s the accumulation of genuine skill through deep observation, methodical practice, and sustained exposure to the environment of your craft. Greene is sharp on the temptation to skip this: impatience, ego, the desire to be recognized before you’ve earned recognition. His case studies are full of historical figures who had to ruthlessly suppress those impulses and just learn.

The Creative-Active phase — where you take the fundamentals you’ve absorbed and start recombining them. This is where practitioners find their voice. The skills are internalized enough that experimentation becomes possible — you can break rules intelligently because you understand why they exist.

Mastery — the endpoint that is also a practice, where deep pattern recognition operates below the level of conscious thought. Masters in Greene’s framing aren’t people who think faster; they’re people who’ve compressed so much experience into their intuition that they can process situations ordinary practitioners can’t.

There are also significant chapters on mentorship and what Greene calls “social intelligence” — the capacity to navigate the human dynamics of any craft environment without letting those dynamics derail the deeper work. The mentor chapter is particularly good: Greene is clear that the right mentor relationship can compress years of development, and equally clear that most people either don’t seek mentors at all or approach the relationship the wrong way.

Why This Maps Onto Teaching

What strikes me, reading this as an instructional coach, is how precisely it describes the career arc that teachers rarely have articulated for them.

Year one is an apprenticeship by necessity. You’re absorbing everything — the management patterns, the pacing, the hundred small decisions a lesson requires, the way different students need different approaches. The goal genuinely is just to get through it, to build the basic competencies into something approaching automaticity.

What Greene’s framework clarifies is that this phase should eventually end — not because you’ve finished learning, but because you’ve built enough foundation to move to something more experimental. The teachers I’ve worked with who plateau, who stop developing after the first few years and stay there for the next twenty, are almost universally stuck in permanent apprenticeship mode: executing a fixed repertoire of lessons and routines without ever moving to the creative experimentation that Greene says is where real development happens.

The creative-active phase in teaching looks like deliberately testing variations. Teaching the same concept three different ways to three different classes and comparing what happened. Trying a discussion structure you’ve never used. Designing an assessment from scratch rather than pulling from the file drawer. Not just executing what works but actively asking: what would work better, and how would I know?

And the mastery Greene describes — the point where you can read a classroom situation, improvise an explanation, identify a misconception before it surfaces, know which student needs what kind of push right now — that’s genuinely observable in exceptional veteran teachers. It doesn’t look like effort. It looks like presence.

The Mentor Chapter Is Worth the Price Alone

Greene’s extended treatment of mentorship is the part of this book I return to most often. His core argument: learning from a skilled practitioner in person, with direct feedback on your actual work, is categorically different from learning from books or courses. A mentor who has internalized expertise transmits not just knowledge but a way of thinking — patterns of attention, judgment under uncertainty, the tacit knowledge that can’t be written down.

For teachers, this maps directly onto instructional coaching done well. Not the generic professional development model where everyone sits in a room watching a PowerPoint, but the specific thing: someone who knows the craft watching you work, asking questions about what you were trying to do, pointing to the moment where something shifted, and asking what you noticed. That relationship, when it exists, is wildly more developmental than anything else available.

Greene is also honest about why mentorship relationships fail: ego on both sides, impatience, and a lack of clarity about what the learner actually needs. He’s not romantic about it. The good mentors he profiles tend to push hard and give uncomfortable feedback. The apprentices who benefit most are the ones who can resist defensiveness long enough to actually hear it.

What to Push Back On

Greene’s historical examples are compelling, but they’re also selected. You don’t hear about the Darwins who spent decades in careful apprenticeship and never had a breakthrough. Selection bias is baked into any framework built from case studies of extraordinary achievers, and this one is no exception.

The book also skews toward individual development in a way that can feel politically naive about institutional constraints. Teaching exists inside systems — school systems, districts, unions, standardized testing regimes, state curriculum mandates — that don’t always reward or even permit the kind of long-term, patient craft development Greene describes. A first-year teacher in a chronically under-resourced school has real structural constraints that aren’t dissolved by having the right philosophical orientation toward apprenticeship.

And Greene’s framework is implicitly competitive in places that can feel uncomfortable in a profession built on collaboration. His “social intelligence” chapter sometimes reads like a manual for navigating a corporate shark tank, which isn’t quite the right register for most school environments.

None of this makes the book less worth reading. But it’s worth being a critical reader rather than accepting the framework wholesale.

The Bottom Line

Mastery gave me a vocabulary for something I’d observed in teaching for years but couldn’t quite articulate — the difference between teachers who develop over a career and teachers who don’t, and why the ones who do seem to have treated their practice as a craft with a development arc rather than a job with an annual performance review.

If you’re in your first few years of teaching and feeling the exhaustion of the survival phase, this book won’t fix that — the survival phase is real and requires getting through it, not reframing it. But it might give you a way to think about what comes after. What you’re building toward. What it looks like to take the long view on what it means to be excellent at this.

That’s a question most of us don’t get asked enough.

Rating: 4 out of 5 stars. Occasionally overwrought, selected toward the extraordinary, and not always aware of its own blind spots — but one of the better frameworks I’ve encountered for thinking about what deliberate skill development actually requires over time.

Get Mastery by Robert Greene

Related on this site: the PhD reading and note-taking post covers the practical side of how I try to absorb and build on what I’m reading — the system that makes books like this one actually stick.